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Normal view

Immunotherapy for virus-related hepatocellular carcinoma: recent progress and future directions

Ann Med. 2026 Dec;58(1):2607229. doi: 10.1080/07853890.2025.2607229. Epub 2025 Dec 26.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, with hepatitis B virus (HBV) and hepatitis C virus (HCV) infections remaining the predominant etiological factors. Chronic viral infection not only drives carcinogenesis but also reshapes the hepatic immune microenvironment, profoundly influencing the efficacy and safety of immunotherapy.

RECENT ADVANCES: Immune checkpoint inhibitors (ICIs) have revolutionized systemic therapy for advanced HCC, with agents targeting PD-1/PD-L1 demonstrating clinical benefit. Combination strategies - such as ICIs with anti-angiogenic therapies, multikinase inhibitors, or locoregional treatments - have shown synergistic efficacy and are now standard of care in certain settings. For virus-related HCC, antiviral therapy improves immune responsiveness and reduces risks such as HBV reactivation, underscoring the need for integrated management.

FUTURE PERSPECTIVES: Emerging therapeutic approaches include next-generation immune checkpoints (e.g. TIM-3, LAG-3, TIGIT), bispecific antibodies, cellular therapies (CAR-T, TCR-T, TILs), and tumor vaccines targeting viral or tumor-associated antigens. Advances in biomarker discovery, including circulating tumor DNA, immune signatures, and microbiome modulation, are expected to guide personalized treatment. Integration of multi-omics and clinical data will further refine patient selection and optimize treatment sequencing.

CONCLUSION: Immunotherapy offers new hope for patients with virus-related HCC, but challenges remain in response heterogeneity, resistance, and toxicity. Individualized strategies that combine immunotherapy with effective antiviral management and biomarker-|guided patient selection are essential. Continued translational and clinical research into virus-immune-tumor interactions will enable safer, more effective, and more durable treatment outcomes, ultimately transforming HCC into a more manageable disease.

PMID:41454610 | PMC:PMC12777805 | DOI:10.1080/07853890.2025.2607229

An integrated bioinformatics and multi-omics investigation of the sirtuin family to identify their prognostic importance in human cancers

Tumour Biol. 2025 Jan-Dec;47:14230380251410470. doi: 10.1177/14230380251410470. Epub 2025 Dec 24.

ABSTRACT

BackgroundIn recent years, the significance of sirtuins in cancer biology has become increasingly evident, but their molecular mechanisms and prognostic impacts remain elusive.ObjectiveThe present study aimed to investigate the differential expression of the sirtuin gene family across cancers and to evaluate their prognostic value.MethodsWe used various bioinformatics databases and methodologies, including Oncomine, GEPIA, OncoDB, cBioPortal, R2 Kaplan-Meier Scanner, STRING, etc., to determine the expression pattern of the sirtuin family genes, along with their mutations and prognostic values in human cancers.ResultsIn the current study, SIRT1, SIRT2, SIRT4, and SIRT5 were downregulated in lymphoma, whereas SIRT6 and SIRT7 were overexpressed. In breast cancer, SIRT3, SIRT5, and SIRT7 were overexpressed, and in terms of kidney cancer, higher expression of SIRT2, SIRT3, and SIRT5 was observed. In contrast, for leukemia, bladder, and brain cancers, most sirtuin family members showed reduced expression. We found that most mutations occurred in uterine cancer, chRCC (chromophobe renal cell carcinoma), DLBCL (diffuse large B-cell lymphoma), melanoma, pRCC (papillary renal cell carcinoma), and esophageal cancer. Moreover, we identified the relevant functional proteins through protein-protein interaction analysis to evaluate copy number alterations (CNAs) in sirtuins. The most frequent alterations were amplifications and deep deletions. Survival analysis demonstrated that SIRT1 and SIRT2 overexpression correlated with improved overall survival in low-grade glioma but predicted poorer outcomes in ovarian cancer. Downregulation of SIRT1, SIRT3, and SIRT5 was associated with better prognosis in DLBCL, while SIRT3 and SIRT4 upregulation predicted favorable survival in testicular germ cell tumors. SIRT6 overexpression was linked to favorable prognosis in esophageal carcinoma and sarcoma, while unfavorable outcomes were observed in hepatocellular carcinoma and cholangiocarcinoma. SIRT7 upregulation was significantly associated with reduced survival in esophageal, liver, and uterine cancers, but surprisingly correlated with improved outcomes in urothelial carcinoma and cervical squamous cell carcinoma.ConclusionsTogether, this multi-omics analysis reveals the correlation and prognostic values of sirtuins across multiple types of human cancers and suggests that sirtuins may serve as promising biomarkers for different cancers.

PMID:41439701 | DOI:10.1177/14230380251410470

Developing and Evaluating Guidelines to Prevent Overdependence on Digital Therapeutics in Children and Adolescents: Randomized Controlled Trial

Background: Digital therapeutics (DTx) for children and adolescents with mental health problems have been developed in the health care industry. Despite reports of side effects from DTx for children and adolescents, there have been no guidelines to address the prevention of DTx overdependence among young users. Objective: This study aimed to identify the requirements for guidelines to prevent DTx overdependence in children and adolescents and to develop and evaluate these guidelines. Methods: We conducted 2 phases. This study first involved a phase I survey to develop guidelines, including assessments of smartphone usage and mental health conditions. The second phase evaluated the guidelines’ effectiveness, reliability, necessity, and satisfaction using a visual analog scale through a randomized controlled trial. Participantsβ€”45 children and adolescents aged 9-16 years and 42 caregiversβ€”were randomly assigned to the experimental and control groups. Results: Phase I revealed that blocking mobile applications and notifications (mean 8.5, SD 1.8) and parental monitoring (mean 8.5, SD 2.1) were effective preventive features. Caregivers, children, and adolescents expressed concerns about the side effects and overdependence of DTx and decreased effects due to nonindividualized guidelines in subjective responses to the phase I survey. Based on these insights, personalized guidelines for phase II were developed, in which overall mean visual analog scale scores for guideline evaluation were higher in the experimental group, except for necessity among caregivers (mean 8.5, SD 1.3 versus mean 8.7, SD 1.2). Conclusions: Both caregivers and children and adolescents demonstrated the need for guidelines to prevent overdependence on DTx distinct from smartphone usage. Tailored guidelines may be acceptable for use in real-world therapeutic protocols. Guidelines to prevent overdependence on DTx in children and adolescents and to achieve a balance between their benefits and risks need to be established. Trial Registration: Clinical Research Information Service (CRiS) of the Republic of Korea KCT0008893; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=25609

Evaluating Peer Online Forums to Support Health: Ethical and Practical Challenges

Many people use peer online forums to seek support for health-related problems. More research is needed to understand the impacts of forum use, and how these are generated. However, there are significant ethical and practical challenges with the methods available to do the required research. We examine the key challenges associated with conducting each of the most commonly used online data collection methods: surveys, interviews, forum post analysis; and triangulation of these methods. Based on our learning from the Improving Peer Online Forums (iPOF) study, an inter-disciplinary realist informed mixed methods evaluation of peer online forums, we outline strategies that can be used to address key issues pertaining to assessing important outcomes, facilitating participation, validating participants (users who consent to take part in one or more parts of the study), protecting anonymity, gaining consent, managing risk, multi-stakeholder engagement, and triangulation. We share this learning to support researchers, reviewers, and ethics committees faced with deciding how best to address these challenges. We highlight the need for ongoing open, transparent discussion to ensure the research field keeps pace with evolving technology design and societal attitudes to online data use.

Context matching is not reasoning when performing generalized clinical evaluation of generative language models

npj Digital Medicine, Published online: 27 December 2025; doi:10.1038/s41746-025-02253-2

Context matching is not reasoning when performing generalized clinical evaluation of generative language models

A novel evaluation benchmark for medical LLMs illuminating safety and effectiveness in clinical domains

npj Digital Medicine, Published online: 26 December 2025; doi:10.1038/s41746-025-02277-8

A novel evaluation benchmark for medical LLMs illuminating safety and effectiveness in clinical domains

Comparison of liquid biopsy-based technologies for cancer screening

27 December 2025 at 19:00

Crit Rev Clin Lab Sci. 2025 Dec 27:1-12. doi: 10.1080/10408363.2025.2606357. Online ahead of print.

ABSTRACT

Circulating plasma DNA has found important applications in diverse medical fields, including prenatal testing, transplantation, and especially cancer. Many companies have developed products for detecting minimal residual disease, selecting or monitoring therapy, assessing prognosis, and confirming diagnosis. One major application is in screening asymptomatic individuals for the presence of cancer. Screening may facilitate better clinical outcomes through earlier interventions. Collectively, these technologies are widely known as "liquid biopsies". After the extraction of free DNA from the circulation, it is analyzed by various molecular techniques to explore differences between DNA originating from normal cells and cancer cells. Circulating plasma DNA originating from tumors (ctDNA) is expected to harbor the same molecular changes as tumor tissue itself. Thus, ctDNA is considered a surrogate of cancer tissue, but without the need to perform invasive biopsies to obtain it. Many new diagnostic companies have taken advantage of this new biomarker and developed technologies for screening for one or multiple cancers. We previously estimated the amount of ctDNA in circulation, which is admixed with DNA originating from normal cells. We concluded that since only a small fraction of the whole plasma (3 liters) is used for testing (3 to 4 mL), it is possible that the retrieved ctDNA may not be enough for cancer diagnosis in all patients. This problem is more acute with small tumors. Here, we mention some companies in the "liquid biopsy" arena and analyze their clinical data to establish if their tests are close to entering the clinic. We conclude from this analysis that current data do not support the use of these technologies for population screening due to many false negative and false positive results.

PMID:41454842 | DOI:10.1080/10408363.2025.2606357

Cancer in a drop: Liquid biopsy highlights from the World Conference on Lung Cancer (WCLC) 2025

J Liq Biopsy. 2025 Nov 29;10:100449. doi: 10.1016/j.jlb.2025.100449. eCollection 2025 Dec.

ABSTRACT

The role of liquid biopsy in oncological care continues to expand, with multiple studies presented at the International Association for the Study of Lung Cancer (IASLC) 2025 World Conference on Lung Cancer (WCLC 2025). This review summarizes recent advances in liquid biopsy for thoracic oncology, encompassing both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). In early detection and screening, proteomic profiling has identified potential biomarkers predictive of future lung cancer risk. The integration of proteomics with clinical and imaging data can improve pulmonary nodule malignancy prediction. In resectable NSCLC, tumour-informed whole-genome sequencing (WGS) assay demonstrated high sensitivity for minimal residual disease (MRD) detection, with MRD clearance following neoadjuvant osimertinib or chemo-immunotherapy associated with favorable outcomes. In advanced NSCLC, longitudinal liquid biopsy analyses reveal dynamic subclonal evolution driving early treatment resistance. Circulating tumor DNA (ctDNA) clearance following targeted therapy in MET exon 14 skipping and BRAF-mutated tumors was associated with improved clinical outcomes. Emerging biomarkers such as ctDNA tumour fraction and circulating microRNA signatures are promising for radiotherapy stratification and prediction of immunotherapy-related toxicities. In SCLC, MRD monitoring enables earlier detection of disease progression and supports ctDNA-guided selection of patients for consolidation immunotherapy following chemotherapy. Overall, these advances demonstrate the expanding role of liquid biopsy in improving early detection, guiding treatment, and improving disease monitoring in lung cancer.

PMID:41438843 | PMC:PMC12720026 | DOI:10.1016/j.jlb.2025.100449

  • βœ‡MIT Technology Review
  • Take our quiz on the year in health and biotechnology Jessica Hamzelou
    In just a couple of weeks, we’ll be bidding farewell to 2025. And what a year it has been! Artificial intelligence is being incorporated into more aspects of our lives, weight-loss drugs have expanded in scope, and there have been some real β€œomg” biotech stories from the fields of gene therapy, IVF, neurotech, and more.Β Β Β  As always, the team at MIT Technology Review has been putting together our 2026 list of breakthrough technologies. That will be published in the new year (watch this space)
     

Take our quiz on the year in health and biotechnology

19 December 2025 at 00:59

In just a couple of weeks, we’ll be bidding farewell to 2025. And what a year it has been! Artificial intelligence is being incorporated into more aspects of our lives, weight-loss drugs have expanded in scope, and there have been some real β€œomg” biotech stories from the fields of gene therapy, IVF, neurotech, and more.Β Β Β 

As always, the team at MIT Technology Review has been putting together our 2026 list of breakthrough technologies. That will be published in the new year (watch this space). In the meantime, my colleague Antonio Regalado has compiled his traditional list of the year’s worst technologies.

I’m inviting you to put your own memory to the test. Just how closely have you been paying attention to the Checkup emails that have been landing in your inbox this year?!

This article first appeared in The Checkup,Β MIT Technology Review’sΒ weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first,Β sign up here.

AI Epidemiology: achieving explainable AI through expert oversight patterns

arXiv:2512.15783v1 Announce Type: new Abstract: AI Epidemiology is a framework for governing and explaining advanced AI systems by applying population-level surveillance methods to AI outputs. The approach mirrors the way in which epidemiologists enable public health interventions through statistical evidence before molecular mechanisms are understood. This bypasses the problem of model complexity which plagues current interpretability methods (such as SHAP and mechanistic interpretability) at the scale of deployed models. AI Epidemiology achieves this population-level surveillance by standardising capture of AI-expert interactions into structured assessment fields: risk level, alignment score, and accuracy score. These function as exposure variables which predict output failure through statistical associations, much like cholesterol and blood pressure act as exposure variables predicting cardiac events. Output-failure associations are subsequently validated against expert overrides and real-world outcomes. The framework places zero burden on experts and provides automatic audit trails by passively tracking expert convergence and divergence with AI recommendations. Since it analyses outputs rather than internal model computations, it also provides governance continuity when institutions update models and switch vendors. Finally, by providing reliability scores and semantic assessments (e.g. 'this recommendation resembles 500 cases overridden by experts due to guideline violations'), it enables experts and institutions to detect unreliable AI outputs before they cause harm. This democratises AI oversight by enabling domain experts to govern AI systems without requiring machine learning expertise.

Small Language Models for Efficient Agentic Tool Calling: Outperforming Large Models with Targeted Fine-tuning

arXiv:2512.15943v1 Announce Type: new Abstract: As organizations scale adoption of generative AI, model cost optimization and operational efficiency have emerged as critical factors determining sustainability and accessibility. While Large Language Models (LLMs) demonstrate impressive capabilities across diverse tasks, their extensive computational requirements make them cost-prohibitive for routine enterprise use. This limitation motivates the exploration of Small Language Models (SLMs), which can deliver comparable performance in targeted applications while drastically reducing infrastructure overhead (Irugalbandara et al., 2023). In this work, we investigate the feasibility of replacing LLM-driven workflows with optimized SLMs. We trained a domain-adapted SLM to execute representative tasks traditionally handled by LLMs, such as document summarization, query answering, and structured data interpretation. As part of the experiment, we investigated the fine-tuning of facebook/opt-350m model (single epoch only) using the Hugging Face TRL (Transformer Reinforcement Learning), specifically the Supervised Fine-Tuning (SFT) trainer. The OPT-350M model was released by Meta AI in 2022 as part of the OPT (Open Pretrained Transformer) family of models. Similar studies demonstrate that even models at the 350M parameter scale can meaningfully contribute to instruction-tuning pipelines (Mekala et al., 2024). Experimental results demonstrated that our fine-tuned SLM achieves exceptional performance with a 77.55\% pass rate on ToolBench evaluation, significantly outperforming all baseline models including ChatGPT-CoT (26.00\%), ToolLLaMA-DFS (30.18\%), and ToolLLaMA-CoT (16.27\%). These findings emphasize that thoughtful design and targeted training of SLMs can significantly lower barriers to adoption, enabling cost-effective, large-scale integration of generative AI into production systems.
  • βœ‡cs.AI, q-bio.NC updates on arXiv.org
  • AI Needs Physics More Than Physics Needs AI Peter Coveney Β· Roger Highfield
    arXiv:2512.16344v1 Announce Type: new Abstract: Artificial intelligence (AI) is commonly depicted as transformative. Yet, after more than a decade of hype, its measurable impact remains modest outside a few high-profile scientific and commercial successes. The 2024 Nobel Prizes in Chemistry and Physics recognized AI's potential, but broader assessments indicate the impact to date is often more promotional than technical. We argue that while current AI may influence physics, physics has signific
     

AI Needs Physics More Than Physics Needs AI

arXiv:2512.16344v1 Announce Type: new Abstract: Artificial intelligence (AI) is commonly depicted as transformative. Yet, after more than a decade of hype, its measurable impact remains modest outside a few high-profile scientific and commercial successes. The 2024 Nobel Prizes in Chemistry and Physics recognized AI's potential, but broader assessments indicate the impact to date is often more promotional than technical. We argue that while current AI may influence physics, physics has significantly more to offer this generation of AI. Current architectures - large language models, reasoning models, and agentic AI - can depend on trillions of meaningless parameters, suffer from distributional bias, lack uncertainty quantification, provide no mechanistic insights, and fail to capture even elementary scientific laws. We review critiques of these limits, highlight opportunities in quantum AI and analogue computing, and lay down a roadmap for the adoption of 'Big AI': a synthesis of theory-based rigour with the flexibility of machine learning.

TIB AIssistant: a Platform for AI-Supported Research Across Research Life Cycles

arXiv:2512.16442v1 Announce Type: new Abstract: The rapidly growing popularity of adopting Artificial Intelligence (AI), and specifically Large Language Models (LLMs), is having a widespread impact throughout society, including the academic domain. AI-supported research has the potential to support researchers with tasks across the entire research life cycle. In this work, we demonstrate the TIB AIssistant, an AI-supported research platform providing support throughout the research life cycle. The AIssistant consists of a collection of assistants, each responsible for a specific research task. In addition, tools are provided to give access to external scholarly services. Generated data is stored in the assets and can be exported as an RO-Crate bundle to provide transparency and enhance reproducibility of the research project. We demonstrate the AIssistant's main functionalities by means of a sequential walk-through of assistants, interacting with each other to generate sections for a draft research paper. In the end, with the AIssistant, we lay the foundation for a larger agenda of providing a community-maintained platform for AI-supported research.

Machine learning-based predictions of healthcare contacts following emergency hospitalisation using electronic health records

npj Digital Medicine, Published online: 17 December 2025; doi:10.1038/s41746-025-02138-4

Machine learning-based predictions of healthcare contacts following emergency hospitalisation using electronic health records

Distributional AGI Safety

arXiv:2512.16856v1 Announce Type: new Abstract: AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an eventual emergence of a monolithic Artificial General Intelligence (AGI). The alternative AGI emergence hypothesis, where general capability levels are first manifested through coordination in groups of sub-AGI individual agents with complementary skills and affordances, has received far less attention. Here we argue that this patchwork AGI hypothesis needs to be given serious consideration, and should inform the development of corresponding safeguards and mitigations. The rapid deployment of advanced AI agents with tool-use capabilities and the ability to communicate and coordinate makes this an urgent safety consideration. We therefore propose a framework for distributional AGI safety that moves beyond evaluating and aligning individual agents. This framework centers on the design and implementation of virtual agentic sandbox economies (impermeable or semi-permeable), where agent-to-agent transactions are governed by robust market mechanisms, coupled with appropriate auditability, reputation management, and oversight to mitigate collective risks.

Data-Chain Backdoor: Do You Trust Diffusion Models as Generative Data Supplier?

arXiv:2512.15769v1 Announce Type: cross Abstract: The increasing use of generative models such as diffusion models for synthetic data augmentation has greatly reduced the cost of data collection and labeling in downstream perception tasks. However, this new data source paradigm may introduce important security concerns. This work investigates backdoor propagation in such emerging generative data supply chains, namely Data-Chain Backdoor (DCB). Specifically, we find that open-source diffusion models can become hidden carriers of backdoors. Their strong distribution-fitting ability causes them to memorize and reproduce backdoor triggers during generation, which are subsequently inherited by downstream models, resulting in severe security risks. This threat is particularly concerning under clean-label attack scenarios, as it remains effective while having negligible impact on the utility of the synthetic data. Furthermore, we discover an Early-Stage Trigger Manifestation (ESTM) phenomenon: backdoor trigger patterns tend to surface more explicitly in the early, high-noise stages of the diffusion model's reverse generation process before being subtly integrated into the final samples. Overall, this work reveals a previously underexplored threat in generative data pipelines and provides initial insights toward mitigating backdoor risks in synthetic data generation.

AI-Powered Dermatological Diagnosis: From Interpretable Models to Clinical Implementation A Comprehensive Framework for Accessible and Trustworthy Skin Disease Detection

arXiv:2512.16235v1 Announce Type: cross Abstract: Dermatological conditions affect 1.9 billion people globally, yet accurate diagnosis remains challenging due to limited specialist availability and complex clinical presentations. Family history significantly influences skin disease susceptibility and treatment responses, but is often underutilized in diagnostic processes. This research addresses the critical question: How can AI-powered systems integrate family history data with clinical imaging to enhance dermatological diagnosis while supporting clinical trial validation and real-world implementation? We developed a comprehensive multi-modal AI framework that combines deep learning-based image analysis with structured clinical data, including detailed family history patterns. Our approach employs interpretable convolutional neural networks integrated with clinical decision trees that incorporate hereditary risk factors. The methodology includes prospective clinical trials across diverse healthcare settings to validate AI-assisted diagnosis against traditional clinical assessment. In this work, validation was conducted with healthcare professionals to assess AI-assisted outputs against clinical expectations; prospective clinical trials across diverse healthcare settings are proposed as future work. The integrated AI system demonstrates enhanced diagnostic accuracy when family history data is incorporated, particularly for hereditary skin conditions such as melanoma, psoriasis, and atopic dermatitis. Expert feedback indicates potential for improved early detection and more personalized recommendations; formal clinical trials are planned. The framework is designed for integration into clinical workflows while maintaining interpretability through explainable AI mechanisms.
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