Gender Inequity in Clinical Clerkships and its Influence on Career Selection: A Cross-Sectional Survey.

Objective: The aim of this study was to identify the frequency, form, and underlying factors contributing to gender inequity experienced by medical undergraduates and assess its influence on their career choices. Method: This was a cross-sectional, retrospective survey with a 100% response rate. This survey was distributed among medical students of clinical years in Karachi's private and government medical colleges from September 10th, 2021-March 30th, 2022. 430 participants were enrolled using a simple-random-sampling-technique. Chi-square/Fisher's Exact tests are employed to assess the relationships between gender and gender-based inequity in various specialties, including their characteristics, influence on career choices, adverse psychological effects, and potential mitigation strategies. Results: Among 430 respondents, 28.6% were male, and 71.4% were female. 89.1% reported gender inequity, evenly distributed in government (80.4%) and private institutions (88.1%). The general surgery and gynecology disciplines stood out, each with a 56% prevalence. In gynecology and surgery clinical-clerkships, both genders experienced similar rates, with females at 54.5% and 42.3%, and males at 56.7% and 61.6%, respectively (P-value = .000*). Disrespect from staff/professors/patients (48.8%) was the most common manifestation, driven by factors like preferences (73.7%), gender superiority (62.6%), societal attitudes (54%), and cultural norms (50.9%). Furthermore, 82.6% of students reported that gender inequity had a negative impact on their career decision (Male = 82.9%;Female = 82.4%, P-value = .899). Additionally, gender inequity also caused demotivation (78.1%), poor self-esteem (67.2%), helplessness/hopelessness (48.6%), and frustration (45.8%). Conclusions: Gender inequity is widely prevalent in the clinical-clerkships, affecting medical students' career decisions and mental health, stressing the need to prioritize and implement solutions at the undergraduate clinical-clerkship level.

Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks.

BACKGROUND OF STUDY: Over the past few decades, the utilization of Artificial Intelligence (AI) has surged in popularity, and its application in the medical field is witnessing a global increase. Nevertheless, the implementation of AI-based healthcare solutions has been slow in developing nations like Pakistan. This unique study aims to assess the opinion of clinical specialists on the future replacement of AI, its associated benefits, and its drawbacks in form southern region of Pakistan. MATERIAL AND METHODS: A cross-sectional selective study was conducted from 140 clinical specialists (Surgery = 24, Pathology = 31, Radiology = 35, Gynecology = 35, Pediatric = 17) from the neglected southern Punjab region of Pakistan. The study was analyzed using χ2 - the test of association and the nexus between different factors was examined by multinomial logistic regression. RESULTS: Out of 140 respondents, 34 (24.3%) believed hospitals were ready for AI, while 81 (57.9%) disagreed. Additionally, 42(30.0%) were concerned about privacy violations, and 70(50%) feared AI could lead to unemployment. Specialists with less than 6 years of experience are more likely to embrace AI (p = 0.0327, OR = 3.184, 95% C.I; 0.262, 3.556) and those who firmly believe that AI knowledge will not replace their future tasks exhibit a lower likelihood of accepting AI (p = 0.015, OR = 0.235, 95% C.I: (0.073, 0.758). Clinical specialists who perceive AI as a technology that encompasses both drawbacks and benefits demonstrated a higher likelihood of accepting its adoption (p = 0.084, OR = 2.969, 95% C.I; 0.865, 5.187). CONCLUSION: Clinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment.

Checkpoint Inhibitor-Related Capillary Leak Syndrome (CLS).

Immune checkpoint inhibitors (ICIs) are now being widely used for the treatment of various malignancies, but they have a distinctive set of side effects due to the overactivation of the immune system, which is important to recognize. Capillary leak syndrome (CLS) is a rare but potentially life-threatening side effect of ICIs that causes a significant increase in the permeability of capillaries, leading to the leakage of plasma-containing proteins from these small vessels. This condition results in several clinical features, including edema, hypotension, hypoalbuminemia, and hemoconcentration. Timely recognition and discontinuation of the offending immunotherapy can optimize outcomes. Treatment is focused on supportive care and prompt initiation of immunosuppressants, such as steroids.

Modelling the GDP of KSA using linear and non-linear NNAR and hybrid stochastic time series models.

BACKGROUND: Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series approach for modeling and forecasting the GDP annual growth rate (%) of Saudi Arabia, a key financial indicator of the country. METHODOLOGY: Stochastic linear and non-linear time series modeling, along with hybrid approaches, are employed and their results are compared. Initially, conventional linear and nonlinear methods such as ARIMA, Exponential smoothing, TBATS, and NNAR are applied. Subsequently, hybrid models combining these individual time series approaches are utilized. Model diagnostics, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), are employed as criteria for model selection to identify the best-performing model. RESULTS: The findings demonstrated that the neural network autoregressive (NNAR) model, as a non-linear approach, outperformed all other models, exhibiting the lowest values of MAE, RMSE and MAPE. The NNAR(5,3) projected the GDP of 1.3% which is close to the projection of IMF benchmark (1.9) for the year 2023. CONCLUSION: The selected model can be employed by economists and policymakers to formulate appropriate policies and plans. This quantitative study provides policymakers with a basis for monitoring fluctuations in GDP growth from 2022 to 2029 and ensuring the sustained progression of GDP beyond 2029. Additionally, this study serves as a guide for researchers to test these approaches in different economic dynamics.

A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player's performance.

BACKGROUND: Prediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player's future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches. METHODOLOGY: To predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series). RESULTS: The study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts. CONCLUSION: In conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance.

Incidence and characteristics of ligamentous knee injuries accompanying a femur shaft fracture and their association with injury mechanism and fracture characteristics: A prospective-observational study in a low-middle-income country.

Objective: This study aims to elaborate the incidence, types, and characteristics of ligamentous knee injuries accompanying femoral-shaft fractures and their association with demographic data, fracture characteristics, and injury mechanism. Methods: This multi-center-prospective-observational study examined patients in the Shaheed Mohtarma Benazir Bhutto Institute of Trauma and Civil Hospital orthopedic wards. Using the consecutive sampling technique, 146 patients with femoral-shaft fractures were recruited, and scrutinized to determine the presence of ligamentous knee injury, through an evaluation form encompassing patients' demographic data, fracture characteristics, knee examinations, and confirmed through magnetic resonance imaging. Knee injuries were detected intra-operatively under regional/general anesthesia and post-operatively utilizing diagnostic maneuvers (varus/valgus stress, Lachman, anterior/posterior drawer, external rotation recurvatum, and McMurray tests) by 2-3 surgeons and confirmed through magnetic resonance imaging. Magnetic resonance imaging was exclusively employed in suspected false positive/negative cases, and when a titanium implant was utilized, that is, 131 cases (89.7%). Chi-square test was used to assess the relationship between incidence and type of ligamentous knee injury with demographic data, injury mechanism, and fracture characteristics. Results: Among the 146 patients with femoral-shaft fractures, 78% and 22% were males and females, with 37% experiencing associated ligamentous knee injury. Medial collateral ligament and anterior cruciate ligament were the commonest types of ligamentous injuries accompanying femoral-shaft fractures, at 44% and 33%. The Chi-square revealed a statistically significant association between the incidence of ligamentous knee injury accompanying femoral-shaft fracture with demographic data, injury mechanism, and fracture characteristics (p-value < 0.05), and was noted to be higher among males (55.6%), participants aged 18-25 years (66.7%), involved in a road traffic accident (88.9%), resulting in a complex (77.8%) and close fracture (88.9%). A similar association was seen between medial collateral ligament and anterior cruciate ligament injuries with age, injury mechanism, and fracture characteristics (p-value < 0.05). Conclusion: Our study revealed the unaddressed fact that Pakistan has a significant incidence of ligamentous knee injuries accompanying femoral-shaft fractures. These insights can empower clinicians/surgeons to understand and manage this condition effectively.

Remimazolam versus propofol for sedation in gastrointestinal endoscopy and colonoscopy within elderly patients: a meta-analysis of randomized controlled trials.

PURPOSE: Propofol has become the sedative of choice for endoscopy and colonoscopy. However, it has shown associations with various adverse effects, specifically in the geriatric population. In contrast, remimazolam is a novel benzodiazepine, demonstrating a superior clinical safety profile. Hence, this systematic review and meta-analysis aims to clarify the efficacy and safety of remimazolam versus propofol in elderly patients (≥ 60 years) undergoing gastrointestinal endoscopic and colonoscopy procedures. METHODS: Electronic databases including PubMed, Cochrane Library, ScienceDirect, and Google Scholar were explored from inception till January 7, 2024. The Cochrane Risk of Bias Tool for Randomized Controlled Trials (RoB-2) was utilized to evaluate the quality of each included study reported in this meta-analysis. RESULTS: Seven randomized control trials were included, resulting in the pooling of 1,466 patients (remimazolam: 731 patients; propofol: 735 patients). Propofol demonstrated a significantly lower time to loss of consciousness (P < 0.00001, 4 studies, 784 patients) and a greater sedation success after first dose (P = 0.05, 5 studies, 1,271 patients). Remimazolam reported a significantly lower risk of bradycardia (P = 0.02, 5 studies, 1,323 patients), hypoxemia (P < 0.00001, 6 studies, 1,389 patients), and pain on injection site (P < 0.00001, 5 studies, 1,184 patients). No statistically significant differences in sedation time, number of supplemental doses, procedural parameters, and other adverse outcomes were reported. CONCLUSION: As per the results of our analyses, propofol demonstrated comparatively superior efficacy, however, remimazolam demonstrated comparatively superior safety. The debatable evidence generated from this meta-analysis may not currently be powerful enough to advocate for the use of remimazolam in elderly patients undergoing gastrointestinal procedures; hence, further comprehensive studies are necessary in order to arrive at a robust conclusion.

A longitudinal prospective study of septoplasty impact on headache and allergic rhinitis in patients with septal deviation.

OBJECTIVE: To measure the severity of allergic rhinitis (AR) and different types of headaches in patients with septal deviation before and after septoplasty. METHODS: This multicentre, prospective, longitudinal, observational study enrolled patients with deviated nasal septum, nasal symptoms and headaches associated with persistent AR lasting at least 2 months without resolution. The nasal obstruction evaluation (NOSE) scale, immunoglobulin-E (Ig-E) levels and visual analogue scale (VAS) for headache pain severity were evaluated before and after septoplasty using Wilcoxon signed-rank test. RESULTS: A total of 196 patients were enrolled in the study (102 males; 94 females). A total of 134 patients (68%) were diagnosed with severe AR and 166 (85%) experienced headaches with AR. The majority (100 of 166 patients; 60%) had sinusoidal headaches, while 25% (42 of 166 patients) reported a combination of sinusoidal headache and migraine and 14% (24 of 166 patients) experienced migraines. A comparison of preoperative and postoperative Ig-E levels, NOSE and VAS scores demonstrated that septoplasty significantly improved AR symptoms and headaches. Although there were significant improvements in headaches overall post-septoplasty, only the sinusoidal components improved, while migraine remained unaffected. CONCLUSION: Septoplasty improved AR and sinusoidal headaches in patients with septal deviation, but migraines remained unaffected.

Impact of trans-anal versus laparoscopic total mesorectal excision on the surgical and pathologic outcomes of patients with rectal cancer: meta-analysis of randomized controlled trials.

BACKGROUND: Despite its profound impact on the oncologic outcomes of rectal cancer, the most optimal surgical approach to total mesorectal excision (TME) has not been identified yet. All previous meta-analyses on this subject have been based on observational studies. This meta-analysis was conducted to assess the surgical and oncologic outcomes of laparoscopic TME (LaTME) compared to trans-anal TME (TaTME), utilizing only randomized controlled trials. DESIGN: Systematic review and meta-analysis of randomized controlled trials. METHODS: We searched electronic databases (MEDLINE, Cochrane CENTRAL, Clinicaltials.gov) from 2010 onwards, for all published clinical trials comparing TaTME to LaTME. Results are presented as risk ratios, with 95% CI, and pooled using the random effects model. RESULTS: A total of 1691 patients, from 6 eligible randomized controlled trials, were included for analysis. Analyzed data showed no significant difference in morbidity (RR: 0.85, p = 0.15), mortality (RR: 0.50, p = 0.44), conversion to open (RR: 0.40, p = 0.07), or anastomotic leakage (RR: 0.73, p = 0.10) between TaTME and LaTME. There was also no difference in the rate of positive distal resection margin (DRM) (RR: 0.55, p = 0.10) or positive circumferential resection margin (CRM) (RR: 0.67, p = 0.30). Patients undergoing TaTME were more likely to have a complete TME (RR: 1.06, p = 0.002) and shorter hospital stays (RR: - 0.97, p < 0.00001). CONCLUSIONS: Patients undergoing TaTME for rectal cancer were more likely to have a complete TME when compared to LaTME, though this did not translate into improved distal or circumferential resection margin. Additionally, TaTME and LaTME had similar surgical outcomes except for shorter length of stay with TaTME.

A hybrid forecasting technique for infection and death from the mpox virus.

Objectives: The rising of new cases and death counts from the mpox virus (MPV) is alarming. In order to mitigate the impact of the MPV it is essential to have information of the virus's future position using more precise time series and stochastic models. In this present study, a hybrid forecasting system has been developed for new cases and death counts for MPV infection using the world daily cumulative confirmed and death series. Methods: The original cumulative series was decomposed into new two subseries, such as a trend component and a stochastic series using the Hodrick-Prescott filter. To assess the efficacy of the proposed models, a comparative analysis with several widely recognized benchmark models, including auto-regressive (AR) model, auto-regressive moving average (ARMA) model, non-parametric auto-regressive (NPAR) model and artificial neural network (ANN), was performed. Results: The introduction of two novel hybrid models, HPF11 and HPF34, which demonstrated superior performance compared to all other models, as evidenced by their remarkable results in key performance indicators such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), is a significant advancement in disease prediction. Conclusion: The new models developed can be implemented in forecasting other diseases in the future. To address the current situation effectively, governments and stakeholders must implement significant changes to ensure strict adherence to standard operating procedures (SOPs) by the public. Given the anticipated continuation of increasing trends in the coming days, these measures are essential for mitigating the impact of the outbreak.

Ovarian Carcinosarcoma and Response to Immunotherapy.

Ovarian carcinosarcoma (OCS) is an uncommon and highly aggressive subtype of ovarian cancer. This form of cancer is characterized by limited treatment options and a poor prognosis. In this report, we present a case study of a 64-year-old female diagnosed with stage III OCS, who underwent debulking surgery and adjuvant chemotherapy, followed by immunotherapy, with encouraging outcomes. Despite the availability of diverse chemotherapy options, the prognosis for patients with OCS remains grim. However, the present case study of a 64-year-old female with OCS illustrates the promising outcomes achieved with immunotherapy. Additionally, this case highlights the significance of microsatellite instability testing in guiding treatment decisions for ovarian cancers of this nature.

An autoregressive distributed lag approach for estimating the nexus between CO2 emissions and economic determinants in Pakistan.

Carbon dioxide (CO2) emissions have become a critical aspect of the economic and sustainable development indicators of every country. In Pakistan, where there is a substantial increase in the population, industrialization, and demand for electricity production from different resources, the fear of an increase in CO2 emissions cannot be ignored. This study explores the link that betwixt CO2 emissions with different significant economic indicators in Pakistan from 1960 to 2018 using the autoregressive distributed lag (ARDL) modelling technique. We implemented the covariance proportion, coefficient of determination, the Durbin Watson D statistics, analysis of variance (ANOVA), variance inflating factor (VIF), the Breusch-Pagan test, the Theil's inequality, the root mean quare error (RMSE), the mean absolute percentage error (MAPE), and the mean absolute error (MAE) for the diagnostics, efficiency, and validity of our model. Our results showed a significant association between increased CO2 emissions and increased electricity production from oil, gas, and other sources. An increase in electricity production from coal resources was seen to have resulted in a decrease in CO2 emissions. We observed that an increase in the gross domestic product (GDP) and population growth significantly contributed to the increased CO2 emissions. The increment in CO2 emissions resulting from industrial growth was not significant. The increment in CO2 emissions in the contemporary year is significantly associated with the preceding year's increase. The rate of increase was very alarming, a sign that no serious efforts have been channelled in this regard to reduce this phenomenon. We call for policy dialogue to devise energy-saving and CO2 emission reduction strategies to minimize the impact of climate change on industrialization, population growth, and GDP growth without deterring economic and human growth. Electricity production from different sources with no or minimal CO2 emissions should be adopted. We also recommend rigorous tree planting nationwide to help reduce the concentration of CO2 in the atmosphere as well as environmental pollution.

The Virtual Face of Colon and Rectal Surgery Training in the USA: An In-Depth Evaluation and Analysis of Fellowship Programs Website Content.

BACKGROUND: With an ever-evolving digital and virtual world hastened by the COVID-19 pandemic, prospective colon and rectal surgery fellowship applicants must rely on online sources of information, such as websites, rather than in-person visits to fellowship programs. This study analyzes and evaluates the content and accuracy of colon and rectal surgery fellowship program websites. METHODS: The Fellowship and Residency Electronic and Interactive Database website provides a complete collection of colon and rectal surgery fellowship websites based in the USA. The accessibility of the websites was verified via Google search, and relevant content for the applicants was evaluated based on 50-point criteria. RESULTS: Analysis of 60 fellowship program websites was conducted, out of which only a fifth (20%) were kept up to date. Twenty-seven (45%) websites fulfilled 50% of the 50-point criteria. The most and least included data points were program overview (69%) and residential/housing information (24%). Most websites contained basic information relevant to international applicants but lacked crucial information such as visa sponsorship (12%) and city information (23%). CONCLUSION: An informative and easily accessible website is essential for prospective applicants to choose the best program for their career goals and academic needs. This study highlighted multiple areas for potential improvement in the colon and rectal surgery program websites. Individual colon and rectal surgery programs may benefit and attract more candidates to their programs through a fully optimized website design and content.

Pakistan CO2 Emission Modelling and Forecasting: A Linear and Nonlinear Time Series Approach.

Pakistan is considered among the top five countries with the highest CO2 emissions globally. This calls for pragmatic policy implementation by all stakeholders to bring finality to this alarming situation since it contributes greatly to global warming, thereby leading to climate change. This study is an attempt to make a comparative analysis of the linear time series models with nonlinear time series models to study CO2 emission data in Pakistan. These linear and nonlinear time series models were used to model and forecast future values of CO2 emissions for a short period. To assess and select the best model among these linear and nonlinear time series models, we used the root mean square error (RMSE) and the mean absolute error (MAE) as performance indicators. The outputs showed that the nonlinear machine learning models are the best among all other models, having the lowest RMSE and MAE values. Based on the forecasted value of the nonlinear machine learning neural network autoregressive model, Pakistan's CO2 emissions will be 1.048 metric tons per capita by 2028. The increasing trend in emissions is a frightening and clear warning, suggesting that innovative policies must be initiated to reduce the trend. We encourage the Pakistan government to price CO2 emissions by companies and entities per ton, adapt electricity production from hydro, wind, and different sources with no emissions of CO2, initiate rigorous planting of more trees in the populated areas of Pakistan as forest covers, provide incentives to companies, organisations, institutions, and households to come out with clean technologies or use technologies with no CO2 emissions or those with lower ones, and fund more studies to develop clean and innovative technologies with less or no CO2 emissions.

Zootherapy as traditional therapeutic strategy in the Cholistan desert of Bahawalpur-Pakistan.

The use of traditional medicines has tremendously increased over the past few decades. Approximately 80% of the world's population relies on traditional medicines for their primary healthcare needs because of their cost effectiveness and efficiency with no or minimal side effects. Zootherapy refers to the use of medicines that are prepared or derived from animals or from their products. The current study documented the folk knowledge related to the practice of various animal-derived products and ethnozoological based drugs used as medicines by the residents of the Cholistan desert of Bahawalpur (Pakistan). In this regard 46 knowledgeable and reliable elderly people, hakims and spiritual healers ranging from 35-60 years of age having knowledge related to zootherapy were included in the current study. A field survey from February 2006 to November 2007 was conducted by interviewing the selected respondents through a structured questionnaire. They provided knowledge regarding the use of animals and their derived products in traditional medicine. The zootherapeutic knowledge was based on both domestic animals as well as wild animals. A total of 20 animal species were included in the study, among which nine animals were domestic while 11 were wild animals. Among selected animals, nine were mammals, four birds, four reptiles and three insects. It was reported that camel was the most commonly used (n = 32 respondents) among mammals while Pigeon (n = 39 respondents), Spiny-tailed lizard (n = 41 respondents) and Indian honey bee (n = 27 respondents) among birds, reptiles and insects, respectively, have significant use for the treatment of different diseases. Based on this communication we could recommend that this type of abandoned knowledge should be considered for the management and conservation of faunistic resources. However, the advantageous role of animals and their products was reported but more extensive research is required to explore the bioactive constituents in the raw material of these animals responsible for their beneficial effects.

Analysis of adolescents' perception and awareness level for Sexual and Reproductive Health Rights in Pakistan.

Background: Awareness of rights is a precondition to establishing rule of law in society. Sexual and Reproductive Health and Rights (SRHR) are closely knitted in the human rights framework as they overlap with other human rights such as the right to health and life. However, awareness about these rights remains a challenge. Considering the importance of these rights this study has measured the awareness of adolescents about SRHR in Bahawalpur (Division) of Pakistan. Methodology: We conducted a cross-sectional study in the Divison of Bahawalpur, Pakistan from October 2019 to December 2019. The study included a sample size of 500 respondents which included 250 young females of age 15-19 and 250 their parents (mother). The reasoning behind including only females in this study was the increased vulnerability and greater impact of SRHR in their life compared to males. The age range 15-19 was primarily selected as females this age will better understand and respond to the questionnaire compared to females in their early adolescent years. The quantitative research was conducted using two-stage cluster sampling. Detailed structured questionnaires were distributed among the respondents to obtain their points of view on the awareness of SRHR. The population was divided into multi-clusters with 25 households comprising 250 households for the division of Bahawalpur. The information was also gathered from the doctors and the teachers through interviews. The data were analyzed using SPSS version 21. Results: The study explored the knowledge and understanding of adolescent SRHR thereby highlighting the key restrictions in Bahawalpur, Pakistan, which prohibit adolescents from gaining access to SRHR and exercising it. There is a significant portion of adolescents who strongly agreed with the importance and awareness level and think they should be more aware of information regarding SRHR. However, they are of the view that they are less independent in practicing them. Conclusion: The study found a low level of awareness about SRHR among young female and their parents in Bahawalpur, Pakistan. It is a need of time and responsibility of the local government of the Bahawalpur region to devise clear and proper policies which give access to these rights. This can be done by including the information on these rights in the course curriculum and teachers keeping in view the cultural and regional restrictions that guide the young female about SRHR.

Relationship between gaming disorder across various dimensions among PUBG players: a machine learning-based cross-sectional study.

Background: PlayerUnknown's battlegrounds (PUBG), a widely played multiplayer online game, has sparked interest and concern regarding its impact on players. This study explored the relationship between factors such as cultivation level, motivation, religious engagement, gaming disorder, and addiction among PUBG players. Methods: This study employed a convenience sampling technique to select a sample of 500 PUBG players. An Artificial Neural Network (ANN) model was used to identify the primary factors contributing to the level of cultivation. Results: Male participants exhibited a higher level of cultivation than their female counterparts did. According to the ANN model, gaming disorder exhibited the greatest normalized importance, with a value of 100%. This was followed by the religious level, which had a normalized importance of 54.6%. Additionally, motivation level and gaming addiction demonstrated normalized importance values of 47.6 and 44.4%, respectively. This study revealed a statistically significant correlation between engaging in PUBG and the cultivation effect observed among respondents. Conclusion: This study highlights several noteworthy factors, including gaming disorder, religious affiliation, motivation level, and gaming addiction. These factors offer valuable insights into understanding gaming behavior and devising effective interventions.

Public knowledge, attitudes, and practices towards herbal medicines; a cross-sectional study in Western Saudi Arabia.

BACKGROUND: Herbal medicines, derived from medicinal plants, are among the most popular alternative remedies around the globe. In Saudi Arabia, herbal medicines are extensively used by public as part of the culture as well as religious norms. Therefore, this study aimed to evaluate knowledge, attitudes, and practices regarding herbal medicines among the general population in Saudi Arabia. METHODS: A descriptive cross-sectional survey study was conducted on the general population of Jeddah city with the help of a validated and self-administered questionnaire. Sample size was calculated to be 450 with subjects between 20 and 60 years of either gender. Descriptive and inferential statistical analysis was performed using SPSS. RESULTS: Results of our data showed that 173 (42.2%) of the total participants used herbal medicines, however, significant association was found between female gender and the usage of herbal medicines (p < 0.001). Approximately, more than half (56.0%) of the respondents agreed that herbal medicines could be used to promote health and treat illnesses, and 45% respondents agreed that herbal medicines are safe. 153 (37.4%) of the participants opined that herbal medicines could be taken with conventional or allopathic medicine. The data also showed a significant (p < 0.05) association between knowledge about the source of herbal medicine and gender where females were found to have more knowledge compared to males. Moreover, a significantly higher number of chronic disease patients were using herbal medicines compared to individuals with no medical problems (p < 0.001). Strikingly, majority (n = 204; 49.9%) of the respondents used herbal medicines as a first choice when sick, while a good number (n = 172; 42.1%) of respondents did not consult doctors before taking herbal medicines. CONCLUSION: The use of herbal medicines is common among the general population of Jeddah. Although most of the participants believed that the herbal medicines are safe and do not require consultation, there is a dire need to increase awareness as well as to establish effective strategies to evaluate the safety, efficacy and quality of the herbal medicines for propitious consequences of this commonly used entity in the local society.

Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases.

COVID-19 continues to pose a dangerous global health threat, as cases grow rapidly and deaths increase day by day. This increasing phenomenon does not only affect economic policy but also international policy around the world. In this paper, Pakistan daily death cases of COVID-19, from February 25, 2020, to March 23, 2022, have been modeled using the long-established autoregressive-integrated moving average (ARIMA) model and the machine learning multilayer perceptron (MLP) model. The most befitting model is selected based on the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Values of the key performance indicator (KPI) showed that the MLP model outperformed the ARIMA model. The MLP model with 20 hidden layers, which emerged as the overall most apt model, was used to predict future daily COVID-19 deaths in Pakistan to enable policymakers and health professionals to put in place systematic measures to reduce death cases. We encourage the Government of Pakistan to intensify its vaccination campaign and encourage everyone to get vaccinated.