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The development of effective and innovative vaccination strategies is urgently needed to better control the spread and transmission of the low-pathogenic avian influenza H9N2 subtype (LPAI-H9N2) in poultry. In addition, the enhancement of innate immunity by some of these innovative inactivated vaccines has not yet been investigated. Here, an experiment was conducted in commercial broiler chickens to compare the immune response to two different inactivated H9N2 vaccines. For this, Group 1 (G1) broilers were vaccinated with vaccine 1 [Nobilis® H9N2-P (pathogen-associated molecular patterns—PAMP) technology], broilers in G2 were vaccinated with vaccine 2 [an inactivated whole H9N2 virus (IWV) autogenous oil emulsion vaccine], while birds in G3 were not vaccinated. The study lasted 34 days. Innate immune parameters (phagocytic activity, nitric oxide, and lysozyme), cytokine signaling (IL-1β, IL-6, IL-8), humoral immunity using the hemagglutination inhibition (HI) test, and the gene expressions of IFN-γ and TLR-21 were assessed. The results showed a significant increase in innate immunity and modulatory cytokines at 24–48 h after the vaccination of G1 broilers, with a continuous increase until the end of the experiment. In addition, a significant increase in geometric mean HI titers was observed in G1 at 11 days post-vaccination (dpv), and a significant (p ˂ 0.05) upregulation of IFN-γ and TLR-21 was observed in the same group, G1, at 31 dpv compared to G2 and G3. Nobilis® H9N2-P may induce faster and stronger innate and active humoral immunity compared to another IWV, which may contribute to the protection of broilers against early H9N2 infections. However, challenge protection studies for several IWV vaccines, including PAMP-H9N2 against LPAI-H9N2, should be further evaluated in both specific pathogen-free (SPF) and commercial broilers.

Concrete is one of the most commonly used materials in civil engineering construction, and it continues to have increased production. This puts pressure on the consumption of its constituent materials, including Portland cement and aggregates. There are environmental consequences related to the increased emission of CO2 that are associated with the production process of Portland cement. This has led to the development and use of alternative cementitious materials, mainly in the form of condensed silica fume, pulverised fuel ash, and ground granulated blast furnace slag. All of these are by-products of the silicon, electrical power generation, and iron production industries, respectively. In recent years, attention has turned to the possible use of sustainable bio-waste materials that might contribute to the replacement of Portland cement in concrete. This research investigates the effects of using rice husk ash as cement replacement material on the 1 to 28-day concrete properties, including the compressive strength, workability, and durability of concrete. The findings indicate that including rice husk ash in concrete can improve its strength at 3–28 days for percentage replacements of 5% to 20% (ranging from 2.4% to 18.7% increase) and improvements from 1 day for 20% replacement (with 11.1% increase). Any percentage replacement with rice husk ash also reduced the air permeability by 21.4% and therefore improved the durability, while there was a small reduction in the workability with increased replacement.

Acute psychological stress (APS) is a complex and multifactorial phenomenon that affects metabolism, necessitating real-time detection and interventions to mitigate its effects on glycemia in people with type 1 diabetes. This study investigates the detection of APS using physiological variables measured by the Empatica E4 wristband and employs explainable machine learning to evaluate the importance of the physiological signals. The extreme gradient boosting model is developed for classification of APS and non-stress (NS) with weighted training, achieving an overall accuracy of 99.93%. The Shapley additive explanations (SHAP) technique is employed to interpret the global importance of the physiological signals, determining the order of importance for the variables from most to least as galvanic skin response (GSR), heart rate (HR), skin temperature (ST), and motion sensors (accelerometer readings). The increase in GSR and HR are positively correlated with the occurrence of APS as indicated by high positive SHAP values. The SHAP technique is also used to explain the local signal importance for particular instances of misclassified samples. The detection of APS can inform multivariable automated insulin delivery systems to intervene to counteract the APS-induced glycemic excursions in people with type 1 diabetes.

Additional Affiliation [...]

The defining characteristic of laterality is the dominance of one side of the brain controlling specific functions of paired organs or on one side of the body. Structural and functional asymmetries are ubiquitous in horses and range from anatomical features (e.g., the length of long bones) to the gathering of sensory information (e.g., which eye is used to observe unfamiliar scenes) and motor functions (e.g., left–right differences in locomotion). There is a common tendency to assign observed structural or functional asymmetries to lateralization, which often involves more than a simple left–right difference in observed behavior. This narrative review explores the concept of laterality relative to the structural and functional asymmetries reported in horses. Inconsistent and poorly defined terminology, a widely disparate methodology, and a lack of standardized thresholds make it difficult to assess the presence or degree of laterality. Within this context, there seems to be limited evidence of laterality in horses and much more prevalent and stronger support for structural and functional asymmetries due to a wide range of well-established behavioral, nociceptive, and biomechanical mechanisms. The authors caution against generalizing the idea that all observed structural or functional asymmetries in horses are due to laterality.

The emergence of diverse content-sharing platforms and social media has rendered the dissemination of fake news and misinformation increasingly widespread. This misinformation can cause extensive confusion and fear throughout the populace. Confronting this dilemma necessitates an effective and accurate approach to identifying misinformation, an intrinsically intricate process. This research introduces an automated and efficient method for detecting false information. We evaluated the efficacy of various machine learning and deep learning models on two separate fake news datasets of differing sizes via holdout cross-validation. Furthermore, we evaluated the efficacy of three distinct word vectorization methods. Additionally, we employed an enhanced weighted voting ensemble model that enhances fake news detection by integrating logistic regression (LR), support vector machine (SVM), gated recurrent unit (GRU), and long short-term memory (LSTM) networks. This method exhibits enhanced performance relative to previous techniques: 98.76% for the PolitiFact dataset and 97.67% for the BuzzFeed dataset. Furthermore, the model outperforms individual components, resulting in superior accuracy, precision, recall, and F1 scores. The enhancements in performance result from the ensemble method’s capacity to use the advantages of each base model, hence providing robust generalization across datasets. Cross-validation was employed to enhance the model’s trustworthiness, validating its capacity to generalize effectively to novel data.

Drowsy driving poses a significant challenge to road safety worldwide, contributing to thousands of accidents and fatalities annually. Despite advancements in driver drowsiness detection (DDD) systems, many existing methods face limitations such as intrusiveness and delayed reaction times. This research addresses these gaps by leveraging facial analysis and state-of-the-art machine learning techniques to develop a real-time, non-intrusive DDD system. A distinctive aspect of this research is its systematic assessment of various machine and deep learning algorithms across three pivotal public datasets, the NTHUDDD, YawDD, and UTA-RLDD, known for their widespread use in drowsiness detection studies. Our evaluation covered techniques including the K-Nearest Neighbors (KNNs), support vector machines (SVMs), convolutional neural networks (CNNs), and advanced computer vision (CV) models such as YOLOv5, YOLOv8, and Faster R-CNN. Notably, the KNNs classifier reported the highest accuracy of 98.89%, a precision of 99.27%, and an F1 score of 98.86% on the UTA-RLDD. Among the CV methods, YOLOv5 and YOLOv8 demonstrated exceptional performance, achieving 100% precision and recall with mAP@0.5 values of 99.5% on the UTA-RLDD. In contrast, Faster R-CNN showed an accuracy of 81.0% and a precision of 63.4% on the same dataset. These results demonstrate the potential of our system to significantly enhance road safety by providing proactive alerts in real time.

Osteopontin (OPN) is a sialylated phosphoprotein highly expressed in atherosclerosis and upregulated in settings of both acute and chronic inflammation. It is hypothesised that plasma levels of OPN may correlate with the presence of coronary artery disease, “CAD”. This offers potential as a point-of-care testing biomarker for early diagnosis, disease monitoring, and prognosis. This review evaluates the current literature on the association between plasma OPN levels and coronary artery disease and what is currently known to support its potential as a biomarker for future practice. Electronic searches of MEDLINE and EMBASE databases were undertaken from inception until July 2024. Thirty-three studies met the inclusion criteria. All studies were observational, with gross heterogeneity in methods used to analyse the association of plasma OPN with clinical characteristics. They included case series, case–control, cross-sectional, and cohort study designs. OPN has been linked to higher cardiovascular risk and unfavourable cardiovascular outcomes. However, the evidence regarding the direct assessment of CAD severity using tools like the SYNTAX or TIMI scores, which focus on anatomical complexity and risk factors, is less definitive. This suggests that OPN may be a more precise reflection of the inflammatory processes and atherosclerotic activity contributing to unfavourable outcomes rather than a direct indicator of the anatomical severity of CAD itself. Consequently, OPN is increasingly perceived as a marker of a poor prognosis rather than a tool for assessing the severity of coronary artery lesions.

With the increasing concern about global warming and future climate change, attention has been drawn to the need to reduce building energy use through improving buildings’ energy efficiency. Existing residential buildings constitute the largest percentage of energy demand and carbon dioxide emissions, and hence, offer significant potential for energy savings and reductions in greenhouse gas emissions. This review aimed to provide an in-depth analysis of current research on improving the energy efficiency of existing residential buildings in Libya and neighbouring Mediterranean countries, with a focus on research methods and tools utilised in this domain. This helped to identify potential areas of intervention to improve the energy efficiency of existing residential stock in Libya. Under identified themes, this study systematically analysed 44 publications of high relevance to the subject area found in Scopus, ScienceDirect, and Google Scholar. The results reveal that while energy retrofitting is a research area of interest in the region considered, studies in the Libyan context are limited. There is also limited attention to achieving net zero energy and embodied carbon reductions, specifically in the Libyan context. Moreover, some weaknesses were identified for most of the studies reviewed, including those in the Libyan context, related to the credibility and reliability of the energy models used in the various literature.

Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose neonatal jaundice non-invasively based on machine learning algorithms created based on a dataset comprising 767 infant images using a computer device and a USB webcam. Methods: The first stage of the proposed system was to evaluate the performance of four machine learning algorithms, namely support vector machine (SVM), k nearest neighbor (k-NN), random forest (RF), and extreme gradient boost (XGBoost), based on a dataset of 767 infant images. The algorithm with the best performance was chosen as the classifying algorithm in the developed application. The second stage included designing an application that enables the user to perform jaundice detection for a patient under test with the minimum effort required by capturing the patient’s image using a USB webcam. Results: The obtained results of the first stage of the machine learning algorithms evaluation process indicated that XGBoost outperformed the rest of the algorithms by obtaining an accuracy of 99.63%. The second-best algorithm was the RF algorithm, which had an accuracy of 98.99%. Following RF, with a slight difference, was the k-NN algorithm. It achieved an accuracy of 98.25%. SVM scored the lowest performance among the above three algorithms, with an accuracy of 96.22%. Based on these obtained results, the XGBoost algorithm was chosen to be the classifier of the proposed system. In the second stage, the jaundice application was designed based on the model created by the XGBoost algorithm. This application ensured it was user friendly with as fast a processing time as possible. Conclusion: Early detection of neonatal jaundice is crucial due to the severity of its complications. A non-invasive system using a USB webcam and an XGBoost machine learning technique was proposed. The XGBoost algorithm achieved 99.63% accuracy and successfully diagnosed 10 out of 10 NICU infants with very little processing time. This denotes the efficiency of machine learning algorithms in healthcare in general and in monitoring systems specifically.

Uterus transplantation (UTx) is a rapidly evolving treatment for uterine factor infertility. New centers offering this treatment must decide whether to utilize living donors, deceased donors, or both. Although limiting UTx to deceased donors eliminates the surgical risks for living donors, an adequate supply of suitable deceased uterus donors in the United States is an emerging concern. Previous studies describing the paucity of deceased uterus donors failed to consider key donor characteristics, potentially overestimating the available organ pool. To estimate the United States’ supply of deceased donor uteri; we extrapolated detailed clinical and demographic information from the regional donor datasets available from three organ procurement organizations to the national Organ Procurement and Transplantation Network donor pool. We estimate there are approximately 3700 possible and 400 optimal uterus donors annually in the United States. Given these projections and the number of women with uterine factor infertility in the U.S. who pursue parenthood through alternative strategies, we conclude that, as uterus transplant transitions from research to established clinical care, demand could quickly exceed the deceased donor supply. The liberalization of deceased donor selection criteria may be insufficient to address this imbalance; therefore, fulfilling the anticipated increased demand for uterus transplantation may require and justify greater use of living donors.

Forecasting stock markets is challenging due to the influence of various internal and external factors compounded by the effects of globalization. This study introduces a data-driven approach to forecast S&P 500 returns by incorporating macroeconomic indicators including gold and oil prices, the volatility index, economic policy uncertainty, the financial stress index, geopolitical risk, and shadow short rate, with ten technical indicators. We propose three hybrid deep learning models that sequentially combine convolutional and recurrent neural networks for improved feature extraction and predictive accuracy. These models include the deep belief network with gated recurrent units, the LeNet architecture with gated recurrent units, and the LeNet architecture combined with highway networks. The results demonstrate that the proposed hybrid models achieve higher forecasting accuracy than the single deep learning models. This outcome is attributed to the complementary strengths of convolutional networks in feature extraction and recurrent networks in pattern recognition. Additionally, an analysis using the Shapley method identifies the volatility index, financial stress index, and economic policy uncertainty as the most significant predictors, underscoring the effectiveness of our data-driven approach. These findings highlight the substantial impact of contemporary uncertainty factors on stock markets, emphasizing their importance in studies analyzing market behaviour.

Flood events pose significant risks to infrastructure and populations worldwide, particularly in Punjab, Pakistan, where critical infrastructure must remain operational during adverse conditions. This study aims to predict flood-prone areas in Punjab and assess the vulnerability of critical infrastructures within these zones. We developed a robust Flood Susceptibility Model (FSM) utilizing the Maximum Likelihood Classification (MLC) model and Analytical Hierarchy Process (AHP) incorporating 11 flood-influencing factors, including “Topographic Wetness Index (TWI), elevation, slope, precipitation (rain, snow, hail, sleet), rainfall, distance to rivers and roads, soil type, drainage density, Land Use/Land Cover (LULC), and the Normalized Difference Vegetation Index (NDVI)”. The model, trained on a dataset of 850 training points, 70% for training and 30% for validation, achieved a high accuracy (AUC = 90%), highlighting the effectiveness of the chosen approach. The Flood Susceptibility Map (FSM) classified high- and very high-risk zones collectively covering approximately 61.77% of the study area, underscoring significant flood vulnerability across Punjab. The Sentinel-1A data with Vertical-Horizontal (VH) polarization was employed to delineate flood extents in the heavily impacted cities of Dera Ghazi Khan and Rajanpur. This study underscores the value of integrating Multi-Criteria Decision Analysis (MCDA), remote sensing, and Geographic Information Systems (GIS) for generating detailed flood susceptibility maps that are potentially applicable to other global flood-prone regions.

Spontaneous abortion, commonly known as miscarriage, is a significant concern during early pregnancy. Histopathological examination of tissue samples is a widely used method to diagnose and classify tissue phenotypes found in products of conception (POC) after spontaneous abortion. Background: Histopathological examination is subjective and dependent on the skill and experience of the examiner. In recent years, artificial intelligence (AI)-based techniques have emerged as a promising tool in medical imaging, offering the potential to revolutionize tissue phenotyping and improve the accuracy and reliability of the histopathological examination process. The goal of this study was to investigate the use of AI techniques for the detection of various tissue phenotypes in POC after spontaneous abortion and evaluate the accuracy and reliability of these techniques compared to traditional manual methods. Methods: We present a novel publicly available dataset named HistoPoC, which is believed to be the first of its kind, focusing on spontaneous abortion (miscarriage) in early pregnancy. A diverse dataset of 5666 annotated images was prepared from previously diagnosed cases of POC from Atia General Hospital, Karachi, Pakistan, for this purpose. The digital images were prepared at 10× through a camera-connected microscope by a consultant histopathologist. Results: The dataset’s effectiveness was validated using several deep learning-based models, demonstrating its applicability and supporting its use in intelligent diagnostic systems. Conclusions: The insights gained from this study could illuminate the causes of spontaneous abortion and guide the development of novel treatments. Additionally, this study could contribute to advancements in the field of tissue phenotyping and the wider application of deep learning techniques in medical diagnostics and treatment.

A 45-day feeding study was carried out to assess the immune-stimulatory effects of capsicum oleoresin when added to rainbow trout diets. A total of 450 fish (mean weight: 155.20 ± 1.96 g) were distributed into 400 L tanks (30 fish/tank) across five experimental groups: control (CT, 0%), C7 (0.7%), C14 (1.4%), C21 (2.1%), and C28 (2.8%). Each group consisted of three replicate tanks. At the end of this period, hemato-biochemical parameters, innate immune responses, and immune-related gene expression levels were evaluated, and a histological examination of head kidney and liver sections was conducted. Finally, fish in all groups were challenged with Lactococcus garvieae and observed for an additional 20 days. The results revealed that oleoresin supplementation enhanced the immune responses of the treated fish, which was evidenced by the increased globulin, total protein, respiratory burst activity, and total immunoglobulin levels. The highest expression levels of the il-8, il-1β, TGF-β, and SAA genes was noticed in the C7 group, as compared with the results for the other groups. The IgT gene expression levels were higher in all experimental groups than in the CT group, and this increase was at the highest level in the C28 group. Following the bacterial challenge, all experimental groups displayed higher survival rates compared to that of the CT group. These values were 75.93, 72.22, 46.30, 33.33, and 29.63% in the C7, C14, C21, C28, and CT groups, respectively, with the C7 group displaying the highest survival rate among the groups. The histological examination of liver and head kidney tissues revealed that higher doses (in the C21 and C28 groups) showed an increase in cytoplasmic vacuolization, which causes adverse effects on fish health. However, the C7 group displayed normal histological structure in both tissues. Taken together, the most favorable immune responses were achieved in the C7 group, suggesting that 0.7% oleoresin could be applied to rainbow trout to boost immunity and protect the fish from diseases.

Autism Spectrum Disorder (ASD) is a complex developmental condition marked by challenges in social interaction, communication, and behavior, often involving restricted interests and repetitive actions. The diversity in symptoms and skill profiles across individuals creates a diagnostic landscape that requires a multifaceted approach for accurate understanding and intervention. This study employed advanced machine-learning techniques to enhance the accuracy and reliability of ASD diagnosis. We used a standard dataset comprising 1054 patient samples and 20 variables. The research methodology involved rigorous preprocessing, including selecting key variables through data mining (DM) visualization techniques including Chi-Square tests, analysis of variance, and correlation analysis, along with outlier removal to ensure robust model performance. The proposed DM and logistic regression (LR) with Shapley Additive exPlanations (DMLRS) model achieved the highest accuracy at 99%, outperforming state-of-the-art methods. eXplainable artificial intelligence was incorporated using Shapley Additive exPlanations to enhance interpretability. The model was compared with other approaches, including XGBoost, Deep Models with Residual Connections and Ensemble (DMRCE), and fast lightweight automated machine learning systems. Each method was fine-tuned, and performance was verified using k-fold cross-validation. In addition, a real-time web application was developed that integrates the DMLRS model with the Django framework for ASD diagnosis. This app represents a significant advancement in medical informatics, offering a practical, user-friendly, and innovative solution for early detection and diagnosis.

This study provides an in-depth analysis of mpox, encompassing its history, characteristics, epidemiology, diagnostics, treatment options, and the ongoing evolution of the virus and its transmission dynamics. Mpox, though once successfully eradicated, has re-emerged with new modes of transmission and a broader host range. Genomic analyses have revealed the virus’s adaptability, posing challenges for diagnostics and vaccine efficacy. The epidemiology has shifted from sporadic zoonotic transmission in rural Africa to a significant presence in urban areas, particularly impacting high-risk populations. Advancements in diagnostics and therapeutics offer hope, but challenges persist. This work underscores the critical need for enhanced surveillance, vaccination strategies, and continued research to bolster global health systems and preparedness for future outbreaks.

The journal retracts the article titled “Crayfish Research: A Global Scientometric Analysis Using CiteSpace” [...]

Piezoelectric energy harvesters are considered a replacement for batteries because they are self-sustainable with low or no maintenance, and suitable for off-grid devices. In this study, two cylinders were placed in a flowing stream of water. The upstream cylinder of 25 mm diameter is fixed at the center of the stream while another cylinder of the same diameter, which is free to oscillate in the crosswise direction of the flow and is placed in the wake of that fixed cylinder. A piezoelectric flag is then placed in the wake of the vibrating cylinder and its distance from the vibrating cylinder was changed to observe the effect of wake length on the energy harvester. The energy harvesting potential of the system is explored for flow velocities ranging from 0.28 m/s to 0.36 m/s. The distance between the cylinders Gx, which is explored for the range of 2.5D–7D, and the distance between the piezoelectric membrane and the vibrating cylinder are expressed as Sx for the same range. The maximum power of 19.17 μW was produced at Sx = 2D and Gx = 3.5D. The maximum power for the baseline case, when two tandemly arranged stationary circular cylinders were used and a piezoelectric membrane was placed in their wake, is 10.24 μW; hence, an increase of 87.2% is observed under the same ambient conditions.

Although antibiotics are the main therapy for bacterial infections, the reports showed that the overuse (or misuse) of antibiotics will results in several problems such as the development of antibiotic-resistant strains, persistence of drug residues, and numerous environmental concerns. Therefore, finding antibiotic alternatives is considered of vital importance. Investigation of the antimicrobial properties of several plant substances and extracts is of great value to replace antibiotics. With this objective, this study aimed to evaluate the antimicrobial activities of an ethanolic extract prepared from olive mill wastewater (OMWW), which is a by-product of olive oil production with considerable environmental burden, against 38 bacterial strains, including fish-associated pathogens, non-pathogenic isolates, collection strains, and one yeast strain, Candida albicans. Disk diffusion, minimum inhibitory concentration (MIC), and minimum bactericidal/fungicidal concentration (MBC/MFC) tests were used to determine the antimicrobial activity of the OMWWE. According to the results, OMWWE provoked strong inhibitory effects against Shewanella baltica strain SY-S145. It also showed a moderate inhibitory effect on Plesiomonas shigelloides strain SY-PS16 and Vibrio anguillarum strain SY-L24. The MIC and MBC of OMWWE on Shewanella baltica SY-S145, Vibrio gigantis strain C24, and V. anguillarum strain SY-L24 were 500 µg/mL. The MIC and MBC on V. parahaemolyticus ATCC 17802 were 1000 µg/mL, whereas the values for Aeromonas salmonicida ATCC 33658 were 500 µg/mL and 1000 µg/mL, respectively. To put it briefly, the OMWW extract showed high antimicrobial activity and can act as an environmentally friendly additive for the control and prevention of diseases caused by A. veronii, A. hydrophila, P. shigelloides, S. baltica, V. anguillarum, and V. parahaemolyticus. Its active agents also prevented infections of both fish-associated pathogens and food spoiling bacteria, which means it can not only help in the disease control mechanism but also in improving the safety of food by reduction of the microbial contamination.

In this study, trapezoidal-type inequalities in fuzzy settings have been investigated. The theory of fuzzy analysis has been discussed in detail. The integration by parts formula of analysis of fuzzy mathematics has been employed to establish an equality. Trapezoidal-type inequality for functions with values in the fuzzy number-valued space is proven by applying the proven equality together with the properties of a metric defined on the set of fuzzy number-valued space and Höler’s inequality. The results proved in this research provide generalizations of the results from earlier existing results in the field of mathematical inequalities. An example is designed by defining a function that has values in fuzzy number-valued space and validated the results numerically using the software Mathematica (latest v. 14.1). The p-levels of the defined fuzzy number-valued mapping have been shown graphically for different values of p∈0,1.

Silica nanoparticles (SNPs) induce reproductive toxicity through ROS production, which significantly limits their application. The protective effects of taurine (Tau) against SNP-induced reproductive toxicity remain unexplored. So this study aims to investigate the impact of Tau on SNP-induced porcine ovarian granulosa cell toxicity. In vitro, granulosa cells were exposed to SNPs combined with Tau. The localization of SNPs was determined by TEM. Cell viability was examined by CCK-8 assay. ROS levels were measured by CLSM and FCM. SOD and CAT levels were evaluated using ELISA and qPCR. Cell apoptosis was detected by FCM, and pro-inflammatory cytokine transcription levels were measured by qPCR. The results showed that SNPs significantly decreased cell viability, while increased cell apoptosis and ROS levels. Moreover, SOD and CAT were decreased, while IFN-α, IFN-β, IL-1β, and IL-6 were increased after SNP exposures. Tau significantly decreased intracellular ROS, while it increased SOD and CAT compared to SNPs alone. Additionally, Tau exhibited anti-inflammatory effects and inhibited cell apoptosis. On the whole, these findings suggest that Tau mitigates SNP-induced cytotoxicity by reducing oxidative stress, inflammatory response, and cell apoptosis. Tau may be an effective strategy to alleviate SNP-induced toxicity and holds promising application prospects in the animal husbandry and veterinary industry.

Nowadays, it is very tough to differentiate between real news and fake news due to fast-growing social networks and technological progress. Manipulative news is defined as calculated misinformation with the aim of creating false beliefs. This kind of fake news is highly detrimental to society since it deepens political division and weakens trust in authorities and institutions. Therefore, the identification of fake news has emerged as a major field of research that seeks to validate content. The proposed model operates in two stages: First, TF-IDF is applied to an entire document to obtain its global features, and its spatial and temporal features are simultaneously obtained by employing Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory with a Gated Recurrent Unit. The Fast Learning Network efficiently classifies the extracted features. Comparative experiments were conducted on three easily and publicly obtainable large-scale datasets for the purposes of analyzing the efficiency of the approach proposed. The results also show how well the model performs compared with past methods of classification.

Neonatal jaundice is a prevalent condition among newborns, with potentially severe complications that can result in permanent brain damage if left untreated during its early stages. The existing approaches for jaundice detection involve invasive procedures such as blood sample collection, which can inflict pain and distress on the patient, and may give rise to additional complications. Alternatively, a non-invasive method using image-processing techniques and implementing kNN, Random Forest, and XGBoost machine learning algorithms as a classifier can be employed to diagnose jaundice, necessitating a comprehensive database of infant images to achieve a diagnosis with high accuracy. This data article presents the NJN collection, a repository of newborn images encompassing diverse birthweights and skin tones, spanning an age range of 2 to 8 days. The dataset is accompanied by an Excel sheet file in CSV format containing the RGB and YCrCb channel values, as well as the status of each sample. The dataset and associated resources are openly accessible at Zenodo website. Moreover, the Python code for data testing utilizing various AI techniques is provided. Consequently, this article offers an unparalleled resource for AI researchers, enabling them to train their AI systems and develop algorithms that can assist neonatal intensive care unit (NICU) healthcare specialists in monitoring neonates while facilitating the fast, real-time, non-invasive, and accurate diagnosis of jaundice.

In this paper, we offer Ostrowski-type inequalities that extend the findings that have been proven for functions of one variable with values in Banach spaces, conducted in a remarkable study by Dragomir, to functions of two variables containing values in the product Banach spaces. Our findings are also an extension of several previous findings that have been established for functions of two variable functions. Prior studies on Ostrowski-type inequalities incriminated functions that have values in Banach spaces or Hilbert spaces. This study is unique and significant in the field of mathematical inequalities, and specifically in the study of Ostrowski-type inequalities, because they have been established for functions having values in a product of two Banach spaces.

In contexts requiring user authentication, such as financial, legal, and administrative systems, signature verification emerges as a pivotal biometric method. Specifically, handwritten signature verification stands out prominently for document authentication. Despite the effectiveness of triplet loss similarity networks in extracting and comparing signatures with forged samples, conventional deep learning models often inadequately capture individual writing styles, resulting in suboptimal performance. Addressing this limitation, our study employs a triplet loss Siamese similarity network for offline signature verification, irrespective of the author. Through experimentation on five publicly available signature datasets—4NSigComp2012, SigComp2011, 4NSigComp2010, and BHsig260—various distance measure techniques alongside the triplet Siamese Similarity Network (tSSN) were evaluated. Our findings underscore the superiority of the tSSN approach, particularly when coupled with the Manhattan distance measure, in achieving enhanced verification accuracy, thereby demonstrating its efficacy in scenarios characterized by close signature similarity.

Software-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper introduces a novel approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system utilizes the IOTA Tangle, a directed acyclic graph (DAG) structure, to improve scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts: Authority, Access Control, and DoS Detector, to ensure trusted and secure network operations, prevent unauthorized access, maintain the integrity of control data, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for the further integration of blockchain technologies in network management.

In the original publication [...]

As the field of artificial intelligence (AI) continues to evolve, its potential applications in various domains, including public policy development, have garnered significant interest. This research aims to investigate the role of AI in shaping public policies through a qualitative examination of secondary data and an extensive bibliographic review. By analyzing the existing literature, government reports, and relevant case studies, this study seeks to uncover the opportunities, challenges, and ethical considerations associated with leveraging AI in the formulation and implementation of public policies. This research will delve into the potential benefits of AI-driven policy analysis, such as enhanced decision-making processes, data-driven insights, and improved policy outcomes. Additionally, it will explore the risks and concerns surrounding AI’s influence on policy, including potential biases, privacy implications, and the need for transparency and accountability. The findings of this study will contribute to the ongoing discourse on the responsible and effective integration of AI in public policy development, fostering informed decision-making and promoting the ethical use of this transformative technology.

Triazine pesticide (atrazine and its derivatives) detection sensors have been developed to thoroughly check for the presence of these chemicals and ultimately prevent their exposure to humans. Sensitive coatings were designed by utilizing molecular imprinting technology, which aims to create artificial receptors for the detection of chlorotriazine pesticides with gravimetric transducers. Initially, imprinted polymers were developed, using acrylate and methacrylate monomers containing hydrophilic and hydrophobic side chains, specifically for atrazine, which shares a basic heterocyclic triazine structure with its structural analogs. By adjusting the ratio of the acid to the cross-linker and introducing acrylate ester as a copolymer, optimal non-covalent interactions were achieved with the hydrophobic core of triazine molecules and their amino groups. A maximum sensor response of 546 Hz (frequency shift/layer height equal to 87.36) was observed for a sensitive coating composed of 46% methacrylic acid and 54% ethylene glycol dimethacrylate, with a demonstrated layer height of 250 nm (6.25 kHz). The molecularly imprinted copolymer demonstrated fully reversible sensor responses, not only for atrazine but also for its metabolites, like des-ethyl atrazine, and structural analogs, such as propazine and terbuthylazine. The efficiency of modified molecularly imprinted polymers for targeted analytes was tested by combining them with a universally applicable quartz crystal microbalance transducer. The stable selectivity pattern of the developed sensor provides an excellent basis for a pattern recognition procedure.

Two new fusarochromanone derivatives, deacetylfusarochromene (1) and deacetamidofusarochrom-2′,3-diene (2), along with the previously reported metabolites fusarochromanone TDP-2 (3), fusarochromene (4), 2,2-dimethyl-5-amino-6-(2′E-ene-4′-hydroxylbutyryl)-4-chromone (5), fusarochromanone (6), (−)-chrysogine (7), and equisetin (8), were isolated from the marine fungus Fusarium equiseti UBOCC-A-117302. The structures of the compounds were determined by extensive spectrometric (HRMS) and spectroscopic (1D and 2D NMR) analyses, as well as specific rotation. Among them, 2 and 5 showed inhibition of three protein kinases with IC50 values ranging from 1.42 to 25.48 μM. Cytotoxicity and antimicrobial activity of all isolated compounds were also evaluated. Six fusarochromanone derivatives (1–6) exhibited diverse activities against three cell lines, RPE-1, HCT-116, and U2OS (IC50 values ranging from 0.058 to 84.380 μM). Equisetin (8) showed bactericidal activities against Bacillus cereus and Listeria monocytogenes (MBC values of 7.8 and 31.25 µM, respectively), and bacteriostatic activity against Enterococcus faecalis (MIC value of 31.25 µM). Compounds 2 and 4 showed bacteriostatic activities against Listeria monocytogenes (MIC of 125 µM).

Fucoxanthin, a carotenoid with remarkable antioxidant properties, has considerable potential for high-value biotechnological applications in the pharmaceutical, nutraceutical, and cosmeceutical fields. However, conventional extraction methods of this molecule from microalgae are limited in terms of cost-effectiveness. This study focused on optimizing biomass and fucoxanthin production from Isochrysis galbana, isolated from the coast of Tadjoura (Djibouti), by testing various culture media. The antioxidant potential of the cultures was evaluated based on the concentrations of fucoxanthin, carotenoids, and total phenols. Different nutrient formulations were tested to determine the optimal combination for a maximum biomass yield. Using the statistical methodology of principal component analysis, Walne and Guillard F/2 media were identified as the most promising, reaching a maximum fucoxanthin yield of 7.8 mg/g. Multiple regression models showed a strong correlation between antioxidant activity and the concentration of fucoxanthin produced. A thorough study of the optimization of I. galbana growth conditions, using a design of experiments, revealed that air flow rate and CO2 flow rate were the most influential factors on fucoxanthin production, reaching a value of 13.4 mg/g. Finally, to validate the antioxidant potential of fucoxanthin, an in silico analysis based on molecular docking was performed, showing that fucoxanthin interacts with antioxidant proteins (3FS1, 3L2C, and 8BBK). This research not only confirmed the positive results of I. galbana cultivation in terms of antioxidant activity, but also provided essential information for the optimization of fucoxanthin production, opening up promising prospects for industrial applications and future research.

In vitro fertilization (IVF) is an established option for the management of infertility in patients with endometriosis, though there remains ongoing debate around the extent to which endometriosis may compromise IVF treatment success, in which fertilization and preimplantation embryo development occur outside the pelvis. Whether endometriosis impacts oocyte and embryo quality and/or endometrial receptivity remains central to this debate. Here, we review the current literature relating to the impact of endometriosis on IVF outcomes and management strategies to consider when performing IVF treatment.

More than 40 species of lampreys (Petromyzontiformes) are known worldwide. Some of them are parasitic and feed on the blood of fish or other aquatic animals. Lampreys spawn once in their lifetime, after which they die. One of the representatives of the ichthyofauna of European rivers is the river lamprey, Lampetra fluviatilis. The river lamprey is now an endangered species due to loss and degradation of their habitats. The present study investigated gonadal development without hormonal stimulation in female river lampreys during puberty under controlled conditions for a period of seven months. Female river lampreys were kept in conditions that mimicked the natural environment. During the November–May period, samples were taken monthly to determine the extent of gonadal development and gastrointestinal and liver changes using histological examination. From the results obtained, the dynamical changes were determined for the following: gonadosomatic index (GSI; %), hepatosomatic index (his; %), and digestivesomatic index (DSI; %). With the gonadal development of female lampreys, an increase in GSI (7–23%; November–May) and a decrease in DSI (0.4–0.1%; November–May) histological changes were observed in the gonads (oocyte development), intestines (over time, decreased lipid vacuoles and enterocyte apoptosis), and in the liver (decreased lipid vacuoles and hepatocyte apoptosis over time) and in the digestive system resulting from its degradation. Also, it was observed that there was a change in the color of the liver to green in April. This study demonstrated the key role of the liver in the oocyte maturation process in this species.

Medical health records hold personal medical information and should only be accessed by authorized medical personnel or concerned patients. The importance of medical health records privacy is increasing as these records are shared in cloud environments. In this paper, we propose an enhanced system for securing patient data (Medical Health Records) embedded in multiple medical images in fragments for secure transmission and public sharing on the cloud or other environments. To protect the patient’s privacy, Medical Records are first encrypted, and then the ciphertext is broken into several fragments based on the number of multimodal medical images of a patient. A key generator randomly selects medical images from the multimodal image data to embed the encrypted patient health record segment using a modified least significant bit embedding process. The proposed technique enables an extra layer of security as even if files fall into the wrong hands and a fragment of the file is decrypted, it will not present any understandable information until all fragments from other medical images are extracted and combined in the correct order. The experiments are performed using multimodal 3255 MRI scans of 21 patients. The robustness of the proposed method was measured using different metrics such as PSNR, MSE, and SSIM. The results show that the proposed system is robust and that image quality is also maintained. To further study the stego image quality, a deep learning-based classification was applied to the images, and the results show that the diagnosis using stego medical images and performance remains unaffected even after embedding the encrypted data.

This study examined the effects of khat chewing on oral gingival conditions by adopting a targeted process which combined physicochemical analyses of the teeth, histopathological examinations of the gums, and bioinformatics modeling. The physicochemical evaluation of teeth in khat consumers compared to non-consumers was carried out using specific analytical techniques; hence, the results of this initial investigation revealed significant erosion of the tooth enamel due to khat chewing, as well as an alteration of the essential chemical composition of the teeth. Additionally, the histopathological analyses complemented preliminary studies by showing severe inflammation of the gums and oral mucosa in khat users. The understanding of these studies was enriched by bioinformatics analysis, where modeling was carried out via computational methods. This analytical phase examined molecular docking mechanisms, including the interaction between cathinone, the main alkaloid of khat, and the protein receptors involved in the protection of gingival tissues against infections. In summary, this multidisciplinary research provided an in-depth view of the oral health issues related to khat chewing, combining experimental studies with bioinformatics perspectives.

Renewable energy sources play a critical role in all governments’ and organizations’ energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids.

Fucoxanthin, a carotenoid widely studied in marine microalgae, is at the heart of scientific research because of its promising bioactive properties for human health. Its unique chemical structure and specific biosynthesis, characterized by complex enzymatic conversion in marine organisms, have been examined in depth in this review. The antioxidant, anti-inflammatory, and anti-cancer activities of fucoxanthin have been rigorously supported by data from in vitro and in vivo experiments and early clinical trials. Additionally, this review explores emerging strategies to optimize the stability and efficacy of fucoxanthin, aiming to increase its solubility and bioavailability to enhance its therapeutic applications. However, despite these potential benefits, challenges persist, such as limited bioavailability and technological obstacles hindering its large-scale production. The medical exploitation of fucoxanthin thus requires an innovative approach and continuous optimization to overcome these barriers. Although further research is needed to refine its clinical use, fucoxanthin offers promising potential in the development of natural therapies aimed at improving human health. By integrating knowledge about its biosynthesis, mechanisms of action, and potential beneficial effects, future studies could open new perspectives in the treatment of cancer and other chronic diseases.

This study aims to develop innovative heterocyclic nanocomposites incorporating silver nanoparticles (SNPs) for potential therapeutic applications targeting infections, gastric ulceration, inflammation, and oxidative damage. By synthesizing new derivatives of spiro-thiazolidine-carbonitrile (Py-ST-X) and incorporating them into Ag nanoparticles (AgNPs) and carbon nanotubes (CNTs), we have prepared Ag@Py-ST-X and Ag@Py-ST-X@CNT nanocomposites, respectively. The physical properties of these materials were studied using XRD, TEM, SEM, and Zeta potential techniques. In our investigation involving rats with gastric ulcers, we observed noteworthy inhibitory effects on gastric acid enzyme activity, specifically H+/K+ATPase, by Ag@Py-ST-NO2 and Ag@Py-ST-Br nanocomposites, demonstrating reductions of 25 and 34%, respectively, compared to untreated ulcers. Nanotubulation of these compounds further improved their inhibitory efficacy to 29 and 45%, respectively. Additionally, these nanoparticles showed the most potent myeloperoxidase (MPO)-inhibitory activity, demonstrating 36 and 49% inhibition, respectively, with nanotubulated versions reaching 44 and 53%. Moreover, Ag@Py-ST-NO2@CNT and Ag@Py-ST-Br@CNT nanotubes showed significant antioxidant activity, reducing thiobarbituric acid reactive substances (TBARS) by 35 and 51%, and hydrogen peroxide (H2O2) levels by 49 and 71%, respectively. These therapeutic effects were confirmed by reductions in gastric surface area (GSA) by 44% and 52%, a decrease in ulcer index (UI) from 80% to 44 and 38%, and an increase in curative index (CI) from 19 to 55 and 62% following administration of Ag@Py-ST-NO2@CNT and Ag@Py-ST-Br@CNT, respectively. Histological studies support these findings, suggesting the potential of these nanocomposites as promising candidates for treating various disorders.

To analyze the security of encryption, an effectual encryption scheme based on colored images utilizing the hybrid pseudo-random binary sequence (HPRBS) and substitution boxes, known as S-boxes, is proposed. The presented work aims to design S-boxes using pseudo-random binary numbers acquired by Linear Feedback Shift Registers (LFSRs) in combination with a modified quadratic chaotic map. Firstly, cryptographically robust S-boxes are constructed by using binary pseudo-random number sequences, and then the cryptographic properties of the presented S-boxes are tested. The suggested S-boxes showed good results. Secondly, an RGB image encryption algorithm utilizing sequences generated by modified quadratic chaotic maps and S-boxes is offered. The new color image encryption techniques comprise two steps, including a permutation and a substitution step. The key association with the content of the image is also addressed. This strategy can result in a “one-time pad” effect and make the algorithm resistant to chosen-plaintext attack (CPA). The proposed scheme has been confirmed to be more valuable than most of the existing schemes. S-boxes are analyzed by the nonlinearity test, bit independence criterion (BIC), linear and differential approximation probabilities (LPs; DPs), and Strict-Avalanche Criterion (SAC) tests. A comparison with different S-boxes presented in the literature is also carried out. The comparison shows encouraging results about the quality of the proposed box. From security and experimental outcomes, the effectiveness of the presented color image encryption technique is verified. The proposed scheme has evident efficiency benefits, which implies that the proposed colored encryption of the image scheme has better potential for application in encryption schemes in real-time.

Sorghum, a cereal grain rich in nutrients, is a major source of phenolic compounds that can be altered by different processes, thereby modulating their phenolic content and antioxidant properties. Previous studies have characterised phenolic compounds from pigmented and non-pigmented varieties. However, the impact of processing via the cooking and fermentation of these varieties remains unknown. Wholegrain flour samples of Liberty (WhiteLi1 and WhiteLi2), Bazley (RedBa1 and RedBa2), Buster (RedBu1 and RedBu2), Shawaya black (BlackSb), and Shawaya short black 1 (BlackSs) were cooked, fermented, or both then extracted using acidified acetone. The polyphenol profiles were analysed using a UHPLC-Online ABTS and QTOF LC-MS system. The results demonstrated that combining the fermentation and cooking of the BlackSs and BlackSb varieties led to a significant increase (p < 0.05) in total phenolic content (TPC) and antioxidant activities, as determined through DPPH, FRAP, and ABTS assays. The 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging activity of WhiteLi1, BlackSb, RedBu2, and BlackSs increased by 46%, 32%, 25%, and 10%, respectively, post fermentation and cooking. Conversely, fermentation only or cooking generally resulted in lower phenolic content and antioxidant levels than when samples were fully processed compared to raw. Notably, most of the detected antioxidant peaks (53 phenolic compounds) were only detected in fermented and cooked black and red pericarp varieties. The phenolic compounds with the highest antioxidant activities in pigmented sorghum included 3-aminobenzoic acid, 4-acetylburtyic acid, malic acid, caffeic acid, and luteolin derivative. Furthermore, the growing location of Bellata, NSW, showed more detectable phenolic compounds following processing compared to Croppa Creek, NSW. This study demonstrates that sorghum processing releases previously inaccessible polyphenols, making them available for human consumption and potentially providing added health-promoting properties.

This study examines the applications, benefits, challenges, and ethical considerations of artificial intelligence (AI) in the banking and finance sectors. It reviews current AI regulation and governance frameworks to provide insights for stakeholders navigating AI integration. A descriptive analysis based on a literature review of recent research is conducted, exploring AI applications, benefits, challenges, regulations, and relevant theories. This study identifies key trends and suggests future research directions. The major findings include an overview of AI applications, benefits, challenges, and ethical issues in the banking and finance industries. Recommendations are provided to address these challenges and ethical issues, along with examples of existing regulations and strategies for implementing AI governance frameworks within organizations. This paper highlights innovation, regulation, and ethical issues in relation to AI within the banking and finance sectors. Analyzes the previous literature, and suggests strategies for AI governance framework implementation and future research directions. Innovation in the applications of AI integrates with fintech, such as preventing financial crimes, credit risk assessment, customer service, and investment management. These applications improve decision making and enhance the customer experience, particularly in banks. Existing AI regulations and guidelines include those from Hong Kong SAR, the United States, China, the United Kingdom, the European Union, and Singapore. Challenges include data privacy and security, bias and fairness, accountability and transparency, and the skill gap. Therefore, implementing an AI governance framework requires rules and guidelines to address these issues. This paper makes recommendations for policymakers and suggests practical implications in reference to the ASEAN guidelines for AI development at the national and regional levels. Future research directions, a combination of extended UTAUT, change theory, and institutional theory, as well as the critical success factor, can fill the theoretical gap through mixed-method research. In terms of the population gap can be addressed by research undertaken in a nation where fintech services are projected to be less accepted, such as a developing or Islamic country. In summary, this study presents a novel approach using descriptive analysis, offering four main contributions that make this research novel: (1) the applications of AI in the banking and finance industries, (2) the benefits and challenges of AI adoption in these industries, (3) the current AI regulations and governance, and (4) the types of theories relevant for further research. The research findings are expected to contribute to policy and offer practical implications for fintech development in a country.

Portland cement (PC) is a common material used in civil infrastructure engineering. Cement production emits roughly 2.2 billion tons of CO2 per year, contributing 8% of global emissions in 2016. This contributes to almost half of the calcination process, and together with thermal combustion, clinker generation could be responsible for 90% of the sector’s emissions. One effective technique for dealing with these industrial by-product wastes is to employ them to make cement replacements such as concrete and mortar, which can be used in a variety of applications. As a result, the purpose of this research is to review the current advancements, challenges, and future perspectives on the utilization of agro-industrial waste (AIW) produced around the world in cement-based products. Geopolymers (GPs), on the other hand, reduce carbon dioxide emissions and have the potential to be a complete or partial replacement for PC in the construction sector. The GP technology enables the use of AIW in combination with an alumina–silicate (A–S) phase with minimal environmental impact. GP-cement is mostly produced by activating alkali silicates or alkali sols with secondary raw materials such as calcined clays, fly ash (FA), zeolite, metakaolin, etc. Mixing various resource materials, including additives, A–S, and alkali sols, alkali concentrations, optimizing the curing temperature, the SiO2/Na2O ratio, microstructural behavior, and other factors, results in GP-cement with outstanding mechanical and durability characteristics. The review concludes that AIW-based geopolymer composites have shown promising results in terms of their mechanical properties, durability, and environmental sustainability, which makes them emerge as promising future building materials with applications in a wide range of industries.

Wearable sensor data can be integrated and interpreted to improve the treatment of chronic conditions, such as diabetes, by enabling adjustments in treatment decisions based on physical activity and psychological stress assessments. The challenges in using biological analytes to frequently detect physical activity (PA) and acute psychological stress (APS) in daily life necessitate the use of data from noninvasive sensors in wearable devices, such as wristbands. We developed a recurrent multi-task deep neural network (NN) with long-short-term-memory architecture to integrate data from multiple sensors (blood volume pulse, skin temperature, galvanic skin response, three-axis accelerometers) and simultaneously detect and classify the type of PA, namely, sedentary state, treadmill run, stationary bike, and APS, such as non-stress, emotional anxiety stress, mental stress, and estimate the energy expenditure (EE). The objective was to assess the feasibility of using the multi-task recurrent NN (RNN) rather than independent RNNs for detection and classification of AP and APS. The multi-task RNN achieves comparable performance to independent RNNs, with the multi-task RNN having F1 scores of 98.00% for PA and 98.97% for APS, and a root mean square error (RMSE) of 0.728 calhr.kg for EE estimation for testing data. The independent RNNs have F1 scores of 99.64% for PA and 98.83% for APS, and an RMSE of 0.666 calhr.kg for EE estimation. The results indicate that a multi-task RNN can effectively interpret the signals from wearable sensors. Additionally, we developed individual and multi-task extreme gradient boosting (XGBoost) for separate and simultaneous classification of PA types and APS types. Multi-task XGBoost achieved F1 scores of 99.89% and 98.31% for the classification of PA types and APS types, respectively, while the independent XGBoost achieved F1 scores of 99.68% and 96.77%, respectively. The results indicate that both multi-task RNN and XGBoost can be used for the detection and classification of PA and APS without loss of performance with respect to individual separate classification systems. People with diabetes can achieve better outcomes and quality of life by including physical activity and psychological stress assessments in treatment decision-making.

Introduction: While most patients with iatrogenic tracheal stenosis (ITS) respond to endoscopic ablative procedures, approximately 15% experience a recalcitrant, recurring disease course that is resistant to conventional management. We aimed to explore genetic profiles of patients with recalcitrant ITS to understand underlying pathophysiology and identify novel therapeutic options. Methods: We collected 11 samples of granulation tissue from patients with ITS and performed RNA sequencing. We identified the top 10 most highly up- and down-regulated genes and cellular processes that these genes corresponded to. For the most highly dysregulated genes, we identified potential therapeutic options that favorably regulate their expression. Results: The dysregulations in gene expression corresponded to hyperkeratinization (upregulation of genes involved in keratin production and keratinocyte differentiation) and cellular proliferation (downregulation of cell cycle regulating and pro-apoptotic genes). Genes involved in retinoic acid (RA) metabolism and signaling were dysregulated in a pattern suggesting local cellular RA deficiency. Consequently, RA also emerged as the most promising potential therapeutic option for ITS, as it favorably regulated seven of the ten most highly dysregulated genes. Conclusion: This is the first study to characterize the role of hyperkeratinization and dysregulations in RA metabolism and signaling in the disease pathophysiology. Given the ability of RA to favorably regulate key genes involved in ITS, future studies must explore its efficacy as a potential therapeutic option for patients with recalcitrant ITS.

This study aims to provide a conceptual analysis of the dynamic transformations occurring in an autonomous vehicle (AV), placing a specific emphasis on the safety implications for pedestrians and passengers. AV, also known as self-driving automobiles, are positioned as potential disruptors in the contemporary transportation landscape, offering heightened safety and improved traffic efficiency. Despite these promises, the intricate nature of road scenarios and the looming specter of misinformation pose challenges that can compromise the efficacy of AV decision-making. A crucial aspect of the proposed verification process is the incorporation of the stop, investigate the source, find better coverage, trace claims, quotes, and media to the original context (SIFT) method. The SIFT method, originally designed to combat misinformation, emerges as a valuable mechanism for enhancing AV safety by ensuring the accuracy and reliability of information influencing autonomous decision-making processes.

Biocide resistance poses a significant challenge in industrial processes, with bacteria like Pseudomonas oleovorans exhibiting intrinsic resistance to traditional antimicrobial agents. In this study, the impact of biocide exposure on the metabolome of two P. oleovorans strains, namely, P. oleovorans P4A, isolated from contaminated coating material, and P. oleovorans 1045 reference strain, were investigated. The strains were exposed to 2-Methylisothiazol-3(2H)-one (MI) MIT, 1,2-Benzisothiazol-3(2H)-one (BIT), and 5-chloro-2-methyl-isothiazol-3-one (CMIT) at two different sub-inhibitory concentrations and the lipids and polar and semipolar metabolites were analyzed by ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry UPLC–Q–TOF/MS. Exposure to the BIT biocide induced significant metabolic modifications in P. oleovorans. Notable changes were observed in lipid and metabolite profiles, particularly in phospholipids, amino acid metabolism, and pathways related to stress response and adaptation. The 1045 strain showed more pronounced metabolic alterations than the P4A strain, suggesting potential implications for lipid, amino acid metabolism, energy metabolism, and stress adaptation. Improving our understanding of how different substances interact with bacteria is crucial for making antimicrobial chemicals more effective and addressing the challenges of resistance. We observed that different biocides trigged significantly different metabolic responses in these strains. Our study shows that metabolomics can be used as a tool for the investigation of metabolic mechanisms underlying biocide resistance, and thus in the development of targeted biocides. This in turn can have implications in combating biocide resistance in bacteria such as P. oleovorans.

Lower back pain (LBP) is a musculoskeletal condition that affects millions of people worldwide and significantly limits their mobility and daily activities. Appropriate ergonomics and exercise are crucial preventive measures that play a vital role in managing and reducing the risk of LBP. Individuals with LBP often exhibit spinal anomalies, which can serve as valuable indicators for early diagnosis. We propose an advanced machine learning methodology for LBP detection that incorporates data balancing and bootstrapping techniques. Leveraging the features associated with spinal anomalies, our method offers a promising approach for the early detection of LBP. Our study utilizes a standard dataset comprising 310 patient records, including spinal anomaly features. We propose an ensemble method called the random forest gradient boosting XGBoost Ensemble (RGXE), which integrates the combined power of the random forest, gradient boosting, and XGBoost methods for LBP detection. Experimental results demonstrate that the proposed ensemble method, RGXE Voting, outperforms state-of-the-art methods, achieving a high accuracy of 0.99. We fine-tuned each method and validated its performance using k-fold cross-validation in addition to determining the computational complexity of the methods. This innovative research holds significant potential to revolutionize the early detection of LBP, thereby improving the quality of life.

Background and Objectives: Functional gastric stenosis, a consequence of sleeve gastrectomy, is defined as a rotation of the gastric tube along its longitudinal axis. It is brought on by gastric twisting without the anatomical constriction of the gastric lumen. During endoscopic examination, the staple line is deviated with a clockwise rotation, and the stenosis requires additional endoscopic manipulations for its transposition. Upper gastrointestinal series show the gastric twist with an upstream dilatation of the gastric tube in some patients. Data on its management have remained scarce. The objective was to assess the efficacy and safety of endoscopic balloon dilatation in the management of functional post-sleeve gastrectomy stenosis. Patients and Methods: Twenty-two patients with functional post-primary-sleeve-gastrectomy stenosis who had an endoscopic balloon dilatation between 2017 and 2023 were included in this retrospective study. Patients with alternative treatment plans and those undergoing endoscopic dilatation for other forms of gastric stenosis were excluded. The clinical outcomes were used to evaluate the efficacy and safety of balloon dilatation in the management of functional gastric stenosis. Results: A total of 45 dilatations were performed with a 30 mm balloon in 22 patients (100%), a 35 mm balloon in 18 patients (81.82%), and a 40 mm balloon in 5 patients (22.73%). The patients’ clinical responses after the first balloon dilatation were a complete clinical response (4 patients, 18.18%), a partial clinical response (12 patients, 54.55%), and a non-response (6 patients, 27.27%). Nineteen patients (86.36%) had achieved clinical success at six months. Three patients (13.64%) who remained symptomatic even after achieving the maximal balloon dilation of 40 mm were considered failure of endoscopic dilatation, and they were referred for surgical intervention. No significant adverse events were found during or following the balloon dilatation. Conclusions: Endoscopic balloon dilatation is an effective and safe minimally invasive procedure in the management of functional post-sleeve-gastrectomy stenosis.

Background and Objectives: Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on healthcare systems and adversely affects the quality of life of patients worldwide. Comorbidities such as chronic kidney disease (CKD), arterial hypertension, and diabetes mellitus (DM) are common among HF patients, as they share similar risk factors. This study aimed to identify the prognostic significance of multiple factors and their correlation with disease prognosis and outcomes in a Jordanian cohort. Materials and Methods: Data from the Jordanian Heart Failure Registry (JoHFR) were analyzed, encompassing medical records from acute and chronic HF patients attending public and private cardiology clinics and hospitals across Jordan. An online form was utilized for data collection, focusing on three kidney function tests, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), and creatinine levels, with the eGFR calculated using the Cockcroft–Gault formula. We also built six machine learning models to predict mortality in our cohort. Results: From the JoHFR, 2151 HF patients were included, with 644, 1799, and 1927 records analyzed for eGFR, BUN, and creatinine levels, respectively. Age negatively impacted all measures (p ≤ 0.001), while smokers surprisingly showed better results than non-smokers (p ≤ 0.001). Males had more normal eGFR levels compared to females (p = 0.002). Comorbidities such as hypertension, diabetes, arrhythmias, and implanted devices were inversely related to eGFR (all with p-values p ≤ 0.001). Higher creatinine levels were linked to hypertension, diabetes, dyslipidemia, arrhythmias, and previous HF history (all with p-values p = 0.049) and mortality (p ≤ 0.001), while BUN levels did not significantly affect these outcomes. Machine learning analysis employing the Random Forest Classifier revealed that length of hospital stay and creatinine >115 were the most significant predictors of mortality. The classifier achieved an accuracy of 90.02% with an AUC of 80.51%, indicating its efficacy in predictive modeling. Conclusions: This study reveals the intricate relationship among kidney function tests, comorbidities, and clinical outcomes in HF patients in Jordan, highlighting the importance of kidney function as a predictive tool. Integrating machine learning models into clinical practice may enhance the predictive accuracy of patient outcomes, thereby supporting a more personalized approach to managing HF and related kidney dysfunction. Further research is necessary to validate these findings and to develop innovative treatment strategies for the CKD population within the HF cohort.