Search Results (810)

Rheumatoid arthritis (RA) is a chronic systemic inflammatory disorder primarily targeting joints, leading to pain, swelling, and stiffness. RA results from the body’s own immune system attacking its own tissues. Currently, there are various treatments available for RA including disease-modifying antirheumatic drugs (DMARDs) and NSAIDs. Leflunomide (LEF) is a USFDA-approved synthetic DMARD which is being widely prescribed for the management of RA; however, it faces several challenges such as prolonged drug elimination, hepatotoxicity, and others. LEF exerts its therapeutic effects by inhibiting dihydroorotate dehydrogenase (DHODH), thereby suppressing pyrimidine synthesis and modulating immune responses. Emerging nanotechnology-based therapies help in encountering the current challenges faced in LEF delivery to RA patients. This review enlists the LEF’s pharmacokinetics, mechanism of action, and clinical efficacy in RA management. A comparative analysis with methotrexate, biologics, and other targeted therapies, highlighting its role in monotherapy and combination regimens and the safety concerns, including hepatotoxicity, gastrointestinal effects, and teratogenicity, is discussed alongside recommended monitoring strategies. Additionally, emerging trends in novel formulations and drug delivery approaches are explored to enhance efficacy and minimize adverse effects. Overall, LEF remains a perfect remedy for RA patients, specifically individuals contraindicated with drugs like methotrexate. The therapeutic applicability of LEF could be enhanced by developing more customized treatments and advanced drug delivery approaches. Full article

According to the latest Verizon DBIR report, credential abuse, including password reuse and human factors in password creation, remains the leading attack vector. It was revealed that most users change their passwords only when they forget them, and 35% of respondents find mandatory password rotation policies inconvenient. These findings highlight the importance of combining technical solutions with user-focused education to strengthen password security. In this research, the “human factor in the creation of usernames and passwords” is considered a vulnerability, as identifying the patterns or rules used by users in password generation can significantly reduce the number of possible combinations that attackers need to try in order to gain access to personal data. The proposed method based on an LSTM model operates at a character level, detecting recurrent structures and generating generalized masks that reflect the most common components in password creation. Open datasets of 31,000 compromised passwords from real-world leaks were used to train the model and it achieved over 90% test accuracy without signs of overfitting. A new method of evaluating the individual password creation habits of users and automatically fetching context-rich keywords from a user’s public web and social media footprint via a keyword-extraction algorithm is developed, and this approach is incorporated into a web application that allows clients to locally fine-tune an LSTM model locally, run it through ONNX, and carry out all inference on-device, ensuring complete data confidentiality and adherence to privacy regulations. Full article

Although platelets have been traditionally thought of to be essential hemostasis mediators, new research shows how important they are for controlling cellular oxidative stress, inflammatory processes, and immunological responses—particularly during major surgery on the abdomen. Perioperative problems are largely caused by the continually changing interaction of inflammatory cytokines, the formation of reactive oxygen species (ROS), and platelet activation. The purpose of this review is to summarize the most recent data regarding the complex function of platelets in abdominal surgery, with an emphasis on how they interact with inflammation and oxidative stress, and to investigate the impact on postoperative therapy and subsequent studies. Recent study data on platelet biology, redox signals, surgical stress, and antiplatelet tactics was reviewed in a systematic manner. Novel tailored therapies, perioperative antiplatelet medication, oxidative biomarkers of interest, and platelet-derived microscopic particles are important themes. In surgical procedures, oxidative stress dramatically increases the reactive capacity of platelets, spurring thromboinflammatory processes that affect cardiac attacks, infection risk, and recovery. A number of biomarkers, including soluble CD40L, thromboxane B2, and sNOX2-derived peptide, showed potential in forecasting results and tailored treatment. Antiplatelet medications are still essential for controlling risk factors for cardiovascular disease, yet using them during surgery necessitates carefully weighing the risks of thrombosis and bleeding. Biomarker-guided therapies, antioxidant adjuncts, and specific platelet inhibitors are examples of evolving tactics. In abdominal procedures, platelets strategically operate at the nexus of oxidative stress, inflammatory processes, and clotting. Improved patient classification, fewer problems, and the creation of individualized surgical care strategies could result from an increased incorporation of platelet-focused tests and therapies into perioperative processes. To improve clinical recommendations, subsequent studies may want to focus on randomized studies, biomarker verification, and using translational approaches. Full article

The covert nature of DNS covert channels makes them a widely utilized method for data exfiltration by malicious attackers. In response to this challenge, the present study proposes a detection methodology for DNS covert channels that employs a Deep Boltzmann Machine with Enhanced Security (DBM-ENSec). This approach entails the creation of a dataset through the collection of malicious traffic associated with various DNS covert channel attacks. Time-dependent grouping features are excluded, and feature optimization is conducted on individual traffic data through feature selection and normalization to minimize redundancy, enhancing the differentiation and stability of the features. The result of this process is the extraction of 23-dimensional features for each DNS packet. The extracted features are converted to gray scale images to improve the interpretability of the model and then fed into an improved Deep Boltzmann Machine for further optimization. The optimized features are then processed by an ensemble of classifiers (including Random Forest, XGBoost, LightGBM, and CatBoost) for detection purposes. Experimental results show that the proposed method achieves 99.92% accuracy in detecting DNS covert channels, with a validation accuracy of up to 98.52% on publicly available datasets. Full article

In today’s increasingly interconnected digital world, cyber threats have grown in frequency and sophistication, making intrusion detection systems a critical component of modern cybersecurity frameworks. Traditional IDS methods, often based on static signatures and rule-based systems, are no longer sufficient to detect and respond to complex and evolving attacks. To address these challenges, Artificial Intelligence and machine learning have emerged as powerful tools for enhancing the accuracy, adaptability, and automation of IDS solutions. This study presents a novel, hybrid ensemble learning-based intrusion detection framework that integrates deep learning and traditional ML algorithms with explainable artificial intelligence for real-time cybersecurity applications. The proposed model combines an Artificial Neural Network and Support Vector Machine as base classifiers and employs a Random Forest as a meta-classifier to fuse predictions, improving detection performance. Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model’s decisions. The framework is deployed using a Flask-based web interface in the Amazon Elastic Compute Cloud environment, capturing live network traffic and offering sub-second inference with visual alerts. Experimental evaluations using the NSL-KDD dataset demonstrate that the ensemble model outperforms individual classifiers, achieving a high accuracy of 99.40%, along with excellent precision, recall, and F1-score metrics. This research not only enhances detection capabilities but also bridges the trust gap in AI-powered security systems through transparency. The solution shows strong potential for application in critical domains such as finance, healthcare, industrial IoT, and government networks, where real-time and interpretable threat detection is vital. Full article

Peripheral artery disease (PAD) is associated with an elevated risk of major adverse cardiovascular events (MACE). Despite this, few reliable biomarkers exist to identify patients at heightened risk of MACE. Growth differentiation factor 15 (GDF15), a stress-responsive cytokine implicated in inflammation, atherosclerosis, and thrombosis, has been broadly studied in cardiovascular disease but remains underexplored in PAD. This study aimed to evaluate the prognostic utility of GDF15 for predicting 2-year MACE in PAD patients using explainable statistical and machine learning approaches. We conducted a prospective analysis of 1192 individuals (454 with PAD and 738 without PAD). At study entry, patient plasma GDF15 concentrations were measured using a validated multiplex immunoassay. The cohort was followed for two years to monitor the occurrence of MACE, defined as stroke, myocardial infarction, or death. Baseline GDF15 levels were compared between PAD and non-PAD participants using the Mann–Whitney U test. A machine learning model based on extreme gradient boosting (XGBoost) was trained to predict 2-year MACE using 10-fold cross-validation, incorporating GDF15 and clinical variables including age, sex, comorbidities (hypertension, diabetes, dyslipidemia, congestive heart failure, coronary artery disease, and previous stroke or transient ischemic attack), smoking history, and cardioprotective medication use. The model’s primary evaluation metric was the F1 score, a validated measurement of the harmonic mean of the precision and recall values of the prediction model. Secondary model performance metrics included precision, recall, positive likelihood ratio (LR+), and negative likelihood ratio (LR-). A prediction probability histogram and Shapley additive explanations (SHAP) analysis were used to assess model discrimination and interpretability. The mean participant age was 70 ± SD 11 years, with 32% (n = 386) female representation. Median plasma GDF15 levels were significantly higher in PAD patients compared to the levels in non-PAD patients (1.29 [IQR 0.77–2.22] vs. 0.99 [IQR 0.61–1.63] pg/mL; p < 0.001). During the 2-year follow-up period, 219 individuals (18.4%) experienced MACE. The XGBoost model demonstrated strong predictive performance for 2-year MACE (F1 score = 0.83; precision = 82.0%; recall = 83.7%; LR+ = 1.88; LR− = 0.83). The prediction histogram revealed distinct stratification between those who did vs. did not experience 2-year MACE. SHAP analysis identified GDF15 as the most influential predictive feature, surpassing traditional clinical predictors such as age, cardiovascular history, and smoking status. This study highlights GDF15 as a strong prognostic biomarker for 2-year MACE in patients with PAD. When combined with clinical variables in an interpretable machine learning model, GDF15 supports the early identification of patients at high risk for systemic cardiovascular events, facilitating personalized treatment strategies including multidisciplinary specialist referrals and aggressive cardiovascular risk reduction therapy. This biomarker-guided approach offers a promising pathway for improving cardiovascular outcomes in the PAD population through precision risk stratification. Full article

Mobile smartphones are prime targets for sophisticated surveillanceware, designed to covertly monitor specific individuals. While mobile operating systems implement various protection mechanisms, their defenses are frequently bypassed due to risky user behaviors or underlying software flaws, leading to persistent successful attacks. This paper addresses the critical research problem of how targeted mobile spyware can be effectively counteracted, particularly given its pervasive and evolving threat amplified by sophisticated evasion techniques. To contribute to this understanding, we comprehensively review mobile surveillanceware variants, namely stalkerware and mercenary spyware. We also critically review mobile OS protection mechanisms, and we detail how surveillanceware bypasses or exploits them. Our analysis reveals that, despite continuous efforts by mobile operating system and device manufacturers, both Android and iOS platforms struggle to protect devices and users, particularly against sophisticated mercenary spyware attacks, remaining vulnerable to these threats. Finally, we systematically review state-of-the-art countermeasures, identify their shortcomings, and highlight unresolved research challenges and concrete directions for future investigation for enhanced prevention and detection. Crucially, this future research must increasingly leverage artificial intelligence, including deep learning and large language models, to effectively keep pace with and overcome the sophisticated tactics employed by modern spyware. Full article

Pedestrian detection systems are widely used in safety-critical domains such as autonomous driving, where deep neural networks accurately perceive individuals and distinguish them from other objects. However, their vulnerability to backdoor attacks remains understudied. Existing backdoor attacks, relying on unnatural digital perturbations or explicit patches, are difficult to deploy stealthily in the physical world. In this paper, we propose a novel backdoor attack method that leverages real-world occlusions (e.g., backpacks) as natural triggers for the first time. We design a dynamically optimized heuristic-based strategy to adaptively adjust the trigger’s position and size for diverse occlusion scenarios, and develop three model-independent trigger embedding mechanisms for attack implementation. We conduct extensive experiments on two different pedestrian detection models using publicly available datasets. The results demonstrate that while maintaining baseline performance, the backdoored models achieve average attack success rates of 75.1% on KITTI and 97.1% on CityPersons datasets, respectively. Physical tests verify that pedestrians wearing backpack triggers could successfully evade detection under varying shooting distances of iPhone cameras, though the attack failed when pedestrians rotated by 90°, confirming the practical feasibility of our method. Through ablation studies, we further investigate the impact of key parameters such as trigger patterns and poisoning rates on attack effectiveness. Finally, we evaluate the defense resistance capability of our proposed method. This study reveals that common occlusion phenomena can serve as backdoor carriers, providing critical insights for designing physically robust pedestrian detection systems. Full article

Background/Objectives: Major adverse cardiovascular events (MACE)—including heart attacks and strokes—are the leading cause of death in patients with peripheral artery disease (PAD), yet biomarker research for MACE prediction in PAD patients remains limited. Inflammatory proteins play a key role in the progression of atherosclerosis and may serve as useful prognostic indicators for systemic cardiovascular risk in PAD. The objective of this study was to evaluate a broad panel of circulating inflammatory proteins to identify those independently associated with 2-year MACE in patients with PAD. Methods: We conducted a prospective cohort study involving 465 patients with PAD. Plasma concentrations of 15 inflammatory proteins were measured at baseline using validated immunoassays. Patients were followed over a two-year period for the development of MACE, defined as a composite endpoint of myocardial infarction, stroke, or mortality. Protein levels were compared between patients with and without MACE using the Mann–Whitney U test. Cox proportional hazards regression was used to determine the independent association of each protein with MACE after adjusting for baseline demographic and clinical variables, including existing coronary and cerebrovascular disease. To validate the findings, a random forest machine learning model was developed to assess the relative importance of each protein for predicting 2-year MACE. Results: The mean age of the cohort was 71 years (SD 10), and 145 participants (31.1%) were female. Over the two-year follow-up, 84 patients (18.1%) experienced MACE. Six proteins were significantly elevated in PAD patients who developed MACE: interferon gamma (IFN-γ; 42.55 [SD 15.11] vs. 33.85 [SD 12.46] pg/mL, p < 0.001), tumor necrosis factor alpha (TNF-α; 9.00 [SD 5.00] vs. 4.65 [SD 4.29] pg/mL, p < 0.001), chemokine (C-X-C motif) ligand 9 (CXCL9; 75.99 [SD 65.14] vs. 5.38 [SD 64.18] pg/mL, p = 0.002), macrophage inflammatory protein-1 beta (MIP-1β; 20.88 [SD 18.10] vs. 15.67 [SD 16.93] pg/mL, p = 0.009), MIP-1δ (25.29 [SD 4.22] vs. 17.98 [SD 4.01] pg/mL, p = 0.026), and interleukin-6 (IL-6; 12.50 [SD 40.00] vs. 6.72 [SD 38.98] pg/mL, p = 0.035). After adjusting for all baseline covariates, only two proteins—TNF-α (adjusted HR 1.66, 95% CI 1.28–2.33, p = 0.001) and IFN-γ (adjusted HR 1.25, 95% CI 1.12–2.29, p = 0.033)—remained significantly and independently associated with 2-year MACE. These findings were corroborated by the random forest model, where TNF-α and IFN-γ received the highest importance scores for predicting 2-year MACE: (TNF-α: 0.15 [95% CI 0.13–0.18], p = 0.002; IFN-γ: 0.19 [95% CI 0.17–0.21], p = 0.001). Conclusions: From a panel of 15 proteins, TNF-α and IFN-γ emerged as inflammatory biomarkers associated with 2-year MACE in PAD patients. Their measurement may aid in cardiovascular risk stratification, helping to identify high-risk individuals who could benefit from early multidisciplinary referrals to cardiology, neurology, and/or vascular medicine specialists to provide intensified medical therapy. Incorporating these biomarkers into PAD management may improve systemic cardiovascular outcomes through more personalized and targeted treatment approaches. Full article

All current transportation systems (vehicles, trucks, planes, etc.) rely on the Global Positioning System (GPS) as their main navigation technology. GPS receivers collect signals from multiple satellites and are able to provide more or less accurate positioning. For civilian applications, GPS signals are sent without any encryption system. For this reason, they are vulnerable to various attacks, and the most prevalent one is known as GPS spoofing. The main consequence is the loss of position monitoring, which may increase damage risks in terms of crashes or hijacking. In this study, we focus on UAV (unmanned aerial vehicle) positioning attacks. We first review numerous techniques for detecting and mitigating GPS spoofing attacks, finding that various types of attacks may occur. In the literature, many studies have focused on only one type of attack. We believe that targeting the study of many attacks is crucial for developing efficient mitigation mechanisms. Thus, we have explored a well-known datasetcontaining authentic UAV signals along with spoofed signals (with three types of attacked signals). As a main contribution, we propose a more interpretable approach to exploit the dataset by extracting individual mission sequences, handling non-stationary features, and converting the GPS raw data into a simplified structured format. Then, we design tree-based machine learning algorithms, namely decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), for the purpose of classifying signal types and to recognize spoofing attacks. Our main findings are as follows: (a) random forest has significant capability in detecting and classifying GPS spoofing attacks, outperforming the other models. (b) We have been able to detect most types of attacks and distinguish them. Full article

Facial authentication has gained widespread adoption as a biometric authentication method, offering a convenient alternative to traditional password-based systems, particularly on mobile devices equipped with front-facing cameras. While this technology enhances usability and security by eliminating password management, it remains highly susceptible to spoofing attacks. Adversaries can exploit facial recognition systems using pre-recorded photos, videos, or even sophisticated 3D models of victims’ faces to bypass authentication mechanisms. The increasing availability of personal images on social media further amplifies this risk, making robust anti-spoofing mechanisms essential for secure facial authentication. To address these challenges, we introduce FaceCloseup, a novel liveness detection technique that strengthens facial authentication by leveraging perspective distortion inherent in close-up shots of real, 3D faces. Instead of relying on additional sensors or user-interactive gestures, FaceCloseup passively analyzes facial distortions in video frames captured by a mobile device’s camera, improving security without compromising user experience. FaceCloseup effectively distinguishes live faces from spoofed attacks by identifying perspective-based distortions across different facial regions. The system achieves a 99.48% accuracy in detecting common spoofing methods—including photo, video, and 3D model-based attacks—and demonstrates 98.44% accuracy in differentiating between individual users. By operating entirely on-device, FaceCloseup eliminates the need for cloud-based processing, reducing privacy concerns and potential latency in authentication. Its reliance on natural device movement ensures a seamless authentication experience while maintaining robust security. Full article

The ability of terrestrial isopods (Crustacea: Isopoda: Oniscidea) to protect themselves effectively from predation by birds has never been tested. They are equipped with glands producing chemical substances; moreover, some species show conspicuous coloration, which might suffice as an aposematic signal. We evaluated the palatability of isopods to birds. We tested the responses of Parus major captured in the wild (and thus possessing some experience with common native isopod species) to the following isopod species: Porcellio scaber (native, inconspicuous), Oniscus asellus (native, moderately conspicuous), Armadillo officinalis (non-native, moderately conspicuous), Armadillidium versicolor (native, conspicuous), and Armadillidium gestroi (non-native, conspicuous). We compared bird responses to isopods with reactions to the Blaptica dubia, an edible roach very similar to isopods in size and appearance. Isopods were better protected from bird attacks than roaches; however, their color pattern did not affect the level of protection. Birds were able to differentiate isopods from the roach; in experiments, where we presented isopod and roach individuals together, the birds hesitated longer in attacking and observed both prey items for a longer time. Non-native species either profited from the generalization of the protection of native isopods or from neophobia. Some isopods elicited significantly more discomfort behavior in birds, suggesting differences in the chemical protection among the tested species. Full article

Background/Objectives: The vestibulo-respiratory reflex regulates the tension of the respiratory muscles, which prevents apneas and awakenings during sleep. This study aimed to determine whether functional deficits in the inner ear disturb sleep quality. Methods: We compared sleep parameters in patients with their first episode of acute inner ear deficit (Group A: sudden idiopathic vertigo attack, sudden sensorineural hearing loss), chronic functional inner ear impairment (Group B: chronic peripheral vertigo, permanent hearing loss), and in healthy individuals (Group C). Polygraphy recordings were performed twice, in Group A at the onset of acute otoneurological symptoms and the second time after their withdrawal with an interval of 1 to 13 days, in Group B after 1 to 6 days, and in Group C after 1 to 8 days. Results: In Group A during the symptomatic night, overall and central apnea-hypopnea indices were significantly higher and snoring time was longer. Group A also had higher central apnea-hypopnea index on the first night compared to healthy individuals. In chronic disorders, sleep recordings showed lower autonomic arousal index than in controls or symptomatic nights in Group A. Conclusions: These findings highlight the severity of sleep apnea indicators in Group A. Our results suggest that acute dysfunction of the inner ear substantially impacts central neuronal signaling responsible for regulating normal sleep-related breathing and leads to a deterioration in sleep quality in contrast to individuals with chronic inner ear impairments. It can also be assumed that people with chronic vertigo or hearing loss experience less interrupted sleep than healthy individuals. Full article

Air pollution is one of the most serious environmental threats, with particulate matter PM₁₀ and PM_2.5 representing its most harmful components, significantly affecting public health. These particles are primarily generated by transport, industry, residential heating, and agriculture, and are associated with increased incidence of respiratory and cardiovascular diseases, asthma attacks, and heart attacks, as well as chronic illnesses and premature mortality. The most vulnerable groups include children, the elderly, and individuals with pre-existing health conditions. This study focuses on the analysis of health risks associated with PM₁₀ and PM_2.5 air pollution in the city of Zvolen, which serves as a representative case due to its urban structure, traffic load, and industrial activity. The aim is to assess the current state of air quality, identify the main sources of pollution, and evaluate the health impacts of particulate matter on the local population. The results will be compared with selected Slovak cities—Banská Bystrica and Ružomberok—to understand regional differences in exposure and its health consequences. The results revealed consistently elevated concentrations of particulate matter (PM) across all analyzed cities, frequently exceeding the guideline values recommended by the World Health Organization (WHO), although remaining below the thresholds set by current national legislation. The lowest average concentrations were recorded in the city of Zvolen (PM₁₀: 20 μg/m³; PM_2.5: 15 μg/m³). These lower values may be attributed to the location of the reference monitoring station operated by the Slovak Hydrometeorological Institute (SHMÚ), situated on J. Alexy Street in the southern part of the city—south of Zvolen’s primary industrial emitter, Kronospan. Due to predominantly southerly wind patterns, PM particles are transported northward, potentially leading to higher pollution loads in the northern areas of the city, which are currently not being monitored. We analyzed trends in PM₁₀ and PM_2.5 concentrations and their relationship with hospitalization data for respiratory diseases. The results indicate a clear correlation between the concentration of suspended particulate matter and the number of hospital admissions due to respiratory illnesses. Our findings thus confirm the significant adverse effects of particulate air pollution on population health and highlight the urgent need for systematic monitoring and effective measures to reduce emissions, particularly in urban areas. Full article

This paper addresses the growing concern surrounding microplastic pollution, particularly within the textile industry, and the associated potential health risks linked to the inhalation and ingestion of microplastic particles. Microplastics, defined as plastic particles smaller than five millimeters, are increasingly found not only in aquatic environments, but also in soils, air, and food. Although research on the health impacts of microplastics is still emerging, early studies indicate that these particles could contribute to health issues, including oxidative stress, inflammation, and cardiovascular diseases. Notably, individuals with higher concentrations of plastics in arterial plaques are more susceptible to heart attacks and strokes. In the textile industry, synthetic fibers such as polyester, nylon, and acrylic release microplastics into the air during production. The paper discusses a study conducted in a textile company that processes polyester yarns, where airborne microplastic concentrations were measured at various locations throughout the day. Particle sizes ranging from 0.3 nm to 10 nm were analyzed, revealing the presence of polyester polymers in the particulate matter. These findings underscore the widespread nature of microplastic pollution, particularly in industrial settings, and raise concerns about the health risks associated with prolonged exposure to airborne microplastics. While further research is necessary to fully understand the extent of these health impacts, preliminary data suggest a troubling link between microplastic inhalation and cardiovascular conditions. Full article