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Received — 8 April 2026 ⏭

A Gamified Mobile Health Intervention to Promote Physical Activity, Executive Function, and Mental Health in College Students: Randomized Controlled Trial

✇Journal of Medical Internet Research
By: Ming Yang · Yingjun Guo · Zhen Li · Lei Jiang · Yanan Gao · Sen Yang · Zhixiong Zhou
8 April 2026 at 01:30
Background: College students commonly experience suboptimal health conditions, including insufficient physical activity (PA), excessive body weight, and declining physical fitness. Traditional interventions face low adherence, while gamified mobile health (mHealth) programs may improve engagement and outcomes. Objective: This study aimed to evaluate the feasibility and effectiveness of a novel gamified, incentive-based mHealth intervention on primary outcomes (PA and adherence) and secondary outcomes (physical fitness, body composition, executive function [EF], and mental health). Methods: A 2-arm parallel-group randomized controlled trial (RCT) was conducted in 2025 at Yantai University with 160 college students (18‐25 years; BMI 18.5‐30.0) who were randomized 1:1 (computer-generated, sex-stratified blocks of 4; concealed allocation) to the intervention group (IG) or control group (CG; n=80 each); major exclusions were contraindications to exercise, severe physical/mental illness, recent PA interventions, or psychotropic medication use. Both used the same fitness watch–app system and identical PA targets (≥150 min moderate-to-vigorous physical activity [MVPA] per week or ≥900 metabolic equivalent-minutes [MET-min] per week); IG additionally received team-based gamification (competition, points/leaderboards, feedback, and rewards), while CG received monitoring only. PA and adherence were monitored throughout the 8-week intervention; other outcomes were assessed at baseline and 8 weeks (fitness, body composition, EF, and mental health). Open-label with blinded outcome assessors/analysts; intention-to-treat (ITT) with multiple imputation. Results: At 8 weeks, data were available for 154 participants (IG 78; CG 76); all 160 were analyzed per ITT. Compared to the CG, the IG demonstrated significantly higher mean levels in all primary PA outcomes over 8 weeks (daily steps: mean 10,356, SD 1245 versus 8242, SD 1087; Δ=2114; =1.81, 95% CI 1.44‐2.18;

Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuanhao Liu · Zihan Zhou · Kaiying Wu · Shuo Liu · Yiyang Huang · Jiajun Guo · Aimin Zhou · Hong Qian
7 April 2026 at 12:00
arXiv:2604.04088v1 Announce Type: cross Abstract: Learner-item cognitive modeling plays a central role in the web-based online intelligent education system by enabling cognitive diagnosis (CD) across diverse online educational scenarios. Although ID embedding remains the mainstream approach in cognitive modeling due to its effectiveness and flexibility, recent advances in language models (LMs) have introduced new possibilities for incorporating rich semantic representations to enhance CD performance. This highlights the need for a comprehensive analysis of how LMs enhance embeddings through semantic integration across mainstream CD tasks. This paper identifies two key challenges in fully leveraging LMs in existing work: Misalignment between the training objectives of LMs and CD models creates a distribution gap in feature spaces; A unified framework is essential for integrating textual embeddings across varied CD tasks while preserving the strengths of existing cognitive modeling paradigms to ensure the robustness of embedding enhancement. To address these challenges, this paper introduces EduEmbed, a unified embedding enhancement framework that leverages fine-tuned LMs to enrich learner-item cognitive modeling across diverse CD tasks. EduEmbed operates in two stages. In the first stage, we fine-tune LMs based on role-specific representations and an interaction diagnoser to bridge the semantic gap of CD models. In the second stage, we employ a textual adapter to extract task-relevant semantics and integrate them with existing modeling paradigms to improve generalization. We evaluate the proposed framework on four CD tasks and computerized adaptive testing (CAT) task, achieving robust performance. Further analysis reveals the impact of semantic information across diverse tasks, offering key insights for future research on the application of LMs in CD for online intelligent education systems.
Received — 6 April 2026 ⏭

Translating ctDNA into cutaneous melanoma care: An international expert survey

✇MRD
By: Glenn Geidel · Nathan Fekade · Katharina Raabe · Daniel J Smit · Laura Adam · Isabel Heidrich · Alessandra Rünger · Julian Kött · Tim Zell · Teresa Amaral · Paolo A Ascierto · Pippa Corrie · Reinhard Dummer · Alexander Eggermont · Jun Guo · Jessica C Hassel · Mathilde Jalving · Douglas B Johnson · Lidija Kandolf · John M Kirkwood · Celeste Lebbé · Rebecca Lee · Georgina V Long · Paul Lorigan · Josep Malvehy · Joanna Mangana · Peter Mohr · Sapna P Patel · Helen Rizos · Caroline Robert · Dirk Schadendorf · Vernon K Sondak · Inge Marie Svane · Alberto Wainstein · Jonathan S Zager · Klaus Pantel · Axel Hauschild · Christoffer Gebhardt · Melanoma World Society Study Group
3 April 2026 at 18:00

Eur J Cancer. 2026 Mar 19;239:116676. doi: 10.1016/j.ejca.2026.116676. Online ahead of print.

ABSTRACT

BACKGROUND: Circulating tumor DNA (ctDNA) is a promising biomarker in melanoma, with higher sensitivity for tumor burden detection than conventional diagnostics. While well established in research, clinical routine implementation remains pending. Key global questions concern optimal clinical applications and barriers to adoption.

METHODS: A web-based survey of 116 members of the Melanoma World Society Study Group assessed international expert opinions on ctDNA utility across predefined clinical scenarios. The questionnaire included 18 general questions on ctDNA use and 5 clinical vignettes with de-identified patient data and retrospectively obtained ctDNA results.

RESULTS: ctDNA was rated most valuable for detecting minimal residual disease (mean score 3.63), surveillance of recurrent disease (3.85), and stage IV melanoma (3.82), with limited utility in early stages. Experts considered ctDNA superior to S100 and LDH for early relapse detection and identifying progressive disease. Most participants (80%) agreed that ctDNA correlates with radiographic response, and 82% favored its integration into routine follow-ups. In urgent high-tumor-burden settings, 82.8% would initiate BRAFi/MEKi therapy based on ctDNA if tissue analysis was pending, and 93.9% if unavailable. For central nervous system lesions, 62% did not support blood ctDNA, while 66% considered cerebrospinal fluid valuable. Pragmatic approaches with small to mid-size targeted panels and short turnaround times were preferred. Major barriers included the need for prospective trials (85%), standardized guidelines (83%), and reimbursement policies (82%).

CONCLUSION: Key opinion leaders regarded ctDNA as a valuable adjunct selected melanoma scenarios. Validation through prospective studies, guideline development, and reimbursement frameworks are essential for broader clinical implementation.

PMID:41932032 | DOI:10.1016/j.ejca.2026.116676

Received — 2 April 2026 ⏭

SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guifeng Deng · Pan Wang · Jiquan Wang · Shuying Rao · Junyi Xie · Wanjun Guo · Tao Li · Haiteng Jiang
1 April 2026 at 12:00
arXiv:2603.26738v2 Announce Type: replace-cross Abstract: While automated sleep staging has achieved expert-level accuracy, its clinical adoption is hindered by a lack of auditable reasoning. We introduce SleepVLM, a rule-grounded vision-language model (VLM) designed to stage sleep from multi-channel polysomnography (PSG) waveform images while generating clinician-readable rationales based on American Academy of Sleep Medicine (AASM) scoring criteria. Utilizing waveform-perceptual pre-training and rule-grounded supervised fine-tuning, SleepVLM achieved Cohen's kappa scores of 0.767 on an held out test set (MASS-SS1) and 0.743 on an external cohort (ZUAMHCS), matching state-of-the-art performance. Expert evaluations further validated the quality of the model's reasoning, with mean scores exceeding 4.0/5.0 for factual accuracy, evidence comprehensiveness, and logical coherence. By coupling competitive performance with transparent, rule-based explanations, SleepVLM may improve the trustworthiness and auditability of automated sleep staging in clinical workflows. To facilitate further research in interpretable sleep medicine, we release MASS-EX, a novel expert-annotated dataset.
Received — 26 March 2026 ⏭

Symbolic Graph Networks for Robust PDE Discovery from Noisy Sparse Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xingyu Chen · Junxiu An · Jun Guo · Yuqian Zhou
25 March 2026 at 12:00
arXiv:2603.22380v1 Announce Type: cross Abstract: Data-driven discovery of partial differential equations (PDEs) offers a promising paradigm for uncovering governing physical laws from observational data. However, in practical scenarios, measurements are often contaminated by noise and limited by sparse sampling, which poses significant challenges to existing approaches based on numerical differentiation or integral formulations. In this work, we propose a Symbolic Graph Network (SGN) framework for PDE discovery under noisy and sparse conditions. Instead of relying on local differential approximations, SGN leverages graph message passing to model spatial interactions, providing a non-local representation that is less sensitive to high frequency noise. Based on this representation, the learned latent features are further processed by a symbolic regression module to extract interpretable mathematical expressions. We evaluate the proposed method on several benchmark systems, including the wave equation, convection-diffusion equation, and incompressible Navier-Stokes equations. Experimental results show that SGN can recover meaningful governing relations or solution forms under varying noise levels, and demonstrates improved robustness compared to baseline methods in sparse and noisy settings. These results suggest that combining graph-based representations with symbolic regression provides a viable direction for robust data-driven discovery of physical laws from imperfect observations. The code is available at https://github.com/CXY0112/SGN
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