NeoAI 1.0: Machine learning-based paradigm for prediction of neonatal and infant risk of death.

BACKGROUND: The Neonatal mortality rate in the United States is 3.8 deaths per 1000 live births, which is comparably higher than other nations. PURPOSE: The aim of the proposed study is to design and develop Artificial Intelligence (AI) models (NeoAI 1.0, Global Biomedical Technologies, Inc., Roseville, CA, USA) on risk variables extracted from the National Center for Health Statistics (NCHS) data from 2014 to 2017 duration, consisting of birth-death infant files to predict neonatal and infant deaths. METHODOLOGY: The NCHS data consisted of 15.8 million live birth records, including 91,773 infant deaths, out of which 61,222 were neonatal (life <28 days) and the rest were non-deaths. We designed and developed two different kinds of systems, labelled as neonatal and infant death systems. The data preparation consisted of balancing the two classes using the Adaptive Synthetic oversampling technique (ADASYN) paradigm. The best features were extracted using mutual information followed by 5-fold cross-validation using four different models, namely AdaBoost, XGBoost, Random Forest, and Logistic Regression based on balanced and unbalanced paradigms. RESULTS: XGBoost gave the best results for the neonatal system with AUC of 0.97 and 0.99 (p < 0.0001), while for the infant system, the scores were 0.91 and 0.99, both systems, without/with ADASYN integration, respectively. Further, there was a 60% increase in F1-score and sensitivity with ADASYN integration. The most important risk factors for classifier models along with feature extraction were maternal age and maternal race by Hispanic classification. Further, gestational age, labour aid and newborn condition were also part of the top five risk factors for these models. CONCLUSIONS: NoeAI showed two independent powerful machine learning (ML) systems and selected the best risk predictors combined with classification models for neonatal and infant deaths. The response time of the online platform was less than a second.

Protein kinase Cζ phosphorylates occludin and promotes assembly of epithelial tight junctions.

Protein kinases play an important role in the regulation of epithelial tight junctions. In the present study, we investigated the role of PKCζ (protein kinase Cζ) in tight junction regulation in Caco-2 and MDCK (Madin-Darby canine kidney) cell monolayers. Inhibition of PKCζ by a specific PKCζ pseudosubstrate peptide results in redistribution of occludin and ZO-1 (zona occludens 1) from the intercellular junctions and disruption of barrier function without affecting cell viability. Reduced expression of PKCζ by antisense oligonucleotide or shRNA (short hairpin RNA) also results in compromised tight junction integrity. Inhibition or knockdown of PKCζ delays calcium-induced assembly of tight junctions. Tight junction disruption by PKCζ pseudosubstrate is associated with the dephosphorylation of occludin and ZO-1 on serine and threonine residues. PKCζ directly binds to the C-terminal domain of occludin and phosphorylates it on threonine residues. Thr403, Thr404, Thr424 and Thr438 in the occludin C-terminal domain are the predominant sites of PKCζ-dependent phosphorylation. A T424A or T438A mutation in full-length occludin delays its assembly into the tight junctions. Inhibition of PKCζ also induces redistribution of occludin and ZO-1 from the tight junctions and dissociates these proteins from the detergent-insoluble fractions in mouse ileum. The present study demonstrates that PKCζ phosphorylates occludin on specific threonine residues and promotes assembly of epithelial tight junctions.