The comparison of the quality of selected brands of antibiotics in Tanzania sourced from different geographical regions.

OBJECTIVES: The quality of amoxicillin capsules, ceftriaxone for injection, and ciprofloxacin tablets was evaluated to determine whether there is any difference in quality when comparing the country of origin. This was undertaken because it has been claimed that antibiotics manufactured in Europe are of superior quality to those originating from Africa or Asia. METHODS: Samples of amoxicillin capsules, ceftriaxone for injection, and ciprofloxacin tablets were collected from three randomly selected wholesale pharmacies in each city, namely Arusha, Dar es Salaam and Mwanza, Tanzania. The collected samples of collected brands were subjected to quality control testing as per their respective pharmacopoeial monographs. Amoxil 250 mg capsules (Glaxo Wellcome, Mayenne, France), Rocephin (Roche, Switzerland) and Cipro-Denk 500 (Allphamed Pharbil Arzneimittel GmbH, Gottingen, Germany) were used as reference brands for the other generic brands of amoxicillin, ceftriaxone and ciprofloxacin, respectively. RESULTS: A total of 31 brands (10 different brands of amoxicillin capsules, 9 of ceftriaxone sodium injections, and 12 of ciprofloxacin tablets) were collected from the targeted regions and subjected to quality control testing. All samples of collected brands complied with the requirements of their respective pharmacopoeial monographs. CONCLUSIONS: There was no significant difference in quality between brands of amoxicillin capsules, ceftriaxone for injection, and ciprofloxacin tablets manufactured in Africa and Asia against those manufactured in Europe in terms of compliance with the respective pharmacopoeial monographs.

Deep Learning for Image-Based Cassava Disease Detection.

Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New deep learning models offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection.