The Impact of COVID-19 on Healthcare Services in Bangladesh: A Qualitative Study on Healthcare Providers'Perspectives.

Objectives: The objective of this study was to explore healthcare providers' experiences in managing the coronavirus disease 2019 (COVID-19) pandemic and its impact on healthcare services. Methods: A qualitative study was conducted with 34 healthcare professionals across 15 districts in Bangladesh. Among the participants, 24 were health managers or administrators stationed at the district or upazila (sub-district) level, and 10 were clinicians providing care to patients with COVID-19. The telephone interviews were conducted in Bangla, audio-recorded, transcribed, and then translated into English. Data were analyzed thematically. Results: Most interviewees identified a range of issues within the health system. These included unpreparedness, challenges in segregating COVID-19 patients, maintaining isolation and home quarantine, a scarcity of intensive care unit (ICU) beds, and ensuring continuity of service for non-COVID-19 patients. The limited availability of personal protective equipment, a shortage of human resources, and logistical challenges, such as obtaining COVID-19 tests, were frequently cited as barriers to managing the pandemic. Additionally, changes in the behavior of health service seekers, particularly increased aggression, were reported. The primary motivating factor for healthcare providers was the willingness to continue providing health services, rather than financial incentives. Conclusions: The COVID-19 pandemic presented a unique set of challenges for health systems, while also providing valuable lessons in managing a public health crisis. To effectively address future health crises, it is crucial to resolve a myriad of issues within the health system, including the inequitable distribution of human resources and logistical challenges.

Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles.

Microvascular complications are one of the key causes of mortality among type 2 diabetic patients. This study was sought to investigate the use of a novel machine learning approach for predicting these complications using only the patient demographic, clinical, and laboratory profiles. A total of 96 Bangladeshi participants with type 2 diabetes were recruited during their routine hospital visits. All patient profiles were assessed by using a chi-squared (χ2) test to statistically determine the most important markers in predicting three microvascular complications: cardiac autonomic neuropathy (CAN), diabetic peripheral neuropathy (DPN), and diabetic retinopathy (RET). A machine learning approach based on logistic regression, random forest (RF), and support vector machine (SVM) algorithms was then developed to ensure automated clinical testing for microvascular complications in diabetic patients. The highest prediction accuracies were obtained by RF using diastolic blood pressure, albumin-creatinine ratio, and gender for CAN testing (98.67%); microalbuminuria, smoking history, and hemoglobin A1C for DPN testing (67.78%); and hemoglobin A1C, microalbuminuria, and smoking history for RET testing (84.38%). This study suggests machine learning as a promising automated tool for predicting microvascular complications in diabetic patients using their profiles, which could help prevent those patients from further microvascular complications leading to early death.

Health management during COVID-19 pandemic-contribution of women health informaticians, medical physicists and veterinarians from Bangladesh and Malaysia during the world crisis.

This article aims to highlight some of the contributions from Bangladeshi and Malaysian women scientists in the fields of health informatics, medical physics and biomedical engineering, and veterinary science in combating the COVID-19 world crisis. The status of COVID-19 situations in Bangladesh and Malaysia in respect to global scenario, some relevant government policies, lessons learnt from previous pandemics, socio-economic impacts of COVID-19, the impact on healthcare system and health management approaches taken by individual/institutional research group led by women scientists during the COVID-19 pandemic have been discussed and demonstrated in this article. These promising activities and initiatives will eventually motivate other women in science and extend their roles from laboratory to society in more aspects.

Computer assisted diagnostic system in tumor radiography.

An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46%, which is a significant improvement than that of the existing results.