An ensemble model for predicting dyslipidemia using 3-years continuous physical examination data.

Background: Dyslipidemia has emerged as a significant clinical risk, with its associated complications, including atherosclerosis and ischemic cerebrovascular disease, presenting a grave threat to human well-being. Hence, it holds paramount importance to precisely predict the onset of dyslipidemia. This study aims to use ensemble technology to establish a machine learning model for the prediction of dyslipidemia.

Fine-grained subphenotypes in acute kidney injury populations based on deep clustering: Derivation and interpretation.

Background: Acute kidney injury (AKI) is associated with increased mortality in critically ill patients. Due to differences in the etiology and pathophysiological mechanism, the current AKI criteria put it an embarrassment to evaluate clinical therapy and prognosis.

Objective: We aimed to identify subphenotypes based on routinely collected clinical data to expose the unique pathophysiologic patterns.

Ethnic disparities in cardiovascular and renal responses to canagliflozin between Asian and White patients with type 2 diabetes mellitus: A post hoc analysis of the CANVAS Program.

Aim: To assess the potential heterogeneity in cardiovascular (CV), renal and safety outcomes of canagliflozin between Whites and Asians, as well as these outcomes in each subgroup.

Materials And Methods: The CANVAS Program enrolled 10 142 patients with type 2 diabetes, comprising 78.34% Whites and 12.

Fine scale hippocampus morphology variation cross 552 healthy subjects from age 20 to 80.

The cerebral cortex varies over the course of a person's life span: at birth, the surface is smooth, before becoming more bumpy (deeper sulci and thicker gyri) in middle age, and thinner in senior years. In this work, a similar phenomenon was observed on the hippocampus. It was previously believed the fine-scale morphology of the hippocampus could only be extracted only with high field scanners (7T, 9.

A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit.

Purpose: This study aimed to construct a mortality model for the risk stratification of intensive care unit (ICU) patients with sepsis by applying a machine learning algorithm.

Methods: Adult patients who were diagnosed with sepsis during admission to ICU were extracted from MIMIC-III, MIMIC-IV, eICU, and Zigong databases. MIMIC-III was used for model development and internal validation.

Assessing the impact of the 2018 tetanus guidelines on knowledge and practices of emergency physicians in trauma patients: a national survey study.

Background: Tetanus remains a significant public health issue in China, with the approach of anti-tetanus prophylaxis in the emergency department resulting in both overuse, particularly of human tetanus immune globulin (TIG), and underuse with the tetanus vaccine. This is largely due to the absence of updated guidelines on tetanus prophylaxis before 2018. Our study aimed to evaluate the effects of the 2018 Chinese tetanus guidelines on the knowledge and practices of emergency physicians about tetanus prevention in trauma patients.

Construction of a Hantaan Virus Phage Antibody Library and Screening for Potential Neutralizing Activity.

China is one of the main epidemic areas for hemorrhagic fever with renal syndrome (HFRS). Currently, there is no human antibody specific to Hantaan virus (HTNV) for the emergency prevention and treatment of HFRS. To prepare human antibodies with neutralizing activity, we established an anti-HTNV phage antibody library using phage display technology by transforming peripheral blood mononuclear cells (PBMCs) of patients with HFRS into B lymphoblastoid cell lines (BLCLs) and extracting cDNA from BLCLs that secreted neutralizing antibodies.

Identifying acute kidney injury subphenotypes using an outcome-driven deep-learning approach.

Objective: Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in kidney function within a few hours or days, leading to kidney failure or damage. Although AKI is associated with poor outcomes, current guidelines overlook the heterogeneity among patients with this condition. Identification of AKI subphenotypes could enable targeted interventions and a deeper understanding of the injury's pathophysiology.

Identification of region-specific amino acid signatures for doxorubicin-induced chemo brain.

Doxorubicin (DOX) is a cornerstone of chemotherapy for solid tumors and leukemias. DOX-induced cognitive impairment, termed chemo brain, has been reported in cancer survivors, whereas its mechanism remains poorly understood. Here we initially evaluated the cognitive impairments of mice treated with clinically relevant, long-term, low-dosage of DOX.

Plasma Pharmacokinetics and Tissue Distribution of Doxorubicin in Rats following Treatment with Astragali Radix.

Doxorubicin (DOX) is an essential component in chemotherapy, and Astragali Radix (AR) is a widely used tonic herbal medicine. The combination of DOX and AR offers widespread, well-documented advantages in treating cancer, e.g.

Open-source algorithm and software for computed tomography-based virtual pancreatoscopy and other applications.

Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases. However, the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures, such as gastroscopy and bronchoscopy, owing to its severe invasiveness. In comparison, virtual pancreatoscopy (VP) has shown notable advantages.

Point-of-Care Blood Coagulation Assay Based on Dynamic Monitoring of Blood Viscosity Using Droplet Microfluidics.

Monitoring of the coagulation function has applications in many clinical settings. Routine coagulation assays in the clinic are sample-consuming and slow in turnaround. Microfluidics provides the opportunity to develop coagulation assays that are applicable in point-of-care settings, but reported works required bulky sample pumping units or costly data acquisition instruments.

Acute kidney injury in traumatic brain injury intensive care unit patients.

Background: The exact definition of Acute kidney injury (AKI) for patients with traumatic brain injury (TBI) is unknown.

Aim: To compare the power of the "Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease" (RIFLE), Acute Kidney Injury Network (AKIN), Creatinine kinetics (CK), and Kidney Disease Improving Global Outcomes (KDIGO) to determine AKI incidence/stage and their association with the in-hospital mortality rate of patients with TBI.

Methods: This retrospective study collected the data of patients admitted to the intensive care unit for neurotrauma from 2001 to 2012, and 1648 patients were included.

Three-Dimensional Digital Reconstruction of the Cerebellar Cortex: Lobule Thickness, Surface Area Measurements, and Layer Architecture.

The cerebellum is ontogenetically one of the first structures to develop in the central nervous system; nevertheless, it has been only recently reconsidered for its significant neurobiological, functional, and clinical relevance in humans. Thus, it has been a relatively under-studied compared to the cerebrum. Currently, non-invasive imaging modalities can barely reach the necessary resolution to unfold its entire, convoluted surface, while only histological analyses can reveal local information at the micrometer scale.

Validated tool for early prediction of intensive care unit admission in COVID-19 patients.

Background: The novel coronavirus disease 2019 (COVID-19) pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2.

Aim: To develop and validate a risk stratification tool for the early prediction of intensive care unit (ICU) admission among COVID-19 patients at hospital admission.

Methods: The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital.

Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study.

Background: Traditional scoring systems for patients' outcome prediction in intensive care units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not reliably predict the clinical prognosis of patients with acute respiratory distress syndrome (ARDS). Thus, none of them have been widely accepted for mortality prediction in ARDS. This study aimed to develop and validate a mortality prediction method for patients with ARDS based on machine learning using the Medical Information Mart for Intensive Care (MIMIC-III) and Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) databases.

Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule.

Background: The widespread coronavirus disease 2019 (COVID-19) has led to high morbidity and mortality. Therefore, early risk identification of critically ill patients remains crucial.

Aim: To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit (ICU) care.

Development and validation of a risk stratification model for predicting the mortality of acute kidney injury in critical care patients.

Background: This study aimed to develop and validate a model for mortality risk stratification of intensive care unit (ICU) patients with acute kidney injury (AKI) using the machine learning technique.

Methods: Eligible data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Calibration, discrimination, and risk classification for mortality prediction were evaluated using conventional scoring systems and the new algorithm.

Virus load and virus shedding of SARS-CoV-2 and their impact on patient outcomes.

Background: Understanding a virus shedding patterns in body fluids/secretions is important to determine the samples to be used for diagnosis and to formulate infection control measures.

Aim: To investigate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding patterns and its risk factors.

Methods: All laboratory-confirmed coronavirus disease 2019 patients with complete medical records admitted to the Shenzhen Third People's Hospital from January 28, 2020 to March 8, 2020 were included.

Microengineered Flexural Post Rings for Effective Blood Sample Fencing and High-Throughput Measurement of Clot Retraction Force.

During blood clotting, clot retraction alters its mechanical properties and critically affects hemostasis. Despite that, existing clot retraction assays hold limitations such as large footprint and low throughput. In this work, we report the design of flexural post rings for a miniaturized assay of clot retraction force (CRF) with high throughput.

The roles of methyl jasmonate to stress in plants.

Plants are constantly exposed to various stresses, which can degrade their health. The stresses can be alleviated by the application of methyl jasmonate (MeJA), which is a hormone involved in plant signalling. MeJA induces synthesis of defensive compounds and initiates the expression of pathogenesis-related genes involved in systemic acquired resistance and local resistance.