Technological improvements have resulted in increased discovery of new microRNAs (miRNAs) and refinement and enrichment of existing miRNA families. miRNA families are important because they suggest a common sequence or structure configuration in sets of genes that hint to a shared function. Exploratory tools to enhance investigation of characteristics of miRNA families and the functions of family-specific miRNA genes are lacking. We have developed, miRNAVISA, a user-friendly web-based tool that allows customized interrogation and comparisons of miRNA families for hypotheses generation, and comparison of per-species chromosomal distribution of miRNA genes in different families. This study illustrates hypothesis generation using miRNAVISA in seven species. Our results unveil a subclass of miRNAs that may be regulated by genomic imprinting, and also suggest that some miRNA families may be species-specific, as well as chromosome- and/or strand-specific.
Background. The respiratory system of children is vulnerable to exposure to particulate matter (PM) with a diameter of less than 2.5 and 10 μm (PM2.5 and PM10) or even lower. Objective. This study assessed PM10 and PM2.5 levels and respiratory health impacts on children in schools located in an industrialized suburb in Kenya. Method. The PM10 and PM2.5 levels were sampled from five public primary schools in Athi River Township and a control school during the wet and dry seasons. Outdoor and classroom samples were collected concurrently on an 8-hour mean during school hours on two consecutive days in each school and analyzed using gravimetric techniques. Five hundred and seventy-eight (n = 578) pupils aged 9–14 years from these schools were also evaluated for symptoms of respiratory illnesses and lung function using a questionnaire and spirometric method, respectively, during the same periods. Results. Indoor median PM10 levels (μg/m3) ranged from 60.8–269.1 and 52.8–232.3 and PM2.5 values (μg/m3) of 17.7–52.4 and 28.5–75.5 during the dry and wet seasons, respectively. The control classrooms had significantly (p <0.05) lower median PM10 levels (μg/m3) of 5.2 and 4.2, and PM2.5 levels (μg/m3) of 3.5 and 3.0 during the respective seasons. Nearly all the classrooms in Athi River schools had PM2.5 and PM10 median levels that exceeded the World Health Organization (WHO) recommended levels. The indoor-to-outdoor ratios varied from 0.35–1.40 and 0.80–2.40 for PM10 and 0.30–0.80 and 0.80–1.40 for PM2.5 during the dry and wet seasons, respectively, suggesting higher levels in the classrooms during the wet season. The relative risk (RR) and odds ratio (OR) presented higher prevalence of respiratory diseases following PM exposure in all the Athi River schools than the control during the dry and wet seasons. At 95% CI, the RR and OR showed strong associations between high PM10 and PM2.5 levels and lung function deficits and vice versa. The association was more prevalent during the wet season. Conclusions. The study calls for effective indoor air management programs in school environments to reduce PM exposure and respiratory health impacts. Participant Consent. Obtained. Ethics Approval. The research permit and approvals were obtained from the University of Nairobi/Kenyatta National Hospital Ethics and Research Committee (KNH-UoN ERC Reference: P599/08/2016) and the National Commission for Science, Technology and Innovation (Reference: NACOSTI/P/18/4268/25724). Competing Interests. The authors declare no competing financial interests.
Background One in four deaths among females of reproductive age is maternal or pregnancy related, thereby making maternal mortality a major global health concern. A disproportionate number of these deaths occur in developing countries. In Kenya, maternal mortality ratio (MMR) has declined from 708 to 378 deaths per 100,000 live births between 2000 and 2021. However, the Sustainable Development Goal (SDG-3.1) target is to reduce global MMR to less than 70 deaths per 100,000 live births by 2030. Here, we quantify and highlight indicators that contribute to differentiated MMR across different geographic regions in Kenya. We anticipate that this will inform targeted interventions and resource allocation for each specific region and fast-track SDG-3 attainment. Methods We leveraged data from the demographic and health survey for Kenya. The correlation in the patterns of the indicators and MMR across the counties and the regions was analyzed and the cumulative contribution by multiple indicators for each county was determined. We then compared the performance of the regions to the national average by calculating the rate ratios. Results Our results highlight how variation in socio-demographic characteristics influence maternal mortality rates across Kenya. We observed a high antenatal clinic attendance rate, but on the contrary very low rates of uptake of modern contraceptives. Infectious diseases (Malaria, TB, and HIV) exhibited an overlap in geographic distribution in coastal counties and counties around lakes. There was a significant correlation between prevalence of malaria and HIV (Pearson’s correlation coefficient r = 0.59), and a moderate positive correlation between prevalence of HIV and TB (Pearson’s correlation coefficient r = 0.41). Gender-based violence during pregnancy was highest in an urban setting (18.1%), and lowest in marginalized rural areas (2.7%). Female genital mutilation had higher rates among those who practice Islam (51.1%), live in rural settings (25.9%), with no education (13%), and in the lowest wealth quintile (6.2%). Conclusion These findings suggest a wide spectrum of direct, indirect, cultural and socio-economic factors collectively contributing to elevated MMR. We disaggregate sub-national disparities and highlight that customized interventions for different sub-populations are required to curtail maternal mortality, and accelerate the attainment of the SDG-3.1 target.
Background: The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (output), hindering the application of machine learning to novel tasks. End-to-end deep learning, which does not require intermediate feature engineering, has been recommended to overcome this challenge but end-to-end deep learning models require large labelled training data sets often unavailable in many medical applications. In this study, we trained machine learning models to predict paediatric hospitalization given raw photoplethysmography (PPG) signals obtained from a pulse oximeter. We trained self-supervised learning (SSL) for automatic feature extraction from PPG signals and assessed the utility of SSL in initializing end-to-end deep learning models trained on a small labelled data set with the aim of predicting paediatric hospitalization.Methods: We compared logistic regression models fitted using features extracted using SSL with end-to-end deep learning models initialized either randomly or using weights from the SSL model. We also compared the performance of SSL models trained on labelled data alone (n=1,031) with SSL trained using both labelled and unlabelled signals (n=7,578). Results: The SSL model trained on both labelled and unlabelled PPG signals produced features that were more predictive of hospitalization compared to the SSL model trained on labelled PPG only (AUC of logistic regression model: 0.78 vs 0.74). The end-to-end deep learning model had an AUC of 0.80 when initialized using the SSL model trained on all PPG signals, 0.77 when initialized using SSL trained on labelled data only, and 0.73 when initialized randomly. Conclusions: This study shows that SSL can improve the classification of PPG signals by either extracting features required by logistic regression models or initializing end-to-end deep learning models. Furthermore, SSL can leverage larger unlabelled data sets to improve performance of models fitted using small labelled data sets.
Introduction: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader and intra-reader variability hinder comparison of results from different studies or centres, which negatively affects efforts to track the burden of chest diseases or evaluate the efficacy of interventions such as vaccines. This study explores machine learning models that could standardize interpretation of CXR across studies and the utility of incorporating individual reader annotations when training models using CXR data sets annotated by multiple readers. Methods: Convolutional neural networks were used to classify CXRs from seven low to middle-income countries into five categories according to the World Health Organization's standardized methodology for interpreting paediatric CXRs. We compared models trained to predict the final/aggregate classification with models trained to predict how each reader would classify an image and then aggregate predictions for all readers using unweighted mean. Results: Incorporating individual reader's annotations during model training improved classification accuracy by 3.4% (multi-class accuracy 61% vs 59%). Model accuracy was higher for children above 12 months of age (68% vs 58%). The accuracy of the models in different countries ranged between 45% and 71%. Conclusions: Machine learning models can annotate CXRs in epidemiological studies reducing inter-reader and intra-reader variability. In addition, incorporating individual reader annotations can improve the performance of machine learning models trained using CXRs annotated by multiple readers.
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