Deep Reinforcement Learning-Based Resource Allocation for UAV-GAP Downlink Cooperative NOMA in IIoT Systems.

This paper studies deep reinforcement learning (DRL)-based joint resource allocation and three-dimensional (3D) trajectory optimization for unmanned aerial vehicle (UAV)-ground access point (GAP) cooperative non-orthogonal multiple access (NOMA) communication in Industrial Internet of Things (IIoT) systems. Cooperative and non-cooperative users adopt different signal transmission strategies to meet diverse, task-oriented, quality-of-service requirements. Specifically, the DRL framework based on the Soft Actor-Critic algorithm is proposed to jointly optimize user scheduling, power allocation, and UAV trajectory in continuous action spaces.

MiR-144-3p participates in the regulation of gestational diabetes mellitus by targeting FOXO1.

Background: The pivotal role of microRNAs (miRNAs) in the pathogenesis of gestational diabetes mellitus (GDM) have been increasingly highlighted in recent studies.

Aims: To investigate miR-144-3p expression in patients with GDM and its potential mechanisms in GDM.

Methods: According to the inclusion criteria, 98 pregnant women with GDM and 98 normal pregnant women were statistically selected.

Nonlinear relationship between hepatic steatosis index and reversion to normal glucose regulation in Chinese adults with prediabetes.

The hepatic steatosis index (HSI) has been demonstrated to have a significant correlation with prediabetes and diabetes; however, its potential association with reversion to normal glucose regulation (NGR) from prediabetes remains insufficiently investigated. The primary objective of this study was to elucidate the relationship between HSI levels and the probability of reversion to NGR among Chinese adults with prediabetes. This retrospective cohort study utilized health examination data from 11,241 Chinese adults with prediabetes.

Construction of a Nomogram Prediction Model for Prognosis in Patients with Large Artery Occlusion-Acute Ischemic Stroke.

Background: Patients with large artery occlusion-acute ischemic stroke (LAO-AIS) can experience adverse outcomes, such as brain herniation due to complications. This study aimed to construct a nomogram prediction model for prognosis in patients with LAO-AIS in order to maximize the benefits for clinical patients.

Methods: Retrospective analysis of 243 patients with LAO-AIS from January 2019 to January 2022 with medical history data and blood examination at admission.

Systemic immune inflammatory index is an independent predictor for the requirement of decompressive craniectomy in large artery occlusion acute ischemic stroke patients after mechanical thrombectomy.

Background And Purpose: Following mechanical thrombectomy (MT), patients with large artery occlusive acute ischemic stroke (LAO-AIS) often have cerebral herniation due to its complications, resulting in poor prognosis. Decompressive craniectomy (DC) can markedly improve patient prognosis. This study aimed to verify the predictive value of clinical parameters such as the systemic immune-inflammatory index (SII) for DC in patients with LAO-AIS after MT.