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Occurrence of Cryptosporidium oocysts in edible frogs (Rana species) sold for human consumption in Hanwa frog market, Zaria, Kaduna State, Nigeria

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Journal of Coastal Life Medicine 2017年第5期5卷 202-205页

作者： Grace Sabo Nok Kia blessing iveren ukuma Mathias Bello Odoba Jerome Unubi Okpanachi Department of Veterinary Public Health and Preventive Medicine Faculty of Veterinary MedicineAhmadu Bello UniversityZariaNigeria

Objective: To determine the occurrence of Cryptosporidium oocysts in edible frogs (Rana spp.) sold at the Hanwa frog market, Zaria, Kaduna State, Nigeria. Methods: A total of 117 frogs (Rana spp.) were randomly collected based on consent and availability at the market. The intestinal contents from the frogs were examined by staining flotation and sedimentation smears with modified Ziehl-Neelsen staining technique and microscopy of the oocysts. Results: Overall, 35.9% of frogs sampled from the Hanwa frog market were positive for Cryptosporidium oocysts. There were more Cryptosporidium oocysts detected by sedimentation test (28.2%) than flotation test (23.9%). There was no significant statistical association between sex of frogs and oocyst detection (χ2 = 0.5349, P > 0.05). Also, there was no correlation between the weights of frogs and the sizes of the Cryptosporidium oocysts detected (r = 0.0109, P > 0.05). Nevertheless, female frogs (40%) and frogs within the weight range 170–219 g were more infected with Cryptosporidium (66.7%). Oocysts size range 6.10–7.00 μm had the highest frequency of 10 (23.8%). By size 28.2% of the oocysts detected suggested infection with Cryptosporidium parvum and Cryptosporidium maleagridis. Conclusions: This study has established that edible frogs (Rana spp.) sold at the Hanwa frog market for human consumption were infected with Cryptosporidium spp. which constitutes a valid public health risk especially for immunocompromised individuals.

关键词： Cryptosporidium oocysts Edible frogs (Rana spp.) Frog market Zoonotic potential Nigeria

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Application of Machine Learning Techniques for Okra Shelf Life Prediction

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Journal of Data Analysis and Information Processing 2021年第3期9卷 136-150页

作者： iveren blessing Iorliam Barnabas Achakpa Ikyo Aamo Iorliam Emmanuel Odeh Okube Kenneth Dekera Kwaghtyo Yahaya I. Shehu Centre for Food Technology and Research (CEFTER) Benue State University Makurdi Nigeria Department of Mathematics & Computer Science BSU Makurdi Nigeria Shehu Shagari College of Education Sokoto Nigeria

The ability of machine learning techniques to make accurate predications is increasing. The aim of this work is to apply machine learning techniques such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, and K-Nearest Neighbour algorithms to predict the shelf life of Okra. Predicting the shelf life of Okra is important because Okra becomes harmful for human consumption if consumed after its shelf life. Okra parameters such as weight loss, firmness, Titrable Acid, Total Soluble Solids, Vitamin C/Ascorbic acid content, and PH were used as inputs into these machine learning techniques. Support Vector Machine, Naïve Bayes and Decision Tree each accurately predicted the shelf life of Okra with accuracies of 100%. However, the Logistic Regression and K-Nearest Neighbour achieved 88.89% and 88.33% accuracies, respectively. These results showed that machine learning techniques especially Support Vector Machine, Naïve Bayes and Decision Tree can be effectively applied for the prediction of Okra shelf life.

关键词： Machine Learning Shelf Life Okra Prediction

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