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Received — 28 September 2025 ⏭ Favourites
  • ✇TechCrunch
  • Everyone’s still throwing billions at AI data centers Theresa Loconsolo
    From $100 billion OpenAI commitments to $100,000 visa fees, this week showed just how much the tech landscape is shifting. On the latest episode of Equity, Anthony Ha and Max Zeff unpack the AI infrastructure gold rush and tech’s talent shuffle. Watch the full episode for more about:   Equity is TechCrunch’s flagship podcast, produced by […]
     
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Everyone’s still throwing billions at AI data centers

✇TechCrunch
By: Theresa Loconsolo
27 September 2025 at 01:30
From $100 billion OpenAI commitments to $100,000 visa fees, this week showed just how much the tech landscape is shifting. On the latest episode of Equity, Anthony Ha and Max Zeff unpack the AI infrastructure gold rush and tech’s talent shuffle. Watch the full episode for more about:   Equity is TechCrunch’s flagship podcast, produced by […]

Association of HTR1F with Prognosis, Tumor Immune Microenvironment, and Drug Sensitivity in Cancer: A Multi-Omics Perspective

✇Omics In Lung
By: Yanjun Gao · Ziyue Zhang · Dafu Ye · Qingqing Li · Yingmei Wen · Shaowen Ma · Bo Zheng · Lei Chen · Yi Yao
27 September 2025 at 18:00

Biomedicines. 2025 Sep 11;13(9):2238. doi: 10.3390/biomedicines13092238.

ABSTRACT

Background:HTR1F (5-Hydroxytryptamine Receptor 1F) encodes a G protein-coupled receptor involved in serotonin signaling. Although dysregulated HTR1F expression has been implicated in certain malignancies, its biological functions and clinical significance across cancer types remain largely unexplored. Methods: We performed an integrative pan-cancer analysis of transcriptomic and pharmacogenomic datasets covering 34 cancer types (PAN-CAN cohort, N = 19,131; normal tissues, G = 60,499). Drug sensitivity and molecular docking analyses were conducted using the GSCALite database. The protein-protein interaction (PPI) network of HTR1F was constructed via the STRING database. Additionally, we evaluated the effects of HTR1F overexpression on proliferation and invasion in human lung squamous cell carcinoma (LUSC) cell lines NCI-H520 and NCI-H226. Results:HTR1F expression was significantly upregulated in 17 cancer types and was associated with poor prognosis, with LUSC showing an AUC of 0.912 for 1-year survival prediction. In LUSC, 695 genes were upregulated and 67 downregulated in response to HTR1F overexpression. HTR1F expression correlated with immune-related genes, immune checkpoints, tumor-infiltrating immune cells, tumor mutation burden (TMB), microsatellite instability (MSI), and drug responses. Genomic alterations, including amplification and deletion, were positively associated with HTR1F expression. Drug sensitivity analysis identified compounds such as sotrastaurin (-10.2 kcal/mol), austocystin D (-9.7 kcal/mol), and tivozanib (-9.3 kcal/mol) as potentially effective inhibitors based on predicted binding affinity. Functional enrichment analyses (GO, KEGG) and GSEA revealed that HTR1F is primarily involved in cell cycle regulation, DNA replication, cellular senescence, and immune-related pathways. Functional validation showed that HTR1F overexpression promotes proliferation of LUSC cells via the MAPK signaling pathway. Conclusions: Our integrative analysis highlights HTR1F as a potential biomarker associated with prognosis, immune modulation, and drug sensitivity across multiple cancer types. These findings provide a foundation for future experimental and clinical studies to explore HTR1F-targeted therapies.

PMID:41007799 | PMC:PMC12467612 | DOI:10.3390/biomedicines13092238

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.

The Impact of Digital Health Interventions on Psychological Health, Self-Efficacy, and Quality of Life in Patients With End-Stage Kidney Disease: Systematic Review and Meta-Analysis

✇Journal of Medical Internet Research
By: Jiayi Zhu · Jianfei Xie · Yating Luo · Xiaoqian Dong · Zitong Lu · Huiyi Zhang · Jingying Wang · Min Liu · Andy SK Cheng
27 September 2025 at 00:00
Background: End-stage kidney disease (ESKD) imposes a significant global health burden, with patients often experiencing poor quality of life (QoL) due to psychological distress and low self-efficacy. Digital health interventions (DHIs) offer potential to address these challenges. However, their effects in this population remain inconsistent, and a comprehensive synthesis of the evidence is lacking. Objective: To assess the impact of DHIs on the psychological health, self-efficacy, and QoL of ESKD patients, and to evaluate engagement, adherence and satisfaction with these interventions. Methods: A comprehensive search was conducted across six electronic databases (PubMed, Web of Science, Cochrane Library, PsycINFO, Embase, and CINAHL) up to January 21, 2025. Randomized controlled trials (RCTs) examining DHIs effects on psychological health, self-efficacy, or QoL in ESKD patients were included. Two reviewers independently screened studies, extracted data, and assessed the risk of bias using the RoB 2 tool. Meta-analysis was performed using Review Manager 5.4, with subgroup analyses by treatment modality, intervention type, and duration. Evidence quality was assessed using the GRADE approach. Results: 23 RCTs involving 2407 ESKD patients from 12 countries were included. DHIs significantly improved depression (SMD: -0.41, 95% CI: [-0.63, -0.19], P=.003) and overall QoL (SMD: 0.55, 95% CI: [0.07, 1.03], P=.03). While DHIs did not significantly improve overall self-efficacy (SMD: 0.56, 95% CI: [-0.06, 1.18], P=.08), a benefit was observed in hemodialysis patients (SMD: 0.59, 95% CI: [0.34, 0.83], P<.001 engagement was favorable with completion rates above adherence of and generally positive patient feedback on dhis. application-based interventions improved self-efficacy ci: p overall qol telemedicine depression video-based due to high heterogeneity risk bias evidence quality rated as low for moderate general anxiety very stress self-efficacy. conclusions: dhis can significantly improve the psychological health eskd patients particularly when tailored needs delivered through interactive platforms such applications telemedicine. suggest good acceptability in clinical practice. however warrants cautious interpretation. future research should involve more high-quality rcts design that address unique elderly peritoneal dialysis kidney transplant recipients. trial: prospero crd42024629357 https:>

Integrative Spatial Omics for Systems-Level Mapping of Pathological Niches

✇(Multiomics OR Omics) AND (Pancreatic)
By: Angela R S Kruse · Roy Lardenoije · Lukasz G Migas · Claire F Scott · Cody Marshall · Morad C Malek · Adel Eskaros · Thai Pham · Kristie Aamodt · Madeline Colley · Lissa Ventura-Antunes · Melissa A Farrow · Raf Van de Plas · Joana Goncalves · Matthew Schrag · Alvin C Powers · Jeffrey M Spraggins
26 September 2025 at 18:00

bioRxiv [Preprint]. 2025 Sep 17:2025.09.12.675904. doi: 10.1101/2025.09.12.675904.

ABSTRACT

Spatial 'omics technologies are a powerful tool for mapping the relationship between cellular organization and molecular distributions in healthy and diseased tissue microenvironments. Here, we describe a novel multimodal pipeline that represents experimental and computational advances for spatiomolecular analysis of tissue samples across molecular classes. This adaptable method integrates matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) lipidomics, spatial transcriptomics (ST), multiplexed immunofluorescence microscopy (MxIF), and histopathological staining to uncover spatiomolecular profiles associated with unique cellular niches and pathological features. We demonstrate the power of this approach using two different complex human disease systems: Alzheimer's disease in human brain tissue and type 2 diabetes mellitus in the human pancreas. By identifying molecular markers associated with disease pathology in the pancreas and brain, we shed light on biologically significant pathways that are impacted in these two spatially complex diseases and highlight the powerful potential of accurate, high-resolution multimodal integration approaches.

PMID:41000710 | PMC:PMC12458195 | DOI:10.1101/2025.09.12.675904

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