Abstract: A training data processing method and an electronic device are provided. The method includes: obtaining medical history data including at least one first disease suffered by a user; setting a plurality of disease types according to a target disease; setting a time interval; obtaining at least one second disease in the time interval from the medical history data; performing a pre-processing operation on the second disease according to the disease types to obtain processed data; and inputting the processed data to a neural network to train the neural network.

Abstract: A method for analyzing an electrocardiography (ECG) signal is provided. The ECG signal is measured through a sensor. One or more specified waveforms in the ECG signal are detected. Multiple ECG features from each specified waveform are captured. The ECG features are input into multiple candidate models to obtain multiple candidate scores. Each candidate score is compared with a standard value to obtain an ideal score among the candidate scores. One of the candidate models corresponding to the ideal score is selected as a risk prediction model to predict disease risk through the risk prediction model.

Abstract: A training data processing method and an electronic device are provided. The method includes: obtaining medical history data including at least one first disease suffered by a user; setting a plurality of disease types according to a target disease; setting a time interval; obtaining at least one second disease in the time interval from the medical history data; performing a pre-processing operation on the second disease according to the disease types to obtain processed data; and inputting the processed data to a neural network to train the neural network.

Abstract: A feature identifying method and an electronic device are provided. The method includes: obtaining a plurality of physiological information obtained by measuring a subject at a plurality of time points in one day; converting the plurality of physiological information into a plurality of correlation features respectively; establishing a plurality of first risk prediction models according to the plurality of correlation features, and identifying at least one first correlation feature from the plurality of correlation features according to the plurality of first risk prediction models; establishing a plurality of second risk prediction models according to the at least one first correlation feature, and identifying, according to the plurality of second risk prediction models, at least one second correlation feature capable of predicting a specific disease from the at least one first correlation feature; and outputting the at least one second correlation feature.

Abstract: Compositions, systems and methods for delivering CRISPR/Cas9-based genome editing system and a donor protein to a cell.

Abstract: A method and an electronic device for selecting influence indicators by using an automatic mechanism are provided. The method includes following steps. Raw data is obtained, where the raw data includes a body-related variable and a plurality of to-be-measured indicators corresponding to the body-related variable. The body-related variable is set as a target parameter. The body-related variable and the to-be-measured indicators are input into a plurality of validation models, and the to-be-measured indicators are sorted according an output result of the validation models to obtain ranking data. Importance of the to-be-measured indicators is calculated by using a screening condition according to the ranking data, so as to select a candidate indicator from the to-be-measured indicators. An influence indicator is determined by calculating a correlation between the candidate indicator and the body-related variable.

Abstract: A model training method and an electronic device are provided. The method includes the following steps: establishing a brain age prediction model according to a training set; adjusting a parameter in the brain age prediction model according to a validation set; inputting a test set into the brain age prediction model with the adjusted parameter to obtain a plurality of first predicted brain ages; determining whether the first predicted brain ages satisfy a first specific condition; and completing training of the brain age prediction model when the first predicted brain ages satisfy the first specific condition.

Abstract: A method for analyzing an electrocardiography (ECG) signal is provided. The ECG signal is measured through a sensor. One or more specified waveforms in the ECG signal are detected. Multiple ECG features from each specified waveform are captured. The ECG features are input into multiple candidate models to obtain multiple candidate scores. Each candidate score is compared with a standard value to obtain an ideal score among the candidate scores. One of the candidate models corresponding to the ideal score is selected as a risk prediction model to predict disease risk through the risk prediction model.

Abstract: A training data processing method and an electronic device are provided. The method includes: obtaining medical history data including at least one first disease suffered by a user; setting a plurality of disease types according to a target disease; setting a time interval; obtaining at least one second disease in the time interval from the medical history data; performing a pre-processing operation on the second disease according to the disease types to obtain processed data; and inputting the processed data to a neural network to train the neural network.

Abstract: A model training method and an electronic device are provided. The method includes the following steps: establishing a brain age prediction model according to a training set; adjusting a parameter in the brain age prediction model according to a validation set; inputting a test set into the brain age prediction model with the adjusted parameter to obtain a plurality of first predicted brain ages; determining whether the first predicted brain ages satisfy a first specific condition; and completing training of the brain age prediction model when the first predicted brain ages satisfy the first specific condition.

Abstract: A method and an electronic device for selecting influence indicators by using an automatic mechanism are provided. The method includes following steps. Raw data is obtained, where the raw data includes a body-related variable and a plurality of to-be-measured indicators corresponding to the body-related variable. The body-related variable is set as a target parameter. The body-related variable and the to-be-measured indicators are input into a plurality of validation models, and the to-be-measured indicators are sorted according an output result of the validation models to obtain ranking data. Importance of the to-be-measured indicators is calculated by using a screening condition according to the ranking data, so as to select a candidate indicator from the to-be-measured indicators. An influence indicator is determined by calculating a correlation between the candidate indicator and the body-related variable.

Abstract: A feature identifying method and an electronic device are provided. The method includes: obtaining a plurality of physiological information obtained by measuring a subject at a plurality of time points in one day; converting the plurality of physiological information into a plurality of correlation features respectively; establishing a plurality of first risk prediction models according to the plurality of correlation features, and identifying at least one first correlation feature from the plurality of correlation features according to the plurality of first risk prediction models; establishing a plurality of second risk prediction models according to the at least one first correlation feature, and identifying, according to the plurality of second risk prediction models, at least one second correlation feature capable of predicting a specific disease from the at least one first correlation feature; and outputting the at least one second correlation feature.

Abstract: The present invention provides an isolated lactic acid bacterium, Lactobacillus plantarum subsp. plantarum PS128 deposited under DSMZ Accession No. DSM 28632, for prophylaxis or treatment of a stress-induced disorder. The present invention further provides a composition for prophylaxis or treatment of a stress-induced disorder that comprises Lactobacillus plantarum subsp. plantarum PS128. Moreover, the present invention provides a method for prophylaxis or treatment of a stress-induced disorder in a subject that comprises a step of administering an effective amount of Lactobacillus plantarum subsp. plantarum PS128 to the subject.

Abstract: An ultrasound stimulation helmet (100) configured to regulate an endogenous neurotrophic factor in a brain or expression of proteins related to a neurodegenerative disease comprises: a main body (1) having a forehead perimeter adjustment knob (12), a back head perimeter adjustment knob (13), a fastening support frame (14), and multiple position adjustment knobs (15); and multiple ultrasound probes (2) detachably mounted on the main body (1), wherein the ultrasound probe (2) has a frequency adjustment button and an intensity adjustment button to respectively control an output frequency and output intensity of the ultrasound probe (2), and other ultrasound parameters and an angle of the ultrasound probe (2) itself are adjustable as well.

Abstract: The present invention relates to use of antipsychotic phenothiazine derivative for treatment of cancer. The invention also provides a use for manufacture a medicament, a pharmaceutical composition and a method for treating a cancer, and/or preventing or delaying cancer recurrence based on trifluoperazine. The invention further provides a use for manufacture a medicament, a pharmaceutical composition and a method for treating cancer based on thioridazine and its enantiomers. Additionally, the invention provides a use for manufacture a medicament, a pharmaceutical composition and a method for treating KRAS mutant NSCLC comprising thioridazine.

Abstract: A method of predicting the risk of a patient for developing phenytoin-induced adverse drug reactions (ADRs), including Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), or drug reactions with eosinophilia and systemic symptoms (DRESS) is disclosed. Genetic polymorphisms of CYP2C genes (including rs1057910 (CYP2C9*3) and rs3758581 on CYP2C19), and HLA alleles (including HLA-B*1502, HLA-B*1301, and HLA-B*5101) can predict adverse reactions caused by phenytoin or fosphenytoin. Accordingly, the present invention provides a kit to assess the risk of a patient for developing adverse reactions in response to phenytoin-related drugs, which comprises the determination of the presence of a specific allele selected from the group consisting of rs1057910 (CYP2C9*3), rs3758581 on CYP2C19, HLA-B*1502, HLA-B*1301, and HLA-B*5101, wherein the presence of at least one allele is indicative of a risk for the adverse drug reactions.

Abstract: A disease suffering probability prediction method and an electronic apparatus are provided. The method includes: determining a path length; obtaining a plurality of first paths conforming to the path length from a plurality of history data of a specific disease; obtaining a plurality of second paths positively related to the specific disease from the plurality of first paths; filtering the plurality of second paths to obtain a plurality of third paths, and establishing a prediction model according to the plurality of third paths; and inputting a path to be predicted to the prediction model and outputting a probability of suffering the specific disease.

Abstract: The invention provides a therapy method for glioblastoma multiforme (GBM). The therapy method uses a low-dose radiation to realize GBM tumor cell-targeted radiotherapy via sequentially administration of radiation enhancer-incorporated nanocarriers, 5-aminolevulinic acid, therapeutic ultrasound and therapeutic radiation.

Abstract: Aspects of the invention include methods of selectively reducing the deleterious activity of mutant extended trinucleotide repeat containing genes in a cell, as well as compositions used in such methods. The deleterious activity (e.g., toxicity and/or dis-functionality of products encoded thereby) of a mutant extended trinucleotide repeat containing gene may be selectively reduced in a variety of different ways, e.g., by selectively decreasing SPT4 mediated transcriptional activity, by enhancing functionality of proteins encoded thereby, etc. Aspects of the invention further include assays for identifying agents that find use in methods of the invention, e.g. as summarized above. Methods and compositions of the invention find use in a variety of different applications, including the prevention or treatment of disease conditions associated with the presence of genes containing mutant extended trinucleotide repeats, such as Huntington's Disease (HD).

Abstract: Aspects of the invention include methods of reducing the deleterious activity of a mutant extended nucleotide repeat (NR) containing target gene in a cell by contacting the cell with an effective amount of a nucleoside agent, as well as compositions used in such methods. The deleterious activity (e.g., toxicity and/or dis-functionality of products encoded thereby) of a mutant extended NR containing target gene may be reduced in a variety of different ways, e.g., by reducing (and in some instances differentially, including selectively, reducing) the production or activity of toxic expression products (e.g., RNA or protein) encoded by the target gene. Kits and compositions for practicing the subject methods are also provided.