The association between serum copper concentration and prevalence of diabetes among US adults with hypertension (NHANES 2011-2016).

The objective of this study was to examine the association between the serum copper concentration and the prevalence of diabetes among US adults with hypertension using the data from the National Health and Nutrition Examination Survey (NHANES). The study population was selected from adults aged over 20 years old in the three survey cycles of NHANES from 2011 to 2016. Logistic regression model analyses were applied to determine the independent risky effect of copper to the prevalence of diabetes. Also, a restricted cubic spline (RCS) model was performed to explore the potential nonlinear association between serum copper concentration and the prevalence of diabetes. A total of 1786 subjects (742 cases and 1044 controls) were included, and 924 were men (51.7%), and 742 (41.5%) were diabetic. Compared with non-diabetic individuals, the concentration of serum copper in diabetic patients with hypertension was higher. After adjusting for age, sex, race, education, marital status, body mass index (BMI), family poverty income ratio (PIR), smoking, alcohol drinking, physical activity, systolic blood pressure (SBP), diastolic blood pressure (DBP), and hyperlipidemia, the highest quartile of serum copper concentration significantly increased the risk of diabetes as compared with the lowest quartile (OR: 1.38, 95% CI: 1.01-1.92, ptrend = 0.036). The results of RCS analysis showed significant non-linear relationship between serum copper concentration and prevalence of diabetes (p-non-linear = 0.010). This study finds that serum copper concentration are significantly associated with risk of diabetes in hypertensive patients, which suggests copper as an important risk factor of diabetes development.

A Retrospective Study on the Time in Range of Blood Glucose and Type 2 Diabetic Peripheral Neuropathy.

Background: Time in range (TIR) is one of the basic indicators to assess glycemic control. In this study, the TIR of DPN patients was used as the observation index to further evaluate the correlation between TIR and DPN, so as to provide new ideas for preventing the occurrence of DPN and delaying its disease progression. Methods: A total of 120 patients with T2DM (T2DM) who were hospitalized in the Endocrinology Department of our hospital from October 2018 to February 2020 were included and divided into two groups according to whether the nerve conduction velocity was normal or not, the diabetic peripheral neuropathy group (DPN) and the other groups. No diabetic peripheral neuropathy group (NDPN). According to the corresponding inclusion and exclusion criteria, the baseline data were recorded, and test indicators such as homocysteine and blood lipids were collected at the same time, and TIR was collected by a transient blood glucose meter. To explore the relationship between TIR and other indicators and peripheral neuropathy in T2DM. Results: A total of 120 T2DM patients participated in the study, including 82 in the DPN group and 38 in the NDPN group. There were no statistically significant differences in basic indicators such as age, height, and weight between the two groups. Glycated hemoglobin (HbA1c) and homocysteine (Hcy) in DPN group were higher than those in NDPN group, while TIR and HDL-C were lower than those in NDPN group (P < 0.05). Logistic regression analysis showed that HbA1c and Hcy were risk factors for DPN, and TIR and HDL-C were protective factors for DPN, with statistical significance (P < 0.05). The prediction results of TIR, Hcy, HDL-C, and HbA1c on diabetic peripheral neuropathy were analyzed by ROC curve, and the prediction results of the five variables were all statistically significant (P < 0.05) and have a better prediction effect. Conclusion: (1) The results of TIR level suggest that the longer the blood sugar is in the good control range, the more beneficial it is to reduce the occurrence of DPN. (2) TIR and HDL-C are protective factors for DPN, and HbA1c and Hcy are risk factors for DPN. (3) The results of ROC curve analysis showed that TIR, Hcy, HbA1c, and HDL-C had a good predictive effect on the occurrence of DPN.

PET/CT-based radiomics analysis may help to predict neoadjuvant chemotherapy outcomes in breast cancer.

Background: The aim of this study was to evaluate the clinical usefulness of radiomics signature-derived 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography-computed tomography (PET-CT) for the early prediction of neoadjuvant chemotherapy (NAC) outcomes in patients with (BC). Methods: A total of 124 patients with BC who underwent pretreatment PET-CT scanning and received NAC between December 2016 and August 2019 were studied. The dataset was randomly assigned in a 7:3 ratio to either the training or validation cohort. Primary tumor segmentation was performed, and radiomics signatures were extracted from each PET-derived volume of interest (VOI) and CT-derived VOI. Radiomics signatures associated with pathological treatment response were selected from within a training cohort (n = 85), which were then applied to generate different classifiers to predict the probability of pathological complete response (pCR). Different models were then independently tested in the validation cohort (n = 39) regarding their accuracy, sensitivity, specificity, and area under the curve (AUC). Results: Thirty-five patients (28.2%) had pCR to NAC. Twelve features consisting of five PET-derived signatures, four CT-derived signatures, and three clinicopathological variables were candidates for the model's development. The random forest (RF), k-nearest neighbors (KNN), and decision tree (DT) classifiers were established, which could be utilized to predict pCR to NAC with AUC ranging from 0.819 to 0.849 in the validation cohort. Conclusions: The PET/CT-based radiomics analysis might provide efficient predictors of pCR in patients with BC, which could potentially be applied in clinical practice for individualized treatment strategy formulation.

Lone pair-π interaction-induced generation of photochromic coordination networks with photoswitchable conductance.

Lone pair-π interaction-induced charge-transfer was successfully used for switching the conductance of a coordination network, through variation of the degree of charge transfer caused by external photostimulation. The underlying mechanism is attributed to the changes in efficient charge-carriers by photoinduced strong charge transfer, which was investigated by in situ UV-Vis absorption, ESR, and computational studies.