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Abstract The present study investigates the efficacy of newly developed Zr-based metal–organic frameworks, specifically MIP-206, and its amine-modified counterpart, MIP-206-NH2, for the re-covery of antimony (Sb) from both synthetic and actual mining wastewater. Batch method studies were employed to examine the effect of waste media pH, Sb concentration, process kinetics, and the performance of the regeneration solution. MIP-206-NH2 exhibited impressive separation capabilities, achieving 102.18 mg/g and 63.23 mg/g for Sb (V) and Sb (III), respectively. In contrast, the pristine MIP-206 reached maximum values of 26.26 mg/g for Sb (V) and 16.95 mg/g for Sb (III). The separation process was well-described by the Langmuir equation, and the kinetics followed the pseudo-second-order model. Although the amine modification resulted in a decrease in the surface area of MIP-206 from 1345.21 to 1169.86 m2/g, SEM and XRD analyses confirmed that the structural integrity of MIP-206-NH2 remained unchanged. In terms of reusability, MIP-206-NH2 maintained up to 90% of its separation performance over 9 cycles, while MIP-206 demonstrated effectiveness for 7 cycles. The regeneration solution exhibited a capacity of approximately 0.63 mol/L for Sb (V) and 0.71 mol/L for Sb (III). Furthermore, MIP-206 and MIP-206-NH2 demonstrated successful application in selectively separating Sb from real mining wastewater.

Abstract Osteoporosis is a major public health concern that significantly increases the risk of fractures. The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. A total of 10,000 complete patient records of primary healthcare data in the German Disease Analyzer database (IMS HEALTH) were included, of which 1293 diagnosed with osteoporosis and 8707 without the condition. The demographic characteristics and chronic disease data, including age, gender, lipid disorder, cancer, COPD, hypertension, heart failure, CHD, diabetes, chronic kidney disease, and stroke were collected from electronic health records. Ten different machine learning algorithms were employed to construct the predictive mode. The performance of the model was further validated and the relative importance of features in the model was analyzed. Out of the ten machine learning algorithms, the Stacker model based on Logistic Regression, AdaBoost Classifier, and Gradient Boosting Classifier demonstrated superior performance. The Stacker model demonstrated excellent performance through ten-fold cross-validation on the training set and ROC curve analysis on the test set. The confusion matrix, lift curve and calibration curves indicated that the Stacker model had optimal clinical utility. Further analysis on feature importance highlighted age, gender, lipid metabolism disorders, cancer, and COPD as the top five influential variables. In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies.

Abstract Most cases of cystic fibrosis (CF) are caused by class 2 mutations in the cystic fibrosis transmembrane regulator (CFTR). These proteins preserve some channel function but are retained in the endoplasmic reticulum (ER). Partial rescue of the most common CFTR class 2 mutant, F508del-CFTR, has been achieved through the development of pharmacological chaperones (Tezacaftor and Elexacaftor) that bind CFTR directly. However, it is not clear whether these drugs will rescue all class 2 CFTR mutants to a medically relevant level. We have previously shown that the nonsteroidal anti-inflammatory drug (NSAID) ibuprofen can correct F508del-CFTR trafficking. Here, we utilized RNAi and pharmacological inhibitors to determine the mechanism of action of the NSAID glafenine. Using cellular thermal stability assays (CETSAs), we show that it is a proteostasis modulator. Using medicinal chemistry, we identified a derivative with a fourfold increase in CFTR corrector potency. Furthermore, we show that these novel arachidonic acid pathway inhibitors can rescue difficult-to-correct class 2 mutants, such as G85E-CFTR > 13%, that of non-CF cells in well-differentiated HBE cells. Thus, the results suggest that targeting the arachidonic acid pathway may be a profitable way of developing correctors of certain previously hard-to-correct class 2 CFTR mutations.

Abstract Excessive secretion of glucagon, a functional insulin antagonist, significantly contributes to hyperglycemia. Glucagon exerts its physiological functions through activation of the glucagon receptor (GCGR). Inhibition of GCGR activity represents a potential therapeutic approach for reducing excess glucose production in diabetes mellitus. Aptamers are short DNA or RNA oligonucleotides evolved from systematic evolution of ligands by exponential enrichment (SELEX). Here, we have successfully selected a DNA aptamer against GCGR by cell-SELEX, which can specifically bind membrane protein of CHO-GCGR cells with a K d of 52.7 ± 5.1 nM. Aptamer-mediated pull-down and gcgr knockdown assay verified that GCGR was the target of aptamer GR-3. Binding analysis revealed that GR-3 could recognize other cells with different affinity according to the level of GCGR protein expressed in these cells. Hepatic tissue imaging suggested that GR-3 could bind the cell membrane of hepatic tissues. With the advantages of small size, high binding affinity, good stability, lack of immunogenicity, and easy synthesis, aptamer GR-3 against GCGR can be a promising tool with the potential to attenuate hyperglycemia in diabetes mellitus.