Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor’s perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and decision making for CVD patients. Simple random sampling was used to select heart disease patients from the Khyber Teaching Hospital and Lady Reading Hospital, Pakistan. ML methods such as decision tree (DT), random forest (RF), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) were implemented for classification and prediction purposes for CVD patients in Pakistan. We performed exploratory analysis and experimental output analysis for all algorithms. We also estimated the confusion matrix and recursive operating characteristic curve for all algorithms. The performance of the proposed ML algorithm was estimated using numerous conditions to recognize the best suitable machine learning algorithm in the class of models. The RF algorithm had the highest accuracy of prediction, sensitivity, and recursive operative characteristic curve of 85.01%, 92.11%, and 87.73%, respectively, for CVD. It also had the least specificity and misclassification errors of 43.48% and 8.70%, respectively, for CVD. These results indicated that the RF algorithm is the most appropriate algorithm for CVD classification and prediction. Our proposed model can be implemented in all settings worldwide in the health sector for disease classification and prediction.

Background: Delivering plant extract at high loading with intact antioxidants and efficient skin permeation always remains a challenge. To address this, we prepared a stable gel formulation containing nanoethosomes loaded with Achillea millefolium L. (AM) extract for topical drug delivery.Method: The AM extract was tested at first for phytochemical analysis, antioxidant activity, total phenolic and flavonoid content, and FTIR examination. The nanoethosomes containing AM extract were synthesized and characterized by size, surface charge, and morphology, and entrapment efficiency (EE) was determined. The optimized nanoethosomes were then incorporated to develop a topical gel formulation and subjected to skin for permeation, pH, viscosity, and organoleptic evaluation for up to three months.Results: The AM ethanolic extract demonstrated 88% free radical scavenging activity and notable phenolic and flavonoid contents of up to 123 mg GAE/g and 42 mg QE/g, respectively. The optimized nanoethosomes encapsulated with AM extract (240 nm) were spherical in shape, with −31.1 mV of surface charge, and showed considerable entrapment efficiency (90%). Furthermore, the selected topical gel remained stable during the study period. The Exvivo permeation study of ethosomal gel showed the highest release percentage of 79.8%.Conclusion: The study concludes that topical gel loaded with nanoethosomes containing AM extract is an encouraging approach for topical drug delivery.

(1) Background: The study aims to examine the use of cell phones on physical and mental health status and their impact on personality among university students. (2) Methods: A cross-sectional study was conducted using a semi-structured questionnaire. The association of physical and mental health variables with the demographic variables was examined using Pearson’s correlation and χ²-test. The binary logistic regression model was further used to predict the probabilities of negative impact on personality due to excessive use of cell phones. (3) Results: A total of 400 participants participated with a mean age of 24.45 ± 3.45 years. The average eye strain was more in High cell phone users HCPU than in LCPU and that difference was significant p = 0.000. The average neck pain was more in (HCPU) than Low cell phone users (LCPU) and there was a significant difference between the two groups with p = 0.006. The average weight gain was more in HCPU than LCPU and that difference was significant p = 0.000. Considering back pain, back pain was found more in HCPU as compared to LCPU with a statistical difference at p = 0.027. Cell phone usage significantly correlated with eye strain (r = 0.577, p = 0.000), neck pain (r = 0.543, p = 0.000), back pain (r = 0.611, p = 0.000), weight gain (r = 0.423, p = 0.000), depression (r = 0.430, p = 0.000), loneliness (r = −0.276, p = 0.002), and mood disorder (r = 0.608, p = 0.000). Eye strain, neck pain, and back pain was observed more in HCPU than in LCPU. HCPU felt they gained more weight when compared to the respondents in the LCPU group. HCPU felt more changes in mood and feeling low when compared to the LCPU, while LCPU group felt more lonelier when compared to the HCPU group. (4) Conclusions: The study highlights a significant association between excess use of cell phones and negative effects on physical and mental health wellbeing. Based on the results, it is recommended that more physical activities and alternative to minimize cell phone usage should be planned for the students. Public health policy makers and stakeholder need to address the ill effects of excessive use of cell phones through novel policies., especially young students, and alternatives to reduce their cell phone activities.

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.

Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and its incidence and prevalence are increasing in many countries. Modeling of CVD plays a crucial role in understanding the trend of CVD death cases, evaluating the effectiveness of interventions, and predicting future disease trends. This study aims to investigate the modeling and forecasting of CVD mortality, specifically in the Sindh province of Pakistan. The civil hospital in the Nawabshah area of Sindh province, Pakistan, provided the data set used in this study. It is a time series dataset with actual cardiovascular disease (CVD) mortality cases from 1999 to 2021 included. This study analyzes and forecasts the CVD deaths in the Sindh province of Pakistan using classical time series models, including Naïve, Holt-Winters, and Simple Exponential Smoothing (SES), which have been adopted and compared with a machine learning approach called the Artificial Neural Network Auto-Regressive (ANNAR) model. The performance of both the classical time series models and the ANNAR model has been evaluated using key performance indicators such as Root Mean Square Deviation Error, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). After comparing the results, it was found that the ANNAR model outperformed all the selected models, demonstrating its effectiveness in predicting CVD mortality and quantifying future disease burden in the Sindh province of Pakistan. The study concludes that the ANNAR model is the best-selected model among the competing models for predicting CVD mortality in the Sindh province. This model provides valuable insights into the impact of interventions aimed at reducing CVD and can assist in formulating health policies and allocating economic resources. By accurately forecasting CVD mortality, policymakers can make informed decisions to address this public health issue effectively.

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, [ ] and [ ], 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.