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PubMed2026

Proteome-level evidence that tebuconazole, both alone and in interaction with thiacloprid, affects epigenetic events in bumblebee heads.

Tebuconazole, a widely used ergosterol biosynthesis-inhibiting fungicide, can affect nontargets, especially when combined with insecticides. We employed label-free quantitative proteomics to investigate the effects of long-term exposure to sublethal concentrations (100 μg/L) of tebuconazole, either by itself or alongside the neonicotinoid thiacloprid (100 μg/L), on the heads of Bombus terrestris workers. A Bayesian factor power analysis revealed that the experiment produced conclusive proteomic results. Tebuconazole treatment revealed eleven differentially abundant proteins, which increased elevenfold with thiacloprid. The proteins that changed in the same direction in both treatments suggest the occurrence of epigenetic events because they are involved in histone trimethylation (H3K4me3), pre-mRNA processing, and folate (vitamin B9) metabolism. Following co-exposure, the abundance of histone H2A.V and its associated proteins was affected. Two important detoxification-related proteins, CYP6BE1 and CYP6AQ1 (honey bee homologs), were identified, as well as proteins that suggest hormonal and neurotoxic effects. Overall, this study suggests that tebuconazole affects key epigenetic processes in bumblebee heads at the proteome level, though this was not confirmed at the biological level or through orthogonal methods. The tested chemicals were previously found to affect trimethylations, but not H3K4me3. We suggest analyzing the different trimethylations, their interplay, and associated hallmarks, such as folate levels. SIGNIFICANCE: The effects of pesticides and their combinations on organisms can be unexpected until they are examined using modern, complex methods. High-throughput proteomics can provide data on important biochemical processes affected by pesticides, offering a different perspective to that at the expression level. Despite their low acute toxicity, a group of fungicides that inhibit (ergo)sterol biosynthesis (EBI or SBI) are considered dangerous to pollinators, including bumblebees. This is due to the increasing toxicity of insecticides through the inhibition of cytochrome P450 detoxification enzymes. We found that tebuconazole had a similar effect on epigenetic events when used alone or in combination with the insecticide thiacloprid. Key proteins suggest that H3K4 histone trimethylation (H3K4me3) was impacted. To our knowledge, this expands the existing evidence suggesting that tebuconazole/triazole fungicides affect histone trimethylation H3K27me3. Since literature shows that thiacloprid affects H3K9me3, it is possible that thiacloprid and tebuconazole interact in these epigenetic events that affect each other. Overall, our results suggest that tebuconazole affects proteins involved in histone trimethylation, pre-mRNA processing, and folate metabolism. These are all hallmarks of epigenetic processes and were further extended by the co-exposure of tebuconazole and thiacloprid to more differently abundant proteins. Additionally, the results provide data on cytochrome P450s of the CYP6 family, which act as detoxifying proteins, as well as proteins that indicate hormonal and neurotoxic effects in bumblebee heads. Finally, the results of the Bayesian power analysis confirmed the meaningfulness of the proteomic data analyzed in this study. If the new findings obtained at the proteome level are verified by different methods, the full extent of the side effects of tebuconazole can be revealed.

Animals↗

PubMed2026

Fructophilic lactic acid bacteria as a window into multi-scale convergent evolution.

Fructophilic lactic acid bacteria (FLAB) are a group of lactic acid bacteria with unique growth characteristics, that is, poor growth on glucose. Their growth is enhanced in the presence of fructose or external electron acceptors. These organisms inhabit fructose-rich environments such as flowers, fruits, and pollinating insects, particularly honey bees. Apilactobacillus spp. and Fructobacillus spp. are representatives of FLAB, although they belong to phylogenetically distant clades. These organisms commonly possess markedly small genomes with a low number of coding DNA sequences. Furthermore, their genomes are characterized by a markedly reduced number of genes involved in carbohydrate transport and metabolism. Genome reduction in FLAB reflects convergent adaptation to fructose-rich environments rather than general genome streamlining. The two distinct FLAB genera, Fructobacillus and Apilactobacillus, independently lost more than 100 genes in statistically similar orders. In contrast, genes involved in carbohydrate and amino acid metabolism exhibited reversed orders of loss between the two genera. Furthermore, FLAB genomes lack an intact bifunctional alcohol/aldehyde dehydrogenase gene (adhE), which causes their poor growth on glucose. A comparative genomic study suggested the evolutionary process underlying adhE gene decay during adaptation to the fructose-rich environments, including pollinating insects. In conclusion, FLAB represent a unique example of habitat-driven convergent reductive evolution that can be investigated across multiple biological scales - from individual genes to whole genomes - in the diverse LAB group with a wide range of habitats, and partially share the fructophilic evolution with eukaryotic yeasts found in fructose-rich habitats.

Fructose↗

PubMed2026

An overview of the use of proteomics and peptidomics to characterize alternative protein foods.

The global protein transition is accelerating the development of alternative protein foods, mainly derived from plants, insects, algae, fungi, and cellular agriculture. Ensuring the authenticity, safety, and nutritional adequacy of these emerging protein matrices requires molecular-level characterization beyond traditional compositional analyses. Proteomics and peptidomics have emerged as transformative analytical platforms capable of decoding the molecular signatures that define protein origin, structural integrity, digestibility, functionality, and health potential. The review comprehensively examines the application of proteomics, and peptidomics for profiling alternative protein foods. Further, the source authentication strategies based on species-specific protein and peptide biomarkers, detection of adulteration in complex matrices, and allergenicity assessment is discussed. Special attention is also given to nutritional proteomics with protein digestibility, gastrointestinal peptide release, and identification of bioactive sequences. SIGNIFICANCE: The importance of this review is that proteomics and peptidomics are becoming central in the management of the fast-growing environment of alternative protein foods, such as plant-based, insect, algal, fungal, and cultured meat products. It provides an explanation of the application of mass spectrometry-based processes to decode molecular signatures defining the origin of proteins, their structural integrity, digestibility, allergenicity, and bioactive properties, and thus directly contribute to safety, nutritional analysis, and authenticity of the product. Presentation of the article includes the integration of the knowledge of traditional muscle foods with alternative systems of proteins, where validated protein and peptide biomarkers are used in authentication, fraud detection, and allergy risk assessment in a wide variety of matrices. It also indicates the role of nutritional proteomics and peptidomics in informing the formulation strategy to promote digestibility and release of health-promoting peptides. In general, this review will guide scientists, the food industry, and regulatory bodies to use modern proteomic technologies in quality assurance, and decision-making, for the advancementof sustainable protein-based foods.

Proteomics↗

PubMed2026

Microbial allies in a cotton pest: A descriptive account of associated microbiota dynamics in Dysdercus cingulatus across development.

BACKGROUND: Hemipteran insects harbour several symbiotic partners, mainly bacteria, which play pivotal roles for hosts like dietary provision, support overall physiology, xenobiotic degradation and manipulate/regulate behaviour. Most of these symbionts usually reside and operate from the digestive tracts of the animals. Cotton is one of the major cash crops in India and Dysdercus cingulatus (D. cingulatus) though a secondary pest, is causing significant destruction of cotton bolls, poor lint quality and reduce oil content of seeds. Premature opening of cotton bolls often leads to bacterial and fungal infections, thus resulting in extensive economic loss worldwide. D. cingulatus is a hemimetabolous insect that comprises of developmental stages like egg, nymph (5 instar stages), and adult. The present work explored the ontogeny specific diversity in the associated microbiota and predicted their probable functional inputs in D. cingulatus. RESULTS: The data obtained using 16S rRNA gene sequencing (NovaSeq 6000) revealed presence of members of Proteobacteria (65.83%), Firmicutes (24%), Actinobacteria (10%) phyla throughout the ontogeny of D. cingulatus. Highest alpha diversity of these symbiotic bacteria was recorded in the third instar nymphs in contrast to rest of the developmental stages. Among all the observed genera, Stenotrophomonas, Hungatella and Glutamicibacter were predominant from egg to adult stages. MicFunPred, a tool used for predicting the probable functional inputs of these symbionts, hinted at their probable stage specific contribution in crucial biochemical pathways such as polyketide biosynthesis, ascorbate/aldarate metabolism, pentose phosphate and glyoxylate cycles, steroid hormone and peptidoglycan biosynthesis, and glycolysis/pyruvate metabolism. CONCLUSIONS: The primary investigations on the ontogenetic composition and diversity of associated microbiota, suggest dynamic shifts in D. cingulatus, concurrent with their probable functions/roles in the host development and metabolism. To the best of our knowledge, this is the first report on symbiotic microbiota variation across the developmental stages of D. cingulatus that provides preliminary descriptive observations that may guide future functional and experimental investigations into microbiota-based pest management.

Animals↗

PubMed2026

Global molecular and serological evidence of dengue and chikungunya infection: a systematic review and meta-analysis of 158,608 tested participants.

INTRODUCTION: Dengue virus (DENV) and chikungunya virus (CHIKV) are Aedes-borne arboviruses with overlapping clinical manifestations, shared vectors, and substantial diagnostic challenges in co-endemic settings. This systematic review and meta-analysis synthesized published evidence on molecular detection, serological positivity, and DENV-CHIKV dual positivity/co-infection in human clinical, surveillance, and community-based study populations. CONTENT: Following PRISMA 2020 guidance, five bibliographic databases (PubMed/MEDLINE, Scopus, Web of Science, ScienceDirect, and Google Scholar) and supplementary grey-literature/preprint sources were searched for English-language studies published from 1 January 1980 to 31 December 2024. No prospective PROSPERO or OSF protocol registration was available. Eligible records reported extractable numerators and denominators for DENV and/or CHIKV in humans using recognized molecular or serological assays. A total of 196 studies comprising 158,608 tested or suspected participants were included in the extraction table. The pooled CHIKV estimate was 14.0 % (95 % CI: 12.0-16.4; I2=97.5 %), with molecular and serological estimates of 9.8 and 15.7 %, respectively. The pooled DENV estimate was 13.8 % (95 % CI: 10.9-17.3; I2=99.0 %), with molecular and serological estimates of 13.1 % (95 % CI: 7.9-21.0) and 14.3 % (95 % CI: 10.2-19.8), respectively. DENV-CHIKV dual positivity/co-infection was 52.9 % (95 % CI: 48.7-57.1) among studies that tested and reported both outcomes. Country-level estimates varied widely and should be interpreted as summaries of available studies rather than nationally representative burden estimates. Funnel-plot asymmetry was statistically significant in DENV analyses but not in the overall CHIKV analysis. SUMMARY: Available evidence indicates extensive but highly heterogeneous DENV and CHIKV positivity across selected clinical and surveillance populations. The pooled estimates should be interpreted cautiously because of substantial between-study heterogeneity, diagnostic variability, outbreak-period sampling, and uneven geographic representation. OUTLOOK: The findings support integrated arboviral surveillance, multiplex diagnostics, and vector-control preparedness in co-endemic regions.

Humans↗

PubMed2026

Construction of an infectious clone of Spodoptera frugiperda densovirus and its biological characteristics.

Densoviruses are highly pathogenic to their insect hosts and have great potential for biocontrol. Spodoptera frugiperda densovirus (SfDV) was isolated from diseased larvae of Spodoptera frugiperda, while its biological functions remain unclear. Herein, we successfully constructed an infectious clone of SfDV. The S. frugiperda larvae transfected with the infectious clone exhibited anorexia, stunted growth, and reduced activity. Histopathological analysis further showed that the epidermis, fat body and trachea were infected instead of muscle and midgut tissues. Transmission electron microscopy (TEM) revealed that numerous virions of about 22 nm were distributed within both the nucleoplasm and cytoplasm of epidermal cells. Moreover, many virions were also found contained within vesicles in the cytoplasm. The replication kinetics of the rescued SfDV (rSfDV) was similar to that of the parental SfDV. The median lethal dose (LD50) and median lethal time (LT50) values of rSfDV were 6.63 × 107 viral genome copies (vgc), 5.23 d, respectively, which were also comparable to those of the parental SfDV. Taken together, the infectious clone of SfDV provides an important tool for further exploring the genome function, pathogenesis, and interactions with its hosts.

Animals↗

PubMed2026

Functional role and regulatory network of miR-22-3p in chicken hepatic lipid metabolism.

Although microRNA-22-3p (miR-22-3p) is abundantly expressed in the avian liver, its epigenetic role in lipid homeostasis remains largely uncharacterized. To elucidate its in vivo function, 14-day-old female Qingyuan Partridge chickens were intravenously injected with lentiviral vectors to establish miR-22-3p overexpression and knockdown models. Phenotypic analysis demonstrated that miR-22-3p knockdown significantly elevated hepatic triglyceride (TG) levels (p < 0.05) and drove marked steatosis, whereas its overexpression reduced TG content. Transcriptome sequencing (RNA-Seq) revealed profound metabolic remodeling, identifying 23 core lipid-associated genes (e.g., ELOVL6, FADS2, ACSBG2, and PTGIS) heavily enriched in steroid biosynthesis, fatty acid metabolism, and elongation pathways. In conclusion, miR-22-3p functions as a bidirectional epigenetic rheostat that negatively regulates hepatic lipid deposition by orchestrating a multilayered polygenic network, providing novel molecular targets for mitigating avian metabolic disorders and optimizing production traits in indigenous poultry breeds.

Animals↗

PubMed2026

Functional neuroimaging subtypes of obsessive-compulsive disorder: A systematic review and meta-analysis.

Obsessive-compulsive disorder (OCD) exhibits substantial clinical heterogeneity that may reflect underlying neurobiological diversity. Neuroimaging-based subtyping may advance precision psychiatry by identifying biologically distinct subgroups with differential treatment responses. This study systematically synthesized evidence from functional neuroimaging subtyping studies in OCD to identify reproducible neurobiological subtypes, characterize their clinical profiles, and establish a consensus-based classification framework. We reviewed 40 original studies employing machine learning, clustering, normative modeling, or classification approaches, encompassing approximately 8,150 patients. Consensus clustering identified three reproducible neurobiological subtypes. The Limbic-Hyperactive subtype, comprising approximately 40% of patients, exhibited amygdala and insula hyperconnectivity, elevated anxiety levels, predominant contamination and washing symptoms, and favorable response to cognitive-behavioral therapy. The Fronto-Striatal-Hypoconnected subtype, comprising approximately 35% of patients, demonstrated reduced orbitofrontal-striatal connectivity, cognitive inflexibility, predominant checking and ordering symptoms, and a favorable response to selective serotonin reuptake inhibitors. The Global-Disrupted subtype, comprising approximately 25% of patients, exhibited widespread connectivity disruption, greater symptom severity, and poor treatment response. Support vector machine classification achieved 81.5% accuracy for subtype assignment, though classification of OCD versus healthy controls showed limited generalizability in multisite settings (AUC 0.567-0.673). These findings support a neuroimaging-based framework for personalized treatment selection but require prospective validation.

Humans↗

PubMed2026

A translational framework for early-phase inner-ear gene therapy: clinical trial design, regulatory strategy, and ethical considerations.

PURPOSE OF REVIEW: Hereditary hearing loss has historically been approached as a diagnostic category rather than a therapeutically modifiable disease. Recent advances in molecular genetics, cochlear gene delivery, and first-in-human clinical trials are changing that. This review summarizes contemporary progress in the genetics of hearing loss, with emphasis on emerging gene-based therapies, clinical trial design, regulatory and ethical considerations, and practical implications for otolaryngologists as biologic treatment enters clinical practice. RECENT FINDINGS: Early clinical trials targeting OTOF -related DFNB9 deafness have demonstrated satisfactory safety profiles and meaningful auditory recovery, establishing the first proof-of-concept for cochlear gene therapy in humans, culminating in the April 2026 FDA approval of Otarmeni. Genetic diagnoses are increasingly informing prognosis, cochlear implant counseling, and therapeutic candidacy. Preclinical research continues to expand toward recessive, dominant, and syndromic hearing loss using gene replacement, antisense, RNA interference, and genome-editing strategies. Substantial challenges remain, including heterogeneous outcome measures, uncertain long-term efficacy, regulatory complexity, and inequitable global access. SUMMARY: The genetics of hearing loss is transitioning from a diagnostic modality to an interventional one. Widespread clinical impact will require advances in vector engineering, equitable implementation, multidisciplinary counseling, and integration with established rehabilitation pathways. For otolaryngologists, genetic literacy is becoming essential to contemporary hearing care.

Humans↗

PubMed2026

Revealing potential biomarkers and metabolic mechanisms of ovarian aging in hens during late laying period based on machine learning and metabolomics.

Ovarian function decline during the late laying period represents a major bottleneck for the economic efficiency of the global poultry industry. However, the underlying metabolic mechanisms and reliable early-warning biomarkers for ovarian aging remain poorly understood. In this study, we performed the first untargeted LC-MS/MS metabolomics analysis of ovarian tissues from Taihe silky fowls at peak laying (30 weeks) and late laying (50 weeks) stages, and employed an ensemble machine learning strategy integrating LASSO, random forest, and support vector machine (SVM) algorithms to identify high-confidence core biomarkers of ovarian aging. Gene expression analysis was further conducted to validate the potential molecular mechanisms. Our results showed that the metabolic profiles of ovarian tissues differed significantly between the two groups. A total of 6 core biomarkers were identified, 4 of which were long-chain acylcarnitines. Mechanistic analysis revealed that downregulation of key genes in the carnitine shuttle system led to impaired mitochondrial fatty acid β-oxidation, which in turn triggered excessive oxidative stress and compromised ovarian endocrine function. In conclusion, this study identifies long-chain acylcarnitines as potential metabolic biomarkers for ovarian aging in Taihe silky fowls. These findings provide novel insights into the metabolic basis of poultry ovarian aging and lay a theoretical foundation for the precise regulation of reproductive performance in indigenous poultry breeds.

Animals↗

PubMed2026

Evolutionary characterization and expression profiling of ACC and FASN genes in Chinese mitten crab Eriocheir sinensis.

Acetyl-CoA carboxylase (ACC) and fatty acid synthase (FASN) are rate-limiting enzymes in the fatty acid biosynthetic pathway, yet their evolutionary relationships, sequence features, and expression profiles remain poorly understood in crustaceans, particularly in the economically important Chinese mitten crab (Eriocheir sinensis). Here, we identified and systematically analyzed ACC and FASN genes in E. sinensis using comparative genomic analyses across 43 species. ACC was highly conserved as a single-copy gene in invertebrates, in contrast to the multiple paralogs observed in vertebrates. Similarly, FASN was generally maintained as a single-copy gene across most taxa but exhibited lineage-specific expansion in certain insect groups. Phylogenetic and structural analysis revealed strong conservation of both genes within crustaceans, supported by multiple conserved motifs and canonical functional domains. Expression profiling showed predominant expression in the hepatopancreas and midgut, suggesting their potential involvement in crustacean lipid metabolism. During the molting cycle, ACC and FASN exhibited higher expression levels during stages C and D, suggesting an increased capacity for fatty acid biosynthesis before molting. In addition, dietary lipid levels experiment revealed that ACC and FASN expression responded dynamically to dietary lipid availability, with increased expression at moderate lipid levels but reduced expression under excessive lipid supplementation, indicating a possible adaptive transcriptional response to lipid status. Collectively, this study provides insights into the evolutionary conservation and expression dynamics of ACC and FASN and improves our understanding of lipid metabolic adaptation in crustaceans.

Animals↗

PubMed2026

Transcriptomic responses to developmental temperature in two field-collected Spodoptera exigua populations from Korea.

The beet armyworm, Spodoptera exigua, is a polyphagous insect whose development and seasonal occurrence are strongly influenced by temperature. However, transcriptomic responses to developmental thermal regimes remain insufficiently characterized in field-collected populations. In this study, we compared two Korean field-collected populations of S. exigua: a Haenam population collected in May and initially maintained at 15 ± 1 °C (HN), and a Jeju population collected in July and initially maintained at 27 ± 1 °C (JJ). F1 larvae from each population were reared under three fluctuating developmental temperature regimes: low (15-21 °C), middle (21-27 °C), and high (27-33 °C), followed by RNA-seq analysis. Differential expression analysis revealed population-associated variation in transcriptomic responses across developmental temperatures. HN exhibited a larger number of differentially expressed genes under the high-temperature regime, suggesting stronger transcriptomic sensitivity to elevated developmental temperature. Functional enrichment analyses identified population-associated differences in pathways related to heat response, oxidative metabolism, cytoskeletal organization, cuticle-associated processes, lipid metabolism, and immune-related functions. In JJ, heat-response and cuticle-related expression patterns were more prominent under warmer developmental conditions, whereas HN showed broader changes in stress- and metabolism-associated pathways under high temperature. Overall, this study provides a comparative transcriptomic analysis of two field-collected S. exigua populations under different developmental temperature regimes and identifies RNA-seq-based molecular response patterns associated with population-specific thermal response profiles.

Animals↗

PubMed2026

Impact of climate change on pediatric health outcomes.

Climate change has become one of the most critical health issues globally in the twenty-first century with children bearing the disproportionate burden of the burden since they are more vulnerable than adults because of their physiological, behavioral, and developmental capacities. It is a systematic review that rates the evidence of the relationship between climatic exposures such as heat, air-pollution, and extreme weather events and pediatric health outcomes. The number of peer-reviewed studies involved was 23 published in 2000-2025, which represented different geographic areas and study designs and assessed acute and chronic health outcomes. The Newcastle-Ottawa Scale and the ROBINS-I tool were used to evaluate the methodological quality, and the majority of the studies had low to moderate risks of bias. The narrative synthesis shows that there are always links between air pollutants especially PM2.5, NO2 and O3 and respiratory morbidity, prevalence of asthma and hospitalization of children. Amplified temperatures as well as heat waves were associated with increased cases of heat illness, dehydration, and febrile state in infants and young children. There were elevated cases of diarrheal and vector-related infections, especially in low-resource settings, which were linked to extreme weather events especially floods. Although the overall results were similar, significant differences in the regions and methods were found, and low-income countries show little evidence. In addition, exposures as analyzed in most studies were usually considered individually, which may have underestimated the cumulative or compound climate risks.

Humans↗

PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗

PubMed2026

An automated geometric modeling framework in GATE for the design and optimization of high-sensitivity converging-beam SPECT collimators.

Objective.The trade-off between detection sensitivity and spatial resolution is a fundamental challenge in designing organ-dedicated Single-photon emission computed tomography (SPECT) collimators. While converging-hole geometries offer a solution, their optimization is often hindered by the lack of flexible computational tools capable of modeling large-scale, non-parallel hole arrays. This study aims to develop an automated geometric modeling framework to facilitate the design and evaluation of complex converging- and diverging-hole collimators within standard Monte Carlo environments.Approach.We developed a specialized modeling framework by implementing custom C++ classes and a vector-based alignment algorithm within GATE. This platform enables automated, orientation-consistent construction of large-scale converging arrays not natively supported by standard implementations. A high-sensitivity pure cone-beam collimator (CBC) was designed using this framework. The evaluation used hot-rod, disc, and Jaszczak phantoms for physical characterization, while XCAT and dedicated brain models were employed for clinical tasks, including cardiac, brain perfusion, and DaTscan SPECT simulations.Main results.The CBC achieved a nearly fourfold sensitivity increase compared to a conventional low-energy high-resolution parallel-hole collimator at a 20 cm radius of rotation, while maintaining comparable spatial resolution. Despite a 52.3% field of view reduction, the CBC yielded a 2.2-fold noise reduction (CV: 11.7% vs 25.9%) and mitigated partial volume effects via geometric magnification. XCAT and brain phantom simulations confirmed enhanced anatomical definition and contrast recovery in cardiac, perfusion, and DaTscan tasks.Significance.This work provides an efficient computational tool for rapid design space exploration of advanced collimator geometries. The results demonstrate that the proposed CBC design offers a significant sensitivity advantage, making it highly suitable for high-performance, small-volume clinical applications such as brain and cardiac molecular imaging.

Tomography, Emission-Computed, Single-Photon↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Orchard netting impacts on biodiversity leading to cascading effects at the ecosystem level.

Agriculture must ensure food production without further compromising the ecosystem functions upon which it depends. Agricultural practices should therefore avoid harming farmland biodiversity, especially of taxa that supply the key ecosystem services (e.g. pollination, pest control and nutrient uptake) that ultimately support crop production. Orchards are among the largest permanent plantations worldwide and are increasingly characterised by the spread of plastic nets used to protect fruits/nuts from either abiotic (anti-hail, anti-rain, shade nets) or biotic (exclusion nets) hazards. Despite having received little attention to date, these nets may impact natural communities, acting both as physical barriers and as drivers of habitat changes to which biota must respond. Species-level responses to netting depend on the organism's ability to enter the netted environment and successfully exploit available resources. Net-mediated ecological filtering and plastic behavioural responses may alter species interactions, leading to cascading ecological impacts that may create species-poorer 'netted communities' with simplified ecological networks. Such changes may erode biological control potential, other ecosystem functions, and overall system stability. We conducted a systematic review on the effects of protection nets on biota, and reported novel empirical evidence on anti-hail nets' impacts on communities of orchard-dwelling birds, flower-visiting insects, and rodents. In total, we identified 48 studies from the literature, however this literature was strongly biased towards apple orchards, western countries, and pest taxa. Net deployment was highly effective in deterring target pest species, in some cases regardless of their original function, as even weather-protection nets limited pest populations. Side effects on non-target taxa were also often reported, such as decreases in pollinators and natural enemies, and/or increases in secondary pests or microbial diseases. However, most assessments largely disregarded non-pest taxa and the broader ecological consequences of netting. The few studies that addressed the effects of nets at the guild/community level, including our empirical study, confirmed that orchard netting resulted in species-poor assemblages, with possible ecosystem-level consequences. We propose that future assessments should pay more attention to the indirect effects of netting on non-target taxa, and on the supply of crop-supporting ecosystem services mediated by wild species occurring in agroecosystems. Due to the trade-offs between these services and net-mediated crop protection, integrated alternatives should be tested to improve the environmental sustainability of food production and biodiversity conservation in farmed landscapes.

Biodiversity↗

PubMed2026

Analysis of end-stage renal disease mediated by cuproptosis-related genes.

OBJECTIVE: The complex pathophysiological mechanism of end-stage renal disease (ESRD) has not been fully understood. Cuproptosis is a newly discovered type of programmed cell death. Therefore, this study attempts to clarify the relationship between cuproptosis-related genes (CRGs) and the phenotype of ESRD. MATERIALS AND METHODS: The National Center for Biological Information Gene Expression Omnibus database was applied to obtain the GSE37171 dataset comprising whole-genome microarray analysis of peripheral blood samples. A 3 : 1 case-control design was employed with 75 ESRD patients and 20 healthy controls who were frequency-matched for age, sex, and ethnicity. Based on differentially expressed genes (DEGs) and genes related to cuproptosis, CRGs were identified. Thereafter, we explored two different subpopulations based on the cuproptosis gene and analyzed their expression and immune infiltration. Genes specific to the CRG cluster were identified through the weighted gene co-expression network analysis algorithm, and the best prediction model was determined and verified by four machine learning methods. RESULTS: The study identified 14 differentially expressed CRGs, among which ATP7B, SLC31A1, LIAS, LIPT1, DLD, MTF1, CDKN2A, DBT, and DLST had relatively high expression levels in the ESRD samples. Compared with the control group, expression levels of FDX1, DLAT, PDHA1, PDHB, and GLS were significantly lower in the ESRD group, and CRGs played a key role in the regulation of immune infiltration in ESRD. Two cuproptosis-related molecular clusters were identified in the ESRD samples. Cluster2 was more correlated with the immune infiltration of ESRD. By analyzing the intersection points between CRG cluster and key genes of ESRD, a total of 888 specific DEGs were identified. Functional differences related to specific DEGs were further explored using gene set variation analysis. Five significant genes (SMC5, USP47, USP53, AGA, and DMXL1) were identified by the support vector machine model as key predictors for ESRD disease risk, achieving an area under the curve (AUC) of 1.00 in internal validation. However, external validation in independent cohorts is required prior to clinical application. Individual gene analysis showed an AUC > 0.81 in discriminating ESRD patients from healthy controls, and the expression of all 5 genes in ESRD patients was significantly lower than in the control group. CONCLUSION: This study clarified the relationship between CRGs and the phenotype of ESRD, analyzed their specific roles in the immune microenvironment, and obtained a predictive model, providing new insights for the study of its potential therapeutic targets.

Humans↗