Dexamethasone Induced Osteocyte Apoptosis in Steroid-Induced Femoral Head Osteonecrosis through ROS-Mediated Oxidative Stress.

OBJECTIVE: Glucocorticoid (GC) overuse is strongly associated with steroid-induced osteonecrosis of the femoral head (SINFH). However, the underlying mechanism of SINFH remains unclear. This study aims to investigate the effect of dexamethasone (Dex)-induced oxidative stress on osteocyte apoptosis and the underlying mechanisms. METHODS: Ten patients with SINFH and 10 patients with developmental dysplasia of the hips (DDH) were enrolled in our study. Sixty rats were randomly assigned to the Control, Dex, Dex + N-Acetyl-L-cysteine (NAC), Dex + Dibenziodolium chloride (DPI), NAC, and DPI groups. Magnetic resonance imaging (MRI) was used to examine edema in the femoral head of rats. Histopathological staining was performed to assess osteonecrosis. Immunofluorescence staining with TUNEL and 8-OHdG was conducted to evaluate osteocyte apoptosis and oxidative damage. Immunohistochemical staining was carried out to detect the expression of NOX1, NOX2, and NOX4. Viability and apoptosis of MLO-Y4 cells were measured using the CCK-8 assay and TUNEL staining. 8-OHdG staining was conducted to detect oxidative stress. 2',7'-Dichlorodihydrofluorescein diacetate (DCFH-DA) staining was performed to measure reactive oxygen species (ROS). The expression of NOX1, NOX2, and NOX4 in MLO-Y4 cells was analyzed by Western blotting. Multiple comparisons were performed using one-way analysis of variance (ANOVA). RESULTS: In patients and the rat model, hematoxylin-eosin (HE) staining revealed a significantly higher rate of empty lacunae in the SINFH group than in the DDH group. Immunofluorescence staining indicated a significant increase in TUNEL-positive cells and 8-OHdG-positive cells in the SINFH group compared to the DDH group. Immunohistochemical staining demonstrated a significant increase in the expression of NOX1, NOX2, and NOX4 proteins in SINFH patients compared to DDH patients. Moreover, immunohistochemical staining showed a significant increase in the proportion of NOX2-positive cells compared to the Control group in the femoral head of rats. In vitro, Dex significantly inhibited the viability of osteocyte cells and induced apoptosis. After Dex treatment, the intracellular ROS level increased. However, Dex treatment did not alter the expression of NOX proteins in vitro. Additionally, NAC and DPI inhibited the generation of intracellular ROS and partially alleviated osteocyte apoptosis in vivo and in vitro. CONCLUSION: This study demonstrates that GC promotes apoptosis of osteocyte cells through ROS-induced oxidative stress. Furthermore, we found that the increased expression of NOXs induced by GC serves as an important source of ROS generation.

Do different types of carbon mitigation regulations have heterogeneous effects on innovation quality?

Carbon peak and carbon neutralization as a global mission cannot be completed without systematically designed carbon mitigation regulations. In order to achieve the carbon emission reduction as formulated in the Paris Agreement and fulfill the promises made at the United Nations General Assembly, the Chinese government has promulgated various types of regulations to curb carbon emission with the hope of realizing the Porter effect. Selecting low-carbon pilot cities and carbon emission trading schema as the research objects, this study employs a differences-in-differences (DID) model to investigate the effects of carbon mitigation regulations on innovation quality and its heterogeneity. The empirical results reveal that market-based carbon mitigation regulations can significantly achieve the Porter effect and improve innovation quality. Furthermore, the government financial situation and the technical efficiency change have important moderating and mediating effects respectively. It is recommended that a full play of the market be given in China for the Porter effect. The main scientific value of this paper is distinguished the heterogenous effect of different types of environmental regulations, which can enhance the pertinence of environmental regulation.

Predict DLBCL patients' recurrence within two years with Gaussian mixture model cluster oversampling and multi-kernel learning.

BACKGROUND AND OBJECTIVE: Diffuse large B-cell lymphoma (DLBCL) is common in adults' non-Hodgkin's lymphoma. Relapse mainly occurs within two years after diagnosis and has a poor prognosis. Relapse after two years is less frequent and has a better prognosis. In this work, we constructed a relapse prediction model for diffuse large B-cell lymphoma patients within two years, expecting to provide a reference for Clinicians to implement individualized treatment. METHOD: We propose a secondary-level class imbalance method based on Gaussian mixture model (GMM) clustering resampling to balance the data. Then use a multi-kernel support vector machine(SVM) to inscribe heterogeneous clinical data. Finally, merging them to identify recurrence patients within two years. RESULTS: Among all the class imbalance methods in this work, Inverse Weighted -GMM +SMOTEENN has the best performance. Compared with NO-GMM (Directl use the SMOTEENN without the GMM clustering process), its Area Under the ROC Curve(AUC) increases by 8.75%, and ECE and brier scores decrease 2.07% and 3.09%, respectively. Among the four classification algorithms in this work, Multiple kernel learning (MKL) has the most minimized brier scores and expected calibration error(ECE), the largest AUC, accuracy, Recall, precision and F1, has the best discrimination and calibration. CONCLUSION: Our inverse weighted -GMM+SMOTEENN+MKL (GMM-SENN-MKL) method can handle data class imbalance and clinical heterogeneity data well and can be used to predict recurrence in DLBCL patients.

Probability calibration-based prediction of recurrence rate in patients with diffuse large B-cell lymphoma.

BACKGROUND: Although many patients receive good prognoses with standard therapy, 30-50% of diffuse large B-cell lymphoma (DLBCL) cases may relapse after treatment. Statistical or computational intelligent models are powerful tools for assessing prognoses; however, many cannot generate accurate risk (probability) estimates. Thus, probability calibration-based versions of traditional machine learning algorithms are developed in this paper to predict the risk of relapse in patients with DLBCL. METHODS: Five machine learning algorithms were assessed, namely, naïve Bayes (NB), logistic regression (LR), random forest (RF), support vector machine (SVM) and feedforward neural network (FFNN), and three methods were used to develop probability calibration-based versions of each of the above algorithms, namely, Platt scaling (Platt), isotonic regression (IsoReg) and shape-restricted polynomial regression (RPR). Performance comparisons were based on the average results of the stratified hold-out test, which was repeated 500 times. We used the AUC to evaluate the discrimination ability (i.e., classification ability) of the model and assessed the model calibration (i.e., risk prediction accuracy) using the H-L goodness-of-fit test, ECE, MCE and BS. RESULTS: Sex, stage, IPI, KPS, GCB, CD10 and rituximab were significant factors predicting the 3-year recurrence rate of patients with DLBCL. For the 5 uncalibrated algorithms, the LR (ECE = 8.517, MCE = 20.100, BS = 0.188) and FFNN (ECE = 8.238, MCE = 20.150, BS = 0.184) models were well-calibrated. The errors of the initial risk estimate of the NB (ECE = 15.711, MCE = 34.350, BS = 0.212), RF (ECE = 12.740, MCE = 27.200, BS = 0.201) and SVM (ECE = 9.872, MCE = 23.800, BS = 0.194) models were large. With probability calibration, the biased NB, RF and SVM models were well-corrected. The calibration errors of the LR and FFNN models were not further improved regardless of the probability calibration method. Among the 3 calibration methods, RPR achieved the best calibration for both the RF and SVM models. The power of IsoReg was not obvious for the NB, RF or SVM models. CONCLUSIONS: Although these algorithms all have good classification ability, several cannot generate accurate risk estimates. Probability calibration is an effective method of improving the accuracy of these poorly calibrated algorithms. Our risk model of DLBCL demonstrates good discrimination and calibration ability and has the potential to help clinicians make optimal therapeutic decisions to achieve precision medicine.

Altered profiles of fecal bile acids correlate with gut microbiota and inflammatory responses in patients with ulcerative colitis.

BACKGROUND: Gut microbiota and its metabolites may be involved in the pathogenesis of inflammatory bowel disease. Several clinical studies have recently shown that patients with ulcerative colitis (UC) have altered profiles of fecal bile acids (BAs). It was observed that BA receptors Takeda G-protein-coupled receptor 5 (TGR5) and vitamin D receptor (VDR) participate in intestinal inflammatory responses by regulating NF-ĸB signaling. We hypothesized that altered profiles of fecal BAs might be correlated with gut microbiota and inflammatory responses in patients with UC. AIM: To investigate the changes in fecal BAs and analyze the relationship of BAs with gut microbiota and inflammation in patients with UC. METHODS: The present study used 16S rDNA sequencing technology to detect the differences in the intestinal flora between UC patients and healthy controls (HCs). Fecal BAs were measured by targeted metabolomics approaches. Mucosal TGR5 and VDR expression was analyzed using immunohistochemistry, and serum inflammatory cytokine levels were detected by ELISA. RESULTS: Thirty-two UC patients and twenty-three HCs were enrolled in this study. It was found that the diversity of gut microbiota in UC patients was reduced compared with that in HCs. Firmicutes, Clostridium IV, Butyricicoccus, Clostridium XlVa, Faecalibacterium, and Roseburia were significantly decreased in patients with UC (P = 3.75E-05, P = 8.28E-07, P = 0.0002, P = 0.003, P = 0.0003, and P = 0.0004, respectively). Proteobacteria, Escherichia, Enterococcus, Klebsiella, and Streptococcus were significantly enriched in the UC group (P = 2.99E-09, P = 3.63E-05, P = 8.59E-05, P = 0.003, and P = 0.016, respectively). The concentrations of fecal secondary BAs, such as lithocholic acid, deoxycholic acid, glycodeoxycholic acid, glycolithocholic acid, and taurolithocholate, in UC patients were significantly lower than those in HCs (P = 8.1E-08, P = 1.2E-07, P = 3.5E-04, P = 1.9E-03, and P = 1.8E-02, respectively) and were positively correlated with Butyricicoccus, Roseburia, Clostridium IV, Faecalibacterium, and Clostridium XlVb (P < 0.01). The concentrations of primary BAs, such as taurocholic acid, cholic acid, taurochenodeoxycholate, and glycochenodeoxycholate, in UC patients were significantly higher than those in HCs (P = 5.3E-03, P = 4E-02, P = 0.042, and P = 0.045, respectively) and were positively related to Enterococcus, Klebsiella, Streptococcus, Lactobacillus, and pro-inflammatory cytokines (P < 0.01). The expression of TGR5 was significantly elevated in UC patients (0.019 ± 0.013 vs 0.006 ± 0.003, P = 0.0003). VDR expression in colonic mucosal specimens was significantly decreased in UC patients (0.011 ± 0.007 vs 0.016 ± 0.004, P = 0.033). CONCLUSION: Fecal BA profiles are closely related to the gut microbiota and serum inflammatory cytokines. Dysregulation of the gut microbiota and altered constitution of fecal BAs may participate in regulating inflammatory responses via the BA receptors TGR5 and VDR.

Applying probability calibration to ensemble methods to predict 2-year mortality in patients with DLBCL.

BACKGROUND: Under the influences of chemotherapy regimens, clinical staging, immunologic expressions and other factors, the survival rates of patients with diffuse large B-cell lymphoma (DLBCL) are different. The accurate prediction of mortality hazards is key to precision medicine, which can help clinicians make optimal therapeutic decisions to extend the survival times of individual patients with DLBCL. Thus, we have developed a predictive model to predict the mortality hazard of DLBCL patients within 2 years of treatment. METHODS: We evaluated 406 patients with DLBCL and collected 17 variables from each patient. The predictive variables were selected by the Cox model, the logistic model and the random forest algorithm. Five classifiers were chosen as the base models for ensemble learning: the naïve Bayes, logistic regression, random forest, support vector machine and feedforward neural network models. We first calibrated the biased outputs from the five base models by using probability calibration methods (including shape-restricted polynomial regression, Platt scaling and isotonic regression). Then, we aggregated the outputs from the various base models to predict the 2-year mortality of DLBCL patients by using three strategies (stacking, simple averaging and weighted averaging). Finally, we assessed model performance over 300 hold-out tests. RESULTS: Gender, stage, IPI, KPS and rituximab were significant factors for predicting the deaths of DLBCL patients within 2 years of treatment. The stacking model that first calibrated the base model by shape-restricted polynomial regression performed best (AUC = 0.820, ECE = 8.983, MCE = 21.265) in all methods. In contrast, the performance of the stacking model without undergoing probability calibration is inferior (AUC = 0.806, ECE = 9.866, MCE = 24.850). In the simple averaging model and weighted averaging model, the prediction error of the ensemble model also decreased with probability calibration. CONCLUSIONS: Among all the methods compared, the proposed model has the lowest prediction error when predicting the 2-year mortality of DLBCL patients. These promising results may indicate that our modeling strategy of applying probability calibration to ensemble learning is successful.

The effectiveness and safety of curcumin as a complementary therapy in inflammatory bowel disease: A protocol of systematic review and meta-analysis.

BACKGROUND: Inflammatory bowel diseases (IBD), which include Crohn disease and ulcerative colitis, affect several million individuals worldwide. Curcumin as a complementary therapy has been used to cure the IBD, yet the efficacy and safety of curcumin remains to be assessed. In this study, we aim to draw up a protocol for systematic review to evaluate the efficacy and safety of curcumin for IBD. METHODS: We will search the following electronic databases from inception to September 31, 2020: PubMed, Cochrane Library, EMBASE, Web of Science, Medline, the China National Knowledge Infrastructure Database, Wan Fang Database, the Chinese Scientific Journal Database, and Chinese Biomedical Literature Database. Clinical trial registrations, potential gray literatures, relevant conference abstracts and reference list of identified studies will also be searched. Relevant randomized controlled clinical trials were enrolled and analyzed. The literature selection, data extraction, and quality assessment will be completed by 2 independent authors. Either the fixed-effects or random-effects model will be used for data synthesis based on the heterogeneity test. Clinical remission will be evaluated as the primary outcome. Clinical response, endoscopic remission, inflammatory markers and adverse events will be assessed as the secondary outcomes. The RevManV.5.3.5 will be used for Meta-analysis. Subgroup analyses of doses, delivery way, frequency of treatment and the degree of IBD severity or different forms of IBD were also conducted. RESULTS: This study will provide a synthesis of current evidence of curcumin for IBD from several aspects, such as clinical remission, clinical response, endoscopic remission, inflammatory markers, and adverse events. CONCLUSION: The conclusion of our study will provide updated evidence to judge whether curcumin is an effective solution to IBD patients. INPLASY REGISTRATION NUMBER: INPLASY202090065.

Physicochemical properties improvement and structural changes of bamboo shoots (Phyllostachys praecox f. Prevernalis) dietary fiber modified by subcritical water and high pressure homogenization: a comparative study.

In presented study, the effects of subcritical water (SW) and high pressure homogenization (HPH) treatments on the physicochemical and structural characteristics of dietary fibers (DFs) from bamboo shoots (Phyllostachys praecox f. Prevernalis) were investigated. The soluble dietary fiber (SDF) content was dramatically increased in SW and HPH treated DFs. Compared with HPH, SW modification enhanced higher physicochemical properties including water holding capacity (WHC), oil holding capacity (OHC) and swelling capacity (SC) of DFs. The abilities of DFs to absorb cholesterol and nitrite ions were both greatly increased after treatments. The results of Scanning electron microscopy (SEM), Fourier transform infrared (FT-IR), and X-ray diffraction (XRD) showed that the structure of DFs were changed by SW and HPH. In conclusion, SW treatment showed better effects on improvement of physicochemical properties of bamboo shoot DFs than that of HPH, and the modified DFs could be a potential new functional foods or food additives.

Establishing a method of HPLC involving precolumn derivatization by 2,2'-dithiobis (5-nitropyridine) to determine the sulfites in shrimps in comparison with ion chromatography.

Although sulfites are widely used in shrimp processing, the contents of residual sulfite need to be strictly controlled due to their potential toxicity. In this paper, a novel method was developed for determination of the free and total sulfites in shrimps. Major procedures of the method includes separation of free and total sulfites with ultrasound-assisted extraction and pH adjustment for 20 min, then a precolumn derivatization was conducted by 2,2'-Dithiobis (5-nitropyridine) and verified by LC-MS, and finally HPLC coupled with an ultraviolet (UV) detector was carried out. Results indicated that the UV absorption wavelength shifted from 213 (sulfites) to 320 nm (new disulfide compounds), significantly reducing the interference of natural occurring compounds and solvents in the matrix. The standard curves exhibited a good linear range of 3.2-51.2 mg/L (R 2 = 0.9996). The limit of detection (LOD) and limit of quantification (LOQ) were 0.3 and 1.0 mg/L, respectively. The contents of free and total sulfite in frozen shrimps were 26.58 ± 0.48 and 31.44 ± 0.83 mg/kg calculated by SO2, respectively. These were similar (p > 0.05) to the data obtained by the method of ion chromatography. In conclusion, the new developed method has been proved to be a reliable and economic method for effective determination of free and total sulfites in the shrimps, and the method could be expanded in determination of the sulfites in other food products.