Liraglutide improved the reproductive function of obese mice by upregulating the testicular AC3/cAMP/PKA pathway.

BACKGROUND: The incidence of male reproductive dysfunction is increasing annually, and many studies have shown that obesity can cause severe harm to male reproductive function. The mechanism of male reproductive dysfunction caused by obesity is unclear, and there is no ideal treatment. Identification of effective therapeutic drugs and elucidation of the molecular mechanism involved in male reproductive health are meaningful. In this study, we investigated the effects of the GLP-1 receptor agonist liraglutide on sex hormones, semen quality, and testicular AC3/cAMP/PKA levels in high-fat-diet-induced obese mice. METHODS: Obese mice and their lean littermates were treated with liraglutide or saline for 12 weeks. Body weight was measured weekly. Fasting blood glucose (FBG) was measured using a blood glucose test strip. The serum levels of insulin (INS), luteinizing hormone (LH), follicle stimulating hormone (FSH), testosterone (T), free testosterone (F-TESTO), estradiol (E2), and sex hormone binding globulin (SHBG) were detected using ELISA. The sperm morphology and sperm count were observed after Pap staining. The mRNA and protein expression levels of testicular GLP-1R and AC3 were measured by RT-qPCR and Western blot, respectively. Testicular cAMP levels and PKA activity were detected using ELISA. RESULTS: Liraglutide treatment can decrease body weight, FBG, INS, HOMA-IR, E2 and SHBG levels; increase LH, FSH, T, and F-TESTO levels; increase sperm count; decrease the sperm abnormality rate; and increase GLP-1R and AC3 expression levels and cAMP levels and PKA activity in testicular tissue. CONCLUSIONS: Liraglutide can improve the sex hormone levels and semen quality of obese male mice. In addition to its weight loss effect, liraglutide can improve the reproductive function of obese male mice, which may also be related to the upregulation of AC3/cAMP/PKA pathway in the testis. This work lays the groundwork for future clinical studies.

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review.

BACKGROUND: Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking. OBJECTIVE: This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care. METHODS: We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes. RESULTS: Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia. CONCLUSIONS: CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.37766/inplasy2022.9.0061.

The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life.

The origin of life involved complicated evolutionary processes. Computer modeling is a promising way to reveal relevant mechanisms. However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-setting for the modeling. Thus, typically, the studies were conducted in a reverse way: the parameter-space was explored to find those parameter values "supporting" a hypothetical scene (that is, leaving the parameter-justification a later job when sufficient knowledge is available). Exploring the parameter-space manually is an arduous job (especially when the modeling becomes complicated) and additionally, difficult to characterize as regular "Methods" in a paper. Here we show that a machine-learning-like approach may be adopted, automatically optimizing the parameters. With this efficient parameter-exploring approach, the evolutionary modeling on the origin of life would become much more powerful. In particular, based on this, it is expected that more near-reality (complex) models could be introduced, and thereby theoretical research would be more tightly associated with experimental investigation in this field-hopefully leading to significant steps forward in respect to our understanding on the origin of life.

Asymmetric Synthesis of Spiro[Azetidine-3,3'-Indoline]-2,2'-Diones via Copper(I)-Catalyzed Kinugasa/C-C Coupling Cascade Reaction.

Spiro[azetidine-indolines] are important scaffolds in diverse bioactive compounds. Current efforts to synthesize spiro[azetidine-indolines] are limited to chiral spiro[azetidine-2,3'-indolines]. Asymmetric synthesis of structurally similar chiral spiro[azetidine-3,3'-indolines] remains unexplored. In this work, the first copper(I)-catalyzed asymmetric Kinugasa/aryl C-C coupling cascade reaction is described. This provides a straightforward access to densely functionalized chiral spiro[azetidine-3,3'-indoline]-2,2'-diones in good yields and with high enantioselectivity.

Chemodiversity of Soil Dissolved Organic Matter.

Dissolved organic matter (DOM) plays a key role in many biogeochemical processes, but the drivers controlling the diversity of chemical composition and properties of DOM molecules (chemodiversity) in soils are poorly understood. It has also been debated whether environmental conditions or intrinsic molecular properties control the accumulation and persistence of DOM due to the complexity of both molecular composition of DOM and interactions between DOM and surrounding environments. In this study, soil DOM samples were extracted from 33 soils collected from different regions of China, and we investigated the effects of climate and soil properties on the chemodiversity of DOM across different regions of China, employing a combination of Fourier transform ion cyclotron resonance mass spectrometry, optical spectroscopy, and statistical analyses. Our results indicated that, despite the heterogeneity of soil samples and complex influencing factors, aridity and clay can account for the majority of the variations of DOM chemical composition. The finding implied that DOM chemodiversity is an ecosystem property closely related to the environment, and can be used in developing large-scale soil biogeochemistry models for predicting C cycling in soils.

Imbalance of erythropoiesis and iron metabolism in patients with thalassemia.

Aim: This study aimed to evaluate the imbalance of erythropoiesis and iron metabolism in patients with thalassemia. Methods: 192 patients with non-transfusion-dependent thalassemia (NTDT), 94 patients with transfusion-dependent thalassemia (TDT) and 101 healthy controls were recruited between June 2013 and December 2016 in the Hematology Department, the First Affiliated Hospital of Guangxi Medical University. The groups were compared in terms of levels of erythropoiesis biomarkers [growth differentiation factor 15 (GDF15), erythropoietin (EPO) and soluble transferrin receptor (sTfR)] and of iron overload biomarkers [serum ferritin (SF), liver iron concentration (LIC) and cardiac T2*] and hepcidin. Results: The levels of GDF15, EPO, sTfR, LIC and SF were significantly higher in patients with thalassemia. The levels of GDF15 and EPO were significantly higher in patients with TDT compared to NTDT. Those with iron overload had higher EPO, GDF15, SF and sTfR levels compared with non-iron overload patients. Hepcidin levels and ratios of hepcidin to erythropoietic activity and to iron biomarker levels were lower in patients with ß-thalassemia intermedia or hemoglobin (Hb) E/ß-thalassemia than in patients with HbH disease. The hepcidin levels were correlated negatively with the levels of EPO, GDF15 and sTfR in patients with NTDT and TDT, but correlated positively with SF and Hb levels only in patients with TDT. Conclusions: Patients with thalassemia showed iron overload, reduced hepcidin levels, and a greater extent of ineffective erythropoiesis. The hepcidin levels were more strongly related to ineffective erythropoiesis compared with iron overload. The imbalance between erythropoiesis and iron metabolism differed across different thalassemia types.

Prevalence of diabetes mellitus in Chinese children with thalassaemia major.

OBJECTIVE: Diabetes mellitus is a common endocrinopathy in patients with ß-thalassaemia major (ß-TM), which is high prevalent in southern China. This study aimed to determine the cause and prevalence of glycaemic disorders in Chinese children with ß-TM. METHODS: In this prospective study, fasting glucose and insulin (FINS) levels were assessed in 267 ß-TM and 80 non-TM control children. Homeostatic model assessment (HOMA) and the quantitative insulin sensitivity check index (QUICKI) were evaluated. Iron overload was assessed by serum ferritin (SF), total units of blood transfused and cardiac T2*. RESULTS: ß-TM had higher FPG (P < 0.001), FINS (P < 0.001) and HOMA-IR (P < 0.05), but lower QUICKI (P < 0.01) than those of controls. The impaired fasting glucose (IFG) was present in 30% of children, whereas 2% had diabetes. The prevalence of IFG in ß-TM group was higher in children aged >10 years (OR 6.5; 95% CI 3.7-11.4; P < 0.001), SF of >2500 µg/l (OR 4.8; 95% CI 2.1-11.1; P < 0.01), serum ALT levels of >50 IU/l (OR 2.1; 95% CI 1.2-3.7; P < 0.05) and cardiac T2* of <20 ms (OR 3.3; 95% CI 1.7-6.6; P < 0. 01). The children on deferiprone (DFP) had a reduced incidence of glycaemic aberrations than those on other chelating agents (OR 0.4; 95% CI 0.23-0.8; P < 0.05). CONCLUSIONS: Our data suggest that IFG occurred in 30% of ß TM children, perhaps due to insulin resistance secondary to iron overload. Deferiprone-containing chelating agent may have a protective effect.

Estimating Grass-Soil Bioconcentration of Munitions Compounds from Molecular Structure.

A partitioning-based model is presented to estimate the bioconcentration of five munitions compounds and two munition-like compounds in grasses. The model uses polyparameter linear free energy relationships (pp-LFERs) to estimate the partition coefficients between soil organic carbon and interstitial water and between interstitial water and the plant cuticle, a lipid-like plant component. Inputs for the pp-LFERs are a set of numerical descriptors computed from molecular structure only that characterize the molecular properties that determine the interaction with soil organic carbon, interstitial water, and plant cuticle. The model is validated by predicting concentrations measured in the whole plant during independent uptake experiments with a root-mean-square error (log predicted plant concentration-log observed plant concentration) of 0.429. This highlights the dominant role of partitioning between the exposure medium and the plant cuticle in the bioconcentration of these compounds. The pp-LFERs can be used to assess the environmental risk of munitions compounds and munition-like compounds using only their molecular structure as input.

Quantum Chemically Estimated Abraham Solute Parameters Using Multiple Solvent-Water Partition Coefficients and Molecular Polarizability.

Polyparameter Linear Free Energy Relationships (pp-LFERs), also called Linear Solvation Energy Relationships (LSERs), are used to predict many environmentally significant properties of chemicals. A method is presented for computing the necessary chemical parameters, the Abraham parameters (AP), used by many pp-LFERs. It employs quantum chemical calculations and uses only the chemical's molecular structure. The method computes the Abraham E parameter using density functional theory computed molecular polarizability and the Clausius-Mossotti equation relating the index refraction to the molecular polarizability, estimates the Abraham V as the COSMO calculated molecular volume, and computes the remaining AP S, A, and B jointly with a multiple linear regression using sixty-five solvent-water partition coefficients computed using the quantum mechanical COSMO-SAC solvation model. These solute parameters, referred to as Quantum Chemically estimated Abraham Parameters (QCAP), are further adjusted by fitting to experimentally based APs using QCAP parameters as the independent variables so that they are compatible with existing Abraham pp-LFERs. QCAP and adjusted QCAP for 1827 neutral chemicals are included. For 24 solvent-water systems including octanol-water, predicted log solvent-water partition coefficients using adjusted QCAP have the smallest root-mean-square errors (RMSEs, 0.314-0.602) compared to predictions made using APs estimated using the molecular fragment based method ABSOLV (0.45-0.716). For munition and munition-like compounds, adjusted QCAP has much lower RMSE (0.860) than does ABSOLV (4.45) which essentially fails for these compounds.

Mechanistic and data-driven perspectives on plant uptake of organic pollutants.

Establishing reliable predictive models for plant uptake of organic pollutants is crucial for environmental risk assessment and guiding phytoremediation efforts. This study compiled an expanded dataset of plant cuticle-water partition coefficients (Kcw), a useful indicator for plant uptake, for 371 data points of 148 unique compounds and various plant species. Quantum/computational chemistry software and tools were utilized to compute various molecular descriptors, aiming to comprehensively characterize the properties and structures of each compound. Three types of models were developed to predict Kcw: a mechanism-driven pp-LFER model, a data-driven machine learning model, and an integrated mechanism-data-driven model. The mechanism-data-driven GBRT-ppLFER model exhibited superior performance, achieving RMSEtrain = 0.133 and RMSEtest = 0.301 while maintaining interpretability. The Shapley Additive Explanation analysis indicated that pp-LFER parameters, ESPI, FwRadicalmax, ExtFP607, and RDF70s are the key factors influencing plant uptake in the GBRT-ppLFER model. Overall, pp-LFER parameter, ESPI, and ExtFP607 show positive effects, while the remaining factors exhibit negative effects. Partial dependency analysis further indicated that plant uptake is not solely determined by individual factors but rather by the combined interactions of multiple factors. Specifically, compounds with ppLFER parameter >4, ESPI > -25.5, 0.098 < FwRadicalmax <0.132, and 2 < RFD70s < 3, are generally more readily taken up by plants. Besides, the predicted Kcw values from the GBRT-ppLFER model were effectively employed to estimate the plant-water partition coefficients and bioconcentration factors across different plant species and growth media (water, sand, and soil), achieving an outstanding performance with an RMSE of 0.497. This study provides effective tools for assessing plant uptake of organic pollutants and deepens our understanding of plant-environment-compound interactions.

Iron Overload Induces Hepatic Ferroptosis and Insulin Resistance by Inhibiting the Jak2/stat3/slc7a11 Signaling Pathway.

Recent studies showed that patients with iron overload had increased risk of insulin resistance or diabetes. Ferroptosis is a new type of cell death mainly caused by iron-dependent oxidative damage. In the present study, we investigated potential mechanisms of iron overload induced hepatic ferroptosis and insulin resistance through in vivo and in vitro experiments. In vivo, the mice models of iron overload were established by intraperitoneal injection of iron dextran. The changes of body weight, serum ferritin and blood glucose were measured. Hematoxylin-eosin (HE) and Perl's stainings were used to observe the pathological changes and iron deposition in the liver of mice. In vitro, HepG2 cells were treated with ferric ammonium citrate (FAC, 9 mmol/L, 24 h) to establish the cell models of iron overload. The labile iron pool, cell viability, glucose consumption and glycogen contents were measured. The ultrastructure of mitochondria was observed by transmission electron microscope (TEM). The malondialdehyde (MDA) and glutathione (GSH) kits were used to detect lipid peroxidation in liver tissues of mice and HepG2 cells. RT-PCR and Western blot were used to detect the mRNA and protein expression levels of ferroptosis factors and JAK2/STAT3 signaling pathway. In this study, we used the iron chelator deferasirox in mice and HepG2 cells. Iron overload caused weight loss, elevated serum ferritin, fasting blood glucose, fasting insulin, HOMA-IR, impaired glucose tolerance, and decreased insulin sensitivity in mice. HE staining and Perls staining showed clumps of iron deposition in the liver of iron overload mice. Iron overload could reduce the glucose consumption, increase MDA contents of HepG2 cells, while reduce glycogen and GSH contents in liver tissues of mice and HepG2 cells. TEM showed deletion of mitochondrial ridge and rupture of outer membrane in HepG2 cells with iron overload. Iron chelator deferasirox could significantly improve the above indicators, which might be related to the activation of JAK2/STAT3/SLC7A11 signaling pathway and hepatic ferroptosis. Iron overload could induce hepatic ferroptosis and insulin resistance by inhibiting the JAK2/STAT3/SLC7A11 signaling pathway, and the iron chelator deferasirox might improve hepatic insulin resistance induced by iron overload.

Fathers' needs of breastfeeding support: Perspective of health nurses.

PURPOSE: To explore the improvement of health education on father's participation in breastfeeding from the perspective of maternal and child health nurses. METHODS: Qualitative phenomenological research was used, and 15 maternal and child health nurses who provided breastfeeding support were invited. With semi-structured deep interviews and on-site recordings, data were analyzed through content analysis. RESULTS: Four main themes were extracted, including 'cultivating fathers' awareness of participation in breastfeeding', 'collaboration of multiple disciplines to improve health education on breastfeeding for fathers in hospital', 'Simulated scenarios to develop fathers' skills in solving breastfeeding problems', and 'establishing a hospital-community interface network to improve breastfeeding continuation care after hospital discharge'. CONCLUSIONS: Medical and health care departments should attach importance to guidance on health education for fathers' breastfeeding participation, cultivate fathers' awareness of participation in breastfeeding, provide multi-disciplinary collaboration-based health education on breastfeeding for fathers from the prenatal period and improve post-discharge health education on breastfeeding. The additional education being suggested would contribute to fathers being able to play an important role in breastfeeding.

Association between polymorphisms of cytokine genes and endometriosis: A comprehensive systematic review and meta-analysis.

INTRODUCTION: There is abundant evidence to suggest that cytokines play a part in the mechanisms responsible for the formation of endometrium heterotopy. Cytokine synthesis is not only determined by the body's immunological reactivity but also by polymorphisms in the immune regulatory genes. The study of these polymorphisms in the immune regulatory genes offers up new possibilities in terms of prognosticating the risk of endometriosis and susceptibility to its treatment. The purpose of this comprehensive systematic review and meta-analysis was to investigate whether or not cytokine gene polymorphisms were linked to an increased chance of endometriosis. METHODS: By searching MEDLINE, Scopus, and Web of Science databases, the relevant studies were identified. The odds ratio (OR) with 95% confidence interval (CI) was used to assess the association between TNF-α/IL-10/IL-6/TGF-ß/IFN-γ/IL-1ß gene polymorphisms and endometriosis risk. RESULTS: A total of 5128 cases and 5334 controls in 32 eligible studies were included in the meta-analysis. Overall, results indicated the negative association between the cytokine gene polymorphisms and endometriosis in the dominant model of TNF-α (rs1799964): [OR] = 0.64, [CI]: 0.46-0.89) and a positive association in IFN-γ a13 allele: OR= 1.45, [CI]: 1.07-1.98; and IL-10 (rs1800872): [OR]= 1.60, [CI]: 1.21-2.12). CONCLUSION: The present study suggests that IL-10 (rs1800872) and IFN-γ a13 allele may be a risk factors for endometriosis. Also, TNF-α (rs1799964) is associated with decreased susceptibility to endometriosis.

GLP-1RA Liraglutide and Semaglutide Improves Obesity-Induced Muscle Atrophy via SIRT1 Pathway.

Background: Obesity is related to the loss of skeletal muscle mass and function (sarcopenia). The co-existence of obesity and sarcopenia is called sarcopenic obesity (SO). Glucagon like peptide-1 receptor agonists (GLP-1RA) are widely used in the treatment of diabetes and obesity. However, the protective effects of GLP-1RA on skeletal muscle in obesity and SO are not clear. This study investigated the effects of GLP-1RA liraglutide and semaglutide on obesity-induced muscle atrophy and explored the underlying mechanisms. Methods: Thirty-six male C57BL/6J mice were randomly divided into two groups and fed a regular diet and a high-fat diet for 18 weeks, respectively. After establishing an obesity model, mice were further divided into six groups: control group, liraglutide (LIRA) group, semaglutide (SEMA) group, high-fat diet (HFD) group, HFD + LIRA group, HFD + SEMA group, and subcutaneous injection for 4 weeks. The body weight, muscle mass, muscle strength, glycolipid metabolism, muscle atrophy markers, myogenic differentiation markers, GLUT4 and SIRT1 were analyzed. C2C12 myotube cells treated with palmitic acid (PA) were divided into four groups: control group, PA group, PA + LIRA group, PA + SEMA group. The changes in glucose uptake, myotube diameter, lipid droplet infiltration, markers of muscle atrophy, myogenic differentiation markers, GLUT4 and SIRT1 were analyzed, and the changes in related indicators were observed after the addition of SIRT1 inhibitor EX527. Results: Liraglutide and semaglutide reduced HFD-induced body weight gain, excessive lipid accumulation and improved muscle atrophy. Liraglutide and semaglutide eliminated the increase of muscle atrophy markers in skeletal muscle and C2C12 myotubes. Liraglutide and semaglutide restored impaired glucose tolerance and insulin resistance. However, these beneficial effects were attenuated by inhibiting SIRT1 expression. Conclusion: Liraglutide and semaglutide protects skeletal muscle against obesity-induced muscle atrophy via the SIRT1 pathway.

Improving predictions and understanding of primary and ultimate biodegradation rates with machine learning models.

This study aimed to develop machine learning based quantitative structure biodegradability relationship (QSBR) models for predicting primary and ultimate biodegradation rates of organic chemicals, which are essential parameters for environmental risk assessment. For this purpose, experimental primary and ultimate biodegradation rates of high consistency were compiled for 173 organic compounds. A significant number of descriptors were calculated with a collection of quantum/computational chemistry software and tools to achieve comprehensive representation and interpretability. Following a pre-screening process, multiple QSBR models were developed for both primary and ultimate endpoints using three algorithms: extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR). Furthermore, a unified QSBR model was constructed using the knowledge transfer technique and XGBoost. Results demonstrated that all QSBR models developed in this study had good performance. Particularly, SVM models exhibited high level of goodness of fit (coefficient of determination on the training set of 0.973 for primary and 0.980 for ultimate), robustness (leave-one-out cross-validated coefficient of 0.953 for primary and 0.967 for ultimate), and external predictive ability (external explained variance of 0.947 for primary and 0.958 for ultimate). The knowledge transfer technique enhanced model performance by learning from properties of two biodegradation endpoints. Williams plots were used to visualize the application domains of the models. Through SHapley Additive exPlanations (SHAP) analysis, this study identified key features affecting biodegradation rates. Notably, MDEO-12, APC2D1_C_O, and other features contributed to primary biodegradation, while AATS0v, AATS2v, and others inhibited it. For ultimate biodegradation, features like No. of Rotatable Bonds, APC2D1_C_O, and minHBa were contributors, while C1SP3, Halogen Ratio, GGI4, and others hindered the process. Also, the study quantified the contributions of each feature in predictions for individual chemicals. This research provides valuable tools for predicting both primary and ultimate biodegradation rates while offering insights into the mechanisms.

Theoretical modeling of molecular fractionation of dissolved organic matter on ferrihydrite and its impact on proton and metal binding properties.

Molecular fractionation of dissolved organic matter (DOM) at the mineral-liquid interfaces in soil changes its molecular composition, thus altering its reactivity, such as proton and metal binding properties. Therefore, a quantitative understanding of compositional change of DOM molecules after adsorptive fractionation by minerals is of great environmental significance for predicting the cycling of organic carbon (C) and metals in the ecosystem. In this study, we conducted adsorption experiments to investigate the adsorption behaviors of DOM molecules on ferrihydrite. The molecular compositions of the original and fractionated DOM samples were analyzed with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). For all DOM molecules, three molecular groups with significantly different chemical properties were identified through Spearman correlation analysis between the relative intensities of DOM molecules and organic C concentrations in solutions after adsorptive fractionation. Three corresponding molecular models for the three molecular groups were constructed based on Vienna Soil-Organic-Matter Modeler and FT-ICR-MS results, which were used as base units to construct molecular models for the original or fractionated DOM samples (model(DOM)). The models well described the chemical properties of the original or fractionated DOM as compared with the experimental data. Furthermore, based on model(DOM), the proton and metal binding constants of DOM molecules were quantified by SPARC chemical reactivity calculations and linear free energy relationships. We found the density of binding sites of the fractionated DOM samples was negatively correlated with the adsorption percentage. Our modeling results suggested that adsorption of DOM on ferrihydrite gradually removed acidic functional groups from the solution, dominated by the adsorption of both carboxyl and phenol groups. This study proposed a new modeling approach to quantify the molecular fractionation processes of DOM on Fe oxides and their impact on proton and metal binding properties, which is expected to be applicable to DOM from different environments.

Iron overload induces islet ß cell ferroptosis by activating ASK1/P-P38/CHOP signaling pathway.

Background: Recent studies have shown that the accumulation of free iron and lipid peroxides will trigger a new form of cell death-ferroptosis. This form of cell death is associated with a variety of diseases, including type 2 diabetes. We hypothesize that iron overload may play a role in driving glucose metabolism abnormalities by inducing endoplasmic reticulum stress that mediates ferroptosis in islet ß cells. In this study, we tested this conjecture from in vivo and in vitro experiments. Methods: We established a mouse iron overload model by intraperitoneal injection of iron dextrose (50 mg/kg) and an iron overload cell model by treating MIN6 cells with ferric ammonium citrate (640 µmol/L, 48 h) in vitro. The iron deposition in pancreatic tissue was observed by Prussian blue staining, and the pathological changes in pancreatic tissues by HE staining and the protein expression level by pancreatic immunohistochemistry. In the cellular experiments, we detected the cell viability by CCK8 and observed the cellular ultrastructure by transmission electron microscopy. We also used MDA and ROS kits to detect the level of oxidative stress and lipid peroxidation in cells. Western blotting was performed to detect the expression levels of target proteins. Results: Iron overload induces MIN6 cell dysfunction, leading to increased fasting blood glucose, impaired glucose tolerance, and significantly decreased insulin sensitivity in mice. This process may be related to the ferroptosis of islet ß cells and the activation of ASK1/P-P38/CHOP signaling pathway.

Light-Propelled Super-Hydrophobic Sponge Motor and its Application in Oil-Water Separation.

Self-propelled separation materials, that is, motor, are one of the keys to realizing smart oil-water separation. Although three-dimensional sponges such as commercial melamine sponge (MS) exhibit excellent oil-water separation ability, they cannot move by themselves on water. Aiming at solving this problem, a polydimethylsiloxane (PDMS) and molybdenum disulfide (MoS2) modified MS motor (PDMS@MS/MoS2) with an asymmetric multilayer structure was prepared, in which the photothermal layer MoS2 provided the propelling force for the motor under infrared light irradiation, and the middle layer PDMS was used as the superhydrophobic modified agent and adhesive agent between commercial MS and MoS2 powder. PDMS coated MS (PDMS@MS) as the superhydrophobic layer showed good superhydrophobic ability (153.1°) and oil-water separation capacity (52.33 g/g to liquid paraffin). Furthermore, the introduction of MoS2 made the speed of the sponge motor reach 8.27 mm s-1 with a removal quantity of 12.20 g/g for cyclohexane. After recycling 8 times, the contact angle, cyclohexane capturing amount, and average velocity of the motor were 150.3°, 11.40 g/g, and 8.41 mm/s, respectively. Meanwhile, PDMS@MS/MoS2 kept a similar light-propelling velocity (∼8 mm) at different pH values and in simulated seawater, demonstrating that the light-propelling motor possessed a good cycle and practical performance, which provides a possibility for the directional light propulsion of a sponge motor in oil-water separation.

The safety and efficacy evaluation of sodium-glucose co-transporter 2 inhibitors for patients with non-alcoholic fatty liver disease: An updated meta-analysis.

BACKGROUND: In recent years, sodium-glucose co-transporter 2 inhibitors (SGLT2is) have been increasingly used in the treatment of patients with non-alcoholic fatty liver disease (NAFLD). This updated meta-analysis aimed to evaluate the efficacy and safety of SGLT2is for patients with NAFLD. METHODS: PubMed, Embase, Cochrane Library, Web of Science, Wan Fang, China National Knowledge Infrastructure and VIP databases were searched for relevant studies from inception to April 30, 2021. Values of weighted mean differences (WMDs) and risk ratios (RRs) were determined for continuous and dichotomous outcomes, respectively. RESULTS: A total of 1,498 patients with NAFLD from 20 studies were included for further analysis. Pooled analyses indicated significant improvements in body mass index [WMD: -0.84 kg/m2, 95% CI (-1.09, -0.60)], alanine aminotransferase [WMD: -4.36 U/L, 95% CI (-7.17, -1.54)], aspartate aminotransferase [WMD: -2.94 U/L, 95% CI (-5.33, -0.55)], fasting plasma glucose [WMD: -4.08 mmol/L, 95% CI (-6.21, -1.95)] and fibrosis-4 index [WMD: -0.08, 95% CI (-0.11, -0.05)] following SGLT2i treatment (p < 0.01 for all above parameters). There was no significant difference in the incidence of total adverse events between the SGLT2i group and the control group (RR = 0.78, 95% CI (0.58, 1.06), p = 0.11]. CONCLUSION: SGLT2is seem to be a promising treatment for patients with NAFLD to improve metabolic and fibrosis indexes without increasing the incidence of adverse events. Most included studies were conducted in NAFLD patients with diabetes. Therefore, the results of this meta-analysis are more applicable to the diabetic population.

Association Between Iron Metabolism and Acute Kidney Injury in Critically Ill Patients With Diabetes.

Objective: Iron overload plays an important role in the pathogenesis of diabetes and acute kidney injury (AKI). The aim of this present study was to explore the relationship between iron metabolism and AKI in patients with diabetes. Methods: The clinical data of diabetes patients from MIMIC-III database in intensive care unit (ICU) were retrospectively analyzed. Regression analyses were used to explore the risk factors of AKI and all-cause death in critical patients with diabetes. Area under the receiver operating characteristic curves (AUROCs) were used to analyze serum ferritin (SF), and regression model to predict AKI in critical patients with diabetes. All diabetes patients were followed up for survival at 6 months, and Kaplan-Meier curves were used to compare the survival rate in patients with different SF levels. Results: A total of 4,997 diabetic patients in ICU were enrolled, with a male-to-female ratio of 1.37:1 and a mean age of 66.87 ± 12.74 years. There were 1,637 patients in the AKI group (32.8%) and 3,360 patients in the non-AKI group. Multivariate logistic regression showed that congestive heart failure (OR = 2.111, 95% CI = 1.320-3.376), serum creatinine (OR = 1.342, 95% CI = 1.192-1.512), Oxford Acute Severity of Illness Score (OR = 1.075, 95% CI = 1.045-1.106), increased SF (OR = 1.002, 95% CI = 1.001-1.003), and decreased transferrin (OR = 0.993, 95% CI = 0.989-0.998) were independent risk factors for AKI in critical patients with diabetes. Multivariate Cox regression showed that advanced age (OR = 1.031, 95% CI = 1.025-1.037), AKI (OR = 1.197, 95% CI = 1.011-1.417), increased Sequential Organ Failure Assessment score (OR = 1.055, 95% CI = 1.032-1.078), and increased SF (OR = 1.380, 95% CI = 1.038-1.835) were independent risk factors for 6-month all-cause death in critical diabetic patients. The AUROCs of SF and the regression model to predict AKI in critical patients with diabetes were 0.782 and 0.851, respectively. The Kaplan-Meier curve showed that the 6-month survival rate in SF-increased group was lower than that in SF-normal group (log-rank χ2 = 16.989, P < 0.001). Conclusion: Critically ill diabetic patients with AKI were easily complicated with abnormal iron metabolism. Increase of SF is an important risk factor for AKI and all-cause death in critically ill patients with diabetes.