Background: The rate of caesarean section (C-section) deliveries in Bangladesh has increased significantly in recent years. While C-sections are sometimes medically necessary, their growing prevalence raises concerns about overuse and accessibility. Various factors, including medical, social, economic, and institutional influences, contribute to the high rate of C-sections in the country. Objective: The objective of this study was to assess the causes and factors influencing the rise of C-sections among pregnant women in Bangladesh. Methodology: This was a randomized prospective study conducted between 2018 and 2020 at Khalishpur Clinic. A total of 532 pregnant women were enrolled, and data were collected from patient records including demographic details, medical history, and reasons for undergoing a C-section. The study analyzed the frequency and distribution of various factors contributing to C-section deliveries, and statistical analysis was performed to identify the most common causes. Results: Among the 532 pregnant women, 413 (77.5%) underwent C-section, and 119 (22.5%) had a normal delivery. The mean age at delivery was 24.2 years (SD 4.6). The leading cause for C-sections was patient request, accounting for 41.5% of cases (n=171), of which 34.1% had no complications and 7.4% had minor complications. Other notable causes included premature rupture of membranes (PROM) (11.9%), post-dated pregnancy (9.7%), and previous C-sections (9.0%). Additionally, 27.6% of C-sections were performed due to other medical reasons. Conclusion: The study found a high rate of C-sections in Bangladesh, with a significant proportion being elective, driven by patient preference. This highlights a cultural shift towards viewing C-sections as a safer, more predictable form of delivery. The findings emphasize the need for improved prenatal care, particularly in rural areas, and greater public awareness regarding the risks and benefits of different delivery methods. Addressing both medical and socio-cultural factors is essential for reducing unnecessary C-sections and promoting safer, evidence-based childbirth practices.

With the advancements in technology, several features are now available for diagnosing heart diseases. However, large data sets have some limitations such as limited storage capacity and long access and processing times. Early diagnosis of heart problems is vital for medical treatment. Heart disease is a severe illness that is on the rise in both developed and developing countries, leading to fatalities. This disease causes the heart to not provide enough blood to various parts of the body, hindering its normal functions. Diagnosing this condition early and accurately is crucial to prevent further harm and potentially save lives. Diagnosis for various forms of heart disease can be detected with numerous medical tests, however, predicting heart disease without such tests is very difficult. Many researchers analyzed the risk factors of this disease and proposed machine learning models for the early detection of heart patients. However, these models suffer from the high dimensionality of data and need to be improved to obtain highly accurate results. The proposal was tested using five different standard data sets from the UCI repository. Our proposal consists of two main processes: the first is the data preprocessing process, and the second is the prediction process. In data preprocessing, the data is prepared for the prediction process, and three different feature selection methods (e.g., PCA) are applied to select the most relevant features from the data. In the prediction process, ten different prediction techniques (for example, Random Forest (RF) and Support Vector Classifier (SVC)) were applied to over-employed datasets. The techniques used were evaluated using four evaluation metrics: accuracy, precision, recall, and F1-score. For this research, we collected the dataset from the UCI repository (Kaggle) and used Random Forest Classification algorithm for predicting heart disease. The predictive model achieved an accuracy of 89.4 percent using Random Forest Classifier’s default setting to predict heart diseases. Furthermore, the research focuses on the opportunity for training and testing using our model with a larger dataset and modifying different hyper parameters for further improvement. The results show that the LASSO method as a feature selection method with RF as a prediction technique produced the best accuracy (100%). Accuracy (99.57%) was obtained for Decision Tree (DT), Gradient Boosting (GB), AdaBoost (AB), Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM). The accuracy of SVC, Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Classifier Bagging Method (SVCBM) was very similar to each other (98.73%).

Background: Brain drain (BD) is the movement of highly educated individuals or professionals from a low socio-economic country to a more advanced socio-economic country for professional opportunities. The aim of this study is to evaluate Reversing Health Sector Brain Drain among Medical Students and House Officers in South-South Nigeria: A Crossectional Study. Method: This was a cross-sectional study involving 350 (final year medical students 200 and house officers 150). A well-structured questionnaire was administered to participants. The study lasted for a period of 2 months. Exclusion criteria was those medical students who were not in final year and doctors that were not house officers. Inclusion criteria were final medical students and house officers. This study used a simple random sample of 200 participants, calculated using the Taro Yamane formula, and data were analyzed with SPSS version 25.0. Results: Study revealed that 94.3% of the participants (final year medical students and house officers) are willing to stay and practice in Nigeria if factors that will enhance better work conditions are put in place. Also, 71.4% of the doctors said better welfare who keep them back in the country and 71.4% of the participants said training and re-training of healthcare professionals is a panacea for brain drain. Conclusion: The study revealed that majority of the participants said the good work condition, attitudinal change by the consultants, provision of jobs for doctors, equipping of hospitals with good facilities and ending kidnapping of doctors are instruments of reversing brain drain in Nigeria.

Proposed by Danish scholar Jan Pedersen, Extralinguistic Cultural Reference is a model concerned with cultural words in film and television subtitles, offering new ideas for the study of cultural word translation in subtitles. Guided by Relevance Theory, this paper analyzes the translation of Extralinguistic Cultural References in the subtitles of Legend of Deification. It is found that the interventional strategies are more used than the minimal change strategies in the ECR translation of the animated film. This paper argues that when translating cultural words in subtitles, translators should take into full consideration the differences between Chinese and Western cultures to give the foreign audience access to a better understanding of the culture in films. Hopefully, it can provide some practical suggestions for cultural word translation in similar mythological animated films.

The purpose of this study is to analyze and find the weaknesses in regulations on the criminal sanction system for personal data protection in Indonesia and how to reconstruct regulations on the criminal sanction system for personal data protection based on the value of justice using constructivism paradigm with a socio-legal research approach method to solve research problems by examining secondary data and primary data by finding the legal reality experienced in the field and qualitative descriptive methods, where later the data will be presented descriptively. The result shows that the protection is weak, because there is incomplete law in Article 12 of the Personal Data Protection Law. This results in uncertainty in the aspect of law enforcement, especially in Article 67 and Article 68 in terms of imposing compensation when there is a failure to protect personal data, added with the inequality in the formulation of sanctions that can be applied to the public and private sectors, legal culture, namely digital literacy in society is still low, so that many individuals do not fully understand the importance of personal data protection and its implications in the digital environment. Therefore, the Legal Reconstruction proposed by the author is carried out on the addition of one paragraph to Article 67 paragraph (4) which contains the Provisions as referred to in paragraph (1), paragraph (2) and paragraph (3). the judge is obliged to determine the amount of compensation payment. Reconstruction is also carried out in Article 68 so that it becomes paragraph (2) The provisions as referred to in paragraph (1) the judge is obliged to determine the amount of compensation payments.

Introduction: In nursing education, students are emboldened to identify their own learning needs in their journey of studying the course, evaluate self-motivation in acquiring new information, and process learning outcomes. This study determines the SDLR in terms of self-management, desire for learning, and self-control domains among nursing students at King Khalid University (KKU) Saudi Arabia. It includes female students enrolled in lower years. Materials and Methods: Descriptive-correlational methods were used to investigate the research. Frequencies, percentages, mean, and standard deviation were employed to measure and examine values using IBM SPSS statistics version 25. Fisher's exact test (FET) was utilized to explore the probability of associations between the participants' demographics and SDLR scales. The Spearman rho (rs) correlation coefficient was applied to analyze the strength of association between variables. The Cronbach's alpha (α) reliability coefficient for the translated tool was .829. Results: The respondents strongly agree they can be trusted to pursue their learning, being confident in their ability to search out information, and being disciplined. Wanting to learn new and enjoy and learn from their mistakes predominates their responses. In self-control, the respondents strongly agree they can find information, make their own decisions, and are responsible for their own decisions/actions. Age in years is statistically significant to the self-directed learning readiness scale. The variable on self-management vs. self-control shows a very high positive correlation. Conclusion: The self-directed learning readiness scale of students unequivocally places them to respond positively and develop more knowledge and skills embedded in the institution.

Introduction: Worldwide, the demand for blood exceeds the supply. Transfusion services face the constant challenge of providing sufficient supplies of quality blood products. The objective of this study was to analyze this gap between demand and supply in the Obstetrics service. Methodology: this is a prospective cross-sectional study carried out in the maternity ward of the Niono district hospital during the 1st semester of 2020. The study population was composed of patients admitted with an obstetric complication requiring a transfusion request not made or partially satisfied. Results: 125/196 patients (63.78%) had a blood request that was unmet or partially met. 220/588 units of blood requested were not served; or 37%. These unmet needs concerned patients who were mainly housewives (98%), unschooled in 87% and residing in rural areas in 89% of cases. 85% of patients had a hemoglobin level below 7g/dl before the blood transfusion. Out of stock was the reason for unmet requests in 88% of cases. 2 cases of death were recorded among the 125 patients, a rate of 1.6%.

The purpose of this study is to find and analyze the weaknesses of the legality of the ratification of the results of the identification of victims who died due to mass disasters in the application of the Disaster Victim Identification procedure and to reconstruct the legality of the ratification of the results of the identification of victims who died due to mass disasters in the application of the Disaster Victim Identification procedure based on the value of justice Using the constructivism paradigm, the sociological legal approach method to solve research problems by examining secondary data and primary data by finding the legal reality experienced in the field and qualitative descriptive methods. The results of the study indicate that the legality of the ratification of the results of the identification of victims who died due to mass disasters in the application of the Disaster Victim Identification procedure is not yet based on the value of justice, due to the lack of distribution of DVI police doctors. Weaknesses in legal substance are Article 157 of Law Number 17 of 2023 concerning Health, Article 473 of Government Regulation Number 28 of 2024 concerning Implementing Regulations of Law Number 17 of 2023 concerning Health and Article 21 of Regulation of the Minister of Health Number 38 of 2022 concerning Medical Services for Legal Interests. Weaknesses in legal culture where legal culture will be aware of disasters and the importance of identification are still lacking. The reconstruction proposed by the author is to regulate those who have the right to issue identification results in DVI operations for mass disasters are DVI Police who have DVI Commander expertise certification which guarantees that the identification results are in accordance with the science used, so that they can be legally accounted for.

Gas flaring and illegal refining in Nigeria’s Niger Delta region contribute significantly to environmental pollution, public health crises, and economic losses. These activities release particulate matter (PM₂.₅, PM₁₀), volatile organic compounds (VOCs), nitrogen oxides (NO), sulfur dioxide (SO₂), carbon monoxide (CO), heavy metals, and dioxins, which have been linked to cardiovascular diseases, respiratory disorders, neurological impairments, and adverse reproductive outcomes. Studies conducted in Rivers, Bayelsa, and Delta States reveal that pollutant levels exceed WHO-recommended safety limits by over 400%, correlating with increased risks of hypertension, myocardial infarction, chronic obstructive pulmonary disease (COPD), neurotoxicity, and adverse pregnancy outcomes (Ehumadu, Uyigue, & Ndekwu, 2021). The economic burden of gas flaring is estimated at $7.4 billion annually due to healthcare costs, lost productivity, and environmental damage (World Bank, 2022). This paper integrates air quality data, pathophysiological mechanisms, and epidemiological evidence to assess health risks.

Background: The postpartum period involves significant physiological changes requiring optimal nutrition and hydration. In African communities, Xylopia aethiopica and Tetrapleura tetraptera traditionally support maternal recovery. Despite their widespread use, scientific validation of their effects on food intake, water consumption, and weight regulation remains limited. This study assessed the effects of Xylopia aethiopica and Tetrapleura tetraptera on food intake, water intake, and weight changes in Wistar rats. Methods: Twenty-four female Wistar rats (87–103 g) were randomised into four groups (n = 6): control (standard rat chow), X. aethiopica (100 mg/kg extract), T. tetraptera (100 mg/kg extract), and a combined group (50 mg/kg of each extract). Food and water intake were recorded daily for 14 days, while body weights were measured at baseline and Day 14. Data were analysed using one-way ANOVA and Tukey’s post hoc tests (p<0.05). Results: All treatment groups demonstrated significantly higher food and water intake than controls, with the X. aethiopica group having the highest intake (food: 95.3 g/day; water: 91.57 ml/day). Weight changes were minimal and statistically insignificant. Conclusion: Xylopia aethiopica and Tetrapleura tetraptera significantly enhance food and water intake without causing excessive weight gain.

Amenorrhea in a woman of reproductive age can be sporadic, temporary or permanent. It can result from lesion affecting the hypothalamic-pituitary-ovarian axis, uterus and vagina. A woman is said to have primary amenorrhoea if she fails to attain menarche at the age of fifteen or at thirteen years without evidence of secondary sexual characteristics. The causes of primary amenorrhoea are broadly classified into constitutional delay, anatomical causes such as Mayer-Rokitansky-Küster-Hauser (MRKH) or genetic causes (gonadal dysgenesis) and other physiological causes including isolated deficiency of gonadotropin releasing hormone. MRKH (Mullerian agenesis) is a rare syndromic disorder that usually occurs when there is agenesis or hypoplasia of the paramesonephric ducts (the precursor for the development of the uterus, fallopian tubes and upper two-third of the vagina). We report on a case of a 22-year-old undergraduate with primary amenorrhoea and subsequent diagnosis of MRKH.

Background: Cardiovascular diseases (CVDs) represent the most prevalent non-communicable diseases worldwide. In 2019, around 17.9 million individuals succumbed to cardiovascular diseases, accounting for 32% of all global fatalities. Mitochondria are essential for maintaining cellular metabolic equilibrium, facilitating cell survival and apoptosis, and generating the majority of cellular energy. Protein–protein interactions (PPIs) play a crucial role in both physiological and pathological processes, with abnormal PPIs linked to numerous disorders, making them prospective pharmacological targets across diverse therapeutic domains. Peptides are highly promising as protein-protein interaction inhibitors due to their capacity to replicate natural interaction patterns and encompass rather extensive interaction regions. Computational methods are extensively employed to accelerate drug discovery by screening prospective lead molecules. Purpose: Current work was designed to check efficacy of Daucus carota flavonoid for cardioprotective activity. Methodology: Scientific validation of the current investigation was done by computational based molecular docking study of lead molecules of Daucus carota pulp against malonyl Co-A decarboxylase enzyme. Result: The flavonoid found in D.carota has been identified as an effective cardioprotective drug and their lead molecules luteolin and apigenin demonstrating effective binding to the target protein malonyl Co-A decarboxylase with binding energies of -7.34 and -7.12 kcal/mol, respectively. Conclusion: The findings indicated that each selected lead chemical for additional investigation shown significant inhibitory activity against malonyl Co-A decarboxylase, hence revealing its cardio protection potential.

Machine learning (ML) has revolutionized predictive healthcare by enhancing early detection, diagnosis, and management of chronic diseases. This study presents a comparative analysis of ML algorithms for diabetes management and breast cancer detection. The research evaluates the effectiveness of Random Forest, Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Linear Regression in predicting patient outcomes. The diabetes dataset includes medical and demographic factors such as age, BMI, HbA1c levels, and glucose levels. In contrast, the breast cancer dataset comprises tumor-related attributes like clump thickness, uniformity of cell size, and marginal adhesion. The results indicate that Random Forest consistently achieves the highest accuracy across both use cases, demonstrating its robustness in handling complex medical datasets. For diabetes prediction, Random Forest outperformed other models with an accuracy of 90.78%, while breast cancer detection achieved a classification accuracy of 96.50%. Logistic Regression and SVM also showed promising results but were less effective in handling non-linear relationships and high-dimensional data. While interpretable, decision Trees and Linear Regression required more extensive datasets to achieve comparable accuracy. This research highlights the potential of machine learning (ML) to enhance public health and lower healthcare costs through early diagnosis and personalized treatment. By integrating predictive models into clinical workflows like Electronic Health Records (EHRs), timely interventions and better resource allocation can be achieved, improving patient outcomes.

The present study investigates the antioxidant and cytotoxic properties of natural phytochemicals and NSAIDs, focusing on their potential anticancer effects against selected cancer cell lines. Luteolin, a dietary flavonoid with known antioxidant and anticancer activities, and piroxicam, a Non-Steroidal Anti-Inflammatory Drug (NSAID) with reported anticancer potential, were examined both individually and in combination against MMP-9. Molecular docking revealed that the piroxicam–luteolin complex demonstrated stable interactions with key residues, including GLU241, ALA242, LEU243, TYR245, MET247, PRO245, HIS226, GLN227, ALA189, LEU188, LEU222, TYR248, and ARG249, with a binding energy of –6.89 kcal/mol, indicating favorable binding affinity. Antioxidant activity assays revealed that luteolin and piroxicam alone exhibited IC₅₀ values of 22.85 ± 0.080 μM and 20.512 ± 0.04 μM, respectively. Notably, their combination reduced the IC₅₀ to 10.89 ± 0.34 μM, suggesting a synergistic enhancement of antioxidant capacity. Similarly, MTT assays demonstrated that luteolin and piroxicam individually displayed cytotoxic effects with IC₅₀ values of 198.3 ± 0.088 μM and 175.5 ± 0.129 μM, while their combination yielded a significantly lower IC₅₀ of 73.3 ± 0.25 μM, confirming a synergistic effect in inhibiting cancer cell proliferation. Furthermore, intracellular ROS estimation revealed effective reduction in ROS levels by luteolin and piroxicam individually, with amplified effects observed upon their combined treatment. These findings indicate that the luteolin–piroxicam combination offers superior antioxidant and cytotoxic activity compared to either compound alone. Thus, this synergistic interaction highlights a promising strategy for developing safe, natural, and effective anticancer therapies, warranting further validation through in vivo studies.

Fetal death/Intra uterine death (IUD) is a significant adverse pregnancy outcome, particularly prevalent in low- and middle-income countries. It is also a good indicator of quality of healthcare available. Fetal autopsy is often required to determine the cause of intrauterine death or miscarriages, provide recurrence risk, and is known to alter the final diagnosis and genetic counseling. Aim: To study the maternal and fetal factors in fetal autopsy lesions. To know the prevalence of various fetal abnormalities in IUD. Material and methods: The present study was a retrospective observational study conducted in the Department of Pathology at Dr. BVP Rural Medical College, Loni, over a duration of 5 years. A total of 78 cases were included, with data collected on the mother's age, gestational age, fetal gender, weight, and other relevant details, alongside records of fetal autopsies. Special attention was given to identifying organ abnormalities in the fetuses. The collected data was compiled in Microsoft Excel, and the analysis was carried out using Openepi version 3.2.1 software. Results: The study found that mean maternal age was 24.8+ 4.5 years, ranging between 18 to 38 years. Mean gestational age was 21.7 +4 weeks. It was seen that among total 78 cases, 40 were males, 30 were females, 6 fetuses were macerated and 2 fetus gender was ambiguous. Conclusion: The study shows a varied distribution of fetal characteristics, with male fetuses being more common and cardiac abnormalities being the most prevalent. These findings emphasize the diverse nature of fetal lesions and their potential effects on fetal development, influenced by gestational age and maternal conditions.