Search Results (15,277)

Voltage security assessment is increasingly challenged by stochastic demand growth and localized stress patterns that are not well represented by deterministic, single-snapshot analyses. This paper proposes a fully steady-state probabilistic stress-testing framework based on Monte Carlo simulation and Newton–Raphson AC power flow, jointly evaluating the minimum bus voltage magnitude $V_{min}$ (voltage-floor adequacy) and the scenario maximum Fast Voltage Stability Index $FVS{I}_{max}$ (worst-case line stress). Stress is injected selectively on screened weak buses by sampling a random stress footprint and intensity across three progressive levels (L1–L3), while preserving the local power factor. The approach is demonstrated on IEEE 14-, 30-, and 118-bus benchmark systems using $N=2000$ realizations per level, with 100% convergence across all cases. Across all systems, results show a consistent, monotone degradation of the voltage floor and a systematic increase in violation risk as stress intensifies. For the IEEE 14 system, the voltage-risk profile escalates rapidly, with $\mathbb{P}(V_{min}<0.90)$ rising from 0.16 (L1) to 0.54 (L3), while the worst-case FVSI tail strengthens markedly (p95 increasing from 0.1455 to 0.2081), indicating a growing likelihood of severe voltage-stress events. In contrast, the IEEE 30 and IEEE 118 systems exhibit milder shifts in central voltage levels but maintain substantial exposure relative to the 0.95 pu planning threshold, with $\mathbb{P}(V_{min}<0.95)$ reaching 0.79 and 0.74 at L3, respectively. Beyond risk magnitudes, the framework reveals a nontrivial structural phenomenon in worst-case line stress: as system size increases, stochastic stress outcomes become increasingly concentrated into a small number of dominant transmission corridors. Recurrence analysis at the highest stress level shows fragmented criticality in IEEE 14 (Top-3 lines sharing criticality), near-total dominance by a single corridor in IEEE 30 (>92% of cases), and complete dominance collapse in IEEE 118 (one corridor governing 100% of $FVS{I}_{max}$ events). These results demonstrate that probabilistic stress-testing can simultaneously quantify voltage-risk escalation and expose hidden structural bottlenecks that remain invisible under deterministic screening, providing a scalable diagnostic tool for planning-stage monitoring and reinforcement prioritization. Full article

Phospodiesterase 4 (PDE4) has long been an attractive target not only for the anti-inflammatory therapy in respiratory diseases, but also for other pathologies such as psoriatic arthritis and atopic dermatitis. In this study, we report the synthesis of 5-acetyl-2-ethyl-6-phenyl-3(2H)-pyridazinones differently substituted at position 4 with a variety of aryl/alkylamines, which act as potent PDE4B1 inhibitors in the low nanomolar range. The selectivity toward PDE4A4, PDE4D3 and HARBS, as well as the ability to inhibit TNFα production in human whole blood (hWB), was also evaluated for the most potent products, resulting in a small cluster of compounds with an interesting profile and two selected products (3a and 3k) have been in depth investigated with additional in vitro tests on metabolism and in vivo studies. Finally, molecular docking and minimization of the ligand-enzyme complexes were carried out. Full article

Psidium cattleyanum Sabine (strawberry guava, araçá) is an ethnomedicinal plant with reputed health benefits; however, its potential for treating skin disorders remains underexplored. This study aimed to characterize the phytochemical profile of P. cattleyanum leaves from Cabo Verde and evaluate their bioactivity relevant to skin health. Phytochemical analysis was performed using high-performance liquid chromatography-mass spectrometry (LC-MS) and spectrophotometric assays. Key biological activities were assessed in vitro, including antioxidant capacity (free radical scavenging assays), anti-aging enzyme inhibition (collagenase, elastase, and tyrosinase), and antibacterial activity against skin pathogens (agar diffusion, minimum inhibitory concentration, and combination studies with standard antibiotics). Cytotoxicity was evaluated using Vero cells (MTT assay). Additionally, a topical cream containing the leaf extract was formulated and subjected to physicochemical stability and sensory testing. LC-MS revealed a rich polyphenolic composition in the leaf extract, including abundant phenolic acids (gallic and ellagic acid derivatives) and flavonoid glycosides. The extract exhibited a high total phenolic content and strong antioxidant activity in DPPH/ABTS assays. It showed potent inhibition of collagenase, elastase, and tyrosinase, indicating an anti-aging effect against wrinkle formation and hyperpigmentation. The extract also demonstrated broad antimicrobial efficacy against skin-associated bacteria, such as Staphylococcus aureus and Cutibacterium acnes, with no antagonism and partial synergism observed when combined with certain antibiotics. The P. cattleyanum extract was successfully incorporated into a cream formulation that remained physically and chemically stable (no phase separation, consistent droplet size, and pH) over 90 days, with good homogeneity and acceptable sensory characteristics (neutral odor, smooth texture, and good spreadability). P. cattleyanum leaves from Cabo Verde are a rich source of bioactive compounds with multifunctional dermatological benefits. This study demonstrates that the P. cattleyanum leaf extract exhibits significant antioxidant, antimicrobial, and anti-aging activities in vitro, supporting its potential use as a natural ingredient for skin care. Full article

Background: Periapical pathosis in periapical radiographs must be properly diagnosed for the success of endodontic treatment but is often muddled by 2D imaging limitations and subjective interpretation. Artificial intelligence (AI) offers a solution, but whether the diagnostic granularity of AI versus human clinicians in everyday clinical practice has been adequately explored remains to be addressed. The purpose of this study was to evaluate the diagnostic accuracy of ChatGPT-5 in detecting periapical radiographic abnormalities compared with the three-expert consensus reference standard. Methods: In this diagnostic accuracy retrospective study, 270 periapical radiographs were independently read by a large language model (ChatGPT-5) and a three-board-certified oral radiologist consensus. The AI was given a standardized prompt to label radiographic features, like the presence of periapical radiolucency, border, shape, and integrity of lamina dura. Diagnostic accuracy, agreement (Cohen’s κ), and predictors of correct AI classification were compared with the expert consensus reference standard. Results: ChatGPT-5 demonstrated high sensitivity (87.5%) but low specificity (12.5%), resulting in an overall diagnostic accuracy of 50.0%. This performance profile reflects a tendency toward over-identification of pathology, with the model classifying 87.5% of radiographs as abnormal compared with 50.0% by expert consensus. Agreement was almost perfect for anatomical localization (arch, κ = 0.857) but poor for binary abnormality detection (κ = 0.000). For morphological descriptors, statistically significant disagreement was observed for lesion border characterization (κ = 0.127; p < 0.001), whereas lesion shape demonstrated only descriptive divergence without reaching statistical significance (κ = 0.359). Root resorption assessment also differed significantly between evaluators (p = 0.046). Regression analysis showed that well-defined corticated borders (OR = 60.25, p < 0.001) and first molar-associated lesions (OR = 32.55, p < 0.001) were significant predictors of correct AI classification. Conclusions: This study demonstrates that while ChatGPT-5 Vision can visually interpret periapical radiographs with high sensitivity, limited specificity and inconsistent morphological feature characterization restrict its reliability for independent clinical diagnosis. The AI system tends to over-diagnose systematically and categorizes lesions more structurally and defined compared to dental experts. AI has the potential for being optimized as a sensitive first-screening test, but its findings must be validated by dental professionals to avoid false positives and ensure proper characterization. Full article

Background/Objectives: Obesity is characterized by chronic low-grade inflammation, which plays a central role in the development of its metabolic complications. The genetic factors influencing this inflammatory phenotype remain incompletely understood. This study aimed to analyze the associations of functional polymorphisms in genes involved in extracellular matrix remodeling (MMP2, MMP9, MMP12, COL1A1), metabolism (MTHFR, CYP3A5), and vascular regulation (NOS3, AGTR1) with plasma cytokine profiles and to identify inflammatory subphenotypes in patients with obesity. Methods: The study included 127 individuals, comprising 73 patients with excess body weight (body mass index, BMI ≥ 25 kg/m²) and 54 individuals with normal weight (BMI 18.5–24.9 kg/m²). Genotyping of selected polymorphisms was performed using real-time PCR. Plasma concentrations of 47 cytokines and chemokines were measured by multiplex immunoassay. Results: Nominally significant associations between genetic variants and cytokine levels were identified. Polymorphisms COL1A1 rs1107946 (CA genotype) and MMP9 rs17576 (AG genotype) were associated with a favorable inflammatory profile (decreased IL-6 and increased IL-10, respectively). In contrast, the AGTR1 rs5186 (AC genotipe) variant was associated with elevated TNF-α, IP-10/CXCL10, while the MTHFR rs1801131 (AC genotipe) variant was linked to increased MIP-1β/CCL4, both reflecting a pro-inflammatory shift. Complex, pleiotropic associations were observed for MMP2 rs243865 (elevated IL-7 and Fractalkine/CX3CL1) and NOS3 rs1799983 (elevated MCP-1/CCL2 and Eotaxin/CCL11). Cluster analysis revealed distinct patient subpopulations with differing inflammatory signatures. In one well-defined subgroup, an exploratory model (test R² = 0.537) identified IL-8, IL-15, and albumin as candidate biomarkers predictive of BMI. Conclusions: The study identifies candidate genetic polymorphisms and inflammatory biomarkers associated with distinct patterns of systemic inflammation in obesity. These hypothesis-generating findings underscore the phenotypic heterogeneity of obesity and provide a basis for further research into the stratification of patients by the risk of developing metabolic complications. Full article

Background/Objectives: Antimicrobial resistance in food–animal environments threatens sustainable production and public health, yet small farms remain poorly characterized as potential reservoirs of antimicrobial resistant bacteria. To address this, we investigated the prevalence and antimicrobial resistance profiles of Escherichia coli, Klebsiella spp., and Enterococcus spp. from small-scale cattle farms in Tennessee, USA. Methods: Over one year, 153 environmental samples (soil, manure, water) were collected from 17 farms. Target bacteria were isolated and confirmed using selective agar, biochemical tests, and PCR, and tested against 12 antibiotics using the Kirby–Bauer disk diffusion test. Multiple Antibiotic Resistance Index (MARI) and multidrug resistance (MDR) profiles were summarized. A complementary farmer survey of 26 farmers captured veterinary access, antibiotic use, manure handling, record keeping, and awareness of antimicrobial resistance. Results: Prevalence was highest for Enterococcus spp. (41.8%), followed by E. coli (23.5%) and Klebsiella spp. (12.4%). Seasonal variation was significant for E. coli and Enterococcus (p < 0.05). Winter manure yielded highest detection of E. coli (55.6%) and Enterococcus (53.8%), whereas Klebsiella peaked in Fall soil (19.1%). Resistance patterns varied across species, with Enterococcus showing consistent resistance to all three. E. coli frequently resisted erythromycin, ampicillin, and azithromycin; and Klebsiella commonly resisted erythromycin, ampicillin, and cefotaxime, though some of these reflect intrinsic resistance rather than acquired clinical resistance. MARI values were 0.92 in manure and soil, identifying them as high-risk reservoirs. We identified 29 distinct MDR pattern. Bipartite network visualization highlighted “resistance hubs” around erythromycin, ampicillin, and vancomycin, particularly in Enterococcus. In our study, 76.9% of farmers consulted veterinarians before antibiotic use, 57.7% kept written antibiotic records, and 65.4% were aware of AMR as a public health issue. Small-scale cattle farms are potential reservoirs of multidrug resistant commensal bacteria. Conclusions: These findings provide an evidence-based foundation to guide targeted antimicrobial stewardship and promote sustainable management practices in small-scale food animal farms. Full article

Background. Over the past decade, the World Health Organization has highlighted the need for rapid molecular diagnostics as first-line tools for detecting Mycobacterium tuberculosis complex (MTBC) to strengthen global tuberculosis control. At the same time, infections caused by non-tuberculous mycobacteria (NTM) have become increasingly prevalent, particularly in low TB-burden countries such as Italy. This changing epidemiological scenario underscores the necessity for fast and reliable methods capable of distinguishing NTM from MTBC, a critical step for guiding appropriate treatment. This study evaluated the diagnostic accuracy and potential applications of the STANDARD™ M10 MTB/NTM assay, which simultaneously detects and differentiates MTBC and NTM. Methods. A total of 155 clinical specimens (78.1% respiratory) from patients with suspected mycobacterial infection were tested by fluorescence microscopy, GeneXpert MTB/RIF Ultra (respiratory samples only), STANDARD™ M10 MTB/NTM and culture, used as the reference method. Results. Culture detected MTBC in 54% and NTM (predominantly slow-growing species) in 46% of samples. STANDARD™ M10 showed overall sensitivity and specificity of 70% and 100%, respectively. For MTBC, sensitivity was 85.1% with almost perfect agreement with culture (κ = 0.866), while for NTM, sensitivity was 50% with moderate agreement (κ = 0.566). Sensitivity decreased in microscopy-negative/culture-positive specimens, particularly for NTM. Compared with GeneXpert MTB/RIF Ultra, STANDARD™ M10 exhibited slightly lower sensitivity for MTBC but retained excellent specificity. Conclusions. STANDARD™ M10 MTB/NTM represents a rapid, fully automated tool to support early etiological diagnosis and MTB/NTM differentiation, mainly in selected samples or high-risk patients, but it does not replace culture or molecular tests providing species identification and MTBC drug-resistance profiling. Full article

Vertigoheel is a multicomponent medicinal product for the treatment of vertigo and dizziness, containing Anamirta cocculus, Conium maculatum, Ambra grisea, and Petroleum rectificatum. Although clinical efficacy has been reported, the chemical composition and underlying mechanisms remain incompletely characterized. Here, we applied ultra-high-performance liquid chromatography coupled to time-of-flight mass spectrometry (UHPLC-ToF-MS) to profile extracts of each ingredient and the final formulations. Untargeted analysis revealed 68,622 molecular features, and multivariate statistics highlighted ingredient-specific metabolites. Representative markers included picrotoxinin and picrotin from Anamirta cocculus, coniine and N-methylconiine from Conium maculatum, ambrinol and ambroxide from Ambra grisea, and santalyl phenylacetate and mercaptostearic acid from Petroleum rectificatum. Two compounds per ingredient were further quantified by targeted UHPLC-MS/MS, confirming their presence in drops and tablets at nanogram-per-dose levels with moderate variability across batches. These findings demonstrate that Vertigoheel retains characteristic constituents from its natural sources in all tested formulations. The established protocol enabled absolute quantification of neuroactive molecules such as picrotoxinin and coniine with minimal work-up. This molecular characterization provides new insight into Vertigoheel’s composition and supports further investigation of its mechanism of action using network pharmacology approaches. Full article

This study examines a top-chord-free open-web steel-truss composite floor in which the concrete slab functionally replaces the traditional top chord and works jointly with vertical square-tube web members and a square-tube bottom chord. Two scaled specimens—with and without concrete infill in the end shear-bending blocks—were fabricated and tested under static loading. The load–deflection response delineates three stages: elastic, elastic–plastic, and failure. Tests show that infilling the shear-bending blocks does not enhance global mechanical performance. In the elastic range, the mid-span open-web section satisfies the plane-section assumption with a linear strain profile, whereas the solid-web section exhibits a bilinear distribution. A validated ANSYS finite-element model reproduces the measured responses and supports a parametric study showing that span-to-depth ratio, opening-to-span ratio, slab (flange) thickness, and width-to-span ratio significantly affect ultimate capacity and deflection. Design recommendations are proposed: span-to-depth ratios of 11–14 and opening-to-span ratios of 0.04–0.07. An equivalent-stiffness-based simplified linear-elastic deflection formula with a reduction factor is derived, which accurately tracks deflection evolution and enables serviceability-driven selection of web spacing and overall structural depth. Full article

Background: Dendritic and histiocytic cell sarcoma (DHCS) and clear cell sarcoma (CCS) are ultra-rare soft-tissue sarcomas characterized by diagnostic ambiguity, limited treatment guidelines, and poor outcomes. Their rarity has restricted the development of evidence-based management strategies, leaving clinical decisions reliant on small case series and institutional experience. DHCS typically presents without a unifying molecular driver and is often misclassified without comprehensive immunophenotyping. CCS is defined by EWSR1-ATF1/CREB1 fusions but exhibits low responsiveness to conventional chemotherapy. There remains a clear need to clarify natural history, therapeutic responses, and molecular characteristics in both. Methods: We conducted a retrospective cohort study of adult patients with histologically confirmed DHCS or CCS seen at The Ohio State University Comprehensive Cancer Center between 2010 and 2022. Demographics, treatment modalities, clinical outcomes, and molecular profiles were extracted and analyzed descriptively. Time to progression (TTP) and progression rates by treatment modality were recorded. A structured literature review was conducted to provide context for the findings. Results: Outcomes are descriptive and cohort-specific, reflecting institutional experience rather than generalizable estimates. Total of 10 patients with DHCS and 5 with CCS were evaluable. Most DHCS patients presented with metastatic disease. Among DHCS patients who received systemic therapies, five of eight (62.5%) experienced progression during or shortly after treatment. Among CCS patients who received systemic therapies, three of four (75%) progressed during or shortly after treatment. Overall mortality occurred in 4 of 10 DHCS patients (40%) and 3 of 5 CCS patients (60%). TP53 mutations were identified in four of seven next-generation sequencing (NGS)-tested DHCS cases, and PD-L1 positivity was detected in five of seven tested DHCS cases and one of five tested CCS cases. Conclusions: Despite multimodal treatment, this referral-based cohort of patients with ultra-rare DHCS and CCS showed high rates of progression and mortality. Our findings underscore the urgent need for multi-institutional collaboration and biomarker-driven clinical trials to guide management of these ultra-rare sarcoma subtypes. Full article

Background: The Glycemic Index (GI) serves as a critical indicator of carbohydrate quality linked to postprandial glycemic response. As “Low-GI” claims proliferate on front-of-pack labels, it remains unclear how consumers value this complex signal. This study quantifies willingness to pay (WTP) for Low-GI labeling and tests a “motivation–capability” mechanism, positing that health orientation motivates label use, while objective Low-GI knowledge facilitates targeted evaluation across nutritional contexts. Methods: A discrete choice experiment was conducted in China using plain yogurt (N = 910). Mixed logit models analyzed how the valuation of the Low-GI claim is moderated by carbohydrate context, health orientation, and objective knowledge. Results: Results indicate a significant average premium for Low-GI labeling, with health orientation acting as a consistent motivational amplifier. Objective knowledge functions as a critical moderator interacting with carbohydrate context, driving label valuation only in specific low- or high-carbohydrate profiles while triggering skepticism in regular carbohydrate ones. Conclusions: These findings suggest that the public health effectiveness of emerging physiological claims depends jointly on consumer motivation and label-specific literacy. Consequently, policy interventions should combine label standardization with targeted education, equipping consumers with the capability to decode the claim’s physiological meaning rather than relying on a generalized health halo. Full article

This study compared force plate-derived lower-limb strength and power metrics between selected and non-selected female basketball players for major international competitions. Thirty-two female players attending the final national team camps for the 2022 World Cup and the 2024 Olympic Games completed isometric mid-thigh pull (IMTP) and countermovement jump (CMJ) testing on dual force plates (1000 Hz). IMTP peak force, rate of force development (RFD) over 0–200 and 0–250 ms, CMJ height, and phase-specific kinetic variables were compared between roster (n = 14) and non-roster (n = 18) players. Eleven roster players had previous World Cup/Olympic experience (1.5 ± 1.2 selections across all 14 players), whereas non-roster players had none. The roster group was older than the non-roster group (26.8 ± 4.2 vs. 22.3 ± 3.1 years, p = 0.002); therefore, between-group comparisons were adjusted for age and playing position using analyses of covariance (ANCOVAs). After adjustment, no between-group differences were observed in IMTP- or CMJ-derived performance outcomes (all p ≥ 0.12; partial η² = 0.00–0.09). Therefore, in this elite cohort, roster status did not reflect force plate metrics but may reflect factors beyond these tests, including age and prior international experience. Full article

Venous thromboembolism (VTE), including pulmonary embolism (PE) and deep vein thrombosis (DVT), remains a major cause of morbidity and mortality worldwide, with significant clinical challenges in diagnosis and risk stratification. Traditional diagnostic tools, including clinical prediction scores, D-dimer testing, and imaging, are limited by suboptimal specificity or sensitivity. In this context, proteomics-based approaches have emerged as powerful tools to elucidate the molecular mechanisms of VTE and to identify novel diagnostic and prognostic biomarkers. This review synthesizes recent advances in proteomic research relevant to VTE. We searched four databases (PubMed, ScienceDirect, Springer Nature, and Wiley) using the keywords “acute pulmonary embolism”, “acute venous thromboembolism”, and “proteomics”. Thirty proteomic studies investigating VTE were examined. Across these studies, proteomic profiling consistently revealed alterations in pathways related to coagulation, inflammation, platelet activation, endothelial dysfunction, and fibrin clot structure. Multiple protein classes, including acute-phase reactants, complement components, coagulation factors, and platelet-derived proteins, have demonstrated potential value in improving diagnostic accuracy and refining prognostic stratification. Proteomic analyses have also revealed distinct molecular signatures between isolated PE and isolated DVT, supporting the concept of biologically heterogeneous VTE phenotypes. Furthermore, emerging evidence from COVID-19–associated thrombosis, cancer-associated VTE, and non-invasive sources such as exhaled breath condensate underscores the expanding clinical relevance of proteomic approaches. Although technical limitations and heterogeneity across studies remain challenges, the integration of proteomic data with clinical and genetic information holds promise for advancing precision medicine in VTE. Full article

Background: Correct preoperative evaluation of glioma grade and molecular profile is a prerequisite for tailored treatment strategies. Specifically, the 1p/19q codeletion status represents a major prognostic and therapeutic marker in low-grade gliomas (LGGs). Nevertheless, its assessment is presently performed through invasive histopathological and genetic studies, thus underlining the need for non-invasive alternative approaches. Methods: We introduce a non-invasive radiomics framework that combines quantitative MRI features with sophisticated ML and DL approaches for glioma grading and 1p/19q codeletion status prediction. High-dimensional radiomic features characterizing tumor geometry, intensity, and texture were derived from preoperative MRI-based tumor delineations. Features were normalized and optimized using correlation-based feature selection. Several traditional ML classifiers were compared and contrasted with DL models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and a CNN-Long Short-Term Memory (LSTM) hybrid model tailored to exploit both spatial feature hierarchies and feature correlations. Model validation was conducted using five-fold cross-validation and an independent test dataset, with accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) metrics. Results: Among all the models tested, the hybrid CNN-LSTM model performed the best, with an accuracy of 88.1% and an AUC of 0.93, outperforming conventional ML approaches and single-model DL architectures. Explainability analysis showed that the radiomic features of tumor heterogeneity and morphology had the most prominent impact on model performance. Conclusions: These findings indicate that the combination of radiomic features with hybrid DL models is capable of making non-invasive predictions of glioma grade and 1p/19q codeletion status. The new computational model has the potential to be used as a supplementary approach in precision neuro-oncology. Full article

This study systematically investigates the role of Ni in Co₂SiO₄ in a bimetallic (Co²⁺,Ni²⁺)₂SiO₄ olivine-type system and the materials’ catalytic efficiency in a model Heck–Mizoroki coupling reaction. Thus, a series of olivines with varying (Co²⁺,Ni²⁺)₂SiO₄ compositions (0–100% Ni) was synthesised and characterised by ICP-OES, FTIR/Raman, P-XRD and XPS analysis. Ideal mixing of metals was achieved with (49:51) Co:Ni. Catalytic testing revealed distinct conversion vs. time profiles, with the (69:31) Co:Ni olivine exhibiting the best overall performance, combining good reactivity with near-perfect selectivity (>99%) and improved stability. Mechanistic pathways were probed through product scope analysis, reactant–product temporal profiling, leaching and radical scavenging experiments. Results suggest a radical-assisted Heck–Mizoroki mechanism. Spectroscopic data correlated Co²⁺ and Ni²⁺ incorporation with M1 and M2 site occupancy, where Ni²⁺ M2 sites enhanced reactant activation and intermediate stability and Co²⁺ in the M1 site enhanced product release, though also homocoupling in Co₂SiO₄. Minimal leaching was observed for all bimetallic catalysts. These findings highlight the tunability of bimetallic olivines for C–C coupling reactions via controlled cation distribution. Full article