Clinical Features and Microvascular Complications Risk Factors of Early-onset Type 2 Diabetes Mellitus

The aim of this research was to study the clinical features and microvascular complications risk factors of early-onset type 2 diabetes mellitus(T2DM).We analyzed the clinical data from 1421 T2DM inpatients at Wuhan Union Hospital.Subjects were divided into early-onset T2DM group(diagnostic age<40 years)and late-onset T2DM group(diagnostic age>40 years).All subjects underwent a standardized assessment of microvascular complications.Data were compared with independent-samples t test or Chi-square test.Multiple logistic regression was used to determine the risk factors of microvascular complications.Patients with early-onset T2DM were more inclined to have a lower systolic blood pressure(SBP),a longer duration of diabetes and higher levels of body mass index(BM1),uric acid(UA),fasting plasma glucose(FPG),total cholesterol(TC),triglyceride(TG)and glycosylated hemoglobin(HbAlc)than those with lateonset T2DM(P<0.05).The prevalence of diabetic retinopathy(DR)was significantly higher and that of diabetic peripheral neuropathy(DPN)was significantly lower in early-onset group than in late-onset group(P<0.05).For DN,UA was an independent risk factor in early-onset T2DM.SBP and TG were independent risk factors in late-onset T2DM.For DR,duration of diabetes and SBP were independent risk factors in early-onset T2DM.Duration of diabetes,SBP and HbAlc were independent risk factors in late-onset T2DM.This study demonstrated that the clinical characteristics of early-onset T2DM were metabolic disorders,including glucose metabolism,lipid metabolism and amino acid metabolism.Early-onset T2DM was more likely to be associated with DR.The potential pathogenesis of early and late-onset T2DM might be different.The management of metabolic risk factors especially HbA1c,SBP,TG and UA is advised to be performed in the early stage of diabetes.

Analysis on the compatibility rules and mechanism of formulae treatment for COVID-2019 based on the TCM inheritance assistance system and network pharmacology

Background:On December 8,2019,Wuhan City,Hubei Province,a new type of coronavirus disease 2019(COVID-2019)was firstly discovered,and COVID-2019 spread rapidly in China.The number of confirmed cases in various provinces and cities rose sharply in China.In clinical treatment,Chinese medicine treatment showed significant efficacy.Since the outbreak,the National Health Commission(NHS)of China has issued seven editions of the“Pneumonitis Diagnosis and Treatment Program for COVID-2019”,at the same time,most provincial health boards and the Chinese Medicine Administration had also released information on the prevention and control scheme of COVID-2019 by Chinese medicine.The purpose of this study is to explore the compatibility rules of the main drugs in the prescription and the potential mechanism on COVID-2019 pneumonia,in order to provide reference for clinical research and new drug development of COVID-2019.Methods:This article uses the TCM inheritance assistance system and network pharmacology BATMAN-TCM online analysis system to collect and summarize the national“Pneumonitis Diagnosis and Treatment Program for COVID-2019(trial version sixth)”and formulae for adult treatment from the TCM prevention program of 23 provinces and cities.Results:We found that the most formulae for the treatment of COVID-2019 were modified on the basis of Maxing Shigan decoction and the top 5 high-frequencyn drugs are Xingren(Armeniacae semen amarum),Mahuang(Ephedrae herba),Gancao(Glycyrrhizae radix et rhizoma),Shigao(Gypsum fibrosum),and Haungqin(Radix scutellariae).High frequency traditional Chinese medicines are mainly used for relieving the symptoms,clearing away heat,eliminating dampness,resolving phlegm,relieving cough and asthma,promoting water and dampness,and tonifying deficiency.Warm medicine and bitter medicine are the most frequently used drugs in four Qi attribute and five flavor attribute,respectively.Most of drugs are belong to lung,stomach and spleen channel.Mahuang(Ephedrae herba),Xingren(Armeniacae semen amarum),Gancao(Glycyrrhizae radix et rhizoma),Shigao(Gypsum fibrosum),Cangzhu(Atractylodis rhizama)and Huoxiang(Pogostemonis herba)are the core drugs for treating COVID-2019.The TTD disease enrichment,target and signal transduction pathways of the six drugs showed that pneumonia and asthma were most closely related to COVID-2019.And the inflammatory reaction-related pathways may be the main pathways through which these drugs function.Conclusions:The modified Maxing Shigan decoction is the main prescription for the treatment of COVID-2019.The Xingren(Armeniacae semen amarum),Gancao(Glycyrrhizae radix et rhizoma),Shigao(Gypsum fibrosum),Cangzhu(Atractylodis rhizama)and Huoxiang(Pogostemonis herba)have certain theoretical and experimental basis for the treatment of COVID-2019 through network pharmacology analysis,but further experiments are needed to verify the effects.

Machine learning-based performance predictions for steels considering manufacturing process parameters:a review

Steels are widely used as structural materials,making them essential for supporting our lives and industries.However,further improving the comprehensive properties of steel through traditional trial-and-error methods becomes challenging due to the continuous development and numerous processing parameters involved in steel production.To address this challenge,the application of machine learning methods becomes crucial in establishing complex relationships between manufacturing processes and steel performance.This review begins with a general overview of machine learning methods and subsequently introduces various performance predictions in steel materials.The classification of performance pre-diction was used to assess the current application of machine learning model-assisted design.Several important issues,such as data source and characteristics,intermediate features,algorithm optimization,key feature analysis,and the role of environmental factors,were summarized and analyzed.These insights will be beneficial and enlightening to future research endeavors in this field.

Composition design of high yield strength points in single-phase Co-Cr-Fe-Ni-Mo multi-principal element alloys system based on electronegativity,thermodynamic calculations,and machine learning

A method which combines electronegativity difference,CALculation of PHAse Diagrams(CALPHAD) and machine learning has been proposed to efficiently screen the high yield strength regions in Co-Cr-Fe-Ni-Mo multi-component phase diagram.First,the single-phase region at a certain annealing temperature is obtained by combining CALPHAD method and machine learning,to avoid the formation of brittle phases.Then high yield strength points in the single-phase region are selected by electronegativity difference.The yield strength and plastic deformation behavior of the designed Co_(14)Cr_(30)Ni_(50)Mo_(6)alloy are measured to evaluate the proposed method.The validation experiments indicate this method is effective to predict high yield strength points in the whole compositional space.Meanwhile,the interactions between the high density of shear bands and dislocations contribute to the high ductility and good work hardening ability of Co_(14)Cr_(30)Ni_(50)Mo_(6)alloy.The method is helpful and instructive to property-oriented compositional design for multi-principal element alloys.