Search Results (1,223)

Objectives: The persistent evolution of software vulnerabilities—spanning novel zero-day exploits to logic-level flaws—continues to challenge conventional cybersecurity mechanisms. Static rule-based scanners and opaque deep learning models often lack the precision and contextual understanding required for both accurate detection and analyst interpretability. This paper presents a hybrid framework for real-time vulnerability detection that improves both robustness and explainability. Methods: The framework integrates semantic encoding via Bidirectional Encoder Representations from Transformers (BERTs), structural analysis using Deep Graph Convolutional Neural Networks (DGCNNs), and lightweight prioritization through Kernel Extreme Learning Machines (KELMs). The architecture incorporates Minimum Intermediate Representation (MIR) learning to reduce false positives and fuses multi-modal data (source code, execution traces, textual metadata) for robust, scalable performance. Explainable Artificial Intelligence (XAI) visualizations—combining SHAP-based attributions and CVSS-aligned pair plots—serve as an analyst-facing interpretability layer. The framework is evaluated on benchmark datasets, including VulnDetect and the NIST Software Reference Library (NSRL, version 2024.12.1, used strictly as a benign baseline for false positive estimation). Results: Our evaluation reports that precision, recall, AUPRC, MCC, and calibration (ECE/Brier score) demonstrated improved robustness and reduced false positives compared to baselines. An internal interpretability validation was conducted to align SHAP/GNNExplainer outputs with known vulnerability features; formal usability testing with practitioners is left as future work. Conclusions: The framework, Designed with DevSecOps integration in mind, the system is packaged in containerized modules (Docker/Kubernetes) and outputs SIEM-compatible alerts, enabling potential compatibility with Splunk, GitLab CI/CD, and similar tools. While full enterprise deployment was not performed, these deployment-oriented design choices support scalability and practical adoption. Full article

Background/Objectives: The aim of our research was to understand how excipients, unit operations, and process parameters impact processability and resulting properties, performance, and stability of tablets containing bovine lactoferrin, a cohesive biomacromolecule. Methods: Microcrystalline cellulose (MCC), croscarmellose (xCMC), lactose (LAC), hydroxypropyl methylcellulose (HPMC), and sodium stearyl fumarate (SSF) were used to produce various tablet formulations containing lactoferrin across a concentration range of 5 to 45%, targeting immediate- or controlled release performance. Tablets were made either by direct compression or via dry granulation followed by tableting. In addition to release performance, tablet attributes were characterized for tensile strength, friability, weight uniformity, and content uniformity. Results: Acceptable tablet tensile strength, friability, and performance were obtained for lactoferrin concentrations ranging from 15 to 45%, using a variety of excipients and manufacturing approaches. In several cases, dry granulation improved content uniformity. Excipient choice and tablet compression force impacted drug release, particularly when MCC alone was used as dry binder for immediate release. Dry granulation impacted tablet tensile properties, but did not significantly impact release performance. Lactoferrin–excipient compatibility was demonstrated for up to 2 years in ambient laboratory conditions. Conclusions: The study demonstrates that robust tablets can be produced using excipients and processes amenable to scale-up for industrial production. Consistent, stable, and suitably performing tablets were successfully produced using a variety of excipients, processing approaches, and across a broad concentration range with this cohesive biomacromolecule active pharmaceutical ingredient (API). Both immediate- and controlled release performance modes were possible. Full article

Background: Acute lung injury (ALI) is a serious respiratory condition. The natural compound 7-Methoxyflavanone (7MF) has a broad spectrum of anti-inflammatory and antioxidant properties. However, its pharmacological effects and underlying mechanisms in alleviating ALI remain poorly understood. Methods: An in vitro LPS-induced RAW264.7 macrophage inflammatory injury assay and an in vivo lipopolysaccharide (LPS)-induced ALI assay in mice were conducted. Results: In vitro experiments showed that 7MF significantly reduced levels of IL1β, IL6, and TNF-α; decreased the expression of COX2 and iNOS, as well as TLR4 and MyD88; suppressed the phosphorylation and degradation of IκBα; and blocked the entry of NF-κB p65 into the nucleus, thereby inhibiting NF-κB signaling. Meanwhile, 7MF also decreased ROS levels; prevented the dissociation of Txnip from Trx-1; and suppressed NLRP3, Caspase-1, Cleaved Caspase-1 p10, NEK7, Caspase-8, Cleaved Caspase-8, IL18, GSDMD, and GSDMD N-terminal expression, and thus inhibited NLRP3 signaling. MCC950, a specific inhibitor of NLRP3, significantly enhanced the pharmacological inhibition of NLRP3 by 7MF. Notably, similar results were confirmed in LPS-induced ALI experiments in mice. Conclusions: The compound 7MF effectively alleviated LPS-induced ALI by suppressing TLR4/NF-κB p65 and ROS/Txnip/NLRP3 signaling pathways. Our findings provide scientific evidence for drug development and treatment of ALI. Full article

The accumulation of plastic waste has intensified the pursuit of biodegradable alternatives, yet standard methods such as CO₂ evolution, oxygen demand, and mass loss fail to fully capture microbial physiological responses during degradation. This study introduces a biochemical assay-based approach to quantify proteins, lipids, and carbohydrates in soil as indicators of microbial activity during polymer biodegradation. For microcrystalline cellulose (MCC), proteins, lipids, and carbohydrates increased by 2.09-, 6.47-, and 11.22-fold, respectively (all p-values < 0.001), closely aligning with CO₂ evolution trends. Non-biodegradable poly(vinyl chloride) (PVC) exhibited no significant changes. Synthesized poly(butylene glutarate) (PBG) also showed significant biomolecule accumulation (up to 2.70-fold) alongside CO₂ production. Biomolecule quantification complements CO₂-based methods by revealing microbial proliferation and metabolic activity that persist beyond the mineralization plateau, offering a more comprehensive assessment of biodegradability. Full article

Background/Objectives: To improve the therapeutic efficacy of albendazole (ABZ) for ileocolonic diseases, its low solubility at higher pH levels should be enhanced. Organic acids have been widely used as pH modifiers to improve the solubility of weakly basic drugs. To achieve an adequate effect of the acidic microenvironmental pH during the drug release, pH modifiers should not leach out from the formulation. In the present work, we aimed to demonstrate that 100% tartaric acid pellets (TAP) can be used as pH-modifier cores, providing an acidic microenvironmental pH to enhance the solubility of albendazole at a higher pH. Methods: This study develops multilayer-coated pellets using TAP as starter cores in a bottom-spray-configured fluidized bed apparatus. The drug-layered TAP were coated with time-dependent and pH-dependent layers. Results: The release of ABZ from tartaric acid-based coated pellets was enhanced compared to that from pellets with the same layering structure but with an inert core of sugar or microcrystalline cellulose (MCC). In vitro experiments showed that tartaric acid remained in the pellet core during the dissolution test at a pH 6.8 medium, which resulted in an enhanced release of albendazole at a higher pH. The application of a combination of time-dependent and pH-dependent polymers aimed not only to prevent the release of albendazole at lower pH levels but also to protect TAP from premature release from the formulation. Conclusions: The application of 100% ready-to-use tartaric acid pellets (TAP) with the applied combination of coatings enhanced the solubility of the weakly basic drug albendazole at higher intestinal pH. Full article

Background and Objectives: This study aims to evaluate the diagnostic potential of routinely available hematological parameters for acute myocardial infarction (AMI) by employing an Explainable Neural Network (ENN) model that combines high predictive accuracy with interpretability. Materials and Methods: A publicly available dataset comprising 981 individuals (477 AMI patients and 504 controls) was analyzed. A broad set of hematological features—including white blood cell subtypes, red cell indices, and platelet-based markers—was used to train an ENN model. Bootstrap resampling was applied to enhance model generalizability. The model’s performance was assessed using standard classification metrics such as accuracy, sensitivity, specificity, F1-score, and Matthews Correlation Coefficient (MCC). SHapley Additive exPlanations (SHAP) were employed to provide both global and individualized insights into feature contributions. Results: The study analyzed hematological and biochemical parameters of 981 individuals. The explainable neural network (ENN) model demonstrated excellent diagnostic performance, achieving an accuracy of 94.1%, balanced accuracy of 94.2%, F1-score of 93.9%, and MCC of 0.883. The AUC was 0.96, confirming strong discriminative ability. SHAP-based explainability analyses highlighted neutrophils (NEU), white blood cells (WBC), RDW-CV, basophils (BA), and lymphocytes (LY) as the most influential predictors. Individual- and class-level SHAP evaluations revealed that inflammatory and erythrocyte-related parameters played decisive roles in AMI classification, while distributional analyses showed narrower parameter ranges in healthy individuals and greater heterogeneity among patients. Conclusions: The findings suggest that cost-effective, non-invasive blood parameters can be effectively utilized within interpretable AI frameworks to enhance AMI diagnosis. The integration of ENN with SHAP provides a dual benefit of diagnostic power and transparent rationale, facilitating clinician trust and real-world applicability. This scalable, explainable model offers a clinically viable decision-support tool aligned with the principles of precision medicine and ethical AI. Full article

Background/Objectives: The Germ Cell Theory of cancer posits that human primordial germ cells (hPGCs) are the cells of origin for malignancies. While this theory is well established for germ cell cancers, a germ cell origin for somatic cancers has been largely overlooked despite clinical observations of malignant somatic transformation (MST), wherein germ cell cancers give rise to diverse somatic cancer phenotypes, often without additional mutations. Methods: To test the Germ Cell Theory experimentally in somatic cancer, we established a virus-driven MST model linking hPGC-like cells (hPGCLCs) to Merkel cell polyomavirus (MCPyV)-positive Merkel cell carcinoma (MCC), a highly aggressive somatic cancer with a germ cell cancer-like, low-mutation epigenetic profile. The MCPyV genome was transduced into human induced pluripotent stem cells (hiPSCs) or hPGC-like cells by lentiviral transfection, followed by xenotransplantation. Results: Virus-positive MCC (VP-MCC)-like tumors were consistently induced without additional oncogenic mutations. These tumors recapitulated VP-MCC’s high-grade neuroendocrine carcinoma histology and molecular profiles. DNA methylation analysis revealed near-complete global hypomethylation in VP-MCC-like tumors, matching the unique epigenetic state of late-stage hPGCs. Notably, pluripotent intermediates were neither necessary nor sufficient for MST; transformation required acquisition of a late-hPGC-like epigenetic state. Conclusions: This is the first MST model of a somatic cancer arising through an aberrant germline-to-soma transition. Our findings unify VP-MCC and germ cell cancer biology, challenge mutation- and soma-centric paradigms, and provide a tractable platform to investigate developmental and epigenetic mechanisms of oncogenesis. This MST model supports a unifying germ cell origin for both germ cell and non-germ cell somatic malignancies. Full article

Multimorbidity, or the presence of two or more co-occurring chronic medical conditions, is extremely prevalent within the United States (US), with disproportionately high incidence rates in individuals with minoritized identities. Sleep disturbances are an empirically supported risk factor contributing to disease status and maintenance throughout the lifespan. Given this, this study examines the relationship between disturbed sleep and multiple chronic conditions (MCCs) in adults using cross-sectional data from (n = 1013) participants enrolled in the Survey of Midlife Development in the US Study (MIDUS-2). Participants within this study were predominantly female (54.9%), white (93.2%), middle-aged (M_AGE = 58 years old), and experienced multimorbidity (56.6%) by having two or more (M_CHRON = 2.25) chronic health conditions in the past year. A negative binomial regression indicated that sleep disturbances significantly predict the number of chronic health conditions, with sleep-disturbed individuals reporting a 41% increase in reported health conditions (IRR = 1.407, p < 0.001). Findings suggest that disturbed sleep is significantly related to disease presence in aging populations and should be addressed through early intervention to mitigate negative health consequences. Full article

Industrial environments frequently rely on analog control instruments due to their reliability and robustness; however, automating the interpretation of these controls remains challenging due to variability in design, lighting conditions, and scale precision requirements. This research investigates the effectiveness of Vision–Language Models (VLMs) for automated interpretation of industrial controls through analysis of three distinct approaches: general-purpose VLMs, fine-tuned specialized models, and lightweight models optimized for edge computing. Each approach was evaluated using two prompting strategies, Holistic-Thought Protocol (HTP) and sequential Chain-of-Thought (CoT), across a representative dataset of continuous and discrete industrial controls. The results demonstrate that the fine-tuned Generative Pre-trained Transformer 4 omni (GPT-4o) significantly outperformed other approaches, achieving low Mean Absolute Error (MAE) for continuous controls and the highest accuracy and Matthews Correlation Coefficient (MCC) for discrete controls. Fine-tuned models demonstrated less sensitivity to prompt variations, enhancing their reliability. In contrast, although general-purpose VLMs showed acceptable zero-shot performance, edge-optimized models exhibited severe limitations. This work highlights the capability of fine-tuned VLMs for practical deployment in industrial scenarios, balancing precision, computational efficiency, and data annotation requirements. Full article

This study investigated the molecular mechanisms by which phthalates induce depression, utilizing network toxicology and molecular docking techniques. By integrating the TargetNet, GeneCards, and PharmMapper databases, 658 potential target genes of phthalates were identified. Additionally, 5433 depression-related targets were retrieved from the GeneCards and OMIM databases. Comparative analysis revealed 360 common targets implicated in both phthalate action and depression. A Protein-Protein Interaction (PPI) network was constructed using the STRING database. Subsequently, the CytoHubba plugin (employing the MCC algorithm) within Cytoscape was used to screen the network, identifying the top 20 hub genes. These core genes include AKT1, CASP3, TNF, TP53, BCL2, and IL6, among others. Validation on the GEO dataset (GSE23848) revealed that the expression of multiple core genes was significantly upregulated in patients with depression (p < 0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that phthalates mainly regulate biological processes such as extracellular stimulus response, lipopolysaccharide metabolism, and chemical synaptic transmission. Depression is mediated by the AGE-RAGE signaling pathway (a complication of diabetes), lipids and atherosclerosis, Endocrine resistance, and the PI3K-Akt signaling pathway. Molecular docking confirmed that phthalates have strong binding activity with key targets (CASP3, TNF, TP53, BCL2, IL6). These findings present a novel paradigm for evaluating the health risks posed by environmental pollutants. Full article

Precise classification of landslide types is critical for targeted hazard mitigation, although the absence of type-specific classifications in many existing inventories limits their utility for effective risk management. This study develops a transferable machine learning approach to identify cliff-type landslides from unclassified records, with a focus on earthquake-prone regions. Using the Forest-based and Boosted Classification and Regression (FBCR) tools in ArcGIS Pro, a model was trained on 167 landslide points and 167 non-landslide points from Tokushima Prefecture, Japan. The model achieved high predictive performance, with 84% accuracy and sensitivity, an F1 score of 84%, and a Matthews correlation coefficient (MCC) of 0.68. The trained model was applied to the Kegalle District, Sri Lanka, and validated against a recently updated inventory specifying landslide types, resulting in an accuracy of 80.1%. It also enabled retrospective identification of cliff-type landslides in older inventories, providing valuable insights for early hazard assessment. Spatial analysis showed strong correspondence between predicted cliff-type zones and key conditioning factors, including specific elevation ranges, steep slopes, high soil thickness, and proximity to roads and buildings. This study integrates FBCR-based modelling with a cross-regional application framework for cliff-type landslide classification, offering a practical, transferable tool for refining inventories, guiding countermeasures, and improving preparedness in regions with similar geomorphological and seismic settings. Full article

Purpose: This study aimed to develop and validate a new machine learning (ML)-based screening tool for a two-step prediction of the need for and type of nutritional therapy (enteral, parenteral, or combined) using Nutrition Risk Screening 2002 (NRS-2002) and other demographic parameters from the Optimal Nutrition Care for All (ONCA) national cohort data. Methods: This multicenter retrospective cohort study included 191,028 patients, with data on age, gender, body mass index (BMI), NRS-2002 score, presence of cancer, and hospital unit type. In the first step, classification models estimated whether patients required nutritional therapy, while the second step predicted the type of therapy. The dataset was divided into 60% training, 20% validation, and 20% test sets. Random Forest (RF), Artificial Neural Network (ANN), deep learning (DL), Elastic Net (EN), and Naive Bayes (NB) algorithms were used for classification. Performance was evaluated using AUC, accuracy, balanced accuracy, MCC, sensitivity, specificity, PPV, NPV, and F₁-score. Results: Of the patients, 54.6% were male, 9.2% had cancer, and 49.9% were hospitalized in internal medicine units. According to NRS-2002, 11.6% were at risk of malnutrition (≥3 points). The DL algorithm performed best in both classification steps. The top three variables for determining the need for nutritional therapy were severe illness, reduced dietary intake in the last week, and mild impaired nutritional status (AUC = 0.933). For determining the type of nutritional therapy, the most important variables were severe illness, severely impaired nutritional status, and ICU admission (AUC = 0.741). Adding gender, cancer status, and ward type to NRS-2002 improved AUC by 0.6% and 3.27% for steps 1 and 2, respectively. Conclusions: Incorporating gender, cancer status, and ward type into the widely used and validated NRS-2002 led to the development of a new scale that accurately classifies nutritional therapy type. This ML-enhanced model has the potential to be integrated into clinical workflows as a decision support system to guide nutritional therapy, although further external validation with larger multinational cohorts is needed. Full article

Rice, wheat, and maize are important food grains consumed by most of the population in Asian countries (like India, Japan, Singapore, Malaysia, China, and Thailand). These crops’ production is affected by biotic and abiotic factors that cause diseases in several parts of the crops (including leaves, stems, roots, nodes, and panicles). A severe infection affects the growth of the plant, thereby undermining the economy of a country, if not detected at an early stage. This may cause extensive damage to crops, resulting in decreased yield and productivity. Early safeguarding methods are overlooked because of farmers’ lack of awareness and the variety of crop diseases. This causes significant crop damage and can consequently lower productivity. In this manuscript, a lightweight vision transformer (MaxViT) with 814.7 K learnable parameters and 85 layers is designed for classifying crop diseases in paddy and wheat. The MaxViT DNN architecture consists of a convolutional block attention module (CBAM), squeeze and excitation (SE), and depth-wise (DW) convolution, followed by a ConvNeXt module. This network architecture enhances feature representation by eliminating redundant information (using CBAM) and aggregating spatial information (using SE), and spatial filtering by the DW layer cumulatively enhances the overall classification performance. The proposed model was tested using a paddy dataset (with 7857 images and eight classes, obtained from local paddy farms in Lalgudi district, Tiruchirappalli) and a wheat dataset (with 5000 images and five classes, downloaded from the Kaggle platform). The model’s classification performance for various diseases has been evaluated based on accuracy, sensitivity, specificity, mean accuracy, precision, F1-score, and MCC. During training and testing, the model’s overall accuracy on the paddy dataset was 99.43% and 98.47%, respectively. Training and testing accuracies were 94% and 92.8%, respectively, for the wheat dataset. Ablation analysis was carried out to study the significant contribution of each module to improving the performance. It was found that the model’s performance was immune to the presence of noise. Additionally, there are a minimal number of parameters involved in the proposed model as compared to pre-trained networks, which ensures that the model trains faster. Full article

Myocardial infarction (MI) remains a leading cause of in-hospital mortality. Early identification of high-risk patients is essential for improving clinical outcomes and optimizing hospital resource allocation. This study presents a machine learning framework for predicting mortality following MI using a publicly available dataset of 1700 patient records, and after excluding records with over 20 missing values and features with more than 300 missing entries, the final dataset included 1547 patients and 113 variables, categorized as binary, categorical, integer, or continuous. Missing values were addressed using denoising autoencoders for continuous features and variational autoencoders for the remaining data. In contrast, feature selection was performed using Random Forest, and PowerTransformer scaling was applied, addressing class imbalance by using SMOTE. Twelve models were evaluated, including Focal-Loss Neural Networks, TabNet, XGBoost, LightGBM, CatBoost, Random Forest, SVM, Logistic Regression, and a voting ensemble. Performance was assessed using multiple metrics, with SVM achieving the highest F1 score (0.6905), ROC-AUC (0.8970), and MCC (0.6464), while Random Forest yielded perfect precision and specificity. To assess generalizability, a subpopulation external validation was conducted by training on male patients and testing on female patients. XGBoost and CatBoost reached the highest ROC-AUC (0.90), while Focal-Loss Neural Network achieved the best MCC (0.53). Overall, the proposed framework outperformed previous studies in key metrics and maintained better performance under demographic shift, supporting its potential for clinical decision-making in post-MI care. Full article

Accurate and early classification of retinal diseases such as diabetic retinopathy, cataract, and glaucoma is essential for preventing vision loss and improving clinical outcomes. Manual diagnosis from fundus images is often time-consuming and error-prone, motivating the development of automated solutions. This study proposes a deep learning-based classification model using a pretrained EfficientNetB3 architecture, fine-tuned on a publicly available Kaggle retinal image dataset. The model categorizes images into four classes: cataract, diabetic retinopathy, glaucoma, and healthy. Key enhancements include transfer learning, data augmentation, and optimization via the Adam optimizer with a cosine annealing scheduler. The proposed model achieved a classification accuracy of 95.12%, with a precision of 95.21%, recall of 94.88%, F1-score of 95.00%, Dice Score of 94.91%, Jaccard Index of 91.2%, and an MCC of 0.925. These results demonstrate the model’s robustness and potential to support automated retinal disease diagnosis in clinical settings. Full article