Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population.

Introduction: Postpartum hemorrhage (PPH) is a significant cause of maternal mortality worldwide, particularly in low- and middle-income countries. It is essential to develop effective prediction models to identify women at risk of PPH and implement appropriate interventions to reduce maternal morbidity and mortality. This study aims to predict the occurrence of postpartum hemorrhage using machine learning models based on antenatal, intrapartum, and postnatal visit data obtained from the Kenya Antenatal and Postnatal Care Research Collective cohort. Method: Four machine learning models - logistic regression, naïve Bayes, decision tree, and random forest - were constructed using 67% training data (1,056/1,576). The training data was further split into 67% for model building and 33% cross validation. Once the models are built, the remaining 33% (520/1,576) independent test data was used for external validation to confirm the models' performance. Models were fine-tuned using feature selection through extra tree classifier technique. Model performance was assessed using accuracy, sensitivity, and area under the curve (AUC) of the receiver operating characteristics (ROC) curve. Result: The naïve Bayes model performed best with 0.95 accuracy, 0.97 specificity, and 0.76 AUC. Seven factors (anemia, limited prenatal care, hemoglobin concentrations, signs of pallor at intrapartum, intrapartum systolic blood pressure, intrapartum diastolic blood pressure, and intrapartum respiratory rate) were associated with PPH prediction in Kenyan population. Discussion: This study demonstrates the potential of machine learning models in predicting PPH in the Kenyan population. Future studies with larger datasets and more PPH cases should be conducted to improve prediction performance of machine learning model. Such prediction algorithms would immensely help to construct a personalized obstetric path for each pregnant patient, improve resource allocation, and reduce maternal mortality and morbidity.

Whole Exome Sequencing Reveals Novel Candidate Genes in Familial Forms of Glaucomatous Neurodegeneration.

Glaucoma is the largest cause of irreversible blindness with a multifactorial genetic etiology. This study explores novel genes and gene networks in familial forms of primary open angle glaucoma (POAG) and primary angle closure glaucoma (PACG) to identify rare mutations with high penetrance. Thirty-one samples from nine MYOC-negative families (five POAG and four PACG) underwent whole-exome sequencing and analysis. A set of prioritized genes and variations were screened in an independent validation cohort of 1536 samples and the whole-exome data from 20 sporadic patients. The expression profiles of the candidate genes were analyzed in 17 publicly available expression datasets from ocular tissues and single cells. Rare, deleterious SNVs in AQP5, SRFBP1, CDH6 and FOXM1 from POAG families and in ACACB, RGL3 and LAMA2 from PACG families were found exclusively in glaucoma cases. AQP5, SRFBP1 and CDH6 also revealed significant altered expression in glaucoma in expression datasets. Single-cell expression analysis revealed enrichment of identified candidate genes in retinal ganglion cells and corneal epithelial cells in POAG; whereas for PACG families, retinal ganglion cells and Schwalbe's Line showed enriched expression. Through an unbiased exome-wide search followed by validation, we identified novel candidate genes for familial cases of POAG and PACG. The SRFBP1 gene found in a POAG family is located within the GLC1M locus on Chr5q. Pathway analysis of candidate genes revealed enrichment of extracellular matrix organization in both POAG and PACG.

Paternal inheritance of diet induced metabolic traits correlates with germline regulation of diet induced coding gene expression.

Inheritance of induced traits through the germline is poorly understood and controversial. The ideal evidence correlating induced and inherited traits with germline gene expression remains largely obscure. Using a Drosophila coding transcriptome level model of paternal high sugar diet induced alterations in triglyceride levels across generations, in conjunction with pre-existing data, we show here highly significant overlap of differentially expressed genes between the ancestral generation, the resulting sperm and embryos, and the future generation individuals. Further, gene ontology and literature-wide overrepresentation analysis reveal association of lipid and carbohydrate metabolism, and immune response, besides others, with differentially expressed genes in the above samples. Analysis of available mouse data on inheritance of diet induced metabolic traits also revealed a similar correlation. Our results support a causal role of sperm borne mRNAs in inheritance of acquired characteristics, consistent with the evidence that these mRNAs are delivered to the oocyte and influence embryonic development.