Lipopolysaccharide (LPS)-induced inflammation in RAW264.7 cells is inhibited by microRNA-494-3p via targeting lipoprotein-associated phospholipase A2.

BACKGROUND: Gram-negative bacterial lipopolysaccharide (LPS) is a major component of inflammation and plays a key role in the pathogenesis of sepsis. According to our previous study, the expression of lipoprotein-associated phospholipase A2 (Lp-PLA2) is significantly upregulated in septic patients and is positively correlated with the severity of this disease. Herein, we investigated the potential roles of Lp-PLA2-targeting microRNAs (miRNAs) in LPS-induced inflammation in murine mononuclear macrophages (RAW264.7 cells). METHODS: In LPS-stimulated RAW264.7 cells, Lp-PLA2 was confirmed to be expressed during the inflammatory response. The function of microRNA-494-3p (miR-494-3p) in the LPS-induced inflammatory response of RAW264.7 cells was determined by the transfection of a miR-494-3p mimic or inhibitor in vitro. RESULTS: Compared to the control, LPS induced a significant increase in the Lp-PLA2 level, which was accompanied by the release of inflammatory mediators. The bioinformatics and qRT‒PCR results indicated that the miR-494-3p level was associated with Lp-PLA2 expression in the LPS-induced inflammatory response of RAW264.7 cells. Dual-luciferase reporter assay results confirmed that the 3'-UTR of Lp-PLA2 was a functional target of microRNA-494-3p. During the LPS-induced inflammatory response of RAW264.7 cells, targeting Lp-PLA2 and transfecting miR-494-3p mimics significantly upregulated the expression of miR-494-3p, leading to a reduction in the release of inflammatory factors and conferring a protective effect on LPS-stimulated RAW264.7 cells. CONCLUSION: By targeting Lp-PLA2, miR-494-3p suppresses Lp-PLA2 secretion, thereby alleviating LPS-induced inflammation, which indicates that miR-494-3p may be a potential target for sepsis treatment.

The predictive value of CHA2DS2-VASc score on the prognosis of patients with atrial fibrillation based on a prospective cohort study.

Background: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia encountered in clinical practice, and it is associated with an increased risk of mortality, stroke, and peripheral embolism. The risk of stroke in AF is heterogeneous and dependent on underlying clinical conditions included in current risk stratification schemes. Recently, the CHA2DS2-VASc score has been incorporated into guidelines to encompass common stroke risk factors observed in routine clinical practice. The aim of this study was to study the predictive value of CHA2DS2-VASc score on the prognosis of patients with AF to determine the correlation of major complications including cerebral infarction and intracranial hemorrhage in patients with AF with oral anticoagulant and antiplatelet aggregation drugs and to identify the risk factors for all-cause mortality. Methods: A prospective study was conducted on 181 patients with AF who underwent physical examinations at Hai'an Qutang Central Hospital from January 2020 to December 2020. The patient's general condition, chronic disease history, CHA2DS2-VASc [congestive heart failure, hypertension, age ≥75 years (doubled), diabetes, stroke (doubled), vascular disease, age 65 to 74 years, and sex category (female)] score, left ventricular ejection fraction (LVEF), lipid metabolism, and oral anticoagulant and antiplatelet aggregation medication during physical examination were recorded. By using telephone meetings to complete the follow-up, we tracked the patient's cerebral infarction, intracranial hemorrhage, and survival status within 2 years of follow-up, statistically analyzed the relationship between AF complications and medication, and grouped patients with AF based on the CHA2DS2-VASc score to evaluate its predictive ability for mortality outcomes in these patients. Results: The patients were divided into four groups according to the medication situation, and the incidence of cerebral infarction in the combination group was significantly lower than that in the non-medication group (0.0% vs. 19.2%; P<0.01). The incidence of intracranial hemorrhage in the combination group was significantly higher than that in the non-drug group (13.8% vs. 0.0%; P<0.01). The logistic regression model indicated that patients with a history of cerebral infarction had an increased risk of death compared to those without a history of cerebral infarction [odds ratio (OR) =7.404; 95% confidence interval (CI): 2.255-24.309]. After grouping according to the CHA2DS2-VASc score, we found that there was a significant difference in the 2-year survival rate between patients with CHA2DS2-VASc score <5 and those with a score ≥5 (P<0.01). The characteristic curve analysis of the participants showed that the CHA2DS2-VASc score had good predictive ability for all-cause mortality in patients with AF (area under the curve =0.754), with a cutoff value of 4, a sensitivity of 62.50%, a specificity of 86.06%, and a 95% CI of 0.684-0.815. Conclusions: The CHA2DS2-VASc score demonstrated high predictive value for all-cause mortality in patients with AF.

The normal value and influencing factors of shear wave elastography in healthy tibial nerves: A cross-sectional study.

Background and Aims: Shear wave elastography is a potential method for evaluating peripheral neuropathy, but lacking reference values. The aim of this study was to measure tibial nerve stiffness in healthy individuals using shear wave elastography and to investigate the influencing factors of tibial nerve stiffness. Methods: Shear wave elastography of bilateral tibial nerves was performed in 50 healthy individuals 4 cm proximal to the medial malleolus. Mean shear modulus data of tibial nerves were obtained and recorded. Intra- and interobserver agreement were assessed using intraclass correlation coefficients. Differences among groups (grouped by laterality, sex, age, and body mass index) were analyzed with independent-samples t-tests and paired t-tests. Effect size (Cohen's d) was also calculated. Results: The intra-and interobserver agreement were moderate (intraclass correlation coefficient, 0.700-0.747) for all participants, and was poor (intraclass correlation coefficient, 0.265-0.088) in very thin people (body mass index <18.5 kg/m2). The shear wave elastography measurements of the tibial nerve did not show a significant difference between legs, sexes, or different age groups. Higher values of tibial nerve stiffness were found in thinner participants. Conclusions: Shear wave elastography is a method to evaluate the stiffness of peripheral nerves. The measurement results were likely influenced by body mass index of the participants.

Prediction of 30-day mortality in heart failure patients with hypoxic hepatitis: Development and external validation of an interpretable machine learning model.

Background: This study aimed to explore the impact of hypoxic hepatitis (HH) on survival in heart failure (HF) patients and to develop an effective machine learning model to predict 30-day mortality risk in HF patients with HH. Methods: In the Medical Information Mart for Intensive Care (MIMIC)-III and IV databases, clinical data and survival situations of HF patients admitted to the intensive care unit (ICU) were retrospectively collected. Propensity Score Matching (PSM) analysis was used to balance baseline differences between HF patients with and without HH. Kaplan Meier analysis and multivariate Cox analysis were used to determining the effect of HH on the survival of CF patients. For developing a model that can predict 30-day mortality in CF patients with HH, the feature recurrence elimination (RFE) method was applied to feature selection, and seven machine learning algorithms were employed to model construction. After training and hyper-parameter optimization (HPO) of the model through cross-validation in the training set, a performance comparison was performed through internal and external validation. To interpret the optimal model, Shapley Additive Explanations (SHAP) were used along with the Local Interpretable Model-agnostic Explanations (LIME) and the Partial Dependence Plot (PDP) techniques. Results: The incidence of HH was 6.5% in HF patients in the MIMIC cohort. HF patients with HH had a 30-day mortality rate of 33% and a 1-year mortality rate of 51%, and HH was an independent risk factor for increased short-term and long-term mortality risk in HF patients. After RFE, 21 key features (21/56) were selected to build the model. Internal validation and external validation suggested that Categorical Boosting (Catboost) had a higher discriminatory capability than the other models (internal validation: AUC, 0.832; 95% CI, 0.819-0.845; external validation: AUC, 0.757 95% CI, 0.739-0.776), and the simplified Catboost model (S-Catboost) also had good performance in both internal validation and external validation (internal validation: AUC, 0.801; 95% CI, 0.787-0.813; external validation: AUC, 0.729, 95% CI, 0.711-0.745). Conclusion: HH was associated with increased mortality in HF patients. Machine learning methods had good performance in identifying the 30-day mortality risk of HF with HH. With interpretability techniques, the transparency of machine learning models has been enhanced to facilitate user understanding of the prediction results.

Performance of a computer aided diagnosis system for SARS-CoV-2 pneumonia based on ultrasound images.

PURPOSE: In this study we aimed to leverage deep learning to develop a computer aided diagnosis (CAD) system toward helping radiologists in the diagnosis of SARS-CoV-2 virus syndrome on Lung ultrasonography (LUS). METHOD: A CAD system is developed based on a transfer learning of a residual network (ResNet) to extract features on LUS and help radiologists to distinguish SARS-CoV-2 virus syndrome from healthy and non-SARS-CoV-2 pneumonia. A publicly available LUS dataset for SARS-CoV-2 virus syndrome consisting of 3909 images has been employed. Six radiologists with different experiences participated in the experiment. A comprehensive LUS data set was constructed and employed to train and verify the proposed method. Several metrics such as accuracy, recall, precision, and F1-score, are used to evaluate the performance of the proposed CAD approach. The performances of the radiologists with and without the help of CAD are also evaluated quantitively. The p-values of the t-test shows that with the help of the CAD system, both junior and senior radiologists significantly improve their diagnosis performance on both balanced and unbalanced datasets. RESULTS: Experimental results indicate the proposed CAD approach and the machine features from it can significantly improve the radiologists' performance in the SARS-CoV-2 virus syndrome diagnosis. With the help of the proposed CAD system, the junior and senior radiologists achieved F1-score values of 91.33% and 95.79% on balanced dataset and 94.20% and 96.43% on unbalanced dataset. The proposed approach is verified on an independent test dataset and reports promising performance. CONCLUSIONS: The proposed CAD system reports promising performance in facilitating radiologists' diagnosis SARS-CoV-2 virus syndrome and might assist the development of a fast, accessible screening method for pulmonary diseases.

An introduction to process tracing as an innovative qualitative research method to explore affective variables in SLA.

When the Complex Dynamic Systems Theory (CDST) enlightened the line of inquiry in education, innovative research methodologies, both quantitative and qualitative, were also introduced. Process tracing, which is among the CDST-compatible qualitative research methods, has just begun to benefit SLA research in the past few years. The present study provides a review of the conceptualization, significance, and procedural features for the implementation of the process tracing analytical method. In doing so, this review suggests a number of practices through which process tracing has been introduced in SLA. Additionally, some practical implications are provided for SLA researchers to enhance their knowledge of this new approach. Finally, future research suggestions for a more advanced use of this method are made in SLA.

Real-Time Elastography: A Web-Based Nomogram Improves the Preoperative Prediction of Central Lymph Node Metastasis in cN0 PTC.

BACKGROUND: Given the difficulty of accurately determining the central lymph node metastasis (CLNM) status of patients with clinically node-negative (cN0) papillary thyroid carcinoma (PTC) before surgery, this study aims to combine real-time elastography (RTE) and conventional ultrasound (US) features with clinical features. The information is combined to construct and verify the nomogram to foresee the risk of CLNM in patients with cN0 PTC and to develop a network-based nomogram. METHODS: From January 2018 to February 2020, 1,157 consecutive cases of cN0 PTC after thyroidectomy and central compartment neck dissection were retrospectively analyzed. The patients were indiscriminately allocated (2:1) to a training cohort (771 patients) and validation cohort (386 patients). Multivariate logistic regression analysis of US characteristics and clinical information in the training cohort was performed to screen for CLNM risk predictors. RTE data were included to construct prediction model 1 but were excluded when constructing model 2. DeLong's test was used to select a forecast model with better receiver operator characteristic curve performance to establish a web-based nomogram. The clinical applicability, discrimination, and calibration of the preferable prediction model were assessed. RESULTS: Multivariate regression analysis showed that age, sex, tumor size, bilateral tumors, the number of tumor contacting surfaces, chronic lymphocytic thyroiditis, and RTE were risk predictors of CLNM in cN0 PTC patients, which constituted prediction model 1. Model 2 included the first six risk predictors. Comparison of the areas under the curves of the two models showed that model 1 had better prediction performance (training set 0.798 vs. 0.733, validation set 0.792 vs. 0.715, p < 0.001) and good discrimination and calibration. RTE contributed significantly to the performance of the prediction model. Decision curve analysis showed that patients could obtain good net benefits with the application of model 1. CONCLUSION: A noninvasive web-based nomogram combining US characteristics and clinical risk factors was developed in the research. RTE could improve the prediction accuracy of the model. The dynamic nomogram has good performance in predicting the probability of CLNM in cN0 PTC patients.