BackgroundHashimoto’s thyroiditis, an autoimmune thyroid disease, shows high morbidity worldwide, particularly in female. Patients with Hashimoto’s thyroiditis have an increasing risk of hypothyroidism during the occurrence and progression of Hashimoto’s thyroiditis. In recent years, metabolomics has been widely applied in autoimmune diseases, especially thyroid disorders. However, metabolites analysis in Hashimoto’s thyroiditis is still absent.MethodsA total of 92 samples were collected, including 35 cases in the control group, 30 cases in the Hashimoto’s thyroiditis with euthyroidism group, and 27 cases in the Hashimoto’s thyroiditis with subclinical hypothyroidism group. SPSS 25.0 for statistical analysis and ROC curve, SIMCA 14.0, Metaboanalysis for multifactor analysis, and Origin 2021 for correlation analysis.Results21 metabolites were identified. 10 metabolites were obtained from control group versus HTE group, 8 serum metabolites were abnormal between control group and HTS group, 3 metabolites were derived from HTE group versus HTS. Kyoto Encyclopedia of Genes and Genomes Enrichment analysis showed that fatty acid degradation, Arginine, and proline metabolism have a significant impact on HTE, while lysine degradation, tyrosine metabolism play an important role HTS group, compared to control group. In the comparison between the HTE and HTS group, Valine, leucine, and isoleucine degradation and Valine, leucine, and isoleucine biosynthesis exists a key role. Correlation analysis shows clinical are not related to metabolites. ROC curve indicates SM, LPC, PC can efficiency in identification patients with HT in different clinical stage from healthy individuals.ConclusionSerum metabolites were changed in HT. Phospholipids such as SM, LPC, PC influence the pathogenesis of Hashimoto’s thyroiditis. Fatty acid degradation and lysine degradation pathways have an impact on different clinical stage of HT.

PurposeThe purpose of this study was to develop and validate a preoperative nomogram of differentiating benign and malignant gallbladder polypoid lesions (GPs) combining clinical and radiomics features.MethodsThe clinical and imaging data of 195 GPs patients which were confirmed by pathology from April 2014 to May 2021 were reviewed. All patients were randomly divided into the training and testing cohorts. Radiomics features based on 3 sequences of contrast-enhanced computed tomography were extracted by the Pyradiomics package in python, and the nomogram further combined with clinical parameters was established by multiple logistic regression. The performance of the nomogram was evaluated by discrimination and calibration.ResultsAmong 195 GPs patients, 132 patients were pathologically benign, and 63 patients were malignant. To differentiate benign and malignant GPs, the combined model achieved an area under the curve (AUC) of 0.950 as compared to the radiomics model and clinical model with AUC of 0.929 and 0.925 in the training cohort, respectively. Further validation showed that the combined model contributes to better sensitivity and specificity in the training and testing cohorts by the same cutoff value, although the clinical model had an AUC of 0.943, which was higher than 0.942 of the combined model in the testing cohort.ConclusionThis study develops a nomogram based on the clinical and radiomics features for the highly effective differentiation and prediction of benign and malignant GPs before surgery.

Objective To identify differentially expressed lncRNA, miRNA, and mRNA during the pathogenesis of gout, explore the ceRNA network regulatory mechanism of gout, and seek potential therapeutic targets. Method First, gout‐related chips were retrieved by GEO database. Then, the analysis of differentially expressed lncRNAs and mRNAs was conducted by R language and other software. Besides, miRNA and its regulated mRNA were predicted based on public databases, the intersection of differentially expressed mRNA and predicated mRNA was taken, and the lncRNA‐miRNA‐mRNA regulatory relationships were obtained to construct the ceRNA regulatory network. Subsequently, hub genes were screened by the STRING database and Cytoscape software. Then the DAVID database was used to illustrate the gene functions and related pathways of hub genes and to mine key ceRNA networks. Results Three hundred and eighty‐eight lncRNAs and 758 mRNAs were identified with significant differential expression in gout patient, which regulates hub genes in the ceRNA network, such as JUN, FOS, PTGS2, NR4A2, and TNFAIP3. In the ceRNA network, lncRNA competes with mRNA for miRNA, thus affecting the IL‐17 signaling pathway, TNF signaling pathway, Oxytocin signaling pathway, and NF‐κB signaling pathway through regulating the cell's response to chemical stress. The research indicates that five miRNAs (miR‐429, miR‐137, miR‐139‐5p, miR‐217, miR‐23b‐3p) and five lncRNAs (SNHG1, FAM182A, SPAG5‐AS1, HNF1A‐AS1, UCA1) play an important role in the formation and development of gout. Conclusion The interaction in the ceRNA network can affect the formation and development of gout by regulating the body's inflammatory response as well as proliferation, differentiation, and apoptosis of chondrocytes and osteoclasts. The identification of potential therapeutic targets and signaling pathways through ceRNA network can provide a reference for further research on the pathogenesis of gout.

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Background TgAb and TPOAb are effective and sensitive diagnose index for HT which is a common AID.TgAb and TPOAb present varying degrees correlatuion with different metabolites in different gender. However, the morbidity in female are higher than male in HT. The research in exploring the correction between metabolic and positive-TgAb or positive-TPOAb in female HT patients is still absent. Methods 14 healthy, 14 TPOAb(+), 4 TgAb(+) patients serum sample were included. Metabolites were detected using the LC-MS. Sstatistical analysis were performed by SPSS. PLS-DA and OPLS-DA were carried by SIMCA. VIP > 1.5 metabolites by OPLS-DA were assessed for statistical significance by t-test or non-parametric test. Enrichment analysis and heatmap of metabolite were conducted by MetaboAnalyst. Correlation analysis was performed by Origin 2021. The ROC curve was established by SPSS. Metabolite point plotting was drawn by Graph prism 9.0. Results Based on VIP > 1.5 and P < 0.05 as selection criteria, 36 metabolites were derived. 13 metabolites were selected from the control vs the TPOAb (+) group, 23 metabolites were identified from the control vs the TgAb (+) group. TgAb and Phenylacetyl-L-glutamine / TPOAb and LPC 16:0 sn-1 performed strong correlation the TPOAb (+) group. Furthermore, TPOAb and LPE 16:1 was presented correlation the TgAb (+) group. Enrichment analysis of metabolic pathways showed that Glycine, serine and threonine metabolism was significant in TPOAb (+), while Galactose metabolism in TgAb (+) group. Conclusion The level of serum metabolites in TPOAb(+) TgAb(-) female patients and TPOAb(-) TgAb(+) female patients are different.