Search Results (104)

Background/Objectives: According to the guidelines established by the European Society of Vascular Surgery (ESVS), a minimum 2 mm diameter is advised for both the radial artery (RA) and cephalic vein (CV) to perform a radio-cephalic arteriovenous fistula (RC-AVF). However, studies have suggested that larger vein diameters, over 2.5 or 3 mm, or even smaller vessel diameters, above 1.6 mm, can yield satisfactory outcomes in both the medium and long term. This study aims to analyze how preoperative vascular mapping influences the long-term RC-AVF failure, considering adherence to guidelines. Methods: This retrospective, monocentric, and observational study enrolled 110 patients with ESKD who were admitted, between 2018 and 2024, to the Vascular Surgery Department at the Emergency County Hospital of Târgu Mureș for the creation of an RC-AVF. Demographic characteristics, comorbidities, preoperative vascular mapping data, and laboratory data were meticulously collected from the hospital’s electronic databases. Patients enrolled in the current study were categorized into two groups based on their adherence to guideline recommendations. Results: Patients whose RC-AVF was created outside guideline recommendations demonstrated smaller arterial (p < 0.001) and venous (p < 0.001) diameters. Additionally, a higher percentage of these patients were on hemodialysis via CVC at the time of RC-AVF creation (p = 0.041), as well as a higher incidence of 6-week AVF maturation failure (p = 0.012) and long-term AVF failure (p = 0.016). In ROC Curve analysis, a threshold of 2.75 mm was established for the RA (AUC: 0.647, p = 0.005) and 2.52 mm for the CV (AUC: 0.677, p = 0.001). Additionally, patients whose RC-AVF procedures adhered to guideline recommendations had a significantly lower risk of long-term RC-AVF failure (HR: 0.44, p = 0.012). This association lost significance after adjusting for cardiovascular risk factors and the presence of CVC at admission (HR: 0.69, p = 0.328). After full adjustment, only the CV remained an independent predictor of long-term successful RC-AVF (HR: 0.68, p = 0.026). In contrast, RA lost significance after adjusting for cardiovascular risk factors and the presence of CVC at admission (HR: 0.71, p = 0.086). Conclusions: In conclusion, this study reveals that only the diameter of the CV is correlated with the long-term failure of RC-AVF, independent of age, gender, diabetes, hypertension, active smoking, and the presence of a CVC at the time of AVF creation. Therefore, while adhering to the threshold diameters of the AR and CV, as recommended by the ESVS guidelines, facilitates the creation of a functional RC-AVF, we assert that additional cofactors, such as demographic data, usual cardiovascular risk factors, or CVC presence, must also be considered to achieve optimal long-term AVF. Full article

Shallow landslides are one of the main geological hazards that occur during heavy rainfall in Yuexi County every year, posing potential risks to the personal and property safety of local residents. A rainfall-induced shallow landslide named Baishizu No. 15 landslide in Yuexi Country was taken as a case study. Based on the field geological investigation, combined with physical and mechanical experiments in laboratory as well as numerical simulation, the failure mechanism induced by rainfall infiltration was studied, and the movement process after landslide failure was inverted. The results show that the pore-water pressure within 2 m of the landslide body increases significantly and the factory of safety (F_s) has a good corresponding relationship with rainfall, which decreased to 0.978 after the heavy rainstorm on July 5 and July 6 in 2020. The maximum shear strain and displacement are concentrated at the foot and front edge of the landslide, which indicates a “traction type” failure mode of the Baishizu No. 15 landslide. In addition, the maximum displacement during landslide instability is about 0.5 m. The residual strength of soils collected from the soil–rock interface shows significant rate-strengthening, which ensures that the Baishizu No. 15 landslide will not exhibit high-speed and long runout movement. The rate-dependent friction coefficient of sliding surface was considered to simulate the movement process of the Baishizu No. 15 landslide by using PFC^2D. The simulation results show that the movement velocity exhibited obvious oscillatory characteristics. After the movement stopped, the landslide formed a slip cliff at the rear edge and deposited as far as the platform at the front of the slope foot but did not block the road ahead. The final deposition state is basically consistent with the on-site investigation. The research results of this paper can provide valuable references for the disaster prevention, mitigation, and risk assessment of shallow landslides on residual soil slopes in the Dabie mountainous region. Full article

Cadmium (Cd) pollution poses a severe threat to rice safety and human health, while traditional linear models exhibit significant limitations in predicting rice Cd accumulation due to environmental complexities. This study systematically evaluated the predictive performance of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Residual Neural Networks (ResNet), using a multi-source soil–rice dataset comprising 57,200 samples from Hunan Province. The results showed that the RF model performed best on the test set (R² = 0.62), with the dominant features being soil’s available Cd (contributing 9.74%) and precipitation during the rice-filling stage (joint contribution of 15.96%). However, the model’s predictive performance experienced a sharp decline on the independent 2023 validation set comprising 393 samples from Yizhang County and Lengshuitan District, with R² values ranging from −0.12 to −0.31. This highlighted the fundamental limitations of static data-driven paradigms. Agronomic management measures, simplified by heterogeneous data and binary encoding, failed to effectively represent the actual intervention intensity. The study demonstrated that while machine learning models captured nonlinear relationships in laboratory environments, they struggled to adapt to the dynamic interactions and spatiotemporal heterogeneity of farmland systems. Future efforts should focus on developing hybrid models guided by mechanistic insights, integrating dynamic environmental processes and real-time data, and promoting localized “one model per region” strategies to enhance predictive robustness. This study provides methodological insights for the technological transformation of agricultural artificial intelligence, emphasizing that the deep integration of data-driven approaches and mechanistic understanding is crucial for overcoming the “last mile” challenge. Full article

Background and Objectives: Cardiac arrhythmias during the SARS-CoV-2 infection may occur due to the direct impact of the virus on the respiratory and cardiovascular systems, as well as through the broader effects of systemic inflammation, or a combination of both. Additional mechanisms include the proarrhythmic effects of COVID-19 pharmacotherapies, drug–drug interactions, and associated autonomic dysfunction. To improve future risk stratification and clinical management, it is essential to accurately assess the risk of arrhythmia in the context of severe infections, in order to develop diagnostic and therapeutic algorithms that support the fastest and safest possible interventions. Materials and Methods: This retrospective observational study included 151 patients diagnosed with COVID-19, who were hospitalized at the Cardiology Clinic of the Timisoara County Emergency Hospital between 2020 and 2022. Results: The most common arrhythmia observed was atrial fibrillation. Elevated inflammatory markers were associated with a higher risk of arrhythmias and increased mortality. Conclusions: The onset of sepsis, as indicated by the laboratory markers, was associated with increased incidence of arrhythmias and unfavorable outcome of the disease. Full article

Background: Prophylactic human papillomavirus (HPV) vaccination substantially alleviates cervical cancer burden. This study aimed to evaluate the safety, tolerability, and immunogenicity of an Escherichia coli-expressed recombinant nonavalent HPV vaccine. Methods: A dose-escalating phase 1 clinical trial was conducted in Sheyang County, Jiangsu Province, China. Each participant received either the test vaccine or the control vaccine (Gardasil 9) following a 0/2/6-month schedule. Adverse reactions (ARs) within 7 days after vaccination, adverse events (AEs) within 30 days, and serious adverse events (SAEs) throughout the study were recorded. Blood parameters were measured before and 3 days after each dose. Serum immunoglobulin G (IgG) and neutralizing antibodies (nAbs) against nine HPV types were analyzed at months 0, 3, and 7. Results: A total of 160 women aged 18–45 years were enrolled, and 155 participants completed the full vaccination regimen. Within 7 days following vaccination, the incidence of ARs ranged from 56.67% to 90.00%, with the low-dose group showing a significantly higher rate than the control group (p = 0.004). Most AEs were mild or moderate, and no vaccine-related SAEs occurred. No significant differences were observed among the four groups regarding the incidence of abnormal laboratory findings. Seroconversion rates for nAbs and IgG against nine HPV types exceeded 97.92% following three doses. High levels of nAbs and IgG were observed at months 3 and 7, with geometric mean titers (GMTs) showing further increases by month 7. Conclusions: This new recombinant nonavalent HPV vaccine exhibits good tolerability and strong immunogenicity among women aged 18–45 years, supporting further efficacy studies in larger populations. Full article

Background: Chronic liver disease (CLD) is a major global health concern, contributing significantly to morbidity and mortality. Cirrhosis and liver cancer are the leading causes of liver-related deaths, with various etiological factors, such as metabolic disorders and alcohol-related and viral hepatitis, driving its global prevalence. Non-invasive biomarkers and scoring systems have emerged as key tools for assessing liver disease severity and differentiating CLD from cirrhosis. This study evaluates biomarkers and non-invasive scores and their utility in distinguishing CLD from cirrhosis. Methods: This retrospective observational study included 250 adult patients hospitalized between January 2021 and December 2023 at Cluj County Emergency Clinical Hospital, Romania. Patients were diagnosed with either cirrhosis or CLD of viral, autoimmune, or primary biliary cholangitis (PBC) etiology. Non-invasive biomarkers, scores, and various hemogram-derived ratios were evaluated. Statistical analysis involved descriptive statistics, comparative tests, and receiver operating characteristic (ROC) curve analysis. Results: Among the 250 patients, 137 had liver cirrhosis (54.8%) and 113 had CLD without cirrhosis (45.2%). Significant differences were observed in laboratory parameters, with cirrhosis patients showing lower hemoglobin, platelet count, and albumin levels alongside higher liver enzymes and INR values. Non-invasive scores such as APRI, FIB-4, and NFS demonstrated higher values in the cirrhosis group, indicating more advanced liver damage. Hemogram-derived ratios, particularly the neutrophil-to-lymphocyte ratio (NLR), were higher in cirrhosis patients. ROC analysis revealed that the Lok index had the highest discriminatory power (AUC 0.89), followed by the King score (AUC 0.864) and the Fibrosis index (AUC 0.856), which effectively distinguished cirrhosis from CLD. Conclusions: This study underscores the utility of non-invasive biomarkers and scoring systems in differentiating CLD from cirrhosis. The Lok index, King score, and Fibrosis index demonstrated excellent diagnostic accuracy, while hemogram-derived ratios, such as NLR, offer insights into systemic inflammation associated with liver disease progression. These findings support the integration of non-invasive markers into clinical practice for improved risk stratification and management of liver diseases. Full article

The aim of this study was to investigate the factors affecting the occurrence of walnut canker disease and the effect of low-temperature treatment on its occurrence in Hotan County, and provide a theoretical basis for the prevention and control of the disease. The occurrence of canker disease was investigated in walnut plantations of different varieties, plant and row spacing, and diameter at breast height (DBH) from April to August of 2023 and 2024. In laboratory research, the spread area of disease spots on isolated branches of peach was studied under different temperature treatments and different treatment times. The occurrences of walnut canker for the two consecutive years were basically the same. The number of new lesions showed an upward trend in April and May and reached its highest in May. The number of new lesions tended to stabilize in June and July in Hotan County. The walnut variety with more severe disease was “Xinfeng”. The disease was most serious in plots with plant spacing and row spacing of 10 m × 7 m. The DBH of the more susceptible walnut trees was >30 cm. Low-temperature treatment and treatment time had a significant impact on the walnut canker lesion expansion area (p < 0.05). The key factor in lesion expansion was low temperature. The maximum lesion expansion area was 1.70 × 10⁴ mm² under treatment at 20 °C for 5 h. The walnut variety and the plant and row spacing were the key factors affecting the occurrence of walnut canker disease, with low temperature being the main factor affecting its spread. Full article

Background/Objectives: Type 2 diabetes mellitus (T2DM), is a rapidly growing global health concern, often accompanied by chronic kidney disease (CKD) and metabolic disturbances. Obesity-related indices, such as the visceral adiposity index (VAI) and body adiposity index (BAI), have been linked to cardiovascular and renal complications in diabetic patients. However, studies integrating both the atherogenic coefficient (AC) and prognostic nutritional index (PNI) for evaluating diabetic nephropathy (DN) remain limited. This study aimed to assess the associations of obesity-related indices with immunological and nutritional factors in patients with T2DM and prediabetes (PreDM). Methods: A retrospective, cross-sectional study was conducted over six months at a university clinical hospital in Dolj County, Romania. The study enrolled 268 newly diagnosed T2DM patients and 150 PreDM patients. Anthropometric parameters, laboratory tests, and demographic data were collected. AC and PNI were calculated using standard formulas, and statistical analyses were performed to determine their associations with metabolic and inflammatory markers. Results: Our study found that T2DM patients had significantly lower PNI values, indicating mild malnutrition, while PreDM patients maintained a normal nutritional status. AC was significantly higher in T2DM patients, correlating with lipid profile alterations and systemic inflammation. Obesity indices, particularly VAI, were significantly elevated in T2DM patients with higher AC values. Statistically significant differences in total cholesterol, low-density lipoprotein cholesterol (LDL-c), and triglycerides were observed between AC subgroups, reinforcing its role in cardiovascular risk assessment. Conclusions: The findings highlight the potential of AC and PNI as biomarkers for assessing nutritional, inflammatory, and lipemic status in diabetic patients. The significant associations between obesity-related indices, lipid profiles, and inflammation markers suggest that early assessment of these parameters may potentially aid in predicting diabetic complications. Further studies are needed to explore the clinical utility of AC and PNI in managing T2DM and CKD progression. Future research should investigate how the lipidic spectrum alters the progression of DN across various patient groups with diabetes and prediabetes Full article

Background and Objectives: This is a retrospective study conducted at the Clinical County Hospital of Craiova, Romania, providing valuable insights into hemorrhagic transformation (HT) in thrombolyzed patients with acute ischemic stroke (AIS). Hemorrhagic complications remain a significant concern after intravenous thrombolysis with recombinant tissue plasminogen activator (rt-PA). This study aims to analyze clinical and biological factors associated with HT following thrombolysis. Materials and Methods: A retrospective analysis was conducted on 356 patients who received rt-PA at the Clinical County Hospital of Craiova between January 2020 and December 2024. Patients were divided into three groups based on CT findings at 24 h post-thrombolysis: no HT, minimal HT, and massive HT. Baseline characteristics were analyzed, including demographics, medical history, NIHSS scores, imaging findings, and laboratory parameters. Statistical analysis was performed using ANOVA and chi-square tests, with a significance threshold of p < 0.05. Results: HT occurred in 12.08% of patients (minimal HT: 8.15%, massive HT: 3.93%). Mortality was significantly higher in the massive HT group (71.43%) compared to minimal HT (41.38%) and non-HT (13.42%) (p < 0.001). Lower platelet count (p = 0.003), elevated blood glucose (p = 0.004), prolonged QT interval (p = 0.004), and reduced fibrinogen levels (p = 0.005) were significantly associated with HT. Other risk factors included atrial fibrillation (p = 0.001), hypertension (p = 0.005), delayed door-to-needle time (p < 0.001), diabetes mellitus (p = 0.007), dense ACM sign on CT (p = 0.003), older age (p < 0.001), obesity (p = 0.001), early neurological deterioration at 2 h/24 h (p < 0.001), elevated GOT (p < 0.001), elevated GPT (p = 0.002), lower LDL cholesterol (p < 0.001), lower total cholesterol (p = 0.001), and lower triglycerides (p < 0.001). Conclusions: Patients with HT had worse clinical outcomes, with massive HT associated with the highest mortality. Risk factors include age, nutritional status, hyperglycemia, and low platelet and fibrinogen levels, among others. Full article

Background and Objectives: Chronic obstructive pulmonary disease (COPD) poses a significant healthcare challenge worldwide, frequently leading to exacerbations necessitating intensive care unit admissions for potentially life-threatening complications. We aimed to investigate correlations between laboratory parameters, bacteriological agents, ventilation mode, and survival rates among COPD patients admitted to the ICU. Materials and Methods: Data were collected from the Pulmonology Department of Mures Clinical County Hospital, Romania, from 1 January 2022 to 30 October 2023. Eighty-four COPD patients required ICU transfer, except for concurrent SARS-CoV-2 infections. Results: Ventilation modes exhibited a significant correlation with specific bacteriological agents, orotracheal intubation being more prevalent in infections with Acinetobacter baumanii, Staphylococcus aureus, and Streptococcus pneumoniae (p < 0.001). Negative cultures were predominantly found in patients managed with non-invasive ventilation. Laboratory parameters revealed an association between elevated white blood cell counts and bacteriological superinfection, particularly with Escherichia coli (p < 0.001). Different bacteriological agents had different survival rates. Patients infected with Acinetobacter baumanii exhibited the highest mortality rate, while those with Staphylococcus aureus had the lowest (p < 0.01). Conclusions: The importance of prompt evaluation of laboratory parameters and bacteriological findings is underscored by these findings, particularly in ICU settings where ventilation and bacteriological profiles influence patient outcomes. The identification of elevated WBC counts is a marker of bacterial superinfection. The association between specific bacterial agents and ventilation modes highlights the importance of tailored treatment based on microbial profiles. These insights can be applied to refine treatment protocols and enhance survival rates in severe COPD exacerbations that require ICU management. Full article

Social conflict over rangeland-use priorities, especially near protected areas, has long pitted environmental and biodiversity conservation interests against livestock livelihoods. Social–ecological conflict limits management adaptation and creativity while reinforcing social and disciplinary divisions. It can also reduce rancher access to land and negatively affect wildlife conservation. Communities increasingly expect research organizations to address complex social dynamics to improve opportunities for multiple ecosystem service delivery on rangelands. In the Greater Yellowstone Ecosystem (GYE), an area of the western US, long-standing disagreements among actors who argue for the use of the land for livestock and those who prioritize wildlife are limiting conservation and ranching livelihoods. Researchers at the USDA-ARS US Sheep Experiment Station (USSES) along with University and societal partners are responding to these challenges using a collaborative adaptive management (CAM) methodology. The USSES Rangeland Collaboratory is a living laboratory project leveraging the resources of a federal range sheep research ranch operating across sagebrush steppe ecosystems in Clark County, Idaho, and montane/subalpine landscapes in Beaverhead County, Montana. The project places stakeholders, including ranchers, conservation groups, and government land managers, in the decision-making seat for a participatory case study. This involves adaptive management planning related to grazing and livestock–wildlife management decisions for two ranch-scale rangeland management scenarios, one modeled after a traditional range sheep operation and the second, a more intensified operation with no use of summer ranges. We discuss the extent to which the CAM approach creates opportunities for multi-directional learning among participants and evaluate trade-offs among preferred management systems through participatory ranch-scale grazing research. In a complex system where the needs and goals of various actors are misaligned across spatiotemporal, disciplinary, and social–ecological scales, CAM creates a structure and methods to focus on social learning and land management knowledge creation. Full article

Wildfires significantly impact human health, air quality, visibility, weather, and climate change and cause substantial economic losses. While state and county-operated air quality monitors provide critical insights during wildfires, they are not available in all regions. This highlights the need for affordable, accessible tools that allow the general public to assess air quality impacts. In this study, we apply machine learning with deep neural networks to diagnose air quality rapidly from sky images taken at the Pacific Northwest National Laboratory in Richland, WA, USA. Using a convolutional neural network (CNN) framework, we trained a deep learning model to classify air quality indices based on sky images. By leveraging transfer learning, our approach fine-tunes a pre-trained model on a small dataset of sky images, significantly reducing training time while maintaining high accuracy. Our results demonstrate the potential of deep learning to provide rapid air quality diagnostics during wildfire episodes, offering early warnings to the public and enabling timely mitigation strategies, particularly for vulnerable populations. Additionally, we show that lower respiratory infections pose the highest health risk during acute smoke exposures. Reactive oxygen species (ROS) from wildfire particles further exacerbate health risks by triggering inflammation and other adverse effects. Full article

Efficient and reliable corn (Zea mays L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-scale area, better representing real-world agricultural conditions and offering more practical relevance for farmers. Therefore, the objective of our study was to determine the best combination of vegetation indices and abiotic factors for predicting corn yield in a rain-fed, production-scale area, identify the most suitable corn growth stage for yield estimation using machine learning, and identify the most effective machine learning model for corn yield estimation. Our study used high-resolution (6 cm) aerial multispectral imagery. Sixty-two different predictors, including soil properties (sand, silt, and clay percentages), slope, spectral bands (red, green, blue, red-edge, NIR), vegetation indices (GNDRE, NDRE, TGI), color-space indices, and wavelengths were derived from the multispectral data collected at the seven (V4, V5, V6, V7, V9, V12, and V14/VT) growth stages of corn. Four regression and machine learning algorithms were evaluated for yield prediction: linear regression, random forest, extreme gradient boosting, and gradient boosting regressor. A total of 6865 yield values were used for model training and 1716 for validation. Results show that, using random forest method, the V14/VT stage had the best yield predictions (RMSE of 0.52 Mg/ha for a mean yield of 10.19 Mg/ha), and yield estimation at V6 stage was still feasible. We concluded that integrating abiotic factors, such as slope and soil properties, significantly improved model accuracy. Among vegetation indices, TGI, HUE, and GNDRE performed better. Results from this study can help farmers or crop consultants plan ahead for future logistics through enhanced early-season yield predictions and support farm profitability and sustainability. Full article

Background and Objectives: Hepatitis C virus (HCV) infection is a major global public health concern, particularly in hemodialysis (HD) patients. This study aims to evaluate the demographic, clinical, and laboratory characteristics of HCV-positive patients undergoing HD and assess the long-term impact of direct-acting antivirals (DAAs) on patient outcomes. Moreover, a narrative review aims to summarize the current knowledge regarding HCV treatment in HD patients. The search in the PubMed, Google Scholar, and Scopus databases identified 48 studies relevant to our topic, 18 regarding clinical history and 29 related to HCV treatment. Methods: A retrospective analysis was performed on 165 HD patients from Bihor County HD centers, Romania, between 2014 and 2024. The cohort was divided into two groups: 54 patients who tested positive for HCV and 111 controls who were HCV-negative. Data collected from GPs included demographic information, comorbidities, laboratory parameters, and psychological assessments. Outcomes were evaluated at over 5 years after DAA treatment. A literature review was conducted using PubMed and Google Scholar to identify relevant studies on HCV in HD patients from 1989 to 2024. Results: Laboratory results showed similar parameters across groups, except for lower serum cholesterol levels in the HCV-positive DAA-treated group vs. HCV-positive non-treated ones (155.607 mg% vs. 170.174 mg%, p = 0.040) and increased ALT levels when comparing the same groups (29.107 vs. 22.261, p = 0.027), whereas comorbidities did not differ significantly. The incidence of malignancies was significantly higher among HCV-positive compared to HCV-negative patients (20.3% vs. 8.1%, p = 0.023), mainly among those treated with DAAs, highlighted by the multivariate analysis. Cardiovascular disease remains the leading cause of mortality regardless of HCV status or the use of antiviral therapy. Psychological assessments revealed more severe depression in HCV-positive patients compared to their HCV-negative counterparts. Conclusions: HCV infection in the hemodialysis population typically follows a subclinical course. At over five years after DAA therapy, the results indicate a stabilization of the liver function and the absence of major complications. However, the incidence of malignancies remains high in HCV-positive patients. Full article

Groundwater conditions are crucial for understanding the evolution of soil salinization. The installation of subsurface pipes significantly alters both the distribution of water and salt in the soil and the groundwater depth; these dynamics and their interrelationships warrant further investigation. To clarify the relationship between groundwater dynamics and changes in water and salt in soil under subsurface pipe salt drainage conditions in the Yinchuan region of Ningxia, groundwater observation wells and soil sample monitoring points were established in Pingluo County. A combined approach of in situ monitoring and laboratory testing was employed to analyze changes in groundwater depth and salinity and their effects on water and salt in soil. The findings revealed that changes in groundwater depth and salinity exhibited clear seasonal patterns. The groundwater depth was deepest at 1.97 m in October and shallowest at 1.62 m in July. The salinity was highest at 22.28 g/L in April and lowest at 18.24 g/L in August. In summer, the groundwater was shallower and had lower salinity, while in other seasons, it was deeper with higher salinity. Soil salinity was lowest in July at 4.58 g/kg and highest in April at over 5.5 g/kg. It decreased with increasing groundwater depth, demonstrating a linear relationship. Additionally, soil salinity and groundwater salinity exhibited synchronous fluctuations, exhibiting an exponential relationship. Based on these observations, a model was developed to describe the relationship among groundwater salinity, groundwater depth, and soil salinity under subsurface pipe salt drainage conditions in the Yinbei region of Ningxia. This model was validated against measured data, yielding a correlation coefficient R² of 0.7238. These findings provide a reference for analyzing the relationship between soil salinity and groundwater in similar regions. Full article