Klotho exerts protection in chronic kidney disease associated with regulating inflammatory response and lipid metabolism.

BACKGROUND: The anti-aging protein Klotho plays a protective role in kidney disease, but its potential as a biomarker for chronic kidney disease (CKD) is controversial. Additionally, the main pathways through which Klotho exerts its effects on CKD remain unclear. Therefore, we used bioinformatics and clinical data analysis to determine its role in CKD. RESULTS: We analyzed the transcriptomic and clinical data from the Nephroseq v5 database and found that the Klotho gene was mainly expressed in the tubulointerstitium, and its expression was significantly positively correlated with estimated glomerular filtration rate (eGFR) and negatively correlated with blood urea nitrogen (BUN) in CKD. We further found that Klotho gene expression was mainly negatively associated with inflammatory response and positively associated with lipid metabolism in CKD tubulointerstitium by analyzing two large sample-size CKD tubulointerstitial transcriptome datasets. By analyzing 10-year clinical data from the National Health and Nutrition Examination Survey (NHANES) 2007-2016, we also found that Klotho negatively correlated with inflammatory biomarkers and triglyceride and positively correlated with eGFR in the CKD population. Mediation analysis showed that Klotho could improve renal function in the general population by modulating the inflammatory response and lipid metabolism, while in the CKD population, it primarily manifested by mediating the inflammatory response. Restricted cubic spline (RCS) analysis showed that the optimal concentration range for Klotho to exert its biological function was around 1000 pg/ml. Kaplan-Meier curves showed that lower cumulative hazards of all-cause mortality in participants with higher levels of Klotho. We also demonstrated that Klotho could reduce cellular inflammatory response and improve cellular lipid metabolism by establishing an in vitro model similar to CKD. CONCLUSIONS: Our results suggest that Klotho exerts protection in CKD, which may be mainly related to the regulation of inflammatory response and lipid metabolism, and it can serve as a potential biomarker for CKD.

Socioeconomic disparities and regional environment are associated with cervical lymph node metastases in children and adolescents with differentiated thyroid cancer: developing a web-based predictive model.

Purpose: To establish an online predictive model for the prediction of cervical lymph node metastasis (CLNM) in children and adolescents with differentiated thyroid cancer (caDTC). And analyze the impact between socioeconomic disparities, regional environment and CLNM. Methods: We retrospectively analyzed clinicopathological and sociodemographic data of caDTC from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2019. Risk factors for CLNM in caDTC were analyzed using univariate and multivariate logistic regression (LR). And use the extreme gradient boosting (XGBoost) algorithm and other commonly used ML algorithms to build CLNM prediction models. Model performance assessment and visualization were performed using the area under the receiver operating characteristic (AUROC) curve and SHapley Additive exPlanations (SHAP). Results: In addition to common risk factors, our study found that median household income and living regional were strongly associated with CLNM. Whether in the training set or the validation set, among the ML models constructed based on these variables, the XGBoost model has the best predictive performance. After 10-fold cross-validation, the prediction performance of the model can reach the best, and its best AUROC value is 0.766 (95%CI: 0.745-0.786) in the training set, 0.736 (95%CI: 0.670-0.802) in the validation set, and 0.733 (95%CI: 0.683-0.783) in the test set. Based on this XGBoost model combined with SHAP method, we constructed a web-base predictive system. Conclusion: The online prediction model based on the XGBoost algorithm can dynamically estimate the risk probability of CLNM in caDTC, so as to provide patients with personalized treatment advice.

Effect of hemoglobin A1c management levels on coronary physiology evaluated by quantitative flow ratio in patients who underwent percutaneous coronary intervention.

AIMS/INTRODUCTION: The coronary physiology and prognosis of patients with different hemoglobin A1c (HbA1c) levels after percutaneous coronary intervention (PCI) are currently unknown. The aim of this study was to assess the effect of different levels of HbA1c control on coronary physiology in patients who underwent PCI for coronary heart disease combined with type 2 diabetes mellitus by quantitative flow ratio (QFR). MATERIALS AND METHODS: Patients who successfully underwent PCI and completed 1-year coronary angiographic follow up were enrolled, clinical data were collected, and QFR at immediate and 1-year follow up after PCI was retrospectively analyzed. A total of 257 patients (361 vessels) were finally enrolled and divided into the hemoglobin A1c (HbA1c)-compliance group (103 patients, 138 vessels) and non-HbA1c-compliance group (154 patients, 223 vessels) according to the HbA1c cut-off value of 7%. We compared the results of QFR analysis and clinical outcomes between the two groups. RESULTS: At 1-year follow up after PCI, the QFR was significantly higher (0.94 ± 0.07 vs 0.92 ± 0.10, P = 0.019) and declined less (0.014 ± 0.066 vs 0.033 ± 0.095, P = 0.029) in the HbA1c-compliance group. Meanwhile, the incidence of physiological restenosis was lower in the HbA1c-compliance group (2.9% vs 8.5%, P = 0.034). Additionally, the target vessel revascularization rate was lower in the HbA1c-compliance group (6.8% vs 16.9%, P = 0.018). Furthermore, HbA1c ≥7% (OR 2.113, 95% confidence interval 1.081-4.128, P = 0.029) and QFR decline (OR 2.215, 95% confidence interval 1.147-4.277, P = 0.018) were independent risk factors for target vessel revascularization. CONCLUSION: Patients with well-controlled HbA1c levels have better coronary physiological benefits and the incidence of adverse clinical outcome events might be reduced.

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.

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.

Mesophilic Argonaute-Based Single Polystyrene Sphere Aptamer Fluorescence Platform for the Multiplexed and Ultrasensitive Detection of Non-Nucleic Acid Targets.

The rapid, simultaneous, and accurate identification of multiple non-nucleic acid targets in clinical or food samples at room temperature is essential for public health. Argonautes (Agos) are guided, programmable, target-activated, next-generation nucleic acid endonucleases that could realize one-pot and multiplexed detection using a single enzyme, which cannot be achieved with CRISPR/Cas. However, currently reported thermophilic Ago-based multi-detection sensors are mainly employed in the detection of nucleic acids. Herein, this work proposes a Mesophilic Argonaute Report-based single millimeter Polystyrene Sphere (MARPS) multiplex detection platform for the simultaneous analysis of non-nucleic acid targets. The aptamer is utilized as the recognition element, and a single millimeter-sized polystyrene sphere (PSmm ) with a large concentration of guide DNA on the surface served as the microreactor. These are combined with precise Clostridium butyricum Ago (CbAgo) cleavage and exonuclease I (Exo I) signal amplification to achieve the efficient and sensitive recognition of non-nucleic acid targets, such as mycotoxins (<60 pg mL-1 ) and pathogenic bacteria (<102 cfu mL-1 ). The novel MARPS platform is the first to use mesophilic Agos for the multiplex detection of non-nucleic acid targets, overcoming the limitations of CRISPR/Cas in this regard and representing a major advancement in non-nucleic acid target detection using a gene-editing-based system.

The identification of a novel mutation (p.I223fs) in WRN associated with Werner syndrome.

PURPOSE: Werner syndrome (WS) is a rare autosomal recessive genetic disease caused by mutations in the WRN gene, and it is characterized by multiple manifestations corresponding to early-onset aging. This study reports the case of a WS patient with a novel WRN mutation. PATIENT AND METHODS: A 36-year-old male patient with WS was evaluated after approval from the local ethics committee. The clinical and biochemical findings of the patient were described. Peripheral blood sample was collected to extract genomic DNA for WRN gene exome sequencing. The three-dimensional (3D) protein structural prediction analysis was performed via the AlphaFold 2.2 program and PyMol software. RESULTS: We report the case of a clinically diagnosed WS patient with consanguineous parents who presented with complex manifestations including early-onset diabetes mellitus, binocular cataracts, cerebral infarction, cerebral atherosclerosis, hypertension, dyslipidemia, hypothyroidism, and suspected meningioma, accompanied by short stature, gray hair, rough skin with subcutaneous fat atrophy, a high-pitched voice, palmoplantar keratoderma, bilateral flat feet, and an indolent deep ulceration on the foot. Exome sequencing identified a novel homozygous frameshift mutation in the WRN gene, c.666-669 del TATT, p.I223fs. The 3D structure prediction showed that premature termination and significant structural changes could occur in the mutant WRN protein. CONCLUSION: We identified a novel homozygous frameshift mutation, p.I223fs, in WRN in a Chinese patient with WS, expanding the spectrum of mutations in WS.

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.

Magnetic Relaxation Switching Immunosensors via a Click Chemistry-Mediated Controllable Aggregation Strategy for Direct Detection of Chlorpyrifos.

Chlorpyrifos (CPF) is the most frequently found organophosphate pesticide residue in solid food samples and can cause increasing public concerns about potential risks to human health. Traditional detection signals of such small molecules are mostly generated by target-mediated indirect conversion, which tends to be detrimental to sensitivity and accuracy. Herein, a novel magnetic relaxation switching detection platform was developed for target-mediated direct and sensitive detection of CPF with a controllable aggregation strategy based on a bioorthogonal ligation reaction between tetrazine (Tz) and trans-cyclooctene (TCO) ligands. Under optimal conditions, this sensor can achieve a detection limit of 37 pg/mL with a broad linear range of 0.1-500 ng/mL in 45 min, which is approximately 51-fold lower than that of the gas chromatography analysis and 13-fold lower than that of the enzyme-linked immunosorbent assay. The proposed click chemistry-mediated controllable aggregation strategy is direct, rapid, and sensitive, indicating great potential for residue screening in food matrices.

Machine learning algorithms are comparable to conventional regression models in predicting distant metastasis of follicular thyroid carcinoma.

OBJECTIVE: Distant metastasis often indicates a poor prognosis, so early screening and diagnosis play a significant role. Our study aims to construct and verify a predictive model based on machine learning (ML) algorithms that can estimate the risk of distant metastasis of newly diagnosed follicular thyroid carcinoma (FTC). DESIGN: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. PATIENTS: A total of 5809 FTC patients were included in the data analysis. Among them, there were 214 (3.68%) cases with distant metastasis. METHOD: Univariate and multivariate logistic regression (LR) analyses were used to determine independent risk factors. Seven commonly used ML algorithms were applied for predictive model construction. We used the area under the receiver-operating characteristic (AUROC) curve to select the best ML algorithm. The optimal model was trained through 10-fold cross-validation and visualized by SHapley Additive exPlanations (SHAP). Finally, we compared it with the traditional LR method. RESULTS: In terms of predicting distant metastasis, the AUROCs of the seven ML algorithms were 0.746-0.836 in the test set. Among them, the Extreme Gradient Boosting (XGBoost) had the best prediction performance, with an AUROC of 0.836 (95% confidence interval [CI]: 0.775-0.897). After 10-fold cross-validation, its predictive power could reach the best [AUROC: 0.855 (95% CI: 0.803-0.906)], which was slightly higher than the classic binary LR model [AUROC: 0.845 (95% CI: 0.818-0.873)]. CONCLUSIONS: The XGBoost approach was comparable to the conventional LR method for predicting the risk of distant metastasis for FTC.

Contamination-free V-shaped ultrafast reaction cascade transferase signal amplification driven CRISPR/Cas12a magnetic relaxation switching biosensor for bacteria detection.

Foodborne pathogenic bacteria seriously endanger human health and must be rapidly identified for control. Magnetic relaxation switching biosensors (MRS) are ideal for rapid bacteria detection due to their high signal-to-noise ratio and immunity to sample matrix signal interference. However, conventional MRS still has some challenges in terms of sensitivity, specificity, and stability due to insufficient cross-linking or non-specific binding of magnetic nanoparticles (MNPs) to the target. To address these challenges, we firstly proposed a novel contamination-free uracil-DNA glycosylase (UDG) assisted V-shaped PCR driven CRISPR/Cas12a-MRS (UPC-MRS) biosensor, which combines contamination-free ultrafast nucleic acid amplification and powerful CRISPR/Cas12a system. It has an extremely specific quadruple signal guarantee realized by the merits of UDG anti-contamination, PCR primer specificity matching, the CRISPR/Cas12a system's precise recognition abilities, and magnetic probe signal unaffected by the sample matrix. As a cascade combined with original terminal deoxynucleotidyl transferase (Tdt)-mediated signal amplification technology, this platform can achieve Salmonella detection at concentrations as low as 53 CFU/mL, which is more sensitive than most existing MRS sensors, and it displays accuracy and applicability in real sample detection. This novel UPC-MRS biosensors avoid the common aerosol pollution problem of previous CRISPR/Cas12a systems which after combining with nucleic acid amplification, hence not only offers an alternative toolbox for Salmonella and other pathogen detection with satisfactory specificity and sensitivity, but also has potential for future applications across diverse fields.

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.

Antibody responses to COVID-19 vaccination in people with obesity: A systematic review and meta-analysis.

COVID-19 vaccine is critical in preventing SARS-CoV-2 infection and transmission. However, obesity's effect on immune responses to COVID-19 vaccines is still unknown. We performed a meta-analysis of the literature and compared antibody responses with COVID-19 vaccines among persons with and without obesity. We used Pubmed, Embase, Web of Science, and Cochrane Library to identify all related studies up to April 2022. The Stata.14 software was used to analyze the selected data. Eleven studies were included in the present meta-analysis. Five of them provided absolute values of antibody titers in the obese group and non-obese group. Overall, we found that the obese population was significantly associated with lower antibody titers (standardized mean difference [SMD] = -0.228, 95% CI [-0.437, -0.019], P < 0.001) after COVID-19 vaccination. Significant heterogeneity was present in most pooled analyses but was reduced after subgroup analyses. No publication bias was observed in the present analysis. The Trim and Fill method did not change the results in the primary analysis. The present meta-analysis suggested that obesity was significantly associated with decreased antibody responses to SARS-CoV-2 vaccines. Future studies should be performed to unravel the mechanism of response to the COVID-19 vaccine in obese individuals.

Association of Glucose-Lowering Drugs With Outcomes in Patients With Diabetes Before Hospitalization for COVID-19: A Systematic Review and Network Meta-analysis.

Importance: Patients with COVID-19 have a high prevalence of diabetes, and diabetes and blood glucose control are determinants of intensive care unit admission and mortality. Objective: To evaluate the association between COVID-19-related adverse outcomes and 8 antihyperglycemic drugs in patients with diabetes who were subsequently diagnosed and hospitalized with COVID-19. Data Sources: Data were retrieved and collected in PubMed, Embase, Cochrane Central Register, Web of Science, and ClinicalTrials.gov from database inception to September 5, 2022. Study Selection: For this systematic review and network meta-analysis, randomized clinical trials and observational studies conducted among patients with diabetes while receiving glucose-lowering therapies for at least 14 days before the confirmation of COVID-19 infection were included after blinded review by 2 independent reviewers and consultations of disagreement by a third independent reviewer. Of 1802 studies initially identified, 31 observational studies met the criteria for further analysis. Data Extraction and Synthesis: This study follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline. Bayesian network meta-analyses were performed with random effects. Main Outcomes and Measures: A composite adverse outcome, including the need for intensive care unit admission, invasive and noninvasive mechanical ventilation, or in-hospital death. Results: Thirty-one distinct observational studies (3 689 010 patients with diabetes hospitalized for COVID-19) were included. The sodium-glucose cotransporter-2 inhibitors (SGLT-2is) were associated with relatively lower risks of adverse outcomes compared with insulin (log of odds ratio [logOR], 0.91; 95% credible interval [CrI], 0.57-1.26), dipeptidyl peptidase-4 inhibitors (logOR, 0.61; 95% CrI, 0.28-0.93), secretagogues (logOR, 0.37; 95% CrI, 0.02-0.72), and glucosidase inhibitors (logOR, 0.50; 95% CrI, 0.00-1.01). Based on the surface under the cumulative ranking curves value, SGLT-2is were associated with the lowest probability for adverse outcomes (6%), followed by glucagon-like peptide-1 receptor agonists (25%) and metformin (28%). A sensitivity analysis revealed that the study was reliable. Conclusions and Relevance: These findings suggest that the use of an SGLT-2i before COVID-19 infection is associated with lower COVID-19-related adverse outcomes. In addition to SGLT-2is, glucagon-like peptide-1 receptor agonists and metformin were also associated with relatively low risk of adverse outcomes.

Exploring risk factors for cervical lymph node metastasis in papillary thyroid microcarcinoma: construction of a novel population-based predictive model.

BACKGROUND: Machine learning was a highly effective tool in model construction. We aim to establish a machine learning-based predictive model for predicting the cervical lymph node metastasis (LNM) in papillary thyroid microcarcinoma (PTMC). METHODS: We obtained data on PTMC from the SEER database, including 10 demographic and clinicopathological characteristics. Univariate and multivariate logistic regression (LR) analyses were applied to screen the risk factors for cervical LNM in PTMC. Risk factors with P < 0.05 in multivariate LR analysis were used as modeling variables. Five different machine learning (ML) algorithms including extreme gradient boosting (XGBoost), random forest (RF), adaptive boosting (AdaBoost), gaussian naive bayes (GNB) and multi-layer perceptron (MLP) and traditional regression analysis were used to construct the prediction model. Finally, the area under the receiver operating characteristic (AUROC) curve was used to compare the model performance. RESULTS: Through univariate and multivariate LR analysis, we screened out 9 independent risk factors most closely associated with cervical LNM in PTMC, including age, sex, race, marital status, region, histology, tumor size, and extrathyroidal extension (ETE) and multifocality. We used these risk factors to build an ML prediction model, in which the AUROC value of the XGBoost algorithm was higher than the other 4 ML algorithms and was the best ML model. We optimized the XGBoost algorithm through 10-fold cross-validation, and its best performance on the training set (AUROC: 0.809, 95%CI 0.800-0.818) was better than traditional LR analysis (AUROC: 0.780, 95%CI 0.772-0.787). CONCLUSIONS: ML algorithms have good predictive performance, especially the XGBoost algorithm. With the continuous development of artificial intelligence, ML algorithms have broad prospects in clinical prognosis prediction.

Comparison of efficacy of deep brain stimulation, repeat transcranial magnetic stimulation, and behavioral therapy in Tourette syndrome: A systematic review and Bayesian Network Meta-Analysis.

Background: Tourette syndrome (TS) is an incurable neuropsychiatric disorder. Deep brain stimulation (DBS), repeat transcranial magnetic stimulation (rTMS), and behavioral therapy (BT) are all effective treatments. However, the comparison of therapeutic effect of these three therapies is lacking. Methods: A systematic literature search was conducted for randomized controlled studies (RCT). A network meta-analysis by R4.04 software according to Bayesian framework were performed. Results were meta-analyzed and network meta-analyzed to evaluate and compare the efficacy of DBS, rTMS and BT in TS patients. Results: A total of 18 randomized controlled studies with 661 participants were included. The Yale Global Tic Severity Scale (YGTSS) and the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) were utilized to evaluate the symptoms of TS. All three treatments improved the tic symptoms of TS [DBS 12.11 (95%CI 7.58-16.65); rTMS 4.96 (95%CI 1.01-10.93); andBT 11.72 (95%CI 10.42-13.01)]; and obsessive-compulsive symptom [DBS 4.9 (95%CI 1.13-8.67); rTMS 5.28 (95%CI 0.21-10.77); and BT 1.61 (95%CI 0.74-2.48)]. The cumulative probability results showed that DBS had the best effect on the improvement of tic symptoms, followed by BT; and rTMS was ranked last. However, in terms of improvement of obsessional symptoms, rTMS was ranked first, DBS was ranked second, and BT was ranked last. In addition, the meta regression analysis of YGTSS in DBS, rTMS and BT has significant difference (P = 0.05). Limitation: Due to the lack of quantitative indicators, we did not perform a network meta-analysis of the side effects of the three treatments. Conclusion: Our study showed that DBS, rTMS, and BT are effective in TS. DBS causes the best improvement in tic symptoms, and rTMS is the most effective in improving the obsessive-compulsive symptoms.

Changes in bone mineral density after bariatric surgery in patients of different ages or patients with different postoperative periods: a systematic review and meta-analysis.

To assess changes in bone mineral density (BMD) following bariatric surgery (BS) in patients with different bone sites, postoperative periods and ages. Twenty-two studies were included. Femoral neck (FN) BMD decreased after surgery (MD, - 0.05 g/cm2, CI - 0.10 to - 0.01, P = 0.03). Postoperative BMD decreased more in the FN and lumbar spine (LS) of patients older than 40 (FNBMD, - 0.07 g/cm2, CI - 0.13 to - 0.00, P = 0.04; LSBMD, - 0.03 g/cm2, CI - 0.05 to - 0.00, P = 0.02) or patients with a postoperative time of greater than 12 months (FNBMD, - 0.06 g/cm2, CI - 0.12 to - 0.01, P = 0.03; LSMD, - 0.04 g/cm2, CI - 0.09 to 0.01, P = 0.12); therefore, post-BS bone loss should be monitored among patients in these groups. Longer follow-ups are needed to determine whether BMD changes or stabilizes.

Surgical Methods and Social Factors Are Associated With Long-Term Survival in Follicular Thyroid Carcinoma: Construction and Validation of a Prognostic Model Based on Machine Learning Algorithms.

Background: This study aimed to establish and verify an effective machine learning (ML) model to predict the prognosis of follicular thyroid cancer (FTC), and compare it with the eighth edition of the American Joint Committee on Cancer (AJCC) model. Methods: Kaplan-Meier method and Cox regression model were used to analyze the risk factors of cancer-specific survival (CSS). Propensity-score matching (PSM) was used to adjust the confounding factors of different surgeries. Nine different ML algorithms,including eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forests (RF), Logistic Regression (LR), Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GaussianNB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP),were used to build prognostic models of FTC.10-fold cross-validation and SHapley Additive exPlanations were used to train and visualize the optimal ML model.The AJCC model was built by multivariate Cox regression and visualized through nomogram. The performance of the XGBoost model and AJCC model was mainly assessed using the area under the receiver operating characteristic (AUROC). Results: Multivariate Cox regression showed that age, surgical methods, marital status, T classification, N classification and M classification were independent risk factors of CSS. Among different surgeries, the prognosis of one-sided thyroid lobectomy plus isthmectomy (LO plus IO) was the best, followed by total thyroidectomy (hazard ratios: One-sided thyroid LO plus IO, 0.086[95% confidence interval (CI),0.025-0.290], P<0.001; total thyroidectomy (TT), 0.490[95%CI,0.295-0.814], P=0.006). PSM analysis proved that one-sided thyroid LO plus IO, TT, and partial thyroidectomy had no significant differences in long-term prognosis. Our study also revealed that married patients had better prognosis than single, widowed and separated patients (hazard ratios: single, 1.686[95%CI,1.146-2.479], P=0.008; widowed, 1.671[95%CI,1.163-2.402], P=0.006; separated, 4.306[95%CI,2.039-9.093], P<0.001). Among different ML algorithms, the XGBoost model had the best performance, followed by Gaussian NB, RF, LR, MLP, LightGBM, AdaBoost, KNN and SVM. In predicting FTC prognosis, the predictive performance of the XGBoost model was relatively better than the AJCC model (AUROC: 0.886 vs. 0.814). Conclusion: For high-risk groups, effective surgical methods and well marital status can improve the prognosis of FTC. Compared with the traditional AJCC model, the XGBoost model has relatively better prediction accuracy and clinical usage.

Hypothalamic Kisspeptin Neurons Regulates Energy Metabolism and Reproduction Under Chronic Stress.

Background: Stress activates the hypothalamic-pituitary-adrenal (HPA) axis, affecting energy homeostasis and reproduction. The aim of this study was to investigate whether stress affected energy metabolism and reproduction through the glucocorticoid receptor on Kisspeptin neurons in the hypothalamus. Methods: Four groups included control group, chronic restraint stress group, Kisspeptin specific glucocorticoid receptor knock out group (KGRKO) and KGRKO+stress group. Body weight, food intake, estrous cycle of female mice, serum sex hormone levels, serum corticosterone and prolactin, Kisspeptin expression in the hypothalamus were measured. Results: The restraint stress group showed a significant weight loss compared with the control group. KGRKO+restraint stress group had a reduced weight loss, suggesting that restraint stress might partially affect the energy metabolism through GR on Kisspeptin neurons. In terms of reproductive function, the restraint stress group and the KGRKO+restraint stress group showed missing pre-estrus period or prolonged estrous cycles. Serum LH and FSH in KGRKO + restraint stress group decreased significantly compared with KGRKO group. However, no significant difference in the level of serum testosterone was observed. After restraint stress, the levels of serum cortisol and prolactin in male and female mice were significantly higher than the control group, and the hypothalamus Kiss1 gene mRNA expression and Kisspeptin protein expression were significantly decreased. Conclusion: Chronic restraint stress induced weight loss and negative changes in reproduction, which were partially mediated by glucocorticoid receptor on Kisspeptin neurons in the hypothalamus.

Association between sleep duration and incidence of type 2 diabetes in China: the REACTION study.

BACKGROUNDS: Inadequate sleep duration is associated with a higher risk of type 2 diabetes and the relationship is nonlinear. We aim to assess the curve relationship between night sleep duration and the incidence of type 2 diabetes in China. METHODS: A cohort of 11,539 participants from the REACTION study without diabetes at baseline (2011) were followed until 2014 for the development of type 2 diabetes. The average number of hours of sleep per night was grouped. Incidence rates and odds ratios (ORs) were calculated for the development of diabetes in each sleep duration category. RESULTS: Compared to people who sleep for 7 to 8 h/night, people with longer sleep duration (≥9 h/night) had a greater risk of type 2 diabetes (OR: 1.27; 95% CI: 1.01-1.61), while shorter sleep (<6 h/night) had no significant difference in risk of type 2 diabetes. When the dataset was stratified based on selected covariates, the association between type 2 diabetes and long sleep duration became more evident among individuals <65 years of age, male, body mass index <24 kg/m 2 or with hypertension or hyperlipidemia, no interaction effects were observed. Furthermore, compared to people persistently sleeping 7 to 9 h/night, those who persistently slept ≥9 h/night had a higher risk of type 2 diabetes. The optimal sleep duration was 6.3 to 7.5 h/night. CONCLUSIONS: Short or long sleep duration was associated with a higher risk of type 2 diabetes. Persistently long sleep duration increased the risk.