Project OPUS: Development and evaluation of an electronic platform for pain management education of medical undergraduates in resource-limited settings

Introduction Pain is a very frequent symptom that is reported by patients when they present to health professionals but remains undertreated or untreated, particularly in low-resource settings including Nigeria. Lack of training in pain management remains the most significant obstacle to pain treatment alongside an inadequate emphasis on pain education in undergraduate medical curricula, negatively impacting on subsequent care of patients. This study aimed to determine the effect of a 12-week structured e-Learning course on the knowledge of pain management among Nigerian undergraduate medical students. Methods Prospective, multisite, pre-post study conducted across five medical colleges in Nigeria. Structured modules covering aspects of pain management were delivered on an e-Learning platform. Pre- and post-test self-assessments were carried out in the 12-week duration of the study. User experience questionnaires and qualitative interviews were conducted via instant messaging to evaluate user experiences of the platform. User experience data was analysed using the UEQ Data Analysis Tool and Framework Analysis. Results A total of 216 of 659 eligible students completed all sections of the e-Learning course. Participant mean age was 23.52 years, with a slight female predominance (55.3%). Across all participants, an increase in median pre- and post-test scores occurred, from 40 to 60 (Z = 11.3, p<0.001, effect size = 1.3), suggestive of increased knowledge acquisition relating to pain management. Participants suggested e-Learning is a valuable approach to delivering pain education alongside identifying factors to address in future iterations. Conclusion e-Learning approaches to pain management education can enhance traditional learning methods and may increase students’ knowledge. Future iterations of e-Learning approaches will need to consider facilitating the download of data and content for the platform to increase user uptake and engagement. The platform was piloted as an optional adjunct to existing curricula. Future efforts to advocate and support integration of e-Learning for pain education should be two-fold; both to include pain education in the curricula of medical colleges across Nigeria and the use of e-Learning approaches to enhance teaching where feasible. Methods: Prospective, multisite, pre-post study conducted across five medical colleges in Nigeria. Structured modules covering aspects of pain management were delivered on an e-Learning platform. Pre- and post-test self-assessments were carried out in the 12-week duration of the study. User experience questionnaires and qualitative interviews were conducted via instant messaging to evaluate user experiences of the platform. User experience data was analysed using the UEQ Data Analysis Tool and Framework Analysis. Results: A total of 216 of 659 eligible students completed all sections of the e-Learning course. Participant mean age was 23.52 years, with a slight female predominance (55.3%). Across all participants, an increase in median pre- and post-test scores occurred, from 40 to 60 (Z=11.3, p<0.001, effect size=1.3), suggestive of increased knowledge acquisition relating to pain management. Participants suggested e-Learning is a valuable approach to delivering pain education alongside identifying factors to address in future iterations. Conclusion: e-Learning approaches to pain management education can enhance traditional learning methods and may increase students’ knowledge. Future iterations of e-Learning approaches will need to consider facilitating the download of data and content for the platform to increase user uptake and engagement. The platform was piloted as an optional adjunct to existing curricula. Future efforts to advocate and support integration of e-Learning for pain education should be two-fold; both to include pain education in the curricula of medical colleges across Nigeria and the use of e-Learning approaches to enhance teaching where feasible

ALGORITHM DEVELOPMENT FOR FINGERPRINT IMAGE ENHANCEMENT USING WAVELET PROCESSING

Abstract — This paper presents a technique for fingerprint image enhancement using wavelet processing. The algorithm developed uses Daubechies ’ wavelets for decomposition as well as reconstruction of the fingerprint image. Experimental results indicate that this algorithm is quite effective, and performs quite competitively with existing methods

Bronchial carcinoid tumors: A rare malignant tumor

Bronchial carcinoid tumors (BCTs) are an uncommon group of lung tumors. They commonly affect the young adults and the middle aged, the same age group affected by other more common chronic lung conditions such as pulmonary tuberculosis. Diagnosis is commonly missed or delayed due to a low index of suspicion. Surgery is the mainstay of treatment with an excellent outcome. There are many reports of this rare group of tumors in the Western and Asian regions. The only report around our sub‑region is a post mortem report of an atypical variant. We wish to report a case of the typical variant and increase our index of suspicion. A 25‑year‑old male presented with a 4 years history of cough and haemoptysis. He was repeatedly treated for bronchial asthma and pulmonary tuberculosis with no improvement of symptoms. Chest X‑ray and chest computed tomography scan revealed a left upper lobe tumor. Histology reported a typical variant of BCT which was confirmed by immunohistochemistry. He had a left upper lobectomy and has done excellently well thereafter. A high index of suspicion is needed to reduce the risk of missing or delaying the diagnosis.Key words: Bronchial carcinoid tumor, diagnosis, outcome, treatment, West Afric

Detecting Cassava Plants under Different Field Conditions Using UAV-Based RGB Images and Deep Learning Models

A significant number of object detection models have been researched for use in plant detection. However, deployment and evaluation of the models for real-time detection as well as for crop counting under varying real field conditions is lacking. In this work, two versions of a state-of-the-art object detection model—YOLOv5n and YOLOv5s—were deployed and evaluated for cassava detection. We compared the performance of the models when trained with different input image resolutions, images of different growth stages, weed interference, and illumination conditions. The models were deployed on an NVIDIA Jetson AGX Orin embedded GPU in order to observe the real-time performance of the models. Results of a use case in a farm field showed that YOLOv5s yielded the best accuracy whereas YOLOv5n had the best inference speed in detecting cassava plants. YOLOv5s allowed for more precise crop counting, compared to the YOLOv5n which mis-detected cassava plants. YOLOv5s performed better under weed interference at the cost of a low speed. The findings of this work may serve to as a reference for making a choice of which model fits an intended real-life plant detection application, taking into consideration the need for a trade-off between of detection speed, detection accuracy, and memory usage

Maximum power point tracking technique based on optimized adaptive differential conductance

Maximum power point (MPP) tracking technique based an optimized adaptive differential conductance technique was developed in this paper. The performance of the algorithm developed in this paper was evaluated at solar irradiance of 1,000, 800 and 600 W/m2 and at temperature of 298, 328 and 358 K. From the simulation results, it was observed that the impedance of the panel decreases as the irradiance increases while the impedance of the load is not affected by the irradiance. This technique was also validated with conventional incremental conductance (INC) technique. From the validation result, the resultant conductance of the optimized adaptive differential conductance technique at MPP is 0.0030 mho higher than resultant conductance at ideal condition while conventional technique has the resultant conductance of 0.0418 mho lower than the resultant conductance at ideal condition. From the analysis, the technique has a relative improvement of 6.0558% compared to the conventional INC technique. The simulation was done using Matrix Laboratory (MATLAB)

Walking balance is mediated by muscle strength and bone mineral density in postmenopausal women: an observational study

Background: Depletion of ovarian hormone in postmenopausal women has been associated with changes in the locomotor apparatus that may compromise walking function including muscle atrophy/weakness, weight gain, and bone demineralization. Therefore, handgrip strength (HGS), bone mineral density (BMD) and body composition [percentage body fat mass (%BFM), fat mass (FM), Fat-free mass (FFM) and body mass index (BMI)], may significantly vary and predict WB in postmenopausal women. Consequently, the study sought to 1. Explore body composition, BMD and muscle strength differences between premenopausal and postmenopausal women and 2. Explore how these variables [I.e., body composition, BMD and muscle strength] relate to WB in postmenopausal women. Method: Fifty-one pre-menopausal (35.74 + 1.52) and 50 postmenopausal (53.32 + 2.28) women were selected by convenience sampling and studied. Six explanatory variables (HGS, BMD, %BFM, FFM, BMI and FM) were explored to predict WB in postmenopausal women: Data collected were analyzed using multiple linear regression, ANCOVA, independent t-test and Pearson correlation coefficient at p < 0.05. Result: Postmenopausal women had higher BMI(t = + 1.72; p = 0.04), %BFM(t = + 2.77; p = .003), FM(t = + 1.77; p = 0.04) and lower HGS(t = − 3.05; p = 0.001),compared to the premenopausal women. The predicted main effect of age on HGS was not significant, F(1, 197) = 0.03, p = 0.06, likewise the interaction between age and %BFM, F(1, 197) = 0.02, p = 0.89; unlike the predicted main effect of %BFM, F(1, 197) = 10.34, p = .002, on HGS. HGS was the highest predictor of WB (t = 2.203; β=0.3046) in postmenopausal women and combined with T-score right big toe (Tscorert) to produce R2 = 0.11;F (2, 47)=4.11;p = 0.02 as the best fit for the predictive model. The variance (R2) change was significant from HGS model (R2 = 0.09;p = 0.03) to HGS + Tscorert model (R2 = 0.11;p = 0.02). The regression model equation was therefore given as: WB =5.4805 + 0.1578(HGS) + (− 1.3532) Tscorert. Conclusion: There are differences in body composition suggesting re-compartmentalization of the body, which may adversely impact the (HGS) muscle strength in postmenopausal women. Muscle strength and BMD areassociated with WB, although, only contribute to a marginal amount of the variance for WB. Therefore, other factors in addition to musculoskeletal health are necessary to mitigate fall risk in postmenopausal women