Background Sleep medicine is an under‐recognised medical specialty in Pakistan and obstructive sleep apnoea (OSA) often goes unnoticed. Final year medical students and junior doctors are the primary medical contact to elicit patient history and physical examination. We aimed to measure the current knowledge of OSA amongst the final year medical students and junior doctors at four university teaching hospitals across three large Pakistani cities. Methods Cross‐sectional survey of final year medical students and junior doctors rotating through medical wards of four university teaching hospitals were conducted during August–October 2019. The knowledge section of the OSA knowledge and attitude (OSAKA) questionnaire was used. Descriptive statistics were used to present the data with Chi‐Square test and independent samples student t‐test to compare the differences between individual items and mean scores of the participants, respectively. Results A total 282 final year medical students and 204 junior doctors completed the survey yielding a response rate of 53% for medical students and 97% for junior doctors. The knowledge of sleep apnoea was poor in both groups of participants with a mean score of 7.6 (42%) on the knowledge scale of OSAKA questionnaire. Medical students scored higher on the item related to snoring as the most prevalent symptom in OSA patients when compared to the junior doctors (χ2 = 8.92, P = 0.003). More junior doctors responded correctly about the role of uvulopalatopharyngoplasty in the management of OSA when compared to the medical students (χ2 = 5.14, P = 0.02). Differences in scores of both groups of participants on other items were small and did not reach statistical significance. Conclusion Final year medical students and junior doctors from a sample of four university teaching hospitals in three large cities of Pakistan have limited knowledge about the diagnosis and management of OSA. The observed limited knowledge of OSA may contribute towards under‐diagnosis of this increasingly prevalent medical condition.

Digital pathology and the use of artificial intelligence constitute undisputedly the future of modern pathology. The outcomes and benefits of the whole slide imaging are beyond the scope of traditional microscopy, which the pathologists were using for decades. COVID-19 pandemic has further highlighted the importance of digital pathology as it offers the pathologists to work from their place of comfort and bridges the gap of physical barriers. In addition to the many advantages, there are certain limitations and challenges, which have to be overcomed particularly in the developing world. The major issue is the cost of scanners and technical support and training of staff. However, despite all these problems and challenges that exist, these can be resolved with the passage of time, where the role of world leader organisations will be of great importance in resolving these challenges.

Low- and middle-income countries (LMICs) represent a big source of data not only for endemic diseases but also for neoplasms. Data is the fuel which drives the modern era. Data when stored in digital form can be used for constructing disease models, analyzing disease trends and predicting disease outcomes in various demographic regions of the world. Most labs in developing countries don’t have resources such as whole slide scanners or digital microscopes. Owing to severe financial constraints and lack of resources, they don’t have the capability to handle large amounts of data. Due to these issues, precious data cannot be saved and utilized properly. However, digital techniques can be adopted even in low resource settings with significant financial constraints. In this review article, we suggest some of the options available to pathologists in developing countries which can enable them to start their digital journey and move forward despite resource-poor health system.

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, with both a high malignant potential and poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance to leiomyosarcoma, often being accompanied by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three biomarkers (i.e., mitosis count, necrosis, and nuclear atypia). Among these biomarkers, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available.

Objective: To assess the coagulation test abnormalities (prothrombin time, activated partial thromboplastin time and fibrin degradation products) in dengue fever patients and its impact on clinical outcomes of the patient. Study Design: Cross-sectional study. Place and Duration of Study: Hematology Department, Pakistan Naval Ship Shifa Hospital, Karachi Pakistan, from Aug 2016 to Feb 2017. Methodology: A total of 135, serologically proven positive dengue-infected IgM cases, based on enzyme-linked immunesorbent assay, were included in the study. Prothrombin time, activated partial thromboplastin time, and fibrin degradation products levels were measured on STA Compact Max® (STAGO) and SysmexCA-1500. Clinical Outcomes of the patient were noted. Results: At the time of admission, elevated prothrombin time was observed in 8(5.6%) cases and remained high at the time of discharge (p-value 0.008). Elevated activated partial thromboplastin time was observed in 57 (42.2%) cases at admission whereas, at discharge, it was observed in 56 (41.5%) cases. Increased level (>5) of fibrin degradation products was seen in 30 (22.2%) cases while at the time of discharge increased level of fibrin degradation products was observed in 25 (18.5%) cases (pvalue 0.007). Out of 135 patients, mortality was observed in two cases and the remaining 133 (98.52%) survived and were discharged. Conclusion: In conclusion, prothrombin time, activated partial thromboplastin time, and fibrin degradation products can be labeled as early predictors of disease severity and their derangements are associated with clinical outcome in dengue infection.