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PubMed2026

Mutual Information-based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG.

Mutual information (MI)-based approaches have increasingly been applied to cancer genomics; however, their use for genome-wide prognostic biomarker discovery remains relatively underexplored. The present article summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG) based on previously published applications in breast cancer, lower-grade glioma, and other cancer datasets. The framework consists of clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches. Previous MI-POG applications identified solute carrier family 20 member 1 (SLC20A1) as a prognostic biomarker in hormone receptor-positive breast cancer. Elevated SLC20A1 expression was associated with unfavorable survival outcomes and was independently validated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Methodological analyses demonstrated how survival endpoints can be integrated into an information-theoretic framework through fixed-time outcome discretization, enabling model-independent assessment of molecular-clinical dependencies. Applications across multiple cancer datasets suggested the potential applicability of the framework across biologically distinct tumor types, although further validation will be required to establish its robustness and generalizability. In conclusion, MI-POG can be formalized as an information-theoretic framework for genome-wide identification of prognostic biomarkers by quantifying molecular-clinical dependencies using mutual information. Representative applications from previously published studies suggest that MI-POG may complement conventional survival-analysis approaches and provide a useful strategy for biomarker discovery, although additional benchmarking and prospective validation will be required.

Humans↗

PubMed2026

Applications of metal-organic frameworks in smart packaging for food freshness indication: a comprehensive review.

Smart packaging is extensively studied for its multifunctional capabilities in antimicrobial activity, preservation, and atmosphere modification. Recently emerged metal-organic frameworks (MOFs) freshness-indicating packaging becomes a key research direction in smart packaging owing to its distinctive functions and physicochemical properties. As multifunctional materials, the unique porous structure and tunable properties of MOFs provide a distinctive approach for developing food packaging applications dedicated to food freshness indication. Existing MOFs-based smart packaging still faces potential safety risks and technical challenges in practical applications, and there remains a lack of integrated discussion that combines synthesis strategies, packaging design, optimization, and safety assessment. This review elaborates on the application of MOFs in freshness-indicating smart packaging, focusing on diverse MOFs synthesis strategies, the formats of smart packaging, types of indicator signals, and qualitative/quantitative analytical methods. It also delves into the methodology concepts of MOFs-based smart packaging and evaluates MOFs safety in food packaging by addressing potential risks. Studies show that MOFs-based smart packaging achieves qualitative and semi-quantitative analysis of food freshness through multiple signal modalities such as visible color change, fluorescence, and photothermal effects. This review emphasizes that safe MOFs design is critically important and should comply with the overall migration limit of <10 mg/dm2 specified in Regulation (EC) No 1935/2004, lanthanide element limit of <0.05 mg/kg, and FDA threshold of 1.5 μg/person/day. Comprehensive safety assessment and intelligent sensing platforms will constitute pivotal directions for advancing MOFs-based smart packaging toward practical application.

Food Packaging↗

PubMed2026

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In vitro fertilisation (IVF) faces persistent challenges including suboptimal embryo selection, unpredictable treatment outcomes, and limited personalisation of protocols. Despite advances in assisted reproductive technology, existing literature exhibits fragmentation: artificial intelligence applications in embryo selection, ovarian stimulation, and endometrial assessment have been developed independently without systematic integration into comprehensive treatment frameworks. Digital twin technology offers unprecedented opportunities to create virtual replicas of biological systems, enabling real-time monitoring, predictive modelling, and personalised treatment strategies. AIM: This narrative review aims to critically examine the current applications of digital twin technology in IVF, evaluate its potential benefits and limitations, synthesize existing evidence into an integrative conceptual model, and identify future directions for implementation in reproductive medicine. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases. A narrative review approach was selected over systematic review to accommodate the heterogeneity of evidence types in this emerging field, including theoretical frameworks, simulation studies, and proof-of-concept implementations that would be excluded from systematic reviews. Search terms included "digital twin," "IVF," "in vitro fertilisation," "assisted reproductive technology," "embryo selection," and "predictive modelling." Studies published between 2015 and 2025 were included, focusing on original research articles, systematic reviews, and proof-of-concept studies describing digital twin applications in reproductive medicine. RESULTS: Digital twin technology in IVF demonstrates significant potential across multiple domains including embryo development simulation, ovarian response prediction, endometrial receptivity modelling, and personalised stimulation protocols. Current applications integrate artificial intelligence, machine learning algorithms, time-lapse imaging, and omics data to create comprehensive virtual models. Early evidence suggests improvements in embryo selection accuracy, ovarian response prediction, and treatment protocol optimization, though large-scale randomized controlled trials remain limited. Implementation challenges include data integration complexity, computational requirements, regulatory considerations, and validation requirements. CONCLUSION: Digital twin technology represents a paradigm shift in IVF practice, offering personalised, predictive, and precision medicine approaches. This review synthesizes existing evidence to propose an integrative conceptual model for digital twin implementation across the IVF treatment spectrum, identifies critical knowledge gaps, and establishes research priorities to advance clinical translation. Despite current limitations, continued advancement promises improved success rates and patient outcomes.

Humans↗

PubMed2025

Speed breeding: protocols, application and achievements.

One of the limiting factors in breeding and genetic research is the time required to develop pure lines. This is due, on the one hand, to the prolonged vegetative period of a single generation and, on the other hand, to the specifics of inbreeding, which typically requires 4-6 consecutive generations of self-pollination in plant material. Researchers have always sought approaches that enable the rapid development of homozygous plant lines. Consequently, methods such as greenhouse cultivation during the autumn-winter period, single-seed descent, shuttle breeding, embryo culture, and doubled haploid technology have been introduced into practice. All these methods have both advantages and limitations. One of the latest approaches facilitating a significant reduction in the vegetative period of plants is speed breeding (SB). This method is based on the application of factors that shorten the time from sowing to flowering, as well as techniques that accelerate the generative phase of development and overcome postharvest dormancy. This review provides a comprehensive list and characterization of all factors that influence the efficiency of speed breeding to varying degrees. Among the factors discussed that reduce the sowing-to-flowering period are photoperiod, light sources, spectral composition and light intensity, temperature, carbon dioxide levels, vernalization, mineral nutrition, substrate volume, mechanical shoot removal, and the use of plant growth regulators. To shorten the generative phase, the review summarizes the application of embryo culture and forced desiccation of immature seeds, along with methods to overcome postharvest dormancy. Additionally, applications of genetic approaches and genetic engineering for shortening generation time in speed breeding are described. The review also consolidates detailed protocols for approximately thirty crops. The high efficiency of speed breeding in reducing both the vegetative period per generation and the time required to develop pure lines has led to its increasing adoption in various research fields. This review highlights the application of speed breeding for hybridization and pure line development, introgression of target alleles, and genomic selection. A list of phenotypic traits exhibiting high correlation between controlled-environment and field conditions is provided.

accelerated flowering↗

PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗

PubMed2026

Memantine and its analogs: Potential applications in cancer therapy.

Drug repurposing refers to the process of using an existing drug or drug candidate for a new treatment or medical condition for which it was not indicated before. The process of "drug repurposing" usually involves an FDA-approved entity which has undergone clinical development and have a with well-established safety and toxicity profile in patients. Several convergent studies show that repurposed drugs may present a promising strategy for the management and therapy of several human cancers. Memantine is used to combat dementia in moderate-to-severe Alzheimer's disease (AD) patients. Several convergent studies show that memantine (and its analogs) may have applications in multiple disease conditions associated with human cancers. Memantine and its related compounds have shown promise as neuroprotective agents, anti-fatigue agents and pain-relieving agents to alleviate the toxic side effects of radiation therapy and chemotherapy. Recent publications have revealed that memantine displays anti-cancer activity by exerting direct growth-suppressive activity on the primary tumor as modulating the genomic/cellular landscape of the tumor microenvironment. Currently, clinical trials are in progress, which aim to evaluate the potential applications of memantine in cancer treatment. The adamantane scaffold in memantine has proved to be a versatile tool for the discovery of synthetic memantine analogs with robust anti-cancer activity. The discovery of second-generation memantine analogs may have wider applications in combating cancer recurrence and addressing clinical challenges in the treatment of drug-resistant and metastatic cancers.

Humans↗

PubMed2026

Biotic and abiotic degradation of PHAs: mechanisms, environments, and potential applications of degradation products.

This review seeks to compile Polyhydroxyalkanoates (PHAs) degradation studies published over the past 20 years. It highlights the effect of physical properties, such as crystallinity and molecular weight, on the decomposition rate of these molecules. Both biotic processes, mediated by bacteria, fungi, and enzymes, as well as abiotic processes, such as hydrolysis and thermal degradation, are analyzed. A repertoire of diverse microorganisms, including their metabolic pathways and enzymes for PHA breakdown, is presented. Furthermore, this review presents the decomposition of PHAs in various environments, such as soil and seawater, highlighting their potential as a sustainable alternative. Finally, the resulting degradation products are described, emphasizing their potential applications in medicine and industry. Although degradation of PHAs has been extensively studied through these years, several knowledge gaps remain undisclosed, including the degradation of diverse polyester monomers. PHAs comprise numerous monomer compositions with variable properties, which present opportunities for different applications but pose a challenge in their degradation. The reader of this review can extract useful information for both the production of PHAs and their potential applications.

Biodegradation↗

PubMed2026

Probiotic-derived extracellular vesicles as food-based nanocarriers: Mechanisms, functional applications, and future perspectives in food systems.

Probiotic-derived extracellular vesicles (PDEVs) are a promising type of postbiotic nanoparticle derived by fermentation of probiotics, and have gained growing interest as a potential application in food science and nutrition. These are lipid bilayer vesicles of nanoscale, which are naturally released by probiotic cells and contain a wide variety of bioactive molecules, such as proteins, nucleic acids, and metabolites. Moreover, PDEVs are highly stable, biocompatible, and can be easily engineered to have surfaces with high functionality, which makes them good candidates in functional engineering. In contrast to traditional live probiotics, PDEVs overcome the difficulties of preserving microbial viability during processing and storage, thus providing superior safety, stability, and predictable biological performance. This is a systematic review of the various functions of PDEVs in food systems. We conclude on the processes through which PDEVs control intestinal barrier integrity, alter gut microbiota composition, and alter host immune responses, and their potential to enhance gut health when added to functional foods. In addition to their health-promoting effects, PDEVs have shown significant potential as natural antimicrobial agents to preserve food and as effective nanocarriers of hydrophobic bioactive compounds, including fucoxanthin, to improve their stability, bioavailability, and targeted delivery. Moreover, PDEVs can be used as new regulators of microbial fermentation. However, it should be noted that a lot of the evidence that is available is still preliminary and the effectiveness of these applications in real food-processing and storage conditions has not been fully proven. Although they have potential, there are a number of challenges that still hinder the widespread use of PDEVs in the food industry. These involve the creation of scalable and cost-effective production processes, batch-to-batch consistency, vesicle stability in a variety of food matrices, and regulatory and safety considerations. Other emerging engineering approaches, such as surface functionalization and cargo loading, are also discussed in this review and could further increase the specificity, functionality, and application versatility of PDEVs in food systems. Moving forward, the incorporation of PDEVs into the next generation functional foods, novel food preservation methods, and customized nutrition plans should be prioritized in future studies. Further developments in these fields can make PDEVs useful platforms at the interface of food microbiology, nanotechnology, and human health.

Probiotics↗

PubMed2026

XsiAMT1.1a was identified as a novel ammonium uptake functional gene and its overexpression combined with GA4 application significantly increased yield in Arabidopsis thaliana.

Nitrogen (N) is a key limiting factor for plant yield. Ammonium is one of the main N forms absorbed by plants. Overexpression of ammonium uptake functional genes, such as ammonium transporter (AMT), can increase yield. However, the AMTs reported to enhance yield significantly is still limited. No researches have focused on the effect of overexpressing AMT combined with hormone application on yield improvement. In this study, we first investigated the role of XsiAMT1.1a, a potential ammonium uptake functional gene in an ammonium preference plant Xanthium sibiricum, in ammonium uptake by the analysis of bioinformatics, gene expression and subcellular localization, and the determination of ammonium uptake rate in endogenous silencing and heterologous overexpression plants. Subsequently, the effect of XsiAMT1.1a overexpression combined with hormone application on yield increase was further investigated in model plant Arabidopsis thaliana. Our results showed that XsiAMT1.1a shared the same conserved domains with AtAMT1 subfamily members and localized on the plasma membrane. XsiAMT1.1a was induced by N deficiency and highly expressed during the reproductive period. XsiAMT1.1a endogenous silencing and heterologous overexpression significantly decreased and increased ammonium uptake rates in X. sibiricum and A. thaliana, respectively. Overexpression of XsiAMT1.1a significantly improved total N accumulation, biomass and yield in A. thaliana, while XsiAMT1.1a overexpression combined with GA4 application had a stronger promoting effect on the above indicators. Our research identified a novel ammonium uptake functional gene, XsiAMT1.1a, and provided a new yield-increasing strategy which was verified in A. thaliana.

Arabidopsis↗

PubMed2026

Efficacy of prescription-eligible digital health applications for depression and generalized anxiety disorder in Germany: a systematic review and meta-analysis.

In Germany, prescription-eligible digital mental health applications (DiGA) were introduced in 2020 as promising interventions to address, among others, depression and anxiety disorders, two of the most prevalent mental health conditions worldwide. Despite growing interest in DiGAs, their overall efficacy remains uncertain. This study aimed to systematically evaluate and quantify the efficacy of prescription-eligible digital interventions for depression and generalized anxiety disorder by synthesizing evidence from randomized controlled trials (19 trials; total N = 4,078; pooled mean age = 38.7 years, SD = 12.1). Here we show that prescription-eligible digital applications for depression and generalized anxiety disorder reduce symptom severity compared with control conditions. For depression, effects were observed both immediately after the intervention (number of apps = 5; k = 17; SMD = - 0.49; 95% CI - 0.65 to - 0.32) and at follow-up (number of apps = 1; k = 4; SMD = - 0.35; 95% CI - 0.46 to - 0.29), while evidence for generalized anxiety disorder was limited due to a small number of available studies (number of studies = 2). These findings support the integration of evidence-based digital tools into mental health treatment strategies in Germany. However, the available evidence is currently dominated by a small number of applications, particularly Deprexis, and should therefore not be interpreted as equally representative of all DiGAs currently listed for depression in Germany. The findings also highlight methodological limitations of current research and underscore the need for real-world evaluations, which address not only efficacy but also the effectiveness, content, quality and implementation.

Generalized Anxiety Disorder↗

PubMed2026

diffMONT: predicting methylation-specific PCR biomarkers based on nanopore sequencing data for clinical application.

MOTIVATION: DNA methylation serves as a key biomarker in clinical diagnostics, especially in cancer detection. With methylation-specific PCR (MSP), a widely used approach, patient samples can be screened fast and efficiently for differential methylation. During MSP, methylated regions are selectively amplified with specific primers. With nanopore sequencing, knowledge about DNA methylation is generated during direct DNA sequencing without needing pretreatment of the DNA. Multiple methods, mainly developed for whole-genome bisulfite sequencing (WGBS) data, exist to predict differentially methylated regions (DMRs) in the genome. However, the predicted DMRs are often very large and not sufficiently discriminating to generate meaningful results in MSP, creating a gap between theoretical cancer marker research and practical application, as no tool currently provides methylation difference predictions tailored for PCR-based diagnostics. RESULTS: Here, we present diffMONT, a tool that predicts differentially methylated regions specifically suited for MSP primer design, enabling rapid translation into practical applications. diffMONT takes into account (i) the specific length of primer and amplicon regions, (ii) the fact that one condition should be unmethylated, and (iii) a minimal required amount of differentially methylated cytosines within the primer regions. We compared the results of diffMONT to metilene and DSS based on a publicly available nanopore sequencing dataset and show that the regions predicted by diffMONT are more specific toward hypermethylated regions. diffMONT accelerates the design of methylation-specific diagnostic assays, bridging the gap between theoretical research and clinical application. AVAILABILITY AND IMPLEMENTATION: The source code for diffMONT, an open-source Python-based tool, is available at https://github.com/rnajena/diffMONT/, with an archived release under https://zenodo.org/records/17641031.

DNA Methylation↗

PubMed2026

Bacterial R-bodies with common morphologies and unrolling dynamics are phylogenetically scattered, indicating extensive lateral gene transfer and wide application potential.

Refractile bodies (R-bodies) of gram-negative bacteria are large proteinaceous assemblies, rolled up in the form of an Archimedean spiral. They exhibit rapid rod-like reversible extension in the micrometer range when cued by chemical environmental triggers and have potential for synthetic biology and biochip applications. Initially described for the Paramecium endosymbionts Caedibacter taeniospiralis and Caedimonas varicaedens, R-bodies have since been discovered in many classes of Pseudomonadota, both in endosymbionts and in non-endosymbionts. However, despite the fact that the genetics and morphologies, as well as the unrolling kinetics of R-bodies from different species, show considerable diversity, no recent study has integrated these aspects into a single framework. The latter would be advantageous for the creation of an R-body biotechnology toolbox, where different properties determine the application area. Here, we have examined the R-bodies from six different Pseudomonadota, comprising both phylogenetically diverse endosymbionts and non-endosymbionts. Comparison of the morphologies of the rolled-up and unrolled forms, obtained using electron microscopy and high-quality images, to their corresponding genetic data indicates that extensive lateral gene transfer has occurred, which confounds a common framework based on these data. However, we have also studied the R-body extension and retraction kinetics using high frame-rate light microscopic video recordings, where we show for the first time that R-bodies can be classified into two classes, showing "fast burst" or "slow" acid-induced extension kinetics, respectively. We propose that this criterion may, in fact, be the most useful for the choice of an R-body tool for biotechnological purposes.IMPORTANCER-bodies are unique proteinaceous macromolecular structures capable of massive reversible extension in response to external environmental triggers without the input of chemical energy. They comprise only a few small polypeptides, which makes them potentially highly amenable to tuning via genetic engineering, as well as being exceptionally stable. These properties would be highly desirable in biotechnology and synthetic biology, as well as in biochip applications, where a controlled mechanical extensor might play an integral part in a nanoscale molecular machine. So far, only R-bodies from a single species, Caedibacter taeniospiralis, have been characterized extensively. However, in recent years, genomic information has revealed that a panoply of R-bodies are widely distributed among gram-negative phyla, although studies have generally not included morphological data. This study brings these two areas together to provide a holistic overview of the field and also reveals new insights into key dynamic aspects of R-body extension.

R-bodies↗

PubMed2026

Advances in the Application of Adenine Base Editor (ABE) in Biology and Medicine: Prospects and Challenges.

Adenine base editors (ABEs), which achieve A·T to G·C conversions in the genome precisely, symbolize a groundbreaking development in genetic engineering across animal, plant, and microbial systems. This review systematically summed up the research progress and current challenges of ABE in medical and biological applications: it outlined the historical context and pivotal milestones of its technological development; it emphasized major therapeutic advances for genetic diseases including spinal muscular atrophy, mitochondrial genetic disorders, and hyperlipidemia; it provided a comprehensive overview of its prospective uses for enhancing genetic traits in agricultural crops, including grains and fruits; this review conducted a multidimensional assessment of ABE performance through systematic comparison with other base editing technologies, comprehensively evaluating both editing efficiency and inherent limitations. It specifically addresses biosecurity risks such as off-target effects and genomic instability. Finally, safety concerns were proposed as the central challenge hindering its clinical translation, although ABE holds immense promise for precision medicine and agricultural breeding. Unlike previous reviews that mainly summarized early ABE development and general applications, this review particularly emphasizes recently engineered ABE systems, translational bottlenecks, delivery strategies, comparative clinical feasibility, and unresolved biosafety challenges that currently limit broader therapeutic and agricultural applications.

Adenine base editors↗

PubMed2026

Digital twins in precision pharmacotherapy: emerging applications, challenges, and future directions.

Digital twin technology, defined as dynamic digital models that represent individual patients, is emerging as a promising paradigm in precision pharmacotherapy. The integration of pharmacokinetic and pharmacodynamic (PK/PD) modeling, clinical data, genomic information, and real-time patient monitoring enables digital twins to shift drug therapy away from population-based averages toward individualized, adaptive decisions. This narrative review explores conceptual frameworks, emerging applications, methodological approaches, clinical value, limitations, and future directions of digital twins in pharmacotherapy, with particular emphasis on the role of clinical pharmacists. Unlike broader digital twin reviews that primarily emphasize technical architectures, disease-specific applications, or pharmaceutical research and development, this review focuses on the clinical-pharmacy translation layer: how digital twin outputs can be interpreted, validated, communicated, and converted into actionable medication decisions at the bedside and across ambulatory care settings. Key applications include precision dosing, polypharmacy management, antimicrobial stewardship, and the optimization of complex therapies, alongside important ethical, regulatory, and implementation challenges.

clinical pharmacy↗

PubMed2026

Genetically Modified and Gene-Edited Organisms-Objectives, Public Perception and Applications.

Genetic modification and genome editing have become important tools in agriculture, animal production, biotechnology, and human medicine, but their safety and societal acceptance remain subjects of debate. This review examines genetically modified (GM) and gene-edited organisms, distinguishing transgenesis from precision genome editing technologies, including CRISPR/Cas9, base editing, and prime editing. Representative applications in crops, livestock, pharmaceutical production, and xenotransplantation are discussed, together with their regulatory framework and public perception. Current scientific assessments indicate that approved GM foods are not inherently more hazardous to human health than their conventional counterparts when evaluated case by case. Potential benefits include improved nutritional quality, biofortification, disease resistance, increased agricultural efficiency, production of therapeutic proteins, and applications in animal health and medicine. Possible concerns include allergenicity, toxicity, unintended genetic or phenotypic effects, altered nutritional composition, environmental consequences, animal welfare issues, and uncertainties associated with long-term or large-scale deployment. Public acceptance varies substantially according to geographical region, application, cultural and ethical considerations, regulatory environment, scientific literacy, and institutional trust. Overall, GM and gene-edited organisms should not be considered a homogeneous category. Their benefits, risks, and societal acceptability depend on the specific organism, genetic modification, intended trait, and context of use, supporting a balanced, evidence-based, and case-specific approach.

acceptance↗

PubMed2026

Induced Pluripotent Stem Cells in Non-Model Species: Applications and Challenges.

Induced pluripotent stem cells have revolutionized biomedical research-yet the vast majority of life on Earth remains beyond their reach. Non-model species lack the annotated genomes, validated reagents, and species-specific culture infrastructure that make iPSC technology routine in humans and mice, and this infrastructure deficit, compounded by genuine biological differences in pluripotency network architecture across taxa, is what has kept the field narrow. The deep conservation of the core pluripotency network across vertebrates suggests that reprogramming may, in principle, be achievable across a far broader range of species than currently demonstrated-though the extent to which this holds across more divergent taxa remains to be established. This review consolidates current progress and future potential of iPSC technology across five domains: technical reprogramming challenges and advances; conservation applications including genetic rescue, in vitro gametogenesis, and de-extinction; medical applications within a one medicine framework; agricultural applications spanning disease resistance, climate resilience, and cultured meat; and species-specific iPSC-derived systems in ecotoxicology. Throughout, we distinguish what has been demonstrated from what remains aspirational and identify the priorities that will determine whether the iPSC revolution can be extended-rigorously and at scale-beyond model organism research.

Induced Pluripotent Stem Cells↗

PubMed2026

Integrated experimental and bioinformatics analysis reveals ECM-integrin and redox signaling associated with PMMA/NiO nanocomposites for craniofacial applications.

BACKGROUND: Poly(methyl methacrylate) (PMMA) is widely used in dental and craniofacial applications; however, its clinical performance is limited by poor surface wettability, moderate mechanical strength, and restricted biological activity. Integrating nanomaterial engineering with computational biology offers an opportunity to better understand biomaterial-cell interactions and support the rational design of functional biomaterials. METHODS: Nickel oxide (NiO) nanoparticles were synthesized via chemical precipitation and incorporated into PMMA to fabricate nanocomposites. Physicochemical characterization included contact angle measurements, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and Vickers hardness testing. Biocompatibility was evaluated using zebrafish embryo developmental assays. To explore biological processes potentially associated with biomaterial-cell interactions, bioinformatics analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and STRING protein-protein interaction (PPI) network analyses were performed. RESULTS: Incorporation of NiO nanoparticles improved the surface and mechanical properties of PMMA, reducing the contact angle from 105.35° to 90.46° and increasing Vickers hardness compared with unmodified PMMA. Structural and morphological analyses confirmed successful synthesis and homogeneous nanoparticle incorporation. Zebrafish embryo studies demonstrated minimal developmental toxicity, supporting the biocompatibility of the nanocomposite. Bioinformatics analyses identified significant enrichment of pathways related to extracellular matrix organization, cell adhesion, focal adhesion, PI3K-Akt signaling, and oxidative stress regulation. Protein-protein interaction analysis revealed highly interconnected networks associated with ECM-integrin signaling and redox homeostasis, highlighting biological processes potentially associated with biomaterial-cell communication. CONCLUSIONS: PMMA/NiO nanocomposites exhibited improved physicochemical performance and favorable biocompatibility characteristics. The integration of experimental characterization with bioinformatics and network-based analyses provides a systems-level perspective on biomaterial-associated cellular processes and identifies ECM-integrin signaling and oxidative stress-related pathways as candidate biological processes for future experimental validation. These findings support the continued development of PMMA/NiO nanocomposites for oral and craniofacial biomedical applications.

Nanocomposites↗

PubMed2026

Applications of artificial intelligence in robot-assisted surgery: a systematic review.

To characterize applications of artificial intelligence (AI) in robot-assisted surgery, summarize technical and clinical performance, and assess the quality of the available evidence. PubMed, Web of Science Core Collection, and Scopus were searched for English-language journal articles published from 1 January 2020 through 31 October 2025. Randomized, observational, model-development, validation, and feasibility studies evaluating AI in robot-assisted surgery or closely related image-guided minimally invasive workflows were eligible. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment. Owing to heterogeneity in surgical procedures, AI tasks, analytical units, validation strategies, and outcomes, findings were synthesized descriptively without statistical pooling. The review was registered in the International Prospective Register of Systematic Reviews (CRD420251175699). Seventeen studies were included: seven clinical prediction or decision-support studies, eight intraoperative recognition, segmentation, or image-guided studies, and two training or workflow studies. Five prediction studies reported area-under-the-curve values of 0.74-0.95. Technical studies reported F1 or Dice scores of 0.525-0.995 and task-specific accuracies of 0.840-0.998. Two randomized studies suggested benefits for personalized suturing feedback and automated camera control, but neither established improved patient outcomes. Only one study had low overall risk of bias; the remaining studies were at high or unclear risk or raised some concerns. AI applications in robot-assisted surgery show promise for prediction, intraoperative perception, training, and workflow support. Evidence primarily demonstrates technical feasibility rather than established clinical effectiveness. Independent multicenter validation and prospective evaluation of patient, educational, and workflow outcomes are required before widespread implementation.

Robotic Surgical Procedures↗