Activation of PPARγ inhibits pro-inflammatory cytokines production by upregulation of miR-124 in vitro and in vivo.

Peroxisome proliferator-activated receptor gamma (PPARγ) and miR-124 have been reported to play important roles in regulation of inflammation. However, the underlying anti-inflammatory mechanisms remain not well understood. In the present study, we demonstrated that the expression level of PPARγ is positively correlated with that of miR-124 in patients with sepsis. Activation of PPARγ upregulates miR-124 and in turn inhibits miR-124 target gene. PPARγ bound directly to PPRE in the miR-124 promoter region, and enhanced the promoter transcriptional activity. PPARγ-induced miR-124 is involved in the suppression of pro-inflammatory cytokine in vitro and in vivo. These results suggest that PPARγ-induced miR-124 inhibits the production of pro-inflammatory cytokines is a novel PPARγ anti-inflammatory mechanism and also indicate that miR-124 may be a potential therapeutic target for the treatment of inflammatory diseases.

Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study.

PURPOSE: The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa. MATERIAL AND METHODS: A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic. RESULTS: The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively. CONCLUSIONS: MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Evaluating social network-based weight loss interventions in Chinese population: An agent-based simulation.

OBJECTIVE: The aim of this study is to assess network-based weight loss interventions in the Chinese setting using agent-based simulation. METHODS: An agent-based model incorporating social, environmental and personal influence is developed to simulate the obesity epidemic through an interconnected social network among a population of 2197 individuals from the nationally representative survey. Model parameters are collected from literature and existing database. To ensure the robustness of our findings, the model is validated against empirical observations and sensitivity analyses are performed on calibrated parameters. RESULTS: When compared with the baseline model, significant weight difference is detected using paired samples t tests for network-based intervention strategies (p<0.05) but no difference is observed for the two conventional intervention strategies including choosing random or high-risk individuals (p>0.05). Targeting the most connected individuals minimizes the average population weight, average BMI, and generates a reduction of 2.70% and 1.38% in overweight and obesity prevalence. CONCLUSIONS: The simulations shows that targeting individuals on the basis of their social network attributes outperforms conventional targeting strategies. Future work needs to focus on how to further leverage social networks to curb obesity prevalence and enhance interventions for other chronic conditions using agent-based simulation.

Efficacy and Safety of Safflower Yellow in Early Diabetic Nephropathy: A Meta-Analysis.

BACKGROUND: Diabetic nephropathy (DN) is a major cause of end-stage renal disease. In order to palliate renal function impairment and reduce kidney related mortality, it is crucial to treating DN patients at the early stage. This study aims to assess the efficacy and safety of conventional therapy combined with safflower yellow versus conventional therapy alone in early DN patients. METHODS: A meta-analysis of randomized controlled trials that compared safflower yellow plus conventional therapy with conventional therapy alone in early DN patients was conducted. Papers were searched using the electronic databases and reference lists. Two reviewers working independently extracted relevant data and carried out risk-of-bias assessments. Statistical analysis was undertaken in Review Manager 5.3. RESULTS: Fourteen trials (1,072 patients) were included in the meta-analysis. Conventional therapy combined with safflower yellow was associated with a higher effective rate (RD, 0.24; 95% CI, 0.17 to 0.30) and a greater decline in urinary albumin excretion rates (SMD, -1.34; 95% CI, -1.77 to -0.92), fasting blood glucose (MD, -0.57; 95% CI, -0.98 to -0.16), serum creatinine (MD, -12.36; 95% CI, -14.66 to -10.06), and blood urea nitrogen (SMD, -0.93; 95% CI, -1.13 to -0.73) in the subgroup with a follow-up time > 15 days. The incidence of adverse events did not differ significantly between these two regimens (RD, -0.01; 95% CI, -0.03 to 0.01). Findings were similar in the subgroup with a follow-up time < 15 days. CONCLUSIONS: Conventional therapy combined with safflower yellow had a more beneficial effect than conventional therapy alone in early DN patients. There were significant differences in effective rate, urinary albumin excretion rates, fasting blood glucose, serum creatinine, and blood urea nitrogen between the two regimens and no significant difference in adverse events. More randomized controlled research using standardized protocols would be needed in the future to compare these two regimens.

Prevalence and patterns of multimorbidity among the elderly in China: a cross-sectional study using national survey data.

OBJECTIVES: Examination of the prevalence and patterns of multimorbidity among the elderly in China. DESIGN: Cross-sectional study. SETTING: More than 10 000 households in 28 of the 34 provinces of mainland China. PARTICIPANTS: 11 707 Chinese adults aged 60 and over. PRIMARY OUTCOME MEASURES: Prevalence and patterns of multimorbidity among the participants. Relative risks were calculated to estimate the probability of up to 14 chronic conditions coexisting with each other. Observed-to-expected (O/E) ratios were used to analyse the patterns of multimorbidity. RESULTS: Multimorbidity was present in 43.6% of respondents from the sample population, with women having the greater prevalence compared with men. There were 804 different comorbidity combinations identified, including 76 dyad combinations and 169 triad combinations. The top 10 morbidity dyads and triads accounted for 69.01% and 47.05% of the total dyad and triad combinations observed, respectively. Among the 14 chronic conditions included in the study, asthma, stroke, heart attack and six other chronic conditions were the main components of multimorbidity due to their high relative risk ratios. The most frequently occurring clusters with higher O/E ratios were stroke along with emotional, nervous, or psychiatric problems; memory-related diseases together emotional, nervous, or psychiatric problems; and memory-related diseases and asthma accompanied by chronic lung diseases and asthma. CONCLUSIONS: The results of this study highlight the high prevalence of multimorbidity in the elderly population in China. Further studies are required to understand the aetiology of multimorbidity, and future primary healthcare policies should be made while taking multimorbidity into consideration.