Search Results (1,454)

Sea level variability in the East Sea (Sea of Japan) and the Northwest Pacific poses challenges for coastal risk management due to the scarcity of long-term observations at remote locations such as Dokdo (Dok Island). This study reconstructs a continuous monthly sea level record at Dokdo from 1993 to 2023 by imputing gaps in 13 nearby Permanent Service for Mean Sea Level tide gauge stations using eight machine learning models and geospatial interpolation methods. The ensemble mean of Machine Learning-based imputations produced physically realistic and temporally coherent timeseries, preserving both seasonal and interannual variability. Sea level at Dokdo, estimated via inverse distance weighting, aligned well with satellite altimetry from Copernicus Marine Service and exhibited strong regional coherence with nearby stations. These results demonstrate that a hybrid framework combining statistical imputation, Machine Learning, and inverse barometric correction can effectively reconstruct sea level in data-sparse marine regions. The methodology provides a scalable tool for monitoring long-term trends and validating satellite and model products in marginal seas like the East Sea. Full article

Annuity insurance is a crucial financial tool for mitigating risks associated with aging, yet it has not gained significant traction in China’s insurance market, especially amid the challenges posed by an aging population. This study develops a discrete-time multi-period life-cycle model to analyze optimal annuity purchases for China’s middle-aged population under disability risk and explores in depth the impact and underlying mechanisms of disability risk on their annuity insurance purchase decisions. Disability is endogenized via two channels: financial-constraint effects (medical costs and pre-retirement income loss) and stochastic health state transitions with recovery and mortality. Using data from China Health and Retirement Longitudinal Study (2018–2020) to estimate age- and gender-specific transition matrices and data from China Household Finance Survey (2019) to link income with initial assets, we solve the model by the endogenous grid method and simulate actuarially fair annuities. The findings reveal substantial under-demand for annuities among China’s middle-aged population. Under inflation, the modest yield premium of annuities over inflation significantly depresses purchases by middle- and low-wealth households, while high-wealth individuals are jointly constrained by rapidly rising health expenditures and inadequate annuity returns. Notably, behavioral patterns could shift fundamentally under a hypothetical zero-inflation scenario. Full article

Background/Objectives: Cardiac autonomic neuropathy (CAN) is a common but also underdiagnosed complication of diabetes mellitus (DM), associated with high cardiovascular risk and mortality. Sudomotor dysfunction can serve as an early indicator of autonomic dysfunction. This study evaluated the association between sudomotor dysfunction and the severity of CAN in patients with type 2 diabetes (T2D). Methods: In this cross-sectional study, 109 patients with T2D were evaluated for diabetic peripheral neuropathy, cardiovascular autonomic dysfunction, and sudomotor dysfunction. Additionally, clinical and biochemical data were collected from patients’ medical records. Results: Sudomotor dysfunction (SUDO+) was present in 59.6% of patients. The presence of SUDO+ was associated with a higher age, longer duration of diabetes, lower eGFR (estimated glomerular filtration rate) values, and more severe signs of peripheral neuropathy. SUDO+ patients showed significantly greater orthostatic systolic and diastolic BP (blood pressure) changes, lower RR interval ratios, and lower feet ESC (electrochemical skin conductance) values. ROC (receiver operating characteristic) analysis for feet ESC in identifying pathological RR ratio showed an AUC of 0.689 (95% CI: 0.593–0.774, p = 0.0022), with a sensitivity of 46.7% and a specificity of 94.7% at a cutoff of ≤68 µS. For orthostatic hypotension and QTc prolongation, the ESC values had limited discriminative power. Chi-squared analysis showed a significant association between feet sudomotor impairment and pathological RR ratio (χ² = 6.521, p = 0.0107). Conclusions: Sudomotor dysfunction is associated with indicators of CAN. SUDOSCAN can be used as a complementary tool for early CAN detection in clinical practice. Full article

Metagenomic next-generation sequencing (mNGS) is increasingly employed for the diagnosis of lower respiratory tract infections (LRTIs). However, the relative diagnostic performance of long-read versus short-read sequencing platforms remains incompletely defined. For this systematic review, a search was conducted in PubMed, Embase, Scopus, Web of Science, and Google Scholar to identify studies directly comparing long-read (e.g., Oxford Nanopore, PacBio) and short-read (e.g., Illumina, Ion Torrent, BGISEQ) metagenomic sequencing for the diagnosis of LRTI. Eligible studies reported diagnostic accuracy or comparative performance between platforms. Risk of bias was evaluated using the QUADAS-2 tool. Thirteen studies met inclusion criteria. Reported platforms included Illumina, Oxford Nanopore, PacBio, Ion Torrent, and BGISEQ-500. A total of 13 studies met inclusion criteria. Across studies reporting sensitivity, average sensitivity was similar for Illumina (71.8%) and Nanopore (71.9%). Specificity varied substantially, ranging from 42.9 to 95% for Illumina and 28.6 to 100% for Nanopore. Concordance between platforms ranged from 56 to 100%. Illumina consistently produced superior genome coverage (approaching 100% in most reports) and higher per-base accuracy, whereas Nanopore demonstrated faster turnaround times (<24 h), greater flexibility in pathogen detection, and superior sensitivity for Mycobacterium species. Risk of bias was frequently high or unclear, particularly in patient selection (6 studies), index test interpretation (5), and flow and timing (4), limiting the robustness of pooled estimates. Long-read and short-read mNGS platforms exhibit comparable strengths in the diagnosis of LRTIs. Illumina remains optimal for applications requiring maximal accuracy and genome coverage, whereas Nanopore offers rapid, versatile pathogen detection, particularly for difficult-to-detect organisms such as Mycobacterium. However, there are certain limitations of the review, including a lack of comparable outcomes reported in all studies; therefore, further research is warranted to address this. Full article

Sudden cardiac death (SCD) is a leading cause of mortality worldwide, with arrhythmia serving as a major precursor. Early and accurate prediction of SCD using non-invasive electrocardiogram (ECG) signals remains a critical clinical challenge, particularly due to the inherent asymmetric and non-stationary characteristics of ECG signals, which complicate feature extraction and model generalization. In this study, we propose a novel SCD prediction framework based on hierarchical feature fusion, designed to capture both non-stationary and asymmetrical patterns in ECG data across six distinct time intervals preceding the onset of ventricular fibrillation (VF). First, linear features are extracted from ECG signals using waveform detection methods; nonlinear features are derived from RR interval sequences via second-order detrended fluctuation analysis (DFA2); and multi-scale deep learning features are captured using a Temporal Convolutional Network-based sequence-to-vector (TCN-Seq2vec) model. These multi-scale deep learning features, along with linear and nonlinear features, are then hierarchically fused. Finally, two fully connected layers are employed as a classifier to estimate the probability of SCD occurrence. The proposed method is evaluated under an inter-patient paradigm using the Sudden Cardiac Death Holter (SCDH) Database and the Normal Sinus Rhythm (NSR) Database. This method achieves average prediction accuracies of 97.48% and 98.8% for the 60 and 30 min periods preceding SCD, respectively. The findings suggest that integrating traditional and deep learning features effectively enhances the discriminability of abnormal samples, thereby improving SCD prediction accuracy. Ablation studies confirm that multi-feature fusion significantly improves performance compared to single-modality models, and validation on the Creighton University Ventricular Tachyarrhythmia Database (CUDB) demonstrates strong generalization capability. This approach offers a reliable, long-horizon early warning tool for clinical SCD risk assessment. Full article

Background/Objectives: Gait dysfunction is a major contributor to disability and reduced quality of life in Parkinson’s disease (PD). Although pharmacological treatments and exercise-based rehabilitation programs provide partial improvement, residual gait dysfunction often persists. Given these limitations, there has been growing interest in non-pharmacological and non-invasive strategies such as vibration therapy (VT). However, previous systematic reviews and meta-analyses have yielded inconsistent findings, largely summarizing the presence or absence of treatment effects without clarifying the clinical or therapeutic conditions under which VT may be most effective. Therefore, this study aimed to systematically review and synthesize evidence on the efficacy of VT for improving gait in PD and to identify clinical and therapeutic factors influencing treatment outcomes. Methods: A systematic search of PubMed, Web of Science, Embase, and the Cochrane Library was conducted with no restrictions on the search period, including studies published up to July 2025. Eligible studies included randomized and quasi-experimental clinical trials that evaluated the effects of VT on gait-related outcomes in patients with Parkinson’s disease. Data extraction followed the PRISMA 2020 guidelines, and the risk of bias was assessed using the Cochrane RoB 2 and ROBINS-I tools. Multilevel random-effects meta-analyses were conducted to estimate pooled effect sizes for gait outcomes, and meta-regression and subgroup analyses were performed based on disease stage, medication status, and vibration parameters. Results: A total of 14 studies (11 randomized and 3 non-randomized) were included. The pooled analysis showed that VT significantly improved gait performance in PD (Hedges’ g = 0.270, 95% CI: 0.115–0.424; 95% PI: −0.166–0.705). The sensitivity analysis restricted to randomized controlled trials yielded a comparable significant effect (g = 0.316, 95% CI: 0.004–0.628). Greater benefits were observed in patients with higher clinical severity, while the moderating effect of levodopa dosage was not significant. Optimal effects were identified with frequencies of 51–100 Hz, session durations ≤3 min, and 2–3 sessions per week. Improvements were evident in gait speed, cycle, and magnitude, whereas no consistent effects were observed for freezing of gait or gait variability. Conclusions: VT yields small but statistically significant improvements in fundamental gait parameters in Parkinson’s disease, particularly under optimized stimulation conditions and in individuals with greater disease severity. Although the pooled effect was modest and the certainty of evidence was rated as very low according to GRADE, these findings cautiously support the potential of vibration-based interventions as a supportive, non-pharmacological, and non-invasive adjunct within broader rehabilitation programs, rather than as a stand-alone treatment. Full article

Early warning of structural damage from induced seismic events requires rapid and reliable ground motion forecasting. This study presents a novel real-time framework that couples a deep learning approach with structural fragility assessment to generate immediate damage alerts following the onset of seismic shaking. Long Short-Term Memory (LSTM) neural networks are employed to predict full S-wave accelerograms from initial P-wave inputs, trained and tested on accelerometric records from induced seismicity scenarios. The predicted S-wave motion is then used as input for a suite of fragility curves in real time to estimate the probability of structural damage for masonry buildings typical in rural areas of geothermal platforms. The proposed method captures both the temporal evolution of shaking and the structural response potential, offering critical seconds of lead time for automated decision-making systems. Results demonstrate high predictive accuracy of the LSTM model and effective early classification of structural risk. This integrated system provides a practical tool for early warning or rapid response in regions experiencing anthropogenic seismicity, such as those affected by geothermal operations. Full article

Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of ≥95% and an atrial fibrillation detection sensitivity/specificity of ≥96%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools. Full article

Background: Despite chemotherapy-related toxicities being more likely in older patients, no routine prediction tool has been validated for the UK population. Previous research within the TOASTIE (tolerance of anti-cancer systemic therapy in the elderly) study found a low predictive performance of the Cancer and Aging Research Group (CARG) score for severe chemotherapy-related toxicities. Building on this, the TOASTIE study dataset was used to assess the viability of developing a predictive model with baseline variables and frailty scores for severe chemotherapy-related toxicities in older patients. Methods: All patients from the TOASTIE dataset were included, with the inclusion/exclusion criteria detailed in the TOASTIE protocol. Demographic factors, self-assessment scores, Rockwood Clinical Frailty Score and researcher’s estimated risks of toxicity were assessed for their association with severe chemotherapy-related toxicities. After data partition into 70:15:15 train/validation/test, models were built on the training dataset using logistic regression (LR), LASSO and random forest (RF). Models were optimized with a validation set with LR and LASSO; cross-validation was used with RF. Model performance was assessed with balanced accuracy, NPV and AUC. Results: Of the 322 patients included, the incidence of severe toxicities was 22% (n = 71). Ten variables were statistically significant, albeit weakly associated with severe toxicities: primarily patient-reported factors, Performance Status and high baseline neutrophil count. LR models gave the best balanced accuracies of 0.6382 (AUC 0.6950, NPV 0.8696) and 0.6469 (AUC 0.6469, NPV 0.4286) with LASSO, and 0.6294 (AUC 0.6557, NPV 0.6557) with RF. Conclusions: Models lack sufficiently robust results for clinical utility. However, a high NPV in predicting no toxicity could help identify lower-risk patients who may not require dose reductions, potentially improving overall outcomes. Full article

The transition toward Industry 4.0 and the emerging concept of Industry 5.0 demand intelligent tools that integrate efficiency, adaptability, and human-centered design. This paper presents a Computer Vision-based framework for automated motion classification in Methods-Time Measurement 2 (MTM-2), with the aim of supporting industrial time studies and ergonomic risk assessment. The system uses a Convolutional Neural Network (CNN) for pose estimation and derives angular kinematic features of key joints to characterize upper limb movements. A two-stage experimental design was conducted: first, three lightweight classifiers—K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), and a Shallow Neural Network (SNN)—were compared, with KNN demonstrating the best trade-off between accuracy and efficiency; second, KNN was tested under noisy conditions to assess robustness. The results show near-perfect accuracy (≈100%) on 8919 motion instances, with an average inference time of 1 microsecond per sample, reducing the analysis time compared to manual transcription. Beyond efficiency, the framework addresses ergonomic risks such as wrist hyperextension, offering a scalable and cost-effective solution for Small and Medium-sized Enterprises. It also facilitates integration with Manufacturing Execution Systems and Digital Twins, and is therefore aligned with Industry 5.0 goals. Full article

Background: The Comprehensive Geriatric Assessment (CGA) has proven to be a valuable tool for providing a more comprehensive health evaluation of allogeneic stem cell transplantation (allo-SCT) recipients. Methods: We prospectively developed and tested a new Simplified Geriatric Score-4 (SGS-4) on 135 consecutive patients aged ≥50 years who underwent allo-SCT between 2020 and 2023. Each CGA component was individually analyzed for its association with overall survival (OS), non-relapse mortality (NRM), and cumulative incidence of relapse (CIR). Then, we performed a two-factor analysis (FA) using oblimin rotation and Bartlett estimation on all CGA components and sex. Based on component weights, a simplified geriatric score-4 score (SGS-4) was created: [Gait Speed] + 2 × [Hand Grip] + Geriatric 8 + 1.5 × [Sex]. ROC analysis defined three fitness groups, frail (≤13), prefrail (>13–22.5), and fit (>22.5). Results: Reduced hand grip strength and impaired mini mental state examination (MMSE) were associated with worse OS and higher NRM. Vulnerable Elders Survey (VES-13) and Fondazione Italiana Linfomi (FIL) scores also indicated poorer OS, though with uneven group sizes. Other CGA domains and the Hematopoietic Cell Transplantation–Comorbidity Index (HCT-CI) showed no significant prognostic value. The SGS-4 effectively stratified patients into three fitness groups, with those in the frail category experiencing lower OS and an increased risk of relapse. Conclusions: The new Simplified Geriatric Score-4 (SGS-4) based on three CGA domains (gait speed, hand grip, Geriatric 8) and sex effectively predicts OS and CIR risk in patients aged ≥50 years undergoing allo-SCT. The study’s small sample size and disease heterogeneity warrant further validation in larger cohorts. Full article

Xylella fastidiosa was first detected in Portugal in 2019 in Lavandula dentata. In response, the national plant health authorities promptly established a Demarcated Zone in the affected area and implemented a series of eradication and control measures, including the systematic removal and destruction of infected and host plants. This study analyzes the economic and operational impacts of these eradication efforts in the northern region of Portugal, with a focus on Demarcated Zones such as the Porto Metropolitan Area, Sabrosa, Alijó, Baião, Mirandela, Mirandela II, and Bougado between 2019 and June 2023. During this period, about 412,500 plants were uprooted. The majority were Pteridium aquilinum (bracken fern), with 360,324 individuals (87.3%), reflecting its wide distribution and the large area affected. Olea europaea (olive tree) was the second most common species removed, with 7024 plants (1.7%), highlighting its economic relevance. Other notable species included Quercus robur (3511; 0.85%), Pelargonium graveolens (3509; 0.85%), and Rosa spp. (1106; 0.27%). Overall, destruction costs were estimated at about EUR 1.04 million, with replanting costs of roughly EUR 6.81 million. In parallel, prospection activities—conducted to detect early signs of infection and monitor disease spread—generated expenses of roughly EUR 5.94 million. While prospecting represents a significant financial investment, the results show that it is considerably more cost-effective than large-scale eradication. Prospection enables early detection and containment, preventing the widespread destruction of vegetation and minimizing disruption to agricultural production, biodiversity, and local communities. Importantly, our findings reveal a sharp decline in confirmed cases in the initial outbreak area—the Porto Demarcated Zone—from 124 cases in 2019 to just 5 in 2023, indicating the effectiveness of the eradication and monitoring measures implemented. However, the presence of 20 active Demarcated Zones across the country as of 2023 highlights the continued risk of spread and the need for sustained vigilance. The complexity of managing Xylella fastidiosa across ecologically and logistically diverse territories justifies the high costs associated with surveillance and targeted interventions. This study reinforces the strategic value of prospection as a proactive and sustainable tool for plant health management. Effective surveillance requires the integration of advanced methodologies aligned with the phenological stages of host plants and the biological cycle of vectors. Targeting high-risk locations, optimizing sample numbers, ensuring diagnostic accuracy, and maintaining continuous training for field teams are critical for improving efficiency and reducing costs. Ultimately, the findings underscore the need to refine and adapt monitoring and eradication strategies to contain the pathogen, safeguard agricultural systems, and prevent Xylella fastidiosa from becoming endemic in Portugal. Full article

Background and Objectives: Albuminuria is a key clinical marker for early detection of diabetic kidney disease (DKD) in individuals with type 2 diabetes mellitus (T2DM). The triglyceride-glucose (TyG) index, a simple surrogate of insulin resistance, has been increasingly investigated for its potential association with renal complications. This study aimed to evaluate the relationship between the TyG index and albuminuria in patients with T2DM and assess its clinical utility as an accessible metabolic marker reflecting early renal involvement. Materials and Methods: This retrospective cross-sectional study included 570 adult patients with confirmed T2DM who were followed at a tertiary internal medicine outpatient clinic between January and December 2024. Participants were classified as albuminuric or non-albuminuric based on spot urine albumin-to-creatinine ratio (ACR) values. Clinical and biochemical parameters were collected from medical records, and the TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Logistic regression models were used to identify independent factors associated with albuminuria. ROC analysis was performed to evaluate the discriminatory accuracy of the TyG index. Results: The median TyG index was significantly higher in the albuminuric group compared to the non-albuminuric group (10.0 vs. 9.1; p < 0.001) and increased progressively with albuminuria severity (p < 0.001). In multivariate logistic regression analysis, elevated TyG index, hyperlipidemia, and reduced estimated glomerular filtration rate were independently associated with albuminuria. When evaluated as a continuous variable, the TyG index showed strong discriminatory ability (area under curve (AUC) = 0.949; 95% confidence interval (CI): 0.933–0.964). Using the optimal cut-off threshold of 9.6, the TyG index maintained high diagnostic performance (AUC = 0.870; 95% CI: 0.839–0.902; sensitivity 87.7%, specificity 86.3%). Subgroup analyses confirmed the robustness of this association across clinical and demographic variables. Conclusions: In this study, higher TyG index values were significantly associated with the presence and severity of albuminuria in individuals with T2DM. While causality cannot be inferred, the findings suggest that the TyG index may serve as a practical, cost-effective tool for identifying patients at increased risk for early diabetic kidney involvement. Prospective longitudinal studies are needed to confirm its predictive value and clinical applicability. Full article

Background/Objectives: In 2019, the European Society of Gynaecological Oncology (ESGO) published a set of quality indicators (QIs) for the surgical management of cervical cancer with the aim of improving clinical practice. The objective of this study is to evaluate the influence of ESGO QIs and clinicopathological factors on progression-free survival (PFS) in patients with early-stage cervical cancer in a retrospective cohort. Methods: A retrospective study was conducted in patients with early-stage cervical cancer who underwent radical surgery with pelvic lymph node assessment at La Paz University Hospital between 2005 and 2022. The cohort was divided into two groups according to the timing of surgery (before vs. after 2010), when MRI was implemented as a standardized diagnostic tool and the multidisciplinary tumor board was established. Univariate and multivariate Cox regression analyses were performed, including demographic and histopathological variables, as well as adherence to ESGO QIs, focusing on those related to the overall management. Hazard ratios and 95% confidence intervals were estimated. Kaplan–Meier survival curves were generated and compared between groups. Results: The implementation of systematic MRI and a multidisciplinary tumor board at our center was associated with a significant reduction in positive surgical margins (p = 0.003) and parametrial invasion (p < 0.001), as well as improved diagnostic accuracy, lowering the rate of upstaging from 31.6% before 2010 to 4.4% thereafter (p < 0.001). PFS in the post-2010 cohort was significantly improved (log-rank p = 0.0408), although no differences in overall survival (OS) were observed (log-rank p = 0.2602). Additionally, cervical conization prior to radical hysterectomy was associated with a markedly reduced risk of recurrence (HR 0.12, p < 0.001), representing the most significant prognostic factor for PFS in our cohort. Conclusions: The correct application of ESGO QIs, along with appropriate staging and pathological assessment, is essential to improve prognosis in cervical cancer. Systematic implementation of these standards is recommended to optimize clinical care. Full article

Background: Chronic pain affects nearly one in five adults worldwide and is increasingly recognized not only as a disease but as a potential risk factor for neurocognitive decline and dementia. While some evidence supports this association, existing systematic reviews are static and rapidly outdated, and none have leveraged advanced methods for continuous updating and robust uncertainty modeling. Objective: This protocol describes a living systematic review with dose–response Bayesian meta-analysis, enhanced by artificial intelligence (AI) tools, to synthesize and maintain up-to-date evidence on the prospective association between any type of chronic pain and subsequent cognitive decline. Methods: We will systematically search PubMed, Embase, Web of Science, and preprint servers for prospective cohort studies evaluating chronic pain as an exposure and cognitive decline as an outcome. Screening will be semi-automated using natural language processing models (ASReview), with human oversight for quality control. Bayesian hierarchical meta-analysis will estimate pooled effect sizes and accommodate between-study heterogeneity. Meta-regression will explore study-level moderators such as pain type, severity, and cognitive domain assessed. If data permit, a dose–response meta-analysis will be conducted. Living updates will occur biannually using AI-enhanced workflows, with results transparently disseminated through preprints and peer-reviewed updates. Results: This is a protocol; results will be disseminated in future reports. Conclusions: This living Bayesian systematic review aims to provide continuously updated, methodologically rigorous evidence on the link between chronic pain and cognitive decline. The approach integrates innovative AI tools and advanced meta-analytic methods, offering a template for future living evidence syntheses in neurology and pain research. Full article