Publications by authors named "Wenting Hua"
Article Synopsis
- This study develops and validates a machine learning (ML) model aimed at predicting the risk of diabetic nephropathy (DN) in patients with type 1 diabetes mellitus (T1DM), focusing on enhancing early detection methods.
- Conducted across 19 hospitals in Gansu Province, China, the study analyzed data from 1368 eligible T1DM patients and utilized techniques like recursive feature elimination and fivefold cross-validation to identify key predictive factors.
- The extreme gradient boosting (XGBoost) model emerged as the most effective with an AUC of 83%, incorporating significant variables such as diabetes duration and blood glucose levels; external validation further confirmed its reliability, though further studies are needed for broader population applicability.
Background: It is controversial whether the level of glycemic control in patients with type 1 diabetes mellitus (T1DM) correlates with reduced cognitive function. This study explored the influence of glycemic management quality on cognitive function in T1DM patients by examining the association between glycemic control level and impaired cognitive function.
Methods: The electronic databases PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, China Science and Technology Journal database, Wanfang database, and China Biology Medicine disc database were systematically searched to identify eligible studies published before January 2023.
A novel magnetic nanoadsorbent (FeO@SiO@PAA-SOH) was synthesized by grafting acrylic acid and sulfonic group to FeO@SiO using a facile cross-link technology. The adsorbent presented water-stability and biocompatibility in wastewater, which exhibited high-selectivity capture for Pb(II) and Cu(II) of 182.5 mg/g and 250.
View Article and Find Full Text PDFLiquid secondary ion mass spectrometry (L-SIMS) of six new functionalized macrocycles was investigated. All six compounds yielded abundant fragment ions and protonation molecular ions [M + H](+) under L-SIMS conditions. The proposed fragmentation mechanisms were supported by high-resolution accurate mass data from Fourier transform ion cyclotron resonance mass spectrometric and MS(n) experiments on using sustained off-resonance irradiation collision-induced dissociation.
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