Multi-step diagnostic method using handheld PCR

Abstract

Presented are computer-based systems and methods in which digital data from a subject is used to provide an initial diagnosis, and in which a polymerase chain reaction (PCR)-based assay is performed at the point of treatment to confirm and refine the initial diagnosis. Advantageously, the polymerase chain reaction (PCR)-based assay is configured as a hand-held, portable device, and uses a plurality of nucleic acid primers selected according to the initial diagnosis. The primers are further selected to enable refinement of the initial diagnosis, thereby enabling generation of a treatment plan and assisting in appropriate treatment at the point of treatment or point of use.

Description

[0001] This application claims priority to our co-pending U.S. provisional patent applications filed October 17, 2023, serial number 63 / 591,045 and November 20, 2023, serial number 63 / 601,127, both of which are incorporated herein by reference. Technical Field

[0002] The field of this invention is devices and methods for confirming and refining initial findings based on digital data using additional digital data from devices at testing or medical points, and particularly when it relates to handheld PCR devices. Background Technology

[0003] The background description includes information that may be used to understand the invention. No information provided herein is acknowledged to be prior art or related to the currently claimed invention, nor is any publication specifically or implicitly referenced considered prior art.

[0004] All publications and patent applications herein are incorporated by reference to the same extent that each individual publication or patent application is specifically and individually indicated to be incorporated by reference. If a definition or use of a term in an incorporated reference is inconsistent with or contradicts the definition of that term provided herein, the definition provided herein shall apply, and the definition in the incorporated reference shall not apply.

[0005] Differential diagnosis has been used for a long time and is typically performed in the physician's office, usually after the physician reviews the patient's medical records and / or test data from blood and / or radiographic tests. Recently, with the advent of computer-aided medicine, computer algorithms can be used to evaluate patient data to suggest possible conditions and, in some cases, treatment options. Indeed, in the case of genomic or transcriptomic testing, computer algorithms are essential for processing massive amounts of data. However, such analyses often do not utilize previously retained patient records or lack analytical depth or contextual awareness, making it impossible to determine appropriate treatment options. For example, when a patient with long-term diabetes presents with diabetic foot ulcers and has previously been treated with an unsuccessful antibiotic, image analysis or other existing digital data alone cannot inform the physician of appropriate treatment options. Therefore, incorrect medications may be administered, prolonging the patient's risk and suffering and delaying appropriate care. On the other hand, when physicians assess diabetic lesions at the point of treatment, immediate analysis is not possible in all or almost all cases, and follow-up tests must be scheduled for a correct diagnosis. These and other difficulties can be further exacerbated when a patient changes healthcare providers and has not yet provided records to the new provider. In almost all of these cases, the patient's medical history is provided only in electronic format and is usually just an incomplete account from the patient. This is especially true where previous medical records included a large amount of clinical data.

[0006] Therefore, even though various systems and methods for medical diagnosis are known in the art, all or almost all of these systems and methods have several drawbacks. Therefore, there remains a need for compositions and methods for improving diagnostic methods, particularly where such methods enable medical point testing or the use of point testing to achieve accurate diagnosis and treatment. Summary of the Invention

[0007] The subject matter of this invention relates to various computer-based systems and methods in which digital data from subjects are used to provide an initial diagnosis, and in which polymerase chain reaction (PCR)-based assays are performed at the point of use or treatment to confirm and refine the initial diagnosis. Advantageously, the PCR-based assays use a variety of nucleic acid primers selected according to the initial diagnosis, and in which the primers are further selected to enable the refinement of the initial diagnosis, thereby enabling the generation of a treatment plan and facilitating appropriate treatment at the point of use or treatment.

[0008] In one aspect of the subject matter of this invention, the inventors envision a computer-aided method for converting diagnostic data into clinically actionable patient data based on polymerase chain reaction (PCR), the method comprising the steps of: (a) obtaining digital diagnostic data from a patient via at least one processor; (b) processing the digital diagnostic data via an implementation of an algorithm, wherein the algorithm probabilistically maps the patient's digital diagnostic data to a first diagnosis; (c) performing spectral analysis on a biosample from the patient on a handheld portable PCR array by determining the expression levels of clinically significant genes, wherein the PCR array includes unique nucleic acid primers for assessing the specific expression of multiple genes associated with the first diagnosis; (d) mapping these expression levels to a genetically informed second diagnosis for the patient via the at least one processor; and (e) providing the genetically informed second diagnosis for the patient to a computer device.

[0009] Most typically, the primary diagnosis will involve an infectious disease, and therefore can involve a bacterial infection, viral infection, fungal infection, or parasitic infection. For example, the primary diagnosis could involve a urinary tract infection or a wound infection. In another example, the primary diagnosis could also involve resistance to treatment for an infectious disease, such as resistance to treatment for a bacterial infection, viral infection, fungal infection, or parasitic infection. Therefore, the primary diagnosis could also involve resistance to antibiotic treatment. In yet another example, the primary diagnosis could involve cancer and / or could include the determination of the type of cancer (e.g., via a diagnostic procedure).

[0010] Depending on the type of diagnosis, the digital diagnostic data can be processed via a digital pathology platform to obtain the primary diagnosis. Most typically, the algorithms used to process the digital diagnostic data can include machine learning algorithms or inference algorithms executed by an inference engine. Therefore, the envisioned digital diagnostic data can include whole-genome data and / or transcriptome sequencing data, or data derived from culturing biological samples of the patient (e.g., urine samples, blood samples, respiratory samples, mucosal samples, or tissue biopsies). In other embodiments, the digital diagnostic data can be derived from radiographic images.

[0011] The method is also envisioned to include steps such as generating a treatment plan at the treatment site based on a second diagnosis, and most typically, administering medication (e.g., antibiotics or chemotherapy drugs) based on that treatment plan.

[0012] In another aspect of the subject matter of this invention, the inventors also envision a computer-aided method for converting polymerase chain reaction (PCR)-based diagnostic data into clinically actionable patient data, the method comprising the steps of: (a) obtaining PCR-based digital diagnostic data from a patient via at least one processor, wherein the PCR-based diagnostic data includes corresponding nucleic acid expression levels of a plurality of genes in a gene array, and wherein the selection of genes in the array is determined by a preliminary first diagnosis; (b) processing the PCR-based digital diagnostic data via an implementation of an algorithm, wherein the algorithm probabilistically maps the PCR-based digital diagnostic data to a second diagnosis; (c) mapping the PCR-based digital diagnostic data to at least a machine learning or artificial intelligence (AI)-based third diagnosis for the patient via the at least one processor and using the second diagnosis; and (d) providing the machine learning or AI-based third diagnosis for the patient to a computer device.

[0013] In a preferred embodiment, PCR-based digital data can be obtained by spectroscopic analysis of a biological sample from the patient on a handheld portable PCR array, and / or the preliminary first diagnosis is derived from digital histopathology. The third diagnosis can then be derived from an inference engine. In another embodiment, the preliminary first diagnosis may be derived from radiographic images or by culturing a biological sample from the patient (e.g., urine, blood, respiratory, mucosal, or tissue biopsy). Additionally, it is envisioned that the preliminary first diagnosis is derived from whole-genome sequencing or transcriptome sequencing, and / or the second and / or third diagnosis includes determination of the patient's antibiotic resistance, chemotherapy resistance, or antifungal resistance. In yet another embodiment, the preliminary first diagnosis may include determination of the cancer type (e.g., via a treatment diagnostic procedure).

[0014] In a further envisioned embodiment, the method may also include the step of generating a treatment plan at the treatment site based on the third diagnosis. Typically, but not necessarily, the algorithm used to process PCR-based digital diagnostic data to form a second diagnosis via a digital pathology platform, and / or the algorithm for processing PCR-based digital diagnostic data, may include a machine learning algorithm or an inference algorithm executed by an inference engine. In yet another aspect of the envisioned method, the plurality of genes in the gene array may be selected from the group consisting of: genes associated with cancer, genes associated with viral infections, genes associated with bacterial infections, genes associated with fungal infections, genes associated with parasitic infections, and genes associated with cardiology.

[0015] In another aspect of the subject matter of this invention, the inventors envision a computer-aided method for converting water or food test data into operable data based on polymerase chain reaction (PCR), the method comprising the steps of: (a) obtaining digital data via at least one processor from measurements selected from the group consisting of: food quality measurements, beverage quality measurements, and water quality measurements, wherein the measurements determine the presence and / or level of at least one biocontaminant in a sample, and wherein the measurements are performed on a portion of the sample; (b) processing the digital data via an implementation of an algorithm, wherein the algorithm probabilistically maps the data to a first determination of biocontamination; (c) performing spectral analysis on another portion of the sample on a handheld portable PCR array to determine the expression levels of genes associated with the presence and quantity of microorganisms, wherein the PCR array includes a variety of unique nucleic acid primers for assessing the expression of a plurality of corresponding genes associated with the first determination; (d) mapping these gene expression levels to a gene-based and operable second determination via the at least one processor; and (e) providing the gene-based and operable second determination of biocontamination to a computer device.

[0016] In some embodiments, the biocontaminant may be bacteria, viruses, yeast, fungi, microorganisms, or parasites. For example, the biocontaminant may be Salmonella. Therefore, the PCR array may include nucleic acid primers specific to Salmonella enterica or Salmonella Bongo serotypes. While other types of assays are also considered suitable, particularly envisioned assays include loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), recombinase-assisted amplification (RAA), rolling circle amplification (RCA), and skip rolling circle amplification (SRCA).

[0017] Such a method is particularly suitable for inpatient and / or outpatient medical facilities (e.g., nursing homes). Alternatively, the envisioned method can be applied to cruise ships or passenger aircraft, or food preparation facilities (e.g., kitchens, restaurants, or cafeterias). As will be readily understood, such a method may additionally include the step of applying a purification scheme to an object based on the second determination, wherein the sample is obtained from the object.

[0018] In another aspect of the subject matter of this invention, the inventors envision a computer-aided method for converting diagnostic data into operable non-human subject data based on polymerase chain reaction (PCR), the method comprising the steps of: (a) obtaining digital diagnostic data from a non-human subject via at least one processor; (b) processing the digital diagnostic data via an implementation of an algorithm, wherein the algorithm probabilistically maps the digital diagnostic data to a first diagnosis; (c) performing spectral analysis on a biological sample from the non-human subject on a handheld portable PCR array by determining the expression levels of genes of nutritional significance, wherein the PCR array includes a variety of unique nucleic acid primers for assessing the expression of a plurality of corresponding genes associated with the first diagnosis; (d) mapping these expression levels to a gene-based second diagnosis for the subject via the at least one processor; and (e) providing the gene-based second diagnosis for the subject to a computer device.

[0019] In one embodiment, the first diagnosis may involve a nutritional deficiency state in the subject, such as a macronutrient deficiency (e.g., protein, fat, and / or carbohydrate deficiency) or a micronutrient deficiency (e.g., vitamin or mineral deficiency). In a further embodiment, digital diagnostic data may be processed via a digital pathology platform to obtain the first diagnosis, and / or the algorithm may include a machine learning algorithm or an inference algorithm executed by an inference engine.

[0020] Most typically, but not always, the primary diagnosis can be derived from whole-genome sequencing or transcriptome sequencing, and / or from culturing a patient's biological sample. Suitable biological samples include, for example, urine samples, blood samples, respiratory samples, mucosal samples, or tissue biopsies. Alternatively, the primary diagnosis can also be derived from radiographic images.

[0021] Preferably, such a method will further include the steps of generating a treatment plan at the treatment site based on the second diagnosis, and / or generating a nutrition plan at the treatment site based on the second diagnosis. Therefore, the envisioned method will also include the step of administering treatment or nutritional supplements to a non-human subject based on the second diagnosis and the treatment or nutrition plan.

[0022] Various objectives, features, aspects, and advantages of the subject matter of the invention will become more apparent from the following detailed description of preferred embodiments. Detailed Implementation

[0023] The inventors have now discovered that prior diagnoses can be readily confirmed and refined using multi-step diagnostic devices, systems, and methods that utilize a treatment-point device (e.g., a portable / handheld PCR array) selected or assembled based on previously known digital diagnostic data, and that the treatment-point device generates PCR-based diagnostic data (typically generated at the treatment point or point of use), which is then mapped to a gene-based diagnosis, thereby using the PCR-based diagnostic data to confirm and preferably also refine the prior diagnosis.

[0024] In this context, it should be particularly understood that the envisioned systems and methods are not limited to providing prior diagnoses, but rather can generate initial diagnoses from previously acquired digital diagnostic data. In this case, it is generally preferred to generate a first or initial diagnosis by probabilistically mapping previously acquired digital diagnostic data using computer algorithms. This avoids potential biases or even erroneous diagnoses that might have been made by previous practitioners.

[0025] After the primary or initial diagnosis is determined, a PCR array is generated, or a pre-manufactured PCR array is selected—wherein the PCR array includes unique nucleic acid primers for evaluating the specific expression of multiple genes associated with the primary diagnosis. As readily understood, the selection of multiple genes may include genes found in a patient (typically a human or other non-human mammal subject) and whose expression is affected in relation to the condition identified in the primary or initial diagnosis. Alternatively or additionally, these genes may also be part of a pathogen that is the cause of the condition identified in the primary or initial diagnosis. Therefore, and from a different perspective, it should be understood that the PCR array will not only be used to query or identify gene expression profiles associated with the condition to confirm the computer-generated diagnosis, but may also be used to further identify or refine the diagnosis to generate a gene-based secondary diagnosis.

[0026] In one exemplary use of the envisioned system and method, a patient may be presented to a physician as a new patient. In this case, the patient has a documented history of diabetic foot ulcer infection and has previously been treated with antibiotics without success. The patient's medical history (including vital signs data, previous diagnoses, current and past medications and dosages, and complete blood counts and blood chemistry) is digitally stored in the patient's insurance provider and medical record storage institution.

[0027] After examining a patient's ulcer and suspecting an infection, the physician uses their treatment point analysis device to request and receive existing digital diagnostic data from the patient. The device then processes the digital diagnostic data using a microprocessor via an implementation of an algorithm that probabilistically maps the data to a first diagnosis, in this example, an initial diagnosis of antibiotic-resistant wound infection. Most typically, this analysis can be performed locally on the device or at least partially remotely on a networked device that collaborates with the treatment point analysis device in terms of information to perform the probabilistic mapping. In most typical embodiments, the physician has a mobile device connected to the treatment point analysis device, which provides recommended PCR array tests to that mobile device. Of course, it should be understood that the treatment point analysis device can also provide such recommendations directly.

[0028] Regardless of the specific mechanism, it should be noted that appropriate PCR array tests can be recommended solely based on existing medical records. Furthermore, it should be understood that the selection of the PCR array can be derived entirely from the patient's existing digital diagnostic data, typically by identifying potential pathogens (e.g., metabolic or genetic diseases, cancer, progressive organ failure, etc.) or causative agents (e.g., bacterial or viral infections, parasitic infections, etc.) associated with the initial diagnosis. This identified pathogen or causative agent then informs the correct selection of nucleic acids for the PCR test. For example, in cases where causative agents are to be detected, DNA-based or rRNA-based PCR can be performed. On the other hand, rtPCR and / or qPCR can be performed, for example, to identify the expression of cell surface markers and / or chemoresistance markers in cancer, where gene expression levels are of particular informational value for a secondary diagnosis.

[0029] To confirm the initial diagnosis and even provide a more detailed diagnosis, the physician can then perform a PCR array test after obtaining a sample from the infected wound (usually via swab or biopsy puncture). The sample thus collected is then placed in sample buffer or lysis buffer to release sufficient nucleic acids for detection. In this context, it should be understood that the nucleic acids used for subsequent PCR testing in this particular example will be the nucleic acids of the pathogenic bacteria. Therefore, the PCR array will contain a variety of nucleic acid primers specific to the most common Gram-positive and Gram-negative bacteria found in wounds, including Staphylococcus aureus, Enterococcus faecalis, Enterococcus faecium, Escherichia coli (E. coli), Klebsiella pneumoniae (KO), Enterobacter spp., Proteus mirabilis, Acinetobacter baumannii, and Pseudomonas aeruginosa. Furthermore, the PCR array will preferably include additional nucleic acid primers that can recognize the presence of resistance genes (such as mecA, mecC, vanA, vanB), as well as other suitable primers.

[0030] Similarly, many alternative PCR arrays are envisioned that can not only rapidly and specifically identify diseases, symptoms, or infections, but also provide more detailed, treatment-related, and actionable information about potential treatment resistance, possible positive or negative treatment responses, cancer subtyping, etc. In most cases, such PCR arrays will be based on currently available molecular diagnostic biomarkers associated with the disease or disorder. However, in other embodiments, the diagnostic biomarkers may also be identified in clinical tests, animal models, and cell-based models. Additionally, it should be understood that the PCR arrays envisioned herein will include one or more positive and negative control primers, wherein the positive control primers typically target ubiquitous human genes with known expression, such as RNase P or other suitable biomarkers. Furthermore, it should be understood that PCR assays can be used as qualitative or quantitative assays. Therefore, it should be noted that the PCR reaction can be a simple amplification method as described above, or a quantitative PCR reaction (e.g., qPCT / rtPCR). In yet another preferred aspect, the PCR array will be configured as a handheld and portable unit containing all the reagents required to perform (quantitative) PCR. There are many known portable lab-on-a-chip devices in the field (see, for example, the NantNudge device), and it is considered that all of these devices are applicable to this paper.

[0031] Returning to the ulcer example, and at the end of the PCR assay, the PCR-based digital diagnostic data can then be processed on a therapeutic point analysis device, and more typically on an information-coupled computing device, which maps expression levels, or expression data, to a gene-based secondary diagnosis for the patient via a processor. In this example, the gene-based secondary diagnosis for the patient can now inform the clinician that the ulcer is infected with Staphylococcus aureus carrying the mecA resistance gene. In this specific case, the physician is now informed of treatment options, such as the use of vancomycin. However, if the digital patient data also indicates kidney damage, alternative treatment options, such as cefuroxime or linezolid, can be selected.

[0032] Therefore, it should be understood that a gene-based second diagnosis for a patient can not only confirm the first or initial diagnosis, but also include additional information obtained from the patient's digital diagnostic data (e.g., allergy-related information, information related to avoiding or using specific medications, etc.). Furthermore, in most cases, a gene-based second diagnosis will refine the initial diagnosis and thus guide physicians to provide more appropriate treatment. From different perspectives, and especially when gene expression data is obtained from the patient via PCR assays, the patient's individual physiological condition can be considered, thereby increasing the likelihood of treatment success. Therefore, the envisioned systems and methods will make treatment more patient-specific and potentially more effective.

[0033] Preferably, but not necessarily, the treatment point analysis device (or the computing device coupled with the treatment point analysis device's information) can transmit the gene-based second diagnosis to another computer, such as a mobile device or tablet used by the treating physician. Additionally, the treatment point analysis device (or the computing device coupled with the treatment point analysis device's information) can also generate treatment recommendations or plans based on the second diagnosis, and the physician can then treat the patient according to these recommendations or plans (e.g., using appropriate antibiotics or chemotherapy drugs).

[0034] Regarding digital diagnostic data from patients, it should be understood that the data may be in various formats. However, the data format specifically envisioned will be one that is recognized and / or used in routine practice. Therefore, the data may be formatted according to standards published by the Society for Clinical Data Management (SCDM) in accordance with the Good Clinical Data Management Practice (GCDMP) guidelines. Thus, appropriate data formats, for example, would include the Harmonized Clinical Data Acquisition Standards (CDASH) standard, the Guidelines for the Implementation of Tabulation Models for Human Clinical Trial Research Data (SDTMIG) standard, or the Clinical Data Exchange Standards Association (CDISC) standard. As readily understood, all digital diagnostic data from patients will be stored and / or transmitted in an encrypted format to comply with relevant patient data privacy regulations.

[0035] It should also be noted that digital diagnostic data from patients will not be limited to a single health issue, but can actually include a comprehensive patient data record. Therefore, it is conceivable that digital diagnostic data from patients will include systematic data, such as age, weight, height, vital sign statistics, as well as results from gross examinations, clinical laboratory data, imaging data (e.g., radiological imaging data), digital histopathology data, and treatment diagnostic data. Furthermore, and where available, digital diagnostic data from patients can also include various omics data, and in particular whole-genome data, exome sequencing data, transcriptome data, proteome data, SNP data, etc. Similarly, the envisioned digital diagnostic data from patients can also include additional molecular data, such as cell surface markers (e.g., cell surface markers associated with cancer type, receptor status, HLA data, etc.). Additionally, the envisioned digital diagnostic data from patients will also include previous treatment data and previous treatment outcomes, especially when it involves drug treatment and the results of such treatment (e.g., resistance to antibiotic treatment or cancer recurrence after treatment with one or more chemotherapy drugs), allergy information, vaccination status, etc.

[0036] Therefore, it should be recognized that digital diagnostic data from patients can be obtained in various ways. For example, digital diagnostic data from patients can be obtained from patient samples (such as urine samples, blood samples, respiratory samples, mucosal samples, or tissue biopsies), or it can be derived by culturing the patient's biological samples. Alternatively or additionally, digital diagnostic data from patients can also be obtained from X-ray or scanning procedures (e.g., CT scans, MRI scans, PET scans, etc.) or digital histopathology platforms or sequencing platforms.

[0037] Depending on the specific digital diagnostic data from the patient, the primary diagnosis (which can be an initial diagnosis, a first diagnosis, or a preliminary diagnosis) can vary significantly. For example, when the data includes imaging, histopathological, and / or omics data, the presumed diagnosis is typically a diagnosis of cancer or a type (subtype) of cancer. On the other hand, when the data is obtained through blood draws and / or cultured samples, the presumed diagnosis is typically a diagnosis of an infection or infectious disease (such as a wound infection, urinary tract infection, etc.) that may be caused by bacteria, viruses, fungi, or parasites. As readily understood, such a diagnosis can be further based on or utilize ICD-10 classification to facilitate computational analysis, which is typically performed using inference engines, machine learning algorithms, and / or probability mappings. Therefore, it should be noted that digital diagnostic data can be mapped entirely to a primary or initial diagnosis by computer. However, human intervention or guidance to obtain a primary or initial diagnosis is also considered appropriate.

[0038] Various systems and methods known in the art exist for associating digital diagnostic data with an initial, first, or preliminary diagnosis, and exemplary systems and methods using inference engines are described in US 9530100, US 9262719, US 10255552, US 10296840, US10762433, and US 10296839, all of which are incorporated herein by reference. Where appropriate, the contemplated algorithms may also include, for example, an interference engine as described in US 9576242, which is also incorporated herein by reference. As should be further understood, the contemplated algorithms may be executed on one or more processors in a treatment point analysis device and / or on one or more remote processors coupled to information of the treatment point analysis device.

[0039] Regardless of how digital diagnostic data is mapped to a primary diagnosis, it is envisioned that this primary diagnosis be transmitted to a medical professional (e.g., via a display of a treatment point analysis device or via a separate computer coupled with information from the treatment point analysis device) for human review. Additionally, the primary diagnosis will typically include information about unique nucleic acid primers used for the PCR reaction, which may include the actual nucleic acid sequence, or encoding of such a sequence, or encoding or instructions for using a specific PCR array containing sequences suitable for treatment point analysis. As readily understood, the primary diagnosis (or the initial determination of biological contamination) will determine the appropriate selection of nucleic acid sequences for subsequent PCR reactions. For example, in confirming the presence of a pathogen, nucleic acid primers may have sequences targeting unique DNA or RNA (and especially rRNA) sequences of the pathogen for qualitative detection. Such detection may also include detecting one or more genes associated with treatment resistance, such as antibiotic resistance genes. In another example, in determining the susceptibility of cancer cells to chemotherapy drugs, nucleic acid primers would have sequences targeting genes and / or RNA known to have a prognosis of treatment resistance or success. From different perspectives, it should be understood that a PCR reaction can be qualitative PCR or quantitative PCR (which can be, for example, rtPCT or qPCR).

[0040] In another envisioned aspect, it is generally preferred that physicians use portable and / or handheld PCR arrays containing nucleic acid primers for PCR reactions. Most typically, but not necessarily, the handheld PCR array will include an array of various nucleic acids associated with a first diagnosis (or a first determination of biocontamination) to confirm that first diagnosis, and most preferably, to further refine that diagnosis. For example, in the case of a suspected infection, the nucleic acid array may include primers as described above for identifying one or more different possible pathogens commonly found in wound infections. In another example, in the case of a suspected cancer type, the nucleic acid array may include primers for identifying a cancer subtype characterized by the presence or absence of cell surface markers (e.g., HER2, estrogen receptor, progesterone receptor) and / or susceptibility to chemotherapy treatment, based on gene expression of genes associated with susceptibility.

[0041] Most typically, but not necessarily, the envisioned portable and / or handheld PCR array will include a preselected set of nucleic acid primers matched to common first diagnoses, and will also include all reagents required to perform the PCR reaction. Such reagents may also include sample preparation buffers, such as lysis buffers, to separate or release circulating or free nucleic acids, as described, for example, in US 11168323, US 11702703, and US 11773447, each of which is incorporated herein by reference. Thus, a typical portable and / or handheld PCR array will include one or more buffers, nucleotides, and polymerases. As noted above, many known portable lab-on-a-chip devices exist in the art (see, for example, the NantNudge device), and all of these are considered suitable for use herein. However, it is further envisioned that in some cases, the PCR array may be directly integrated with the analytical device, while in other cases, the PCR array may be informationally coupled to such a device (e.g., via cable, Bluetooth, Wi-Fi, etc.). Regardless of the actual PCR array used, it should be noted that profiling of biological samples from patients can be performed on a handheld portable PCR array by determining the expression levels of clinically significant genes. This PCR array includes unique nucleic acid primers for assessing the specific expression of multiple genes relevant to the primary diagnosis. Finally, it should be recognized that the primary diagnosis will typically also include the reaction conditions of the PCR reaction (most typically the time and temperature of denaturation, annealing / extension steps, and the number of cycles).

[0042] As previously mentioned, the envisioned analytical device will include a processor programmed to execute an algorithm that calculates the expression levels of clinically significant genes (and / or determines the presence of these genes) and maps these gene expression levels to a gene-based secondary diagnosis. Thus, based on the presence of certain genes and the expression levels of certain genes, along with a first diagnosis, the processor then maps these expression levels (or gene presence) to a gene-based secondary diagnosis for that patient. Preferably, but not necessarily, the mapping can be done via an inference engine, via machine learning, and / or via probabilistic mapping, and can use the same or different processors. From a different perspective, the same treatment point device not only allows receiving initial digital diagnostic data and generating a first diagnosis, but also enables generating a gene-based secondary diagnosis for the patient based on PCR results from a (handheld portable) PCR array. Advantageously, in this process, the initial diagnosis is confirmed and even refined into a gene-based secondary diagnosis.

[0043] Typically, validation and refinement will be performed on one or more processors (onboard and / or remotely) using algorithms as already noted above and described, for example, in US 9262719, US 9530100, US 9576242, US 10255552, US10296839, US 10296840, and US 10762433 (each of which is incorporated herein by reference). Alternatively or additionally, validation of the initial hypothesis may be performed using systems and methods as described in US 10354194, and refinement (e.g., for assigning cancer scores) may be performed using systems and methods described in US 2020 / 0335215, both of which are incorporated herein by reference. Other suitable algorithms, systems and methods are described in US 2020 / 0356883 and US 2023 / 0034330, both of which are incorporated herein by reference.

[0044] In yet another envisioned embodiment, the treatment point analysis device can directly provide a patient-specific, gene-based second diagnosis to a practicing physician or a computer device, which can be a single, networked computer, or a handheld mobile device, such as a tablet or mobile phone. Most typically, the analysis device will also generate a treatment plan at the treatment point based on the second diagnosis, guiding healthcare professionals to select and administer appropriate pharmaceutical agents or treatments. Thus, based on PCR data obtained from the sample and the analysis in the device, medication, such as antibiotics or chemotherapy drugs, can subsequently be administered to the patient. From another perspective, healthcare professionals only need to analyze the treatment point device and the PCR array to generate actionable diagnostic information that can be used to treat the patient at the treatment point.

[0045] Therefore, and in view of the considerations provided above, the inventors have also conceived of a computer-aided method for converting polymerase chain reaction (PCR)-based diagnostic data into clinically actionable patient data. For example, PCR-based digital diagnostic data can be obtained from a PCR array as described above, and then processed in a processor via a probabilistic algorithm to establish a genetically based second diagnosis. Then, where desired, this second diagnosis can be further mapped via the processor using machine learning or artificial intelligence to generate a machine learning-based third diagnosis or an artificial intelligence (AI)-based third diagnosis for the patient. Then, most typically, this machine learning-based third diagnosis or AI-based third diagnosis is transmitted to another computer device as described above for guiding the treatment of the patient. The refinement / mapping of the second diagnosis is typically performed via an inference engine or machine learning algorithm.

[0046] While the systems and methods presented herein are generally envisioned for use in the medical diagnosis and treatment of humans, it should also be understood that these methods are applicable to the analysis and processing of data from non-human subjects, following essentially the same approach described above. Advantageously, and particularly where the non-human subjects are companion animals (e.g., dogs, cats, horses, etc.) or livestock (e.g., pigs, cattle, poultry, etc.), the conditions tested need not be limited to infections or cancer or other serious diseases, but may also include various nutritional deficiencies, such as macronutrient (e.g., protein, carbohydrate, lipid) deficiencies and micronutrient (e.g., vitamin or mineral) deficiencies.

[0047] Furthermore, and beyond medical applications, it should be understood that the systems and methods presented herein can also be used in the fields of environmental testing and remediation, with particularly suitable environmental applications including water quality testing (e.g., drinking water, freshwater lakes and streams, open ocean and marine waters), soil testing (e.g., agriculture, horticulture), food and beverage testing, air testing, and especially in cases where testing involves microbial or viral contaminants. For example, the envisioned systems and methods can be readily used with food or environmental samples to identify and characterize pathogenic contaminants such as pathogenic bacteria, viruses, yeasts, fungi, microorganisms, and / or parasites. In food testing, among other pathogens, the biological contaminant may be Salmonella (e.g., entero Salmonella or Salmonella Bongos serotype), and therefore PCR primers can be selected accordingly. Therefore, such testing will generally be more qualitative than quantitative, and the envisioned PCR assays will specifically include loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), recombinase-assisted amplification (RAA), rolling circle amplification (RCA), or skip rolling circle amplification (SRCA). However, quantitative testing is also considered suitable herein. Depending on the results of the analysis, it should be recognized that the envisioned method may therefore also include the step of applying a purification protocol to the object based on a second determination, wherein the sample is obtained from the object. Finally, and regardless of the type of test performed and whether for medical or non-medical purposes, it is envisioned that treatment points or points of use will include inpatient and / or outpatient medical facilities, nursing homes, passenger aircraft, food preparation facilities (e.g., kitchens, restaurants, or cafeterias), and military facilities.

[0048] It should be noted that any language relating to computers should be interpreted as including any suitable combination of computing devices, including servers, interfaces, systems, databases, agents, peer nodes, engines, modules, controllers, or other types of computing devices operating individually or in combination. It should be understood that computing devices include processors configured to execute software instructions stored in tangible, non-transitory computer-readable storage media (e.g., hard disk drives, solid-state drives, RAM, flash memory, ROM, etc.). The software instructions preferably configure the computing device to provide tasks, responsibilities, or other functions, as discussed below with respect to the disclosed devices. In particularly preferred embodiments, various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, which may be based on HTTP, HTTPS, AES, public-key exchange, web service APIs, known financial transaction protocols, or other electronic information exchange methods. Data exchange preferably occurs within a packet-switched network (Internet, LAN, WAN, VPN, or other types of packet-switched networks).

[0049] In some embodiments, the figures representing the amount or characteristics of components (such as concentration, reaction conditions, etc.) used to describe and claim certain embodiments of the invention should be understood to be modified in some cases by the term “about.” As used herein, the terms “about” and “approximately” when referring to a specified measurable value (such as a parameter, quantity, duration of time, etc.) are intended to cover the specified value and variations of that specified value and variations relative to that specified value, such as + / - 10% or less, alternatively + / - 5% or less, alternatively + / - 1% or less, alternatively + / - 0.1% or less, and such variations relative to that specified value, provided that such variations are suitable for implementation in the disclosed embodiments. Thus, the values ​​referred to by the modifiers “about” or “approximately” are also specifically disclosed themselves. The description of value ranges herein is intended only as a shorthand method for individually referring to each individual value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were described separately herein.

[0050] As used herein, the term “administration” of a pharmaceutical composition or drug refers to both direct and indirect administration of the pharmaceutical composition or drug, wherein direct administration of the pharmaceutical composition or drug is typically performed by a healthcare professional (e.g., physician, nurse, etc.), and wherein indirect administration includes steps of providing the pharmaceutical composition or drug to a healthcare professional or making the pharmaceutical composition or drug available to a healthcare professional for direct administration (e.g., via injection, infusion, oral delivery, local delivery, etc.). It should be further noted that the terms “prognosis” or “prediction” of a condition, susceptibility to disease development, or response to anticipated treatment are intended to encompass predictive actions or predictions (rather than treatment or diagnosis) of a condition, susceptibility, and / or response (including the rate of progression, improvement, and / or duration of a subject’s condition).

[0051] Unless otherwise specified herein or otherwise clearly contradicted by the context, all methods described herein may be performed in any suitable order. The application of any and all instances or exemplary language (such as "for example") provided with respect to certain embodiments herein is intended only to better illustrate the invention and not to limit the scope of the invention as otherwise claimed. The language in the specification should not be construed as indicating that any unclaimed element is necessary for the practice of the invention.

[0052] As used herein and throughout the claims, unless the context clearly indicates otherwise, the meanings of “a” and “the” include plural pronouns. Similarly, as used herein, unless the context clearly indicates otherwise, the meaning of “in” includes both “in” and “on”. As also used herein, and unless the context clearly indicates otherwise, the term “coupled to” is intended to include both direct coupling (where two elements coupled to each other are in contact with each other) and indirect coupling (where at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously.

[0053] It will be apparent to those skilled in the art that further modifications are possible beyond those already described without departing from the inventive concept described herein. Therefore, the subject matter of the invention is not limited except within the scope of the appended claims. Furthermore, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms "comprises" and "comprising" should be interpreted as referring to an element, component, or step in a non-exclusive manner, indicating that the referenced element, component, or step may be present or utilized, or combined with other elements, components, or steps not explicitly referenced. When the specification or claims refer to at least one item selected from the group consisting of A, B, C, ..., and N, the text should be interpreted as requiring only one element from that group, rather than A plus N, or B plus N, etc.

Claims

1. A computer-aided method for converting diagnostic data into clinically actionable patient data based on polymerase chain reaction (PCR), the method comprising: a) Obtaining digital diagnostic data from the patient via at least one processor; b) Processing the digital diagnostic data by implementing an algorithm, wherein the algorithm probabilistically maps the digital diagnostic data to a first diagnosis; and c) By determining the expression levels of clinically significant genes, a spectroscopic analysis of biological samples from the patient was performed on a handheld portable PCR array, wherein the PCR array included unique nucleic acid primers for assessing the specific expression of multiple genes associated with the primary diagnosis; d) Mapping these expression levels to a gene-based second diagnosis for the patient via the at least one processor; and e) Provide the patient with the genetically based second diagnosis to a computer device.

2. The method as described in claim 1, wherein, The primary diagnosis involved an infectious disease.

3. The method as described in claim 2, wherein, The primary diagnosis involves bacterial infection, viral infection, fungal infection, or parasitic infection.

4. The method of claim 3, wherein, The primary diagnosis involved a urinary tract infection.

5. The method of claim 1, wherein, The primary diagnosis involves resistance to treatment for infectious diseases.

6. The method of claim 1, wherein, This primary diagnosis involves resistance to treatment for bacterial, viral, fungal, or parasitic infections.

7. The method of claim 6, wherein, The primary diagnosis involved resistance to antibiotic treatment.

8. The method of claim 6, wherein, The primary diagnosis involves resistance to treatment for urinary tract infections.

9. The method of claim 1, wherein, The first diagnosis involves cancer and / or includes the determination of the type of cancer, and optionally, the determination of the type of cancer includes a treatment diagnostic procedure.

10. The method of claim 1, wherein, The digital diagnostic data is processed through a digital pathology platform to obtain the primary diagnosis.

11. The method of claim 1, wherein, This algorithm includes machine learning algorithms.

12. The method of claim 1, wherein, The algorithm includes inference algorithms executed by the inference engine.

13. The method of claim 1, wherein, This digital diagnostic data includes whole-genome sequencing or transcriptome sequencing.

14. The method of claim 1, wherein, This digital diagnostic data was derived by culturing the patient's biological sample.

15. The method of claim 14, wherein, The biological sample includes urine sample, blood sample, respiratory sample, mucosal sample, or tissue biopsy.

16. The method of claim 1, wherein, This digital diagnostic data is derived from radiographic images.

17. The method of claim 1, further comprising generating a treatment plan at the treatment point based on the second diagnosis.

18. The method of claim 17, further comprising administering the drug based on the treatment plan.

19. The method of claim 18, wherein, The drug is an antibiotic or chemotherapy drug.

20. A computer-aided method for converting polymerase chain reaction (PCR)-based diagnostic data into clinically actionable patient data, the method comprising: a) Obtaining PCR-based digital diagnostic data from a patient via at least one processor, wherein the PCR-based diagnostic data includes the corresponding nucleic acid expression levels of multiple genes in a gene array, and wherein the selection of genes in the array is determined by a preliminary first diagnosis; b) Processing the PCR-based digital diagnostic data via an implementation of an algorithm, wherein the algorithm probabilistically maps the PCT-based digital diagnostic data to a secondary diagnosis; and c) Mapping the PCR-based digital diagnostic data to at least a third diagnosis based on machine learning or artificial intelligence (AI) for the patient via the at least one processor and using the second diagnostic method; and d) Provide the computer device with the third diagnosis based on machine learning or AI for the patient.

21. The method of claim 20, wherein, The PCR-based digital data was obtained by performing spectral analysis on biological samples from the patient on a handheld portable PCR array.

22. The method of claim 20, wherein, The preliminary first diagnosis was derived from digital histopathology.

23. The method of claim 20, wherein, The third diagnosis was derived from the inference engine.

24. The method of claim 20, wherein, The initial diagnosis was derived from radiographic images.

25. The method of claim 20, wherein, The initial diagnosis was made by culturing the patient's biological sample.

26. The method of claim 25, wherein, The biological sample includes urine sample, blood sample, respiratory sample, mucosal sample, or tissue biopsy.

27. The method of claim 20, wherein, This preliminary first diagnosis is derived from whole-genome sequencing or transcriptome sequencing.

28. The method of claim 20, wherein, The second and / or third diagnosis includes determining the patient's antibiotic resistance.

29. The method of claim 20, wherein, The second and / or third diagnosis includes determining the patient's chemotherapy resistance.

30. The method of claim 20, wherein, The second and / or third diagnosis includes determining the patient's antifungal resistance.

31. The method of claim 20, wherein, This initial first diagnosis includes determining the type of cancer.

32. The method of claim 31, wherein, The determination of cancer type includes treatment and diagnostic procedures.

33. The method of claim 20, further comprising generating a treatment plan at the treatment point based on the third diagnosis.

34. The method of claim 20, wherein, The PCR-based digital diagnostic data is processed by a digital pathology platform to arrive at the second diagnosis.

35. The method of claim 20, wherein, This algorithm includes machine learning algorithms.

36. The method of claim 20, wherein, The algorithm includes inference algorithms executed by the inference engine.

37. The method of claim 20, wherein, The genes in this gene array are selected from the following groups: genes associated with cancer, genes associated with viral infections, genes associated with bacterial infections, genes associated with fungal infections, genes associated with parasitic infections, and genes associated with cardiology.

38. A computer-aided method for converting water or food test data into actionable data based on polymerase chain reaction (PCR), the method comprising: a) Obtaining digital data from measurements selected from the group consisting of food quality measurements, beverage quality measurements, and water quality measurements via at least one processor, wherein the measurements determine the presence and / or level of at least one biological contaminant in a sample, and wherein the measurements are performed on a portion of the sample. b) The digital data is processed by implementing an algorithm, wherein the algorithm probabilistically maps the data to a first determination of biological contamination; c) Perform spectral analysis on another portion of the sample on a handheld portable PCR array to determine the expression levels of genes associated with the presence and quantity of microorganisms, wherein the PCR array includes a variety of unique nucleic acid primers for assessing the expression of the corresponding plurality of genes associated with the first determination; d) Mapping these gene expression levels to a second, gene-based, operable determination via the at least one processor; and e) Providing a computer device with this gene-based and operable second determination of biological contamination.

39. The method of claim 38, wherein, This biocontaminant includes bacteria, viruses, yeast, fungi, microorganisms, or parasites.

40. The method of claim 38, wherein, The biological contaminant is Salmonella.

41. The method of claim 40, wherein, The PCR array includes nucleic acid primers that are specific to either entero Salmonella or Bongo Salmonella serotypes.

42. The method of claim 38, wherein, The assay includes loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), recombinase-assisted amplification (RAA), rolling circle amplification (RCA), or skip rolling circle amplification (SRCA).

43. The method of claim 38, used in inpatient and / or outpatient medical facilities.

44. The method of claim 38, wherein, The medical facility includes a nursing home.

45. The method of claim 38, wherein it is used on a cruise ship or a passenger aircraft.

46. ​​The method of claim 38, wherein it is used in a food preparation facility.

47. The method of claim 47, wherein, The food preparation facility includes a kitchen, restaurant, or cafeteria.

48. The method of claim 38, further comprising the step of applying a purification scheme to the object based on the second determination, wherein, The sample was obtained from this object.

49. A computer-aided method for converting diagnostic data into operable non-human subject data based on polymerase chain reaction (PCR), the method comprising: a) Obtaining digital diagnostic data from non-human subjects via at least one processor; b) Processing the digital diagnostic data by implementing an algorithm, wherein the algorithm probabilistically maps the digital diagnostic data to a first diagnosis; and c) By determining the expression levels of genes of nutritional significance, a spectroscopic analysis of biological samples from the non-human subject was performed on a handheld portable PCR array, wherein the PCR array included a variety of unique nucleic acid primers for assessing the expression of multiple corresponding genes associated with the first diagnosis; d) Mapping these expression levels to a gene-based second diagnosis for the subject via the at least one processor; and e) Provide the subject with the gene-based second diagnosis to a computer device.

50. The method of claim 49, wherein, The primary diagnosis involved a nutritional deficiency in the subject.

51. The method of claim 50, wherein, The primary diagnosis included the identification of a significant deficiency of certain nutrients in the subject's body.

52. The method of claim 51, wherein, The primary diagnosis includes the determination of a protein deficiency in the subject's body.

53. The method of claim 51, wherein, The primary diagnosis includes determining that the subject has a lack of fat in his body.

54. The method of claim 51, wherein, The primary diagnosis includes determining a carbohydrate deficiency in the subject's body.

55. The method of claim 50, wherein, The primary diagnosis includes determining a micronutrient deficiency in the subject's body.

56. The method of claim 55, wherein, The primary diagnosis includes determining a vitamin deficiency in the subject's body.

57. The method of claim 55, wherein, The primary diagnosis includes determining a mineral deficiency in the subject's body.

58. The method of claim 49, wherein, The digital diagnostic data is processed through a digital pathology platform to obtain the primary diagnosis.

59. The method of claim 49, wherein, The algorithm includes machine learning algorithms or inference algorithms executed by an inference engine.

60. The method of claim 49, wherein, The primary diagnosis was derived from whole-genome sequencing or transcriptome sequencing.

61. The method of claim 49, wherein, The initial diagnosis was made by culturing the patient's biological sample.

62. The method of claim 55, wherein, The biological sample includes urine sample, blood sample, respiratory sample, mucosal sample, or tissue biopsy.

63. The method of claim 49, wherein, The initial diagnosis was derived from radiographic images.

64. The method of claim 49, further comprising generating a treatment plan at the treatment point based on the second diagnosis.

65. The method of claim 49, further comprising generating a nutrition plan at the treatment site based on the second diagnosis.

66. The method of claim 64 or claim 65, comprising the step of administering a treatment or nutritional supplement to the non-human subject based on the second diagnosis and treatment or nutrition plan.