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Received — 19 January 2026 ⏭ Favourites

Japanese AI Agent System on Human Papillomavirus Vaccination: System Design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junyu Liu · Siwen Yang · Dexiu Ma · Qian Niu · Zequn Zhang · Momoko Nagai-Tanima · Tomoki Aoyama
19 January 2026 at 13:00
arXiv:2601.10718v1 Announce Type: new Abstract: Human papillomavirus (HPV) vaccine hesitancy poses significant public health challenges, particularly in Japan where proactive vaccination recommendations were suspended from 2013 to 2021. The resulting information gap is exacerbated by misinformation on social media, and traditional ways cannot simultaneously address individual queries while monitoring population-level discourse. This study aimed to develop a dual-purpose AI agent system that provides verified HPV vaccine information through a conversational interface while generating analytical reports for medical institutions based on user interactions and social media. We implemented a system comprising: a vector database integrating academic papers, government sources, news media, and social media; a Retrieval-Augmented Generation chatbot using ReAct agent architecture with multi-tool orchestration across five knowledge sources; and an automated report generation system with modules for news analysis, research synthesis, social media sentiment analysis, and user interaction pattern identification. Performance was assessed using a 0-5 scoring scale. For single-turn evaluation, the chatbot achieved mean scores of 4.83 for relevance, 4.89 for routing, 4.50 for reference quality, 4.90 for correctness, and 4.88 for professional identity (overall 4.80). Multi-turn evaluation yielded higher scores: context retention 4.94, topic coherence 5.00, and overall 4.98. The report generation system achieved completeness 4.00-5.00, correctness 4.00-5.00, and helpfulness 3.67-5.00, with reference validity 5.00 across all periods. This study demonstrates the feasibility of an integrated AI agent system for bidirectional HPV vaccine communication. The architecture enables verified information delivery with source attribution while providing systematic public discourse analysis, with a transferable framework for adaptation to other medical contexts.

AnyECG: Evolved ECG Foundation Model for Holistic Health Profiling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jun Li · Hongling Zhu · Yujie Xiao · Qinghao Zhao · Yalei Ke · Gongzheng Tang · Guangkun Nie · Deyun Zhang · Jin Li · Canqing Yu · Shenda Hong
19 January 2026 at 13:00
arXiv:2601.10748v1 Announce Type: cross Abstract: Background: Artificial intelligence enabled electrocardiography (AI-ECG) has demonstrated the ability to detect diverse pathologies, but most existing models focus on single disease identification, neglecting comorbidities and future risk prediction. Although ECGFounder expanded cardiac disease coverage, a holistic health profiling model remains needed. Methods: We constructed a large multicenter dataset comprising 13.3 million ECGs from 2.98 million patients. Using transfer learning, ECGFounder was fine-tuned to develop AnyECG, a foundation model for holistic health profiling. Performance was evaluated using external validation cohorts and a 10-year longitudinal cohort for current diagnosis, future risk prediction, and comorbidity identification. Results: AnyECG demonstrated systemic predictive capability across 1172 conditions, achieving an AUROC greater than 0.7 for 306 diseases. The model revealed novel disease associations, robust comorbidity patterns, and future disease risks. Representative examples included high diagnostic performance for hyperparathyroidism (AUROC 0.941), type 2 diabetes (0.803), Crohn disease (0.817), lymphoid leukemia (0.856), and chronic obstructive pulmonary disease (0.773). Conclusion: The AnyECG foundation model provides substantial evidence that AI-ECG can serve as a systemic tool for concurrent disease detection and long-term risk prediction.

Multi-Stage Patient Role-Playing Framework for Realistic Clinical Interactions

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shijie Jiang · Zefan Zhang · Kehua Zhu · Tian Bai · Ruihong Zhao
19 January 2026 at 13:00
arXiv:2601.10951v1 Announce Type: cross Abstract: The simulation of realistic clinical interactions plays a pivotal role in advancing clinical Large Language Models (LLMs) and supporting medical diagnostic education. Existing approaches and benchmarks rely on generic or LLM-generated dialogue data, which limits the authenticity and diversity of doctor-patient interactions. In this work, we propose the first Chinese patient simulation dataset (Ch-PatientSim), constructed from realistic clinical interaction scenarios to comprehensively evaluate the performance of models in emulating patient behavior. Patients are simulated based on a five-dimensional persona structure. To address issues of the persona class imbalance, a portion of the dataset is augmented using few-shot generation, followed by manual verification. We evaluate various state-of-the-art LLMs and find that most produce overly formal responses that lack individual personality. To address this limitation, we propose a training-free Multi-Stage Patient Role-Playing (MSPRP) framework, which decomposes interactions into three stages to ensure both personalization and realism in model responses. Experimental results demonstrate that our approach significantly improves model performance across multiple dimensions of patient simulation.

MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management

✇cs.AI, q-bio.NC updates on arXiv.org
By: Miriam K. Wolff · Peter Calhoun · Eleonora Maria Aiello · Yao Qin · Sam F. Royston
19 January 2026 at 13:00
arXiv:2601.11505v1 Announce Type: cross Abstract: Progress in Type 1 Diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D management datasets. Current datasets differ substantially in structure and are time-consuming to access and process, which impedes data integration and reduces the comparability and generalizability of algorithmic developments. This work aims to establish a unified and accessible data resource for T1D algorithm development. Multiple publicly available T1D datasets were consolidated into a unified resource, termed the MetaboNet dataset. Inclusion required the availability of both continuous glucose monitoring (CGM) data and corresponding insulin pump dosing records. Additionally, auxiliary information such as reported carbohydrate intake and physical activity was retained when present. The MetaboNet dataset comprises 3135 subjects and 1228 patient-years of overlapping CGM and insulin data, making it substantially larger than existing standalone benchmark datasets. The resource is distributed as a fully public subset available for immediate download at https://metabo-net.org/ , and with a Data Use Agreement (DUA)-restricted subset accessible through their respective application processes. For the datasets in the latter subset, processing pipelines are provided to automatically convert the data into the standardized MetaboNet format. A consolidated public dataset for T1D research is presented, and the access pathways for both its unrestricted and DUA-governed components are described. The resulting dataset covers a broad range of glycemic profiles and demographics and thus can yield more generalizable algorithmic performance than individual datasets.
Received — 18 January 2026 ⏭ Favourites
  • ✇Latest Science News -- ScienceDaily
  • AI maps the hidden forces shaping cancer survival worldwide
    Researchers have turned artificial intelligence into a powerful new lens for understanding why cancer survival rates differ so dramatically around the world. By analyzing cancer data and health system information from 185 countries, the AI model highlights which factors, such as access to radiotherapy, universal health coverage, and economic strength, are most closely linked to better survival in each nation.
     
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AI maps the hidden forces shaping cancer survival worldwide

✇Latest Science News -- ScienceDaily
17 January 2026 at 22:26
Researchers have turned artificial intelligence into a powerful new lens for understanding why cancer survival rates differ so dramatically around the world. By analyzing cancer data and health system information from 185 countries, the AI model highlights which factors, such as access to radiotherapy, universal health coverage, and economic strength, are most closely linked to better survival in each nation.

Wearable device derived electrocardiographic age and its association with atrial fibrillation

✇npj Digital Medicine
By: Seung Hyun Park · Ju Hyun Jin · Jongwoo Kim · Dongha Lee · Daein Kim · Jaeseong Jang · Hee Tae Yu · Seng Chan You · Boyoung Joung
17 January 2026 at 08:00

npj Digital Medicine, Published online: 17 January 2026; doi:10.1038/s41746-026-02344-8

Wearable device derived electrocardiographic age and its association with atrial fibrillation

From OpenAI’s offices to a deal with Eli Lilly — how Chai Discovery became one of the flashiest names in AI drug development

✇TechCrunch
By: Lucas Ropek
17 January 2026 at 04:14
The startup has partnered with Eli Lilly and enjoys the backing of some of Silicon Valley's most influential VCs.
  • ✇TechCrunch
  • The AI healthcare gold rush is here Theresa Loconsolo
    AI companies are clustering around healthcare and fast.  In just the past week, OpenAI bought health startup Torch, Anthropic launched Claude for healthcare, and Sam Altman-backed MergeLabs closed a $250 million seed round at an $850 million valuation. The money and products are pouring into health and voice AI, but so are concerns about hallucination risks, inaccurate medical information, and […]
     
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The AI healthcare gold rush is here

✇TechCrunch
By: Theresa Loconsolo
17 January 2026 at 02:57
AI companies are clustering around healthcare and fast.  In just the past week, OpenAI bought health startup Torch, Anthropic launched Claude for healthcare, and Sam Altman-backed MergeLabs closed a $250 million seed round at an $850 million valuation. The money and products are pouring into health and voice AI, but so are concerns about hallucination risks, inaccurate medical information, and […]
  • ✇STAT
  • STAT+: JPM Week 2026 is over. It was fantastic. Here’s why Adam Feuerstein
    This is the online version of Adam’s Biotech Scorecard, a subscriber-only newsletter. STAT+ subscribers can sign up here to get it delivered to their inbox. A chill week No M&A. Some news but not a deluge. The weather was amazing. I took a walk in Golden Gate Park and came across the sculpture above, whatever it is.  The entire week was, dare I say it, chill. Just the way I wanted it to be. Continue to STAT+ to read the full story…
     
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STAT+: JPM Week 2026 is over. It was fantastic. Here’s why

✇STAT
By: Adam Feuerstein
16 January 2026 at 19:30

This is the online version of Adam’s Biotech Scorecard, a subscriber-only newsletter. STAT+ subscribers can sign up here to get it delivered to their inbox.

A chill week

No M&A. Some news but not a deluge. The weather was amazing. I took a walk in Golden Gate Park and came across the sculpture above, whatever it is. 

The entire week was, dare I say it, chill. Just the way I wanted it to be. 

Continue to STAT+ to read the full story…

© Josh Edelson/AFP/Getty Images

Cellular neighborhoods in cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Lichun Ma · Barbara Xiong · Meng Liu · Kai Tan
16 January 2026 at 19:00

Nat Cancer. 2026 Jan 16. doi: 10.1038/s43018-025-01107-w. Online ahead of print.

ABSTRACT

The concept of cellular neighborhoods, defined as recurring structures within the tissue with characteristic cell compositions and interactions, has transformed our understanding of the complexity and dynamics of tumor ecosystems. Recent advances in spatial omics and computational modeling have enabled high-resolution mapping of these neighborhoods, providing unprecedented insights into their roles in shaping tumor heterogeneity, evolution and therapeutic responses. Despite these advances, a unified framework for interpreting cellular neighborhoods remains lacking. This Perspective synthesizes emerging concepts and insights, focusing on the definition and classification of cellular neighborhoods in cancer, computational methods for identifying and comparing them, and their clinical relevance.

PMID:41545713 | DOI:10.1038/s43018-025-01107-w

Contaminating plasmid sequences and disrupted vector genomes in the liver following adeno-associated virus gene therapy

✇Nature Medicine
By: Sarah Buddle · Li-An K. Brown · Sofia Morfopoulou · Oscar Enrique Torres Montaguth · Mariacristina Scoto · Vanessa Herder · Anil Dhawan · Julianne R. Brown · Laura Atkinson · Angelika Kopec · Dee Davis · Nathaniel Storey · Luis Campos · Neil Sebire · Hannah Macpherson · Jasmaine Lee · Richard Orton · Giovanni Baranello · Patawee Asamaphan · Georgios Ilia · Rajvinder Karda · Holly Belfield · Malcolm Gracie Semple · J. Kenneth Baillie · John Counsell · Simon N. Waddington · Emma C. Thomson · Francesco Muntoni · Judith Breuer
16 January 2026 at 08:00

Nature Medicine, Published online: 16 January 2026; doi:10.1038/s41591-025-04073-z

Analyses of liver biopsies from a child with spinal muscular atrophy treated with adeno-associated virus gene therapy who developed hepatitis reveal contaminating manufacturing plasmids and disrupted vector genomes, possibly resulting from recombination events.

Clinical proteomics in cardiovascular medicine: Current capabilities, limitations, and future directions

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Bhawana Singh · Oleg A Karpov · Manuel Mayr
15 January 2026 at 19:00

Atherosclerosis. 2026 Jan 8;413:120637. doi: 10.1016/j.atherosclerosis.2026.120637. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Commercial high-throughput proteomics platforms, such as Olink and SomaLogic, enable large-scale epidemiological studies with integrated multi-omics measurements. While these proteomics approaches have been widely applied in biobanks, issues of data quality remain underappreciated. In this review, we discuss these limitations and outline a way forward for realizing the clinical translation of proteomics as a comprehensive 'liquid health check'.

METHODS: We reviewed the recent literature for artificial intelligence (AI) and multi-omics, particularly proteomics in atherosclerotic cardiovascular disease (ASCVD).

RESULTS: AI-driven multi-omics analyses have the potential to advance our understanding of multifactorial causes of ASCVD, including aging. Emerging concepts such as "ageotypes" suggest the potential for personalized intervention to slow aging processes. Commercial proteomics platforms have accelerated biomarker discovery in ASCVD, but challenges remain in clinical translation. Limited correlation between Olink and SomaLogic necessitates orthogonal validation of findings. Platform-specific issues, such as epitope effects and cross-reactivity, can yield divergent protein quantitative trait loci for the same protein, complicating causal inference. While tissue proteomics provides complementary insights to plasma proteomics, reliance on autopsy samples raises concerns about protein degradation and measurement reliability. Increasingly, single-cell and spatial proteomics are being explored to better capture plaque heterogeneity, complementing bulk proteomics in larger cohorts.

CONCLUSION: Beyond risk prediction, proteomics offers opportunities to elucidate disease mechanisms and enable drug repurposing. To realize the clinical potential of plasma proteomics, absolute or reliably recalibratable relative quantification will be required to guide patient care. Ultimately, the clinical value of proteomics will be determined by the quality rather than the quantity of protein measurements.

PMID:41539063 | DOI:10.1016/j.atherosclerosis.2026.120637

Received — 16 January 2026 ⏭ Favourites

“I Want to Spend My Time Living”—Experiences With a Digital Outpatient Service With a Mobile App for Tailored Care Among Adults With Long-Term Health Service Needs: Qualitative Study Using Thematic Analysis

✇Journal of Medical Internet Research
By: Heidi Holmen · Erik Fosse
16 January 2026 at 05:30
Background: Digital health services are increasingly used in hospital-based outpatient care, offering remote monitoring, patient-reported outcomes, information sharing, and asynchronous communication. While expected to improve self-management, timeliness, and efficiency, the success of digital health interventions relies on patients’ health literacy and digital health literacy. While some research has addressed potential associations between digital health interventions and patients’ health outcomes, research on patients’ experiences remains limited. Objective: The aim of this study was to explore and gain in-depth knowledge about the experiences of patients with chronic or long-term conditions enrolled in a 6-month digital outpatient care intervention for tailored care and health literacy. Methods: We conducted an exploratory qualitative interview study with 17 strategically recruited adult patients with cancer, interstitial lung disease, epilepsy, or complicated pain who used a digital outpatient service for 6 months. Individual telephone interviews were conducted using a semistructured guide, transcribed verbatim, and analyzed with thematic analysis to generate codes and themes. Participants had a median age of 62 years (minimum-maximum 36-83 years), with 8 females and 9 males. Results: The thematic analysis led to 1 main theme “Digital outpatient care as a flexible service supporting patients’ self-management,” informed by 3 subthemes “The ongoing nature of managing a chronic condition and how the digital service meet the patients’ desire for autonomy in their care,” “Digital tools flexibly address the patients’ unique needs, but reliability depends on patient interaction,” and “Digital services enhance the patients’ sense of safety through easy access to a relation with competent healthcare workers.” The themes highlight patients’ appreciation for greater flexibility in their care and their desire to self-manage with the support of easily accessible health care workers, ultimately supporting their health literacy. Patients recognized the importance of actively engaging with the digital solution to fully benefit from its opportunities and emphasized the critical role of health care workers in fostering their sense of security. Conclusions: Digital outpatient care was experienced as flexible and supportive for patients with long-term conditions. The increased possibility of interacting with health care workers was welcomed by the patients, and the combination of flexibility, self-monitoring, and addressing concerns regarding their self-management may increase the patients experience of autonomy. As health literacy likely plays a role in patients’ ability to effectively engage with digital tools and self-manage their conditions, future research should explore how varying levels of health literacy influence these outcomes. In addition, research should address whether such digital outpatient clinics are positive for a wider range of patients, associated health outcomes, and any positive effects on a health system level. Trial Registration: ClinicalTrials.gov NCT05068869; https://clinicaltrials.gov/ct2/show/NCT05068869
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  • Exclusive eBook: How AGI Became a Consequential Conspiracy Theory MIT Technology Review
    In this exclusive subscriber-only eBook, you’ll learn about how the idea that machines will be as smart as—or smarter than—humans has hijacked an entire industry.by Will Douglas Heaven October 30, 2025 ACCESS EBOOK Table of Contents: How Silicon Valley got AGI-pilled The great AGI conspiracy How AGI hijacked an industry The great AGI conspiracy, concluded Related Stories: How AGI became the most consequential conspiracy theory of our time The New Conspiracy Age
     
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Exclusive eBook: How AGI Became a Consequential Conspiracy Theory

✇MIT Technology Review
By: MIT Technology Review
16 January 2026 at 01:16

In this exclusive subscriber-only eBook, you’ll learn about how the idea that machines will be as smart as—or smarter than—humans has hijacked an entire industry.

by Will Douglas Heaven October 30, 2025

Table of Contents:

  • How Silicon Valley got AGI-pilled
  • The great AGI conspiracy
  • How AGI hijacked an industry
  • The great AGI conspiracy, concluded

Related Stories:

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Molecular features of early- vs. late-onset gastric cancer: a systematic review and meta-analysis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xinmei Lin · Wenhan Shi · Zitong Zhou · Degang Li · Qiuyuan Yao · Yu Sun
15 January 2026 at 19:00

BMC Cancer. 2026 Jan 14. doi: 10.1186/s12885-026-15567-5. Online ahead of print.

ABSTRACT

BACKGROUND: Early-onset gastric cancer (EOGC), diagnosed before age 50, is characterized by distinct clinicopathological features, though its molecular landscape remains poorly defined.

METHODS: A systematic literature search of PubMed, Embase, and Web of Science identified studies comparing molecular characteristics of EOGC and late-onset gastric cancer (LOGC). Meta-analyses assessed differences in The Cancer Genome Atlas (TCGA) molecular subtypes, gene mutations, therapeutic biomarkers, and serum tumor markers. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated; heterogeneity was assessed using the I2 statistic.

RESULTS: EOGC was associated with a higher prevalence of the genomically stable (GS) subtype (OR = 1.71, 95% CI: 1.37-2.12) and a lower prevalence of the chromosomal instability (CIN) subtype (OR = 0.62, 95% CI: 0.50-0.77). CDH1 mutations were more frequent in EOGC (OR = 3.44, 95% CI: 2.85-4.16), while HER2 expression (OR = 0.54, 95% CI: 0.43-0.67), dMMR/MSI-H status (OR = 0.25, 95% CI: 0.12-0.53), and p53 expression (OR = 0.56, 95% CI: 0.39-0.82) were significantly lower. Serum markers including CEA and CA19-9 were also less frequently elevated in EOGC.

CONCLUSION: EOGC represents a biologically distinct subset of gastric cancer with unique genomic and immunological features. These findings support age-specific diagnostic approaches and emphasize the value of multiomic strategies to uncover the mechanisms driving early-onset disease.

PMID:41535782 | DOI:10.1186/s12885-026-15567-5

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