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Background: Acceptability of malaria chemoprevention interventions by caregivers is crucial for overall programme success. This study assessed coverage and acceptability of Seasonal Malaria Chemoprevention (SMC) in selected communities in the Northern part of Ghana. Methods: An analytical cross-sectional design was conducted from "July 23rd to August 4th, 2020—a 12-day period that covered 5 days of the first SMC implementation cycle and 7 days post-implementation. Using a stratified multi-stage sampling technique, a total of 495 caregivers providing care for 569 eligible children aged 3–59 months from randomly selected households in the study communities were enrolled into the study. Acceptability of SMC was assessed on a set of 19 questionnaire items-8 of the items measured caregivers' perceptions and 11 items measured children's reaction to administered medicines. Univariable and stepwise multivariable logistic regression analyses were performed to assess the predictors of acceptability of SMC at a 95% confidence interval and a p-value of 0.05. Results: SMC coverage was 95.1% (541/569). Caregivers had a good level of knowledge of SMC (n = 475; 96.0%; 95% CI 94.2—97.7%) and a good perception of SMC (n = 471; 95.2%; 95% CI 93.3–97.0). Seven out of ten caregivers (70.9%; 95% CI 66.9–74.9%) had good acceptability of SMC. For 7 out of 28 children who did not receive the SMC intervention, their caregivers intentionally refused them the intervention. Of those that received the treatment, 17.2% (n = 85; 95%CI 13.8–20.5%) of caregivers had at least one leftover amodiaquine tablet after the third day of treatment. Caregivers who practice Christianity or Islam had better acceptability than caregivers who practice African traditional religion (p < 0.001). Conclusion: Health authorities and stakeholders can work towards bridging the gap between knowledge and SMC treatment practices of caregivers through continuous education, adherence counseling, and effective monitoring of SMC practices in malaria-endemic countries. [ABSTRACT FROM AUTHOR]

Embolism, hyperglycemia, high intraocular pressure-induced increased reactive oxygen species (ROS) production, and microglial activation result in endothelial/retinal ganglion cell death. Here, we conducted in vitro and in vivo ischemia/reperfusion (I/R) efficacy studies of a hybrid antioxidant–nitric oxide donor small molecule, SA-10, to assess its therapeutic potential for ocular stroke. Methods: To induce I/R injury and inflammation, we subjected R28 and primary microglial cells to oxygen glucose deprivation (OGD) for 6 h in vitro or treated these cells with a cocktail of TNF-α, IL-1β and IFN-γ for 1 h, followed by the addition of SA-10 (10 µM). Inhibition of microglial activation, ROS scavenging, cytoprotective and anti-inflammatory activities were measured. In vivo I/R-injured mouse retinas were treated with either PBS or SA-10 (2%) intravitreally, and pattern electroretinogram (ERG), spectral-domain optical coherence tomography, flash ERG and retinal immunocytochemistry were performed. Results: SA-10 significantly inhibited microglial activation and inflammation in vitro. Compared to the control, the compound SA-10 significantly attenuated cell death in both microglia (43% vs. 13%) and R28 cells (52% vs. 17%), decreased ROS (38% vs. 68%) production in retinal microglia cells, preserved neural retinal function and increased SOD1 in mouse eyes. Conclusion: SA-10 is protective to retinal neurons by decreasing oxidative stress and inflammatory cytokines. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the prevalence of artifacts in optical coherence tomography (OCT) images with acceptable signal strength and evaluate the performance of supervised deep learning models in improving OCT image quality assessment., Methods: We conducted a retrospective study on 4555 OCT images from 546 patients, with each image having an acceptable signal strength (≥6). A comprehensive analysis of prevalent OCT artifacts was performed, and five pretrained convolutional neural network models were trained and tested to infer images based on quality., Results: Our results showed a high prevalence of artifacts in OCT images with acceptable signal strength. Approximately 21% of images were labeled as nonacceptable quality. The EfficientNetV2 model demonstrated superior performance in classifying OCT image quality, achieving an area under the receiver operating characteristic curve of 0.950 ± 0.007 and an area under the precision recall curve of 0.985 ± 0.002., Conclusions: The findings highlight the limitations of relying solely on signal strength for OCT image quality assessment and the potential of deep learning models in accurately classifying image quality., Translational Relevance: Application of the deep learning-based OCT image quality assessment models may improve the OCT image data quality for both clinical applications and research.