Objective To investigate the effect of hemoglobin A1c variability on the incidence of malignant tumors in type 2 diabetes mellitus (T2DM) patients, and to provide a reference for the prevention and treatment of diabetes. Methods A retrospective cohort study was used to integrate Shanghai malignant tumor registration information and T2DM patient management information of Minhang Districts in Shanghai. A record linkage was carried out using ID numbers for patients who had been diagnosed through the Cancer Registry System. Average Real Variability（ARV）was used to assess the variability for hemoglobin A1c results, with no less than 3 measurements and no more than 1 follow-up visit per year. Cox proportional hazards regression models and Restricted Cubic Splines (RCS) were used for statistical analysis. ARV was grouped using quartile classification, represented by Q1, Q2, Q3, and Q4, respectively. Cox regression was performed using unadjusted model I and model II adjusted for gender, age, and group. Results A total of 2 762 research subjects were included in the present study, with 45.84% males and 54.16% females. The average age was (63.58±10.11) years, and an average follow-up time was (9.96±3.66) years. Malignant tumor incidence rate was 345.45/100 000 person years. There was statistical significance among different ARV groups (P=0.01). COX regression analysis of model I and model II showed that compared with Q1, the risk of Q4 group was significantly increased, with HRs of 2.72 (1.56-4.74) and 2.68 (1.53-4.68), respectively. The RCS graph's analysis showed that except for the ≥65-year-old group, the risk of tumor incidence gradually increased with the increase of ARV coefficient. Conclusion The variability of hemoglobin A1c is positively associated with the risk of occurrence of malignant tumors in type 2 diabetes patients. It is necessary to strengthen the monitoring of hemoglobin A1c and to reduce the health hazards caused by fluctuations in hemoglobin A1c.

Objective To investigate the effect of hemoglobin A1c variability on the incidence of malignant tumors in type 2 diabetes mellitus (T2DM) patients, and to provide a reference for the prevention and treatment of diabetes. Methods A retrospective cohort study was used to integrate Shanghai malignant tumor registration information and T2DM patient management information of Minhang Districts in Shanghai. A record linkage was carried out using ID numbers for patients who had been diagnosed through the Cancer Registry System. Average Real Variability（ARV）was used to assess the variability for hemoglobin A1c results, with no less than 3 measurements and no more than 1 follow-up visit per year. Cox proportional hazards regression models and Restricted Cubic Splines (RCS) were used for statistical analysis. ARV was grouped using quartile classification, represented by Q1, Q2, Q3, and Q4, respectively. Cox regression was performed using unadjusted model I and model II adjusted for gender, age, and group. Results A total of 2 762 research subjects were included in the present study, with 45.84% males and 54.16% females. The average age was (63.58±10.11) years, and an average follow-up time was (9.96±3.66) years. Malignant tumor incidence rate was 345.45/100 000 person years. There was statistical significance among different ARV groups (P=0.01). COX regression analysis of model I and model II showed that compared with Q1, the risk of Q4 group was significantly increased, with HRs of 2.72 (1.56-4.74) and 2.68 (1.53-4.68), respectively. The RCS graph's analysis showed that except for the ≥65-year-old group, the risk of tumor incidence gradually increased with the increase of ARV coefficient. Conclusion The variability of hemoglobin A1c is positively associated with the risk of occurrence of malignant tumors in type 2 diabetes patients. It is necessary to strengthen the monitoring of hemoglobin A1c and to reduce the health hazards caused by fluctuations in hemoglobin A1c.

Objective·To explore the correlation between body compositions and cardiovascular fitness(CRF)in patients with coronary heart disease(CHD).Methods·The CHD patients(CHD group)who underwent elective percutaneous coronary intervention treatment at Renji Hospital,Shanghai Jiao Tong University School of Medicine from October 2022 to June 2023 as well as healthy people(control group)were selected.All the participants completed cardiopulmonary exercise testing(CPET)to determine CRF and bioelectrical impedance analysis(BIA)to determine body compositions on the same day.Results·A total of 191 patients with coronary heart disease and 188 healthy individuals were included.There was no statistically significant difference in baseline characteristics between the two groups.Compared with the control group,the CRF indicators of the CHD group were significantly reduced(all P<0.05).In terms of body composition indicators,the trunk muscle mass(TMM)of the CHD group was significantly lower than that of the control group(P<0.01),and the trunk fat mass(TFM)was significantly higher than that of the control group(P<0.01).Correlation analysis showed that TMM(R=0.538),lower limbs muscle mass(LMM)(R=0.754),and lower limbs fat mass(LFM)(R=0.593)were positively correlated with peak oxygen uptake per kilogram of bodyweight(VO2peak/kg)in the CHD group(all P<0.01),while TFM(R=-0.563)was negatively correlated with VO2peak/kg(P<0.01).There was no statistically significant correlation between other body composition indicators and VO2peak/kg.According to VO2peak/kg,the CHD patients were divided into low CRF group,medium CRF group,and high CRF group.The results showed that there were statistically significant differences in LMM,TMM,LFM,and TFM among the three groups of patients(all P<0.05).Multiple linear regression analysis suggested that age,gender,TMM,TFM,LMM,and LFM were related factors of VO2peak/kg in the patients with CHD.The VO2peak/kg of CHD patients increased with the increase of TMM,LMM,and LFM and the decrease of age and TFM;the female patients had lower VO2peak/kg compared to the males.Conclusion·The CRF of CHD patients is significantly lower than that of the healthy population,with higher TFM and lower TMM;in the CHD patients,CRF is negatively correlated with TFM and positively correlated with TMM,LMM,and LFM.

Objective·To analyze the differences and classify hypertrophic cardiomyopathy(HCM)patients and healthy controls(HC)using short-axis cine cardiac magnetic resonance(CMR)images-derived radiomics features.Methods·One hundred HCM subjects were included,and fifty HC were randomly selected at 2∶1 ratio during January 2018 to December 2021 in the Department of Cardiology,Renji Hospital,Shanghai Jiao Tong University School of Medicine.The CMR examinations were performed by experienced radiologists on these subjects.CVI 42 post-processing software was used to obtain left ventricular morphology and function measurements,including left ventricular ejection fraction(LVEF),left ventricular end-diastolic volume(LVEDV)and left ventricular end-diastolic mass(LVEDM).The 3D radiomic features of the end-diastolic myocardial region were extracted from short-axis images CMR cine.The distribution of the radiomic features in the two groups was analysed and machine learning models were constructed to classify the two groups.Results·One hundred and seven 3D radiomic features were selected and extracted.After exclusion of highly correlated features,least absolute shrinkage and selection operator(LASSO)was used,and a 5-fold cross-validation was performed.There were still 11 characteristics with non-zero coefficients.The K-best method was used to decide the top 8 features for subsequent analysis.Among them,four features were significantly different between the two groups(all P<0.05).Support vector machine(SVM)and random forest(RF)models were constructed to discriminate the two groups.The results showed that the maximum area under the curve(AUC)for the single-feature model(first order grayscale:entropy)was 0.833(95%CI 0.685?0.968)and the maximum accuracy for the multi-feature model was 83.3%with an AUC of 0.882(95%CI 0.705?0.980).Conclusion·There are significant differences in both left ventricular function and left ventricular morphology between HCM and HC.The 3D myocardial radiomic features of the two groups are also significantly different.Although single feature is able to distinguish the two groups,the combination of multi-features show better classification performance.

Objective To investigate the effect of Alogliptin benzoate on the serum autophagy markers in type 2 diabetes mellitus(T2DM)patients.Methods Eighty newly diagnosed T2DM patients who visited the Department of Endocrinology in Baoding No.1 Central Hospital from December 2021 to October 2022 were randomly divided into a group treated with Metformin(Met group,n=40)and a group treated with Met and Alog(Met+Alog group,n=40).The differences in BMI,WHR,FPG,HbA1c,Atg7 and Beclin-1 between two groups before and after 12 weeks of treatment were compared.Results After treatment,the levels of Atg7 and Beclin-1 increased in both groups(P<0.05),while FPG,HbA1c and HOMA-IR decreased(P<0.05).After treatment,Atg7,Beclin-1 and HDL-C in Met+Alog group were higher than those in Met group(P<0.05).Pearson correlation analysis showed that Atg7 was negatively correlated with BMI,FPG and HbA1c(P<0.05);Beclin-1 was positively correlated with HDL-C(P<0.05),and negatively correlated with BMI,FPG,HbA1c,and TG(P<0.05).Meta linear regression analysis showed that BMI was the influencing factor of Atg7,while BMI and HDL-C were the influencing factors of Beclin-1.Conclusion Alogliptin benzoate may improve islet β cell function by up-regulating the expression of autophagy related factors Atg7 and Beclin-1 in patients with T2DM.

AIM To identify the chemical components of Longmu Qingxin Mixture by UPLC-Q-TOF-MS and study its material basis for the treatment of attention deficit hyperactivity disorder.METHODS The sample was detected by mass spectrometry in positive and negative ion mode on a Waters CORTECS? UPLC? T3 chromatographic column.The data were analyzed with Peakview 1.2 software and matched with the Natural Products HR-MS/MS Spectral Library 1.0 database,and the components were identified in combination with literature reports.The material basis of Longmu Qingxin Mixture for the treatment of attention deficit hyperactivity disorder was analysed according to the identified components.RESULTS Forty chemical components were identified,including 11 flavonoids,6 monoterpene glycosides,4 triterpene saponins,3 phenolic acids,6 alkaloids etc.,which mainly derived from Radix Astragali,Radix Paeoniae Alba,Radix Scutellariae,licorice root,Ramulus Uncariae cum,etc.,baicalein,formononetin,astragaloside Ⅳ and rhynchophylline may be the material basis for the therapeutic effect of Longmu Qingxin Mixture.CONCLUSION UPLC-Q-TOF-MS can quickly identify the chemical components of Longmu Qingxin Mixture.Flavonoids,triterpene saponins and alkaloids may be the material basis for Longmu Qingxin Mixture for the treatment of attention deficit hyperactivity disorder,which can provide the basis for its material basis research,quality standard establishment and pharmacological study of the dismantled formula.

Antisense oligonucleotides are a type of gene therapy that targets mRNA and inhibits gene expression. They have been applied in the treatment of various diseases, but there are still problems with poor enzyme stability and high dosage in vivo, due to the shortage of appropriate delivery system. Insulin-like growth factor 1 receptor (IGF1R) is a cell surface receptor with tyrosine kinase activity. Its expression is abnormal in a variety of malignant tumors. It mediates the malignant proliferation, migration and invasion of tumor cells through a variety of ways. In this study, an antisense oligonucleotide (ASO, N04) targeting IGF1R mRNA was designed and chemically modified (PS, 2'-OMOE), then neutral cytidine lipid DNCA and cystine backbone cationic lipid CLD (Mix) were used to encapsulate ASOs. The particle size, polymer dispersity index and ζ potential of the formulations were 151 nm, 0.18 and -3.9 mV. The nanoparticles entered liver cancer cells (HepG-2, Huh-7), silenced target mRNA, arrested cell cycle in S phase, promoted apoptosis, and inhibited the proliferation efficiently. These results indicate that Mix/N04_MOE5 has great potential in tumor treatment, which provides a basis for further research on novel agents against hepatocellular carcinoma.

Objective The purpose of this study was to investigate the bacterial communities of biting midges and ticks collected from three sites in the Poyang Lake area,namely,Qunlu Practice Base,Peach Blossom Garden,and Huangtong Animal Husbandry,and whether vectors carry any bacterial pathogens that may cause diseases to humans,to provide scientific basis for prospective pathogen discovery and disease prevention and control. Methods Using a metataxonomics approach in concert with full-length 16S rRNA gene sequencing and operational phylogenetic unit(OPU)analysis,we characterized the species-level microbial community structure of two important vector species,biting midges and ticks,including 33 arthropod samples comprising 3,885 individuals,collected around Poyang Lake. Results A total of 662 OPUs were classified in biting midges,including 195 known species and 373 potentially new species,and 618 OPUs were classified in ticks,including 217 known species and 326 potentially new species.Surprisingly,OPUs with potentially pathogenicity were detected in both arthropod vectors,with 66 known species of biting midges reported to carry potential pathogens,including Asaia lannensis and Rickettsia bellii,compared to 50 in ticks,such as Acinetobacter lwoffii and Staphylococcus sciuri.We found that Proteobacteria was the most dominant group in both midges and ticks.Furthermore,the outcomes demonstrated that the microbiota of midges and ticks tend to be governed by a few highly abundant bacteria.Pantoea sp7 was predominant in biting midges,while Coxiella sp1 was enriched in ticks.Meanwhile,Coxiella spp.,which may be essential for the survival of Haemaphysalis longicornis Neumann,were detected in all tick samples.The identification of dominant species and pathogens of biting midges and ticks in this study serves to broaden our knowledge associated to microbes of arthropod vectors. Conclusion Biting midges and ticks carry large numbers of known and potentially novel bacteria,and carry a wide range of potentially pathogenic bacteria,which may pose a risk of infection to humans and animals.The microbial communities of midges and ticks tend to be dominated by a few highly abundant bacteria.

Objective·To analyze the epidemiological characteristics of risk factors for cardiovascular diseases and malignant tumors based on the Shanghai community elderly cohort.Methods·The study subjects were selected from the Shanghai community elderly cohort established from February to August 2019,with a total of 17 948 people.The study subjects were divided into 4 groups according to self-reported presence or absence of tumors and/or cardiovascular diseases during the baseline survey:tumor-free and non-cardiovascular disease group,single cardiovascular disease group,single tumor group and tumor cardiovascular disease co-occurrence group.The differences among the four groups of subjects were collected and compared in terms of demographic characteristics and physiological indicators,daily living habits(smoking,drinking tea,drinking coffee,drinking carbonated drink,drinking alcohol,sedentary time,physical activity level and sleep quality),past medical history,psychological status(depression and anxiety)and dietary compliance.Results·Among the study subjects,60.1%of tumor patients were complicated with cardiovascular diseases.The differences among the four groups of subjects in age,gender,educational level,pre-retirement occupation,waist circumference,hip circumference and body mass index were statistically significant(all P<0.05).Compared with the tumor-free and non-cardiovascular disease group,the single cardiovascular disease group,single tumor group and tumor cardiovascular disease co-occurrence group all exhibited lower proportions of smoking and high physical activity levels(all P<0.05),and higher proportion of sedentary time exceeding 4 h/d and poor sleep quality(all P<0.05);the proportion of subjects with past medical histories including hyperlipidemia,peripheral vascular disease,endocrine system disease,respiratory system disease,urinary system disease and digestive system disease of the single cardiovascular disease group and the tumor cardiovascular disease co-occurrence group was higher(all P<0.05),and the proportion of subjects with depression and anxiety was also higher(all P<0.05).Furthermore,compared with the tumor-free and non-cardiovascular disease group,the single cardiovascular disease group had lower compliance rates of poultry,fish,fruit and liquid milk(all P<0.05).Among the four groups,only the compliance rate of vegetable intake exceeded 50%,while the compliance rates of poultry,fish,fruit,liquid milk and tubers were all below 20%.Conclusion·In the elderly population of Shanghai communities,over half of malignant tumor patients are concomitant with cardiovascular diseases.Unhealthy daily habits are prevalent among those with cardiovascular diseases,tumors and tumor-cardiovascular disease co-occurrence.The intake of many foods in the elderly of the community do not reach the levels recommended by Chinese Dietary Guidelines.

Objective·To develop a machine learning approach for early identification of metabolic syndromes associated with inflammatory metabolic state changes in breast cancer patients after neoadjuvant therapy,using common laboratory and transthoracic echocardiography indices.Methods·Female patients with primary invasive breast cancer diagnosed at the Department of Breast Surgery,Renji Hospital,Shanghai Jiao Tong University School of Medicine,between September 2020 and September 2022,were included.General patient information,laboratory test results,and transthoracic echocardiography data were collected.After feature extraction,five machine learning algorithms,including random forest(RF),gradient boosting(GB),support vector machine(SVM),K-nearest neighbor(KNN),and decision tree(DT),were applied to construct a prediction model for the changes of the patients' metabolic state after neoadjuvant therapy,and the prediction performances of the five models were compared.Results·A total of 232 cases with valid clinical data were included,comprising 135 cases before neoadjuvant therapy and 97 cases after completing 4 cycles of neoadjuvant therapy.Feature extraction identified five key features:white blood cell count,hemoglobin,high-density lipoprotein(HDL),interleukin-2 receptor,and interleukin-8.In the multi-feature analysis,the area under the receiver operating characferistic curve(AUC)was higher in the combination of white blood cell count,hemoglobin and HDL compared to the combination of interleukin-2 receptor and interleukin-8(RF:0.928 vs 0.772,GB:0.900 vs 0.792,SVM:0.941 vs 0.764,KNN:0.907 vs 0.762,DT:0.799 vs 0.714).The RF,SVM,and GB models showed higher AUC(0.928,0.941,0.900)and accuracy(0.914,0.897,0.776).The SVM model exhibited superior accuracy in the training data compared to the RF and GB models(P=0.394,0.122 and 0.097,respectively).Conclusion·The SVM model can be used to establish a prediction model for identifying breast cancer patients at high risk of developing inflammatory metabolic state-related metabolic syndrome after neoadjuvant therapy by incorporating five common clinical indicators,namely,white blood cell count,hemoglobin,high-density lipoprotein,interleukin-2 receptor,and interleukin-8.SVM modeling may be useful for clinicians to establish individualized screening protocols based on a patient's inflammatory metabolic state.