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XR-DT: Extended Reality-Enhanced Digital Twin for Agentic Mobile Robots

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianyi Wang · Jiseop Byeon · Ahmad Yehia · Huihai Wang · Yiming Xu · Tianyi Zeng · Ziran Wang · Junfeng Jiao · Christian Claudel
arXiv:2512.05270v1 Announce Type: cross Abstract: As mobile robots increasingly operate alongside humans in shared workspaces, ensuring safe, efficient, and interpretable Human-Robot Interaction (HRI) has become a pressing challenge. While substantial progress has been devoted to human behavior prediction, limited attention has been paid to how humans perceive, interpret, and trust robots' inferences, impeding deployment in safety-critical and socially embedded environments. This paper presents XR-DT, an eXtended Reality-enhanced Digital Twin framework for agentic mobile robots, that bridges physical and virtual spaces to enable bi-directional understanding between humans and robots. Our hierarchical XR-DT architecture integrates virtual-, augmented-, and mixed-reality layers, fusing real-time sensor data, simulated environments in the Unity game engine, and human feedback captured through wearable AR devices. Within this framework, we design an agentic mobile robot system with a unified diffusion policy for context-aware task adaptation. We further propose a chain-of-thought prompting mechanism that allows multimodal large language models to reason over human instructions and environmental context, while leveraging an AutoGen-based multi-agent coordination layer to enhance robustness and collaboration in dynamic tasks. Initial experimental results demonstrate accurate human and robot trajectory prediction, validating the XR-DT framework's effectiveness in HRI tasks. By embedding human intention, environmental dynamics, and robot cognition into the XR-DT framework, our system enables interpretable, trustworthy, and adaptive HRI.
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M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG

✇cs.AI, q-bio.NC updates on arXiv.org
By: David Anugraha · Patrick Amadeus Irawan · Anshul Singh · En-Shiun Annie Lee · Genta Indra Winata
arXiv:2512.05959v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance in visual question answering (VQA), yet they remain constrained by static training data. Retrieval-Augmented Generation (RAG) mitigates this limitation by enabling access to up-to-date, culturally grounded, and multilingual information; however, multilingual multimodal RAG remains largely underexplored. We introduce M4-RAG, a massive-scale benchmark covering 42 languages and 56 regional dialects and registers, comprising over 80,000 culturally diverse image-question pairs for evaluating retrieval-augmented VQA across languages and modalities. To balance realism with reproducibility, we build a controlled retrieval environment containing millions of carefully curated multilingual documents relevant to the query domains, approximating real-world retrieval conditions while ensuring consistent experimentation. Our systematic evaluation reveals that although RAG consistently benefits smaller VLMs, it fails to scale to larger models and often even degrades their performance, exposing a critical mismatch between model size and current retrieval effectiveness. M4-RAG provides a foundation for advancing next-generation RAG systems capable of reasoning seamlessly across languages, modalities, and cultural contexts.
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The AI Productivity Index (APEX)

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bertie Vidgen · Abby Fennelly · Evan Pinnix · Julien Benchek · Daniyal Khan · Zach Richards · Austin Bridges · Calix Huang · Ben Hunsberger · Isaac Robinson · Akul Datta · Chirag Mahapatra · Dominic Barton · Cass R. Sunstein · Eric Topol · Brendan Foody · Osvald Nitski
arXiv:2509.25721v4 Announce Type: replace-cross Abstract: We present an extended version of the AI Productivity Index (APEX-v1-extended), a benchmark for assessing whether frontier models are capable of performing economically valuable tasks in four jobs: investment banking associate, management consultant, big law associate, and primary care physician (MD). This technical report details the extensions to APEX-v1, including an increase in the held-out evaluation set from n = 50 to n = 100 cases per job (n = 400 total) and updates to the grading methodology. We present a new leaderboard, where GPT5 (Thinking = High) remains the top performing model with a score of 67.0%. APEX-v1-extended shows that frontier models still have substantial limitations when performing typical professional tasks. To support further research, we are open sourcing n = 25 non-benchmark example cases per role (n = 100 total) along with our evaluation harness.
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Concept-Guided Backdoor Attack on Vision Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoyu Shen · Weimin Lyu · Haotian Xu · Tengfei Ma
arXiv:2512.00713v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have achieved impressive progress in multimodal text generation, yet their rapid adoption raises increasing concerns about security vulnerabilities. Existing backdoor attacks against VLMs primarily rely on explicit pixel-level triggers or imperceptible perturbations injected into images. While effective, these approaches reduce stealthiness and remain vulnerable to image-based defenses. We introduce concept-guided backdoor attacks, a new paradigm that operates at the semantic concept level rather than on raw pixels. We propose two different attacks. The first, Concept-Thresholding Poisoning (CTP), uses explicit concepts in natural images as triggers: only samples containing the target concept are poisoned, causing the model to behave normally in all other cases but consistently inject malicious outputs whenever the concept appears. The second, CBL-Guided Unseen Backdoor (CGUB), leverages a Concept Bottleneck Model (CBM) during training to intervene on internal concept activations, while discarding the CBM branch at inference time to keep the VLM unchanged. This design enables systematic replacement of a targeted label in generated text (for example, replacing "cat" with "dog"), even when the replacement behavior never appears in the training data. Experiments across multiple VLM architectures and datasets show that both CTP and CGUB achieve high attack success rates while maintaining moderate impact on clean-task performance. These findings highlight concept-level vulnerabilities as a critical new attack surface for VLMs.
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Critical Appraisal Tools for Evaluating Artificial Intelligence in Clinical Studies: Scoping Review

✇Journal of Medical Internet Research
By: Juan B Cabello · Vicente Ruiz Garcia · Miguel Torralba · Miguel Maldonado Fernandez · Marimar Ubeda · Eukene Ansuategui · Luis Ramos-Ruperto · Jose I Emparanza · Iratxe Urreta · Maria Teresa Iglesias · Jose I Pijoan · Amanda Burls
Background: Health research that uses predictive and/or generative AI is rapidly growing. Just as in traditional clinical studies, the way in which AI studies are conducted can introduce systematic errors. Transmission of this AI evidence into clinical practice and research needs critical appraisal tools for clinical decision makers and researchers. Objective: To identify existing tools for critical appraisal of clinical studies that use artificial intelligence (AI) and examine the concepts and domains these tools explore. Methods: Inclusion criteria in PCC framework P: (population) Artificial intelligence clinical studies. C (Concept): tools for critical appraisal and associated constructs such as: quality, reporting, validity, risk of bias, and applicability. C (context): in clinical practice context. In addition, bias classification and Chatbot assessment studies were included. We searched in medical and engineering databases (MEDLINE, EMBASE, CINAHL, PsycINFO and IEEE). We included clinical primary research with tools for critical appraisal. Classic reviews and systematic reviews were included in first phase of screening. They were excluded in the secondary phase, after identifying new tools by forward snowballing. We excluded non-human, computer and mathematical research, and letters, opinion papers and editorials. We used Rayyan for screening. Data extraction was done by two observers and discrepancies were solved by discussion. The protocol was previously registered in OSF (https://doi.org/10.17605/OSF.IO/ETYDS). We adhered to the PRISMA extension for Scoping reviews and to the PRISMA-Search extension for Reporting Literature in Systematic Reviews. Results: We retrieved 4393 unique records for screening. After excluding 3803 records, 119 were selected for full-text screening. From these, 59 were excluded. After inclusion of 10 studies via other methods, a total of 70 records were finally included. 46 of them were reporting guidelines (15 tools for critical appraisal, 2 for quality of study and 2 for risk of bias). Nine papers ware focused on bias classification or mitigation. We found 15 Chatbots assessment studies or systematic reviews of Chatbots studies (6 and 9 respectively) which are a very heterogeneous group. Conclusions: The results picture a landscape of the evidence tools where reporting tools predominate, followed by critical appraisal and risk of bias tools, and few tools for risk of bias. The mismatch of bias in AI and epidemiology should be considered for critical appraisal, especially regarding fairness and the mitigation bias in the AI. Finally, Chatbot assessment studies is a vast and evolving field in which progress in design, reporting and critical appraisal is necessary and urgent. Clinical Trial: https://doi.org/10.17605/OSF.IO/ETYDS
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Exploring a Digital Health Solution to Collect and Manage Health-Related Needs for Patients Who Undergo Complex Surgery: Mixed Methods Study

✇Journal of Medical Internet Research
By: Naleef Fareed · Humza Asgher · Diamantis Tsilimigras · Odysseas P Chatzipanagiotou · Giovanni Catalano · Bridget Hartwell · Kathleen Bolton · Timothy M Pawlik
Background: Patients who undergo complex surgery (e.g., esophagectomy, liver resection) often experience substantial burden of health-related needs (medical, social, and behavioral health). A closed loop digital solution could facilitate the collection and resolution of health-related needs by care team members for patients who undergo complex surgery. A digital solution may facilitate adherence to a clear treatment plan and concomitantly reduce surgical complications and readmissions associated with unmet health-related needs, which remain persistent challenges across health care settings. Objective: To establish problems and gaps in the collection, integration, and management of health-related needs and identify a set of user specifications for a digital solution to collect and manage health-related needs, specifically medical, social, and behavioral needs for patients who undergo complex surgery. Methods: We applied the Double Diamond Framework and organized the study into two sequential phases: (1) qualitative methods to discover patients’ and care team members’ perspectives on health-related needs; (2) participatory design sessions to gain feedback and sentiment about ideal features of a digital solution. Both phases were conducted between December 2023 and March 2025. We supplemented both phases with analysis of electronic health record (EHR) data for patients who underwent complex surgery at our academic medical center (AMC). Results: Extensive themes emerged from interviews with patients (n=20) and care team members (n=24), capturing their health-related and surgical experiences as well as desired features for a proposed digital solution. Our swim lane diagram demonstrated four critical gaps in workflow: (1) heterogeneity in the approach to screening, monitoring, and managing health-related needs; (2) patients felt uncomfortable reporting health-related needs, particularly behavioral and social needs, to their care team; (3) lack of access to referral resources to resolve needs; and (4) the need for a closed loop intervention for patients and care team members. A subset of participants from Phase 1 (n=5 patients and n=9 care team members) provided feedback on preferred features, drawing from digital tools currently available in the EHR at our AMC. Among four existing EHR tools tested, there were notable variations in how patients and care team members felt about their potential use. Participants also provided extensive feedback for preferred components (e.g., goals and active plans) that should be available in an existing or custom digital solution to manage health-related needs. Findings from the qualitative interviews and design sessions were corroborated with EHR documentation. Conclusions: Digital solutions could provide a streamlined approach for collection and management of health-related needs in surgery, with the goal of addressing unmet needs and improving patient activation. This approach is critical to ensure patients, especially patients who undergo complex surgery, have positive health outcomes. We identified preferences for specific features in a proposed digital solution based on our systematic assessment that will inform future work.
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AI-driven transfer learning and classical molecular dynamics for strategic therapeutic repurposing and rational design of antiviral peptides targeting monkeypox virus DNA polymerase

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Leona Dcunha · Sonet Daniel Thomas · Levin John · Amritha Thaikkad · Dileep Francis · Rajesh Raju · Nik Hirani · Abhithaj Jayanandan

Comput Biol Med. 2025 Dec 7;200:111372. doi: 10.1016/j.compbiomed.2025.111372. Online ahead of print.

ABSTRACT

The emergence of monkeypox virus (MPXV) as a global health threat has necessitated the rapid identification of novel antiviral therapeutics. Currently, no FDA-approved drugs are specifically designed against the disease. We used an in-house deep learning pharmacophore model for screening a library of 1974 FDA-approved drugs targeting the active site of MPXV DNA polymerase. Three drugs exhibited the strongest binding affinities, outperforming the control drug, Cidofovir diphosphate, and forming stable interactions with key active site residues. Among them, Paromomycin emerged as the most favourable drug, demonstrating stable, persistent, and adaptable interactions in molecular dynamics simulation. In parallel, we developed a novel automated peptide-generating AI pipeline that integrates active-site residues with knowledge-guided amino acid selection to generate and evaluate synthetic peptides. Cysteine-Phenylalanine-Cysteine (CFC), together with a panel of candidates, emerged through rational balancing of physicochemical properties and drug-likeness for accelerated therapeutic discovery. Synthetic peptides were evaluated to further understand the binding efficacies with DNA polymerase. CFC peptide demonstrated strong binding affinity (-8.08 kcal/mol) through stable interactions with key catalytic residues ASP549, ARG634 and LYS661, while MMGBSA analysis confirmed favourable binding energy (-33.02 kcal/mol). Consistent results in MD simulations indicate functional binding without destabilisation. Although ADMET predictions for CFC revealed limitations in permeability and oral bioavailability, its favourable binding profile and reduced predicted toxicity support its potential as a novel antiviral lead.

PMID:41360016 | DOI:10.1016/j.compbiomed.2025.111372

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A typology of physician input approaches to using AI chatbots for clinical decision-making

✇npj Digital Medicine
By: Rachel Siden · Hannah Kerman · Robert J. Gallo · Joséphine A. Cool · Jason Hom · Ethan Goh · Neera Ahuja · Paul Heidenreich · Lisa Shieh · Daniel Yang · Jonathan H. Chen · Adam Rodman · Laura M. Holdsworth

npj Digital Medicine, Published online: 05 December 2025; doi:10.1038/s41746-025-02184-y

A typology of physician input approaches to using AI chatbots for clinical decision-making
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A Definition of AGI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dan Hendrycks · Dawn Song · Christian Szegedy · Honglak Lee · Yarin Gal · Erik Brynjolfsson · Sharon Li · Andy Zou · Lionel Levine · Bo Han · Jie Fu · Ziwei Liu · Jinwoo Shin · Kimin Lee · Mantas Mazeika · Long Phan · George Ingebretsen · Adam Khoja · Cihang Xie · Olawale Salaudeen · Matthias Hein · Kevin Zhao · Alexander Pan · David Duvenaud · Bo Li · Steve Omohundro · Gabriel Alfour · Max Tegmark · Kevin McGrew · Gary Marcus · Jaan Tallinn · Eric Schmidt · Yoshua Bengio
arXiv:2510.18212v3 Announce Type: replace Abstract: The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.
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Scaling Multimodal Search and Recommendation with Small Language Models via Upside-Down Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yu-Chen Lin · Sanat Sharma · Hari Manikandan · Jayant Kumar · Tracy Holloway King · Jing Zheng
arXiv:2502.09854v2 Announce Type: replace-cross Abstract: In this work, we investigate how small language models (SLMs) can be scaled to support multimodal search and recommendation use cases while remaining efficient enough for real-time, resource-constrained deployments. We present a framework that combines upside-down reinforcement learning with synthetic data distillation from a large language model (Llama-3) to train a 100M-parameter GPT-2 model for multitask prompt generation. Despite being up to 80 times smaller than state-of-the-art large language models (LLMs), our SLM achieves relevance and diversity scores within 6% of competitive baselines such as Llama-3 8B, Qwen3 8B, and Ministral 8B. These results demonstrate that SLMs can effectively handle multimodal search and recommendation tasks, while dramatically reducing inference latency and memory overhead. Our study highlights the potential of lightweight models as practical engines for scalable multimodal discovery, bridging the gap between cutting-edge research and real-world multimodal applications such as media recommendations and creative content generation.
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Privacy is All You Need: Revolutionizing Wearable Health Data with Advanced PETs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Karthik Barma
arXiv:2503.03428v2 Announce Type: replace-cross Abstract: In a world where data is the new currency, wearable health devices offer unprecedented insights into daily life, continuously monitoring vital signs and metrics. However, this convenience raises privacy concerns, as these devices collect sensitive data that can be misused or breached. Traditional measures often fail due to real-time data processing needs and limited device power. Users also lack awareness and control over data sharing and usage. We propose a Privacy-Enhancing Technology (PET) framework for wearable devices, integrating federated learning, lightweight cryptographic methods, and selectively deployed blockchain technology. The blockchain acts as a secure ledger triggered only upon data transfer requests, granting users real-time notifications and control. By dismantling data monopolies, this approach returns data sovereignty to individuals. Through real-world applications like secure medical data sharing, privacy-preserving fitness tracking, and continuous health monitoring, our framework reduces privacy risks by up to 70 percent while preserving data utility and performance. This innovation sets a new benchmark for wearable privacy and can scale to broader IoT ecosystems, including smart homes and industry. As data continues to shape our digital landscape, our research underscores the critical need to maintain privacy and user control at the forefront of technological progress.
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Challenges and Limitations of Generative AI in Synthesizing Wearable Sensor Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Flavio Di Martino · Franca Delmastro
arXiv:2505.14206v2 Announce Type: replace-cross Abstract: The widespread adoption of wearable sensors has the potential to provide massive and heterogeneous time series data, driving the use of Artificial Intelligence in human sensing applications. However, data collection remains limited due to stringent ethical regulations, privacy concerns, and other constraints, hindering progress in the field. Synthetic data generation, particularly through Generative Adversarial Networks and Diffusion Models, has emerged as a promising solution to mitigate both data scarcity and privacy issues. However, these models are often limited to narrow operational scenarios, such as short-term and unimodal signal patterns. To address this gap, we present a systematic evaluation of state-of-the-art generative models for time series data, explicitly assessing their performance in challenging scenarios such as stress and emotion recognition. Our study examines the extent to which these models can jointly handle multi-modality, capture long-range dependencies, and support conditional generation-core requirements for real-world wearable sensor data generation. To enable a fair and rigorous comparison, we also introduce an evaluation framework that evaluates both the intrinsic fidelity of the generated data and their utility in downstream predictive tasks. Our findings reveal critical limitations in the existing approaches, particularly in maintaining cross-modal consistency, preserving temporal coherence, and ensuring robust performance in train-on-synthetic, test-on-real, and data augmentation scenarios. Finally, we present our future research directions to enhance synthetic time series generation and improve the applicability of generative models in the wearable computing domain.
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Harnessing cuproptosis for pancreatic cancer therapy: From molecular insights to clinical prospects

✇(Multiomics OR Omics) AND (Pancreatic)
By: Ali Darzi · Siavash Boroumandi · Pouriya Khajegi · Mohammad Reza Dehdashti · Pedram Abdali · Mohammad Moein Amerinia · Fatemeh Kheirkhah · Mohammad Mahdi Ajalli · Mahla Shokouhfar

Biomed Pharmacother. 2025 Dec 2;193:118852. doi: 10.1016/j.biopha.2025.118852. Online ahead of print.

ABSTRACT

Pancreatic cancer (PC) remains a high-fatality malignancy with limited clinical progress, characterized by aggressive biology, marked resistance to standard therapies, and dismal outcomes. Even with state-of-the-art resection, radiotherapy, and multidrug chemotherapy, median survival benefits are modest, highlighting an urgent need for mechanism-based interventions. Cuproptosis, a newly delineated modality of regulated cell death initiated by intracellular copper accumulation and mitochondrial stress, presents a biologically coherent therapeutic avenue. Distinct from apoptosis, necroptosis, and ferroptosis, cuproptosis is driven by the direct binding of copper to lipoylated enzymes of the tricarboxylic acid (TCA) cycle, resulting in bioenergetic failure, misfolded protein aggregation, and collapse of cytotoxic proteostasis. Converging studies suggest that copper disequilibrium and metabolic reprogramming are recurrent features of PC, potentially contributing to malignant progression, immune evasion, and chemoresistance. These insights motivate two complementary strategies: first, therapeutic manipulation of copper flux, via chelators, ionophores, or transport modulators, to selectively trigger cuproptosis in tumor cells; and second, sensitization of mitochondrial metabolism, through targeting lipoic-acid pathway components, pyruvate utilization, or TCA load, to lower the threshold for cuproptotic killing. In parallel, multi-omic interrogation of cuproptosis-associated genes, proteins, and metabolites may yield prognostic and predictive biomarkers, enabling risk-adapted treatment selection and rational combinations with cytotoxic, targeted, or immunotherapeutic modalities. This review synthesizes recent advances on cuproptosis in PC and outlines its translational potential as both a therapeutic target and a biomarker framework.

PMID:41337879 | DOI:10.1016/j.biopha.2025.118852

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Digital Biometrics in Predicting Risk for Obstructive Sleep Apnea and Hypertension: Decentralized, Prospective Cohort Study

✇Journal of Medical Internet Research
By: Natalia Orendain · Samantha Spierling Bagsic · Edward Ramos · Jay Pandit · Robert L Owens · Stuti Jaiswal
Background: Sleep is an important component of human health and can be measured longitudinally using digital activity trackers. Further, decentralized digital research has the potential to provide a real-world picture of sleep in large populations. Objective: This study examined whether longitudinal sleep patterns from activity trackers could predict risk of obstructive sleep apnea (OSA) and hypertension, as defined the Berlin questionnaire and self report, respectively. Methods: We recruited adults ≥18 years nationwide to join our sleep-focused smartphone-based study, called the Research Framework for Exploring Sleep Health (REFRESH). Our sample of N= 391 adults is comprised predominately of females (68%; n = 247 out of 364) at a mean age of 48 years (standard deviation (SD) = 13.62). Participants were asked to fill out health-related surveys, including the Berlin questionnaire, and the Horne-Ostberg questionnaire for chronotype. Participants were asked to link their own activity tracker to the application to collect longitudinal sleep data. Results: We analyzed sleep data from 391 participants, among a predominately White (65%; n = 231 out of 353) followed by multiracial (17%; n = 61 out of 353) and Hispanic or Latino (6.5%; n = 23 out of 353) cohort. Collinearity testing showed that OSA risk and self-reported hypertension could be considered independently. Holding body mass index (BMI) at fixed value, the odds of having high OSA risk increased by 159% for every one-hour increase in weekday sleep variability (odds ratio (OR) = 2.592, 95% confidence interval (CI) [1.613, 4.400]; P
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Multi-omic profiling provides insights into the heterogeneity, microenvironmental features, and biomarker landscape of small-cell lung cancer

✇Omics In Lung
By: Mingchao Xie · Miljenka Vuko · Shashank Saran · Siyu Liu · Andrew G Chambers · Hana Baakza · Helen K Angell · Felicia Ng · Carl M Gay · Robert J Cardnell · Felix J Segerer · Alma Andoni · Jaime Rodriguez-Canales · Paul M Waring · Markus Schick · J Carl Barrett · Lauren A Byers · Giulia Fabbri

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

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Integrative Analysis of Multi-Omics Data for Biomarker Discovery

✇Omics in Hepatocellular
By: Rency S Varghese · Xinran Zhang · Sarada Giridharan · Hoi Yan Katharine Chau · Radhika Unnikrishnan · Md Mamunur Rashid · Muhammad Salman Sajid · Habtom W Ressom

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

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CACARA: Cross-Modal Alignment Leveraging a Text-Centric Approach for Cost-Effective Multimodal and Multilingual Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Diego A. B. Moreira · Alef I. Ferreira · Jhessica Silva · Gabriel O. dos Santos · Gustavo Bonil · Jo\~ao Gondim · Marina dos Santos · Helena Maia · Simone Hashiguti · N\'adia da Silva · Carolina Scarton · Helio Pedrini · Sandra Avila
arXiv:2512.00496v1 Announce Type: cross Abstract: As deep learning models evolve, new applications and challenges are rapidly emerging. Tasks that once relied on a single modality, such as text, images, or audio, are now enriched by seamless interactions between multimodal data. These connections bridge information gaps: an image can visually materialize a text, while audio can add context to an image. Researchers have developed numerous multimodal models, but most rely on resource-intensive training across multiple modalities. Similarly, extending these models to new languages often follows the same resource-heavy training strategy. In this work, we propose a multimodal and multilingual architecture, CACARA, trained through emergent alignment learning, enabling the seamless integration of new modalities into an existing bimodal/multimodal model without requiring full retraining. This work breaks new ground by demonstrating that this emergent alignment paradigm can unlock multilingual capabilities from monolingual training. By fine-tuning the newly incorporated modality only on data aligned with the English language, our model develops support for over 100 languages without explicit multilingual pretraining or tuning of the text encoder. Such emergent multimodal and multilingual properties are gained efficiently, preserving previously learned knowledge at a training cost comparable to that of a monolingual model. Our strategy achieves up to a 14.24 percentage points improvement in R@1 audio-to-text retrieval, outperforming state-of-the-art multimodal models -- all without the heavy computational cost of retraining across every modality and language.
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Deep Learning-Based Computer Vision Models for Early Cancer Detection Using Multimodal Medical Imaging and Radiogenomic Integration Frameworks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Emmanuella Avwerosuoghene Oghenekaro
arXiv:2512.00714v1 Announce Type: cross Abstract: Early cancer detection remains one of the most critical challenges in modern healthcare, where delayed diagnosis significantly reduces survival outcomes. Recent advancements in artificial intelligence, particularly deep learning, have enabled transformative progress in medical imaging analysis. Deep learning-based computer vision models, such as convolutional neural networks (CNNs), transformers, and hybrid attention architectures, can automatically extract complex spatial, morphological, and temporal patterns from multimodal imaging data including MRI, CT, PET, mammography, histopathology, and ultrasound. These models surpass traditional radiological assessment by identifying subtle tissue abnormalities and tumor microenvironment variations invisible to the human eye. At a broader scale, the integration of multimodal imaging with radiogenomics linking quantitative imaging features with genomics, transcriptomics, and epigenetic biomarkers has introduced a new paradigm for personalized oncology. This radiogenomic fusion allows the prediction of tumor genotype, immune response, molecular subtypes, and treatment resistance without invasive biopsies.
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