The search for optimal nocturnal diurnal heart rate Index targets in ICU patients: a retrospective observational study from large ICU database.

Background: Circadian rhythms play a crucial role in cardiovascular health, with the nocturnal diurnal heart rate index (NDHRI) reflecting significant circadian variations. However, the optimal NDHRI target in Intensive Care Unit (ICU) patients remains undefined. This study aims to establish an evidence-based NDHRI target range and assess its association with mortality.

Automated Detection of Epiretinal Membranes in OCT Images Using Deep Learning.

Introduction: Development and validation of a deep learning algorithm to automatically identify and locate epiretinal memberane (ERM) regions in OCT images.

Methods: OCT images of 468 eyes were retrospectively collected from a total of 404 ERM patients. One expert manually annotated the ERM regions for all images.

Deep Learning Promotes the Screening of Natural Products with Potential Microtubule Inhibition Activity.

Natural microtubule inhibitors, such as paclitaxel and ixabepilone, are key sources of novel medications, which have a considerable influence on anti-tumor chemotherapy. Natural product chemists have been encouraged to create novel methodologies for screening the new generation of microtubule inhibitors from the enormous natural product library. There have been major advancements in the use of artificial intelligence in medication discovery recently.

MiR-501-5p alleviates cardiac dysfunction in septic patients through targeting NR4A3 to prevent its binding with Bcl-2.

Sepsis-induced myocardial dysfunction is a common complication in septic patients. To date, a limited number of biomarkers that could predict cardiomyocyte apoptosis have been explored. In this study, we successfully established a cecal ligation and puncture (CLP)-induced septic model, and it was found that miR-501-5p expression was down-regulated in peripheral blood samples of septic patients with cardiac dysfunction, lipopolysaccharide (LPS)-induced cardiomyocytes, and the myocardium and peripheral blood in the septic model.

A Deep Learning-Based Framework for Accurate Evaluation of Corneal Treatment Zone After Orthokeratology.

Purpose: Given the robust effectiveness of inhibiting myopia progression, orthokeratology has gained increasing popularity worldwide. However, identifying the boundary and the center of reshaped corneal area (i.e.

Identification of Barrett's esophagus in endoscopic images using deep learning.

Background: Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images.

Methods: 443 endoscopic images from 187 patients of BE were included in this study. The gastroesophageal junction (GEJ) and squamous-columnar junction (SCJ) of BE were manually annotated in endoscopic images by experts.

Prognostic value of the combined variability of mean platelet volume and neutrophil percentage for short-term clinical outcomes of sepsis patients.

Introduction: In this single center retrospective cohort study, 784 patients with sepsis were enrolled and followed up for at least 30 days. The selected endpoint was an all-cause mortality event.

Method: The relationship between MPV-CV + NEU%-CV and all-cause mortality (in-hospital and 30-day) was analyzed by categorizing the patients into four groups according to MPV-CV and NEU%-CV values.

Optimal teicoplanin loading regimen to rapidly achieve target trough plasma concentration in critically ill patients.

Teicoplanin is used for the treatment of Methicillin-resistant Staphylococcus aureus infection. It has been demonstrated that conventional loading regimen was insufficient for teicoplanin to achieve target trough plasma concentration (C  > 10 mg/L). Therefore, a Chinese expert group recommended an optimal loading dose regimen of teicoplanin to treat severe Gram-positive infection.