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Received — 14 October 2025 ⏭ Favourites

Small airway disease as a key factor in COPD: new perspectives and insights

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Ruoyi Zhou · Haojie Wang · Yulu Zhang · Jieming Mai · Liuliu Yang
13 October 2025 at 18:00

Front Med (Lausanne). 2025 Sep 26;12:1648612. doi: 10.3389/fmed.2025.1648612. eCollection 2025.

ABSTRACT

Small airways-defined as bronchioles <2 mm in internal diameter that lack cartilaginous support-are frequently involved in the earliest stages of chronic obstructive pulmonary disease (COPD). While COPD is defined per GOLD by persistent post-bronchodilator airflow limitation, small-airway dysfunction can precede spirometric abnormality, motivating earlier, imaging- and physiology-based detection (Agustí et al., 2023). Pathological progression typically begins with loss and stenosis of terminal bronchioles, followed by mucus retention/plugging, fibrotic remodeling, chronic inflammation, microvascular abnormalities, and cellular senescence, ultimately resulting in irreversible impairment of gas exchange. Early diagnosis remains difficult, but a suite of advanced non-invasive modalities-including impulse oscillometry system/forced oscillation techniques (IOS/FOT), single- and multiple-breath washout tests, high-resolution CT with parametric response mapping (PRM), nuclear medicine approaches (e.g., SPECT), dynamic measurements of lung compliance, and Fluorine-19 (19F) MRI-combined with artificial intelligence markedly improve the sensitivity and specificity for detecting small-airway disease. Therapeutic strategies that target cellular senescence and fibrotic pathways-such as senolytics and antifibrotic interventions-are showing promise, particularly approaches that clear senescent cells or block pro-fibrotic signaling. The integration of single-cell omics, high-resolution microvascular imaging, and molecularly targeted therapies is expected to accelerate precision diagnostics and enable personalized early interventions. This review summarizes recent insights into small-airway physiology, key pathophysiological and molecular mechanisms, and current pharmacological strategies, and emphasizes the clinical principle of "early detection, early diagnosis, early intervention" for managing COPD-related small-airway disease.

PMID:41080967 | PMC:PMC12510933 | DOI:10.3389/fmed.2025.1648612

Received — 28 September 2025 ⏭ Favourites

Using Large Language Models to Assess the Consistency of Randomized Controlled Trials on AI Interventions With CONSORT-AI: Cross-Sectional Survey

✇Journal of Medical Internet Research
By: Xufei Luo · Zeming Li · Zhenhua Yang · Bingyi Wang · Yanfang Ma · Fengxian Chen · Qi Wang · Long Ge · James Zou · Lu Zhang · Yaolong Chen · Zhaoxiang Bian
27 September 2025 at 00:15
Background: Chatbots based on large language models (LLMs) have shown promise in evaluating the consistency of research. Previously, researchers used LLM to assess if randomized controlled trial (RCT) abstracts adhered to the CONSORT-Abstract guidelines. However, the consistency of artificial intelligence (AI) interventional RCTs align with the CONSORT-AI standards by LLMs remains unclear. Objective: The aim of this study is to identify the consistency of randomized controlled trials on AI interventions with CONSORT-AI using chatbots based on LLMs. Methods: This cross-sectional study employed six LLM models to assess the consistency of RCTs on AI interventions. The sample selection is based on articles published in JAMA Network Open, which included a total of 41 RCTs. All queries were submitted to LLMs through an API interface with a temperature setting of 0 to ensure deterministic responses. One researcher posed the questions to each model, while another independently verified the responses for validity before recording the results. The Overall Consistency Score (OCS), recall, inter-rater reliability and consistency of contents were analyzed. Results: We found gpt-4-0125-preview has the best average OCS (86.5%, 95%CI: 82.5%-90.5% and 81.6%, 95% CI: 77.6%-85.6%), followed by gpt-4-1106-preview(80.3%, 95%CI: 76.3%-84.3% and 78.0%, 95% CI: 74.0%-82.0%). The model with the worst average OCS is gpt-3.5-turbo-0125 (61.9%, 95%CI: 57.9%-65.9% and 63.0%, 95% CI: 59.0%-67.0%). Among the 11 unique items of CONSORT-AI, Item 2 (“State the inclusion and exclusion criteria at the level of the input data”) received the poorest overall evaluation across six models, with an average OCS of 48.8%. For other items, those with an average OCS greater than 80% across the six models included Items 1, 5, 8, and 9. Conclusions: GPT-4 variants demonstrate strong performance in assessing the consistency of RCTs with CONSORT-AI. Nonetheless, refining the prompts could enhance the precision and consistency of the outcomes. While AI tools like GPT-4 variants are valuable, they are not yet fully autonomous in addressing complex and nuanced tasks such as adherence to CONSORT-AI standards. Therefore, integrating AI with higher levels of human supervision and expertise will be crucial to ensuring more reliable and efficient evaluations, ultimately advancing the quality of medical research.
Received — 10 July 2025 ⏭ Favourites

Clinical performance evaluation of a plasma dual-target methylation test for the detection of primary liver cancer: a multicenter study

✇Most Recent Articles: Clinical Epigenetics
By: Zhigao Xu · Wanping Chen · Hongsheng Wei · Kui Tan · Lianglu Zhang · Lanlan Dong · Wenjin Liang · Xin Zhou · Tao Zhang · Jian Chen · Chuang Peng · Zhongxin Wang · Shaojun Ye and Qifa Ye
7 July 2025 at 08:00
Primary liver cancer (PLC) is a global health concern. The plasma dual-target methylation (PDTM) test, which interrogates the methylation status of GNB4 and Riplet, exhibits a commendable ability to discriminate ...
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