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BACKGROUND: Myocardial injury has been found using magnetic resonance imaging in recovered coronavirus disease 2019 (COVID-19) patients unselected or with ongoing cardiac symptoms. PURPOSE: To evaluate for the presence of myocardial involvement in recovered COVID-19 patients without cardiovascular symptoms and abnormal serologic markers during hospitalization. STUDY TYPE: Prospective. POPULATION: Twenty-one recovered COVID-19 patients and 20 healthy controls (HC). FIELD STRENGTH/SEQUENCE: 3.0 T, cine, T2-weighted imaging, T1 mapping, and T2 mapping. ASSESSMENT: Cardiac ventricular function includes end-diastolic volume, end-systolic volume, stroke volume, cardiac output, left ventricle (LV) mass, and ejection fraction (EF) of LV and right ventricle (RV), and segmental myocardial T1 and T2 values were measured. STATISTICAL TESTS: Student's t-test, univariate general linear model test, and chi-square test were used for analyses between two groups. Ordinary one-way analyses of variance or Kruskal-Wallis H test were used for analyses between three groups, followed by post-hoc analyses. RESULTS: Fifteen (71.43%) COVID-19 patients had abnormal magnetic resonance findings, including raised myocardial native T1 (5, 23.81%) and T2 values (10, 47.62%), decreased LVEF (1, 4.76%), and RVEF (2, 9.52%). The segmental myocardial T2 value of COVID-19 patients (49.20 [46.1, 54.6] msec) was significantly higher than HC (48.3 [45.2, 51.7] msec) (P < 0.001), while the myocardial native T1 value showed no significant difference between COVID-19 patients and HC. The myocardial T2 value of serious COVID-19 patients (52.5 [48.1, 57.1] msec) was significantly higher than unserious COVID-19 patients (48.8 [45.9, 53.8] msec) and HC (48.3 [45.2, 51.7]) (P < 0.001). COVID-19 patients with abnormally elevated D-dimer, C-reactive protein, or lymphopenia showed higher myocardial T2 values than without (all P < 0.05). DATA CONCLUSION: Cardiac involvement was observed in recovered COVID-19 patients with no preexisting cardiovascular disease, no cardiovascular symptoms, and elevated serologic markers of myocardial injury during the whole course of COVID-19. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.

Super-resolution (SR) in medical imaging is an emerging application in medical imaging due to the needs of high quality images acquired with limited radiation dose, such as low dose Computer Tomography (CT), low field magnetic resonance imaging (MRI). However, because of its complexity and higher visual requirements of medical images, SR is still a challenging task in medical imaging. In this study, we developed a deep learning based method called Medical Images SR using Generative Adversarial Networks (MedSRGAN) for SR in medical imaging. A novel convolutional neural network, Residual Whole Map Attention Network (RWMAN) was developed as the generator network for our MedSRGAN in extracting the useful information through different channels, as well as paying more attention on meaningful regions. In addition, a weighted sum of content loss, adversarial loss, and adversarial feature loss were fused to form a multi-task loss function during the MedSRGAN training. 242 thoracic CT scans and 110 brain MRI scans were collected for training and evaluation of MedSRGAN. The results showed that MedSRGAN not only preserves more texture details but also generates more realistic patterns on reconstructed SR images. A mean opinion score (MOS) test on CT slices scored by five experienced radiologists demonstrates the efficiency of our methods.

This paper aims to analyze the existing problems during the teaching process of the course Data Communication Technology. Specifically, this paper will take the ICT integration between industry and education in ZTE as an example to explore the joint construction of “data communication technology” course between school and enterprise. Based on that, the idea of course construction will be put forward, which is meant to carry out project-based teaching practice by the MIMPS and reconstruct the teaching content. This not only optimizes the teaching method as well as the online and offline mixed teaching based on the BOPPS model, but also cultivates the communication technology talents with strong engineering practice ability and innovation capability.

Most of the hybrid CSI beamforming schemes consider the channel environment as the ideal uncorrelated channel, and do not take into account the long-term spatial correlation information of users. Most of them adopt the assumption that the users in the cell are evenly distributed around the base station, which is inconsistent with the actual wireless communication environment. According to the characteristics of non-uniform distribution of users around the base station, this paper studies two non-uniform distribution random beamforming algorithms and their implementation.

In order to better save energy consumption, it is imperative to implement household heating measurement and charge according to heat consumption in central heating buildings. This paper presents a household heat metering system based on CC1110 wireless single chip microcomputer. The wireless temperature collector selects the temperature sensor PT100 to collect the temperature information, and the wireless data transceiver receives the data of the wireless temperature collector, and distributes the heat through the thermal energy distributor to realize the household heat metering. The system is easy to install and has a good performance in the field of household heat metering of old buildings. It has practical application value.

Objectives To retrospectively analyze the most common imaging features on CT at baseline and as they evolve with time as the disease progresses or resolves in a cohort of patients affected with 2019 coronal virus disease (COVID-19) pneumonia in Zhuhai, China.Methods We evaluated 38 patients with COVID-19 in the authors’ institution from Jan 1 to Jan 31, 2020. Cases were confirmed by real-time RT-PCR and were analyzed for epidemiological, demographic, clinical, and radiological features. Outcomes were followed up until Feb 18, 2020. Results 38 initial scans and 62 follow-up scans were obtained. 28 (74%) patients had the history of travel to or residence in Hubei Province of China in 14 days prior to the illness onset. Common findings included ground-glass opacification (GGO), sometimes mixed with consolidation, and interlobular septal and intralobular interstitial thickening. Follow-up imaging often demonstrated peripheral GGO and consolidations spreading to the remainder of the lungs and the increasing consolidative component reflecting the progression of the disease. 8 patients (21%) whose swabs or serum were positive for COVID-19 had no imaging findings on CT throughout the disease course. After treatment the serum and sputum tests became negative for COVID-19 in 32(84%) cases. 28(74%) patients were discharged and three (8%) of them were transferred to the Observation Ward, while seven (18%) patients were kept in Isolation Ward. Conclusion The commonest pattern observed was GGO alone or GGO mixed with consolidation predominantly in lower and peripheral lungs. The follow-up CT scan is crucial for the diagnosis and evaluation of the disease process.

In our study, the carbon nanodots (CDs) were synthesized by one-step solvothermal method using resorcinol as the only presusor. The obtained CDs contained abundant unsaturated oxygen-containing groups resulting from the surface oxidation. A novel, simple, and real-time fluorescent assay for the detection of water in various organic solvents was thus established by reducing the surface oxidation states. Excellent reversibility can be readily achieved by the external stimulus water and N,N'-dicyclohexylcarbodiimide (DCC). The water-induced sensitive (limit of detection = 0.006%, v/v, in ethanol) and ultrafast (1 s) response in emission properties was capable of water determination in spirit samples in both solution and solid-state paper test strips.