Multidisciplinary Digital Publishing Institute (Switzerland)
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Elevated Antibiotic Resistance in Escherichia coli from Surface Waters Impacted by Concentrated Animal Feeding Operations in California and Michigan
No full textMonitoring of antimicrobial resistance (AMR) in the environment is needed, but standardized methods are lacking. This study evaluated a cost-efficient, kit-based approach for detecting antibiotic-resistant Escherichia coli (AR-E. coli) and predicting multidrug-resistant E. coli (MDR-E. coli) in concentrated animal feeding operations (CAFO)-impacted surface waters in Tulare, California, and Clayton, Michigan. Eighteen Tulare and six Clayton samples were analyzed using modified IDEXX Colilert-18 kits with selective antibiotics. In Round 1 of the study, 702 isolates were tested against 12 antibiotics using disk diffusion to identify ideal additions. No and low resistance to AMP was seen at the unimpacted and less-impacted sites, respectively, while up to 34% of isolates at impacted sites were resistant to AMP. The percentage of isolates resistant to erythromycin was 16% or lower at less-impacted sites, but it ranged up to 64% at impacted sites. In Round 2, 1002 isolates (1704 total) were characterized to compare modified IDEXX results with culture-based results. Resistance to ampicillin (AMP-E. coli-col) detected by IDEXX strongly correlated with MDR prevalence in Clayton (R2 = 0.71, p = 0.00197), while extended-spectrum beta-lactamase-producing E. coli (ESBL-E. coli-col) correlated with MDR prevalence in Tulare (R2 = 0.70, p < 0.0001). This observation appears to be site-specific. Findings suggest both IDEXX- and culture-based methods can serve as AMR screening tools at CAFO-impacted sites. This work supports community-based monitoring and informs standardized approaches for tracking AMR in inland waters
SCUBE-1 as a Biomarker Predictor for the Home Follow-Up and Hospitalization of SARS-CoV-2 Patients
No full textBackground/Objectives: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to pose a significant global health challenge due to its high transmissibility and potential for severe clinical outcomes. Early identification of patients at risk of hospitalization is essential for effective triage in emergency departments and for the optimal allocation of healthcare resources. Methods: This prospective study included 84 patients aged over 18 years who presented to the emergency department on 23 December 2020, with suspected SARS-CoV-2 infection. Initially, 100 patients were evaluated, and 16 were excluded based on predefined exclusion criteria. The mean age of the participants was 53.65 ± 13.62 years, and 39 (46.4%) were women. Results: At admission, the mean signal peptide, CUB domain, EGF (SCUBE-1) level among SARS-CoV-2 patients was 0.16 ± 0.08 ng/mL. There was no significant difference in SCUBE-1 levels between patient and control groups (n = 59 vs. 25), but levels differed significantly between hospitalized and home-treated patients (n = 37 vs. 22; p = 0.001). Neutrophil count (p = 0.001) and NLR (p = 0.010) were higher in patients than controls and also higher in hospitalized than home-treated patients (p = 0.003 and p = 0.015). ROC analysis revealed that SCUBE-1 predicted hospitalization with 84.6% sensitivity and 88.9% specificity. A positive correlation was observed between SCUBE-1 levels and length of hospital stay (p = 0.007, r = 0.554), with a median stay of 9.0 (5.0–11.0) days. Conclusions: SCUBE-1 levels were significantly associated with disease severity in SARS-CoV-2 patients and may serve as a promising biomarker to support clinical decision-making for hospitalization versus home-based management
Multimodal Management of Extreme Hypertriglyceridemia in a Child with Recurrent Pancreatitis: Clinical Challenges and Solutions
No full textBackground: Severe hypertriglyceridemia (SHTG) in children is a rare but clinically significant disorder associated with recurrent acute pancreatitis and substantial morbidity. Early identification and prompt management are essential to prevent pancreatic and systemic complications. Methods: We report the case of an 11-year-old female with a history of xanthogranulomatous pancreatitis who presented with extreme hypertriglyceridemia, with fasting triglyceride levels exceeding 4000 mg/dL. Results: The patient was treated acutely with continuous intravenous aspart insulin (0.1 U/kg/hour) and adjusted 10% glucose infusion, with hourly glucose and potassium monitoring, leading to a rapid and marked reduction in triglyceride levels—55% reduction within the first 24 h, 76% at 48 h, and 82% after 96 h of treatment. No hypoglycemia or other adverse effects were observed. Nutritional management included a low–long-chain triglyceride (LCT) diet enriched with medium-chain triglycerides (MCTs) and omega-3 fatty acids, providing essential calories while minimizing chylomicron production. Over a 12-month follow-up, the patient remained asymptomatic, with sustained lipid normalization and no recurrence of pancreatitis. Conclusions: This case underscores the therapeutic value of combining pharmacologic and dietary strategies in pediatric SHTG. Evidence from pediatric and adult studies supports the role of insulin infusion for acute triglyceride lowering and MCT-based nutritional therapy for long-term control. Our findings highlight the need for early, individualized, and multidisciplinary management and emphasize the potential future role of emerging targeted therapies in addressing refractory pediatric hypertriglyceridemia
First High-Density Linkage Map and Quantitative Trait Loci for Disease Resistance in Striped Catfish Pangasianodon hypophthalmus
No full textWhile striped catfish (Pangasianodon hypophthalmus) is an economically important aquaculture species, its genomic resources remain limited. To date, linkage maps, QTL (quantitative trait loci) analyses, and the identification of candidate genes associated with disease resistance traits are very limited. Therefore, the present study aimed to construct a high-density linkage map and identify candidate genes for this species. Our analysis was conducted on a pedigree population consisting of 560 individuals (490 offspring and 70 parents for 40 families), whose genomes were analyzed using a genotyping-by-sequencing platform. After stringent filtering, 9882 high-quality SNPs were retained for linkage analysis. Linkage analysis placed 8786 markers onto 30 linkage groups (LGs), with an average density of 0.43 SNPs per cM. Recombination rates varied across the 30 linkage groups (LGs), averaging of 3.6 cM/Mb in males, 6.7 cM/Mb in females, and 5.1 cM/Mb when sex-averaged. Using the linkage map, our QTL analysis identified three significant QTLs for disease resistance to Edwardsiella ictaluri, the causative agent of Bacillary Necrosis of Pangasius (BNP). The QTLs were located on LG1, LG9 and LG29, and their peak markers explained 17.03% of the phenotypic variance. An LD-based interval of approximately ±25 kb surrounding the QTL peak was identified as the putative candidate region. However, subsequent genome-wide association analysis did not identify significant SNP effects within these regions, suggesting that the QTLs may represent polygenic or small-effect loci that are detectable only in linkage-based analyses. In summary, this study presents the first high-density SNP-based linkage map for striped catfish and reports significant QTL and associated candidate genes related to disease resistance and growth traits. These findings provide valuable insights into the genetic architecture of economically important traits in P. hypophthalmus. Nevertheless, further validation in independent populations is required before incorporating these markers into selective breeding programs
Regionalized Life Cycle Analysis of Ecosystem External Cost Associated with Land-Use Change in Photovoltaic Systems
No full textThis article presents a methodology for assessing the ecosystem external costs linked to land-use changes caused by utility-scale photovoltaic systems using a regionalized life cycle approach. The core scientific challenge is to integrate a typically non-site-specific method—life cycle assessment—with a site-specific evaluation of ecosystem services affected by land-use changes. The methodology does not model specific agricultural practices. The approach is applied to three configurations of solar-tracking photovoltaic plants installed on arable land: ground-mounted photovoltaics, elevated agrivoltaics, and spaced agrivoltaics. For each configuration, the external costs or benefits per megawatt-hour (MWh) produced are estimated, allowing a comparative life cycle analysis. The findings show that the elevated agrivoltaic system is the only configuration resulting in a net loss of ecosystem service value, albeit marginal (−0.2 EUR/MWh). In contrast, the ground-mounted system yields a net benefit (approximately 1 EUR/MWh), followed by spaced agrivoltaics (0.1 EUR/MWh). These outcomes are mainly driven by the construction and operational phases, while the impacts from component production, transport, and end-of-life stages are significantly lower. The methodology offers a replicable framework for integrating the monetary evaluation of ecosystem services into life cycle assessments of land-intensive renewable energy systems
Water Functional Zoning Framework Based on Machine Learning: A Case Study of the Yangtze River Basin
No full textWater functional zoning plays a crucial role in water resource allocation, pollution prevention, and ecological protection. With the increasing intensity of human activities, there is a significant mismatch between current water functional zoning and the economic, social development needs and ecological protection goals. Existing water functional zoning methods mainly rely on expert experience for qualitative judgment, which is highly subjective and inefficient. In response, this paper presents a transferable quantitative feature system and introduces a machine learning-based progressive zoning framework for water functions, validated through a case study of the Yangtze River Basin. The results show that the overall accuracy of the framework is 0.78, which is 4–7% higher compared to traditional single models. In terms of spatial distribution, the transformation of protection and reserved zones in 2020 mainly occurred in the middle and lower reaches, where human activities are frequent, particularly in Sichuan and Jiangxi provinces. The development zones are highly concentrated in the downstream areas, with some regions transitioning into protection or reserved zones, mainly in Hubei and Chongqing provinces. Adjustments to buffer zones are primarily concentrated along inter-provincial boundary areas, such as the junction between Hubei and Anhui provinces. This framework helps managers quickly identify key areas for optimizing water functional zones, providing valuable reference for the precise management of water resources and the formulation of ecological protection strategies in the basin
Ultrasound- and CT-Guided Medial-to-Lateral Radiofrequency Ablation of the Infraorbital Nerve for Persistent Idiopathic Dentoalveolar Pain: A Trajectory-Based Approach
No full textPersistent Idiopathic Dentoalveolar Pain (PIDAP) is a persistent idiopathic toothache that frequently remains unresponsive to medical therapy. Precise targeting of the infraorbital nerve is essential for successful intervention, yet anatomical variability often limits the consistency of conventional radiofrequency ablation (RFA). This report describes a medial-to-lateral ultrasound- and computed tomography-guided approach, intended to align with the natural orientation of the infraorbital canal and potentially enhance electrode–nerve contact. A 48-year-old woman with refractory maxillary incisor pain underwent RFA after only transient benefit from a diagnostic nerve block. Ultrasound enabled accurate identification of the infraorbital foramen and confirmed the canal’s medial-to-lateral course, which then guided CT-assisted needle advancement into the orbitomaxillary segment. The patient experienced immediate analgesia. Pain reduction was maintained at the one-month follow-up. At the two-month assessment, although a mild symptom rebound was observed, no procedure-related complications were noted. This trajectory-based medial-to-lateral technique offers an anatomically grounded alternative for infraorbital nerve RFA and may represent a valuable option for refractory PIDAP
Association Between HLA Alleles and IgA Nephropathy in a Taiwanese Population
No full textGenetic associations with IgA nephropathy (IgAN), particularly in the human leukocyte antigen (HLA) region, vary across ethnic groups. This study investigated the association of HLA alleles with the diagnosis, pathological findings, and prognosis of biopsy-proven IgAN in a Taiwanese population. A case-control study was conducted using data from the Taiwan Precision Medicine Initiative, including 157 patients with biopsy-proven IgAN and 1570 age- and sex-matched controls. Genetic data were obtained from single-nucleotide polymorphism arrays, and HLA imputation was performed. Most single-nucleotide polymorphisms associated with IgAN were located within the HLA region on chromosome 6. Frequencies of several alleles (including C*08:01, DQA1*03:01, and DQB1*04:01) were significantly higher in the IgAN group. Conversely, frequencies of alleles such as B*58:01 and DQB1*02:01 were significantly lower. This study identified novel risk and protective HLA alleles for IgAN in a Taiwanese population
Innovative Cyber-Physical/Electronic AI-Assisted Digital Twin Model of Small Energy Harvesting Cantilever Power Generators
No full textThe paper deals with the design of a Digital Twin model of an energy harvesting cantilever beam for low frequency energy harvesting applications and specifically with a digital model matching simulations corresponding with Finite Element Method solutions in order to validate the model. The physical behavior is based on the main parameters to be investigated. The finite elements analysis is geometrically and parametrically carried out for a small PZT5A device of the orders of millimeters and is optimized to take into consideration the relationships between tip displacement, generated voltages and vibration gravitational forces for standard industrial applications in the acceleration range between 0.5 and 2 g. Then a procedure to integrate the Digital Twin into a design framework has been developed, including an artificial intelligence algorithm that supports the modelling of the real behavior of the device. The paper is devoted to help researchers involved in a Digital Twin adoption in the field of electronic design and of the physical characterization of low frequency energy harvesting devices exclusively using open-source tools
A Comprehensive Performance Evaluation of YOLO Series Algorithms in Automatic Inspection of Printed Circuit Boards
No full textConsidering the rapid iteration of you-only-look-once (YOLO)-series algorithms, this paper aims to provide a data-driven performance spectrum and selection guide for the latest YOLO series algorithm (YOLOv8 to YOLOv13) in printed circuit board (PCB) automatic optical inspection (AOI) through systematic benchmarking. A comprehensive evaluation of the six state-of-the-art YOLO series algorithms is conducted on a standardized dataset containing six typical PCB defects: missing hole, mouse bite, open circuit, short circuit, spur, and spurious copper. An innovative dual-cycle comparative experiment (100 rounds and 500 rounds) is designed, and a systematic assessment is performed across multiple dimensions, including accuracy, efficiency, and inference speed. The experimental results have revealed significant variations in algorithm performance with training cycles: under short-term training (100 rounds), YOLOv13 achieves leading detection performance (mAP50 = 0.924, mAP50-95 = 0.484) with the fewest parameters (2.45 million); after full training (500 rounds), YOLOv10 achieves the highest overall accuracy (mAP50 = 0.946, mAP50-95 = 0.526); additionally, YOLOv11 shows the optimal speed-accuracy balance after long-term training, while YOLOv12 excels in short-term training; moreover, “open circuit” and “spur” are evaluated as the most challenging defect categories to detect. The findings given in this paper indicate the absence of a universally applicable “all-in-one” algorithm and propose a clear algorithm selection roadmap: YOLOv10 is recommended for offline analysis scenarios prioritizing extreme accuracy; YOLOv13 is the top choice for applications requiring rapid iteration with tight training time constraints; and YOLOv11 is the best option for high-throughput online inspection PCB production lines