Search Results (345,765)

In 2023, Tropical Cyclone Freddy caused severe damage in southern Malawi, flooding much of the lowland area near Lake Chilwa and displacing many residents. This study evaluates long-term, region-specific mitigation strategies to lessen future risks, using a novel approach that combines drone and satellite data, building footprints, and 3D simulations to analyze how building elevation affects flood damage and assess Property-Level Flood Risk Adaptation measures. Results show a significant difference in ground elevation between affected and unaffected buildings, with damaged structures generally at lower levels. The 3D simulation confirmed a water-level rise of approximately 3.0 m caused by Freddy. Scenario analysis indicates that elevating buildings by 2.0, 2.5, and 3.0 m could reduce direct flood exposure and 64%, 76%, and 91% of damage, respectively. These insights can inform the development of targeted regional risk-mitigation strategies through Property-Level Flood Risk Adaptation in high-risk areas. Full article

Background: Bayesian model-informed precision dosing (MIPD) is increasingly used to individualize drug therapy; therefore, this review aimed to identify and characterize its implementation in routine clinical practice. Methods: A focused systematic review was conducted. Web of Science Core Collection and PubMed were searched from inception to February 2026. Eligible studies were original research articles evaluating Bayesian MIPD in routine clinical practice using software platforms that supported dosing decisions. Data were synthesized descriptively. No formal risk-of-bias assessment was performed due to heterogeneity in study design. Results: Fifteen studies met the inclusion criteria. Anti-infective therapy predominated, particularly vancomycin (n = 11), with additional studies involving busulfan, mycophenolate mofetil, amikacin, and tobramycin. Commonly reported software platforms included InsightRx (n = 6) and DoseMeRx (n = 4), along with Abbottbase, NextDose, and ISBA. MIPD was mainly applied with therapeutic drug monitoring, reflecting predominant a posteriori use in routine care. Across studies, implementation was associated with improved pharmacokinetic target attainment, while a subset reported clinical benefits, including reduced nephrotoxicity and favorable effectiveness-related outcomes. Pharmacist involvement was commonly described. Conclusions: Published evidence indicates that Bayesian MIPD is being implemented in routine clinical settings, but current published experience is dominated by vancomycin-focused studies. Although the evidence base remains limited, it has grown since 2020 and suggests that software-supported Bayesian dosing can improve pharmacokinetic target attainment and may support better clinical outcomes. Full article

Heart failure with reduced ejection fraction (HFrEF) is increasingly recognized as a systemic metabolic disorder. The aim of this study was to characterize amino acid-related metabolic differences between heart failure with moderately reduced ejection fraction (HFmrEF) (LVEF 40–49%) and HFrEF (LVEF < 40%) and to derive a biologically interpretable composite metabolomic index capable of discriminating between these two stages of systolic dysfunction. We conducted a cross-sectional metabolomic analysis of 42 patients stratified by left ventricular ejection fraction (LVEF < 40% vs. 40–49%). The reference group comprised patients with mildly reduced ejection fraction (LVEF 40–49%), without inclusion of individuals with preserved or normal cardiac function. Targeted amino acid profiling was performed using liquid chromatography-tandem mass spectrometry (LC–MS/MS). Metabolites were standardized and analyzed individually and in combination. A composite index (Heart Failure Amino Acid-Derived Systolic Index: HASI-40), integrating markers of proteolysis and metabolic resilience, was derived to distinguish patients with HFrEF from those with HFmrEF. Discrimination was assessed using receiver operator curve (ROC) analysis with internal validation and multivariable adjustment. Patients with LVEF < 40% exhibited a coordinated metabolic phenotype characterized by reduced methionine, sarcosine, serine, and taurine. While individual metabolites did not retain significance after multiple-testing correction, the composite HASI-40 index remained strongly associated with HFrEF (OR 5.56, 95% CI: 1.70–18.14; p = 0.004), although the wide confidence interval indicates limited precision due to sample size. The index demonstrated good discrimination with an area under the curve (AUC) of 0.862, which improved when combined with age (AUC 0.932). The index represents a standardized composite measure and does not define a diagnostic cutoff for individual patients. These findings suggest that HFmrEF and HFrEF exhibit partially distinct metabolic phenotypes despite overlapping clinical characteristics. These findings suggest that HASI-40 captures metabolic differences between patients with HFmrEF (LVEF 40–49%) and those with HFrEF (LVEF < 40%), reflecting progression toward more advanced systolic dysfunction. However, due to the absence of a control group with preserved ejection fraction, small sample size, and lack of external validation, the index should be considered exploratory and hypothesis-generating rather than clinically applicable. Full article

Building damage and collapse are the main sources of serious casualties and financial losses during earthquakes, which are among the most destructive natural disasters that endanger human life and property. Therefore, quick and precise post-earthquake building damage assessment is essential for risk assessment and emergency action. Convolutional neural networks (CNNs) primarily concentrate on local features and frequently ignore global contextual information within and across buildings, despite the fact that deep learning-based techniques allow automated damage identification. Transformer-based approaches, on the other hand, are good at capturing global dependencies, but their large memory and processing costs restrict their usefulness. As a result, existing networks still struggle to achieve an effective balance between accuracy and efficiency. To address this issue, this study proposes a lightweight and efficient network for post-earthquake building damage assessment. Specifically, we develop a two-stage method based on EfficientViM with an encoder–decoder architecture. In the encoder, Mamba is introduced to extract multi-scale change features with long-range dependencies, leveraging the state space model to preserve global modeling capability while significantly reducing computational complexity. In the decoder, two lightweight modules are designed to further enhance discriminative capability and computational efficiency. The network finally outputs building localization and pixel-level building damage, respectively. Experiments were conducted on four earthquake events from the BRIGHT dataset using a three-for-training and one-for-testing cross-event rotation evaluation strategy. The results demonstrate that LEViM-Net requires only 30.94 M parameters and 27.10 G FLOPs. In addition, for the Türkiye earthquake event, the proposed method achieves an F1 score of 80.49%, an overall accuracy (OA) of 88.17%, and a mean intersection over union (mIoU) of 49.73%. The proposed model enables efficient remote-sensing-based mapping of macroscopic and image-visible building damage, providing timely support for early-stage emergency response. Full article

Background: To investigate the effect of angiotensin-(1-7) [Ang-(1-7)] on serum metabolomics in obese type 2 diabetic (T2DM) mice. Methods: Four-week-old male C57BL/6 mice were fed a high-fat diet and intraperitoneally injected with streptozotocin (35 mg/kg) to establish an obese T2DM model. Mice were randomized into control, T2DM and T2DM+Ang-(1-7) groups (n = 6). Body weight and blood glucose were recorded weekly. At 10 weeks, blood glucose, serum inflammatory factors, lipid profiles, and pancreatic β-cell insulin secretion were detected; serum metabolite alterations were analyzed via untargeted metabolomics. Results: 1. Ang-(1-7) intervention decreased blood glucose (p < 0.05) and CRP levels (p < 0.01), and alleviated dyslipidemia (p < 0.05 or p < 0.01), as well as β-cell morphology and insulin expression in obese T2DM mice. 2. Non-targeted metabolomics analysis suggested that Ang-(1-7) may alleviate abnormal amino acid metabolic pathways by regulating levels of metabolites such as L-valine, L-proline, L-histidine, and glutamic acid. This intervention also tended to reduce multiple lipid metabolites, including Omega-3 Arachidonic Acid Ethyl Ester, phosphatidylcholine, and glycerophosphocholine, thereby participating in the modulation of lipid metabolism balance. KEGG enrichment analysis further indicated that Ang-(1-7) was involved in the regulation of protein digestion and the absorption pathway, as well as the HIF-1 signaling pathway related to oxidative stress, bile acid metabolism pathway, and other signaling pathways, and improving the insulin secretion pathway, pyrimidine metabolism, and TCA cycle energy metabolism pathway. Conclusions: Ang-(1-7) may partially improve metabolic disturbances in obese T2DM mice, which is potentially associated with the modulation of multiple metabolic processes, including amino acid metabolism, lipid metabolism, insulin secretion, and TCA cycle energy metabolism. Full article

Introduction: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is widely used for diagnosing pulmonary diseases causing mediastinal lymphadenopathy. However, non-diagnostic results may occur. This study investigated factors associated with non-diagnostic cytological results in EBUS-TBNA. Methods: This retrospective study included patients who underwent EBUS-TBNA at a tertiary hospital between March 2019 and December 2023. Data on demographics, biopsy techniques, cyto-/histopathological results, sonographic lymph node measurements, and pre-procedural PET-CT SUVmax values were recorded. Cytological results were classified as diagnostic or non-diagnostic. We analyzed the characteristics and associated factors of patients who were non-diagnostically identified. Results: Among 776 patients undergoing EBUS-TBNA, 502 (64.7%) were male, with a mean age of 61.5 ± 12.6 years. A total of 1110 lymph nodes were sampled. Of the patients, 14.1% had a non-diagnostic cytology. Among the diagnosed patients, cytological findings showed 58.9% non-malignant, 41.1% malignant. The most sampled station was station 7 (72.9%), with an average of 5.9 ± 1.4 aspirations. Diagnostic cases had significantly more aspirations (p = 0.022) and sampled larger lymph node sizes (p < 0.001). Each 1 mm increase in lymph node size raised the likelihood of diagnostic results by 1.04 times (adjOR = 1.04, 95% CI = 1.02–1.08, p = 0.002). The largest lymph node size significantly predicted diagnostic results (AUROC = 0.611, p < 0.001). A cut-off of 19.55 mm had 67.0% sensitivity and 52.2% specificity. Conclusion: Sampled larger lymph nodes increase diagnostic yield in EBUS-TBNA, reducing the need for repeat procedures and enabling earlier treatment, thereby decreasing morbidity and mortality. Full article

Alveolar ridge preservation (ARP) aims to reduce post-extraction bone resorption and facilitate implant placement. Among alloplastic grafts, β-tricalcium phosphate (β-TCP) is widely used due to its osteoconductive properties and complete resorbability. This systematic review evaluated the clinical effectiveness of β-TCP for ARP, focusing on ridge dimensional changes assessed by cone–beam computed tomography (CBCT). Electronic searches were performed in major scientific databases up to April 2026. Randomized controlled trials (RCTs) reporting CBCT-based dimensional outcomes after at least 4 months were included. Five RCTs met the inclusion criteria. Considerable heterogeneity was observed in biomaterial formulations, socket management, and outcome assessment. When used alone, β-TCP showed variable results, ranging from greater ridge resorption compared with xenograft to outcomes comparable with those of freeze-dried bone allograft. More consistent findings were reported when β-TCP was used in combination with other biomaterials, with outcomes generally comparable to those of deproteinized bovine bone mineral (DBBM). Overall, β-TCP may have a potential role in alveolar ridge preservation; however, evidence remains limited and heterogeneous. Differences between β-TCP alone and composite formulations should be carefully considered, and no definitive conclusions can be drawn regarding its comparative predictability versus xenografts. Further RCTs are needed to clarify its clinical effectiveness and identify optimal applications. Full article

Vegetables constitute an essential component of the daily diet in Albania; however, they also represent a major pathway of human exposure to pesticide residues. This study investigates the presence of pesticide residues in widely used vegetables, including leafy, fruity, root, and bulb types, and evaluates the potential dietary health risks associated with their consumption. Vegetable samples were analyzed using gas chromatography–tandem mass spectrometry (GC-MS/MS) and liquid chromatography–tandem mass spectrometry (LC-MS/MS), for the presence of 417 pesticide analytes, ensuring high analytical sensitivity and reliability. Pesticide residues were present, with 42 distinct compounds, including metabolites, found in all the analyzed samples. Notably, some of the detected substances are not currently authorized for use as plant protection products, suggesting either environmental persistence or regulatory non-compliance. Exceedances of European Union maximum residue limits (MRLs) were most frequently detected in leafy vegetables (42.31%), followed by fruity vegetables (18.75%), whereas no MRL exceedances were observed in root and bulb vegetables. According to the dietary exposure assessment conducted using European Food Safety Authority Pesticide Residue Intake Model (EFSA PRIMo model v.3.1), chronic dietary exposure to pesticide residues was below the acceptable daily intake (ADI). According to this assessment, the acute exposure exceeded the acute reference dose (ARfD) for several pesticide–vegetable combinations, particularly among children. This highlights the need for ongoing monitoring and better agricultural management techniques to reduce potential health risks related to pesticide residues in vegetables. The study results indicate the need to strengthen national monitoring programs, enforce pesticide regulations more strictly, and promote the wider adoption of integrated pest management strategies to reduce dietary pesticide exposure and protect public health in Albania. Full article

Objectives: To validate claims-based algorithms for identifying thyroid eye disease (TED) cases and assess whether machine learning can improve case identification in a large, linked real-world dataset. Methods: Using a large, linked database from Komodo Health^® and Academy IRIS^® Registry, we evaluated six rule-based algorithms incorporating Graves’ disease (GD), eye symptoms and signs. The IRIS Registry’s curated data, based on confirmed TED diagnoses from medical notes, served as the reference standard. Additionally, we developed supervised machine learning models using demographic, diagnostic, procedural, and medication data. Feature selection was performed using recursive feature elimination to rank predictive codes and construct a simplified, interpretable model. Cross-validation was used to assess model performance and compare performance with the rule-based algorithms. Results: The rule-based algorithms demonstrated a trade-off between sensitivity and specificity, with some achieving high specificity but limited sensitivity. Algorithm 1 had the highest sensitivity (48.7%) but lower specificity (59.9%) and PPV (75.8%). Algorithms 2–5 demonstrated higher specificity (87.2–93.5%) but lower sensitivity (17.8–27.0%). Algorithm 6 improved sensitivity (33.4%) compared to Algorithms 2–5 while maintaining high specificity (86.8%) and a strong PPV (86.7%). Machine learning models demonstrated similar trade-offs. One model achieved improved specificity (77.2%) with sensitivity of 49.3%, outperforming Algorithm 1 in specificity while matching its sensitivity. Another model maximized specificity (91.7%) and PPV (89.8%) at a reduced sensitivity of 28.5%. These results highlight the flexibility of machine learning models in adjusting performance to address different research objectives. Conclusions: This study evaluated existing rule-based algorithms for identifying TED cases in claims data, revealing trade-offs between sensitivity and specificity. Machine learning models provide additional flexibility, allowing performance to be tailored to specific research use cases. While no single method consistently outperformed others across all metrics, both rule-based and machine learning approaches demonstrated value in improving TED case identification using real-world data sources. Full article

In red tea production, fermentation is critical for flavor. However, manual determination of its stages is inaccurate and inefficient, often spoiling flavor and lowering product value. To solve this, this study combines CNN and knowledge distillation to build a lightweight classification model, AT-ShuffleNet, for accurate, efficient stage identification in real processing. It collected images of Fuding and Tieguanyin tea at different fermentation stages. ResNet (teacher model) and ShuffleNet v2-0.5 (student model) were used for distillation. Focal and Poly Losses optimized both models to tap distillation potential. STD, MGD, SPKD, ATD, and KD methods were tested at various ratios to find the optimal strategy, forming AT-ShuffleNet. The lightweight model performed well: P (89.11%), R (90.16%), Kappa (89.29%), ACC (91.2%), F1 (89.53%). It addresses manual limitations, enabling accurate classification and reducing deployment issues in unstructured environments. For industrial validation, it was deployed on edge devices and integrated into a self-developed WeChat mini-program. Full article

Aspartate–glutamate carrier 1 (AGC1) deficiency is a rare neurometabolic disorder caused by biallelic pathogenic variants in SLC25A12. Clinically, it is characterized by early-onset developmental and epileptic encephalopathy, often associated with hypomyelination and reduced brain N-acetylaspartate. AGC1 loss reduces malate–aspartate shuttle flux, limiting cytosolic NAD⁺ regeneration and impairing neuronal redox coupling, ATP supply, and aspartate-dependent biosynthesis during brain development. We integrate human genetics with mechanistic evidence from mammalian, Drosophila melanogaster, and Saccharomyces cerevisiae models to describe conserved transport principles and species-specific regulation underlying selective central nervous system vulnerability. We review the management of AGC1 deficiency, focusing on ketogenic therapy. Published reports show reproducible seizure reduction and, in some patients, improved myelination and N-acetylaspartate. However, these responses are heterogeneous and appear to depend on the timing, duration, and stability of ketosis. Preclinical evidence suggests that β-hydroxybutyrate may contribute to metabolic support in AGC1 deficiency. Prospective studies should test disease modification using standardized endpoints plus MRI/¹H-MRS and ketosis measures. Full article

Layered saline soils containing weakly permeable interlayers exhibit restricted infiltration, surface salt accumulation, and limited deep salt discharge. This study investigated how weakly permeable interlayer thickness, hydraulic-parameter scenario, hole diameter, hole spacing, and irrigation salinity affect soil water redistribution, salt leaching, and profile desalination under vertical-hole treatment. Pilot-scale soil-box experiments were used for model calibration and validation, and HYDRUS-3D simulations were then used for controlled-condition scenario analysis and preliminary layout screening. The weakly permeable interlayer reduced hydraulic connectivity, increased water retention above the interface, and intensified surface salt enrichment, with stronger effects at greater thickness. Vertical holes improved hydraulic continuity and promoted downward percolation and salt leaching, but their effectiveness depended on layout. At a spacing of 30 cm, increasing hole diameter from 5 to 10 cm increased the mean desalination rate from 7.07% to 13.44% in the surface layer and from 4.06% to 18.61% in the deep layer. Irrigation salinity had little effect on water content but increased soil salt accumulation. Under the assumed conceptual cost–performance framework, the 10 cm diameter and 30 cm spacing combination showed the highest composite performance within the tested parameter range. These findings provide a mechanistic basis and preliminary layout-screening reference for vertical-hole treatment in layered saline soils with weakly permeable interlayers. Full article

Background/Objectives: Blood transfusion is a crucial component in the treatment of individuals with sickle cell disease [SCD]; nonetheless, multiple transfusions can lead to considerable complications, notably alloimmunization. However, the prevalence of alloimmunization and its predictors remain incompletely explained. This review aimed to determine its global prevalence and identify associated risk factors. Method: Our protocol was registered in PROSPERO [ID: CRD420251167042] in accordance with the PRISMA 2020 criteria. A thorough literature search was conducted across PubMed, Embase, Web of Science, Scopus, and the Cochrane Library to identify studies reporting the prevalence of alloimmunization in adults with confirmed sickle cell disease who have received blood transfusions. This search included all publications up to 16 April 2026. Two reviewers independently screened and extracted data, and the Newcastle–Ottawa Scale was used to evaluate the study’s quality. After the Freeman–Tukey transformation, a random-effects model was used to estimate the pooled prevalence. We examined disparities among groups and geographies, study designs, and matching procedures to determine their differences. We additionally employed meta-regression to identify potential predictors. Results: Nine studies [n = 1711; 1978–2026] met the inclusion criteria. The overall rate of alloimmunization was 28.9% [95% CI 22.4–35.4; I² = 88.5%]. The most prevalent antibodies were those of the Rh and Kell systems, with anti-E antibodies being the most frequent, followed by anti-C and anti-K antibodies. A higher number of transfusions and the HbSβ⁰ genotype were both persistent risk factors, while older age at first transfusion appeared protective. Extended antigen matching dramatically reduced prevalence, though approximately 9% of individuals remained affected. Conclusions: Alloimmunization continues to challenge transfusion management in adults with SCD. Broader implementation of extended antigen matching and genotype-informed transfusion strategies may help mitigate this risk. Full article

Although Graph Neural Networks (GNNs) are widely regarded as an ideal tool for capturing spatial dependencies in river basins, their effectiveness in hydrological forecasting is severely challenged by a topology paradox: under a purely data-driven paradigm, GNNs fail to spontaneously learn physical laws, generating predictions that lack physical interpretability and frequently violate mass conservation. To address this fundamental problem, this paper proposes a physics-informed graph learning framework integrated with an explicit, differentiable water balance constraint (WB-GNN). By reconstructing the continuity equation into a differentiable loss function, we directly embed physical conservation as a strong inductive bias into the neural network’s training objective. We comprehensively evaluated the model on two large-sample datasets (LamaH-CE and CAMELS) against state-of-the-art baselines, including EA-LSTM and unconstrained Pure-GNN. Quantitative results demonstrate that the proposed physical constraint successfully awakens the potential of river network topology. On the LamaH-CE dataset, WB-GNN achieved a Nash-Sutcliffe Efficiency (NSE) of 0.86 and a Root Mean Square Error (RMSE) of 9.2 ${\mathrm{m}}^3/\mathrm{s}$, outperforming both the domain-specific EA-LSTM (NSE: 0.83) and the unconstrained Pure-GNN (NSE: 0.74). Crucially, the introduction of the differentiable constraint reduced the Physical Inconsistency Ratio (PIR) by an order of magnitude-from 39.8% in the unconstrained model to just 4.3%. Similar robust improvements were validated across the highly heterogeneous CAMELS dataset. These quantifiable results confirm that the proposed method not only achieves superior forecasting accuracy but also fundamentally guarantees physical trustworthiness, making it highly robust for critical decision-making in extreme flood events. Full article

The rapid proliferation of Internet of Things (IoT) devices in cloud-based e-learning platforms has posed significant security risks, particularly in protecting learner information, authentication of devices, and safe communication in the highly heterogeneous learning settings. Current cryptographic solutions are largely based on classical public-key infrastructure (PKI) protocols such as RSA and ECC, which will become vulnerable with the advent of large-scale quantum computers capable of executing Shor’s algorithm. In addition, traditional perimeter-based security models are inadequate for handling the dynamics, scattered, and resource-limited characteristics of IoT-enabled educational systems. As a solution to these problems, this paper introduces ZeroTrustEdu, a scalable zero-trust cryptographic solution that combines lightweight post-quantum key management with adaptive trust scoring of cloud-connected IoT e-learning infrastructure. The proposed framework makes three fundamental contributions namely: (1) a hierarchical zero-trust security model with no implicit trust, operating across device, edge, and cloud layers; (2) a lightweight key distribution protocol based on the Module-Lattice Key Encapsulation Mechanism (ML-KEM) compliant with NIST FIPS 203 standards and (3) an adaptive behavioral trust scoring engine that dynamically adjusts device and user trust levels based on real-time interaction analytics. The architecture is evaluated using extensive NS-3 network simulations with up to 100,000 concurrent IoT nodes with formal security analysis under Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) threat models. Comparative evaluation against RSA-2048, ECC-P256, and AES-256 baselines demonstrates that, ZeroTrustEdu delivers a 62% ± 3% (95% CI, 10 independent runs) reduction in ML-KEM encapsulation latency (12.8 ms for key encapsulation/decapsulation, contributing to a complete device authentication latency of 47.3 ms including ML-DSA signature operations), 45% reduced communication overheads, and 38% reduction in energy consumption on ARM Cortex-M4 constrained devices compared to RSA-2048 and achieves provable post-quantum security reducible to the hardness of the Module Learning With Errors (MLWE) problem. These findings demonstrate that the proposed architecture provides a viable, scalable, and quantum-resilient security solution for next-generation IoT-enabled e-learning environments. The cryptographic security of ZeroTrustEdu is guaranteed at the primitive level through NIST-standardized ML-KEM (FIPS 203) and ML-DSA (FIPS 204), with IND-CCA2 and EUF-CMA security formally proven in the respective standards; full protocol-level formal verification using automated theorem provers (ProVerif, Tamarin) is identified as valuable future work to rule out protocol-composition vulnerabilities beyond primitive-level guarantees. Full article