Multi-scale modeling of the circadian modulation of learning and memory

Women's and health professionals' perceptions, beliefs and barriers to cervical cancer screening uptake in Southern Ethiopia: a qualitative study

Cervical cancer remains a public health problem worldwide. Screening for cervical cancer is poorly implemented in resource-limited settings. In Ethiopia, evidence from the community and health professionals regarding implementation of the screening programme is lacking. The objective of this study was to explore women's and health professionals' perceptions, beliefs, and barriers in relation to cervical screening in Southern Ethiopia. Five focus group discussions among women and six key informant interviews with health professionals were conducted from June to July 2022 to gather the required data from a total of 42 participants. The participants were purposively selected from a diverse group to ensure varied viewpoints. Data were collected through group discussions and face-to-face interviews using a semi-structured interview guide. The interview sessions were tape-recorded. The data were analysed using a thematic approach. Women demonstrated a low level of awareness and perceived risk. Also, the perceived benefit of screening for cervical cancer during healthy periods was low. Individual and system-level barriers to screening include low awareness, stigma, poor perceptions towards health screening and causes of cervical cancer, low risk perception and competing domestic priorities, shortage of trained human and other resources, human resource turnover, low implementation and lack of close follow-up of screening programmes. In summary, lack of awareness, misconceptions, and poor perceptions were common. Screening implementation and uptake were low due to individual, psychosocial, and system-related barriers. Therefore, behavioural change communication and system-strengthening efforts need to be in place to effectively tackle the observed gaps.

Detection of glaucoma using retinal fundus images: A comprehensive review

Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.