PubMed HealthSearch

SEARCH · PubMed Health

Results for “artificial selection”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

From bottleneck to boom: Polyploidy, genetic instability and response to artificial selection resolve the peanut paradox.

This study, the second in a three-part series, shows how peanut's polyploid origin enabled rapid diversification and enhanced domestication potential. Building on the knowledge that cultivated peanut (Arachis hypogaea) originated from a narrow hybridization between Arachis duranensis and Arachis ipaënsis less than 10 000 years ago, we are confronted with a paradox: how did such a narrow origin give rise to so much diversity-two subspecies, six botanical varieties, and thousands of landraces differing in growth habit, seed size, and pod morphology? Although several diploid Arachis species were cultivated earlier, only the allotetraploid became fully domesticated and widely adopted. The global success of peanut, despite its narrow genetic origin, suggests that polyploidization itself facilitated domestication. To test this hypothesis, we investigated how the two diploid progenitors and neoallotetraploids derived from a single hybridization and polyploidization event responded under artificial selection. In a pollinator-free greenhouse, we advanced lineages of the neoallotetraploid and its diploid parents over 6 years, selecting for divergent seed weights. The neoallotetraploid showed a much stronger response to artificial selection than its diploid parents, while also spontaneously generating diverse phenotypic variation-including flower color, pod reticulation, and chlorophyll content-traits that distinguish A. hypogaea subspecies and landraces. These traits mirrored directional shifts in parental genome dosage caused by homoeologous exchange, supporting a causal connection with phenotype. These findings offer a compelling rationale for a domestication advantage in polyploid peanut, and provide a living demonstration of how a single ancestral tetraploid, despite an extreme genetic bottleneck, generates a phenotypic boom.

Arachis

Shared candidate genes associated with variation in egg size in cold-adapted and artificially selected Drosophila melanogaster.

The development of most multicellular organisms begins with oogenesis, the production of the egg. In D. melanogaster, egg size is a highly polygenic trait closely related to fitness. Elements of shifts in egg size have been widely studied and modeled, but the genes underlying this variation are still poorly understood. This study aimed to identify candidate genes associated with processes underlying egg-size variation using D. melanogaster as a model. In selection experiments, we generated large-egg populations from a shared ancestral population using both cold-adaptation and artificial selection, and identified candidate genes for the large-egg phenotype. Using whole-genome DNA sequencing and strict computational filtering, we uncovered single-nucleotide polymorphisms in 10 genes. Characterization of these candidates revealed functions in cytoskeletal dynamics, DNA replication and repair, intracellular signaling, and stem cell maintenance and differentiation. RT-PCR and qPCR were used to validate gene expression differences between cold-adapted lines and the Oregon R control (OrR) in a subset of the candidates. In RT-PCR, stathmin demonstrated a modified expression pattern in all cold-adapted lines relative to OrR controls. In qPCR experiments, Pde1c had significantly higher expression (p&#x202f;<&#x202f;0.05) in the cold-adapted flies compared to OrR controls for all three fly cages tested. For Ino80, significantly higher expression was observed for one of three cages while one cage showed lower expression. We have assembled a candidate list we hope will be a useful resource for researchers across specialties, from germ cells to cytoskeletal dynamics, to further investigate the genetic and developmental aspects of variation in egg size in D. melanogaster.

Animals

Advances in genomics-driven genetic decoding and genomic design breeding in tomato.

Tomatoes are highly nutritious and represent one of the important vegetable fruits worldwide. Both historically and moving forward, genetic decoding and precision breeding remain fundamental to tomato improvement. Here, we summarize pivotal advances in decoding tomato genomes across domestication, improvement and evolution processes and provide a perspective on future breeding through precision design. In-depth population genetic studies have revealed how artificial selection systematically prioritized yield-related alleles at the cost of narrowing genetic diversity, especially at flavor-related loci-highlighting the urgent need to reconcile these trade-offs. Comparative genomics across species, viewed through an evolutionary lens, has uncovered critical insights into functional genes, deepening our understanding of the genetic architecture and regulatory mechanisms underlying key traits. Collectively, these advances have enabled precise identification and functional characterization of key genetic elements, paving the way for systematic redomestication of tomato through precision genomic design. Looking ahead, more efficient and precise breeding strategies will be required to accelerate genetic gains in tomato in the coming decades. The integration of recent genomic advances, coupled with genomic selection and artificial intelligence, into genomic design breeding offers a transformative framework, unlocking unprecedented opportunities for developing highly flavorful and consumer-customized tomato varieties.

Journal Article

Evolutionary consequences of domestication on the selective effects of new amino acid changing mutations in canids.

The domestication of wild canids led to dogs no longer living in the wild but instead residing alongside humans. Extreme changes in behavior and diet associated with domestication may have led to the relaxation of the selective pressure on traits that may be less important in the domesticated context. Thus, here we hypothesize that strongly deleterious mutations may have become less deleterious in domesticated populations. We test this hypothesis by estimating the distribution of fitness effects (DFE) for new amino acid changing mutations using whole-genome sequence data from 24 gray wolves and 61 breed dogs. We find that the DFE is strikingly similar across canids, with 26-28% of new amino acid changing mutations being neutral/nearly neutral (|s| < 1e-5), and 41-48% under strong purifying selection (|s| > 1e-2). Our results are robust to different model assumptions suggesting that the DFE is stable across short evolutionary timescales, even in the face of putative drastic changes in the selective pressure caused by artificial selection during domestication and breed formation. On par with previous works describing DFE evolution, our data indicate that the DFE of amino acid changing mutations depends more strongly on genome structure and organismal characteristics, and less so on shifting selective pressures or environmental factors. Given the constant DFE and previous data showing that genetic variants that differentiate wolf and dog populations are enriched in regulatory elements, we speculate that domestication may have had a larger impact on regulatory variation than on amino acid changing mutations.

Journal Article

Selection of GhTT2-A07 promoter enhances fiber quality in improved cotton varieties.

Modern cultivated cotton fibers are predominantly white with enhanced quality compared to their wild ancestors. However, the molecular mechanisms and evolutionary drivers linking fiber color to quality remain least focused. In this study, we identified FQC1 (Fiber Quality and Color 1), a major quantitative trait locus (QTL) on chromosome A07 that concurrently regulates both fiber quality and pigmentation. Through map-based cloning, we revealed that Gossypium hirsutum TRANSPARENT TESTA2-A07 (GhTT2-A07), an R2R3-MYB transcription factor, resides within this locus. GhTT2-A07 modulates fiber development by directly activating genes in the general phenylpropanoid pathway, thereby promoting the metabolic flux toward downstream secondary metabolites. Variations in the GhTT2-A07 promoter led to its reduced expression in modern white cotton cultivars. This down-regulation suppresses the accumulation of S/G/H-type lignin monomers and proanthocyanidins, resulting in altered secondary cell wall composition and ultimately enhancing the quality of mature white fibers. Population genetic analyses further indicate that the white-fiber allele GhTT2-A07W has been fixed in modern breeding genotypes, underscoring the impact of artificial selection during cotton domestication. Overall, our study elucidates the biochemical and molecular mechanisms underlying fiber quality and pigmentation in cotton, clarifies the selection criteria for high-quality white fibers in modern cultivars, and provides a theoretical basis for future targeted genetic improvement of cotton fibers.

Alleles

Genome Wide Analysis Reveals Divergence and Ancestral Origins of Min Pigs.

The Min pig, a representative northern Chinese indigenous breed, carries a unique ancestral background shaped by the historical phylogeography of Northeast Asia. This study aimed to dissect the population structure, temporal genetic divergence, and ancestral composition of Min pigs, trace their evolutionary origin, and identify trait-linked functional genes, providing information regarding their evolutionary history and conservation. We analyzed 61 Min pigs sampled across nearly 20&#x2009;years and 701 reference pigs comprising other Chinese indigenous breeds, Western commercial lines, and Chinese wild boars, using PCA, NJ phylogenetic analysis, Admixture, TreeMix, D-statistic, f4-ratio, and combined selection signature scans (sliding-window FST, XP-EHH, and &#x3c0;-ratio). Clear genetic stratification was observed among Min pig subpopulations, reflecting long-term divergence under natural and artificial selection. PCA and Admixture (K&#x2009;=&#x2009;2-4) separated East Asian indigenous and Western ancestral components, verifying an admixed Northeast Asian origin with a dominant ancient East Asian component and a Western component. Compared with early-2000s Min pigs, contemporary individuals are genetically closer to Western breeds and exhibit a more scattered structure due to shifted ancestral component proportions, further confirmed by D-statistic and f4-ratio values. We identified 321 differentiated SNP loci based on the Animal QTL database, corresponding to core candidate genes (AKT3, ACACA, MAP3K5, FGFR4, C3, and SERPINC1) enriched for meat quality, growth, reproduction, immunity, energy metabolism, and MAPK/PI3K-Akt/AMPK pathways. This study reveals Min pigs' admixed origin and temporal divergence, clarifying their Northeast Asian evolution and providing molecular markers for genetic monitoring and conservation.

Animals

Genetic diversity, population structure in a historical panel of Brazilian soybean cultivars.

Soybean [Glycine max (L.) Merrill] is one of the most widely grown legumes in the world, with Brazil being its largest producer and exporter. Breeding programs in Brazil have resulted from multiple cycles of selection and recombination starting from a small number of USA cultivar ancestors in the 1950s and 1960s years. This process has led to the successful adaptation of this crop to tropical conditions, a phenomenon known as tropicalization. Many studies describe a narrow genetic background in Brazilian soybean cultivars. Various factors can affect the genetic diversity in species, especially in cultivated crops, such as the reproduction type, artificial selection, and the number and sources of variability in the breeding programs. In turns, the genetic diversity can affect the linkage disequilibrium blocks (LD) patterns and, consequently, molecular breeding strategies for selection of target loci for agronomic traits. We used high-throughput genotyping with SoySNP50K Illumina SNP markers to assess a collection of 370 Brazilian soybean accessions covering more than 60 years of soybean breeding in Brazil. Our goal was to investigate population structure and genetic diversity in the Brazilian germplasm, detect patterns of LD blocks, and identify regions presenting signals of selective swaps linked with quantitative trait loci (QTLs) of agronomic interest. Population structure analysis revealed two major groups among all genotypes, primarily differentiated by the year of release, separating old and new cultivars (before and after 2000&#xb4;s years), and by growth habit (stem termination type-SST). The group I comprises about 75% of the panel and includes cultivars release before 2000`s years, including the oldest cultivars released in Brazil, most of which exhibit a determinate growth habit and maturity groups VI and VII. Group II includes only 83 materials, but shows higher levels of diversity than group I, representing most recent introductions in Brazilian germplasm. Further analysis of substructure within Group I, identified seven subgroups with no clear trend for segregation based on maturity group, STT or year of release. Instead, these subgroups were based on the contribution of key donors of disease resistance and adaptability, as soybean cultivation expanded from the South to Central region of Brazil. This finding is consistent with the history of soybean expansion in Brazil. We identified 123 genomic regions under selection among the groups of Brazilian cultivars associated with 440 quantitative trait loci (QTLs), revealing regions fixed across the breeding process associated with yield, disease resistance, water efficiency use, and others.

Glycine max

Seed shattering habit in millets and the secrets of the abscission layer - a comprehensive review.

Though seed shattering continues to be a significant barrier affecting yield stability and harvesting efficiency in millets and other grasses, millets are increasingly acknowledged as climate-resilient, nutrient-rich 2007cereal crops with the potential to strengthen global nutritional and food security under the combined pressures of climate change, population growth, and limited natural resources. Since strong artificial selection favoured non-shattering phenotypes during domestication, seed shattering, an adaptive trait in wild species that promotes seed dispersal through the formation and activation of specialised abscission layers, became a distinguishing feature of cultivated cereals. With a focus on the morphological, physiological, hormonal, and genetic modulation of the abscission zone, this article summarizes the state of the art regarding seed shattering in millets. Abscission layer morphology, location, and lignification vary greatly among grasses, from well-defined lignified zones in rice and sorghum to non-lignified and anatomically subtle zones in Setaria and Panicum species. Cell wall-modifying enzymes like polygalacturonases, cellulases, expansins, and pectin methylesterases that mediate middle lamella degradation are modulated by coordinated hormonal signalling involving auxin, ethylene, and abscisic acid, which controls the timing and progression of cell separation at the physiological level. Domestication-related genes, including SH1, qSH1, SH4, and LES1, demonstrate convergent evolutionary mechanisms controlling abscission layer development in a variety of grass lineages at the molecular level. Understanding these regulatory networks has been greatly enhanced by recent developments in transcriptomics, functional genomics, and genome sequencing in both model species and underused millets. The role of millets as climate-smart cereals for sustainable future agriculture is reinforced by the integration of anatomical, physiological, and genetic insights, which offer a solid basis for targeted breeding and genome-editing strategies intended to improve seed retention, enhance yield stability, and increase harvest efficiency.

Abscission Layer

Selection of geographical populations suitable for artificial breeding of the Northeast China Brown Frog (Rana dybowskii).

Amphibians, as a group greatly disturbed by human activities, are at increased risk of extinction. Rana dybowskii is an anuran species with both ecological and economic significance. Due to environmental changes and human overexploitation, it has been classified as Near-Threatened. This study integrates morphological and molecular immunological approaches to identify R. dybowskii populations with greater survival and disease resistance, based on 32 morphological traits and MHC class I and II polymorphism. Morphological results showed that compared with other populations, Yichun (YC) population had the highest fatness, the lowest IOD/HW, and the largest HW/SVL, HL/SVL, HW/HL, SL/TL. It indicates that YC population shows larger body size, wider vision and stronger jumping ability. The polymorphism of MHC I gene was the highest in Shangzhi (SZ) population, and the polymorphism of MHC II gene was the highest in YC population. Moreover, duplication, selection, and recombination occurred during evolution of MHC class I and II genes. Since both SZ and YC populations scored higher in this category (the variant sites, nucleotide polymorphism, amino-acid divergence/nucleotide divergence, dN/dS, Tajima' D, etc.), they were more resistant to disease. All in all, these results indicated that YC population of the Lesser Khingan Mountains had good morphology and immune results, and R. dybowskii in the Lesser Khingan Mountains might be more suitable to be the original population of artificial breeding, which provided a theoretical basis for the realization of artificial breeding in the next step.

Ranidae

Haplotype stacking to improve stability of stripe rust resistance in wheat.

Genotype-by-environment interaction analysis and haplotype-level characterisation provide novel insights into the stability of stripe rust resistance. Breeding selection strategies are proposed to achieve rapid and stable genetic gains across environments. This study investigated stripe/yellow rust (YR) responses in the Vavilov wheat diversity panel evaluated across 11 field experiments conducted in Australia and Ethiopia during 2014-2021. Genotype-by-environment interaction (GEI) was analysed using a factor analytic (FA) model. Genotype-level selection was performed with overall performance (OP) and root-mean-square deviation (RMSD), which reflected average performance and stability of YR resistance across environments, respectively. Genomic estimated breeding values (GEBV) for these traits were calculated and compared with those from a multi-trait GBLUP model with average performance represented by the mean GEBV across environments and stability by the standard deviation of GEBV across environments. The FA-based and multi-trait GBLUP GEBV had high correlations. Haplotypes with large effects on OP and RMSD were identified using the local GEBV method. Favourable haplotypes were then used for stacking in breeding simulations, using the Vavilov collection as a base. Compared to truncation selection, optimal haplotype selection (OHS) using an artificial intelligence (AI)-based algorithm achieved longer-term genetic gains for both OP and RMSD (after many generations) by initially selecting founder parents that maximised favourable haplotypes. Simulations using YR responses from diverse environments that mimicked fluctuating environmental conditions across seasons were conducted to evaluate strategies for selection of YR resistance that is stable across years. Strategies which gave most weight to OP, but some weight to RMSD were optimal in these conditions, and substantially reduced variation of performance across years. This study provides useful information for breeding cultivars with both high YR resistance and high stability of resistance across environments.

Triticum

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

Genetic Diversity Analysis of Red Fox Populations (Vulpes vulpes L., 1758) in Natural and Anthropogenic Isolation.

This study presents a comparative analysis of the genetic structure and diversity of three red fox (Vulpes vulpes L.) populations representing different microevolutionary scenarios: panmixia (free-ranging Belarusian foxes), geographic isolation (free-ranging Scottish foxes), and anthropogenic selection (farm-bred foxes). Using a validated set of STR markers, multivariate statistical analysis was conducted to assess the genetic structure and the degree of genetic erosion across the studied groups. The wild red fox population in Belarus has been shown to maintain a state close to panmixia (PHWE&#x2009;=&#x2009;0.090), characterized by a high effective population size (Ne&#x2009;=&#x2009;694) and high allelic diversity. The island population from Scotland exhibits moderate gene pool depletion (Ne&#x2009;=&#x2009;75.9) and a pronounced heterozygote deficiency (FIS&#x2009;=&#x2009;0.18). Critical genetic erosion, which was characterized by a minimal effective population size (Ne&#x2009;=&#x2009;60.2) and allelic fixation, was detected in the farm-bred group. The genetic distance between farm-bred and wild foxes (FST&#x2009;=&#x2009;0.279; p&#x2009;=&#x2009;0.001) reflects both the phylogeographic divergence between the Nearctic ancestors of farmed lineages and Palearctic wild populations, and the consequences of prolonged anthropogenic isolation, genetic drift, and selective breeding. These data indicate that artificial isolation and the impacts of genetic drift and targeted selection lead to a substantial depletion of the species' adaptive potential.

Animals

AWGE-ESPCA: An edge sparse PCA model based on adaptive noise elimination regularization and weighted gene network for Hermetia illucens genomic data analysis.

Hermetia illucens is an important insect resource. Studies have shown that exploring the effects of Cu2+-stressed on the growth and development of the Hermetia illucens genome holds significant scientific importance. There are three major challenges in the current studies of Hermetia illucens genomic data analysis: firstly, the lack of available genomic data which limits researchers in Hermetia illucens genomic data analysis. Secondly, to the best of our knowledge, there are no Artificial Intelligence (AI) feature selection models designed specifically for Hermetia illucens genome. Unlike human genomic data, noise in Hermetia illucens data is a more serious problem. Third, how to choose those genes located in the pathway enrichment region. Existing models assume that each gene probe has the same priori weight. However, researchers usually pay more attention to gene probes which are in the pathway enrichment region. Based on the above challenges, we initially construct experiments and establish a new Cu2+-stressed Hermetia illucens growth genome dataset. Subsequently, we propose AWGE-ESPCA: an edge Sparse PCA model based on adaptive noise elimination regularization and weighted gene network. The AWGE-ESPCA model innovatively proposes an adaptive noise elimination regularization method, effectively addressing the noise challenge in Hermetia illucens genomic data. We also integrate the known gene-pathway quantitative information into the Sparse PCA(SPCA) framework as a priori knowledge, which allows the model to filter out the gene probes in pathway-rich regions as much as possible. Ultimately, this study conducts five independent experiments and compared four latest Sparse PCA models as well as representative supervised and unsupervised baseline models to validate the model performance. The experimental results demonstrate the superior pathway and gene selection capabilities of the AWGE-ESPCA model. Ablation experiments validate the role of the adaptive regularizer and network weighting module. To summarize, this paper presents an innovative unsupervised model for Hermetia illucens genome analysis, which can effectively help researchers identify potential biomarkers. In addition, we also provide a working AWGE - ESPCA model code in the address: https://github.com/yhyresearcher/AWGE_ESPCA.

Animals

Smarter stomata: emergent technologies unlocking yield potential in a changing climate.

Stomata, the gatekeepers of leaf gas exchange, regulate carbon dioxide uptake and water loss, functions increasingly critical as crops face more frequent, intense heat and drought. Under dry conditions, stomatal conductance (g s) typically decreases, limiting carbon assimilation and yield. Heat stress, in contrast, elicits variable g S responses: sometimes increasing to facilitate transpirational cooling, while at other times decreasing, especially when combined with drought. Heat and drought also induce complex, context-dependent shifts in stomatal anatomy. Smaller, denser stomata improve drought resilience in some cases, while reduced density confers greater tolerance in others. The optimal stomatal ideotype remains unknown, and different or even opposing traits may confer resilience dependent on the environmental scenario. Substantial genotypic variation in g s and stomatal anatomy, high heritability and co-localized quantitative trait loci for stomatal traits and yield highlight their untapped potential as breeding targets for climate-resilient crops. However, stomatal traits remain largely absent from breeding pipelines due to challenges of phenotyping at scale. This is changing rapidly. Advances in deep learning, porometry, digital microscopy, and remote sensing now enable high-throughput measurement of stomatal physiology and anatomy. Next-generation breeding technologies including clustered regularly interspaced short palindromic repeats (CRISPR), multi-omics approaches, and artificial intelligence-driven ideotype selection models could revolutionize breeding, allowing precise engineering of stomatal traits for resilience to environmental stress. The time has come to move beyond characterizing stomatal traits and start actively incorporating them into breeding strategies. By leveraging these technologies, stomatal traits can become high value targets, unlocking their potential to enhance crop performance in a hotter, drier future.

abiotic stress

Biomarker-guided selection of intravesical therapy in high-risk non-muscle invasive bladder cancer: A contemporary review.

High-risk non-muscle invasive bladder cancer poses therapeutic challenges, with significant rates of recurrence and progression with standard intravesical bacillus Calmette-Gu&#xe9;rin (BCG) therapy. Current surveillance strategies lack accurate risk stratification models to predict individual treatment response and personalized treatment options. Simultaneously, there are no well-validated alternatives to replace the current gold-standard approach based on clinical and pathologic features. This review examines emerging biomarkers and advanced technologies with the potential to enhance patient selection and personalize intravesical therapy in HR-NMIBC. Artificial intelligence(AI)-driven histopathologic tools, such as the computer histological AI biomarker, have demonstrated the ability to identify non-responders to standard therapy using whole-slide digital pathology images. In parallel, radiomics-enhanced imaging has shown promise in assessing tumor biology and immune microenvironment features predictive of BCG responsiveness. Liquid biopsy, especially urine tumor DNA analysis, is now available in the arsenal to detect minimal residual disease, stratify recurrence risk, and predict treatment response even before clinical or radiographic evidence of recurrence. Tissue-based genomic profiling has also revealed molecular alterations associated with treatment resistance, though additional validation is needed. Together, these next-generation biomarkers may represent a pivotal shift toward precision oncology in bladder cancer and their incorporation into NMIBC future clinical guidelines is both anticipated and necessary.

BCG-unresponsive disease

Suppression of AAV-Delivered Transgene Expression Using Artificial MicroRNAs Delivered by an Alternative AAV Serotype.

Adeno-associated virus (AAV) gene transfer vectors mediate long-term expression in nondividing cells, an advantage for treating chronic disorders. However, current platforms lack a way to selectively shut down transgene expression if adverse effects arise. To create an "off switch," we hypothesized that incorporating unique artificial microRNA (amiRNA) target sequences into an AAV expression cassette would allow subsequent suppression of transgene expression using a second AAV vector encoding the cognate amiRNA. We introduced 22-nt sequences absent from human and mouse transcriptomes into the 3' untranslated region (UTR) of a therapeutic AAV cassette. To identify optimal amiRNAs, two tandem copies of each amiRNA were cloned into the 3'UTR of an mCherry reporter gene. In vitro assessment of six amiRNA/target pairs using a dual luciferase assay identified four amiRNAs that efficiently suppressed reporter expression. Cells cotransfected with target site 3 (TS3) and amiRNA-T3B showed the greatest reduction in luciferase activity (80%, p < 0.0001) and were selected for further study. The "off-switch" system was then evaluated using an AAV5 therapeutic vector expressing a recombinant humanized anti-IgE monoclonal antibody (AAV5-TBG-anti-IgE-TS3), designed for long-term suppression of allergen-induced reactions. Co-transfection of HEK293T cells with anti-IgE-TS3 and amiRNA-T3B significantly reduced anti-IgE mRNA and protein levels relative to a control amiRNA (p < 0.0001). In vivo testing in Balb/c mice (n = 5) involved intravenous administration of AAV5-anti-IgE-TS3 (3.2 &#xd7; 1010 gc), followed 4 weeks later by an AAVrh.10 amiRNA vector (AAVrh.10-TBG-amiRNA-T3B; 1 &#xd7; 1011 gc). Control mice receiving only the therapeutic vector expressed 18.4 &#xb1; 13.8 &#xb5;g/mL serum anti-IgE at 10 weeks. In contrast, mice receiving the amiRNA "off" vector showed marked suppression of anti-IgE (0.3 &#xb1; 0.15 &#xb5;g/mL, p < 0.0001). These findings provide proof-of-concept that AAV-delivered amiRNAs can selectively switch off transgene expression, offering a strategy to improve the safety of AAV-mediated gene therapies.

Dependovirus

Age-related differences in semen quality in Holstein-Friesian bulls: a paired within-bull comparison of early and mature reproductive stages.

Genomic selection has changed dairy cattle breeding by increasing the use of young bulls for artificial insemination and shortening the reproductive lifespan of sires. Under these conditions, semen quality at the beginning of commercial use has become an important practical issue. Semen samples from 39 fertile Holstein-Friesian bulls used for commercial AI were collected between 2013 and 2016, during the introduction of genomic selection in Poland. This paired within-bull study compared semen collected from the same bulls at an early reproductive stage (13-20 months; young bulls, YB) and at full maturity (5-6 years; mature bulls, MB). The evaluation included conventional ejaculate traits, CASA-derived motility and kinematic descriptors, mtDNA copy number, and mitochondrial content per sperm cell. Importantly, all ejaculates met the quality requirements for commercial insemination. Ejaculate volume, sperm concentration, mitochondrial DNA copy number, and mitochondrial content did not differ significantly between age groups. The CASA-derived sperm movement profile, in contrast, differed with age. Semen from young bulls showed a higher proportion of progressively motile spermatozoa, whereas semen from mature bulls showed higher velocity-related parameters, including VSL, VCL, and STR. These findings indicate that bull age mainly affected sperm movement characteristics rather than semen output or mitochondrial content. Overall, the results support the use of young bulls in artificial insemination programs and show that age-related differences in semen quality are expressed mainly through changes in the post-thaw sperm motility and kinematic profile.

Animals

A systematic approach to standardizing the visual appearance of endometriotic lesions for artificial intelligence recognition.

INTRODUCTION: Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion recognition. Standardization of the visual classification of lesions, in the form of an ontology, is an essential prerequisite to enable medical experts to annotate surgical data consistently and subsequently allow engineers to train and build an artificial intelligence tool for endometriosis recognition. MATERIAL AND METHODS: A systematic search was conducted in the MEDLINE (via PubMed), EMBASE, and the Cochrane Library databases up to May 2022, aiming to identify studies describing the laparoscopic visual appearance of superficial endometriosis, endometriomas, and deep infiltrating endometriosis. The accumulated data in the literature concerning the visual appearance of the different forms of endometriosis were used to create an ontology that could be used for artificial intelligence applications. RESULTS: Out of 932 articles screened, 35 studies were selected based on the inclusion criteria of human subjects with histologically confirmed endometriosis lesions visualized via laparoscopy. The selected studies were reviewed to develop a visual ontology of endometriosis lesions observed via laparoscopy. The lesions were categorized into 4 classes and further subdivided into 11 subclasses: superficial (black, red, white, or subtle), adhesions (dense or filmy), deep (obliteration, retraction, or deformation), and ovarian (endometrioma or chocolate fluid). The positive predictive value (PPV) varied across lesion types: black lesions (PPV 47%-97%), red lesions (PPV 33%-100%), white lesions (PPV 20%-81%), and ovarian endometriosis (PPV 42%-98%). Nonspecific lesions such as adhesions (PPV 16%-50%) and subtle superficial lesions (PPV 0%-67%) presented lower PPVs. Deep endometriosis lesions, often buried within organs, required indirect signs (obliteration, retraction, deformation) for identification. CONCLUSIONS: The visual ontology proposed in this systematic search could facilitate the detection and classification of endometriosis lesions using artificial intelligence. This study highlights the challenges of reaching a consensus on lesion recognition and classification in AI projects due to the diverse visual presentations of endometriosis.

Humans