Search Results (4,337)

This study aimed to determine the presence of relevant infectious and parasitic agents (IPAs) in managed honeybees from Central Italy and the Republic of Kosovo and Albania to assess the overall health status of local apiaries by determining the contamination levels and co-occurrence. Therefore, pathogens and parasites such as Paenibacillus larvae, Melissococcus plutonius, Vairimorpha apis, V. ceranae, the acute bee paralysis virus (ABPV), black queen cell virus (BQCV), chronic bee paralysis virus (CBPV), deformed wing virus variants DWV-A and DWV-B, and the parasitoid flies Megaselia scalaris and Senotainia tricuspis were detected by quantitative polymerase chain reaction (qPCR) and reverse transcriptase qPCR (RT-qPCR) in clinically healthy adult honeybees collected from 187 apiaries in the Abruzzo and Molise regions of Central Italy, 206 apiaries in the Republic of Kosovo in 2022 and 2023 and 18 apiaries in Albania in 2022. The percentages of positive samples and contamination for V. ceranae, P. larvae and DWV-B were significantly higher in the Republic of Kosovo and Albania, while the percentages of samples positive for M. plutonius, CBPV, DWV-A, and the parasitoid flies were higher in Central Italy. Additionally, P. larvae and some viruses showed significantly different occurrence rates between the two years in Italy and the Republic of Kosovo. The co-occurrence of IPAs also differed between the two geographic areas. Their varying distribution could depend on epidemiological dynamics, climatic factors, and management practices specific to each country, whose relative impact should be defined to guide targeted interventions to reduce honeybee mortality. Full article

Since the molecular mechanisms underlying sex determination in Procambarus clarkii are still unclear, it is important to investigate the genetic basis of sex determination in crustaceans. Currently, the molecular mechanisms of sex determination and the gender-specific markers in this species remain poorly understood. In this study, a total of 14,046,984 SNPs and 2,160,652 InDels were identified through genome-wide resequencing of 89 individuals (45 females and 44 males). Further analysis confirmed that the candidate chromosome was Chr38, the sex determination system was identified as XY, and the sex determination region was located at Chr38: 6,000,000–21,100,000 bp. A pair of sex-specific molecular markers has been identified based on a 21 bp female-specific insertion within the candidate sex-determining region. Additionally, SOAT, NPC1, PTGS2, FANCD1, and VAlRS were identified as candidate sex-determining genes through the screening of candidate genes and RT-qPCR validation analysis. These findings provide a robust foundation for investigating sex-determining mechanisms in crustaceans. Through the integration of genome-wide association studies (GWAS), selection signals, and transcriptome analysis, we identified, for the first time, genes associated with sex determination, growth, and immunity. These genes represent promising candidates for further functional studies and genetic improvement in Procambarus clarkii. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

Background/Objectives: Accurate identification of dental malocclusions from routine clinical photographs can be time-consuming and subject to interobserver variability. A YOLOv11-based deep learning approach is presented and evaluated for automatic malocclusion detection on routine intraoral photographs, testing the hypothesis that training on a structured annotation protocol enables reliable detection of multiple clinically relevant malocclusions. Methods: An anonymized dataset of 5854 intraoral photographs (frontal occlusion; right/left buccal; maxillary/mandibular occlusal) was labeled according to standardized instructions derived from the Index of Orthodontic Treatment Need (IOTN) A total of 17 clinically relevant classes were annotated with bounding boxes. Due to an insufficient number of examples, two malocclusions (transposition and non-occlusion) were excluded from our quantitative analysis. A YOLOv11 model was trained with augmented data and evaluated on a held-out test set using mean average precision at IoU 0.5 (mAP50), macro precision (macro-P), and macro recall (macro-R). Results: Across 15 analyzed classes, the model achieved 87.8% mAP50, 76.9% macro-P, and 86.1% macro-R. The highest per-class AP₅₀ was observed for Deep bite (98.8%), Diastema (97.9%), Angle Class II canine (97.5%), Anterior open bite (92.8%), Midline shift (91.8%), Angle Class II molar (91.1%), Spacing (91%), and Crowding (90.1%). Moderate performance included Anterior crossbite (88.3%), Angle Class III molar (87.4%), Head bite (82.7%), and Posterior open bite (80.2%). Lower values were seen for Angle Class III canine (76%), Posterior crossbite (75.6%), and Big overjet (75.3%). Precision–recall trends indicate earlier precision drop-off for posterior/transverse classes and comparatively more missed detections in Posterior crossbite, whereas Big overjet exhibited more false positives at the chosen threshold. Conclusion: A YOLOv11-based deep learning system can accurately detect several clinically salient malocclusions on routine intraoral photographs, supporting efficient screening and standardized documentation. Performance gaps align with limited examples and visualization constraints in posterior regions. Larger, multi-center datasets, protocol standardization, quantitative metrics, and multimodal inputs may further improve robustness. Full article

Cervical cancer is a largely preventable disease, with over 90% of cases caused by persistent infection with human papillomavirus (HPV). Despite the availability of HPV vaccination and cervical screening, incidence rates in Canada have been rising since 2015, particularly among underserved populations. This study investigates contributing factors behind cervical cancer diagnoses in Eastern Ontario over a two-year period to identify gaps leading to failures in prevention and screening. A retrospective chart review was conducted for cervical cancer cases diagnosed between January 2022 and December 2023 at two regional cancer centres in Eastern Ontario. Cases were categorized as screen-detected, inadequately screened, or system failure, based on prior screening history and care processes. Data was collected on patient, screening, and cancer characteristics. Of 132 cases, 22 (16.7%) were screen-detected, 73 (55.3%) were inadequately screened, and 37 (28.0%) were attributed to healthcare system failure. Later-stage disease was significantly more common in the latter two groups. Thirty-one (23.5%) cases presented with palliative diagnoses, and 18 (13.6%) individuals died within 2.5 years. Inadequate screening was associated with rurality, deprivation, and lack of a primary care provider. System failures included false-negative Pap tests, loss to follow-up, and misapplication of screening guidelines. This study evaluated failures in cervical cancer prevention, which led to cervical cancer diagnoses in Eastern Ontario. Gaps included suboptimal screening participation, lack of access to care, health care system breakdowns, and limitations of the Pap test. Findings provide concrete suggestions for eliminating cervical cancer in Canada. Full article

Non-communicable diseases (NCDs) such as Diabetes Mellitus (DM) and Sickle Cell Disease (SCD) pose an escalating health challenge in Africa, underscored by diagnostic deficiencies, inadequate surveillance, and limited health system capacity that contribute to late diagnoses and consequent preventable complications. This review adopts a comparative framework that considers DM and SCD as complementary indicator diseases, both metabolic and genetic, and highlights intersecting diagnostic, infrastructural, and governance hurdles relevant to AI-enabled screening in resource-constrained environments. The study synthesizes epidemiological data across both African and high-income regions and methodically catalogs machine learning (ML) and deep learning (DL) research by clinical application, including risk prediction, image-based diagnostics, remote patient monitoring, privacy-preserving learning, and governance frameworks. Our key observations reveal significant disparities in disease detection and health outcomes, driven by underdiagnosis, a lack of comprehensive newborn screening for SCD, and fragmented diabetes surveillance systems in Africa, despite the availability of effective diagnostic technologies in other regions. The reviewed literature on ML/DL shows high algorithmic accuracy, particularly in diabetic retinopathy screening and emerging applications in SCD microscopy. However, most studies are constrained by small, single-site datasets that lack robust external validation and do not align well with real-world clinical workflows. The review identifies persistent implementation challenges, including data scarcity, device variability, limited connectivity, and inadequate calibration and subgroup analysis. By integrating epidemiological insights into AI diagnostic capabilities and health system realities, this work extends beyond earlier surveys to offer a comprehensive, Africa-centric, implementation-focused synthesis. It proposes actionable operational and policy recommendations, including offline-first deployment strategies, federated learning approaches for low-bandwidth scenarios, integration with primary care and newborn screening initiatives, and enhanced governance structures, to promote equitable and scalable AI-enhanced diagnostics for NCDs. Full article

Aspergillus fumigatus, a saprophytic fungus, causes aspergillosis, primarily affecting the immunocompromised. The efficacy of triazole antifungals is compromised by resistance that has developed both clinically and environmentally. Widespread agricultural use of similar triazole fungicides selects for resistant genotypes, leading to potential food contamination and compromising treatment. This study assessed the presence of azole-resistant A. fumigatus in minimally processed food items commonly consumed in Brazil. A total of 25 commercial samples, including black pepper, yerba mate, and green coffee beans, were collected from different regions. Forty-two A. fumigatus isolates were recovered and screened for susceptibility to agricultural and clinical triazoles by determining EC₅₀ values for tebuconazole (0.04–0.7 µg/mL), itraconazole (0.06–0.5 µg/mL), and voriconazole (0.07–0.15 µg/mL). Sequence analysis of the CYP51A gene revealed the presence of M172V mutation, none of which are associated with resistance. Microsatellite genotyping indicated high genotypic diversity and genetic relatedness among isolates from different food sources. Although no azole-resistant phenotypes were identified, the consistent recovery of A. fumigatus from products not directly exposed to azole fungicides highlights the need for continued surveillance. Agricultural environments remain critical hotspots for the emergence and dissemination of resistance, reinforcing the importance of integrated One Health strategies in antifungal resistance monitoring. Full article

Hemotropic mycoplasmas (hemoplasmas) are uncultivable, cell wall-less bacteria that parasitizeon the surface of red blood cells of mammals, potentially causing anemia and other systemic signs. While widely distributed among domestic and wild animals, their occurrence in equids remains poorly understood, and no species has been identified as host-specific to horses or donkeys. This study presents the first systematic survey of hemoplasmas in equids from southeastern Europe and only the second molecularly confirmed case in horses in Europe. A total of 843 equids (817 horses and 26 donkeys) from different regions of Croatia, representing various ages, uses, and husbandry systems, were screened for hemoplasmas by PCR targeting the 16S rRNA gene. Only one horse tested positive, identified as Mycoplasma wenyonii, a hemoplasma typically associated with cattle. The estimated prevalence was 0.12% (95% CI: 0.003–0.68%). No donkeys were infected. The extremely low prevalence observed here—the lowest reported in any study detecting hemoplasma-positive horses—supports the hypothesis that equids do not harbor host-specific hemoplasma species and may only sporadically acquire infections from other hosts via spillover. This finding underscores the apparent absence of persistent hemoplasma lineages adapted to equids and highlights the need for further research on their epidemiology, host specificity, and transmission dynamics. Full article

Background/Objectives: The high volatility of volatile drugs significantly restricts their clinical applicability. Although excipients capable of strong interactions can reduce volatilization, conventional screening methods rely on empirical trial-and-error, resulting in low efficiency and high resource consumption. To address this limitation, this study introduces an artificial intelligence (AI)-driven strategy for screening drug–excipient interactions. Using d-borneol as a model drug, this approach aims to efficiently identify strongly interacting excipients and develop stable nano-formulations. Methods: High-throughput simulations were performed using the Protenix structure prediction model to evaluate interactions between d-borneol and 472 FDA-approved excipients. The top 50 candidate excipients were selected based on these simu-lations. Molecular docking and stability experiments were conducted to validate the predictions. Results: Molecular docking and stability experiments confirmed the consistency between predicted and experimental results, validating the model’s reliability. Among the candidates, soybean phospholipid (PC) was identified as the optimal excipient. A lyophilized liposomal formulation prepared with PC significantly suppressed the volatilization of d-borneol and improved both thermal and storage stability. Mechanistic investigations indicated that d-borneol stably incorporates into the hydro-phobic region of phospholipids, enhancing membrane ordering via hydrophobic interactions without disturbing the polar headgroups. Conclusions: This study represents the first application of a structure prediction model to excipient screening for volatile drugs. It successfully addresses the stability challenges associated with d-borneol and offers a new paradigm for developing nano-formulations for volatile pharmaceuticals. Full article

Tibia morphology is a significant factor in poultry germplasm and market traits. Copy number variation (CNV) has been identified as a structural source of genetic variation for complex traits. We profiled genome-wide CNVs in Dong Tao chickens and nine other local breeds and performed a breed-based case–control CNV-GWAS (Dong Tao vs. reference breeds). We sequenced 152 chickens, including 46 Dong Tao, and annotated genes and pathways. A total of 22,972 CNVs were detected, of which 2193 were retained after filtration across 33 chromosomes, with sizes ranging from 2 kilobases to 12.8 megabases. Principal component analysis indicated an overall weakness in the breed structure and a sex-related trend within Dong Tao. A deletion on chromosome 3 at 36,529,501 to 36,539,000 was observed in Dong Tao. The exploratory screen identified 44 CNV regions at nominal significance (p < 0.05), distinguishing Dong Tao from other breeds. Thirty-seven regions contained 99 genes, including CHRM3 within the chromosome 3 deletion and CRADD overlapping two CNVs. Enrichment analysis indicated thiamine metabolism and growth hormone receptor signalling as the primary pathways of interest, with TPK1, SOCS2, and FHIT identified as potential candidates. These results provide a CNV landscape for Dong Tao and prioritize variant regions and pathways potentially relevant to its robust tibia morphology; however, no direct CNV–tibia phenotype regression was performed. Full article

Background: Depression and eating disorders (EDs) represent significant and often multiple public health concerns. Healthcare workers, including medical residents, were affected by several stressors that the COVID-19 pandemic has engendered and amplified, potentially exacerbating mental health issues. Despite this, limited evidence is available regarding the association between depressive symptoms and possible EDs among Public Health Residents (PHRs). Methods: A nationwide cross-sectional study, the ‘Public Health Residents Anonymous Survey in Italy (PHRASI),’ was conducted between June and July 2022. A total of 379 PHRs participated in this study, filling in a self-administered questionnaire which included the PHQ-9 for assessing depressive symptoms and the SCOFF (Sick, Control, One, Fat, Food) test as a screening tool for possible EDs. Multivariable logistic regression evaluated associations between sociodemographic and training/work-related factors, depressive symptoms, and EDs. Results: Overall, 40.6% of respondents screened positive for possible EDs. Depressive symptoms had a positive association with possible EDs (aOR = 2.76; 95% CI = 1.55–4.93). Other factors associated with higher ED odds included region of residence (aOR = 1.92; 95% CI = 1.06–3.47), intention to repeat the test for another postgraduate course (aOR = 3.22; 95% CI = 1.25–8.3), and working more than 40 h per week (aOR = 1.91; 95% CI = 1.19–3.07). Conversely, having more than one child (aOR = 0.32; 95% CI = 0.13–0.78) was associated with lower odds. Conclusions: The findings highlight a significant association between depressive symptoms and positive screening for possible EDs, underscoring the need for integrated mental health support and preventive interventions within medical residency programmes, especially in the context of public health crises. Full article

This study optimized a homogeneous photo-Fenton process for the simultaneous degradation of the emerging pesticides atrazine and methomyl in water using Response Surface Methodology (RSM). A synthetic agricultural effluent containing 2.0 mg L⁻¹ of each pesticide (COD = 103.2 mg O₂ L⁻¹; TOC = 26.1 mg C L⁻¹; BOD₅ = 45.8 mg O₂ L⁻¹) was treated in a recirculating UV–H₂O₂/Fe²⁺ reactor. A 2³ factorial design with replication and five central points identified the H₂O₂/Fe²⁺ ratio and irradiation time as the main factors controlling mineralization, achieving up to 88.9% COD removal in the best screening run. Steepest-ascent experiments were then performed to approach the region of maximum response, followed by a rotatable Central Composite Design (20 runs). The resulting quadratic model explained 98.14% of the COD variance (R² = 0.9814; adjusted R² = 0.9646; predicted R² = 0.8591; CV = 0.2736%) and predicted a maximum COD removal of 94.5% at a volumetric flow rate of 0.466 L min⁻¹, a Fenton ratio of 12.713 mg mg⁻¹, and a treatment time of 71.0 min. Experimental validation under these optimized conditions yielded highly reproducible removals of 94.2 ± 0.04% COD and 81% TOC, confirming the predictive capability of the RSM model and demonstrating a high degree of organic mineralization. The response surfaces revealed that increasing the Fenton ratio enhances oxidation up to an optimum, beyond which hydroxyl-radical self-scavenging slightly decreases efficiency. Overall, the integration of multivariable experimental design and RSM provided a robust framework to maximize photo-Fenton performance with moderate reagent consumption and operating time, consolidating this process as a viable alternative for the mitigation of pesticide-laden agricultural wastewaters. Full article

The forest fly (Hippobosca equina) is an obligate haematophagous dipteran insect (order Diptera) that primarily infests horses and may contribute to the circulation of vector-borne pathogens. This study aimed to investigate the presence of Anaplasma phagocytophilum, Borrelia burgdorferi s.l., Babesia caballi, and Theileria equi, important vector-borne pathogens of equids, in forest flies collected from horses in endemic areas of Spain. A total of 170 forest flies were collected from 39 equids across four geographical regions in Spain (Segovia, Madrid, Toledo, and Menorca) and blood samples were collected from 27 of these horses. All flies were morphologically and molecularly identified as H. equina, and DNA extracted from flies and equine blood was screened using multiplex real-time and nested PCR, followed by sequencing and phylogenetic analysis. Neither flies nor horses tested positive for A. phagocytophilum, whereas one fly was positive for B. burgdorferi s.l. (0.6%). In contrast, T. equi and B. caballi DNA were detected in 11.2% and 1.2% of flies, respectively, and all positive flies were collected from horses positive for equine piroplasmosis (T. equi/B. caballi infection), with identical 18S rRNA sequences between hosts and flies. Nested PCR showed a higher detection rate than real-time PCR for the detection of these piroplasms in flies and blood samples. These findings provide the first molecular evidence of EP pathogens in H. equina and support further investigation into the epidemiological importance of forest flies in equine pathogen surveillance. Full article

This study presents the first comprehensive assessment of per- and polyfluoroalkyl substances (PFAS) in surface waters of northern Serbia (Middle Danube region), combining targeted analysis of 25 PFAS with high-resolution mass spectrometry suspect screening (SSA) at 12 settlement-adjacent sites on major rivers and part of the Danube–Tisa–Danube (DTD) canal network. The sum of 10 quantified PFAS showed pronounced spatial variability: the Great Bačka Canal (GBC) exhibited the highest mean and maximum values (18.4 ng/L and 52.6 ng/L, respectively); the Danube averaged 9.05 ng/L (2.92–22.2 ng/L); the Tisa averaged 10.5 ng/L (4.53–16.5 ng/L); and the Sava and Tamiš exhibited the lowest means (~5.4 ng/L each). In total, 19 of 24 sites exceeded the proposed EU group Environmental Quality Standard (EQS) of 4.4 ng/L, expressed as PFOA-equivalents, with exceedances of 5.4–20.2 ng/L; PFOS exceeded the 0.65 ng/L inland surface water annual average (AA) EQS in 17 samples. SSA expanded coverage beyond targets, revealing ultra-/short-chain PFAS and replacements, with TFA as the most abundant (337–1165 ng/L; mean 513 ng/L) and notable maxima for PFPrA (51.3 ng/L), ADONA (24.9 ng/L), and TFMS (11.2 ng/L). Compared with European freshwaters, the maximum obtained here lies in the lower-mid part of the reported range, consistent with short-chain perfluoroalkyl carboxylic acids (PFCA) dominance and diffuse-source influences. Full article

To address traffic safety hazards from asphalt pavement icing in Xinjiang’s cold regions and inefficiencies of conventional deicing and imprecise geothermal deicing systems, this study focused on local asphalt surfaces. Using “outdoor qualitative screening and indoor quantitative verification”, key variables were identified via controlled tests and their coupling effects on the time to complete icing were quantified through an L16(4⁴) orthogonal test (a 4-factor, 4-level design encompassing 16 test groups). A Backpropagation (BP) neural network model (3 inputs, 5 hidden neurons, and a learning rate of 0.7) optimized with 64 datasets was established to predict the time to complete icing of asphalt pavements, achieving a prediction accuracy (PA) of 90.7% for the time to complete icing and a mean error of merely 0.71 min. Dynamic icing risk thresholds (high/medium/low) were established via K-means clustering and statistical tests, enabling data-driven precise activation and on-demand regulation of geothermal deicing systems. This resolves energy waste and deicing delays, offering technical support for efficient geothermal utilization in cold-region transportation infrastructure, and provides a scalable “factor screening + model prediction” framework for asphalt pavement anti-icing practice. Full article