Mental health status among COVID-19 patients survivors of critical illness in Saudi Arabia:A 6-month follow-up questionnaire study

BACKGROUND Psychological assessment after intensive care unit(ICU)discharge is increasingly used to assess patients'cognitive and psychological well-being.However,few studies have examined those who recovered from coronavirus disease 2019(COVID-19).There is a paucity of data from the Middle East assessing the post-ICU discharge mental health status of patients who had COVID-19.AIM To evaluate anxiety and depression among patients who had severe COVID-19.METHODS This is a prospective single-center follow-up questionnaire-based study of adults who were admitted to the ICU or under ICU consultation for>24 h for COVID-19.Eligible patients were contacted via telephone.The patient’s anxiety and depression six months after ICU discharge were assessed using the Hospital Anxiety and Depression Scale(HADS).The primary outcome was the mean HADS score.The secondary outcomes were risk factors of anxiety and/or depression.RESULTS Patients who were admitted to the ICU because of COVID-19 were screened(n=518).Of these,48 completed the questionnaires.The mean age was 56.3±17.2 years.Thirty patients(62.5%)were male.The main comorbidities were endocrine(n=24,50%)and cardiovascular(n=21,43.8%)diseases.The mean overall HADS score for anxiety and depression at 6 months post-ICU discharge was 11.4(SD±8.5).A HADS score of>7 for anxiety and depression was detected in 15 patients(30%)and 18 patients(36%),respectively.Results from the multivariable ordered logistic regression demonstrated that vasopressor use was associated with the development of anxiety and depression[odds ratio(OR)39.06,95% confidence interval:1.309-1165.8;P<0.05].CONCLUSION Six months after ICU discharge,30% of patients who had COVID-19 demonstrated a HADS score that confirmed anxiety and depression.To compare the psychological status of patients following an ICU admission(with vs without COVID-19),further studies are warranted.

Predicting potential invasion risks of Leucaena leucocephala(Lam.)de Wit in the arid area of Saudi Arabia

The presence of invasive plant species poses a substantial ecological impact,thus comprehensive evaluation of their potential range and risk under the influence of climate change is necessary.This study uses maximum entropy(MaxEnt)modeling to forecast the likelihood of Leucaena leucocephala(Lam.)de Wit invasion in Saudi Arabia under present and future climate change scenarios.Utilizing the MaxEnt modeling,we integrated climatic and soil data to predict habitat suitability for the invasive species.We conducted a detailed analysis of the distribution patterns of the species,using climate variables and ecological factors.We focused on the important influence of temperature seasonality,temperature annual range,and precipitation seasonality.The distribution modeling used robust measures of area under the curve(AUC)and receiver-operator characteristic(ROC)curves,to map the invasion extent,which has a high level of accuracy in identifying appropriate habitats.The complex interaction that influenced the invasion of L.leucocephala was highlighted by the environmental parameters using Jackknife test.Presently,the actual geographic area where L.leucocephala was found in Saudi Arabia was considerably smaller than the theoretical maximum range,suggesting that it had the capacity to expand further.The MaxEnt model exhibited excellent prediction accuracy and produced reliable results based on the data from the ROC curve.Precipitation and temperature were the primary factors influencing the potential distribution of L.leucocephala.Currently,an estimated area of 216,342 km^(2)in Saudi Arabia was at a high probability of invasion by L.leucocephala.We investigated the potential for increased invasion hazards in the future due to climate change scenarios(Shared Socioeconomic Pathways(SSPs)245 and 585).The analysis of key climatic variables,including temperature seasonality and annual range,along with soil properties such as clay composition and nitrogen content,unveiled their substantial influence on the distribution dynamic of L.leucocephala.Our findings indicated a significant expansion of high risk zones.High-risk zones for L.leucocephala invasion in the current climate conditions had notable expansions projected under future climate scenarios,particularly evident in southern Makkah,Al Bahah,Madina,and Asir areas.The results,backed by thorough spatial studies,emphasize the need to reduce the possible ecological impacts of climate change on the spread of L.leucocephala.Moreover,the study provides valuable strategic insights for the management of invasion,highlighting the intricate relationship between climate change,habitat appropriateness,and the risks associated with invasive species.Proactive techniques are suggested to avoid and manage the spread of L.leucocephala,considering its high potential for future spread.This study enhances the overall comprehension of the dynamics of invasive species by combining modeling techniques with ecological knowledge.It also provides valuable information for decision-making to implement efficient conservation and management strategies in response to changing environmental conditions.

Factors That Affect Consumption Patterns and Market Demands for Honey in the Kingdom of Saudi Arabia

Despite the significant annual consumption of honey in Saudi Arabia, information gaps remain with regard to the marketing and market structure of honey along the value chain. This study analyzed the major factors that influenced the consumption, expenditure patterns, and demand of honey in Saudi Arabia. This study forecasted the near-future expected market demands for honey in Saudi Arabia by collecting and analyzing the primary data using questionnaires. A total of 331 respondents from representative regions and large cities were randomly selected and interviewed. The data were analyzed using qualitative and quantitative methods as well as appropriate econometric models. Respondents characterized honey quality using organoleptic words, and these characterizations varied based on the relative significance of perception parameters. Taste, aroma, physical state, and color had aggregated average scores of 4.58, 4.44, 3.54, and 3.28, respectively. In addition to the above parameters, honey source, brand name, and confidence in the producers influenced its perceived quality. The major outlets for honey in Saudi Arabia included producers, specialized honey stores, and auction markets in major cities during the harvesting seasons. Medication, food, and sweetening were the major motivations for buying honey in the Saudi market, with aggregate scores of 4.52, 3.71, and 1.52, respectively. Significant honey price variations were observed within and among different honeys and packaging volumes;this finding might be due to factors such as botanical and geographical origins, package volume size economics (i.e., bulk purchases), honey variety blending, brand names, and producer policies. The average price of locally produced honey was approximately $73 per kg, which is 10 times more than the average price of honey in the US and the EU. The estimated consumption/income elasticity was 0.27. These results suggest that honey is a basic commodity in Saudi Arabia. Based on econometric model forecasts, the Saudi market demand for honey is expected to reach approximately 29,784 tons in 2025.

Structure and Performance of the Retail Outlets of Honey in the Kingdom of Saudi Arabia

The study aimed at analyzing the most important factors affecting the retail marketing of honey in Saudi Arabia. Cross sectional survey was employed using field interview (direct contact) with a random sample of 343 retail outlets represented all its forms and patterns in seven major cities in Saudi Arabia. Measurements like market share, Gini coefficient and Herfindahl Hirschman Index (HHI) were used to estimate the indicators of market structure and its performance. The result showed that the structure of honey retail market is generally closer to pure competition with a small concentration in some areas. Gini coefficients of concentration, as well as the values of the Herfindahl Hirschman Index (HHI) were relatively low for retailers. The average marketing margin was about SAR 6.4/Kg for local honey while the marketing margin of imported honey was about SAR 113/Kg. The high profit margin variation between local and imported honey is due to the high marketing costs, lower supplied quantity and higher demand for local honey. The estimated cost of marketing of onekilogram of honey was about 8 SAR at the retail level. From retailers’ point of view, low quality, lack of marketing services, improper display, high rental property, lack of knowledge and experience of the consumer about properties and quality characteristics honey, high prices and shortage of some varieties of honey in some specific season are the most important problems of honey marketing in the Saudi market. Setting priorities between different brands of honey plays a noticeable role in marketing. Some honey producers and traders were very concerned about unfair competition of honey market through high promotion as a brand based on their long history in the production and supply of high-quality varieties of honey from specific and well-known varieties. Hence, intensive awareness creation effort through training, exhibition, media and advertisement are required to improve the perception of consumers towards the local honey.

Physicians’ Instructions to Patients Regarding Medications in a Saudi University Hospital

Objectives: To estimate the proportion of patients who received instructions regarding their medications’ functions, methods of administration, dosages, adverse effects, drug-drug interactions, as well as to identify the sources of knowledge concerning medications’ instructions. Methods: A cross-sectional study was carried out in King Khalid University Hospital (KKUH) in the out-patient pharmacy in 2013. The data collection method includes personal interview with patients who are randomly selected from adults above 18 years of age. The interview was conducted among patients and any person who attends the consultation. Results: The sample was 274 patients. Patients who received instructions for drugs’ functions 208 (75.9%), method of administration 229 (83.6%), doses of drugs 220 (80.3%), drugs’ adverse effects 47 (17.1%), and drug-drug interactions 41 (15%). Sources of medications’ instructions were physicians (73.6%), pharmacists (42.3%), patient information leaflets (PILs) (40.5%) and family or friends (12.8%). Conclusion: The provided instructions about prescribed medications to patients in KKUH were incomplete that may lead to therapeutic failure.

Tuberculosis: Awareness among Students in a Saudi University

Background: Knowledge plays a vital role in influencing the behavior and practices of individuals. Tuberculosis (TB) is a major public health problem. Our objective is to identify the extent of awareness about TB among King Saud University students, and to compare knowledge about tuberculosis amongst different University tracks. Methods: This study was conducted using a cross-sectional approach including 530 students in three different academic tracks: Health, Scientific, and Humanitarian tracks. For data collection, a structured questionnaire was developed through revision of the literature which contained three different parts;demographic data of subjects, knowledge about TB and attitude toward TB. Results: The established scoring system revealed a poor grade of knowledge at 51.4%. Tuberculosis knowledge was significantly higher amongst the track of Health colleges (46.7% compared with 27.2% for the track of Science and 26% for the track of Humanitarian studies). However, some of the issues were answered fittingly in higher magnitude by the two other non-health tracks;there was no significant difference in gender-specific awareness level (48% for both). Conclusion: Although the Health track has better knowledge in general (46.7%), the level of awareness of Tuberculosis is poor among King Saud University students. Moreover, the level of awareness differs among the three tracks, which are health, humanitarian, and science. The health track showed the highest level of awareness.

Optimal Groundwater Irrigation Allocation of Al-Wajid Aquifer in the Kingdom of Saudi Arabia

Groundwater is the primary water source in the Kingdom of Saudi Arabia. As result of lack of basic knowledge on irrigation practices, massive abstractions of groundwater occurred in 1980＇s. A Decision Support Linear Goal Programming （LGP） model was developed to determine optimal groundwater irrigation levels, to assess the implications for water management policies, and to estimate welfare impact on producer surplus. Due to the reductions of groundwater in 1980＇s, the Al-Wajid aquifer water levels have dropped in agricultural areas by more than 200 m. Results from this study estimate that the total groundwater of the Al-Wajid aquifer that can be saved is equal to 66 MCM for the first scenario, 147 MCM for the second scenario, and 229 MCM for the third scenario. Regarding the welfare analysis impacts, it is clear that the total gross margin is decreasing up to 7.7% at the end of the year of scenario Ⅲ. Therefore, the third scenario with a water saving increase to 18.1% is recommended as a directive for agricultural policy formation in the future.

The Impact of a Clinical Pharmacist in the Emergency Department of an Academic Hospital in the Kingdom of Saudi Arabia

Introduction： The department of emergency medicine （DEM） has a high-risk environment due to its unique and complex workflow. Many high-risk medications are ordered and administered at patients’ bedsides without being checked by a pharmacist first, which may lead to an increase in the incidence of patient medication errors （MEs）. Objective： The current study evaluated the needs of the clinical pharmacy service in the DEM at King Saud University Medical City （KSUMC）, Riyadh, Saudi Arabia. Methods： A cross-sectional retrospective study was conducted between Jan 2016 to Dec 2017 and the documentation of clinical pharmacist interventions was extracted from Esihi database. Results： A total of 2,255 interventions for 862 patients were documented. The recommended interventions were as follows： 645 （dose adjustments）, 108 （therapeutic substitutions）, and 354 interventions （initiating drug therapy）. Adverse drug reactions （ADRs） were reported in 16 patients, and drug interactions were managed in 26 patients. The DEM responded to 713 information inquires and 290 pharmacokinetic consultations. Drug discontinuations included 39 incidents （where unjustified drug prescription occurred）, 37 （where contraindications were involved）, and 19 （where duplicate therapy was involved）. The most common interventions were related to the following drugs： antibiotics （34%）, anticoagulants （15%）, and anticonvulsants （10%）. The acceptance rates for the EM clinical pharmacist recommendations increased from 93.9% in 2016 to 99% in 2017. The most common outcome for interventions was to optimize the therapeutic effects of the drugs that were administered （73%）. Reconciliation was done in 796 patients. Conclusions： The clinical pharmacy service plays a critical role in the management of patients in the emergency department （ED）.

Facies development and sedimentology of the Middle Miocene carbonates of the Raghama Formation, northeastern Saudi Arabia

Ragahama Formation comprises a siliciclastic continental deposits followed by marine carbonates, representing prograding alluvial fans from adjacent high hinterlands seaward into lagoons and fringing reef environments. The present work aimed to document the facies development and sedimentology of the Raghama carbonates exposed along the eastern coastal plain of the Red Sea, northwestern Saudi Arabia. Four stratigraphic sections were measured and sampled(D1–D4) and thin sections and major and trace element analyses were prepared and applied for petrographic and geochemical approaches. The carbonates were subdivided into three successive fore-reef, reef-core, and back-reef depositional facies. Sandy stromatolitic boundstone, microbial laminites, dolomitic ooidal grainstone, bioclastic coralline algal wackestone, sandy bioclastic wackestone, and coral boundstones were the reported microfacies types. Petrographic analysis reveals that the studied carbonates were affected by dissolution, dolomitization, and aggrading recrystallization, which affects both the original micrite matrix and grains or acts as fracture and veinlet filling leading to widespread vuggy and moldic porosity. No evidence of physical compaction, suggesting rapid lithification and recrystallization during early diagenesis and prior to substantial burial and intensive flushing by meteoric waters. Most of the original microstructure of corals were leached and destructed. This is indicated by the higher depletion in Sr and Ca levels and increase in Mg,Na, Fe, and Mn levels, especially in section D1, in comparison with the worldwide carbonates.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Transforming Healthcare:AI-NLP Fusion Framework for Precision Decision-Making and Personalized Care Optimization in the Era of IoMT

In the rapidly evolving landscape of healthcare,the integration of Artificial Intelligence(AI)and Natural Language Processing(NLP)holds immense promise for revolutionizing data analytics and decision-making processes.Current techniques for personalized medicine,disease diagnosis,treatment recommendations,and resource optimization in the Internet of Medical Things(IoMT)vary widely,including methods such as rule-based systems,machine learning algorithms,and data-driven approaches.However,many of these techniques face limitations in accuracy,scalability,and adaptability to complex clinical scenarios.This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the IoMT.Through the integration of advanced data analytics methodologies with NLP capabilities,we propose a comprehensive framework designed to enhance personalized medicine,streamline disease diagnosis,provide treatment recommendations,and optimize resource allocation.Using a systematic methodology data was collected from open data repositories,then preprocessed using data cleaning,missing value imputation,feature engineering,and data normalization and scaling.Optimization algorithms,such as Gradient Descent,Adam Optimization,and Stochastic Gradient Descent,were employed in the framework to enhance model performance.These were integrated with NLP processes,including Text Preprocessing,Tokenization,and Sentiment Analysis to facilitate comprehensive analysis of the data to provide actionable insights from the vast streams of data generated by IoMT devices.Lastly,through a synthesis of existing research and real-world case studies,we demonstrated the impact of AI-NLP fusion on healthcare outcomes and operational efficiency.The simulation produced compelling results,achieving an average diagnostic accuracy of 93.5%for the given scenarios,and excelled even further in instances involving rare diseases,achieving an accuracy rate of 98%.With regard to patient-specific treatment plans it generated them with an average precision of 96.7%.Improvements in early risk stratification and enhanced documentation were also noted.Furthermore,the study addresses ethical considerations and challenges associated with deploying AI and NLP in healthcare decision-making processes,offering insights into risk-mitigating strategies.This research contributes to advancing the understanding of AI-driven optimization algorithms in healthcare data analytics,with implications for healthcare practitioners,researchers,and policymakers.By leveraging AI and NLP technologies in IoMT environments,this study paves the way for innovative strategies to enhance patient care and operational effectiveness.Ultimately,this work underscores the transformative potential of AI-NLP fusion in shaping the future of healthcare.

Structural Analysis for Qazzaz Metamorphic Core Complex,Northwestern Arabian Shield,Saudi Arabia

Identifying deformational mechanisms and associated structures at various scales,ranging from regional-scale structures to microscopic fabric,is crucial for the assessment of tectonic development.Thirty-three samples were taken from the Qazzaz metamorphic core complex to estimate the finite strain for felsic and mafic minerals.These samples included gneisses rocks,monzogranite,and metavolcano-sedimentary rocks for both the Thalbah and Bayda groups.Using the Rf/j and Fry methods,the axial ratios(XZ)range about 2.20 to 7.10 and 1.90 to 9.10,respectively.For various rock units,the strain measurements show moderate to highly deformation.Most of the observed samples show shallow WNW dipping along a N to WNW trend of finite strain(X).The short axes(Z)based to be subvertical foliation related with a subhorizontal foliation.The results demonstrate that contacts generated at semi-brittle to ductile deformation and that the strain of magnitude has the same value for different lithologic units.It concluded that nappe generation in orogens results from pure shear deformation.

The Correlation between Psychological Resilience and Professional Quality of Life in Saudi Psychiatric Nurses:A Cross-Sectional Study

In recent years,significant focus has been placed on resilience,with ongoing studies aiming to identify strategies for reducing the negative effects of job stress and protecting nurses from negative psychosocial outcomes.Nevertheless,as far as this topic is concerned,no research has yet been conducted in the context of Saudi psychiatric nurses.The aim of this research,therefore,is to determine how Saudi Arabian psychiatric nurses’professional quality of life and psychological resilience relate to one another.To this aim,a cross-sectional study has been performed in Saudi Arabia incorporating a population of 167(a 66.4%response rate)mental health nurses taken from the largest psychiatric hospital in the capital city Riyadh.Data collection was performed using the popular Arabic version of the Connor–Davidson Resilience and the Arabic version of the Professional Quality of Life Scales.To assess the variables related to both Professional Quality of Life and psychological resilience levels,statistical analyses such as Pearson correlation,ANOVA,t-tests,and linear regression were utilized.The findings show compassion satisfaction and burnout to have a moderate inverse relationship(r=−0.502),while compassion satisfaction(CS)and resilience have a moderate positive correlation(r=0.594).Compared to their morning-shift counterparts,nurses working night shifts reported higher mean scores for resilience(72.70),CS(40.20),burnout(24.52),and compassion fatigue(CF)(28.11).Participants with less than five years’experience in a psychiatric clinic had the highest mean resilience score(72.96).Finally,a positive relationship between resilience and compassion satisfaction(CS)was revealed using linear regression analysis(β=0.536,t=7.080,p=0.000).The study reveals significant differences in the scores assigned to resilience and work-life quality based on educational qualifications,shift-work type,and experiences.Burnout and compassion satisfaction(CS)are found to be significantly correlated,as are compassion satisfaction(CS)and resilience,and burnout and compassion fatigue(CF).

CHART:Intelligent Crime Hotspot Detection and Real-Time Tracking Using Machine Learning

Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the data.While some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal dynamics.This research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these challenges.The proposed methodology begins with comprehensive data collection from the police database.The dataset includes detailed attributes such as crime type,location,time and demographic information.The key steps in the proposed framework include:Data Preprocessing,Feature Engineering that leveraging domain-specific knowledge to extract and transform relevant features.Heat Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various areas.The Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),respectively.By designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime analysis.Visualization of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhancing overall crime prevention and response efforts.

Gut microbiota predicts the diagnosis of ulcerative colitis in Saudi children

BACKGROUND Ulcerative colitis(UC)is an immune-mediated chronic inflammatory condition with a worldwide distribution.Although the etiology of this disease is still unknown,the understanding of the role of the microbiota is becoming increasingly strong.AIM To investigate the predictive power of the gut microbiota for the diagnosis of UC in a cohort of newly diagnosed treatment-naïve Saudi children with UC.METHODS The study population included 20 children with a confirmed diagnosis of UC and 20 healthy controls.Microbial DNA was extracted and sequenced,and shotgun metagenomic analysis was performed for bacteria and bacteriophages.Biostatistics and bioinformatics demonstrated significant dysbiosis in the form of reduced alpha diversity,beta diversity,and significant difference of abundance of taxa between children with UC and control groups.The receiver operating characteristic curve,a probability curve,was used to determine the difference between the UC and control groups.The area under the curve(AUC)represents the degree of separability between the UC group and the control group.The AUC was calculated for all identified bacterial species and for bacterial species identified by the random forest classification algorithm as important potential biomarkers of UC.A similar method of AUC calculation for all bacteriophages and important species was used.RESULTS The median age and range were 14(0.5-21)and 12.9(6.8-16.3)years for children with UC and controls,respectively,and 40%and 35%were male for children with UC and controls,respectively.The AUC for all identified bacterial species was 89.5%.However,when using the bacterial species identified as important by random forest classification algorithm analysis, the accuracy increased to 97.6%. Similarly, the AUC for all theidentified bacteriophages was 87.4%, but this value increased to 94.5% when the important bacteriophagebiomarkers were used.CONCLUSIONThe very high to excellent AUCs of fecal bacterial and viral species suggest the potential use of noninvasivemicrobiota-based tests for the diagnosis of unusual cases of UC in children. In addition, the identification ofimportant bacteria and bacteriophages whose abundance is reduced in children with UC suggests the potential ofpreventive and adjuvant microbial therapy for UC.

Summary of the current guidelines for managing iatrogenic colorectal perforations and the evolving role of endoluminal vacuum therapy

Colonoscopy represents a safe procedure that is widely used in medical practice either to diagnose or treat various gastrointestinal diseases.During the last few years,the incidence rate of perforations in colonoscopic procedures has increased,especially in therapeutic colonoscopies.The recent advancements in endoscopic techniques and gastrointestinal tumoral resection procedures such as endoscopic mucosal resection,endoscopic full-thickness resection,and endoscopic submucosal dissection(ESD)could be a risk factor for this increased risk.The incidence rate of mortality of serious colonoscopic perforations is 7.1%.The management plan for these perforations starts with conservative treatment in mild cases,endoscopic closure,and surgical management in severe cases.Recently,endoluminal vacuum therapy was found to be effective in the management of colorectal perforations and this has been reported in multiple case reports.This editorial provides an overview of the current guidelines for the management of iatrogenic colorectal perforations.These insights are from the perspectives of endoscopists and gastroenterologists.We also present a management algorithm based on the guidelines of the European Society of Gastrointestinal Endoscopy,the American Gastroenterological Association,and the World Society of Emergency Surgery.We also discussed in brief the use of endoluminal vacuum therapy in colorectal perforations.

A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

Global Piecewise Analysis of HIV Model with Bi-Infectious Categories under Ordinary Derivative and Non-Singular Operator with Neural Network Approach

This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.

Impact of Pollutant Concentration and Particle Deposition on the Radiative Flow of Casson-Micropolar Fluid between Parallel Plates

Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing pollution scales is made easier by analysing waste discharge concentrations.The waste discharge concentration analysis is useful for assessing how effectively wastewater treatment techniques reduce pollution levels.This study aims to explore the Casson micropolar fluid flow through two parallel plates with the influence of pollutant concentration and thermophoretic particle deposition.To explore the mass and heat transport features,thermophoretic particle deposition and thermal radiation are considered.The governing equations are transformed into ordinary differential equations with the help of suitable similarity transformations.The Runge-Kutta-Fehlberg’s fourthfifth order technique and shooting procedure are used to solve the reduced set of equations and boundary conditions.The integration of a neural network model based on the Levenberg-Marquardt algorithm serves to improve the accuracy of predictions and optimize the analysis of parameters.Graphical outcomes are displayed to analyze the characteristics of the relevant dimensionless parameters in the current problem.Results reveal that concentration upsurges as the micropolar parameter increases.The concentration reduces with an upsurge in the thermophoretic parameter.An upsurge in the external pollutant source variation and the local pollutant external source parameters enhances mass transport.The surface drag force declines for improved values of porosity and micropolar parameters.

Decoding molecular mechanisms:brain aging and Alzheimer's disease

The complex morphological,anatomical,physiological,and chemical mechanisms within the aging brain have been the hot topic of research for centuries.The aging process alters the brain structure that affects functions and cognitions,but the worsening of such processes contributes to the pathogenesis of neurodegenerative disorders,such as Alzheimer's disease.Beyond these observable,mild morphological shifts,significant functional modifications in neurotransmission and neuronal activity critically influence the aging brain.Understanding these changes is important for maintaining cognitive health,especially given the increasing prevalence of age-related conditions that affect cognition.This review aims to explore the age-induced changes in brain plasticity and molecular processes,differentiating normal aging from the pathogenesis of Alzheimer's disease,thereby providing insights into predicting the risk of dementia,particularly Alzheimer's disease.