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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Decrease in leptin mediates rat bone metabolism impairments during high-fat diet-induced catch-up growth by modulating the OPG/RANKL balance.

Due to catch-up growth (CUG), there are adverse effects on human health. However, there is little information about its influence on bone metabolism. This study aimed to investigate the effects of leptin on bone metabolism and formation during high-fat diet (HFD)-induced CUG. We randomly divided male Wistar rats (5 weeks old) into four groups: control (CTL), caloric restriction and normal chow (RN), caloric restriction (4 weeks), and HFD (RH), and RH + leptin antagonist (RH + LEPA). We monitored body weights, biochemical markers, and epididymal and perirenal fat in these rats. We then performed Hematoxylin and Eosin (H&E) staining to evaluate bone metabolism. We detected osteoprotegerin (OPG) and receptor activator of nuclear factor-kappa b ligand (RANKL) by qRT-PCR and immunohistochemistry (IHC). We found that HFD increased the body weights in rats. In RN, RH, and RH + LEPA groups, major biochemical markers of bone metabolism in rat serum were significantly altered. We found that epididymal and perirenal fat tissues of RH and RH + LEPA groups were higher than those in the RN group. Severe bone formation impairment in the distal diaphysis and metaphysis of the left femora and lumbar vertebra was seen in the RH group compared to RN, which was even aggravated by a leptin antagonist. OPG in the left femora and lumbar vertebra was lower in RH than the RN group. The leptin antagonist decreased OPG during CUG in the RH group, whereas RANKL expression showed an opposite alteration. During HFD-induced CUG, bone formation was mediated by OPG and RANKL and was affected by the leptin content.

Reductive release of Fe mineral-associated organic matter accelerated by oxalic acid.

The properties and composition of soil dissolved organic matter (DOM) are highly affected by the adsorption and desorption of organic matter (OM) on soil minerals and heterotrophic microbial respiration. Organic acids (e.g., oxalic acid), components of root exudates, have been revealed to liberate organic matter (OM) by the dissolution of protective mineral phases and stimulate microbial degradation of OM. However, the effects of organic acids on the properties and composition of soil DOM molecules and the related mechanisms are still poorly understood. In this study, we conducted microcosm incubation experiments with and without oxalic acid addition, and aimed to elucidate the variations of DOM properties and composition, employing a combination of Fourier transform ion cyclotron resonance mass spectrometry, optical spectroscopy, and bacterial community composition analysis. Our results indicated that the released OM from the direct dissolution of protective mineral phases by oxalic acid further stimulated the microbial reductive release of Fe mineral-associated OM under anoxic conditions. Furthermore, the addition of oxalic acid enhanced the degradation of aliphatic compounds and lignins with low O/C ratios, and increased the accumulation of lignins with high O/C ratios, tannins, and condensed aromatics. Linking the bacterial community composition to DOM molecular properties and composition further suggested that the enhanced reductive release of Fe mineral-associated OM was highly related to the increased abundances of Proteobacteria and Actinobacteria. Overall, oxalic acid induced long-lasting impacts on soil DOM properties and composition under anoxic soil conditions in our study. We expect that our results will contribute to understanding the dynamics of soil DOM molecules in the environment.

Development and Validation of a Prediction Model for Survival in Diabetic Patients With Acute Kidney Injury.

Objective: We aimed to analyze the risk factors affecting all-cause mortality in diabetic patients with acute kidney injury (AKI) and to develop and validate a nomogram for predicting the 90-day survival rate of patients. Methods: Clinical data of diabetic patients with AKI who were diagnosed at The First Affiliated Hospital of Guangxi Medical University from April 30, 2011, to April 30, 2021, were collected. A total of 1,042 patients were randomly divided into a development cohort and a validation cohort at a ratio of 7:3. The primary study endpoint was all-cause death within 90 days of AKI diagnosis. Clinical parameters and demographic characteristics were analyzed using Cox regression to develop a prediction model for survival in diabetic patients with AKI, and a nomogram was then constructed. The concordance index (C-index), receiver operating characteristic curve, and calibration plot were used to evaluate the prediction model. Results: The development cohort enrolled 730 patients with a median follow-up time of 87 (40-98) days, and 86 patients (11.8%) died during follow-up. The 90-day survival rate was 88.2% (644/730), and the recovery rate for renal function in survivors was 32.9% (212/644). Multivariate analysis showed that advanced age (HR = 1.064, 95% CI = 1.043-1.085), lower pulse pressure (HR = 0.964, 95% CI = 0.951-0.977), stage 3 AKI (HR = 4.803, 95% CI = 1.678-13.750), lower 25-hydroxyvitamin D3 (HR = 0.944, 95% CI = 0.930-0.960), and multiple organ dysfunction syndrome (HR = 2.056, 95% CI = 1.287-3.286) were independent risk factors affecting the all-cause death of diabetic patients with AKI (all p < 0.01). The C-indices of the prediction cohort and the validation cohort were 0.880 (95% CI = 0.839-0.921) and 0.798 (95% CI = 0.720-0.876), respectively. The calibration plot of the model showed excellent consistency between the prediction probability and the actual probability. Conclusion: We developed a new prediction model that has been internally verified to have good discrimination, calibration, and clinical value for predicting the 90-day survival rate of diabetic patients with AKI.

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.

Guava Leaf Extract Attenuates Insulin Resistance via the PI3K/Akt Signaling Pathway in a Type 2 Diabetic Mouse Model.

BACKGROUND AND OBJECTIVE: Insulin resistance is well known to exhibit essential effects on the progression of diabetes mellitus (DM). Guava leaf was also reported to exhibit anti-diabetic effects including decreasing blood glucose. Therefore, this present study aims to explore the role guava leaf extract (GLE) plays in insulin resistance and its mechanism of action via the PI3K/Akt signaling pathway. METHODS: KK-Ay mice is a spontaneous genetic type 2 diabetes mouse model induced by feeding a high fat and high sugar diet. Mice were randomly assigned into three groups: diabetic mice (DM), DM + MET (diabetic mice treated with metformin) and DM + GLE (diabetic mice treated with GLE) groups. After 8 weeks of treatment, body weight and levels of fasting plasma glucose (FPG), fasting insulin and lipids in plasma were measured. Mice were sacrificed and mRNA and protein expression of insulin receptor substrate1 (IRS1), phosphatidylinositol 3-kinase (PI3K) and serine/threonine kinase protein B (Akt) in livers were measured. RESULTS: GLE markedly reduced body weight, FPG, fasting insulin and insulin resistance index but increased the insulin sensitivity index of diabetic KK-Ay mice. Moreover, GLE upregulated the expression of IRS-1, PI3K and Akt mRNAs in livers of diabetic KK-Ay mice. In addition, GLE also elevated IRS-1, PI3K, Akt, p-PI3K and p-Akt protein expression in their livers. The results of the DM + MET group were similar to those of the DM + GLE group. CONCLUSION: GLE plays anti-diabetic roles by ameliorating insulin resistance in KK-Ay diabetic mice and this is related to the activation of PI3K/Akt signaling pathway.

Impact of Age on the Prognosis of Papillary Thyroid Carcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) is a common subtype of thyroid carcinoma with a rising incidence rate. The purpose of this study was to provide a better understanding of age and PTC using the Surveillance, Epidemiology, and End Results (SEER) Program. METHODS: The study derived patients' information from the SEER Program (2010-2015). Chi-square or Fisher exact tests, and Kaplan-Meier method were used to analyze survival. Prognostic factors associated with survival were analyzed by Cox multivariate regression. RESULTS: A total of 1738 records were included from SEER, with 1079 PTC in the age group <55 years, and 659 PTC in the age group ≥55 years. The 5-year survival rate was 94% and the overall survival (OS) curve in different age groups indicated that patients younger than 55yr have a longer survival time (P < 0.05). In multivariate Cox regression, age, M stage and surgery treatment were independent risk factors (P < 0.05). Regarding PTC-specific survival, age and surgery treatment were the two main independent prognostic factors in multivariate regression. However, AJCC and M stage were not in the disease specific survival. CONCLUSION: Age is a prognostic factor in OS and PCT specific survival. AJCC I stage and surgery treatment are also significant in predicting prognosis.