Knowledge, attitude and practices about malaria among members of a university community in Kwara State, Nigeria

Malaria is still the leading cause of morbidity and mortality in many tropical regions despite global efforts towards its eradication. This study investigates knowledge and practices about malaria among members of Kwara State University community, Nigeria. Questionnaires were administered to 518 undergraduate students and 91 staff randomly from the university community. Majority (99.63 %) of students and staff (98.91 %) agreed that malaria is caused by mosquito bite, two hundred and twenty two (42.09 %) of the students and 69.23 % of staff identified the female Anopheles mosquito as the vector of malaria. A total of 32.82 % picked stagnant water; 27.03 % water retaining containers and 38.19 % plant/vegetation as mosquito vector habitat. The majority of students (83.78 %) and staff (97.80 %) also responded that night was the common vector biting period. On malaria symptoms the respondents ranked fever (71.81 %), body pain (31.85 %) and headaches (29.53 %) while staff responses followed similar trends. On the medication employed for the treatment of malaria, ACT, (53.86 %), Artesunate (25.09 %), Sulphadoxine-pyrimethamine (15.44 %) and paracetamol (10.81%) attested to by the students while those of the staff was in the order 53.84 % ACT, 29.67 % Artesunate and 18.68 % Sulphadoxine-pyrimethamine. The result elucidates that most respondents had fair knowledge about malaria, its vector, habitat, prevention and control, but the majority had poor practices towards malaria. Therefore, education and interventions aimed at social and behaviour change are necessary to address and fill the gaps highlighted, conscious efforts toward enlightenment of the people of the university community and its environment is a necessary step among suggestions proffered.Keywords: Malaria, Community, Container survey, Knowledge, Practic

Pattern of Skin Cancers in a Tertiary Medical Center in Southwest Nigeria

Background: Skin cancers are the most common malignancies in the western world, and their incidence is increasing globally. However, the data about the pattern in sub-Saharan Africa are limited. This study evaluates the pattern of primary skin malignancies in a tertiary medical center located in a sub-urban area. Methods: The histo-pathological records of patients managed for malignancies from January 2012 to December 2020 were retrieved from the pathology department of a tertiary medical center in Ekiti State, Southwest Nigeria. All primary skin cancers seen within this study period were extracted from the records and then reviewed retrospectively. Results: The male-to-female ratio of primary skin malignancies was 1:1.06, and the mean age of patients was 57.2 ± 17years. All patients were black Africans who were mainly of the Yoruba ethnicity (97.2%). Squamous cell cancer had the highest frequency (34.7%), followed by melanoma (27.8%), dermatofibrosarcoma (12.5%), and basal cell carcinoma (11.1%). The most commonly affected anatomic region is the lower limbs (50.6%). Conclusion: The pattern of primary skin cancers seen in black Africans differ from that of Caucasians: however, larger community-based studies in our environment is recommended to provide more conclusive information about the pattern of skin cancers

Child malaria treatment decisions by mothers of children less than five years of age attending an outpatient clinic in south-west Nigeria: an application of the PEN-3 cultural model

<p>Abstract</p> <p>Background</p> <p>Using the PEN-3 cultural model, this study sought to understand mothers treatment decisions about their child febrile illness by examining positive health beliefs and practices held by mothers, examine existential (unique) practices that are indigenous to mothers and have no harmful health consequences, and explore negative beliefs and practices that limit recommended responses to febrile illness in children.</p> <p>Methods</p> <p>This qualitative study was conducted in the paediatric section of an outpatient clinic in south-west Nigeria. A total of 123 mothers with children less than five years of age with febrile illness diagnosed as malaria by physicians were individually interviewed on their treatment-seeking practices prior to visiting the clinic and their reasons for attendance at the clinic.</p> <p>Results</p> <p>For some mothers interviewed, effective treatment from the clinic for their child's febrile illness, coupled with physician's approach with malaria diagnosis and treatment practices was important in generating positive maternal treatment-seeking responses to child febrile illness. In addition, beliefs related to a child teething highlighted existential decisions with treatment-seeking for child febrile illness in this setting. Finally, the belief that febrile illness is not all that severe despite noticeable signs and symptoms was a concerning negative perception shared by some mothers in this study.</p> <p>Conclusion</p> <p>The findings highlight the need to consider not only the responses that may serve as barriers to effective treatment, but also an acknowledgment of the positive and existential responses that are equally critical in influencing mothers' management of malaria in their children.</p

MasakhaNEWS: News Topic Classification for African languages

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach.Comment: Accepted to IJCNLP-AACL 2023 (main conference

MasakhaNEWS:News Topic Classification for African languages

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach

Production of Reinforced Polyester Composite from Okra Fibre and Sawdust

This research presents properties of okra and sawdust reinforced polyester composite. The compatibility of the simple woven okra and sawdust with polyester was enhanced with stearic acid treatment. FTIR analysis confirmed decrease in hydrophilicity of the fibre and dust. Six composite samples; pure polyester, sawdust reinforced polyester composite, okra reinforced polyester composite, 10% sawdust in okra fibre reinforced composite, 20% sawdust in okra fibre reinforced composite and 30% sawdust in okra fibre reinforced composites were fabricated and characterized. The morphological analysis showed that the homogeneity of polyester in the samples reduces with increase in sawdust filler (10-30 wt%). Water absorption was highest (1.6%) in 30% sawdust in okra. The densities of all the composites were between 3.5 – 4.5 kg/m3. The sawdust reinforced composite recorded low impact energy of 0.25 J while the woven okra fibre reinforced polyester recorded the highest impact energy of 9.9 J. Hardness property reduced as the biomass content increased. Unreinforced polyester recorded the highest average elongation of 25% (1400 µm) and reduced elongation as filler increased. The storage modulus was highest for unreinforced composite at 40oC but as the temperature reached 81oC the storage modulus of unreinforced polyester dropped lower than the sawdust composite. The damping factor (1.41) was highest for 20 wt% sawdust/okra polyester composite. This research concludes that sawdust and okra are suitable for lightweight and energy damping materials in automobile applications