Efficacy of rituximab in treating steroid-resistant Graves' orbitopathy in active moderate-to-severe and sight-threatening forms: A retrospective observation from China.

Background And Objectives: The efficacy of rituximab (RTX) in treating steroid-resistant Graves' orbitopathy (GO) has been limitedly studied in Asians. Moreover, RTX has been considered even less for patients with steroid-resistant dysthyroid optic neuropathy (DON) who failed to undergo orbital decompression surgery for physical or financial reasons, or who responded poorly to the procedure. This study aimed to investigate the efficacy of RTX in treating steroid-resistant active moderate-to-severe and sight-threatening GO in a Chinese population.

COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring.

While no longer a public health emergency of international concern, COVID-19 remains an established and ongoing global health threat. As the global population continues to face significant negative impacts of the pandemic, there has been an increased usage of point-of-care ultrasound (POCUS) imaging as a low-cost, portable, and effective modality of choice in the COVID-19 clinical workflow. A major barrier to the widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians who can interpret POCUS examinations, leading to considerable interest in artificial intelligence-driven clinical decision support systems to tackle this challenge.

Neural networks based on attention architecture are robust to data missingness for early predicting hospital mortality in intensive care unit patients.

Background: Although the machine learning model developed on electronic health records has become a promising method for early predicting hospital mortality, few studies focus on the approaches for handling missing data in electronic health records and evaluate model robustness to data missingness. This study proposes an attention architecture that shows excellent predictive performance and is robust to data missingness.

Methods: Two public intensive care unit databases were used for model training and external validation, respectively.

Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit.

Background: Clinical decision of extubation is a challenge in the treatment of patient with invasive mechanical ventilation (IMV), since existing extubation protocols are not capable of precisely predicting extubation failure (EF). This study aims to develop and validate interpretable recurrent neural network (RNN) models for dynamically predicting EF risk.

Methods: A retrospective cohort study was conducted on IMV patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.

Discovery of TIGIT inhibitors based on DEL and machine learning.

Drug discovery has entered a new period of vigorous development with advanced technologies such as DNA-encoded library (DEL) and artificial intelligence (AI). The previous DEL-AI combination has been successfully applied in the drug discovery of classical kinase and receptor targets mainly based on the known scaffold. So far, there is no report of the DEL-AI combination on inhibitors targeting protein-protein interaction, including those undruggable targets with few or unknown active scaffolds.

Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis.

Background: Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis.

Methods: Two ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III).

An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data.

With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are deficient in dynamic scalability and the correlations between environmental constraints and personal choices have not been investigated. This paper proposes an improved HMM-based (hidden Markov model) method for planning personalized routes with crowd sourcing spatiotemporal data.

Th17 and Treg lymphocytes in obesity and Type 2 diabetic patients.

Assumption that the pathogenesis of obesity-associated type 2 diabetes (T2DM) encompasses inflammation and autoimmune aspects is increasingly recognized. In the state of obesity and T2DM, the imbalance of T helper 17 (Th17) cells and regulatory T (Treg) cells are observed. These alterations reflect a loss of T cell homeostasis, which may contribute to tissue and systemic inflammation and immunity in T2DM.