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BackgroundHigh rates of non-prescription dispensing of antimicrobials have led to a significant increase in the antimicrobial overuse and misuse in Saudi Arabia (SA). The objective of this study was to evaluate the antimicrobial utilization following the enforcement of a new prescription-only antimicrobial dispensing policy in the community pharmacy setting in SA.MethodsData were extracted from the IQVIA database between May 2017 and May 2019. The antimicrobial utilization rates, based on sales, defined daily dose in grams (DDD), DDD/1000 inhabitants/day (DID), and antimicrobial-claims for the pre-policy (May 2017 to April 2018) and post-policy (June 2018 to May 2019) periods were assessed.ResultsOverall antimicrobial utilization declined slightly (~9-10%) in the post-policy versus pre-policy period (sales, 31,334 versus 34,492 thousand units; DDD, 183,134 versus 202,936), with higher claims (~16%) after policy implementation. There was a sudden drop in the utilization rate immediately after policy enforcement; however, the values increased subsequently, closely matching the pre-policy values. Utilization patterns were similar in both periods; penicillin was the most used antimicrobial (sales: 11,648-14,700-thousand units; DDD: 71,038-91,227; DID: 2.88-3.78). For both periods, the highest dip in utilization was observed in July (sales: 1,027-1,559 thousand units; DDD: 6,194-9,399), while the highest spike was in March/October (sales: 3,346-3,884 thousand units; DDD: 22,329-19,453).ConclusionNon-prescription antimicrobial utilization reduced minimally following policy implementation in the community pharmacies across SA. Effective implementation of prescription-only regulations is necessary.

Strawberry is a well-known source of natural antioxidants with excellent free radical scavenging capacity. This study determined the effects of chitosan application in field condition on plant growth, fruit yield and antioxidant activities in strawberry fruit. Foliar applications of chitosan on strawberry significantly increased plant growth and fruit yield (up to 42% higher) compared to untreated control. Increased fruit yield was attributed to higher plant growth, individual fruit weight and total fruit weight/plant due to the chitosan application. Surprisingly, the fruit from plants sprayed with chitosan also had significantly higher contents (up to 2.6-fold) of carotenoids, anthocyanins, flavonoids and phenolics compared to untreated control. Total antioxidant activities in fruit of chitosan treated plants were also significantly higher (ca. 2-fold) (p< 0.05) than untreated control. To the best of our knowledge, this is the first report of chitosan applied on field plants providing significant improvement of both yield and health benefiting biochemical contents in strawberry fruit. Further study on the elucidation of mechanisms involved with enhancement of growth, yield and biochemical contents by chitosan is needed to promote sustainable production of strawberry.

In recent years, renewable energy sources have been considered the most encouraging resources for grid and off-grid power generation. This paper presents an improved current control strategy for a three-phase photovoltaic grid-connected inverter (GCI) under unbalanced and nonlinear load conditions. It is challenging to suppress the harmonic content in the output current below a pre-set value in the GCI. It is also difficult to compensate for unbalanced loads even when the grid is under disruption due to total harmonic distortion (THD) and unbalanced loads. The primary advantage and objective of this method is to effectively compensate for the harmonic current content of the grid current and microgrid without the use of any compensation devices, such as active and passive filters. This method leads to a very low THD in both the GCI currents and the current exchanged with the grid. The control approach is designed to control the active and reactive power and harmonic current compensation, and it also corrects the system unbalance. The proposed control method features the synchronous reference frame (SRF) method. Simulation results are presented to demonstrate the effective performance of the proposed method.

Child open defecation is common in low-income countries and can lead to fecal exposure in the domestic environment. We assessed associations between child feces management practices vs. measures of contamination and child diarrhea among households with children

Intrusion detection plays a significant role in the provision of information security. The most critical element is the ability to precisely identify different types of intrusions into the network. However, the detection of intrusions poses a important challenge, as many new types of intrusion are now generated by cyber-attackers every day. A robust system is still elusive, despite the various strategies that have been proposed in recent years. Hence, a novel deep-learning-based architecture for detecting intrusions into a computer network is proposed in this paper. The aim is to construct a hybrid system that enhances the efficiency and accuracy of intrusion detection. The main contribution of our work is a novel deep learning-based hybrid architecture in which PSO is used for hyperparameter optimisation and three well-known pre-trained network models are combined in an optimised way. The suggested method involves six key stages: data gathering, pre-processing, deep neural network (DNN) architecture design, optimisation of hyperparameters, training, and evaluation of the trained DNN. To verify the superiority of the suggested method over alternative state-of-the-art schemes, it was evaluated on the KDDCUP'99, NSL-KDD and UNSW-NB15 datasets. Our empirical findings show that the proposed model successfully and correctly classifies different types of attacks with 82.44%, 90.42% and 93.55% accuracy values obtained on UNSW-B15, NSL-KDD and KDDCUP'99 datasets, respectively, and outperforms alternative schemes in the literature. [ABSTRACT FROM AUTHOR]

In this study, we tested the overall hypothesis that CC expansion and early embryo development would be improved by including follicular fluid (FF) from small or large follicles in the oocyte maturation medium. In the first experiment, FF aspirated from bovine abattoir ovaries was added to the maturation medium at 0, 25, 50, 75 or 100%. Images of individual COCs were captured at 0, 6, 12 and 19 hours (h) of the maturation period and analyzed to calculate change in the total area over time. Cumulus cell expansion was greatest in COCs matured in 75% and 50% FF, and these differences were detectable at 12 (75% FF only) and 19 h (50% and 75% FF) of maturation. The improvement in CC expansion was greatest when FF from small follicles was used. Treatments for the subsequent experiments were selected based upon the results of the first experiment. Oocyte nuclear maturation rates were observed after supplementing the maturation medium with 0 or 75% FF and maturing for 19 h. The rate of nuclear maturation as determined by the presence or absence of the first polar body was similar between control (0% FF) and treated (75% FF) groups. In the final experiment, COCs were matured in 0%, 50% or 75% FF in preparation for IVF. Duration of the maturation period (12, 19 or 22 h) and size of the follicles from which FF was collected (small or large) also varied. In general, FF supplementation at 50% did not affect the zygotes' developmental potential (neither increased nor decreased). Supplementation of maturation medium with 75% FF from small follicles consistently reduced measures of embryo development while 75% FF from large follicles yielded mixed results. It is concluded that FF supplementation improves CC expansion, but the greater CC expansion does not benefit subsequent embryo development. Notably, however, the 50% FF treatment did not reduce blastocyst rates, indicating that FF can be included in maturation media at concentrations of 50% or less with no detriment to IVF outcomes. [ABSTRACT FROM AUTHOR]

This article describes the development of priorities and actions to improve the state of research, policy, and practice related to accessible housing in Canada for persons with disability or with accessible housing needs. A modified Delphi approach with an expert cross-sectoral panel was used to gain convergence on a set of priorities for advancing the accessible housing field in Canada. This included circulating an anonymous pre-meeting survey (N = 49) followed by an in-person planning meeting (N = 45). The expert panel at the in-person meeting identified three clusters of priorities from an initial list of 21 priorities, which included: 1) engaging with all levels of government to support accessible housing efforts; 2) developing educational resources to raise awareness about accessible housing, and creating services to facilitate locating and acquiring accessible housing; and 3) fostering meaningful engagement across key interest groups and sectors to find solutions to enact positive change in this space. The findings provide an initial roadmap for bringing greater cohesion to the accessible housing field, which will enable cross-sectoral partnerships and collective action towards informing the next generation of accessible housing standards, regulations and practices for people with accessible housing needs. [ABSTRACT FROM AUTHOR]

Introduction: Dengue continues to be a major public health concern in Malaysia, as evidenced by the significant surge in cumulative dengue case numbers and deaths in 2023 compared to the previous year. While previous studies have explored the interplay of abiotic and biotic factors of mosquito density and dengue cases on a local scale in Malaysia, there is a notable gap in the research focusing on adult Aedes mosquito populations. Aims: This study aims to contribute to the existing knowledge by investigating the association and time lags (TLs) between daily microclimate (DM), mosquito indices (MIs), and dengue cases at the residence level. Methods: In this longitudinal study, field data were collected over 26 weeks using data loggers, gravid oviposit sticky (GOS) traps, and non-structural 1 (NS1) test kits in both non-dengue hotspot (NDH) and dengue hotspots (DH). The collected data encompassed DM variables, vegetation cover (VC), MIs, and number of dengue cases. An autocorrelation analysis was conducted to determine the TLs between MIs and their preceding values, while a cross-correlation analysis revealed the TLs between MIs and DM variables. Results: The study indicated there are positive correlations between the adult index (AI) of Ae. albopictus, their preceding values and rainfall at an NDH. Conversely, the AIs of total Aedes at the DH exhibited positive correlations with their preceding values, temperature, rainfall, and maximum relative humidity (RH), but negative correlations with the mean and maximum RH. The dengue-positive trap index (DPTI) of total Aedes at DHs demonstrated positive associations with their preceding values, mean temperature, minimum temperature, maximum RH, and rainfall, with negative correlations observed for the maximum temperature, mean RH, and minimum RH. Similar trends were identified for the Ae. aegypti and Ae. albopictus at DHs. The association between dengue cases, DM, and MIs was inconclusive due to underreported cases. Conclusions: This study highlighted the DM and TLs of dengue virus-infected and non-infected adult female Aedes mosquitoes using onsite data collection. Furthermore, this study presents a replicable methodology that can be adopted by researchers worldwide for investigating the dynamics of dengue transmission in similar settings. The findings offer valuable insights for decision-makers, providing them with evidence-based information to implement targeted interventions and strategies aimed at controlling Aedes mosquito populations and mitigating the spread of dengue virus infections. [ABSTRACT FROM AUTHOR]

This study assessed the risks of soil pollution by heavy metals in Chilmari Upazila, northern Bangladesh, using the static environmental resilience (Pi) model of soil. Geostatistical modeling and self-organizing maps (SOM) identified pollution areas and spatial patterns, while a positive matrix factorization (PMF) model revealed pollution sources. The results showed that the average concentrations of Cr, Pb and As were well above background levels. Agricultural and industrial soils were mainly contaminated with Cr, Pb and As according to the Nemerow Pollution Index (NPI), Ecological Risk (ER) and Pi Index. Over 70% of the sites were contaminated with Pb and Cr, while co-contamination was particularly high. A one-way ANOVA showed significant correlations between Pb, Cu and Zn levels and human activities. The PMF analysis revealed that industrial effluents, agrochemicals and lithogenic sources were the main contributors to soil contamination with 16%, 41% and 43%, respectively. The SOM analysis revealed three distinct spatial patterns (Pb-Zn, Cr-Cu-Ni and Co-Mn-As), which are consistent with the PMF results. These results emphasize the need for stringent measures to reduce industrial emissions and remediate soil contamination in order to improve soil quality and food security. [ABSTRACT FROM AUTHOR]

Background: Paroxysmal tachycardia encompasses various heart rhythm disorders that cause rapid heart rates. Its episodic occurrence makes it difficult to identify and measure its prevalence and trends in the population. Additionally, there is limited data on disparities and trends in mortality due to paroxysmal tachycardia, which is essential for assessing current medical approaches and identifying at-risk populations. Methods: Our study examined death certificates from 1999 to 2020 using the CDC WONDER Database to identify deaths caused by paroxysmal tachycardia in individuals aged 25 and older, using the ICD-10 code I47. Age-adjusted mortality rates (AAMRs) and annual percent changes (APC) were calculated by year, gender, age group, race/ethnicity, geographic location, and urbanization status. Trends in AAMRs were analyzed using the Joinpoint Regression Program to identify significant changes and inflection points in mortality trends throughout the study period. Results: Between 1999 and 2020, 155,320 deaths were reported in patients with paroxysmal tachycardia. Overall, AAMR decreased from 4.8 to 3.7 per 100,000 population between 1999 and 2020, despite showing a significant increase from 2014 to 2020 (APC: 4.33; 95% CI: 3.53 to 5.56). Men had consistently higher AAMRs than women (4.7 vs. 2.2). Furthermore, we found that AAMRs were highest among Non-Hispanic (NH) Black or African Americans and lowest in NH Asian or Pacific Islanders (4 vs. 1.9). Nonmetropolitan areas had higher AAMRs than metropolitan areas (3.6 vs. 3.2). Conclusions: Our analysis showed a significant decrease in mortality from paroxysmal tachycardia since 1999, although there has been a slight increase in recent years. However, disparities remain, with higher AAMRs among men, NH Black or African Americans, and residents of non-metropolitan areas. These findings call for immediate public health actions to curb the rising trends and reduce potential disparities. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD) greatly influence morbidity and mortality, with COPD patients frequently suffering from cardiovascular comorbidities like coronary heart disease and stroke. This study analyzes mortality trends and disparities among individuals in the United States (US) affected by both CVD and COPD. Methods: This study analyzed death certificates from the CDC WONDER database for individuals aged 25 and older who died between 1999 and 2020 with both CVD (ICD I00-I99) and COPD (ICD J41-J44). Age-adjusted mortality rates (AAMRs) and annual percent change (APC) were calculated by year, sex, age group, race/ethnicity, geographic region, and urbanization status. Results: Between 1999 and 2020, there were 3,590,124 reported deaths due to coexisting CVD and COPD, with overall AAMR slightly changing from 82.2 to 81.2 per 100,000 population, and a notable rise from 2018 to 2020 (APC: 5.28; 95% CI: 1.83 to 7.22) coinciding with the onset of COVID-19 pandemic. A similar surge in mortality was observed across multiple demographic subgroups, particularly among older adults. Disparities across age groups, sex, race, and geographic location were also observed in the mortality rates due to CVD and COPD. When analyzed by age group, older adults exhibited the highest AAMR at 824.1. Men had higher AAMRs than women (96.5 vs. 60.7). Ethnoracial analysis showed that non-Hispanic (NH) White individuals had the highest AAMRs (82.0), followed by NH American Indian or Alaska Native (74.5), NH Black (63.6), Hispanic (38.1), and NH Asian or Pacific Islander (25.1) individuals. Additionally, non-metropolitan areas had higher AAMRs compared to metropolitan areas (96.2 vs. 70.9). Conclusions: The findings suggest that mortality rates for CVD and COPD have increased in recent years, coinciding with the onset of the COVID-19 pandemic, which may have exacerbated outcomes in vulnerable populations. The study highlights the need for targeted interventions to address the overlapping impacts of CVD and COPD, especially in high-risk groups. [ABSTRACT FROM AUTHOR]

Oil fields located in cold environments and deep-sea locations often face challenges with paraffin wax buildup in pipelines during long-distance crude oil transportation. Various strategies have been employed to address this issue, with chemical methods being the most effective and economical. However, traditional chemical inhibitors present problems due to their high toxicity and low biodegradability, leading to increased operational costs and environmental concerns. This study focuses on developing an eco-friendly paraffin inhibitor system using three different concentrations of Glycine and Palm-based Methyl Ester Sulfonate (MES). Experiments were conducted on crude oil samples from the Dulang Oilfield. The experimental measurements include wax appearance temperature (WAT), pour point temperature (PPT), and rheological tests in the absence and presence of the proposed inhibitors. The results revealed that both Glycine and MES can effectively reduce WAT, viscosity, and yield point. Specifically, 10% Glycine was the best inhibitor, reducing WAT by 23.3%. However, MES (1%, 5%, and 10%) demonstrated greater overall effectiveness, with an average WAT reduction of 13.76% compared to Glycine's 10.85%. MES also shows a better performance in reducing viscosity and yield stress. While PPT results were insignificant, MES is recommended as a flow improver rather than a pour point depressant. The successful development of these newly formulated chemical inhibitors promises an environmentally sustainable and economically efficient approach to maximizing oil production from mature fields while mitigating paraffin precipitation. [ABSTRACT FROM AUTHOR]

The Weibull distribution is an important continuous distribution that is cardinal in reliability analysis and lifetime modeling. On the other hand, it has several limitations for practical applications, such as modeling lifetime scenarios with non-monotonic failure rates. However, accurate modeling of non-monotonic failure rates is essential for achieving more accurate predictions, better risk management, and informed decision-making in various domains where reliability and longevity are critical factors. For this reason, we introduce a new three parameter lifetime distribution—the Modified Kies Weibull distribution (MKWD)—that is able to model lifetime scenarios with non-monotonic failure rates. We analyze the statistical features of the MKWD, such as the quantile function, median, moments, mean, variance, skewness, kurtosis, coefficient of variation, index of dispersion, moment generating function, incomplete moments, conditional moments, Bonferroni, Lorenz, and Zenga curves, and order statistics. Various measures of uncertainty for the MKWD such as Rényi entropy, exponential entropy, Havrda and Charvat entropy, Arimoto entropy, Tsallis entropy, extropy, weighted extropy and residual extropy are computed. We discuss eight different parameter estimation methods and conduct a Monte Carlo simulation study to evaluate the performance of these different estimators. The simulation results show that the maximum likelihood method leads to the best results. The effectiveness of the newly suggested model is demonstrated through the examination of two different sets of real data. Regression analysis utilizing survival times data demonstrates that the MKWD model offers a superior match compared to other current distributions and regression models. [ABSTRACT FROM AUTHOR]

Background: The quality and safety education for nurses (QSEN) competency program represents a valuable initiative in nursing practice and education, equipping nurses with the essential knowledge, attitude, and skills (KAS) required to deliver safe, efficient, and patient-centered care. Purpose: This study aims to determine the impact of QSEN competency on the KAS of nurses in Palestine. Method: A quasi-experimental pre-test and post-test design with two groups was used utilizing a questionnaire to collect data from 164 Junior nurses in two governmental hospitals within the period of 25th, January to the 10th February 2024. Patricia Benner's theory suggests that a strong educational foundation and diverse experiences enable nurses to enhance their patient care knowledge and abilities over time. Results: The findings indicate that nurses in Palestine can benefit from targeted interventions and QSEN educational programs aimed at improving their patient-centered care competence, as post-test scores show a significant rise over pre-test scores. Junior nurses who participated in the QSEN program experienced a 57% increase in knowledge, a 57% increase in skills, and a 64% increase in attitudes. The intervention significantly improved knowledge (77.02 vs. 49.19, p < 0.001), quality and safety skills (70.16 vs. 44.61, p < 0.001), and attitudes (75.47 vs. 46.16, p < 0.001) among participants post-procedure, indicating a substantial positive impact on these areas, demonstrating the effectiveness of the educational intervention. Conclusion: The study demonstrates that an educational intervention improves junior nurses' KSAs for six QSEN competencies, leading to higher average scores in quality and safety competence subscales, thereby enhancing staff satisfaction, and reducing medical errors, and patient safety. [ABSTRACT FROM AUTHOR]

Methotrexate (MTX) is classified as an antimetabolite. It's commonly used to treat lung cancer. MTX is an immunosuppressant following the above-mentioned mechanism of action due to its poor selectivity. The tricky move is to investigate the solid dispersions and coating using a co-delivery system of MTX and Ambroxol (ABL). ABL is known for its, anticancer and preferential pulmonary distribution after oral administration. The goals was development were the MTX physiochemical modulation for pulmonary enhanced distribution, MTX resistance modulation and long-acting system development using ABL middle coating and HPMC outer coating. The selection of the optimum MTX-ABL dispersion was done based on the FT-IR characterization. The MTX-release analysis results for the optimized MTX-ABL solid dispersion and the HPMC-coated MTX-ABL gel product were tested for release in the gastrointestinal simulated media to select the most optimum HPMC amounts to coat the MTX-ABL optimum solid dispersion. Moreover, different characterizations of FT-IR, X-ray diffraction and Scanning electron microscopy investigations were completed for the MTX, ABL, the ABL-MTX optimized solid dispersion and the optimum MTX-ABL-HPMC gel. The cytotoxicity assay and the ELISA to assess the levels of BAX, BCL-2, TGF-β and FR-ɑ after the MTX, ABL and the optimized MTX-ABL solid dispersion groups were tested against lung cancer cells, A549 cells, for 24 h. The sustained release character and HPMC-ABL encapsulation of MTX were confirmed. The MTX-ABL solid dispersion showed less MTX resistance without the need to use the high MTX concentrations in comparison to the MTX alone. The apoptotic, anti-metastatic, and MTX preferential lung cancer uptake profiles were higher using the MTX-ABL solid dispersion than in the MTX or ABL. The MTX-ABL-HPMC gel could serve as an alternative to the MTX-oral tablets available in the markets with enhanced efficacy and safety profile. [ABSTRACT FROM AUTHOR]

Recently, distributed systems have become the backbone of technological development. It serves as the foundation for new trends technologies such as blockchain, the internet of things and others. A distributed system provides fault tolerance and decentralization, where a fault in any component does not result in a whole system failure. In addition, deep learning model enables processing data to find patterns, which helps in classification, regression, prediction, and clustering. This work employs deep learning to handle faults within distributed systems in three scenarios. Firstly, a faulty processor may not be able to produce the right output. Therefore, deep learning model uses the inputs and outputs of other processors to find patterns and produces the proper output of the faulty processor. Secondly, if a faulty possessor corrupts its inputs as well, then the deep learning model learns from the inputs and the outputs of successful processors and produces the proper output of the faulty processor, even with corrupted inputs. Thirdly, for unrelated data, in which the patterns of the input of the faulty processors differ from the patterns of the inputs of successful ones. In this case, the model is able to discover the new pattern and to be labeled as unknown. In the experiments, we use deep learning models like VGG16, VGG19, AlexNet LSTM and ResNet34, to investigate the performance of the deep learning in the three mentioned scenarios. For unstructured datasets, the accuracy of the models is affected by the size of the faulty data. The accuracy of all models lies between 60% when the size of the faulty data is 90%, and 96%, when the size of the faulty data is 90%. The structured datasets are not significantly affected by the portion of the faulty data and the accuracy reaches 99%. [ABSTRACT FROM AUTHOR]

Diabetes, a chronic metabolic condition characterised by persistently high blood sugar levels, necessitates early detection to mitigate its risks. Inadequate dietary choices can contribute to various health complications, emphasising the importance of personalised nutrition interventions. However, real-time selection of diets tailored to individual nutritional needs is challenging because of the intricate nature of foods and the abundance of dietary sources. Because diabetes is a chronic condition, patients with this illness must choose a healthy diet. Patients with diabetes frequently need to visit their doctor and rely on expensive medications to manage their condition. It is challenging to purchase medication for chronic illnesses on a regular basis in underdeveloped nations. Motivated by this concept, we suggest a hybrid model that, rather than depending solely on medication to evade a visit to the doctor, can first anticipate diabetes and then suggest a diet and exercise regimen. This research proposes an optimized approach by harnessing machine learning classifiers, including Random Forest, Support Vector Machine, and XGBoost, to develop a robust framework for accurate diabetes prediction. The study addresses the difficulties in predicting diabetes precisely from limited labeled data and outliers in diabetes datasets. Furthermore, a thorough food and exercise recommender system is unveiled, offering individualized and health-conscious nutrition recommendations based on user preferences and medical information. Leveraging efficient learning and inference techniques, the study achieves a meager error rate of less than 30% using an extensive dataset comprising over 100 million user-rated foods. This research underscores the significance of integrating machine learning classifiers with personalized nutritional recommendations to enhance diabetes prediction and management. The proposed framework has substantial potential to facilitate early detection, provide tailored dietary guidance, and alleviate the economic burden associated with diabetes-related healthcare expenses. [ABSTRACT FROM AUTHOR]

Background and purpose: The most widely used social media platform for video content is YouTubeTM. The present study evaluated the quality of information on YouTubeTM on artificial intelligence (AI) in dentistry. Methods: This cross-sectional study used YouTubeTM (https://www.youtube.com) for searching videos. The terms used for the search were "artificial intelligence in dentistry," "machine learning in dental care," and "deep learning in dentistry." The accuracy and reliability of the information source were assessed using the DISCERN score. The quality of the videos was evaluated using the modified Global Quality Score (mGQS) and the Journal of the American Medical Association (JAMA) score. Results: The analysis of 91 YouTube™ videos on AI in dentistry revealed insights into video characteristics, content, and quality. On average, videos were 22.45 minutes and received 1715.58 views and 23.79 likes. The topics were mainly centered on general dentistry (66%), with radiology (18%), orthodontics (9%), prosthodontics (4%), and implants (3%). DISCERN and mGQS scores were higher for videos uploaded by healthcare professionals and educational content videos(P<0.05). DISCERN exhibited a strong correlation (0.75) with the video source and with JAMA (0.77). The correlation of the video's content and mGQS, was 0.66 indicated moderate correlation. Conclusion: YouTube™ has informative and moderately reliable videos on AI in dentistry. Dental students, dentists and patients can use these videos to learn and educate about artificial intelligence in dentistry. Professionals should upload more videos to enhance the reliability of the content. [ABSTRACT FROM AUTHOR]

Background: Poststroke depression (PSD) is a highly prevalent and serious mental health condition affecting a significant proportion of stroke survivors worldwide. While its exact causes remain under investigation, managing PSD presents a significant challenge. Aim: This study aimed to evaluate the prevalence and predictors of depression among Bangladeshi stroke victims. Methods: A cross-sectional study was carried out with 725 stroke victims who were receiving medical care at three designated tertiary care hospitals in Sylhet from January to December 2023. Depression and disability were measured using the Patient Health Questionnaire-9 and the Modified Rankin Scale. Logistic regression analysis was employed to examine the predictors linked to depression. Results: According to the study, 80.8% of individuals had moderate to severe disability, and 58.1% of them experienced a moderate to severe level of depression. Individuals who had hemorrhagic stroke (AOR 1.31, 95% CI: 0.77–2.25), repeated episodes (AOR 3.41, 95% CI: 1.89–6.14), tobacco use (AOR 1.76, 95% CI: 1.16–2.67), or coexisting health conditions (AOR 1.68, 95% CI: 1.00–2.82) exhibited elevated levels of depression. Participants whose medical expenses covered by relatives or others were six times more likely to experience depressive symptoms (AOR 6.32, 95% CI: 1.61–24.76). Individuals who did not receive rehabilitation services had two times greater odds of being depressed (OR 1.85, 95% CI: 1.23–2.77, p = 0.003). Consequently, individuals with low functional status had eleven times greater levels of depression (AOR 11.03, 95% CI: 7.14–17.04). Conclusion: More than half of the participants in this present study reported moderate to extreme levels of depression which is a serious health issue among Bangladeshi stroke survivors. Understanding the predictors of depression linked to stroke could enhance the effectiveness of therapeutic interventions for this condition. In addition, multidisciplinary teams should work collaboratively to address this serious issue. [ABSTRACT FROM AUTHOR]

Protein glycosylation, a vital post-translational modification, is pivotal in various biological processes and disease pathogenesis. Computational approaches, including protein language models and machine learning algorithms, have emerged as valuable tools for predicting O-GlcNAc sites, reducing experimental costs, and enhancing efficiency. However, the literature has not reported the prediction of O-GlcNAc sites through the evolutionary scale model (ESM). Therefore, this study employed the ESM-2 model for O-GlcNAc site prediction in humans. Approximately 1100 O-linked glycoprotein sequences retrieved from the O-GlcNAc database were utilized for model training. The ESM-2 model exhibited consistent improvement over epochs, achieving an accuracy of 78.30%, recall of 78.30%, precision of 61.31%, and F1-score of 68.74%. However, compared to the traditional models which show an overfitting on the same data up to 99%, ESM-2 model outperforms in terms of optimal training and testing predictions. These findings underscore the effectiveness of the ESM-2 model in accurately predicting O-GlcNAc sites within human proteins. Accurately predicting O-GlcNAc sites within human proteins can significantly advance glycoproteomic research by enhancing our understanding of protein function and disease mechanisms, aiding in developing targeted therapies, and facilitating biomarker discovery for improved diagnosis and treatment. Furthermore, future studies should focus on more diverse data types, longer protein sequence lengths, and higher computational resources to evaluate various parameters. Accurate prediction of O-GlcNAc sites might enhance the investigation of the site-specific functions of proteins in physiology and diseases. [ABSTRACT FROM AUTHOR]

Cryptococcosis is a lethal mycosis instigated by the pathogenic species Cryptococcus neoformans and Cryptococcus gattii, primarily affects the lungs, manifesting as pneumonia, and the brain, where it presents as meningitis. Mortality rate could reach 100% if infections remain untreated in cryptococcal meningitis. Treatment options for cryptococcosis are limited and and there are no licensed vaccines clinically available to treat or prevent cryptococcosis. Our study utilizes an integrated bioinformatics approaches to develop a polyvalent multiepitope subunit vaccine focusing on the key virulent proteins Heat shock transcription factor and Chaperone DnaK of both C. neoformans and C. gatti. Then in silico analysis was done to predict highly antigenic epitopes by assessing antigenicity, transmembrane topology screening, allergenecity, toxicity, and molecular docking approaches. Following this analysis, we designed two vaccine constructs integrating a compatible adjuvant and suitable linkers. These constructs exhibited notable characteristics including high antigenicity, non-toxicity, solubility, stability, and compatibility with Toll-like receptors (TLRs). The interaction between both vaccine constructs and TLR2, TLR3, and TLR9 was assessed through molecular docking analysis. Molecular dynamics simulations and MM-PBSA calculations suggest the substantial stabilizing property and binding affinity of Vaccine Construct V1 against TLR9. Both the vaccines revealed to have a higher number of interchain hydrogen bond with TLR9. These findings serve as a crucial stepping stone towards a comprehensive solution for combating cryptococcus infections induced by both C. neoformans and C. gattii. Further validation through in vivo studies is crucial to confirm the effectiveness and potential of the vaccine to curb the spread of cryptococcosis. Subsequent validation through in vivo studies is paramount to confirm the effectiveness and potential of the vaccine in reducing the spread of cryptococcosis. [ABSTRACT FROM AUTHOR]

Background: Anaphylaxis is a severe, life-threatening allergic reaction requiring prompt treatment with epinephrine. However, gaps in public understanding exist globally. To guide future education efforts, this study assessed anaphylaxis awareness among adults in Al-Ahsa, Saudi Arabia. Methods: A cross-sectional survey assessed knowledge among 380 adults using a validated questionnaire. Convenience sampling was used with data collected via online platforms. Descriptive statistics were calculated, and associations were tested. We analyzed the data using SPSS software. Results: Knowledge was suboptimal, with a mean score of 34.5/60. Most identified the anaphylaxis definition (76.1%) and rapid symptom onset (78.9% within minutes). But just 37.9% recognized epinephrine as a first-line treatment and only 17.4% had auto-injector training. Nearly half (48.4%) needed to learn the proper EpiPen injection site. Under half understood the need for long-term allergen avoidance. Older age, more education, and Job status were associated with higher scores. Conclusion: This study reveals critical gaps in anaphylaxis knowledge among adults in Al-Ahsa, Saudi Arabia. Poor understanding of emergency treatment and long-term management highlights the urgent need for improved public awareness. Targeted educational initiatives must emphasize prompt epinephrine administration, proper auto-injector use, and allergen avoidance. Collaborative efforts engaging healthcare professionals, schools, and policymakers are essential to enhance anaphylaxis preparedness through focused training and tailored awareness campaigns. This will empower the Saudi public to recognize symptoms quickly and respond effectively, saving lives from this dangerous allergic reaction. [ABSTRACT FROM AUTHOR]

Learning Management System (LMS) is a major tool used in most universities and institutions for online/distance education purposes. A variety of LM systems are being used in different universities and institutions, and these LM systems are also updating their versions and patches to stay in the competition. Hence, the selection of an appropriate LMS is an important task as it is going to influence the academic proceedings of an academic institution. There are a few online comparison sheets available for different LMS, which have been scored based on online feedback, but those online comparisons lack a comprehensive features list for the comparison, and are unable to provide a generalized scoring function to compute the suitability score for an LMS. This article proposes a framework to evaluate an LMS based on a detailed list of features and functions. This evaluation is scored through a generalized scoring function that computes the quantitative score of LM systems. Lastly, some widely used open access and proprietary LM systems have been evaluated using the proposed framework and scoring function. [ABSTRACT FROM AUTHOR]

Background: COVID-19 has been linked to hyperglycemia and diabetes, with noteworthy variation in outcomes. This study aimed to compare the sociodemographic factors, clinical characteristics, and in-hospital and short-term post-discharge outcomes between COVID-19 patients with new onset hyperglycemia and pre-existing diabetes patients in tertiary care hospitals in Bangladesh. Methods: A prospective observational study was conducted among adult COVID-19 patients with new onset hyperglycemia or pre-existing diabetes admitted to the COVID-19 unit of Dhaka Medical College Hospital between April 2021 and October 2021. Patients were conveniently selected from indoors. Bivariate analysis was used to compare sociodemographic and clinical characteristics at admission and short-term outcomes. The Cox proportional hazard model was used to examine factors associated with time to death in the hospital. All statistical analyses were performed using Stata Version 17. Results: A total of 169 patients were included. Of these, 29 died in the hospital, and four left against medical advice. Out of the 136 survivors, 135 came for follow-up two weeks after discharge. At baseline, 30.18% of patients had new onset hyperglycemia, and 69.8% had pre-existing diabetes. The average age of patients was 56.38 ± 14.21 years, and 60.36% were male. A significantly higher proportion of COVID-19 patients with new onset hyperglycemia were smokers than those with pre-existing diabetes (p = 0.003). However, pre-existing diabetes was associated with higher lung involvement (p = 0.047) and comorbidities (p = 0.002). Age, income over 35,000 BDT (USD 335.5$), and a BMI over 25 kg/m2 emerged as significant predictors of prolonged hospital stay and mortality. Post-discharge follow-up indicated that new-onset hyperglycemia resolved in 8.89% of patients, whereas 19.26% continued to exhibit hyperglycemia, with smoking being a significant determinant of its persistence (p = 0.001). Conclusion: In conclusion, our investigation illuminates the clinical trajectory of new-onset hyperglycemia in the context of COVID-19 and reinforces the necessity for diligent monitoring and management post-discharge. Therefore, close monitoring and follow-up of COVID-19 patients is recommended for the early detection and management of hyperglycemia and the prevention of diabetes development in the long run. [ABSTRACT FROM AUTHOR]

Fishmeal (FM) is a key component of commercial fish feeds, but due its unsustainable supply, the search for quality alternatives of FM has become a significant area of investigation worldwide. The insect-based proteins such as black soldier fly larvae (BSFL) are being recognized as an alternative ingredient. However, anti-nutritional factors in these alternatives may negatively affect nutrient utilization in fish. Incorporating exogenous protease enzymes as feed additives could be a promising way to improve the digestibility of these alternative ingredients. Therefore, this study aimed to evaluate the impact of dietary inclusion of BSFL meal, combined with protease supplementation, on various parameters of striped catfish (Pangasius hypophthalmus). Five diets were formulated: a control diet (FM-based), two diets with 30% and 60% BSFL inclusion (BSF30 and BSF60), and two diets with the same inclusion levels plus the incorporation of exogenous protease (BSF30P and BSF60P). One hundred fifty fish (8.26±0.02 g) were arbitrarily allocated to five groups, each with three replicates. The fish were provided with their respective diets twice per day for 60 days. In comparison to the control, fish-fed diets supplemented with protease demonstrated statistically significant lower levels of feed conversion ratio and higher growth performance regarding the final body weight and weight gain. Lipase activity in the BSF60P group was notably greater than in the control group. Protease supplementation significantly enhanced the apparent digestibility coefficient of protein, intestinal protease activity, and crude protein content in the whole body. Most hematological and biochemical parameters remained unaffected except for substantially lower triglyceride and cholesterol levels in the highest BSFL inclusion groups. These groups also showed a reduction in crude fat contents. While glutathione peroxidase and malondialdehyde levels did not change significantly from the control, the liver tissues of fish fed BSF60P diets showed considerably higher levels of antioxidant enzymes such as catalase and superoxide dismutase. The findings suggest that including 60% of a BSFL based diet, along with exogenous protease supplementation, is feasible without compromising the growth performance and health of striped catfish. [ABSTRACT FROM AUTHOR]

To explore an alternative strategy to chemotherapy to combat oral cancer, natural products and their derivates constitute one promising approach. In the last previous study, we have demonstrated the potential anti-tumor properties of anethole; an aromatic compound abundantly present in nature that serves as a major active ingredient found in plants like anise and fennel. In the current study, we aimed to investigate how this molecule inhibits oral cancer cell proliferation and induces apoptosis. This will be carried out by a transcriptomic study of its effects on the expression profile of cell cycle and apoptosis regulation genes in gingival cancer cells. cell cycle. Ca9-22 cells were treated with 10 μM of anethole (IC50) and cell proliferation was evaluated by MTT assay. The percentage of cells in different stages of the cell cycle was measured by flow cytometry. Cytotoxicity was evaluated by LDH assay and apoptosis was investigated by Pi/Annexin V assay following 24-hour treatment. Furthermore, we employed PCR array analysis to investigate alterations in the expression levels of oncogenes and tumor suppressor genes associated with cell cycle regulation and apoptosis. Finally, Gene-gene interactions were examined using the Gene MANIA database. Our findings demonstrate that anethole significantly attenuated the proliferation of Ca9-22 cells, leading to disturbances in cell cycle progression and eliciting cellular toxicity and apoptosis. By a double normalizing with two housekeeping genes (Actin and GAPDH), we show that, treatment with 10 μM of anethole alters (more than two-fold) the expression of 13 genes involved in the control of the cell cycle (8 were up regulated and 5 were down regulated) and 7 genes involved in the regulation of apoptosis (4 were up regulated and 3 downregulated by anethole). Finally, each group of genes modulated by anethole forms a network of connections between them or with other genes. Our study suggests that anethole holds promise as a potential alternative treatment for oral cancer by its ability to modify numerous oncogenes and tumor suppressor genes implicated in the cell cycle regulation and induction of apoptosis in oral cancer cells. These findings underscore the significance of further research into the potential therapeutic application of anethole as an alternative drug for managing oral cancer. [ABSTRACT FROM AUTHOR]

Background: A Partial pulpotomy technique is an alternative choice of treatment for immature permanent teeth with exposed vital pulps. This study aimed to compare the histopathological pulpal response of the primary teeth of puppies after partial pulpotomy and full pulpotomy using mineral trioxide aggregate (MTA). Materials and methods: 72 primary premolars from experimental puppies aged 6–8 weeks were equally divided into test and control groups, as MTA partial pulpotomy (MTA-PP) and MTA full pulpotomy (MTA-FP). After 60 days, the teeth were extracted and examined histologically. The specimens were assessing the extent and intensity of inflammatory response (acute or chronic), necrosis, fibrosis, calcific bridge (presence or absence), pulp calcification, and pathological root resorption. Results: The Inflammation intensity was lower (2.8%) in MTA-PP than in MTA-FP (16.7%) (P = 0.004). MTA-FP also had a significantly higher percentage of localized (27.8%) and diffuse (11.1%) necrosis (P = 0.014), whereas both groups (P = 0.290) had an equal fibrosis degree. A calcific bridge was observed in 72.2% of cases. Meanwhile, pathological internal root resorption was more evident in MTA-FP (63.9%) than in MTA-PP (19.4%) (P <0.0001). Conclusion: Partial MTA pulpotomy procedure demonstrated promising histological findings that can be applied to vital-pulp therapy in primary teeth. [ABSTRACT FROM AUTHOR]

Background: The prevalence and impact of obesity and overweight is one of the main concerns among researchers worldwide. There are several consequences of overweight and obesity among students in developing countries like Bangladesh. Therefore, this study aims to explore the prevalence and impact of overweight or obesity on individuals' self-esteem and academic performance among university students in Bangladesh. Methods and materials: This study is based on primary data that was collected from 600 students of Jahangirnagar University using a well-structured questionnaire. The questionnaire included demographic information, the Rosenberg Self-Esteem Scale (RSES), and the International Physical Activity Questionnaire (IPAQ-7). The summary statistics, the Fisher exact test, the Chi-square test, and the Pearson correlation matrix were computed. A logistic regression model assessed the relationship between low self-esteem and other variables such as demographic characteristics, BMI status, and physical activities. Results: The findings indicate that 38.2% of the students are overweight or obese, and the prevalence rate is high among males. The lowest academic performance across all disciplines is observed among obese students. A negative correlation is observed between BMI and self-esteem (r = -0.41) and academic performance is negatively linked with overweight or obese (r = -0.26) Results depict that overweight/obese students are 7.11 times more likely to have low self-esteem (AOR: 7.11, 95% CI: 4.59–11.02) compared to normal students. Conclusions: Overweight and obese students are more prone to lower academic performance and low self-esteem, considering physical inactivity and irregular dietary patterns. Therefore, university authorities ought to encourage students to engage in physical activities and should create awareness among students about the beneficial effects of nutritious foods and the importance of physical exercise. [ABSTRACT FROM AUTHOR]

The epidermal growth factor receptor (EGFR), a crucial component of cellular signaling pathways, is frequently dysregulated in a range of cancers. EGFR targeting has become a viable approach in the development of anti-cancer medications. This study employs an integrated approach to drug discovery, combining multiple computational methodologies to identify potential EGFR inhibitors. The co-crystal ligand for the EGFR protein (R85) (PDB ID: 7AEI) was employed as a model for developing pharmacophore hypotheses. Nine databases underwent a ligand-based virtual screening, and 1271 hits meeting the screening criteria were chosen. EGFR protein crystal structure was obtained from the PDB database (PDB ID: 7AEI) and prepared. The hit compounds identified during virtual screening were docked to the prepared EGFR receptor to predict binding affinities by using the glide tool's standard precision mode. The top ten compounds were chosen, and their affinities of binding ranged from -7.691 to -7.338 kcal/mol. The ADMET properties of the selected compounds were predicted, and three compounds MCULE-6473175764, CSC048452634, and CSC070083626 showed better QPPCaco values compared to other identified compounds, so these were selected for further stability analysis. To confirm the stability of the protein-ligand complexes, a 200 ns molecular dynamics (MD) simulation was run using the binding sites of the top three compounds against the EGFR receptor. These results suggest that the selected compounds may be lead compounds in suppressing the biological activity of EGFR, additional experimental investigation is required. [ABSTRACT FROM AUTHOR]

This study investigates the impact of environmental, social, and governance (ESG) scores on dividend policy, while taking into account the moderating effect of financial sustainability. It examines data from companies listed on the Saudi Exchange, during the period spanning the years from 2013 to 2022. According to the findings of panel regression analysis, there is a strong positive correlation between ESG performance and dividend payments. In essence, businesses that exhibit strong ESG practices continuously maintain dividend payments as a way of demonstrating their dedication to both stakeholders and shareholders. Furthermore, financial sustainability exerts an enhancing influence on the ESG–dividend relationship, indicating that the positive effect of ESG on dividend yields is significant in financially sustainable companies compared to their peers. It is noteworthy that these conclusions hold up well even when put through sensitivity studies using different estimating methods. The implications of these results extend to a broad spectrum of stakeholders, including investors, management, analysts, and policymakers. They provide valuable insights for companies and markets seeking to expand their ESG initiatives. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the relationships between kinesiophobia, proprioception, and limits of stability in elderly individuals post-THR. Specifically, it sought to assess the direct and indirect effects of kinesiophobia on proprioception through mediating factors such as pain intensity, functional mobility, and psychological well-being. A cross-sectional observational study was conducted with 100 participants (50 post-THR patients and 50 asymptomatic elderly controls) at King Khalid University Hospital. Kinesiophobia was measured using the Tampa Scale for Kinesiophobia (TSK), proprioception was assessed via a digital inclinometer, and limits of stability were evaluated using computerized dynamic posturography. Post-THR patients exhibited significantly higher levels of kinesiophobia (p < 0.001) and impaired proprioception (p < 0.001) compared to controls. Mediation analyses revealed that pain intensity, functional mobility, and psychological well-being partially mediated the relationship between kinesiophobia and proprioception. The Sobel tests confirmed significant mediation effects for pain intensity (Z = 3.88, p = 0.021), functional mobility (Z = 2.96, p = 0.013), and psychological well-being (Z = 2.84, p = 0.015). Kinesiophobia significantly impairs proprioception and balance in elderly individuals post-THR, with these effects being partially mediated by pain intensity, functional mobility, and psychological well-being. These findings highlight the importance of addressing psychological factors in rehabilitation programs to enhance proprioceptive function and improve postural stability, thereby optimizing recovery outcomes in the post-THR population. [ABSTRACT FROM AUTHOR]

Accurate and flexible 3D pose estimation for virtual entities is a strenuous task in computer vision applications. Conventional methods struggle to capture realistic movements; thus, creative solutions that can handle the complexities of genuine avatar interactions in dynamic virtual environments are imperative. In order to tackle the problem of precise 3D pose estimation, this work introduces TRI-POSE-Net, a model intended for scenarios with limited supervision. The proposed technique, which is based on ResNet-50 and includes integrated Selective Kernel Network (SKNet) blocks, has proven to be efficient for feature extraction customised specifically to pose estimation scenarios. Furthermore, trifocal tensors and their trio-view geometry allow us to generate 3D ground truth poses from 2D poses, resulting in more refined triangulations. Through the proposed approach, the 3D poses can be estimated from a single 2D RGB image. Moreover, the proposed approach was evaluated on the HumanEva-I dataset yielding a Mean-Per-Joint-Position-Error (MPJPE) of 47.6 under self-supervision and an MPJPE of 29.9 under full supervision. In comparison with the other works, the proposed work has performed well in the self-supervision paradigm. [ABSTRACT FROM AUTHOR]

The rapid advancement of additive manufacturing (AM) requires researchers to keep up with these advancements by continually improving the AM processes. Improving manufacturing processes involves evaluating the process outputs and their conformity to the required specifications. Process capability indices, calculated using critical quality characteristics (QCs), have long been used in the evaluation process due to their proven effectiveness. AM processes typically involve multi-correlated critical QCs, indicating the need to develop a multivariate process capability index (MPCI) rather than a univariate capability index, which may lead to misleading results. In this regard, this study proposes a general methodological framework for evaluating AM processes using MPCI. The proposed framework starts by identifying the AM process and product design. Fused Deposition Modeling (FDM) is chosen for this investigation. Then, the specification limits associated with critical QCs are established. To ensure that the MPCI assumptions are met, the critical QCs data are examined for normality, stability, and correlation. Additionally, the MPCI is estimated by simulating a large sample using the properties of the collected QCs data and determining the percent of nonconforming (PNC). Furthermore, the FDM process and its capable tolerance limits are then assessed using the proposed MPCI. Finally, the study presents a sensitivity analysis of the FDM process and suggestions for improvement based on the analysis of assignable causes of variation. The results revealed that the considered process mean is shifted for all QCs, and the most variation is associated with part diameter data. Moreover, the process data are not normally distributed, and the proposed transformation algorithm performs well in reducing data skewness. Also, the performance of the FDM process according to different designations of specification limits was estimated. The results showed that the FDM process is incapable of different designs except with very coarse specifications. [ABSTRACT FROM AUTHOR]

Sonneratia apetala, a nutrient-rich mangrove fruit, presents an opportunity for innovative food product development, offering potential health benefits and economic value through the creation of jam, jelly, and pickle. This innovative invention reveals the nutritional content including vitamins and minerals of Sonneratia apetala jam, jelly, and pickles from Nijhum Dwip in Hatiya Upazila, Noakhali District. These products contain Na, Mg, K, Ca, Mn, Fe, Cu, and Zn, which are essential for human nutrition. The texture and sensory qualities of the products depend on their Total Soluble Solids (TSS), acidity, moisture, pH, and total sugar content, with each parameter receiving an average score of 7 to 8 out of 9 (hedonic scale). Trace amounts of Cd, Cr, Pb, and Hg were found to be significantly below the safe consumption limits. F-, Cl-, SO42-, soluble and total PO43- concentrations were also below safety thresholds. The moisture, ash, protein, fat, fiber, pectin, sugar, carbohydrate, and caloric values highlight the dietary benefits and energy content of these products. The products exhibited higher levels of vitamin C and minerals compared to other citrus fruits. All tested parameters met safe consumption standards, ensuring product safety. These products underwent testing for Heterotrophic Bacterial Count to guarantee their safety. A one-year shelf life is ensured by conducting quarterly storage data checks and organoleptic tests by a 10-member jury panel. The one-way ANOVA test for sensory analysis and shelf life detection indicates statistically significant results. These products help mitigate nutrient deficiencies and promote health by regulating the diet. Applying this technology in grassroots jam, jelly, and pickle production could potentially boost the local economy by approximately $10,000 annually through the creation of small industries among the coastal population. [ABSTRACT FROM AUTHOR]

Pioglitazone, an anti-diabetic drug, has been previously shown to ameliorate kidney damage through anti-inflammatory and antioxidant effects. In this study, we employed an integrative bioinformatics approach to study the possible mechanisms involved in the mitigative effect of pioglitazone against colistin-induced nephrotoxicity. Next, we validated the results obtained from the bioinformatics study by pre-treating human kidney-2 (HK-2) cell line with pioglitazone 100 μM for 30 minutes and then treating the cells with colistin sulfate 1200 μM for 24 hours. Inflammatory signaling by cytokines and the nuclear factor erythroid 2 related factor 2 (Nrf2) signaling pathways were predicted to be involved in the ameliorative effect of pioglitazone against colistin-induced nephrotoxicity. The nuclear factor kappa B subunit p65 (NF-κB p65) and Nrf2 were among the predicted transcription factors regulating the hub genes. Moreover, miR-24, miR-16, and miR-21 were identified as potential pathogenic miRNAs regulating the hub genes. In contrast, miR-17, miR-27a, and miR-146a were identified as potential protective miRNAs. The in vitro study indicated that pioglitazone pre-treatment increased cell viability in HK-2 cells exposed to colistin. Pioglitazone pre-treatment reduced the expression of pro-inflammatory cytokine genes (IL6 and TNF). Moreover, pioglitazone reduced the protein expression of NF-κB p65 and increased the protein expression of Nrf2. The protective effect of pioglitazone against colistin-induced toxicity in HK-2 cells is related to its anti-inflammatory and antioxidant activity through modulating NF-κB-mediated inflammatory signaling and Nrf2-mediated antioxidative stress signaling. [ABSTRACT FROM AUTHOR]

Background: Rosemary (Rosmarinus officinalis) has a rich historical use for various reasons due to its beneficial characteristics including stimulating hair growth, providing antioxidant and antibacterial effects, and functioning as a skin conditioner and fragrance enhancer. The plant is cultivated in Jordan and using it is gaining popularity among the population. Aim of the study: To assess the knowledge, attitude and perception of rosemary use among Jordanians. Materials and methods: A cross-sectional study was conducted targeting at least 385 participants via social media platforms. Face and content validity of the questionnaire was performed by independent researchers. The questionnaire was divided into three main sections including the demographics section, the knowledge section and the attitude toward rosemary use. Statistical analysis was conducted using SPSS including descriptive statistics, chi-square tests, and multiple linear regression. Results: The study's participants (n = 407) had a mean age of 30.98 years old (SD = 12.76). The mean knowledge score for the participants ranged from -5 to 7, with a mean of 2.96 (SD = 2.61), with younger participants exhibiting higher knowledge scores regarding rosemary. Approximately half of the participants (48.2%) reported using rosemary for medical purposes. About three-quarters reported using rosemary oil, but a higher percentage reported using the leaves. Around three quarters administered it orally or applied it topically. Most participants obtained rosemary by growing it at home or from herbalists. Family and friends were the main influencers for rosemary users. Most of the study participants strongly agreed/agreed that plants possess a healing power (94.4%). The most reported reason for using rosemary was improving the hair condition (82.1%) followed by gastrointestinal problems (67.9%). Conclusions: The study highlights the widespread use of rosemary for different reasons through different methods of application. Addressing misconceptions and enhancing knowledge dissemination may allow informed decision-making and promote the safe and effective use of herbal therapies. [ABSTRACT FROM AUTHOR]

Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.

The early identification of pests and diseases in crops now presents a significant challenge. Different methods have been used to resolve this problem. Sticky traps and black light traps, used to identify diseases and for field monitoring, are examples of a manual procedure for analysing the diseases. A lot of time is required, and it is less effective to manually inspect larger crop fields manually. To serve requires a professional, so it is, therefore, costly. The use of sticky traps, where by bugs stick to the material upon contact, is one method of disease monitoring. A camera is used to take a picture of the sticky trap. From the picture using the average disease count, this image is then processed to ascertain the pet density for a specific time period. Such manual methods, as well as providing an effective outcome also pose a danger to the environment. This is because farmers spray pesticides in large quantities as a preventative measure. Various approaches have been used to identify diseases, including image processing and sophisticated algorithms. The most effective method of disease identification from crops is automatic detection using methods of image processing and classification algorithms for the diseases to be categorised based on different picture attributes. With a stacking stacking hybrid learning with scratch and transfer learning strategies, which is utilised in this work, a model that has already been trained is used to learn on images of diverse fruit plant leaves from the Plant Village dataset, spanning both safe samples and various illnesses. This reasearch paper used ensemble CNN and we achieved accuracy between 99.75% to 100%. [ABSTRACT FROM AUTHOR]

A common conundrum discussed in economic research revolves around the fact that nations endowed with plentiful natural resources often exhibit a lower gross domestic product (GDP). This conundrum is commonly called the "resource curse", where most empirical studies about the effects primarily focused on developed economies. At the same time, limited data is available regarding a burgeoning oil-exporting nation like the Republic of Yemen. This research endeavor aims to investigate the relationship between oil price Changes and Yemen's economic growth. Utilizing annual data spanning from 1990 to 2019, the study employs the auto-regressive distributed lag (ARDL) model to establish the long-term connection between oil price volatility and economic growth over both short and long timeframes. This study's outcomes indicate that oil price Changes have a significant positive relationship with Yemen's economic growth in both the long and short run. Oil rents show a significant negative relationship with economic growth in both the long and short run. The results of GLM, RLS, and GMM robustness checks are consistent with our model results. Based on these findings, we suggest that Yemen should diversify its economy by investing in agriculture and tourism, and focus on human capital, education, and research and development. These steps could reduce the economy's dependence on oil and enhance sustainable economic growth. These empirical insights and suggestions are particularly useful for policymakers as they help build sound external and economic policies to sustain long-term economic growth. [ABSTRACT FROM AUTHOR]

Tamarix articulate from the Tamaricaece family is a halophytic plant. This plant is commonly called Athal or Tamarix in different Arabic and Asian countries. Due to the high load of polyphenolic phytochemicals, the plant has been used as a therapeutic option against several diseases for decades. The plant is an anti-inflammatory, anti-bacterial, anti-viral, anti-cancer, anti-oxidant, and anti-inflammatory. In this work, the 222 phytochemical compounds of T. articulate from our previous study are used in different bioinformatic and biophysics techniques to explore their biological potency against different anti-bacterial, anti-cancer and anti-viral targets. By doing so, it was found that Riddelline ranked as the best binding molecule of biological macromolecules selected herein in particular the bacterial targets. The binding energy value of the compound for the KdsA enzyme was -14.64 kcal/mol, KdsB (-13.09 kcal/mol), MurC (-13.67 kcal/mol), MurD (-13.54 kcal/mol), MurF (-14.20 kcal/mol), Polo-like kinase 1 (Plk1) (-12.34 kcal/mol), Bcl-2 protein (-13.39 kcal/mol), SARS-CoV-2 main protease enzyme (-12.67 kcal/mol), and Human T cell leukemia virus protease (-13.67 kcal/mol). The mean Rg value of KdsA-Riddelline complex and KdsA-FPE complex is 32.67 Å, and average RMSD of KdsA-Riddelline complex and KdsA-FPE complex is 2.31 Å, respectively. The binding energy complexes was found to be dominated by van der Waals (-71.98 kcal/mol for KdsA-Riddelline complex and -65.09 kcal/mol for KdsA-FPE complex). The lead compound was also unveiled to show favorable druglike properties and pharmacokinetics. Together, the data suggest the good anti-bacterial activities of the T. articulate phytochemicals and thus can be subjected to experimental in vitro and in vivo investigations. [ABSTRACT FROM AUTHOR]

This paper addresses the computational methods and challenges associated with prime number generation, a critical component in encryption algorithms for ensuring data security. The generation of prime numbers efficiently is a critical challenge in various domains, including cryptography, number theory, and computer science. The quest to find more effective algorithms for prime number generation is driven by the increasing demand for secure communication and data storage and the need for efficient algorithms to solve complex mathematical problems. Our goal is to address this challenge by presenting two novel algorithms for generating prime numbers: one that generates primes up to a given limit and another that generates primes within a specified range. These innovative algorithms are founded on the formulas of odd-composed numbers, allowing them to achieve remarkable performance improvements compared to existing prime number generation algorithms. Our comprehensive experimental results reveal that our proposed algorithms outperform well-established prime number generation algorithms such as Miller-Rabin, Sieve of Atkin, Sieve of Eratosthenes, and Sieve of Sundaram regarding mean execution time. More notably, our algorithms exhibit the unique ability to provide prime numbers from range to range with a commendable performance. This substantial enhancement in performance and adaptability can significantly impact the effectiveness of various applications that depend on prime numbers, from cryptographic systems to distributed computing. By providing an efficient and flexible method for generating prime numbers, our proposed algorithms can develop more secure and reliable communication systems, enable faster computations in number theory, and support advanced computer science and mathematics research. [ABSTRACT FROM AUTHOR]

Herbal supplements containing diverse phytochemicals have been proven to improve digestibility, beneficial serum metabolites, milk yield, and quality in cows. However, the temperature sensitivity of phytochemicals in herbs complicates their practical use as livestock supplements. In response, this study aimed to assess how shade-dried plantain, lemongrass, and their combination influence cow performance, digestibility, serum and milk antioxidants, and milk fatty acids. Forty multiparous mid-lactating Holstein-tropical crossbred cows were randomly assigned to four groups (n = 10) for 63 days in a completely randomized design with covariates adjustment. The control group received a basal diet (14.9% crude protein, 11.0 MJ metabolizable energy/kg dry matter (DM)) without herbs. The herbs-supplemented groups received shade-dried powder (per cow/day) of either 100 g plantain (plantain), 100 g lemongrass (lemongrass), or 50 g each of plantain and lemongrass (combined), along with the basal diet. Compared with the control, the plantain or lemongrass group presented improved performance, i.e., DM consumption (3–4%), milk (10–11%) and its components yield (p ≤ 0.05). Similar to the control, the combined group had no effect on performance or digestibility, whereas the digestibility of all nutrients in the plantain or lemongrass group substantially increased. Herbs-supplementation reduced serum bad-cholesterol and urea-nitrogen while increasing good-cholesterol and albumin compared with the control (p < 0.01). Notably, 4–8% serum and 8–23% milk antioxidants were greater in the herbs-supplemented groups than the control, while the lemongrass had the optimum milk β-carotene. Substantially, herbs-supplementation resulted in a 6–10% higher unsaturated fatty acids (USFAs), whereas the combined group presented a 3.56-fold greater n-3 fatty acids than the control. Significantly, the plantain or lemongrass group presented better benefit‒cost‒ratio than the combined and control. Therefore, shade-dried herbs-supplementation improved the beneficial serum metabolites, serum and milk antioxidants, and milk USFAs in cows. Additionally, shade-dried plantain or lemongrass enhanced cow DM consumption, digestibility, milk yield, and profitability. [ABSTRACT FROM AUTHOR]

Background: The study's primary goal is to ascertain whether there is a relationship between the processed green tea methanolic extract's (GTME) phytochemical components and its potential effectiveness against human liver cancer cells. The GTME's phytochemical composition was identified using gas chromatography-mass spectrometry, and the extract's capacity to lower cellular proliferation and cause apoptosis in HepG2 cancerous liver cell lines was checked. Results: The findings of the gas chromatography-mass chromatogram showed that GTME included bioactive antioxidants and anticancer substances. Additionally, utilizing the MTT, comet assay, and acridine assay, GTME revealed a selective cytotoxic impact with a significant IC50 value (27.3 µg/ml) on HepG2 cells without any harmful effects on WI-38 healthy cells. Also, compared to untreated cells, the extract-treated HepG2 cells had an upsurge in the proportion of cells that have undergone apoptosis and displayed a comet nucleus, which is a sign of DNA damage. In addition, HepG2 cells treated with GTME revealed a stop in the G1 phase and sub-G1 apoptotic cells (37.32%) in a flow cytometry analysis. Furthermore, reactive oxygen species were shown to be responsible for HepG2 apoptosis, and the tested extract significantly reduced their levels in the treated cells. Lastly, compared to untreated cells in treated HepG2 cells, GTME significantly changed protein expression levels linked with cell cycle arrest in the G1 phase and apoptosis. Conclusion: These findings provided information about the processes through which the GTME inhibited the growth of HepG2. Therefore, it has potential as an effective natural therapy for the treatment of human liver cancer. However, to validate these findings, animal models must be used for in vivo studies. [ABSTRACT FROM AUTHOR]

Psoriasis is chronic immune-mediated inflammatory disorder characterized by various comorbidities, erythematous plaques with silvery scale which can lead to psoriatic arthritis. The phosphodiesterase 4 (PDE4) protein is a potential drug target to control Psoriasis. In the current study, pharmacophore-based virtual screening of Diversity library of ChemDiv database was first performed, and then the screened hits were docked to the active site of PDE4 to choose the best binding modes. Forty-six hits generated during the virtual screening were prepared and docked to the PDE4 receptor by SP docking module of glide. The binding affinities of the selected hits were calculated by molecular docking and based on the affinities, ten hits were selected for the bioactivity scores prediction and ADMET analysis. Based on the ADMET profiling, four hits D356-2630, C700-2058, G842-0420 and F403-0203 were processed to MD simulations for stability analysis. The outcomes showed that these compounds showed strong binding with proteins with better binding free energies. Based on the results of our study, we proposed that these hits can function as lead in the biological assays and in vitro studies are required to develop the novel drug candidates. [ABSTRACT FROM AUTHOR]

Background: Breast cancer patients face significant psychological challenges, including difficulties in accepting the diagnosis, treatment, and long-term impact of the disease. Acceptance and Commitment Therapy (ACT) has shown promise in enhancing acceptance and psychological flexibility in various populations. This study aims to investigate the effectiveness of ACT in promoting disease acceptance among breast cancer patients through a randomized controlled trial. Methods: This study will recruit 90 breast cancer patients and randomly allocate them to an ACT intervention or control group. The ACT intervention, focusing on acceptance, mindfulness, value clarification, and committed action, will be delivered over 4 weeks. Meanwhile, the control group will receive standard care with non-therapeutic intervention. The study's primary outcome is disease acceptance, while secondary outcomes include depression, anxiety, social support, quality of life (QoL), and psychological inflexibility. Data will be collected at three points: baseline, post-intervention, and three-month follow-up. Statistical analysis will compare outcomes between groups to evaluate the effectiveness and mechanism of this intervention using covariance and mediation analysis. Discussion: This study evaluates the effectiveness of ACT in promoting disease acceptance among breast cancer patients. It hypothesizes that the ACT group will show higher disease acceptance and improvements in social support, QoL, and psychological flexibility compared to the control group. The findings will contribute to research on psychological interventions and demonstrate ACT's effectiveness in enhancing disease acceptance. Trial registration: The research project is registered in the ClinicalTrials (NCT05327153). [ABSTRACT FROM AUTHOR]

Background: Ocular emergencies (OE) include ocular trauma, infections, retinal detachment, and uveitis. Due to the limited number of studies describing ocular emergencies requiring Emergency Department (ED) visits in Saudi Arabia, there is a need for further studies addressing this topic. Therefore, this study aimed to assess and describe the incidence, clinical presentation, and causes of ocular-related ED visits at a tertiary hospital in Riyadh, Saudi Arabia. Methods: This was a retrospective cohort study. Medical records were reviewed for all patients (all age groups) patients presenting at the ED from 1 January 2019 to 31 October 2023 where data for patients with any ocular emergency was extracted using an Excel sheet. Data analysis was performed using RStudio (R version 4.3.1). Baseline information was described using frequencies and proportions. Types of Ocular emergencies were described using the International Classification of Diseases, 10th Revision (ICD-10) codes and presented using frequencies and proportions. The distribution of ocular emergencies across months and age groups was expressed using chart figures. Results: A total of 15,321 ocular-related ED visits were included in this study. Almost 51% of patients were males. The mean age at diagnosis was 25.5 ± 22.1 years. More than half of the patients (51.0%) were diagnosed during childhood (<18 years), while proportions of older ages declined gradually. Average presentations of ocular emergencies in a single month ranged from 200 to 400. Conjunctival disorders including conjunctivitis (29.8%) were the most frequent ocular emergencies. Conclusion: The findings of this study showed that ocular emergencies are more prevalent in younger patients (aged <18 years), more commonly due to conjunctivitis. This highlights the need for policymakers to assess the causes of such emergency in this population and implement prevention strategies. Moreover, an average of 200 to 400 emergency visits per month are ocular-related. This finding could help policymakers understand the burden of ocular emergencies on the ED and the pressure that could add to the ED staff to provide appropriate care for these patients. [ABSTRACT FROM AUTHOR]

Background: To align with the 2030 vision of the World Health Organization (WHO) to ensure 90% of girls receive the HPV vaccine before turning 15, Bangladesh has recently started the (HPV) vaccine campaign nationwide. Therefore, our study aimed to assess the level of its acceptance among parents of eligible daughters in Bangladesh. Methods: This nationwide cross-sectional study was conducted among the self-identified parents of daughters in the age group of 9–15 years between June 28 and August 2, 2023. A total of 2,151 parents were conveniently selected from all eight divisions of Bangladesh. Data was collected through face-to-face interviews using a semi-structured questionnaire. The Health Belief Model was used to appraise respondents' beliefs concerning HPV and its vaccination. Adjusted odds ratios (AOR) with a corresponding 95% confidence interval (CI), and a p-value of <0.05 was considered statistically significant. R Studio (Version 2023.09.0+463) was used as an interface for data analysis, while R (the programming language) was used for statistical computations. Results: The mean age of the study participants was 38.18 (±5.86) years. The overall acceptance rate of the HPV vaccine was 86.61% (95%CI: 85.09–88.02). The vaccine acceptance level was positively associated with all the domains of the HBM (p<0.001), except in the perceived barrier domain (p = 0.489). After adjustment for other factors, it was found that higher age was associated with a decreased acceptance (AOR: 0.92; 95% CI: 0.89–0.95). The urban residents exhibited 42% lower odds of vaccine acceptance than rural (AOR: 0.58; 95% CI: 0.36–0.92). Similarly, participants of the middle-income group had 44% lower odds than the lower-income group (AOR: 0.56; 95% CI: 0.32–0.97). Conclusion: Our study found a reasonably good level of acceptance of the HPV vaccine among the parents of eligible daughters. Multiple factors such as younger age, urban residence, belonging to the middle income group, history of regular routine health check-ups, knowledge of cervical cancer, positive perception about benefits of the vaccine, and positive cues to actions were associated with HPV vaccine acceptance. [ABSTRACT FROM AUTHOR]

Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of 'very high' and 'high' class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities. [ABSTRACT FROM AUTHOR]