Search Results (3,656)

The rapid emergence of New Psychoactive Substances (NPS), particularly pyrrolidinophenone derivatives, poses a significant challenge for public health and forensic toxicology. While their neuropharmacological profiles as dopamine transporter inhibitors are well-documented, their cardiac toxicity remains poorly understood. This study employs a multiparametric New Approach Methodology (NAM) using zebrafish embryos to integrate neurobehavioral and cardiotoxic endpoints for comparative hazard prioritization. We evaluated nine pyrrolidine-containing cathinones, including α-PVP, MDPV, α-PiHP, MDPiHP, α-D2PV, 3-Cl-, 4-Cl-, and 3,4-Cl-α-PVP, and 4-F-3-Me-α-PVP, on locomotor activity and cardiac rhythmicity using high-speed video microscopy and dynamic pixel analysis. Across the series, compounds induced concentration-dependent negative chronotropy and, in most cases, locomotor suppression. Crucially, we identified a functional dissociation between atrial rate control and atrioventricular (AV) conduction. The 3,4-dichloro substitution (3,4-Cl-α-PVP) was the most potent inducer of negative chronotropy (EC₅₀ = 52.6 μM), whereas 4-Cl-α-PVP exhibited a distinct pro-arrhythmic liability, increasing the incidence of 2:1 AV block. Time-course locomotor profiling indicated that α-PVP and chlorinated analogs were among the most potent behavioral modifiers. Using a Functional Safety Index (AV block EC₅₀/locomotor EC₅₀-like), we show that most compounds exhibit wide separations between neurobehavioral inhibition and severe conduction impairment, while specific substitutions, particularly para-chlorination, are associated with comparatively reduced functional separation between these endpoints within the assay. Overall, these data demonstrate that subtle structural changes within the pyrrolidinophenone scaffold can shape distinct arrhythmic phenotypes and functional safety profiles, supporting zebrafish-based integrated screening as a rapid platform for prioritizing emerging synthetic cathinones with comparatively higher cardiac liability within this experimental framework. Full article

Soft-ground-story configurations in high-rise buildings present a critical vulnerability during seismic events, often leading to disproportionate structural damage and collapse. This study focuses on the systematic identification of soft-ground-story high-rise structures in Bucharest, a city located in a high seismic hazard zone influenced by Vrancea intermediate-depth earthquakes. The research employs a multi-step methodology combining field surveys, structural documentation, and analysis of architectural layouts from various sources to detect soft-ground-story irregularities across the urban building stock in Bucharest. The findings reveal that such configurations remain prevalent in mixed-use structures along major boulevards, where open ground floors were historically favoured for commercial purposes. The results provide a database of soft-ground-story high-rise buildings in Bucharest, highlighting their prevalence in distinct urban districts and their potential impact on seismic risk. Quantitative screening indicators, vertical element area ratio and mean axial stress in ground-story columns, are proposed for rapid vulnerability assessment. Dynamic measurements confirm a 33–38% increase in fundamental eigenperiods after the 1977 earthquake, indicating moderate-to-extensive damage states. These findings underscore the urgent need for targeted retrofitting strategies and inform seismic risk mitigation policies. The study provides a foundation for future integration of advanced diagnostic tools, such as image-based deep learning and vibration monitoring, into citywide seismic resilience planning. Full article

Background/Objectives: Acute promyelocytic leukemia (APL) is a high-risk subtype of acute myeloid leukemia and requires rapid diagnosis to avoid early mortality. Current clinical diagnostic and genetic tests are time-consuming, expensive, and complex. Notably, all these tests depend on bone marrow aspiration and are intensely invasive, resulting in poor patient compliance. This study aimed to develop a rapid, explainable, and accurate auxiliary tool for cell-level detection of abnormal promyelocytes in peripheral blood smears, which can serve as a key clue for suspecting APL. Methods: We developed a multi-stage deep learning (DL) model that automatically read images of peripheral blood smears (PBSs), accurately segmented cells, and identified abnormal promyelocytes using only image data. We retrospectively reviewed a total of 114 bone marrow smears (42 APL patients and 72 non-APL patients) and 158 PBSs (30 APL patients and 128 non-APL patients) at the Fifth Affiliated Hospital of Harbin Medical University and collected 223,123 cell images for training. Then, the efficacy of EfficientDet in APL screening was evaluated with an additional 150 PBSs (50 from APL patients and 100 from non-APL patients) and finally compared with manual microscopy. Results: EfficientDet exhibited superior overall screening performance compared with pathologists in the identification of abnormal promyelocytes. Conclusions: Our findings suggest that the DL approach we describe herein is promising as a practical tool for abnormal promyelocyte detection and early APL screening, raising attention to suspected cases of APL for expert evaluation and further reducing diagnostic delays. Full article

Background/Objectives: Malnutrition is common among women undergoing gynaecologic oncology (GO) surgery and is associated with increased morbidity, prolonged hospitalisation, and reduced survival. Nevertheless, the optimal nutritional screening tools remain uncertain. Methods: We conducted a narrative review of commonly used nutritional screening and assessment tools in surgical GO patients. To highlight practical challenges in accurately identifying at risk individuals, we incorporated findings from our clinical audit. Results: There was a considerable variation between tools. While many tools were associated with adverse outcomes, their clinical value in this population was unclear. The presence of ascites and rapid deterioration in oral intake may contribute to under-recognition of at-risk patients, as illustrated by our audit findings. Emerging strategies including determining body composition from routine pre-operative Computed Tomography (CT) scans, which have shown statistical associations with survival and toxicity in observational studies, but their clinical utility is not yet established. Conclusions: Although several screening tools were statistically associated with adverse outcomes, robust data on their clinical utility are lacking. Current tools may inadequately factor for the specific considerations when screening this group. Consequently, nutritionally vulnerable surgical GO patients requiring nutritional intervention may be missed. As no gold standard currently exists for this population, bespoke, objective approaches and prospective studies are urgently needed to address disease-specific nutritional considerations. Full article

Existing appearance-based and video-based gaze estimation methods mainly rely on frame-wise prediction or local-window temporal fusion, which limits their ability to model long-range dependencies and to explicitly suppress output-level jitter. This leaves a gap in unified temporal gaze estimation frameworks that jointly address contextual feature aggregation and prediction-level stabilization. To address this limitation, we propose a unified state-space temporal gaze estimation framework to improve both angular accuracy and temporal consistency. Specifically, consecutive eye image sequences are mapped into a shared latent state space, where spatial appearance cues and inter-frame dynamics are jointly modeled. A feature-level temporal aggregation module is further designed to adaptively reweight historical observations for the current estimate, and a prediction-level temporal correction module is introduced to suppress short-term fluctuations while preserving rapid gaze shifts. On the TEyeD dataset after quality screening, the proposed method achieves a 3D gaze MAE of $0.533°$, compared with $0.96°$ for Model-aware and $3.18°$–$3.47°$ for the ResNet baselines reported in the original TEyeD paper, while maintaining manageable deployment overhead. These results indicate that the proposed framework provides a favorable balance between estimation accuracy, temporal stability, and practical efficiency. Full article

Soil salinity remains a major constraint to rice productivity, particularly during early developmental stages when plants are highly sensitive to osmotic and ionic stress. In this study, we evaluated 201 genetically diverse rice genotypes from the 3K Rice Diversity Panel to investigate stage-specific mechanisms of salinity tolerance and develop machine learning-based predictive models for rapid phenotypic screening. Morphological and physiological traits were measured under control and saline conditions at germination and early seedling stages to derive Stress Tolerance Indices (STIs). The average membership function value (AMFV), calculated from multi-trait STI profiles, effectively captured variation in salinity responses and enabled classification of genotypes into five tolerance categories. Genome-wide association analysis using high-density SNP markers identified 36 significant marker–trait associations, including potentially novel SNPs on chromosomes 1 and 12. Several loci co-localized with candidate genes (LTR1, LGF1, OsCPS4, OsNCX7, and OsNHX4), while functional SNPs within genes (OsDRP2C, RLCK168, and OsMed37_2) and non-synonymous variants (qSVII11.1 and qSNaK3.1) further supported their candidacy in salinity tolerance. Mining favourable SNPs of causal genes identified superior multilocus combinations consistent with STI-based phenotypic patterns, with genotype 91-382 emerging as the strongest performer, exhibiting enhanced Na⁺ exclusion, K⁺ retention, and biomass resilience across developmental stages. To address multicollinearity among STI traits, we applied cross-validated LASSO (germination) and Elastic Net (early seedling) models, achieving high predictive accuracy and revealing a developmental shift from biomass-driven tolerance at germination to ion-regulatory processes at the seedling stage. Independent validation showed strong agreement between predicted and observed AMFVs. By integrating physiological indices, GWAS-derived SNP signals, and regularized machine learning approaches, this study provides a robust framework for identifying elite donors and accelerating breeding for salt-tolerant rice. Full article

Dengue virus (DENV), an important mosquito-borne orthoflavivirus, represents a growing global threat due to its geographic expansion and recent outbreaks worldwide. In resource-limited endemic settings, the development of affordable diagnostic assays is needed. In this study, we developed and validated a colorimetric reverse transcription loop-mediated isothermal amplification assay (RT-LAMP) for the detection of DENV type 3 (DENV-3) using 95 previously diagnosed clinical samples from Southeastern Mexico. Primers targeting the 3′ untranslated region (3′ UTR) of DENV-3 were designed, and assay conditions were standardized. The colorimetric RT-LAMP DENV-3 system achieved a preliminary limit of detection of 1 × 10³ copies per reaction, with 90.7% sensitivity and 100% specificity. The colorimetric format enabled visual readout without specialized equipment, supporting its potential applicability in point-of-care and resource-limited settings. The developed colorimetric RT-LAMP detection for DENV-3 is intended as a rapid screening/triage tool that can trigger confirmatory testing or public-health actions. Full article

Many advances in enhanced oil recovery (EOR) take advantage of the unique properties of nanomaterials to improve characterization of formation properties, achieve conformance control during flood operations, and extend the controlled release time of polymers. Magnetite nanoparticles (nMag) have been employed in these processes due to their low cost, low toxicity, and ability to be engineered to meet desired needs, especially with the application of a magnetic field. Similarly, silica dioxide (SiO₂) and aluminum oxide (Al₂O₃) nanoparticles have been evaluated for the delivery of scale and asphaltene inhibitors. However, the injection of nanoparticles into porous media comes with the risk of formation damage due to particle deposition, which can lead to increased injection pressures and reductions in permeability. The goal of this study was to develop a method to evaluate and assess nanoparticle formulations for their potential to cause formation damage. A screening apparatus was constructed to hold small sandstone discs (~2 mm) or cores (~2.5 cm) for rapid testing with minimal material use and the capability to be used with either aqueous brine solutions or non-polar solvents as the mobile phase. Image analysis of the disc and pressure measurements demonstrated increasing deposition of nMag and face-caking when the salinity was increased from 500 mg/L NaCl (8.56 mM) to API brine (2.0 M). Similarly, when the injected concentration of silica nanoparticles in 500 mg/L NaCl was increased from 1 to 10 wt%, the back pressure increased by 55 psi, and face-caking was observed. The screening test results were consistent with traditional core-flood tests and was able to be modified to accommodate organic liquid mobile phases. The screening test results closely matched nanoparticle transport and retention measured in sandstone cores, confirming the ability of the system to rapidly screen nanoparticle formulations for potential formation damage. Full article

Background/Objectives: Vestibular disorders can have functional consequences for children, including balance and gross motor delays, academic difficulties and behavioral manifestations; however, they are frequently undiagnosed in children. The purposes of this study were to evaluate the feasibility and clinical utility of performing a non-instrumented dynamic visual acuity (DVA) test as a primary screening tool for children and to examine typical performance on this non-instrumented DVA test in a large sample of children ages 2 through 13 years. Methods: A clinical DVA test was administered to a convenience sample of 208 children aged 2–13 years. Static visual acuity was assessed using a standard Snellen or LEA eye chart, depending on the child’s ability to read letters. Dynamic visual acuity was then measured while the examiner manually rotated the child’s head at 2 Hz (240 bpm). DVA was calculated as the number of lines of visual acuity lost with head movement. Results: All children aged 4 years and older and 67% of 3-year-olds successfully completed DVA testing. Most 2-year-olds and 33% of 3-year-olds were unable to complete DVA testing. Although the number of visual acuity lines lost with rotational head oscillations at 2 Hz varied between age groups, Kruskal–Wallis test indicated no significant difference in DVA scores between age groups (K = 12.721, DF = 9, P = 0.176). Ninety percent of children who were able to perform DVA testing lost two or fewer lines of visual acuity with head rotations consistent with adult norms. Conclusions: This method of DVA testing is an easily accessible and promising clinically feasible screening tool for identifying children with vestibular dysfunction. The authors recommend widespread vestibular screening of children to facilitate rapid referral for diagnosis and treatment of children with vestibular dysfunction. Full article

Background: Sub-Saharan Africa is undergoing a rapid epidemiological transition marked by a growing burden of non-communicable diseases, including breast, cervical, ovarian, and uterine cancers, which constitute major causes of morbidity and mortality among women in the region; however, comprehensive assessments of long-term trends and regional heterogeneity remain limited. This study examines the burden and temporal trends of breast, cervical, ovarian, and uterine cancers across sub-Saharan Africa from 1990 to 2023. Methods: A retrospective ecological analysis was conducted using data from the latest Global Burden of Disease 2023 study. Age-standardised incidence rates, mortality rates, and disability-adjusted life year rates were estimated for breast, cervical, ovarian, and uterine cancers across 48 sub-Saharan African countries and four sub-regions. Temporal trends were assessed from 1990 to 2023, with percentage changes calculated to characterise epidemiological transitions. Geographic variation and age-specific patterns were examined to identify high-burden settings and priority populations. Results: Between 1990 and 2023, the burden of all four cancers increased substantially across sub-Saharan Africa, with significant regional and country-level heterogeneity. Breast cancer exhibited the largest absolute burden, with incidence increasing by over 120 percent and mortality by more than 80 percent, particularly in Central and Western Africa. Cervical cancer remained the leading cause of cancer-related mortality among women in Eastern and Southern Africa, despite evidence of stabilisation or decline in selected countries. Ovarian and uterine cancers demonstrated sustained upward trends, especially in Central Africa, with high mortality-to-incidence ratios indicating late diagnosis and limited treatment access. Across all cancer types, Central and Eastern sub-Saharan Africa consistently experienced the highest disability-adjusted life year burdens. Conclusions: The burden of the selected cancers in sub-Saharan Africa has increased markedly over the past three decades, with persistent regional inequities reflecting gaps in prevention, early detection, and treatment capacity. Strengthening cancer surveillance systems, expanding equitable access to screening and vaccination programmes, and improving diagnostic and treatment infrastructure are critical to reversing current trends. These findings provide region-specific evidence to guide cancer control priorities and resource allocation across sub-Saharan Africa. Full article

Background/Objectives: Group A Streptococcus (GAS) pharyngitis is a frequent cause of morbidity in pediatric populations, which requires timely identification to prevent complications such as acute rheumatic fever. Rapid antigen detection tests (RADTs) are practical alternatives to throat culture. This study evaluates the diagnostic performance of the RapidFor™ Strep A test. Methods: This prospective clinical study enrolled 389 pediatric patients aged < 18 years with symptoms suggestive of streptococcal pharyngitis. Two throat swabs were collected from each patient: one for rapid antigen testing with RapidFor™ Strep A and one for culture. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Results: Throat culture was positive in 95 of 389 patients (24.4%). The RapidFor™ Strep A test demonstrated a sensitivity of 98.95% (95% confidence interval [CI]: 94.28–99.81%) and a specificity of 96.26% (95% CI: 93.43–97.90%). The PPV was 89.52%, and the NPV was 99.65%. Agreement with culture was excellent (κ = 0.919); in particular, false-positive results accounted for 2.8% and false-negative results accounted for 1.05%. Fever was the strongest clinical indicator associated with positive results. Conclusions: The RapidFor™ Strep A test showed very high diagnostic accuracy compared with throat culture, including an excellent NPV (99.6%), which supports its reliability for ruling out GAS pharyngitis in pediatric settings. The test is an effective screening tool that facilitates timely antibiotic therapy. Full article

The rapid expansion of global commerce has escalated the complexity of money laundering schemes, making the detection of illicit transfers an urgent but highly challenging research problem. In operational anti-money laundering (AML) systems, the extreme rarity of illicit transactions often overwhelms compliance teams with false positives, leading to severe “alert fatigue.” To address this critical bottleneck, this paper introduces an enhanced, probability-driven risk-prioritization framework utilizing an XGBoost classifier integrated with Bayesian Optimization (BO-XGBoost). By optimizing directly for the Area Under the Precision–Recall Curve (PR-AUC), the model is specifically tailored to rank high-risk anomalies under severe class imbalance. We validate the proposed approach on a rigorously resampled transaction dataset simulating a realistic 5% laundering rate. The BO-XGBoost model demonstrates exceptional prioritization capability, achieving an ROC-AUC of 0.9686 and a PR-AUC of 0.7253. Most notably, it attains a near-perfect Precision@1%, meaning the top 1% of flagged transactions are 100% true illicit activities, entirely eliminating false positives at the highest priority tier. Comparative and SHAP-based interpretability analyses confirm that BO-XGBoost easily outperforms sequence-heavy deep learning baselines. Crucially, it matches computationally expensive stacking ensembles in peak predictive precision while significantly surpassing them in operational efficiency, indicating its immense promise for resource-optimized, real-world compliance screening. Full article

This paper presents a fuzzy satisfaction-based intelligent framework for early-stage multiobjective sizing of a buck DC–DC converter under uncertain operating conditions. Lightweight closed-form estimators are used to evaluate inductor current ripple, output voltage ripple, and efficiency, including an explicit decomposition of ripple into capacitive and ESR-induced components to distinguish capacitance-dominated and ESR-dominated regimes. Engineering targets for ripple, efficiency, and passive size/cost pressure are mapped to reproducible piecewise membership functions and aggregated into a bounded overall satisfaction score using a weighted geometric operator; alternative non-compensatory and OWA-type aggregators are considered for sensitivity analysis. The resulting nonconvex design problem is solved via a compact two-stage derivative-free strategy that combines global screening with an interpretable Takagi–Sugeno (TSK) rule-based refinement layer, which generates bounded, physics-consistent updates of the design variables and supports rapid feasibility restoration followed by preference-driven tuning. Uncertainty in operating conditions and parameter drift is addressed through scenario evaluation and worst-case or average-case aggregation of satisfaction, linking the fuzzy decision objective to robust scenario design. Numerical studies for a 24 ± 4 V to 12 V converter illustrate regime-dependent adaptation: in low-ESR conditions, ripple improvement is driven mainly by capacitance/frequency adjustments, while in high-ESR conditions, the rule base shifts corrections toward inductor and frequency choices that reduce ESR-dominated ripple. Full article

Dopaminergic neurodegeneration is a hallmark of Parkinson’s disease (PD), and environmental contaminants have been implicated in disrupting dopaminergic pathways. However, practical in vivo workflows for rapid, standardized, and accessible assessment of dopaminergic neurotoxicity remain limited. In this study, we built on our laboratory’s established high-throughput framework and implemented a high-content imaging workflow to quantify DA neurodegeneration in Caenorhabditis elegans following exposure to representative per- and polyfluoroalkyl substances (PFAS). We evaluated the neurotoxic effects of perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexanesulfonic acid (PFHxS), perfluorohexanoic acid (PFHxA), and three PFAS mixtures with environmentally relevant component ratios. Functional relevance was assessed using dopamine-dependent behavioral endpoints, including basal slowing response (BSR) and area-restricted search (ARS). PFOS exhibited the greatest potency, followed by PFHxS, PFHxA, and PFOA, based on morphological degeneration and benchmark concentration modeling. Structural neuronal damage was significantly associated with behavioral impairment. Under mixture conditions, neurotoxicity was more strongly associated with PFOS molar fraction than with total PFAS concentration (ΣPFAS), suggesting a composition-dependent toxicity profile. Collectively, these findings establish a scalable in vivo framework for assessing PFAS-induced dopaminergic neurotoxicity and support the potential use of this platform for screening environmental pollutants with dopaminergic neurotoxic potential. Full article

Rational design of high-performance viscosifying polymers is critical for enhancing supercritical CO₂ flooding efficiency in enhanced oil recovery (EOR). Traditional experimental and simulation approaches are limited in exploring the vast design space of polymer architecture, flexibility, and intermolecular interactions. This work presents an integrated machine learning (ML) and mesoscopic simulation framework using Dissipative Particle Dynamics (DPD) to accelerate the development of tailored polymeric thickeners. We systematically investigate synergistic effects of linear and branched polymer blends on solvent viscosity under Poiseuille flow, representative of flow in micro-fractures and pore throats. Key molecular descriptors are varied to generate a comprehensive rheological database. This data trains a deep neural network (DNN) surrogate model linking molecular parameters to macroscopic viscosity. The DNN is coupled with gradient ascent optimization for inverse design, enabling rapid virtual screening of thousands of formulations. A focused case study demonstrates that the star-like architectures with associative cores and semi-flexible backbones outperform linear analogs for supercritical CO₂ viscosity enhancement. The optimal candidate—a four-arm star polymer with linear side chains—was validated by DPD simulation. This multiscale “simulation-to-surrogate” methodology bridges molecular design with continuum-scale flow behavior, offering a transformative tool for formulating cost-effective, efficient, and sustainable next-generation EOR chemicals. Full article