Survival benefit of postoperative radioiodine therapy among patients with intermediate-risk differentiated thyroid carcinoma.

BACKGROUND: The 2015 American Thyroid Association (ATA) guidelines proposed the use of the ATA Risk Stratification System and American Joint Committee on Cancer Tumor-Node-Metastasis (AJCC/TNM) Staging System for postoperative radioiodine decision-making. However, the management of patients with intermediate-risk differentiated thyroid carcinoma (DTC) is not well defined. In this study, we aimed to evaluate the therapeutic efficacy of radioactive iodine therapy (RAIT) among various subgroups of patients with intermediate-risk DTC after surgery. METHODS: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database (2010-2015). The DTC patients with intermediate risk of recurrence were divided into two groups (treated or not treated with radioactive iodine (RAI)). As the treatment was not randomly assigned, stabilized inverse probability treatment weighting (sIPTW) was used to reduce selection bias. We used the Kaplan-Meier method and log-rank test to analyze overall survival (OS) and cancer-specific survival (CSS). RESULTS: Kaplan-Meier analysis after sIPTW found a significant difference in OS and CSS between no RAIT and RAIT (log-rank test, P < 0.0001; P = 0.0019, respectively). The Kaplan-Meier curves of CSS in age cutoff of 55 years showed a significant association between no RAIT and RAIT (log-rank test, P = 0.0045). Univariate and multivariate Cox regression showed RAIT was associated with a reduced risk of mortality compared with no RAIT (hazard ratio [HR] 0.59, 95% confidence interval [95% CI 0.44-0.80]). Age (≥ 55) years showed a worse CSS regardless of whether or not a patient was treated or not treated with RAI ([HR] 8.91, 95% confidence interval [95% CI 6.19-12.84]). CONCLUSIONS: RAIT improves OS and CSS in patients with intermediate-risk DTC after surgery. 55 years is a more appropriate prognostic age cutoff for the relevant classification systems and is a crucial consideration in RAI decision-making. Therefore, we need individualized treatment plans.

Serum vitamin D is substantially reduced and predicts flares in diabetic retinopathy patients.

AIMS/INTRODUCTION: Research has suggested that vitamin D deficiency is associated with diabetic retinopathy (DR). Our study aimed to determine whether vitamin D deficiency is the cause of diabetic retinopathy or if diabetic retinopathy reduces vitamin D levels. MATERIALS AND METHODS: Participants with type-2 diabetes were recruited for this prospective observational clinical study and were divided into a diabetic group without retinopathy and a diabetic group with retinopathy, with additional healthy volunteers serving as a control group. The differences in clinical characteristics among the three groups were also compared. Patients without retinopathy were then followed for 1 year to monitor the incidence of diabetic retinopathy. After follow-up, participants were divided into subgroups based on whether diabetic retinopathy occurred. The baseline data of the subgroups were compared, and the independent risk factors were analyzed. RESULTS: Vitamin D levels were generally low. Participants with diabetic retinopathy had significantly lower vitamin D levels than did those without retinopathy (P < 0.01). A comparison of the two subgroups revealed lower baseline vitamin D concentrations in the new-DR subgroup than in the non-DR subgroup (P < 0.01). Vitamin D deficiency and elevated HbA1c levels were found to be independent risk factors for diabetic retinopathy (OR = 0.935, 95% CI: 0.867-0.981, P = 0.006; OR = 2.208, 95% CI: 1.764-2.764, P < 0.01). The limit of vitamin D intake according to the receiver-operating characteristic (ROC) curve was 26.01 ng/mL, and the area under the ROC curve was 0.603 (95% CI: 0.559-0.706, P = 0.002). CONCLUSIONS: Vitamin D levels were significantly lower in patients diagnosed with diabetic retinopathy. More importantly, vitamin D deficiency may accelerate the onset of diabetic retinopathy.

Modulation of adipose tissue metabolism by exosomes in obesity.

Obesity and its related metabolic complications represent a significant global health challenge. Central to this is the dysregulation of glucolipid metabolism, with a predominant focus on glucose metabolic dysfunction in the current research, whereas adipose metabolism impairment garners less attention. Exosomes (EXs), small extracellular vesicles (EVs) secreted by various cells, have emerged as important mediators of intercellular communication and have the potential to be biomarkers, targets, and therapeutic tools for diverse diseases. In particular, EXs have been found to play a role in adipose metabolism by transporting cargoes such as noncoding RNAs (ncRNA), proteins, and other factors. This review article summarizes the current understanding of the role of EXs in mediating adipose metabolism disorders in obesity. It highlights their roles in adipogenesis (encompassing adipogenic differentiation and lipid synthesis), lipid catabolism, lipid transport, and white adipose browning. The insights provided by this review offer new avenues for developing exosome-based therapies to treat obesity and its associated comorbidities.

Elevated serum CA199 levels in patients suffering type 2 diabetes vs. various types of cancer.

AIMS: Carbohydrate antigen 199 (CA199) is a standard tumor marker, and recent studies have found elevated in CA199 levels in patients with diabetes. However, there is no systematic measurement and comparison of serum CA199 levels in patients with diabetes and cancer. Here, a detailed description of the changes in serum CA199 levels in patients with type 2 diabetes and various cancers was explored. METHODS: A total of 5,641 participants were screened for clinical laboratory test results of serum CA199 levels over the past three years (2020-2023). This study included 2,464 healthy controls, 688 patients with type 2 diabetes, and 2,489 patients with 16 different types of cancer. Each type of cancer had more than 30 independent serum CA199 level test results. The serum CA199 levels were compared between cancer groups, type 2 diabetes patients, and healthy controls. Additionally, the CA199 levels of cancer patients were compared with those of patients with type 2 diabetes. RESULTS: The serum CA199 levels of esophagus cancer, lung cancer, pancreatic cancer, ovarian cancer, breast cancer, rectum cancer, prostate cancer, bladder cancer, liver cancer, gastric cancer, cervical cancer, colon cancer, lymphoma, thyroid cancer, intracranial tumors, and nasopharyngeal laryngeal cancer were found to be elevated compared to healthy controls (P < 0.01). In addition, the serum CA199 levels of patients with type 2 diabetes were also significantly elevated compared to healthy controls (P < 0.01). Moreover, the degree of elevation in serum CA199 levels in patients with type 2 diabetes was not significantly different from that observed in some types of cancer, such as esophagus cancer (P = 0.163), breast cancer (P = 0.927), prostate cancer (P = 1.000), bladder cancer (P = 0.406), Lymphoma (P = 0.975), thyroid cancer (P = 1.000), intracranial tumors (P = 0.161), nasopharyngeal and laryngeal cancer (P = 1.000). CONCLUSIONS: Serum CA199 levels also increase in type 2 diabetes, and the magnitude of the increase is similar to that seen in some cancers.

Comparison of in-patient glucose team based management with conventional blood glucose management- a retrospective study from China.

BACKGROUND: Glycemic control for patients with diabetes in the surgical department is often unsatisfactory. Compounding this issue is the fact that conventional glucose management models are often inefficient and difficult to monitor over time. OBJECTIVE: To investigate the impact of inpatient glucose team-based management on glycemic control and hospital days in surgical patients with diabetes. METHODS: A retrospective analysis was conducted on 4156 patients with diabetes in the surgical department who received inpatient management of diabetes at a tertiary medical center from June 2020 to May 2022. Based on whether they received inpatient glucose team-based management, the surgical patients with diabetes were divided into two groups: the inpatient glucose team-based management (GM group, consisting of 1698 participants) and the conventional blood glucose management group (control group, consisting of 2458 participants). We compared the two groups in terms of glycemic control, hospital days, and health-care costs. Multiple logistic regression analysis was performed to build the hospital days prediction model and nomogram. Finally, the performance of the model was evaluated. RESULTS: The rate of glucose detection was higher in the GM group at 2 h postprandial (P < 0.01). The incidence of hypoglycemia and severe hyperglycemia, blood glucose attainment time, pre-operative preparation days, hospital days, and health-care costs were lower in the GM group than in the control group (P < 0.01). The linear regression model revealed that blood glucose attainment time, incidence of hypoglycemia (< 3.9mmol/L), preoperative preparation days, perioperative complications, and health-care costs were the factors influencing the hospital days (Total Point 83.4 points, mean hospital days 9.37 days). Receiver operating characteristic (ROC) curve analysis demonstrated that the nomogram had good accuracy for predicting hospital days (area under the ROC curve 0.83, 95% confidence interval [CI], 0.74 to 0.92). CONCLUSION: Inpatient glucose team-based management demonstrated significant improvements in glycemic control among surgical patients with diabetes, resulting in reduced hospital days and associated costs. The developed nomogram also exhibited promising potential in predicting hospital days, offering valuable clinical applications.

Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort.

BACKGROUND: The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model may suffer from overfitting or poor interpretability. METHODS: This study employed nine commonly used machine learning methods to construct overweight risk models. The general community are the target of this study, and a total of 10,905 Chinese subjects from Ningde City in Fujian province, southeast China, participated. The best model was selected through appropriate verification and validation and was suitably explained. RESULTS: The overweight risk models employing machine learning exhibited good performance. It was concluded that CatBoost, which is used in the construction of clinical risk models, may surpass previous machine learning methods. The visual display of the Shapley additive explanation value for the machine model variables accurately represented the influence of each variable in the model. CONCLUSIONS: The construction of an overweight risk model using machine learning may currently be the best approach. Moreover, CatBoost may be the best machine learning method. Furthermore, combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

Diagnostic biomarker for type 2 diabetic peripheral neuropathy via comprehensive bioinformatics analysis.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common complication of Type 2 diabetes mellitus (T2DM), which frequently results in disabling neuropathic pain and lower-limb amputation. The identification of noninvasive biomarkers for DPN may help early detection and individualized treatment of DPN. METHODS: In this study, we identified differentially expressed genes (DEGs) between DPN and the control based on blood-source (GSE95849) and tissue-source gene expression profiles (GSE143979) from the Gene Expression Omnibus (GEO) database using limma, edgeR, and DESeq2 approaches. KEGGG and GO functional enrichments were performed. Hub genes and their correlation with infiltrating immune cells were analyzed. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to quantify hub gene expression. RESULTS: In total, 144 DEGs between DPN and the control were identified. Functional enrichment revealed that the DEGs were mainly enriched in immune-related pathways like the Fc epsilon receptor Ig signaling pathway. By protein-protein interaction (PPI) network analysis, FCER1G, SYK, ITGA4, F13A1, MS4A2, and PTK2B were screened as hub genes with higher expression in DPN patients, among which half were immune genes (FCER1G, PTK2B, and SYK). RT-qPCR demonstrated that mRNA expression of FCER1G, PTK2B, and SYK was significantly increased in patients with DPN compared with both diabetic nonperipheral neuropathy (DNN) and normal subjects. The area under the receiver operating characteristic (ROC) curve of FCER1G, PTK2B, and SYK was 0.84, 0.81, and 0.73, respectively, suggesting their great advantages as diagnostic biomarkers to predict the progression of neuropathy in T2DM. Further analysis indicated that the expression of FCER1G, PTK2B, and SYK was negatively correlated with the cell proportion of significantly altered resting natural killer cells, T follicular helper cells, and activated mast cells, but positively correlated with monocytes. CONCLUSIONS: Our findings demonstrated FCER1G, PTK2B, and SYK are potential diagnostic biomarkers and therapeutic targets for DPN, which provides new insight into DPN pathogenesis and therapies.

Predicting risk of obesity in overweight adults using interpretable machine learning algorithms.

Objective: To screen for predictive obesity factors in overweight populations using an optimal and interpretable machine learning algorithm. Methods: This cross-sectional study was conducted between June 2011 and January 2012. The participants were randomly selected using a simple random sampling technique. Seven commonly used machine learning methods were employed to construct obesity risk prediction models. A total of 5,236 Chinese participants from Ningde City, Fujian Province, Southeast China, participated in this study. The best model was selected through appropriate verification and validation and suitably explained. Subsequently, a minimal set of significant predictors was identified. The Shapley additive explanation force plot was used to illustrate the model at the individual level. Results: Machine learning models for predicting obesity have demonstrated strong performance, with CatBoost emerging as the most effective in both model validity and net clinical benefit. Specifically, the CatBoost algorithm yielded the highest scores, registering 0.91 in the training set and an impressive 0.83 in the test set. This was further corroborated by the area under the curve (AUC) metrics, where CatBoost achieved 0.95 for the training set and 0.87 for the test set. In a rigorous five-fold cross-validation, the AUC for the CatBoost model ranged between 0.84 and 0.91, with an average AUC of ROC at 0.87 ± 0.022. Key predictors identified within these models included waist circumference, hip circumference, female gender, and systolic blood pressure. Conclusion: CatBoost may be the best machine learning method for prediction. Combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

Association of altered fibrinogen indexes levels as a potential biomarker in determining the possible onset of diabetic retinopathy.

Research suggests that fibrinogen was related to diabetic retinopathy (DR). Then, the relationship between functional indices of fibrinogen and detailed staging of DR has not been explored. Type 2 diabetic and healthy control subjects (n = 960) were recruited in a cross-sectional study. Participants with type 2 diabetes mellitus were categorized into five stages according to their fundus lesions, and fibrinogen (Fib) and its functional indices (angle α and k value) were measured. The angle α levels increased in diabetic subjects with retinopathy compared with those without, and it was significantly elevated early in retinopathy. In contrast, the k value levels slightly decreased. Despite observing an increase in angle α levels and a decrease in k value levels during the later stages of retinopathy compared to the earlier stages, there was no statistically significant difference in the later stages. The association of the angle α and k value with DR was independent of the hyperglycaemic state and other potential confounders (OR = 1.672, 95% CI 1.489-1.876, P < 0.01; OR = 0.013, 95% CI 0.004-0.041, P < 0.01). The angle α levels and k value levels were closely correlated with retinopathy (r = 0.593, P < 0.00; r = - 0.646, P < 0.01). The ROC curve indicated that the diagnostic value of angle α and k value were (AUC = 0.897, P < 0.001; AUC = 0.859, P < 0.001). Fibrinogen function indexes, such as angle α and k value, may be valuable for the early diagnosis of DR but do not directly assess the severity of DR.

Long noncoding RNAs as potential diagnostic biomarkers for diabetes mellitus and complications: A systematic review and meta-analysis.

AIMS: Long noncoding RNAs (lncRNAs) may be associated with the development of type 2 diabetes mellitus and its complications; however, the findings remain controversial. We aimed to synthesize the available data to assess the diagnostic utility of lncRNAs for identification of type 2 diabetes mellitus and its consequences. MATERIALS AND METHODS: We performed a systematic review and meta-analysis, searching PubMed, Embase, and Web of Science for articles published from September 11, 2015 to December 27, 2022. We evaluated human case-control or cohort studies on differential lncRNA expression in type 2 diabetes mellitus or its associated comorbidities. We excluded studies if they were non-peer reviewed or published in languages other than English. From 2387 identified studies, we included 17 (4685 participants). RESULTS: Analysis of the pooled data showed that lncRNAs had a diagnostic area under the curve (AUC) of 0.84 (95% CI: 0.80-0.87), with a sensitivity of 0.79 (95% CI: 0.74-0.83) and a specificity of 0.75 (95% CI: 0.69-0.80). LncRNAs had an AUC of 0.65 for the diagnosis of prediabetes, with 82% sensitivity and 65% specificity. CONCLUSIONS: LncRNAs may be promising diagnostic markers for type 2 diabetes mellitus and its complications.

Anti-Inflammatory Phomalones from the Deep-Sea-Derived Fungus Trichobotrys effuse FS522.

Four new phomalones A-D (1-4), together with five known analogues (5-9) were isolated from the deep-sea-derived fungus Trichobotrys effuse FS522. Their structures of the new compounds established by analysis of their NMR and HR-ESI-MS spectroscopic data, and the absolute configurations of 2 was determined by electronic circular dichroism (ECD) calculations. compounds 4, 6 and 8 substantially inhibited the production of nitric oxide (NO) with IC50 values of 4.64, 13.90, and 34.07 µM.

TSHR-based chimeric antigen receptor T cell specifically deplete auto-reactive B lymphocytes for treatment of autoimmune thyroid disease.

Graves' disease (GD) is a prominent antibody-mediated autoimmune disorder characterized by stimulating antibodies (TRAb) that target the thyroid-stimulating hormone receptor (TSHR). Targeting and eliminating TRAb-producing B lymphocytes hold substantial therapeutic potential for GD. In this study, we engineered a novel chimeric antigen receptor T cell (CAR-T) therapy termed TSHR-CAR-T. This CAR-T construct incorporates the extracellular domain of the TSH receptor fused with the CD8 transmembrane and intracellular signal domain (4-1BB). TSHR-CAR-T cells demonstrated the ability to recognize and effectively eliminate TRAb-producing B lymphocytes both in vitro and in vivo. Leveraging this autoantigen-based chimeric receptor, our findings suggest that TSHR-CAR-T cells offer a promising and innovative immunotherapeutic approach for the treatment of antibody-mediated autoimmune diseases, including GD.

A novel type of Brassica napus with higher stearic acid in seeds developed through genome editing of BnaSAD2 family.

KEY MESSAGE: Modifications of multiple copies of the BnaSAD2 gene family with genomic editing technology result in higher stearic acid content in the seed of polyploidy rapeseed. Solid fats from vegetable oils are widely used in food processing industry. Accumulating data showed that stearic acid is more favorite as the major composite among the saturate fatty acids in solid fats in considerations of its effects on human health. Rapeseed is the third largest oil crop worldwide, and has potential to be manipulated to produce higher saturated fatty acids as raw materials of solid fats. Toward that end, we identified four SAD2 gene family members in B. napus genome and established spatiotemporal expression pattern of the BnaSAD2 members. Genomic editing technology was applied to mutate all the copies of BnaSAD2 in this allopolyploid species and mutants at multiple alleles were generated and characterized to understand the effect of each BnaSAD2 member on blocking desaturation of stearic acid. Mutations occurred at BnaSAD2.A3 resulted in more dramatic changes of fatty acid profile than ones on BnaSAD2.C3, BnaSAD2.A5 and BnaSAD2.C4. The content of stearic acid in mutant seeds with single locus increased dramatically with a range of 3.1-8.2%. Furthermore, combination of different mutated alleles of BnaSAD2 resulted in more dramatic changes in fatty acid profiles and the double mutant at BnaSAD2.A3 and BnaSAD2.C3 showed the most dramatic phenotypic changes compared with its single mutants and other double mutants, leading to 11.1% of stearic acid in the seeds. Our results demonstrated that the members of BnaSAD2 have differentiated in their efficacy as a Δ9-Stearoyl-ACP-Desaturase and provided valuable rapeseed germplasm for breeding high stearic rapeseed oil.

Klotho reduces the risk of osteoporosis in postmenopausal women: a cross-sectional study of the National Health and Nutrition Examination Survey (NHANES).

BACKGROUND: Osteoporosis (OP) is one of the diseases that endanger the health of the elderly population. Klotho protein is a hormone with anti-aging effects. A few studies have discussed the relationship between Klotho and OP. However, there is still a lack of research on larger populations. This study aims to evaluate the association between OP and Klotho in American postmenopausal women. METHODS: This is a retrospective study. We searched the National Health and Nutrition Examination Survey (NHANES) database and collected data of 3 survey cycles, finally involving 871 postmenopausal women over 50 years old in the present study. All participants took dual-energy X-ray absorptiometry examination and serum Klotho testing at the time of investigation. After adjusting the possible confounding variables, a multivariate regression model was employed to estimate the relationship between OP and Klotho proteins. Besides, the P for trend and restricted cubic spline (RCS) were applied to examine the threshold effect and calculate the inflection point. RESULTS: Factors influencing the occurrence of OP included age, ethnicity, body mass index and Klotho levels. Multivariate regression analysis indicated that the serum Klotho concentration was lower in OP patients than that in participants without OP (OR[log2Klotho] = 0.568, P = 0.027). The C-index of the prediction model built was 0.765, indicating good prediction performance. After adjusting the above-mentioned four variables, P values for trend showed significant differences between groups. RCSs revealed that when the Klotho concentration reached 824.09 pg/ml, the risk of OP decreased drastically. CONCLUSION: Based on the analysis of the data collected from the NHANES database, we propose a correlation between Klotho and postmenopausal OP. A higher serum Klotho level is related to a lower incidence of OP. The findings of the present study can provide guidance for research on diagnosis and risk assessment of OP.

Role of apolipoproteins in the pathogenesis of obesity.

Obesity is a state presented by excessive accumulation and abnormal distribution of body fat, with metabolic disorders being one of its distinguishing features. Obesity is associated with dyslipidemia and apolipoproteins are important structural components of plasma lipoproteins. Apolipoproteins influence the progression of obesity by a variety of mechanisms including lipid metabolism, energy expenditure and inflammatory response. In this review, we discuss the role of apolipoproteins in obesity, mechanisms of action as well as their potential as new therapeutic targets.

Impact of Visceral Obesity on Structural and Functional Alterations of Gut Microbiota in Polycystic Ovary Syndrome (PCOS): A Pilot Study Using Metagenomic Analysis.

Objective: We aimed to identify structural and functional alterations of gut microbiota associated with visceral obesity in adult women with polycystic ovary syndrome (PCOS). Methods: Twenty-seven adults with PCOS underwent stool and fasting blood collection, oral glucose tolerance testing, and visceral fat area (VFA) measurement via dual-bioimpedance technique. Metagenomic analysis was used to analyze gut microbiota. Results: PCOS patients were divided into three groups: visceral obesity group (PCOS-VO, n=9, age 28.33±5.68 years, BMI 37.06±4.27 kg/m2, VFA 128.67±22.45 cm2), non-visceral obesity group (PCOS-NVO, n=10, age 25.40±4.53, BMI 30.74±3.95, VFA 52.00±24.04), normal BMI group (PCOS-NB, n=8, age 27.88±2.53, BMI 21.56±2.20, VFA 27.00±21.18), with no statistical difference in age (P>0.05) and significantly statistical differences in BMI and VFA (P<0.05). The groups showed a significant difference in microbial ß-diversity between PCOS-VO and PCOS-NVO (P=0.002) and no difference between PCOS-NVO and PCOS-NB (P=0.177). Bacteroidetes was the phylum with the highest relative abundance among all patients, followed by Firmicutes. Those with visceral obesity had a higher abundance of Prevotella, Megamonas, and Dialister genera, positively correlated with metabolic markers (r>0.4, P<0.05), and lower abundance of Phascolarctobacterium and Neisseria genera, negatively correlated with metabolic markers (r<-0.4, P<0.05). Functional annotation analysis showed significant differences in relative abundance of ribosome pathway, fatty acid biosynthesis pathway, and sphingolipid signaling pathway between groups, affecting lipid homeostasis and visceral fat accumulation. Conclusion: Alteration in ß-diversity of gut microbiota exists in PCOS with visceral obesity versus those without visceral obesity and relates to functional differences in ribosomes, fatty acid biosynthesis, and sphingolipid signaling pathways.

Self-Supporting Co/CeO2 Heterostructures for Ampere-Level Current Density Alkaline Water Electrolysis.

Remodeling the active surface through fabricating heterostructures can substantially enhance alkaline water electrolysis driven by renewable electrical energy. However, there are still great challenges in the synthesis of highly reactive and robust heterostructures to achieve both ampere-level current density hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Herein, we report a new Co/CeO2 heterojunction self-supported electrode for sustainable overall water splitting. The self-supporting Co/CeO2 heterostructures required only low overpotentials of 31.9 ± 2.2, 253.3 ± 2.7, and 316.7 ± 3 mV for HER and 214.1 ± 1.4, 362.3 ± 1.9, and 400.3 ± 3.7 mV for OER at 0.01, 0.5, and 1.0 A·cm-2, respectively, being one of the best Co-based bifunctional electrodes. Electrolyzer constructed from this electrode acting as an anode and cathode merely required cell voltages of 1.92 ± 0.02 V at 1.0 A·cm-2 for overall water splitting. Multiple characterization techniques combined with density functional theory calculations disclosed the different active sites on the anode and cathode, and the charge redistributions on the heterointerfaces that can optimize the adsorption of H and oxygen-containing intermediates, respectively. This study presents the tremendous prospective of self-supporting heterostructures for effective and economical overall water splitting.

Molecule-Enhanced Electrocatalysis of Sustainable Oxygen Evolution Using Organoselenium Functionalized Metal-Organic Nanosheets.

Remolding the reactivity of metal active sites is critical to facilitate renewable electricity-powered water electrolysis. Doping heteroatoms, such as Se, into a metal crystal lattice has been considered an effective approach, yet usually suffers from loss of functional heteroatoms during harsh electrocatalytic conditions, thus leading to the gradual inactivation of the catalysts. Here, we report a new heteroatom-containing molecule-enhanced strategy toward sustainable oxygen evolution improvement. An organoselenium ligand, bis(3,5-dimethyl-1H-pyrazol-4-yl)selenide containing robust C-Se-C covalent bonds equipped in the precatalyst of ultrathin metal-organic nanosheets Co-SeMON, is revealed to significantly enhance the catalytic mass activity of the cobalt site by 25 times, as well as extend the catalyst operation time in alkaline conditions by 1 or 2 orders of magnitude compared with these reported metal selenides. A combination of various in situ/ex situ spectroscopic techniques, ab initio molecular dynamics, and density functional theory calculations unveiled the organoselenium intensified mechanism, in which the nonclassical bonding of Se to O-containing intermediates endows adsorption-energy regulation beyond the conventional scaling relationship. Our results showcase the great potential of molecule-enhanced catalysts for highly efficient and economical water oxidation.

Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study.

AIMS/INTRODUCTION: To compare the application value of different machine learning (ML) algorithms for diabetes risk prediction. MATERIALS AND METHODS: This is a 3-year retrospective cohort study with a total of 3,687 participants being included in the data analysis. Modeling variable screening and predictive model building were carried out using logistic regression (LR) analysis and 10-fold cross-validation, respectively. In total, six different ML algorithms, including random forests, light gradient boosting machine, extreme gradient boosting, adaptive boosting (AdaBoost), multi-layer perceptrons and gaussian naive bayes were used for model construction. Model performance was mainly evaluated by the area under the receiver operating characteristic curve. The best performing ML model was selected for comparison with the traditional LR model and visualized using Shapley additive explanations. RESULTS: A total of eight risk factors most associated with the development of diabetes were identified by univariate and multivariate LR analysis, and they were visualized in the form of a nomogram. Among the six different ML models, the random forests model had the best predictive performance. After 10-fold cross-validation, its optimal model has an area under the receiver operating characteristic value of 0.855 (95% confidence interval [CI] 0.823-0.886) in the training set and 0.835 (95% CI 0.779-0.892) in the test set. In the traditional LR model, its area under the receiver operating characteristic value is 0.840 (95% CI 0.814-0.866) in the training set and 0.834 (95% CI 0.785-0.884) in the test set. CONCLUSIONS: In the real-world epidemiological research, the combination of traditional variable screening and ML algorithm to construct a diabetes risk prediction model has satisfactory clinical application value.