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JP-7854722-B2 - Devices and methods for detecting pathogens

JP7854722B2JP 7854722 B2JP7854722 B2JP 7854722B2JP-7854722-B2

Inventors

  • イェリネク，ラズ
  • シャウロフ，ニツァン

Assignees

  • ビー．ジー．ネゲブ テクノロジーズ アンド アプリケーションズ リミテッド， アット ベン‐グリオン ユニバーシティー

Dates

Publication Date
20260507
Application Date
20211111
Priority Date
20201111

Claims (20)

  1. It is a sensor array, The sensor array comprises a plurality of capacitive sensors, Each capacitive sensor is electrically connected to two electrodes and includes a sensing element containing a carbon dot. The plurality of capacitance sensors comprises at least a first sensor, a second sensor, and a third sensor. The carbon dots of the first sensor, the second sensor, and the third sensor each independently contain hydrophilic surface groups, and each independently contains nitrogen and oxygen atoms. The hydrophilic surface groups of the carbon dots are predetermined to have (i) different surface polarities and (ii) different sensitivities of the first sensor, the second sensor, and the third sensor to volatile compounds (VC). The nitrogen content of the carbon dots in the first sensor, the second sensor, and the third sensor is 13-20 at%, 10-12.9 at%, and 1-9.9 at%, respectively. Sensor array.
  2. The sensor array according to claim 1, wherein the hydrophilic surface group comprises one of an amine group, an imine group, a carbonyl group, a carboxyl group, and a hydroxyl group, or any combination thereof.
  3. The sensor array according to claim 1 or 2, wherein (i) the carbon content and (ii) the atomic ratios between the total nitrogen and oxygen content of the carbon dots of the first sensor, the second sensor, and the third sensor are 0.2 to 1.9, 2 to 2.9, and 3 to 8, respectively.
  4. The sensor array according to claim 1, wherein the carbon dots lack metal.
  5. The sensor array according to claim 2, wherein the w/w ratios between the amine group and the imine group of the carbon dots of the first sensor, the second sensor, and the third sensor are 1.5 to 3, 0.8 to 1.4, and 0.2 to 0.8, respectively.
  6. The sensor array according to claim 1, wherein the oxygen content of the carbon dots in the first sensor, the second sensor, and the third sensor is 20-30 at%, 10-20 at%, and 1-9.9 at%, respectively.
  7. The sensor array according to claim 1, wherein the carbon content of the carbon dots in the first sensor, the second sensor, and the third sensor is 40-65 at%, 66-79 at%, and 80-95 at%, respectively.
  8. The sensor array according to claim 1, wherein the sensor array is configured to selectively detect a target VC originating from a microorganism.
  9. The sensor array according to claim 1, wherein the VC includes polar VC, non-polar VC, or both.
  10. The sensor array according to claim 9, wherein the first sensor is characterized by its sensitivity to the polar VC, and the polar VC optionally comprises an amine, hydroxyl, carboxyl, carbonyl, or any combination thereof.
  11. The sensor array according to claim 9, wherein the third sensor is characterized by its sensitivity to the nonpolar VC, and the nonpolar VC optionally comprises an aromatic ring, an alkyl chain, or both.
  12. The sensor array according to claim 1, wherein the electrodes are configured to receive electricity.
  13. The sensor array according to claim 1, wherein each of the carbon dots is characterized by a different water contact angle.
  14. The sensor array according to claim 1, wherein the different polarities of each of the carbon dots are predetermined so that each of the plurality of capacitance sensors can generate a different capacitance signal in response to VC.
  15. A method for predicting the presence of at least one target microorganism, which can be executed by at least one processor, the method being: The steps include receiving multiple capacitance signals generated by multiple capacitance sensors in response to exposure of a sample to multiple capacitance sensors, A step of introducing the plurality of capacitance signals into a machine learning (ML) model, wherein the ML model is trained to predict the presence and concentration of a target microorganism in the sample based on the capacitance signals; and a step of generating an index of the presence or concentration of one or more of the target microorganisms in the sample based on the predictions. The plurality of capacitance sensors are the sensor array described in claim 1. method.
  16. The method according to claim 15, wherein the introduction includes extracting at least one capacitance feature from the plurality of capacitance signals, and the ML model is trained to predict the presence of the target microorganism based on the capacitance feature.
  17. The method according to claim 16, wherein the extraction of capacitance features includes sampling the capacitance signal at predetermined time intervals and generating capacitance features representing the vector of the sampled capacitance signals.
  18. The method according to claim 17, wherein the capacitance feature is associated with a specific capacitance sensor.
  19. The method according to claim 16, wherein the extraction of capacitance features includes calculating the maximum change in capacitance and generating a capacitance feature representing the calculated maximum capacitance.
  20. Training the aforementioned ML model is The method according to claim 15, comprising receiving a plurality of training samples, each of which is labeled by (i) the presence of the microorganism of interest in the sample and optionally by (ii) its concentration, or both (i) and (ii); and using the labeled training samples as supervisory data for training the ML model to predict the presence or concentration of the microorganism of interest in the samples.

Description

Cross-reference of related applications This application claims priority to U.S. Provisional Application No. 63/112,260, titled "DEVICE AND METHODS FOR DETECTING BACTERIA," filed on 11 November 2020, the contents of which are incorporated herein by reference in their entirety. In some embodiments, the present invention relates to a device and method for monitoring microbial contamination, such as by detecting volatile compounds derived from microorganisms. Bacteria are known to release a variety of volatile molecules, the type and concentration of which are strain-dependent. Bacterial volatile compounds such as alcohols, aldehydes, and ketones have been used as microbial biomarkers, and bacterial volatile metabolite mixtures have been employed as a unique "odor profile" medium for bacterial identification. In particular, colorimetric arrays have been developed to sense volatile compounds, allowing for differentiation between different bacterial strains. Optical and chemically resistant gas sensing methods have also been used for bacterial analysis utilizing specific biomarkers. A fundamental limitation of these vapor-based bacterial detection schemes is that they cannot be used for continuous monitoring because samples must be collected (usually manually) and analyzed in ex situ. This aspect excludes a wide range of critical bacterial sensing applications in healthcare, environmental monitoring, and homeland security. Among the various gas sensing technologies developed, the "artificial nose" is attracting significant attention. The artificial nose aims to effectively mimic the function of physiological organs, particularly their exceptional selectivity between different vapor molecules and gas mixtures. Reported artificial nose platforms rely on various physical mechanisms, including changes in the electrical resistance of conductivity sensors, the absorption and desorption of heat in calorimetry sensors, and changes in the electrical conductance of semiconductor field-effect transistors. The C-dot IDE capacitive artificial nose has been successfully applied to continuous monitoring and bacterial identification, highlighting its availability as a general platform for sensors. There remains a significant need for devices and methods for non-invasive bacterial growth detection, including the continuous monitoring and identification of different bacteria. In some embodiments, the present invention relates to an apparatus for determining the presence of an analyte at a given location, a system comprising the same, and a method for using the same. In some embodiments, the present invention is partly based on research showing that the apparatus of the present invention, for example, the C-dot IDE capacitive nose, has been successfully employed for continuous real-time monitoring of bacterial growth. Importantly, the recorded unique capacitance signals enable the identification of different Gram-positive and Gram-negative bacteria. Generally, the novel capacitive C-dot-based nose can be easily implemented as a portable vapor sensor for continuous non-invasive monitoring and identification of bacterial growth in a variety of applications, including medical diagnostics, food processing, and environmental monitoring. This invention can be used, for example, as a safety measure where it is necessary to eliminate the possibility of bacterial presence. Such safety-related applications may include water safety, food safety, and human health safety (e.g., hospitals or airports). This invention can be used for the measurement and monitoring of analytes, such as microorganisms and/or VOCs secreted therefrom, in a wide range of locations and applications, including water safety, food safety, hospitals, veterinary medicine, and airports. In some embodiments, applying the method of the present invention is a prerequisite for decision-making, such as determining whether an analyte is present and which analyte is present, and therefore, an action to neutralize the analyte is taken. In some embodiments, the training phase includes recording capacitance values under baseline conditions. In some embodiments, the baseline conditions are referred to as or used as reference values (e.g., white (blanc) measurements or standard measurements). In some embodiments, any capacitance output at a location calculated or determined according to the methods disclosed herein is compared to a white measurement or standard measurement. In some embodiments, the method includes receiving a capacitance value and comparing it to a white measurement or standard measurement to obtain a relative capacitance value or an absolute capacitance value, e.g., an unknown measurement or analyte. In some embodiments, baseline conditions are provided under specific relative humidity conditions. In some embodiments, baseline conditions include multiple relative humidity conditions. In some embodiments, the relative humidity is at least 10%, at least 40%, at least 60