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Received — 1 November 2025 ⏭ Favourites

Animal models in tuberculosis metabolomics: a systematic review of current evidence and the road to translational relevance

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Rochelle Caudron · Ilse Du Preez · Laneke Luies · Monique Opperman
31 October 2025 at 18:00

Front Mol Biosci. 2025 Oct 15;12:1688882. doi: 10.3389/fmolb.2025.1688882. eCollection 2025.

ABSTRACT

BACKGROUND: Animal models are important for tuberculosis (TB) research, offering controlled settings to study disease mechanisms. However, their ability to replicate TB-induced metabolic responses in humans is uncertain. This systematic review evaluated the current use of animal models in metabolomics studies aimed at characterising active pulmonary TB.

METHODS: PubMed, Scopus, and Web of Science were systematically searched for metabolomics studies of pulmonary TB in humans and animal models, following PRISMA guidelines. Eligible studies were screened, and quality was assessed using QUDOMICS and STAIR tools. Data were synthesised by species, sample matrix, experimental design, and reported differential metabolites. Differential metabolite names were compared between species and subjected to pathway analysis in MetaboAnalyst 6.0.

RESULTS: Of the 80 eligible studies, nine involved animal models, predominantly mice. These models captured only 4.7% of human TB-associated differential metabolites, with the highest overlap (3.8%) in mouse lung tissue. Despite low concordance at metabolite level, conserved disruptions were observed in amino acid, glutathione, and one-carbon metabolism pathways. Interspecies variation was evident, influenced by host species, sample matrix, infection protocol, and analytical method.

CONCLUSION: Animal models partially replicated key metabolic features of human TB, particularly at the pathway level. However, variability across studies hampers current translational interpretation. Broader model use, standardised protocols, and integrated multi-platform omics approaches are needed to improve the relevance and comparability of animal models in TB metabolomics research.

PMID:41169614 | PMC:PMC12568366 | DOI:10.3389/fmolb.2025.1688882

Digital Health Technology Compliance With Clinical Safety Standards In the National Health Service in England: National Cross-Sectional Study

✇Journal of Medical Internet Research
By: Youssof Oskrochi · Elliott Roy-Highley · Keith Grimes · Sam Shah
1 November 2025 at 03:30
Background: To be authorized for use in the National Health Service (NHS) in England, digital health technologies (DHTs) must meet 2 mandatory clinical risk management standards, Data Coordination Board (DCB) 0129 and 0160, demonstrating that risks from design and use have been assessed and mitigated. NHS organizations must not procure a DHT without DCB0129 assurance and must not deploy one without DCB0160 assurance. Despite legal requirement, no public data exist on how many DHTs are in use in the NHS or how many are assured. Objective: This study aimed to determine the number of DHTs in use in the NHS in England and assess their assurance status against mandated clinical safety standards. Methods: In early 2025, 239 NHS organizations in England received a freedom of information notice requesting information on the number of DHTs they were using and their assurance against DCB0129 and DCB0160 standards. Results: Of the 239 NHS organizations, 204 (85.4%) responded, of which 178 (87.3%) provided full or partial data, covering 14,747 DHT deployments. The mean number of deployed DHTs per organization was 82.8 (SD 146.1; 95% CI 61.4-104.3) with substantial variation between NHS provider trusts (mean 107.1, SD 161.1; 95% CI 79.8-134.3), ambulance trusts (mean 13.0, SD 8.2; 95% CI 7.6-18.4), and integrated care boards (mean 8.1, SD 16.0; 95% CI 2.8-13.5). Overall organizational compliance rates were low, with a median of 25.6% (IQR 7.8%-55.7%) deployed DHTs being fully assured; for NHS provider trusts compliance was lower at 24.5% (IQR 8.1%-50%). A total of 13 (6.4%) of the 204 organizations reported that all their DHTs were fully assured, while 16 (7.8%) reported that none were assured. Across all DHTs with reported assurance data, 17.3% (95% CI 16.6%-18.1%) were fully assured against both standards, 13.3% were partially assured against one standard, and 70.1% (95% CI 69.1%-71.1%) had no documented assurance. Conclusions: This is the first study to quantify both the scale of DHT deployment in NHS organizations in England and the extent of compliance with mandatory safety standards. More than 10,000 DHTs currently in use lack documented assurance against clinical safety standards. In a typical NHS trust, 3 out of 4 digital tools influencing patient care do not demonstrate compliance with minimum legal or clinical safety requirements. These findings raise significant concerns about the risks posed to patients by these technologies; the capacity of organizations to assess and mitigate them; and the legal ramifications of when, not if, harm occurs. Crucially, failure to assure digital technologies poses a significant risk to one of the core ambitions of the NHS 10-Year Health Plan for England; safely transitioning from analogue to digital care models. These findings are unlikely to be unique to the NHS and should prompt health care systems worldwide to assess the risks posed by their DHT deployments.

Opportunities and Challenges for Designing in Connected Health: Insights From an Expert Workshop

✇Journal of Medical Internet Research
By: Shweta Premanandan · Awais Ahmad · Alex Jaranka · Ece Üreten · Eunji Lee · Jennifer Gross · Åsa Cajander · Sofia Ouhbi · Marta Lárusdóttir · Owen Eriksson · Sten-Erik Öhlund
1 November 2025 at 03:00
Health care increasingly depends on information and communication technology. This offers both opportunities and challenges when designing connected health systems. While individual studies examined particular cases, there is a limited synthesis of insights across projects. The objective of this paper is to explore these opportunities and challenges by examining 6 diverse connected health projects and synthesizing lessons from an expert workshop. To achieve this, we conducted a full-day workshop that brought together 6 connected health projects. The workshop used an iterative and participatory process which included paper submissions and presentations and facilitated discussions, and a gallery walk to enable cross-case comparison and collaborative reflection. Thematic analysis of workshop outputs was then used to synthesize key opportunities and challenges in designing connected health systems. The 6 projects represented a variety of design methods and approaches to connected health, and their discussion surfaced both opportunities and challenges in this domain. Key opportunities include improving data integration and usability, enhancing collaboration across stakeholders, using a user-centered and iterative design process, addressing complexity in sociotechnical systems, sustainability, and adopting digital infrastructures for seamless communication. Participants also identified important challenges, namely exchange of information, interoperability, and communication; ethical considerations, rules, and regulations; understanding design, evaluation, and standards; actionable data, reliability, quality, and trust in data; and stakeholder involvement. The contribution of this paper lies in the synthesis of insights across multiple projects and perspectives to provide practical guidance for researchers, designers, and policymakers. By highlighting opportunities and challenges in designing connected health systems, the findings emphasize the importance of patient-centered, sustainable, and collaborative design approaches while also pointing to the need to address persistent barriers. Advancing connected health will require adopting iterative and inclusive design processes that prioritize patient-centeredness, sustainability, and collaboration across health care systems.
Received — 31 October 2025 ⏭ Favourites

An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jorge Martinez-Gil · Mario Pichler · Nefeli Bountouni · Sotiris Koussouris · Marielena M\'arquez Barreiro · Sergio Gusmeroli
31 October 2025 at 12:00
arXiv:2510.25813v1 Announce Type: new Abstract: We present a novel framework for Industry 5.0 that simplifies the deployment of AI models on edge devices in various industrial settings. The design reduces latency and avoids external data transfer by enabling local inference and real-time processing. Our implementation is agent-based, which means that individual agents, whether human, algorithmic, or collaborative, are responsible for well-defined tasks, enabling flexibility and simplifying integration. Moreover, our framework supports modular integration and maintains low resource requirements. Preliminary evaluations concerning the food industry in real scenarios indicate improved deployment time and system adaptability performance. The source code is publicly available at https://github.com/AI-REDGIO-5-0/ci-component.

SciTrust 2.0: A Comprehensive Framework for Evaluating Trustworthiness of Large Language Models in Scientific Applications

✇cs.AI, q-bio.NC updates on arXiv.org
By: Emily Herron · Junqi Yin · Feiyi Wang
31 October 2025 at 12:00
arXiv:2510.25908v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated transformative potential in scientific research, yet their deployment in high-stakes contexts raises significant trustworthiness concerns. Here, we introduce SciTrust 2.0, a comprehensive framework for evaluating LLM trustworthiness in scientific applications across four dimensions: truthfulness, adversarial robustness, scientific safety, and scientific ethics. Our framework incorporates novel, open-ended truthfulness benchmarks developed through a verified reflection-tuning pipeline and expert validation, alongside a novel ethics benchmark for scientific research contexts covering eight subcategories including dual-use research and bias. We evaluated seven prominent LLMs, including four science-specialized models and three general-purpose industry models, using multiple evaluation metrics including accuracy, semantic similarity measures, and LLM-based scoring. General-purpose industry models overall outperformed science-specialized models across each trustworthiness dimension, with GPT-o4-mini demonstrating superior performance in truthfulness assessments and adversarial robustness. Science-specialized models showed significant deficiencies in logical and ethical reasoning capabilities, along with concerning vulnerabilities in safety evaluations, particularly in high-risk domains such as biosecurity and chemical weapons. By open-sourcing our framework, we provide a foundation for developing more trustworthy AI systems and advancing research on model safety and ethics in scientific contexts.

Human-AI Complementarity: A Goal for Amplified Oversight

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rishub Jain · Sophie Bridgers · Lili Janzer · Rory Greig · Tian Huey Teh · Vladimir Mikulik
31 October 2025 at 12:00
arXiv:2510.26518v1 Announce Type: new Abstract: Human feedback is critical for aligning AI systems to human values. As AI capabilities improve and AI is used to tackle more challenging tasks, verifying quality and safety becomes increasingly challenging. This paper explores how we can leverage AI to improve the quality of human oversight. We focus on an important safety problem that is already challenging for humans: fact-verification of AI outputs. We find that combining AI ratings and human ratings based on AI rater confidence is better than relying on either alone. Giving humans an AI fact-verification assistant further improves their accuracy, but the type of assistance matters. Displaying AI explanation, confidence, and labels leads to over-reliance, but just showing search results and evidence fosters more appropriate trust. These results have implications for Amplified Oversight -- the challenge of combining humans and AI to supervise AI systems even as they surpass human expert performance.

Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Reda El Makroum · Sebastian Zwickl-Bernhard · Lukas Kranzl
31 October 2025 at 12:00
arXiv:2510.26603v1 Announce Type: new Abstract: The electricity sector transition requires substantial increases in residential demand response capacity, yet Home Energy Management Systems (HEMS) adoption remains limited by user interaction barriers requiring translation of everyday preferences into technical parameters. While large language models have been applied to energy systems as code generators and parameter extractors, no existing implementation deploys LLMs as autonomous coordinators managing the complete workflow from natural language input to multi-appliance scheduling. This paper presents an agentic AI HEMS where LLMs autonomously coordinate multi-appliance scheduling from natural language requests to device control, achieving optimal scheduling without example demonstrations. A hierarchical architecture combining one orchestrator with three specialist agents uses the ReAct pattern for iterative reasoning, enabling dynamic coordination without hardcoded workflows while integrating Google Calendar for context-aware deadline extraction. Evaluation across three open-source models using real Austrian day-ahead electricity prices reveals substantial capability differences. Llama-3.3-70B successfully coordinates all appliances across all scenarios to match cost-optimal benchmarks computed via mixed-integer linear programming, while other models achieve perfect single-appliance performance but struggle to coordinate all appliances simultaneously. Progressive prompt engineering experiments demonstrate that analytical query handling without explicit guidance remains unreliable despite models' general reasoning capabilities. We open-source the complete system including orchestration logic, agent prompts, tools, and web interfaces to enable reproducibility, extension, and future research.

Identity Management for Agentic AI: The new frontier of authorization, authentication, and security for an AI agent world

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tobin South · Subramanya Nagabhushanaradhya · Ayesha Dissanayaka · Sarah Cecchetti · George Fletcher · Victor Lu · Aldo Pietropaolo · Dean H. Saxe · Jeff Lombardo · Abhishek Maligehalli Shivalingaiah · Stan Bounev · Alex Keisner · Andor Kesselman · Zack Proser · Ginny Fahs · Andrew Bunyea · Ben Moskowitz · Atul Tulshibagwale · Dazza Greenwood · Jiaxin Pei · Alex Pentland
31 October 2025 at 12:00
arXiv:2510.25819v1 Announce Type: cross Abstract: The rapid rise of AI agents presents urgent challenges in authentication, authorization, and identity management. Current agent-centric protocols (like MCP) highlight the demand for clarified best practices in authentication and authorization. Looking ahead, ambitions for highly autonomous agents raise complex long-term questions regarding scalable access control, agent-centric identities, AI workload differentiation, and delegated authority. This OpenID Foundation whitepaper is for stakeholders at the intersection of AI agents and access management. It outlines the resources already available for securing today's agents and presents a strategic agenda to address the foundational authentication, authorization, and identity problems pivotal for tomorrow's widespread autonomous systems.

Multi-Agent Reinforcement Learning for Market Making: Competition without Collusion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziyi Wang · Carmine Ventre · Maria Polukarov
31 October 2025 at 12:00
arXiv:2510.25929v1 Announce Type: cross Abstract: Algorithmic collusion has emerged as a central question in AI: Will the interaction between different AI agents deployed in markets lead to collusion? More generally, understanding how emergent behavior, be it a cartel or market dominance from more advanced bots, affects the market overall is an important research question. We propose a hierarchical multi-agent reinforcement learning framework to study algorithmic collusion in market making. The framework includes a self-interested market maker (Agent~A), which is trained in an uncertain environment shaped by an adversary, and three bottom-layer competitors: the self-interested Agent~B1 (whose objective is to maximize its own PnL), the competitive Agent~B2 (whose objective is to minimize the PnL of its opponent), and the hybrid Agent~B$^\star$, which can modulate between the behavior of the other two. To analyze how these agents shape the behavior of each other and affect market outcomes, we propose interaction-level metrics that quantify behavioral asymmetry and system-level dynamics, while providing signals potentially indicative of emergent interaction patterns. Experimental results show that Agent~B2 secures dominant performance in a zero-sum setting against B1, aggressively capturing order flow while tightening average spreads, thus improving market execution efficiency. In contrast, Agent~B$^\star$ exhibits a self-interested inclination when co-existing with other profit-seeking agents, securing dominant market share through adaptive quoting, yet exerting a milder adverse impact on the rewards of Agents~A and B1 compared to B2. These findings suggest that adaptive incentive control supports more sustainable strategic co-existence in heterogeneous agent environments and offers a structured lens for evaluating behavioral design in algorithmic trading systems.

The Quest for Reliable Metrics of Responsible AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Theresia Veronika Rampisela · Maria Maistro · Tuukka Ruotsalo · Christina Lioma
31 October 2025 at 12:00
arXiv:2510.26007v1 Announce Type: cross Abstract: The development of Artificial Intelligence (AI), including AI in Science (AIS), should be done following the principles of responsible AI. Progress in responsible AI is often quantified through evaluation metrics, yet there has been less work on assessing the robustness and reliability of the metrics themselves. We reflect on prior work that examines the robustness of fairness metrics for recommender systems as a type of AI application and summarise their key takeaways into a set of non-exhaustive guidelines for developing reliable metrics of responsible AI. Our guidelines apply to a broad spectrum of AI applications, including AIS.

Integrating Genomics into Multimodal EHR Foundation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jonathan Amar · Edward Liu · Alessandra Breschi · Liangliang Zhang · Pouya Kheradpour · Sylvia Li · Lisa Soleymani Lehmann · Alessandro Giulianelli · Matt Edwards · Yugang Jia · David Nola · Raghav Mani · Pankaj Vats · Jesse Tetreault · T. J. Chen · Cory Y. McLean
31 October 2025 at 12:00
arXiv:2510.23639v2 Announce Type: replace-cross Abstract: This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships between clinical data and genetic predispositions. The methodology extends advancements in generative AI to the EHR foundation model space, enhancing predictive capabilities and interpretability. Evaluation on AoU data demonstrates the model's predictive value for the onset of various conditions, particularly Type 2 Diabetes (T2D), and illustrates the interplay between PRS and EHR data. The work also explores transfer learning for custom classification tasks, showcasing the architecture's versatility and efficiency. This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies, laying the groundwork for more personalized, equitable, and actionable real-world evidence generation in healthcare.
Received — 30 October 2025 ⏭ Favourites

Multi-omic profiling reveals age-related immune dynamics in healthy adults

✇Nature - Issue - nature.com science feeds
By: Qiuyu Gong · Mehul Sharma · Marla C. Glass · Emma L. Kuan · Aishwarya Chander · Mansi Singh · Lucas T. Graybuck · Zachary J. Thomson · Christian M. LaFrance · Samir Rachid Zaim · Tao Peng · Lauren Y. Okada · Palak C. Genge · Katherine E. Henderson · Elisabeth M. Dornisch · Erik D. Layton · Peter J. Wittig · Alexander T. Heubeck · Nelson M. Mukuka · Julian Reading · Garrett Strawn · Teminijesu Titus-Adewunmi · Kathleen Abadie · Charles R. Roll · Veronica Hernandez · Vaishnavi Parthasarathy · Tyanna J. Stuckey · Blessing Musgrove · Elliott Swanson · Cara Lord · Morgan D. A. Weiss · Cole G. Phalen · Regina R. Mettey · Kevin J. Lee · John B. Johanneson · Erin K. Kawelo · Jessica Garber · Upaasana Krishnan · Megan Smithmyer · E. John Wherry · Laura A. Vella · Sarah E. Henrickson · Mackenzie S. Kopp · Adam K. Savage · Lynne A. Becker · Paul Meijer · Ernest M. Coffey · Jorg J. Goronzy · Mikael Sigvardsson · Cate Speake · Thomas F. Bumol · Ananda W. Goldrath · Troy R. Torgerson · Xiao-jun Li · Peter J. Skene · Jane H. Buckner · Claire E. Gustafson
29 October 2025 at 08:00

Nature, Published online: 29 October 2025; doi:10.1038/s41586-025-09686-5

This multi-omic longitudinal analysis of the healthy human peripheral immune system constructs the Human Immune Health Atlas and assembles data on immune cell composition and state changes with age, including responses to cytomegalovirus infection and influenza vaccination.

Advancing Non-Small-Cell Lung Cancer Management Through Multi-Omics Integration: Insights from Genomics, Metabolomics, and Radiomics

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Martina Pierri · Giovanni Ciani · Maria Chiara Brunese · Gianluigi Lauro · Stefania Terracciano · Maria Iorizzi · Valerio Nardone · Maria Giovanna Chini · Giuseppe Bifulco · Salvatore Cappabianca · Alfonso Reginelli
29 October 2025 at 18:00

Diagnostics (Basel). 2025 Oct 14;15(20):2586. doi: 10.3390/diagnostics15202586.

ABSTRACT

The integration of multi-omics technologies is transforming the landscape of cancer management, offering unprecedented insights into tumor biology, early diagnosis, and personalized therapy. This review provides a comprehensive overview of the current state of omics approaches, with a particular focus on the application of genomics, NMR-based metabolomics, and radiomics in non-small cell lung cancer (NSCLC). Genomics currently represents one of the most established omics technologies in oncology, as it enables the identification of genetic alterations that drive tumor initiation, progression, and therapeutic response. Interestingly, genomic analyses have revealed that many tumors harbor mutations in genes encoding metabolic enzymes, thus establishing a tight connection between genomics and tumor metabolism. In parallel, metabolomics profiling-by capturing the metabolic phenotype of tumors-has, in recent years, identified specific biomarkers associated with tumor burden, progression, and prognosis. Such findings have catalyzed growing interest in metabolomics as a complementary approach to better characterize cancer biology and discover novel diagnostic and therapeutic targets. Moreover, radiomics, through the extraction of quantitative features from standard imaging modalities, captures tumor heterogeneity and contributes predictive information on tumor biology, treatment response, and clinical outcomes. As a non-invasive and widely available technique, radiomics has the potential to support longitudinal monitoring and individualized treatment planning. Both metabolomics and radiomics, when integrated with genomic data, could support a more comprehensive understanding of NSCLC and pave the way for the development of non-invasive, predictive models and personalized therapeutic strategies. In addition, we explore the specific contributions of these technologies in enhancing clinical decision-making for lung cancer patients, with particular attention to their potential in early diagnosis, treatment selection, and real-time monitoring.

PMID:41153258 | DOI:10.3390/diagnostics15202586

Received — 29 October 2025 ⏭ Favourites

BLM$_1$: A Boundless Large Model for Cross-Space, Cross-Task, and Cross-Embodiment Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wentao Tan · Bowen Wang · Heng Zhi · Chenyu Liu · Zhe Li · Jian Liu · Zengrong Lin · Yukun Dai · Yipeng Chen · Wenjie Yang · Enci Xie · Hao Xue · Baixu Ji · Chen Xu · Zhibin Wang · Tianshi Wang · Lei Zhu · Heng Tao Shen
29 October 2025 at 12:00
arXiv:2510.24161v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have advanced vision-language reasoning and are increasingly deployed in embodied agents. However, significant limitations remain: MLLMs generalize poorly across digital-physical spaces and embodiments; vision-language-action models (VLAs) produce low-level actions yet lack robust high-level embodied reasoning; and most embodied large language models (ELLMs) are constrained to digital-space with poor generalization to the physical world. Thus, unified models that operate seamlessly across digital and physical spaces while generalizing across embodiments and tasks remain absent. We introduce the \textbf{Boundless Large Model (BLM$_1$)}, a multimodal spatial foundation model that preserves instruction following and reasoning, incorporates embodied knowledge, and supports robust cross-embodiment control. BLM$_1$ integrates three key capabilities -- \textit{cross-space transfer, cross-task learning, and cross-embodiment generalization} -- via a two-stage training paradigm. Stage I injects embodied knowledge into the MLLM through curated digital corpora while maintaining language competence. Stage II trains a policy module through an intent-bridging interface that extracts high-level semantics from the MLLM to guide control, without fine-tuning the MLLM backbone. This process is supported by a self-collected cross-embodiment demonstration suite spanning four robot embodiments and six progressively challenging tasks. Evaluations across digital and physical benchmarks show that a single BLM$_1$ instance outperforms four model families -- MLLMs, ELLMs, VLAs, and GMLMs -- achieving $\sim\!\textbf{6%}$ gains in digital tasks and $\sim\!\textbf{3%}$ in physical tasks.

Integrating Genomics into Multimodal EHR Foundation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jonathan Amar · Edward Liu · Alessandra Breschi · Liangliang Zhang · Pouya Kheradpour · Sylvia Li · Lisa Soleymani Lehmann · Alessandro Giulianelli · Matt Edwards · Yugang Jia · David Nola · Raghav Mani · Pankaj Vats · Jesse Tetreault · T. J. Chen · Cory Y. McLean
29 October 2025 at 12:00
arXiv:2510.23639v1 Announce Type: cross Abstract: This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships between clinical data and genetic predispositions. The methodology extends advancements in generative AI to the EHR foundation model space, enhancing predictive capabilities and interpretability. Evaluation on AoU data demonstrates the model's predictive value for the onset of various conditions, particularly Type 2 Diabetes (T2D), and illustrates the interplay between PRS and EHR data. The work also explores transfer learning for custom classification tasks, showcasing the architecture's versatility and efficiency. This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies, laying the groundwork for more personalized, equitable, and actionable real-world evidence generation in healthcare.

Closing Gaps: An Imputation Analysis of ICU Vital Signs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alisher Turubayev · Anna Shopova · Fabian Lange · Mahmut Kamalak · Paul Mattes · Victoria Ayvasky · Bert Arnrich · Bjarne Pfitzner · Robin P. van de Water
29 October 2025 at 12:00
arXiv:2510.24217v1 Announce Type: cross Abstract: As more Intensive Care Unit (ICU) data becomes available, the interest in developing clinical prediction models to improve healthcare protocols increases. However, the lack of data quality still hinders clinical prediction using Machine Learning (ML). Many vital sign measurements, such as heart rate, contain sizeable missing segments, leaving gaps in the data that could negatively impact prediction performance. Previous works have introduced numerous time-series imputation techniques. Nevertheless, more comprehensive work is needed to compare a representative set of methods for imputing ICU vital signs and determine the best practice. In reality, ad-hoc imputation techniques that could decrease prediction accuracy, like zero imputation, are still used. In this work, we compare established imputation techniques to guide researchers in improving the performance of clinical prediction models by selecting the most accurate imputation technique. We introduce an extensible and reusable benchmark with currently 15 imputation and 4 amputation methods, created for benchmarking on major ICU datasets. We hope to provide a comparative basis and facilitate further ML development to bring more models into clinical practice.

The Role of Omentin in Gastrointestinal Cancer: Diagnostic, Prognostic, and Therapeutic Perspectives

✇Omics in Gastric
By: Adam Mylonakis · Maximos Frountzas · Irene Lidoriki · Alexandros Kozadinos · Maria Evangelia Koloutsou · Angeliki Margoni · Areti Kalfoutzou · Dimitrios Theodorou · Konstantinos G Toutouzas · Dimitrios Schizas
28 October 2025 at 18:00

Metabolites. 2025 Sep 30;15(10):649. doi: 10.3390/metabo15100649.

ABSTRACT

Background/Objectives: Omentin, also known as intelectin-1, is a secreted adipokine with anti-inflammatory, insulin-sensitizing, and immune-modulatory functions, primarily expressed in visceral adipose tissue. While omentin has been associated with favorable metabolic outcomes, its role in cancer pathogenesis appears context-dependent and remains poorly understood. This review investigates the biological functions, expression patterns, and clinical relevance of omentin across gastrointestinal malignancies. Methods: A comprehensive review of the literature was conducted using PubMed, Scopus, and Web of Science up to August 2025 to evaluate the role of omentin in gastrointestinal cancers. Both preclinical and clinical studies evaluating omentin, its analogues and omentin-enhancing agents in gastric, colorectal, hepatic, pancreatic, and esophageal cancers were included. Results: Omentin exhibits anti-proliferative, anti-inflammatory, and anti-angiogenic effects within the tumor microenvironment in several GI malignancies. However, evidence also indicates a dual role. High intratumoral omentin expression correlates with improved prognosis in colorectal, gastric, and hepatic cancers; in contrast, elevated circulating levels-particularly in colorectal and pancreatic cancers-have been paradoxically associated with increased cancer risk and poor outcomes. Mechanistically, omentin modulates PI3K/Akt, NF-κB, AMPK, and oxidative stress pathways, and interacts with TMEM207. However, most available studies are small-scale and heterogeneous, with methodological inconsistencies and limited multi-omics integration, leaving major knowledge gaps. Conclusions: This review highlights omentin's distinct systemic and local roles across GI cancers, underscoring its translational implications. Omentin emerges as a promising but context-dependent biomarker and therapeutic target, with future research needed to address heterogeneity, standardize assays, and validate its clinical utility in large-scale prospective studies.

PMID:41149627 | PMC:PMC12566161 | DOI:10.3390/metabo15100649

Navigating Cancer Complexity: Integrative Multi-Omics Methodologies for Clinical Insights

✇(Multiomics OR Omics) AND (Pancreatic)
By: Martina Catalano · Alberto D'Angelo · Francesco De Logu · Romina Nassini · Daniele Generali · Giandomenico Roviello
28 October 2025 at 18:00

Clin Med Insights Oncol. 2025 Oct 21;19:11795549251384582. doi: 10.1177/11795549251384582. eCollection 2025.

ABSTRACT

Recent advancements in cancer multi-omics have transformed our understanding of cancer biology by integrating genomics, transcriptomics, proteomics, and metabolomics. These integrative approaches have led to the identification of novel biomarkers and therapeutic targets, offering deeper insights into the molecular intricacies of various cancers, including breast, lung, gastric, pancreatic, and glioblastoma. Despite these advances, challenges remain, such as the integration of disparate data types and the interpretation of complex biological interactions. However, developments in proteogenomics and mass spectrometry have enhanced the correlation between molecular profiles and clinical features, refining the prediction of therapeutic responses. Future research in cancer drug discovery is poised to benefit from multi-omics approaches, improving the precision and efficacy of personalized therapies. By developing integrative network-based models, researchers aim to address challenges related to heterogeneity, reproducibility, and data interpretation. A standardized framework for multi-omics data integration could revolutionize cancer research, optimizing the identification of novel drug targets and enhancing our understanding of cancer biology. This complete approach holds the promise of advancing personalized therapies by fully characterizing the molecular landscape of cancer, ultimately improving patient outcomes through more effective and targeted treatment strategies. This narrative review underscores the potential of multi-omics approaches to transform cancer research and improve patient outcomes through more precise and effective treatments.

PMID:41147019 | PMC:PMC12553891 | DOI:10.1177/11795549251384582

Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siddharth Mehrotra · Jin Huang · Xuelong Fu · Roel Dobbe · Clara I. S\'anchez · Maarten de Rijke
29 October 2025 at 12:00
arXiv:2510.21293v2 Announce Type: replace Abstract: Background: Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT. However, current research often adopts techno-centric approaches, focusing primarily on technical attributes such as reliability, robustness, and fairness, while overlooking the sociotechnical dimensions critical to understanding AI trustworthiness in real-world contexts. Objectives: This scoping review aims to examine how the AIES and FAccT communities conceptualize, measure, and validate AI trustworthiness, identifying major gaps and opportunities for advancing a holistic understanding of trustworthy AI systems. Methods: We conduct a scoping review of AIES and FAccT conference proceedings to date, systematically analyzing how trustworthiness is defined, operationalized, and applied across different research domains. Our analysis focuses on conceptualization approaches, measurement methods, verification and validation techniques, application areas, and underlying values. Results: While significant progress has been made in defining technical attributes such as transparency, accountability, and robustness, our findings reveal critical gaps. Current research often predominantly emphasizes technical precision at the expense of social and ethical considerations. The sociotechnical nature of AI systems remains less explored and trustworthiness emerges as a contested concept shaped by those with the power to define it. Conclusions: An interdisciplinary approach combining technical rigor with social, cultural, and institutional considerations is essential for advancing trustworthy AI. We propose actionable measures for the AI ethics community to adopt holistic frameworks that genuinely address the complex interplay between AI systems and society, ultimately promoting responsible technological development that benefits all stakeholders.

Navigating Cancer Complexity: Integrative Multi-Omics Methodologies for Clinical Insights

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Martina Catalano · Alberto D'Angelo · Francesco De Logu · Romina Nassini · Daniele Generali · Giandomenico Roviello
28 October 2025 at 18:00

Clin Med Insights Oncol. 2025 Oct 21;19:11795549251384582. doi: 10.1177/11795549251384582. eCollection 2025.

ABSTRACT

Recent advancements in cancer multi-omics have transformed our understanding of cancer biology by integrating genomics, transcriptomics, proteomics, and metabolomics. These integrative approaches have led to the identification of novel biomarkers and therapeutic targets, offering deeper insights into the molecular intricacies of various cancers, including breast, lung, gastric, pancreatic, and glioblastoma. Despite these advances, challenges remain, such as the integration of disparate data types and the interpretation of complex biological interactions. However, developments in proteogenomics and mass spectrometry have enhanced the correlation between molecular profiles and clinical features, refining the prediction of therapeutic responses. Future research in cancer drug discovery is poised to benefit from multi-omics approaches, improving the precision and efficacy of personalized therapies. By developing integrative network-based models, researchers aim to address challenges related to heterogeneity, reproducibility, and data interpretation. A standardized framework for multi-omics data integration could revolutionize cancer research, optimizing the identification of novel drug targets and enhancing our understanding of cancer biology. This complete approach holds the promise of advancing personalized therapies by fully characterizing the molecular landscape of cancer, ultimately improving patient outcomes through more effective and targeted treatment strategies. This narrative review underscores the potential of multi-omics approaches to transform cancer research and improve patient outcomes through more precise and effective treatments.

PMID:41147019 | PMC:PMC12553891 | DOI:10.1177/11795549251384582

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