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: 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 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: 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 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: 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 using machine learning algorithms in analyzing laboratory test results of body fluid to detect microbes in the body fluid includes using a body fluid detection module for analytic measurements in body fluid of a person to create biological samples; sending the biological samples of a plurality of persons and corresponding microbes infection statuses to perform machine learning algorithms to establish a microbes in body fluid prediction model; and sending data obtained from the body fluid detection of a patient for testing to the microbes in body fluid prediction model for operation and analysis in order to determine whether the microbes is present in body fluid.

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 of establishing a machine learning model for cancer anticipation includes collecting test results of a plurality of tumor markers of a plurality of eligible individuals and corresponding conditions of cancer; performing a variable selection process on the collected data to select a plurality of robust variables; and using the selected variables, numerals, and conditions of cancer by cooperating with a machine learning method to establish a cancer anticipation model. A method of detecting cancer by using a plurality of tumor markers in a machine learning model for cancer anticipation is also provided.