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At least 19 recordsLinked to original sources
PubMed1978

Perceptual versus mediational learning in a total change concept-shift paradigm.

The experiment investigated the effects of language acquisition by children in Grades 1 to 4 on performance in a concept-shift task in which the relevant stimulus attributes were either the colour of ink in which a word was written or the meaning of the word. Both English stream and French Immersion children served as subjects. The results indicated a developmental sequence from perceptual learning to verbal mediation. This process was demonstrated at an earlier stage in the French Immersion students who formed a more highly selected group, and intellectual or socio-economic explanations for these differences may be feasible. The relative speed of acquisition of intradimensional and extradimensional shifts interacts with the perceptual/mediational process.

Child↗
PubMed2026

Paradigm shift for cry gene expression in Bacillus thuringiensis.

In most Bacillus thuringiensis strains, the cry genes are transcribed by RNA polymerases containing sporulation-sigma factors E or K, leading to the formation of an insecticidal crystal within the mother cell along spore development. The kurstaki HD1 strain, a parent of commercial strains, also releases the insecticidal proteins Cry1I and Vip3A in the extracellular medium. vip3A expression is activated by the transcriptional regulator VipR at the onset of the stationary phase. Here, we expanded the VipR regulon in strain HD1 by identifying the VipR-binding box upstream from the cry2Aa, cry2Ab, and cry1Ia genes, and conducting transcription assays. Unexpectedly, a VipR box was located in the promoter of a putative N-acetylmuramoyl-l-alanine amidase (ami) gene upstream from cry1Ac in strain kurstaki HD73, closely related to the HD1 but devoid of vipR. Introduction of vipR in this strain led to the expression of the ami-cry1Ac operon, resulting in an early and increased production of Cry1Ac. We demonstrated that Cry1Ac was also produced in a VipR-dependent manner in an HD73 ∆spo0A mutant. Similarly, an HD1 ∆spo0A strain produces all the insecticidal proteins encoded in its genome, including cry2Ab, previously considered unexpressed. A genomic analysis also revealed the presence of putative VipR-binding sequences in lepidopteran-active strains, upstream from cry genes such as cry1E, cry1F, cry9D, and cry9E. Overall, our results break the dogma on the regulation of cry1A and cry2A genes and provide evidence of sporulation-independent Cry toxin production in biopesticidal Bt strains.IMPORTANCEBacillus thuringiensis is a remarkably efficient entomopathogen due to its ability to produce various insecticidal proteins, such as Cry or Vip. This property has made it a highly effective biopesticide used worldwide. Our work modifies the paradigm of cry1 and cry2 genes being regulated solely by sporulation-specific sigma factors and thus exclusively expressed during this process. Indeed, we demonstrated that the VipR regulator controls the transcription of vip3Aa, cry2Aa, cry2Ab, cry1Ia, and the ami-cry1A operons encoded by a strain closely related to that of commercial biopesticides and specifically turns on their expression from the onset of the stationary phase, leading to the production of insecticidal crystals independently of sporulation. By providing new knowledge on the regulation of insecticidal protein genes, these findings bring new insight for the genetic improvement of Bt strains used as commercial biopesticides.

Bacillus thuringiensis↗
PubMed2026

Quantitative assessment of the fingerprint evidential value using machine learning.

Fingerprints as physical evidence have long supported criminal investigation and adjudication. In practice, however, fingerprint identification relies mainly on examiners' experience. Furthermore, expert opinions tend to be categorical, even though the opinions with the same conclusion could differ substantially in evidential strength. To quantitatively assess fingerprint evidential value, this study proposes a machine learning-based framework as an interpretable decision-support tool. A lightweight residual one-dimensional convolutional neural network was constructed, incorporating channel recalibration and a similarity-driven attention mechanism to learn adaptive contribution weights for different matched minutiae (minutiae for short). Controlled experiments revealed that the predicted evidential value increased with the number of minutiae and was significantly influenced by the quality of minutiae. With 10 minutiae, the mean predicted scores were 4.49, 7.00, and 9.09 for blurred, moderately blurred, and clear minutiae, respectively. Multiple regression analysis indicated that replacing a pair of blurred minutiae with a pair of clear minutiae increased the score by 0.492, whereas replacing it with a pair of moderately blurred minutiae increased the score by only 0.216. By mapping predicted scores to graded levels of evidential strength, the framework contributes to a paradigm shift from categorical expert opinions to graded ones, helping courts evaluate fingerprint evidence more scientifically.

Humans↗
PubMed2025

The Application of Preventive Medicine in the Future Digital Health Era.

A number of seismic shifts are expected to reshape the future of medicine. The global population is rapidly aging, significantly impacting the global disease burden. Medicine is undergoing a paradigm shift, defining and diagnosing diseases at earlier stages and shifting the health care focus from treating diseases to preventing them. The application and purview of digital medicine are expected to broaden significantly. Furthermore, the COVID-19 pandemic has further accelerated the shift toward predictive, preventive, personalized, and participatory (P4) medicine, and has identified health care accessibility, affordability, and patient empowerment as core values in the future digital health era. This "left shift" toward preventive care is anticipated to redefine health care, emphasizing health promotion over disease treatment. In the future, the traditional triad of preventive medicine-primary, secondary, and tertiary prevention-will be realized with technologies such as genomics, artificial intelligence, bioengineering and wearable devices, and telemedicine. Breast cancer and diabetes serve as case studies to demonstrate how these technologies such as personalized risk assessment, artificial intelligence-assisted and app-based technologies, have been developed and commercialized to provide personalized preventive care, identifying those at a higher risk and providing instructions and interventions for healthier lifestyles and improved quality of life. Overall, preventive medicine and the use of advanced technology will hold great potential for improving health care outcomes in the future.

Humans↗
PubMed2026

Interpreting cancer genetics through a two-step "evolutionary cascade hypothesis": bridging neutral and selective perspectives.

BACKGROUND: DNA mutations are the fundamental engines of cancer, driving its initiation and progression. The forces that fuel malignancy are also the architects of evolution, shaping life through genetic variations. Mutations, in fact, can emerge naturally from endogenous processes, such as oxidative DNA damage or errors in replication, as well as induced by external factors, including cosmic radiation and chemical carcinogens. MAIN BODY: A key question in cancer research is whether tumor evolution is primarily governed by selective bottlenecks, neutral evolution, or dynamic genetic plasticity. In this work, we examine cancer as a disease driven by evolutionary processes rooted in fundamental biological requirements, including sustained proliferation and nutrient utilization. We hypothesize that the accumulation of mutations activates an evolutionary switch, enabling tumor cells to acquire an enhanced capacity for survival, adaptation, and growth at rates far exceeding typical evolutionary timescales. We propose the "evolutionary cascade hypothesis," a unifying framework that integrates these models into a coherent sequence. At its core lies the failure of DNA repair mechanisms, representing a critical transition in cancer progression. This shift marks the transition from an initial non-Darwinian, neutral phase to a Darwinian, more deterministic phase. CONCLUSIONS: As predictive models of tumor evolution advance through genomic big data and artificial intelligence-driven analysis, the future of cancer treatment may extend beyond targeting individual mutations to disrupting the underlying evolutionary mechanisms that sustain malignancy. This paradigm shift could redefine therapeutic strategies and ultimately improve patient outcomes.

Humans↗
PubMed2026

Repair and regeneration across the lifespan: an ontogenetic perspective.

The capacity for tissue repair and regeneration undergoes a profound and progressive decline across the human lifespan, representing a fundamental driver of aging and chronic disease. This review establishes a comprehensive ontogenetic framework by mapping the continuous biological transition from the flawless, scarless regenerative plasticity of embryonic development to the irreversible fibrotic scarring and organ failure characteristic of senescence. We synthesize the hierarchical collapse of reparative networks across multiple biological scales. Importantly, this ontogenetic decline should not be interpreted as a purely degenerative trajectory but rather as a dynamic systems-level reprogramming in which evolutionary trade-offs prioritize tumor suppression, immune surveillance, and reproductive fitness over long-term regenerative fidelity. Recognizing this adaptive reallocation of biological resources reframes aging not simply as failure but as a predictable recalibration of repair hierarchies. At the molecular and cellular levels, the accumulation of genomic instability, unresolvable DNA damage, and mitochondrial dysfunction gradually overwhelms intracellular quality-control mechanisms. Concurrently, epigenetic drift and chronic, low-grade systemic inflammation ("inflammaging") dismantle the stem cell niche, driving adult stem cell exhaustion and shifting wound healing away from functional tissue replacement toward maladaptive fibrosis. Furthermore, we examine divergent, organ-specific repair trajectories. By contrasting the severe regenerative restrictions of the adult central nervous system and myocardium with the persistent, yet exhaustible, resilience of the liver, we elucidate the unique intrinsic and microenvironmental barriers that impede structural and functional recovery. Finally, we evaluate the clinical paradigm shift from passive management of age-related degeneration to active restoration of tissue integrity. By integrating systemic geroscience-which addresses the global hallmarks of aging-with targeted bioengineering and in vivo epigenetic modulation, contemporary regenerative medicine seeks to recreate permissive, youthful microenvironments. Ultimately, mastering these ontogenetic principles holds unprecedented potential to reactivate endogenous repair pathways, mitigate multi-organ collapse, and significantly extend human functional healthspan.

DNA repair↗
PubMed2026

Acute leukemia therapy at a crossroads: from conventional chemotherapy to the era of precision medicine.

Since the discovery of cytotoxic agents in the mid-20th century, acute leukemia has consistently served as a model for oncology research. As the Human Genome Project and subsequent genomic profiling elucidated the landscape of somatic mutations and cytogenetic aberrations driving leukemogenesis, the development of molecularly targeted therapies has dramatically accelerated, yielding significant improvements in patient outcomes. In acute myeloid leukemia (AML), the emergence of selective inhibitors targeting high-frequency alterations such as FLT3, NPM1, and IDH1/2 has redefined the standard of care, demonstrating superior efficacy when combined with conventional intensive chemotherapy or hypomethylating agents. Simultaneously, for acute lymphoblastic leukemia (ALL), in addition to the significant improvements achieved by tyrosine kinase inhibitors (TKIs) for BCR-ABL-positive ALL, the advent of CD19- or CD22-targeted monoclonal antibodies and CAR-T cell therapies has marked an epoch-making milestone, representing a major paradigm shift in the management of relapsed or refractory cases. Bridging these two distinct lineages, menin inhibitors have emerged as a novel class of agents targeting a common pathogenic mechanism in KMT2A-rearranged AML/ALL and NPM1-mutated AML, exhibiting promising antileukemic activity across these subtypes. In this review, we describe the evolution of leukemia therapy-highlighting historical trajectory across AML, APL, and ALL from uniform cytotoxic chemotherapy to molecularly targeted agents, antibody-based therapies, and chemo-free paradigms, while outlining future perspectives for precision hematology.

Acute lymphoblastic leukemia↗
PubMed2026

Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.

The escalating generation of global vegetable waste represents a critical loss of bioactive resources, necessitating a paradigm shift from passive disposal to active nutrient upcycling. However, the industrial conversion of this heterogeneous biomass into standardized functional food ingredients is currently impeded by significant techno-economic barriers, primarily structural recalcitrance, compositional inconsistency, and the presence of toxic fermentation inhibitors. This review provides a comprehensive analysis of the synergistic application of microbial engineering and artificial intelligence (AI) to resolve these bioprocessing bottlenecks within a food-to-food closed-loop framework (as shown in the graphical abstract). We evaluate recent advances in engineering food-grade microbial chassis (e.g., Saccharomyces cerevisiae and Escherichia coli) to enhance lignocellulose degradation and stress tolerance. Concurrently, we examine the integration of AI across the entire value chain, covering deep learning-based rational enzyme design, genome-scale metabolic modeling, and intelligent process control for precision fermentation. Current evidence demonstrates that the hardware-software coupling of engineered strains and AI algorithms significantly enhances conversion efficiency and process robustness. Key findings highlight that AI-driven Design-Build-Test-Learn cycles facilitate the de novo creation of enzymes with superior kinetics and strains with adaptive stress response capabilities against toxins. Moreover, dynamic digital twin models effectively mitigate the impact of substrate variability, ensuring the batch-to-batch consistency required for food applications. We conclude that this data-driven synergistic paradigm is pivotal for establishing a resilient circular bioeconomy, enabling the reliable bioconversion of waste into high-value single-cell proteins, natural flavor additives, and sustainable packaging materials.

Artificial Intelligence↗
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↗
PubMed2026

GLP-1 Receptor Agonist and GIP/GLP-1 Receptor Dual Agonist Therapeutics at the Intersection of Alcohol Use Disorder, Obesity, and Cardiometabolic Dysfunction.

The co-occurrence of metabolic dysfunction and heavy alcohol consumption contributes substantially to global morbidity, particularly through its impact on liver disease progression. Glucagon-like peptide-1 receptor (GLP1R) agonists and glucose-dependent insulinotropic polypeptide receptor (GIPR)/GLP1R dual agonists, currently approved for the treatment of diabetes and obesity and under investigation for metabolic dysfunction-associated steatohepatitis, are considered for repurposing to reduce alcohol consumption and stabilize metabolic health in heavy-drinking populations. This review summarizes existing evidence, highlights ongoing research, and outlines key unanswered questions regarding this therapeutic potential and the paradigm shift toward metabolic circuit-based interventions in addiction treatment. The dual-target GIPR/GLP1R approach could fill a critical gap for individuals struggling with both heavy alcohol consumption and metabolic dysfunction.

Alcohol use disorder↗
PubMed2026

Generative artificial intelligence for enzyme design and biocatalysis.

Sparked by innovations in generative artificial intelligence (AI), the field of protein design has undergone a paradigm shift with an explosion of new models for optimizing existing enzymes or creating them from scratch. After more than one decade of low success rates for computationally designed enzymes, generative AI models are now frequently used for designing proficient enzymes. Here, we provide a comprehensive overview and classification of generative AI models for enzyme design, highlighting models with experimental validation relevant to real-world settings and outlining their respective limitations. We argue that generative AI models now have the maturity to create and optimize enzymes for industrial applications. Wider adoption of generative AI models with experimental feedback loops can speed up the development of biocatalysts and serve as a community assessment to inform the next generation of models.

Biocatalysis↗
PubMed2026

The 21st International ``Ponte di Legno'' Childhood Acute Lymphoblastic Leukemia Workshop Report: Progress and Emerging Opportunities.

The 21st Ponte di Legno Working Group meeting convened in Orlando, USA, on December 4-5, 2025, bringing together leading childhood acute lymphoblastic leukemia (ALL) investigators from major global consortia. In response to the transformative advances in childhood ALL treatment, particularly the integration of immunotherapy into frontline therapy, the group revisited and updated its mission statement. The revised mission emphasizes collaborative studies on rare leukemia subsets, harmonized toxicity reporting, particularly for immunotherapy-related toxicities, and unrestricted worldwide collaboration. In addition, sharing data and strategies for integrating novel agents will be integral to optimizing future trial design. Key scientific topics included rare genetic subgroups, T-cell ALL genomics, treatment-related toxicity benchmarking, and central nervous system (CNS) disease management challenges. A major focus was immunotherapy integration into frontline therapy, particularly blinatumomab as an emerging standard of care and inotuzumab ozogamicin as an investigational agent, and their potential to enable chemotherapy de-escalation. Additional discussions addressed immunotherapy-specific toxicities. The integration of immunotherapy into frontline ALL therapy represents a paradigm shift with potential to improve outcomes while reducing treatment burden. However, careful attention to CNS disease control, emerging toxicities, and preservation of the remarkable achievements in childhood ALL therapy remains essential as the field advances.

Blinatumomab↗
PubMed2025

Genetics first approach: Expanding the utility of genetic testing by nongeneticist physicians.

PURPOSE: The increasing demand for genetic testing and a global shortage of geneticists has significantly strained health care systems worldwide. This highlighted the need for new strategies aiming to increase testing accessibility, reduce wait times, and enhance patient care quality. METHODS: We implemented a 4-step program, "Genetics First," to empower nongeneticist physicians (NGPs) to play an active role in the process of genetic consultation and testing. The steps included (1) establishing criteria to identify suitable clinical domains, (2) selecting clinical indications within the domain through expert panel review, (3) designing tailored education and workflows for NGPs across indications, and (4) monitoring test outcomes and providing further support for complex cases. Test outcomes were compared between NGPs and clinical geneticists. RESULTS: Endocrinology was selected as the first domain, with 114 endocrinologists who completed the program. During the study, 260 gene panels were performed for monogenic diabetes, with NGPs initiating 68% of tests, leading to a 107% increase in referrals. The diagnostic yield was 30%, with no significant difference between NGP- and clinical geneticist-initiated tests. CONCLUSION: This study demonstrates the feasibility and impact of involving NGPs in genetic testing, offering a paradigm shift that could expand access to genetic testing and improve patients' care and clinical outcomes.

Humans↗
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↗
PubMed2026

"Updates on diagnostic and prognostic molecular biomarkers of CNS tumors".

The diagnosis and classification of central nervous system (CNS) tumors has undergone a paradigm shift over the past decade, evolving from a purely histology-based approach to an integrated framework that incorporates molecular and epigenetic features. This review summarizes recent updates in key genomic and epigenomic biomarkers across major CNS tumor categories, with a focus on their diagnostic, prognostic, and therapeutic implications. DNA methylation profiling has emerged as a valuable tool for tumor classification, subgrouping, and grading, complementing traditional histopathologic assessment. Across diffuse gliomas, newly characterized molecular alterations have refined grading criteria and clarified the boundaries between tumor types, including important caveats about the use of individual molecular features as sole diagnostic criteria. In ependymomas, medulloblastomas, atypical teratoid/rhabdoid tumors, meningiomas, pineal tumors, and embryonal tumors, methylation profiling now defines biologically and clinically meaningful subgroups that inform risk stratification and treatment selection. The emerging recognition of mismatch repair-deficient gliomas and fusion-driven tumor entities further underscores the expanding complexity of CNS tumor taxonomy. As molecular technologies continue to advance, the integration of genomic, epigenomic, histopathologic, and clinical data will be essential to improving diagnostic precision, guiding therapy, and ultimately enhancing patient outcomes.

Embryonal↗
PubMed2026

Precision Engineering of Evolution-Resilient Rice against Bacterial Blight.

The persistent conflict between rice and Xanthomonas oryzae pv. oryzae (Xoo), the causal agent of bacterial blight, exemplifies a dynamic genetic arms race in agriculture. The cyclical deployment and erosion of major resistance (R) genes highlight the high adaptive potential of Xoo and the need for strategies that are durable rather than absolute. This review synthesizes a paradigm shift from reactive, single R-gene deployment toward proactive engineering of evolution-resilient resistance. We explore the molecular-genetic basis of Xoo adaptability, including TAL effector diversification, non-TAL virulence functions, genome variation, and immune suppression mechanisms. In response, we propose a framework for durable disease management with three connected components: precision disarmament through editing of susceptibility-gene effector-binding elements and executor/decoy designs; smart induction through targeted delivery and immune priming; and ecological fortification through protective microbiomes. We also discuss the limits, trade-offs, and field-validation requirements of these approaches. Integrating frontier technologies with evolutionary genetics, predictive genomics, and pathogen population dynamics can help develop rice varieties and deployment systems that are more difficult for Xoo populations to overcome.

CRISPR↗
PubMed2026

Advances in Single-Molecule Immunoassay: From Counting Strategies to CRISPR-Enhanced Biosensing.

Single-molecule immunoassays (SMIs) overcome the sensitivity limitations of conventional bulk measurements by enabling a paradigm shift from analog to digital signal readouts, thereby facilitating highly sensitive quantification of ultra-low-abundance biomarkers for precision diagnostics. This review provides a systematic overview of recent advances in SMI technologies and the conceptual framework underlying their evolution. First, discretization strategies for single-molecule counting are classified into hard discretization, based on physical confinement, and soft discretization, based on spatiotemporal isolation, within heterogeneous and homogeneous assay systems, respectively. The fundamental mechanisms by which these strategies mitigate diffusion limitations and enhance signal-to-noise ratios are discussed. Second, the integration of SMIs with CRISPR-based diagnostic systems (CRISPR-dx) is examined, with particular emphasis on their complementary roles in target recognition and signal amplification. Finally, recent applications of SMIs in the diagnosis of oncological, neurological, infectious, and cardiovascular diseases are summarized, along with a critical discussion of current engineering challenges and future directions toward clinical translation.

Immunoassay↗