Mice infected with Mycobacterium tuberculosis are resistant to secondary infection with SARS-CoV-2 (preprint)

Mycobacterium tuberculosis (Mtb) and SARS-CoV-2 (CoV2) are the leading causes of death due to infectious disease. Although Mtb and CoV2 both cause serious and sometimes fatal respiratory infections, the effect of Mtb infection and its associated immune response on secondary infection with CoV2 is unknown. To address this question we applied two mouse models of COVID19, using mice which were chronically infected with Mtb. In both model systems, Mtb-infected mice were resistant to secondary CoV2 infection and its pathological consequences, and CoV2 infection did not affect Mtb burdens. Single cell RNA sequencing of coinfected and monoinfected lungs demonstrated the resistance of Mtb-infected mice is associated with expansion of T and B cell subsets upon viral challenge. Collectively, these data demonstrate that Mtb infection conditions the lung environment in a manner that is not conducive to CoV2 survival.

The Effect of the Physical Fitness and Breathing Training Program in COVID-19 patients in Wuhan, China: prospective study (preprint)

Background Corona Virus Disease 2019 (COVID-19) has adverse effects on patients’ respiratory system. Therefore the pulmonary rehabilitation is particularly necessary for COVID-19 patients. A recent qualitative study indicated that patients perceived the impact of fatigue on their daily lives to be a key factor in decreasing their quality of life. This study aimed to investigate the knowledge and needs of physical fitness and breathing training, and to explore the impact of physical fitness and breathing training on COVID-19 patients. Methods From Feb 16, 2020 to Apr 6, 2020, a self-designed questionnaire was used to investigate the knowledge and needs of physical fitness and breathing training in COVID-19 inpatients. And then the participants received an intervention about physical fitness and breathing training which lasted 2 weeks. The 9-item Functional Assessment of Chronic Illness Therapy-Fatigue scale (FACIT-F) was used to measure COVID-19 related fatigue before and after the intervention. Findings According to a 5-point Likert scale, the 133 COVID-19 patients had only an "not really" and "uncertain" knowledge of physical fitness and breathing training (2.47±1.17). 86.98% of the patients expected to receive guidance of physical fitness and breathing training through video teaching. Differences were observed in fatigue, General fatigue component (15.0(8.0) vs. 19.0(10.0), P<0.01); Functional ability component (4.0 (3.0) vs. 7.0(4.0), P<0.01); Psychological component (6.0(5.0) vs.7.0 (5.0), P<0.01), after the intervention, Moderate degree (36.09% vs. 28.57%) alleviated to mild degree (51.12% vs. 66.91%). SpO2-% 86 ((75, 89) vs. 92(89, 98), P<0.001), and Oxygen flow-L/min (2(0,4) vs. 8(3,9), P<0.001). In our study, 130 healthcare professionals took part in this program. None of the participants reported covid-19 related symptoms. When the participants returned home, they all tested negative for SARSCoV- 2 specific nucleic acids and IgM or IgG antibodies (95% confidence interval 0.0 to 0.7%).Interpretation COVID-19 patients had insufficient knowledge of physical fitness and breathing training for pulmonary rehabilitation. Patients should be guided to receive training, which can be benefit for patients.

A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients (preprint)

Objective: To analyze and compare the imaging workflow, radiation dose and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. Materials and Methods: 127 adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time and off-center distance, of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups.Results: The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP:1.56cm±0.83 vs. MP: 4.05cm±2.40, p<0.001) and higher proportion of positioning accuracy (AP: 99% vs. MP: 92%), resulted in 16% radiation dose reduction (AP: 6.1mSv±1.3 vs. MP: 7.3mSv±1.2, p<0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas.Conclusion: The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose, optimizing imaging workflow and image quality in imaging the chest. This technique has important added clinical value in imaging COVID-19 patients to reduce the cross-infection risks.