Search Results (20,905)

Background/Objective: Sarcopenia is a predictor of poor surgical outcomes in older adults. The Psoas Muscle Index (PMI), calculated from routine preoperative CT scans, has been proposed as an imaging-based marker of physiological reserve, but its diagnostic utility in vascular surgery remains unclear. We aimed to assess the predictive value of PMI for early complications following elective abdominal aortic aneurysm (AAA) repair in two European centers. Methods: We retrospectively analyzed 245 patients who underwent open or endovascular AAA repair between 2018 and 2022 in Poland and The Netherlands. PMI was measured at the level of third lumbar vertebrae (L3) level, normalized to height, and stratified into center-specific tertiles. Early complications were compared across tertiles, procedures, and centers. Multivariate logistic regression was used to adjust for age, comorbidities, and procedure type. Results: Low PMI was significantly associated with early complications in EVAR patients at the Polish center (p = 0.004). No associations were found in open repair or at the Dutch center. Mean PMI values did not differ significantly between centers. Conclusions: PMI may serve as a context-dependent imaging biomarker for early risk stratification following AAA repair, particularly in endovascular cases. Its predictive value is influenced by institutional and procedural factors, highlighting the need for prospective validation and standardization before clinical adoption. Full article

Background/Objectives: Pediatric allogeneic hematopoietic stem cell transplantation (allo-HSCT) is complicated by iron overload and hypomagnesemia, both contributing to immune dysfunction and post-transplant morbidity. The combined impact of these metabolic disturbances on pediatric allo-HSCT outcomes remains unexplored. This study aims to determine whether hypomagnesemia can serve as a prognostic biomarker for delayed immune reconstitution and explores its interplay with iron overload in predicting post-transplant complications and survival outcomes. Methods: A retrospective analysis was conducted on 163 pediatric allo-HSCT recipients. Serum magnesium levels were measured at defined intervals post-transplant, and outcomes were correlated with CD4+ T cell recovery, time to engraftment, incidence of graft-versus-host disease (GVHD), and survival within 12 months. Iron status, including siderosis severity, was evaluated using imaging and laboratory parameters obtained from clinical records. Results: Patients who died within 12 months post-transplant exhibited significantly lower magnesium levels. Hypomagnesemia was associated with delayed CD4+ T cell recovery, prolonged engraftment, and an increased risk of acute GVHD. A strong inverse correlation was observed between magnesium levels and the severity of siderosis. Iron overload appeared to exacerbate magnesium deficiency. Additionally, the coexistence of hypomagnesemia and siderosis significantly increased the risk of immune dysfunction and early mortality. No significant association was found with chronic GVHD. Conclusions: Hypomagnesemia is a significant, early predictor of poor outcomes in pediatric allo-HSCT, particularly in the context of iron overload, underscoring the need for early intervention, including iron chelation and MRI, to improve outcomes. Full article

Background: Post-hepatectomy liver failure (PHLF) is the most worrisome complication after a major hepatectomy and is the leading cause of postoperative mortality. The most important predictor of PHLF is the future liver remnant (FLR), the volume of the liver that will remain after the hepatectomy, representing a major concern for hepatobiliary surgeons, radiologists, and patients. Therefore, an accurate preoperative assessment of the FLR and the prediction of PHLF are crucial to minimize risks and enhance patient outcomes. Recent radiomics and deep learning models show potential in predicting PHLF and the FLR by integrating imaging and clinical data. However, most studies lack external validation and methodological homogeneity and rely on small, single-center cohorts. This review outlines current CT-based approaches for surgical risk stratification and key limitations hindering clinical translation. Methods: A literature analysis was performed on the PubMed Dataset. We reviewed original articles using the subsequent keywords: [(Artificial intelligence OR radiomics OR machine learning OR deep learning OR neural network OR texture analysis) AND liver resection AND CT]. Results: Of 153 pertinent papers found, we underlined papers about the prediction of PHLF and about the FLR. Models were built according to machine learning (ML) and deep learning (DL) automatic algorithms. Conclusions: Radiomics models seem reliable and applicable to clinical practice in the preoperative prediction of PHLF and the FLR in patients undergoing major liver surgery. Further studies are required to achieve larger validation cohorts. Full article

A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors. Full article

Background: Comprehensive Geriatric Assessment (CGA) is essential for evaluating older cancer patients, but significant gaps persist in both research and clinical practice. This study aimed (I) to identify the CGA elements that most influence anti-cancer treatment decisions in older patients and (II) to explore the predictive value of CGA components for mortality. Methods: This observational study included older patients with newly diagnosed, histologically confirmed solid or hematological cancers, recruited consecutively from 2003 to 2023. Participants were followed for four years. The data collected included CGA measures of functional (Activities of Daily Living-ADL), cognitive (Mini-Mental State Examination-MMSE), and emotional (Geriatric Depression Scale-GDS) domains. Patients were categorized into frail, vulnerable, or fit groups based on Balducci’s criteria. Statistical analyses included decision tree modeling and Cox regression to identify predictors of mortality. Results: A total of 7022 patients (3222 females) were included, with a mean age of 78.3 ± 12.9 years. The key CGA factors influencing treatment decisions were ADL (first step), cohabitation status (second step), and age (last step). After four years, 21.9% patients had died. Higher GDS scores (OR 1.04, 95% CI 1.01–1.07, p = 0.04) were independently associated with survival in men and living with family members (OR 1.67, 95% CI 1.35–2.07, p < 0.001) in women. Younger patients (<77 years) showed both MMSE and GDS as significant risk factors for mortality. Conclusions: Functional capacity, cohabitation status, and GDS scores are crucial for guiding treatment decisions and predicting mortality in older cancer patients, emphasizing the need for a multidimensional geriatric assessment. Full article

To enhance agricultural soil health and soil organic carbon (SOC) sequestration, it is important to accurately measure SOC. The aim of this study was to compare common methods for measuring SOC in soils in order to determine the most effective approach among different agricultural land types. The measurement methods of loss-on-ignition (LOI), automated dry combustion (Dumas), and real-time near-infrared spectroscopy (NIRS) were compared. A total of 95 soil core samples, ranging in clay and calcareous content, were collected across a range of agricultural land types from forty-eight fields across five farms in the Southwest of England. There were similar and positive correlations between all three methods for measuring SOC (ranging from r = 0.549 to 0.579; all p < 0.001). On average, permanent grass fields had higher SOC content (6.6%) than arable and temporary ley fields (4.6% and 4.5%, respectively), with the difference of 2% indicating a higher carbon storage potential in permanent grassland fields. Newly predicted conversion equations of linear regression were developed among the three measurement methods according to all the fields and land types. The correlation of the conversation equations among the three methods in permanent grass fields was strong and significant compared to those in both arable and temporary ley fields. The analysed results could help understand soil carbon management and maximise sequestration. Moreover, the approach of using real-time NIRS analysis with a rechargeable portable NIRS soil device can offer a convenient and cost-saving alternative for monitoring preliminary SOC changes timely on or offsite without personnel risks from the high-temperature furnace and chemical reagent adopted in the LOI and Dumas processes, respectively, at the laboratory. Therefore, the study suggests that faster, lower-cost, and safer methods like NIRS for analysing initial SOC measurements are now available to provide similar SOC results as traditional soil analysis methods of the LOI and Dumas. Further studies on assessing SOC levels in different farm locations, land, and soil types across seasons using NIRS will improve benchmarked SOC data for farm stakeholders in making evidence-informed agricultural practices. Full article

Detection of surface defects can significantly elongate mechanical service time and mitigate potential risks during safety management. Traditional defect detection methods predominantly rely on manual inspection, which suffers from low efficiency and high costs. Some machine learning algorithms and artificial intelligence models for defect detection, such as Convolutional Neural Networks (CNNs), present outstanding performance, but they are often data-dependent and cannot provide guarantees for new test samples. To this end, we construct a detection model by combining Mask R-CNN, selected for its strong baseline performance in pixel-level segmentation, with Conformal Risk Control. The former evaluates the distribution that discriminates defects from all samples based on probability. The detection model is improved by retraining with calibration data that is assumed to be independent and identically distributed (i.i.d) with the test data. The latter constructs a prediction set on which a given guarantee for detection will be obtained. First, we define a loss function for each calibration sample to quantify detection error rates. Subsequently, we derive a statistically rigorous threshold by optimization of error rates and a given guarantee significance as the risk level. With the threshold, defective pixels with high probability in test images are extracted to construct prediction sets. This methodology ensures that the expected error rate on the test set remains strictly bounded by the predefined risk level. Furthermore, our model shows robust and efficient control over the expected test set error rate when calibration-to-test partitioning ratios vary. Full article

Primary and secondary immunodeficiencies comprise a wide array of illnesses marked by immune system abnormalities, resulting in heightened vulnerability to infections, autoimmunity, and cancers. Notwithstanding progress in diagnostic instruments and an enhanced comprehension of the underlying pathophysiology, delayed diagnosis and underreporting persist as considerable obstacles. The implementation of artificial intelligence into clinical practice has surfaced as a viable method to enhance early detection, risk assessment, and management of immunodeficiencies. Recent advancements illustrate how artificial intelligence-driven models, such as predictive algorithms, electronic phenotyping, and automated flow cytometry analysis, might enable early diagnosis, minimize diagnostic delays, and enhance personalized treatment methods. Furthermore, artificial intelligence-driven immunopeptidomics and phenotypic categorization are enhancing vaccine development and biomarker identification. Successful implementation necessitates overcoming problems associated with data standardization, model validation, and ethical issues. Future advancements will necessitate a multidisciplinary partnership among physicians, data scientists, and governments to effectively use the revolutionary capabilities of artificial intelligence, therefore ushering in an age of precision medicine in immunodeficiencies. Full article

Phospholipase A1 (Ves a 1), a major toxin from Vespa affinis venom, poses significant risks to allergic individuals. Nevertheless, the epitope determinants of Ves a 1 have not been characterized. Thus, identifying its linear B-cell epitopes is crucial for understanding envenomation mechanisms. In this study, we predicted and identified B-cell epitopes EP5 and EP6 as potential candidates. EP5 formed an α-helix at the active site of Ves a 1, whereas EP6 adopted an extended loop conformation. Both synthetic peptides were synthesized and evaluated for their inhibitory effects using immune-inhibitory assays with polyclonal antibodies (pAbs) targeting both native (nVes a 1) and recombinant (rVes a 1) forms. The Ves a 1 polyclonal antibodies (pAb-nVes a 1 and pAb-Ves a 1) were produced, and their specificity binding to Ves a 1 was confirmed by Western blot. Next, ELISA inhibition assays showed that EP5 and EP6 significantly blocked pAb binding to both nVes a 1 and rVes a 1. Dot blot and Western blot assays supported these findings, particularly with stronger inhibition toward rVes a 1. Furthermore, enzymatic assays indicated that nVes a 1 and rVes a 1 retained phospholipase activity. Immunoinformatics docking showed that EP5 and EP6 specifically bind to a single-chain variable fragment antibody (scFv) targeting Naja naja PLA2. Molecular analysis revealed similar amino acid interactions to the template, suggesting effective paratope–epitope binding. These results support the potential of EP5 and EP6 for future diagnosis and therapy of V. affinis venom allergy. Full article

Background: BK polyomavirus (BKPyV) reactivation is a common complication after kidney transplantation and may result in nephropathy and graft loss. As there is no effective antiviral therapy, management focuses on early detection and reduction of immunosuppression, which increases the risk of rejection. Identifying patients at higher risk remains challenging. Monitoring BKPyV-specific T-cell responses could aid in predicting reactivation. This study evaluated the usefulness of ELISpot to monitor BKPyV-specific cellular immunity before and after kidney transplantation. Methods: A prospective multicenter study was conducted between October 2020 and March 2022. ELISpot assays were performed prior to transplantation and two months afterward. Results: Seventy-two patients were included, with a median age of 56 years; 61% were men, and 24% had undergone previous transplantation. Nine patients developed presumptive BKPyV-nephropathy. No significant differences were found in donor type, induction therapy, or rejection rates between patients with or without nephropathy (p = 0.38). Based on ELISpot results, patients were classified into three groups according to their risk of BKPyV-nephropathy. The high-risk group included those who changed from positive to negative at 2 months post-transplant, representing 40% of presumptive BKPyV-nephropathy cases. Patients who remained negative at 2 months were classified as moderate risk (14.5%), while those with a positive ELISpot at 2 months comprised the low-risk group (0%). In the logistic regression analysis, both the ELISpot risk category [OR 19 (CI 1.7–2.08)] and the use of mTOR inhibitors from the start of transplantation [OR 0.02 (CI 0.01–0.46)] were significantly associated with BKPyV-nephropathy. Conclusions: Monitoring BKPyV-specific T cells with ELISpot before and after kidney transplantation may help stratify patients by risk of reactivation. Loss of BKPyV immunity at two months is associated with nephropathy, while mTOR-based immunosuppression appears protective. This strategy could guide personalized immunosuppression and surveillance. Full article

Objectives: This study sought to echocardiographic manifestations and the related risk factors affecting the prognosis of isolated congenitally corrected transposition of the great arteries (CCTGA). Methods: A total of 143 patients (≥18 years of age) were diagnosed with isolated CCTGA at Anzhen Hospital. The patients were classified as the operation group and the non-operation group depending on whether they had undergone tricuspid valve surgery. The echocardiographic data and follow-up were compared, and the primary outcomes examined were defined as death or heart transplantation. Results: The average age of 143 patients with isolated CCTGA was 39.93 ± 13.50 years. Tricuspid valve surgery was performed in 31 patients with isolated CCTGA, and 112 patients did not undergo tricuspid valve surgery. The incidence of tricuspid valve structural changes in the operation group was 39.1%, and this group had higher numbers of patients with right ventricular diastolic diameter, right ventricular systolic diameter, left atrial dimensions, and regurgitation before surgery compared with the non-operation group (p < 0.05). Follow-up results showed no significant difference in the number of death/heart transplantations, and the incidence of systemic ventricular ejection fraction (SVEF) < 40% between the two groups. The survival rate of the surgery group was higher than that of the non-surgery group, although not statistically significant (p = 0.123). Age, right ventricular end-diastolic diameter, and decreased SVEF at the first diagnosis are independent predictive risk factors for major adverse outcomes. Conclusions: Adult patients with isolated CCTGA may have structural abnormalities in their tricuspid valves. There were no significant differences in the incidence of adverse outcomes, morphological right ventricular systolic dysfunction, and survival between the surgery group and the non-surgery group. However, this study is a retrospective study, and the sample size of the surgical group is relatively small, which may limit the generalizability of the research conclusions. In the future, a prospective, large-scale study will be conducted to evaluate the therapeutic effect of tricuspid valve surgery on such patients. Full article

Blasting is an effective technique for fragmenting rock in open-pit mining operations. Blasting operations produce either boulders or fine fragments, both of which increase costs and pose environmental risks. As a result, predicting the mean fragmentation size (MFS) distribution of rock is critical for assessing blasting operations’ quality and mitigating risks. Due to the limitations of empirical and statistical models, several researchers are turning to artificial intelligence (AI)-based techniques to predict the MFS distribution of rock. Thus, this study uses three AI tree-based algorithms—extra trees (ET), gradient boosting (GB), and random forest (RF)—to predict the MFS distribution of rock. The prediction accuracy of the models is optimized utilizing the tree-structured Parzen estimators (TPEs) algorithm, which results in three models: TPE-ET, TPE-GB, and TPE-RF. The dataset used in this study was collected from the published literature and through the data augmentation of a large-scale dataset of 3740 blast samples. Among the evaluated models, the TPE-ET model exhibits the best performance with a coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and max error of 0.93, 0.04, 0.03, and 0.25 during the testing phase. Moreover, the block size (XB, m) and modulus of elasticity (E, GPa) parameters are identified as the most influential parameters for predicting the MFS distribution of rock. Lastly, an interactive web application has been developed to assist engineers with the timely prediction of MFS. The predictive model developed in this study is a reliable intelligent model because it combines high accuracy with a strong, explainable AI tool for predicting MFS. Full article

Background/Objectives: Heart rate variability (HRV) has been widely investigated as a predictor of disease and mortality across diverse patient populations; however, there remains no consensus on the optimal set or combination of time and frequency domain nor on nonlinear features for reliable prediction across clinical contexts. Given the relevance of the COVID-19 pandemic and the unique clinical profiles of these patients, this retrospective observational study explored the potential of HRV analysis for early prediction of in-hospital mortality using ECG signals recorded during the initial moments of ICU admission in COVID-19 patients. Methods: HRV indices were extracted from four ECG leads (I, II, III, and aVF) using sliding windows of 2, 5, and 7 min across observation intervals of 15, 30, and 60 min. The raw data posed significant challenges in terms of structure, synchronization, and signal quality; thus, from an original set of 381 records from 321 patients, after data pre-processing steps, a final dataset of 82 patients was selected for analysis. To manage data complexity and evaluate predictive performance, two feature selection methods, four feature reduction techniques, and five classification models were applied to identify the optimal approach. Results: Among the feature aggregation methods, compiling feature means across patient windows (Method D) yielded the best results, particularly for longer observation intervals (e.g., using LDA, the best AUC of $0.82\pm 0.13$ was obtained with Method D versus $0.63\pm 0.09$ with Method C using 5 min windows). Linear Discriminant Analysis (LDA) was the most consistent classification algorithm, demonstrating robust performance across various time windows and further improvement with dimensionality reduction. Although Gradient Boosting and Random Forest also achieved high AUCs and F1-scores, their performance outcomes varied across time intervals. Conclusions: These findings support the feasibility and clinical relevance of using short-term HRV as a noninvasive, data-driven tool for early risk stratification in critical care, potentially guiding timely therapeutic decisions in high-risk ICU patients and thereby reducing in-hospital mortality. Full article

This study was conducted using Geographic Information Systems (GISs), Remote Sensing (RS) techniques, and the SLEUTH model based on Cellular Automata (CA) to analyze the spatial and temporal dynamics of urban growth in Sanliurfa Province and to create future projections. The model in question simulates urban sprawl by using Slope, Land Use/Land Cover (LULC), Excluded Areas, urban areas, transportation, and hill shade layers as inputs. In addition, disaster risk areas and public policies that will affect the urbanization of the city were used as input layers. In the study, the spatial pattern of urbanization in Sanliurfa was determined by using Landsat satellite images of six different periods covering the years 1985–2025. The Analytical Hierarchy Process (AHP) method was applied within the scope of Multi-Criteria Decision Analysis (MCDA). Weighting was made for each parameter. Spatial analysis was performed by combining these values with data in raster format. The results show that the SLEUTH model successfully reflects past growth trends when calibrated at different spatial resolutions and can provide reliable predictions for the future. Thus, the proposed model can be used as an effective decision support tool in the evaluation of alternative urbanization scenarios in urban planning. The findings contribute to the sustainability of land management policies. Full article

Background/Objectives: Given the heterogeneous nature of long COVID, its treatment and management remain challenging. This study aimed to investigate whether poor pre-pandemic sleep quality, its deterioration during the peak of the pandemic, and circadian preference increase the risk of long COVID symptoms. Methods: An online survey was conducted between 9 October and 12 December 2022, with 384 participants who had recovered from COVID-19 at least three months prior to data collection. Participants were categorized based on the presence of at least one long COVID symptom. Logistic regression models assessed associations between sleep-related variables and long COVID symptoms. Results: Participants with long COVID symptoms reported significantly poorer sleep quality, higher perceived stress, greater somatic and cognitive pre-sleep arousal, and elevated levels of post-traumatic stress symptoms, anxiety, depression, and aggression. Fatigue (39.8%) and memory problems (37.0%) were the most common long COVID symptoms. Sleep deterioration during the pandemic peak was reported by 34.6% of respondents. Pre-pandemic poor sleep quality, its deterioration during the pandemic, and poor sleep at the time of the survey were all significantly associated with long COVID. An extreme morning chronotype consistently predicted long COVID symptoms across all models, while an extreme evening chronotype was predictive only when accounting for sleep quality changes during the pandemic. COVID-19 frequency, severity, financial impact, and somatic pre-sleep arousal were significant predictors in all models. Conclusions: Poor sleep quality before the pandemic and its worsening during the pandemic peak are associated with a higher likelihood of long COVID symptoms. These findings underscore the need to monitor sleep health during pandemics and similar global events to help identify at-risk individuals and mitigate long-term health consequences, with important clinical and societal implications. Full article