Browsing by Author "He, Ping"
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Item Open Access Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning.(International urology and nephrology, 2022-09) Hui, Dongna; Sun, Yiyang; Xu, Shixin; Liu, Junjie; He, Ping; Deng, Yuhui; Huang, Huaxiong; Zhou, Xiaoshuang; Li, RongshanBackground
The heterogeneity of Type 2 Diabetes Mellitus (T2DM) complicated with renal diseases has not been fully understood in clinical practice. The purpose of the study was to propose potential predictive factors to identify diabetic kidney disease (DKD), nondiabetic kidney disease (NDKD), and DKD superimposed on NDKD (DKD + NDKD) in T2DM patients noninvasively and accurately.Methods
Two hundred forty-one eligible patients confirmed by renal biopsy were enrolled in this retrospective, analytical study. The features composed of clinical and biochemical data prior to renal biopsy were extracted from patients' electronic medical records. Machine learning algorithms were used to distinguish among different kidney diseases pairwise. Feature variables selected in the developed model were evaluated.Results
Logistic regression model achieved an accuracy of 0.8306 ± 0.0057 for DKD and NDKD classification. Hematocrit, diabetic retinopathy (DR), hematuria, platelet distribution width and history of hypertension were identified as important risk factors. Then SVM model allowed us to differentiate NDKD from DKD + NDKD with accuracy 0.8686 ± 0.052 where hematuria, diabetes duration, international normalized ratio (INR), D-Dimer, high-density lipoprotein cholesterol were the top risk factors. Finally, the logistic regression model indicated that DD-dimer, hematuria, INR, systolic pressure, DR were likely to be predictive factors to identify DKD with DKD + NDKD.Conclusion
Predictive factors were successfully identified among different renal diseases in type 2 diabetes patients via machine learning methods. More attention should be paid on the coagulation factors in the DKD + NDKD patients, which might indicate a hypercoagulable state and an increased risk of thrombosis.Item Open Access Mutations in NGLY1 cause an inherited disorder of the endoplasmic reticulum-associated degradation pathway.(Genet Med, 2014-10) Enns, Gregory M; Shashi, Vandana; Bainbridge, Matthew; Gambello, Michael J; Zahir, Farah R; Bast, Thomas; Crimian, Rebecca; Schoch, Kelly; Platt, Julia; Cox, Rachel; Bernstein, Jonathan A; Scavina, Mena; Walter, Rhonda S; Bibb, Audrey; Jones, Melanie; Hegde, Madhuri; Graham, Brett H; Need, Anna C; Oviedo, Angelica; Schaaf, Christian P; Boyle, Sean; Butte, Atul J; Chen, Rui; Chen, Rong; Clark, Michael J; Haraksingh, Rajini; FORGE Canada Consortium; Cowan, Tina M; He, Ping; Langlois, Sylvie; Zoghbi, Huda Y; Snyder, Michael; Gibbs, Richard A; Freeze, Hudson H; Goldstein, David BPURPOSE: The endoplasmic reticulum-associated degradation pathway is responsible for the translocation of misfolded proteins across the endoplasmic reticulum membrane into the cytosol for subsequent degradation by the proteasome. To define the phenotype associated with a novel inherited disorder of cytosolic endoplasmic reticulum-associated degradation pathway dysfunction, we studied a series of eight patients with deficiency of N-glycanase 1. METHODS: Whole-genome, whole-exome, or standard Sanger sequencing techniques were employed. Retrospective chart reviews were performed in order to obtain clinical data. RESULTS: All patients had global developmental delay, a movement disorder, and hypotonia. Other common findings included hypolacrima or alacrima (7/8), elevated liver transaminases (6/7), microcephaly (6/8), diminished reflexes (6/8), hepatocyte cytoplasmic storage material or vacuolization (5/6), and seizures (4/8). The nonsense mutation c.1201A>T (p.R401X) was the most common deleterious allele. CONCLUSION: NGLY1 deficiency is a novel autosomal recessive disorder of the endoplasmic reticulum-associated degradation pathway associated with neurological dysfunction, abnormal tear production, and liver disease. The majority of patients detected to date carry a specific nonsense mutation that appears to be associated with severe disease. The phenotypic spectrum is likely to enlarge as cases with a broader range of mutations are detected.