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PubMed1988

Embryonal carcinoma in two cases of androgen insensitivity syndrome: clinical, endocrinological and pathological features.

Embryonal carcinoma in two cases of complete androgen insensitivity syndrome (CAIS) is reported. In both cases gonadectomy carried out for prophylactic purposes led to the discovery of a localized embryonal carcinoma with areas of anaplastic seminoma in one case. In non-neoplastic tissue, gonad morphology in both cases was typical of AIS. Prevalently hypotrophic aspects, especially in the interstitial gland, were found in case 2. This may explain the different endocrine profile in the two cases before gonadectomy. Our study, aside from series of psycho-sexual problems, shows, according to all Authors, that the most serious complication is the high risk of malignancy after puberty in patients with AIS.

Adolescent↗
PubMedDate not supplied

Androgen insensitivity syndrome in the era of molecular genetics and the Internet: a point of view.

The era of molecular genetics has seen the discovery of a great deal of scientific information about the androgen receptor (AR) and about the many AR mutations that have been identified in patients with Androgen Insensitivity Syndrome (AIS). In families with well-characterised mutations, carriers can now be identified and prenatal testing can be offered. An unexpected finding is that an AR mutation also causes X-linked spinobulbar muscular atrophy. The intersex community has established two influential support groups, the AIS Support Group (which has branches in the UK, North America and Australia) and the Intersex Society of North America (ISNA). It is ironic that at a time when advances in biomedical science regarding AIS are a source of pride, these support groups are accusing the medical profession of having ignored the real needs of patients with AIS. Since the support groups are willing to assist the medical profession to develop better approaches to the management of intersex disorders, a collaborative approach is likely to be mutually beneficial for patients and physicians. ISNA has alienated many doctors by advocating a radical approach, namely that surgery should not be performed to 'correct' ambiguous genitalia until the individual is old enough to express a gender preference. Many children born in developing countries have either no genital surgery to correct ambiguity, or surgery is carried out very late. Long term outcome studies, carried out in developing countries and sensitive to the cultural background, would provide information on how non-Western societies can accept genital abnormalities that would be considered unacceptable in the West.

Androgen-Insensitivity Syndrome↗
PubMed2026

Application of emerging technologies in the antiviral field.

Viral diseases pose a serious threat to global public health, agriculture, and biosecurity. Conventional antiviral strategies are often limited by an incomplete understanding of disease mechanisms, poor targeting precision, and slow response times. Emerging technologies are now reshaping the landscape of antiviral research. This review examines the roles of four key frontiers, including organoid models, gene editing, AI-driven molecular design, and synthetic biology. Organoids provide physiologically relevant platforms that model virus-host interactions and disease progression. Viral infections remain a major challenge to human and animal health, agriculture, and biosecurity. Progress in antiviral research is constrained by the complexity of viral pathogenesis, the diversity and rapid evolution of viruses, and the limited translational relevance of some traditional model systems. Recent advances in organoid technology, gene editing, artificial intelligence, and synthetic biology are expanding the toolkit available for antiviral research and development. In this review, we discuss how these four technological frontiers contribute to disease modeling, target discovery, molecular design, and translational innovation. Organoids, in particular, provide physiologically relevant systems for investigating viral infection, tissue tropism, host responses, and pathogenesis. Gene editing tools, such as CRISPR, enable precise manipulation of host and viral genomes, facilitating the development of resistant organisms and next-generation vaccine platforms. AI technologies, including AlphaFold for structure prediction and platforms for de novo protein design, address long-standing bottlenecks in structural biology and offer powerful means to engineer antiviral proteins, antibodies, and vaccine antigens. Synthetic biology, guided by the Design-Build-Test-Learn cycle, integrates computational design, genetic assembly, and functional validation into a cohesive pipeline. Together, these technologies form a synergistic workflow that spans disease modeling, target discovery, molecular design, construction, testing, and iterative optimization. This integrated approach is shifting antiviral development from traditional empirical methods toward more precise, intelligent strategies. The review also highlights ongoing challenges in integration and scalability, stressing that high-quality biological datasets and stronger interdisciplinary collaboration are essential for realizing translational potential. By presenting a cohesive view of these converging methodologies, this review offers a framework to guide the intelligent evolution of antiviral strategies in both human and animal health.

Antiviral↗
PubMed1999

Atheroprotective mechanisms of HDL.

The aim of this review was to bring together results obtained from studies on different aspects of HDL as related to CHD and atherosclerosis. As atherosclerosis is a multistep process, the various components of HDL can intervene at different stages, such as induction of monocyte adhesion molecules, prevention of LDL modification and removal of excess cholesterol by reverse cholesterol transport. Transgenic technology has provided a model for atherosclerosis, and permitted evaluation of the contributions of different HDL components towards the global effect. The availability of apo AIV transgenic mice amplified the results obtained from apo AI overexpressors with respect to prevention of atherosclerosis. Prevention of atherosclerosis in apo E deficient mice by relatively small amounts of macrophage derived apo E may open new possibilities for therapeutic intervention. Contrary to early notions, increased plasma levels of CETP, even in the presence of low but functionally normal HDL, were atheroprotective. The extent to which paraoxonase and apo J participate in prevention of human atherosclerosis needs further evaluation. The findings that LCAT overexpression in rabbits was atheroprotective in contrast to increase in atherosclerosis in h LCAT tg mice, which was only partially corrected by CETP expression, call for some caution in the extrapolation of results from transgenic animals to humans. The important discovery of SR-BI as the receptor for selective uptake of CE from HDL revived interest in the clearance of CE from plasma. This pathway supplies also the vital precursor for steroidogenesis in adrenals and gonads and was shown to be dependent on apo AI.

Animals↗
PubMed2004

Comparative PRNP genotyping of U.S. cattle sires for potential association with BSE.

The recent discovery of significant associations between bovine spongiform encephalopathy (BSE) susceptibility in German cattle and the frequency distributions of insertion/deletion (indel) polymorphisms within the bovine PRNP gene prompted an evaluation of 132 commercial U.S. artificial insemination (AI) sires from 39 breeds. Forward primer sequences from published primer sets targeting indels within the putative bovine PRNP promoter, intron 1, and the 3' UTR (untranslated region) were synthesized with unique 5' fluorescent labels and utilized to develop a rapid multiplexed PCR assay for identifying BSE-associated indels as well as facilitating polymorphism analyses and/or marker-assisted selection. Significant differences ( p < 0.05 all tests) were detected between the frequencies of bovine PRNP promoter alleles for 48 healthy German cattle previously described and 132 commercial U.S. cattle sires. The frequency of the 23-bp promoter allele observed for commercial U.S. cattle sires strongly resembled that recently described for 43 BSE-affected German cattle. No significant difference ( p = 0.051) was detected between the distributions of promoter genotypes for healthy German cattle and our panel of commercial U.S. cattle sires. Interestingly, significant differences ( p < 0.01; p < 0.02) were also noted between the frequencies and distributions of intron 1 alleles and genotypes, respectively, for BSE-affected German cattle and our panel of U.S. cattle sires. No significant allelic or genotypic differences were detected for the 14-bp 3' UTR indel for any given comparison between German cattle and commercial U.S. cattle sires.

Alleles↗
PubMed2026

Next-generation brain proteomics: Integrating single-cell, spatial, and multi-omics for clinical biomarker discovery.

The mammalian brain's functional complexity arises from the sophisticated architecture of neurons and glia. This network is essentially defined by its dynamic proteome, which reveals the functional execution underlying neural computation and disease. This review integrates the technological leap in neuroproteomics. It has moved beyond bulk tissue proteome cataloguing to high-sensitivity single-cell and spatial resolution. We detail how next-generation platforms, such as TIMS-PASEF and Orbitrap-Astral, have enabled deeper and faster phenotypic profiling of limited brain samples. However, the proteome coverage remains constrained by dynamic range, sample loss, ionisation bias and incomplete detection of low-abundance regulatory proteins. We further examine how such studies have revealed the proteomic remodelling that drives lineage specification and synaptic plasticity by linking temporal protein expression waves to biological function. Crucially, we delineate the clinical translational trajectory, illustrating how aberrant signatures are verified in cerebrospinal fluid (CSF) and validated in plasma to support precision medicine. Finally, we argue for the necessity of "fused" multi-omics integration and Artificial Intelligence (AI) to decode the non-linear molecular logic of brain pathology.

Humans↗
PubMed1999

Is imitation learning the route to humanoid robots?

This review investigates two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It is postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. Imitation learning focuses on three important issues: efficient motor learning, the connection between action and perception, and modular motor control in the form of movement primitives. It is reviewed here how research on representations of, and functional connections between, action and perception have contributed to our understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution has provided a hypothetical neural basis of imitation. Computational approaches to imitation learning are also described, initially from the perspective of traditional AI and robotics, but also from the perspective of neural network models and statistical-learning research. Parallels and differences between biological and computational approaches to imitation are highlighted and an overview of current projects that actually employ imitation learning for humanoid robots is given.

Journal Article↗
PubMed2004

The lethal impacts of Roundup and predatory stress on six species of North American tadpoles.

The decline in amphibians across the globe has sparked a search for the causes, and recent evidence suggests a connection with pesticides. However, for most pesticides, tests on amphibians are rare and conducted only for short durations (1 to 4 days) and without natural stressors. Recent studies have discovered that the stress of predator cues in the water can make insecticides much more lethal to larval amphibians, but it is unknown whether this phenomenon can be generalized to other types of pesticides. Using six species of North American amphibian larvae (Rana sylvatica, R. pipiens, R. clamitans, R. catesbeiana, Bufo americanus, and Hyla versicolor), I examined the impact of a globally common herbicide (Roundup) on the survival of tadpoles for 16 days with and without the chemical cues emitted by predatory newts (Notophthalmus viridescens). LC50(16-d) estimates varied from 0.55 to 2.52 mg of active ingredient (AI)/L, which was considerably lower than the few previous studies using Roundup (1.5 to 15.5 mg AI/L). Moreover, in one of the six species tested (R. sylvatica), the addition of predatory stress made Roundup twice as lethal. This discovery suggests that synergistic interactions between predatory stress and pesticides may indeed be a generalizable phenomenon in amphibians that occurs with a wide variety of pesticides.

Animal Communication↗
PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗
PubMed2004

Impact of avian influenza on U.S. poultry trade relations-2002: H5 or H7 low pathogenic avian influenza.

Avian influenza (AI) viruses are Type A influenza viruses of the Orthomyxoviridae family. There are 15 subtypes of the virus widespread in migratory waterfowl throughout the world. It has become increasingly evident that some low pathogenic avian influenza (LPAI) H5 or H7 viruses have the capacity to mutate into the more virulent strains that cause extensive economic losses and high mortality. Recent AI disease outbreaks in several countries have increased attention and concern over low pathogenic H5 and H7 AI viruses. This heightened international concern increases the risk of unnecessary trade bans. For the US poultry industry, avian influenza continues to be a challenge to the flow of trade. On one hand, there is the increased focus of world attention on the H5 and H7 low pathogenic AI virus and the possibility of mutation. On the other hand, there are the factors contributing to our finding of infected flocks. Among these, perhaps the most important is the ever-present reservoir of virus in the migratory waterfowl population. With the discovery of exposed flocks comes the threat of trade bans.

Animals↗
PubMedDate not supplied

Sucrose metabolism of perennial ryegrass in relation to cold acclimation.

Sugar metabolism is one of the important factors involved in winter hardiness and since the discovery of sucrose biosynthesis, considerable advances have been made in understanding its regulation and crucial role. This investigation examined the changes in activities of sucrose metabolizing enzymes and sugar content during cold hardening of perennial ryegrass (Lolium perenne L.). Changes in acid invertase (AI), sucrose synthase (SS) and sucrose phosphate synthase (SPS) along with all the three soluble sugars glucose, fructose and sucrose were measured in leaves and stem base tissue during cold acclimation. Although fructans were the predominant carbohydrate the changes in glucose, fructose and sucrose were significant. All the three soluble sugars in both leaf and stem tissues started to decrease from the first day and continued up to day 7 and thereafter started to increase until day 28. AI in the soluble fraction showed a higher activity than that in the cell wall bound fraction. In both the leaf and stem bases soluble AI activity increased during the first week and after that it started to decrease gradually. On the other hand both the SS and SPS increased gradually throughout the acclimation period. Sucrose content was negatively correlated with AI and positively correlated with SS and SPS accounting well for the relation between the substrate and enzyme activity. These results suggest that AI, SS and SPS in ryegrass are regulated by cold acclimation and play an important role in sugar accumulation and acquisition of freezing tolerance.

Acclimatization↗
PubMed2026

PepGen: conditional generation of peptides for MHC binding.

MOTIVATION: Peptide-MHC II binding drives adaptive immunity, yet discovery of novel binder peptides remains challenging due to open binding grooves of MHC-II that accommodate variable-length peptides. While discriminative models perform well, they are unfeasible for generation via enumeration due to vast peptide space (2013&#x2248;8&#xd7;1016 for peptides of length 13 amino acids). Generative AI approaches could accelerate binder design to enable vaccines targeted to particular MHC-II alleles or optimize other peptide chemical properties. RESULTS: We introduce PepGen, the first protein language model for MHC II peptide generation building on Generalized Language Modeling. PepGen conditions on alleles, arbitrary partial peptides including putative TCR-interacting motifs, and continuous binding affinity. Across multiple benchmarks including infilling and de novo generation, PepGen outperformed frequency sampling, Gibbs clustering, and autoregressive baselines. Adjusted log-probabilities enable good classification performance. Experimental validation confirmed that the SARS-CoV-2 peptide TEGALNTPKDHIGTR binding the HLA-DQA101:03-DQB106:03 allele can be redesigned to bind the HLA-DQA101:02-DQB105:02 allele. PepGen generated three putative TCR-motif-preserving binders gaining up to 70% of original MFI. Overall, PepGen provides scalable, motif-constrained MHC II peptide redesign and de novo generation, validated through thorough benchmarks and functional assays. AVAILABILITY AND IMPLEMENTATION: Code and Data are available at https://github.com/DaniTheOrange/PepGen.

Peptides↗
PubMed2003

The Ai project: historical and ecological contexts.

This paper aims to review a long-term research project exploring the chimpanzee mind within historical and ecological contexts. The Ai project began in 1978 and was directly inspired by preceding ape-language studies conducted in Western countries. However, in contrast with the latter, it has focused on the perceptual and cognitive capabilities of chimpanzees rather than communicative skills between humans and chimpanzees. In the original setting, a single chimpanzee faced a computer-controlled apparatus and performed various kinds of matching-to-sample discrimination tasks. Questions regarding the chimpanzee mind can be traced back to Wolfgang Koehler's work in the early part of the 20th century. Yet, Japan has its unique natural and cultural background: it is home to an indigenous primate species, the Japanese snow monkey. This fact has contributed to the emergence of two previous projects in the wild led by the late Kinji Imanishi and his students. First, the Koshima monkey project began in 1948 and became famous for its discovery of the cultural propagation of sweet-potato washing behavior. Second, pioneering work in Africa, starting in 1958, aimed to study great apes in their natural habitat. Thanks to the influence of these intellectual ancestors, the present author also undertook the field study of chimpanzees in the wild, focusing on tool manufacture and use. This work has demonstrated the importance of social and ecological perspectives even for the study of the mind. Combining experimental approaches with a field setting, the Ai project continues to explore cognition and behavior in chimpanzees, while its focus has shifted from the study of a single subject toward that of the community as a whole.

Adaptation, Psychological↗
PubMed2024

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Humans↗
PubMed2026

Biological Foundation Models for Complex Disease Research and Clinical Translation.

Complex diseases, including cancer, rare genetic disorders, neurodevelopmental and psychiatric conditions, and neurodegenerative diseases, arise from interactions among genetic variation, gene regulation, and cellular states that are difficult to capture using a single data type or biological scale. Biological foundation models address this challenge by treating nucleotides and genes as tokens and learning representations that can be transferred to downstream biomedical and clinical tasks. In this review, we examine two major model classes, genomic sequence foundation models and cell foundation models, and compare their tokenization strategies, model architectures, pretraining objectives, and adaptation methods. We summarize their emerging applications in regulatory variant interpretation, disease-associated cell-state analysis, drug-response prediction, and therapeutic target discovery across complex diseases. We distinguish applications supported by experimental or retrospective validation from those that remain primarily computational or conceptual. We further discuss key challenges to clinical translation, including multimodal data integration, model interpretability, benchmarking, patient-specific prediction, and privacy protection. We highlight future opportunities to integrate biological foundation models with emerging frameworks of medical digital twins, agentic AI, and federated learning. By linking model design to translational goals, this review provides a practical framework for evaluating biological foundation models and their readiness for complex disease research and clinical use.

biological foundation model↗
PubMed2004

Use of single nucleotide polymorphism arrays to identify a novel region of loss on chromosome 6q in squamous cell carcinomas of the oral cavity.

BACKGROUND: A subset of patients with oral cavity squamous cell carcinoma(SCC), often of young age yet lacking a history of carcinogen exposure, has been identified, with no clear etiology for tumor development. METHODS: To identify somatic genetic alterations unique to this patient population, we performed a high throughput single nucleotide polymorphism (SNP) analysis, quantitative PCR of the E6 and E7 regions of human papillomavirus (HPV) 16, sequencing of the IVSF-4+ locus of the FANC-C gene, and microsatellite analysis for 18 nonsmoking patients, age 23 to 57 years (median age, 39 years). We compared these results with oral SCC from 17 patients 47 to 81 (median, 64) years of age with significant tobacco exposure (>40 pack-years) to identify unique genetic alterations for each group. RESULTS: SNP analysis demonstrated variable rates of allelic imbalance (AI) and no significant difference in terms of AI patterns between the two groups. However, we found an elevated rate of AI in chromosomal arms 6q (47% [17 of 36]) by performing microsatellite analysis of both groups. Only one tumor demonstrated the presence of HPV 16, and none of the tumors demonstrated mutations in the IVSF-4+ region of FANC-C. CONCLUSIONS: Despite variable marker density, SNP array analysis is an emerging technique for genome-wide assessment and is a useful tool for discovery of novel sites of allelic loss in oral SCC, including a novel region of allelic loss on chromosome 6q.

Adult↗
PubMed2005

Control of gene expression and assembly of chromosomal subdomains by chromatin regulators with antagonistic functions.

Epigenetic regulation of higher-order chromatin structure controls gene expression and the assembly of chromosomal domains during cell division, differentiation, and development. The proposed "histone code" integrates a complex system of histone modifications and chromosomal proteins that establish and maintain distinctive types of chromatin, such as euchromatin, heterochromatin, and centromeric (CEN) chromatin. The reversible nature of histone acetylation, phosphorylation, and (most recently discovered) methylation are mechanisms for controlling gene expression and partitioning the genome into functional domains. Many different regions of the genome contain similar epigenetic marks (histone modifications), raising the question as to how they are independently specified and regulated. In this review, we will focus on several recent discoveries in chromatin and chromosome biology: (1) identification of long-elusive histone "de-methylating" enzymes that affect chromatin structure, and (2) assembly and maintenance of chromatin domains, specifically heterochromatin and euchromatin, through a dynamic equilibrium of modifying enzymes, histone modifications, and histone variants identified biochemically and genetically.

Animals↗
PubMed2026

Data-driven approaches in green microbiology: strategies for plant growth-promoting bacteria.

Plant growth-promoting bacteria (PGPB) are gaining attention as scalable biological solutions to enhance crop productivity and resilience. However, accurately identifying and characterizing PGPB remains challenging, particularly under variable environmental conditions where microbial functions are context-dependent and shaped by complex plant-microbe interactions. Advances in high-throughput sequencing have shifted the field from culture-dependent approaches to genome-informed strategies, enabling large-scale taxonomic and functional profiling. Although trait-based databases support the prediction of plant-beneficial genes, they capture only a fraction of the underlying biological complexity and often require labor-intensive analyses. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to integrate genomic, physiological, and ecological data, enabling the prioritization of candidate strains with plant growth-promoting potential. To evaluate advances in the field, we conducted a systematic review of studies integrating ML and DL with PGPB characterization, assessing algorithm selection, performance, and target plant systems. Across 248 observations, only 6.0% of studies directly addressed PGPB screening, whereas the majority (77.4%) focused on plant disease detection, revealing a substantial gap in the application of AI to beneficial microorganisms for plant growth. Convolutional neural networks (CNNs) were the most frequently applied algorithms, largely driven by image-based phenotyping tasks. Overall, the field is constrained by limited datasets, high computational demands, and challenges in modeling multispecies and host-associated interactions. We highlight the need for integrative and interpretable ML and DL frameworks that bridge genomic data and functional validation. Such approaches represent a promising path toward scalable, data-driven discovery and deployment of bioinoculants in sustainable agriculture.

Agriculture↗