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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.
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.

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.

Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yihe Deng · I-Hung Hsu · Jun Yan · Zifeng Wang · Rujun Han · Gufeng Zhang · Yanfei Chen · Wei Wang · Tomas Pfister · Chen-Yu Lee
31 October 2025 at 12:00
arXiv:2510.25992v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle with problems that require multi-step reasoning. For small-scale open-source models, Reinforcement Learning with Verifiable Rewards (RLVR) fails when correct solutions are rarely sampled even after many attempts, while Supervised Fine-Tuning (SFT) tends to overfit long demonstrations through rigid token-by-token imitation. To address this gap, we propose Supervised Reinforcement Learning (SRL), a framework that reformulates problem solving as generating a sequence of logical "actions". SRL trains the model to generate an internal reasoning monologue before committing to each action. It provides smoother rewards based on the similarity between the model's actions and expert actions extracted from the SFT dataset in a step-wise manner. This supervision offers richer learning signals even when all rollouts are incorrect, while encouraging flexible reasoning guided by expert demonstrations. As a result, SRL enables small models to learn challenging problems previously unlearnable by SFT or RLVR. Moreover, initializing training with SRL before refining with RLVR yields the strongest overall performance. Beyond reasoning benchmarks, SRL generalizes effectively to agentic software engineering tasks, establishing it as a robust and versatile training framework for reasoning-oriented LLMs.

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.

Multi-Agent Evolve: LLM Self-Improve through Co-evolution

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yixing Chen · Yiding Wang · Siqi Zhu · Haofei Yu · Tao Feng · Muhan Zhang · Mostofa Patwary · Jiaxuan You
31 October 2025 at 12:00
arXiv:2510.23595v3 Announce Type: replace Abstract: Reinforcement Learning (RL) has demonstrated significant potential in enhancing the reasoning capabilities of large language models (LLMs). However, the success of RL for LLMs heavily relies on human-curated datasets and verifiable rewards, which limit their scalability and generality. Recent Self-Play RL methods, inspired by the success of the paradigm in games and Go, aim to enhance LLM reasoning capabilities without human-annotated data. However, their methods primarily depend on a grounded environment for feedback (e.g., a Python interpreter or a game engine); extending them to general domains remains challenging. To address these challenges, we propose Multi-Agent Evolve (MAE), a framework that enables LLMs to self-evolve in solving diverse tasks, including mathematics, reasoning, and general knowledge Q&A. The core design of MAE is based on a triplet of interacting agents (Proposer, Solver, Judge) that are instantiated from a single LLM, and applies reinforcement learning to optimize their behaviors. The Proposer generates questions, the Solver attempts solutions, and the Judge evaluates both while co-evolving. Experiments on Qwen2.5-3B-Instruct demonstrate that MAE achieves an average improvement of 4.54% on multiple benchmarks. These results highlight MAE as a scalable, data-efficient method for enhancing the general reasoning abilities of LLMs with minimal reliance on human-curated supervision.

Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xi Li · Ruofan Mao · Yusen Zhang · Renze Lou · Chen Wu · Jiaqi Wang
31 October 2025 at 12:00
arXiv:2406.05948v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs), especially those accessed via APIs, have demonstrated impressive capabilities across various domains. However, users without technical expertise often turn to (untrustworthy) third-party services, such as prompt engineering, to enhance their LLM experience, creating vulnerabilities to adversarial threats like backdoor attacks. Backdoor-compromised LLMs generate malicious outputs to users when inputs contain specific "triggers" set by attackers. Traditional defense strategies, originally designed for small-scale models, are impractical for API-accessible LLMs due to limited model access, high computational costs, and data requirements. To address these limitations, we propose Chain-of-Scrutiny (CoS) which leverages LLMs' unique reasoning abilities to mitigate backdoor attacks. It guides the LLM to generate reasoning steps for a given input and scrutinizes for consistency with the final output -- any inconsistencies indicating a potential attack. It is well-suited for the popular API-only LLM deployments, enabling detection at minimal cost and with little data. User-friendly and driven by natural language, it allows non-experts to perform the defense independently while maintaining transparency. We validate the effectiveness of CoS through extensive experiments on various tasks and LLMs, with results showing greater benefits for more powerful LLMs.

Vital Insight: Assisting Experts' Context-Driven Sensemaking of Multi-modal Personal Tracking Data Using Visualization and Human-In-The-Loop LLM

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiachen Li · Xiwen Li · Justin Steinberg · Akshat Choube · Bingsheng Yao · Xuhai Xu · Dakuo Wang · Elizabeth Mynatt · Varun Mishra
31 October 2025 at 12:00
arXiv:2410.14879v4 Announce Type: replace-cross Abstract: Passive tracking methods, such as phone and wearable sensing, have become dominant in monitoring human behaviors in modern ubiquitous computing studies. While there have been significant advances in machine-learning approaches to translate periods of raw sensor data to model momentary behaviors, (e.g., physical activity recognition), there still remains a significant gap in the translation of these sensing streams into meaningful, high-level, context-aware insights that are required for various applications (e.g., summarizing an individual's daily routine). To bridge this gap, experts often need to employ a context-driven sensemaking process in real-world studies to derive insights. This process often requires manual effort and can be challenging even for experienced researchers due to the complexity of human behaviors. We conducted three rounds of user studies with 21 experts to explore solutions to address challenges with sensemaking. We follow a human-centered design process to identify needs and design, iterate, build, and evaluate Vital Insight (VI), a novel, LLM-assisted, prototype system to enable human-in-the-loop inference (sensemaking) and visualizations of multi-modal passive sensing data from smartphones and wearables. Using the prototype as a technology probe, we observe experts' interactions with it and develop an expert sensemaking model that explains how experts move between direct data representations and AI-supported inferences to explore, question, and validate insights. Through this iterative process, we also synthesize and discuss a list of design implications for the design of future AI-augmented visualization systems to better assist experts' sensemaking processes in multi-modal health sensing data.

Epistemic Diversity and Knowledge Collapse in Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dustin Wright · Sarah Masud · Jared Moore · Srishti Yadav · Maria Antoniak · Chan Young Park · Isabelle Augenstein
31 October 2025 at 12:00
arXiv:2510.04226v4 Announce Type: replace-cross Abstract: Large language models (LLMs) tend to generate lexically, semantically, and stylistically homogenous texts. This poses a risk of knowledge collapse, where homogenous LLMs mediate a shrinking in the range of accessible information over time. Existing works on homogenization are limited by a focus on closed-ended multiple-choice setups or fuzzy semantic features, and do not look at trends across time and cultural contexts. To overcome this, we present a new methodology to measure epistemic diversity, i.e., variation in real-world claims in LLM outputs, which we use to perform a broad empirical study of LLM knowledge collapse. We test 27 LLMs, 155 topics covering 12 countries, and 200 prompt variations sourced from real user chats. For the topics in our study, we show that while newer models tend to generate more diverse claims, nearly all models are less epistemically diverse than a basic web search. We find that model size has a negative impact on epistemic diversity, while retrieval-augmented generation (RAG) has a positive impact, though the improvement from RAG varies by the cultural context. Finally, compared to a traditional knowledge source (Wikipedia), we find that country-specific claims reflect the English language more than the local one, highlighting a gap in epistemic representation

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.

Nanomaterial-assisted immunodiagnostic profiling and therapeutic targeting of hepatocellular carcinoma: from molecular biomarkers to clinical applications

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Jiping Luo · Jianzeng Ye · Kaipeng Huang · Ziyu Cheng · Liming Liu · Xianpeng Li
30 October 2025 at 18:00

Front Immunol. 2025 Oct 14;16:1668630. doi: 10.3389/fimmu.2025.1668630. eCollection 2025.

ABSTRACT

AIMS AND OBJECTIVES: This study aimed to identify immunologically relevant transcriptomic and proteomic biomarkers in hepatocellular carcinoma (HCC) and to characterize their B-cell epitopes for potential integration into nanomaterial-based biosensors and immunomodulatory platforms for early diagnosis and targeted therapy.

METHODS: We conducted a comprehensive multi-omics analysis by integrating transcriptomic (TCGA-LIHC) and proteomic data to identify differentially expressed genes (DEGs) in HCC. Protein-protein interaction networks and pathway enrichment were used to prioritize hub genes. Five candidate biomarkers, RFC2, HSP90AB1, YWHAZ, CYP2E1, and ADH4, were selected for qRT-PCR and serum ELISA validation in clinical cohorts comprising 85 HCC patients and 50 healthy controls. B-cell epitope prediction was performed using BepiPred 2.0 and validated through synthetic peptide-based ELISA in the same cohort to assess immunoreactivity. Diagnostic performance was evaluated using ROC curve analysis.

RESULTS: RFC2, HSP90AB1, and YWHAZ were significantly upregulated (|log2FC|>0.2) and showed high serological expression, whereas CYP2E1 and ADH4 were consistently downregulated. Predicted B-cell epitopes from RFC2, HSP90AB1, and YWHAZ exhibited strong immunoreactivity (AUC>0.84), indicating their diagnostic potential. Enrichment analysis revealed that upregulated DEGs were involved in cell cycle and mitotic progression, while downregulated genes were linked to immune suppression and metabolic dysfunction. These validated immunogenic epitopes offer promising anchors for nanomaterial-functionalized biosensors, such as gold nanoparticle-conjugated ELISA, graphene-based electrochemical platforms, and peptide-coated quantum dots, for ultrasensitive and multiplexed HCC detection.

CONCLUSION: By integrating transcriptomic and proteomic screening with epitope-level validation, we identified a novel panel of immunogenic biomarkers suitable for nanomaterial-enabled diagnostics in HCC. These findings support the translational potential of peptide-nano scaffold conjugates in developing minimally invasive, immune-responsive biosensing and therapeutic tools tailored for early-stage liver cancer management.

PMID:41164201 | PMC:PMC12558944 | DOI:10.3389/fimmu.2025.1668630

Toward governance of artificial intelligence in pediatric healthcare

✇npj Digital Medicine
By: Felix Richter · Emma Holmes · Florian Richter · Katherine Guttmann · Son Q. Duong · Sandeep Gangadharan · Eric E. Schadt · Hojjat Salmasian · Bruce D. Gelb · Benjamin S. Glicksberg
30 October 2025 at 08:00

npj Digital Medicine, Published online: 30 October 2025; doi:10.1038/s41746-025-02000-7

Toward governance of artificial intelligence in pediatric healthcare
Received — 30 October 2025 ⏭ Favourites
  • ✇MIT Technology Review
  • Building a high performance data and AI organization (2nd edition) MIT Technology Review Insights
    Four years is a lifetime when it comes to artificial intelligence. Since the first edition of this study was published in 2021, AI’s capabilities have been advancing at speed, and the advances have not slowed since generative AI’s breakthrough. For example, multimodality— the ability to process information not only as text but also as audio, video, and other unstructured formats—is becoming a common feature of AI models. AI’s capacity to reason and act autonomously has also grown, and organizati
     
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Building a high performance data and AI organization (2nd edition)

✇MIT Technology Review
By: MIT Technology Review Insights
29 October 2025 at 23:00

Four years is a lifetime when it comes to artificial intelligence. Since the first edition of this study was published in 2021, AI’s capabilities have been advancing at speed, and the advances have not slowed since generative AI’s breakthrough. For example, multimodality— the ability to process information not only as text but also as audio, video, and other unstructured formats—is becoming a common feature of AI models. AI’s capacity to reason and act autonomously has also grown, and organizations are now starting to work with AI agents that can do just that.

Amid all the change, there remains a constant: the quality of an AI model’s outputs is only ever as good as the data
that feeds it. Data management technologies and practices have also been advancing, but the second edition of this study suggests that most organizations are not leveraging those fast enough to keep up with AI’s development. As a result of that and other hindrances, relatively few organizations are delivering the desired business results from their AI strategy. No more than 2% of senior executives we surveyed rate their organizations highly in terms of delivering results from AI.

To determine the extent to which organizational data performance has improved as generative AI and other AI advances have taken hold, MIT Technology Review Insights surveyed 800 senior data and technology executives. We also conducted in-depth interviews with 15 technology and business leaders.

Key findings from the report include the following:

• Few data teams are keeping pace with AI. Organizations are doing no better today at delivering on data strategy than in pre-generative AI days. Among those surveyed in 2025, 12% are self-assessed data “high achievers” compared with 13% in 2021. Shortages of skilled talent remain a constraint, but teams also struggle with accessing fresh data, tracing lineage, and dealing with security complexity—important requirements for AI success.

• Partly as a result, AI is not fully firing yet. There are even fewer “high achievers” when it comes to AI. Just 2% of respondents rate their organizations’ AI performance highly today in terms of delivering measurable business results. In fact, most are still struggling to scale generative AI. While two thirds have deployed it, only 7% have done so widely.

Download the report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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

Prospective proteomics for discovering biomarkers in lung adenocarcinoma: a literature review

✇Omics In Lung
By: Yichen Wang · Mingyue Xu · Xiaoyu Wei · Haitao Huang · Qi Chen · Baofu Chen · Xiaohong Bao · Jicheng Li
29 October 2025 at 18:00

Transl Cancer Res. 2025 Sep 30;14(9):6102-6117. doi: 10.21037/tcr-2025-1092. Epub 2025 Sep 26.

ABSTRACT

BACKGROUND AND OBJECTIVE: Lung adenocarcinoma (LUAD), as the main subtype of non-small cell lung cancer (NSCLC), faces clinical challenges including molecular heterogeneity, late diagnosis, and aggressive growth, leading to a low 5-year survival rate. Biomarkers are critical for early detection, accurate differentiation of benign/malignant lesions, and guiding personalized treatment strategies. Proteomic technologies using liquid biopsy show potential by analyzing protein changes and post-translational modifications (PTMs) to identify novel biomarkers and unravel cancer mechanisms. This review examines proteomic advances in LUAD, compares platform strengths, lists validated protein markers, and discusses challenges like specificity and regulations. It aims to develop a precision medicine framework by integrating multi-omics data for improved diagnosis and treatment.

METHODS: This study conducted a literature review by searching the PubMed and Web of Science databases for original articles written in English from 2002 to 2025, using the keywords "lung adenocarcinoma" OR "LUAD" AND "biomarkers" AND "proteomics" OR "SomaScan" OR "spatial proteomics" to identify the latest research findings in the field of proteomics technology and LUAD biomarkers. The included studies mainly focused on the current landscape of biomarkers in the diagnosis, treatment, and prognosis of LUAD.

KEY CONTENT AND FINDINGS: This review discusses high-throughput methods for comprehensive protein profiling in accessible biospecimens (tissues, blood, urine) to identify biomarkers for LUAD. We systematically evaluate emerging proteomic strategies, including mass spectrometry (MS), proximity extension assays (PEAs), spatial proteomics techniques, and SomaScan platforms-coupled with innovative computational frameworks have revolutionized biomarkers discovery and their translational potential in developing precision diagnostics and targeted therapies. Additionally, the review addresses challenges in integrating proteomics with genomics, transcriptomics, and metabolomics, offering new methodologies and expanding research in life sciences. As technological advancements continue, it is anticipated that more potential biomarkers will be conducted to validate the broader application in LUAD treatment, addressing early-stage disease complexities and aiding in selecting more effective treatment strategies.

CONCLUSIONS: By synthesizing cutting-edge evidence on proteome-driven LUAD biomarkers, this review elucidates actionable strategies to refine early detection protocols and mechanism-informed personalized treatment frameworks, directly advancing precision oncology initiatives for this prevalent malignancy through biomarker-guided clinical decision-making and multi-omics integration.

PMID:41158224 | PMC:PMC12554480 | DOI:10.21037/tcr-2025-1092

  • ✇STAT
  • STAT+: Natera, known for spotting cancer recurrence, wades into early detection Elaine Chen
    Want to stay on top of the science and politics driving biotech today? Sign up to get our biotech newsletter in your inbox. Good morning. It seems everyone I know has been getting sick lately — hope you are all taking care of yourselves! Onto the news today. BridgeBio notches another Phase 3 win BridgeBio said this morning that its investigational drug succeeded in a late-stage trial of patients with autosomal dominant hypocalcemia type 1, a rare genetic condition that causes low calciu
     
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STAT+: Natera, known for spotting cancer recurrence, wades into early detection

✇STAT
By: Elaine Chen
29 October 2025 at 21:26

Want to stay on top of the science and politics driving biotech today? Sign up to get our biotech newsletter in your inbox.

Good morning. It seems everyone I know has been getting sick lately — hope you are all taking care of yourselves! Onto the news today.

BridgeBio notches another Phase 3 win

BridgeBio said this morning that its investigational drug succeeded in a late-stage trial of patients with autosomal dominant hypocalcemia type 1, a rare genetic condition that causes low calcium levels in the blood.

Continue to STAT+ to read the full story…

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Improving Recruitment Into Research Studies via Electronically Collected Patient-Entered Data: Mixed Methods Study

✇Journal of Medical Internet Research
By: Irene Katzan · WH Wilson Tang · Andrew Schuster · Ryan Honomichl · Misti Allison · Renee Feldman · Michelle Gandolf · Benjamin Walter · Brittany Lapin
29 October 2025 at 20:15
Background: Patient recruitment remains a critical challenge in clinical research. Although the integration of electronically collected patient-entered data within clinical practices enables innovative recruitment approaches, existing methods present challenges such as increased patient burden and potential violation of autonomy. A more nuanced approach involves identifying patient attributes associated with higher propensity for research participation, enabling research teams to efficiently prioritize outreach efforts. Objective: This study aims to (1) develop patient-reported questions reflecting perceptions about research participation and (2) determine whether patient responses are predictive of interest in joining a precision medicine registry. Methods: This mixed methods study used an exploratory sequential design in 2 phases. Phase 1 involved cognitive interviews with 32 patients recruited through the Cleveland Clinic Healthcare Partners program to develop “research perception” questions. Participants evaluated 9 candidate questions that were based on a literature review of research participation factors. Three questions were selected for implementation. Phase 2 was a cross-sectional cohort study incorporating these 3 questions into routine electronic questionnaires completed by primary care patients through the patient portal. The study population included 1077 patients who completed both “research perception” and “research recruitment” questions between August 2018 and April 2019. Diagnostic accuracy was assessed using receiver operating characteristic curve analysis, and multivariable logistic regression models evaluated associations while adjusting for demographic and health factors. Results: Phase 1 revealed strong research support among participants, with 97% (31/32) agreeing that research should be part of the institution’s mission and 100% (32/32) affirming that research enhances patient care. Phase 2 included 1077 patients (mean age 48.3, SD 16.3 years; 625/1065 female, 58.68%; 661/1005 White, 65.77%), of whom 278 (25.8%) expressed interest in being contacted about the precision medicine registry. Patients expressing interest were older and had worse self-reported health, more depressive symptoms, and greater social needs. “Strongly agree” and “very important” responses to any “research perception” question were significantly associated with study interest, with adjusted odds ratios ranging from 6.36 (95% CI 2.77-14.6) to 17.6 (95% CI 5.08-61.1; P<.001). The “research perception” questions demonstrated high sensitivity (>80%) but limited specificity (24%-31%). Conclusions: Patient-reported questions assessing research participation likelihood can help identify patients more likely to enroll in clinical studies. This approach enables effective recruitment prioritization while preserving patient autonomy and reducing patient burden. High sensitivity makes these questions valuable as screening tools, although limited specificity suggests use for prioritizing rather than excluding participants. Further validation across different trial types and populations is warranted.
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