Method and device for non-invasive detection of pathogens in wounds - Patents.com

Abstract

According to an embodiment of the present invention, at least one sensor detects one or more gases released from one or more pathogens in the wound causing the infection. The at least one sensor includes a sensing material that changes one or more properties in response to the presence of the one or more gases. At least one processor analyzes information from the at least one sensor to identify the one or more pathogens and determine the presence of infection in the wound. The one or more pathogens are identified based on a pattern of changes in the one or more properties indicative of the corresponding pathogen. The at least one sensor may be disposed in one of a wearable device, a portable device, and a wound dressing. Additionally, a negative pressure source may be utilized to apply negative pressure to the wound to promote healing.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part of U.S. patent application Ser. No. 17 / 751,207, filed May 23, 2022, entitled "Noninvasive Device for Monitor, Detection, and Diagnosis of Diseases and Human Performance," which is a continuation-in-part of U.S. Provisional Patent Application Ser. No. 63 / 192,005, filed May 22, 2021, entitled "Noninvasive Wearable Intelligent Sensor for Rapid Monitoring, Screening, and Diagnosis of Diseases from Skin," which is a continuation-in-part of U.S. Provisional Patent Application Ser. No. 63 / 192,006, filed May 22, 2021, entitled "Noninvasive Wearable / Portable Intelligent Sensor for Rapid Monitoring, Screening, and Diagnosis of Diseases from Skin," and which is a continuation-in-part of U.S. Provisional Patent Application Ser. No. 63 / 192,006, filed May 22, 2021, entitled "Method and Device for Non-Invasive Detecting and Identifying Pathogens in Real-Time This application claims priority to U.S. Provisional Patent Application No. 63 / 269,151, entitled "Method and Apparatus for Treating Wounds," filed March 10, 2022, the disclosures of which are incorporated herein by reference in their entireties.

[0002] This application also claims priority to U.S. Provisional Patent Application No. 63 / 269,151, entitled "Method and Device for Non-Invasive Detecting and Identifying Pathogen in Real-Time in Wounds," filed on March 10, 2022, and U.S. Provisional Patent Application No. 63 / 434,064, entitled "Integrated Sensor in Negative Pressure Wound Device for Wound Monitoring and Early Detection of Infection," filed on December 20, 2022, the disclosures of which are incorporated by reference in their entireties herein.

[0003] Embodiments of the present invention relate to detecting and identifying pathogens for early detection of wound infection in real time. [Background technology]

[0004] Chronic skin wound infections and surgical site infections pose a significant burden to the U.S. healthcare system and can result in increased morbidity and mortality. Common pathogens associated with chronic, superficial and deep surgical site infections include, but are not limited to, Staphylococcus epidermidis (SE), Streptococcus pyogenes (SP), Enterococcus faecium (EF), Staphylococcus aureus (SA), Klebsiella pneumoniae (KP), Acinetobacter baumannii (AB), Pseudomonas aeruginosa (PA), Enterobacter species (ES), Escherichia coli (EC), Proteus mirabilis (PM), Salmonella typhimurium (SM), Enterobacter cloaca (E. cl), and Acetinobacter anitratus (AA).

[0005] Negative pressure wound devices (NPWDs) are commonly used to treat wounds because they help remove excess fluid and promote healing. However, wounds can become infected, which can significantly delay healing and cause serious complications if not treated properly. Early detection of wound infection is important to ensure timely treatment and optimal patient outcomes.

[0006] Current diagnostic methods to identify and confirm infection include visual inspection, as well as culture-based and molecular methods. These techniques are time and resource consuming, and require some sample transport. Many also have limited sensitivity and specificity inherent in sample processing and user error, requiring complex laboratory science experience and equipment.

[0007] Thus, limitations of these approaches include the potential for delayed diagnosis and often empirical treatment prior to confirmation of the infectious agent, increasing the risk of suboptimal antibiotic selection, which often leads to the development of antibiotic resistance and increased mortality. Furthermore, these approaches may not provide real-time results, making it difficult to rapidly detect and treat infections.

[0008] Volatile organic compounds (VOCs) as diagnostic tools include a diverse group of carbon-based molecules, including alcohols, isocyanates, ketones, aldehydes, hydrocarbons, and sulfides, which are volatile at ambient temperatures. VOC detection has the advantages of being painless, non-invasive, and reproducible. There is growing evidence that VOCs and their combinations are intrinsic to various disease states, and their early detection may represent a useful means of diagnosis. VOCs have been identified as potential biomarkers in the diagnosis of lung cancer, breast cancer, asthma, and diabetes.

[0009] Pathogens also produce VOCs, and current volatile detection by breath testing is at the cutting edge of this technology for diagnosing infectious diseases. The ability to rapidly detect microbial VOCs that may allow for pathogen identification has enormous implications for infection management, from triage, point of trauma care in austere environments, to hospital, clinic and home environments. If a patient's wounds could be accurately monitored from the early stages, through discharge from the hospital and into home use, appropriate antimicrobial therapy could be initiated early enough to prevent serious infection, and the status of the infection could be continuously monitored. Summary of the Invention

[0010] According to one embodiment of the present invention, a system detects a wound infection. The system includes at least one sensor and at least one processor. The at least one sensor detects one or more gases released from one or more pathogens in the wound causing the infection. The at least one sensor includes a sensing material that changes one or more properties in response to the presence of the one or more gases. The at least one processor analyzes information from the at least one sensor to identify the one or more pathogens and determine the presence of an infection in the wound. The one or more pathogens are identified based on a pattern of changes in the one or more properties indicative of the corresponding pathogen. In one embodiment, the at least one sensor is disposed in one of a wearable device, a portable device, and a wound dressing. In one embodiment, the system further includes a negative pressure source that applies negative pressure to the wound to promote healing. An embodiment of the present invention further includes a method and an apparatus having a memory device including software executable by the at least one processor to detect a wound infection in substantially the same manner as described above. [Brief description of the drawings]

[0011] Generally, like reference numbers in the various drawings are utilized to indicate like elements.

[0012] [Figure 1] FIG. 2 is a diagram of an example of a wearable device, according to an embodiment of the present invention.

[0013] [Diagram 2] FIG. 2 illustrates components of a device according to one embodiment of the present invention.

[0014] [Diagram 3] 4 is a flow diagram of an exemplary method for preparing a sensing material of a sensor in a sensor array for use in an embodiment of the present invention.

[0015] [Figure 4]FIG. 1 shows TEM images of sensing materials and VOC gas responses in an exemplary sensor array.

[0016] [Diagram 5] FIG. 1 illustrates a process of building a model / classifier and using the model / classifier to diagnose wound infection according to an embodiment of the present invention.

[0017] [Figure 6A] FIG. 1 illustrates a portable device according to one embodiment of the present invention.

[0018] [Figure 6B] FIG. 6B illustrates the use of the handheld device of FIG. 6A to detect infection in a wound, according to one embodiment of the present invention.

[0019] [Figure 7A] FIG. 6B shows an example of the analysis of VOC patterns of bacteria including Escherichia coli (E. coli), Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA) in a wound infection using the device of FIG. 6A. [Figure 7B] FIG. 6B shows an example of the analysis of VOC patterns of bacteria including Escherichia coli (E. coli), Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA) in a wound infection using the device of FIG. 6A.

[0020] [Figure 8] FIG. 1 illustrates a wearable device integrated into a dressing system for real-time monitoring of wound infection, according to one embodiment of the present invention.

[0021] [Figure 9A] FIG. 9 shows an example of a VOC pattern of a pathogen causing a wound infection detected by the wearable device of FIG. [Figure 9B] FIG. 9 shows an example of a VOC pattern of a pathogen causing a wound infection detected by the wearable device of FIG. [Figure 9C]FIG. 9 shows an example of a VOC pattern of a pathogen causing a wound infection detected by the wearable device of FIG. [Figure 9D] FIG. 9 shows an example of a VOC pattern of a pathogen causing a wound infection detected by the wearable device of FIG.

[0022] [Figure 10A] FIG. 1 illustrates analyte sensor measurements from an exemplary embodiment of the present invention.

[0023] [Figure 10B] FIG. 13 illustrates sensor data from an exemplary embodiment of the present invention projected onto a set of primary components.

[0024] [Figure 10C] 11A-11C show sensor data from an exemplary embodiment of the present invention projected onto different sets of primary components.

[0025] [Figure 10D] FIG. 1 illustrates the accuracy of bacterial predictions of an exemplary embodiment.

[0026] [Figure 11] FIG. 1 illustrates another exemplary wearable device for detecting wound infection, according to an embodiment of the present invention.

[0027] [Figure 12A] FIG. 1 illustrates a negative pressure wound device, according to one embodiment of the present invention.

[0028] [Figure 12B] FIG. 12B illustrates the use of the negative pressure wound device of FIG. 12A in accordance with one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] One embodiment of the present invention provides real-time detection of the presence or absence of infection in a wound of a subject, and identification of the presence of one or more specific pathogens in the wound.

[0030] Embodiments of the present invention relate to a wearable or portable device or system comprising a sensor array having a plurality of sensors, a detection mechanism and a pattern recognition analyzer, and its use for diagnosing wound infection in a non-invasive real-time manner.

[0031] The device of the present invention may be embedded in a wound dressing or in close proximity to a wound. The device is composed of a MACchip sensor array module, a microcontroller unit (MCU) module, a digital signal processing circuit (DSC), an analog-to-digital converter (ADC), a communication interface (USB, Bluetooth, and WiFi), an "on / off switch", and a user interface. The sensor array module comprises a multi-component nanostructured material-based sensor or sensors in combination with pattern recognition and machine learning or other algorithms. The device may be utilized to provide convenient, non-invasive, real-time detection of wound infection from early onset to all subsequent infection stages, thereby enabling caregivers to provide effective, timely treatment.

[0032] Embodiments of the present invention detect the onset of direct infection of a wound bed and provide simultaneous identification of active microorganisms for wound infection management in hospital or other settings.Embodiments of the present invention provide a non-invasive technology utilizing nanomaterial-based sensors that can detect early stages of infection before symptoms occur, allowing consistent monitoring through all stages of infection.

[0033] As used herein, the singular forms "a," "an," and "the" include both the singular and plural of the referent unless the context clearly dictates otherwise.

[0034] The term "optional" or "optionally" means that the subsequently described event, circumstance, or substituent may or may not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not occur.

[0035] The recitation of numerical ranges by endpoints includes all numbers and fractions subsumed within the respective ranges, as well as the recited endpoints.

[0036] The terms "about" or "approximately" as used herein when referring to a measurable value, such as a parameter, amount, duration, and the like, are meant to encompass variations of and variations from the specified value, such as variations of no more than + / -10%, no more than + / -5%, no more than + / -1%, and no more than + / -0.1% of the specified value, as appropriate for practice in embodiments of the invention. It is to be understood that the value to which the modifier "about" or "approximately" refers is itself specifically, and preferably, disclosed.

[0037] The terms "subject," "individual," and "patient" are used interchangeably herein to refer to a vertebrate, preferably a mammal, more preferably a human. Mammals include, but are not limited to, murines, simians, humans, farm animals, sport animals, and pets. Also included are tissues, cells, and their progeny, of biological entities obtained in vivo or cultured in vitro.

[0038] The word "exemplary" is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete manner.

[0039] When any element is referred to as being "attached," "connected," "coupled," "contacting," etc., "on" another element, it will be understood that it may be directly attached, connected, coupled, or in contact with the other element, or that there may be intervening elements. In contrast, when any element is referred to as being, for example, "directly on," "directly attached to," "directly connected to," "directly coupled to," or "in direct contact with" another element, no intervening elements are present. Those skilled in the art will also understand that references to structures or features located "adjacent" another feature may have portions that overlap or underlie the adjacent feature.

[0040] The term "real-time" is used to describe a process of sensing, processing, or transmitting information in a time frame equal to or less than the minimum timescale for which the information is required. For example, real-time monitoring of pulse rate may utilize a single average pulse rate measurement per minute averaged over 30 seconds, as instantaneous pulse rate is often not useful to the end user. Typically, averaged physiological and environmental information is more relevant than instantaneous changes. Thus, in the context of some embodiments of the present invention, signals may be processed over seconds or even minutes to generate a "real-time" response.

[0041] The terms "infection" and "bacterial infection" refer to the presence and / or colonization of pathogenic bacteria in or on a subject in sufficient numbers or amounts to be pathogenic and to cause disease, injury or harm to a subject infected with the bacteria. An infected subject is said to be "infected" with a pathogen. As used herein, a pathogenic bacterium, or "pathogen" for short, is a bacterium known to cause a bacterial infection in a subject.

[0042] Terms such as "comprise," "comprising," "include," "including," and the like are used to specify the presence of stated elements, steps, operations, and / or components, but do not exclude the presence or addition of one or more other elements, steps, operations, and / or components. Terms such as "first," "second," and the like may be used to describe various elements, but are not intended to be limiting. Such terms are used only to distinguish one element from another.

[0043] Various embodiments are described below. It should be noted that a particular embodiment is not intended as an exhaustive description or as a limitation to the broad aspects described herein. An aspect described in connection with a particular embodiment is not necessarily limited to that embodiment and can be implemented in any other embodiment. Throughout this specification, reference to "one embodiment," "embodiment," or "exemplary embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," or "exemplary embodiment" in various places throughout this specification do not necessarily all refer to the same embodiment, but may. Furthermore, particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments, as would be apparent to one of ordinary skill in the art from this disclosure. Furthermore, some embodiments described herein include some features included in other embodiments, while others do not, meaning that combinations of features of different embodiments are within the scope of the invention. For example, in the appended claims, any of the claimed embodiments may be used in any combination.

[0044] Embodiments of the present invention may be used to detect and identify wound infections using VOCs released from pathogens in subjects (e.g., in the skin of palms, fingers, ears, nose, face, eyes, arms, legs, chest, chest, back, abdomen, and / or feet), thus enabling real-time monitoring of dynamic changes of VOCs. High performance nanosensors combined with pattern recognition and machine learning algorithms enable early detection of wound infections.

[0045] The device of an embodiment of the present invention may be used for detection and identification of pathogens in a subject in real time. The device comprises at least one sensor array, a microcontroller unit (MCU), a digital signal processing circuit (DSC), an analog-to-digital converter (ADC), a communication interface (USB, Bluetooth, WiFi), and an "on-off switch". The results are transferred in real time via wireless communication to a mobile phone or laptop and / or to a designated server for data analysis and storage using a user interface. Information includes vital signs (such as skin temperature), VOC information, environmental conditions (time, temperature, humidity, and / or pressure). Based on the collected information, a comprehensive information library may be built to support pattern recognition and machine learning algorithms for early detection of wound infection.

[0046] In one embodiment, a method or process for diagnosing a wound infection includes applying a device on or near the wound, such as attaching or embedding the device in a wound dressing system, wound healing system, or wound management system, detecting metabolite VOC gases formed from metabolite VOC gases released from the wound in real time using a nanostructured sensor array, analyzing electrical properties in response, and recognizing and identifying pathogens using pattern recognition and machine learning algorithms. Additionally, the method may further include diagnosing bacterial infection and / or identification in one of internal medicine, rheumatology, physics, rehabilitation, clinical research, and basic research in the fields of immunology and / or microbiology, and evaluating the effectiveness of a drug on a subject that is known to kill or inhibit the growth of bacteria that cause the infection.

[0047] One embodiment of the present invention provides a wearable and / or portable device for rapid screening and diagnosis of pathogens in vivo and in vitro. The device comprises a housing with an open structure and at least one open end of the device disposed on the housing with a nanosensor and / or biosensor for detecting VOCs released from the pathogen for data acquisition. The data is provided to a remote server connected to the nanosensor and / or biosensor for data acquisition in the device of the housing for processing and transmitted to an acquisition unit.

[0048] In some embodiments, the device comprises a sensor array module for VOC detection, sensors for monitoring vital signs (e.g., heart rate, blood pressure, respiratory rate, blood oxygen saturation, and / or skin temperature and / or body temperature), artificial intelligence / machine learning algorithms, and an intuitive, user-friendly interface.

[0049] FIG. 1 shows an example of a device 100 that may be worn by a user or subject, according to an embodiment of the present invention. The device 100 includes a top cover 105 with an indicator 110, such as an OLED (or an LCD display with which a user may interact), that indicates the status of the device, a bottom cover 115 with two cup-shaped inlets 120 adapted to be attached to the surface of a test subject, and electronics 125 and a battery 130 housed between the top and bottom covers. The cup-shaped inlets are adapted to allow VOCs released from the wound of the test subject to enter the device. The electronics include two sensor arrays 135, each having a number of sensors for detecting VOCs, and a chip containing electronics for recording (e.g., in a non-volatile memory), processing (e.g., by a processor), and transmitting (e.g., via a Bluetooth card 140, a WiFi card, etc.) data. The battery provides power to the electronics. In this embodiment, the cup-shaped inlets on the bottom cover are connected to the sensor array in the electronics via a conduit (e.g., a tube) so that the sensor array is exposed to the VOCs immediately entering the inlets. The device has straps 145 for attaching it to the limbs or torso of a human or mammalian subject (e.g., to the palms, fingers, ears, nose, face, eyes, arms, legs, chest, chest, back, abdomen, and / or feet).

[0050] In certain embodiments, the inlet of the device is covered by a waterproof and / or breathable membrane. The sensor array includes a plurality of VOC sensors that respond to VOCs and generate a signal when exposed to gases, vapors, or odors, including VOCs, released from pathogens in the subject's wound, and one or more physiological sensors. The device can be integrated with a plurality of commercially available physiological sensors for monitoring vital signals. Such vital signals may include heart rate, pulse rate, respiratory rate, blood oxygen saturation, blood pressure, hydration level, stress, position and balance, body tension, neurological function, brain activity, blood pressure, intracranial pressure, auscultatory information, skin and body temperature, sleep, cholesterol, lipids, blood panels, body fat density, and / or muscle density. Additional sensors may be installed to monitor environmental conditions such as temperature, humidity, and / or pressure. The collected data may be used for wound infection detection and pattern recognition and machine learning algorithms that may perform other functions.

[0051] FIG. 2 is a block diagram illustrating various components of a device 200 for detecting wound infection, according to an embodiment of the present invention. The device 200 may correspond to any of the devices described herein (e.g., FIGS. 1, 6A, 6B, 8, 11, 12A, and 12B). The device includes a sensor array 205 including multiple VOC sensors and, optionally, one or more physiological and / or environmental sensors. The multiple sensor signals are sent to a sensor signal processing circuit 210 for processing. A switch channel 215 selects the signal from one of the sensors at a time and sends it to one of the analog-to-digital converters (ADC) 220 for conversion to a digital signal. The digital signal passes through a serial peripheral interface (SPI) 225 into a microcontroller unit (MCU) 230. The MCU 230 may be connected to a flash memory or RAM 235 for data storage and / or retrieval. In the embodiment of FIG. 2, the MCU 230 is connected to the MCU 240 via a USB interface 245. The MCU 240 is part of a communication module 250, which transfers the processed output to a nearby device via a communication device such as a Bluetooth or WiFi card, etc. The communication module 250 is further connected to its own Flash / RAM memory 255 and to user interfaces such as a liquid crystal display (LCD) 260, a capacitive touch panel (CTP) 265, and an on-off switch 270.

[0052] A battery 275 and power management circuitry 280 control the power supply to the sensor array and other electronics. Note that a user may control the sensor array 205 by sending commands through a user interface. The device may also have embedded firmware that runs on the device.

[0053] In certain embodiments, the raw or processed data detected by the sensor array 205 is wirelessly transferred in real time to a mobile phone or laptop and / or to a designated server for data analysis and storage. The transferred data may include vital signs (such as heart rate, blood pressure, respiratory rate, blood oxygen saturation, and / or skin temperature and / or body temperature), VOC information, and environmental conditions (time, temperature, humidity, and / or pressure). Based on the collected information, a comprehensive database can be built to support pattern recognition and machine learning algorithms for early detection of wound infection.

[0054] In some embodiments, the device measures VOCs based on a nanostructured sensor array. The sensor array comprises a plurality of sensors, e.g., 2-6-8-12-32 or more sensors having a sensing material, each sensor comprising a material that changes a particular property, e.g., resistance, when in contact with a particular VOC. The sensing material comprises at least one or more of mesoporous, macroporous, microporous, nanoporous, nonporous, hierarchically porous materials, including mesoporous / macroporous hierarchical structures, microporous / macroporous hierarchical structures, microporous / mesoporous / macroporous hierarchical structures, microporous / mesoporous / macroporous hierarchical structures, or mixed nanoporous structures. The mesoporous structure is a configuration selected from an ordered mesoporous structure with a regular pore arrangement, a string-like mesoporous structure with uniform pore size but with a wide range of irregularities, or a non-ordered mesostructure with a pore size of 2-50 nm. The macroporous structure is selected from an ordered macroporous structure or a non-ordered macroporous structure with a pore size of 50 nm to 50 μm. In further embodiments, the pore size of the sensing material is in the range of 0.4 to 2 nm, 2 to 50 nm, 50 nm to 200 nm, 200 nm to 500 nm, 500 nm to 1 μm, 1 to 50 μm. The specific surface area is in the range of 1 to 1000 m. 2 / g.

[0055] In some embodiments, the sensing material is selected from the group consisting of tin (Sn), terbium (Tb), cobalt (Co), zinc (Zn), indium (In), copper (Cu), nickel (Ni), chromium (Cr), manganese (Mn), tungsten (W), titanium (Ti), vanadium (V), iron (Fe), aluminum (Al), gallium (Ga), silver (Ag), gold (Au), palladium (Pd), rhodium (Rh), ruthenium (Ru), molybdenum (Mo), niobium (Nb), zirconium (Zr), yttrium (Y), lanthanum (La), platinum (Pt), silicon (Si), cerium (Cu ... The present invention includes monionary, binary, ternary, quaternary, pentanary, hexanionary, heptanionary and octanionary multi-component metal oxides selected from the group consisting of elements of Ca, Ca(II), Ca(II) and Ca(II) with different compositions of each chemical element, such as CoZnInSnOx, CuSnCoInOx, CoZnCrNiOx, SnTbCoOx, SnTbZnOx, CoTbInOx, CoNiTbOx, CoCeNiCuOx, ZnSnTeOx, CoZnInOx, CuSnInOx, CoCrNiOx, SnWLaOx, SnInLaCoOx, CoOx, CoTbOx, etc., where x=0.01-1.

[0056] The sensor array (e.g., sensor array 205) comprises at least one or two or three or four or eight or twelve or more gas sensors, which can be used to detect one or more gases from a metabolic product gas mixture released from a pathogen of a wound infection.

[0057] In some embodiments, the sensor further comprises a substrate and a plurality of electrodes on the substrate.

[0058] In some embodiments, the sensor is configured in a form selected from the group consisting of a capacitive sensor, a resistive sensor, a chemiresistive sensor, an impedance sensor, and a field effect transistor sensor. Each possibility represents a separate embodiment of the invention. In an exemplary embodiment, the sensor is configured as a chemiresistive sensor.

[0059] In certain embodiments, the sensor further comprises a detection mechanism comprising a device for measuring changes in resistance, conductance, alternating current (AC), frequency, capacitance, impedance, inductance, mobility, potential, optical property, or voltage threshold, with each possibility representing a separate embodiment of the present invention.

[0060] In certain embodiments, a sensor array is provided for diagnosing wound infection caused by a pathogen in a subject, the sensor array having a plurality of sensors, e.g., 2-6-8-12-32-48, consisting essentially of at least two of a sensing material, a substrate, a plurality of electrodes on the substrate, and a detection mechanism.

[0061] In certain embodiments, upon contact with at least one VOC on the sensing material indicative of a wound infection caused by a pathogen such as Staphylococcus epidermidis, Streptococcus pyogenes, Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter species, Escherichia coli, Proteus mirabilis, Salmonella typhimurium, Enterobacter cloaca, Acetinobacter nitratus, Lactobacillus delbrueckii, Gardnerella vaginalis, and antibiotic resistant bacterial strains, the electrical conductivity between the electrodes changes, thereby providing a measurable signal indicative of the wound infection.

[0062] The gas sensor may comprise a sensing material. In some examples, the sensing material comprises at least one or more of mesoporous, macroporous, microporous, nanoporous, non-porous, hierarchically porous materials, including mesoporous / macroporous hierarchical structures, microporous / macroporous hierarchical structures, microporous / mesoporous hierarchical structures, microporous / mesoporous / macroporous hierarchical structures, or mixed nanoporous structures. The mesoporous structure is selected from an ordered mesoporous structure having a regular pore arrangement, a string-like mesoporous structure having a uniform pore size but no regularity over a wide range, or a non-ordered mesostructure having a pore size of 2-50 nm. The macroporous structure is selected from an ordered macroporous structure or a non-ordered macroporous structure having a pore size of 50 nm-50 μm. In further embodiments, the pore size of the sensing material is in the range of 0.4-2 nm, 2-50 nm, 50 nm-200 nm, 200 nm-500 nm, 500 nm-1 μm, 1-50 μm. The specific surface area is in the range of 1-1000 m 2 / g.

[0063] The sensing material of some embodiments of the present invention may be in the form of a nanomaterial, examples of which include nanowires, nanorods, nanospheres, nanoporous, nanoplates, nanosheets, nanomeshes, nanotubes, nanohollow spheres, nanopolyhedrons, and nanospools.

[0064] In some embodiments, the sensing material is selected from the group consisting of tin (Sn), terbium (Tb), cobalt (Co), zinc (Zn), indium (In), copper (Cu), nickel (Ni), chromium (Cr), manganese (Mn), tungsten (W), titanium (Ti), vanadium (V), iron (Fe), aluminum (Al), gallium (Ga), silver (Ag), gold (Au), palladium (Pd), rhodium (Rh), ruthenium (Ru), molybdenum (Mo), niobium (Nb), zirconium (Zr), yttrium (Y), lanthanum (La), platinum (Pt), silicon (Si), cerium (Cu ... The present invention includes monionary, binary, ternary, quaternary, pentanary, hexanionary, heptanionary and octanionary multi-component metal oxides selected from the group consisting of elements of Ca, Ca(II), Ca(II) and Ca(II) with different compositions of each chemical element, such as CoZnInSnOx, CuSnCoInOx, CoZnCrNiOx, SnTbCoOx, SnTbZnOx, CoTbInOx, CoNiTbOx, CoCeNiCuOx, ZnSnTeOx, CoZnInOx, CuSnInOx, CoCrNiOx, SnWLaOx, SnInLaCoOx, CoOx, CoTbOx, etc., where x=0.01-1.

[0065] In some embodiments, the sensing material comprises monatomic, binary, ternary, quaternary, pentanary, and hexanary or single or multi-component monometallic nanoparticles selected from the group of elements silver (Ag), gold (Au), palladium (Pd), rhodium (Rh), ruthenium (Ru), platinum (Pt), osmium (Os), and iridium (Ir). The size of the nanoparticles ranges from 0.5 nm to 500 nm. The shape of the nanoparticles can be nanospheres, nanorods, nanowires, nanodots, nanostars, nanosheets, nanopartes, nanotubes, nanohollow spheres, nanotubes, and nanopolyhedrons. Some examples are Pt nanospheres, Au nanodots, Ag nanowires, Ag-Au nanotubes, and Os nanorods.

[0066] In some embodiments, the sensing material comprises a carbon-based material, which may be carbon black, activated carbon, microporous carbon, mesoporous carbon, micromesoporous carbon, pyrolytic carbon, carbon nanotubes, carbon nanofibers, carbon nanospheres, carbon nanosheets, carbon nanowires, carbon nanorods, graphene, graphene oxide, and reduced graphene oxide. The carbon-based material may be doped with sulfur, nitrogen, oxygen, boron, fluorine, phosphorus, selenium, chlorine, and the like.

[0067] In some embodiments, the sensing material comprises a carbon material functionalized with an organic agent including amines, fatty acids, alcohols, thiols, aldehydes, phenols, esters, epoxies, polymers, silane coupling agents, and mixtures thereof.

[0068] In some embodiments, the sensing material comprises a carbon material having a monoatomic metal in the carbon skeleton, the monoatomic metal being selected from the group of elements: tin (Sn), terbium (Tb), cobalt (Co), zinc (Zn), indium (In), copper (Cu), nickel (Ni), chromium (Cr), manganese (Mn), tungsten (W), titanium (Ti), vanadium (V), iron (Fe), aluminum (Al), gallium (Ga), silver (Ag), gold (Au), palladium (Pd), rhodium (Rh), ruthenium (Ru), molybdenum (Mo), niobium (Nb), zirconium (Zr), yttrium (Y), lanthanum (La), platinum (Pt), silicon (Si), cerium (Ce), tellurium (Te), osmium (Os), and iridium (IR).

[0069] In some embodiments, the sensing material includes both a metal and a metal oxide as components to form a composite sensing material having metal nanoparticles and a metal oxide.

[0070] In some embodiments, the sensing material includes a carbon-based material as a building block to form a composite sensing material with metal nanoparticles and metal oxides.

[0071] In some embodiments, the sensing material comprises a conjugated polymer such as a polythiophene material, a polyaniline material, a polypyrrole material, a polycarbazole material, etc. In some embodiments, the conjugated polymer is mixed with a carbon-based material.

[0072] 3 shows a flow diagram of an exemplary method 300 for preparing sensing materials for sensors in a sensor array (e.g., forming a three-dimensional macroporous / mesoporous material array). In step 305, a pore-forming template solution is prepared by using one or a mixture of two or more of polymer nanospheres, carbon black, carbon nanotubes, carbon nanofibers, carbon nanospheres, and polymethylmethacrylate (PMMA) microspheres, polystyrene nanospheres, latex spheres, and inorganic nanoparticles, silica nanoparticles, carbon nanoparticles, carbon dots, carbon nanocells, polymers with amphoteric solvents. In step 310, precursor solutions are prepared to generate target products by using one or more mixtures of metal species, graphene oxide, maxine, carbon nanotubes, metal nanoparticles, oligomeric organosilicates (1.4-bis(triethoxysilyl)benzene, 1.2-bis(triethoxysilyl)ethane, bis[(3-trimethoxysilyl)propylamine (amine), ethyltriethoxysilane) with an amphoteric solvent. In step 315, each of the precursor solution and the pore-forming template solution is placed in an individual one of the channels of the deposition device as an independent solution. In step 320, different proportions of each independent solution are deposited on the substrate to generate different combinations and compositions of as-synthesized films on the spots of the array. Each dot has an independent composition of at least one precursor solution and at least one pore-forming template solution. In step 325, the amphoteric solvent is evaporated and composite meso / macro structures can be formed into the as-synthesized film array. In step 330, the as-synthesized membrane array is heated to remove the organic pore-forming template and / or the membrane array is treated with aqueous NaOH or HF to remove the silica template and generate the 3D macroporous and mesoporous structured material array.

[0073] FIG. 4 shows an exemplary TEM image of the sensing material in the sensor array and the response of VOC gases.

[0074] An embodiment of the invention detects and identifies wound infections caused by pathogens in a subject. The method (e.g., implemented by a device described herein) includes providing a sensor array comprising a plurality of sensors, e.g., 2-6-8-12-32 sensors, where the sensors comprise at least one or more of mesoporous, macroporous, microporous, nanoporous, non-porous, hierarchically porous materials, including mesoporous / macroporous hierarchical structures, microporous / macroporous hierarchical structures, microporous / mesoporous hierarchical structures, microporous / mesoporous / macroporous hierarchical structures, or mixed nanoporous structures. The mesoporous structure is a configuration selected from an ordered mesoporous structure with a regular pore arrangement, a string-like mesoporous structure with uniform pore size but with a wide range of irregularities, or a non-ordered mesostructure with pore size between 2 and 50 nm. The macroporous structure is selected from an ordered macroporous structure or a non-ordered macroporous structure with a pore size of 50 nm to 50 μm. In further embodiments, the pore size of the sensing material is in the range of 0.4 to 2 nm, 2 to 50 nm, 50 nm to 200 nm, 200 nm to 500 nm, 500 nm to 1 μm, 1 to 50 μm. The specific surface area is in the range of 1 to 1000 m. 2 / g.

[0075] The sensing materials are tin (Sn), terbium (Tb), cobalt (Co), zinc (Zn), indium (In), copper (Cu), nickel (Ni), chromium (Cr), manganese (Mn), tungsten (W), titanium (Ti), vanadium (V), iron (Fe), aluminum (Al), gallium (Ga), silver (Ag), gold (Au), palladium (Pd), rhodium (Rh), ruthenium (Ru), molybdenum (Mo), niobium (Nb), zirconium (Zr), yttrium (Y), lanthanum (La), platinum (Pt), silicon (Si), cerium (Ce (Cu), Tb, TbZnOx, CoTbInOx, CoNiTbOx, CoCeNiCuOx, ZnSnTeOx, CoZnInOx, CuSnInOx, CoCrNiOx, SnWLaOx, SnInLaCoOx, CoOx, CoTbOx, etc., with different compositions of each chemical element, where x=0.01-1.

[0076] The sensor array is exposed to a metabolite gas mixture released from a pathogen of a wound infection in a subject. Response parameters from the sensors in the sensor array are measured and analyzed upon exposure to the test VOC using a detection mechanism to generate a response pattern. The response pattern is analyzed with a reference signal obtained from a control sample. The pathogen is recognized and identified using pattern recognition and machine learning algorithms. Infections may be diagnosed and / or bacteria may be identified in one of the following fields: internal medicine, rheumatology, physical therapy, rehabilitation, clinical research, and basic research in the fields of immunology and / or microbiology. The effectiveness of a drug on a subject may be evaluated if the drug is known to kill or inhibit the growth of bacteria causing the infection.

[0077] In some embodiments, measuring the plurality of response parameters includes measuring, extracting, filtering, amplifying, and processing the plurality of electrical signals from the sensor.

[0078] In some embodiments, the response elicitation parameter is selected from the group consisting of: a normalized change in the sensor signal at the peak of the exposure, a normalized change in the sensor signal at the middle of the exposure, a normalized change in the sensor signal at the end of the exposure, and an area under the curve of the sensor signal.

[0079] Pattern recognition and machine learning algorithms are applied to train the data and generate correlations between specific infection problems and patterns of signals. The algorithms include at least one algorithm selected from the group consisting of artificial neural network algorithms such as Naive Bayes, Principal Component Analysis (PCA), Support Vector Machine (SVM), Multilayer Perception (MLP), Generalized Regression Neural Network (GRNN), Fuzzy Inference System (FIS), Self-Organizing Map (SOM), Radial Bias Function (RBF), Genetic Algorithm (GAS), Neuro-Fuzzy System (NFS), Adaptive Resonance Theory (ART), Partial Least Squares (PLS), Multiple Linear Regression (MLR), Principal Component Regression (PCR), Discriminant Function Analysis (DFA), Linear Discriminant Analysis (LDA), Cluster Analysis, and Nearest Neighbor. In one embodiment, the at least one algorithm is Principal Component Analysis (PCA).

[0080] Once the model is set up using VOC patterns (and optionally vital signs and / or environmental conditions) as input, the system can generate early diagnostic results for wound infection purposes.

[0081] Pathogens of wound infections include, but are not limited to, Acetinobacter anitratus, Acinetobacter baumannii, Actinomyces israelii, Agrobacterium radiobacter, Agrobacterium tumefaciens, Anaplasma phagocytophilum, Azorhizobium carinodans, Azotobacter vinelandii, Bacillus anthracis, Bacillus brevis, Bacillus cereus, Bacillus fusiformis, Bacillus licheniferii, and the like. Myss, Bacillus megaterium, Bacillus mycoides, Bacillus stearothermophilus, Bacillus subtilis, Bacillus thuringiensis, Bacteroides fragilis, Bacteroides gingivalis, Bacteroides melaninogenicus, Bartonella henselae, Bartonella quintana, Bordetella bronchiseptica, Bordetella pertussis, Borrelia burgdorferi, Brucella abortus, Brucella meliloti, tensis, Brucella suis, Burkholderia mallei, Burkholderia pseudomallei, Burkholderia cepacia, Callimatobacterium granulomatis, Campylobacter coli, Campylobacter fetus, Campylobacter jejuni, Campylobacter pylori, Chlamydia trachomatis, Chlamydophila pneumoniae, Chlamydophila psittaci, Clostridium botulinum, Clostridium difficile, Clostridium perfringens, Clostridium tetani, Corynebacterium diphtheriae, Corynebacterium fujiforme, Coxiella burnetii, Ehrlichia chaffeensis, Enterobacter cloacae, Enterococcus avium, Enterococcus durans, Enterococcus faecalis, Enterococcus faecium, Enterococcus gallinum, Enterococcus malaratus, Escherichia coli, Francisella tularensis tularensis, Fusobacterium nucleatum, Enterobacter species, Gardnerella vaginalis, Haemophilus ducreyi, Haemophilus influenzae, Haemophilus parainfluenzae, Bordetella pertussispertussis), Haemophilus vaginalis, Helicobacter pylori, Klebsiella pneumoniae, Lactobacillus acidophilus, Lactobacillus bulgaricus, Lactobacillus casei, Lactobacillus delbrueckii, Lactococcus lactis, Legionella pneumophila, Listeria monocytogenes, Methanobacterium extroquens, Microbacterium multiforme, Micrococcus luteus, Moraxella catarrhalis, Morganella morganii, Mycobacterium avium, Mycobacterium bovis, Mycobacterium diphtheriae, Mycobacterium intracellulare, Mycobacterium leprae, Mycobacterium leprae, Mycobacterium smegmatis, Mycobacterium tuberculosis, Mycoplasma fermentans, Mycoplasma genitalium, Mycoplasma hominis, Mycoplasma penetrans, Mycoplasma pneumoniae, Mycoplasma mexicanus, Neisseria gonorrhoeae, Neisseria meningitidis, Pasteurella multocida, Francisella tularensistularensis), Porphyromonas gingivalis, Prevotella melaninogenica, Proteus vulgaris, Proteus mirabilis, Proteus pennellii, Providencia stuartii, Pseudomonas aeruginosa, Pseudomonas aeruginosa, Rhizobium radiobacter, Rickettsia prowazekii, Rickettsia apsittaci, Rickettsia quintana, Rickettsia rickettsii, Rickettsia atracomae, Locarimaea henselae, Locarimaea quintana, Locarimaea dentata cariosa, Salmonella enteritidis, , Salmonella typhi, Salmonella typhimurium, Salmonella typhimurium, Serratia marcescens, Rhodophyton moniliforme, Spirochete voltans, Staphylococcus aureus, Staphylococcus epidermidis, Stenotrophomonas maltophilia, Streptococcus agalactiae, Streptococcus avium, Streptococcus bovis, Streptococcus cricetus, Streptococcus faecium, Streptococcus faecalis, Streptococcus fels, Streptococcus gallinarum, Streptococcus lactis, Streptococcus maltophilia, Streptococcus galactiae ... Tococcus mitiol, Streptococcus mitis, Streptococcus mutans, Streptococcus oralis, Streptococcus pneumoniae, Streptococcus pyogenes, Streptococcus latus, Streptococcus salivarius, Streptococcus sanguis, Streptococcus sobrinus, Treponema pallidum, Treponema denticola, Vibrio cholerae, Vibrio comma, Vibrio parahaemolyticus, Vibrio vulnificus, Yersinia parahaemolyticus, Yersinia pestis, and Yersinia pseudomyxii. , and / or are known to comprise one or more antibiotic resistant strains derived from known species, and / or are known to comprise one or more extended-spectrum β-lactamase producing strains derived from known species, in particular the one or more extended-spectrum β-lactamase producing strains are selected from the group consisting of extended-spectrum β-lactamase producing Escherichia coli, and extended-spectrum β-lactamase producing Klebsiella pneumoniae.

[0082] Antibiotic-resistant bacterial strains include carbapenem-resistant Acinetobacter baumannii, carbapenem-resistant Pseudomonas aeruginosa, vancomycin-resistant Enterococcus faecium, methicillin-resistant Staphylococcus aureus, vancomycin-resistant Staphylococcus aureus, clarithromycin-resistant Helicobacter pylori, fluoroquinolone-resistant Campylobacter coli, fluoroquinolone-resistant Campylobacter fetus, fluoroquinolone-resistant Campylobacter jejuni, fluoroquinolone-resistant Campylobacter pylori, fluoroquinolone-resistant Salmonella enteritidis, fluoroquinolone-resistant Salmonella typhi, fluoroquinolone-resistant Salmonella typhimurium, cephalosporin-resistant Neisseria gonorrhoeae, fluoroquinolone-resistant Neisseria gonorrhoeae, penicillin-nonsusceptible pneumoniae, ampicillin-resistant Haemophilus influenzae, fluoroquinolone-resistant Shigella, carbapanem-resistant Escherichia coli, and Calcitonin-resistant Escherichia coli. cephalosporin-resistant Escherichia coli, cephalosporin-resistant Klebsiella pneumoniae, cephalosporin-resistant Enterobacter cloacae, cephalosporin-resistant Salmonella typhimurium, cephalosporin-resistant Proteus vulgaris, cephalosporin-resistant Proteus mirabilis, cephalosporin-resistant Proteus pennelli, cephalosporin-resistant Providencia stuartii, and cephalosporin-resistant Morganella morganii.

[0083] FIG. 5 is a flow diagram illustrating a method 500 for diagnosing wound infection using VOC data (e.g., for a wearable / portable device 505). First, a sufficient number of data samples, i.e., a discovery cohort, from both healthy controls (clean wounds) and patients with known health conditions (wound infection) are collected in step 510. The data samples include VOC data patterns, or VOC data patterns and vital signs and / or environmental conditions. Mathematical algorithms are used in steps 525, 530, 535, and 540 to train the data, identify unique patterns between healthy controls and patients, and generalize the classifier. The mathematical algorithm may be one or more of PCA, Naive Bayes, Support Vector Machine (SVM), Multi-Layer Perception (MLP), Generalized Regression Neural Network (GRNN), Fuzzy Inference System (FIS), Self-Organizing Map (SOM), Radial Bias Function (RBF), Genetic Algorithm (GAS), Neuro-Fuzzy System (NFS), Adaptive Resonance Theory (ART), Partial Least Squares (PLS), Multiple Linear Regression (MLR), Principal Component Regression (PCR), Discriminant Function Analysis (DFA), Linear Discriminant Analysis (LDA), Cluster Analysis, and Nearest Neighbor. The classifier is preferably a machine learning model, but may alternatively be a mathematical equation of vital signs and / or skin (or pathogen)-VOC parts to predict wound infection.

[0084] In the discovery cohort, a portion of the data is randomly assigned to the training set in step 515, while the remaining is assigned to the test set in step 520. The optimal classifier is generated in the training set using the test set. The value of the area under the ROC curve (AUC) of the patients is determined. Then, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the device are evaluated for both the training and test groups. For example, a 5-fold cross-validation (random selection of one fold of samples for testing and the remaining 4 folds for training) can be applied to calculate the classification performance of the training set.

[0085] Once the mathematical model (aka classifier) ​​is generated in step 545, one or more independent clinical cohorts are collected to validate the model. In this process, model parameters are refined (in steps 525, 530, 535, 540) and patients may be further stratified into subtypes using different parameter sets. After model validation and refinement, a user can input VOC data or vital signs, or both (and optionally environmental conditions) from subjects into the model / classifier and the model may predict a health condition (or wound infection) in step 550.

[0086] 6A and 6B show an exemplary portable device and its method of use according to an embodiment of the present invention. The portable device 600 includes a handle 630 coupled to the underside of a processing device (e.g., a personal digital assistant (PDA) 620). The portable device includes a disposable suction cup 605 adapted to cover the surface of a subject, a fan 610 adapted to generate a slight vacuum in the suction cup 605, at least one sensor array module 615, and a PDA 620 having an interface with which a user can interact. The sensor array module 615 is similar to that described above (e.g., the sensor array 205). It includes at least one sensor array, at least one sensor signal processing circuit, at least one switch channel circuit, at least one analog-to-digital converter (ADC), at least one microcontroller unit (MCU), at least one power management system, and at least one USB interface. The signal processing circuit may include a voltage divider circuit to measure the resistance of each sensor of the sensor array. To ensure ADC accuracy, multiple ADC and switch channels are used for data acquisition and digitization. The MCU collects the digital signal from the ADC and sends it to the system-on-chip (SOC) on the PDA620.

[0087] The sensor array module 615 is attached to the suction cup 605. By adopting the suction cup 605, interference from the environment, such as hand sweat, dirt, and temperature changes, is reduced. To enhance the sensitivity of the device, a fan 610 is used to generate a slight negative pressure in the suction cup so that the VOCs emitted from the palm mostly enter the portable device. The PDA 620 includes a SOC with 1-1 / wireless / USB communication function, a central processing unit (CPU), a memory, and an OLED or LCD screen. The data can be transferred to a terminal (e.g., PC) or cloud database by USB cable or wireless communication. The PDA runs an APP to provide human-computer interface, test data collection, and data transfer for further analysis. The test results may be displayed on the PDA as a number or visually using color-coded messages (e.g., white with a data of 0 may mean blank, green with a data of 0.1-2.9 may mean healthy (no infection), yellow with a data of 3-4.9 may mean continue testing, and red with a data above 5.0 may mean a warning). For example, when the data is above 5.0, the PDA alerts the user by displaying a red message.

[0088] Variations of the device 600 are multiple. For example, the device may rely on diffusion without a fan to draw a vacuum. Additionally, the device may be handheld or stationary. In some embodiments, the device has a pressure sensor that can detect changes in ambient pressure and bring the device from standby mode to operating mode. Thus, when a body part of the subject (e.g., hand, forehead) covers the suction cup 605, the change in pressure may bring the device into operating mode. In one embodiment, the device may be automatically turned off when data is sufficient for a readout or after a predefined period in standby mode. The device may have a manual input option where the user may manually set the duration of the test, for example, 0.001 to 30 minutes. Additionally, a health management system may be installed on a remote device (e.g., a server or a smartphone) to analyze and visualize the sensor readout.

[0089] 7A and 7B show an analysis of VOC patterns detected using a handheld device 600 for monitoring the growth of three bacteria (E. coli, Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA)) in wound infections. Each dot represents the device readout of a VOC in the primary space.

[0090] 8 shows an exemplary embodiment of a wearable device 850 for real-time monitoring of wound infection. The wearable device 850 is integrated into a dressing system 800 for detection, identification, and monitoring of bacteria causing wound infection. The dressing system 800 includes a wound dressing 810 for covering a wound and a wearable device 850 coupled to the wound dressing and placed on or near the wound.

[0091] Similar to that described with respect to FIG. 1 or FIG. 2, the wearable device 850 includes a sensor array, a sensor signal processing circuit (voltage divider circuit), a four-channel switching circuit, four 14-bit analog-to-digital converters (ADCs), a microcontroller unit (MCU), and a USB interface. The sensor array may include a 3×3 array with nine different gas sensors and one physiological sensor (skin temperature). Each of the nine sensors has a different nanostructured multi-component metal oxide or a different amount (e.g., concentration) of nanostructured multi-component metal oxide configuration or composition. The voltage divider circuit is used to process the change in characteristics (e.g., voltage change, resistance change, impedance change, combinations thereof, etc.) in each sensor in the sensor array. To ensure ADC accuracy, four ADCs and a four-channel switching circuit are used for data acquisition and digitization.

[0092] The MCU collects the digital signal from the ADC and sends it to the system. The data is wirelessly transferred to the PC and cloud database. The controller unit includes an 8-bit microcontroller with a Wi-Fi communication module, a liquid crystal display (LCD), a capacitive touch panel (CTP), and an "on-off switch". The communication unit transmits and receives radio waves at a specific frequency. The communication and power module (rechargeable battery) contains the power supply and is also responsible for data acquisition and transmission, and can connect with the PC via the Wi-Fi communication module. The CTP and LCD provide the function of the human interaction interface.

[0093] The wearable device 850 continuously detects VOCs emitted from ESKAPEE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter species, and Escherichia coli) for 36 hours. The 16 sensors had complete profiles for all experiments.

[0094] Figures 9A-D show the 16 sensor measurements of four analytes (Enterococcus faecium, Klebsiella pneumoniae, Acinetobacter baumannii, and Enterobacter species) within 36 hours. Each ESKAPEE pathogen can be distinguished by its unique pattern within the first 12 hours. To proceed with AI-based detection, a 9-class support vector machine (SVM) classifier was built to identify the sensor profile of the bacterial strain. To avoid overfitting artifacts, the dataset from 75% of the samples sampled every 0.5 hours from the sensor profile was used as the training set to train the SVM model. The remaining 25% of the sample data was used as the test set to validate the SVM model. The average accuracy per bacteria was extremely high, reaching an overall accuracy of 97.08.

[0095] Another exemplary embodiment shows the detection and discrimination of seven bacteria in wound infections. Figures 10A-10D show the differentiation of common wound infection pathogens including Enterococcus faecium (EF), Staphylococcus aureus (SA), Klebsiella pneumoniae (KP), Acinetobacter baumannii (AB), Pseudomonas aeruginosa (PA), Enterobacter species (ES), and Escherichia coli (EC) (ESKAPEE) using the device and PCA analysis. Each dot represents a sensor array readout of a bacterium in the primary space. Each of the ESKAPEE bacteria was cultured on an individual LB agar plate for 24 hours prior to qualitative analysis of the initial emission of VOCs from the organism. Culture medium on Luria Broth (LB) agar plates without bacteria was used as a negative control for background signal. Cultures were measured continuously for 48 hours by the MACchip array. Measurements of a 5:5 mixed PA-SA strain and an empty plate were also included. An AI algorithm was applied to separate the signals from individual strains into clearly distinguishable clusters.

[0096] The device of this exemplary embodiment uses a nanocomposite MACchip sensor array of 32 sensors. The MACchip sensor array integrates three types of sensors fabricated by a high-throughput 3D nanoprinting process using 200 different customized inks. The three types of sensors (and inks) use macroporous, mesoporous, microporous, and macro / mesoporous nanocrystalline (MAC) films embedded with metal oxides, MAC embedded with graphene and nanoparticles, and carbon black and conductive polymer composites. The sensor materials allow for different and complementary sensing capabilities. The sensors are designed to easily absorb chemical vapors, and after chemical binding, the electrical properties of the sensor change, generating a signal. Software with AI algorithms evaluates the presence / pattern of odors for specific chemical pathogens and presents the results to the patient and / or caregiver.

[0097] Sensing materials such as CoZnInSnOx, CuSnCoInOx, CoZnCrNiOx, SnTbCoOx, SnTbZnOx, CoTbInOx, CoNiTbOx, CoCeNiCuOx, ZnSnTeOx, CoZnInOx, CuSnInOx, CoCrNiOx, SnWLaOx, SnInLaCoOx, CoOx, CoTbOx, etc. have different compositions of each chemical element, x=0.01~1, and form conductive paths between electrodes to produce sensors in the MACchip sensor array. For quaternary metal oxides, the molar ratios of each element are 1:1:1:1, 1:1:0.5:0.5, 0.5:0.5:1:1, 0.1:0.1:1:1, and 1:1:0.1:0.1. For the ternary metal oxides, the molar ratios of each element are 1:1:1, 1:0.5:0.5, 1:0.1:0.1, 0.5:0.5:1, 0.1:0.1:1, and 0.1:1:0.1. The pore size of the multi-component is between 2 and 100 nm.

[0098] VOCs from laboratory strains representing all ESKAPEE bacteria, mixed cultures, and empty LB-agar plates are continuously monitored for 48 h. 19 sensors had complete profiles for all experiments. Figure 10A shows 19 sensor measurements of 9 analytes within 18 h. In the hierarchical clustering heatmap, the bacterial profiles tend to cluster in unique patterns for each bacterium. The patterns were visualized using principal component analysis (PCA), which condenses high-dimensional data into low dimensions but preserves the highest variation. Figure 10B, Figure 10C project the entire sensor data (19 sensors, 7 bacteria, PA-SA mixed bacteria, blank plate, 0.5 h sampling interval) onto two principal components (PC1 and PC2) and (PC1 and PC3), respectively. Each ESKAPEE bacterium, mixed culture, and empty plate can be distinguished by a unique pattern within the first hour, with only two clusters (EC and EF, etc.) partially overlapping.

[0099] To proceed with AI-based detection, a nine-class support vector machine (SVM) classifier was constructed to distinguish the sensor profiles of the seven bacterial strains, the PA-SA mixed strain, and the blank plate. To avoid overfitting artifacts, the dataset (sampled every 0.5 h from the sensor profiles, a total of 804 samples for the seven bacteria, the mixed strain, and the blank plate) was randomly divided as a training set (75% of the samples) and a test set (25% of the samples), and the SVM model was trained on the training set and validated using the test set. After feature selection, nine best features were extracted from the 32 sensors. The prediction accuracy for each bacterial strain was recorded, and this process was repeated 10,000 times. The average accuracy for each bacteria was extremely high, reaching an overall accuracy of 97.08% (Figure 10D).

[0100] FIG. 11 shows a further embodiment of a wearable device 1100 for real-time monitoring. In this illustrative example, the wearable device 1100 is a wearable wristwatch. The wearable device 1100 comprises a sensor array, a sensor signal processing circuit (voltage divider circuit), a four-channel switching circuit, an analog-to-digital converter (ADC), a microcontroller unit (MCU), and a USB interface as part of the electronics 1125 (similar to FIG. 1 or FIG. 2). As an example, the sensor array comprises two different gas sensors and one physiological sensor (heart rate). Each of the two sensors has a different nanostructured multi-component metal oxide or a different amount (e.g., concentration) of nanostructured multi-component metal oxide configuration or composition.

[0101] The voltage divider circuit is used to process the electrical changes (e.g., voltage changes, resistance changes, impedance changes, combinations thereof, etc.) in each sensor of the sensor array. The MCU collects the digital signal from the ADC and sends it to the system. The data is analyzed using artificial intelligence algorithms and wirelessly transferred to a PC and / or a cloud database. The controller unit includes an 8-bit microcontroller with a WiFi communication module, a liquid crystal display (LCD) 1130, a capacitive touch panel (CTP) 1135, and an "on-off switch". The communication and power module (e.g., rechargeable battery) 1140 contains the power supply and is also responsible for data acquisition and transmission, and can connect with a PC via the Wi-Fi communication module. The CTP and / or LCD provide the function of the human interaction interface.

[0102] In some embodiments, the negative pressure wound device may include the above-mentioned devices for wound infection detection and be used for negative pressure wound therapy.

[0103] Negative pressure wound therapy (NPWT) is a treatment that involves the use of devices to apply negative pressure to wound dressings to promote wound healing and reduce the risk of infection. NPWT has been shown to be effective in a variety of wound types, including chronic wounds, traumatic wounds, and surgical wounds.

[0104] Despite the benefits of NPWT, wound infection remains a common complication of the therapy. Wound infections can result in delayed healing and may require additional treatment, such as the use of antibiotics.

[0105] Thus, one embodiment of the present invention provides a negative pressure wound device including one or more integrated gas sensors for early detection and prediction of wound infections caused by pathogens (such as bacteria and fungi).

[0106] Negative pressure wound devices are designed to continuously monitor wound healing and provide real-time data on the presence and levels of gases indicative of infection, allowing timely intervention to prevent or treat infection (e.g., wound disinfection, dressing changes, medications, etc.) and improving the overall treatment of the wound.

[0107] One embodiment of the present invention relates to a negative pressure wound device incorporating a gas sensor for continuous monitoring of wound healing and early detection of wound infection caused by pathogens (such as bacteria and fungi). The negative pressure wound device includes a flexible wound dressing connected to a vacuum pump and a gas sensor system or module. The gas sensor module is configured to detect the presence of certain gases, such as hydrogen, oxygen, methane, carbon dioxide, ammonia and volatile organic compounds, that may indicate the presence of infection in the wound. The gas sensor module is also configured to detect the presence of certain gases produced by bacteria and fungi, allowing for early detection and prediction of certain types of infection. The gas sensor module is connected to a control unit that analyzes the gas levels and alerts a caregiver if an infection is detected or predicted. The negative pressure wound device may also include a display for displaying the results of the gas sensor readings and may be configured to transmit the sensor readings to a remote device for analysis and / or storage. The device may be connected to a remote monitoring system or a wireless communication system for transmitting data to a healthcare provider. Incorporating a gas sensor module into a negative pressure wound device can provide real-time wound monitoring, enabling early detection and prediction of wound infection, which can improve patient care and outcomes and may help reduce antibiotic use.

[0108] In one embodiment, the negative pressure wound device includes a housing that encloses components of the device, including a negative pressure source and a gas sensor. The negative pressure source applies negative pressure to the wound site, which helps remove excess fluid and promote healing. The gas sensor is configured to measure levels of specific gases produced by bacteria and fungi present in the wound. Data from the gas sensor is transmitted to a control unit configured to receive and analyze the data. The control unit is also responsible for controlling the negative pressure source in response to data from the gas sensor. For example, the negative pressure source may be enabled in response to detection of an infection, adjustments to pumping flow rate may be possible in response to progression or remission of an infection, etc.

[0109] The device includes a flexible wound dressing connected to a vacuum pump and a gas sensor module. The gas sensor is disposed within a housing of the device and can be of various types including an electrochemical sensor, a metal oxide sensor, an infrared sensor, or an optical sensor.

[0110] The gas sensor is configured to detect gases present in the wound dressing, such as hydrogen, oxygen, methane, carbon dioxide, ammonia, and volatile organic compounds (VOCs), which may indicate the presence of infection in the wound. VOCs include, but are not limited to, aldehydes, alcohols, ketones, acids, sulfur-containing compounds, esters, hydrocarbons and nitrogen-containing compounds, propene, acetaldehyde, ethanol, acetonitrile, (E)-2-butene, (Z)-2-butene, 2-propenal, n-propanol, acetone, 2-propanol, dimethylsulfide, 1-pentene, isoprene, n-pentane, 1,3-dioxolane, 2-methyl-2-propenal, 2-methyl-propanal, 3-butene-2-ene, 2-methyl-propanal ... ion, 2-methylfuran, n-butanal, 2-butanone, 3-methylfuran, ethyl acetate, 2-butenal, 2-methyl-1,3-dioxolane, 2-methyl-2-pentene, 2,3-dimethyl-2-butene, (E)-2-methyl-1,3-pentadiene, (Z)-2-methyl-1,3-pentadiene, 3-methylbutanal, 2-methylbutanal, isopropyl acetate, 2-pentanone, 2,5-dimethylfuran, allyl methyl sulfide, n-pentanal, 3-methyl- 2-Butenal, 1-heptene, 2-heptene, n-heptane, 2-ethylbutanal, 4-methyl-3-penten-2-one, isobutyl acetate, 2-hexanone, n-hexanal, gamma-butyrolactone, n-butyl acetate, (E)-2-hexenal, 1-octene, n-octane, 2-heptanone, n-heptanal, benzaldehyde, 1-nonene, n-nonane, 6-methyl-5-hepten-2-one, 2-pentyl-furan, b-pinene, n-octanal , p-cymene, DL-limonene, styrene, eucalyptol, n-nonanal, 2-ethylethanol, 3-methylhexane, butyraldehyde, ethylbenzene, ethyl butanoate, toluene, undecane, H2O, CO, NO, N2O, NO2, ammonia, acetophenone, 4-methylphenol, dodecane, dimethylpyrazine, 2-pentanol, 2-butanol, 2-pentene, 2-methylbutyl isobutyrate, 2-methoxy-5-methylthiophene, amyl isovalerate;2-Methylbutyl 2-methylbutyrate, 6-tridecane, 3-methyl 1H-pyrrole, 2-methyl-3-(2-propenyl)pyrazine, 2,3-dimethyl-5-isopentylpyrazine, methylthioacetate, methylthiocyanate, hydrogen cyanide, 2-aminoacetophenone, 1-undecene, formaldehyde, dimethyl ether, carbon dioxide, pentafluoropropionamide, methylcyclohexane, 2-methylbutanol, N-propyl acetate, butanal, 2,5-dimethyltetrahydrofuran, carbon disulfide, methyl propanoate, methyl butanoate, 6-methyl-5-hepten-2-one, 2,5-dimethylpyrazine, hydrogen sulfide, propanol panol, indole, 1,1,2,2-tetrachloroethane, butanol, 2-tridecenone, 3-hydroxy-2-butanone, 1-hydroxy-2-propanone, 3-nitro-benzenesulfonic acid, isobutyric acid, methyl ester, 1,2-dimethyl-benzene, 2-ethyl-1-hexanol, 3-methylbutanoic acid isopentyl, 2,4-dinitro-benzenesulfonic acid, decanal, 2-methyl-1-propanol, 2-phenylethanol, 1,4-dichlorobenzene, 2-methylbutanoic acid, methyl mercaptan, 2-nonanone, 3-methyl-1-butanol, 3-methylbutanoic acid, dimethyl trisulfide, dimethyl disulfide, and acetic acid;

[0111] In yet other embodiments, one or more of the multiple sensor arrays include multiple physiological sensors, each physiological sensor adapted to detect at least one parameter selected from heart rate, pulse rate, respiratory rate, blood oxygen saturation, blood pressure, hydration level, stress, position and balance, body tension, neurological function, brain activity, blood pressure, cranial pressure, auscultatory information, skin and body temperature, eye muscle movement, sleep, cholesterol, lipids, blood panel, body fat density, muscle density, temperature, humidity, and pressure.

[0112] Some embodiments of the negative pressure wound device can measure skin and body temperature (-15°C to 45°C), heart rate, humidity (0-99%), and various concentrations of VOCs. The VOC detection limit can be in the range of 0.1 ppb to 5000 ppm, for example, 0.1 ppb-1 ppb, 1 ppb-5 ppb, 5 ppb-10 ppb, 10 ppb-50 ppb, 50 ppb-100 ppb, 100 ppb-200 ppb, 200 ppb-300 ppb, 300 ppb-500 ppb, 500 ppb-1 ppm, 1 ppm-2 ppm, 2 ppm-5 ppm, 5 ppm-10 ppm, 10 ppm-100 ppm, 100 ppm-200 ppm, 200 ppm-500 ppm, 500 ppm-1000 ppm, 1000 ppm-2000 ppm, and 2000 ppm-5000 ppm.

[0113] The gas sensor system may also be configured to detect the presence of specific gases produced by bacteria and fungi, allowing for early detection and prediction of certain types of infection.

[0114] The negative pressure wound device may also include a display for displaying the results of the gas sensor readings and may be configured to transmit the sensor readings to a remote device for analysis and / or storage. The remote device may be configured to provide an indication of wound infection and pathogen type based on analysis of the gas sensor readings.

[0115] The negative pressure wound device may also include a treatment module for timely intervention to prevent or treat infection based on data from the gas sensor.

[0116] In one embodiment, the negative pressure wound device includes a housing and the wound dressing is attached to the housing. The housing may be shaped and sized to fit over the wound and the wound dressing may be made of a porous material that allows the passage of gas.

[0117] The pump is in communication with the housing and is configured to apply negative pressure to the wound dressing. The pump may be a mechanical or an electrical pump and may be powered by a battery or other power source.

[0118] The negative pressure wound device also includes a processor connected to the gas sensor system. The processor is configured to analyze data collected by the gas sensor system and generate an alert when the data indicates the presence of a bacterial or fungal infection at the wound site. The alert may be presented on a display connected to the processor. The display may be a separate display unit or may be integrated into the negative pressure wound device.

[0119] The negative pressure wound device may also include a wireless communication module (such as a Bluetooth or WiFi system) for transmitting data from the gas sensor to a remote location for analysis and treatment recommendations. A battery provides power to the negative pressure wound device and the gas sensor.

[0120] In addition to the above features, the negative pressure wound device may also include machine learning algorithms to analyze data from the gas sensor to predict the likelihood of infection and suggest treatment options. The device may also include a database for storing and organizing data from the gas sensor, and a dashboard for displaying real-time data and providing alerts when levels of gas indicative of infection are detected. The device may also include a reporting module for generating reports on the data from the gas sensor and the effectiveness of the treatment intervention.

[0121] The negative pressure wound device may also include a display for displaying the results of the gas sensor readings. The display may be a visual display, such as an LCD screen, or an audio display, such as a speaker.

[0122] The negative pressure wound device may also include a transmitter for transmitting the gas sensor readings to a remote device for analysis and / or storage, which may be a computer, smartphone, or other device with internet connectivity.

[0123] In use, the negative pressure wound device is applied to a wound and the pump is activated to apply negative pressure to the wound dressing. The gas sensor detects gas present within the wound dressing and transmits the sensor reading to a display and / or remote device. The resulting gas sensor reading may be used to determine the presence of a wound infection and prompt appropriate treatment.

[0124] The integration of gas sensors into negative pressure wound devices allows for rapid, accurate detection of wound infections, improving patient outcomes and reducing the risk of complications. The devices are easy to use and allow for continuous monitoring of the wound, enabling healthcare providers to rapidly address any potential infections.

[0125] An embodiment of the present invention includes a system for early detection and prediction of wound infections caused by bacteria and fungi. The system includes a negative pressure wound device comprising a housing, a negative pressure source, and one or more gas sensors for detecting gases produced by bacteria and fungi present in the wound. A control unit is configured to receive data from the gas sensor and control the negative pressure source in response to the data. A user interface displays the data from the gas sensor, and a treatment module provides timely intervention to prevent or treat the infection based on the data from the gas sensor. A communication module transmits data from the gas sensor to a remote location for analysis and treatment recommendation. Machine learning algorithms may be used to analyze the data from the gas sensor to predict the likelihood of infection and suggest treatment (e.g., wound disinfection, dressing change, medication, etc.).

[0126] An embodiment of the present invention includes a method for early detection and prediction of wound infections caused by bacteria and fungi. The wound site is continuously monitored using one or more gas sensors integrated into a negative pressure wound device. The levels of certain gases produced by bacteria and fungi present in the wound are measured, and real-time data regarding the presence and levels of gases indicative of infection is provided to a control unit. The negative pressure applied by the negative pressure wound device is adjusted in response to data from the gas sensors. The data from the gas sensors is displayed on a user interface. Based on the data from the gas sensors, timely interventions are made to prevent or treat the infection (e.g., wound disinfection, dressing change, medication, etc.).

[0127] 12A and 12B, a negative pressure wound device 1200 includes a housing 1205 to which a wound dressing 1210 is attached. The wound dressing is made of a porous material that allows gas to pass through. A pump 1215 is in communication with the housing and configured to apply negative pressure to the wound dressing. The pump may be a mechanical or electrical pump and may be powered by a battery or other power source.

[0128] The gas sensor 1220 is disposed within the housing 1205 and configured to detect gases present within the wound dressing 1210. The gas sensor may include sensors for detecting hydrogen and methane, which may be indicative of anaerobic bacteria present in the wound. The gas sensor may also include sensors for detecting other gases, which may be indicative of a wound infection. The housing 1205 is a container that houses the wound dressing 1210 and the gas sensor 1220. The gas sensor 1220 is disposed within the housing 1205 in proximity to the wound dressing 1210. This allows the sensor to detect gases produced by bacteria and fungi within the wound and provide real-time data regarding the presence and levels of gases indicative of infection.

[0129] The gas sensor 1220 communicates with a processor of the device configured to process the sensor readings and transmit the results to a display and / or transmitter of the device. The display may be a visual display, such as an LCD screen, or an audio display, such as a speaker. The user interface of the display may display real-time data regarding the level of gas detected by the sensor, as well as other relevant information, such as the type of bacteria or fungus present in the wound, and any recommended interventions or treatments.

[0130] The transmitter is configured to transmit the sensor readings to a remote device 1230 for analysis (determination of wound infection) and / or storage. The remote device may be a computer, smartphone, or other device with internet connectivity. The gas sensors and other device components may be substantially similar to those described above (FIGS. 1 and 2). The user interface of the remote device may display real-time data regarding the levels of gas detected by the sensor, as well as other relevant information, such as the type of bacteria or fungus present in the wound, and any recommended interventions or treatments.

[0131] Based on the collected information including VOC patterns (and optionally vital signs and / or environmental conditions), machine learning algorithms such as Naive Bayes, Principal Component Analysis, Multinomial Logistic Regression, and Support Vector Machines may be applied to train the data and generate correlations between specific infection issues with peak patterns (and optional pump adjustments and / or treatments). Interventions and treatments can be customized based on the specific needs of the patient and the type of infection present in the wound. For example, if data from the gas sensor indicates the presence of anaerobic bacteria, interventions may include disinfecting the wound and applying an antibiotic effective against anaerobic bacteria. Alternatively, if the data suggests the presence of other types of bacteria or fungi, a different treatment plan may be recommended. The negative pressure wound device can be designed to adjust the level of negative pressure based on the level of gas detected by the sensor. For example, if the gas sensor detects high levels of hydrogen or methane gas, which may indicate the presence of anaerobic bacteria, the device can automatically increase the level of negative pressure to help remove the bacteria and promote wound healing.

[0132] Once the model is set up using VOC patterns (and optionally vital signs and / or environmental conditions) as input, the system can generate early diagnosis results for wound infection purposes (and optionally pump adjustments, interventions, and / or treatments).

[0133] In use, the negative pressure wound device 1200 is applied to a wound and the pump 1215 is activated to apply negative pressure to the wound dressing 1210. The gas sensor 1220 detects gas present in the wound dressing (substantially as described above) and transmits a sensor reading to a processor. The processor processes the sensor reading and transmits the results to a display and / or transmitter, substantially as described above. The gas sensor reading results may be displayed on a display and / or transmitted to a remote device 1230 for analysis and storage. The results may be used to determine the presence of a wound infection and prompt appropriate treatment. For example, the pump may be enabled in response to the detection of an infection and adjustments of the pumping rate may be allowed in response to the progression or remission of the infection. A negative pressure wound device with a gas sensor provides for bacterial detection, similar to that shown in Figures 10A-10D above.

[0134] The integration of gas sensors into negative pressure wound devices allows for rapid, accurate detection of wound infections, improving patient outcomes and reducing the risk of complications. The devices are easy to use and allow for continuous monitoring of the wound, enabling healthcare providers to rapidly address any potential infections.

[0135] The gas sensor may be configured to detect other gases in addition to hydrogen, methane, carbon dioxide, ammonia and microbial volatile organic compounds. The gas sensor may also be configured to detect multiple gases simultaneously or sequentially.

[0136] The negative pressure wound device may also include additional sensors or monitoring systems, such as temperature or pH sensors, to provide additional information about the wound environment. The device may also include a user interface, such as buttons or a touch screen, to allow the user to adjust the negative pressure or other device settings.

[0137] The negative pressure wound device may also include a wireless or wired connection to a remote device, such as a computer or smartphone, for transmitting the gas sensor readings and other device data. The remote device may include software for analyzing the gas sensor readings and providing an indication of wound infection or other conditions. The remote device may also include a database for storing the gas sensor readings and other device data for future reference or analysis.

[0138] The negative pressure wound device may also include a power source, such as a battery or an external power source, to power the device and its components. The device may include a charging system, such as a USB port or a charging port, to recharge the power source.

[0139] The negative pressure wound device may also include a waterproof or water-resistant casing or housing to protect the device and its components from moisture or other environmental factors. The casing or housing may also be sterilizable or disposable to reduce the risk of infection or contamination.

[0140] The negative pressure wound device may also include a timer or other tracking system to monitor the duration of treatment and prompt the user to change the wound dressing or other components as necessary. The device may also include a memory or other storage system for storing gas sensor readings and other device data for future reference or analysis.

[0141] The negative pressure wound device may also include a user manual or other instructions to aid the user in proper operation and maintenance of the device. The instructions may include information on how to apply the device to a wound, how to activate and adjust the negative pressure, and how to interpret gas sensor readings and other device data. The instructions may also include safety alerts and precautions to prevent the user from injuring themselves or damaging the device.

[0142] Negative pressure wound devices allow for continuous monitoring of the wound, enabling healthcare providers to quickly address any potential infections and improve patient outcomes.

[0143] Negative pressure wound devices may be designed and manufactured using a variety of materials and techniques to meet the needs and preferences of the user.

[0144] Wound dressings may be made of porous and absorbent materials, such as foam or gauze, to allow the passage of gases and the absorption of exudate.

[0145] Gas sensors may be made from highly sensitive and durable materials, such as semiconductors or metal oxides, to enable accurate and reliable readings.

[0146] The pump may be made from durable and reliable materials, such as metal or plastic, to ensure long term performance.

[0147] Negative pressure wound devices may also be designed to be user friendly and easy to operate, with clear and intuitive controls and displays. The devices may be ergonomically designed to conform comfortably to the wound and minimize the risk of discomfort or irritation to the user.

[0148] The negative pressure wound device may also be designed to be compatible with a variety of wound dressings and other accessories, such as adhesive patches or wraps, to allow for flexibility and customization. The device may also be designed to be compatible with a variety of power sources, such as batteries or external power sources, to allow for convenient and reliable operation.

[0149] In terms of manufacturing, the negative pressure wound devices may be made using a variety of techniques, such as injection molding, blow molding, or extrusion, to create the desired shape and size. The devices may also undergo a variety of quality control measures to ensure they meet required performance and safety standards.

[0150] The negative pressure wound device may include a housing element that is made up of, but is not limited to, a chip system, a battery, IoT functionality, a fan system of various shapes, and a port system. In some embodiments, such devices are connected to, but are not limited to, sterile tubing, capillary systems, gas permeable membranes, membranes, and cast systems. Such devices may include, but are not limited to, skin-safe adhesives, elastic bands, or combinations thereof to allow attachment anywhere on the human body.

[0151] In one embodiment of the negative pressure wound device, during operation, at least one of the one or more processors generates data by executing a method selected from Naive Bayes, Principal Component Analysis (PCA), Support Vector Machine (SVM), Multi-Layer Perception (MLP), Generalized Regression Neural Network (GRNN), Fuzzy Inference System (FIS), Self-Organizing Map (SOM), Radial Bias Function (RBF), Genetic Algorithm (GAS), Neuro-Fuzzy System (NFS), Adaptive Resonance Theory (ART), Partial Least Squares (PLS), Multiple Linear Regression (MLR), Principal Component Regression (PCR), Discriminant Function Analysis (DFA), Linear Discriminant Analysis (LDA), Cluster Analysis, and Nearest Neighbor.

[0152] It will be understood that the embodiments described above and illustrated in the drawings represent only a few of the many ways in which embodiments for non-invasive detection of pathogens in wounds may be implemented.

[0153] In some embodiments, detecting a gas mixture of pathogens includes exposing the gas mixture to a sensor array comprising 2-6-8-12-32 or more sensors with a sensing material at an operating temperature of 250° C. or less, the sensing material comprising at least one or more of mesoporous, macroporous, microporous, nanoporous, non-porous, hierarchically porous materials, including mesoporous / macroporous hierarchical structures, microporous / macroporous hierarchical structures, microporous / mesoporous hierarchical structures, microporous / mesoporous / macroporous hierarchical structures, or mixed nanoporous structures. The mesoporous structure is a configuration selected from an ordered mesoporous structure with a regular pore arrangement, a string-like mesoporous structure with uniform pore size but with a wide range of irregularities, or a non-ordered mesostructure with pore size of 2-50 nm. The macroporous structure is selected from an ordered macroporous structure or a non-ordered macroporous structure with a pore size of 50 nm to 50 μm. In further embodiments, the pore size of the sensing material is in the range of 0.4 to 2 nm, 2 to 50 nm, 50 nm to 200 nm, 200 nm to 500 nm, 500 nm to 1 μm, 1 to 50 μm. The specific surface area is in the range of 1 to 1000 m. 2 / g.

[0154] In some embodiments, the sensing material is selected from the group consisting of tin (Sn), terbium (Tb), cobalt (Co), zinc (Zn), indium (In), copper (Cu), nickel (Ni), chromium (Cr), manganese (Mn), tungsten (W), titanium (Ti), vanadium (V), iron (Fe), aluminum (Al), gallium (Ga), silver (Ag), gold (Au), palladium (Pd), rhodium (Rh), ruthenium (Ru), molybdenum (Mo), niobium (Nb), zirconium (Zr), yttrium (Y), lanthanum (La), platinum (Pt), silicon (Si), cerium (Cu ... The present invention includes monionary, binary, ternary, quaternary, pentanary, hexanionary, heptanionary and octanionary multi-component metal oxides selected from the group consisting of elements of Ca, Ca(II), Ca(II) and Ca(II) with different compositions of each chemical element, such as CoZnInSnOx, CuSnCoInOx, CoZnCrNiOx, SnTbCoOx, SnTbZnOx, CoTbInOx, CoNiTbOx, CoCeNiCuOx, ZnSnTeOx, CoZnInOx, CuSnInOx, CoCrNiOx, SnWLaOx, SnInLaCoOx, CoOx, CoTbOx, etc., where x=0.01-1.

[0155] In some embodiments, the set of detected sensor array signals may be obtained in response to changes in the electrical resistance of the sensing material.

[0156] The method of some embodiments may include exposing the sensor array to a gas mixture released from a wound infection pathogen. As used herein, the gas mixture may include VOCs or vapors from a subject, for example, from the subject's skin or exhaled breath. In some cases, the VOCs or vapors are released from the subject's skin. The skin may be any wound on the subject, for example, the subject's palm, finger, arm, leg, back, abdomen, or foot. In some examples, the gas mixture includes a variety of odors from chemical classes, such as aldehydes, alcohols, ketones, acids, sulfur-containing compounds, esters, hydrocarbons, and nitrogen-containing compounds.

[0157] The method of some embodiments may be carried out at relatively low operating temperatures. In some embodiments, the operating temperature may be up to 250°C, up to 2000°C, up to 1500°C, up to 1000°C, up to 80°C, up to 60°C, up to 50°C, up to 40°C, up to 30°C, up to 20°C, or up to 10°C. In some examples, the operating temperature may range from about -30°C to about 40°C, e.g., from about 0°C to 30°C, from about 10°C to about 30°C, or from about 20°C to about 25°C. In some examples, the operating temperature may be 50°C or lower.

[0158] The method, gas sensor, and device of some embodiments may detect the relative level of the gas mixture. In some cases, the method and device of some embodiments may be able to detect VOCs at concentrations of 5000 parts per million (ppm) or less, 4000 ppm or less, 3000 ppm or less, 2000 ppm or less, 1000 ppm or less, 500 ppm or less, 250 ppm or less, 100 ppm or less, 50 ppm or less, 10 ppm or less, 1 ppm or less, 800 parts per billion (ppb) or less, 600 ppb or less, 500 ppb or less, 400 ppb or less, 200 ppb or less, 100 ppb or less, 80 ppb or less, 60 ppb or less, 40 ppb or less, 20 ppb or less, 10 ppb or less, or 1 ppb or less of gas in the gas mixture. In some cases, the method, sensor, and device of some embodiments may be configured to have a detection limit of 5000 ppm or less of gas in the gas mixture. "Detection limit" refers to the lowest amount of a substance that can be distinguished from the absence of that substance (e.g., a blank value). In certain cases, some embodiments of the gas sensor or device are configured to have a detection limit of 1000 ppm or less, 500 ppm or less, such as 400 ppm or less, including 300 ppm or less, 200 ppm or less, 100 ppm or less, 75 ppm or less, 50 ppm or less, 25 ppm or less, 20 ppm or less, 15 ppm or less, 10 ppm or less, 5 ppm or less, 1 ppm or less, 500 ppb or less, 100 ppb or less, 50 ppb or less, 10 ppb or less, or 1 ppb or less. In certain cases, some embodiments of the gas sensor or device are configured to have a detection limit of 1 ppm or less. In certain cases, the gas sensor or device is configured to detect at least 1 ppb, at least 10 ppb, at least 50 ppb, at least 100 ppb, at least 500 ppb, at least 1 ppm, at least 5 ppm, at least 10 ppm, at least 15 ppm, at least 20 ppm, at least 25 ppm, at least 50 ppm, at least 75 ppm, at least 100 ppm, or at least 200 ppm of VOCs.

[0159] In some embodiments, the sensor array may detect both gas mixtures that contain or accumulate volatile compounds and gas mixtures that do not contain or accumulate volatile compounds.

[0160] The subject may refer to animals, including mammals, preferably humans, to which the methods of some embodiments may be applied. Mammalian species that may benefit from embodiments of the invention include, but are not limited to, apes, chimpanzees, orangutans, humans, monkeys, domestic animals (e.g., pets), such as dogs, cats, guinea pigs, hamsters, and larger domestic animals, such as cows, horses, goats, sheep, etc. The subject may also be any wild animal, as embodiments of the invention may be used for tracking purposes.

[0161] In some embodiments, the identification of bacteria may be based on volatile organic or inorganic compounds obtained from the pathogen of a wound infection in a subject having a sensor array. The sensor array may be any device that can generate an electrical signal that changes in response to interaction with the volatile compounds of interest. The sensor signal may be any observable change in one or more quantifiable entities, such as resistance, voltage, frequency, etc.

[0162] In some embodiments, the reference signal includes known pathogens such as bacteria and fungi. Reference signals can be established using standard samples obtained from in vitro grown pathogens or from in vivo infected and non-infected patients or animals that are evaluated by embodiments of the invention and / or by conventional techniques to identify pathogens found therein.

[0163] The data acquisition unit may include a nanosensor, a biosensor, readout circuitry, one or more microcontrollers (eg, signal converters, signal processing, control circuitry, power control integrated circuits, etc.), a communication unit, and a battery.

[0164] In some embodiments, the devices of some embodiments include one or more of the following characteristics or attributes: skin and body temperature (-15°C to 45°C), heart rate, humidity (0-99%), concentration of volatile organic compounds (1 ppb to 5000 ppm), provide audible and inaudible alarms or color alerts, or other visualization or notification when pathogens are detected, and can be directly integrated into existing products (e.g., wound dressing systems, wound healing systems, wound management systems).

[0165] The device of some embodiments includes one or more of the gas sensors described above. In certain embodiments, the device further comprises a power source, a display, a computer, a microcontroller unit, a readout circuit, a communication module (e.g., a wireless communication module), a memory, or any combination thereof. The gas sensor and / or device may detect and differentiate between two or more pathogens (e.g., bacteria, fungi, etc.) in a polymicrobial infection based on the pattern of changes in the sensing material properties.

[0166] The device of some embodiments may be standalone and / or may be incorporated into (e.g., as part of) and / or interoperable with interactive mobile devices or applications with Internet of Things (IoT) characteristics. In some embodiments, the device may be integrated into or part of a specialized wound dressing system, a hypothermia bag, a transport chamber, a smart phone, a wearable device, a health care device, a medical device, fitness equipment (e.g., treadmills, ellipticals, etc.), or combinations thereof. The device may detect VOCs, such as VOCs from exhaled breath or VOCs emitted from the skin (e.g., the skin of the palms, fingers, ears, nose, face, eyes, arms, legs, chest, chest, back, abdomen, or feet of a subject).

[0167] The device of some embodiments may be a wearable device. In some cases, the gas sensor array sensitive to wound infection pathogens that emit VOCs using the sensing material herein may be manufactured as a wearable device. Examples of wearable devices include armbands, sleeves, jackets, glasses, eyewear, goggles, gloves, watches, wristbands, bracelets, earphones, earbuds, clothing, hats, headbands, headsets, bras, and jewelry.

[0168] The device of some embodiments may be a portable device. In some cases, the sensing material of the present specification may be used to fabricate a gas sensor array sensitive to VOCs released by wound infection pathogens by deposition as a portable device. Examples of portable devices include key chains, breath test devices, etc.

[0169] The devices of some embodiments may be disposable devices (e.g., wound dressings, etc.), where the disposable device (and the sensor or sensors) are configured for disposable use and may be replaced after each use.

[0170] Some embodiment devices may function with relatively low power consumption, for example, some embodiment devices may have a power consumption of up to 500 μAmp, up to 400 μAmp, up to 300 μAmp, up to 200 μAmp, up to 20 μAmp, up to 10 μAmp, up to 9 μAmp, up to 8 μAmp, up to 7 μAmp, up to 6 μAmp, up to 5 μAmp, up to 4 μAmp, up to 3 μAmp, up to 2 μAmp, or up to 1 μAmp.

[0171] The devices of some embodiments can be relatively small in size. For example, the devices can be up to 300 cm 3 , up to 200cm 3 , up to 100cm 3 , up to 30cm 3 , max. 20cm 3 , up to 15cm 3, up to 10cm 3 , up to 8cm 3 , up to 6cm 3 , up to 5cm 3 , up to 4cm 3 , up to 3cm 3 , maximum 2cm 3 , or up to 1 cm 3 may have a volume of

[0172] In some cases, the devices of some embodiments are intelligent. For example, the devices may be configured to calibrate (e.g., self-calibrate). Calibration may be performed based on reference information specific to an individual user.

[0173] The device of some embodiments may be configured to digitally read the VOC concentration. The device may convert the signal from one form to another. For example, the device may convert an analog signal to a digital signal and / or convert the digital signal into a measurement of the energy expenditure and / or metabolic profile of a user subject.

[0174] The device of some embodiments may transmit data wirelessly, for example via the Internet, Bluetooth, Bluetooth Low Energy (BLE), or a combination thereof. The device may be configured to connect to a smartphone or computer (e.g., a laptop) and to use (e.g., wear) the device to visualize, monitor, and / or analyze the onset and progression of a subject's infection, antibiotic treatment, metabolic profile, and physiological state, or a combination thereof.

[0175] Data from some embodiment devices can be shared with medical professionals in real time to ensure accurate and appropriate treatment.

[0176] The device (e.g., FIG. 1, FIG. 6A, FIG. 6B, FIG. 8, FIG. 11, FIG. 12A, and FIG. 12B) may determine an action to intervene and / or treatment as described above. Based on the collected information including the VOC patterns (and optionally vital signs and environmental conditions), machine learning algorithms such as Naive Bayes, Principal Component Analysis, Multinomial Logistic Regression, and Support Vector Machines may be applied in embodiments to train the data and generate correlations between specific infection issues with peak patterns (and optional intervention actions, pressure source control, and / or treatment). Once the model is set with the VOC patterns (and optionally vital signs and / or environmental conditions) as input, the system can generate early diagnosis results for wound infection objectives (optionally pressure source adjustment, intervention actions, and / or treatment).

[0177] The device of some embodiments may continuously sense the presence of a wound infection, for example, the device of some embodiments may monitor pathogen growth in real time from incubation, through colonization, to infection.

[0178] In some embodiments, the one or more pathogens include at least one from the group of bacteria and fungi. Bacteria include Escherichia coli, Salmonella enterica, Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes, Neisseria gonorrhoeae, Neisseria meningitidis, Haemophilus influenzae, Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterococcus faecalis, Enterococcus faecium, Clostridioides difficile, Campylobacter jejuni, Listeria monocytogenes, Vibrio cholerae, Vibrio parahaemolyticus, Mycobacterium tuberculosis, Mycobacterium leprae, Helicobacter pylori, Bordetella pertussis, Legionella pneumophila, Shigella spp., Yersinia pestis, Francisella tularensis, tularensis), Brucella spp., Borrelia burgdorferi, Chlamydia trachomatis, Chlamydia pneumoniae, Coxiella burnetidus, Rickettsia rickettsii, Rickettsia prowazekii, Bartonella henselae, Burkholderia pseudomallei, Burkholderia malayi, Acinetobacter baumannii, Moraxella catarrhalis, Nocardia spp., Propionibacterium acnes, Actinomyces spp., Treponema pallidum, Treponema denticola, Fusobacterium spp., Porphyromonas spp., Prevotella spp., Bacteroides fragilis, Bacteroides taiotaomicron, Capnocytophaga spp., Pasteurella multocida, Actinobacillus spp., Streptococcus spp., These include Bacillus moniliformis, Erysipelothrix rhusiopathiae, Lactobacillus spp., Corynebacterium jeikeium, Nocardia asteroidea, Mycoplasma pneumoniae, Ureaplasma urealyticum, Legionella long beach, Legionella bozemanii, Legionella dumophila, Legionella mikdadei, Legionella anisa, Legionella filei, Legionella gormani, Legionella jordanis, Legionella londiniensis, Legionella machacerni, Legionella oakridgegensis, Legionella quatielensis, Legionella brillens, Legionella sainterensi, Legionella steigerwartii, Legionella taurinensis, and Legionella waswartii.

[0179] Fungi include Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, Histoplasma capsulatum, Blastomyces dermatitidis, Coccidioides immitis, Candida glabrata, Candida tropicalis, Candida parapsilosis, Candida krusei, Trichophyton rubrum, Trichophyton rotundus, Microsporum canis, Epidermophyton rotundus, Pneumocystis jirovecii, and Fusarius Musorani, Fusarium oxysporum, Rhizopus oryzae, Mucor spp., Sedosporium prolificans, Sporothrix schenckii, Paracoccidioides braziliensis, Candida dubliniensis, Candida lustaniae, Candida guilliermondii, Candida kefia, Candida famata, Candida lipolytica, Candida utilis, Candida zeylanoides, Candida rugosa, Candida norvegicus Candida periculosa, Candida salmonis, Candida stellatoidea, Candida zonata, Aspergillus flavus, Aspergillus niger, Aspergillus terreus, Candida haemuloni, Candida orthopsilosis, Candida metapsilosis, Candida auris, Trichosporon asahi, Trichosporon cutanum, Trichosporon mucoides, Trichosporon ovoides, Trichosporon asteroides, Geotrichum candidum, Geotrichum capitatum, Paecilomyces, Acremonium, Alternaria, Cladosporium, Penicillium, Aspergillus nidulans, Aspergillus versicolor, Exophiala dermatitidis, Exophiala jenselmei, Exophiala spinifera, Exophiala xenobiotica, Candida utilis var.utilis), Candida glabrata var. bracalensis, Trichosporondohaense, Trichosporondomesticum, Trichosporon japonicum, Trichosporon moniliforme, Trichosporon mucoidum, Trichosporon pullulans, Rhizomucor psillus, Rhizomucor variabilis, Cunninghamella verutretiae, Cunninghamella echinalate, Cunninghamella blakesleyana, Absidia crimbifera, Mucor circinelloides, Mucor racemosus, Succinia basiformis, Rhizopus microspores, and Rhizopus spp.

[0180] Various modifications and variations of the described embodiments will be apparent to those skilled in the art without departing from the scope and spirit of the present invention. Although the present invention has been described in connection with specific embodiments, it will be understood that the invention is capable of further modifications and that the claimed invention should not be unduly limited to such specific embodiments. Indeed, various modifications of the described aspects for carrying out the embodiments of the present invention that are obvious to those skilled in the art are intended to be within the scope of the present invention. This application is generally intended to cover any variation, use, or adaptation of the embodiments of the present invention, including such departures from the present disclosure that are within known and customary practice within the technical field to which the present invention pertains and that may be applied to the essential features of the present specification before being described, in accordance with the principles of the present invention.

Claims

1. 1. A method for detecting a wound infection comprising: receiving information from at least one sensor relating to the detection of one or more gases emitted by one or more pathogens in the wound causing the infection, the at least one sensor including a sensing material that changes one or more properties in response to the presence of the one or more gases; analyzing the information from the at least one sensor via at least one processor to identify the one or more pathogens and determine the presence of the infection in the wound, wherein the one or more pathogens are identified based on a pattern of changes in the one or more characteristics indicative of a corresponding pathogen; A method comprising:

2. The at least one sensor further provides one or more measurements from a group of physiological parameters and environmental conditions, and analyzing the information includes:

2. The method of claim 1, comprising: analyzing the information and measurements from the at least one sensor to identify the one or more pathogens and determine the presence of the infection in the wound, wherein the one or more pathogens are identified based on a pattern of changes in the one or more characteristics and the measurements indicative of the corresponding pathogen.

3. The one or more pathogens include two or more pathogens from the group of bacteria and fungi, and the method further comprises:

10. The method of claim 1, further comprising the step of detecting and distinguishing between the two or more pathogens in a polymicrobial infection.

4. analyzing the information, 2. The method of claim 1, further comprising: analyzing the information with a machine learning model that correlates the patterns of change in the one or more characteristics with patterns of the corresponding pathogens.

5. The at least one sensor is disposed within one of a wearable device, a portable device, a disposable device, and a wound dressing, and the method further comprises:

10. The method of claim 1, further comprising monitoring pathogen growth in real time from incubation, colonization, to infection.

6. The method of claim 1 , wherein the at least one sensor is further configured to distinguish between two or more pathogens in a polymicrobial infection.

7. The method of claim 1 , wherein the information from the at least one sensor is monitored in real time.

8. The method of claim 1 , wherein the at least one sensor is disposed within a wound dressing, the wound dressing and the at least one sensor being configured for single use.

9. The method of claim 1 , wherein the one or more properties of the sensing material that are changed include conductivity, capacitance, resistance, or impedance.

10. The method of claim 1 , further comprising the step of providing an alert or notification to a health care provider or a patient based on the information from the at least one sensor.

11. 2. The method of claim 1, wherein the one or more pathogens include at least one from the group of bacteria and fungi, and the analyzing step further comprises identifying the one or more pathogens based on a pattern of changes in the one or more characteristics corresponding to the bacteria and fungi.

12. The one or more pathogens include at least one from the group of bacteria and fungi, and the bacteria are selected from the group consisting of Escherichia coli, Salmonella enterica, Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes, Neisseria gonorrhoeae, Neisseria meningitidis, Haemophilus influenzae, Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterococcus faecalis, Enterococcus faecium, Clostridioides difficile, Campylobacter jejuni, Listeria monocytogenes, Vibrio cholerae, Vibrio parahaemolyticus, Mycobacterium tuberculosis, Mycobacterium leprae, Helicobacter pylori, Bordetella pertussis, Legionella pneumophila, Shigella spp., Yersinia pestis, Francisella tularensis, tularensis), Brucella spp., Borrelia burgdorferi, Chlamydia trachomatis, Chlamydia pneumoniae, Coxiella burnetidus, Rickettsia rickettsii, Rickettsia prowazekii, Bartonella henselae, Burkholderia pseudomallei, Burkholderia malayi, Acinetobacter baumannii, Moraxella catarrhalis, Nocardia spp., Propionibacterium acnes, Actinomyces spp., Treponema pallidum, Treponema denticola, Fusobacterium spp., Porphyromonas spp., Prevotella spp., Bacteroides fragilis, Bacteroides taiotaomicron, Capnocytophaga spp., Pasteurella multocida, Actinobacillus spp., Streptobacillus moniliformis, Erysipelothrix rhusiopathiae, Lactobacillus spp., Corynebacterium diphtheriae, Corynebacterium and the fungus may be selected from the group consisting of Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, Histoplasma capsulatum, Blastomyces dermatitidis, Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, Histoplasma capsulatum, Blastomyces dermatitidis, Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, Histoplasma capsulatum, Blastomyces dermatitidis,Coccidioides immitis, Candida glabrata, Candida tropicalis, Candida parapsilosis, Candida krusei, Trichophyton rubrum, Trichophyton nigricans, Microsporum canis, Epidermophyton nigricans, Pneumocystis jiroveci, Fusarium solani, Fusarium oxysporum, Rhizopus oryzae, Mucor spp., Sedosporium prolificans, Sporothrix schenckii, Paracoccidioides braziliensis, Candida dubliniensis, Candida rusitaniae, Candida guilliermondii, Candida cef Candida famata, Candida lipolytica, Candida utilis, Candida zeylanoides, Candida rugosa, Candida norvegensis, Candida peliculosa, Candida salmon, Candida stellatoidea, Candida zonata, Aspergillus flavus, Aspergillus niger, Aspergillus terreus, Candida haemuloni, Candida orthopsilosis, Candida metapsilosis, Candida auris, Trichosporon asahi, Trichosporon cutanum, Trichosporon mucoides, Trichosporon o Voides, Trichosporon asteroides, Geotrichum candidum, Geotrichum capitatum, Paecilomyces spp., Acremonium spp., Alternaria spp., Cladosporium spp., Penicillium spp., Aspergillus nidulans, Aspergillus versicolor, Exophiala dermatitidis, Exophiala jenselmei, Exophiala spinifera, Exophiala xenobiotica, Candida utilis var. utilis, Candida glabrata var. bracalensis, Trichosporon flies 10. The method of claim 1, comprising the genus Rhizomucor psillus, Trichosporon japonicum, Trichosporon moniliforme, Trichosporon mucoidum, Trichosporon pullulans, Rhizomucor psillus, Rhizomucor variabilis, Cunninghamella verutretiae, Cunninghamella echinalate, Cunninghamella blakesleyana, Absidia crimbifera, Mucor circinelloides, Mucor racemosus, Succinia basiformis, Rhizopus microspores, and Rhizopus spp.

13. The at least one sensor is disposed in a wound dressing, and the method comprises: The method of claim 1 , further comprising applying negative pressure to the wound via a negative pressure source to promote healing.

14. adjusting a flow rate of the negative pressure source based on the information from the at least one sensor; The method of claim 13 further comprising:

15. determining, via the at least one processor, a treatment for the wound based on the information from the at least one sensor; The method of claim 1 further comprising:

16. 1. A system for detecting a wound infection, comprising: at least one sensor for detecting one or more gases released from one or more pathogens in the wound causing the infection, said at least one sensor comprising a sensing material that changes one or more properties in response to the presence of said one or more gases; at least one processor configured to analyze information from the at least one sensor to identify the one or more pathogens and determine the presence of the infection in the wound, wherein the one or more pathogens are identified based on a pattern of changes in the one or more characteristics indicative of a corresponding pathogen; A system comprising:

17. The at least one sensor further provides one or more measurements from the group of physiological parameters and environmental conditions, and analyzing the information. and analyzing the information and measurements from the at least one sensor to identify the one or more pathogens and determine the presence of the infection in the wound, wherein the one or more pathogens are identified based on a pattern of changes in the one or more characteristics and the measurements indicative of the corresponding pathogen.

18. The one or more pathogens include at least one from the group of bacteria and fungi, and the bacteria are selected from the group consisting of Escherichia coli, Salmonella enterica, Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes, Neisseria gonorrhoeae, Neisseria meningitidis, Haemophilus influenzae, Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterococcus faecalis, Enterococcus faecium, Clostridioides difficile, Campylobacter jejuni, Listeria monocytogenes, Vibrio cholerae, Vibrio parahaemolyticus, Mycobacterium tuberculosis, Mycobacterium leprae, Helicobacter pylori, Bordetella pertussis, Legionella pneumophila, Shigella spp., Yersinia pestis, Francisella tularensis, tularensis), Brucella spp., Borrelia burgdorferi, Chlamydia trachomatis, Chlamydia pneumoniae, Coxiella burnetidus, Rickettsia rickettsii, Rickettsia prowazekii, Bartonella henselae, Burkholderia pseudomallei, Burkholderia malayi, Acinetobacter baumannii, Moraxella catarrhalis, Nocardia spp., Propionibacterium acnes, Actinomyces spp., Treponema pallidum, Treponema denticola, Fusobacterium spp., Porphyromonas spp., Prevotella spp., Bacteroides fragilis, Bacteroides taiotaomicron, Capnocytophaga spp., Pasteurella multocida, Actinobacillus spp., Streptobacillus moniliformis, Erysipelothrix rhusiopathiae, Lactobacillus spp., Corynebacterium diphtheriae, Corynebacterium and the fungus may be selected from the group consisting of Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, Histoplasma capsulatum, Blastomyces dermatitidis, Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, Histoplasma capsulatum, Blastomyces dermatitidis, Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, Histoplasma capsulatum, Blastomyces dermatitidis,Coccidioides immitis, Candida glabrata, Candida tropicalis, Candida parapsilosis, Candida krusei, Trichophyton rubrum, Trichophyton pedunculosis, Microsporum canis, Epidermophyton purpurea, Pneumocystis jiroveci, Fusarium solani, Fusarium oxysporum, Rhizopus oryzae, Mucor spp., Sedosporium prolificans, Sporothrix schenckii, Paracoccidioides braziliensis, Candida dubliniensis, Candida rusitaniae, Candida guilliermondii, Candida kefii A, Candida famata, Candida lipolytica, Candida utilis, Candida zeylanoides, Candida rugosa, Candida norvegensis, Candida peliculosa, Candida salmon, Candida stellatoidea, Candida zonata, Aspergillus flavus, Aspergillus niger, Aspergillus terreus, Candida haemuloni, Candida orthopsilosis, Candida metapsilosis, Candida auris, Trichosporon asahi, Trichosporon cutanum, Trichosporon mucoides, Trichosporon oboi des, Trichosporon asteroides, Geotrichum candidum, Geotrichum capitatum, Paecilomyces spp., Acremonium spp., Alternaria spp., Cladosporium spp., Penicillium spp., Aspergillus nidulans, Aspergillus versicolor, Exophiala dermatitidis, Exophiala jenselmei, Exophiala spinifera, Exophiala xenobiotica, Candida utilis var. utilis, Candida glabrata var. bracalensis, Trichosporon dojacensis 17. The system of claim 16, comprising Trichosporon domesticum, Trichosporon japonicum, Trichosporon moniliforme, Trichosporon mucoidum, Trichosporon pullulans, Rhizomucor psillus, Rhizomucor variabilis, Cunninghamella verutretiae, Cunninghamella echinalate, Cunninghamella blakesleyana, Absidia crimbifera, Mucor circinelloides, Mucor racemosus, Succinia basiformis, Rhizopus microspores, and Rhizopus spp.

19. Analysing the information 20. The system of claim 16, further comprising: analyzing the information with a machine learning model that correlates the patterns of change in the one or more characteristics with patterns of the corresponding pathogens.

20. 17. The system of claim 16, wherein the at least one sensor is disposed within one of a wearable device, a portable device, a disposable device, and a wound dressing.

21. The system of claim 20 , wherein the at least one processor is located in a remote device.

22. The at least one sensor is disposed within the wound dressing, and the system comprises:

21. The system of claim 20, further comprising a negative pressure source to apply negative pressure to the wound to promote healing.

23. The at least one processor:

23. The system of claim 22, further configured to adjust a flow rate of the negative pressure source based on the information from the at least one sensor.

24. The at least one processor: The system of claim 16 , further configured to determine a treatment for the wound based on the information from the at least one sensor.

25. Software executable by at least one processor, the software including: receiving information regarding detection of one or more gases emitted from one or more pathogens in the wound causing infection from at least one sensor, the at least one sensor including a sensing material that changes one or more properties in response to the presence of the one or more gases; analyzing the information from the at least one sensor to identify the one or more pathogens and determine the presence of the infection in the wound, wherein the one or more pathogens are identified based on a pattern of changes in the one or more characteristics indicative of a corresponding pathogen; and a memory device containing software to cause the apparatus to perform the steps of the method of the present invention.

26. wherein the one or more pathogens include at least one from the group of bacteria and fungi, and analyzing the information comprises:

26. The apparatus of claim 25, further comprising: analyzing the information with a machine learning model that correlates the pattern of changes in the one or more characteristics with a pattern of the corresponding pathogen.

27. a negative pressure source applies negative pressure to the wound to promote healing, the software further causing the at least one processor to:

26. The apparatus of claim 25, further comprising: an apparatus for adjusting a flow rate of the negative pressure source based on the information from the at least one sensor.

28. The software further causes the at least one processor to:

26. The apparatus of claim 25, further comprising: determining a treatment for the wound based on the information from the at least one sensor.

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