Heart-targeted amelioration of sepsis-induced myocardial dysfunction by microenvironment responsive nitric oxide nanogenerators in situ.

BACKGROUND: A balanced endogenous level of bioavailable nitric oxide (NO) plays a key role in maintaining cardiovascular homeostasis. The bioactive NO level in the cardiomyocytes was much reduced during sepsis. However, it is clinically challenging for the NO gas therapy due to the lack of spatial and temporal release system with precise control. The purpose of this study is to design a NO-releasing biomaterial with heart-targeted capability responsive to the infectious microenvironment, thus ameliorating lipopolysaccharide (LPS)-induced cardiac dysfunction. RESULTS: The heart-targeted NO delivery and in situ releasing system, PCM-MSN@LA, was synthesized using hollow mesoporous silica nanoparticles (MSN) as the carrier, and L-arginine (LA) as the NO donor. The myocardial delivery was successfully directed to heart by specific peptide (PCM) combined with low-intensity focused ultrasound (LIFU) guidance. The myocardial system synthesized NO from the LA released from PCM-MSN@LA in the presence of increased endogenous nitric oxide synthase (NOS) activity induced by LPS. This targeted NO release in situ achieved extraordinary protective effects against LPS-challenged myocardial injury by reducing the recruitment of inflammatory cells, inhibiting oxidative stress and maintaining the mitochondria integrity. In particular, this protection was not compromised by simultaneous circulation collapse as an adverse event in the context. CONCLUSIONS: PCM-MSN@LA + LIFU exhibited extraordinary cardioprotective effects against severe sepsis in the hearts of LPS-treated animals without the side effect of NO diffusion. This technology has great potential to be served as a novel therapeutic strategy for sepsis-induced myocardial injury.

A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19.

Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.

Correlation between early features and prognosis of symptomatic COVID-19 discharged patients in Hunan, China.

To determine the correlation between the clinical, laboratory, and radiological findings and the hospitalization days in Coronavirus Infectious Disease-19 (COVID-19) discharged patients. We retrospectively identified 172 discharged patients with COVID-19 pneumonia from January 10, 2020, to February 28, 2020, in Hunan province. The patients were categorized into group 1 (≤ 19 days) and group 2 (> 19 days) based on the time from symptom onset to discharge. Cough during admission occurred more commonly in group 2 (68.4%) than in group 1 (53.1%, p = 0.042). White blood cell (p = 0.045), neutrophil counts (p = 0.023), Alanine aminotransferase (p = 0.029), Aspartate aminotransferase (p = 0.027) and Lactate dehydrogenase (p = 0.021) that were above normal were more common in group 2. Patients with single lesions were observed more in group 1(17.7%, p = 0.018) and multiple lesions observed more in group 2(86.8%, p = 0.012). The number of lobes involved (p = 0.008) in the CT score (p = 0.001) for each patient was all differences between the two groups with a statistically significant difference. Mixed ground-glass opacity (GGO) and consolidation appearances were observed in most patients. GGO components > consolidation appearance was more common in group 1 (25.0%) than in group 2 (8.0%) with a significant difference (0.015), GGO < consolidation was more common in group 2(71.1%, p = 0.012). From the logistic regression analysis, the CT score (OR, 1.223; 95% CI, 1.004 to 1.491, p = 0.046) and the appearance of GGO > consolidation (OR, 0.150; 95% CI, 0.034 to 0.660, p = 0.012) were independently associated with the hospitalization days. Thus, special attention should be paid to the role of radiological features in monitoring the disease prognosis.

Clinical and radiological changes of hospitalised patients with COVID-19 pneumonia from disease onset to acute exacerbation: a multicentre paired cohort study.

OBJECTIVES: To analyse clinical and radiological changes from disease onset to exacerbation in coronavirus infectious disease-19 (COVID-19) patients. METHODS: We reviewed clinical histories of 276 patients with confirmed COVID-19 pneumonia and extracted data on patients who met the diagnostic criteria for COVID-19 severe/fatal pneumonia and had an acute exacerbation starting with mild or common pneumonia. RESULTS: Twenty-four patients were included. Of these, 8% were smokers, 54% had been to Wuhan, and 46% had comorbidities. Before acute exacerbation, elevated lactate dehydrogenase (232.9 ± 88.7) was present, and chest CT scans showed the number of involved lobes was 4 (2-5) and total CT score was 6 (2-8). Following acute exacerbation, patients were likely to have more clinical symptoms (p < 0.01) and abnormal laboratory changes (p < 0.01). The number of involved lobes and CT score after an exacerbation significantly increased to 5 (5-5) and 12 (9-14), respectively. Receiver operating characteristic (ROC) curve showed that, when the cutoff value of CT score was 5, the sensitivity and specificity for severe pneumonia were 90% and 70%, respectively. CT findings of ground glass opacity with consolidations (91.7%), bilateral distribution (100.0%), and multifocal lesion (100.0%) were features in found in patients after exacerbation. CONCLUSIONS: There are significant changes in clinical, laboratory, and CT findings in patients from disease onset to exacerbation. An increase in the number of involved lobes or an increased CT score from the baseline may predict poor clinical outcomes. Combining an assessment of CT changes with clinical and laboratory changes could help clinical teams evaluate the prognosis. KEY POINTS: • The common chest CT signs of COVID-19 pneumonia after exacerbation were ground glass opacity (GGO) with consolidation, bilateral distribution, and multifocal lesions. • An increase in number of involved lobes or an increased CT score from the baseline may predict a poor clinical outcome. • Worsened symptoms and abnormal laboratory results are also associated with poor prognosis.