Search Results (1,148)

During August 2025, a single adult yellow-bellied watersnake, Nerodia erythrogaster transversa was found dead on the road in Montgomery County, Arkansas, USA, salvaged, and its feces examined for coccidian parasites. Fecal material from the rectum was placed in a vial of 2.5% potassium dichromate and examined by light microscopy. Partially sporulated oocysts were initially found and allowed to completely sporulate. The snake was found to be passing a new species of Eimeria. Oocysts of Eimeria speairsi sp. n. were ovoidal to spheroidal with a slightly rough bi-layered wall, measured (L × W) 28.0 × 18.2 µm, and had a length/width (L/W) ratio of 1.5; a micropyle, oocyst residuum, and polar granule was absent. Sporocysts are ellipsoidal and measured 13.7 × 8.6 µm, L/W ratio of 1.6; a flattened Stieda body was present but sub-Stieda and para-Stieda bodies were absent. The sporocyst residuum was composed of various-sized granules in a compact rounded or irregular mass, sometimes dispersed between the sporozoites. A 412 bp sequence of the SSU rRNA gene produced for E. speairsi sp. n. showed a relatively low level of similarity. The species description is based primarily on oocyst morphology and partial SSU rRNA sequence data from the single host snake. In addition, an updated summary of the coccidians of North American watersnakes is provided. Full article

Patterns of resource selection are driven by the decision-making processes of animals occurring at multiple scales from where to establish a home range (i.e., second order selection) to which resource patches to use within the home range (i.e., third order selection). Elk (Cervus canadensis) were reintroduced to southwestern Virginia, USA, from 2012 to 2014 following successful translocations onto reclaimed surface coal mines in the region. We sought to understand how elk have acclimated following their translocation using location data from GPS-collared adult female elk (n = 33) collected from 2019 to 2022 along with remotely sensed terrain and land cover data. We utilized continuous-time movement models paired with generalized linear mixed-effects modeling to describe seasonal resource selection at second and third orders. At both scales of selection and throughout the year, female elk selected reclaimed surface mines, conifer forests, ridgetops, and areas with lower terrain roughness, while avoiding mixed hardwood and oak (Quercus spp.) forests. Unmined open land was only selected at the third order during periods of forage scarcity (i.e., winter) and increased metabolic requirements (i.e., late gestation). Although surface coal mining leaves legacy environmental impacts on the landscape, management of these sites provides benefits to elk and maintains open habitat that is otherwise limited. Full article

Background: Anemia is a major public health issue, particularly among women of reproductive age (WRA) in low- and middle-income countries (LMICs). Pakistan’s National Nutrition Survey (NNS) 2011 showed a high prevalence of vitamin B₁₂ (B₁₂) and folate deficiency among WRA, necessitating further investigation in subsequent surveys. Methods: Blood samples from 31,828 WRA (15–49 years old) were collected using a stratified multi-stage sampling technique in NNS-2018. We conducted a secondary analysis using population-weighted logistic regression to assess the association of potential factors with B₁₂ and folate deficiency. B₁₂ (n = 4442) and folate (n = 12,662) samples were measured using an electrochemiluminescence immunoassay and a Centers for Disease Control and Prevention, USA (CDC)-approved microbiologic assay, respectively. Results: Folate deficiency was present in 44.7% WRA, and 20.2% had B₁₂ deficiency. Provincial distribution was associated with folate deficiency, i.e., Sindh (OR = 1.140, 95% CI 1.018, 1.285), Baluchistan (OR = 1.237, 95% CI 1.052, 1.453), and Islamabad (OR = 1.524, 95% CI 1.109, 2.092), while B₁₂ deficiency was prevalent in Islamabad (OR = 1.673, 95% CI 1.122, 2.497), Gilgit Baltistan (OR = 2.472, 95% CI 1.197, 5.106), and newly merged districts of KPK (OR = 1.584, 95% CI 0.977, 2.570). Rural residence (OR = 1.407, 95% CI 1.125, 1.760), obesity (OR = 1.649, 95% CI 1.282, 2.122), and overweight (OR = 1.560, 95% CI 1.262, 1.928) were associated with B₁₂ deficiency. Conclusions: Our results show regional and demographic differences in the prevalence of folate and B₁₂ deficiencies among WRA. This underscores the need for targeted nutritional interventions and further longitudinal studies to identify potentially associated factors. Full article

The Epi2SensA method is a method similar to the validated EpiSensA assay for assessing the skin sensitization potential of chemicals. The Epi2SensA protocol includes adaptation (changes to exposure conditions and the controls) for using an alternative reconstructed human epidermis (RhE) model, the EpiDerm™ model. The interlaboratory validation study evaluated the reliability and predictive capacity of Epi2SensA according to OECD Performance Standards. Four laboratories (Mattek, Now Part of Sartorius, Eurofins Munich, Burleson Research Technologies, Inc., and Food and Drug Safety Center) conducted blinded testing of 20 coded reference substances representing various chemical categories and sensitization potencies. Statistical analysis using modified acceptance criteria (a 60% cell viability threshold) and a modified prediction model (requiring at least two positive gene markers) demonstrated substantially improved performance compared to the original EpiSensA criteria. The between-laboratory reproducibility (BLR) was 85%, the average within-laboratory reproducibility (WLR) was 83.3%, and the average predictivity parameters were 88.1% for sensitivity, 88.9% for specificity, and 88.3% for accuracy. Epi2SensA achieved performance metrics comparable to the validated reference method (EpiSensA), supporting regulatory acceptance of the Epi2SensA assay using the EpiDerm™ model (Mattek Corporation, Now Part of Sartorius, Ashland, MA, USA) as an alternative RhE source for OECD TG 442D skin sensitization testing. Full article

Rocky shores are characterized by rough, multi-scale bathymetric variations that result in enhanced wave energy dissipation by bottom friction compared to sandy beaches. Realistic SWAN simulations of surface gravity waves across the rocky shores of Monterey (CA, USA) are conducted, and model results are compared to 20 inner-shelf observational sites spanning 34–5 m water depth. The wave field was highly variable during the study, including alternately low energy waves dominated by southern swell and higher energy local waves aligned with strong north-westerly winds. Including a modified bottom friction parameterization is required for the model to reproduce bulk wave statistics with high skill across the entire inner shelf. The SWAN simulation with the default bottom friction parameterization overestimates significant wave height relative to observations because the friction factor $f_e$ parameterization has a maximum value of 0.3. Additional simulations included two empirical formulations relating $f_e$ to the normalized wave excursion $A_b/k_N$ in the large roughness regime $A_b/k_N<1$. Both simulations incorporate a higher $f_e$ that is required to model strong bottom friction dissipation over rocky seabeds. The higher friction factors, with 80% falling within the range 0.43 to 5.38, are associated with variability in the normalized orbital excursion within $0.1<A_b/k_N<1$. This range corresponds to a large bottom roughness length scale, $k_N=0.5$ m, characteristic of rocky shore environments. Full article

Background/Objectives: This study aimed to automatically classify Zirconia-based fixed restorations and porcelain-fused-to-metal (PFM) restorations on panoramic radiographs using an artificial intelligence-based model. Unlike previous studies that mainly focused on classifying types of restorations (e.g., crowns, fillings, implants), this research concentrated on material-based differentiation, aiming to provide a more specific contribution to clinical decision support systems. Method: Panoramic radiographs obtained from the archive of Süleyman Demirel University Faculty of Dentistry were included in this study. Radiographs with poor image quality or insufficient visibility of the restoration area were excluded. A total of 593 cropped region-of-interest (ROI) images, labeled by expert prosthodontists using ImageJ software (version 1.54r; National Institutes of Health, Bethesda, MD, USA), were included in the analysis. In order to reduce class imbalance, data augmentation was applied only for images in the Zirconia-based fixed restorations class. By using various image processing techniques such as rotation, reflection and brightness change, the number of samples in the zirconia-based restorations class was increased and thus a balanced dataset was obtained with a close number of samples for both classes. For model training, the pre-trained VGG16 architecture was used with a transfer learning method, and the final layers were retrained and fine-tuned. The model was configured specifically for binary classification. The entire dataset was randomly split into 70% training, 20% validation, and 10% testing. Model performance was evaluated using accuracy, F1-score, sensitivity, and specificity. Results: The model correctly classified 90 out of 94 images in the test dataset, achieving an overall accuracy rate of 96%. For both classes, the precision, recall, and F1-score values were measured in the range of 95% to 96%. Additionally, the Area Under the Curve (AUC) of the ROC curve was calculated as 0.994, and the Average Precision (AP) score was determined to be 0.995. According to the confusion matrix results, only 4 images were misclassified, consisting of 2 false positives and 2 false negatives. Conclusions: The deep learning model demonstrated high accuracy in differentiating zirconia and metal-supported porcelain restorations on panoramic radiographs, suggesting that material-based AI classification may support clinical decision-making in restorative dentistry. Full article

Liver disease encompasses a wide range of conditions, each requiring tailored therapeutic approaches. This review describes and critically discusses treatments with robust evidence for improving liver health. Ursodeoxycholic acid (UDCA) is a drug approved by the Food and Drug Administration of the USA to treat primary biliary cholangitis (PBC). In addition, UDCA has been demonstrated to protect against metabolic dysfunction-associated steatohepatitis, fibrosis, and drug-induced liver injury (DILI). The mechanism of action of UDCA has been attributed not only to decreasing the effects of toxic bile acids but also to protecting mitochondrial integrity and function, as well as to antioxidant, anti-inflammatory, and anti-apoptotic activities. UDCA can scavenge reactive oxygen species (ROS) and activate the nuclear factor-E2-related factor-2 (Nrf2) pathway, thereby exerting antioxidant activity. The anti-inflammatory activity of UDCA is associated with its ability to inhibit the nuclear factor-κB pathway. Pirfenidone is a well-recognized antifibrotic drug for the treatment of idiopathic pulmonary fibrosis; its effects on liver fibrosis have also been demonstrated. Pirfenidone exerts anti-inflammatory effects by attenuating the nucleotide-binding oligomerization domain-like receptor 3 inflammasome signaling pathway. The antioxidant actions of pirfenidone are associated with its ability to upregulate the Nrf2 pathway. Both the anti-inflammatory and antioxidant properties of pirfenidone act together to attenuate lung and liver fibrosis, decreasing transforming growth factor-β levels, inhibiting profibrogenic hepatic stellate cell activation, and increasing extracellular matrix degradation. Methyltransferases utilize S-adenosyl-L-methionine (SAM) as a methyl donor for most transmethylation reactions in the body. SAM increases reduced glutathione (GSH) levels, exerting important antioxidant effects. Evidence indicates that SAM prevents fibrosis and attenuates hepatocellular carcinoma development, improving patient survival. N-acetylcysteine (NAC) is a precursor to L-cysteine and GSH and is used in clinical settings to treat cancer, nephropathy, heart disease, pulmonary fibrosis, polycystic ovary syndrome, and influenza. Regarding the liver, NAC is the most accepted treatment for DILI, especially after paracetamol overdose. Owing to its antioxidant and anti-inflammatory actions, NAC has been successfully used to treat chronic liver injuries, including hepatosteatosis and fibrosis. Therefore, ursodeoxycholic acid, pirfenidone, S-adenosyl-L-methionine, and N-acetylcysteine could represent therapeutic strategies for the treatment of liver pathologies. Full article

Dual-purpose (DP) crops diversify farm income, facilitating livestock and grain production. Dual-purpose wheat (Triticum aestivum L.), i.e., grazing or clipping herbage early in the season followed by grain harvest, is commonly used in the Great Plains of the United States of America (USA), but the use of DP crops, including oat (Avena sativa L.), in the southeastern USA is limited. This 2-year study assessed agronomic and nutritive value responses and grain production of one wheat (AGS 4023) and four oat (Brooks, Horizon 214, Horizon 306, and Horizon 578) cultivars under two management strategies (dual-purpose [DP, clipping for forage and subsequent grain harvest] or grain production [GP]. Treatments were arranged in a randomized complete block design with three replications. Seasonal forage accumulation ranged from 4140 to 5460 kg ha⁻¹ yr⁻¹ among cultivars. Greater concentration of NDF (p < 0.001) was observed in year 2 than in year 1 (46.3% vs. 40.9%, respectively), but cultivars did not differ. Crude protein concentration ranged from 18.6% to 21% among cultivars. Grain yield was greater for H578 than all other cultivars (7400 versus an average of 5100 kg DM ha⁻¹). Grain yield reduction for DP ranged from 27 to 45% compared with GP. These results demonstrate the potential of selected cultivars (e.g., H578) for use as DP crops in the region. Recognizing some reduction in grain yield compared with GP, DP cropping increases flexibility for producers, facilitating the use of integrated crop-livestock systems that diversify income streams and enhance the circularity of agroecosystems. Full article

Seizure clusters (SCs) are an acute and transient increase in seizure frequency relative to an individual patient’s baseline and are associated with an increased risk of injury, morbidity, and potentially mortality if not promptly and adequately treated. Despite their clinical importance, the management of SCs remains highly heterogeneous, primarily due to the absence of a universally accepted definition, which is determined also by the wide variability in seizure semiology and baseline individual burden;, as well as by differences in care settings. Outpatient treatment relies largely on caregivers’ ability to recognize SCs and administer rescue medication, whereas inpatient management may also involve invasive routes of administration. We conducted a literature review identifying 32 original articles addressing the treatment of SCs. The analysis focused on definitions, efficacy outcomes, and adverse events across three clinical scenarios: outpatient, Emergency Department (EDs) and Epilepsy Monitoring Units. The results show that in the outpatient setting, the available evidence suggests that diazepam nasal spray (DZP-NS), midazolam nasal spray (MDZ-NS), and oral lorazepam (LZP) solution may demonstrate comparable efficacy and safety. However, comparisons are limited by heterogeneity in studies’ designs, patient populations and outcome definitions, as well as by the absence of head-to-head trials. Moreover, geographic differences in drug availability (e.g., USA vs. Europe) limit the development of universally applicable treatment protocols. Consequently, the off-label use of oral benzodiazepines, including clobazam, clonazepam, and lorazepam, remains common when oral therapy is feasible, despite limited evidence. The implementation of a patient-specific Acute Seizure Action Plan (ASAP) incorporating an individualized SC definition is recommended. In contrast, inpatient management shows greater consensus, largely reflecting first-line treatment paradigms for status epilepticus. These include prompt intravenous benzodiazepine administration, followed by the intravenous loading of antiseizure medications such as brivaracetam or lacosamide in cases of seizure recurrence. In ED settings, “empirical” definitions of SCs (i.e., more than three seizures within 24 h) may facilitate timely intervention. Full article

Suspended sediment load (Qs) is an important parameter in the analysis of hydrological processes and management of water resources. Direct methods of measuring Qs are costly and require precise instruments, which makes their application limited, especially in remote regions. Indirect methods, on the other hand, discover the relationships between river hydrological parameters and Qs. Machine learning-based models are among the empirical data mining approaches that have been employed for the prediction of Qs under various conditions. Ensemble models, e.g., XGBoost (Python 3.12.3 with XGBoost version 3.1.0), are among the widely used machine learning approaches in the hydrologic context. A challenging step in establishing such models is conducting suitable hyperparameter tuning. A modeling study is reported here that combines the metaheuristic red fox algorithm (RFO) with XGBoost to improve Qs prediction. Daily observations of 21 years from Illinois State, USA (12 rivers), were used to assess the proposed methodology. Hydrologic data, including water stage, temperature, sediment concentration and river water flowrate were used as input variables when defining two input configurations. The obtained results reveal that the proposed RFO-XGBoost model outperformed the standalone XGBoost model in all the studied sites for both input configurations. However, the performance improvement percentage fluctuated among the sites. It was found that the model improvement was primarily affected by river hydrologic characteristics. A SHAP analysis revealed river flowrate as the most empirically influential input parameter in the model’s predictions of Qs. Uncertainty analysis through the Monte Carlo simulations further confirmed the proposed model’s enhanced performance and robustness. Full article

A productivity simulation for hydraulically fractured wells with complex fracture geometry involves a heavy computational burden and is therefore not suitable for engineering-scale fracture-optimization designs and production-analysis applications. This paper develops a productivity-prediction surrogate model based on a deep convolution–bidirectional gated recurrent unit temporal network (DC-BiGRU) framework where a deep convolutional neural network is used to extract features from fracture images, while a BiGRU model was designed to fully capture valuable information from the production sequence. Some additional inputs, e.g., cluster spacing and stage spacing, that account for different fracture-placement designs in horizontal wells were also considered. A large number of shale-gas production data samples at different times were generated using a fractured-horizontal-well productivity simulator under diverse hydraulic-fracture geometries and bottom-hole flowing pressures. The surrogate model had relative errors below 10% with an average error of about 6%. Compared to high-fidelity capacity prediction simulators, the computational efficiency of the deep learning surrogate models was improved by two to three orders of magnitude. The runtime of the high-fidelity numerical simulator was about 20 min, while the surrogate model, which was run on an NVIDIA Tesla P100 GPU (NVIDIA, Santa Clara, CA, USA), took less than 1 s, which is almost negligible. The proposed surrogate model resolved the low efficiency of the productivity simulation for complex-fracture hydraulic fracturing wells in unconventional reservoirs, enabling rapid dynamic forecasting of fractured-well productivity. Full article

Wire-arc coatings have received substantial attention for corrosion protection; however, poor bonding often leads to delamination, corrosion initiation, and costly re-coating of structural components. This review combines bibliometric mapping with a focused technical synthesis to clarify how bonding performance has been studied in wire-arc coatings. Specifically, publication trends, keyword co-occurrence networks, and country-level co-authorship maps are used to map the evolution of the field and position adhesion-related studies within the broader literature. The analysis of 762 wire-arc coating publications from Web of Science (among 13,314 thermal spray coating records) reveals that research is centered on microstructure, mechanical properties, and corrosion resistance, with growing links to wire-based additive manufacturing. Keyword co-occurrence networks demonstrate clear process–structure–property relationships, while country-level collaboration maps highlight the leadership of China, the USA, and Germany. Critical to note, only eight publications systematically investigate the combined effects of substrate roughness, coating thickness, and Zn-Al coating composition on bond strength—representing less than 0.01% of the thermal spray literature. This pronounced research gap underscores the novelty of the present review, which synthesizes existing knowledge on adhesion mechanisms, identifies key process parameters, and establishes a research agenda to optimize wire-arc coatings for infrastructure corrosion protection. The technical synthesis highlights that adhesion is governed by the coupled effects of surface preparation (roughness and topography), coating build-up (thickness), and spray conditions (e.g., standoff distance and substrate preheating), which together influence coating microstructure and failure modes. These findings provide a structured framework to guide parameter selection for durable coatings. Full article

Satellite-derived bathymetry holds significant value for acquiring nearshore bathymetric data. However, in coastal waters, bathymetry is affected by in-water particle scattering and seafloor substrate variability, leading to spatial inconsistency between the logarithmic green band profile derived from multispectral satellite imagery and the actual water depth profile. According to the position information of interpolated points and the inverse distance square relationship with the surrounding 16 points from low-reference bathymetric data (such as the bathymetric map from GEBCO, NOAA Electronic Navigational Charts), this model adopts a third-order inverse distance square bicubic convolution interpolation method to resample a high-resolution bathymetric map with the size of the satellite image. Normalized underwater topography trend data (derived from the low-resolution reference bathymetric map) were combined with normalized green band data to compute an averaged dataset. In this way, a linear bathymetric model was constructed. We invert this model’s parameters and calculate the water depth by using the average data and reference points from reference bathymetric data. Validation tests were conducted across three test areas using independent validation bathymetric data: Weizhou Island, China (Case II waters); Saipan, Northern Mariana Islands, USA (Case I waters); and Molokai Island, Hawaii, USA (Case I waters). Each test area was studied using five error analysis methods (i.e., scatterplot, error histogram, regional bathymetric error, three check lines, and seven check points). Compared to four classic bathymetric models (i.e., single-band model, log-ratio model, ratio-log model, and multi-band model), the proposed model achieved lower root mean square errors (RMSE) of 2.08 m, 1.40 m, and 2.01 m in the three test areas, representing reductions of 35%, 43%, 45%, and 20% and overall averages of 48%, 62%, 64%, and 43%, respectively. Its goodness of fit (R²) reached 0.87, 0.97, and 0.97, showing improvements of at least 5%, 5%, 9%, and 9% and overall averages of 17%, 77%, 84%, and 12%, respectively. The results demonstrate that the proposed model significantly improves bathymetry accuracy while maintaining algorithmic simplicity, providing a new model for acquiring nearshore foundational bathymetric maps. Full article

Early in 2020, the WHO recommended that existing drugs be evaluated as a repurposed resource to fight the SARS-CoV-2 pandemic. Here, we investigate the trends of using repurposed and off-label drugs among people living with HIV in Central and Eastern Europe (CEE). From November 2020 to May 2021, data on the clinical outcomes of HIV-positive patients diagnosed with COVID-19 were collected on eCRFs (SurveyMonkey^® platform, Inc. San Mateo, CA, USA). Factors associated with the off-label drugs available at this time (chloroquine, hydroxychloroquine, favipiravir, oseltamivir, and lopinavir/ritonavir) were identified using logistic regression models. Of the 557 HIV-positive patients assessed with COVID-19 disease, 67 (12.0%) received off-label drugs, as well as 11.6% (16/138) of hospitalized and 12.2% (51/419) of ambulatory patients (p = 0.8564). In the adjusted logistic regression model, higher odds of off-label drug use were found in patients who had their diagnoses confirmed by an RT PCR test (aOR 5.08 [95%CI 1.17–22.0], p = 0.0396), and who came from a non-EU region (aOR 6.79 [95%CI 3.51–13.1], p < 0.0001). The only factor decreasing the odds of off-label drug use was co-infection (aOR 0.31 [95%CI 0.10–0.94], p < 0.0395). In a cohort of HIV patients from the CEE, 12% were prescribed off-label drugs for COVID-19. Symptomatic patients with confirmed SARS-CoV-2 infection or who were from non-EU countries were more likely to receive a repurposed drug. Drug repurposing is an immediate solution to emerging pandemics. All data regarding the safety and effectiveness of such use should be monitored, reported, and publicly available. Access patterns within and outside the EU should be analyzed to prevent potential inequalities in access to care during epidemics in European settings. Full article

Background/Objectives: Despite the publication of several systematic reviews on adolescent health literacy, comprehensive evaluations of the relationship between health literacy and health-related behaviors are still limited. This systematic review sought to synthesize and critically appraise the available evidence on associations between health literacy and health behaviors among adolescents. Methods. A systematic search of three databases (Scopus, PubMed, and PsycINFO) was conducted in accordance with PRISMA guidelines. Thirty-seven eligible cross-sectional studies were selected for qualitative synthesis. Methodological quality was evaluated using the Newcastle–Ottawa Scale adapted for cross-sectional studies. Results: The 37 included studies encompassed 71,558 adolescents (mean age range 11.0–17.0 years) and were conducted primarily in Europe (n = 22), with additional studies from the USA (n = 5), Asia (n = 8), and cross-cultural settings (n = 2). Across studies, 11 HL instruments were used (including two eHealth literacy measures), most commonly the Health Literacy for School-aged Children scale (n = 14). Physical activity (n = 22), nutrition-related indicators (n = 26), and smoking/alcohol/drug outcomes (n = 16) were assessed using heterogeneous operationalisations. Overall, higher HL was more often associated with healthier behavioral profiles, with more consistent patterns for nutrition-related outcomes. Findings for physical activity and substance use were more heterogeneous and, in some cases, varied depending on the HL measurement approach (subjective vs. objective) and the behavioral reference period. Conclusions: Current evidence indicates that higher health literacy in adolescents is generally associated with more favorable health behaviors, particularly regarding nutrition-related indicators. However, study heterogeneity and the predominance of cross-sectional designs limit comparability and causal inference. Future research should prioritize standardized measurement tools and longitudinal designs to clarify directionality and underlying mechanisms. Full article