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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Exosomes are widely distributed extracellular vesicles (EVs), which are currently a major research hotspot for researchers based on their wide range of sources, stable membrane structure, low immunogenicity, and containing a variety of biomolecules. A large number of literatures have shown that exosomes and exosome cargoes (especially microRNAs) play an important role in the activation of inflammation, development of tumor, differentiation of cells, regulation of immunity and so on. Studies have found that exosomes can stimulate the immune response of the body and participate in the occurrence and development of various diseases, including autoimmune diseases. Furthermore, the potential of exosomes as therapeutic tools in various diseases has also attracted much attention. Autoimmune thyroid disease (AITD) is one of the most common autoimmune diseases, mainly composed of Graves' disease (GD) and Hashimoto's thyroiditis (HT), which affects the health of many people and has a genetic predisposition, but its pathogenesis is still being explored. Starting from the relevant biological characteristics of exosomes, this review summarizes the current research status of exosomes and the association between exosomes and some diseases, with a focus on the situation of AITD and the potential role of exosomes (including substances in their vesicles) in AITD in combination with the current published literature, aiming to provide new directions for the pathogenesis, diagnosis or therapy of AITD. Supplemental data for this article is available online at [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 β-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 β-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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Organosulfur natural products (NPs) refer to the different kinds of small molecular-containing sulfur (S) elements. Sulfur-containing NPs tightly link to the biochemical processes and play an important role in the pharmaceutical industry. The majority of S-containing NPs are generally isolated from Alliaceae plants or bacteria, and those from fungi are still relatively rare. In recent years, an increasing number of S-containing metabolites have been discovered in marine and terrestrial fungi, but there is no comprehensive and targeted review to summarize the studies. In order to make it more straightforward to better grasp the fungal-derived S-containing NPs and understand the particularity of marine S-containing NPs compared to those from terrestrial fungi, we summarized the chemical structures and biological activities of 89 new fungal-derived S-containing metabolites from 1929 when the penicillin was discovered to the present in this current review. The structural and bioactive diversity of these S-containing metabolites were concluded in detail, and the preliminary mechanism for C-S bond formation in fungi was also discussed briefly. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate whether T4 + T3 combination therapy had advantages in improving psychological health compared with T4 monotherapy. Methods: We searched PubMed, Embase, Cochrane Library, and Web of Science from January 2000 to March 2021, and updated in September 2021. The inclusion criteria (prospective study, published in English, had a T4 + T3 combination therapy test group and a T4 monotherapy control group, patients aged ≥18 years and with overt primary hypothyroidism, and published after January 2000) were applied by two reviewers; any disagreement was resolved by a third reviewer. The two reviewers independently extracted data using a standard data form and assessed the risk of bias using the Cochrane risk of bias tool. Coprimary outcomes included the psychological health measures of depression, fatigue, pain, anxiety and anger, measured using validated and reliable instruments. Results: Eighteen of 2029 studies (883 patients) were included in the meta‐analysis. No significant difference was found between T4 + T3 combination therapy and T4 monotherapy with regard to depression (standardized mean difference [SMD]: −0.06, 95% confidence interval [CI]: −0.18; 0.07), fatigue (SMD: 0.06, 95% CI: −0.13; 0.26), pain (SMD: −0.01, 95% CI: −0.24; 0.22), anxiety (SMD: 0.01, 95% CI: −0.15; 0.17) and anger (SMD: 0.05, 95% CI: −0.15; 0.24). Methodological heterogeneity had no influence on the results. The patients preferred combination therapy significantly. Conclusions: Compared with T4 monotherapy, T4 + T3 combination therapy had no significant advantage in improving psychological health. For patients who are unsatisfied with LT4 monotherapy, the patient and the physician should make a joint decision concerning therapy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]