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Everyone’s still throwing billions at AI data centers

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

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

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Application of the Supportive Accountability Model in Digital Health Interventions: Scoping Review

Background: Digital health interventions (DHIs) harness technological innovation to address challenges in the accessibility and scalability of health care. However, the effectiveness of DHIs is challenged by low user engagement and adherence, as users tend to drop out over time. The supportive accountability model (SAM) is a theoretical framework designed to enhance adherence to DHIs by incorporating structured human support. Objective: Guided by SAM, this scoping review answers the following research questions: (1) What is the extent of research on human support factors and their influence on engagement with and adherence to DHIs? and (2) What is the extent of research applying SAM (ie, accountability, bond, and legitimacy) to improve engagement with and adherence to DHIs? Methods: Our search strategy followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We conducted our literature search using 6 databases selected based on relevance to our research topic: MEDLINE, PsycINFO, Embase, CINAHL, Scopus, and ClinicalTrials.gov. Search terms included (“human support” OR “supportive accountability”) AND (engagement OR adherence) AND intervention, applied to titles, abstracts, and keywords. Hand-searching was also used to identify additional relevant articles. Two authors (SPYC and GK) screened articles in multiple rounds using predefined inclusion and exclusion criteria. The final sample consisted of 36 empirical, peer-reviewed articles published in scholarly journals. All articles examined human-supported DHIs. Results: Implementation of human support among the interventions varied by the source, delivery method, and frequency and duration of support. Overall, there were inconsistencies in the application of SAM to intervention designs. Support was provided by 4 main groups: peers and peer specialists, health experts and practitioners, trained coaches, and members of the research study team. Modes of communication included phone or video calls, as well as text-based support, such as messaging or email. The frequency and duration of support varied across studies and were influenced by the communication method used, with more structured and frequent contact occurring in interventions that relied on synchronous support, such as phone or video calls. In addition, we found that some studies used human support as the primary mode of intervention delivery rather than as an adjunctive tool, focusing on improving engagement and adherence, as proposed by SAM. Aside from accountability, there was also a lack of explicit focus on other constructs within the model (eg, bond and legitimacy). Conclusions: This scoping review highlights the current use of human support to promote DHI adherence and reveals gaps in the application of SAM. Future research should address all core SAM components—not just accountability—and ensure human support is used as an adjunct to enhance engagement. These steps can help maximize the impact of DHIs on health care access and outcomes.
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Using Large Language Models to Assess the Consistency of Randomized Controlled Trials on AI Interventions With CONSORT-AI: Cross-Sectional Survey

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.
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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

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:>
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Integrative Spatial Omics for Systems-Level Mapping of Pathological Niches

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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FUSION: a web-based application for in-depth exploration of multi-omics data with brightfield histology

Nat Commun. 2025 Sep 25;16(1):8388. doi: 10.1038/s41467-025-63050-9.

ABSTRACT

Spatial technologies examining the cell and tissue microenvironment at near single-cell resolution are revealing important molecular insights. However, few tools enable integrated, interactive analysis of spatial-omics with tissue morphology in the same functional tissue unit. Here, we present FUSION (Functional Unit State Identification in Whole Slide Images), a web-based platform for visualizing and analyzing spatial-omics data with high-resolution histology. FUSION provides workflows for assessing cell compositions, quantitative morphometrics, and comparative tissue analyses. We demonstrate applicability across spatial assays, including 10x Visium, Visium HD, 10x Xenium, Cell DIVE, and PhenoCycler, applied to healthy and diseased tissues from kidney, small intestine, lung, and skin in the Human BioMolecular Atlas Program. FUSION is cloud-based, open-source, and accessible at https://fusion.hubmapconsortium.org/ , hosting over 50 paired datasets and tutorials. In a series of use cases, we show its capacity to distinguish renal glomeruli injury states, quantify morphometric changes, and characterize fibrosis with immune infiltration.

PMID:40998789 | PMC:PMC12462499 | DOI:10.1038/s41467-025-63050-9

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Application of Nudges to Design Clinical Decision Support Tools: Systematic Approach Guided by Implementation Science

Background: Clinical decision support (CDS) is one strategy to increase evidence-based practices by clinicians. Despite its potential, CDS tools have had mixed results and are often disliked by clinicians. Principles from behavioral economics, including “nudges,” may improve the effectiveness and clinician satisfaction of CDS tools. Objective: This paper outlines a pragmatic approach grounded in implementation science to identify and prioritize how to incorporate different types of nudges into CDS tools. Methods: We applied the Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments and Ego (MINDSPACE) nudge framework and the Practical, Robust Implementation and Sustainability Model (PRISM) implementation science framework to systematically and pragmatically identify and prioritize different types of nudges for CDS tools. A case example of a CDS tool to improve guideline-concordant prescribing for patients with heart failure was used to illustrate how these frameworks can be applied in real-life scenarios. We describe a process of how these frameworks can be used pragmatically by clinicians and informaticists or more technical CDS builders to apply nudge theory to CDS tools. Results: Four iterative steps guided by PRISM were defined: 1) engage partners for user-centered design, 2) develop a shared understanding of the nudge types, 3) determine the nudge type for the overarching CDS format, and 4) brainstorm and prioritize nudge types and forms to address each modifiable contextual issue. These steps are iterative and intended to be adapted to align with the local resources and needs of various clinical scenarios and settings. We provide illustrative examples of how this approach was applied to the case example, including who we engaged, details of nudge design decisions, and lessons learned. Conclusions: We present a pragmatic approach to guide the selection and prioritization of nudges, informed by implementation science. This approach can be used to comprehensively and systematically consider key issues in designing CDS to optimize clinician satisfaction, effectiveness, equity, and sustainability while minimizing the potential for unintended consequences. The findings can be adapted and generalized to other health settings and clinical situations, advancing the goals of learning health systems to expedite the translation of evidence into practice.
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Understanding the Role of Clinical Decision Support Systems Among Hospital Nurses Using the FITT (Fit Between Individuals, Tasks, and Technology) Framework: Qualitative Study

Background: Clinical decision support systems (CDSSs) have gained prominence in health care, aiding professionals in decision-making and improving patient outcomes. While physicians often use CDSSs for diagnosis and treatment optimization, nurses rely on these systems for tasks such as patient monitoring, prioritization, and care planning. In nursing practice, CDSSs can assist with timely detection of clinical deterioration, support infection control, and streamline care documentation. Despite their potential, the adoption and use of CDSSs by nurses face diverse challenges. Barriers such as alarm fatigue, limited usability, lack of integration with workflows, and insufficient training continue to undermine effective implementation. In contrast to the relatively extensive body of research on CDSS use by physicians, studies focusing on nurses remain limited, leaving a gap in understanding the unique facilitators and barriers they encounter. Objective: This study aimed to explore the facilitators and barriers influencing the adoption and use of CDSSs by nurses in hospitals, using an extended Fit Between Individuals, Tasks, and Technology (FITT) framework. Methods: A qualitative study was conducted using semistructured interviews with 22 nurses from across the Netherlands, representing 3 hospital types: general (n=9), top-clinical (n=12), and academic (n=1). The sample included a diverse mix of practicing nurses, nurses-in-training, and clinical nurse information officers, with clinical experience ranging from 1.5 to 38 years. Interview transcripts were analyzed thematically, beginning with an inductive coding approach to identify key factors. These were then categorized deductively using the extended FITT framework. In total, 988 code instances were examined. To ensure analytical rigor, the coding process was separately conducted by 2 researchers and reviewed by an expert panel. Results: A total of 26 distinct factors were identified, categorized into 4 FITT dimensions: technology-individual, technology-task, task-individual, and organizational context. Of these, 11 factors were facilitators (eg, cognition, clarification, and prevention), 7 were barriers (eg, alarm fatigue, poor design, and limited digital proficiency), and 8 were both facilitators and barriers depending on the context (eg, acceptance, workload, and training). In addition, key value tensions emerged, such as the balance between standardization and professional autonomy, and the trade-off between enhanced decision support and increased administrative burden. Conclusions: The findings underscore the complexity of CDSS adoption in nursing practice, highlighting the interaction of facilitators and barriers across FITT dimensions. Practical recommendations include participatory design processes, targeted training programs, advanced alert management systems, and strong organizational support. Addressing value tensions and aligning CDSS functionality with nurses’ workflows can enhance adoption and optimize patient outcomes. Trial Registration:
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Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis

npj Digital Medicine, Published online: 25 September 2025; doi:10.1038/s41746-025-01852-3

Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis
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Quality safety and disparity of an AI chatbot in managing chronic diseases: simulated patient experiments

npj Digital Medicine, Published online: 25 September 2025; doi:10.1038/s41746-025-01956-w

Quality safety and disparity of an AI chatbot in managing chronic diseases: simulated patient experiments
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Ophthalmic drug discovery and development using artificial intelligence and digital health technologies

npj Digital Medicine, Published online: 25 September 2025; doi:10.1038/s41746-025-01954-y

Ophthalmic drug discovery and development using artificial intelligence and digital health technologies
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Opinion: My patient almost quit a clinical trial to save her job

When one of my patients was first diagnosed with sarcoidosis, her specialist gave her two options: take steroids or join a clinical trial. She opted for the trial, hoping it might lead to better treatment for herself and others.

However, her initial excitement waned as the logistical demands began to take a toll on her personal and professional life. With twice-monthly appointments, each trial day required an early two-hour drive, followed by eight hours of appointments, and concluded with a long drive home — all while managing symptoms of the disease. The trial sponsor eventually covered an overnight hotel room to ease her travel, but that doubled her time away from work and further amplified her stress.

Read the rest…

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Molecular advances in early-stage and locally advanced non-small cell lung carcinoma: Shaping the future of precision oncology-systematic review

Sci Prog. 2025 Jul-Sep;108(3):368504251383055. doi: 10.1177/00368504251383055. Epub 2025 Sep 24.

ABSTRACT

ObjectiveTo synthesize recent molecular advances that inform diagnosis, risk-stratification, and perioperative treatment in early-stage and locally advanced non-small cell lung carcinoma (NSCLC), with emphasis on comprehensive genomic profiling, minimal residual disease (MRD) detection by circulating tumor DNA (ctDNA), and the translation of biomarkers into targeted and immunotherapy strategies.MethodsSystematic review registered in PROSPERO (CRD420251076423). Searches of PubMed, Scopus, Web of Science, and Embase (January 2015-April 2025) followed PRISMA 2020/PRISMA-S. From 4640 records, 890 duplicates were removed; 3750 titles/abstracts were screened; 150 full texts were assessed; 75 studies met inclusion criteria. Risk of bias used Newcastle-Ottawa Scale (NOS) for observational studies and Cochrane RoB 2 tool for randomized controlled trials; certainty was summarized with GRADE where applicable.ResultsActionable alterations (e.g. EGFR, ALK, KRAS, MET, RET, BRAF, NTRK) are prevalent in early-stage NSCLC and comparable to advanced disease, supporting routine comprehensive genomic profiling in curative-intent settings. Next-generation sequencing (NGS) and ctDNA enable the detection of MRD, earlier relapse prediction, and dynamic treatment monitoring. Perioperative strategies integrating targeted therapy and immunotherapy (e.g. adjuvant EGFR-TKI, neoadjuvant chemo-immunotherapy) improve pathological and disease-free outcomes in selected biomarker-defined populations. Evidence profiles generally show low-to-moderate risk of bias and moderate-to-high certainty for key outcomes related to profiling and MRD, with heterogeneity across platforms and endpoints.ConclusionsMolecular advances-particularly broad NGS and ctDNA-based MRD-are reshaping the perioperative management of early and locally advanced NSCLC, enabling precision selection for targeted and immunotherapy approaches. Standardization of testing workflows and reporting, and cost-effective implementation are priorities for equitable adoption and for future trials that combine NGS, MRD, and multi-omic/AI-driven risk stratification.

PMID:40990633 | PMC:PMC12461064 | DOI:10.1177/00368504251383055

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Expanding care coordination in an integrated health system through causal machine learning

npj Digital Medicine, Published online: 24 September 2025; doi:10.1038/s41746-025-01925-3

Expanding care coordination in an integrated health system through causal machine learning
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