Search Results (2,486)

Background: China’s influenza vaccine (InfV) has undergone multiple iterations and numerous technological breakthroughs, providing tremendous impetus and solid support for the development of China’s health sector. As the number of vaccinated individuals continues to rise, the importance of ongoing surveillance and evaluation of vaccine safety has become increasingly prominent, forming part of efforts to maintain public trust in the national immunization program and ensure its sustainability. Methods: From 2016 to 2025, data on suspected adverse events following immunization (AEFIs) related to InfV administration were extracted from the Chinese National Immunization Information System (CNIIS). Data on InfV vaccination doses were obtained from the Anhui Provincial Immunization Information Management System. A descriptive statistical method was used to analyze the distribution characteristics of AEFIs, and the chi-square test was applied to evaluate differences in reporting rates. Results: Between 2016 and 2025, a total of 4026 AEFI reports related to InfV were monitored through the CNIIS. The overall reporting rate was 34.40 per 100,000 doses. Specifically, common adverse reactions and rare adverse reactions accounted for 95.88% (3860 cases) and 3.38% (136 cases), with reporting rates of 32.98 per 100,000 doses and 1.16 per 100,000 doses, respectively. Among common adverse reactions, the reporting rates of fever (axillary temperature ≥ 38.6 °C), local redness and swelling at the injection site (diameter > 5.0 cm), and local induration (diameter > 5.0 cm) were 9.62 per 100,000 doses, 1.96 per 100,000 doses, and 1.20 per 100,000 doses, respectively. Among rare adverse reactions, the reporting rates of allergic rash, angioedema, anaphylactic shock, febrile convulsions, anaphylactoid purpura, thrombocytopenic purpura, epilepsy, Guillain–Barré syndrome, and aseptic abscess were 0.98, 0.05, 0.03, 0.03, 0.02, 0.02, 0.01, 0.01, and 0.01 per 100,000 doses, respectively. No cases were reported for subunit inactivated influenza vaccine (IIV, Subunit). Statistically significant differences were observed in the reporting rates of allergic rash across different types of InfV (χ² = 36.83, p < 0.05), with trivalent inactivated influenza vaccine (IIV3, Split) and trivalent live attenuated influenza virus vaccine (LAIV3) showing the highest reporting rates. Most adverse events following vaccination occurred within 24 h after inoculation. Conclusions: From 2016 to 2025, the overall reporting rate of AEFIs after InfV administration in Anhui Province was within an acceptable range. Common adverse reactions were common, while rare adverse reactions were few, mainly consisting of allergic reactions. These results indicate that InfV has a favorable safety profile, and continuous strengthening of AEFI surveillance for InfV and improvement of surveillance quality are warranted. Full article

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

Background: Anaplasmosis is an emerging tick-borne infection that typically presents as a non-specific febrile illness, with variable degrees of cytopenias and liver tests abnormalities. Severe complications remain atypical and uncommon. Case Report: We report a case of acute pulmonary embolism (PE) occurring during confirmed anaplasmosis in a 73-year-old male with no traditional thromboembolic risk factors. The patient presented with fever, constitutional symptoms, thrombocytopenia, leukopenia, and abnormal liver tests, raising suspicion for a tick-borne illness. Despite early clinical improvement on doxycycline, persistent tachycardia triggered further evaluation and uncovered an acute PE. Comprehensive workup at admission and repeated 14 months later excluded inherited and acquired thrombophilias, malignancies, autoimmune diseases, and alternative infectious etiologies. The patient was treated with doxycycline 100 mg orally twice daily for 10 days and anticoagulation with unfractionated heparin followed by 6 months of apixaban for a first episode of provoked PE. He attained complete clinical recovery without recurrence of thrombosis at the two-year follow-up. Discussion: Infectious diseases are increasingly recognized as contributors to thrombosis through inflammation-mediated hypercoagulability and endothelial dysfunction. Pulmonary involvement in anaplasmosis typically manifests as pneumonitis, pneumonia or acute respiratory distress syndrome, but thrombotic complications such as PE are exceedingly rare. This case highlights a rare but clinically significant vascular complication of anaplasmosis and underscores the importance of considering thromboembolic events in patients with persistent or unexplained tachycardia. Conclusions: As the incidence of anaplasmosis continues to rise, greater awareness of its potential cardiovascular manifestations is essential. Early recognition and prompt treatment with doxycycline remain critical, while further studies are needed to better define the thrombotic risk associated with this infection. Full article

Porcine reproductive and respiratory syndrome (PRRS), caused by PRRS virus (PRRSV), poses a serious threat to the global swine industry. Although modified live virus (MLV) vaccines have been widely used in the field for PRRS prevention for decades, the safety and efficacy of these vaccines have long been controversial. Here, we report a rare recombination pattern in China: the emergence of a novel NADC30-like PRRSV strain recombined from two MLV-like strains. Genome comparative analysis reveals that the SCMS2025 isolate has a non-continuous 136-amino acid deletion in the NSP2 protein and shares the highest nucleotide identity of 87.6% with lineage 5 (L5) strains. Phylogenetic analysis showed that SCMS2025 was classified into L1 (NADC30-like) strains based on ORF5 genotyping, whereas it belonged to a single branch between L1 and L5 strains based on the complete genomic sequences. Strikingly, genomic recombination analysis revealed that the newly emerged PRRSV isolate likely resulted from complex recombination events between NADC30-like and two MLV-like strains (RespPRRS MLV and TJbd14-1 MLV-like strains). Furthermore, SCMS2025 infection caused transient overt clinical signs followed by rapid recovery, indicating that the novel PRRSV isolate is a low pathogenic strain. Notably, all SCMS2025-inoculated piglets remained seronegative for PRRSV-specific antibodies throughout the entire 14-day observation period, suggesting a delayed onset of the host humoral immune response. Our study provides evidence for the ongoing evolution of PRRSV through inter lineage recombination and highlights the urgent need for safe and effective vaccines. Full article

Equipment fault prediction in maglev train systems poses substantial challenges: fault events are inherently rare, class distributions are severely imbalanced, and individual equipment units are subject to complex spatial and functional couplings that single-device statistical approaches fundamentally cannot capture. To address these challenges, this study proposes a Knowledge Graph-driven Graph Neural Network (KG-GNN) framework. A fault knowledge graph encompassing equipment, fault, temporal, and environmental entities is constructed to unify multi-source maintenance data. Graph connectivity is established via three spatial relation types (co-location, co-zone, and co-level), with edge weights derived from Laplacian-smoothed Lift scores quantifying fault co-occurrence strength. A two-layer GATv2Conv-based graph attention network is designed: the first layer employs four-head attention with explicit edge-weight integration to capture heterogeneous neighborhood influences, while the second layer produces compact node embeddings via single-head attention. A Top-20 sparsification strategy suppresses weak-association noise, and training under severe class imbalance is stabilized through Focal Loss and F2-Score-guided early stopping. On the test set, the proposed method achieves an F2-Score of 0.5703, Recall of 0.6825, and AUC-ROC of 0.9329 (single-run evaluation); multi-seed evaluation (5 seeds) yields F2 = 0.5645 ± 0.0035, Recall = 0.6789 ± 0.0095, and AUC-ROC = 0.9298 ± 0.0026, outperforming the MLP baseline by 18.3% in F2-Score and substantially exceeding GCN (F2 = 0.1476 ± 0.0176) and GATConv (F2 = 0.4284 ± 0.0097). Ablation studies confirm the individual contributions of authentic graph topology, precise edge weighting, and graph sparsification to overall performance. Full article

Air quality forecasting and environmental health research at urban and regional scales depend on the combination of measurements from heterogeneous sensor networks, yet the construction of integrated multi-source datasets is rarely described or released as a self-contained deliverable. This paper presents an open dataset that combines four sensor-derived sources covering the whole of Spain over the period from 2022 to 2024: hourly air quality observations from the 588 stations of the national network operated by the Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO), daily meteorological records from the Agencia Estatal de Meteorología (AEMET), daily mobility indicators derived from anonymised mobile telephony events published by the Ministerio de Transportes y Movilidad Sostenible (MITMA) at the municipality level, and a calendar of national and Autonomous Community public holidays. The processing pipeline harmonises sources that differ in temporal resolution, spatial codification and quality regime into a tidy hourly table indexed by station and timestamp, with a fixed feature schema of 56 variables per record. Air quality stations are paired with their nearest AEMET station through a three-tier distance rule, and the daily exogenous features are aligned to the air quality time axis through a two-variant temporal-alignment scheme (lag-and-expand to the hourly grid for the hourly release, same-calendar-day join for the daily release). A complementary daily resolution variant of the dataset is also released, with 72 columns and the same feature schema except for the air quality block, which is aggregated to daily mean, minimum and maximum. The integrated dataset contains approximately 15 million hourly records across the 588 stations and is released on Zenodo (DOI 10.5281/zenodo.20196221) under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. It is intended as a substrate for research on air quality forecasting, environmental epidemiology and multi-source data fusion at the nationwide scale. Full article

Price spikes are rare but economically significant events observed across electricity, financial, commodity, and cryptocurrency markets. Their abrupt magnitude, heavy-tailed distributions, and severe class imbalance make them difficult to forecast using conventional time-series methods. This systematic literature review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, synthesises recent deep learning approaches to forward-looking price-spike prediction and classification. Searches of Scopus, Web of Science, and IEEE Xplore identified studies published between 2020 and 2026. Following screening and full-text eligibility assessment of approximately 300 studies, only 20 met the inclusion criteria and were included in the final synthesis, comprising 19 peer-reviewed papers and one doctoral thesis. The review develops a structured taxonomy spanning spike definitions, task formulations, model architectures, input design, and evaluation practices. A central finding is that predictive performance is driven more by problem formulation, label construction, and evaluation design than by model architecture. While architectures have diversified to include recurrent networks, transformers, graph neural networks, and hybrid frameworks, improvements are often attributable to differences in how the prediction problem is defined rather than the models themselves. Key limitations stem from inconsistent spike definitions and insufficient treatment of class imbalance, leading to a misalignment between modelling objectives and evaluation practices, further exacerbated by the absence of standardised benchmarks. These issues hinder comparability and can lead to overstated model performance by masking poor detection of rare but economically critical spike events. The review therefore identifies clear directions for future research, including standardised spike labelling, adoption of rare-event-appropriate evaluation frameworks, and problem formulations that explicitly target extreme-event prediction. Full article

Fibroblast growth factor receptor 2 (FGFR2) inhibitors such as pemigatinib are targeted therapies for cholangiocarcinoma with FGFR2 alterations. While generally well tolerated, they are associated with unique adverse events. Calciphylaxis, a potentially fatal vascular calcification disorder, is a rare complication. We present a 43-year-old woman with metastatic intrahepatic cholangiocarcinoma harboring an FGFR2 fusion who developed calciphylaxis after seven months of pemigatinib therapy. Despite drug discontinuation, antibiotics, and multidisciplinary supportive care, she deteriorated rapidly and died from sepsis and advanced disease. Histopathological analysis confirmed dermal and vascular calcifications consistent with calciphylaxis. This case highlights the importance of early recognition of cutaneous lesions in patients on FGFR inhibitors. Prompt cessation of therapy, management of metabolic derangements, and consideration of sodium thiosulfate may be lifesaving. Full article

Flood hazards are intensifying across Africa due to rapid urban expansion and hydro-climatic variability. This study develops a multi-metric geospatial framework combining extreme value analysis, hydrograph-based metrics, and dependence modelling to quantify flood magnitude, frequency, timing, and joint risk dynamics. Daily precipitation and streamflow reanalysis data (1985–2025) were analyzed for two contrasting Tanzanian catchments: the large Rufiji basin (RU) and the smaller Mirongo catchment (MW). Annual maxima were modelled using the Generalized Extreme Value (GEV) distribution, complemented by flow duration curves, peak-over-threshold detection, and regression-copula dependence analysis. Results reveal strong hydrological contrasts. RU exhibits amplified rare-event growth (design floods from ~2850 to 11,770 m³/s), extended recession persistence (>100 days), low flashiness, and long rainfall-runoff lags (~15 days), indicating storage-dominated behavior. MW shows smaller design floods (~80 to 370 m³/s), higher flashiness, and short lags (~4 days), reflecting rapid, rainfall-driven response. Gaussian copula parameters indicate moderate dependence in both basins (0.32 and 0.34), suggesting that joint dependence alone does not distinguish flood mechanisms without complementary metrics. The proposed framework improves basin-specific flood risk profiling and supports geospatial early-warning system design in data-scarce Sub-Saharan environments. Full article

Analysis of freely moving Drosophila captures complex movement behaviors. However, previous experiments have been limited by the inability to distinguish between falls and downward jumps (downjumps). Here, individual flies moving freely in a culture vial were recorded using a single 58 fps video camera. Upward jumps were readily identified by positive movement in the vertical direction. Several statistical and machine learning methods were used to distinguish between falls and downjumps, including Principal Component Analysis (PCA), K-Means Clustering, Uniform Manifold Approximation and Projection (UMAP), Hierarchical Density-Based Spatial Clustering with Applications to Noise (HDBSCAN) and Shapley Additive Explanations (SHAP). Falls were abundant and characterized by an initial velocity consistent with simple acceleration due to gravity. Downjumps were more rare, and were characterized by a greater initial velocity, indicating active propulsion by the fly. Aged flies took longer to resume movement after a fall, suggesting possible negative effects of falls. Falls in young w[1118]-strain flies exhibited mid-event velocities that were lower than expected, indicating some compensatory behavior that was reduced in aged flies. These methods should facilitate future studies of the effects of aging and neurodegenerative disease models on locomotor behaviors and falls, including the testing of potential interventions. Full article

Aging disrupts sleep, but how these changes are structured across circadian time, vigilance states, and sex remains poorly understood, because most prior studies used single-sex cohorts and few days of recordings. We continuously recorded 14 days of EEG/EMG in 24 C57BL/6J mice using a balanced 2 × 2 design (young vs. old; male vs. female; n = 6/group). A comprehensive multiscale analysis of the extended dataset enabled detailed reconstruction of 24 h sleep–wake architecture, better characterization of natural day-to-day variability including across multiple estrous cycles, and detection of rare bouts and transition events. Across seven levels of analysis, from circadian profiles to EEG spectral parameterization, the strongest aging effect was a dark-phase-specific 17–18% loss of theta-dominant active wake (TDW) in both sexes, with reciprocal increases in quiet wake (nTDW) and NREM sleep. We also identified a recurring N-shaped structural motif at the dark-to-light transition, where age-related and several sex-associated differences were most apparent. Broadly, old mice exhibited (i) shorter TDW bouts; (ii) a shift in NREM exit kinetics toward wakefulness; (iii) more brief and poorly consolidated “out-block” NREM episodes; and (iv) a slowing of waking theta and higher low-frequency TDW power. Variance decomposition indicated that statistical power depends more on sample size than on recording length. Together, aging reflects a coordinated, circadian-phase-specific reorganization of sleep–wake architecture. Sex-related and interaction findings should be interpreted as hypothesis-generating pending larger cohorts. Full article

Purpose: To report intraoperative safety and postoperative complications after small-incision lenticule extraction using the 2 MHz femtosecond platform (SMILE Pro; VisuMax 800) in routine practice. Methods: Retrospective consecutive case series at a single center. All planned SMILE Pro procedures were analyzed (916 eyes from 482 patients). Outcomes included completion rate, intraoperative events, postoperative complications stratified as <3 and >3 months, and retreatment rate. Results: Baseline age was 32.9 ± 6.9 years; average preoperative refraction was −3.60 ± 1.90/−0.87 ± 0.76 D (sphere/cylinder) with best corrected visual acuity of −0.08 ± 0.07 logMAR. Procedures were completed in 911 of 916 eyes (99.45%). Suction loss occurred in six eyes (0.66%); one was completed after redocking, four were converted (two ICL, two femtosecond LASIK) and one did not receive a second procedure. No failed lenticule separations occurred. Retreatment was performed in 14 eyes (1.54%): 11 re-LASIK, 2 ICL, and 1 cataract extraction. Early postoperative events (<3 months) were mainly superficial punctate keratitis (3.51%) and dry eye (1.32%); beyond 3 months, events remained uncommon (dry eye 1.65%, photopsia/halo/glare 0.88%). No severe or sight-threatening complications were observed. Conclusions: SMILE Pro on the VisuMax 800 showed a high completion rate, rare intraoperative disruption, low retreatment, and rare, mostly mild postoperative events. These findings support a favorable early safety profile in routine practice; longer-term follow-up is warranted. Full article

Direct flood loss estimation for industrial complexes is jointly sensitive to terrain representation, rainfall magnitude, infiltration assumptions, and depth–damage function selection, yet these uncertainties are rarely evaluated together. We quantify their combined effects for the Gumi National Industrial Complex (GNIC), South Korea, using five DEM resolutions (0.5–10 m), six rainfall return periods (10–200 years plus the observed July 2024 event), and three infiltration regimes (5, 10, 20 mm h⁻¹), yielding 90 hydrodynamic realisations from a GPU-accelerated 2D shallow-water model. Each was combined with a harmonised inventory of 16,463 buildings (replacement value 43.07 trillion KRW) and three vulnerability-function families (HAZUS-MH, JRC Huizinga, Korean MD-FDA), producing 270 loss estimates under a common dimensionless transformation. A three-way ANOVA on log-transformed damage confirmed highly significant main effects of resolution, rainfall, and infiltration across all functions, more than an order of magnitude larger than interactions, and robust to heteroscedasticity-consistent and permutation tests. Coarsening the DEM from 0.5 to 10 m reduced expected annual loss (EAL) by 55–57%, while inter-function depth–damage divergence exceeded four-fold at shallow inundation. Validation against the July 2024 event gave the best skill at 2 m resolution (critical success index 0.80, accuracy 0.86). Multi-family residential and heavy industry accounted for 83–89% of total EAL. These results show that terrain resolution and damage-function selection are first-order, statistically independent controls on industrial flood loss, and that omitting any sensitivity axis can bias EAL by more than two-fold. Full article

Catamenial pneumothorax is a rare form of recurrent spontaneous pneumothorax occurring in women in temporal association with the menstrual cycle, most commonly within 72 h before or after the onset of menstruation, and is frequently encountered as part of thoracic endometriosis syndrome. Thoracic endometriosis represents an extrapelvic manifestation of endometriosis in which ectopic endometrial tissue may involve the pleura, diaphragm, lung parenchyma, or airways, leading to cyclic pleuropulmonary symptoms. The clinical spectrum includes catamenial pneumothorax, catamenial hemothorax, catamenial hemoptysis, and pulmonary endometriotic nodules. This narrative review critically analyzes the diagnostic challenges and limitations of imaging modalities in thoracic endometriosis, with particular emphasis on diagnostic delay, radiological pitfalls, and the discrepancy between morphological detection and etiological confirmation. Chest radiography and computed tomography are useful for documenting acute thoracic events, whereas magnetic resonance imaging may provide additional tissue characterization in selected cases, particularly when hemorrhagic or diaphragmatic lesions are suspected. However, imaging findings are often nonspecific, temporally variable, and insufficient to establish the diagnosis when interpreted in isolation. Recognition of thoracic endometriosis therefore requires correlation of imaging findings with menstrual cyclicity, gynecological history, clinical phenotype, and, when indicated, surgical and histopathological assessment. The available evidence remains limited by retrospective designs, small case series, inconsistent diagnostic criteria, and lack of validated thoracic-specific imaging pathways. Accordingly, an integrated clinical–radiological–surgical approach should be regarded as a pragmatic diagnostic framework rather than a validated algorithm. Such an approach may improve clinical suspicion, reduce diagnostic delay, and support more appropriate multidisciplinary management of this underrecognized condition. Full article

Reliable prediction of post-initiation run-out distance of rainfall-induced landslides is essential for hazard assessment, evacuation planning, and disaster-risk mitigation. However, most existing data-driven approaches formulate run-out prediction as a deterministic regression problem and therefore provide limited information on predictive uncertainty, rare long-runout events, and explainable decision support. To address these limitations, this study proposes CQR-EVT-XAI, a trustworthy AI framework that integrates Quantile LightGBM, Conformalized Quantile Regression (CQR), Extreme Value Theory (EVT), and Explainable Artificial Intelligence (XAI) for uncertainty-aware and explainable landslide run-out risk prediction. Based on 10,158 rainfall-induced landslide samples, physics-informed features are constructed from elevation difference H, source area A, source volume V, and mean slope angle $\theta$. The proposed framework generates calibrated prediction intervals, threshold-based exceedance probabilities, upper-tail risk indicators, and interpretable risk levels. The CQR-LightGBM median model achieves high point-prediction accuracy, with $R^2$ = 0.939, RMSE = 18.03 m, and MAE = 6.55 m. Conformal calibration improves the empirical coverage of the nominal 90% and 95% prediction intervals from 0.813 to 0.903 and from 0.876 to 0.953, respectively. Tail-risk analysis shows that the upper prediction bound ${\widehat{L}}_{95}$ effectively identifies extreme long-runout events, achieving recall values of 0.974 and 0.900 for L > 300 m and L > 500 m, respectively. SHAP analysis reveals that elevation difference H, source volume V, and energy-related derived features dominate both median run-out prediction and upper-tail risk behavior, while slope-related variables mainly influence predictive uncertainty and exceedance-risk levels. These results demonstrate that the proposed CQR-EVT-XAI framework provides a practical workflow for calibrated uncertainty quantification, tail-risk identification, and explainable decision support in rainfall-induced landslide run-out risk assessment. Full article