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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

Development of a Data-Based Method for Predicting Nursing Workload in an Acute Care Hospital: Methodological Study

✇Journal of Medical Internet Research
By: Mark McMahon · Sylvie Plate · Tobias Herz · Gabi Brenner · Michael Kleinknecht-Dolf · Michael Krauthammer
17 September 2025 at 03:45
Background: Determining effective nurse staffing levels is crucial for ensuring quality patient care and operational efficiency within hospitals. Traditional workload prediction methods often rely on professional judgment or simple volume-based approaches, which can be inaccurate. Machine learning offers a promising avenue for more data-driven and precise predictions, by using historical nursing workload data to forecast future patient care requirements, which could help with staff planning while also improving patient outcomes and nurse well-being. Objective: This methodological study aims to use nursing activity data, specifically LEP (Leistungserfassung in der Pflege; “documentation of nursing activities”), to predict future workload requirements using machine learning techniques. Methods: We conducted a retrospective observational study at the University Hospital of Zürich, using nursing workload data for inpatients across eight wards, collected between 2017 and 2021. Data were transformed to represent nursing workload per ward and shift, with three shifts per day. Variables used in modeling included historical workload trends, patient characteristics, and upcoming operations. Machine learning models, including linear regression variants and tree-based methods (Random Forest and XGBoost), were trained and tested on this dataset to predict workload 72 hours in advance, on a shift-by-shift basis. Model performance was assessed using mean absolute error (MAE) and mean absolute percentage error (MAPE), and results were compared against a baseline of assuming no change in workload from the time of prediction. Prediction accuracy was further evaluated by categorizing future workload changes into decreased, similar, or increased workload relative to current shift levels. Results: Our findings demonstrate that machine learning models consistently outperform the baseline across all wards. The best-performing model was the Lasso Regression model, which achieved an average improvement in accuracy of 25.0% compared to the baseline. When used to predict upcoming changes in workload levels, the model achieved strong classification performance, giving an average AUROC of 0.79 and precision values between 66.2% and 75.3%. Crucially, the model severely misclassified—predicting an upcoming increase as a decrease, and vice versa—in just 0.17% of cases, highlighting potential reliability for using the model in practice. Key variables identified as important for predictions include historical shift workload averages and overall ward workload trends. Conclusions: This study suggests the potential of machine learning to enhance nurse workload prediction, while highlighting the need for refinement. Limitations due to potential discrepancies between recorded nursing activities and the actual workload highlight the need for further investigation into data quality. To maximize impact, future research should focus on: 1) utilizing more diverse data, 2) more advanced machine learning architecture that perform time-series modelling, 3) addressing data quality concerns, and 4) conducting controlled trials for real-world evaluation.
Received — 16 September 2025 ⏭ Favourites

A Practical Guide to Using Futures Methods in Health Care: Approaches, Applications, and Case Studies

✇Journal of Medical Internet Research
By: Bertalan Mesko · Tamás Kristóf · Pranavsingh Dhunnoo · Nóra Árvai · Gellért Katonai
16 September 2025 at 21:00
Researchers and health care institutions have increasingly applied structured futures methods—such as the futures wheel, scenario analysis, forecasting, and horizon scanning—to systematically explore, generate, and prepare for multiple possible futures. However, discussions around the future of medicine, specialties, or therapeutic areas have often relied on the subjective opinions or perspectives of key opinion leaders rather than on future strategies, policies, visions, and scenarios that are grounded in rigorous and established methods. This underscores the need for futures methods to be widely adopted and effectively incorporated into both medical practice and health care policymaking. Integrating structured foresight techniques into strategic planning enables clinicians and policymakers to transition from reactive decision-making to proactive, plausible approaches that shape a more resilient and adaptive health care system. Our goal with this paper is to provide a methodological guide that is supported by case studies, demonstrating how futures methods can be systematically applied in health care. By offering practical examples, we intend to empower medical professionals, health care leaders, researchers, patients, and policymakers with the tools to anticipate and navigate future challenges and opportunities more effectively.

Primary care detection of Alzheimer’s disease using a self-administered digital cognitive test and blood biomarkers

✇Nature Medicine
By: Pontus Tideman · Linda Karlsson · Olof Strandberg · Susanna Calling · Ruben Smith · Patrik Midlöv · Philip B. Verghese · Joel B. Braunstein · Niklas Mattsson-Carlgren · Erik Stomrud · Sebastian Palmqvist · Oskar Hansson
15 September 2025 at 08:00

Nature Medicine, Published online: 15 September 2025; doi:10.1038/s41591-025-03965-4

A brief, self-administered digital cognitive test, in combination with a blood test, accurately detects clinical Alzheimer’s disease in primary care.
Received — 15 September 2025 ⏭ Favourites

Prognostic Value of Circulating Tumor DNA in HR+/HER2- Stage I-III Breast Cancer: A Systematic Review

✇MRD
By: Ismail Ajjawi · Mariya Rozenblit · Alejandro Rios-Hoyo · Maryam B Lustberg
13 September 2025 at 18:00

Cancers (Basel). 2025 Aug 29;17(17):2831. doi: 10.3390/cancers17172831.

ABSTRACT

Background: Hormone receptor-positive (HR+), HER2-negative breast cancer accounts for the majority of breast cancer diagnoses. While outcomes have improved with neoadjuvant and adjuvant therapies, the risk of late recurrence persists, and there remains a critical need for reliable biomarkers to guide prognosis and post-treatment surveillance. Circulating tumor DNA (ctDNA), detectable via liquid biopsy, has emerged as a promising tool for monitoring minimal residual disease and predicting survival outcomes. This systematic review evaluates the association between ctDNA detection during neoadjuvant or adjuvant treatment and survival outcomes in early-stage HR+/HER2- breast cancer. Methods: This systematic review was conducted in accordance with PRISMA guidelines. A comprehensive literature search of Ovid MEDLINE and Embase was conducted to identify studies published through 3 May 2024 that evaluated ctDNA as a prognostic biomarker in stage I-III HR+/HER2- breast cancer. We included studies reporting recurrence-free survival, invasive disease-free survival, or overall survival and excluded non-original studies, conference abstracts, and non-English articles. Data extraction and qualitative synthesis were performed, and the risk of bias was qualitatively assessed across studies. No review protocol was registered. Results: Eleven studies comprising 1644 patients met the inclusion criteria. In the neoadjuvant setting, ctDNA positivity prior to treatment initiation was associated with inferior survival outcomes. In the adjuvant setting, detection of ctDNA during or after treatment was consistently linked to poorer recurrence-free and invasive disease-free survival. Across studies, ctDNA detection was a significant negative prognostic marker. Conclusions: This systematic review supports the prognostic value of ctDNA in HR+/HER2- early-stage breast cancer. Limitations include small sample sizes, observational study designs, and heterogeneity in ctDNA assays. Standardization of ctDNA testing methods and further prospective trials are needed to validate its clinical utility and explore its potential role in guiding therapeutic interventions.

PMID:40940926 | PMC:PMC12427406 | DOI:10.3390/cancers17172831

Functions of the global health system in a new era

✇Nature Medicine
By: Kumanan Rasanathan · Keith Cloete · Githinji Gitahi · Octavio Gómez-Dantés · Diah Saminarsih · Soumya Swaminathan · Amirhossein Takian · John-Arne Røttingen
11 September 2025 at 08:00

Nature Medicine, Published online: 11 September 2025; doi:10.1038/s41591-025-03936-9

In an irrevocably changed landscape, reform of the global health system needs to answer key questions on functions, what should be delivered in different contexts and at different levels, and how the system should operate.

Interventions Based on Biofeedback Systems to Improve Workers’ Psychological Well-Being, Mental Health, and Safety: Systematic Literature Review

✇Journal of Medical Internet Research
By: Simão Ferreira · Matilde A Rodrigues · Catarina Mateus · Pedro Pereira Rodrigues · Nuno Barbosa Rocha
13 September 2025 at 04:00
Background: In modern, high-speed work settings, the significance of mental health disorders is increasingly acknowledged as a pressing health issue, with potential adverse consequences for organizations, including reduced productivity and increased absenteeism. Over the past few years, various mental health management solutions, such as biofeedback applications, have surfaced as promising avenues to improve employees’ mental well-being. However, most studies on these interventions have been conducted in controlled laboratory settings. Objective: This review aimed to systematically identify and analyze studies that implemented biofeedback-based interventions in real-world occupational settings, focusing on their effectiveness in improving psychological well-being and mental health. Methods: A systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed and EBSCO databases for studies published between 2012 and 2024. Inclusion criteria were original peer-reviewed studies that focused on employees and used biofeedback interventions to improve mental health or prevent mental illness. Exclusion criteria included nonemployee samples, lack of a description of the intervention, and low methodological quality (assessed using the Physiotherapy Evidence Database [PEDro] checklist). Data were extracted on study characteristics, intervention type, physiological and self-reported outcomes, and follow-up measures. Risk of bias was assessed, and VOSviewer was used to visualize the distribution of research topics. Results: A total of 9 studies met the inclusion criteria. The interventions used a range of delivery methods, including traditional biofeedback, mobile apps, mindfulness techniques, virtual reality, and cerebral blood flow monitoring. Most studies focused on breathing techniques to regulate physiological responses (eg, heart rate variability and respiratory sinus arrhythmia) and showed reductions in stress, anxiety, and depressive symptoms. Mobile and app-directed interventions appeared particularly promising for improving resilience and facilitating recovery after stress. Of the 9 studies, 8 (89%) reported positive outcomes, with 1 (11%) study showing initial increases in stress due to logistical limitations in biofeedback access. Sample sizes were generally small, and long-term follow-up data were limited. Conclusions: Biofeedback interventions in workplace settings show promising short-term results in reducing stress and improving mental health, particularly when incorporating breathing techniques and user-friendly delivery methods such as mobile apps. However, the field remains underexplored in occupational contexts. Future research should address adherence challenges, scalability, cost-effectiveness, and long-term outcomes to support broader implementation of biofeedback as a sustainable workplace mental health strategy.

The Impact of Artificial Intelligence on Lung Cancer Diagnosis and Personalized Treatment

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yaman Ayasa · Diyar Alajrami · Mayar Idkedek · Kareem Tahayneh · Firas Abu Akar
13 September 2025 at 18:00

Int J Mol Sci. 2025 Aug 31;26(17):8472. doi: 10.3390/ijms26178472.

ABSTRACT

Lung cancer is the leading cause of cancer mortality globally, despite the advancements in screening and management. Survival rates for lung cancer remain suboptimal, largely due to late-stage diagnoses and tumor heterogeneity. Recent advancements in artificial intelligence and radiomics provide a promising outlook for lung cancer screening, diagnosis, personalized treatment, and prognosis. These advances use large-scale clinical and imaging datasets that help identify patterns and predictive features that may be missed by human interpretation. Artificial intelligence tools hold the potential to take clinical decision-making to another level, thus improving patient outcomes. This review summarizes current evidence on the applications, challenges, and future directions of artificial intelligence (AI) in lung cancer care, with an emphasis on early diagnosis and personalized treatment. We examine recent developments in AI-driven approaches, including machine learning and deep neural networks, applied to imaging (radiomics), histopathology, biomarker analysis, and multi-omic data integration. AI-based models demonstrate promising performance in early detection, risk stratification, molecular profiling (e.g., programmed death-ligand 1 (PD-L1) and epidermal growth factor receptor (EGFR) status), and outcome prediction. These tools may enhance diagnostic accuracy, optimize therapeutic decisions, and ultimately improve patient outcomes. However, significant challenges remain, including model heterogeneity, limited external validation, generalizability issues, and ethical concerns related to transparency and clinical accountability. AI holds transformative potential for lung cancer care but requires further validation, standardization, and integration into clinical workflows. Multicenter collaborations, regulatory frameworks, and explainable AI models will be essential for successful clinical adoption.

PMID:40943394 | PMC:PMC12429163 | DOI:10.3390/ijms26178472

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progression

✇(Multiomics OR Omics) AND (Pancreatic)
By: Rebecca Melton · Sara Jimenez · Weston Elison · Luca Tucciarone · Abigail Howell · Gaowei Wang · Denise Berti · Elisha Beebe · Michael Miller · Chun Zeng · Carolyn McGrail · Kennedy VanderStel · Katha Korgaonkar · Ruth Elgamal · Hannah Mummey · Joshua Chiou · Emily Griffin · Irina Kusmartseva · Mark Atkinson · Sebastian Preissl · Fabian J Theis · Maike Sander · Kyle J Gaulton
10 September 2025 at 18:00

Sci Adv. 2025 Sep 12;11(37):eady0080. doi: 10.1126/sciadv.ady0080. Epub 2025 Sep 10.

ABSTRACT

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here, we performed single-nucleus multiomics and spatial transcriptomics in up to 32 nondiabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine subtypes. β, Acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in β cells. Last, single-cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting β cell regulation. Overall, these results revealed drivers of T1D in the pancreas, which form the basis for therapeutic targets for disease prevention.

PMID:40929272 | PMC:PMC12422192 | DOI:10.1126/sciadv.ady0080

The Impact of Artificial Intelligence on Lung Cancer Diagnosis and Personalized Treatment

✇Omics In Lung
By: Yaman Ayasa · Diyar Alajrami · Mayar Idkedek · Kareem Tahayneh · Firas Abu Akar
13 September 2025 at 18:00

Int J Mol Sci. 2025 Aug 31;26(17):8472. doi: 10.3390/ijms26178472.

ABSTRACT

Lung cancer is the leading cause of cancer mortality globally, despite the advancements in screening and management. Survival rates for lung cancer remain suboptimal, largely due to late-stage diagnoses and tumor heterogeneity. Recent advancements in artificial intelligence and radiomics provide a promising outlook for lung cancer screening, diagnosis, personalized treatment, and prognosis. These advances use large-scale clinical and imaging datasets that help identify patterns and predictive features that may be missed by human interpretation. Artificial intelligence tools hold the potential to take clinical decision-making to another level, thus improving patient outcomes. This review summarizes current evidence on the applications, challenges, and future directions of artificial intelligence (AI) in lung cancer care, with an emphasis on early diagnosis and personalized treatment. We examine recent developments in AI-driven approaches, including machine learning and deep neural networks, applied to imaging (radiomics), histopathology, biomarker analysis, and multi-omic data integration. AI-based models demonstrate promising performance in early detection, risk stratification, molecular profiling (e.g., programmed death-ligand 1 (PD-L1) and epidermal growth factor receptor (EGFR) status), and outcome prediction. These tools may enhance diagnostic accuracy, optimize therapeutic decisions, and ultimately improve patient outcomes. However, significant challenges remain, including model heterogeneity, limited external validation, generalizability issues, and ethical concerns related to transparency and clinical accountability. AI holds transformative potential for lung cancer care but requires further validation, standardization, and integration into clinical workflows. Multicenter collaborations, regulatory frameworks, and explainable AI models will be essential for successful clinical adoption.

PMID:40943394 | PMC:PMC12429163 | DOI:10.3390/ijms26178472

Received — 9 September 2025 ⏭ Favourites

Liquid biopsy- A pivotal test to help navigate clinical decisions at a precision center in India!

✇MRD
By: Sewanti Limaye · Aditya Shreenivas · Darshana Patil · Janani Sambath · Irene A George · Anjali Parab · Aakriti Datta · Punita Jain · Shambhavi Singh · Chetan Madre · Pritam Kataria · Darshit Shah · Niyati Shah · Ruturaj Deshpande · Rajan Datar · Prashant Kumar
8 September 2025 at 18:00

J Liq Biopsy. 2025 Aug 9;9:100323. doi: 10.1016/j.jlb.2025.100323. eCollection 2025 Sep.

ABSTRACT

Liquid biopsy, specifically circulating tumor DNA (ctDNA) analysis, has emerged as a transformative tool in precision oncology, providing real-time, minimally invasive characterizations of the tumor and tumor dynamics. While tissue biopsy is a critical tool for baseline diagnosis of malignancy, it is often limited by sampling constraints and an inability to capture tumor heterogeneity. In this study, we explored the clinical utility of serial ctDNA testing in guiding therapeutic decisions across a cohort of 30 patients with diverse solid tumors. Our real-world analysis demonstrates that ctDNA profiling meaningfully influenced treatment escalation, de-escalation, disease monitoring, and early relapse prediction. Cases where ctDNA positivity indicated minimal residual disease prompted timely escalation of therapy, while ctDNA clearance allowed safe treatment de-intensification, minimizing toxicity without compromising outcomes. Longitudinal ctDNA monitoring provided a dynamic, non-invasive method for assessing treatment response and detecting recurrence months before radiological progression. Our study highlights the potential of integrating liquid biopsy into routine clinical practice to enable dynamic treatment monitoring, early detection of therapeutic resistance, and more informed, personalized decision-making across various cancer types.

PMID:40919127 | PMC:PMC12409318 | DOI:10.1016/j.jlb.2025.100323

The WHO global landscape of cancer clinical trials

✇Nature Medicine
By: Raffaella Casolino · Lambed Tatah · Sarah Charnaud · Marilina Santero · Andrè Ilbawi · Anna Laura Ross
9 September 2025 at 08:00

Nature Medicine, Published online: 09 September 2025; doi:10.1038/s41591-025-03926-x

This Review of the WHO’s International Clinical Trials Registry Platform presents a snapshot of the global cancer trial landscape and provides critical empirical evidence to inform policy, practice and investment.

Building the world’s first truly global medical foundation model

✇Nature Medicine
By: Yih Chung Tham · Jocelyn Hui Lin Goh · Paul Nderitu · Yukun Zhou · An Ran Ran · Sahana Srinivasan · Gabriel Dawei Yang · Gatera Fiston Kitema · Polly Rawlinson · Hongyang Jiang · Ke Zou · Carol Y. Cheung · Pearse A. Keane · Hatem M. Abdelakher · Eberechukwu Achigbu · Referano Agustiawan · Islam Sh Ahmed · Shehab Ahmad Al-Abed · Minhaj Nur Alam · Sura Yousef Alaroud · Abdulrahman F. Albloushi · Tsedeke Asaminew Alemu · Nchifor Alice · Faisal Alkathery · Otim Ambrose · Kwesi N. Amissah-Arthur · Damara Andalia · Fares Antaki · Louis Arnould · Simon Arunga · Olufemi Emmanuel Babalola · Vilma Jurate Balciuniene · Covadonga Bascaran · Ciara Bergin · Carolina Bernal-Morales · Ansgar Beuse · Mukharram M. Bikbov · Guzel M. Bikbova · Iryna Blagun · Damir Bosnar · Rupert Bourne · Tasanee Braithwaite · Iva Bulat · Philip Burgess · Matthew J. Burton · Avshalom Caspi · Ling-Ping Cen · Manju Chandran · Robert T. Chang · Kao-Jung Chang · Varun Chaudhary · David Ziyou Chen · Haoyu Chen · Xi Chen · Xinjian Chen · Xuejing Chen · Youxin Chen · Qingyu Chen · Ching-Yu Cheng · Kai Xiong C
8 September 2025 at 08:00

Nature Medicine, Published online: 08 September 2025; doi:10.1038/s41591-025-03859-5

Building the world’s first truly global medical foundation model
  • ✇STAT
  • STAT+: FDA greenlights trial of gene-edited pig kidneys as treatment for end-stage kidney disease Eric Boodman and Megan Molteni
    DOVER, N.H. — Not long after he woke from surgery in June, Bill Stewart made a  pact with his newest organ. He wasn’t sure how long the thing would last. The doctors had been up-front from the get-go: It could be three months or six, one year or four. Still, the uncertainty hit him as he started getting back preliminary lab results, which were okay but left room for improvement. “My pig kidney and I had a little conversation while I was laying there. I just basically said, ‘I’m going to do every
     
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STAT+: FDA greenlights trial of gene-edited pig kidneys as treatment for end-stage kidney disease

✇STAT
By: Eric Boodman and Megan Molteni
8 September 2025 at 20:00

DOVER, N.H. — Not long after he woke from surgery in June, Bill Stewart made a  pact with his newest organ. He wasn’t sure how long the thing would last. The doctors had been up-front from the get-go: It could be three months or six, one year or four. Still, the uncertainty hit him as he started getting back preliminary lab results, which were okay but left room for improvement. “My pig kidney and I had a little conversation while I was laying there. I just basically said, ‘I’m going to do everything I can to make sure that you stay healthy, and I appreciate you doing everything you can to keep me upright and breathing,” Stewart said.

It’s been almost three months, and Stewart is home, back to work, and has even been able to go e-biking on a lakeside trail with his wife, blessedly untethered to the grueling schedules of dialysis for the first time in years, all thanks to a gene-edited Yucatan miniature pig named Lavender. 

Stewart is the most recent recipient of a pig kidney — but chances are, he won’t hold that distinction for long. On Monday, eGenesis, a Cambridge-based biotechnology company, announced that it had been cleared by the Food and Drug Administration to begin a trial of kidneys from donor pigs that have been CRISPR’d to make their organs more human-friendly. Now, Massachusetts researchers will be performing more surgeries like Stewart’s to see whether these animal parts could serve as a lifeline for people with end-stage renal disease.

Continue to STAT+ to read the full story…

© Cheryl Senter for STAT

Antibody–bottlebrush prodrug conjugates for targeted cancer therapy

✇Nature Biotechnology - Issue - nature.com science feeds
By: Bin Liu · Hung V.-T. Nguyen · Yivan Jiang · Aiden X. Wang · Valerie Lensch · Zehao Sun · Zane H. Boyer · Philip A. Raftopoulos · Yutong Dai · Piper L. MacNicol · Yuyan Wang · Nidhi Jyotsana · Wencong Wang · Sachin Bhagchandani · Sanjana Hemdev · Peyton Shieh · Samantha L. Kristufek · Magalie Boucher · Michael Downes · Ronald M. Evans · David W. C. MacMillan · Jeremiah A. Johnson
9 September 2025 at 08:00

Nature Biotechnology, Published online: 09 September 2025; doi:10.1038/s41587-025-02772-z

Antibody–bottlebrush conjugates expand the options for drug cargos compared to antibody–drug conjugates.
Received — 8 September 2025 ⏭ Favourites

Clinical applications of cell-free DNA-based liquid biopsy analysis

✇MRD
By: Shu Yazaki · Momoko Tokura · Hisaki Aiba · Yuki Kojima · Kouya Shiraishi
7 September 2025 at 18:00

Transl Oncol. 2025 Nov;61:102519. doi: 10.1016/j.tranon.2025.102519. Epub 2025 Sep 6.

ABSTRACT

Liquid biopsies, particularly those involving circulating tumor DNA (ctDNA) from patient blood, have emerged as crucial and minimally invasive adjuncts to standard tissue-based testing. ctDNA testing enables the identification of actionable mutations for targeted therapy and can be routinely used when tissue samples are unavailable for genotyping. Compared to tissue-based testing, ctDNA testing has the advantages of capturing spatial or temporal genomic heterogeneity and facilitating repeated assessments. The utility of liquid biopsies extends to multiple clinical applications, including cancer diagnosis, treatment monitoring, and minimal residual disease (MRD) detection. Numerous clinical trials are currently evaluating treatment strategies using ctDNA testing. In particular, the implementation of adjuvant treatment escalation or de-escalation based on MRD detection could dramatically transform future approaches to solid tumor treatment. Various ctDNA assays have been developed, and it is important to understand their strengths and weaknesses for effective clinical applications. Furthermore, ctDNA testing faces several technical challenges, including low sensitivity in detecting copy number alterations and fusions, as well as the possibility of detecting mutations associated with clonal hematopoiesis of indeterminate potential. In this review, we comprehensively discuss the methodologies and recent advancements in cfDNA-based liquid biopsies for cancer patients, covering diagnosis, genomic profiling, and treatment monitoring. Furthermore, we explore clinical trial designs employing ctDNA testing and anticipate forthcoming changes in patient care.

PMID:40915174 | PMC:PMC12450568 | DOI:10.1016/j.tranon.2025.102519

Article: Virtual Panel: How Software Engineers and Team Leaders Can Excel with Artificial Intelligence

✇InfoQ
By: Ben Linders · Courtney Nash · Mandy Gu · Hien Luu
5 September 2025 at 17:00

Artificial intelligence is impacting the individual work of software developers, how professionals work together in teams, and how software teams are being managed. In this panel, we'll discuss how artificial intelligence is reshaping software development, and what mindset and skills are required for software developers and engineering leaders to become adaptable and resilient in the age of AI.

By Ben Linders, Courtney Nash, Mandy Gu, Hien Luu
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