Hemodynamics, anatomy, and outcomes of quadricuspid aortic valves: Multimodality imaging assessment.

BACKGROUND: Quadricuspid aortic valve (QAV) is a rare congenital heart disease with a limited body of literature. This retrospective cohort study investigates QAV morphology, function, and clinical outcomes. METHODS: Echocardiography was used to assess valvular function. Morphological characteristics such as phenotypes, raphe, regurgitant orifice area (ROA), and aortic dilation (diameter >40 â€‹mm) were assessed by cardiac CT. Patients were followed up for the combined event of all-cause death and aortic valve replacement (AVR). RESULTS: Ninety QAV patients (screened from 322385 CT scans) were included (mean age 55.2 â€‹± â€‹13.6 years, 61.1 â€‹% male). Isolated significant aortic regurgitation (AR) was present in 75.6 â€‹% of patients. The cohort was dominated by type I (four equal leaflets, 37.8 â€‹%) and type II (3 larger and 1 smaller leaflets, 42.2 â€‹%) QAV. Fused raphe was present in 26.7 â€‹% of patients. ROACT was correlated with AR severity and aortic dilation (41.1 â€‹%, n â€‹= â€‹37). Among patients without AVR at baseline (n â€‹= â€‹60), one died and 17 underwent AVR during a median follow-up of 35.0 months (IQR:17.3-62.8). ROACT was associated with an increasing risk of combined event (as a categorical variable with a cut-off of 21.4 â€‹mm2, HR â€‹= â€‹4.25, 95%CI 1.49-12.17, p â€‹= â€‹0.007; as a continuous variable (per mm2 increment), HR â€‹= â€‹1.04, 95%CI 1.01-1.07, p â€‹= â€‹0.003). Additionally, ROACT had incremental prognostic value when added to the AR severity model (area under the receiver-operating characteristic curve increased from 86.8 to 88.4, p â€‹= â€‹0.004). CONCLUSION: QAV is characterized by variable anatomy, progressive AR, concomitant cusp fusion and aortic enlargement. ROACT may be a potential ancillary prognostic marker in patients with QAV.

Butachlor (BTR) degradation by dielectric barrier discharge plasma in soil: Affecting factors, degradation route, and toxicity assessment.

Over the past few decades, the frequent and excessive usage of pesticides has had detrimental effects on the soil and other habitats. In terms of removing organic contaminants from soil, non-thermal plasma has become one of the most competitive advanced oxidation methods. The study used dielectric barrier discharge (DBD) plasma to repair soil contaminated by butachlor (BTR). BTR degradation was investigated in actual soil under various experimental parameters. According to the results, DBD plasma treatment at 34.8 W destroyed 96.10% of BTR within 50 min, and this degradation was consistent with the model of first order kinetics. Boosting the discharge power, lowering the initial BTR concentration, using appropriate soil moisture content and air flow rate, and using oxygen as the working gas for discharge are all beneficial to the degradation of BTR. The changes in soil dissolved organic matter (DOM) before and after plasma treatment were assessed using a total organic carbon (TOC) analyzer. A Fourier transform infrared (FTIR) spectroscopy and an Ultra Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC-MS) were employed to investigate the degradation of BTR. A wheat growth test showed that the best growth was achieved at 20 min of plasma soil remediation, but too long treatment would lower soil pH and thus affect wheat growth.

Development and quantitative assessment of deep learning-based image enhancement for optical coherence tomography.

PURPOSE: To develop a deep learning-based framework to improve the image quality of optical coherence tomography (OCT) and evaluate its image enhancement effect with the traditional image averaging method from a clinical perspective. METHODS: 359 normal eyes and 456 eyes with various retinal conditions were included. A deep learning framework with high-resolution representation was developed to achieve image quality enhancement for OCT images. The quantitative comparisons, including expert subjective scores from ophthalmologists and three objective metrics of image quality (structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and contrast-to-noise ratio (CNR)), were performed between deep learning method and traditional image averaging. RESULTS: With the increase of frame count from 1 to 20, our deep learning method always obtained higher SSIM and PSNR values than the image averaging method while importing the same number of frames. When we selected 5 frames as inputs, the local objective assessment with CNR illustrated that the deep learning method had more obvious tissue contrast enhancement than averaging method. The subjective scores of image quality were all highest in our deep learning method, both for normal retinal structure and various retinal lesions. All the objective and subjective indicators had significant statistical differences (P < 0.05). CONCLUSION: Compared to traditional image averaging methods, our proposed deep learning enhancement framework can achieve a reasonable trade-off between image quality and scanning times, reducing the number of repeated scans.

Establishment and assessment of a nomogram for predicting blood transfusion risk in posterior lumbar spinal fusion.

BACKGROUND: The aim of this study was to determine the risk factors and develop a nomogram for blood transfusions after posterior lumbar spinal fusion (PSL). METHODS: We conducted a retrospective, single-center study based on 885 patients receiving PSL, and data was obtained from May 2015 to September 2019. Univariable and multivariable logistics regression analysis were conducted to identify risk factors for blood transfusion, and a nomogram was constructed to individually evaluate the risk of blood transfusion. Discrimination, calibration, and clinical usefulness were validated by the receiver operating characteristics (ROC), C-index, calibration plot, and decision curve analysis, respectively. Bootstrapping validation was performed to assess the performance of the model. RESULTS: Of 885 patients, 885 were enrolled in the final study population, and 289 received blood transfusion. Statistical analyses showed that low preoperative hemoglobin (Hb), longer time to surgery, operative time, levels of fusion > 1, longer surgery duration, and higher total intraoperative blood loss (IBL) were the risk factors for transfusion. The C-index was 0.898 (95% CI 0.847-0.949) in this dataset and 0.895 in bootstrapping validation, respectively. Calibration curve showed satisfied discrimination and calibration of the nomogram. Decision curve analysis (DCA) shown that the nomogram was clinical utility. CONCLUSIONS: In summary, we investigated the relationship between the blood transfusion requirement and predictors: levels of fusion, operative time, time to surgery, total intraoperative EBL, and preoperative Hb level. Our nomogram with a robust performance in the assessment of risk of transfusion can contribute to clinicians in making clinical decision. However, external validation is still needed in the further.