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Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model’s efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.

Effective requirement collection and prioritization are paramount within the inherently distributed nature of the Internet of Things (IoT) application. Current methods typically categorize IoT application requirements subjectively into inessential, desirable, and mandatory groups. This often leads to prioritization challenges, especially when dealing with requirements of equal importance and when the number of requirements grows. This increases the complexity of the Analytical Hierarchical Process (AHP) to O(n2) dimensions. This research introduces a novel framework that integrates an enhanced AHP with an advanced grouping model to address these issues. This integrated approach mitigates the subjectivity found in traditional grouping methods and efficiently manages larger sets of requirements. The framework consists of two main modules: the Pre-processing Module and the Prioritization Module. The latter includes three units: the Grouping Processing Unit (GPU) for initial classification using a new grouping approach, the Review Processing Unit (RPU) for post-grouping assessment, and the AHP Processing Unit (APU) for final prioritization. This framework is evaluated through a detailed case study, demonstrating its ability to effectively streamline requirement prioritization in IoT applications, thereby enhancing design quality and operational efficiency.

The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively.

Background This study aims to access the perspective of medical students toward practicing neurosurgery after MBBS and also to identify factors responsible for low affinity among medical students in pursuing neurosurgery as a career. In this cross-sectional study, medical students were surveyed via pre-tested questionnaire, with a four-point Likert scale to determine their influence on student’s consideration of neurosurgery as a career. Data analysis was performed using SPSS software. Results The survey was completed by 185 students out of which 56.2% students considered neurosurgery as a career. Although more than 90% students acknowledged that neurosurgery training is too prolonged and it can also impede family life but huge prestige and income is attached to neurosurgery, 35.7% students shared that neurosurgery exposure and teaching is not adequate enough for them in order to have a positive influence toward neurosurgery as a career. Conclusion Additional studies are required to further explore how participation in a formal neurosurgery experience can alter medical students’ perceptions and influences their consideration of neurosurgery as career choice.

Respiratory protection is key in infection prevention of airborne diseases, as highlighted by the COVID-19 pandemic for instance. Conventional technologies have several drawbacks (i.e., cross-infection risk, filtration efficiency improvements limited by difficulty in breathing, and no safe reusability), which have yet to be addressed in a single device. Here, we report the development of a filter overcoming the major technical challenges of respiratory protective devices. Large-pore membranes, offering high breathability but low bacteria capture, were functionalized to have a uniform salt layer on the fibers. The salt-functionalized membranes achieved high filtration efficiency as opposed to the bare membrane, with differences of up to 48%, while maintaining high breathability (> 60% increase compared to commercial surgical masks even for the thickest salt filters tested). The salt-functionalized filters quickly killed Gram-positive and Gram-negative bacteria aerosols in vitro, with CFU reductions observed as early as within 5 min, and in vivo by causing structural damage due to salt recrystallization. The salt coatings retained the pathogen inactivation capability at harsh environmental conditions (37 °C and a relative humidity of 70%, 80% and 90%). Combination of these properties in one filter will lead to the production of an effective device, comprehensibly mitigating infection transmission globally.

Background While gender disparities in surgery are documented worldwide, it is unclear to what extent women consider surgery as a career before embarking on their medical school journey. This study aimed to report the percentage of pre-medical women in Pakistan who intend to eventually specialize in surgery and assess the factors motivating and deterring this decision. Methods An online survey was conducted among female pre-medical (high school) students across Pakistan. Multivariable logistic regression was performed to determine motivating and deterring factors associated with the intention to pursue surgery. Results Out of 1219 female high-school students, 764 (62.7) intended to join medical school. Among these 764, only 9.8% reported an exclusive intent to pursue surgery, while just 20.3% reported considering other specialties in addition to surgery. Significant motivators to pursue surgery exclusively were the intellectual satisfaction of pursuing surgery (adjusted odds ratio: 2.302), having opportunities to travel internationally for work (2.300) and use cutting-edge technology (2.203), interest in the specialty of surgery (2.031), the social prestige of becoming a surgeon (1.910), and considering one’s personality well-suited to surgery (1.888). Major deterrents included the lack of interest in surgery (adjusted odds ratio: 3.812), surgical education and training being too difficult (2.440) and lengthy (1.404), and the risk of aggressive behavior from patients (2.239). Conclusion Even before entering medical school, most female pre-medical students have already decided against considering a future surgical career. Deterrents likely stem from women being pressured to conform to deep-seated societal expectations to dedicate their time and energy to domestic responsibilities.

Background Endometrial carcinoma (EC) accounts for 5.8% of all cancers in Saudi females. Although most ECs are sporadic, 2–5% tend to be familial, being associated with Lynch syndrome and Cowden syndrome. In this study, we attempted to uncover the frequency, spectrum and phenotype of germline mutations in the proofreading domain of POLE and POLD1 genes in a large cohort of ECs from Middle Eastern region. Methods We performed Capture sequencing and Sanger sequencing to screen for proofreading domains of POLE and POLD1 genes in 432 EC cases, followed by evaluation of protein expression using immunohistochemistry. Variant interpretation was performed using PolyPhen-2, MutationAssessor, SIFT, CADD and Mutation Taster. Results In our cohort, four mutations (0.93%) were identified in 432 EC cases, two each in POLE and POLD1 proofreading domains. Furthermore, low expression of POLE and POLD1 was noted in 41.1% (170/1414) and 59.9% (251/419) of cases, respectively. Both the cases harboring POLE mutation showed high nuclear expression of POLE protein, whereas, of the two POLD1 mutant cases, one case showed high expression and another case showed low expression of POLD1 protein. Conclusions Our study shows that germline mutations in POLE and POLD1 proofreading region are a rare cause of EC in Middle Eastern population. However, it is still feasible to screen multiple cancer related genes in EC patients from Middle Eastern region using multigene panels including POLE and POLD1 .

Background: The provision of dental care in Pakistan is limited, with less than 5% of the population having access to qualified dental practitioners. The lack of contextually relevant local dental guidelines further adds to the problem. We developed clinical practice guidelines (CPGs) and referral pathways to improve primary care for common oral diseases. Methods: Using the GRADE-ADOLOPMENT approach, recommendations from source guidelines (developed in Europe and the United States) were adopted (retained as is or with minor changes), adapted (modified according to the local context), or excluded (omitted due to lack of local relevance). The guidelines included diseases such as periodontitis, dental pain, intraoral swelling, and oral cavity malignancies. The end result was a set of locally relevant CPGs, which were then used to formulate referral pathways, with the incorporated suggestions being based on a thorough evidence review process. Results: We included four recommendations, three of which were adopted with minor modifications to the referral pathway. These changes focused on assessing potentially malignant oral conditions and counseling for risk factors. No content changes were needed for the CPGs of the other two disorders. We developed referral pathways for three specific oral conditions, detailing primary care physicians’ roles in diagnosis, initial treatment, and timely referral. Conclusion: Contextually relevant dental CPGs and referral pathways can improve patient outcomes in Pakistan. Our study produced four additional recommendations focused on risk factor counselling and mitigation, which could potentially reduce the burden of oral malignancies in our local population.

Objectives: This study was aimed to compare the body mass index (BMI) and waist-to-hip ratio (WHR) in their ability to predict type 2 diabetes risk in a large prospective cohort of men and women in Pakistan. Methods: This was a case-control study conducted at Diabetic and medical OPD of GTTH. Anthropometric measures including BMI and WHR were analyzed. Student’s t-test, Chi-squared test along with Cramer’s V value, was applied to evaluate association between variables. Receiver operating curve (ROC) was used to assess anthropometric measures. Results: The study included 804 diabetics and 396 non-diabetics between 30–60 years of age. Comparing the BMI parameters it was found that 717 (89·2%) in diabetic group were overweight or obese (p-value < 0·001). On comparing the WHR, 97·9% diabetics had increased WHR (p-value <0·001). Both BMI & WHR were further compared using ROC curve which found out that WHR had an area under ROC of 0·720 & BMI has 0·680, suggesting that WHR is more better predictor of diabetes as compared to BMI. Conclusions: Both BMI and WHR were strong discriminators of T2DM but WHR was found superior according to ROC value. Family history is significantly associated in patients with diabetes. doi: https://doi.org/10.12669/pjms.35.5.759 How to cite this:Qureshi SS, Amer W, Kaleem M, Beg BM. Adult anthropometry in Type 2 diabetic population: A case-control study. Pak J Med Sci. 2019;35(5):1284-1289. doi: https://doi.org/10.12669/pjms.35.5.759 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This study was aimed to compare the body mass index (BMI) and waist-to-hip ratio (WHR) in their ability to predict type 2 diabetes risk in a large prospective cohort of men and women in Pakistan. This was a case-control study conducted at Diabetic and medical OPD of GTTH. Anthropometric measures including BMI and WHR were analyzed. Student's t-test, Chi-squared test along with Cramer's V value, was applied to evaluate association between variables. Receiver operating curve (ROC) was used to assess anthropometric measures. The study included 804 diabetics and 396 non-diabetics between 30-60 years of age. Comparing the BMI parameters it was found that 717 (89.2%) in diabetic group were overweight or obese (p-value < 0.001). On comparing the WHR, 97.9% diabetics had increased WHR (p-value <0.001). Both BMI & WHR were further compared using ROC curve which found out that WHR had an area under ROC of 0.720 & BMI has 0.680, suggesting that WHR is more better predictor of diabetes as compared to BMI. Both BMI and WHR were strong discriminators of T2DM but WHR was found superior according to ROC value. Family history is significantly associated in patients with diabetes.