Association between metabolic score of visceral fat and carotid atherosclerosis in Chinese health screening population: a cross-sectional study.

BACKGROUND: The metabolic score for visceral fat (METS-VF) quantifies the cumulative burden of visceral and intra-abdominal adipose tissues. However, the relationship between the METS-VF and carotid atherosclerosis (CAS) has not been extensively explored. Therefore, this study aimed to investigate the association between the METS-VF and CAS. METHODS: This cross-sectional study enrolled 7089 Chinese adults who underwent physical examinations at the Zhenhai Lianhua Hospital, Zhejiang, China, in 2020. Multivariable logistic regression analysis was used to explore the linear relationship between METS-VF and CAS. Generalised additive models (GAM) were employed to evaluate potential nonlinear associations. The inflection points of METS-VF were determined using segmented logistic regression analysis optimised for maximum likelihood ratios and recursive algorithms. RESULTS: Multivariable logistic regression analysis revealed a positive correlation between METS-VF and CAS (odds ratio [OR]: 1.824, 95% confidence interval [CI]: 1.753-1.899; P < 0.001). The GAM analysis confirmed a nonlinear association between them [effective degrees of freedom: 4.803, χ2: 876.7, P < 0.001], with an inflection point at a METS-VF of 8.09 (P < 0.001 for log-likelihood ratio test). Below this inflection point, METS-VF exhibited a significant positive association with CAS risk (OR: 1.874, 95% CI: 1.796-1.954; P < 0.001). Conversely, no significant association was observed when METS-VF ≥ 8.09 (OR: 0.998, 95% CI: 0.786-1.268; P = 0.989). CONCLUSIONS: METS-VF and CAS demonstrated a positive non-linear correlation, with the curve indicating a saturation effect at METS-VF = 8.09.

Development and Internal Validation of a Risk Prediction Model for Carotid Atherosclerosis in the Hyperuricemia Population.

Purpose: The aim of this study was to identify independent risk factors for carotid atherosclerosis (CAS) in a population with hyperuricemia (HUA) and develop a CAS risk prediction model. Patients and Methods: This retrospective study included 3579 HUA individuals who underwent health examinations, including carotid ultrasonography, at the Zhenhai Lianhua Hospital in Ningbo, China, in 2020. All participants were randomly assigned to the training and internal validation sets in a 7:3 ratio. Multivariable logistic regression analysis was used to identify independent risk factors associated with CAS. The characteristic variables were screened using the least absolute shrinkage and selection operator combined with 10-fold cross-validation, and the resulting model was visualized by a nomogram. The discriminative ability, calibration, and clinical utility of the risk model were validated using the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results: Sex, age, mean red blood cell volume, and fasting blood glucose were identified as independent risk factors for CAS in the HUA population. Age, gamma-glutamyl transpeptidase, serum creatinine, fasting blood glucose, total triiodothyronine, and direct bilirubin, were screened to construct a CAS risk prediction model. In the training and internal validation sets, the risk prediction model showed an excellent discriminative ability with the area under the curve of 0.891 and 0.901, respectively, and a high level of fit. Decision curve analysis results demonstrated that the risk prediction model could be beneficial when the threshold probabilities were 1-87% and 1-100% in the training and internal validation sets, respectively. Conclusion: We developed and internally validated a risk prediction model for CAS in a population with HUA, thereby contributing to the CAS early identification.

Predicting the risk of subclinical atherosclerosis based on interpretable machine models in a Chinese T2DM population.

Background: Cardiovascular disease (CVD) has emerged as a global public health concern. Identifying and preventing subclinical atherosclerosis (SCAS), an early indicator of CVD, is critical for improving cardiovascular outcomes. This study aimed to construct interpretable machine learning models for predicting SCAS risk in type 2 diabetes mellitus (T2DM) patients. Methods: This study included 3084 T2DM individuals who received health care at Zhenhai Lianhua Hospital, Ningbo, China, from January 2018 to December 2022. The least absolute shrinkage and selection operator combined with random forest-recursive feature elimination were used to screen for characteristic variables. Linear discriminant analysis, logistic regression, Naive Bayes, random forest, support vector machine, and extreme gradient boosting were employed in constructing risk prediction models for SCAS in T2DM patients. The area under the receiver operating characteristic curve (AUC) was employed to assess the predictive capacity of the model through 10-fold cross-validation. Additionally, the SHapley Additive exPlanations were utilized to interpret the best-performing model. Results: The percentage of SCAS was 38.46% (n=1186) in the study population. Fourteen variables, including age, white blood cell count, and basophil count, were identified as independent risk factors for SCAS. Nine predictors, including age, albumin, and total protein, were screened for the construction of risk prediction models. After validation, the random forest model exhibited the best clinical predictive value in the training set with an AUC of 0.729 (95% CI: 0.709-0.749), and it also demonstrated good predictive value in the internal validation set [AUC: 0.715 (95% CI: 0.688-0.742)]. The model interpretation revealed that age, albumin, total protein, total cholesterol, and serum creatinine were the top five variables contributing to the prediction model. Conclusion: The construction of SCAS risk models based on the Chinese T2DM population contributes to its early prevention and intervention, which would reduce the incidence of adverse cardiovascular prognostic events.

Rotational diffusion of colloidal microspheres near flat walls.

Recently, colloids with an off-center fluorescent core and homogeneous composition have been developed to measure the rotational diffusivity of microparticles using 3D confocal microscopy in refractive index-matched suspensions. Here, we show that the same particles may be imaged using a standard fluorescence microscope to yield their rotational diffusion coefficients. Trajectories of the off-center core may be combined with known expressions for the correlation decay of particle orientations to determine an effective rotational diffusivity. For sedimented particles, we also find the rotational diffusivity about axes perpendicular and parallel to the interface by adding some bright field illumination and simultaneously tracking both the core and the particle. Trajectories for particles of different sizes yield excellent agreement with hydrodynamic models of rotational diffusion near flat walls, taking the sedimentation-diffusion equilibrium into account. Finally, we explore the rotational diffusivity of particles in crowded two-dimensional monolayers, finding a different reduction of the rotational motion about the two axes depending on the colloidal microstructure.

Development of autoimmune thyroid disease after COVID-19 infection: case report.

Background: SARS-CoV-2 could trigger multiple immune responses, leading to several autoimmune diseases, including thyroid diseases. Many cases of thyroid diseases caused by COVID-19 infection have been reported. Here, we describe the disease development of patients with autoimmune thyroid disease after COVID-19 infection. Methods: The clinical characteristics, diagnosis and treatment of five different patients with autoimmune thyroid disease after COVID-19 infection were reported. Results: Female patients with primary autoimmune thyroid disease which have been stable for many years were reported. One month after COVID-19 infection, the disease has undergone different evolution. Case 1, a patient with history of long-term stable Hashimoto's thyroiditis, suddenly suffered from Graves disease after COVID-19 infection. Case 2, a patient with history of long-term stable Hashimoto's thyroiditis with thyroid nodules, suddenly suffered from Graves disease after infection. Case 3, a patient with history of long-term stable Graves disease, suddenly suffered from worsening after infection. The above three cases showed thyroid-stimulating antibodies were enhanced. Case 4, a patient with history of previous hypothyroidism had an increase in thyroid-related antibody (TPOAb and TRAb) activity after infection, followed by a marked worsening of hypothyroidism. Case 5, a patient with no history of thyroid disease suddenly developed controllable "thyrotoxicosis" after infection, suggesting the diagnosis of painless thyroiditis. Conclusion: The five case reports show a different development of the primary autoimmune thyroid disease after COVID-19 infection. The change in the trend of thyroid disease is closely related to the immune response induced by SARS-CoV-2 infection.

Interleukin-41 as a biomarker of the anti-inflammatory response associated with hyperuricemia.

BACKGROUND: Interleukin (IL)-41 is a recently discovered secreted protein that is expressed in a variety of tissues, and it is associated with several immune and metabolic diseases. However, IL-41 has not been studied in hyperuricemia (HUA). METHODS: Forty-four HUA patients and 44 healthy controls (HCs) were included in this study, and we collected theirgeneral and biochemical parameters, including white blood cell, neutrophil, lymphocyte, and platelet counts, mean platelet volume, platelet distribution width, serum creatinine, blood urea nitrogen, fasting blood glucose, total triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein, total protein, albumin, alkaline phosphatase, gamma-glutamyltransferase, and hemoglobin concentration. The level of serum IL-41 was determined using an enzyme-linked immunosorbent assay. Multivariate logistic regression analysis was exploited to identify the independent risk factors associated with HUA, and the clinical diagnostic value of IL-41 was analyzed by applying the receiver operating characteristic (ROC) curve. We assessed the association between IL-41 and clinical parameters with Spearman's rank correlation. RESULTS: Circulating IL-41 levels were significantly higher in HUA patients than in the HCs group (460.3 pg/mL vs. 261.3 pg/mL, respectively; P < 0.001). The area under the ROC curve (AUC) for IL-41 in HUA patients was 0.7443 (with a cut-off value of 311.055 pg/mL, a sensitivity of 68.18 %, and a specificity of 72.73 %), while the AUC for IL-41 combined with the platelet count was 0.8109. Correlation analysis revealed that the circulating IL-41 level was positively correlated with age in HCs and HUA patients. CONCLUSIONS: We herein demonstrated that serum IL-41 was elevated in HUA patients and that it may constitute a novel biomarker of anti-inflammatory response related to HUA.

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events. OBJECTIVE: This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk. METHODS: The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)-recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets. RESULTS: The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model. CONCLUSIONS: Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events.

High free triiodothyronine, and free-triiodothyronine-to-free-thyroxine ratio are associated with metabolic syndrome in a euthyroid employee population: the Zhejiang Zhenhai study.

Objective: The aim of this study was to elaborate the link of thyroid hormones (THs) and metabolic syndrome (MetS) in a Chinese euthyroid employee population with MetS component(s). Methods: An annual health checkup was performed on employees in 2019. Anthropometric parameters, metabolic parameters, and thyroid function were measured. A questionnaire was used in conjunction with Zhenhai Lianhua Hospital database to receive employees' medication records and thyroid surgical history records. Results: A total of 5486 eligible employees were included; the prevalence of MetS was generally higher in males than in females (38.9 vs. 30.4%, P < 0.001). Among employees with central obesity, hypertriglyceridemia, hyperglycemia, hypertension, and low high-density lipoprotein cholesterol (HDL-C), the prevalence of MetS was 68.8, 63.6, 68.2, 48.8, and 60.0% in males and 72.6, 63.3, 61.3, 42.3, and 42.3% in females, respectively. Logistic regression analysis showed that thyroid-stimulating hormone and free thyroxine (FT4) quartiles had no significant impact on MetS. Free triiodothyronine/free thyroxine (FT3/FT4) and free triiodothyronine (FT3)) quartiles were positively associated with the increased odds ratio (OR) for MetS and dyslipidemia (hypertriglyceridemia and low HDL-C), regardless of gender. In males, FT3 and FT3/FT4 quartiles were positively associated with the OR for central obesity, whereas FT4 quartiles were negatively associated; both FT3 and FT4 quartiles were positively associated with increased OR of hyperglycemia, while similar results were not observed in females. Interaction analysis indicated no significant effect of gender and TH interactions on risk of MetS. Conclusion: High FT3 and FT3/FT4 were strongly linked with MetS and dyslipidemia in our study, even in the euthyroid individuals. Tighter control of thyroid function was necessary for those with preexisting MetS component(s).

The Metabolic Score for Insulin Resistance Index is Superior to the Triglyceride and Glucose Index in Identifying Nonalcoholic Fatty Liver Disease in Hyperuricemia Subjects.

Purpose: Hyperuricemia (HUA) is a recognized risk factor for non-alcoholic fatty liver disease (NAFLD). Early diagnosis and the identification of predictive factors are important for the HUA population. This study aimed to investigate the potential relationship between the metabolic score for insulin resistance (METS-IR) index and the triglyceride and glucose (TyG) index with NAFLD in individuals with HUA. Patients and Methods: A total of 5537 HUA participants were included in the study. Hepatic ultrasonography was conducted following the latest diagnostic criteria to diagnose NAFLD. The relationship between the TyG and METS-IR index and NAFLD was evaluated by multivariable logistic regression analysis and restricted cubic spline model (RCS). In addition, the area under the curve (AUC), positive likelihood ratio, and negative likelihood ratio were explored to compare the predictive value of IR surrogates for NAFLD with HUA. The AUCs of the two indicators were compared using the DeLong test. Results: Multivariable logistic regression analysis revealed that elevated TyG (OR = 2.285, 95% CI: 1.525, 3.428) and METS-IR (OR = 1.242, 95% CI: 1.219, 1.266) indices significantly increase the risk of NAFLD. Meanwhile, the RCS analysis revealed a -log-shaped nonlinear relationship between NAFLD risk and the METS-IR index (P non-linear < 0.001), contrasting with the linear association observed with the TyG index (P non-linear = 0.763). Notably, the risk of NAFLD demonstrated a significant escalation when the METS-IR index exceeded the threshold of 39.208 (OR=1). Compared to the TyG index (AUC = 0.734, 95% CI: 0.721~0.748), the METS-IR index (AUC = 0.821, 95% CI: 0.810~0.832) demonstrated superior predictive value for NAFLD in individuals with HUA according to the DeLong test. Conclusion: In the HUA population, the METS-IR index has a higher predictive value for NAFLD than the TyG index, contributing to early diagnosis and disease prevention.

Development and internal validation of a risk model for hyperuricemia in diabetic kidney disease patients.

Purpose: This research aimed to identify independent risk factors for hyperuricemia (HUA) in diabetic kidney disease (DKD) patients and develop an HUA risk model based on a retrospective study in Ningbo, China. Patients and methods: Six hundred and ten DKD patients attending the two hospitals between January 2019 and December 2020 were enrolled in this research and randomized to the training and validation cohorts based on the corresponding ratio (7:3). Independent risk factors associated with HUA were identified by multivariable logistic regression analysis. The characteristic variables of the HUA risk prediction model were screened out by the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation, and the model was presented by nomogram. The C-index and receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow test, and decision curve analysis (DCA) were performed to evaluate the discriminatory power, degree of fitting, and clinical applicability of the risk model. Results: Body mass index (BMI), HbA1c, estimated glomerular filtration rate (eGFR), and hyperlipidemia were identified as independent risk factors for HUA in the DKD population. The characteristic variables (gender, family history of T2DM, drinking history, BMI, and hyperlipidemia) were screened out by LASSO combined with 10-fold cross-validation and included as predictors in the HUA risk prediction model. In the training cohort, the HUA risk model showed good discriminatory power with a C-index of 0.761 (95% CI: 0.712-0.810) and excellent degree of fit (Hosmer-Lemeshow test, P > 0.05), and the results of the DCA showed that the prediction model could be beneficial for patients when the threshold probability was 9-79%. Meanwhile, the risk model was also well validated in the validation cohort, where the C-index was 0.843 (95% CI: 0.780-0.906), the degree of fit was good, and the DCA risk threshold probability was 7-100%. Conclusion: The development of risk models contributes to the early identification and prevention of HUA in the DKD population, which is vital for preventing and reducing adverse prognostic events in DKD.

FK866 inhibits colorectal cancer metastasis by reducing NAD+ levels in cancer-associated fibroblasts.

BACKGROUND: Extraintestinal metastasis is the main therapeutic challenge for colorectal cancer, the third most common cancer worldwide. Various components of the tumor microenvironment, especially cancer-associated fibroblasts (CAFs), play important roles in tumor metastasis. NAMPT is often overexpressed in tumor tissues and is associated with poorer prognosis. However, the specific roles of NAMPT as well as NAD+ in tumor metastasis are relatively unknown. Therefore, we investigated the role of NAMPT and related NAD+ metabolism in cancer-associated fibroblasts mediated colorectal cancer metastasis. OBJECTIVE: This study sought to explore the molecular mechanism of FK866 in CAFs cell and colorectal cancer proliferation and metastasis. METHODS: The expression of NAMPT in clinical tissues were detected by immunohistochemically analysis. To investigate the role of NAMPT and NAD+ in the interactions between cancer cells and cancer-associated fibroblasts in tumor microenvironment, we isolated CAFs from normal and cancer tissues of clinical colorectal cancer patients. CAFs were treated with different concentrations of FK866, inhibitor of NAMPT, then the NAD+ content was detected using kits, the expression of CAFs activity and stemness indexes was assessed by Western blot and immunofluorescence. The secreted factors of these cells were analyzed by cellular inflammatory factor microarrays. The migration of SW480 after co-cultured with FK866-treated CAFs was detected by Transwell. Finally, high-throughput sequencing was performed to identify the proteins that are associated with the effect of altered NAD+ in CAFs on the migration of cancer cells. RESULTS: NAMPT expression is significantly higher in colorectal cancer tissues, especially in metastatic cancer patients, than that in normal tissues. Inhibition of NAMPT by FK866 in CAFs decreases the expression of activity indicators (α-SMA, PDGFRß), stemness indicators (BMI-1, OCT4), inflammatory factors and chemokines. Meanwhile, FK866 treatment inhibits the migration ability of SW480 cells co-cultured with CAFs. Finally, high-throughput sequencing reveals that PITX3 are down-regulated after NAD+ reduction in CAFs, which could be reversed by adding NAM, a raw material for NAD+ synthesis. CONCLUSION: Inhibition of the NAMPT-mediated NAD+ synthesis by FK866 may decrease the activation and stemness of CAFs, reduce the secretion of inflammatory and chemokines by suppressing the expression of PITX3, resulting in the suppression of colorectal cancer metastasis.

Moxidectin induces autophagy arrest in colorectal cancer.

Colorectal cancer (CRC) is a cancer with a high morbidity and mortality worldwide. Hence, developing new therapeutic drugs for CRC is very important. Moxidectin (MOX) has shown good anti-glioblastoma effect both in vitro and in vivo. This study aimed to elucidate the anti-CRC effect of MOX and its potential mechanism by investigating the influence of MOX on the viability, apoptosis, necrosis and autophagy of colorectal cancer cells (HCT15 and SW620) and its underlying mechanisms. It was found that MOX can induce autophagy arrest, promote autophagy initiation, inhibit autophagic flux and cell proliferation, simultaneously PI3K-Akt-mTOR signaling pathway and microtubule acetylation. Furthermore, MOX suppressed the growth of xenograft tumors, which was consistent with the in vitro results.

Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population.

Purpose: This study aimed to identify independent risk factors for carotid atherosclerosis (CAS) and construct and validate a CAS risk prediction model based on the Chinese population. Methods: This retrospective study included 4,570 Chinese adults who underwent health checkups (including carotid ultrasound) at the Zhenhai Lianhua Hospital, Ningbo, China, in 2020. All the participants were randomly assigned to the training and validation sets at a ratio of 7:3. Independent risk factors associated with CAS were identified using multivariate logistic regression analysis. The least absolute shrinkage and selection operator combined with 10-fold cross-validation were screened for characteristic variables, and nomograms were plotted to demonstrate the risk prediction model. C-index and receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the risk model's discrimination, calibration, and clinical applicability. Results: Age, body mass index, diastolic blood pressure, white blood cell count, mean platelet volume, alanine transaminase, aspartate transaminase, and gamma-glutamyl transferase were identified as independent risk factors for CAS. In the training, internal validation, and external validation sets, the risk model showed good discriminatory power with C-indices of 0.961 (0.953-0.969), 0.953 (0.939-0.967), and 0.930 (0.920-0.940), respectively, and excellent calibration. The results of DCA showed that the prediction model could be beneficial when the risk threshold probabilities were 1-100% in all sets. Finally, a network computer (dynamic nomogram) was developed to facilitate the physicians' clinical operations. The website is https://nbuhgq.shinyapps.io/DynNomapp/. Conclusion: The development of risk models contributes to the early identification and prevention of CAS, which is important for preventing and reducing adverse cardiovascular and cerebrovascular events.

The potential value of low-level serum interleukin-38 for the clinical diagnosis and risk prediction of hyperuricemia.

BACKGROUND: Interleukin (IL)-38, a novel anti-inflammatory cytokine in the IL-1 family, has an important role in various autoimmune/inflammatory diseases. However, the level of serum IL-38 in hyperuricemia (HUA) and its clinical applications are still unknown. METHODS: Forty-four HUA patients and 43 healthy controls were enrolled in this study. The levels of serum IL-38 in the participants were detected by enzyme-linked immunosorbent assay. Multivariable logistic regression analysis was used to identify independent risk factors associated with HUA. The least absolute shrinkage and selection operator combined with 10-fold cross-validation was used to screen the characteristic variables for modeling and the logistic regression model was visualized by nomogram. The discrimination, degree of concordance, and clinical applicability of the models were evaluated by receiver operator characteristic curve, calibration plot, and decision curve analysis, respectively. RESULTS: Compared with that in healthy controls, the level of serum IL-38 was reduced in HUA patients (275.09 ± 294.89 vs 505.99 ± 312.94, P < 0.01). The area under the curve of serum IL-38 was 0.768 (cutoff value: 246.91 pg/ml). IL-38, platelet count, mean platelet volume, and total protein were identified as independent risk factors for HUA. The risk model showed an excellent clinical differentiation value (area under the curve: 0.961) and degree of fit. Decision curve analysis indicated that the prediction model could be beneficial for patients when the threshold probability was 1%-95%. CONCLUSIONS: Low level of serum IL-38 shows some potential in the clinical diagnosis and risk prediction of HUA. In addition, as a novel biomarker of HUA, serum IL-38 would contribute to the in-depth study of its pathogenesis in the future.

Metabolomics: A New Tool to Reveal the Nature of Diabetic Kidney Disease.

Metabolomics is a field of systems biology that draws on the scientific methods of other groups to qualitatively or quantitatively characterize small molecule metabolites in organisms, revealing their interconnections with the state of the organism at an overall relative macroscopic level. Diabetic kidney disease (DKD) is well known as a chronic metabolic disease, and metabolomics provides an excellent platform for its clinical study. A growing number of metabolomic analyses have revealed that individuals with DKD have metabolic disturbances of multiple substances in their bodies. With the continuous development and improvement of metabolomic analysis technology, the application of metabolomics in the clinical research of DKD is also expanding. This review discusses the recent progress of metabolomics in the early diagnosis, disease prognosis, and pathogenesis of DKD at the level of small molecule metabolites in vivo.

Minocycline binds and inhibits LYN activity to prevent STAT3-meditated metastasis of colorectal cancer.

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Metastasis is a major cause of CRC recurrence and mortality. Several antibiotic drugs have been reported to exert potential anticancer activities, however, whether and how the tetracycline antibiotic minocycline exhibit tumor suppressive effect on CRC remains unknown. Here, we found that minocycline markedly inhibits the epithelial-mesenchymal transition (EMT) process and metastasis of CRC cells both in vitro and in vivo. Using chemical proteomics screening combined with docking analysis and site-directed mutagenesis, we identified LYN as a direct bind target of minocycline, and Ala255 of LYN is required for minocycline binding. Mechanistically, minocycline binding inactivates LYN, leading to STAT3 inactivation and EMT suppression, thereby inhibits CRC metastasis. Tissue microarray analysis further confirmed the clinical relevance of LYN-STAT3 axis in the EMT and progression of CRC. In addition to CRC, minocycline also significantly prevents EMT process and inhibits the metastasis of several other cancer types. Our findings elucidate the mechanism of action of minocycline for the inhibition of CRC metastasis by LYN binding, and suggest that repurposing minocycline may represent a promising strategy for the treatment of advanced CRC and other cancer types.

LncRNA TUG1 functions as a ceRNA for miR-1-3p to promote cell proliferation in hepatic carcinogenesis.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterised by high malignancy, metastasis and recurrence, but the specific mechanism that drives these outcomes is unclear. Recent studies have shown that long noncoding RNAs (lncRNAs) can regulate the proliferation and apoptosis of hepatic cells. METHODS: We searched for lncRNAs and microRNAs (miRNAs), which can regulate IGF1 expression, through a bioinformatics website, and predicted that lncRNA taurine-upregulated gene 1 (TUG1) would have multiple targets for miR-1-3p binding, meaning that lncRNA TUG1 played an adsorption role. A double luciferase assay was used to verify the targeting relationship between lncRNA TUG1 and miR-1-3p. Western blotting and qPCR were used to verify the targeting relationship between miR-1-3p and IGF1, and qPCR was used to verify the regulatory relationship between the lncRNA TUG1-miR-1-3p-IGF1 axis. CCK-8 was used to detect the growth activity of miRNA-transfected L-O2 cells, and flow cytometry was used to detect cell cycle changes and apoptosis. RESULT: The proliferation cycle of L-O2 cells transfected with miR-1-3p mimics was significantly slowed. Flow cytometry showed that the proliferation of L-O2 cells was slowed, and the apoptosis rate was increased. In contrast, when L-O2 cells were transfected with miR-1-3p inhibitor, the expression of IGF1 was significantly upregulated, and the cell proliferation cycle was significantly accelerated. Flow cytometry showed that the cell proliferation rate was accelerated, and the apoptosis rate was reduced. CONCLUSION: LncRNA TUG1 can adsorb miR-1-3p as a competitive endogenous RNA (ceRNA) to promote the expression of IGF1 and promote cell proliferation in hepatic carcinogenesis.

Role of Apolipoprotein A1 in PPAR Signaling Pathway for Nonalcoholic Fatty Liver Disease.

Peroxisome proliferator-activated receptors (PPARs) have been suggested to play crucial roles in the pathology of NAFLD with a vague understanding of the underlying mechanism. Here, we integrated large-scale literature data and clinical data to explore the potential role of the PPAR-APOA1 signaling pathway in the pathology of NAFLD. First, the signaling pathway connecting PPARs, APOA1, and NAFLD was constructed. Then, we employed clinical data to explore the association between APOA1 levels and NAFLD. In addition, we built the APOA1-driven pathway analysis to explore the potential mechanism of the APOA1-NAFLD association. Pathway analysis showed that APOA1 serves as a hubprotein connecting PPARs and NAFLD through a beneficial modulation of 16 out of 21 NAFLD upstream regulators. Each relationship within the composed pathway was supported by results from multiple previous studies. Clinical data analysis showed that an increase of APOA1 level was associated with a significantly decreased NAFLD prevalence (χ 2 = 292.109; P < 0.001). When other confounding factors were adjusted, serum APOA1 level was shown as an independent risk factor for the prevalence of NAFLD (P value<.0001; OR = 0.562). Our results suggested that the three PPARs (PPARA, PPARD, and PPARG) might promote the expression and molecular transportation of APOA1 to form a PPAR-APOA1 signaling pathway that demonstrated a beneficial role in the pathogenesis of NAFLD.

Low levels of serum IL-39 are associated with autoimmune thyroid disease.

BACKGROUND: Interleukin (IL)-39 is a novel member of IL-12 cytokine family, but its role in autoimmune thyroid diseases (AITD) is unclear. The aim of the present study was to determine serum levels of IL-39 in Hashimoto's thyroiditis (HT) and Graves' disease (GD) patients. METHODS: A total of 48 patients with HT, 50 patients with GD, and 45 healthy controls (HCs) were recruited for this study. Levels of serum IL-39 were determined by ELISA. RESULTS: Compared with HC group, levels of serum IL-39 in patients with HT (p < 0.05) and GD (p < 0.01) were drastically reduced. Among patients with HT, serum IL-39 levels had a positive correlation with white blood cell count (WBC) count and free triiodothyronine level. Among patients with GD, the levels of IL-39 in serum were positively correlated with WBC count and C-reactive protein levels. CONCLUSIONS: IL-39 may be a new potential predictor for patients with HT and GD.

The Emerging Roles of IL-36, IL-37, and IL-38 in Diabetes Mellitus and its Complications.

Diabetes mellitus is a metabolic disease caused by a combination of genetic and environmental factors. The importance of the inflammatory response occurring in the pancreas and adipose tissue in the occurrence and progression of diabetes has been gradually accepted. Excess blood glucose and free fatty acids produce large amounts of inflammatory cytokines and chemokines through oxidative stress and endoplasmic reticulum stress. There is sufficient evidence that proinflammatory mediators, such as interleukin (IL)-1ß, IL-6, macrophage chemotactic protein-1, and tumor necrosis factor-α, are engaged in insulin resistance in peripheral adipose tissue and the apoptosis of pancreatic ß-cells. IL-36, IL-37, and IL-38, as new members of the IL-1 family, play an indispensable role in the regulation of immune system homeostasis and are involved in the pathogenesis of inflammatory and autoimmune diseases. Recently, the abnormal expression of IL-36, IL-37, and IL-38 in diabetes has been reported. In this review, we discuss the emerging functions, potential mechanisms, and future research directions on the role of IL-36, IL-37, and IL-38 in diabetes mellitus and its complications.