Publisher Correction: Deep-learning-based virtual screening of antibacterial compounds

7 November 2025 at 08:00

Nature Biotechnology, Published online: 07 November 2025; doi:10.1038/s41587-025-02941-0

Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification

cs.AI, q-bio.NC updates on arXiv.org

By: Shaghayegh Kolli · Richard Rosenbaum · Timo Cavelius · Lasse Strothe · Andrii Lata · Jana Diesner

6 November 2025 at 13:00

arXiv:2511.03217v1 Announce Type: cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information. At the same time, knowledge-graph-based fact-checkers deliver precise and interpretable evidence, yet suffer from limited coverage or latency. By integrating LLMs with knowledge graphs and real-time search agents, we introduce a hybrid fact-checking approach that leverages the individual strengths of each component. Our system comprises three autonomous steps: 1) a Knowledge Graph (KG) Retrieval for rapid one - hop lookups in DBpedia, 2) an LM-based classification guided by a task-specific labeling prompt, producing outputs with internal rule-based logic, and 3) a Web Search Agent invoked only when KG coverage is insufficient. Our pipeline achieves an F1 score of 0.93 on the FEVER benchmark on the Supported/Refuted split without task- specific fine - tuning. To address Not enough information cases, we conduct a targeted reannotation study showing that our approach frequently uncovers valid evidence for claims originally labeled as Not Enough Information (NEI), as confirmed by both expert annotators and LLM reviewers. With this paper, we present a modular, opensource fact-checking pipeline with fallback strategies and generalization across datasets.

Site-specific DNA insertion into the human genome with engineered recombinases

Nature Biotechnology - Issue - nature.com science feeds

By: Alison Fanton · Liam J. Bartie · Juliana Q. Martins · Vincent Q. Tran · Laine Goudy · Courtney Kernick · Matthew G. Durrant · Jingyi Wei · Zev Armour-Garb · April Pawluk · Silvana Konermann · Alexander Marson · Luke A. Gilbert · Theodore L. Roth · Patrick D. Hsu

6 November 2025 at 08:00

Nature Biotechnology, Published online: 06 November 2025; doi:10.1038/s41587-025-02895-3

Engineered DNA recombinases efficiently and specifically insert genetic cargos without the use of landing pads.

Improving dataset transparency in dermatologic Artificial Intelligence using a dataset nutrition label

npj Digital Medicine

By: Yingjoy Li · Matthew Taylor · Kasia S. Chmielinski · Allan C. Halpern · Roxana Daneshjou · Jenna C. Lester · Veronica Rotemberg

5 November 2025 at 08:00

npj Digital Medicine, Published online: 05 November 2025; doi:10.1038/s41746-025-02125-9

Biased and poorly documented dermatology datasets pose risks to the development of safe and generalizable artificial intelligence (AI) tools. We created a Dataset Nutrition Label (DNL) for multiple dermatology datasets to support transparent and responsible data use. The DNL offers a structured, digestible summary of key attributes, including metadata, limitations, and risks, enabling data users to better assess suitability and proactively address potential sources of bias in datasets.

Evaluating clinical AI summaries with large language models as judges

npj Digital Medicine

By: Emma Croxford · Yanjun Gao · Elliot First · Nicholas Pellegrino · Miranda Schnier · John Caskey · Madeline Oguss · Graham Wills · Guanhua Chen · Dmitriy Dligach · Matthew M. Churpek · Anoop Mayampurath · Frank Liao · Cherodeep Goswami · Karen K. Wong · Brian W. Patterson · Majid Afshar

5 November 2025 at 08:00

npj Digital Medicine, Published online: 05 November 2025; doi:10.1038/s41746-025-02005-2

Evaluating clinical AI summaries with large language models as judges

Liquid biopsy in gastrointestinal oncology: clinical applications and translational integration of ctDNA, CTCs, and sEVs

MRD

By: Rita Palieri · Maria De Luca · Francesco Balestra · Giorgia Panzetta · Claudio Lotesoriere · Federica Rizzi · Angela Dalia Ricci · Rita Mastrogiacomo · Maria Lucia Curri · Luigi Andrea Laghi · Gianluigi Giannelli · Nicoletta Depalo · Maria Principia Scavo

5 November 2025 at 19:00

Oncol Rev. 2025 Oct 20;19:1702932. doi: 10.3389/or.2025.1702932. eCollection 2025.

ABSTRACT

BACKGROUND AND AIMS: Liquid biopsy offers a minimally invasive tool to detect actionable mutations, monitor minimal residual disease (MRD), and guide therapy in gastrointestinal (GI) cancers. We critically review the clinical utility of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and small extracellular vesicles (sEVs) across GI malignancies and propose a framework for their integration into clinical practice.

METHODS: We synthesized evidence from over 200 studies, including prospective trials and translational research, to assess diagnostic accuracy, prognostic value, and clinical actionability of each biomarker type in esophageal, gastric, colorectal, pancreatic, hepatocellular, and biliary cancers.

RESULTS: ctDNA has shown strong potential for MRD detection and treatment monitoring, particularly in colorectal and pancreatic cancer. CTCs offer insights into metastatic risk and therapeutic resistance, while sEVs provide molecular cargo relevant to immunomodulation and disease progression. Emerging microfluidics and AI-driven multi-omics approaches may overcome current limitations.

CONCLUSION: The integration of liquid biopsy technologies into GI oncology holds promise for early detection and precision therapy. We propose a five-phase clinical roadmap and outine the key research gaps that need to be addressed before widespread implementation in routine care.

PMID:41190015 | PMC:PMC12580207 | DOI:10.3389/or.2025.1702932

Combining International Standards to Develop Clinical Decision Support for Parent Smoking Cessation in Pediatrics

Journal of Medical Internet Research

By: Jeritt G Thayer · Brian P Jenssen · Alexander G Fiks · Dean Karavite · Janani Ramachandran · Shannon Kelleher · Ekaterina Nekrasova · Jeremy E Drehmer · Emara Nabi-Burza · Jonathan P Winickoff · Robert W Grundmeier

6 November 2025 at 05:30

Smoking has severe health consequences, and secondhand smoke (SHS) exposure among children increases the risk of sudden infant death syndrome, chronic respiratory diseases, such as asthma, and lung cancer in adulthood. For many parents, pediatricians are the primary source of interaction with the healthcare system. Nevertheless, in pediatric settings, appropriate tobacco treatments are rarely, if ever, provided to parents who smoke. To best address tobacco use among parents, it is ideal to develop scalable solutions that are coordinated across health systems, community partners, and national services within pediatric settings. We describe our experience developing and implementing a parent tobacco treatment platform (PTTP) within a pediatric institution that leverages multiple international standards to support interoperability, with the overarching goal of providing a model for how such work can be approached. The clinical decision support (CDS) system includes clinician- and patient-facing components, connects parents to three different treatment options (nicotine replacement therapy, text-based counseling, and telephonic counseling), and incorporates three international standards (Fast Healthcare Interoperability Resources [FHIR], SMART on FHIR, and CDS Hooks). FHIR is used across all components. SMART on FHIR is limited to the clinician-facing tool, and CDS Hooks is used in the patient-facing portion. While healthcare interoperability standards supported a significant portion of the overall system, non-standard technologies and enhancements of existing standards were also required. Further, no connections with community partners could use existing interoperability standards. Over one year, the CDS was used in 194,946 visits, identified 7,847 parents who smoke, and connected 2,954 parents to 6,320 distinct treatment services, a significant improvement compared to prior efforts. Our project demonstrates that building CDS systems using international standards, such as SMART on FHIR, FHIR, and CDS Hooks, is possible, but challenges remain. Limits in the CDS Hooks standard to support common workflows and a lack of communication standards used by 3rd parties outside the healthcare system represent areas for future work. To support these requirements, additional EHR-specific records and communication mechanisms were required.

A multimodal whole-slide foundation model for pathology

Nature Medicine

By: Tong Ding · Sophia J. Wagner · Andrew H. Song · Richard J. Chen · Ming Y. Lu · Andrew Zhang · Anurag J. Vaidya · Guillaume Jaume · Muhammad Shaban · Ahrong Kim · Drew F. K. Williamson · Harry Robertson · Bowen Chen · Cristina Almagro-Pérez · Paul Doucet · Sharifa Sahai · Chengkuan Chen · Christina S. Chen · Daisuke Komura · Akihiro Kawabe · Mieko Ochi · Shinya Sato · Tomoyuki Yokose · Yohei Miyagi · Shumpei Ishikawa · Georg Gerber · Tingying Peng · Long Phi Le · Faisal Mahmood

5 November 2025 at 08:00

Nature Medicine, Published online: 05 November 2025; doi:10.1038/s41591-025-03982-3

Pretrained using 335,645 whole-slide images, a foundation model is developed to provide representations for slide- and patient-level tasks. It is capable of performing clinical tasks and generating reports even in data-scarce scenarios, such as rare cancer diagnosis and survival prediction, without requiring further fine-tuning.

Fair human-centric image dataset for ethical AI benchmarking

5 November 2025 at 08:00

Nature, Published online: 05 November 2025; doi:10.1038/s41586-025-09716-2

The Fair Human-Centric Image Benchmark (FHIBE, pronounced ‘Feebee’)—an image dataset that implements best practices for consent, privacy, compensation, safety, diversity and utility—can be used responsibly as a fairness evaluation dataset for many human-centric computer vision applications.

How can we assess human-agent interactions? Case studies in software agent design

cs.AI, q-bio.NC updates on arXiv.org

By: Valerie Chen · Rohit Malhotra · Xingyao Wang · Juan Michelini · Xuhui Zhou · Aditya Bharat Soni · Hoang H. Tran · Calvin Smith · Ameet Talwalkar · Graham Neubig

5 November 2025 at 13:00

arXiv:2510.09801v2 Announce Type: replace Abstract: LLM-powered agents are both a promising new technology and a source of complexity, where choices about models, tools, and prompting can affect their usefulness. While numerous benchmarks measure agent accuracy across domains, they mostly assume full automation, failing to represent the collaborative nature of real-world use cases. In this paper, we make two major steps towards the rigorous assessment of human-agent interactions. First, we propose PULSE, a framework for more efficient human-centric evaluation of agent designs, which comprises collecting user feedback, training an ML model to predict user satisfaction, and computing results by combining human satisfaction ratings with model-generated pseudo-labels. Second, we deploy the framework on a large-scale web platform built around the open-source software agent OpenHands, collecting in-the-wild usage data across over 15k users. We conduct case studies around how three agent design decisions -- choice of LLM backbone, planning strategy, and memory mechanisms -- impact developer satisfaction rates, yielding practical insights for software agent design. We also show how our framework can lead to more robust conclusions about agent design, reducing confidence intervals by 40% compared to a standard A/B test. Finally, we find substantial discrepancies between in-the-wild results and benchmark performance (e.g., the anti-correlation between results comparing claude-sonnet-4 and gpt-5), underscoring the limitations of benchmark-driven evaluation. Our findings provide guidance for evaluations of LLM agents with humans and identify opportunities for better agent designs.

Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments

cs.AI, q-bio.NC updates on arXiv.org

By: Angelos Alexopoulos · Agorakis Bompotas · Nikitas Rigas Kalogeropoulos · Panagiotis Kechagias · Athanasios P. Kalogeras · Christos Alexakos

4 November 2025 at 13:00

arXiv:2511.00094v1 Announce Type: cross Abstract: Robotic systems have become integral to smart environments, enabling applications ranging from urban surveillance and automated agriculture to industrial automation. However, their effective operation in dynamic settings - such as smart cities and precision farming - is challenged by continuously evolving topographies and environmental conditions. Traditional control systems often struggle to adapt quickly, leading to inefficiencies or operational failures. To address this limitation, we propose a novel framework for autonomous and dynamic reconfiguration of robotic controllers using Digital Twin technology. Our approach leverages a virtual replica of the robot's operational environment to simulate and optimize movement trajectories in response to real-world changes. By recalculating paths and control parameters in the Digital Twin and deploying the updated code to the physical robot, our method ensures rapid and reliable adaptation without manual intervention. This work advances the integration of Digital Twins in robotics, offering a scalable solution for enhancing autonomy in smart, dynamic environments.

Will Humanity Be Rendered Obsolete by AI?

cs.AI, q-bio.NC updates on arXiv.org

By: Mohamed El Louadi · Emna Ben Romdhane

4 November 2025 at 13:00

arXiv:2510.22814v2 Announce Type: replace Abstract: This article analyzes the existential risks artificial intelligence (AI) poses to humanity, tracing the trajectory from current AI to ultraintelligence. Drawing on Irving J. Good and Nick Bostrom's theoretical work, plus recent publications (AI 2027; If Anyone Builds It, Everyone Dies), it explores AGI and superintelligence. Considering machines' exponentially growing cognitive power and hypothetical IQs, it addresses the ethical and existential implications of an intelligence vastly exceeding humanity's, fundamentally alien. Human extinction may result not from malice, but from uncontrollable, indifferent cognitive superiority.

The Denario project: Deep knowledge AI agents for scientific discovery

cs.AI, q-bio.NC updates on arXiv.org

By: Francisco Villaescusa-Navarro · Boris Bolliet · Pablo Villanueva-Domingo · Adrian E. Bayer · Aidan Acquah · Chetana Amancharla · Almog Barzilay-Siegal · Pablo Bermejo · Camille Bilodeau · Pablo C\'ardenas Ram\'irez · Miles Cranmer · Urbano L. Fran\c{c}a · ChangHoon Hahn · Yan-Fei Jiang · Raul Jimenez · Jun-Young Lee · Antonio Lerario · Osman Mamun · Thomas Meier · Anupam A. Ojha · Pavlos Protopapas · Shimanto Roy · David N. Spergel · Pedro Taranc\'on-\'Alvarez · Ujjwal Tiwari · Matteo Viel · Digvijay Wadekar · Chi Wang · Bonny Y. Wang · Licong Xu · Yossi Yovel · Shuwen Yue · Wen-Han Zhou · Qiyao Zhu · Jiajun Zou · \'I\~nigo Zubeldia

3 November 2025 at 13:00

arXiv:2510.26887v1 Announce Type: new Abstract: We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.

Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence

cs.AI, q-bio.NC updates on arXiv.org

By: L\'ivia Dutra · Arthur Lorenzi · La\'is Berno · Franciany Campos · Karoline Biscardi · Kenneth Brown · Marcelo Viridiano · Frederico Belcavello · Ely Matos · Ol\'ivia Guaranha · Erik Santos · Sofia Reinach · Tiago Timponi Torrent

3 November 2025 at 13:00

arXiv:2510.26969v1 Announce Type: cross Abstract: We introduce a methodology for the identification of notifiable events in the domain of healthcare. The methodology harnesses semantic frames to define fine-grained patterns and search them in unstructured data, namely, open-text fields in e-medical records. We apply the methodology to the problem of underreporting of gender-based violence (GBV) in e-medical records produced during patients' visits to primary care units. A total of eight patterns are defined and searched on a corpus of 21 million sentences in Brazilian Portuguese extracted from e-SUS APS. The results are manually evaluated by linguists and the precision of each pattern measured. Our findings reveal that the methodology effectively identifies reports of violence with a precision of 0.726, confirming its robustness. Designed as a transparent, efficient, low-carbon, and language-agnostic pipeline, the approach can be easily adapted to other health surveillance contexts, contributing to the broader, ethical, and explainable use of NLP in public health systems.

A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification

cs.AI, q-bio.NC updates on arXiv.org

By: Flavio Corradini · Flavio Gerosa · Marco Gori · Carlo Lucheroni · Marco Piangerelli · Martina Zannotti

3 November 2025 at 13:00

arXiv:2410.22377v3 Announce Type: replace-cross Abstract: In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture, at once, dependencies among variables and across time points. The objective of this systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application domains of GNNs for time series classification and forecasting. A database search was conducted, and 366 papers were selected for a detailed examination of the current state-of-the-art in the field. This examination is intended to offer to the reader a comprehensive review of proposed models, links to related source code, available datasets, benchmark models, and fitting results. All this information is hoped to assist researchers in their studies. To the best of our knowledge, this is the first and broadest systematic literature review presenting a detailed comparison of results from current spatio-temporal GNN models applied to different domains. In its final part, this review discusses current limitations and challenges in the application of spatio-temporal GNNs, such as comparability, reproducibility, explainability, poor information capacity, and scalability. This paper is complemented by a GitHub repository at https://github.com/FlaGer99/SLR-Spatio-Temporal-GNN.git providing additional interactive tools to further explore the presented findings.

Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimates

cs.AI, q-bio.NC updates on arXiv.org

By: Tien Huu Do · Antoine Masquelier · Nae Eoun Lee · Jonathan Crowther

3 November 2025 at 13:00

arXiv:2507.23607v2 Announce Type: replace-cross Abstract: Clinical trials are a systematic endeavor to assess the safety and efficacy of new drugs or treatments. Conducting such trials typically demands significant financial investment and meticulous planning, highlighting the need for accurate predictions of trial outcomes. Accurately predicting patient enrollment, a key factor in trial success, is one of the primary challenges during the planning phase. In this work, we propose a novel deep learning-based method to address this critical challenge. Our method, implemented as a neural network model, leverages pre-trained language models (PLMs) to capture the complexities and nuances of clinical documents, transforming them into expressive representations. These representations are then combined with encoded tabular features via an attention mechanism. To account for uncertainties in enrollment prediction, we enhance the model with a probabilistic layer based on the Gamma distribution, which enables range estimation. We apply the proposed model to predict clinical trial duration, assuming site-level enrollment follows a Poisson-Gamma process. We carry out extensive experiments on real-world clinical trial data, and show that the proposed method can effectively predict the number of patients enrolled at a number of sites for a given clinical trial, outperforming established baseline models.

A Process Mining-Based System For The Analysis and Prediction of Software Development Workflows

cs.AI, q-bio.NC updates on arXiv.org

By: Ant\'ia Dorado · Iv\'an Folgueira · Sof\'ia Mart\'in · Gonzalo Mart\'in · \'Alvaro Porto · Alejandro Ramos · John Wallace

3 November 2025 at 13:00

arXiv:2510.25935v2 Announce Type: replace-cross Abstract: CodeSight is an end-to-end system designed to anticipate deadline compliance in software development workflows. It captures development and deployment data directly from GitHub, transforming it into process mining logs for detailed analysis. From these logs, the system generates metrics and dashboards that provide actionable insights into PR activity patterns and workflow efficiency. Building on this structured representation, CodeSight employs an LSTM model that predicts remaining PR resolution times based on sequential activity traces and static features, enabling early identification of potential deadline breaches. In tests, the system demonstrates high precision and F1 scores in predicting deadline compliance, illustrating the value of integrating process mining with machine learning for proactive software project management.

On the limitation of evaluating machine unlearning using only a single training seed

cs.AI, q-bio.NC updates on arXiv.org

By: Jamie Lanyon · Axel Finke · Petros Andreou · Georgina Cosma

3 November 2025 at 13:00

arXiv:2510.26714v2 Announce Type: replace-cross Abstract: Machine unlearning (MU) aims to remove the influence of certain data points from a trained model without costly retraining. Most practical MU algorithms are only approximate and their performance can only be assessed empirically. Care must therefore be taken to make empirical comparisons as representative as possible. A common practice is to run the MU algorithm multiple times independently starting from the same trained model. In this work, we demonstrate that this practice can give highly non-representative results because -- even for the same architecture and same dataset -- some MU methods can be highly sensitive to the choice of random number seed used for model training. We therefore recommend that empirical comparisons of MU algorithms should also reflect the variability across different model training seeds.

International expert consensus on the clinical integration of circulating tumor cells in solid tumors

31 October 2025 at 18:00

Eur J Cancer. 2025 Dec 9;231:116050. doi: 10.1016/j.ejca.2025.116050. Epub 2025 Oct 20.

ABSTRACT

BACKGROUND: Circulating tumor cells (CTCs) are a versatile biomarker in solid tumors. Extensive research supports their clinical relevance and led to regulatory approval in breast, prostate, and colorectal cancers. However, clinical adoption remains limited mainly due to the lack of consensus and standardized technologies. Additionally, CTC research lacks unified direction. To address these gaps, an international expert panel was established to assess the current and future clinical utility of CTCs.

METHODS: A panel of 11 CTC experts identified key areas of controversy, informing a structured survey distributed to 55 international multidisciplinary experts. Consensus was predefined as ≥ 70 % agreement. Areas without consensus were discussed in a virtual meeting, leading to final statements on the clinical integration of CTCs.

RESULTS: Thirty-seven experts completed the survey. Consensus was reached on the clinical utility of CTCs for prognosis and treatment monitoring in metastatic breast (BC) and prostate (PC) cancers, including AR-V7 testing in metastatic castration-resistant PC for therapy selection. In other tumors, CTCs remain investigational. Experts agreed that while clinical utility is not yet established in early-stage disease, CTCs show promise in early BC, especially combined with cell-free DNA (cfDNA) for minimal residual disease detection. CellSearch® is currently the only platform with high-level evidence for clinical use, though emerging technologies are promising. Key challenges include improving detection sensitivity/specificity, standardizing workflows, generating robust data, and clinician education. Experts emphasized shifting from enumeration to phenotypic and molecular characterization, particularly for treatment guidance, and highlighted the complementary role of CTCs and cfDNA, advocating for integrated liquid biopsy approaches.

CONCLUSIONS: This consensus offers practical guidance for clinical integration of CTCs and outlines strategic research priorities to unlock their full potential in precision oncology.

PMID:41172567 | DOI:10.1016/j.ejca.2025.116050

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

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