Method and system for detecting endometriosis using in VIVO bioelectronic sensor

Abstract

A method of detecting the presence or absence of endometriosis is provided herein that includes exposing a biological chemosensory array to a test volatile organic compound (VOC) mixture, obtaining a test neuronal response, comparing the test neuronal response to a control response, and outputting a result based on the comparison. A system for detecting the presence or absence of endometriosis is also provided herein, which includes an odor stimulus delivery component for delivering a test VOC mixture to a biological chemosensory array, a neuron probe for detecting a test neuronal response, and a processor for storing the test neuronal response.

Description

METHOD AND SYSTEM FOR DETECTING ENDOMETRIOSIS USING IN VIVO BIOELECTRONIC SENSORCROSS-REFERENCE TO RELATED PATENT APPLICATIONS

[0001] This patent application claims the benefit of U.S. Provisional Patent Application No. 63 / 546,819 filed on 1 November 2023, the entire content of which is hereby incorporated by reference.GOVERNMENT SUPPORT STATEMENT

[0002] This invention was made with government support under 1 R21 HD 114955-01 and R01 HD 099090 awarded by the National Institute of Health. The government may have certain rights in the invention.FIELD

[0003] This disclosure generally relates to a method and system for detecting the presence or absence of endometriosis in a subject by exposing insect antennae to volatile organic compound (VOC) mixtures obtained from the subject, detecting test neuronal responses, and comparing the test neuronal responses to endometriosis and non-endometriosis control neuronal responses.BACKGROUND

[0004] This section provides background information related to the present disclosure which is not necessarily prior art.

[0005] Endometriosis is a chronic inflammatory estrogen-dependent disease characterized by the presence of endometrial tissue outside the uterus. It affects approximately 10-15% of women of reproductive age with symptoms including chronic pelvic pain, dysmenorrhea, dyspareunia, and infertility. Diagnosis of endometriosis is often delayed an average of 7-10 years after the onset of symptoms due to heterogeneity of symptoms and absence of biomarkers.

[0006] Furthermore, the etiology and pathogenesis of endometriosis remains vague and the current gold standard for diagnosis is laparoscopic surgery which is associated with high cost and surgical risk. Thus, there is a need in the art for new and improved methods and systems for detecting endometriosis non-invasively.SUMMARY

[0007] This section provides a general summary of the disclosure and is not a comprehensive disclosure of its full scope or all of its features.

[0008] In certain aspects, the present disclosure provides a method of detecting a presence or absence of endometriosis. The method includes exposing a biological chemosensory array to a test volatile organic compound (VOC) mixture, obtaining a test neuronal response from the biological chemosensory array, comparing the test neuronal response to one or more control neuronal response obtained from one or more endometriosis control VOC mixture and one or more non-endometriosis control VOC mixture, and outputting a result based on the comparison, wherein the result indicates a presence or absence of endometriosis or a probability of a presence or absence of endometriosis.

[0009] Also provided herein is a method of diagnosing endometriosis based on the presence of endometriosis or the probability of the presence of endometriosis obtained from the method of detecting endometriosis. Additionally, a method of treating and / or preventing endometriosis based on an endometriosis diagnosis obtained from the method of diagnosing endometriosis is provided herein.

[0010] Also provided herein is a system for detecting the presence or absence of endometriosis. The system includes an odor stimulus delivery component for delivering a test VOC mixture to one or more biological chemosensory array, a neuron probe for detecting one or more test neuronal response from the one or more biological chemosensory array, and at least one processor which stores the one or more test neuronal response in memory. The one or more biological chemosensory array is stabilized by a stabilizing component, and the test neuronal response is one or more neuronal voltage signal.

[0011] In some embodiments, the VOC mixture is a gas VOC mixture emitted from a biological sample, such as breath, urine, sweat, or blood. The biological chemosensory array is obtained from an insect antennal lobe, such as from a locust antenna.

[0012] Further areas of applicability will become apparent from the description provided herein. The description and specific examples in this summary are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are for illustrative purposes only of selected embodiments and not all possible implementations and are not intended to limit the scope of the present disclosure.

[0014] FIG. 1 is an example schematic of the experimental setup used in Examples 1-5. In this setup, cell culture headspace gas mixtures are delivered to the chemosensory array (antenna) and neural responses are recorded from the antennal lobe region of the locust brain using a multielectrode array. Clean air from a compressed air cylinder enters an olfactometer with a flow rate, L. The entire flow rate, L, is delivered directly to the locust’s antenna prior to cell culture headspace stimulus. At the start of the stimulus, the olfactometer diverts a portion of the air, x, into the headspace of the cell culture flask. The clean air (L-x) and the odor laden air (x) are combined at the final valve and delivered to the antenna. A multielectrode array placed within the locust antennal lobe records the cell culture headspace evoked neural responses. At the bottom, a representative peri- stimulus voltage trace (PSVT) is shown with a 4 second odor presentation window in gray.

[0015] FIG. 2 includes representative images of the four cell cultures used in Examples 1-5. Immortalized human ectopic endometriotic epithelial cells (12Z) and stromal cells (iEc-ESC) are shown on the top. Immortalized normal endometrial epithelial cells (H1657-EEC) and stromal cells (H1644-ESC) are shown on the bottom. All the images are shown at 72-hour post-seeding. The white scale bar indicates 100 pm.

[0016] FIG. 3 includes representative extracellular cell culture headspace-evoked neural voltage responses from a single location within the locust antennal lobe as described in Example 1. The grey box indicates the 4 second stimulus presentation window.

[0017] FIG. 4 includes distinct neural spiking responses to all cell culture and media headspace gas mixtures as described in Example 1. Representative raster plots from one neuron spike sorted from the voltage trace shown in FIG. 3 shows spiking patterns from five trials of each cell line and media described in Example 1. Each black line in the raster plot indicates an action potential, or spiking event, from the neuron. Peri-stimulus time histograms (PSTHs) are shown above each raster plot for all cell cultures and media. Trial-averaged PSTHs are plotted with the shaded region indicating the standard error of the mean (SEM). The gray box indicates the 4 second stimulus presentation window.

[0018] FIG. 5A is a graph demonstrating that dimensionality reduction of high dimensional neural response vectors via principal component analysis (PCA) shows distinct temporal neuraltrajectories in the PCA subspace from Example 2. Arrows specify the direction of the neural trajectories from the origin which indicates the start of the time window. The time window plotted is 0.25 - 1.5 seconds after stimulus onset. Data was RMS filtered from 36 electrophysiological recording locations for analysis.

[0019] FIG. 5B is a graph demonstrating that dimensionality reduction of high dimensional neural response vectors via linear discriminant analysis (LDA) shows distinct neural response clusters in the LDA subspace from Example 2. Each point in the LDA subspace indicates a 50- millisecond time bin within the time window of 0.25-1.5 seconds after stimulus onset. Data was RMS filtered from 36 electrophysiological recording locations for analysis.

[0020] FIG. 6A is a confusion matrix summarizing the high dimensional leave-one-trial-out analysis for the classification of 50 millisecond time bins within the time window of 0.25-1.5 seconds after stimulus onset from Example 2. Data was RMS filtered from 36 electrophysiological recording locations for analysis.

[0021] FIG. 6B is a confusion matrix summarizing the high dimensional leave-one-trial-out analysis for the classification of 1.25 second trials for the entire time window of 0.25-1.5 seconds after stimulus onset from Example 2. Data was RMS filtered from 36 electrophysiological recording locations for analysis.

[0022] FIG. 7 A includes raster plots of spike sorted neuron responses to a single odor from antennal lobe recordings in locust described in Example 2. Each neuron’s response was recorded for five trials. The responses are all aligned using the stimulus onset as the reference point and binned into discrete, non-overlapping, 50 millisecond time bins.

[0023] FIG. 7B shows how the number of spiking events in each time bin is counted and used to populate a three-dimensional matrix (neurons x trials x time bins) described in Example 2.

[0024] FIG. 7C shows how four trials are selected and averaged together to create the training template, with the fifth trial being left out to create the testing template, known as a leave-one- trial-out (LOTO) analysis described in Example 2. Each time bin, denoted by the different shades of grey, is analyzed separately.

[0025] FIG. 8 shows the training and testing templates for each time bin and both odors, solid and checkered circles, respectively, are visualized as high dimensional points, with the number of dimensions equal to the number of neurons, as described in Example 2. Testing templates (checkered) are compared to each training template (solid) and assigned using the smallest Euclidean distance. For time bin a (left) the light grey testing template would be assigned to lightgrey, and the dark grey testing template would be assigned to dark grey. Time bin b, (middle) light grey is assigned to dark grey and dark grey is assigned to dark grey. Time bin c, (right) light grey is assigned to light grey and light grey is assigned to dark grey.

[0026] FIG. 9A and FIG. 9B show how a confusion matrix can be populated by the assignments shown in FIG. 7A-C, and FIG. 8. FIG. 9A shows testing templates for each odor are on the x-axis and their assignments are on the y-axis, as described in Example 2. For two of the time bins (‘a’ and ‘c’), the green testing template was assigned to light grey, however for one time bin (‘b’) light grey was mis-assigned to dark grey. The assignments for each of the odors was counted. FIG. 9B shows that instead of counting each individual bin, the mode of the bin assignments can be used to assign an entire trial. For the left-out trial visualized here, the mode for the light grey testing template is light grey (two out of three) and the mode for the dark grey testing template is dark grey (two out of three). After completing the analysis for a single left out trial, it can be iterated until each trial has been left out once and used as a testing template. Each time using the other four trials to create the training template. Additionally, this can be expanded to include any number of odors.

[0027] FIG. 10 includes representative images of the four cell lines at two different time points, as described in Example 3. Immortalized human ectopic endometriotic epithelial cells (12Z) and stromal cells (iEc-ESC) are shown on the top. Immortalized normal endometrial epithelial cells (H1657-EEC) and stromal cells (H1644-ESC) are shown on the bottom. All the images are shown at either 24 or 72-hour post-seeding. The white scale bar indicates 100 pm.

[0028] FIG. 11 includes distinct and tuned neural spiking responses to all cell culture and media headspace gas mixtures for two representative neurons at multiple time points, as described in Example 3. Representative raster plots show spiking patterns from five trials of each cell line and media. Each black line in the raster plot indicates an action potential, or spiking event, from the respective neuron. Peri-stimulus time histograms (PSTHs) are shown above each raster plot. Trial-averaged PSTHs are plotted with the shaded region indicating the standard error of the mean (SEM). The gray box indicates the 4 second stimulus presentation window.

[0029] FIG. 12 is a graph representing dimensionality reduction of high dimensional neural response vectors via linear discriminant analysis (LDA) showing distinct neural response clusters in the LDA subspace, as described in Example 4. Each point in the LDA subspace indicates a 50- millisecond time bin within the time window of 0.25-1.25 seconds after stimulus onset. Data was RMS filtered from 36 electrophysiological recording locations for analysis.

[0030] FIG. 13A is a confusion matrix summarizing the high dimensional leave-one-trial-out analysis for the classification of 50 millisecond time bins within the time window of 0.25-1.25 seconds after stimulus onset, as described in Example 4. Data was RMS filtered from 36 electrophysiological recording locations for analysis.

[0031] FIG. 13B is a confusion matrix summarizing the high dimensional leave-one-trial-out analysis for the classification of 1 second trials for the entire time window of 0.25-1.25 seconds after stimulus onset, as described in Example 4. Data was RMS filtered from 36 electrophysiological recording locations for analysis.

[0032] FIG. 14A is a graph representing dimensionality reduction of high dimensional neural response vectors via principal component analysis (PC A) showing distinct temporal neural trajectories in the PCA subspace, as described in Example 5. Arrows specify the direction of the neural trajectories from the origin which indicates the start of the time window. The time window plotted is 0.5 - 2.25 seconds after stimulus onset. Data was RMS filtered from 22 electrophysiological recording locations for analysis.

[0033] FIG. 14B is a graph representing dimensionality reduction of high dimensional neural response vectors via linear discriminant analysis (LDA) showing distinct neural response clusters in the LDA subspace, as described in Example 5. Each point in the LDA subspace indicates a 50- millisecond time bin within the time window of 0.5-2.25 seconds after stimulus onset. Data was RMS filtered from 22 electrophysiological recording locations for analysis.

[0034] FIG. 15A is a confusion matrix summarizing the high dimensional leave-one-trial-out analysis for the classification of 50 millisecond time bins within the time window of 0.5-2.25 seconds after stimulus onset, as described in Example 5. Data was RMS filtered from 22 electrophysiological recording locations for analysis.

[0035] FIG. 15B is a confusion matrix summarizing the high dimensional leave-one-trial-out analysis for the classification of 1.75 second trials for the entire time window of 0.5-1.75 seconds after stimulus onset, as described in Example 5. Data was RMS filtered from 22 electrophysiological recording locations for analysis.

[0036] FIG. 16 is a functional block diagram of an example system for detecting the presence or absence of endometriosis used in the working examples herein.

[0037] FIG. 17 is a schematic showing an example experimental set up for collecting mouse urine as described in Example 6.

[0038] FIG. 18 is a schematic showing an example experimental set up for laparoscopy and inoculation with menstrual tissue and laparotomies in baboons as described in Example 7.

[0039] In the drawings, reference numbers may be reused to identify similar and / or identical elements.DETAILED DESCRIPTIONA. Introduction

[0040] VOCs are gaseous chemicals that represent the metabolic processes in the body. VOC gas mixtures have distinct smell profiles, and these profiles can be detected using chemical analysis of biological samples. The biological olfactory system in insects can be used for detection and classification of complex mixtures of VOCs. For example, studies using gas chromatographymass spectrometry (GC-MS) and other chemical detection systems have identified changes in VOCs in the breath of patients with cancer and other diseases. However, no VOCs have been classified as biomarkers of endometriosis.

[0041] The inventors developed an insect brain-based gas sensing system and computational analytical technique for non-invasive detection and diagnosis of endometriosis. Biological olfactory systems have evolved to detect minute differences in complex VOC mixtures in natural environments. Here, the challenge of detecting endometriosis non-invasively through a novel olfactory neuron-based sensor is addressed, where the capacity of the entire biological olfactory sensory system of an insect brain is leveraged and neural responses are analyzed to discriminate gas mixtures emitted from endometriotic vs. non-endometriotic biological samples.

[0042] The results demonstrate that this novel biosensing technology can discriminate between multiple types of endometriotic and non-endometriotic cell lines. VOC mixtures emitted from cell cultures were delivered to the biological chemosensory array (antennae) and neuronal responses (extracellular neuronal voltage signals) were obtained from the insect (locust) antennal lobe. Individual neurons can distinguish each cell line by its ‘smell’ (emitted VOC mixture). By combining the neural responses across experiments, high-dimensional population neural response templates were obtained that were used to classify unknown gas mixtures to achieve high classification accuracy. This innovative approach harnesses the full power of a biological olfactory system for the detection of endometriosis via VOC gas mixture analysis and presents a novel detection tool to aid in the diagnosis and treatment of endometriosis.

[0043] Example embodiments are provided so that this disclosure will be thorough and will fully convey the scope to those who are skilled in the art. Numerous specific details are set forthsuch as examples of specific components, methods, and systems to provide a thorough understanding of embodiments of the present disclosure. It will be apparent to those skilled in the art that specific details need not be employed, that example embodiments may be embodied in many different forms and that neither should be construed to limit the scope of the disclosure. In some example embodiments, well-known processes, well-known device structures, and well- known technologies are not described in detail.B. Definitions

[0044] The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms “comprises,” “comprising,” “including,” and “having,” are inclusive and therefore specify the presence of stated features, elements, compositions, steps, integers, operations, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Although the open-ended term “comprising,” is to be understood as a non-restrictive term used to describe and claim various embodiments set forth herein, in certain aspects, the term may alternatively be understood to instead be a more limiting and restrictive term, such as “consisting of’ or “consisting essentially of.” Thus, for any given embodiment reciting compositions, materials, components, elements, features, integers, operations, and / or process steps, the present disclosure also specifically includes embodiments consisting of, or consisting essentially of, such recited compositions, materials, components, elements, features, integers, operations, and / or process steps. In the case of “consisting of,” the alternative embodiment excludes any additional compositions, materials, components, elements, features, integers, operations, and / or process steps, while in the case of “consisting essentially of,” any additional compositions, materials, components, elements, features, integers, operations, and / or process steps that materially affect the basic and novel characteristics are excluded from such an embodiment, but any compositions, materials, components, elements, features, integers, operations, and / or process steps that do not materially affect the basic and novel characteristics can be included in the embodiment.

[0045] Any method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is also to be understood that additional or alternative steps may be employed, unless otherwise indicated.

[0046] The use of the term "a" or "an" when used in conjunction with the term "comprising" in the claims and / or the specification may mean "one," but it is also consistent with the meaning of "one or more," "at least one," and "one or more than one." As such, the terms "a," "an," and "the" include plural referents unless the context clearly indicates otherwise. Thus, for example, reference to "a compound" may refer to one or more compounds, two or more compounds, three or more compounds, four or more compounds, or greater numbers of compounds.

[0047] The use of the term "at least one" will be understood to include one as well as any quantity more than one, including but not limited to, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 100, etc. The term "at least one" may extend up to 100 or 1000 or more, depending on the term to which it is attached; in addition, the quantities of 100 / 1000 are not to be considered limiting, as higher limits may also produce satisfactory results. In addition, the use of the term "at least one of X, Y, and Z" will be understood to include X alone, Y alone, and Z alone, as well as any combination of X, Y, and Z. The use of ordinal number terminology (i.e., "first," "second," "third," "fourth," etc.) is solely for the purpose of differentiating between two or more items and is not meant to imply any sequence or order or importance to one item over another or any order of addition, for example.

[0048] The use of the term "or" in the claims is used to mean an inclusive "and / or" unless explicitly indicated to refer to alternatives only or unless the alternatives are mutually exclusive. For example, a condition "A or B" is satisfied by any of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

[0049] As used herein, any reference to "one embodiment," "an embodiment," "some embodiments," "one example," "for example," or "an example" means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearance of the phrase "in some embodiments" or "one example" in various places in the specification is not necessarily all referring to the same embodiment, for example. Further, all references to one or more embodiments or examples are to be construed as non-limiting to the claims.

[0050] Throughout this disclosure, the term "about" is used to indicate that a value includes the inherent variation of error for a composition / apparatus / device, the method being employed to determine the value, or the variation that exists among the study subjects. For example, but not by way of limitation, when the term "about" is utilized, the designated value may vary by plus or minus twenty percent, or fifteen percent, or twelve percent, or eleven percent, or ten percent, or nine percent, or eight percent, or seven percent, or six percent, or five percent, or four percent, orthree percent, or two percent, or one percent from the specified value, as such variations are appropriate to perform the disclosed methods and as understood by persons having ordinary skill in the art. Particularly in reference to a given quantity, number or percentage, “about” is meant to encompass deviations of plus or minus ten percent (± 10). For example, about 5% encompasses any value between 4.5% to 5.5%, such as 4.5, 4.6, 4.7, 4.8, 4.9, 5, 4.1, 5.2, 5.3, 5.4, or 5.5. Accordingly, unless otherwise indicated, the numerical parameters set forth in this specification and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by the presently disclosed subject matter.

[0051] The term "or combinations thereof" as used herein refers to all permutations and combinations of the listed items preceding the term. For example, "A, B, C, or combinations thereof" is intended to include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. Continuing with this example, expressly included are combinations that contain repeats of one or more item or term, such as BB, AAA, AAB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context.

[0052] As will be understood by one skilled in the art, for any and all purpose, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Furthermore, as will be understood by one skilled in the art, a range includes each individual member.

[0053] The term “olfactory receptor” or “odorant receptor” as used herein refers to chemoreceptors expressed in the cell membranes of olfactory receptor neurons and are responsible for the detection of odorants (for example, compounds that have an odor, such as VOCs) which give rise to the sense of smell. Activated olfactory receptors trigger nerve impulses which transmit information about odor to the brain. In insects, olfactory receptors are members of an unrelated group of ligand-gated ion channels.

[0054] The term “in vivo” as used herein refers to experiments on living organisms. Thus, in some embodiments a neural response may be taken from a live insect, such as a locust.

[0055] The term “endometriosis” refers to an often painful condition in which tissue that is similar to the inner lining of the uterus grows outside of the uterus. Endometriosis often affects the ovaries, fallopian tubes, and the tissue lining the pelvis. In some cases, endometriosis growth may be found beyond the area where pelvic organs are located. The terms “endometriotic” and“non-endometriotic” refer to biological samples, cells, and / or VOC mixtures that are obtained from a subject with endometriosis.

[0056] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by a person of ordinary skill in the art. In particular, this disclosure utilizes routine techniques in the field of VOC detection and in vivo bioelectronic sensors.C. Method of Detecting Endometriosis, Endometriotic Cancer and / or Ovarian Cancer

[0057] Provided herein is a method for detecting a presence or absence of endometriosis, endometriotic cancer, and / or ovarian cancer.

[0058] For detecting endometriosis, the method includes exposing a biological chemosensory array to a test VOC mixture, obtaining a test neuronal response from the biological chemosensory array, comparing the test neuronal response to one or more control neuronal response obtained from one or more endometriosis control VOC mixture and one or more non-endometriosis control VOC mixture, and outputting a result based on the comparison. The result can indicate a presence or absence of endometriosis or a probability of a presence or absence of endometriosis.

[0059] For detecting endometriotic cancer and / or ovarian cancer, the method includes exposing a biological chemosensory array to a test VOC mixture, obtaining a test neuronal response from the biological chemosensory array, comparing the test neuronal response to one or more control neuronal response obtained from one or more endometriotic cancer and / or ovarian cancer control VOC mixture and one or more non-endometriotic cancer and / or non-ovarian cancer control VOC mixture, and outputting a result based on the comparison. The result can indicate a presence or absence of endometriotic cancer and / or ovarian cancer or a probability of a presence or absence of endometriotic cancer and / or ovarian cancer.Endometriosis

[0060] In some embodiments, the endometriosis may be superficial endometriosis, which may be found mainly on the pelvic peritoneum. In some embodiments, the endometriosis may be cystic ovarian endometriosis (endometrioma), which may be found in the ovaries. In some embodiments, the endometriosis may be deep endometriosis, which may be found in the recto-vaginal septum, bladder, and bowel. In some embodiments, the endometriosis may be peritoneal endometriosis, which may affect the lining of the abdominal cavity. In some embodiments, the endometriosis may be ovarian endometriomas, where lesions may grow inside the ovaries. In someembodiments, the endometriosis may be deep infiltrating endometriosis (DIE), which may involve nodules comprising endometrial-like, fibromuscular, and adipose tissues.

[0061] In some embodiments, the probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. In some embodiments, the probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 40%. In some embodiments, the probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 50%. In some embodiments, the probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 60%. In some embodiments, the probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 70%. In some embodiments, the probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 80%. In some embodiments, the probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 90%. In some embodiments, the probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 99%. In some embodiments, the probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer may be about 100%.

[0062] In some embodiments, the probability of an absence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%. In some embodiments, the probability of an absence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 40%. In some embodiments, the probability of an absence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 50%. In some embodiments, the probability of an absence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 60%. In someembodiments, the probability of an absence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 70%. In some embodiments, the probability of an absence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 80%. In some embodiments, the probability of an absence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 90%. In some embodiments, the probability of an absence of endometriosis, endometriotic cancer, and / or ovarian cancer may be at least about 99%. In some embodiments, the probability of an absence of endometriosis, endometriotic cancer, and / or ovarian cancer may be about 100%.VOC mixture and olfactometer

[0063] VOCs are organic compounds that have a high vapor pressure at room temperature. VOCs may be produced by one or more biological processes in an organism and can be released to the exterior in several ways. For example, VOCs may be emitted from an organism via exhaled breath, perspiration, urine, feces, or lacrimal fluid.

[0064] In some embodiments, a VOC mixture may be a VOC gas or liquid mixture. In certain embodiments, the VOC mixture may be a VOC gas mixture. In some embodiments, the VOC mixture may be emitted from a biological sample. A VOC mixture may contain at least 2, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 VOCs.

[0065] In some embodiments, the VOC mixture is in a container or vessel, such as a cell culture flask. In some embodiments, the VOC mixture is delivered to the biological chemosensory array via one or more tube. In some embodiments, the VOC mixture is delivered to the biological chemosensory array via an olfactometer system.

[0066] In some embodiments, the olfactometer system may be a flow-olfactometer system. In some embodiments, air may flow continuously through or over a biological sample emitting a VOC mixture, and is then transported (e.g., via tubing) to the biological chemosensory array. Thus, the olfactometer system may allow precision cell culture headspace stimulus delivery. The headspace refers to air / space immediately above, for example within about 0-24 inches above, the biological sample containing a VOC mixture. In some embodiments, the headspace is contained in a container or vessel with the biological sample. In some embodiments, the olfactometer may use vacuum pressure to switch between test VOC mixture flow and zero contaminant air (i.e., does not contain VOCs). In some embodiments, air flow to the biological chemosensory array may be continuous.

[0067] In some embodiments, the olfactometer system may include a stimulus air flow line and a dilution air flow line. In some embodiments the stimulus air flow line may include a VOC mixture. In some embodiments, the dilution air flow line may be added to the stimulus air flow line to dilute the VOC mixture. In some embodiments, the dilution air flow line may include zero contaminant air. In some embodiments, the dilution air flow line may be added upstream of the cell culture vessel. In some embodiments, the dilution air flow line may be added downstream of the cell culture vessel.

[0068] In some embodiments, air may pass through the flow line(s) at about 150-400 standard cubic centimeters per minute (seem). In certain embodiments, air may pass through the flow line(s) at about 200 seem. In some embodiments, the air flow line is about 1 / 8 in. to about 1 / 16 in. diameter. In certain embodiments, the air flow line is about 1 / 16 in. in diameter. In some embodiments, the end of the stimulus air flow line is placed about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, or 10 cm from the biological chemosensory array. In certain embodiments, the end of the stimulus air flow line may be placed about 2-3 cm from the biological chemosensory array. In certain embodiments, the stimulus flow line may be placed about 2-3 cm from the most distal part of the biological chemosensory array. In some embodiments, the stimulus air flow line may be delivered to the biological chemosensory array for about 1-5 seconds. In certain embodiments, the stimulus air flow line may be delivered to the biological chemosensory array for about 4 seconds. In some embodiments, stimulus delivery may be repeated at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 times. In certain embodiments, stimulus delivery may be repeated at least 5 times. In some embodiments, the interstimulus interval (i.e., time between two stimulus (i.e., test VOC mixture) delivery intervals) may be at least about 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, or 90 seconds. In certain embodiments, the interstimulus delivery may be about 60 seconds. In some embodiments, before and / or after stimulus air flow (i.e., test VOC mixture air flow) is delivered to the biological chemosensory array, constant air flow may be maintained that does not contain the test VOC mixture (e.g., zero contaminant air). Thus, in some embodiments, the stimulus air flow delivery may be “turned on” and / or “turned off’ while the zero contaminant air maintains a constant flow. In some embodiments, keeping a constant air flow may reduce confounding neuronal responses due to changes in air pressure via mechanosensory detection. In some embodiments a funnel positioned behind the biological chemosensory array may be used to remove VOCs. In some embodiments, the funnel may include a vacuum. In some embodiments,the funnel may be about 2-10 inches in diameter. In certain embodiments, the funnel may be about 6 inches in diameter.Biological sample

[0069] In some embodiments, the method of detecting endometriosis, endometriotic cancer, and / or ovarian cancer is non-invasive. In other words, in some embodiments, the method of detecting endometriosis, endometriotic cancer, and / or ovarian cancer does not introduce instruments into a subject. Instead, a biological sample is taken from a subject to use in the disclosed method.

[0070] Thus, in some embodiments, the VOC mixture may be emitted from one or more biological sample. In some embodiments, the VOC mixture may be emitted from one or more endometriotic, endometriotic cancer, and / or ovarian cancer biological sample. In some embodiments, the VOC mixture may be emitted from one or non-endometriotic, non- endometriotic cancer, and / or non-ovarian cancer (e.g., healthy) biological sample. In some embodiments, the test VOC mixture may be obtained from one or more endometriotic, endometriotic cancer, and / or ovarian cancer biological sample. In some embodiments, the control VOC mixture may be obtained from one or more non-endometriotic, non-endometriotic cancer, and / or non-ovarian cancer biological sample.

[0071] In some embodiments, the biological sample may be cheek tissue, blood (e.g., whole blood, plasma, dried blood spot, etc.), organ tissue, feces, skin, hair, breath, urine, sweat, or a combination thereof. In some embodiments, the biological sample may be breath, urine, sweat, blood, or a combination thereof. In certain embodiments, the biological sample may be breath.

[0072] “Subject,” ‘ ‘individual,” and “patient” interchangeably refer to a mammal, for example, a human or a non-human primate, but also domesticated mammals (e.g., canine or feline), laboratory mammals (e.g., mouse, rat, rabbit, hamster, guinea pig), and agricultural mammals (e.g., equine, bovine, porcine, ovine). In some embodiments, the subject may be human (e.g., adult female, adolescent female, , female child). Alternatively, in some embodiments, the subject may be a non-human animal. For example, in some embodiments, the non-human animal may be a mouse or primate. In certain embodiments, the subject can be under the care of a physician or other health worker. In certain embodiments the subject may not be under the care of a physician or other health worker.

[0073] In some embodiments, a subject may be suffering from endometriosis, endometriotic cancer, and / or ovarian cancer. In some embodiments, the subject may be a female and / or may have a uterus. In some embodiments, the subject may be of reproductive age. In someembodiments, the subject may present symptoms such as chronic pelvic pain, dysmenorrhea, dyspareunia, and / or infertility.Biological chemosensory array

[0074] The biological chemosensory array includes one or more insect antenna. Thus, in some embodiments, the insect may be any insect with one or more antenna.

[0075] Thus, the insect may belong to the order Protura, Collembola, Diplura, Microcoryphia, Thysanura, Ephemeroptera, Odonata, Orthoptera, Phasmatodea, Grylloblattodea, Mantophasmatodea, Dermaptera, Plecoptera, Embiidina, Zoraptera, Isoptera, Mantodea, Blattodea, Hemiptera, Thysanoptera, Psocoptera, Phthiraptera, Coleoptera, Neuroptera, Hymenoptera, Trichoptera, Lepidoptera, Siphonaptera, Mecoptera, Strepsiptera, or Diptera.

[0076] In certain embodiments, the insect may belong to the order Orthoptera, Hymenoptera, or Diptera. In some embodiments, the insect belonging to the order Orthoptera may be a cricket, grasshopper, locust. In some embodiments, the insect may belong to the order Acrididae and / or the suborder Caelifera. In certain embodiments, the locust may be post-fifth instar locust (Schistocerca americana). In some embodiments, locusts may have about 142 olfactory receptors. In some embodiments, the insect belonging to the order Hymenoptera may be a honeybee or an ant. In some embodiments, honeybees may have about 170 olfactory receptors. In some embodiments, ants may have about 400 olfactory receptors. In some embodiment, the insect belonging to the order Diptera may be a fruit fly. In some embodiments, the insect may belong to the Family Drosophilidae. In some embodiments, fruit flies may have about 60 olfactory receptors.

[0077] In some embodiments, the insect, insect head, and / or insect antennae may be stabilized via a stabilizing component. In some embodiments, the stabilizing component may be a surgical platform. In some embodiments, the insect may be stabilized and / or immobilized with wax. In some embodiments, the exoskeleton between the antennae was removed. In some embodiments, glandular tissue was removed until the insect brain was fully visible. In some embodiments, treatment with protease is used to remove the neural sheath on the antennal lobe.Olfactory receptors

[0078] In some embodiments, the biological chemosensory array (antenna) may have olfactory receptors, also known as odorant receptors, which are chemoreceptors expressed in the cell membranes of olfactory receptor neurons and are responsible for the detection of VOCs. Olfactory receptor neurons contain olfactory receptor neurons. A single olfactory receptor neuron may contain multiple olfactory receptors. In some embodiments, the terms “olfactory receptorneuron” and “olfactory receptor” may be used interchangeably. Thus, olfactory receptors may be used to detect VOCs in control and / or test VOC mixtures.

[0079] In some embodiments, the insect may have at least about 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 210,220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400,410, 420, 430, 440, 450, 460, 470, 480, 490, or 500 olfactory receptors. In some embodiments, the insect may have at least about 142 olfactory receptors. In some embodiments, the insect may have at least about 170 olfactory receptors. In some embodiments, the insect may have at least about 400 olfactory receptors. In some embodiments, the insect may have at least about 60 olfactory receptors. In certain embodiments, the insect may have at least about 100 olfactory receptors.Neuronal response

[0080] In some embodiments, at least one neuronal response may be obtained from one or more insect antennal lobe. In some embodiments, the neuronal response may be obtained from one insect antennal lobe. Thus, in some embodiments, a neuronal response may be triggered by contact of one or more olfactory receptors with one or more VOCs in a VOC test or VOC control mixture.

[0081] In some embodiments, at least one neuronal response may be an extracellular neuronal voltage signal. In some embodiments, extracellular recording may use an electrode probe inserted into living tissue to measure electrical activity coming from adjacent cells, such as neurons. In some embodiments, the probe may be inserted into the antennal lobe of an insect, such as a locust. In certain embodiments, a 16-channel silicon probe with impedances between 100 and 400 kQ may be inserted into the antennal lobe for all neural recordings. In certain embodiments, a 16- channel silicon probe with impedances between 200 and 300 k may be inserted into the antennal lobe for all neural recordings.

[0082] In some embodiments, the neuronal response may be obtained from an insect in vivo. In other words, in some embodiments, the neuronal response may be taken from a live insect (e.g.,a locust). Thus, in some embodiments, the biological chemosensory array (antenna) may not be detached from the insect.

[0083] In some embodiments, the olfactory receptors may transform chemical stimuli (e.g., via contact with one or more VOCs) into electrical signals that are transmitted to the antennal lobe which contains a dense interconnected network of excitatory projection neurons and inhibitory local neurons. In some embodiments, voltage signals may be sampled at about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 kHz and then digitized. In some embodiments, voltage signals may be sampled at about 20 kHz. In some embodiments, the digitized signals may be transmitted to a recording controller before being visualized and / or stored.

[0084] In some embodiments, in the antennal lobe, neural codes may take the form of spatiotemporal patterns of activity distributed across the projection neurons and inhibitory local neurons. Thus, the collective VOC-evoked population response may contain information about the VOC identity, intensity, timing, or a combination thereof. In some embodiments, Thus, the collective VOC-evoked population response may be reliable over repeated trials.

[0085] In some embodiments, one or more neuronal response may be recorded. In some embodiments, a neuronal response recording may be obtained before, during, or after stimulus delivery. In some embodiments, a neuronal response recording may be obtained during stimulus delivery. In other words, a neuronal response recording may be taken during the timeframe in which a test VOC mixture is delivered to the biological chemosensory array. In some embodiments, neuronal responses recorded during test VOC mixture delivery may be referred to as a test neuronal response.

[0086] In some embodiments, a neuronal response may be recorded before, during, or after the timeframe in which a control VOC mixture is delivered to the biological chemosensory array. In some embodiments, a neuronal response may be recorded during the timeframe in which a control VOC mixture is delivered to the biological chemosensory array. In some embodiments, neuronal responses recorded during control VOC mixture delivery may be referred to as a control neuronal response. As mentioned herein, the control VOC mixture may be an endometriosis, endometriotic cancer, and / or ovarian cancer control VOC mixture or a non-endometriosis, non- endometriotic cancer, and / or non-ovarian cancer control VOC mixture. Thus, a control neuronal response may be obtained from an endometriosis, endometriotic cancer, and / or ovarian cancer control VOC mixture (i.e., an endometriosis, endometriotic cancer, and / or ovarian cancer controlneuronal response) or a control neuronal response may be obtained from a non-endometriosis, non-endometriotic cancer, and / or non-ovarian cancer control VOC mixture (i.e., a non- endometriosis, non-endometriotic cancer, and / or non-ovarian cancer control VOC mixture).Comparison results

[0087] In some embodiments, one or more test neuronal response may be compared to one or more control neuronal response. In some embodiments, at least 1, 2 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 neuronal responses may be recorded for each of the test and the control VOC mixtures. In some embodiments, at least five neuronal responses may be recorded for each of the test and the control VOC mixtures. In some embodiments, the neuronal response recordings are averaged together prior to comparison. In some embodiments, more than one insect is used to generate the neuronal responses. In some embodiments, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 insects may be used for each of the test and the control VOC mixtures. In certain embodiments, 5 locusts may be used to obtain neural recordings for each of the test and control VOC mixtures.

[0088] In some embodiments, dimensionality reduction analysis may be performed, for example via principal component analysis (PCA) and / or linear discriminant analysis (LDA), to visualize a population of neuronal responses. In some embodiments, one or more test neuronal response may be compared to one or more non-endometriosis control neuronal response and / or one or more endometriosis control neuronal response. Thus, in some embodiments, using dimensionality reduction analysis, the test neuronal response may be classified as endometriotic or non-endometriotic. Thus, in some embodiments, using dimensionality reduction analysis, the test neuronal response may be classified as endometriotic or non-endometriotic based on a probability of being endometriotic or non-endometriotic, respectively. Additionally, statistical tests, such as a leave one trial out (LOTO) analysis, may be used to determine the accuracy of each prediction and / or the probability that a test VOC mixture is either endometriotic or non- endometriotic.

[0089] A method, system, and computational analytical technique for detecting human lung cancer VOCs using honeybee olfactory neural circuitry is described in Parnas et al., (2024) Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor. Biosensors and Bioelectronics. 261, 1-18., which is herein incorporated by reference in its entirety. Such methods and computational analytical techniques may be used with system and methods described herein.Clinical Workflow

[0090] In some embodiments, an example clinical workflow may include patients visiting a healthcare provider for symptoms related to endometriosis, endometriotic cancer, and / or ovarian cancer. At this initial point of contact, if the provider suspects an endometriosis, endometriotic cancer, and / or ovarian cancer diagnosis, a test using the method described herein can be ordered with sample collection occurring at the location of the provider. For sample collection, in some embodiments, exhaled breath or urine samples can be collected at any location (e.g., at-home, hospital, clinic, community healthcare setting, retail pharmacy, or other). In some embodiments, the urine or breath samples can be collected and sent to a central location for analysis. In some embodiments, within a few days, the patients may get their results back that will deliver the probability of endometriosis, endometriotic cancer, and / or ovarian cancer. In some embodiments, the method for detecting endometriosis, endometriotic cancer, and / or ovarian cancer may be done in a laboratory. For example, in some embodiments, the method may use specific infrastructure in order to obtain neural recordings from the insect.Optional treatment / prevention step

[0091] Optionally, in some embodiments, when the method of detecting endometriosis results in a presence of endometriosis or a probability of a presence of endometriosis in a sample obtained by a subject, the method may further include a treatment / prevention step. For example, where the test VOC mixture is obtained from a subject, the method may also include providing clinical care and / or treatment to the subject if the result indicates a presence or probability of a presence of endometriosis. Treating and / or preventing endometriosis is further described in Sec. E.D. Method of Diagnosing Endometriosis, Endometriotic Cancer, and / or Ovarian Cancer

[0092] Also provided herein is a method for diagnosing endometriosis, endometriotic cancer, and / or ovarian cancer.

[0093] The method for diagnosing endometriosis includes exposing a biological chemosensory array to a test volatile organic compound (VOC) mixture, obtaining a test neuronal response from the biological chemosensory array, comparing the test neuronal response to one or more control neuronal response obtained from one or more endometriosis control VOC mixture and one or more non-endometriosis control VOC mixture, and outputting a result based on the comparison. The result can indicate a presence or absence of endometriosis or a probability of a presence or absence of endometriosis.

[0094] The method for diagnosing endometriotic cancer and / or ovarian cancer includes exposing a biological chemosensory array to a test volatile organic compound (VOC) mixture, obtaining a test neuronal response from the biological chemosensory array, comparing the test neuronal response to one or more control neuronal response obtained from one or more endometriotic cancer and / or ovarian cancer control VOC mixture and one or more non- endometriotic cancer and / or non-ovarian cancer control VOC mixture, and outputting a result based on the comparison. The result can indicate a presence or absence of endometriotic cancer and / or ovarian cancer or a probability of a presence or absence of endometriotic cancer and / or ovarian cancer.

[0095] Thus, the present disclosure provides the method described in Sec. C, where the VOC mixture is obtained from a subject, further comprising diagnosing the subject with endometriosis, endometriotic cancer, and / or ovarian cancer if the result indicates a presence or probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer.

[0096] Optionally, in some embodiments, the method may further include a treatment / prevention step. For example, where the test VOC mixture is obtained from a subject, the method may also include providing clinical care and / or treatment to the subject if the subject is diagnosed with endometriosis, endometriotic cancer, and / or ovarian cancer. Treating and / or preventing is further described in Sec. E.E. Method of Treating and / or Preventing Endometriosis, Endometriotic Cancer, and / or Ovarian Cancer

[0097] Additionally, the present disclosure provides the method described in Sec. C, where the VOC mixture is obtained from a subject, further comprising providing clinical care and / or treatment to the subject if the result indicates a presence or probability of a presence of endometriosis, endometriotic cancer, and / or ovarian cancer.

[0098] In some embodiments, “treat” and “treatment” refer to an approach for obtaining beneficial or desired results, including clinical results. In some embodiments, the subject in need of treatment may include a subject diagnosed as having, or suspected to have, endometriosis. In some embodiments, prevention includes treatment of endometriosis that causes the clinical symptoms of the endometriosis not to develop or progress, or treatment of endometriosis that reduces the occurrence of endometriosis.

[0099] In some embodiments, the clinical care and / or treatment may include performing one or more surgical procedure on a subject, such as laparoscopy to remove endometrial tissue, hysterectomy to remove the uterus, fallopian tubes, and / or ovaries, etc.

[0100] In some embodiments, the clinical care and / or treatment may include one or more dietary and / or nutritional changes for the subject. For example, in some embodiments, a subject may eat a high fiber diet that includes fruits, vegetables, beans, whole grains, and nuts. In some embodiments, a subject may eat a high fiber diet that includes fruits, vegetables, beans, whole grains, and nuts. In some embodiments, a subject may limit saturated fat based foods by eating low-fat dairy products and selecting lean meats. In some embodiments, a subject may eat more sources of omega-3 fats, such as fish (salmon, mackerel, herring, and sardines), fish oil, canola oil, flaxseeds, etc. In some embodiments, a subject may eat magnesium-rich foods (e.g., pumpkin seeds, sunflower seeds, black beans, avocado, almonds, bananas, and spinach) to help soothe the uterus and reduce pain.

[0101] In some embodiments, the clinical care and / or treatment may include administering to a subject one or more hormone therapy, such as a hormonal contraceptive, Gonadotropin-releasing hormone (Gn-RH) agonist or antagonist, progestin therapy, aromatase inhibitor, or a combination thereof. In some embodiments, the clinical care and / or treatment may include administering to a subject one or more fertility treatment.

[0102] In some embodiments, the clinical care and / or treatment may include administering to a subject an effective amount of an endometriosis medication, such as an analgesic (e.g. Ibuprofen, Naproxen, etc.).

[0103] In some embodiments, administration may be in an “effective amount” or a “therapeutically effective amount” (as the case may be), this being sufficient to show benefit to the individual / subject. The actual amount administered, and rate and time-course of administration, may depend on the nature and severity of endometriosis.

[0104] A pharmaceutical composition including a hormone therapy and / or an endometriosis, endometriotic cancer, and / or ovarian cancer medication may be administered as an individual therapeutic agent or in combination with other therapeutic agents, and may be administered sequentially or concurrently with an existing therapeutic agent and once or multiple times.

[0105] Administration of the pharmaceutical composition can be systemic, mucosal and / or proximal to the location of a target site (e.g., near endometriotic tissue). Suitable routes of administration will be apparent to those of skill in the art. Various acceptable methods of administration include, but are not limited to, intravenous administration, intraperitoneal administration, intramuscular administration, intranodal administration, intracoronary administration, intraarterial administration (e.g., into a carotid artery), subcutaneous administration, retroorbital administration, transdermal delivery, intratracheal administration,subcutaneous administration, intraarticular administration, intraventricular administration, inhalation (e.g., aerosol), intracranial, intraspinal, intraocular, aural, intranasal, oral, pulmonary administration, impregnation of a catheter, and direct injection into a tissue. In one aspect, routes of administration include: intravenous, intraperitoneal, subcutaneous, intradermal, intranodal, intramuscular, transdermal, inhaled, intranasal, oral, intraocular, intraarticular, intracranial, and intraspinal. Parenteral delivery can include intradermal, intramuscular, intraperitoneal, intrapleural, intrapulmonary, intravenous, subcutaneous, atrial catheter and venal catheter routes. Aural delivery can include ear drops, intranasal delivery can include nose drops or intranasal injection, and intraocular delivery can include eye drops. Aerosol (inhalation) delivery can also be performed using methods standard in the art.

[0106] A suitable amount of the pharmaceutical composition to be administered can be determined by routine experiments with animal models. Such models include, without implying any limitation, rabbit, sheep, mouse, rat, dog and non-human primate models. Example unit dose forms for injection include sterile solutions of water, physiological saline or mixtures thereof. The pH of such solutions should be adjusted to about 7.4. Suitable carriers for injection include hydrogels, devices for controlled or delayed release, polylactic acid and collagen matrices. For injection, the pharmaceutical composition may be provided, for example, in a pre-filled syringe Suitable pharmaceutically acceptable carriers for topical application include those which are suitable for use in lotions, creams, gels and the like. If the composition is to be administered orally, tablets, capsules and the like may be used as a unit dose form. The pharmaceutically acceptable carriers for the preparation of unit dose forms which can be used for oral administration are well known in the prior art. The choice thereof will depend on secondary considerations such as taste, costs and storability, which are not critical for the purposes of the present disclosure, and can be made without difficulty by a person skilled in the art.F. System for Detecting Endometriosis, Endometriotic Cancer, and / or Ovarian Cancer

[0107] FIG. 16 illustrates a block diagram of an example system 202, which may be used for detecting the presence or absence or a probability of a presence or absence of endometriosis, endometriotic cancer, and / or ovarian cancer as described in Sec. C. For example, the method described herein for detecting a presence or absence of endometriosis, endometriotic cancer, and / or ovarian cancer may be carried out via a system 202 for detecting endometriosis, endometriotic cancer, and / or ovarian cancer. The system 202 includes an odor stimulus delivery component 206 for delivering a test VOC mixture 207 to one or more biological chemosensory array 204, a neuron probe 203 for detecting one or more test neuronal response from the one ormore biological chemosensory array 204, and at least one processor 216 which stores the one or more test neuronal response in memory. The biological chemosensory array 204 can be stabilized by a stabilizing component 205, and the test neuronal response can be one or more neuronal voltage signal.

[0108] As mentioned in Sec. C, the VOC mixture may be a VOC gas mixture. In some embodiments, the VOC mixture may be emitted from one or more biological sample, such as from breath, urine, sweat, or blood. In some embodiments, the biological sample may be obtained from a mouse, primate, or human.

[0109] Also as mentioned in Sec. C, the biological chemosensory array 204 may be one or more insect antenna. In some embodiments, the neuronal response may be obtained from an insect antennal lobe. In some embodiments, the insect may belong to the order Orthoptera, Hymenoptera, or Diptera. In some embodiments, an insect belonging to the order Orthoptera may be a locust. In some embodiments, an insect belonging to the order Hymenoptera may be a honeybee or an ant. In some embodiments, an insect belonging to the order Diptera may be a fruit fly. In some embodiments, the insect may have at least about 100 olfactory receptors. In some embodiments, a neuronal response may be obtained from an insect in vivo.

[0110] In some embodiments, the system may be used to diagnose, treat, and / or prevent endometriosis, endometriotic cancer, and / or ovarian cancer. The method of diagnosing endometriosis, endometriotic cancer, and / or ovarian cancer is described in Sec. D, while the method of treating and / or preventing endometriosis, endometriotic cancer, and / or ovarian cancer is described in Sec. E.

[0111] The system 202 may be configured to control operation of one or more aspects of the system, such as a neuron probe 203. As shown in FIG. 16, the system 202 includes one or more processors 216 (which may be referred to as a central processor unit (CPU) that is in communication with memory 214 including optional read only memory (ROM) 218 and optional random access memory (RAM) 220, and optional secondary storage (222) (such as disk drives). The processor 216 may be implemented as one or more CPU chips. The system 202 further includes optional input / output (VO) devices 224, and network connectivity devices (e.g., a communication interface) 212.

[0112] In various implementations, the memory 214 may be configured to store computerexecutable instructions, and one or more processors 216 may be configured to execute the instructions control operation of the neuron probe 203. The neuron probe 203 may include a sensorassociated with the biological chemosensory array 204 and configured to sense at least one parameter of the biological chemosensory array 204.

[0113] The instructions may include receiving the at least one parameter of the biological chemosensory array 204 as detected by the sensor and implementing one or more control operations according to the at least one parameter. For example, the one or more processors 216 may be configured to determine one or more neuronal response (voltage signal) from a biological chemosensory array 204, to determine a presence or absence of endometriosis or a probability of a presence or absence or endometriosis.

[0114] The neuron probe 203 may include any suitable sensors, such as a commercial Neuronexus 16-channel silicon probe (A2x2-tet-3mm-150-150-121) with impedances between 200 and 300 k . This sensor may be used to detect or determine one or more voltage signals from the biological chemosensory array 204.

[0115] In various implementations, the memory 214 may be configured to store an artificial intelligence algorithm. The one or more processors 216 may be configured to use the artificial intelligence algorithm to calculate at least one value based on a sensed or determined parameter, and control operation of the neuron probe 203, etc. The artificial intelligence algorithm may include any suitable algorithm, such as a machine learning model. For example, a machine learning model may be trained to predict a presence or absence of endometriosis or a probability of a presence or absence of endometriosis according to historical values. The historical values may indicate levels of voltage signals obtained from the biological chemosensory array 204 when it is exposed to test VOC mixtures and / or control VOC mixtures (e.g., endometriosis and nonendometriosis controls).

[0116] Any suitable machine learning models may be used, and the models may be trained in any suitable fashion. For example, historical data may be separated into training data and test data, where the training data is used to train the model, and the test data is used to test the model performance and prediction accuracy. Typically, the set of training data is selected to be larger than the set of test data, depending on the desired model development parameters. Separating a portion of the acquired data as test data allows for testing of the trained model against actual historical output data, to facilitate more accurate training and development of the model. This arrangement may allow the system to simulate an outcome of the machine learning prediction when it processes a new voltage signal, etc. in the future. The model may be trained using any suitable machine learning model techniques, including those described herein, such as randomforest logistic regression, decision tree (for example, a light gradient boosted free), and neural networks.

[0117] The trained model may be tested using the test data, and the results of the output data from the tested model may be compared to actual historical outputs of the test data, to determine a level of accuracy. The model results may be evaluated using any suitable machine learning model analysis, such as cumulative gain and lift charts. Lift is a measure of the effectiveness of a predictive model calculated as the ratio between the results obtained with and without the predicative model (for example, by comparing the tested model outputs to the actual outputs of the test data). Cumulative gains lift charts provide visual aids for measuring model performance. Both charts include a lift curve and a baseline, where a greater area between the lift curve and the base line indicates a stronger model.

[0118] After evaluating the model test results, the model may be deployed if the model test results are satisfactory. Deploying the model may include using the model to make predictions for a large-scale input dataset with unknown outputs, such as determining if a test VOC mixture is obtained from a biological sample from a subject with or without endometriosis. If the evaluation of the model test results is unsatisfactory, the model may be developed further using different parameters, using different modeling techniques, or using other model types.

[0119] One example machine learning model is a recurrent neural-network-based model, which may be to directly predict dependent variables without casting relationships between variables into mathematical formula. The neural network model includes a large number of virtual neurons operating in parallel and arranged in layers. The first layer is the input layer and received raw input data. Each successive layer modifies outputs from a preceding layer and sends them to a next later. The last layer is the output layer and produces output of the system.

[0120] In some embodiments, a convolutional neural network may be implemented. Similar to LSTM neural networks, convolutional neural networks include an input layer, a hidden layer, and an output layer. However, in a convolutional neural network, the output layer includes one fewer output than the number of neurons in the hidden layer and each neuron is connected to each output. Additionally, each input in the input layer is connected to each neuron in the hidden layer.

[0121] In various implementations, each input node in the input layer may be associated with a numerical value, which can be any real number. In each layer, each connection that departs from an input node has a weight associated with it, which can also be any real number. In the input layer, the number of neurons equals number of features (columns) in a dataset. The output layer may have multiple continuous outputs.

[0122] As mentioned above, the layers between the input and output layers are hidden layers. The number of hidden layers can be one or more (one hidden layer may be sufficient for many applications). A neural network with no hidden layers can represent linear separable functions or decisions. A neural network with one hidden layer can perform continuous mapping from one finite space to another. A neural network with two hidden layers can approximate any smooth mapping to any accuracy.

[0123] The number of neurons can be optimized. At the beginning of training, a network configuration is more likely to have excess nodes. Some of the nodes may be removed from the network during training that would not noticeably affect network performance. For example, nodes with weights approaching zero after training can be removed (this process is called pruning). The number of neurons can cause under-fitting (inability to adequately capture signals in dataset) or over-fitting (insufficient information to train all neurons; network performs well on training dataset but not on test dataset).

[0124] Various methods and criteria can be used to measure performance of a neural network model. For example, root mean squared error (RMSE) measures the average distance between observed values and model predictions. Coefficient of Determination (R2) measures correlation (not accuracy) between observed and predicted outcomes. This method may not be reliable if the data has a large variance. Other performance measures include irreducible noise, model bias, and model variance. A high model bias for a model indicates that the model is not able to capture true relationship between predictors and the outcome. Model variance may indicate whether a model is not stable (a slight perturbation in the data will significantly change the model fit).

[0125] Referring again to FIG. 16, the secondary storage 222 may include one or more disk drives or tape drives. The secondary storage 222 may be used for non-volatile storage of data and as an over-flow data storage device if RAM 220 is not large enough to hold all working data. The secondary storage 222 may be used to store programs which are loaded into RAM 220 when such programs are selected for execution.

[0126] In this embodiment, the secondary storage 222 has a processing component 222a comprising non-transitory instructions operative by the processor 216 to perform various operations of the methods of the present disclosure. The ROM 218 is used to store instructions and perhaps data which are read during program execution. The secondary storage 222, the memory 214, the RAM 220, and / or the ROM 218 may be referred to in some contexts as computer readable storage media and / or non-transitory computer readable media.

[0127] The optional I / O devices 224 may include printers, video monitors, liquid crystal displays (LCDs), plasma displays, touch screen displays, keyboards, keypads, switches, dials, mice, track balls, voice recognizers, card readers, paper tape readers, or other suitable input devices.

[0128] The network connectivity devices 212 may take the form of modems, modem banks, Ethernet cards, universal serial bus (USB) interface cards, serial interfaces, token ring cards, fiber distributed data interface (FDDI) cards, wireless local area network (WLAN) cards, radio transceiver cards. The devices 212 may promote radio communications using protocols, such as code division multiple access (CDMA), global system for mobile communications (GSM), longterm evolution (LTE), worldwide interoperability for microwave access (WiMAX), near field communications (NFC), radio frequency identity (RFID), and / or other air interface protocol radio transceiver cards, and other suitable network devices. These network connectivity devices 212 may enable the processor 216 to communicate with the Internet and / or one or more intranets. With such a network connection, it is contemplated that the processor 216 might receive information from the network, might output information to the network in the course of performing the abovedescribed method operations, etc. Such information, which is often represented as a sequence of instructions to be executed using processor 216, may be received from and outputted to the network, for example, in the form of a computer data signal embodied in a carrier wave.

[0129] The processor 216 executes instructions, codes, computer programs, scripts which it accesses from hard disk, floppy disk, optical disk (these various disk based systems may all be considered secondary storage 222), flash drive, memory 214, ROM 218, RAM 220, the network connectivity devices 212, etc. While only one processor 216 is shown, multiple processors may be present. Thus, while instructions may be discussed as executed by a processor, the instructions may be executed simultaneously, serially, or otherwise executed by one or multiple processors.

[0130] Although the system 202 is described with reference to a computing device, it should be appreciated that the system may be formed by two or more computing devices in communication with each other that collaborate to perform a task. For example, but not by way of limitation, an application may be partitioned in such a way as to permit concurrent and / or parallel processing of the instructions of the application. Alternatively, the data processed by the application may be partitioned in such a way as to permit concurrent and / or parallel processing of different portions of a dataset by the two or more computers.

[0131] In an embodiment, virtualization software may be employed by the system 202 to provide the functionality of a number of servers that is not directly bound to the number ofcomputers in the system 202. The functionality disclosed above may be provided by executing an application and / or applications in a cloud computing environment). Cloud computing may include providing computing services via a network connection using dynamically scalable computing resources. A cloud computing environment may be established by an enterprise and / or may be hired on an as-needed basis from a third party provider.

[0132] It is understood that by programming and / or loading executable instructions onto the system 202, at least one of the CPU 216, the memory 214, the ROM 218, and the RAM 220 are changed, transforming the system 202 in part into a specific purpose machine and / or apparatus having the novel functionality taught by the present disclosure. It is fundamental to the electrical engineering and software engineering arts that functionality that can be implemented by loading executable software into a computer can be converted to a hardware implementation by well- known design rules.

[0133] The foregoing description is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses. The broad teachings of the disclosure can be implemented in a variety of forms. Therefore, while this disclosure includes particular examples, the true scope of the disclosure should not be so limited since other modifications will become apparent upon a study of the drawings, the specification, and the following claims. It should be understood that one or more steps within a method may be executed in different order (or concurrently) without altering the principles of the present disclosure. Further, although each of the embodiments is described above as having certain features, any one or more of those features described with respect to any embodiment of the disclosure can be implemented in and / or combined with features of any of the other embodiments, even if that combination is not explicitly described. In other words, the described embodiments are not mutually exclusive, and permutations of one or more embodiments with one another remain within the scope of this disclosure.

[0134] Spatial and functional relationships between elements (for example, between modules) are described using various terms, including “connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitly described as being “direct,” when a relationship between first and second elements is described in the above disclosure, that relationship encompasses a direct relationship where no other intervening elements are present between the first and second elements, and also an indirect relationship where one or more intervening elements are present (either spatially or functionally) between the first and second elements. The phrase at least one of A, B, and C should be construed to mean a logical (A OR B OR C), using a non-exclusive logicalOR, and should not be construed to mean “at least one of A, at least one of B, and at least one of C.”

[0135] In the figures, the direction of an arrow, as indicated by the arrowhead, generally demonstrates the flow of information (such as data or instructions) that is of interest to the illustration. For example, when element A and element B exchange a variety of information but information transmitted from element A to element B is relevant to the illustration, the arrow may point from element A to element B. This unidirectional arrow does not imply that no other information is transmitted from element B to element A. Further, for information sent from element A to element B, element B may send requests for, or receipt acknowledgements of, the information to element A. The term subset does not necessarily require a proper subset. In other words, a first subset of a first set may be coextensive with (equal to) the first set.

[0136] In this application, including the definitions below, the term “module” or the term “controller” may be replaced with the term “circuit.” The term “module” may refer to, be part of, or include processor hardware (shared, dedicated, or group) that executes code and memory hardware (shared, dedicated, or group) that stores code executed by the processor hardware.

[0137] The module may include one or more interface circuits. In some examples, the interface circuit(s) may implement wired or wireless interfaces that connect to a local area network (LAN) or a wireless personal area network (WPAN). Examples of a LAN are Institute of Electrical and Electronics Engineers (IEEE) Standard 802.11-2016 (also known as the WIFI wireless networking standard) and IEEE Standard 802.3-2015 (also known as the ETHERNET wired networking standard). Examples of a WPAN are IEEE Standard 802.15.4 (including the ZIGBEE standard from the ZigBee Alliance) and, from the Bluetooth Special Interest Group (SIG), the BLUETOOTH wireless networking standard (including Core Specification versions 3.0, 4.0, 4.1, 4.2, 5.0, and 5.1 from the Bluetooth SIG).

[0138] The module may communicate with other modules using the interface circuit(s). Although the module may be depicted in the present disclosure as logically communicating directly with other modules, in various implementations the module may actually communicate via a communications system. The communications system includes physical and / or virtual networking equipment such as hubs, switches, routers, and gateways. In some implementations, the communications system connects to or traverses a wide area network (WAN) such as the Internet. For example, the communications system may include multiple LANs connected to each other over the Internet or point-to-point leased lines using technologies including Multiprotocol Label Switching (MPLS) and virtual private networks (VPNs).

[0139] In various implementations, the functionality of the module may be distributed among multiple modules that are connected via the communications system. For example, multiple modules may implement the same functionality distributed by a load balancing system. In a further example, the functionality of the module may be split between a server (also known as remote, or cloud) module and a client (or, user) module.

[0140] The term code, as used above, may include software, firmware, and / or microcode, and may refer to programs, routines, functions, classes, data structures, and / or objects. Shared processor hardware encompasses a single microprocessor that executes some or all code from multiple modules. Group processor hardware encompasses a microprocessor that, in combination with additional microprocessors, executes some or all code from one or more modules. References to multiple microprocessors encompass multiple microprocessors on discrete dies, multiple microprocessors on a single die, multiple cores of a single microprocessor, multiple threads of a single microprocessor, or a combination of the above.

[0141] Shared memory hardware encompasses a single memory device that stores some or all code from multiple modules. Group memory hardware encompasses a memory device that, in combination with other memory devices, stores some or all code from one or more modules.

[0142] The term memory hardware is a subset of the term computer-readable medium. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave); the term computer-readable medium is therefore considered tangible and non-transitory. Non-limiting examples of a non-transitory computer-readable medium are nonvolatile memory devices (such as a flash memory device, an erasable programmable read-only memory device, or a mask readonly memory device), volatile memory devices (such as a static random access memory device or a dynamic random access memory device), magnetic storage media (such as an analog or digital magnetic tape or a hard disk drive), and optical storage media (such as a CD, a DVD, or a Blu-ray Disc).

[0143] The apparatuses and methods described in this application may be partially or fully implemented by a special purpose computer created by configuring a general purpose computer to execute one or more particular functions embodied in computer programs. The functional blocks and flowchart elements described above serve as software specifications, which can be translated into the computer programs by the routine work of a skilled technician or programmer.

[0144] The computer programs include processor-executable instructions that are stored on at least one non-transitory computer-readable medium. The computer programs may also include orrely on stored data. The computer programs may encompass a basic input / output system (BIOS) that interacts with hardware of the special purpose computer, device drivers that interact with particular devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, etc.

[0145] The computer programs may include: (i) descriptive text to be parsed, such as HTML (hypertext markup language), XML (extensible markup language), or JSON (JavaScript Object Notation), (ii) assembly code, (iii) object code generated from source code by a compiler, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. As examples only, source code may be written using syntax from languages including C, C++, C#, Objective C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, JavaScript®, HTML5 (Hypertext Markup Language 5th revision), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, MATLAB, SIMULINK, and Python®.EXAMPLES

[0146] The following examples are merely illustrative, and do not limit this disclosure in any way.EXAMPLE 1: Endometriotic vs. healthy emitted cell culture gas mixtures elicit distinct neural responses in the locust antennal lobe

[0147] Locust antennal lobe neural response patterns to endometriotic and healthy emitted cell culture gas mixtures were investigated. Volatile chemical gas mixtures contained within the cell culture flasks were delivered to the locust antenna via an olfactometer system and neural responses were recorded from the locust antennal lobe (FIG. 1).

[0148] Cell culture headspace delivery. A commercial olfactometer (Aurora Scientific, 220A) was used for precision cell culture headspace stimulus delivery (FIG. 1). At the start of each set of trials, 200 standard cubic centimeters per minute (seem) of zero contaminant air was passed through the air flow line via a 1 / 16 in. diameter PTFE flow line to the locust antenna demoted the stimulus flow line. The end of the stimulus flow line was placed approximately 2-3 cm from the most distal antennal segment. An additional 200 seem of zero contaminant air was passed through a separate flow line to the exhaust denoted the dilution flow line. Five seconds before odor stimulus delivery, 40% (80 seem) of the dilution flow line was redirected through the cell culture flow line directly upstream of the cell culture flasks. The dilution flow line and the cell cultureflow line joined downstream of the cell culture flasks. This allowed for the complete mixing of the 80 seem cell culture headspace flow with the 120 seem of the dilution flow line’s clean air. The air-volatiles mixture primed the line with volatiles up to the final valve, where the combined cell culture headspace + dilution flow was delivered to the exhaust. Upon stimulus onset, the final valve redirected the clean air flow to the exhaust and the cell culture headspace + dilution flow to the locust antenna via the stimulus flow line. After 4 s of constant flow and stimulus delivery, the final valve redirected the clean air flow back to the locust antenna via the stimulus flow line, alleviating potential headspace gas depletion during the cell culture headspace delivery. This protocol was designed to keep a constant flow rate through the stimulus flow line, thereby eliminating any potentially confounding neuronal responses due to changes in air pressure via mechanosensory detection. A 6-inch diameter funnel pulling a slight vacuum was positioned immediately behind the locust the locust during cell culture headspace stimulus delivery to ensure rapid removal of volatiles. Each 4 second-duration stimulus was repeated 5 times with an interstimulus interval of 60 seconds. The order of the stimuli was pseudorandomized for each experiment.

[0149] Electrophysiology. All neural recordings were performed on post-fifth instar locust (Schistocerca americana) of either sex raised in a crowded colony. For in vivo extracellular neural recordings, locusts were immobilized on a surgical platform and antennae were stabilized. A batik wax bowl around the head was constructed to isolate the region and then filled with a room temperature, physiologically balanced locust saline solution. The removal of the exoskeleton between the antennae was performed and glandular tissue was removed until the brain was fully visible. Treatment with protease was done to remove the neural sheath on the antennal lobes. Following surgery, the animals were placed in a Faraday cage isolated bench and a silver-chloride ground wire was placed in the saline bath. A commercial Neuronexus 16-channel silicon probe (A2x2-tet-3mm-150-150-121) with impedances between 200 and 300 k was inserted into the antennal lobe (FIG. 1) for all neural recordings. Voltage signals were sampled at 20 kHz and then digitized using an Intan pre-amplifier board (C3334 RHD 32-channel head stage). The digitized signals were transmitted to the Intan recording controller (C3100 RHD USB interface board) before being visualized and stored using the Intan graphical user interface and EabView data acquisition system.

[0150] Two endometriotic cell lines (12Z and iEc-ESC) and two healthy cell lines (H1657- EEC and H1644-ESC), with one epithelial and one stromal cell type for each, were grown in identical cell culture medium after seeding each cell line at the same initial cell number (FIG. 2). All cells were cultured at 37°C 5% CO2. 12z cells were maintained in DMEM / F12 mediasupplemented with 10% charcoal stripped fetal bovine serum, 1% penicillin / streptomycin, and 0.1% sodium pyruvate. H1644 iESC and iEcESC were cultured in DMEM / F12 media supplemented with 10% charcoal stripped fetal bovine serum, 1% penicillin / streptomycin, 0.1% sodium pyruvate, and lOug / mL hygromycin B. H1657 iEEC were cultured in DMEM / F12 media supplemented with 2% charcoal stripped fetal bovine serum, 1% penicillin / streptomycin, and 0.1% sodium pyruvate. All cell lines were grown T75 flasks until about 90-100% confluency.

[0151] Each of the cell lines were plated in their own individual T25 flask. All cell lines were trysinized using 2x Trypsin-EDTA (0.5%) to lift cells from the bottom of the T75 flasks. Cells were counted using Invtrogen Countess II Cell Counter and plated at 3xl05cells / flask (6xl04cells / mL) in the T25 flask. The cells were cultured in 5mL DMEM / F12 media supplemented with 10% charcoal stripped fetal bovine serum, 1% penicillin / streptomycin, and 0.1% sodium pyruvate for 72hrs. A T25 flask containing 5mL DMEM / F12 media supplemented with 10% charcoal stripped fetal bovine serum, 1% penicillin / streptomycin, and 0.1% sodium pyruvate and no cells was also prepared at the same time and maintained at the same time as the flasks containing cells.

[0152] All four cell lines were grown in individual airtight flasks for 72 hours. Volatile chemical gas mixtures emitted from the cell culture were delivered in precise amounts to the locust antenna for 4 seconds. Simultaneously, in vivo extracellular neural recordings were obtained from the locust antennal lobe.

[0153] Volatile chemical gas mixtures emitted from each of the cell cultures and media elicited changes in the neural spiking responses in the locust antennal lobe. Distinct VOC-evoked neural responses were observed to each of the cell lines and media as displayed in the voltage traces (FIG. 3). From the voltage traces shown in FIG. 3, individual neurons were obtained via spike sorting and observed the firing rate by plotting peri-stimulus time histograms and raster plots.

[0154] Spike Sorting'. All neural data was imported into MATLAB after high pass filtering using a 300Hz Butterworth filter to eliminate frequencies below 300 Hz. The data was analyzed by custom-written codes in MATLAB R2023b. All data was processed with Igor Pro for spike sorting analysis using previously described methods (Pouzat, Mazor et al. 2002). Spiking events were identified using a detection threshold between 2.5 and 3.5 standard deviation (SD) of baseline fluctuations. Individual projection neurons were identified if they passed the following criteria: cluster separation > 5 SD, inter-spike intervals (ISI) < 10%, and spike waveform variance < 10%.

[0155] Root mean squared (RMS) transformation'. All neural data was imported into MATLAB after high pass filtering using a 300Hz Butterworth filter to eliminate frequencies below 300 Hz. The filtered data was trimmed to the time window of interest and all data were passed through a 500-point continuous moving root mean squared (RMS) filter followed by a smoothing step via a 500-point continuous moving average filter. Stimulus-specific baseline values were calculated as the average voltage over all time bins for the 2 seconds prior to the stimulus onset. Baseline responses were averaged over all trials and subsequently subtracted from the data to obtain the change in root mean squared values. These values were binned into nonoverlapping 50 millisecond bins and the average of each bin was computed. For each recording location, root mean squared transformed voltage data of each 4-channel tetrode were averaged together.

[0156] The representative neuron displayed a unique and distinct response to each of the cell lines and media (FIG. 4). For example, the neuron shown in FIG. 4, the 12Z endometriotic epithelial cell line showed a high spiking frequency with the onset of the emitted cell culture gas mixture stimulus while H1657-EEC (healthy epithelial cell line) had a smaller spiking frequency. Thus, it was determined that individual neurons display distinct cell culture headspace-evoked neural responses to endometriotic and healthy cell lines. The ability of a single neuron to have varied responses to different cell lines showcases the differentiation of each volatile gas mixtures emitted from various cell lines at the individual neuron level.EXAMPLE 2: Spatiotemporal neural responses at the population level can be leveraged to differentiate and classify endometriotic vs healthy emitted cell culture gas mixtures

[0157] It is presumed that in the locust antennal lobe, the identity of an odor is not encoded by a single neuron but by a population of neurons via spatiotemporal neural responses (Stopfer, Jayaraman et al. 2003, Mazor and Laurent 2005, Broome, Jayaraman et al. 2006, Saha, Leong et al. 2013, Saha, Mehta et al. 2020). Therefore, next, a population of antennal lobe projections neurons and their responses to each of the cell line emitted gas mixtures was examined over a specific time window showing the evolution of the population response over time. Using dimensionality reduction techniques such as principal component analysis (PCA) and linear discriminant analysis (LDA) the data from a high dimensional dataset (36 dimensions) wasreduced to a low dimensional subspace to visualize the population neural responses in a 3- dimensional PCA or LDA space (FIG. 5A and FIG. 7B).

[0158] Dimensionality reduction analyses'. Two methods of dimensionality reduction were performed - Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In PCA, baseline subtracted, spike sorted or RMS transformed neural signals were binned in 50 millisecond non-overlapping time bins and averaged across trials (n = 5, each cell culture was repeated 5 times with a 60 second inter- stimulus interval). Stimulus-specific baseline values were calculated as the average voltage over all time bins for the 2 seconds prior to the stimulus onset. The neural responses were pooled across electrophysiological experiments to generate a matrix, where each element in the matrix corresponds to the neural responses of one location or neuron in one 50 millisecond time bin. Similar neural population time-series data matrices were generated for each cell culture and media stimulus. PCA dimensionality reduction analysis was performed on the time-series data involving all cell cultures including media control and directions of maximum variance were found. The consequent high-dimensional vector in each time bin was projected along the eigenvectors of the covariance matrix. Only the three dimensions with the highest eigenvalues were used for visualization and data points in subsequent time bins were connected to produce low-dimensional neural trajectories. The trajectories were smoothed using a third order IIR Butterworth filter (Half Power Frequency = 0.15). Lastly, all trajectories were shifted to begin at the origin to analyze stimulus- specific response dynamics and trajectory divergence. For LDA, the same neural population time-series data matrix was used. Here, the separation between interclass distances was maximized while minimizing the within class distances. For visualization purposes, time bins were plotted as unique points in this transformed LDA space and stimulus- specific clusters became noticeable. All dimensionality reduction analyses were accomplished using custom written MATLAB (R2023b) codes.

[0159] Using these techniques, unique and separate trajectories can be qualitatively seen in 3-dimensional PCA subspace that represent the population neural responses to each of the cell lines and media over 1.25 seconds during the odor stimulus. The angles in which these trajectories move in different directions signify that these cell lines are eliciting unique and distinct responses in the neuronal population (FIG. 5A). Furthermore, using LDA, it was observed that distinct clusters of 50 millisecond time bins (25 total time bins for each cell line) displaying that the population neural responses were able to differentiate each of the cell lines and media tested (FIG. 5B).

[0160] Next, the classification success of each of the cell lines and media was predicted using a high dimensional, leave-one-trial-out (LOTO) analysis (for a schematic of leave-one-trial-out quantitative analysis, see FIG. 7A-C, FIG. 8, and FIG. 9A-B) based on 50 millisecond time bins or 1.25 second trials in high dimensional space (36 dimensions).

[0161] Quantitative classification analysis: leave-one-trial-out: To achieve a quantitative estimate of classification performance, a leave-one-trial-out analysis was performed (FIG. 5A-B and FIG. 6A-B). During each iteration, population neural response time-series data from one trial was used as the test data and the remaining four trials were used to train a linear classifier. The linear algorithm generated a model based on the training data set within the original high dimensional encoding state space to effectively classify testing data. By considering time bins as points in a high-dimensional space, an average response vector was calculated for the training data corresponding to each stimulus. These neural templates were then used to classify individual time bins (50 millisecond duration) of each testing data set. The minimal Euclidean or Manhattan distances between each point corresponding to a time bin of the testing data and the previously calculated average response vectors for the training data were used to assign class identities. The Euclidean (L2) or the Manhattan (L1) norm was used to quantify classifier predictability. This type of analysis is termed a bin-wise classification. Furthermore, a winner-take-all approach, was also incorporated to calculate the most likely predicted class for each trial. This was performed by considering the mode of all predicted time bins as the trial-wise class identifier. Model performance was illustrated using a confusion matrix, which compared the predicted responses to the true class labels. For example, a fully diagonal matrix indicates 100% classification accuracy. Quantitative classification analysis was performed using custom written MATLAB (R2020a) codes.

[0162] To achieve this, training templates were created that consisted of the average spiking events of 50 millisecond time bins across four trials with the 5th trial being left out to be used as a test template for each stimulus (FIG. 7A-C). This resulted in 5 training templates and 5 testing templates due to the 4 cell lines and media used in this study. Both the average of the four trials (training template) and the 5th trial (testing template) consists of 50 millisecond time bins over a specified duration of stimulus exposure time. Neural responses were segregated in 50 millisecond time bins due the 20 Hz local field oscillation present in the locust antennal lobe (Stopfer, Bhagavan et al. 1997, Stopfer and Laurent 1999, Perez-Orive, Mazor et al. 2002). The averaged 50 millisecond time bins from the training template were used to classify the 50 millisecond time bins from the test template in a time binned-matched manner by considering each time bin as a point in the high dimensional space (FIG. 8). The Euclidean distances between each test time binand all 5 training time bins were calculated with the test time bin being assigned to the cell line or media with the associated closest training time bin. This was repeated for the total number of time bins (1.25 seconds = 25-time bins, 50 milliseconds each) and cycled through each of the 5 trials so that each trial (1-5) will have the chance of being the trial that is left out (or test trial). In this way, test template (trials 1-5) for each cell line and media were classified in a bin-wise manner and summarized the percentages of time bins classified for each biomarker in a confusion matrix (FIG. 6A and FIG. 9A). Using this method, it was observed that most of the time bins were classified correctly as indicated by the high diagonal values in the confusion matrix as shown in FIG. 6A with an accuracy of 42.88%. Then, the entire test trial was classified by taking the mode of all of the 50 millisecond time bin classifications in a single trial (FIG. 8) and assigning the whole trial to the stimulus associated with the mode of the time bin classification as shown in FIG. 6B. Thus, it was determined that spatiotemporal population neural responses distinguish and classify emitted endometriotic and healthy cell culture gas mixtures. The trial-wise classification results achieved 96% accuracy. To attain this high accuracy, experimental data from several electrophysiological recordings (following the “ Electrophysiology” methods described in EXAMPLE 1) were combined.EXAMPLE 3: Endometriotic vs. healthy emitted cell culture gas mixtures at multiple time points elicit distinct and tuned neural responses in the locust antennal lobe

[0163] It was next investigated whether individual neurons would elicit distinct responses to the same cell lines but at multiple time points. Two endometriotic cell lines (12Z and iEc-ESC) and two healthy cell lines (H1657-EEC and H1644-ESC), were grown in identical cell culture medium after seeding each cell line at the two different time points (24 and 72 hour) before electrophysiological recordings with the same initial cell number (FIG. 10). All eight cell lines were grown in individual airtight flasks for either 24 or 72 hours. Volatile chemical gas mixtures emitted from the cell culture were delivered in precise amounts to the locust antenna for 4 seconds following the ”Cell culture headspace delivery” methods described in EXAMPLE 1. Simultaneously, in vivo extracellular neural recordings were obtained from the locust antennal lobe. Volatile chemical gas mixtures emitted from each of the cell cultures at the two time points and media elicited changes in the neural spiking responses in the locust antennal lobe as shown in FIG. 11. Two representative spike sorted neurons (following the “ Spike Sorting” methods described in EXAMPLE 1) are shown in FIG. 11. Representative neuron 1, shows higher spiking responses to the healthy cell lines regardless of the time point in comparison to the endometrioticcell lines indicating that neuron is “tuned” towards the volatile chemicals emitted from the healthy cell lines. In comparison representative neuron 2, shows higher spiking responses to the 72-hour time point for all cell lines in comparison to the 24-hour time point indicating that this neuron is “tuned” towards the concentration of volatiles emitted from the 72-hour time point regardless if the cell line is healthy or endometriotic. Both of these neurons show the importance of recording from a population of neurons in the antennal lobe and how these neurons display district and tuned neural responses to volatile chemical gas mixtures. Thus, it was determined that individual neurons display distinct and tuned responses to endometriotic and healthy cell culture emitted gas mixtures at multiple time points.EXAMPLE 4: Multiple time points can be differentiated and classified by the spatiotemporal neural responses to endometriotic vs healthy cell culture emitted gas mixtures

[0164] It was anticipated that based on the distinct and tuned neural responses to the emitted volatile chemical gas mixtures corresponding to each cell line at multiple timepoints, a populationbased analysis can be utilized to differentiate and classify each cell line. To investigate this, the dimensionality reduction technique, linear discriminant analysis (LDA), was used to reduce the total dimensionality of the data (36 dimensions) down to 3 and visualized the population responses in 3-dimensional LDA subspace (FIG. 12). This allowed us to begin to see the separation between clusters for each of the cell lines in particular for the 12Z, H1657-EEC, and H1644-ESC cell lines separating out towards the top left, top right and bottom right, respectively, of the LDA subspace (FIG. 12). This separation can be seen regardless of the time point (24 hour or 72 hour) for each of these three cell lines. Next, it was investigated whether all of the cell lines at the multiple timepoints could be classified in high dimensional space using the quantitative leave-one-trial-out (LOTO) analysis. In the bin-wise confusion matrix, an accuracy of 41.33% was seen (FIG. 13A), however in the trial-wise confusion matrix, an accuracy of 88.89% was seen (FIG. 13B). Thus, it was determined that spatiotemporal population neural responses distinguish and classify emitted endometriotic and healthy cell culture gas mixtures at multiple time points.

[0165] This reiterates the importance of spatiotemporal responses in the identification of odors in the antennal lobe and how those temporal responses can be utilized for the classification of volatile chemical gas mixtures via computational methods. Overall, by combining the neural responses across experiments, population neural response templates were obtained and used to classify unknown gas mixtures to achieve high classification accuracy. This innovative insect-olfaction based disease detection approach leverages an entire biological olfactory system for the detection of endometriosis.EXAMPLE 5: Co-culture of endometriotic and healthy cell cultures can be distinguished and classified by the locust olfactory system

[0166] Next, whether combining the healthy and endometriotic cell culture within a single flask at different ratios could be distinguished by the insect-based sensor was investigated. To do this endometriotic and healthy epithelial cell were co-cultured at different ratios (0% - 100%) within the same flasks and delivered the emitted cell culture gas mixtures to the locust antenna (following the “Cell culture headspace delivery” methods described in EXAMPLE 1) for electrophysiological recordings from the antennal lobe.

[0167] The 12z RFP cells and H1657 iEEC were trysinized using 2x Trypsin-EDTA (0.5%) to lift cells from the bottom of the T75 flasks. Cells were counted using Invtrogen Countess Cell II Counter. The cells were combined at 25%, 50%, 75% concentration with a total cell density at 3xl05cells / flask (6xl04cells / mL) in the T25 flask. A flask containing only 12z RFP cells and a flask containing only Hl 657 iEEC were prepared at the same time. The cells were cultured in 5mL DMEM / F12 media supplemented with 10% charcoal stripped fetal bovine serum, 1% penicillin / streptomycin, and 0.1% sodium pyruvate for 72 hours. A T25 flask containing 5mL DMEM / F12 media supplemented with 10% charcoal stripped fetal bovine serum, 1% penicillin / streptomycin, and 0.1% sodium pyruvate and no cells was also prepared at the same time and maintained at the same time as the flasks containing cells. After 72 hours cells were imaged and transported to the Saha lab for analysis.

[0168] At the population level, visualizing the spatiotemporal neural responses in PCA and LDA (following the “Dimensionality reduction analyses” methods described in EXAMPLE 2) subspace, distinct neural trajectories and clusters, respectively, are gain seen, indicating that the population of antennal lobe projection neurons and their responses can differentiate each of the cell co-culture ratios (FIG. 14A-B). Using a quantitative high dimensional LOTO analysis (as described in the “Quantitative classification analysis: leave-one-trial-out” methods described in EXAMPLE 2), a 38.95% accuracy was obtained in the bin-wise confusion matrix while an accuracy of 85% was obtained (FIG. 15A-B). Overall, by combining the neural responses across experiments, population neural response templates were obtained and used to classify unknown gas mixtures to achieve high classification accuracy for various cell culture timepoints and cocultures. Thus, it was determined that Spatiotemporal population neural responses distinguish andclassify emitted endometriotic and healthy cell co-culture gas mixtures. This innovative approach harnesses leverages a biological olfactory system for the detection of endometriosis via volatile gas mixture analysis and lays the foundation for a novel detection tool to aid in the diagnosis of this complex disease.EXAMPLE 6: Detecting endometriosis vs non-endometriosis in samples obtained from mice

[0169] Mice are maintained in a designated laboratory animal care facility. Pgr cre / + Rosa 26 mT / mG mouse models are used. This mouse has a double-fluorescent Cre reporter and has the ability to express membrane-targeted tandem dimer Tomato (mT) prior to Cre-mediated excision and membrane-targeted green fluorescent protein (mG) after excision (FIG. 17). Six- to eight- week-old mice are injected with estradiol (E2) (O.lpg / mouse) every 24 hours for 3 days, and then surgical induction of endometriosis is performed. Endometriotic lesions are established by inoculating endometrial tissue into the peritoneal cavity. To access the peritoneal cavity, mice undergo a laparotomy under anesthesia and a midventral incision (1 cm) is performed to expose the uterus and intestine. The left uterine horn is removed and placed in a petri dish containing sterile PBS. The uterine horn is opened longitudinally and then cut into small fragments. The fragments suspended in 0.5 mL sterile PBS are injected into the peritoneal cavity of the same mouse from which the uterus is taken for an autologous implantation, and the abdominal cavity is gently massaged to disperse the tissue. The abdominal incision and wound are closed with sutures and skin is closed with surgical wound clips, respectively. After a designated time (15 days, 1 month and 3 months) the animals are restrained using the scruffing method. The scruffing method refers to a method of restraining an animal by firmly grasping the loose skin the at the back of its neck, between the thumb and the forefinger, to create a “tent” of skin to hold the animal still. Such method allows for procedures such as injections and / or examinations. To collect urine, the mouse is held over a Petri dish and the belly of the animal is lightly stroked to stimulate the bladder. Urine is collected into a 1.5mL microcentrifuge tube. Any urine that collected on the petri dish is collected using a 200uL pipet and added to the sample in the microcentrifuge tube. To increase volume samples, urine is collected for 3 days prior to endometriosis induction as well as for 3 days prior to tissue collection. Samples are either stored for 24 hours at 4°C and then stored at - 80°C until analysis or samples are stored at -80°C until analysis.

[0170] Experimental methods as described in EXAMPLES 1-5 using cell culture may be used for detecting a presence or absence of endometriosis in biological samples obtained from mice. Inother words, the cell cultures used in EXAMPLES 1-5 may be replaced with one or more biological sample obtained from one or more mice.EXAMPLE 7: Detecting endometriosis vs non-endometriosis in samples obtained from baboons

[0171] Endometriosis is experimentally induced in female baboons (Papio anubi.s) by intraperitoneal inoculation (i.p.) with menstrual tissue on two consecutive cycles. Menstrual tissue is inserted via an endometrial suction curette (e.g., PIPELLE®) though the cervix. On the second day of visible menses, the menstrual tissue present in the uterine cavity is aspirated. Six endometrial suction curettes of menstrual tissue are deposited into the peritoneal cavity of the baboons via laparoscopic guidance. This is done on two consecutive menstrual cycles (FIG. 18). In the cycle before the induction of endometriosis and prior to surgery to harvest control eutopic endometrium, a urinary catheter is inserted into the bladder and urine is withdrawn with a 10-cc syringe, which is immediately aliquoted and frozen at -80°C on day 10 post ovulation. Endometriosis is then induced in the animals by intraperitoneal inoculation (i.p.) of autologous menstrual tissue on 2 consecutive cycles. Following laparoscopic confirmation of endometriosis at the second inoculation, the animals are sampled at 3-month intervals post inoculation and euthanized at 15 months as required by the Institutional Animal Care and Use Committee. Prior to each surgery a urinary catheter is inserted into the bladder of the sedated animal and urine aspirated and stored in aliquots at -80°C.

[0172] Experimental methods as described in EXAMPLES 1-5 using cell culture may be used for detecting a presence or absence of endometriosis in biological samples obtained from baboons. In other words, the cell cultures used in EXAMPLES 1-5 may be replaced with one or more biological sample obtained from one or more baboon.

Claims

WHAT IS CLAIMED IS:

1. A method of detecting a presence or absence of endometriosis, the method comprising: exposing a biological chemosensory array to a test volatile organic compound (VOC) mixture; obtaining a test neuronal response from the biological chemosensory array; comparing the test neuronal response to one or more control neuronal response obtained from one or more endometriosis control VOC mixture and one or more non-endometriosis control VOC mixture; and outputting a result based on the comparison, wherein the result indicates a presence or absence of endometriosis or a probability of a presence or absence of endometriosis.

2. The method of claim 1, wherein at least one VOC mixture is a VOC gas mixture.

3. The method of claim 1 or claim 2, wherein the VOC mixture is emitted from one or more biological sample.

4. The method of claim 3, wherein the biological sample is breath, urine, sweat, or blood.

5. The method of claim 3 or claim 4, wherein the biological sample is obtained from a mouse, primate, or human.

6. The method of any one of the previous claims, wherein the biological chemosensory array is one or more insect antenna.

7. The method of claim 6, wherein at least one neuronal response is obtained from an insect antennal lobe.

8. The method of claim 6 or claim 7, wherein the insect belongs to the order Orthoptera, Hymenop tera, or Diptera.

9. The method of claim 8, wherein the insect belonging to the order Orthoptera is a locust.

10. The method of claim 8, wherein the insect belonging to the order Hymenoptera is a honeybee or an ant.

11. The method of claim 8, wherein the insect belonging to the order Diptera is a fruit fly.

12. The method of any one of claims 6-10, wherein the insect has at least about 100 olfactory receptors.

13. The method of any one of the previous claims, wherein at least one neuronal response is an extracellular neuronal voltage signal.

14. The method of any one of the previous claims, wherein the neuronal response is obtained from an insect in vivo.

15. A system for detecting the presence or absence of endometriosis, the system comprising: an odor stimulus delivery component for delivering a test volatile organic compound (VOC) mixture to one or more biological chemosensory array; wherein the one or more biological chemosensory array is stabilized by a stabilizing component; a neuron probe for detecting one or more test neuronal response from the one or more biological chemosensory array; and at least one processor which stores the one or more test neuronal response in memory, wherein the test neuronal response is one or more neuronal voltage signal.

16. The system of claim 15, wherein at least one VOC mixture is a VOC gas mixture.

17. The system of claim 15 or claim 16, wherein the VOC mixture is emitted from one or more biological sample.

18. The system of claim 17, wherein the biological sample is breath, urine, sweat, or blood.

19. The system of claim 17 or claim 18, wherein the biological sample is obtained from a mouse, primate, or human.

20. The system of any one of claims 15-19, wherein the biological chemosensory array is one or more insect antenna.

21. The system of claim 20, wherein at least one neuronal response is obtained from an insect antennal lobe.

22. The system of claim 20 or claim 21, wherein the insect belongs to the order Orthoptera, Hymenop tera, or Diptera.

23. The system of claim 22, wherein the insect belonging to the order Orthoptera is a locust.

24. The system of claim 22, wherein the insect belonging to the order Hymenoptera is a honeybee or an ant.

25. The system of claim 22, wherein the insect belonging to the order Diptera is a fruit fly.

26. The system of any one of claims 20-24, wherein the insect has at least about 100 olfactory receptors.

27. The system of any one of claims 15-26, wherein the neuronal response is obtained from an insect in vivo.

28. The system of any one of claims 15-27, for use in the diagnosis, prevention, and / or treatment of endometriosis.

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