Abstract: A method and a kit for detecting Mycobacterium tuberculosis are provided. The method includes a step of performing a nested qPCR assay to a specimen. The nested qPCR assay includes a first round of amplification using external primers and a second round of amplification using internal primers and a probe. The external primers have sequences of SEQ ID NOs. 1 and 2, and the internal primers and the probe have sequences of SEQ ID NOs. 3 to 5.

Abstract: An apparatus and method for examining an organism surface includes a pliable member including a contact layer, a plurality of channels, and a reaction layer, wherein the plurality of channels are disposed between the contact layer and the reaction layer; each of the channels having a size in the range of micrometers and formed of biocompatibility materials, and the contact layer including a plurality of spherical projections having a size in the range of micrometers and formed of biocompatible materials, and the reaction layer including examining molecules and a reaction unit.

Abstract: Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership and/or a category with a time range for follow up testing or reclassification with newly measured input factors.

Abstract: A method of creating characteristic profiles of mass spectra and identification model for analyzing and identifying microorganisms includes collecting m/z data of microorganisms having same features from MALDI-TOF MS; classifying the microorganisms; classifying the collected set of m/z data as a plurality of subsets; creating modified subsets by applying KDE to the subsets; creating first characteristic MS profiles based on the modified subsets; summarizing into a second characteristic MS profile; repeating above six steps to create second characteristic MS profiles; creating a training set comprising first matched vectors; training a machine learning system using the training set to establish a feature classification model; using MALDI-TOF MS to analyze microorganisms having unknown features; comparing m/z of MALDI-TOF MS spectrum of the microorganisms having unknown features with second characteristic MS profiles to obtain second matched vectors; using the feature classification model analyzing the second m

Abstract: An apparatus of examining organism surface includes a pliable member including a contact layer, a plurality of channels, and a reaction layer in which the channels are disposed between the contact layer and the reaction layer; the channels have a size in the range of micrometers and are formed of biocompatibility materials; the contact layer includes a plurality of spherical projections for examination having a size in the range of micrometers and being formed of biocompatibility materials; and the reaction layer includes examining molecules and a reaction unit.

Abstract: A method and a kit for detecting Mycobacterium tuberculosis are provided. The method includes a step of performing a nested qPCR assay to a specimen. The nested qPCR assay includes a first round of amplification using external primers and a second round of amplification using internal primers and a probe. The external primers have sequences of SEQ ID NOs. 1 and 2, and the internal primers and the probe have sequences of SEQ ID NOs. 3 to 5.

Abstract: A method of identifying properties of microorganisms using ODEP force includes obtaining a sample solution of microorganisms including microorganisms having predetermined properties; pre-processing the sample solution of microorganisms to obtain a sample solution of microorganisms to be tested having predetermined electrical properties; placing the sample solution of microorganisms to be tested on a channel of a chip member; activating a light image module having combinations of different strengths of ODEP force to analyze the sample solution of microorganisms to be tested on the channel in which microorganisms in the sample solution of microorganisms to be tested are configured to generate a predetermined distribution and a predetermined presentation due to the predetermined electrical properties; and identifying properties of the microorganisms based on the predetermined distribution and the predetermined presentation.

Abstract: An immunoassay includes forming first mixtures by reacting a combination reagent with serum of a subject; simultaneously forming second mixtures by reacting randomly selected platelet samples with the serum wherein in each mixture there are immunity compounds formed by combining the platelet antigens with predetermined antibodies in the serum, and other platelet antigens and other antibodies in the serum not forming the immunity compounds; preparing an interception device including receptacles and a filter net; placing each mixture in one receptacle; washing the mixtures wherein the mixtures forming the immunity compounds are intercepted by the filter net with others passing through; adding a signal sensing reagent to each receptacle; reacting the signal sensing reagent with the intercepted mixtures forming the immunity compounds to form final products; and performing a signal sensing to determine whether the final products contain anti-platelet antibodies and determine compatibility of cross matching of resp

Abstract: A method and a kit for detecting mycobacterium are provided. The method includes steps of: providing a sample; providing a pair of primers, which is selected from a group consisting of a sequence having about 45% to 100% similar to SEQ ID NO. 1, a sequence having about 60% to 100% similar to SEQ ID NO. 2, a sequence complementary thereof; performing a polymerase chain reaction by using the set of primers and the sample to obtain a product; and analyzing the product to detect the presence of the mycobacterium.

Abstract: A test sheet for occult blood includes a permeable substrate layer, a test layer, and an outer layer. The permeable substrate layer and the test layer are jointed side by side or stacked from top to bottom on the outer layer. The permeable substrate layer is configured to carry biologic sample, and the test layer includes test agent. The outer layer protects the user from the biologic sample. At least one fold line is formed on the front surface of the connection of the permeable substrate layer and the test layer, or alternatively, on the front surface of the permeable substrate layer. When the test sheet is folded along the fold line, the biologic sample permeates the permeable substrate layer and reacts with the test agent. If the reaction causes color change, the biologic sample is determined to contain occult blood.

Abstract: A method of establishing a coronary artery disease (CAD) prediction model for CAD screening includes establishing a data set in a computer equipment; entering the data set and corresponding future CAD condition of asymptomatic individuals into a machine learning component; selecting a plurality of robust variables from the clinical data and the cardiovascular markers of the cardiovascular markers panel by using feature selection methods; establishing the CAD prediction model by using machine learning methods; uploading new clinical data and new results of the cardiovascular markers to the cloud-based platform when any asymptomatic individuals undergo the health examination, and performing calculation and analysis by the CAD prediction model; and notifying the asymptomatic individuals of having a high risk of encountering a CAD event or not in a certain period of follow-up time.

Abstract: A method for establishing a computer-aided data interpretation model for immune diseases by immunomarkers and visualization is revealed. First combine a plurality of immunomarkers into an immunomarker panel. Then collect test data of a plurality of subjects measured by the immunomarker panel, and disease diagnosis information of the subjects for establishment of an immunomarker-panel testing database. Next new subjects are tested by the immunomarker panel. The data obtained and the corresponding information in the immunomarker-panel testing database are processed by unsupervised machine learning algorithm to get a computer-aided data interpretation model showing comparison of case distribution patterns. The method provides real-time analysis of multiple data to medical professionals for their reference. Thereby the correctness, the timeliness and the reproducibility of the interpretation result for the diagnosis and treatment of immune diseases are all improved.

Abstract: A method of creating characteristic profiles of mass spectra and identification model for analyzing and identifying microorganisms includes obtaining data of MALDI-TOF MS of microorganisms having same features; using a kernel density estimation to generate characteristic profiles of an m/z of the data; creating a characteristic MS profile based on the m/z; repeating above three step until characteristic MS profiles of features of the microorganisms is obtained; comparing m/z of MALDITOF MS spectrum of known microorganisms with the characteristic profiles to obtain first matched vectors; using a machine learning method to establish a feature classification model; using MALDI-TOF MS to analyze microorganisms having unknown features; comparing the m/z of MALDI-TOF MS spectrum of the microorganisms having unknown features with the characteristic MS profiles to obtain second matched vectors; using the feature classification model to analyze the second matched vectors; and identifying the microorganisms having the

Abstract: A method of creating characteristic peak profiles of mass spectra and identification model for analyzing and identifying microorganisms are provided. MALDI-TOF MS data of microorganisms having the same feature are gathered. Discretization of the data is performed. Density-based clustering is used to find m/z values of spectral peaks with high probability of occurrence from the discretized data. A characteristic MS peak profile is created for every specific feature of microorganisms. Every such a characteristic profile forms a feature template. The mass spectrum of each known isolate is matched against all the feature templates and a number of matched vectors are obtained. The matched vectors are then concatenated into a single “integrated vector.” Then, a machine learning method and the integrated vectors generated from all known isolates are used to create a classification model for microorganism identification.

Abstract: A method for detecting different properties in a microorganism population by ODEP force includes obtaining a microorganism sample solution having a plurality of microorganisms to be tested; pre-processing the microorganism sample solution to obtain a microorganism sample solution to be tested including the microorganisms having electrical properties differences; placing the microorganism sample solution to be tested in a channel of an ODEP device and activating an optical projection device to form at least one optical projection directed to the ODEP device; flowing the microorganism sample solution to be tested from one end of the channel to the other end thereof, and exerting an ODEP force on the optical projection device to generate a force having a direction different from a flowing direction of the microorganism sample solution to be tested; and detecting heterogeneity of the microorganisms based on strength differences of the ODEP force exerted on the respective microorganisms.

Abstract: An immunoassay includes forming first mixtures by reacting a combination reagent with serum of a subject; simultaneously forming second mixtures by reacting randomly selected platelet samples with the serum wherein in each mixture there are immunity compounds formed by combining the platelet antigens with predetermined antibodies in the serum, and other platelet antigens and other antibodies in the serum not forming the immunity compounds; preparing an interception device including receptacles and a filter net; placing each mixture in one receptacle; washing the mixtures wherein the mixtures forming the immunity compounds are intercepted by the filter net with others passing through; adding a signal sensing reagent to each receptacle; reacting the signal sensing reagent with the intercepted mixtures forming the immunity compounds to form final products; and performing a signal sensing to determine whether the final products contain anti-platelet antibodies and determine compatibility of cross matching of resp

Abstract: A method for establishing a computer-aided data interpretation model for immune diseases by immunomarkers and visualization is revealed. First combine a plurality of immunomarkers into an immunomarker panel. Then collect test data of a plurality of subjects measured by the immunomarker panel, and disease diagnosis information of the subjects for establishment of an immunomarker-panel testing database. Next new subjects are tested by the immunomarker panel. The data obtained and the corresponding information in the immunomarker-panel testing database are processed by unsupervised machine learning algorithm to get a computer-aided data interpretation model showing comparison of case distribution patterns. The method provides real-time analysis of multiple data to medical professionals for their reference. Thereby the correctness, the timeliness and the reproducibility of the interpretation result for the diagnosis and treatment of immune diseases are all improved.

Abstract: A method and a kit for detecting mycobacterium are provided. The method includes steps of: providing a sample; providing a pair of primers, which is selected from a group consisting of a sequence having about 45% to 100% similar to SEQ ID NO. 1, a sequence having about 60% to 100% similar to SEQ ID NO. 2, a sequence complementary thereof; performing a polymerase chain reaction by using the set of primers and the sample to obtain a product; and analyzing the product to detect the presence of the mycobacterium.

Abstract: A coronary artery disease (CAD) screening method includes 1) collecting clinical information of asymptomatic individuals and testing a plurality of samples of the individuals by using a cardiovascular markers panel including a plurality of cardiovascular markers; 2) entering the clinical information and the test results and the corresponding CAD states of the individuals into a machine learning platform; 3) selecting a plurality of roust variables from the clinical information and the cardiovascular markers of the cardiovascular markers panel by using feature selection methods; 4) using a machine learning algorithm embedded in the machine learning platform to establish a CAD prediction model; and 5) entering clinical information and sample data obtained by using the cardiovascular markers panel for an individual being screened into the CAD prediction model for calculation and analysis, thereby determining whether the individual being screened has CAD or not.

Abstract: A method of predicting daily living activities performance of a person with disabilities includes establishing a rehabilitation assessments panel based on a plurality of rehabilitation evaluation scales and laboratory data; evaluating a plurality of persons with disabilities by the rehabilitation assessments panel; entering assessment results and the corresponding activities of daily living (ADL) performance into a machine learning platform; utilizing variable selection methods to select a plurality of variables having optimal classification performance from the rehabilitation assessments panel; executing a machine learning algorithm to create an ADL prediction model based on the selected variables; evaluating a participant in terms of the rehabilitation assessments panel; and entering assessment results into the ADL prediction model for calculation, thereby obtaining a prediction result of future ADL performance for the participant.