Search Results (8,775)

This study provides a detailed comparative analysis of a three-hybrid intrusion detection method aimed at strengthening network security through precise and adaptive threat identification. The proposed framework integrates an Autoencoder-Gaussian Mixture Model (AE-GMM) with two supervised learning techniques, XGBoost and Logistic Regression, combining deep feature extraction with interpretability and stable generalization. Although the downstream classifiers are trained in a supervised manner, the hybrid intrusion detection nature of the framework is preserved through unsupervised representation learning and probabilistic modeling in the AE-GMM stage. Two benchmark datasets were used for evaluation: NSL-KDD, representing traditional network behavior, and UNSW-NB15, reflecting modern and diverse traffic patterns. A consistent preprocessing pipeline was applied, including normalization, feature selection, and dimensionality reduction, to ensure fair comparison and efficient training. The experimental findings show that hybridizing deep learning with gradient-boosted and linear classifiers markedly enhances detection performance and resilience. The AE–GMM-XGBoost model achieved superior outcomes, reaching an F1-score above 0.94 ± 0.0021 and an AUC greater than 0.97 on both datasets, demonstrating high accuracy in distinguishing legitimate and malicious traffic. AE-GMM-Logistic Regression also achieved strong and balanced performance, recording an F1-score exceeding 0.91 ± 0.0020 with stable generalization across test conditions. Conversely, the standalone AE-GMM effectively captured deep latent patterns but exhibited lower recall, indicating limited sensitivity to subtle or emerging attacks. These results collectively confirm that integrating autoencoder-based representation learning with advanced supervised models significantly improves intrusion detection in complex network settings. The proposed framework therefore provides a solid and extensible basis for future research in explainable and federated intrusion detection, supporting the development of adaptive and proactive cybersecurity defenses. Full article

Background: Root canal sealers may come into direct contact with periapical tissues, particularly in cases of apical extrusion, potentially influencing periapical healing and treatment outcomes. Cytotoxicity assessment represents a clinically relevant parameter when selecting endodontic sealers. However, evidence derived from in vitro studies remains heterogeneous and challenging to interpret from a clinical perspective. Therefore, the aim of this systematic review was to critically evaluate the in vitro cytotoxicity of all root canal sealers that have been commercially marketed over the years, excluding experimental materials, and to contextualize the findings in relation to clinically relevant experimental conditions. Methods: This systematic review was conducted according to PRISMA guidelines and preregistered on the Open Science Framework. PubMed, Scopus, and the Cochrane Library were searched up to 30 November 2025. In vitro studies evaluating the cytotoxicity of commercially available root canal sealers using validated cell viability or proliferation assays were included. Data extraction focused on sealer composition, setting condition, extraction protocols, exposure parameters, and cytotoxic outcomes. Due to marked methodological heterogeneity, a qualitative synthesis was performed. Results: Ninety-eight in vitro studies were included. All categories of root canal sealers demonstrated some degree of cytotoxicity, particularly when tested in freshly mixed conditions, at higher extract concentrations, or after prolonged exposure. Bioactive and calcium silicate-based sealers generally showed a more favorable cytotoxicity profile compared with conventional materials, especially after complete setting and at diluted concentrations, although cytotoxic effects were reported under specific experimental conditions. Resin-based sealers, including AH Plus, exhibited condition-dependent cytotoxicity, while zinc oxide–eugenol and glass ionomer sealers tended to display higher cytotoxic potential. Conclusions: In vitro cytotoxicity of root canal sealers varies according to material composition and experimental conditions. Bioactive sealers generally exhibit a more favorable biological profile, which may be clinically relevant in situations involving sealer extrusion or prolonged tissue contact. Standardized testing protocols and further translational studies are required to support evidence-based clinical material selection. Full article

Objective: the aim of this systematic review and meta-analysis was to evaluate the effectiveness of photobiomodulation therapy (PBM) on perineal pain and wound healing following episiotomy. Methods: Electronic databases were searched for randomized controlled trials (RCTs) and non-RCTs investigating PBM after episiotomy. Risk of bias was assessed using RoB 2 for RCTs and ROBINS-I for non-RCTs. The certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Meta-analyses were performed using random-effects models. Results: Eight studies were included in the systematic review, and six studies were included in the meta-analysis. According to RoB 2, one trial was judged at low risk of bias, two trials raised some concerns, and three trials were at high risk of bias. ROBINS-I showed the serious risk of bias of two non-RCTs. GRADE for both pain and wound-healing outcomes was rated as very low. Meta-analysis of pain showed no significant difference between PBM and control groups (SMD = 0.04; 95% CI −0.60 to 0.68; p = 0.89), with considerable heterogeneity (I² = 91%). For wound-healing outcomes, meta-analysis showed no significant difference (MD = 0.94; 95% CI −0.69 to 2.56; p = 0.26), with substantial heterogeneity (I² = 94%). Conclusions: PBM therapy did not demonstrate a significant benefit for reducing perineal pain or improving wound healing after episiotomy compared with control interventions. Interpretation of these findings should be made cautiously due to small study numbers, substantial heterogeneity, and the inability to perform sensitivity or subgroup analyses, highlighting the need for high-quality RCT with standardized PBM protocols before clinical recommendations can be made. Full article

Objectives: Open repair remains widely used for TAAA (Thoracoabdominal Aortic Aneurysm), but disease progression may require reoperation via repeat thoracotomy, which is technically challenging. These procedures involve increased risks due to adhesions and altered anatomy. This study aims to evaluate the impact of repeat thoracotomy on early surgical outcomes in TAAA patients. Methods: We conducted a retrospective cohort study of 214 patients who underwent open TAAA repair between June 1996 and March 2023. Among them, 30 underwent repeat thoracotomy (RT), and 184 underwent their first thoracotomy (FT). To reduce baseline discrepancies, propensity score matching (3:1) was performed, resulting in 22 RT patients matched with 45 FT patients. The primary outcome was 30-day mortality, while secondary outcomes included postoperative complications. Results: In the matched cohort, the median operative time was longer in the RT group (500 min, IQR [476.0–552.0]) compared to the FT group (459.0 min, IQR [426.5–514.0]; p = 0.037). Thirty-day mortality was similar between groups (RT: 4.5%, FT: 2.2%, p = 0.433). No cases of paraplegia occurred. Postoperative bleeding was observed more frequently in the RT group (RT: 13.6% vs. FT: 2.2%, p = 0.050), suggesting a potential difference, but statistical significance was not reached. Other major complications showed no significant differences. Conclusions: In this propensity score matched analysis, repeat thoracotomy was not associated with statistically significant differences in early outcomes following open TAAA repair. These findings should be interpreted cautiously and suggest that prior left thoracotomy may not be associated with worse early outcomes in experienced centers. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Climate change-induced extreme weather events increasingly threaten public health, with a particularly acute impact on the mental well-being of urban populations. This study evaluates regional disparities in mental health outcomes associated with climate-induced extreme weather in urban environments, where social and infrastructural vulnerabilities exacerbate environmental stressors. We synthesized data from cohort and cross-sectional studies using both traditional frequentist and Bayesian meta-analytic frameworks to assess the mental health sequelae of extreme weather events (e.g., heatwaves, floods, droughts, and storms). The traditional meta-analysis indicated a significant increase in the odds of adverse mental health outcomes (OR = 1.32, 95% CI: 1.07–1.57). However, this global estimate was characterized by extreme heterogeneity (I² = 95.8%), indicating that the risk is not uniform but highly context-dependent. Subgroup analyses revealed that this risk is concentrated in specific regions; the strongest associations were observed in Africa (OR = 2.23) and Europe (OR = 2.26). Conversely, the Bayesian analysis yielded a conservative estimate, suggesting a slight reduction in odds (mean OR = 0.92, 95% CrI: 0.87–0.98). This divergence is driven by the Bayesian model’s shrinkage of high-magnitude outliers toward the high-precision data observed in resilient, high-income settings (e.g., USA). Given the extreme heterogeneity observed (I² = 95.8%), we caution against interpreting either pooled estimate as a universal effect size. Instead, the regional subgroup findings—particularly the consistently elevated risks in Africa and Europe—offer more stable and policy-relevant conclusions. These findings emphasize urgent, context-specific interventions in urban areas facing compounded climate social risks. Full article

Background/Objectives: People often engage in small, repetitive movements—or “fidgeting”—while listening. This behavior has traditionally been regarded as a sign of inattention. However, recent perspectives suggest that these movements may support engagement and arousal regulation. Yet, little is known about how different types of fidgeting affect the allocation of cognitive resources during auditory processing. This study examined whether hand and leg fidgeting influence pupil-linked arousal and auditory task performance. Methods: Young, healthy adults aged 18–26 years completed four auditory processing tasks while performing either hand fidgeting (manipulating a small fidget toy) or leg fidgeting (very light ergometer pedaling). A control group did not fidget. Pupil-linked arousal was assessed using changes in pupil diameter, and listening performance was evaluated across tasks of varying difficulty. Results: Both forms of fidgeting caused pupil dilation compared to the control group, particularly in the case of Hand Fidgeting during the listening task with speech in noise and the fast speech task. Despite these physiological changes, there were no measurable differences in auditory task performance across conditions. Conclusions: Fidgeting modulates pupil-linked arousal without impairing auditory processing in young, healthy adults. Hand fidgeting may help sustain engagement during demanding listening tasks. However, because the fidgeting was intentional and task performance approached ceiling or floor levels, these findings should be interpreted as preliminary. Future studies should examine whether fidgeting supports arousal maintenance or listening performance in individuals with attentional vulnerabilities or auditory processing difficulties. Full article

Background: Triple-negative breast cancers (TNBCs) are among the most aggressive breast tumors, due not only to the absence of clinically functional biomarkers used in other molecular subtypes, but also their marked heterogeneity and pronounced migratory and invasive behavior. The search for new molecules of interest for risk prediction, diagnosis and therapy stems from the class of long non-coding RNAs (lncRNAs), which often display context-dependent (“dual”) functions and tissue specificity. Among them, lncRNA LINC01133 stands out for its dysregulation across cancer, although its molecular role in TNBC remains unclear. Methods: In the present study, we used the human TNBC cell line Hs578T to generate a cell panel comprising the parental line (Hs578T_wt), the control line (Hs578T_ctr), and the LINC01133 knockout line (Hs578T_ko). Subsequently, we performed bulk RNA-Seq to identify KO-associated Differentially Expressed Genes (DEGs) using ko_vs_ctr as the primary contrast. Functional interpretation was achieved by Over-Representation Analysis (ORA) using Gene Ontology. We then conducted a comparative patient-cohort analysis using TCGA-BRCA Basal-like/TNBC cases (TCGA/BRCA n = 1098; Basal-like/TNBC n = 199), classified with the AIMS algorithm, and evaluated concordance between KO-associated signatures and patient tumor expression patterns via trend-based analyses across the LINC01133 expression levels and associated genes. Results: A total of 265 KO-dominant DEGs were identified in Hs578T_ko, reflecting transcriptional changes consistent with tumor progression, with enrichment of pathways associated with LINC01133 knockout including cell adhesion, cell–cell interactions, epithelial–mesenchymal transition (EMT), and extracellular matrix (ECM) remodeling. The main DEGs included ITIH5, GLUL, CACNB2, PDX1, ASPN, PTGER3, MFAP4, PI15, EPHB6, and CPA3 with additional candidates, such as KAZN and the lncRNA gene SSC4D, which have been implicated in migration/invasion, ECM remodeling, or signaling across multiple tumor contexts. Translational analyses in TCGA-BRCA basal-like tumors suggested a descriptive association in which lower LINC01133 levels were accompanied by shifts in the expression trends of genes linked to ECM/EMT programs and modulation of genes related to cell adhesion and protease inhibition. Conclusions: These results suggest a transcriptional model in which LINC01133 is associated with TNBC-related gene expression programs in a concentration-dependent manner, with loss of LINC01133 being associated with a transcriptomic shift toward pro-migratory/ECM remodeling signatures. While functional validation is required to establish causality, these data support LINC01133 as a molecule of interest in breast cancer research. Full article

(1) Background: Urban traffic micro-environments show strong spatial and temporal variability. Short and intensive campaigns remain a practical approach for understanding exposure patterns in complex environments, but they need clear and interpretable summaries that are not limited to simple site or time segmentation. (2) Methods: We carried out a multi-site campaign across five traffic-affected micro-environments, where measurements covered several pollutants, gases, and meteorological variables. A machine learning framework was introduced to learn interpretable operational regimes as recurring multivariate states using clustering with stability checks, and then we evaluated their added explanatory value and cross-site transfer using a strict site hold-out design to avoid information leakage. (3) Results: Five regimes were identified, representing combinations of emission intensity and ventilation strength. Incorporating regime information increased the explanatory power of simple NO₂ models and allowed the imputation of missing H₂S day using regime-aware random forest with an $R^2$ near 0.97. Regime labels remained identifiable using reduced sensor sets, while cross-site forecasting transferred well for NO₂ but was limited for PM, indicating stronger local effects for particles. (4) Conclusions: Operational-regime learning can transform short multivariate campaigns into practical and interpretable summaries of urban air pollution, while supporting data recovery and cautious model transfer. Full article

Dynamic contrast-enhancement (DCE) modality of MRI is typically considered secondary in prostate cancer (PCa) diagnostics, due to the common interpretation that its diagnostic power is lower than that of other modalities like T2-weighted (T2W) or diffusion-weighted imaging (DWI). To challenge this paradigm, this study introduces a novel concept of a difference map, which relies exclusively on DCE-MRI for the localization of peripheral zone prostate cancer using functional data analysis-based (FDA) signal processing. The proposed workflow uses discrete voxel-level DCE time–signal curves that are transformed into a continuous functional form. First-order derivatives are then used to determine patient-specific time points of greatest enhancement change that adapt to the intrinsic characteristics of each patient, producing diffmaps that highlight regions with pronounced enhancement dynamics, indicative of malignancy. A subsequent normalization step accounts for inter-patient variability, enabling consistent interpretation across subjects and probabilistic PCa localization. The approach is validated on a curated dataset of 20 patients. Evaluation of eight workflow variants is performed using weighted log loss, the best variant achieving a mean log loss of 0.578. This study demonstrates the feasibility and effectiveness of a single-modality, automated, and interpretable approach for peripheral prostate cancer localization based solely on DCE-MRI. Full article

Clinical rotations are an integral part of the veterinary curriculum. Their effect on knowledge, skills and self-perception, however, has been poorly investigated. The aim of this study was to evaluate the effect of a structured small animal clinical rotation on veterinary students in these three areas. Participating students were asked to complete an online questionnaire with questions assessing knowledge, skills and self-perception before and after their clinical rotation. A total of 61 students completed the questionnaire before and 43 after the clinical rotation, leading to 41 pre-post matches for self-assessment of skills and self-perception and 39 pairs for knowledge-based questions. The percentage of correctly answered knowledge-based questions increased statistically significantly but only by one correct answer on average. Participants rated their skills in performing specific tasks significantly higher after the clinical rotation compared to before. All participants assessed themselves as competent at history taking, performing a general examination and endotracheal intubation after the clinical rotation. However, 30–40% of participants disagreed at least partly with the sentence that they can perform neurological and ophthalmological examinations as well as interpret blood results on their own after the clinical rotation. Participants rated themselves significantly higher regarding their self-perception in the role of small animal practitioner after the clinical rotation than at the start of the rotation. The study indicated that the clinical rotation improved students’ self-assessment of their skills and attitude but did not lead to a significant improvement in knowledge. Full article

Background and Objectives: Acute pancreatitis (AP) is an acute inflammatory disease ranging from mild, self-limiting forms to severe presentations associated with high morbidity and mortality. Early prognostic assessment is crucial for guiding clinical management. This study aimed to evaluate the prognostic value of the red cell distribution width-to-albumin ratio (RDW/Alb, RAR) in relation to clinically relevant outcomes, including intensive care unit (ICU) admission and in-hospital mortality, in patients with AP. Materials and Methods: This retrospective study included 282 patients diagnosed with AP who were hospitalized at Mersin University Hospital between January 2019 and February 2024. Clinical, laboratory, and radiological data were retrospectively analyzed. The predictive performance of RAR was evaluated and compared with established clinical scoring systems, including bedside index for severity in acute pancreatitis (BISAP), systemic inflammatory response syndrome (SIRS), harmless acute pancreatitis score (HAPS), and pancreatitis activity scoring system (PASS). Results: The median RDW-to-albumin ratio (RAR) was 3.9 (range: 2.6–36.7). Receiver operating characteristic (ROC) curve analysis demonstrated that RAR showed good predictive performance for ICU admission (Area Under the Curve (AUC): 0.781; p < 0.001; optimal cut-off: 4.15) and high predictive performance for in-hospital mortality (AUC: 0.927; p < 0.001; optimal cut-off: 5.26). RAR exhibited limited but statistically significant discriminatory performance when compared with the BISAP score (AUC: 0.591; p = 0.017), whereas no significant predictive performance was observed in relation to PASS, HAPS, or SIRS scores. Conclusions: Within the context of this retrospective cohort, RAR is a simple, inexpensive, and readily available biomarker that may be associated with ICU admission and in-hospital mortality in patients with AP. Given the absence of standard severity endpoints such as persistent organ failure or pancreatic necrosis, these findings should not be interpreted as evidence of conventional disease severity prediction but rather as hypothesis-generating observations that warrant validation in larger prospective studies. Full article

Background: Seaweed consumption is a major source of dietary iodine in Korea, particularly among lactating women during the postpartum period. This practice raises concerns regarding short-term iodine excess and its potential effects on thyroid function. We examined the prevalence of thyroid disease and hormone abnormalities 1 year after childbirth among postpartum women with varying levels of seaweed-derived iodine intake. Methods: Between 17 July 2021 and 10 December 2021, 147 postpartum women were enrolled within two weeks after childbirth at postpartum care centers in Korea, which provide structured residential maternal and infant care, including standardized meals, during the early postpartum period. Participants provided informed consent and completed baseline questionnaires and dietary assessments. Iodine intake, including seaweed soup consumption during the 8-week postpartum period, and infant growth indicators were evaluated. A total of 81 participants completed the 1-year follow-up. At follow-up, dietary records, thyroid disease prevalence, hormone levels, urinary iodine concentration, and infant growth indicators were assessed. Results: At 1 year, none of the 81 participants had thyroid disease. The prevalence of abnormal triiodothyronine (T3), free thyroxine (FT4), and thyroid-stimulating hormone (TSH) was analyzed by iodine-intake quartiles, revealing no significant differences (T3: p = 0.4175; FT4: p = 0.1591; TSH: p = 0.9344). Conclusions: These findings suggest that the evidence regarding an association between short-term postpartum iodine intake and thyroid outcomes one year after childbirth remains inconclusive. Owing to the limited sample size, high attrition, and observational design, the study may have been underpowered to detect clinically meaningful differences, and potential effects cannot be excluded. Therefore, these results should be interpreted cautiously, and larger, well-designed longitudinal studies with repeated thyroid assessments are needed to better clarify the long-term implications of postpartum iodine exposure. Full article

Background and Objectives: Aneuploidy is the leading cause of implantation failure and miscarriage, with prevalence increasing with maternal age. Embryos classified as chaotic, characterized by the presence of five or more chromosomal abnormalities, and those with complex aneuploidies, defined by two to four abnormalities, represent a controversial category in preimplantation genetic testing for aneuploidy (PGT-A), as the potential for misclassification remains a significant concern. Materials and Methods: We performed a retrospective study at the Calla IVF Center, Oradea, analyzing 230 blastocysts grouped by maternal age (25–30, 31–35, 36–40, and 41–50 years). A trophoblast biopsy was performed on days 5–7, and the samples were analyzed by next-generation sequencing (NGS). Embryos were classified as euploid, aneuploid, mosaic, or chaotic. The 19 embryos initially diagnosed as chaotic were thawed and subjected to re-biopsy. Statistical analysis included descriptive statistics (chi-square tests and ANOVA) and multivariable regression models, with p < 0.05 as the criterion for statistical significance. Results: Aneuploidy increased with maternal age, from 29.6% in women aged 25–30 years to 68.7% in those aged 41–50 (p = 0.002). Poor-quality blastocysts exhibited higher aneuploidy rates (72.4%) than good-quality embryos (34.6%; p = 0.004). Chaotic embryos comprised 8.3% of the cohort. Upon re-biopsy, none were confirmed as euploid; all remained abnormal and were reassigned to aneuploid, mosaic, or persistently chaotic categories. This finding suggests that apparent euploid results reported elsewhere may reflect technical variability and sampling limitations in PGT-A rather than accurate chromosomal normalization. Conclusions: The prevalence of aneuploid embryos showed a progressive increase with advancing maternal age. Chaotic embryos are heterogeneous, and re-biopsy may help refine the interpretation of complex PGT-A profiles, supporting its role as a diagnostic and quality control tool rather than a strategy to identify euploid embryos. Our study offers novel insights through age-stratified analysis, the integration of morphology with genetics in a Romanian IVF cohort, and a detailed evaluation of chaotic embryos, providing clinical recommendations for patient counseling and embryo selection. Full article

This study examines reservoir-triggered seismicity (RTS) in Poland and Vietnam. The current state of individual RTS seismic networks necessitates detecting earthquakes from only a few stations. The number of P waves is often inadequate for phase association and event location, which underscores the importance of identifying S waves. Given that individual RTS cases may consist of only hundreds of events, it is crucial for algorithms to be trained on small datasets or to detect effectively using external, global training data. To evaluate this, we compared the efficiency of a deep learning global detection model, transfer learning to the RTS database, a specialized neural network designed for RTS, and manual detection of seismic signals. Transfer learning efficiency was database dependent. Additional interpretation and parametrization of detection results are assumed. Therefore, the emphasis is on phase detection, rather than phase picking accuracy, and detection sensitivity is more important than its specificity. Phase association plays a vital role in detecting seismic signals, facilitating the elimination of most false picks. As a result, the comparisons of detections were based on parameters related to the location of seismic events. The findings indicate that neither the automatic signal detection methods nor the manual methods alone are sufficient. However, their combination significantly enhances detectability. The final catalogs cover up to 30% more events compared to the previous manual. It fulfills the main aim of applying a neural network detector, which is to increase the number of seismic events in the catalog. It may also be further utilized in the research of the triggering process, such as identifying fluid paths and determining fault geometry. Full article