Objective: Early detection and intervention of developmental disabilities (DDs) are critical to improving the long-term outcomes of afflicted children. In this study, our objective was to utilize facial landmark features from mobile application to distinguish between children with DDs and typically developing (TD) children. Methods: The present study recruited 89 children, including 33 diagnosed with DD, and 56 TD children. The aim was to examine the effectiveness of a deep learning classification model using facial video collected from children through mobile-based application. The study participants underwent comprehensive developmental assessments, which included the child completion of the Korean Psychoeducational Profile-Revised and caregiver completing the Korean versions of Vineland Adaptive Behavior Scale, Korean version of the Childhood Autism Rating Scale, Social Responsiveness Scale, and Child Behavior Checklist. We extracted facial landmarks from recorded videos using mobile application and performed DDs classification using long short-term memory with stratified 5-fold cross-validation. Results: The classification model shows an average accuracy of 0.88 (range: 0.78–1.00), an average precision of 0.91 (range: 0.75–1.00), and an average F1-score of 0.80 (range: 0.60–1.00). Upon interpreting prediction results using SHapley Additive exPlanations (SHAP), we verified that the most crucial variable was the nodding head angle variable, with a median SHAP score of 2.6. All the top 10 contributing variables exhibited significant differences in distribution between children with DD and TD (p<0.05). Conclusion The results of this study provide evidence that facial landmarks, utilizing readily available mobile-based video data, can be used to detect DD at an early stage.

BACKGROUND/OBJECTIVES: Obesity-induced steatohepatitis accompanied by activated hepatic macrophages/Kupffer cells facilitates the progression of hepatic fibrinogenesis and exacerbates metabolic derangements such as insulin resistance. Heme oxyganase-1 (HO-1) modulates tissue macrophage phenotypes and thus is implicated in protection against inflammatory diseases. Here, we show that the flavonoid quercetin reduces obesity-induced hepatic inflammation by inducing HO-1, which promotes hepatic macrophage polarization in favor of the M2 phenotype. MATERIALS/METHODS: Male C57BL/6 mice were fed a regular diet (RD), high-fat diet (HFD), or HFD supplemented with quercetin (HF+Que, 0.5g/kg diet) for nine weeks. Inflammatory cytokines and macrophage markers were measured by ELISA and RT-PCR, respectively. HO-1 protein was measured by Western blotting. RESULTS: Quercetin supplementation decreased levels of inflammatory cytokines (TNFα, IL-6) and increased that of the anti-inflammatory cytokine (IL-10) in the livers of HFD-fed mice. This was accompanied by upregulation of M2 macrophage marker genes (Arg-1, Mrc1) and downregulation of M1 macrophage marker genes (TNFα, NOS2). In co-cultures of lipid-laden hepatocytes and macrophages, treatment with quercetin induced HO-1 in the macrophages, markedly suppressed expression of M1 macrophage marker genes, and reduced release of MCP-1. Moreover, these effects of quercetin were blunted by an HO-1 inhibitor and deficiency of nuclear factor E2-related factor 2 (Nrf2) in macrophages. CONCLUSIONS: Quercetin reduces obesity-induced hepatic inflammation by promoting macrophage phenotype switching. The beneficial effect of quercetin is associated with Nrf2-mediated HO-1 induction. Quercetin may be a useful dietary factor for protecting against obesity-induced steatohepatitis.
Animals ; Blotting, Western ; Coculture Techniques ; Cytokines ; Diet ; Diet, High-Fat ; Down-Regulation ; Enzyme-Linked Immunosorbent Assay ; Fatty Liver ; Heme Oxygenase-1* ; Heme* ; Hepatocytes ; Humans ; Inflammation* ; Insulin Resistance ; Liver ; Macrophages* ; Male ; Mice ; NF-E2-Related Factor 2 ; Obesity ; Phenotype* ; Quercetin* ; Up-Regulation