Search Results (581)

Urban cycling is increasingly recognised as a key component of sustainable urban mobility, yet many medium-sized cities lack integrated analytical tools to support evidence-based cycling infrastructure planning. This study proposes a GIS-based decision-support framework integrating a relative cycling impedance (Bike Cost) model, GIS-based network analysis, Origin–Destination (OD) Cost Matrix analysis, frequency-based corridor identification, and sensitivity analysis to evaluate cycling accessibility and prioritise cycling investments. The framework was applied to the central district of Burdur, Türkiye, using neighbourhood centres as origins and primary schools, middle schools, high schools, the university, parks, and tourism destinations as six destination categories. The results reveal that Burdur’s compact urban structure provides relatively high cycling accessibility within the urban core. In contrast, peripheral neighbourhoods experience lower accessibility because of fragmented network connectivity and higher cycling impedance. Spatial comparison with the existing cycling infrastructure revealed that the identified high-priority corridors do not overlap with the current cycling network, highlighting a clear mismatch between existing infrastructure and the corridors of greatest strategic importance. Sensitivity analysis confirmed that the priority corridors remained highly stable under alternative Bike Cost weighting scenarios, demonstrating the robustness of the proposed framework. These findings indicate that improving network continuity and connectivity is as important as expanding cycling infrastructure. The proposed framework provides a transferable and reproducible methodology for supporting evidence-based cycling infrastructure planning and sustainable urban mobility in medium-sized cities. Full article

We present a nanovoid-assisted ring-core fiber designed for low-bending-loss few-mode transmission. We develop a 3D model that accounts for stress-induced perturbation in bending-loss evaluation. Our model yields closer agreement with published experimental data compared to conventional conformal mapping. The nanovoid ring-core fiber reduces the bending loss of higher-order modes by up to three orders of magnitude, and improves the degeneracy of the higher-order pair nearly ten-fold at tight bending radii. The proposed fiber maintains the same intermodal separation compared to the reference ring-core fiber across all investigated radii. Full article

Background/Objectives: Dental age estimation is important in clinical and forensic practice, particularly when skeletal indicators are unavailable or compromised. Machine-learning models often achieve lower regression errors than conventional dental methods; however, whether this translates into better classification performance at legally relevant age thresholds remains unclear. This study compared the Demirjian and Willems methods with several machine learning models for overall accuracy and threshold-specific performance at the jurisdiction-specific ages of 12 and 15 years. Methods: A total of 1384 panoramic radiographs from individuals aged 8.00–15.99 years were retrospectively evaluated. The developmental stages of the seven left mandibular permanent teeth and sex were used as model inputs. Linear Regression, Decision Tree, Random Forest, Support Vector Regression, Multilayer Perceptron, Gradient Boosting, and XGBoost were trained using cross-validation and evaluated on an internal holdout set. Performance was assessed using regression errors, age-group-specific bias, sensitivity, specificity, balanced accuracy, and likelihood ratios. Results: Machine-learning models generally produced lower errors than conventional methods. In the holdout set, the lowest mean absolute error was 0.512 years for Support Vector Regression and Gradient Boosting, followed by 0.519 years for XGBoost, compared with 0.649 and 0.654 years for the Willems and Demirjian methods. However, lower regression error did not consistently improve threshold-specific performance. At 12 years, machine learning models increased sensitivity but reduced specificity and positive likelihood ratios relative to Willems. At 15 years, Linear Regression and Random Forest produced no positive predictions, whereas the better-performing models showed results similar to Willems. Conclusions: Lower regression error does not necessarily indicate better forensic threshold-specific classification performance. Dental age-estimation models should therefore be validated using threshold-specific likelihood ratios, classification metrics, and age-group-specific bias in addition to overall prediction errors. Full article

This study compared net photosynthesis (A), water-use efficiency (WUE), intrinsic water-use efficiency (iWUE), and A–${\mathrm{C}}_i$ curve-derived photosynthetic parameters (maximum carboxylation rate, $V_{\mathrm{cmax}}$; maximum electron transport rate, $J_{\max }$) across nine mangrove and semi-mangrove species at a tropical coastal site in Bali, Indonesia. Gas exchange measurements were conducted using portable photosynthesis systems (LI-6400 and LI-6800), and A–${\mathrm{C}}_i$ curves were fitted to the Farquhar–von Caemmerer–Berry (FvCB) model. Sonneratia alba Sm. exhibited the highest A (15.29 ± $2.39$ μmol ${\mathrm{m}}^{-2}{\mathrm{s}}^{-1}$), suggesting comparatively high photosynthetic performance, while Hibiscus tiliaceus L. and Pongamia pinnata (L.) Pierre showed the highest iWUE values (88–$97$ μmol ${\mathrm{mol}}^{-1}$), indicating relatively efficient carbon gain per unit stomatal conductance. Significant overall interspecific variation was detected in $V_{\mathrm{cmax}}$ and $J_{\max }$ ($p=0.003$ and $p=0.016$, respectively). The observed interspecific variation in A, iWUE, $V_{\mathrm{cmax}}$, and $J_{\max }$ suggests differences in photosynthetic characteristics and leaf-level water-use efficiency among species. These ecophysiological baseline data may contribute to species selection for mangrove restoration and to understanding physiological responses to environmental change in tropical coastal forests. Full article

Contemporary intensive care operates in an environment with high-complexity cases, large volumes of information, and vast physiological, biological, and therapeutic data, collected from laboratory results, investigations, and therapies for organ support, as well as from systems that operate in parallel. The lack of interoperability contributes to information overload, alarm fatigue, and delayed decision-making. The Smart ICU concept has been developed to address these limitations by integrating medical devices, information systems, and artificial intelligence into a unified system that allows interoperable data integration and predictive analytics. Aim: The purpose of this article is to provide a narrative review of the Smart ICU concept, with a specific focus on the cardiac intensive care unit. It summarizes Smart ICU architecture, data integration, clinical support, and applicability in monitoring perioperative inflammation in cardiac surgery. We describe the Smart ICU architecture, from data acquisition to storage and analytics, highlighting the differences between Smart ICU, artificial intelligence, and Tele-ICU, and we underline predictive analytics as a supportive tool, as well as its influence on clinical outcomes. Cardiac ICU application: Cardiac ICUs offer a data-dense, temporally well-defined model following cardiac surgery with cardiopulmonary bypass, where data concerning patients’ hemodynamics, perfusion data, and biological and inflammatory markers intertwine. Cardiac Smart ICU models could recognize early signs of hemodynamic compromise and low cardiac output states and identify early indicators of post-cardiac surgery complications. Neutrophil activation and complete blood count-derived indices may be used as dynamic biological data for Smart Cardiac ICU models. Conclusion: The Smart Cardiac ICU may support earlier risk stratification, and therefore earlier diagnostic and therapeutic interventions, but its clinical value requires prospective, multicenter validation. Cardiopulmonary bypass-induced inflammation may offer an ideal setting to integrate physiological, procedural, and immunological data into bedside predictive models. Full article

Background: Computed tomography (CT)-derived body composition has emerged as a promising prognostic biomarker in pancreatic ductal adenocarcinoma (PDAC). However, most previous studies have relied on baseline measurements and evaluated either resected or metastatic disease separately. We aimed to investigate the prognostic significance of both baseline and longitudinal CT-derived body composition changes in clinically distinct but therapeutically homogeneous cohorts of patients with PDAC receiving FOLFIRINOX. Methods: This retrospective single-center study included 98 consecutive patients with histologically confirmed PDAC treated with FOLFIRINOX between 2018 and 2025. Forty-seven patients underwent curative-intent resection followed by adjuvant modified FOLFIRINOX, whereas 51 patients with unresectable metastatic disease received first-line FOLFIRINOX. Skeletal muscle index (SMI) and visceral adipose tissue (VAT) were quantified on serial CT scans obtained at the third lumbar vertebral level before treatment and during therapy. Follow-up CT scans suitable for longitudinal body composition analysis were available for 46 of 51 patients (90.2%) in the metastatic cohort. Overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS) were estimated using the Kaplan–Meier method. Univariable and multivariable Cox proportional hazards regression analyses were performed to identify independent prognostic factors. Results: Baseline CT-derived body composition parameters were not independently associated with survival in either cohort. In the resected cohort, preservation of visceral adiposity during treatment (follow-up VAT > 100 cm²) independently predicted improved OS in a multivariable model including three covariates (HR 0.460, 95% CI 0.220–0.960; p = 0.039). Median DFS and OS were 11.7 months (95% CI 6.5–16.9) and 20.9 months (95% CI 12.1–29.7), respectively. In the metastatic cohort, treatment-related skeletal muscle loss (ΔSMI) independently predicted inferior OS in a multivariable model including four covariates (HR 0.949, 95% CI 0.913–0.986; p = 0.008), while lung metastasis was also independently associated with worse survival (HR 5.792, 95% CI 1.880–17.841; p = 0.002). Median PFS and OS were 9.7 months (95% CI 8.0–11.4) and 12.7 months (95% CI 9.9–15.5), respectively. Conclusions: Longitudinal CT-derived body composition changes may provide additional prognostic information beyond baseline measurements in patients with PDAC receiving FOLFIRINOX. Preservation of visceral adiposity was associated with improved survival following curative-intent resection, whereas greater treatment-related skeletal muscle loss was associated with poorer survival in metastatic disease. These findings suggest that the prognostic relevance of body composition may vary according to disease stage and should be considered hypothesis-generating pending validation in larger prospective multicenter studies. Full article

Background: Photodynamic therapy (PDT) is a cancer treatment that combines a photosensitizer (PS), light, and oxygen to generate reactive oxygen species (ROS), leading to tumor cell death. PDT efficacy depends largely on PS accumulation within tumors, prompting the development of third-generation PSs and nanotechnology-based delivery systems. Among these, BODIPYs (4,4-difluoro-4-bora-3a,4a-diaza-s-indacene) are promising PSs due to their favorable photophysical properties, while liposomes improve drug delivery, cellular uptake, and sustained release profiles. This study describes the synthesis of two novel BODIPY derivatives differing in the position of a methyl ester group on the meso-phenyl ring, their incorporation into liposomes, and evaluation of PDT efficacy. Methods: Cellular uptake of BODIPY-loaded liposomes, intracellular ROS generation, apoptosis, necrosis, and lipid peroxidation were assessed by flow cytometry in colorectal and ovarian cancer cell lines. The antitumor activity of the liposomal formulations was further evaluated in both 2D and 3D models using MTT and clonogenic assays. The involvement of ferroptosis and necroptosis in PDT-induced cell death was also investigated. Results: Liposomal formulations significantly enhanced cellular uptake compared with free compounds. Following light activation, both formulations induced potent antitumor effects through multiple cell death mechanisms, including canonical and non-canonical pathways, and maintained strong efficacy in 3D tumor spheroids. Conclusions: Liposome-encapsulated BODIPYs represent promising PDT agents by improving cellular uptake and eliciting robust antitumor activity through complementary cell death mechanisms. Furthermore, the methyl ester substituent on the meso-phenyl ring provides a versatile platform for future conjugation with targeting ligands, supporting the development of third-generation, tumor-targeted photosensitizers and warranting further preclinical investigation. Full article

Background: Geranium robertianum L. (GR) is a perennial herbaceous plant that belongs to the Geraniaceae family, widely distributed across temperate regions of Europe, Asia, North Africa, and America. It has been used in traditional ethnomedicine for its antibacterial, haemostatic, and anti-allergic properties in the treatment of oropharyngeal conditions, wounds, and inflammatory diseases. Beyond these traditional applications, pharmacological studies have documented several biological activities of its extracts and constituents. Objective: The aim of this review is to evaluate the therapeutic potential of GR by examining the phytochemical composition and the pharmacological properties relevant to dental medicine. Methods: A literature screening was conducted (PubMed, Google Scholar, PubMed Central), with focus on studies concerning phytochemical composition, pharmacological effects, and potential applications of GR and its major bioactive constituents in oral health. Results: Phytochemical analysis reveals that GR is characterized by a rich content of polyphenols, particularly tannins, flavonoids, and phenolic acids, with geraniin, ellagic acid, gallic acid, quercetin, and kaempferol identified as its principal bioactive constituents. Recent studies have confirmed that these constituents are responsible for the plant’s therapeutic potential in oral medicine. These compounds possess a variety of effects, from antibacterial and anti-inflammatory properties to antifungal and antiviral activities, as well as antioxidant, wound-healing, and anticancer benefits relevant to dental medicine. Conclusions: Despite its long-standing traditional use, GR remains largely underexplored in the context of oral health. This gap in the literature highlights GR as a promising phytotherapeutic candidate, calling for further in vitro and in vivo studies in order to investigate its efficacy and establish its mechanism of action in dental medicine. Full article

Background/Objectives: Dental caries is one of the most common oral diseases worldwide, and early diagnosis is essential for effective treatment. However, detecting carious lesions in panoramic radiographs is challenging because of low image contrast, anatomical complexity, and overlapping structures. This study aimed to develop an improved object detection framework for dental caries localization in panoramic radiographs. Methods: A modified YOLOv8 architecture was developed by integrating ConvNeXtV2 blocks into the Cross-Stage Partial (C2f) modules to enhance feature extraction and gradient propagation. To improve detection performance and reduce overfitting, key training hyperparameters were optimized using a genetic algorithm. The proposed model was evaluated on a dataset of 474 panoramic dental radiographs annotated by experts using bounding boxes. Results: The optimized model achieved a box precision of 78.4%, a recall of 53.6%, and a mean average precision at 50% intersection-over-union threshold (AP50) of 62.6%. Compared with the baseline YOLOv8 model, the proposed approach improved precision and AP50. Visual and quantitative analyses demonstrated that the ConvNeXtV2-enhanced architecture enabled more accurate localization of carious regions. Conclusions: The results indicate that combining ConvNeXtV2-based architectural enhancement with genetic algorithm-based hyperparameter optimization is an effective strategy for dental caries localization in panoramic radiographs. Although recall remains a limitation, the proposed framework shows potential as a computer-aided diagnostic tool for supporting clinical caries assessment. Full article

Traditional Romanian Pălincă is a protected fruit spirit obtained from fermented fruit matrices, whereas honey- and botanical-enriched Pălincă-based beverages require integrated compositional and technological characterization. This study investigated the physicochemical, chromatic, antioxidant, and ICP-MS elemental profiles of experimental beverages prepared from grape- and plum-based Pălincă distillates enriched with honey, blackthorn, rosehip, or wood-associated materials under controlled artisanal conditions. Chemometric tools were applied to evaluate ingredient- and process-related differentiation, while target hazard quotient and hazard index calculations were retained as a concise screening-level elemental safety assessment. Honey enrichment produced the strongest compositional changes, mainly reflected by increased soluble solids, conductivity, turbidity, chromatic intensity, and mineral content, together with lower final alcoholic strength. Fruit-macerated and mixed honey-botanical beverages were associated with higher phenolic content and antioxidant activity. ICP-MS profiling highlighted potassium as the dominant macroelement, while Cu, Fe, Zn, and Mn were the main technological trace elements. Potentially toxic elements remained at low concentrations, and the screening-level THQ/HI assessment indicated low non-carcinogenic elemental risk under the applied exposure scenario. Overall, the results show that ICP-MS elemental profiling can support the technological and ingredient-related differentiation of experimental Pălincă-based beverages, while toxicological interpretation should remain limited to elemental exposure screening. Full article

Background/Objective: Early identification of high-risk patients with acute upper gastrointestinal bleeding (UGIB) is essential for appropriate triage, timely intervention, and optimal resource allocation in the emergency department (ED). Existing risk scores have limitations in practicality and predictive performance. We aimed to develop and internally validate a novel pre-endoscopic score based solely on readily available ED variables to predict clinically significant high-risk outcomes in UGIB. To develop a novel pre-endoscopic risk score using routinely available emergency department parameters and to evaluate its predictive performance for clinically significant high-risk outcomes in patients with acute upper gastrointestinal bleeding. Methods: This retrospective single-center prognostic model development study with internal validation included 312 adults with endoscopy-confirmed non-variceal UGIB presenting to a tertiary ED between January 2023 and January 2025. High-risk status was defined as the occurrence of at least one of the following during hospitalization: blood transfusion, endoscopic hemostatic intervention, intensive care unit admission, rebleeding within 30 days, or in-hospital mortality. This composite endpoint included both clinical outcomes and management-related interventions. Independent predictors were identified using multivariable logistic regression. The HOLD-B score (Hemoglobin, Lactate, Diastolic blood pressure, and Blood urea nitrogen) was derived from significant variables. Discriminative performance was evaluated using receiver operating characteristic (ROC) analysis and compared with established scores. Results: Of the study population, 51.6% were classified as high risk. Hemoglobin ≤8.05 g/dL, blood urea nitrogen >44.5 mg/dL, lactate >2.15 mmol/L, and diastolic blood pressure ≤67.5 mmHg were independent predictors of high-risk status. The HOLD-B score (0–10 points) demonstrated good discriminative performance (AUC 0.798; 95% CI 0.749–0.846), outperforming Rockall (0.687), GBS (0.733), AIMS65 (0.576), ABC (0.626), ABL (0.716), and Pre-RS (0.599). At a cutoff ≥4, the HOLD-B score yielded 71.4% sensitivity and 77.5% specificity. However, in a sensitivity analysis excluding blood transfusion from the composite endpoint, the AUC decreased from 0.798 to 0.676, and hemoglobin was no longer an independent predictor, indicating that part of the observed model performance may have been influenced by the inclusion of transfusion in the composite endpoint. DeLong analysis demonstrated statistically significant differences compared with several established scores (p < 0.01). Conclusions: In this endoscopy-confirmed non-variceal UGIB cohort, the HOLD-B score demonstrated promising discriminative performance for predicting a clinically significant composite outcome using only emergency department variables. Because this composite outcome included management-related interventions in addition to clinical events, the model should not be interpreted as predicting individual clinical endpoints. Prospective external validation in broader emergency department populations is required before routine clinical implementation. Full article

Intensive care medicine represents one of the most challenging setting in modern healthcare, where specific mechanisms intertwine and form a dynamic biological model, where organ dysfunction can easily evolve to multi-organ dysfunction, continuously reshaping the patient’s clinical course. The critically ill patient represents a biological system resulted from interaction between maladaptive and adaptative mechanisms, therefore generates a large volume of data that can exceeds human cognitive capacity. Artificial intelligence can integrate multimodal physiological, laboratory, and clinical data into a dynamic representation of the patient’s biological trajectory. AI-tools and machine learning technologies have evolved to potential clinical support tools, with great perspectives for future implementation, but currently with limited use in clinical practice. This article is a narrative review of artificial intelligence in ICU, aiming to present current evidence and limitations. Full article

Maternal obesity and a Western diet high in fat and sugars are increasing worldwide and may contribute to rising neurodevelopmental and neurobehavioral disorders in offspring. Both human and animal studies link these factors to adverse outcomes. However, the cellular mechanisms driving altered early-life neurogenesis, particularly in the hippocampus, remain unclear. To assess fructose effects on neural stem cell (NSC) differentiation and neuronal morphology, hippocampal NSCs from E12.5 C57BL/6 mouse embryos were cultured and treated with fructose (8.75 or 12.5 mM) for 7 days. Neuronal and astrocyte populations, along with neuronal morphology, were analyzed by immunofluorescence, and protein expression was assessed by Western blot. Fructose treatment significantly reduced neuronal and increased astrocyte counts, leading to a decreased neuron-to-astrocyte ratio compared to controls. These findings were supported by decreased MAP2 (neuronal) and increased GFAP (astrocyte) protein expression. Furthermore, fructose also significantly reduced neurite lengths without affecting neurite number. Morphological analysis revealed decreased soma size, reduced area/perimeter, and altered soma area-to-perimeter ratios, indicating impaired structural integrity. Thus, fructose exposure shifts NSC differentiation toward an astroglial lineage while suppressing neuronal development and impairs neuronal growth and structural complexity. Future studies are necessary to determine the influence of these cellular changes on synaptic plasticity and learning, as well as memory. Full article

Emergency triage artificial intelligence (AI) is usually evaluated as a centralized prediction problem, yet many hospitals cannot pool patient-level records across governance boundaries. We studied a narrower and safety-relevant task: within-dataset federated simulation for detecting Emergency Severity Index (ESI)-2/ESI-1 encounters using information available before or during triage. Zendal is a reproducible within-dataset federated evaluation framework for critical emergency triage escalation. It defines a triage-time feature boundary, site-aligned cross-silo splits, training-split-only imputation and scaling, validation-selected operating thresholds, bootstrap uncertainty, decision-curve analysis, calibration analysis, local-only baselines, per-site and subgroup diagnostics, and leave-one-site-out transfer stress testing. In 558,029 public emergency department encounters, the endpoint was binary critical-band ESI assignment (ESI-2/ESI-1 vs. ESI-3/ESI-4/ESI-5), not an independently adjudicated clinical outcome. The feature set contained 240 pre-triage variables: demographics, arrival metadata, previous utilization counts, triage vital signs, and chief-complaint indicators; disposition, diagnoses, medications, procedures, imaging, laboratories, and target-derived imputations were excluded. A centralized logistic reference reached AUROC 0.879 (95% CI: 0.876–0.882), average precision 0.767, sensitivity 0.900 (95% CI: 0.895–0.905), specificity 0.654 (95% CI: 0.649–0.658), and positive predictive value (PPV) 0.530 at a validation-selected threshold targeting at least 90% sensitivity. Federated logistic training across three site-aligned clients nearly matched the centralized reference: AUROC 0.878 (95% CI: 0.875–0.880), average precision 0.767, sensitivity 0.900 (95% CI: 0.896–0.904), specificity 0.650 (95% CI: 0.645–0.655), and PPV 0.527. Post hoc Platt scaling reduced the federated expected calibration error from 0.160 to 0.011, supporting recalibration before direct risk interpretation. Model-guided escalation produced a positive mean decision-curve net benefit across thresholds 0.05–0.50 (0.164 for federated logistic vs. −0.003 for flag-all and 0 for flag-none). In leave-one-site-out transfer tests, federated source-site models retained AUROC 0.846–0.876 and sensitivity 0.871–0.909 on held-out sites, but the low-prevalence site had PPV 0.107 and negative net benefit. An open MIMIC-IV-ED Demo smoke test supported schema feasibility but was too small for external validation. Zendal is therefore a transparent framework for no-raw-row-exchange triage model evaluation, not a deployment-ready clinical system or a demonstration of formal privacy protection. Full article

Background: The aim of this study was to evaluate the clinical and diagnostic utility of metabolic and systemic inflammatory marker indices in adolescent patients with polycystic ovary syndrome (PCOS). Methods: This retrospective case–control study included 63 adolescent girls diagnosed with PCOS, based on the strict presence of both menstrual irregularity and hyperandrogenism, and 63 healthy controls matched for age and body mass index (BMI). Fasting blood glucose (FBG), fasting insulin (FI), and lipid profiles were measured. Metabolic indices, including the Triglyceride-Glucose (TyG) index, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), and Metabolic Score for Insulin Resistance (METS-IR), as well as systemic inflammatory indices (Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and the Aggregate Index of Systemic Inflammation (AISI)), were calculated. The diagnostic performance of these markers was evaluated using Receiver Operating Characteristic (ROC) curves and binary logistic regression analyses. Results: No statistically significant difference was observed between the PCOS and control groups in terms of age and BMI (p > 0.05). FBG levels (p = 0.001) and the TyG index (p = 0.028) were found to be significantly higher in the PCOS group. However, no significant differences were observed between the groups in terms of other metabolic indices (HOMA-IR, METS-IR) and systemic inflammatory markers (SII, SIRI, AISI). In the ROC analysis, only the TyG index demonstrated statistically significant but weak discriminatory power for PCOS (AUC = 0.614; 95% CI: 0.515–0.712; p = 0.028). Furthermore, binary logistic regression analysis revealed that the TyG index was not an independent predictor of PCOS after adjustment. Conclusions: While early metabolic signals such as elevated FBG and TyG index were detected, the evaluated systemic inflammatory indices did not demonstrate significant differences. However, the weak discriminatory capacity of the TyG index and the failure of other composite indices restrict their clinical utility. Therefore, these biomarkers cannot be recommended as reliable standalone diagnostic tools in adolescent PCOS. Full article