The application of metagenomics, radiomics and machine learning for diagnosis of sepsis.

Introduction: Sepsis poses a serious threat to individual life and health. Early and accessible diagnosis and targeted treatment are crucial. This study aims to explore the relationship between microbes, metabolic pathways, and blood test indicators in sepsis patients and develop a machine learning model for clinical diagnosis.

Osteocyte-derived exosomes regulate the DLX2/wnt pathway to alleviate osteoarthritis by mediating cartilage repair.

Background: Chondrocyte viability, apoptosis, and migration are closely related to cartilage injury in osteoarthritis (OA) joints. Exosomes are identified as potential therapeutic agents for OA.

Objective: This study aimed to investigate the role of exosomes derived from osteocytes in OA, particularly focusing on their effects on cartilage repair and molecular mechanisms.

Development of rapid nucleic acid testing techniques for common respiratory infectious diseases in the Chinese population.

Most respiratory viruses can cause serious lower respiratory diseases at any age. Therefore, timely and accurate identification of respiratory viruses has become even more important. This study focused on the development of rapid nucleic acid testing techniques for common respiratory infectious diseases in the Chinese population.

Association of fresh vegetable and salt-preserved vegetable consumptions with estimated glomerular filtration rate.

Objective: This study aimed to investigate the relationship between the consumption of fresh and salt-preserved vegetables and the estimated glomerular filtration rate (eGFR), which requires further research.

Methods: For this purpose, the data of those subjects who participated in the 2011-2012 and 2014 surveys of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and had biomarker data were selected. Fresh and salt-preserved vegetable consumptions were assessed at each wave.

Deubiquitinase USP29 correlates RORγt expression and its association with thymoma myasthenia gravis.

Objective: The objective of this study was to examine the expression of deubiquitylases USP29 in thymomas with myasthenia gravis (MG) and research associated immunological processes.

Methods: 69 MG patients with thymomas, 21 thymoma patients without MG, and 31 healthy controls were classified into three groups (categories): group with MG-associated thymoma (MG-T), group with non-MG-associated thymoma (NMG-T), and group with healthy controls (HC). In thymomas, the mRNA and protein levels of RORγt and USP29 were examined by real-time reverse transcription polymerase chain reaction (real-time RT-PCR) and western blotting.

Explainable Dynamic Multimodal Variational Autoencoder for the Prediction of Patients With Suspected Central Precocious Puberty.

Central precocious puberty (CPP) is the most common type of precocious puberty and has a significant effect on children. A gonadotropin-releasing hormone (GnRH)-stimulation test is the gold standard for confirming CPP. This test, however, is costly and unpleasant for patients.

Machine learning identifies girls with central precocious puberty based on multisource data.

Objective: The study aimed to develop simplified diagnostic models for identifying girls with central precocious puberty (CPP), without the expensive and cumbersome gonadotropin-releasing hormone (GnRH) stimulation test, which is the gold standard for CPP diagnosis.

Materials And Methods: Female patients who had secondary sexual characteristics before 8 years old and had taken a GnRH analog (GnRHa) stimulation test at a medical center in Guangzhou, China were enrolled. Data from clinical visiting, laboratory tests, and medical image examinations were collected.

Early and Accurate Prediction of Clinical Response to Methotrexate Treatment in Juvenile Idiopathic Arthritis Using Machine Learning.

Accurately predicting the response to methotrexate (MTX) in juvenile idiopathic arthritis (JIA) patients before administration is the key point to improve the treatment outcome. However, no simple and reliable prediction model has been identified. Here, we aimed to develop and validate predictive models for the MTX response to JIA using machine learning based on electronic medical record (EMR) before and after administering MTX.

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study.

Background: Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis-gonadotropin-releasing hormone (GnRH)-stimulation test or GnRH analogue (GnRHa)-stimulation test-is expensive and makes patients uncomfortable due to the need for repeated blood sampling.

Objective: We aimed to combine multiple CPP-related features and construct machine learning models to predict response to the GnRHa-stimulation test.

Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence.

Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found.

Comprehensive Analysis of Differentially Expressed Unigenes under NaCl Stress in Flax ( L.) Using RNA-Seq.

Flax ( L.) is an important industrial crop that is often cultivated on marginal lands, where salt stress negatively affects yield and quality. High-throughput RNA sequencing (RNA-seq) using the powerful Illumina platform was employed for transcript analysis and gene discovery to reveal flax response mechanisms to salt stress.

QTL Mapping of Fiber-Related Traits Based on a High-Density Genetic Map in Flax ( L.).

Unlabelled: A genetic map is an important and valuable tool for quantitative trait locus (QTL) mapping, marker-assisted selection (MAS)-based breeding, and reference-assisted chromosome assembly. In this study, 112 F plants from a cross between L. "DIANE" and "NY17" and parent plants were subjected to high-throughput sequencing and specific-locus amplified fragment (SLAF) library construction.

Developing a Machine Learning System for Identification of Severe Hand, Foot, and Mouth Disease from Electronic Medical Record Data.

Children of severe hand, foot, and mouth disease (HFMD) often present with same clinical features as those of mild HFMD during the early stage, yet later deteriorate rapidly with a fulminant disease course. Our goal was to: (1) develop a machine learning system to automatically identify cases with high risk of severe HFMD at the time of admission; (2) compare the effectiveness of the new system with the existing risk scoring system. Data on 2,532 HFMD children admitted between March 2012 and July 2015, were collected retrospectively from a medical center in China.

Interrelationship of rotavirus infection and Creatine Kinase-MB isoenzyme levels in children hospitalized with acute gastroenteritis in Guangzhou, China, 2012-2015.

Elevated levels of Creatine Kinase-MB (CK-MB) Isoenzyme are a common phenomenon among rotavirus (RV) diarrhea. However, few studies have addressed this issue using large sample size. In current study, 1,118 children (age <5 years) hospitalized with diarrhea in Guangzhou Women and Children's Medical Center from 2012 to 2015 were finally included.

Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia.

The prediction of relapse in childhood acute lymphoblastic leukemia (ALL) is a critical factor for successful treatment and follow-up planning. Our goal was to construct an ALL relapse prediction model based on machine learning algorithms. Monte Carlo cross-validation nested by 10-fold cross-validation was used to rank clinical variables on the randomly split training sets of 336 newly diagnosed ALL children, and a forward feature selection algorithm was employed to find the shortest list of most discriminatory variables.