An AI-Digital Pathology Algorithm Predicts Survival after Radical Prostatectomy from the PLCO Trial.

Purpose: Clinical variables alone have limited ability to determine which patients will have recurrence after radical prostatectomy (RP). We evaluated the ability of locked multimodal artificial intelligence (MMAI) algorithms trained on prostate biopsy specimens to predict prostate cancer specific mortality (PCSM) and overall survival (OS) among patients undergoing radical prostatectomy with digitized RP specimens.

Materials And Methods: The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Randomized Controlled Trial randomized subjects from 1993-2001 to cancer screening or control.

Digital channel-enabled distributed force decoding via small datasets for hand-centric interactions.

Tactile interfaces are essential for enhancing human-machine interactions, yet achieving large-scale, precise distributed force sensing remains challenging due to signal coupling and inefficient data processing. Inspired by the spiral structure of and the processing principles of neuronal systems, this study presents a digital channel-enabled distributed force decoding strategy, resulting in a phygital tactile sensing system named PhyTac. This innovative system effectively prevents marker overlap and accurately identifies multipoint stimuli up to 368 regions from coupled signals.

Physical activity alleviated associations of oxidation capacity of the atmosphere with platelet-based inflammatory indicators: findings from the Henan Rural Cohort Study.

: several adverse effects of ozone (O) and nitrogen dioxide (NO) are assessed using combined oxidant capacity (O) and redox-weighted oxidant capacity (Owtx) as surrogates. However, the associations of oxidant capacity (O and Owtx) with platelet-based inflammatory indicators and the potential modifying role of physical activity (PA) remain unclear. : 31 318 participants were selected from the baseline survey of the Henan Rural Cohort Study.

Identification of pain-related long non-coding RNAs for pulpitis prediction.

Objectives: We investigated the recently generated RNA-sequencing dataset of pulpitis to identify the potential pain-related lncRNAs for pulpitis prediction.

Materials And Methods: Differential analysis was performed on the gene expression profile between normal and pulpitis samples to obtain pulpitis-related genes. The co-expressed gene modules were identified by weighted gene coexpression network analysis (WGCNA).

A brain-computer interface system for lower-limb exoskeletons based on motor imagery and stacked ensemble approach.

Existing lower limb exoskeletons (LLEs) have demonstrated a lack of sufficient patient involvement during rehabilitation training. To address this issue and better incorporate the patient's motion intentions, this paper proposes an online brain-computer interface (BCI) system for LLE based motor imagery and stacked ensemble. The establishment of this online BCI system enables a comprehensive closed-loop control process, which includes the collection and decoding of brain signals, robotic control, and real-time feedback mechanisms.