Showing papers by "Mahmudur Rahman published in 2023"

Availability of medical abortion medicines in eight countries: a descriptive analysis of key findings and opportunities

Abstract: Abstract Background In recent years a growing number of manufacturers and medical abortion products have entered country markets and health systems, with varying degrees of quality and accessibility. An interplay of factors including pharmaceutical regulations, abortion laws, government policies and service delivery guidelines and provider’s knowledge and practices influence the availability of medical abortion medicines. We assessed the availability of medical abortion in eight countries to increase understanding among policymakers of the need to improve availability and affordability of quality-assured medical abortion products at regional and national levels. Methods Using a national assessment protocol and an availability framework, we assessed the availability of medical abortion medicines in Bangladesh, Liberia, Malawi, Nepal, Nigeria, Rwanda, Sierra Leone and South Africa between September 2019 and January 2020. Results Registration of abortion medicines—misoprostol or a combination of mifepristone and misoprostol—was established in all countries assessed, except Rwanda. Mifepristone and misoprostol regimen for medical abortion was identified on the national essential medicines list/standard treatment guidelines for South Africa as well as in specific abortion care service and delivery guidelines for Bangladesh, Nepal, Nigeria, and Rwanda. In Liberia, Malawi, and Sierra Leone—countries with highly restrictive abortion laws and no abortion service delivery guidelines or training curricula—no government-supported training on medical abortion for public sector providers had occurred. Instead, training on medical abortion was either limited in scope to select private sector providers and pharmacists or prohibited. Community awareness activities on medical abortion have been limited in scope across the countries assessed and where abortion is broadly legal, most women do not know that it is an option. Conclusion Understanding the factors that influence the availability of medical abortion medicines is important to support policymakers improve availability of these medicines. The landscape assessments documented that medical abortion commodities can be uniquely impacted by the laws, policies, values, and degree of restrictions placed on service delivery programs. Results of the assessments can guide actions to improve access.

Upper Limb Joint Angle Estimation Using Wearable IMUs and Personalized Calibration Algorithm

Abstract: In physical therapy, exercises improve range of motion, muscle strength, and flexibility, where motion-tracking devices record motion data during exercises to improve treatment outcomes. Cameras and inertial measurement units (IMUs) are the basis of these devices. However, issues such as occlusion, privacy, and illumination can restrict vision-based systems. In these circumstances, IMUs may be employed to focus on a patient’s progress quantitatively during their rehabilitation. In this study, a 3D rigid body that can substitute a human arm was developed, and a two-stage algorithm was designed, implemented, and validated to estimate the elbow joint angle of that rigid body using three IMUs and incorporating the Madgwick filter to fuse multiple sensor data. Two electro-goniometers (EGs) were linked to the rigid body to verify the accuracy of the joint angle measuring algorithm. Additionally, the algorithm’s stability was confirmed even in the presence of external acceleration. Multiple trials using the proposed algorithm estimated the elbow joint angle of the rigid body with a maximum RMSE of 0.46°. Using the IMU manufacturer’s (WitMotion) algorithm (Kalman filter), the maximum RMSE was 1.97°. For the fourth trial, joint angles were also calculated with external acceleration, and the RMSE was 0.996°. In all cases, the joint angles were within therapeutic limits.

Challenges of online and face-to-face learning and its relationship with learner’s motivation among undergraduate students during the post-COVID era

Abstract: Background: Online and face-to-face learning challenges influence students’ motivation. However, limited studies have yet been conducted to correlate students’ motivation with online and face-to-face learning challenges, especially in Malaysia. This study examined the challenges faced by learners during face-to-face and online learning and its relationship with learners’ motivation. Methods: This cross-sectional- correlation study was conducted among year-3 to year-5 undergraduate students at the Universiti Malaysia Sarawak irrespective of gender and nationality. A total of 475 students’ data were collected using a validated self-administered questionnaire. Collected data were analysed using IBM SPSS version 27.0. Pearson’s moment correlation was used to examine the association between students’ motivation and online and face-to-face learning. A p value of less than 0.05 was considered statistically significant. Results: Technological challenge was weakly positively correlated with age (p<0.05), gender (p<0.05) and amotivation (p<0.01). However, no statistically significant correlation was found with extrinsic, intrinsic motivation and CGPA (p>0.05). Among the domains of challenges, the technological challenges were strongly positively correlated with the individual (p<0.001), domestic (p<0.001), institutional (p<0.001) and community (p<0.001) challenges. This study found that extrinsic motivation was positively correlated with intrinsic motivation, but both extrinsic and intrinsic motivation were negatively correlated with amotivation. All domains of challenges were positively correlated with amotivation of students. Conclusions: Universities could organise strategies to improve the current teaching and learning methods to boost students’ extrinsic and intrinsic motivation.

Unbiased Pain Assessment through Wearables and EHR Data: Multi-attribute Fairness Loss-based CNN Approach

Abstract: The combination of diverse health data (IoT, EHR, and clinical surveys) and scalable-adaptable Artificial Intelligence (AI), has enabled the discovery of physical, behavioral, and psycho-social indicators of pain status. Despite the hype and promise to fundamentally alter the healthcare system with technological advancements, much AI adoption in clinical pain evaluation has been hampered by the heterogeneity of the problem itself and other challenges, such as personalization and fairness. Studies have revealed that many AI (i.e., machine learning or deep learning) models display biases and discriminate against specific population segments (such as those based on gender or ethnicity), which breeds skepticism among medical professionals about AI adaptability. In this paper, we propose a Multi-attribute Fairness Loss (MAFL) based CNN model that aims to account for any sensitive attributes included in the data and fairly predict patients' pain status while attempting to minimize the discrepancies between privileged and unprivileged groups. In order to determine whether the trade-off between accuracy and fairness can be satisfied, we compare the proposed model with well-known existing mitigation procedures, and studies reveal that the implemented model performs favorably in contrast to state-of-the-art methods. Utilizing NIH All-Of-US data, where a cohort of 868 distinct individuals with wearables and EHR data gathered over 1500 days has been taken into consideration to analyze our suggested fair pain assessment system.

Hyperautomation in Super Shop Using Machine Learning

Abstract: The purpose of this research was to determine how we can optimize both customer and seller experiences in a super shop using hyperautomation technology. Here, a smart bot was employed to speed up responses of simple consumer queries by utilizing natural language processing in real time. We also used machine learning frameworks, such as XGBoost, linear regression, random forest, and hybrid models together, to predict future product demand. In addition, data mining methods, such as the Apriori algorithm, FP growth algorithm, and GSP algorithm, were used to find out which algorithm can be used to determine the right way to place a product to increase the super shop sale.

Long‐COVID causal impacts on cognitive ability of mild‐cognitive impaired population from large‐scale electronic health record using heterogeneous causal effect model

Abstract: Identification of long‐COVID impacts on human cognitive ability is important but Randomized Controlled Trial (RCT) is not possible. Virtual RCT (VRCT) can be done from electronic health record (EHR) which requires to address heterogeneity appropriately.

Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding

Abstract: Deep learning advancements have revolutionized scalable classification in many domains including computer vision. However, when it comes to wearable-based classification and domain adaptation, existing computer vision-based deep learning architectures and pretrained models trained on thousands of labeled images for months fall short. This is primarily because wearable sensor data necessitates sensor-specific preprocessing, architectural modification, and extensive data collection. To overcome these challenges, researchers have proposed encoding of wearable temporal sensor data in images using recurrent plots. In this paper, we present a novel modified-recurrent plot-based image representation that seamlessly integrates both temporal and frequency domain information. Our approach incorporates an efficient Fourier transform-based frequency domain angular difference estimation scheme in conjunction with the existing temporal recurrent plot image. Furthermore, we employ mixup image augmentation to enhance the representation. We evaluate the proposed method using accelerometer-based activity recognition data and a pretrained ResNet model, and demonstrate its superior performance compared to existing approaches.

Pathogenic Potential of Composted and Sundried Human Excreta from Eco-toilets in Bangladesh

Abstract: Background: The concept of eco-toilets in Bangladesh was promoted by an NGO (Practical Action, Bangladesh). The ecotoilets are especially built to collect human excreta for using them as manure on the fields after appropriate treatment for 12 months. Objectives: This study was designed to see the physical properties appropriate for fields and pathogenic potential to ensure the treated human excreta are safe for cultivator’s handling. Methodology: The study was carried out in three phases during 2010 to 2014. A total of 210 composted faecal excreta were examined for physical properties like colour, odour, odour intensity, moisture, bacteriological examination for faecally transmitted potential bacteria and parasitological examination for ova / cysts of faecally - transmitted parasites to recommend whether these composted excreta were suitable for use as manure by the cultivators. Results: Majority of the specimens in all three phases contained remarkable moisture: 22.3-80.0% in all specimens in phase I, a considerable number (16, 53.3%) contained 25% moisture in phase II and high moisture content (66.4%-78.9%) in 6(50.0%) of specimens in phase III. Bacteriological examination revealed no pathogenic organism in any specimen of the phases- although, some specimens in all phases showed growth of non-coliform bacteria. Parasitological examinations in all phases revealed ova/larvae of helminths of Ascaris lumbricoides (AL)/ Trichuris trichiura (TT)/ Strongyloides stercoralis (SS). No cyst or trophozoite of any pathogenic parasite, or oocyst of the cryptosporidium was found. Conclusion: The composted excreta could not be adequately treated by sun-drying as indicated by high moisture content and were found not suitable to use as manure by cultivators in the field. J Monno Med Coll June 2022;8(2): 44-47

Effect of excessive use of agrochemicals on farming practices: Bangladesh perspectives

Abstract: Agrochemicals are the part and parcel of modern farming practices. The study attempted to explore the effects of excessive use of agrochemicals oin farming practices. A structured questionnaire and face-to-face interview were conducted in collecting data from randomly selected 150 farmers of Satkhira sadar upazila under Satkhira district of Bangladesh during November 2022. The perceived effect of excessive use of agrochemicals was the focus variable whereas the selected socio-economic attributes of the participants were chosen as explanatory variables. The effect of overuse of agrochemicals was assessed employing a 4-point rating scale and ranked by calculating perceived effect index while the independent variables were measured using appropriate scoring techniques and scales. The findings revealed that, majority (48.7%) of the respondents perceived high positive effects of excessive use of agrochemicals on farming practices while a large portion (59.3%) of them perceived medium negative effects. It was also found that, the most positive effect perceived was increase productivity and cropping intensity (91.8%) while the worst effect was perceived as poisonous to human, animal, and soil microorganisms (82.7%). The results also explored that the main cause of overuse of agrochemical is provision of higher yield (94.7%) whereas the best management practice suggested by the farmers was agricultural training (95.3%). However, perceived effect of agrochemical usage was found positively correlated with respondents’ educational qualification, farming experience, agricultural training, extension media contact, knowledge, and awareness. So, appropriate strategies along with sustainable farming system is crucial to protect our environment and save our upcoming generations.