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This study examined the incidence, characteristics, and risk factors of new gastrointestinal disorders (GID) associated with SARS-CoV-2 infection up to 3.5 years post-infection. This retrospective study included 35,102 COVID-19 patients and 682,594 contemporary non-COVID-19 patients without past medical history of GID (controls) from the Montefiore Health System in the Bronx (3/1/2020 to 7/31/2023). Comparisons were made with unmatched and propensity-matched (1:2) controls. The primary outcome was new GID which included peptic ulcer, inflammatory bowel disease, irritable bowel syndrome, diverticulosis, diverticulitis, and biliary disease. Multivariate Cox proportional hazards model analysis was performed with adjustment for covariates. There were 2,228 (6.34%) COVID-19 positive patients who developed new GID compared to 38,928 (5.70%) controls. COVID-19 patients had an elevated risk of developing new GID (adjusted HR = 1.18 (95% CI 1.12–1.25) compared to propensity-matched controls, after adjusting for confounders that included smoking, obesity, diabetes, hypertension. These findings underscore the need for additional research and follow-up of at-risk individuals for developing GID post infection.

In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research.

The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet technique was utilized for the investigation. The study adapts the fine-tune model to learn the pertinent patterns from mel spectrogram images specific to this bird species classification task. The fine-tuned EfficientNet model combined with a type of Recurrent Neural Networks (RNNs) namely Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). RNNs are employed to capture the temporal dependencies in audio signals, thereby enhancing bird species classification accuracy. The dataset utilized in this work contains nearly 17,000 bird sound recordings across a diverse range of species. The experiment was conducted with several combinations of EfficientNet and RNNs, and EfficientNet-B7 with GRU surpasses other experimental models with an accuracy of 84.03% and a macro-average precision score of 0.8342.

Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10.

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

This study aims to investigate new tissue conditioner (TC) formulations involving chitosan nanoparticles (CSNPs) and essential oils (EO) for their antifungal potential, release kinetics, and hardness. CSNPs were synthesized, and the separate solutions of CSNP were prepared with two types of EO, i.e., Oregano oil and Lemongrass. The EO was loaded separately in two concentrations (200 [mu]L and 250 [mu]L). The blank and EO-loaded CSNPs were screened against Candida albicans (C. albicans), and their minimum inhibitory concentration was established. GC Reline.sup.[TM] (GC corporation, USA) TC was considered a control group, whereby the four experimental groups were prepared by mixing CSNPs/EO solutions with TC powder. The antifungal effectiveness (C. albicans) and release kinetics behavior (1-6 h, 24 h, 48 h, and 72 h) was investigated. The Shore A hardness of control and experimental groups was evaluated in dry and wet modes (deionized water and artificial saliva). For statistical analysis, SPSS version 22 was used to do a one-way ANOVA post-hoc Tukey's test. Compared to the control group, TCs containing blank CSNPs and CSNPs loaded with EO showed 3 and 5 log reductions in C. albicans growth, respectively. A significantly high antifungal effect was observed with TC containing lemongrass essential oil (200 [mu]L). The continuous release of EO was detected for the first 6 hours, whereas completely stopped after 48 hours. Mean hardness values were highest for dry samples and lowest for samples stored in artificial saliva. The statistically significant difference within and between the study groups was observed in mean and cumulative essential oils release and hardness values of TCs over observed time intervals irrespective of storage media. TCs containing essential-oil-loaded CSNPs seem a promising alternative treatment of denture-induced stomatitis, however, a further biological analysis should be taken.

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson’s patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson’s dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson’s disease analysis.

The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals.

Abstract Cisplatin (CP) is a commonly used, powerful antineoplastic drug, having numerous side effects. Casticin (CAS) is considered as a free radical scavenger and a potent antioxidant. The present research was planned to assess the curative potential of CAS on CP persuaded renal injury in male albino rats. Twenty four male albino rats were distributed into four equal groups. Group-1 was considered as a control group. Animals of Group-2 were injected with 5mg/kg of CP intraperitoneally. Group-3 was co-treated with CAS (50mg/kg) orally and injection of CP (5mg/kg). Group-4 was treated with CAS (50mg/kg) orally throughout the experiment. CP administration substantially reduced the activities of catalase (CAT), superoxide dismutase (SOD), peroxidase (POD), glutathione S-transferase (GST), glutathione reductase (GSR), glutathione (GSH) content while increased thiobarbituric acid reactive substances (TBARS), and hydrogen peroxide (H2O2) levels. Urea, urinary creatinine, urobilinogen, urinary proteins, kidney injury molecule-1 (KIM-1), and neutrophil gelatinase-associated lipocalin (NGAL) levels were substantially increased. In contrast, albumin and creatinine clearance was significantly reduced in CP treated group. The results demonstrated that CP significantly increased the inflammation indicators including nuclear factor kappa-B (NF-κB), tumor necrosis factor-α (TNF-α), Interleukin-1β (IL-1β), Interleukin-6 (IL-6) levels and cyclooxygenase-2 (COX-2) activity and histopathological damages. However, the administration of CAS displayed a palliative effect against CP-generated renal toxicity and recovered all parameters by bringing them to a normal level. These results revealed that the CAS is an effective compound having the curative potential to counter the CP-induced renal damage. Resumo A cisplatina (CP) é uma droga antineoplásica poderosa, comumente usada, com vários efeitos colaterais. Casticin (CAS) é considerado um eliminador de radicais livres e um potente antioxidante. A presente pesquisa foi planejada para avaliar o potencial curativo da CAS em lesão renal induzida por PC em ratos albinos machos. Vinte e quatro ratos albinos machos foram distribuídos em quatro grupos iguais. O Grupo 1 foi considerado grupo controle. Os animais do Grupo 2 foram injetados com 5 mg / kg de PB por via intraperitoneal. O Grupo 3 foi cotratado com CAS (50 mg / kg) por via oral e injeção de CP (5 mg / kg). O Grupo 4 foi tratado com CAS (50 mg / kg) por via oral durante todo o experimento. A administração de CP reduziu substancialmente as atividades de catalase (CAT), superóxido dismutase (SOD), peroxidase (POD), glutationa S-transferase (GST), glutationa redutase (GSR), glutationa (GSH), enquanto aumentou as substâncias reativas ao ácido tiobarbitúrico (TBARS) e níveis de peróxido de hidrogênio (H2O2). Os níveis de ureia, creatinina urinária, urobilinogênio, proteínas urinárias, molécula 1 de lesão renal (KIM-1) e lipocalina associada à gelatinase de neutrófilos (NGAL) aumentaram substancialmente. Em contraste, a albumina e a depuração da creatinina foram significativamente reduzidas no grupo tratado com PC. Os resultados demonstraram que a CP aumentou significativamente os indicadores de inflamação, incluindo fator nuclear kappa-B (NF-κB), fator de necrose tumoral-α (TNF-α), interleucina-1β (IL-1β), interleucina-6 (IL-6) níveis e atividade da ciclooxigenase-2 (COX-2) e danos histopatológicos. No entanto, a administração de CAS apresentou um efeito paliativo contra a toxicidade renal gerada por CP e recuperou todos os parâmetros, trazendo-os a um nível normal. Estes resultados revelaram que o CAS é um composto eficaz com potencial curativo para combater o dano renal induzido por CP.