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

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 "Suitable 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 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 December 20, 2022, the disclosures of which are incorporated herein by reference in their entireties.

[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 place a significant burden on 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 dressings (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, if not treated appropriately, can lead to serious complications. Early detection of wound infection is important to ensure timely treatment and optimal patient outcomes.

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

[0007] Therefore, limitations of these approaches include the potential for delayed diagnosis and the frequent administration of empirical treatment before 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, noninvasive, and reproducible. There is increasing 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 forefront of this technology for diagnosing infectious diseases. The ability to rapidly detect microbial VOCs, which may allow for pathogen identification, has enormous implications for infection management, from triage and point-of-care in austere settings to hospital, clinic, and home environments. If a patient's wound can be accurately monitored from its inception through discharge from the hospital and into home use, appropriate antimicrobial therapy can be initiated early enough to prevent serious infection, and the status of the infection can be continuously monitored. Summary of the Invention

[0010] In accordance with one embodiment of the present invention, a system for detecting wound infection includes at least one sensor and at least one processor. The at least one sensor detects one or more gases released by one or more pathogens in the wound that cause 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 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. Embodiments of the present invention also include methods and apparatuses having a memory device including software executable by the at least one processor to detect wound infection in substantially the same manner as described above. [Brief explanation of the drawings]

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

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

[0013] [Figure 2] FIG. 1 illustrates components of a device according to one embodiment of the present invention.

[0014] [Figure 3] 1 is a flow diagram of an exemplary method for preparing a sensing material of sensors in a sensor array for use in embodiments of the present invention.

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

[0016] [Figure 5] FIG. 1 illustrates a process for building a model / classifier and using the model / classifier to diagnose wound infection according to one 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] 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. 8. [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. 8. [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. 8. [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. 8.

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

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

[0024] [Figure 10C] 10A-10C illustrate 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] 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 INVENTION

[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 adjacent to the 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), communication interfaces (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 stages of infection, thereby enabling caregivers to provide effective and timely treatment.

[0032] Embodiments of the present invention detect the onset of direct wound bed infection 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 throughout all stages of infection.

[0033] As used herein, the singular forms "a," "an," and "the" include both the singular and the 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] As used herein, the term "about" or "approximately" when referring to a measurable value such as a parameter, amount, duration, etc., is 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, 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 intervening elements may be present. 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" to 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 needed. 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 a period of 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 quantities to be pathogenic and 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 limiting. Such terms are used only to distinguish one element from another.

[0043] Various embodiments are described below. Note that specific embodiments are not intended as exhaustive or as limitations on the broad aspects described herein. An aspect described in connection with a particular embodiment is not necessarily limited to that embodiment and may be practiced in any other embodiment. Throughout this specification, references to "one embodiment," "an embodiment," or "an exemplary embodiment" mean 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 "an exemplary embodiment" in various places throughout this specification do not necessarily all refer to the same embodiment, although they 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 from 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 emitted from pathogens in a subject (e.g., in the skin of the palms, fingers, ears, nose, face, eyes, arms, legs, chest, chest, back, abdomen, and / or feet), thus enabling real-time monitoring of dynamic changes in VOCs. High-performance nanosensors combined with pattern recognition and machine learning algorithms enable early detection of wound infections.

[0045] The device of one embodiment of the present invention may be used for real-time pathogen detection and identification in a subject. The device comprises at least one sensor array, a microcontroller unit (MCU), a digital signal processor (DSC), an analog-to-digital converter (ADC), a communication interface (USB, Bluetooth, WiFi), and an "on-off switch." 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, and environmental conditions (time, temperature, humidity, and / or pressure). Based on the collected information, a comprehensive information library may be constructed 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 to or near a wound, such as by 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 nanostructure 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 in a subject that is known to kill or inhibit the growth of infection-causing bacteria.

[0047] One embodiment of the present invention provides a wearable and / or portable device for rapid in vivo and in vitro pathogen screening and diagnosis. The device comprises a housing having an open structure and at least one open end of the device having a nanosensor and / or biosensor disposed on the housing for detecting VOCs emitted from pathogens for data acquisition. The data is provided to a remote server connected to the nanosensor and / or biosensor for data acquisition within the device in the housing for processing and transmission 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 and / or body temperature), artificial intelligence / machine learning algorithms, and an intuitive, user-friendly interface.

[0049] FIG. 1 illustrates an example of a device 100 that may be worn by a user or test subject, according to one embodiment of the present invention. The device 100 includes a top cover 105 having an indicator 110, such as an OLED (or an LCD display that the user can interact with), that indicates the device's status; a bottom cover 115 having two cup-shaped inlets 120 adapted for attachment 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 test subject's wound to enter the device. The electronics include two sensor arrays 135, each having multiple sensors for detecting VOCs, and a chip containing electronics for recording (e.g., in 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 powers the electronics. In this embodiment, the cup-shaped inlets on the bottom cover are connected to the sensor arrays in the electronics via conduits (e.g., tubing) so that the sensor arrays are immediately exposed to VOCs 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 device's inlet is covered by a waterproof and / or breathable membrane. The sensor array includes multiple VOC sensors that respond to VOCs and generate a signal when exposed to gases, vapors, or odors, including VOCs released by pathogens in the subject's wound, and one or more physiological sensors. The device can be integrated with multiple 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, auscultation information, skin and body temperature, sleep, cholesterol, lipids, blood panel, body fat density, and / or muscle density. Additional sensors may be installed to monitor environmental conditions, such as temperature, humidity, and / or pressure. Collected data can be used for wound infection detection and can be used in pattern recognition and machine learning algorithms, which may perform other functions.

[0051] FIG. 2 is a block diagram illustrating various components of a device 200 for detecting wound infection, according to one embodiment of the present invention. 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. Multiple sensor signals are sent to sensor signal processing circuitry 210 for processing. Switch channel 215 selects the signal from one of the sensors at a time and sends it to one of analog-to-digital converters (ADCs) 220 for conversion to a digital signal. The digital signal passes through a serial peripheral interface (SPI) 225 to a microcontroller unit (MCU) 230. MCU 230 may be connected to flash memory or RAM 235 for data storage and / or retrieval. In the embodiment of FIG. 2, MCU 230 is connected to 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 card or a WiFi card, which 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 the device.

[0053] In certain embodiments, raw or processed data detected by the sensor array 205 is wirelessly transmitted in real time to a mobile phone or laptop and / or a designated server for data analysis and storage. The transmitted data may include vital signs (such as heart rate, blood pressure, respiratory rate, blood oxygen saturation, and / or skin 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 constructed 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 includes multiple sensors, e.g., 2 to 6 to 8 to 12 to 32 or more sensors, each having a sensing material, where each sensor changes a specific property, e.g., resistance, when contacted with a specific VOC. The sensing material includes 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, or mixed nanoporous structures. The mesoporous structure is selected from an ordered mesoporous structure with a regular pore arrangement, a string-like mesoporous structure with uniform pore sizes but with a wide range of irregularities, or a disordered mesostructure with pore sizes of 2 to 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, or 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), and cerium (Cu). The present invention includes uni-, binary, ternary, quaternary, pentanary, hexanion, septanion and octanionary multi-component metal oxides selected from the group consisting of elements of cerium (Ce), tellurium (Te), and arsenic (Ar), 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, two, three, four, eight, twelve or more gas sensors, which can be used to detect one or more gases from a metabolite gas mixture released by 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 capacitance sensor, a resistive sensor, a chemiresistive sensor, an impedance sensor, and a field effect transistor sensor. Each possibility represents a separate embodiment of the present 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 a wound infection caused by a pathogen in a subject, the sensor array having, for example, 2 to 6 to 8 to 12 to 32 to 48 sensors 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 pathogens 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 anitratus, Lactobacillus delbrueckii, Gardnerella vaginalis, and antibiotic-resistant bacterial strains, a change in electrical conductivity between the electrodes is observed, thereby providing a measurable signal indicative of the wound infection.

[0062] The gas sensor may include a sensing material. In some examples, the sensing material includes 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 hierarchical structures, and microporous / mesoporous / macroporous hierarchical structures, or mixed nanoporous structures. The mesoporous structure is selected from an ordered mesoporous structure with a regular pore arrangement, a string-like mesoporous structure with uniform pore sizes but extensive irregularity, or a disordered mesostructure with pore sizes between 2 and 50 nm. The macroporous structure is selected from an ordered macroporous structure or a disordered macroporous structure with pore sizes between 50 nm and 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, or 1 to 50 μm. The specific surface area is in the range of 1 to 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 nanospheres.

[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), and cerium (Cu). The present invention includes uni-, binary, ternary, quaternary, pentanary, hexanion, septanion and octanionary multi-component metal oxides selected from the group consisting of elements of cerium (Ce), tellurium (Te), and arsenic (Ar), 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 mono-, bi-, ternary, quaternary, pentanary, and hexanary or single- or multi-component monometallic nanoparticles selected from the group consisting of silver (Ag), gold (Au), palladium (Pd), rhodium (Rh), ruthenium (Ru), platinum (Pt), osmium (Os), and iridium (Ir). The nanoparticles range in size from 0.5 nm to 500 nm. The nanoparticles may be shaped as nanospheres, nanorods, nanowires, nanodots, nanostars, nanosheets, nanoparticulates, nanotubes, hollow nanospheres, nanotubes, or nanopolyhedrons. Some examples include 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, etc.

[0067] In some embodiments, the sensing material comprises a carbon material functionalized with an organic agent including an amine, a fatty acid, an alcohol, a thiol, an aldehyde, a phenol, an ester, an epoxy, a polymer, a silane coupling agent, and mixtures thereof.

[0068] In some embodiments, the sensing material comprises a carbon material having a monoatomic metal in its 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 oxide.

[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 chart 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, polymethyl methacrylate (PMMA) microspheres, polystyrene nanospheres, latex spheres, and inorganic nanoparticles, silica nanoparticles, carbon nanoparticles, carbon dots, carbon nanocells, and polymers containing amphoteric solvents. In step 310, precursor solutions are prepared to generate target products by using one or a mixture of two or more of the following: metal species, graphene oxide, maxine, carbon nanotubes, metal nanoparticles, and oligomeric organosilicates (1,4-bis(triethoxysilyl)benzene, 1,2-bis(triethoxysilyl)ethane, bis[(3-trimethoxysilyl)propylamine (amine)], and ethyltriethoxysilane) together with an amphiprotic solvent. In step 315, the precursor solution and the pore-forming template solution are each placed as separate solutions into individual channels of a deposition apparatus. In step 320, different proportions of each separate 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 amphiprotic solvent is evaporated, and composite meso / macrostructures can be formed into the as-synthesized film array. In step 330, the as-synthesized film array is heated to remove the organic pore-forming template and / or the film array is treated with aqueous NaOH or HF to remove the silica template and produce a 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 present invention detects and identifies a wound infection caused by a pathogen in a subject. The method (e.g., implemented by a device described herein) includes providing a sensor array including a plurality of sensors, e.g., 2 to 6 to 8 to 12 to 32 sensors, wherein the sensors comprise 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, or mixed nanoporous structures. The mesoporous structure is selected from an ordered mesoporous structure with a regular pore arrangement, a string-like mesoporous structure with uniform pore sizes but with a wide range of irregularities, or a disordered mesostructure with pore sizes 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, or 1 to 50 μm. The specific surface area is in the range of 1 to 1000 m. 2 / g.

[0075] Sensing materials include 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), and cerium (Ce ), tellurium (Te), and other elemental group, including monian, binary, ternary, quaternary, pentanary, hexanion, septanion, and octanionary multicomponent metal oxides 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.

[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 using 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 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 signal patterns. 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] Wound infection pathogens 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 lichenifer Mys, 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 psittacosis, 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 malartus, Escherichia coli, Francisella tularensis tularensis), Fusobacterium nucleatum, Enterobacter species, Gardnera vaginalis, Haemophilus ducreyi, Haemophilus influenzae, Haemophilus parainfluenzae, Bordetella pertussis (Haemophiluspertussis), 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 tularensis (Pasteurellatularensis), Porphyromonas gingivalis, Prevotella melaninogenica, Proteus vulgaris, Proteus mirabilis, Proteus pennellii, Providencia stuartii, Pseudomonas aeruginosa, Pseudomonas aeruginosa, Pseudomonas aeruginosa, Rhizobium radiobacter, Rickettsia prowazekii, Rickettsia apsittaci, Rickettsia quintana, Rickettsia rickettsii, Rickettsia atracomae, Localimaea henselae, Localimaea quintana, Localimaea dentata cariosa, Salmonella enteritidis , Salmonella typhi, Salmonella typhimurium, Salmonella typhimurium, Serratia marcescens, Rhodophyton erythrophytum, Spirirum 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 Tococcus mitior, 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 enterocolitica, Yersinia pestis, and Yersinia pseudocystica and / or known to comprise one or more antibiotic-resistant strains derived from known species, and / or known to comprise one or more extended-spectrum beta-lactamase-producing strains derived from known species, in particular, the one or more extended-spectrum beta-lactamase-producing strains selected from the group consisting of extended-spectrum beta-lactamase-producing Escherichia coli and extended-spectrum beta-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 typhi, 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. The bacterial strain is selected from the group consisting of carbapanem-resistant Klebsiella pneumoniae, carbapanem-resistant Enterobacter cloacae, carbapanem-resistant Salmonella typhimurium, carbapanem-resistant Proteus vulgaris, carbapanem-resistant Proteus mirabilis, carbapanem-resistant Proteus pennelli, carbapanem-resistant Providencia stuartii, carbapanem-resistant Morganella morganii, 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] 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 infections) are collected in step 510. The data samples include VOC data patterns, or VOC data patterns as well as 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 fractions to predict wound infection.

[0084] In the discovery cohort, a portion of the data is randomly assigned to a training set in step 515, while the remainder is assigned to a test set in step 520. The optimal classifier is developed in the training set using the test set. The area under the ROC curve (AUC) value for the patients is determined. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the device are then 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 (also known as a 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, from subjects (and optionally environmental conditions) into the model / classifier, and the model may predict a health condition (or wound infection) in step 550.

[0086] 6A and 6B illustrate an exemplary portable device and method of use according to one 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 create a slight vacuum within the suction cup 605, at least one sensor array module 615, and the 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 for measuring the resistance of each sensor in 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 PDA 620.

[0087] The sensor array module 615 is attached to a suction cup 605. The suction cup 605 reduces environmental interference, such as hand sweat, dirt, and temperature changes. To enhance the device's sensitivity, a fan 610 is used to generate a slight negative pressure within the suction cup so that most VOCs emitted from the palm of the hand enter the handheld device. The PDA 620 includes a SOC with I / O / wireless / USB communication capabilities, a central processing unit (CPU), memory, and an OLED or LCD screen. Data can be transferred to a terminal (e.g., a PC) or a cloud database via a USB cable or wireless communication. The PDA runs an APP to provide a human-computer interface, test data collection, and data transfer for further analysis. Test results may be displayed on the PDA as numbers 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 device 600 are numerous. For example, the device may rely on diffusion without a fan to draw a vacuum. Furthermore, the device may be handheld or stationary. In some embodiments, the device has a pressure sensor that can detect changes in ambient pressure and transition the device from standby mode to operational mode. Thus, when a subject's body part (e.g., hand, forehead) covers suction cup 605, the change in pressure may transition the device to operational mode. In one embodiment, the device may automatically turn off when data is sufficient for a readout or after a predetermined period in standby mode. The device may have a manual input option that allows the user to manually set the duration of the test, for example, between 0.001 and 30 minutes. Additionally, a health management system may be installed on a remote device (e.g., a server or 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 main volume.

[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 detecting, identifying, and monitoring bacteria that cause wound infection. The dressing system 800 includes a wound dressing 810 that covers a wound and a wearable device 850 that is coupled to the wound dressing and placed on or near the wound.

[0091] Similar to that described with reference 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 multicomponent metal oxide configuration or composition, or a different amount (e.g., concentration) of nanostructured multicomponent metal oxide. The voltage divider circuit is used to process changes in characteristics (e.g., voltage change, resistance change, impedance change, combinations thereof, etc.) of 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 transmits it to the system. The data is transferred wirelessly to a PC and a 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) provides a built-in power supply and is also responsible for data acquisition and transmission, and can connect to a PC via the Wi-Fi communication module. The CTP and LCD provide the function of a human interaction interface.

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

[0094] Figures 9A-9D show the measurement results of 16 sensors for 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 advance AI-based detection, a nine-class support vector machine (SVM) classifier was constructed to identify the sensor profiles of bacterial strains. To avoid overfitting artifacts, the dataset from 75% of the samples sampled every 0.5 hours from the sensor profiles 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 bacterium was extremely high, reaching an overall accuracy of 97.08.

[0095] Another exemplary embodiment demonstrates the detection and differentiation 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 within the primary cavity. Each ESKAPEE bacterium was cultured on an individual LB agar plate for 24 hours prior to qualitative analysis of the initial VOC emissions from the organism. Culture medium on a Luria Broth (LB) agar plate without bacteria was used as a negative control for background signal. The cultures were continuously measured by the MACchip array for 48 hours. 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] This exemplary embodiment of the device uses a 32-sensor nanocomposite MACchip sensor array. 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 a carbon black and conductive polymer composite. The sensor materials enable different and complementary sensing capabilities. The sensors are designed to readily absorb chemical vapors, which, upon chemical bonding, change the electrical properties of the sensor, generating a signal. Software with AI algorithms evaluates the odor presence / pattern for specific chemical pathogens and presents the results to the patient and / or caregiver.

[0097] The sensing materials, such as CoZnInSnOx, CuSnCoInOx, CoZnCrNiOx, SnTbCoOx, SnTbZnOx, CoTbInOx, CoNiTbOx, CoCeNiCuOx, ZnSnTeOx, CoZnInOx, CuSnInOx, CoCrNiOx, SnWLaOx, SnInLaCoOx, CoOx, and CoTbOx, have different compositions of each chemical element, x = 0.01 to 1, which form conductive paths between electrodes to create 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 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 2-100 nm.

[0098] VOCs from laboratory strains representing all ESKAPEE bacteria, mixed cultures, and empty LB-agar plates were continuously monitored for 48 hours. Nineteen sensors had complete profiles for all experiments. Figure 10A shows 19 sensor measurements of nine analytes within 18 hours. In the hierarchical clustering heatmap, 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 while preserving the highest variability. Figures 10B and 10C project the entire sensor data (19 sensors, seven bacteria, PA-SA mixed bacteria, blank plate, 0.5-hour sampling interval) onto two principal components (PC1 and PC2) and (PC1 and PC3), respectively. Each ESKAPEE bacterium, mixed culture, and empty plate could be distinguished by unique patterns within the first hour, with only two clusters (e.g., EC and EF) partially overlapping.

[0099] To advance AI-based detection, we constructed a nine-class support vector machine (SVM) classifier to distinguish between the sensor profiles of the seven bacterial strains, the PA-SA mixed strain, and the blank plate. To avoid overfitting artifacts, we randomly divided the dataset (sampled every 0.5 h from the sensor profiles, totaling 804 samples for the seven bacteria, the mixed strain, and the blank plate) into a training set (75% of the samples) and a test set (25% of the samples). The SVM model was trained on the training set and validated using the test set. After feature selection, the 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 includes a sensor array, a sensor signal processing circuit (a 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 2). As an example, the sensor array includes two different gas sensors and one physiological sensor (heart rate). Each of the two sensors has a different nanostructured multi-component metal oxide configuration or composition, or different amounts (e.g., concentrations) of the nanostructured multi-component metal oxide.

[0101] A voltage divider circuit is used to process electrical changes (e.g., voltage changes, resistance changes, impedance changes, combinations thereof, etc.) in each sensor of the sensor array. The MCU collects digital signals from the ADC and transmits them 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 Wi-Fi 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., a rechargeable battery) 1140 provides a built-in power source and is also responsible for data acquisition and transmission, and can connect to a PC via the Wi-Fi communication module. The CTP and / or LCD provide the functionality of a human interaction interface.

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

[0103] Negative pressure wound therapy (NPWT) is a treatment method that involves the use of a device to apply negative pressure to a wound dressing 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, traumatic, and surgical wounds.

[0104] Despite the benefits of NPWT, wound infection remains a common complication of the therapy. Wound infection can lead to 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 that includes 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 dressing incorporating a gas sensor for continuous monitoring of wound healing and early detection of wound infections caused by pathogens (such as bacteria and fungi). The negative pressure wound dressing includes a vacuum pump and a flexible wound dressing connected to a gas sensor system or module. The gas sensor module is configured to detect the presence of specific 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 specific 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 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 can 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, allowing for 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 device components, 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 the detection of an infection, or the pumping rate may be adjusted in response to the progression or remission of the 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 electrochemical, metal oxide, infrared, or optical sensors.

[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, dimethyl sulfide, 1-pentene, isoprene, n-pentane, 1,3-dioxolane, 2-methyl-2-propenal, 2-methyl-propanal, 3-butene-2-ene, 2-methyl-2-propanal, 2-methyl-2-propanal, 3-butene-2-ene, 2-methyl-2-propanal, 2-methyl-2-propanal, 2-methyl-2-propanal, 2-methyl-2-propanal, 2-methyl-2-propane ... 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, methyl thiol acetate, methyl thiocyanate, 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, propanediol Indol, 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, isopentyl 3-methylbutanoate, 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 another embodiment, one or more of the plurality of sensor arrays includes a plurality of 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, nerve function, brain activity, blood pressure, cranial pressure, auscultation 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. VOC detection limits can range from 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] Gas sensor systems can 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 gas to pass through.

[0117] A 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 electric 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 for analyzing 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 gas levels 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 therapeutic interventions.

[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 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 quickly 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 including 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 sensors and control the negative pressure source in response to the data. A user interface displays the data from the gas sensors, and a treatment module takes timely intervention to prevent or treat infection based on the data from the gas sensors. A communications module transmits data from the gas sensors to a remote location for analysis and treatment recommendations. Machine learning algorithms may be used to analyze the data from the gas sensors to predict the likelihood of infection and suggest treatments (e.g., wound disinfection, dressing changes, medications, 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 specific 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 intervention (e.g., wound disinfection, dressing change, medication, etc.) is performed to prevent or treat the infection.

[0127] 12A and 12B, a negative pressure wound device 1200 includes a housing 1205, and a wound dressing 1210 attached to the housing. 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 is 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 that 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 device processor configured to process the sensor readings and transmit the results to the device's display and / or transmitter. The display may be a visual display, such as an LCD screen, or an audio display, such as a speaker. The display user interface 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 intervention or treatment.

[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 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 intervention or treatment.

[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 patient's specific needs and the type of infection present in the wound. For example, if data from a gas sensor indicates the presence of anaerobic bacteria, intervention 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. Negative pressure wound devices 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 configured with VOC patterns (and optionally vital signs and / or environmental conditions) as input, the system can generate early diagnostic 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 the 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 resulting gas sensor reading 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 the pumping rate may be adjusted in response to the progression or remission of the infection. A negative pressure wound device with a gas sensor provides 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 quickly 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] Negative pressure wound devices 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 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 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 assist the user in properly operating and maintaining 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 injury to the user or damage to the device.

[0142] Negative pressure wound devices allow for continuous monitoring of the wound, enabling healthcare providers to quickly address any potential infection 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] With regard to manufacturing, 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] Negative pressure wound devices may include housing elements that are comprised of, but are 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 to 6 to 8 to 12 to 32 or more sensors using 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, nonporous, hierarchically porous materials, or mixed nanoporous structures, including mesoporous / macroporous hierarchical structures, microporous / macroporous hierarchical structures, microporous / mesoporous hierarchical structures, microporous / mesoporous / macroporous hierarchical structures, etc. 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 sizes but with a wide range of irregularities, or a disordered mesostructure with pore sizes 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, or 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), and cerium (Cu). The present invention includes uni-, binary, ternary, quaternary, pentanary, hexanion, septanion and octanionary multi-component metal oxides selected from the group consisting of elements of cerium (Ce), tellurium (Te), and arsenic (Ar), 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] In some embodiments, the method may include exposing a sensor array to a gas mixture emitted from a wound infection pathogen. As used herein, a 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 emitted from the subject's skin. The skin may be any wound on the subject, for example, the subject's palm, fingers, arms, legs, back, abdomen, or feet. 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 performed 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] Some embodiment methods, gas sensors, and devices may detect relative levels of a gas mixture. In some cases, some embodiment methods and devices may be capable of detecting VOCs at concentrations of 5,000 parts per million (ppm) or less, 4,000 ppm or less, 3,000 ppm or less, 2,000 ppm or less, 1,000 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 a gas mixture. In some cases, some embodiment methods, sensors, and devices may be configured to have a detection limit of 5,000 ppm or less of gas in a 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 any animal, including mammals, preferably humans, to which the methods of some embodiments may be applied. Mammalian species that may benefit from embodiments of the present invention include, but are not limited to, apes, chimpanzees, orangutans, humans, monkeys, domestic animals (e.g., pets), such as dogs, cats, guinea pigs, and hamsters, and large domestic animals, such as cows, horses, goats, and sheep. The subject may also be any wild animal, as embodiments of the present invention may be used for tracking purposes.

[0161] In some embodiments, bacterial identification may be based on volatile organic or inorganic compounds derived from the pathogen of a wound infection in a subject having a sensor array. The sensor array may be any device capable of generating 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, or frequency.

[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 pathogens grown in vitro or from infected and non-infected patients or animals in vivo, that are evaluated by embodiments of the present invention and / or by conventional techniques for identifying pathogens found therein.

[0163] The data acquisition unit may include a nanosensor, a biosensor, a readout circuit, one or more microcontrollers (e.g., 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%), and volatile organic compound concentration (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 distinguish 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 smartphone, a wearable device, a healthcare device, a medical device, fitness equipment (e.g., a treadmill, an elliptical, etc.), or a combination thereof. The device may detect VOCs, for example, VOCs from exhaled breath or VOCs emitted from the skin (e.g., the skin of a subject's palms, fingers, ears, nose, face, eyes, arms, legs, chest, chest, back, abdomen, or feet).

[0167] The device of some embodiments may be a wearable device. In some cases, the gas sensor array sensitive to VOC-emitting wound infection pathogens using the sensing material herein may be fabricated as a wearable device. Examples of wearable devices include armbands, sleeves, jackets, eyeglasses, 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 as a portable device by deposition. Examples of portable devices include key chains, breath test devices, etc.

[0169] The device in some embodiments may be a disposable device (e.g., a wound dressing, etc.), where the disposable device (and one or more sensors) are configured for single-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 , maximum 200cm 3 , up to 100cm 3 , up to 30cm 3 , maximum 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 The volume may be

[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] In some embodiments, the device 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 to a measure of the user subject's energy expenditure and / or metabolic profile.

[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) so that the device can be used (e.g., worn) to visualize, monitor, and / or analyze a subject's onset and progression of infection, antibiotic treatment, metabolic profile, and physiological state, or a combination thereof.

[0175] In some embodiments, device data can be shared with medical professionals in real time to ensure accurate and appropriate treatment.

[0176] The device (e.g., FIGS. 1, 6A, 6B, 8, 11, 12A, and 12B) may determine an intervening action and / or treatment as described above. Based on the collected information, including 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 interventions, pressure source control, and / or treatments). Once a model is established using 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 adjustments, interventions, and / or treatments).

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

[0178] In some embodiments, the one or more pathogens comprise at least one from the group consisting 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 mallei, 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 diphtheriae, Corynebacterium jeikeium, Nocardia asteroidea, Mycoplasma pneumoniae, Ureaplasma urealyticum, Legionella long beach, Legionella bozemanii, Legionella dumophila, Legionella mikdadei, Legionella anisa, Legionella fileyi, Legionella gormani, Legionella jordanis, Legionella londiniensis, Legionella machacerni, Legionella oakridgegensis, Legionella quateirensis, Legionella brilulens, 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 rubrum, Microsporum canis, Epidermophyton, Pneumocystis jiroveci, and Fusarius Musorani, Fusarium oxysporum, Rhizopus oryzae, Mucor spp., Sedosporium prolificans, Sporothrix schenckii, Paracoccidioides brasiliensis, Candida dubliniensis, Candida dalsitaniae, Candida guillemondii, 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 psilus, Rhizomucor variabilis, Cunninghamella verrutretiae, Cunninghamella echinalate, Cunninghamella blakesleyana, Absidia crimbifera, Mucor circinelloides, Mucor racemosus, Succenia 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 invention. While the invention has been described in connection with specific embodiments, it will be understood that the invention is capable of further modifications, and that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described aspects for carrying out embodiments of the invention that are obvious to those skilled in the art are intended to be within the scope of the invention. This application is generally intended to cover any variation, use, or adaptation of embodiments of the invention in accordance with the principles of the invention, including such departures from the present disclosure as may be within known and customary practice within the technical field to which the invention pertains and as may be applied to the essential features herein before set forth.

Claims

1. 1. A method for detecting a wound infection, comprising: applying negative pressure to the infected wound using a negative pressure source in a standby mode; detecting a change in said negative pressure with a pressure sensor to activate an operating mode; in said operational mode, receiving information from at least one sensor relating to the detection of one or more gases emitted by one or more pathogens in said wound, 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; 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 change 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: 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 change in the one or more characteristics and the measurements indicative of the corresponding pathogen; the one or more pathogens are selected from bacteria present in monomicrobial infections, fungi present in monomicrobial infections, and both bacteria and fungi present in polymicrobial infections; The method of claim 1.

3. analyzing the information, 10. 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.

4. 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 based on the information from the at least one sensor.

5. 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.

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

7. The one or more pathogens comprise at least one from the group consisting 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 burnetii, Rickettsia rickettsii, Rickettsia prowazekii, Bartonella henselae, Burkholderia pseudomallei, Burkholderia mallei, 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, the fungus may be selected from the group consisting of Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, Histoplasma capsulatum, Blastomyces dermatitidis, ...Coccidioides immitis, Candida glabrata, Candida tropicalis, Candida parapsilosis, Candida krusei, Trichophyton rubrum, Trichophyton rubrum, Microsporum canis, Epidermophyton, Pneumocystis jiroveci, Fusarium solani, Fusarium oxysporum, Rhizopus oryzae, Mucor, Scedosporium prolificans, Sporothrix schenckii, Paracoccidioides brasiliensis, Candida dubliniensis, Candida dalusitaniae, Candida guilliermondii, Candida keff 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 jensermae, 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 domesticum, Trichosporon japonicum, Trichosporon moniliforme, Trichosporon mucoidum, Trichosporon pullulans, Rhizomucor psillus, Rhizomucor variabilis, Cunninghamella verrutretiae, Cunninghamella echinalate, Cunninghamella blakesleyana, Absidia crimbifera, Mucor circinelloides, Mucor racemosus, Succenia baciformis, Rhizopus microspores, and Rhizopus spp.

8. The at least one sensor is disposed in a wound dressing, and the method comprises: adjusting a flow rate of the negative pressure source based on the information from the at least one sensor; The method of claim 1 further comprising:

9. 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:

10. An apparatus comprising: a negative pressure source; and a memory device containing software executable by at least one processor, the negative pressure source is configured to apply negative pressure to the infected wound, thereby switching the device from a standby mode to an operating mode; The software causes the at least one processor to: In the operating mode, receiving information regarding the detection of one or more gases emitted from one or more pathogens in the wound 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 change in the one or more characteristics indicative of a corresponding pathogen; An apparatus for carrying out the above.

11. The device described in claim 10, which adjusts the flow rate of the negative pressure source based on the information from the at least one sensor.

Images