Health Protection of CT Radiographers During the Outbreak of COVID-19: Application of Automatic Positioning Technology for Relocatable CT in the Fang Cang Hospital.

Background: To investigate the value of automatic positioning technology in improving the protection of radiographers in the relocatable CT room of a Fang Cang hospital during the outbreak of coronavirus disease 2019 (COVID-19). Methods: The National Emergency Medical Team of our hospital assumed command of Wuchang Fang Cang Hospital and treated confirmed COVID-19 patients with mild symptoms. Relocatable CT was used to examine patients in this hospital. Automatic positioning technology was applied to avoid close contact between medical staff and patients and to protect medical staff more effectively. Results: Seven hundred lung CT scans acquired from 269 patients were completed from February 17 to 26, 2020 with automatic positioning technology for relocatable CT in a Fang Cang hospital. All scans were conducted successfully using automatic positioning technology. All patients entered the scanning room from a separate door. All the position lines were accurate, and all images met the requirement for diagnosis of COVID-19, with satisfied quality. None of our medical staff had any close contact with patients. Conclusion: Automatic positioning technology applied to relocatable CT can minimize the close contact between technologists and patients and effectively improve the protection of medical staff without sacrificing image quality.

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.

Shikonin Ameliorates LPS-Induced Cardiac Dysfunction by SIRT1-Dependent Inhibition of NLRP3 Inflammasome.

Shikonin (SHI) is an anti-inflammatory agent extracted from natural herbs. It is still unknown whether SHI ameliorates lipopolysaccharide (LPS)-induced cardiac dysfunction. This study aims to explore the protective effects of SHI on LPS-induced myocardial injury and its mechanism. The LPS-induced cardiac dysfunction mouse model was employed to investigate the protective effects of SHI. In the present study, we found that SHI treatment improved the survival rate and cardiac function and remarkably ameliorated the release of inflammatory cytokines and macrophage infiltration in heart tissue of LPS-treated mice. SHI also reduced lactate dehydrogenase (LDH) and cardiac troponin (cTn) release, cell inflammation, and apoptosis in LPS plus adenosine triphosphate (ATP)-treated H9c2 cells. In addition, SHI significantly upregulated silent information regulator 1 (SIRT1) expression and suppressed the upregulation of NOD-like receptor protein 3 (NLRP3), cleaved caspase-1, and caspase-1 activity in heart tissues induced by LPS. Meanwhile, we got the same results in LPS plus ATP-treated H9c2 cells in vitro. Further, SIRT1 inhibitor or siRNA partially blocked SHI-mediated upregulation of SIRT1 expression and downregulation of NLRP3, cleaved caspase-1, and caspase-1 activity in heart tissues induced by LPS. Therefore, we conclude that SHI ameliorates LPS-induced cardiac dysfunction by inhibiting SIRT1-dependent activation of NLRP3 inflammasomes and might be a promising therapeutic strategy for the treatment of LPS-induced cardiac dysfunction.

Antibody seroconversion in asymptomatic and symptomatic patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

OBJECTIVES: Asymptomatic and symptomatic patients may transmit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but their clinical features and immune responses remain largely unclear. We aimed to characterise the clinical features and immune responses of asymptomatic and symptomatic patients infected with SARS-CoV-2. METHODS: We collected clinical, laboratory and epidemiological records of patients hospitalised in a coronavirus field hospital in Wuhan. We performed qualitative detection of anti-SARS-CoV-2 immunoglobulin M (IgM) and immunoglobulin G (IgG) using archived blood samples. RESULTS: Of 214 patients with SARS-CoV-2, 26 (12%) were asymptomatic at hospital admission and during hospitalisation. Most asymptomatic patients were ≤ 60 years (96%) and females (65%) and had few comorbidities (< 16%). Serum levels of white and red blood cells were higher in asymptomatic than in symptomatic patients (P-values < 0.05). During hospitalisation, IgG seroconversion was commonly observed in both asymptomatic and symptomatic patients (85% versus 94%, P-value = 0.07); in contrast, IgM seroconversion was less common in asymptomatic than in symptomatic patients (31% versus 74%, P-value < 0.001). The median time from the first virus-positive screening to IgG or IgM seroconversion was significantly shorter in asymptomatic than in symptomatic patients (median: 7 versus 14 days, P-value < 0.01). Furthermore, IgG/IgM seroconversion rates increased concomitantly with the clearance of SARS-CoV-2 in both asymptomatic and symptomatic patients. At the time of virus clearance, IgG/IgM titres and plasma neutralisation capacity were significantly lower in recovered asymptomatic than in recovered symptomatic patients (P-values < 0.01). CONCLUSION: Asymptomatic and symptomatic patients exhibited different kinetics of IgG/IgM responses to SARS-CoV-2. Asymptomatic patients may transmit SARS-CoV-2, highlighting the importance of early diagnosis and treatment.

Association between Cardiovascular Burden and Requirement of Intensive Care among Patients with Mild COVID-19.

BACKGROUND: Information regarding the impact of cardiovascular (CV) conditions on disease progression among patients with mild coronavirus disease 2019 (COVID-19) is limited. METHODS: This study evaluated the association of underlying CV conditions with disease progression in patients with mild COVID-19. The primary outcome was the need to be transferred to the designated hospital for intensive care due to COVID-19 disease progression. The patients were divided into with and without CV conditions as well as stable and intensive care groups. RESULTS: Of the 332 patients with mild COVID-19, the median age was 51 years (IQR, 40-59 years), and 200 (61.2%) were female. Of the 48 (14.5%) patients with CV conditions, 23 (47.9%) progressed to severe disease status and required intensive care. Compared with patients without CV conditions, patients with CV conditions were older and more likely to have fatigue, chest tightness, and myalgia. The rate of requiring intensive care was significantly higher among patients with CV conditions than in patients without CV conditions (47.92% vs. 12.4%; P < 0.001). In subgroup analysis, the rate of requiring intensive care was also higher among patients with either hypertension or coronary heart disease (CHD) than in patients without hypertension or CHD. The multivariable regression model showed that CV condition served as an independent risk factor for intensive care (odds ratio (OR), 2.652 (95% CI, 1.019-6.899)) after adjustment for various cofounders. CONCLUSIONS: Patients with mild COVID-19 complicating CV conditions are susceptible to develop severe disease status and requirement for intensive care.

[In vitro evaluation, cellular uptake and anti-acute myocardial ischemia effect of puerarin PEG-PLGA micelles].

PUE@PEG-PLGA micelles has excellent characteristics such as small particle size, high drug loading and slow drug release. The results of TEM electron microscopy showed that PUE@PEG-PLGA micelles had obvious core-shell structure. The critical micelle concentration(CMC) of PEG-PLGA micelles determined by pyrene assay was about 4.8 mg·L~(-1). Laser confocal experiments showed that PEG-PLGA micelles can enhance the cellular uptake of coumarin-6 and aggregate around the mitochondria; quantitative results of extracellular drug residues also indirectly confirmed that PEG-PLGA micelles can promote cellular uptake of the drug. Acute ischemic myocardial model rats were prepared by coronary artery ligation, and then the model rats were randomly divided into six groups: Sham operation group, model group, puerarin(PUE) group, as well as low-, mid-, and high-dose PUE@PEG-PLGA micelles groups. Drugs were given by iv administration 5 min after the ligation. The ST segment changes in the electrocardiogram were monitored; serum creatine kinase(CK), lactate dehydrogenase(LDH), aspartate aminotransferase(AST), and malondialdehyde(MDA) levels were detected and myocardial infarct size was also measured. Both PUE and PUE@PEG-PLGA micelles can reduce the elevated ST segment, reduce serum CK, LDH, AST and MDA levels, and reduce myocardial infarct size. The efficacy of PUE@PEG-PLGA medium and high dose groups was significantly better than that in the PUE group, and the efficacy in PUE@PEG-PLGA low dose group was basically equivalent to that in the PUE group. PUE@PEG-PLGA micelles can greatly improve the cardiomyocytes uptake of PUE, enhance the anti-acute myocardial ischemia effect of drugs, and reduce its dosage.

[Optimization of diagnosis indicator selection and inspection plan by 3.0T MRI in breast cancer].

OBJECTIVE: To optimize 3.0T MRI diagnosis indicator in breast cancer and to select the best MRI scan program. METHODS: Totally 45 patients with breast cancers were collected, and another 35 patients with benign breast tumor served as the control group. All patients underwent 3.0T MRI, including T1- weighted imaging (T1WI), fat suppression of the T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), 1H magnetic resonance spectroscopy (1H-MRS) and dynamic contrast enhanced (DCE) sequence. With operation pathology results as the gold standard in the diagnosis of breast diseases, the pathological results of benign and malignant served as dependent variables, and the diagnostic indicators of MRI were taken as independent variables. We put all the indicators of MRI examination under Logistic regression analysis, established the Logistic model, and optimized the diagnosis indicators of MRI examination to further improve MRI scan of breast cancer. RESULTS: By Logistic regression analysis, some indicators were selected in the equation, including the edge feature of the tumor, the time-signal intensity curve (TIC) type and the apparent diffusion coefficient (ADC) value when b=500 s/mm2. The regression equation was Logit (P)=-21.936+20.478X6+3.267X7+ 21.488X3. CONCLUSION: Valuable indicators in the diagnosis of breast cancer are the edge feature of the tumor, the TIC type and the ADC value when b=500 s/mm2. Combining conventional MRI scan, DWI and dynamic enhanced MRI is a better examination program, while MRS is the complementary program when diagnosis is difficult.