Normal view
-
cs.AI, q-bio.NC updates on arXiv.org
-
AI-exposed jobs deteriorated before ChatGPT
arXiv:2601.02554v1 Announce Type: cross Abstract: Public debate links worsening job prospects for AI-exposed occupations to the release of ChatGPT in late 2022. Using monthly U.S. unemployment insurance records, we measure occupation- and location-specific unemployment risk and find that risk rose in AI-exposed occupations beginning in early 2022, months before ChatGPT. Analyzing millions of LinkedIn profiles, we show that graduate cohorts from 2021 onward entered AI-exposed jobs at lower rates
-
cs.AI, q-bio.NC updates on arXiv.org
-
A Multicenter Benchmark of Multiple Instance Learning Models for Lymphoma Subtyping from HE-stained Whole Slide Images
arXiv:2512.14640v1 Announce Type: cross Abstract: Timely and accurate lymphoma diagnosis is essential for guiding cancer treatment. Standard diagnostic practice combines hematoxylin and eosin (HE)-stained whole slide images with immunohistochemistry, flow cytometry, and molecular genetic tests to determine lymphoma subtypes, a process requiring costly equipment, skilled personnel, and causing treatment delays. Deep learning methods could assist pathologists by extracting diagnostic information
A Multicenter Benchmark of Multiple Instance Learning Models for Lymphoma Subtyping from HE-stained Whole Slide Images
-
cs.AI, q-bio.NC updates on arXiv.org
-
Beyond Task Completion: An Assessment Framework for Evaluating Agentic AI Systems
arXiv:2512.12791v2 Announce Type: replace-cross Abstract: Recent advances in agentic AI have shifted the focus from standalone Large Language Models (LLMs) to integrated systems that combine LLMs with tools, memory, and other agents to perform complex tasks. These multi-agent architectures enable coordinated reasoning, planning, and execution across diverse domains, allowing agents to collaboratively automate complex workflows. Despite these advances, evaluation and assessment of LLM agents and
Beyond Task Completion: An Assessment Framework for Evaluating Agentic AI Systems
-
Nature - Issue - nature.com science feeds
-
Somatic evolution following cancer treatment in normal tissue
Nature, Published online: 10 December 2025; doi:10.1038/s41586-025-09792-4High-depth sequencing of non-cancerous tissue from patients with metastatic cancer reveals single-base mutational signatures of alcohol, smoking and cancer treatments, and reveals how exogenous factors, including cancer therapies, affect somatic cell evolution.
Somatic evolution following cancer treatment in normal tissue
Nature, Published online: 10 December 2025; doi:10.1038/s41586-025-09792-4
High-depth sequencing of non-cancerous tissue from patients with metastatic cancer reveals single-base mutational signatures of alcohol, smoking and cancer treatments, and reveals how exogenous factors, including cancer therapies, affect somatic cell evolution.Bridging Rural America’s Digital Divide in Health Care
-
Omics In Lung
-
Multi-omic profiling provides insights into the heterogeneity, microenvironmental features, and biomarker landscape of small-cell lung cancer
Mol Cancer. 2025 Dec 2. doi: 10.1186/s12943-025-02514-4. Online ahead of print.ABSTRACTBACKGROUND: Greater understanding of differential therapeutic sensitivity, specifically to immunotherapy, in small-cell lung cancer (SCLC) is required.METHODS: We explored SCLC heterogeneity through integrated molecular characterization of tumor tissue samples from 159 treatment-naive patients, utilizing genetic, epigenetic, transcriptional, and proteomic profiling, immunohistochemistry staining for multiple b
Multi-omic profiling provides insights into the heterogeneity, microenvironmental features, and biomarker landscape of small-cell lung cancer
Mol Cancer. 2025 Dec 2. doi: 10.1186/s12943-025-02514-4. Online ahead of print.
ABSTRACT
BACKGROUND: Greater understanding of differential therapeutic sensitivity, specifically to immunotherapy, in small-cell lung cancer (SCLC) is required.
METHODS: We explored SCLC heterogeneity through integrated molecular characterization of tumor tissue samples from 159 treatment-naive patients, utilizing genetic, epigenetic, transcriptional, and proteomic profiling, immunohistochemistry staining for multiple biologically relevant markers including transcriptional subtype-defining proteins, and spatial immune profiling using multiplex immunofluorescence.
RESULTS: Multi-omics analysis confirmed high heterogeneity across/within neuroendocrine and non-neuroendocrine subtypes. Methylomics analysis identified four methylome clusters that may enhance subtype prediction, prognosis, and longitudinal monitoring of subtype evolution. Immunohistochemistry analysis showed high MHC-I expression in non-neuroendocrine subtypes, which have greatest potential benefit from adding immunotherapy to chemotherapy; high DLL3 expression associated with neuroendocrine subtypes and an immune-cold tumor microenvironment. Multiplex immunofluorescence demonstrated associations of MHC-I with spatial arrangement and phenotypic features of immune cells in the tumor microenvironment of high-MHC-I-expressing SCLC, providing mechanistic rationale for MHC-I as a potential biomarker of immunotherapy response.
CONCLUSIONS: This multimodal profiling analysis provides further insights into the biologic complexity of SCLC and highlights potential therapeutic vulnerabilities of distinct disease subtypes.
PMID:41331472 | DOI:10.1186/s12943-025-02514-4
-
Omics in Hepatocellular
-
Integrative Analysis of Multi-Omics Data for Biomarker Discovery
Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-7. doi: 10.1109/EMBC58623.2025.11254134.ABSTRACTThe complexity of biological systems and the limitations of analyzing individual omics studies for biomarker discovery have raised the need for a holistic approach by multi-omics integration. By integrating data from multiple layers, researchers can gain insights into the entire system rather than just individual components. Also, integrative analysis can help identify molecular signatures that a
Integrative Analysis of Multi-Omics Data for Biomarker Discovery
Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-7. doi: 10.1109/EMBC58623.2025.11254134.
ABSTRACT
The complexity of biological systems and the limitations of analyzing individual omics studies for biomarker discovery have raised the need for a holistic approach by multi-omics integration. By integrating data from multiple layers, researchers can gain insights into the entire system rather than just individual components. Also, integrative analysis can help identify molecular signatures that are more accurate in predicting disease onset, progression, and response to treatment, leading to better-targeted therapies and personalized medicine. In this paper, we explored statistical and deep learning methods for integrative analysis of metabolomics, lipidomics, peptidomics, proteomics, and glycoproteomics data acquired by LC-MS/MS analysis of serum samples from 20 hepatocellular carcinoma (HCC) cases and 20 patients with liver cirrhosis (CIRR). The goal is to identify a panel of multi-omics features that distinguish HCC cases from cirrhotic controls. A pathway analysis using these features identified biological pathways such as LXR/RXR Activation and Acute Response signaling as significantly enriched in our multi-omics datasets.
PMID:41336317 | PMC:PMC12694951 | DOI:10.1109/EMBC58623.2025.11254134
-
MRD
-
DNA-Based Liquid Biopsy for Evaluating Surgical and Postsurgical Outcomes in Gynecologic Malignancies: A Systematic Review
J Clin Lab Anal. 2025 Dec 1:e70139. doi: 10.1002/jcla.70139. Online ahead of print.ABSTRACTINTRODUCTION: DNA-based liquid biopsies, including circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA), are emerging as minimally invasive biomarkers for monitoring surgical and postsurgical outcomes in gynecologic malignancies. These tools offer the potential to guide early intervention, refine risk stratification, and improve prognostic accuracy. This systematic review aimed to assess the clinical ut
DNA-Based Liquid Biopsy for Evaluating Surgical and Postsurgical Outcomes in Gynecologic Malignancies: A Systematic Review
J Clin Lab Anal. 2025 Dec 1:e70139. doi: 10.1002/jcla.70139. Online ahead of print.
ABSTRACT
INTRODUCTION: DNA-based liquid biopsies, including circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA), are emerging as minimally invasive biomarkers for monitoring surgical and postsurgical outcomes in gynecologic malignancies. These tools offer the potential to guide early intervention, refine risk stratification, and improve prognostic accuracy. This systematic review aimed to assess the clinical utility of DNA-based liquid biopsies in evaluating recurrence, surgical success, and preoperative diagnosis in gynecologic cancers.
METHODS: A systematic review was conducted in accordance with PRISMA guidelines, covering studies published from 2017 to 2025. Literature searches were performed in PubMed, Scopus, and Web of Science. A total of 32 eligible observational studies involving 3210 patients with ovarian, endometrial, uterine, and other gynecologic malignancies were included. Study quality was assessed using the Newcastle-Ottawa Scale (NOS).
RESULTS: The studies showed a broad geographic and methodological diversity, with a median NOS score of 7. CtDNA and cfDNA demonstrated promise in three key areas: (1) Recurrence prediction-postoperative ctDNA positivity was associated with higher relapse rates and reduced disease-free survival; (2) Monitoring surgical outcomes and treatment response-ctDNA dynamics more accurately reflected tumor burden than traditional markers like CA125; (3) Preoperative diagnostic support-cfDNA methylation profiling and cfDNA/CA125 models enhanced malignancy detection and risk stratification. Ovarian and endometrial cancers were most frequently studied.
CONCLUSIONS: DNA-based liquid biopsies show strong potential in perioperative care for gynecologic cancers. Their integration into clinical workflows could improve the detection of minimal residual disease and inform individualized surgical planning.
PMID:41327898 | DOI:10.1002/jcla.70139
-
cs.AI, q-bio.NC updates on arXiv.org
-
CLINB: A Climate Intelligence Benchmark for Foundational Models
arXiv:2511.11597v1 Announce Type: new Abstract: Evaluating how Large Language Models (LLMs) handle complex, specialized knowledge remains a critical challenge. We address this through the lens of climate change by introducing CLINB, a benchmark that assesses models on open-ended, grounded, multimodal question answering tasks with clear requirements for knowledge quality and evidential support. CLINB relies on a dataset of real users' questions and evaluation rubrics curated by leading climate s
CLINB: A Climate Intelligence Benchmark for Foundational Models
-
cs.AI, q-bio.NC updates on arXiv.org
-
REFA: Reference Free Alignment for multi-preference optimization
arXiv:2412.16378v4 Announce Type: replace-cross Abstract: To mitigate reward hacking from response verbosity, modern preference optimization methods are increasingly adopting length normalization (e.g., SimPO, ORPO, LN-DPO). While effective against this bias, we demonstrate that length normalization itself introduces a failure mode: the URSLA shortcut. Here models learn to satisfy the alignment objective by prematurely truncating low-quality responses rather than learning from their semantic co
REFA: Reference Free Alignment for multi-preference optimization
-
Journal of Medical Internet Research
-
Wearable Artificial Intelligence for Epilepsy: Scoping Review
Background: Epilepsy affects approximately 50 million people globally and imposes a substantial clinical and societal burden, requiring continuous and personalized monitoring for effective management. Wearable artificial intelligence (AI) technologies offer a promising solution by leveraging physiological signals and machine learning for seizure detection and prediction. While various approaches have been proposed, a comprehensive overview summarizing these advances and challenges is still neede
Wearable Artificial Intelligence for Epilepsy: Scoping Review
-
cs.AI, q-bio.NC updates on arXiv.org
-
Identity Management for Agentic AI: The new frontier of authorization, authentication, and security for an AI agent world
arXiv:2510.25819v1 Announce Type: cross Abstract: The rapid rise of AI agents presents urgent challenges in authentication, authorization, and identity management. Current agent-centric protocols (like MCP) highlight the demand for clarified best practices in authentication and authorization. Looking ahead, ambitions for highly autonomous agents raise complex long-term questions regarding scalable access control, agent-centric identities, AI workload differentiation, and delegated authority. Th
Identity Management for Agentic AI: The new frontier of authorization, authentication, and security for an AI agent world
-
cs.AI, q-bio.NC updates on arXiv.org
-
Epistemic Diversity and Knowledge Collapse in Large Language Models
arXiv:2510.04226v4 Announce Type: replace-cross Abstract: Large language models (LLMs) tend to generate lexically, semantically, and stylistically homogenous texts. This poses a risk of knowledge collapse, where homogenous LLMs mediate a shrinking in the range of accessible information over time. Existing works on homogenization are limited by a focus on closed-ended multiple-choice setups or fuzzy semantic features, and do not look at trends across time and cultural contexts. To overcome this,
Epistemic Diversity and Knowledge Collapse in Large Language Models
-
Nature - Issue - nature.com science feeds
-
Multi-omic profiling reveals age-related immune dynamics in healthy adults
Nature, Published online: 29 October 2025; doi:10.1038/s41586-025-09686-5This multi-omic longitudinal analysis of the healthy human peripheral immune system constructs the Human Immune Health Atlas and assembles data on immune cell composition and state changes with age, including responses to cytomegalovirus infection and influenza vaccination.
Multi-omic profiling reveals age-related immune dynamics in healthy adults
Nature, Published online: 29 October 2025; doi:10.1038/s41586-025-09686-5
This multi-omic longitudinal analysis of the healthy human peripheral immune system constructs the Human Immune Health Atlas and assembles data on immune cell composition and state changes with age, including responses to cytomegalovirus infection and influenza vaccination.-
Nature Biotechnology - Issue - nature.com science feeds
-
Elucidating lipid nanoparticle properties and structure through biophysical analyses
Nature Biotechnology, Published online: 23 October 2025; doi:10.1038/s41587-025-02855-xGuidance for optimizing lipid nanoparticle formulations is derived using sophisticated biophysical techniques.
Elucidating lipid nanoparticle properties and structure through biophysical analyses
Nature Biotechnology, Published online: 23 October 2025; doi:10.1038/s41587-025-02855-x
Guidance for optimizing lipid nanoparticle formulations is derived using sophisticated biophysical techniques.-
Journal of Medical Internet Research
-
Effectiveness of a Digital Therapy on 6-Month Weight Loss in People With Obesity: The Digital Therapy to Promote Weight Loss in Patients With Obesity by Increasing Their Adherence to Treatment (DEMETRA) Randomized Clinical Trial
Background: Obesity is a chronic, relapsing disease influenced by environmental, lifestyle, biological, and genetic factors, affecting over 1 billion people globally. Treatment for adults typically involves multicomponent lifestyle interventions—diet, physical activity, and behavior change—for at least 6-12 months. However, adherence is often low, and in-person sessions can be time-consuming and costly. Digital therapeutics (DTx), which enhance patient engagement and support long-term outcomes,
Effectiveness of a Digital Therapy on 6-Month Weight Loss in People With Obesity: The Digital Therapy to Promote Weight Loss in Patients With Obesity by Increasing Their Adherence to Treatment (DEMETRA) Randomized Clinical Trial
-
Nature - Issue - nature.com science feeds
-
Parity and lactation induce T cell mediated breast cancer protection
Nature, Published online: 20 October 2025; doi:10.1038/s41586-025-09713-5Parity and lactation induce T cell mediated breast cancer protection
Parity and lactation induce T cell mediated breast cancer protection
Nature, Published online: 20 October 2025; doi:10.1038/s41586-025-09713-5
Parity and lactation induce T cell mediated breast cancer protection-
AAAS: Table of Contents
-
A human pan-disease blood atlas of the circulating proteome
Science, Ahead of Print.
A human pan-disease blood atlas of the circulating proteome
-
Nature Medicine
-
Genomically matched therapy in advanced solid tumors: the randomized phase 2 ROME trial
Nature Medicine, Published online: 29 September 2025; doi:10.1038/s41591-025-03918-xIn the proof-of-concept phase 2 ROME trial, comprehensive genomic profiling followed by molecular tumor board evaluation and randomization of patients with metastatic solid cancer to receive personalized therapy or standard of care led to a significantly higher objective response rate and longer progression-free survival in patients who received personalized therapy.
Genomically matched therapy in advanced solid tumors: the randomized phase 2 ROME trial
Nature Medicine, Published online: 29 September 2025; doi:10.1038/s41591-025-03918-x
In the proof-of-concept phase 2 ROME trial, comprehensive genomic profiling followed by molecular tumor board evaluation and randomization of patients with metastatic solid cancer to receive personalized therapy or standard of care led to a significantly higher objective response rate and longer progression-free survival in patients who received personalized therapy.-
Omics in Hepatocellular
-
Multi-Omics Feature Selection to Identify Biomarkers for Hepatocellular Carcinoma
Metabolites. 2025 Aug 28;15(9):575. doi: 10.3390/metabo15090575.ABSTRACTINTRODUCTION: Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, ranks as the third leading cause of mortality globally. Patients diagnosed with HCC exhibit a dismal prognosis mostly due to the emergence of symptoms in the advanced stages of the disease. Moreover, conventional biomarkers demonstrate insufficient efficacy in the early detection of HCC, hence highlighting the need for the identification o
Multi-Omics Feature Selection to Identify Biomarkers for Hepatocellular Carcinoma
Metabolites. 2025 Aug 28;15(9):575. doi: 10.3390/metabo15090575.
ABSTRACT
INTRODUCTION: Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, ranks as the third leading cause of mortality globally. Patients diagnosed with HCC exhibit a dismal prognosis mostly due to the emergence of symptoms in the advanced stages of the disease. Moreover, conventional biomarkers demonstrate insufficient efficacy in the early detection of HCC, hence highlighting the need for the identification of novel and more effective biomarkers.
METHODS: In this paper, we investigate methods for integration of multi-omics data we generated by both untargeted and targeted mass spectrometric analysis of serum samples from HCC cases and patients with liver cirrhosis. Specifically, the performances of several feature selection methods are evaluated on their abilities to identify a panel of multi-omics features that distinguish HCC cases from cirrhotic controls.
RESULTS: The integrative analysis identified key molecules associated with liver including such as leucine and isoleucine as well as SERPINA1, which is involved in LXR/RXR Activation and Acute Response signaling. A new method that uses recursive feature selection in conjunction with a transformer-based deep learning model as an estimator led to more promising results compared to other deep learning methods that perform disease classification and feature selection sequentially.
CONCLUSIONS: The findings in this study reinforce the importance of adapting or extending deep learning models to support robust feature selection, especially for integration of multi-omics data with limited sample size to avoid the risk of overfitting and the need for evaluation of the multi-omics features discovered in this study via blood samples from a larger and independent cohort to identify robust biomarkers for HCC.
PMID:41002959 | PMC:PMC12471784 | DOI:10.3390/metabo15090575