Explore the vast range of titles available.

Breast cancer remains a worldwide public health dilemma and is currently the most common tumour in the globe. Awareness of breast cancer, public attentiveness, and advancement in breast imaging has made a positive impact on recognition and screening of breast cancer. Breast cancer is life-threatening disease in females and the leading cause of mortality among women population. For the previous two decades, studies related to the breast cancer has guided to astonishing advancement in our understanding of the breast cancer, resulting in further proficient treatments. Amongst all the malignant diseases, breast cancer is considered as one of the leading cause of death in post menopausal women accounting for 23% of all cancer deaths. It is a global issue now, but still it is diagnosed in their advanced stages due to the negligence of women regarding the self inspection and clinical examination of the breast. This review addresses anatomy of the breast, risk factors, epidemiology of breast cancer, pathogenesis of breast cancer, stages of breast cancer, diagnostic investigations and treatment including chemotherapy, surgery, targeted therapies, hormone replacement therapy, radiation therapy, complementary therapies, gene therapy and stem-cell therapy etc for breast cancer.

Flavanols, a common class of secondary plant metabolites, exhibit several beneficial health properties by acting as antioxidant, anticarcinogen, cardioprotective, anti-microbial, anti-viral, and neuroprotective agents. Furthermore, some flavanols are considered functional ingredients in dairy products. Based on their structural features and health-promoting functions, flavanols have gained the attention of pharmacologists and botanists worldwide. This review collects and summarizes 121 flavanols comprising four categories: flavan-3-ols, flavan-4-ols, isoflavan-4-ols, and flavan-3,4-ols. The research of the various structural features and pharmacological activities of flavanols and their derivatives aims to lay the groundwork for subsequent research and expect to provide mentality and inspiration for the research. The current study provides a starting point for further research and development.

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.

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. 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. 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. 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.

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 χ 2 - 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.

Background Air pollution is a serious environmental factor associated with higher rates of illness and death from respiratory, cardiovascular, and other non-communicable diseases. Accurate prediction is vital for assessing health risks, which would guide the public health experts and shape sustainable policies. Aim This study aims to provide a modelling approach by comparing traditional, non-parametric, and machine learning based hybrid models, the first of its kind to forecast PM 2.5 levels across the Gulf Cooperation Council (GCC) nations, to inform data-driven environmental health strategies and public policy development. Method The study utilized the annual PM 2.5 dataset from the World Bank database for GCC countries covering the span of 1991–2021. The traditional models, like ARIMA, Naïve, exponential smoothing, non-parametric model, NPAR, and machine learning based hybrid models, were applied. The model accuracy was evaluated by RMSE, MAE, MAPE, nRMSE, and Diebold–Mariano (DM) test. A Rolling Cross-validation procedure was performed to validate the models. Key results The study showed that Qatar consistently showed the highest PM 2.5 levels at 87.90 ± 5.78 µg/m³, followed by Bahrain (67.42 ± 4.59 µg/m³) and Kuwait (58.00 ± 5.95 µg/m³). The modelling approach concluded that machine learning based hybrid models performed well across all competing models, with NPAR-NNAR showing the lowest RMSE (KSA = 2.0181, Qatar = 2.2852, Bahrain = 1.3145, and Kuwait = 2.299), while NAIVE-NNAR performed best for UAE = 1.0462 and Oman = 1.6522. The forecasting results showed that GCC countries may experience variations in PM2.5 levels, with Qatar and Bahrain facing the highest concentrations among them in the coming decades. Major Implication This is the first multi-country study across the GCC to forecast PM 2.5 , showing a significant step forward for environmental health planning. The higher accuracy of modelling approaches is important for improving early warning capabilities, anticipating pollution trends that directly affect respiratory and cardiovascular health outcomes.

Background of the study Player Unknown’s Battlegrounds (PUBG), a popular and widely played multiplayer online game, has generated interest and concern about its effects on the physical health of its players. This study explores the relationship between factors like cultivation level, gaming disorder, migraine and associated symptoms, sleep quality, and life quality of PUBG players. Methods This cross-sectional study included 500 PUBG players, categorized into Lower, Medium, and High PUBG Users. Data were collected using a self-administered questionnaire, including the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and a gaming disorder screening tool. The Multilayer Perceptron (MLP) methodology was applied to analyze the factors influencing migraine symptoms, sleep quality, and quality of life. Results Participants of the study were categorized into lower PUBG users (LPU), medium PUBG users (MPU), and high PUBG users (HPU). Among study participants reporting migraine pain, 259 (51.8%) reported that they were HPU. By examining daytime sleepiness using the ESS, Higher normal Day sleep (DS) was observed in 78 (15.9%) HPU. The cultivation level of PUBG showed a very weak positive correlation with experiencing migraine pain or associated headache symptoms ( r  = 0.034, p  = 0.454). In contrast, the gaming disorder of PUBG showed a weak negative correlation with PSQI ( r = -0.092, p  = 0.041). The higher levels of gaming disorder are slightly associated with poorer sleep quality. The results of the MLP model suggested that daily PUBG use was the most contributing factor to migraine and related symptoms followed by gaming disorder, gaming addiction, PSQI, and ESS. Conclusion The study concluded that PUBG playing contributes to migraine and its associated symptoms although is not significant it contributes to less sleep quality and lower quality of life.

Different allopathic drugs are being used for the treatment of diabetes mellitus but more emphasis are being placed on the use of medicinal plants, herbs, and natural extracts of fruits and vegetables due to their easy availability, easy consummation with low cost, and with no well-reported side effects. White skinned sweet potato (WSSP; Ipomoea batatas L.) peel-off was selected to find out its antidiabetic potential as well as to explore the effects on selected biochemical parameters in diabetes-induced Wistar rats. In young (3–4 months) and old (up to 1 year) diabetic Wistar rats, it was found that WSSP (I. batatas L.) peel-off significantly (P < 0.05) decreased blood glucose level, protein glycation level, total cholesterol, triglycerides, and low-density lipoprotein (LDL)-cholesterol. A significant (P < 0.05) increase in high-density lipoprotein (HDL)-cholesterol level after treatment was also reported. Furthermore, it was also found that WSSP peel-off also had beneficial effects on total protein concentration, albumin, globulin, and liver enzymes (serum glutamic oxaloacetic transaminase (SGOT) and serum glutamic pyruvic transaminase (SGPT)). It might be concluded that antidiabetic potential of WSSP extract is due to the presence of bioactive compounds like glycoprotein, anthocyanins, alkaloids, and flavonoids, which act as insulin-like molecules or insulin secretagogues constituents in sweet potatoes peel-off and these antidiabetic proteins were extracted out in more concentration in methanol due to its organic nature. Further research is needed to purify and quantify the antidiabetic components responsible for antidiabetic effects of WSSP and it should be available in compact dose form for the treatment of diabetic patients.

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.

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.