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Received — 5 October 2025 ⏭ Favourites

Exploring Attitudes and Obstacles Around Digital Public Health Tools: Insights From a Statewide Cross-Sectional Survey on Washington’s Vaccine Verification System

✇Journal of Medical Internet Research
By: Andrea R Molino · Debra Revere · Rebecca A Hills · Adam S Elder · Laura M West · Bryant T Karras · Chris Baumgartner · Janet G Baseman
3 October 2025 at 23:00
Background: Development and use of digital public health tools surged during the COVID-19 pandemic. Among these tools, vaccine verification systems emerged as alternatives to paper vaccine records, aiming to help limit the spread of disease. In November 2021, the Washington State Department of Health launched “WA Verify,” a QR code–based vaccine verification system built on the SMART Health Card framework, providing residents with a convenient way to store and share proof of vaccination digitally. However, WA Verify was developed and deployed before assessments and public input regarding potential adoption challenges—such as concerns about privacy, surveillance, data sharing, trust in the technology, and the managing organizations—could be completed. Objective: This analysis used statewide survey data from Washington to identify and characterize barriers and facilitators to the adoption of WA Verify, and to understand how factors such as data privacy, security, attitudes toward public health policies and communication, and technological proficiency may influence acceptance and uptake of digital public health tools. Methods: A cross-sectional statewide survey was distributed between September 2022 and January 2023 to a random sample of 5000 Washington households. Respondents were categorized into 3 groups based on their responses indicating WA Verify “users,” “potential users,” or “unlikely users.” Comparisons were made between groups regarding experiences with and opinions on COVID-19 vaccine and test verification, public health policies, communication, digital tools, technological proficiency, sociodemographic characteristics, and health history. Poststratification weights were applied to reduce nonresponse bias. Results: Of the 1401 respondents, 359 (25.6% unweighted, 25.8% weighted) were users, 662 (47.3% unweighted, 49.8% weighted) were potential users, and 380 (27.1% unweighted, 24.4% weighted) were unlikely users. All percentages reported are based on weighted data. Compared with users and potential users, unlikely users were more likely to oppose policies requiring proof of COVID-19 vaccination or negative test results (users: 6.0%, potential users: 13.6%, unlikely users: 65.9%). Unlikely users were more likely to cite concerns about personal health data security and phone hacking or tracking, though these concerns were also notable among potential users and users. Users and potential users were more likely to perceive a digital vaccine verification system as convenient (users: 96.5%, potential users: 92.3%, unlikely users: 38.1%) and indicated openness to receiving relevant information from a range of sources. Unlikely users were more likely to report not owning a smartphone and demonstrated lower technological proficiency (users: 12.3%, potential users: 15.9%, unlikely users: 32.3%), indicating a technological divide between groups. Conclusions: While nearly three-quarters of respondents had either already adopted or were willing to adopt a tool like WA Verify, concerns about data security, lower technological proficiency, and distrust of public health characterized those least likely to adopt such tools. Identifying barriers to adoption among “unlikely users” is essential for developing effective communication strategies—such as targeted marketing and community engagement—to improve adoption and ensure equitable access to public health technologies.
Received — 25 September 2025 ⏭ Favourites

The Biodiversity Cell Atlas: mapping the tree of life at cellular resolution

✇Nature - Issue - nature.com science feeds
By: Arnau Sebé-Pedrós · Amos Tanay · Mara K. N. Lawniczak · Detlev Arendt · Stein Aerts · John Archibald · Maria Ina Arnone · Mark Blaxter · Phillip Cleves · Susana M. Coelho · Mafalda Dias · Casey Dunn · Anamaria Elek · Jonathan Frazer · Toni Gabaldón · Jesse Gillis · Xavier Grau-Bové · Roderic Guigó · Oliver Hobert · Jaime Huerta-Cepas · Manuel Irimia · Allon Klein · Harris Lewin · Christopher J. Lowe · Heather Marlow · Jacob M. Musser · László G. Nagy · Sebastián R. Najle · Lior Pachter · Sadye Paez · Irene Papatheodorou · Michael J. Passalacqua · Nikolaus Rajewsky · Seung Y. Rhee · Thomas A. Richards · Tatjana Sauka-Spengler · Lauren M. Saunders · Eve Seuntjens · Jordi Solana · Yuyao Song · Ulrich Technau · Bo Wang
24 September 2025 at 08:00

Nature, Published online: 24 September 2025; doi:10.1038/s41586-025-09312-4

The Biodiversity Cell Atlas aims to create comprehensive single-cell molecular atlases across the eukaryotic tree of life, which will be phylogenetically informed, rely on high-quality genomes and use shared standards to facilitate comparisons across species.
Received — 24 September 2025 ⏭ Favourites

Comparative Evaluation of a Medical Large Language Model in Answering Real-World Radiation Oncology Questions: Multicenter Observational Study

✇Journal of Medical Internet Research
By: Fabio Dennstädt · Max Schmerder · Elena Riggenbach · Lucas Mose · Katarina Bryjova · Nicolas Bachmann · Paul-Henry Mackeprang · Maiwand Ahmadsei · Dubravko Sinovcic · Paul Windisch · Daniel Zwahlen · Susanne Rogers · Oliver Riesterer · Martin Maffei · Eleni Gkika · Hathal Haddad · Jan Peeken · Paul Martin Putora · Markus Glatzer · Florian Putz · Daniel Hoefler · Sebastian M Christ · Irina Filchenko · Janna Hastings · Roberto Gaio · Lawrence Chiang · Daniel M Aebersold · Nikola Cihoric
24 September 2025 at 03:30
Background: Large language models (LLMs) hold promise for supporting clinical tasks, particularly in data-driven and technical disciplines such as radiation oncology. While prior evaluation studies have focused on examination-style settings for evaluating LLMs, their performance in real-life clinical scenarios remains unclear. In the future, LLMs might be used as general AI assistants to answer questions arising in clinical practice. It is unclear how well a modern LLM, locally executed within the infrastructure of a hospital, would answer such questions compared with clinical experts. Objective: This study aimed to assess the performance of a locally deployed, state-of-the-art medical LLM in answering real-world clinical questions in radiation oncology compared with clinical experts. The aim was to evaluate the overall quality of answers, as well as the potential harmfulness of the answers if used for clinical decision-making. Methods: Physicians from 10 departments of European hospitals collected questions arising in the clinical practice of radiation oncology. Fifty of these questions were answered by 3 senior radiation oncology experts with at least 10 years of work experience, as well as the LLM Llama3-OpenBioLLM-70B (Ankit Pal and Malaikannan Sankarasubbu). In a blinded review, physicians rated the overall answer quality on a 5-point Likert scale (quality), assessed whether an answer might be potentially harmful if used for clinical decision-making (harmfulness), and determined if responses were from an expert or the LLM (recognizability). Comparisons between clinical experts and LLMs were then made for quality, harmfulness, and recognizability. Results: There were no significant differences between the quality of the answers between LLM and clinical experts (mean scores of 3.38 vs 3.63; median 4.00, IQR 3.00-4.00 vs median 3.67, IQR 3.33-4.00; P=.26; Wilcoxon signed rank test). The answers were deemed potentially harmful in 13% of cases for the clinical experts compared with 16% of cases for the LLM (P=.63; Fisher exact test). Physicians correctly identified whether an answer was given by a clinical expert or an LLM in 78% and 72% of cases, respectively. Conclusions: A state-of-the-art medical LLM can answer real-life questions from the clinical practice of radiation oncology similarly well as clinical experts regarding overall quality and potential harmfulness. Such LLMs can already be deployed within the local hospital environment at an affordable cost. While LLMs may not yet be ready for clinical implementation as general AI assistants, the technology continues to improve at a rapid pace. Evaluation studies based on real-life situations are important to better understand the weaknesses and limitations of LLMs in clinical practice. Such studies are also crucial to define when the technology is ready for clinical implementation. Furthermore, education for health care professionals on generative AI is needed to ensure responsible clinical implementation of this transforming technology.
Received — 19 September 2025 ⏭ Favourites

Large Language Models’ Clinical Decision-Making on When to Perform a Kidney Biopsy: Comparative Study

✇Journal of Medical Internet Research
By: Michael Toal · Christopher Hill · Michael Quinn · Ciaran O'Neill · Alexander P Maxwell
19 September 2025 at 04:15
Background: Artificial intelligence (AI) and Large Language models (LLMs) are increasing in sophistication and are being integrated into many disciplines. The potential for LLMs to augment clinical decisions is an evolving area of research. Objective: This study compared the responses of over 1000 kidney specialist physicians (nephrologists) to outputs of commonly used LLMs using a questionnaire determining when a kidney biopsy should be performed. Methods: This research group completed a large online questionnaire for nephrologists to determine when a kidney biopsy should be performed. The questionnaire was co-designed with patient participation, refined through multiple iterations, then piloted locally before international dissemination. It was the largest international study in the field and demonstrated variation between human clinicians in biopsy propensity relating to human factors such as sex and age, as well as systemic factors such as country, job seniority and technical proficiency. The same questions were put to both human doctors and LLMs in an identical order in a single session. Eight commonly used LLMs were interrogated: Chat GPT 3.5, Mistral Hugging Face, Perplexity, Microsoft Co-pilot, Llama 2, GPT 4.0, MedLM and Claude 3. The most common response given by clinicians (human mode) to each question was taken as the baseline for comparison. Questionnaire responses to the indications and contraindications for biopsy generated a score (0-44) reflecting biopsy propensity, in which a higher score was used as a surrogate marker for an increased tolerance of potential associated risks. Results: The ability of LLMs to reproduce human expert consensus varied widely with some models demonstrating a balanced approach to risk in a similar manner to humans, whilst other models reported outputs at either end of the spectrum for risk tolerance. In terms of agreement with the human mode, Chat GPT 3.5 and GPT 4.0 (Open AI) had the highest levels of alignment, with the human mode selected in 6/11 questions. The total biopsy propensity score generated from the human mode was 23/44. Both Open AI models produced similar propensity scores between 22 and 24, however Llama 2 and MS Co-pilot also reported scores within this range, but with poorer response alignment to the human mode at only 2/11 questions. The most risk averse model in this study was MedLM with a propensity score of 11 and the least risk averse model was Claude 3 with a score of 34. Conclusions: LLM outputs demonstrated a modest ability to replicate human clinical decision making in this study, however the performance varied widely between LLM models. Questions with more uniform human responses produced LLM outputs with greater alignment, whereas in questions with low levels of human consensus there was poor output alignment. This may limit the practical use of LLMs in real world clinical practice.

Clinical implementation of an AI-based prediction model for decision support for patients undergoing colorectal cancer surgery

✇Nature Medicine
By: Andreas Weinberger Rosen · Ilze Ose · Mikail Gögenur · Lars Peter Kloster Andersen · Rasmus Dahlin Bojesen · Rasmus Peuliche Vogelsang · Martin Høyer Rose · Philip Wallentin Steenfos · Lasse Bremholm Hansen · Helle Skadborg Spuur · Ines Raben · Søren Thorgaard Skou · Ellen Astrid Holm · Karina Mortensen · Trine Kjær · Jens Ravn Eriksen · Andinoos Eros Tsouchnikas · Angelina Astrid Righult · Emine Ceren Ayhan · Sule Eraslan · Enise Gögenur · Christina Alexandersen · Dilara Seyma Gögenur · Amalie Thomsen Nielsen · Ida Kolukisa Saqi · Lea Maria Elisabeth Löffler · Xenia Lea Schefte · Oliver Wedel Fischer · Tina Fransgård · Morten Holt Thomsen · Michael Seiersen · Steffen Kirstein Brisling · Stine Ydegaard Turiño · Nina Abild Frederiksen · Ismail Gögenur
18 September 2025 at 08:00

Nature Medicine, Published online: 18 September 2025; doi:10.1038/s41591-025-03942-x

A model developed with data from 19,403 patients with colorectal cancer for prediction of 1-year mortality is used as a decision support tool in a prospective cohort, showing promising results in reducing postoperative complications.
Received — 17 September 2025 ⏭ Favourites

New Doc on the Block: Scoping Review of AI Systems Delivering Motivational Interviewing for Health Behavior Change

✇Journal of Medical Internet Research
By: Zev Karve · Jacob Calpey · Christopher Machado · Michelle Knecht · Maria Carmenza Mejia
17 September 2025 at 04:00
Background: Artificial intelligence (AI) is increasingly used in digital health, particularly through large language models (LLMs), to support patient engagement and behavior change. One novel application is the delivery of motivational interviewing (MI), an evidence-based, patient-centered counseling technique designed to enhance motivation and resolve ambivalence around health behaviors. AI tools, including chatbots, mobile apps, and web-based agents, are being developed to simulate MI techniques at scale. While these innovations are promising, important questions remain about how faithfully AI systems can replicate MI principles or achieve meaningful behavioral impact. Objective: This scoping review aimed to summarize existing empirical studies evaluating AI-driven systems that apply MI techniques to support health behavior change. Specifically, we examined the feasibility of these systems; their fidelity to MI principles; and their reported behavioral, psychological, or engagement outcomes. Methods: We systematically searched PubMed, Embase, Scopus, Web of Science, and Cochrane Library for empirical studies published between January 1, 2018, and February 25, 2025. Eligible studies involved AI-driven systems using natural language generation, understanding, or computational logic to deliver MI techniques to users targeting a specific health behavior. We excluded studies using AI solely for training clinicians in MI. Three independent reviewers screened and extracted data on study design, AI modality and type, MI components, health behavior focus, MI fidelity assessment, and outcome domains. Results: Of the 1001 records identified, 15 (1.5%) met the inclusion criteria. Of these 15 studies, 6 (40%) were exploratory feasibility or pilot studies, and 3 (20%) were randomized controlled trials. AI modalities included rule-based chatbots (9/15, 60%), LLM-based systems (4/15, 27%), and virtual or mobile agents (2/15, 13%). Targeted behaviors included smoking cessation (6/15, 40%), substance use (3/15, 20%), COVID-19 vaccine hesitancy, type 2 diabetes self-management, stress, mental health service use, and opioid use during pregnancy. Of the 15 studies, 13 (87%) reported positive findings on feasibility or user acceptability, while 6 (40%) assessed MI fidelity using expert review or structured coding, with moderate to high alignment reported. Several studies found that users perceived the AI systems as judgment free, supportive, and easier to engage with than human counselors, particularly in stigmatized contexts. However, limitations in empathy, safety transparency, and emotional nuance were commonly noted. Only 3 (20%) of the 15 studies reported substantially significant behavioral changes. Conclusions: AI systems delivering MI show promise for enhancing patient engagement and scaling behavior change interventions. Early evidence supports their usability and partial fidelity to MI principles, especially in sensitive domains. However, most systems remain in early development, and few have been rigorously tested. Future research should prioritize randomized evaluations; standardized fidelity measures; and safeguards for LLM safety, empathy, and accuracy in health-related dialogue. Trial Registration: OSF Registries 10.17605/OSF.IO/G9N7E; https://osf.io/g9n7e
Received — 15 September 2025 ⏭ Favourites
Received — 8 September 2025 ⏭ Favourites

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

✇Omics In Lung
By: Zijian Zhang · Xiang Wang · Chenyang Li · Robert Taylor Ripley · Jia Wu · Jianjun Zhang · Christopher I Amos · Chao Cheng
5 September 2025 at 18:00

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zijian Zhang · Xiang Wang · Chenyang Li · Robert Taylor Ripley · Jia Wu · Jianjun Zhang · Christopher I Amos · Chao Cheng
5 September 2025 at 18:00

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

Received — 5 September 2025 ⏭ Favourites

Scalable generation and functional classification of genetic variants in inborn errors of immunity to accelerate clinical diagnosis and treatment

✇Cell
By: Zachary H. Walsh · Chris J. Frangieh · Neeharika Kothapalli · Jay Levy · Clarissa K. Heck · Johannes C. Melms · Ron S. Gejman · Parin Shah · Jared M. Pollard · Akul Naik · Sarah L. Grauman · Lei Haley Huang · Ashley Lee · Dusan Bogunovic · Joshua D. Milner · Benjamin Izar
20 June 2025 at 08:00
In lieu of traditional genetic variant testing approaches, an approach using scalable variant classification in primary human T cells with a clinically relevant readout can inform rapid diagnosis and treatment of inborn errors of immunity.
Received — 3 September 2025 ⏭ Favourites

Deep hierarchical subtyping of multi-organ systemic sclerosis trajectories - a EUSTAR study

✇npj Digital Medicine
By: Cécile Trottet · Manuel Schürch · Ahmed Allam · Liubov Petelytska · Ivan Castellví · Radim Bečvář · Jeska de Vries-Bouwstra · Florenzo Iannone · Patricia Carreira · Marie-Elise Truchetet · Giovanna Cuomo · Elena Rezus · Francesco Paolo Cantatore · Carmen Pilar Simeón-Aznar · Magda Parvu · Marta Dzhus · Oliver Distler · Anna-Maria Hoffmann-Vold · Michael Krauthammer · Silvia Bellando-Randone · Ulrich Andreas Walker · Maurizio Cutolo · Simona Rednic · Yannick Allanore · Carlomaurizio Montecucco · Srdjan Novak · Gábor Kumánovics · Przemyslaw Kotyla · Elisabetta Zanatta · Katja Perdan Pirkmajer · Gianluca Moroncini · Paolo Airó · Mislav Radic · Alexandra Balbir-Gurman · Nico Hunzelmann · Luca Idolazzi · Josko Mitrovic · Christopher Denton · Madelon Vonk · Jelena Colic · Joerg Henes · Ivan Foeldvari · Gianluigi Bajocchi · Tânia Santiago · Bojana Stamenkovic · Maria De Santis · Claudia Ickinger · Lidia P. Ananieva · Klaus Sondergaard · Gabriella Szucs · David Launay · Valeria Riccieri · Andra Balanescu · Ana Maria Gheorghiu · Christina Bergmann · Luc Moutho
1 September 2025 at 08:00

npj Digital Medicine, Published online: 01 September 2025; doi:10.1038/s41746-025-01962-y

Deep hierarchical subtyping of multi-organ systemic sclerosis trajectories - a EUSTAR study
Received — 2 September 2025 ⏭ Favourites
Received — 29 August 2025 ⏭ Favourites

GeneBits: ultra-sensitive tumour-informed ctDNA monitoring of treatment response and relapse in cancer patients

✇MRD
By: Julian Broche · Olga Kelemen · Aishwarya Sekar · Leon Schütz · Francesc Muyas · Andrea Forschner · Christopher Schroeder · Stephan Ossowski
27 August 2025 at 18:00

J Transl Med. 2025 Aug 27;23(1):964. doi: 10.1186/s12967-025-06993-3.

ABSTRACT

BACKGROUND: Circulating tumour DNA (ctDNA) in liquid biopsies has emerged as a powerful biomarker in cancer patients. Its relative abundance in cell-free DNA serves as a proxy for the overall tumour burden. Here we present GeneBits, a method for cancer therapy monitoring and relapse detection. GeneBits employs tumour-informed enrichment panels targeting 20-100 somatic single-nucleotide variants (SNVs) in plasma-derived DNA, combined with ultra-deep sequencing and unique molecular barcoding. In conjunction with the newly developed computational method umiVar, GeneBits enables accurate detection of molecular residual disease and early relapse identification.

RESULTS: To assess the performance of GeneBits and umiVar, we conducted benchmarking experiments using three different commercial cell-free DNA reference standards. These standards were tested with targeted next-generation sequencing (NGS) workflows from both IDT and Twist, allowing us to evaluate the consistency and accuracy of our approach across different oligo-enrichment strategies. GeneBits achieved comparable depth of coverage across all target sites, demonstrating robust performance independent of the enrichment kit used. For duplex reads with ≥ 4x UMI-family size, umiVar achieved exceptionally low error rates, ranging from 7.4×10-7 to 7.5×10-5. Even when including mixed consensus reads (duplex & simplex), error rates remained low, between 6.1×10-6 and 9×10-5. Furthermore, umiVar enabled variant detection at a limit of detection as low as 0.0017%, with no false positive calls in mutation-free reference samples. In a reanalysed melanoma cohort, variant allele frequency kinetics closely mirrored imaging results, confirming the clinical relevance of our method.

CONCLUSION: GeneBits and umiVar enable highly accurate therapy and relapse monitoring in plasma as well as identification of molecular residual disease within four weeks of tumour surgery or biopsy. By leveraging small, tumour-informed sequencing panels, GeneBits provides a targeted, cost-effective, and scalable approach for ctDNA-based cancer monitoring. The benchmarking experiments using multiple commercial cell-free DNA reference standards confirmed the high sensitivity and specificity of GeneBits and umiVar, making them valuable tools for precision oncology. UmiVar is available at https://github.com/imgag/umiVar .

PMID:40866952 | PMC:PMC12382282 | DOI:10.1186/s12967-025-06993-3

Received — 14 August 2025 ⏭ Favourites
Received — 1 August 2025 ⏭ Favourites

NAVIGATOR: A regional multimodal imaging biobank initiative powered by AI tools for precision medicine in oncology

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Gayane Aghakhanyan · Andrea Barucci · Maria Antonietta Pascali · Massimiliano Assante · Giulio Bagnacci · Elena Bertelli · Francesca Pia Caputo · Maria Emanuela Cuibari · Emanuele Carlini · Roberto Carpi · Claudia Caudai · Dania Cioni · Sara Colantonio · Valentina Colcelli · Andrea Dell'Amico · Vittoria Del Vecchio · Domenico Di Gangi · Lorenzo Faggioni · Valentina Formica · Roberto Francischello · Luca Frosini · Christina Kotsa · Giuseppe Lipari · Paolo Manghi · Vito Di Martino · Chiara Marzi · Maria Antonietta Mazzei · Francesco Mangiacrapa · Nunzia Di Meglio · Vittorio Miele · Elisa Molinaro · Fabiola Paiar · Pasquale Pagano · Giancarlo Panichi · Filippo Pasquinelli · Biagio Peccerillo · Armando Perrella · Tommaso Piccioli · Alfredo Oliviero · Matteo Olivoni · Davide Rucci · Marco Tampucci · Lorenzo Tumminello · Federico Volpini · Alberto Zanuzzi · Salvatore Claudio Fanni · Emanuele Neri · NAVIGATOR Consortium Group
31 July 2025 at 18:00

Eur J Radiol. 2025 Jul 22;191:112327. doi: 10.1016/j.ejrad.2025.112327. Online ahead of print.

ABSTRACT

The NAVIGATOR project established an Italian regional imaging biobank and interactive research platform designed to support precision oncology through the integration of multimodal imaging, clinical, and omics data. The platform goes beyond a static repository, offering a secure Virtual Research Environment (VRE) where users can upload data, test AI algorithms, and execute complete analytical pipelines. The platform incorporates artificial intelligence (AI)-driven radiomics and deep learning methodologies to enable biomarker extraction, disease stratification, and predictive modeling. This manuscript presents the development and implementation of the NAVIGATOR infrastructure, including its data governance framework, ethical and legal considerations, and application to three oncological use cases: prostate, rectal, and gastric cancers. To date, the biobank has collected imaging and clinical data from over 700 patients across these cohorts. AI models were deployed within a dedicated VRE to facilitate image analysis, feature extraction, and classification tasks. The project addresses critical challenges related to data harmonization, regulatory compliance, privacy safeguards and fairness in AI systems. NAVIGATOR demonstrates the feasibility of integrating AI methodologies within imaging biobanks and provides a scalable framework to advance oncological research and support clinical decision-making.

PMID:40743874 | DOI:10.1016/j.ejrad.2025.112327

Received — 24 July 2025 ⏭ Favourites

High-Sensitive Spatial Proteomics for Pancreatic Cancer Progression Analysis

✇(Multiomics OR Omics) AND (Pancreatic)
By: Jongmin Woo · Zhenyu Sun · Yingwei Hu · Trung Alvin Hoàng · Decapite Christine · Smith Katelyn · Singhi Aatur · Randall E Brand · Daniel W Chan · Qing Kay Li · Ralph H Hruban · Hui Zhang
14 July 2025 at 18:00

bioRxiv [Preprint]. 2025 May 5:2025.05.01.651678. doi: 10.1101/2025.05.01.651678.

ABSTRACT

Pancreatic cancer remains as one of the most challenging malignancies to diagnose and treat due to the late development of symptoms and limited early diagnostic options. Intraductal papillary mucinous neoplasms (IPMNs) are non-invasive precursors to invasive pancreatic ductal adenocarcinoma (PDAC)and an understanding of the changes in patterns of protein expression that accompany the progression from normal ductal (ND) cell, to IPMN to PDAC may provide avenues for improved earlier detection. In this study, we present an optimized spatial tissue proteomics workflow, termed SP-Max (Spatial Proteomics Optimized for Maximum Sensitivity and Reproducibility in Minimal Sample), designed to maximize protein recovery and quantification from limited laser micro dissected (LMD) samples. Our workflow enabled the identification of more than 6,000 proteins and the quantification of over 5,200 protein groups from FFPE tissue contours of pancreatic tissues. Comparative analyses across ND, IPMN, and PDAC revealed critical molecular differences in protein pathways and potential markers of progression. SP-Max provides a systematic, reproducible approach that significantly enhances our ability to study precancerous lesions and cancer progression in pancreatic tissues at unprecedented resolution.

PMID:40654937 | PMC:PMC12247709 | DOI:10.1101/2025.05.01.651678

Received — 3 July 2025 ⏭ Favourites

The Somatic Mosaicism across Human Tissues Network

✇Nature - Issue - nature.com science feeds
By: Tim H. H. Coorens · Ji Won Oh · Yujin Angelina Choi · Nam Seop Lim · Boxun Zhao · Adam Voshall · Alexej Abyzov · Lucinda Antonacci-Fulton · Samuel Aparicio · Kristin G. Ardlie · Thomas J. Bell · James T. Bennett · Bradley E. Bernstein · Thomas G. Blanchard · Alan P. Boyle · Jason D. Buenrostro · Kathleen H. Burns · Fei Chen · Rui Chen · Sangita Choudhury · Harsha V. Doddapaneni · Evan E. Eichler · Gilad D. Evrony · Melissa A. Faith · Thomas G. Fazzio · Robert S. Fulton · Manuel Garber · Nils Gehlenborg · Soren Germer · Gad Getz · Richard A. Gibbs · Raquel G. Hernandez · Fulai Jin · Jan O. Korbel · Dan A. Landau · Heather A. Lawson · Niall J. Lennon · Heng Li · Yan Li · Po-Ru Loh · Gabor Marth · Michael J. McConnell · Ryan E. Mills · Stephen B. Montgomery · Pradeep Natarajan · Peter J. Park · Rahul Satija · Fritz J. Sedlazeck · Diane D. Shao · Hui Shen · Andrew B. Stergachis · Hunter R. Underhill · Alexander E. Urban · Melissa W. VonDran · Christopher A. Walsh · Ting Wang · Tao P. Wu · Chenghang Zong · Eunjung Alice Lee · Flora M. Vaccarino · Richard S. Conroy · Brionn
2 July 2025 at 08:00

Nature, Published online: 02 July 2025; doi:10.1038/s41586-025-09096-7

The Somatic Mosaicism across Human Tissues Network aims to create a reference catalogue of somatic mosaicism across different tissues and cells within individuals.
Received — 30 June 2025 ⏭ Favourites

Obesity influences the biological response to injury: a multi-omics analysis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Terry R Schaid · Christopher Erickson · Angelo D'Alessandro · Kirk Hansen · Otto Thielen · William Hallas · Lauren Gallagher · Alexis Cralley · Ian LaCroix · Benjamin Ramser · Benjamin Stocker · Sanchayita Mitra · Preston Stafford · Monica Dzieciatkowska · Christopher Silliman · Mitchell Cohen
27 June 2025 at 18:00

Eur J Trauma Emerg Surg. 2025 Jun 27;51(1):238. doi: 10.1007/s00068-025-02922-7.

ABSTRACT

PURPOSE: Obesity is a prevalent disease, but its influence on post-injury biology remains unclear. In this study, we aimed to characterize the independent effect of obesity on the proteomic and metabolomic signatures of trauma.

METHODS: Plasma was obtained on arrival from injured patients at a Level 1 Trauma Center and analyzed with modern mass spectrometry-based proteomics and metabolomics. Samples obtained after start of transfusion were excluded. Patients were stratified by "obesity" (body mass index [BMI]≥30 kg/m2) vs. "no obesity" (BMI < 30 kg/m2). In sub-group analyses, patients were sub-stratified by Low Injury/Low Shock (ISS < 15, base excess [BE]≥-6mEq/L) and High Injury/High Shock (ISS≥15, BE<-6). Multiple regression was used to adjust the omics data for significant covariates prior to performing ome-wide analyses.

RESULTS: There were 183 patients included (48 [26%] with obesity and 135 [74%] without). After covariate-adjustment, multiple proteins and metabolites were correlated with ISS and/or BE and were significantly different from Low Injury/Low Shock to High Injury/High Shock only in patients with obesity. This obesity-specific omics response to injury was characterized by increased inflammation, hypercoagulability, altered nitrogen metabolism, and mitochondrial dysfunction. Patients with obesity also exhibited excessive injury-provoked tissue destruction and organ damage compared to patients without obesity. In injury severity-adjusted analyses, the obesity signature consistently displayed markers of hemolysis, likely reflecting a pre-injury hemolytic propensity.

CONCLUSION: Obesity is independently associated with altered post-injury biology, which likely underlies unique pathology in trauma patients with obesity. Identifying this aberrant response to injury is the first step in developing personalized therapies for this patient population.

PMID:40576654 | DOI:10.1007/s00068-025-02922-7

Received — 12 May 2025 ⏭ Favourites
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  • Biomarkers in adjuvant and neoadjuvant treatment of melanoma Julian Kött · Christoffer Gebhardt
    Dermatologie (Heidelb). 2025 Jun;76(6):361-364. doi: 10.1007/s00105-025-05506-z. Epub 2025 May 7.ABSTRACTBACKGROUND: Personalized treatment of melanoma is becoming increasingly more important. Biomarkers offer the possibility of controlling treatment more precisely and reducing side effects.OBJECTIVE: The aim of this text is to provide an overview of current tissue-based, blood-based and radiological biomarkers and their clinical application in melanomas.MATERIAL AND METHODS: A literature resear
     
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Biomarkers in adjuvant and neoadjuvant treatment of melanoma

✇MRD
By: Julian Kött · Christoffer Gebhardt
7 May 2025 at 18:00

Dermatologie (Heidelb). 2025 Jun;76(6):361-364. doi: 10.1007/s00105-025-05506-z. Epub 2025 May 7.

ABSTRACT

BACKGROUND: Personalized treatment of melanoma is becoming increasingly more important. Biomarkers offer the possibility of controlling treatment more precisely and reducing side effects.

OBJECTIVE: The aim of this text is to provide an overview of current tissue-based, blood-based and radiological biomarkers and their clinical application in melanomas.

MATERIAL AND METHODS: A literature research and analysis of current studies on biomarkers in adjuvant and neoadjuvant treatment of melanomas were carried out and relevant congress contributions were additionally included.

RESULTS: Tissue-based programmed cell death 1 ligand 1 (PD-L1) expression, interferon gamma (IFNγ) signature, gene expression profiles (GEP) and tumor mutational burden (TMB) are of prognostic and predictive relevance. Blood-based circulating tumor DNA (ctDNA) in the sense of a liquid biopsy should be emphasized as a personalized biomarker for longitudinal tracking during treatment or aftercare. Positron emission tomography computed tomography (PET-CT) and body composition enable an improved assessment of treatment efficiency. There are currently no data from prospective validation studies on these biomarkers; initial data from the NivoMela study are awaited.

CONCLUSION: The combination of tissue-based, blood-based and radiological biomarkers in terms of multiparametric approaches is promising but further prospective validation is needed for broad clinical use. These are currently not comprehensively implemented in the clinical routine in centers or in remuneration procedures.

PMID:40335648 | DOI:10.1007/s00105-025-05506-z

Received — 13 April 2025 ⏭ Favourites

Cross-sectional and longitudinal association of seven DNAm-based predictors with metabolic syndrome and type 2 diabetes

✇Most Recent Articles: Clinical Epigenetics
By: Suet Mei Chew · Alexander Teumer · Pamela R. Matías‑García · Christian Gieger · Juliane Winckelmann · Karsten Suhre · Christian Herder · Wolfgang Rathmann · Annette Peters and Melanie Waldenberger
8 April 2025 at 08:00
To date, various epigenetic clocks have been constructed to estimate biological age, most commonly using DNA methylation (DNAm). These include “first-generation” clocks such as DNAmAgeHorvath and “second-gener...
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