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PubMed2026

The Subtle Crisis: Public Domain Genomes and the Ethics of Translational Infrastructure.

Public domain human genomic resources are infrastructure: tools researchers use to ask basic biological questions whose answers are then translated into products and care. Translational science now asks them to support an expanding set of tasks, including clinical variant interpretation for diverse populations, pharmacogenomic prescribing, polygenic risk prediction, and the training of clinical artificial intelligence. The corpus of public domain genomes, due in large part to upstream recruitment choices, is not fit for these purposes, and the gap between discovery and translation is widening. This essay argues that closing the gap requires treating public domain genomic infrastructure as a particular object of translational bioethics rather than a technical precondition for it. The limited number of genomes in the public domain relative to the broader genomic record, and the typology-friendliness of how that record represents human variation, are two faces of the same set of upstream choices. Reversing them is not a matter of more sampling under existing terms; it is a matter of building infrastructure of a particular kind; infrastructure made from people. That category, common in genomics but absent from the rest of science, demands an ethical apparatus the field has not yet built. Here we consider the commitments such an apparatus requires, and argue that where, how, and with whom we build genomic infrastructure is itself an ethics question the field has largely declined to ask.

Humans↗
PubMed2025

Delivery of genome editors with engineered virus-like particles.

Genome editing technologies have revolutionized biomedical sciences and biotechnology. However, their delivery in vivo remains one of the major obstacles for clinical translation. Here, we introduce various emerging genome editing systems and review different delivery systems have been developed to realize the promise of in vivo gene editing therapies. In particular, we focus on virus-like particles (VLPs), an emerging delivery platform and provide in depth analysis on recent advancements to improve VLPs delivery potential and highlight opportunities for future improvements. To this end, we also provide detail workflows for engineered VLP (eVLP) selection, production, and purification, along with methods for characterization and validation.

Gene Editing↗
PubMed2026

Expanding the human proteome with microproteins and peptideins.

A major scientific drive is to characterize the protein-coding genome, which is a primary basis for studying human health. But the fundamental question remains of what has been missed in previous analyses. Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states1-3, with major implications for biomedical science. However, a key gap in knowledge has been which ncORFs produce small microproteins or alternative protein molecules that contribute to the human proteome. Here we report the collaborative efforts of the TransCODE Consortium4 to produce a consensus landscape of protein-level evidence for ncORFs. We show that about 25% of a set of 7,264 ncORFs gives rise to detectable peptides in a large-scale analysis of 95,520 proteomics experiments. We develop an annotation framework for ncORF-encoded microproteins as human proteins and codify the new conceptual model of 'peptideins' as microproteins that have indeterminate potential as functional proteins. To probe the biological implications of peptideins, we create an evolutionary analysis approach, termed ORF relative branch length (ORBL), and determine that evolutionary constraint is common and associates with observation of ncORF-derived peptides. We then characterize a pan-essential cellular phenotype for one peptidein from the OLMALINC long non-coding RNA. Overall, we generate public research tools supported by GENCODE and PeptideAtlas and advance biomedical discovery for understudied components of the human proteome.

Humans↗
PubMed2025

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data.

The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.

Journal Article↗
PubMed2026

Temporal mismatch in allogeneic iPSC therapies: biological risks and implications for clinical translation.

INTRODUCTION: The clinical translation of pluripotent stem cell-derived therapies has entered a new phase following conditional approval of first-in-class allogeneic induced pluripotent stem cell (iPSC)-derived products in Japan. These approvals highlight both the therapeutic promise of iPSC technologies and regulatory challenges associated with evaluating complex cell-based interventions. AREAS COVERED: This report examines the evidentiary basis supporting recent approvals and reviews key biological characteristics of allogeneic iPSC-derived therapies, including pluripotency-associated instability, immunological constraints, and manufacturing-related genomic variability. Drawing on recent clinical studies and relevant experimental literature, we analyze how these multilayered risks evolve over extended time horizons and assess their implications for the interpretation of early-phase clinical data and current regulatory frameworks. EXPERT OPINION: We argue that the central challenge extends beyond limited clinical evidence to a fundamental mismatch between the temporal dynamics of biological risk and the duration of conventional clinical evaluation. As a result, early clinical observations may systematically underestimate long-term risks. Conditional approval pathways should therefore incorporate safeguards aligned with this temporal uncertainty, including long-term follow-up, rigorous post-approval evaluation, and enhanced transparency in biological and manufacturing data. Aligning regulatory design with intrinsic properties of pluripotent stem cell-derived therapies will be essential for ensuring safe and responsible clinical translation.

Humans↗
PubMed2026

Bridging Organ-on-a-Chip and Omics: A Multi-Dimensional Frontier in Biomedical Research.

Organ-on-a-Chip (OOC) technology offers a powerful platform for replicating human tissue-specific microenvironments, thereby narrowing the translational gap between conventional biomedical models and actual human physiology. Concurrently, omics technologies deliver comprehensive molecular-level insights into biological systems. This review highlights the transformative potential of integrating OOC platforms with high-throughput omics methodologies. We systematically examine the classification, structural configurations, and engineering principles underlying OOC systems, alongside the defining attributes of key omics domains-genomics, transcriptomics, proteomics, and metabolomics. The convergence of dynamic OOC models with advanced omics technologies enables high-resolution, multi-dimensional analyses across numerous biomedical applications, including drug metabolism, disease mechanisms, environmental toxicity assessments, and host-microbiome interactions. This interdisciplinary integration is driving a paradigm shift in precision and translational medicine. However, several challenges remain to be addressed, such as the development of whole-organ mimetics, adaptation of sample collection techniques, and real-time artificial intelligence-based integration of biosensor data with multi-omics datasets. Addressing these hurdles will be vital for unlocking the full potential of this technological synergy in biomedical science.

Multiomics↗
PubMed2025

Advancing the fight against tuberculosis: integrating innovation and public health in diagnosis, treatment, vaccine development, and implementation science.

Tuberculosis (TB) remains one of the leading causes of infectious disease mortality worldwide, increasingly complicated by the emergence of drug-resistant strains and limitations in existing diagnostic and therapeutic strategies. Despite decades of global efforts, the disease continues to impose a significant burden, particularly in low- and middle-income countries (LMICs) where health system weaknesses hinder progress. This comprehensive review explores recent advancements in TB diagnostics, antimicrobial resistance (AMR surveillance), treatment strategies, and vaccine development. It critically evaluates cutting-edge technologies including CRISPR-based diagnostics, whole-genome sequencing, and digital adherence tools, alongside therapeutic innovations such as shorter multidrug-resistant TB regimens and host-directed therapies. Special emphasis is placed on the translational gap-highlighting barriers to real-world implementation such as cost, infrastructure, and policy fragmentation. While innovations like the Xpert MTB/RIF Ultra, BPaLM regimen, and next-generation vaccines such as M72/AS01E represent pivotal progress, their deployment remains uneven. Implementation science, cost-effectiveness analyses, and health equity considerations are vital to scaling up these tools. Moreover, the expansion of the TB vaccine pipeline and integration of AI in diagnostics signal a transformative period in TB control. Eliminating TB demands more than biomedical breakthroughs-it requires a unified strategy that aligns innovation with access, equity, and sustainability. By bridging science with implementation, and integrating diagnostics, treatment, and prevention within robust health systems, the global community can accelerate the path toward ending TB.

diagnostic innovation↗
PubMed2026

Copper-Containing Surface Engineering for Soft-Tissue Biomedical Devices: Structure-Function Relationships and Ion Release-Driven Biological Performance, A Systematic Review.

Copper and copper-based materials have gained increasing attention for the functional modification of implantable medical devices intended for prolonged soft-tissue contact, including vascular stents, catheters, and intrauterine devices. Owing to their broad-spectrum antimicrobial activity, redox reactivity, and involvement in angiogenesis and cellular signaling, copper-based systems offer significant potential for multifunctional surface engineering. However, achieving a balance between antibacterial efficacy, corrosion behavior, controlled ion release, and cytocompatibility remains a critical challenge. This PRISMA-compliant systematic review analyzes copper-containing materials and surface modification strategies for soft-tissue biomedical applications. A structured search of Scopus, Web of Science, and PubMed (2015-2025) identified 65 eligible studies. The review encompasses bulk copper-containing alloys, electrochemical and chemical surface modification techniques, physical vapor deposition approaches, and advanced hybrid systems integrating copper with polymers, hydrogels, or metal-phenolic networks. Across the reviewed literature, antibacterial performance was strongly dependent on copper concentration, microstructural distribution, and spatiotemporal ion release profiles. Moderate, well-controlled copper incorporation frequently improved antibacterial efficacy while maintaining acceptable hemocompatibility and cytocompatibility, particularly in vascular and blood-contacting devices. In contrast, excessive copper loading often accelerated corrosion and induced adverse cellular responses. Emerging multifunctional architectures demonstrated improved regulation of biological interactions, enabling simultaneous antibacterial, antithrombotic, and proendothelial effects. Overall, copper-based surface technologies represent a versatile platform for soft-tissue implant modification. Future translational progress will require precise control of copper release kinetics and comprehensive long-term in vivo validation to ensure safety and sustained therapeutic performance. From the authors' perspective, the most promising future direction involves multifunctional copper-based hybrid coatings capable of dynamically regulating ion release, host tissue integration, and antibacterial performance simultaneously. Strategies integrating hierarchical architectures, stimulus-responsive release systems, and clinically scalable fabrication methods are expected to play a key role in translating copper-containing surfaces from experimental concepts toward commercially viable soft-tissue biomedical devices.

Copper↗
PubMed2025

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins.

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

GENCODE↗
PubMed2026

Harnessing the Power of Large Language Models for Drug Discovery: A Systematic Review of Current Applications and Future Directions.

INTRODUCTION: The demand for inventive approaches to drug discovery has increased due to the rising costs, time, and failure rates in pharmaceutical research. Large Language Models (LLMs), with their sophisticated natural language processing and generative capabilities, have become potent instruments that have the potential to revolutionize biomedical research. The function of LLMs in different phases of drug development is methodically examined in this article. METHODS: The PRISMA 2020 principles were adhered to in this systematic study. A thorough search for research published between 2018 and 2025 was done using PubMed, Scopus, Web of Science, and Google Scholar. The search terms "large language model," "transformer," "drug discovery," and important sub-domains (such as "de-novo design" and "ADMET") were merged, and two reviewers independently screened the results. Predetermined inclusion and exclusion criteria were used to filter studies for relevance. 98 studies out of the 1,285 records that were initially retrieved met the requirements for the final qualitative synthesis. RESULTS: 98 studies that demonstrated the use of LLMs in various drug discovery domains were found during the review. These covered molecular generation, genomics, protein-ligand modeling, ADME/T and toxicity profiling, drug-target interaction and DTI prediction, and biomedical text mining. 42 different LLM-based tools were mapped, including BioBERT, SciSpacy, Drug- LLM, DNA-BERT, GPT-4, and ChatGPT. Predictive accuracy, hypothesis creation, target prioritization, and multi-modal data integration all showed notable gains with these techniques. DISCUSSION: By providing scalable, precise, and effective solutions for data-driven drug discovery, LLMs are revolutionizing the pharmaceutical industry. They allow for the creation of hypotheses and individualized insights across multi-modal biological data, and they perform better than conventional approaches in a number of subdomains. Improvements in performance were task-dependent; the most consistent gains occurred for biomedical text mining, disease-genedrug relationship mapping and drug-target interaction prediction tasks. Yet most evidence for clinical applications is still derived from retrospective studies and benchmark datasets, suggesting a higher need for prospective validation. CONCLUSION: There is revolutionary potential in incorporating LLMs into drug discovery processes. Clinical translation and regulatory uptake will depend heavily on collaborative validation, ethical deployment, and standardization as models become more multimodal and interpretable. Before normal use, extensive prospective benchmarking and head-to-head comparisons with established chemoinformatics pipelines are necessary.

De novo design↗
PubMed2026

Research updates in cystic fibrosis related diabetes: Understanding pathophysiology, expanding animal and human islet models, and advancing clinical and translational research.

In 2024-2025, the Cystic Fibrosis Foundation (US) and Cystic Fibrosis Trust (UK) hosted an International CFRD Consortium round-table webinar series for basic science, translational, and clinical researchers with the goal of sharpening mechanistic understanding of CFRD pathogenesis and prioritizing therapeutic development. This review summarizes the research priorities identified in the International CFRD Consortium, including (i) further investigation into the role of pancreatic fibrosis, vascular abnormalities, and α-cell dysfunction in the development of CFRD; (ii) the creation and refinement of novel animal and human cell- and tissue-based models to understand the complex interplay of exocrine and endocrine cells in the CF pancreas; (iii) development and validation of circulating and imaging biomarkers, together with dynamic glucose testing to explore β-cell function and kinetics in people with CF across the dysglycemia spectrum; and (iv) prospective clinical studies to guide CFRD treatment options and investigate the changing landscape of aging, increasing prevalence of obesity and diabetes and their complications in the era of cystic fibrosis transmembrane conductance regulator (CFTR) modulators. Collectively, these priorities aim to accelerate transition from mechanism to intervention and expand evidence-based care for people with CF at risk of, or living with, CFRD.

Humans↗