Laparoscopic common bile duct exploration (LCBDE) remains an effective single-stage strategy for choledocholithiasis, but declining utilization has raised concerns about diminishing operative experience and training exposure among contemporary surgeons. We evaluated institutional trends in LCBDE, intraoperative cholangiography (IOC), and resident participation and identified predictors of successful duct clearance. This retrospective single-center cohort study (2012-2021) included adults undergoing laparoscopic or robotic cholecystectomy. Patients were grouped as single-stage LCBDE with cholecystectomy or two-stage management with ERCP and endoscopic sphincterotomy followed by cholecystectomy. Cases were identified using CPT and ICD procedure codes cross-referenced with operating room logs. Data were abstracted from the electronic medical record. Primary outcomes were temporal trends in LCBDE, IOC utilization, and resident participation. Secondary outcomes were predictors of successful LCBDE. LCBDE succeeded in 240/291 cases (82.4%). Failures occurred exclusively during transcystic exploration, most commonly due to an inability to cannulate the cystic duct (43%). Median length of stay was shorter after successful LCBDE (1 vs 3 days, p < 0.001). Surgeons in successful cases had greater experience (median 17.5 vs 12.9 years, p = 0.03). On multivariable analysis, surgeon experience independently predicted success (OR 1.04 per year, 95% CI 1.004-1.078), whereas surgeon specialty was not significant after adjustment. Of 303 LCBDE patients, 291 met inclusion criteria; among 339 two-stage patients, 257 met inclusion criteria. Institutionally, annual LCBDE volume declined (trend p < 0.05), IOC utilization decreased (66% in 2018 to 54% in 2021), and resident involvement fell (91.8% in 2014 to 69.6% in 2021), with per-resident exposure declining from 2.2 to 0.9 cases per year. LCBDE achieves high duct-clearance rates and shorter hospitalization when successful. Surgeon experience, rather than specialty, independently predicts success, underscoring a clinically meaningful learning curve. Declines in LCBDE volume, IOC utilization, and resident exposure highlight the need for training strategies including simulation, standardized workflows, and improved case access to preserve competency.
Soil enzyme activity is a key determinant of crop productivity, as it regulates nutrient cycling, organic matter decomposition, and nitrogen transformation. The existing Machine Learning (ML) and Deep Learning (DL) approaches for soil fertility assessment often underutilize biochemical indicators owing to noise, missing data, and complex feature interactions. These models treat Feature Selection (FS) and Hyperparameter Tuning (HPT) as separate process. This limits the overall model performance. Conventional Sand Cat Swarm Optimization (SCO) method suffers from rigid exploration-exploitation transitions and premature convergence. To overcome these limitations, an Improved Sand Cat Swarm Optimization (Improved SCSO) based framework is implemented in this work. The proposed work sequentially performs FS and HPT within a unified optimization process and includes a stochastic escape-from-worst update mechanism. Cosine-modulated search behavior is incorporated in the model to enhance exploration. Exploitation and convergence stability are improved by Time-adaptive best-solution inheritance strategy. Enzyme-related soil attributes were explicitly incorporated into the optimization process, enabling the selection of biologically meaningful features. A correlation-based filtering step was applied to remove redundant features and improve the prediction consistency. The optimized feature subset was evaluated using multiple ML, DL and Hybrid models have also been assessed to understand the predictive performance of proposed framework. The model was evaluated using stratified k-fold cross-validation with Accuracy, Precision, Recall, and F1-score. The experimental results show that the proposed framework consistently outperforms traditional SCO-based methods. Gradient Boost (GB) achieved the highest accuracy of 98.48%, followed by hybrid models such as Decision Tree (DT) + Random Forest (RF) (98.38%) and GB + RF (98.28%) respectively. In addition, a dynamic crop mapping strategy was developed to estimate crop suitability based on predicted fertility levels and enzyme activity, thereby improving its practical application. Overall, the proposed framework improves prediction accuracy and interpretability, providing an effective solution for soil fertility assessment and data-driven crop recommendation.
A large clinical-genomic database was used to assess circulating tumor DNA (ctDNA) pre- and post-CDK4/6 inhibitor (CDK4/6i) plus endocrine therapy (ET) treatment in a cohort of patients with HR+/HER2- metastatic breast cancer (mBC). A panel of putative resistance alterations to CDK4/6i + ET (CDK4/6i+ET-R) was developed based upon previous studies. Patients with ≥1 baseline CDK4/6i + ET-R were compared to patients without CDK4/6i + ET-R, and univariate and multivariate analyses were performed to assess differences in real-world time to treatment discontinuation (rwTTD), real-world time to next treatment (rwTTNT), and overall survival (OS). ESR1 and RB1 alterations were significantly more frequent post-CDK4/6i. Patients with CDK4/6i + ET-R mutations prior to CDK4/6i treatment had significantly worse outcomes in terms of time on CDK4/6i and OS, suggesting that this resistance signature could prove useful in better refining personalized treatment selection in future clinical practice, though further exploration is needed.
Multilevel image thresholding is an important segmentation technique that partitions an image into meaningful regions in applications such as object recognition, medical imaging, and satellite image analysis. However, conventional techniques are limited by their poor sensitivity to initial conditions, lack of sufficient spatial contextual information, and slow convergence. The proposed method uses a novel hybrid model based on the Artificial Hummingbird Algorithm (AHA) to address the limitations of existing approaches. Here, Latin Hypercube, Sobol, Halton, and Sierpinski strategies are used during the population initialization phase to improve population diversity and search space coverage. This improves the exploration capability of the algorithm and supports better search space. The proposed methodology also uses spatial contextual information to improve the quality of segmentation. It also incorporates a relationship between neighboring pixels in order to retain more structure and enhance the visual performance of the output. For the exploitation phase, the Great Deluge Algorithm (GDA) is utilized as the optimization algorithm. Furthermore, the use of GDA serves as an adaptive acceptance function which reduces the chance of getting stuck during the search process. Minimum Cross Entropy Measure (MCEM) is used as the objective function to obtain optimal threshold values. Different evaluation metrics have been used to compare the results of the proposed method with other existing metaheuristic algorithms. The code is available at https://github.com/suprajatirumalasetti/AHA_GDA_Image_Segmentation_Code.
The pessimistic conclusions from previous research on the Expressive Power of translating approaches for knowledge graph completion are investigated and rethought. To this end, a novel model RosE is formulated by introducing two degrees of freedom and outperforms traditional translation-based models on widely used datasets such as FB15k, WN18, FB15k237, and WN18RR. Every new freedom is a vector in the model, the operation of which multiplies with entity and relation embeddings, rotating them to a new position. Consequently, the head entity and relation embedding are equal to the tail entity. Fortunately, the intrinsic limitations merely exist in this research line when the model is trained in real vector space, not in other spaces such as trigonometric functions and complex. The experimental and theoretical results, together with the newly proposed model RosE, also confirm this conclusion. Therefore, the findings in this work do not discourage further exploration in this research line, but rather avoid those with discouraging outcomes. In short, this paper clarifies that the limitations of the translation approach for knowledge graph completion are specific conditions that only involve partial models. That is, the research line of translation approach is still promising when certain known pitfalls are avoided.
The Eastern Desert of Egypt is a hyper-arid region where groundwater serves as the primary alternative to surface water for sustainable development. However, comprehensive assessments of groundwater potential across this vast and geologically complex region remain limited. This study addresses this gap by developing a spatial model for delineating groundwater potential zones (GWPZ) using an integrated approach that combines remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP). Seven key thematic layers, precipitation, lithology, slope, drainage density, soil type, land use/land cover (LULC), and lineament density, are selected, standardized, and weighted based on hydrogeological relevance. These layers are integrated through a weighted overlay analysis in a GIS environment. The resulting GWPZ map is initially classified into five categories: very high, high, moderate, low, and very low potential. The very high and very low potential zones have very small areas in the final output due to the region's arid conditions and hydrogeological limitations. The remaining zones covered the study area as follows: high potential (7.7%), moderate (54.5%), and low potential (37.7%). Model reliability is assessed through two complementary validation approaches. First, 370 groundwater wells with available location data are spatially overlaid on the GWPZ map, showing limited overlap with high recharge zones, as most wells target deep fossil aquifers not influenced by present-day surface conditions. Second, a supplementary validation using three independent surface-derived indicators: Topographic Wetness Index, curvature, and lineament-stream intersection density, demonstrated strong agreement with the GWPZ output. The integration of these two validation methods confirms the robustness of the model for mapping shallow groundwater recharge potential in arid environments. This framework offers a scalable, data-driven approach to support groundwater exploration and strategic water resource planning in similar regions worldwide.
The growing interest in plant-based therapeutics has led to increased exploration of medicinal flora for their nutritional and pharmacological potential. The objective of this study was to determine the nutritional composition, phytochemical profile, and antioxidant activity of Cotoneaster microphyllus from Shimla, Himachal Pradesh. The proximate analysis revealed high levels of ash and fat in the leaves, while high fiber levels in the fruits. According to mineral profiling, leaves showed an abundance of Mg, Ca, Na, and Zn, while fruits indicated predominant presence of P and K. Phytochemical extractions were performed using hydromethanol, methanol, and aqueous solvents, with hydromethanol extract exhibiting the highest phytochemical content and antioxidant activity, followed by methanol and aqueous extracts. DPPH and FRAP antioxidant assays confirmed that C. microphyllus scavenges free radicals and reducing antioxidant potential effectively. Based on GC-MS and LC-MS analyses, cyclosiloxanes and phthalate ester compounds were identified via GC-MS and 50 unique compounds were identified via LC-MS, reported for the first time in Cotoneaster. UHPLC was also used to quantify chlorogenic acid, with fruit extracts showing the highest concentration. In this study, we provide a novel insight into the phytochemical composition and bioactive potential of C. microphyllus. There is a significant lack of systematic biochemical and functional evaluation of this species, so this study represents the first comprehensive integration of nutrition profiling, multi-solvent phytochemical quantification, and advanced characterization (GC-MS, LC-MS, and UHPLC) of different plant parts. These findings provide new insight into phytochemical composition of C. microphyllus and point to its potential as a source of bioactive chemicals with potential pharmacological and nutraceutical applications, which need for more biological validation.
The Score Committee of the European Foot and Ankle Society (EFAS) developed, validated, and published the EFAS Score in 16 languages. Currently, the Norwegian version completed data acquisition and was further validated. The data were collected pre-operatively and post-operatively at a minimum follow-up of 3 months and mean follow-up of 6 months. Item reduction, scale exploration, confirmatory analyses and responsiveness were performed using classical test theory and item response theory. The internal consistency was confirmed in the Norwegian version (Cronbach's Alpha 0.86). The Standard Error of Measurement (SEM) was 0.31 and is similar to other language versions. Between baseline and follow-up, 77% of patients showed an improvement on their EFAS score, with good responsiveness (effect size 1.05). The Norwegian EFAS Score version was successfully validated in patients with a wide variety of foot and ankle pathologies. All score versions are freely available at www.efas.net.
Long-duration spaceflight produces structural, functional, and hemodynamic brain changes driven by microgravity, radiation, elevated CO2, and isolation. Consequences include Spaceflight-Associated Neuro-Ocular Syndrome, vestibular imbalance, orthostatic intolerance, and cognitive disturbance. We consolidate current evidence, present a cerebrovascular physiologic framework, and discuss emerging countermeasures-including lower body negative pressure, artificial gravity, advanced neuromonitoring, and synthetic torpor-needed to safeguard neurological health on exploration-class missions.
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Pediatric acute kidney injury (AKI) is a complex syndrome that affects a significant proportion of hospitalized children. AKI carries significant short- and long-term sequelae for survivors. As kidney tubular biomarkers and risk prediction tools have become more widely available, our ability to identify those at higher risk of AKI has improved. However, AKI prevention in clinical practice relies heavily on traditional kidney protection strategies: fluid management, hemodynamic optimization, and nephrotoxin avoidance. In this review, we explore contemporary risk stratification tools in AKI and briefly review key aspects of AKI pathophysiology relevant to prevention. In addition, this review summarizes established kidney-protective strategies and appraises pharmacological and extracorporeal interventions for preventing AKI in children. Early intervention frameworks, emerging clinical trials, and targeted preventive strategies suggest meaningful progress in the field. Ongoing research continues to refine our approach to pediatric AKI prevention and offers cautious optimism for improved outcomes.
E-cigarettes pose health risks and no established benefits to people who have never used tobacco. Australia introduced legislation in 2021, which was strengthened in 2024, to limit e-cigarette sales to pharmacists among other restrictions. This study explored the impact of these reforms. Weighted data from serial cross-sectional annual population-representative surveys (Health Omnibus Survey (2014-2017) and Population Health Survey Module System (2018-2024)) of South Australians aged 15 years and older (n=32 737) were used to assess e-cigarette use and cessation attempts overall by tobacco use status and demographic characteristics. The prevalence of current e-cigarette use was steady between 2014 (1.2%, 95% CI 0.7% to 1.8%) and 2020 (2.6%, 95% CI 1.7% to 3.5%), then doubled to 6.1% in 2023 (95% CI 4.6% to 7.7%) before declining in 2024 to 4.2% (95% CI 3.4% to 5.1%). Changes were most pronounced among those aged 15-29 years, where the prevalence of e-cigarette use and having never used tobacco rose 12-fold from 1.4% (95% CI 0 to 3.3%) in 2020 to 17.0% (95% CI 8.8% to 25.3%) in 2023, before declining to 5.5% (95% CI 3.3% to 7.7%) in 2024. Half of participants using e-cigarettes (49.3%) had ever attempted to quit vaping, though unprompted awareness of doctors or pharmacists as available sources of support was low (2.8%). Early results indicate that Australia's strengthened vaping reforms correspond with reductions in e-cigarette prevalence in South Australia, driven mainly by declines among youth who have never used tobacco. Continued monitoring is needed to consolidate evidence of this public health gain alongside improved awareness of cessation support services.
This study examines the relationship between religious denomination, religious attendance, and mammography screening among White and Black women in Brazil, using baseline data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil, 2015-2016). The sample comprised 3120 women aged 50-69 who self-identified as White or Black (including Brown). Mammography use was categorized as never, within the past two years (in accordance with national guidelines), or more than two years ago. Multinomial logistic regression models assessed associations, controlled by sociodemographic, and health-related factors. Results show that Evangelical women were significantly more likely than Catholics to have never undergone a mammogram (RRR = 1.53) and to be overdue (RRR = 1.52). Higher religious attendance was associated with a lower risk of never having been screened. These findings underscore a dual role of religion: it can either facilitate preventive care through, for instance, social support and health-promoting norms, or hinder it when certain beliefs-such as "faith healing"-discourage medical engagement. Furthermore, racial disparities persist within Evangelicals. Among Black women, Evangelical affiliation was associated with a higher likelihood of never being screened, while high religious attendance substantially reduced this risk. Among White women, Evangelical affiliation did not influence initiation of screening but was linked to nearly double the risk of being overdue, with no significant effect of attendance. Ultimately, our findings underscore the complex intersection of religion, race, and health. This study makes a novel contribution by being the first in Brazil to assess how religious denomination and religiosity relate to mammography screening among racial groups. Future research on religion and health should account for racial disparities.
This research aimed to address existing methodological limitations in research on mindful eating behaviours in children, to enhance future research in this area. This manuscript presents the development and the assessment of the validity and reliability of a Mindful Eating Behaviour Scale for children and parents across five studies. Study 1 assessed the scale's content validity using ratings from experts in the field. Study 2 evaluated the internal consistency of the scales. Study 3 explored temporal stability using a test-retest design with a 2-week interval. Studies 4 and 5 examined the criterion-related validity of the scales. Study 1 demonstrated that the items reflected the definition of mindful eating behaviour and were suitable for children. Study 2 showed that the scale and the subscales, "sensory attention" and "non-judgemental awareness" demonstrated good internal consistency. Using exploratory factor analysis, the scale supported the presence of two factors, "sensory attention" and "non-judgemental awareness". Study 3 showed that the scale was a reliable measure over time. Studies 4 and 5 showed that the scales are suitable measures for mindful eating behaviour. The scales demonstrated correlations between mindfulness and other eating behaviours, but further research is required, as only limited associations were observed. The current research presents new psychometric scales for mindful eating in children. The studies show that the scale is valid and reliable. No level of evidence: basic science.
Time efficient and reliable pipelines for quantitative evaluation of structural brain MRI are essential to utilize the potential of morphometry tools for large scale research projects as well as to pave the path towards future clinical applications. In our work, we have explored this idea by evaluating three deep learning models for brain segmentation and cortex parcellation (DeepSCAN, FastSurferCNN and QuickNAT) as input for an 11-min surface reconstruction pipeline adapted from the well studied open source software package FreeSurfer. Performance was assessed using both, large publicly available human MRI datasets and a synthetic dataset with known metrics and reference surfaces. Evaluation criteria included closeness to the surface reconstruction by FreeSurfer's full recon-all pipeline, reproducibility within same-session rescans, performance stability across a wide age range, sensitivity to variations of the grey-white contrast in the MRI and accuracy regarding metrics of synthetic surfaces. Metrics derived from the DeepSCAN-based pipeline demonstrated the highest agreement with FreeSurfer in the human data and the greatest fidelity to the expected metrics in the synthetic dataset. Our findings identify the DeepSCAN-based surface reconstruction pipeline as a rapid, yet reliable alternative to established research-grade structural MRI processing. Time expenditure and reliability suggest it is suitable for research applications with high-throughput requirements. This is an essential first step towards necessary subsequent studies aimed at evaluating robustness, pathological variability, and utility in the context of clinical diagnostics.
Primary biliary cholangitis (PBC) is a chronic cholestatic liver disease characterized by autoimmune-mediated destruction of intrahepatic bile ducts, leading to fibrosis, cirrhosis, and liver failure. Ursodeoxycholic acid remains the first-line treatment, but up to 40% of patients respond inadequately and continue to experience fatigue and pruritus. This therapeutic gap has recently been addressed by the approval of two new drugs, elafibranor and seladelpar, which activate peroxisome proliferator-activated receptors (PPARs). This review explores recently unveiled molecular mechanisms underlying the effectiveness of PPAR-targeting drugs in PBC, focusing on their effects on cellular immune regulation, bile acid production and toxicity, and hepatic fibrosis. Additionally, we examine current knowledge and ongoing challenges that will influence the roles of PPAR agonists in improving PBC treatment.
"Spin" refers to the manipulation of language to suggest benefits when none exist, commonly observed in randomized controlled trials (RCTs). This study investigates the extent, strategies, and reporting quality of spin in digital dental implant RCT abstracts, and explores study characteristics associated with spin. RCTs related to digital dental implants were retrieved from PubMed, Web of Science, Embase, Scopus, and the Cochrane Library. Spin was identified using predefined strategies, and logistic regression was performed to assess factors associated with its occurrence. Reporting quality was evaluated with the original 16-item CONSORT checklist for abstracts. We analyzed 19,795 articles, and a total of 127 abstracts were finally included in the reporting quality analyses, with spin identified in 51 abstracts (40.2%), where 24 abstracts (18.9%) exhibited spin in both "Results and conclusions" sections. A significantly lower presence of spin was observed in studies reporting exact p-values (OR: 0.317; 95% CI: 0.134-0.727; p = 0.007) and shorter abstract word limits for journal submission (moderate: OR = 0.185, p = 0.01; low: OR = 0.138, p = 0.02). The prevalence of spin in digital implant surgery RCTs is concerning. Given that busy clinicians often rely solely on abstracts for clinical interpretation, such distorted reporting may mislead practitioners into adopting expensive or complex digital technologies without robust evidence of superiority. It is essential for all stakeholders to prioritize transparent reporting to prevent biased clinical decision-making and safeguard the integrity of evidence-based dentistry.
Chimeric antigen receptor T-cell (CAR-T) therapy was initially used to treat B-cell malignancies, and it is now considered an effective treatment option for multiple sclerosis (MS). CAR-T therapy selectively targets and depletes pathogenic B cells within lymphoid tissue and the central nervous system (CNS), showing promise for achieving deep, sustained remission and long-term treatment-free disease control in patients with refractory MS. A comprehensive analysis was carried out by searching multiple keywords with combinations such as "CAR-T", "MS", "Demyelination", "Autoimmunity", "CD19", "Inflammation", "B cells", T cells", "Neurodegeneration ", "Neurological Disorders", "Immunity", etc. The review included preclinical and clinical research articles publicly available till March 2026. This study was conducted to explore the mechanisms of action, clinical effectiveness, safety profile, and prospects for CAR-T treatment for MS. From the beginning clinical testing indicates that CD19 targeted CAR-T cells can efficiently and permanently destroy through B-cells, leading to a significant decrease in disease progression, a recovery of impairment, and an immense reduction in inflammatory markers in individuals who have progressive MS. New techniques for engineering such as allogeneic CAR-T cells and enhanced CRISPR-based safety switches, are being investigated for making things safer and easier for individuals. CAR-T treatment represents a revolutionary approach for individuals with refractory MS. With ongoing improvements in safety and specificity, it has the potential to transform the therapeutic paradigm toward a sustainable immunological reset and prolonged remission in clinical neuroimmunology.
The linear carbon allotrope carbyne has been predicted to display outstanding electrical and mechanical properties, but its preparation and characterization are hindered by synthetic challenges. Although oligoyne and [n]cumulene models of carbyne have been explored, the end-groups used to avoid decomposition have a profound effect on their electronic configuration. Here we show that transmetallation of linear carbon fragments from Au(I) species to Au(0) electrodes delivers stable Au|CC…CC|Au devices. Scanning tunnelling microscope break junction techniques were used to characterize charge-transport behaviour in these one-dimensional carbon chains (up to 16 atoms) free of end-capping groups. Shorter chains exhibited oligoyne-like behaviour, with conductance attenuation as a function of length, whereas longer chains show evidence of bond-length equalization towards a cumulenic structure, with remarkably enhanced charge transport. The direct contact between the electrode and the carbon fragment at the Au|C interfaces grant high conductance and quasi-ballistic transport to one-dimensional carbon chains, providing a pathway to advanced carbon-based nanoelectronics based on the stabilization of carbyne within the junction environment.
Cement is one of the most used materials in the construction industry. Its production contributes about 8% to the global emissions. This study explores the use of eggshell powder (ESP) that contains over 80% calcium carbonate (CaCO3) as a partial substitute for ordinary Portland cement (OPC), such as Types B and D, to substitute limestone within the cement matrix. The physical (consistency and setting time), compressive strength, and chemical (Scanning Electron Microscope with Energy Dispersive Spectroscopy, (SEM-EDS), and X-ray Diffraction (XRD), X-ray fluorescence (XRF)) properties were evaluated against British, Indian, and established standards in the literature. The results showed that cement consistency (31-35%) and setting times were within acceptable limits according to BS EN 197-1 (initial ≥ 60 min) and IS 8112 (initial ≥ 30 min, final ≤ 600 min). The mechanical strength of the samples exceeded the required strength (42.5 N/mm2) recommended by the British Standards, except for samples B10N and D10N, which showed strength reductions of 8.4% and 12.3%, respectively. The SEM-EDS and XRD analyses confirmed a high CaCO3 content in the samples. The study suggests that incorporating ESP into OPC should not exceed 5% by weight, as higher proportions could negatively impact the cement's physical and strength properties. This approach will promote environmental sustainability by using agro-waste while ensuring the cement remains suitable for construction.