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BACKGROUND: As a part of natural disease progression, acute kidney injury (AKI) can develop into chronic kidney disease via renal fibrosis and inflammation. LTBP4 (latent transforming growth factor beta binding protein 4) regulates transforming growth factor beta, which plays a role in renal fibrosis pathogenesis. We previously investigated the role of LTBP4 in chronic kidney disease. Here, we examined the role of LTBP4 in AKI. METHODS: LTBP4 expression was evaluated in human renal tissues, obtained from healthy individuals and patients with AKI, using immunohistochemistry. LTBP4 was knocked down in both C57BL/6 mice and human renal proximal tubular cell line HK-2. AKI was induced in mice and HK-2 cells using ischemia-reperfusion injury and hypoxia, respectively. Mitochondrial division inhibitor 1, an inhibitor of DRP1 (dynamin-related protein 1), was used to reduce mitochondrial fragmentation. Gene and protein expression were then examined to assess inflammation and fibrosis. The results of bioenergetic studies for mitochondrial function, oxidative stress, and angiogenesis were assessed. RESULTS: LTBP4 expression was upregulated in the renal tissues of patients with AKI. Ltbp4 -knockdown mice showed increased renal tissue injury and mitochondrial fragmentation after ischemia-reperfusion injury, as well as increased inflammation, oxidative stress, and fibrosis, and decreased angiogenesis. in vitro studies using HK-2 cells revealed similar results. The energy profiles of Ltbp4-deficient mice and LTBP4-deficient HK-2 cells indicated decreased ATP production. LTBP4-deficient HK-2 cells exhibited decreased mitochondrial respiration and glycolysis. Human aortic endothelial cells and human umbilical vein endothelial cells exhibited decreased angiogenesis when treated with LTBP4-knockdown conditioned media. Mitochondrial division inhibitor 1 treatment ameliorated inflammation, oxidative stress, and fibrosis in mice and decreased inflammation and oxidative stress in HK-2 cells. CONCLUSIONS: Our study is the first to demonstrate that LTBP4 deficiency increases AKI severity, consequently leading to chronic kidney disease. Potential therapies focusing on LTBP4-associated angiogenesis and LTBP4-regulated DRP1-dependent mitochondrial division are relevant to renal injury.

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BackgroundChildren with intractable functional constipation (IFC) who are refractory to traditional pharmacological intervention develop severe symptoms that can persist even in adulthood, resulting in a substantial deterioration in their quality of life. In order to better manage IFC patients, efficient subtyping of IFC into its three subtypes, normal transit constipation (NTC), outlet obstruction constipation (OOC), and slow transit constipation (STC), at early stages is crucial. With advancements in technology, machine learning can classify IFC early through the use of validated questionnaires and the different serum concentrations of gastrointestinal motility-related hormones.MethodA hundred and one children with IFC and 50 controls were enrolled in this study. Three supervised machine-learning methods, support vector machine, random forest, and light gradient boosting machine (LGBM), were used to classify children with IFC into the three subtypes based on their symptom severity, self-efficacy, and quality of life which were quantified using certified questionnaires and their serum concentrations of the gastrointestinal hormones evaluated with enzyme-linked immunosorbent assay. The accuracy of machine learning subtyping was evaluated with respect to radiopaque markers.ResultsOf 101 IFC patients, 37 had NTC, 49 had OOC, and 15 had STC. The variables significant for IFC subtype classification, according to SelectKBest, were stool frequency, the satisfaction domain of the Patient Assessment of Constipation Quality of Life questionnaire (PAC-QOL), the emotional self-efficacy for Functional Constipation questionnaire (SEFCQ), motilin serum concentration, and vasoactive intestinal peptide serum concentration. Among the three models, the LGBM model demonstrated an accuracy of 83.8%, a precision of 84.5%, a recall of 83.6%, a f1-score of 83.4%, and an area under the receiver operating characteristic curve (AUROC) of 0.89 in discriminating IFC subtypes.ConclusionUsing clinical characteristics measured by certified questionnaires and serum concentrations of the gastrointestinal hormones, machine learning can efficiently classify pediatric IFC into its three subtypes. Of the three models tested, the LGBM model is the most accurate model for the classification of IFC, with an accuracy of 83.8%, demonstrating that machine learning is an efficient tool for the management of IFC in children.