Search Results (11,915)

Background: Lidocaine, classified as an amide-type agent, and tetracaine, designated as an ester-type agent, are frequently co-formulated for dermatologic procedures. Despite the extensive literature on the pharmacokinetics (PK) of these substances, there is a paucity of head-to-head comparisons of intravenous (IV) and topical administration in the same preclinical model. Absolute bioavailability (F%) is imperative for optimizing formulation design and safety. Methods: A single-dose, single-sequence, three-period pilot study was performed in male Göttingen mini-pigs. The first period of the study involved the intravenous bolus administration of lidocaine HCl and tetracaine HCl, with a dosage of 1 mg/kg for each agent. In Period 2, the topical application of Pliaglis (a combination of 7% lidocaine and 7% tetracaine, with a concentration of 10 g/100 cm² and a duration of 60 min) was utilized. In Period 3, the pharmacokinetic profile of Z4T4L4 (a formulation comprising 4% lidocaine HCl and 4% tetracaine HCl) was assessed under the same experimental conditions. Blood samples were collected up to 24 h after the administration of the drug; skin biopsies were obtained 90 min after the application of the test substance. Plasma and skin concentrations were measured by means of validated liquid chromatography–tandem mass spectrometry (LC–MS/MS). PK parameters were derived using a noncompartmental analysis approach, while F% was calculated through AUC comparison with IV dosing. Results: Subsequent to intravenous administration, the mean elimination half-lives of lidocaine and tetracaine were determined to be 1.62 h and 1.85 h, respectively. Pliaglis demonstrated higher skin concentrations of lidocaine (358 μg/g) and tetracaine (465 μg/g) compared to Z4T4L4 (33.6 μg/g and 46.1 μg/g, respectively). Despite lower skin levels, Z4T4L4 produced higher F% (lidocaine: 1.98% vs. 1.41%; tetracaine: 3.34% vs. 1.26%). The time to maximum plasma concentration (T_max) for lidocaine was found to be 2–4 h (Pliaglis) and 2–8 h (Z4T4L4), while for tetracaine, it was 1–8 h (Pliaglis) and 2–8 h (Z4T4L4). Conclusions: In this preliminary study, which included three subjects, Z4T4L4 exhibited a numerical tendency towards increased systemic bioavailability in comparison with Pliaglis. This observation was noted despite the fact that Z4T4L4 resulted in markedly lower skin concentrations. Due to the exploratory nature of the pilot study (n = 3), observed differences are reported as numerical trends. The data suggest that Z4T4L4 may enhance systemic absorption while reducing skin retention, highlighting a potential formulation-dependent dissociation between local concentration and systemic bioavailability. These preliminary findings provide in vivo evidence of a divergence between eutectic-based tissue retention and enhancer-driven systemic flux. This highlights that formulation design fundamentally dictates the safety profile of local anesthetics, necessitating a balance between local efficacy and systemic safety. Full article

This study presents a controlled benchmarking analysis of min–max scaling, Z-score normalization, and an adaptive preprocessing pipeline that combines percentile-based ROI cropping with histogram standardization. The evaluation was conducted across four public chest X-ray (CXR) datasets and three convolutional neural network architectures under controlled experimental settings. The adaptive pipeline generally improved accuracy, F1-score, and training stability on datasets with relatively stable contrast characteristics while yielding limited gains on MIMIC-CXR due to strong acquisition heterogeneity. Ablation experiments showed that histogram standardization provided the primary performance contribution, with ROI cropping offering complementary benefits, and the full pipeline achieving the best overall performance. The computational overhead of the adaptive preprocessing was minimal (+6.3% training-time cost; 5.2 ms per batch). Friedman–Nemenyi and Wilcoxon signed-rank tests confirmed that the observed improvements were statistically significant across most dataset–model configurations. Overall, adaptive normalization is positioned not as a novel algorithmic contribution, but as a practical preprocessing design choice that can enhance cross-dataset robustness and reliability in chest X-ray classification workflows. Full article

Factor V (FV) Leiden and F2 G20210A are inherited genetic risk factors that are in the first line of laboratory tests for thromboembolic diseases. Their detection relies on PCR assays, which are subject to contamination, as well as pipetting error, when manually performed and require individual assays for each gene. In this article, we report an improved PCR and restriction endonuclease assay for the simultaneous detection of the FV Leiden and F2 G20210A variants, based on multiplex amplification with fluorescent primers, digestion control, identity monitoring, and contamination tracking. Full article

Background: Anatomopathological reports are largely unstructured, which limits automated data extraction, interoperability, and large-scale research. Manual extraction and standardization are costly and difficult to scale. Objective: We developed and evaluated an automated pipeline for entity extraction and multi-ontology normalization of anatomopathological reports. Methods: A corpus of 560 reports from the Military Hospital of Tunis, Tunisia, was manually annotated for three entity types: sample type, test performed, and finding. The entity extraction utilized BioBERT v1.1, while the normalization combined BioClinicalBERT multi-label classification with retrieval-augmented generation, incorporating both dense and BM25 sparse retrieval over SNOMED CT, LOINC, and ICD-11. The performance was measured using precision, recall, F1-score, and statistical tests. Results: BioBERT achieved high extraction performance (F1: 0.97 for the sample type, 0.98 for the test performed, and 0.93 for the finding; overall 0.963, 95% CI: 0.933–0.982), with low absolute errors. For terminology mapping, the combination of BioClinicalBERT and dense retrieval outperformed the standalone and BM25-based approaches (macro-F1: 0.6159 for SNOMED CT, 0.9294 for LOINC, and 0.7201 for ICD-11). Cohen’s Kappa ranged from 0.7829 to 0.9773, indicating substantial to near-perfect agreement. Conclusions: The pipeline provides robust automated extraction and multi-ontology coding of anatomopathological entities, supporting transformer-based named entity recognition with retrieval-augmented generation. However, given the limitations of this study, multi-institutional validation is needed before clinical deployment. Full article

The excessively concentrated ripening period of mulberries causes seasonal surplus. Fruit mulberry (Morus spp.) exhibits the unique trait of “simultaneous flowering and leaf flushing”, rendering budburst timing closely correlated with fruit ripening time. Thus, deciphering the molecular mechanism underlying winter bud dormancy maintenance in fruit mulberry is urgently needed. Herein, an F₁ hybrid population comprising 337 individuals, derived from Morus wittiorum (♀) and ‘322’ (♂), was utilized as research material. Through Bulked Segregant Analysis Sequencing (BSA-Seq), we successfully mapped a dormancy-associated QTL interval designated as LB (Late Burst), spanning 9,990,001–11,990,000 bp on Chromosome 13. Integrating Weighted Gene Co-expression Network Analysis (WGCNA) results, MaSVP was identified as a candidate gene within this interval. Virus-induced gene silencing (VIGS) of MaSVP in winter buds of Morus wittiorum significantly accelerated budburst compared to the control, demonstrating that MaSVP represses winter bud dormancy release and plays a crucial role in regulating dormancy maintenance in fruit mulberry. Dynamic expression profiling of dormancy-related genes revealed that the transcript levels of MaSVP, MaSAPK3, MaCASL2, and MaPYR8 were significantly downregulated (Tukey’s test, p < 0.05) as budburst approached, whereas those of MaFT and MaGA20ox1-D were significantly upregulated (Tukey’s test, p < 0.05). These results indicate that winter bud dormancy maintenance in Morus wittiorum is associated with abscisic acid (ABA) and gibberellin (GA) metabolism. Collectively, this study provides critical insights into the biological basis of winter bud dormancy maintenance in fruit mulberry and offers valuable genetic resources for breeding late-maturing cultivars. Full article

This study develops a machine-learning-based framework for multiclass soil classification using Cone Penetration Test (CPT) data, aiming to overcome the limitations of traditional empirical Soil Behavior Type (SBT) charts and improve the automation, continuity, robustness, and reliability of stratigraphic interpretation. A dataset of 340 CPT soundings from 26 sites in Shanghai is compiled, and a sliding-window feature engineering strategy is introduced to transform point measurements into local pattern descriptors. An XGBoost-based multiclass classifier is then constructed using fifteen engineered features, integrating second-order optimization, regularized tree structures, and probability-based decision functions. Results demonstrate that the proposed method achieves strong classification performance across nine soil categories, with an overall classification accuracy of approximately 92.6%, an average F1-score exceeding 0.905, and a mean Average Precision (mAP) of 0.954. The confusion matrix, P–R curves, and prediction probabilities show that soil types with distinctive CPT signatures are classified with near-perfect confidence, whereas transitional clay–silt facies exhibit moderate but geologically consistent misclassification. To evaluate depth-wise prediction reliability, an Accuracy Coverage Rate (ACR) metric is proposed. Analysis of all CPTs reveals a mean ACR of 0.924, and the ACR follows a Weibull distribution. Feature importance analysis indicates that depth-dependent variables and smoothed p_s statistics are the dominant predictors governing soil behavior differentiation. The proposed XGBoost-based framework effectively captures nonlinear CPT–soil relationships, offering a practical and interpretable tool for high-resolution soil classification in subsurface investigations. Full article

Background/Objectives: Early-stage diabetes often presents with subtle symptoms, making timely screening challenging. This study aimed to develop an interpretable and robust machine learning framework for early-stage diabetes risk prediction using integrated statistical and machine learning–based feature selection, and to evaluate its generalizability using real-world hospital data. Methods: A Union Feature Selection approach was constructed by combining logistic regression significance testing with ReliefF and MRMR feature importance scores. Five machine learning models—Decision Tree, Naïve Bayes, SVM, KNN, and Neural Network—were trained on the UCI Early Stage Diabetes dataset (N = 520) under multiple feature-selection scenarios. External validation was performed using retrospective hospital records from the University of Phayao (N = 60). Model performance was assessed using accuracy, precision, recall, and F1-score. Results: The union feature-selection approach identified four core predictors—polyuria, polydipsia, gender, and irritability—with additional secondary features providing only marginal improvements. Among the evaluated models, Naïve Bayes demonstrated the most stable external performance, achieving 85% test accuracy, balanced precision, recall, and F1-score, along with a moderate AUC of 0.838, indicating reliable discriminative ability in real-world hospital data. In contrast, SVM, KNN, and Neural Network models, despite exhibiting very high internal validation performance (>96%) under optimally selected ML features, showed marked performance decline during external validation, highlighting their sensitivity to distributional shifts between public and clinical datasets. Conclusions: The combined statistical–ML feature selection method improved interpretability and stability in early-stage diabetes prediction. Naïve Bayes demonstrated the strongest generalizability and is well suited for real-world screening applications. The findings support the use of integrated feature selection to develop efficient and clinically relevant risk assessment tools. Full article

The rapid growth of online chess has intensified the challenge of distinguishing engine-assisted from authentic human play, exposing the limitations of existing approaches that rely solely on deterministic evaluation metrics. This study introduces a proof-of-concept hybrid framework for discriminating between engine-like and human-like chess play patterns, integrating Stockfish’s deterministic evaluations with stylometric behavioral features derived from the Maia engine. Key metrics include Centipawn Loss (CPL), Mismatch Move Match Probability (MMMP), and a novel Curvature-Based Stability (ΔS) indicator. These features were incorporated into a convolutional neural network (CNN) classifier and evaluated on a controlled benchmark dataset of 1000 games, where ‘suspicious’ gameplay was algorithmically generated to simulate engine-optimal patterns, while ‘clean’ play was modeled using Maia’s human-like predictions. Results demonstrate the framework’s ability to discriminate between these behavioral archetypes, with the hybrid model achieving a macro F1-score of 0.93, significantly outperforming the Stockfish-only baseline (F1 = 0.87), as validated by McNemar’s test (p = 0.0153). Feature ablation confirmed that Maia-derived features reduced false negatives and improved recall, while ΔS enhanced robustness. This work establishes a methodological foundation for behavioral pattern discrimination in chess, demonstrating the value of combining deterministic and human-centric modeling. Beyond chess, the approach offers a template for behavioral anomaly analysis in cybersecurity, education, and other decision-based domains, with real-world validation on adjudicated misconduct cases identified as the essential next step. Full article

Imaging logging serves as a critical technology for identifying and characterizing fractures in unconventional oil and gas reservoirs. Despite significant progress in deep learning for automated fracture recognition in this field, the integration of fracture interpretation with large language models remains insufficient. To address this, this paper constructs a Chinese fracture image–text pair dataset covering multiple scenarios and proposes “FracLogGPT”, a three-stage multimodal large language model with a parameter scale of approximately 7 billion. Using Qwen2.5-VL-7B as the baseline model, this study employs Domain-Adaptive pre-training (DAPT) to tailor the model to geological and logging contexts. Efficient Supervised Fine-Tuning (SFT) is achieved via the LoRA method, while output style alignment is accomplished through Direct Preference Optimization (DPO) combined with expert preference data. Experimental results on an independent test set show that FracLogGPT achieves a Count-F1 of 0.70 for fracture-count classification, with location and morphology consistency accuracies of 0.49 and 0.43, respectively, and higher text-level BLEU and ROUGE-L scores than larger, non-domain-adapted external models evaluated under the same conditions. Comparative experiments across stages validate the effectiveness of the proposed workflow. In summary, “FracLogGPT” achieves automated identification and expert-like description of imaging logging fractures with approximately 7 billion parameters, providing a reusable training pathway and evaluation workflow for intelligent imaging logging interpretation. Full article

Pillar stability prediction is essential for underground mining safety, yet it remains challenging due to limited data, class imbalance, and insufficient interpretability. This study proposes an integrated Borderline-SMOTE-Osprey Optimization Algorithm-Super Learner framework (BSMOTE-OOA-SL) for hard-rock pillar stability prediction. The framework combines five heterogeneous base learners (ANN, GBDT, KNN, RF, and SVM), applies Borderline-SMOTE within training folds to alleviate class imbalance, and employs the Osprey Optimization Algorithm (OOA) for systematic hyperparameter optimization. The model is evaluated using a dataset of 241 pillar cases from seven underground mines. Statistical experiments based on multiple random train–test splits show that the proposed framework consistently outperforms individual base learners in terms of Accuracy, Macro-Precision, Macro-Recall, and Macro-F1, demonstrating improved robustness and generalization. Ablation results indicate that the joint use of Borderline-SMOTE and OOA leads to quantitative performance gains of 10.21%, 12.25%, 12.61%, and 12.86% in Accuracy, Macro-Precision, Macro-Recall, and Macro-F1, respectively. Under a representative data split, the model achieves an overall accuracy of 95.92%, with strong class-wise Precision, Recall, and F1-score across all stability categories, and AUC values exceeding 0.9 for all classes (reaching 1.0 for the Failed category). SHAP-based interpretability analysis identifies stress-related indicators—particularly average pillar stress, Stress/UCS ratio, and UCS—as the dominant factors governing pillar stability. Overall, the proposed BSMOTE-OOA-SL framework provides a robust, interpretable, and statistically reliable solution for hard-rock pillar stability prediction. Full article

A new composite microwave–dielectric system, (1 − x)Li_2.08TiO₃-xLi₂ZnTi₃O₈ (x = 0.3–0.7), was systematically investigated to identify the optimal composition for low-temperature co-fired ceramic (LTCC) applications by correlating sintering behavior, microstructural evolution, and microwave–dielectric properties. Although the undoped compositions exhibited excellent intrinsic dielectric performance, they required sintering at 1100 °C, making them incompatible with Ag-based LTCC processing. Among the investigated formulations, 0.6Li_2.08TiO₃–0.4Li₂ZnTi₃O₈ was identified as the most suitable base composition. To reduce the sintering temperature, 0.3–1.0 wt.% V₂O₅ was introduced as a sintering aid, enabling densification at 900 °C for 30 min (97.0% relative density) while preserving the coexistence of Li_2.08TiO₃ and Li₂ZnTi₃O₈ without XRD-detectable secondary phases. Microstructural observations indicated that V₂O₅ promoted liquid-phase sintering, leading to enhanced densification and Li_2.08TiO₃-selective abnormal grain coarsening without altering the intrinsic permittivity. Complementary dilatometry provided process-level evidence for this liquid-phase sintering mechanism: large total shrinkage at 900 °C ($∆L/L_o$≈ −17–19%), earlier ${\mathrm{T}}_{\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{t}}$/${\mathrm{T}}_{\mathrm{p}\mathrm{e}\mathrm{a}\mathrm{k}}$ with ${\mathrm{T}}_{\mathrm{p}\mathrm{e}\mathrm{a}\mathrm{k}}$ lowered by ~250 °C, and an increased ${\mathrm{R}}_{\mathrm{p}\mathrm{e}\mathrm{a}\mathrm{k}}$, collectively supporting 900 °C/30 min as the practical firing window. The optimized 0.6Li_2.08TiO₃–0.4Li₂ZnTi₃O₈ composition containing 0.3 wt.% V₂O₅ exhibits excellent microwave–dielectric properties (${\epsilon }_{\mathit{r}}$ = 23.32, Q × f = 68,400 GHz, and ${\tau }_{\mathit{f}}$ = −1.55 ppm/°C). Higher V₂O₅ contents (>0.3 wt.%) caused a gradual reduction in Q × f due to increasing microstructural non-uniformity. Ag co-firing tests confirmed electrode stability with no interfacial reactions at 900 °C for 30 min. Overall, 0.3 wt.% V₂O₅-assisted 0.6Li_2.08TiO₃–0.4Li₂ZnTi₃O₈ provides a practical sub-950 °C processing window that satisfies key LTCC requirements, including moderate permittivity, high Q × f, near-zero ${\tau }_{\mathit{f}}$, and compatibility with Ag electrodes. Full article

Objectives: Combining two pharmacophores into one molecule with multiple applications presents interest in the field of medicinal chemistry. Quinazolinones are among privileged scaffolds due to their wide biological activities, whereas hydrazones are versatile linkers with pharmacological potential. Thus, this article focused on a green method for the synthesis of new N-acyl-hydrazones of 2-(2-methyl-4-oxoquinazolin-3(4H)-yl)acetohydrazide and the exploration of their biological potential. Methods: The novel N-acyl-hydrazones (1a–1f) were synthesized under microwave irradiation, using various substituted salicylaldehydes and benzaldehydes. The products were characterized by FT-IR, ¹H-NMR, ¹³C-NMR, and HRMS. Their pharmacological profile was assessed by in silico methods and docking simulations. Biological evaluation included antioxidant, antimicrobial, and cytotoxic activities, as well as preliminary toxicity on Artemia franciscana. Results: Spectroscopic data indicated syn-E and anti-E isomers. Compound 1c showed the highest antioxidant activity. Antimicrobial assays indicated narrow-spectrum activity, with compounds 1a and 1b being most effective against C. albicans and S. aureus. Biofilm inhibition assays revealed that 1a and 1c interfered with microbial adhesion, highlighting their potential in combating biofilm-associated infections. Cytotoxicity tests on HT-29 and A431 cancer cell lines showed selective anticancer effects for compounds 1a–1d, with minimal toxicity on normal Vero cells, especially for 1b and 1d. Toxicity against Artemia franciscana correlated with in vitro cytotoxicity data, revealing low lethality for all N-acyl-hydrazones. Docking studies indicate that the antibacterial activity may involve inhibition of S. aureus DNA gyrase B, whereas the cytotoxic effects could be mediated by interaction with the EGFR kinase. Conclusions: These findings may increase the chances of identifying a lead compound in this class, supporting the further development of selected N-acyl-hydrazones and their pharmacological exploration. Full article

Background/Objectives: Understanding how plant-derived extracts influence the rheological and sensory behavior of emulsions is crucial for developing stable and consumer-appealing formulations. Although walnut leaf extract (Juglans regia L.) is recognized for its bioactive properties, its structural impact on cosmetic emulsions has not been systematically characterized. This study aimed to investigate the effect of increasing walnut leaf extract concentration on the rheological profile, mechanical integrity during application, and sensory performance of oil-in-water creams. Methods: Four emulsion formulations (F1–F4) containing 0%, 1%, 3%, and 5% walnut leaf extract were prepared using Olivem 1000 and Olivem 300 as emulsifiers. Rheological measurements included amplitude sweep, flow curve, frequency sweep, and thixotropy tests to assess viscoelasticity, flow behavior, and recovery. A sensory evaluation was conducted by trained panelists to correlate rheological parameters with perceived product attributes. Results: All formulations exhibited pseudoplastic, shear-thinning behavior in well-structured cosmetic emulsions during application. The addition of walnut extract significantly modified rheological responses: at 1% concentration, an increase in storage modulus (G′) and shear-thinning ratio (η₀/η∞) indicated structural reinforcement and improved spreadability, whereas higher concentrations (3–5%) led to structural softening and faster thixotropic recovery. The frequency sweep revealed a concentration-dependent shift from elastic- to viscous-dominant behavior. Sensory analysis confirmed these trends, with higher extract levels reducing stickiness and greasiness while enhancing absorption. Conclusions: Walnut leaf extract shows a concentration-dependent influence on the rheological behavior of the emulsions, strengthening the network structure at low levels while promoting softening and faster structural recovery at higher concentrations. The strong correlation between rheological and sensory parameters underscores the potential of walnut extract as a multifunctional ingredient for designing well-structured, non-greasy, and consumer-preferred cosmetic creams. Full article

A series of novel triazone derivatives containing acylhydrazone and phenoxypyridine motifs were designed, synthesized, and evaluated for their biological activities. The bioassay results indicated that most of the target compounds exhibited excellent insecticidal activities against bean aphids. In particular, compounds 3i and 3e showed excellent aphicidal activities comparable to pymetrozine, thus emerging as novel insecticidal lead compounds. Additionally, compounds 3c (60%), 3e (60%), and 3f (60%) exhibited good larvicidal activities against C. pipiens pallens at 0.5 mg/kg. Further fungicidal activity tests revealed that most derivatives exhibited broad-spectrum fungicidal activities. A total of twelve compounds exhibited better fungicidal activities against cercospora arachidicola hori than carbendazim, and eight compounds exhibited better fungicidal activities against fusarium moniliforme than carbendazim. This work suggests that compound 3e could serve as an insecticidal lead compound for further structural optimization. Full article

Facial expression recognition (FER) is crucial for affective computing and human–computer interaction; however, it is still difficult to achieve under various conditions in the real world, such as lighting, occlusion, and pose. This work presents a lightweight hybrid network, SE-Hybrid + Face-ViT, which merges convolutional and transformer architectures through multi-level feature fusion and adaptive channel attention. The network includes a convolutional stream to capture the fine-grained texture of the image and a retrained Face-ViT branch to provide the high-level semantic context. Squeeze-and-Excitation (SE) modules adjust the channel responses at different levels, thus allowing the network to focus on the emotion-salient cues and suppress the redundant features. The proposed architecture, trained and tested on the large-scale AffectNet benchmark, achieved 70.45% accuracy and 68.11% macro-F1, thereby outperforming the latest state-of-the-art models such as TBEM-Transformer, FT-CSAT, and HFE-Net by around 2–3%. Grad-CAM-based visualization of the model confirmed accurate attention to the most significant facial areas, resulting in better recognition of subtle expressions such as fear and contempt. The findings indicate that SE-Hybrid + Face-ViT is a computationally efficient yet highly discriminative FER strategy that successfully addresses the issue of how to preserve details while globally reasoning with contextual information locally. Full article