Genotype 4 Hepatitis E virus replicates in the placenta, causes severe histopathological damage, and vertically transmits to fetuses.

BACKGROUND: Hepatitis E virus (HEV) infection in pregnant women causes adverse pregnancy outcomes, including maternal death, premature delivery, stillbirth, and fetal infection. However, the pathogenesis of maternal and fetal HEV infection is unclear. METHODS: Placenta and placental appendixes were collected from HEV-4 infected pregnant women to explore the vertical transmission of HEV from mothers to fetuses. RESULTS: HEV-4 replicated in the placenta, placental membrane, and umbilical cord and was vertically transmitted from mothers to fetuses. HEV-4 placental infection resulted in serious histopathological damage, such as fibrosis and calcification, and severe inflammatory responses. Adverse maternal outcomes were observed in 38.5% of HEV-4 infected pregnant women. The distinct cytokine/chemokine expression patterns of HEV-infected pregnant women and nonpregnant women may contribute to the adverse pregnancy outcomes. Furthermore, the impaired maternal and fetal innate immune responses against HEV-4 facilitated viral replication during pregnancy. CONCLUSION: HEV-4 replicates in the placenta and is vertically transmitted from mothers to fetuses, causing severe histopathological damage.

The association of coagulation indicators with in-hospital acute kidney injury and malignant events of patients with acute aortic dissection: a retrospective cohort study.

Background: Acute kidney injury (AKI) is a prevalent complication of acute aortic dissection (AAD) and is associated with poor outcomes. The onset of AAD may result in endothelial injury due to the formation of the false lumen, which can activate the coagulation pathway and lead to coagulation dysfunction. It serves as a valuable diagnostic and prognostic marker for AAD, but also plays a role in the pathological mechanisms underlying AKI. We aimed to investigate the potential value of coagulation indicators at admission for assessing in-hospital AKI and malignant events after AAD. Methods: We identified patients with AAD admitted to the First Affiliated Hospital of Shantou University Medical College from January 2015 to October 2020 and divided them into two groups according to coagulation function. Univariable and multivariable analyses were used to analyze the association between coagulation indicators and AKI and malignant events in patients with AAD. Chi-squared or Fisher exact test and receiver operating characteristic (ROC) curve analysis was conducted to assess the value of coagulation indicators in predicting in-hospital AKI and malignant events. Results: A total of 487 patients were enrolled in this study, including 309 cases with normal coagulation. After the multivariable adjustment, the incidence of in-hospital AKI in the abnormal coagulation group was significantly higher [model 1: 2.061 (1.214-3.501), P=0.007; model 2: 1.833 (1.058-3.177), P=0.031; model 3: 1.836 (1.048-3.216), P=0.034]. The incidence of malignant events was higher in the abnormal prothrombin time (PT) group [model 1: 4.283 (0.983-18.665), P=0.053; model 2: 7.342 (1.467-36.749), P=0.015; model 3: 6.996 (1.377-35.537), P=0.019]. Chi-squared and Fisher exact test showed that PT and abnormal coagulation score (ACS) were statistically different among the AKI groups and malignant event groups. Under ROC analysis, coagulation indicators were helpful to predict AKI (AUC =0.668; P<0.001). Conclusions: Our study confirmed the presence of coagulation dysfunction is associated with an increased risk of AKI and malignant events. It suggested the severity of coagulation dysfunction is positively correlated with the incidence of in-hospital AKI in AAD patients. These results highlight the importance of considering coagulation dysfunction as a potential mechanism underlying AKI and malignant events after AAD.

Myocardial strain analysis of echocardiography based on deep learning.

Background: Strain analysis provides more thorough spatiotemporal signatures for myocardial contraction, which is helpful for early detection of cardiac insufficiency. The use of deep learning (DL) to automatically measure myocardial strain from echocardiogram videos has garnered recent attention. However, the development of key techniques including segmentation and motion estimation remains a challenge. In this work, we developed a novel DL-based framework for myocardial segmentation and motion estimation to generate strain measures from echocardiogram videos. Methods: Three-dimensional (3D) Convolutional Neural Network (CNN) was developed for myocardial segmentation and optical flow network for motion estimation. The segmentation network was used to define the region of interest (ROI), and the optical flow network was used to estimate the pixel motion in the ROI. We performed a model architecture search to identify the optimal base architecture for motion estimation. The final workflow design and associated hyperparameters are the result of a careful implementation. In addition, we compared the DL model with a traditional speck tracking algorithm on an independent, external clinical data. Each video was double-blind measured by an ultrasound expert and a DL expert using speck tracking echocardiography (STE) and DL method, respectively. Results: The DL method successfully performed automatic segmentation, motion estimation, and global longitudinal strain (GLS) measurements in all examinations. The 3D segmentation has better spatio-temporal smoothness, average dice correlation reaches 0.82, and the effect of target frame is better than that of previous 2D networks. The best motion estimation network achieved an average end-point error of 0.05 ± 0.03 mm per frame, better than previously reported state-of-the-art. The DL method showed no significant difference relative to the traditional method in GLS measurement, Spearman correlation of 0.90 (p < 0.001) and mean bias -1.2 ± 1.5%. Conclusion: In conclusion, our method exhibits better segmentation and motion estimation performance and demonstrates the feasibility of DL method for automatic strain analysis. The DL approach helps reduce time consumption and human effort, which holds great promise for translational research and precision medicine efforts.

Using a non-invasive multi-sensor device to evaluate left atrial pressure: an estimated filling pressure derived from ballistocardiography.

BACKGROUND: Heart failure is a global health problem, and elevated left atrial pressure (LAP) is a precursor to identifying decompensated heart failure. At present, out-of-hospital monitoring of patients with heart failure is mostly based on the patient's symptoms and signs, and the use of non-invasive technology is scarce. In this study, a non-invasive ballistocardiography (BCG) device was used to collect thoracic vibration signals generated by heartbeat. We collected these signals from more than 1,000 adults, including those with different heart diseases, and used a sensor system and a composite index related to LAP recognition named the LAP-index, to analyze them. This study aimed to verify the reliability and accuracy of the LAP-index in identifying elevated LAP within heart failure patients. METHODS: We prospectively included 158 patients with various extent of diastolic function, some of whom had various underlying diseases, and collected BCG and echocardiographic data using a cross-section methodology. The BCG signal was recorded from multiple optical fiber vibration sensors placed on the back of each patient. We adopted the 2016 ASE/EACVI echocardiography guideline as the standard for determining LAP level from echocardiography parameters. To evaluate the diagnostic efficacy of the LAP-index, we drew a receiver operating characteristic (ROC) curve and calculated the area under the ROC curve (AUC). RESULTS: The LAP-index of the 158 patients ranged from 6 to 32. Of them, 39 were diagnosed as high LAP by echocardiography, and 119 cases had normal or slightly elevated LAP. Comparison of the LAP-index results and echocardiographic results revealed the ROC c-statistic of the LAP-index for identifying high LAP was 0.86 (95% CI: 0.79-0.93; P<0.0001). When the LAP-index was at the best cut-off value of 15.5, the positive agreement rate between it and echocardiography LAP was 0.85, the negative agreement rate was 0.80, and the overall agreement rate was 0.81. CONCLUSIONS: The sensor system and the LAP-index, a composite index derived from BCG, have high reliability and accuracy in identifying elevated LAP, which provides a novel possibility for the non-invasive detection of hemodynamic congestion in heart failure patients.

Ballistocardiography to identify high left atrial pressure in patients with heart failure.

BACKGROUND: Due to a high prevalence and morbidity rate, heart failure (HF) constitutes an immense economic burden on the global health care system. An increase in left atrial pressure (LAP) precedes the occurrence of any HF symptoms. In this study, we applied a novel non-invasive method of ballistocardiography (BCG) to extract early diastolic ventricular vibration waves [the BCG-B3 index, which corresponds to the third heart sound (S3) at the end of the rapid filling phase of diastole]. This study evaluated the predictive value of the BCG-B3 index for LAP in HF patients. METHODS: A total of 83 HF patients and 20 patients with underlying diseases were prospectively enrolled, and their cross-sectional BCG and echocardiography (ECHO) data were collected. BCG obtains a signal through a high-precision fiber-optic sensor placed on the patient's back. LAP or pulmonary capillary wedge pressure (PCWP) was estimated by the ratio of mitral inflow peak early diastolic velocity to the early diastolic velocity of the mitral annulus (E/e') or the Nagueh equation (LAP = 1.24 × E/e' + 1.9). To evaluate the diagnostic efficacy of the BCG-B3 index, a receiver operating characteristic (ROC) curve was plotted, and the area under the ROC curve (AUC) was calculated. The best cutoff value for the BCG-B3 index was determined by the maximum Youden index. RESULTS: The correlation coefficient between the BCG-B3 index and E/e' ratio was 0.51 (P<0.01). Under an optimal cutoff value of 55.13, the BCG-B3 index showed a positive consistency value of 0.93, a negative consistency value of 0.53, and an overall consistency value of 0.82 for identification of significantly elevated LAP. CONCLUSIONS: The BCG-B3 index derived by noninvasive BCG using a built-in fiber-optic sensor has important diagnostic value for identifying significantly elevated LAP in HF patients with high accuracy. BCG examination is not limited by place or the doctor's experience. Therefore, BCG can provide timely assessments for HF patients, enabling early diagnosis and treatment.