Establishment and Validation of a Predictive Model for Long-Term Severe Functional Tricuspid Regurgitation after Mitral Valve Replacement.

BACKGROUND: The objective was to develop and validate an individualized nomogram to predict severe functional tricuspid regurgitation (S-FTR) after mitral valve replacement (MVR) via retrospective analysis of rheumatic heart disease (RHD) patients' pre-clinical characteristics. METHODS: Between 2001-2015, 442 MVR patients of RHD were examined. Transthoracic echocardiography detected S-FTR, and logistic regression model analyzed its independent predictors. R software established a nomogram prediction model, and Bootstrap determined its theoretical probability, which subsequently was compared with the actual patient probability to calculate the area under the curve (AUC) and calibration plots. Decision curve analysis (DCA) identified its clinical utility. RESULTS: Ninety-six patients developed S-FTR during the follow-up period. Both uni- and multivariate analyses found significant correlations between S-FTR occurrence with gender, age, atrial fibrillation (AF), pulmonary arterial hypertension (PH), left atrial diameter (LAD), and tricuspid regurgitation area (TRA). The individualized nomogram model had the AUC of 0.99 in internal verification. Calibration test indicated high agreement of predicted and actual S-FTR onset. DCA also showed that utilization of those six aforementioned factors was clinically useful. CONCLUSION: The nomogram for the patient characteristics of age, gender, AF, PH, LAD, and TRA found that they were highly predictive for future S-FTR onset within 5 years. This predictive ability therefore allows clinicians to optimize postoperative patient care and avoid unnecessary tricuspid valve surgeries.

Identification critical host factors for Japanese encephalitis virus replication via CRISPR screening of human sgRNA library.

Japanese encephalitis virus (JEV) is a pathogen with a substantial impact on both livestock and human health. However, the critical host factors in the virus life cycle remain poorly understood. Using a library comprising 123411 small guide RNAs (sgRNAs) targeting 19050 human genes, we conducted a genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-based screen to identify essential genes for JEV replication. By employing knockout or knockdown techniques on genes, we identified eleven human genes crucial for JEV replication, such as prolactin releasing hormone receptor (PRLHR), activating signal cointegrator 1 complex subunit 3 (ASCC3), acyl-CoA synthetase long chain family member 3 (ACSL3), and others. Notably, we found that PRLHR knockdown blocked the autophagic flux, thereby inhibiting JEV infection. Taken together, these findings provide effective data for studying important host factors of JEV replication and scientific data for selecting antiviral drug targets.

Left atrial strain predicts risk and prognosis in patients with acute coronary syndrome: A retrospective study with external validation.

Objectives: To explore the association between left atrial (LA) strain and the GRACE score in patients with acute coronary syndrome (ACS) and to investigate the utility of LA function in predicting short-term adverse cardiovascular events post ACS. Methods: This retrospective study included ACS patients who underwent coronary angiography (CAG) in two independent cohorts from October 2020 to July 2022. The patients were classified into low-intermediate risk group and high-risk group based on the GRACE score. All participants underwent a transthoracic echocardiogram, with LA strain analysis before CAG. Correlation analysis was used to determine the relationship between LA strain and the GRACE score. The predictive value of LA strain was examined utilizing the area under the curve (AUC). Participants were followed for 10.5 ± 2.9 months for the primary endpoint of major adverse cardiovascular events (MACE). Results: A total of 229 patients were included in this study, including 196 in the primary group and 33 in the validation group. Spearman's correlation analysis showed there was a moderate negative correlation between the GRACE and left atrial reservoir strain (LASr) in both the primary (r = -0.63, P < 0.001) and validation (r = -0.73, P < 0.001) cohorts. Receiver operator characteristic (ROC) curve analysis showed that the AUC of LASr for prediction of the high-risk group was 0.86. Taking LASr 19.6% as the cut-off value, the sensitivity and specificity were 0.71 and 0.92, respectively. The cut-off value of 19.6% remains good at identifying high-risk group in the validation group (AUC = 0.87, sensitivity: 77.8%, specificity: 95.8%). Furthermore, 49 patients reached the endpoint in the primary cohort during the follow-up. On multivariable regression analysis, LASr (P = 0.03) was the independent echocardiographic predictor for the primary endpoint, rather than left atrial volume index (LAVI). Conclusions: LASr can identify high-risk patients with ACS as defined by the GRACE score and may be superior to Max LAVI in predicting incidents of MACE in the short-term following ACS.