Chest computed tomography and pulmonary function analysis of coronavirus disease 2019 survivors in 1 month follow-up period after cure and discharge

OBJECTIVE: The objective is to explore the chest computed tomography (CT) and pulmonary function findings of coronavirus disease 2019 (COVID-19) survivors 1 month after discharge. METHODS: We retrospectively analyzed the chest CT and pulmonary function findings of 158 (COVID-19) survivors who were discharged and followed up at Wuhan Red Cross Hospital from April 9 to May 10, 2020. RESULTS: Follow-up chest CT revealed that pulmonary lesions in all patients were clearly absorbed and dissipated. One month after discharge, 125 (79.1%) patients had residual imaging abnormalities, mainly light ground-glass opacities (44.9%) and light patchy shadows (38.6%). Other features included fibrous cord lesions in 90 cases (57.0%), pulmonary nodules in 46 cases (29.1%), pleural thickening in 44 cases (27.8%), and mediastinal lymphadenopathy in 81 cases (51.3%). Among 158 COVID-19 survivors, 124 were tested for pulmonary function. Pulmonary function tests revealed pulmonary function impairment in 10 patients (8.1%), restrictive ventilation dysfunction in 16 (12.9%), and pulmonary diffusion dysfunction in 48 (38.7%). Residual lung lesions and pleural thickening were negatively correlated with DLCO% (P < 0.05). CONCLUSION: COVID-19 patients have characteristic findings on chest CT and pulmonary function testing. Identifying these findings is helpful to dynamically monitor the disease prognosis.

Development of humanized ACE2 mouse and rat models for COVID‐19 research

To date (17 November 2021), there has been more than 254 million confirmed cases of COVID‐19, and more than 5 million death globally (World Health Organization. https://covid19.who.int/). The virus causing COVID‐19 is called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2). SARS‐CoV‐2 infects host cells by the binding of its spike protein to the cellular surface protein angiotensin‐converting enzyme 2 (ACE2). The predicted 29 amino acid residues of ACE2 that interact with SARS‐CoV‐2 spike protein receptor binding domain (RBD) vary between human ACE2 and mouse or rat ACE2. Therefore, wildtype mice and rats show lower SARS‐CoV‐2 infection rate and mild symptoms compared to what is seen in humans. Small animal models that recapitulate human COVID‐19 disease are urgently needed for better understanding the transmission and therapeutic measurement. Currently, scientists use either mouse‐adapted SAS‐CoV‐2 (SAS‐CoV‐2 MA) models or random transgenic mouse models that artificially express human ACE2 under the control of cytokeratin 18 promoter or a constitutive promoter. SAS‐CoV‐2 MA may not completely reflect all aspects of the original human‐tropic SAS‐CoV‐2 and the current transgenic human ACE2 mouse models typically have high mortality rate caused by neuroinvasion and encephalitis due to very high human ACE2 expression. To overcome these limitations, we have developed humanized ACE2 mouse and rat models using CRISPR‐Cas9. Specifically, we inserted a ~3kb human ACE2 cDNA cassette into the mouse and rat Ace2 gene loci to ensure that human ACE2 expression is under the control of rodent Ace2 promoter and regulatory elements, while simultaneously disabling the rodent Ace2 gene. To accomplish this, CRISPR gRNAs targeting close to the translation initiation site of Ace2 were screened in cultured mouse and rat cells. Then CRISPR/Cas9 complex and donor DNA were subsequently microinjected into one‐cell stage embryos which were subsequently implanted into pseudo pregnant females. Resulting pups were screened for correct knockin by junction PCR and insert PCR, and the PCR products were Sanger sequenced. Targeted Locus Amplification (TLA) further confirmed the integration sites and transgene sequence. RT‐qPCR and Western blot analysis data showed that, in our models, human ACE2 is expressed in tissues expressing endogenous Ace2 (such as lung, kidney, and GI tract), while rodent endogenous Ace2 is absent from these tissues. Further breeding data indicated that both hemizygous and homozygous humanized ACE2 animals appear to be normal and fertile. Most importantly, animals displayed symptoms after infection with SARS‐CoV‐2. In summary, these data suggest that our humanized ACE2 models can be valuable for COVID‐19 research.

Ludangshen oral liquid for treatment of convalescent COVID-19 patients: a randomized, double-blind, placebo-controlled multicenter trial.

OBJECTIVE: To explore the effect of Ludangshen oral liquid for treatment of convalescent patients with coronavirus disease 2019 (COVID-19) with randomized, double-blind, placebo-controlled multicenter method. METHODS: 200 convalescent COVID-19 patients who had symptoms related to decreased digestive and respiratory function were randomly divided to either receive Ludangshen oral liquid or placebo for 2 weeks. The severity of clinical symptoms including fatigue, anorexia, abdominal distension, loose stools, and shortness of breath were assessed by visual analogue scale and observed at before and after treatment. The improvement and resolution rates of clinical symptoms were evaluated. Full analysis set (FAS) and per-protocol set (PPS) were used for statistical analyses. Adverse events were recorded during the study. RESULTS: 8 patients did not complete the study. After 2 weeks of treatment, both FAS and PPS results showed that patients in Ludangshen group had significantly lower score of fatigue, anorexia, loose stools, and shortness of breath than placebo group (P < 0.05), while there was no significant difference in distention (P > 0.05). The improvement rate of fatigue, anorexia, distension, loose stools and shortness of breath were significantly higher in Ludangshen group (P < 0.05), as well as the resolution rates (P < 0.05) except for shortness of breath (P > 0.05). There were two cases of adverse events, with one nose bleeding in Ludangshen group and one headache in placebo group. CONCLUSION: The study suggested that two weeks of Ludangshen oral liquid treatment may have certain effects for convalescent COVID-19 patients on improving digestive and respiratory symptoms including fatigue, anorexia, loose stools and shortness of breath, which may be one of the choices for management of convalescent COVID-19 patients with digestive and respiratory symptoms.

Extended graphical lasso for multiple interaction networks for high dimensional omics data.

There has been a spate of interest in association networks in biological and medical research, for example, genetic interaction networks. In this paper, we propose a novel method, the extended joint hub graphical lasso (EDOHA), to estimate multiple related interaction networks for high dimensional omics data across multiple distinct classes. To be specific, we construct a convex penalized log likelihood optimization problem and solve it with an alternating direction method of multipliers (ADMM) algorithm. The proposed method can also be adapted to estimate interaction networks for high dimensional compositional data such as microbial interaction networks. The performance of the proposed method in the simulated studies shows that EDOHA has remarkable advantages in recognizing class-specific hubs than the existing comparable methods. We also present three applications of real datasets. Biological interpretations of our results confirm those of previous studies and offer a more comprehensive understanding of the underlying mechanism in disease.

Progressive back-projection network for COVID-CT super-resolution.

BACKGROUND AND OBJECTIVE: Recently, the COVID-19 epidemic has become more and more serious around the world, how to improve the image resolution of COVID-CT is a very important task. The network based on progressive upsampling for COVID-CT super-resolution increases the reconstruction error. This paper proposes a progressive back-projection network (PBPN) for COVID-CT super-resolution to solve this problem. METHODS: In this paper, we propose a progressive back-projection network (PBPN) for COVID-CT super-resolution. PBPN is divided into two stages, and each stage consists of back-projection, deep feature extraction and upscaling. We design an up-projection and down-projection residual module to minimize the reconstruction error and construct a residual attention module to extract deep features. In each stage, firstly, PBPN performs back-projection to extract shallow features by two up-projection and down-projection residual modules; then, PBPN extracts deep features from the shallow features by two residual attention modules; finally, PBPN upsamples the deep features through sub-pixel convolution. RESULTS: The proposed method achieves the improvements of about 0.14~0.47 dB/0.0012~0.0060 for × 2 scale factor, 0.02~0.08 dB/0.0024~0.0059 for × 3 scale factor, and 0.08~0.41 dB/ 0.0040~0.0147 for × 4 scale factor than state-of-the-art methods (Bicubic, SRCNN, FSRCNN, VDSR, LapSRN, DRCN and DSRN) in terms of PSNR/SSIM on benchmark datasets. CONCLUSIONS: The proposed mehtod obtains better performance for COVID-CT super-resolution and reconstructs high-quality high-resolution COVID-CT images that contain more details and edges.

Leptin correlates with monocytes activation and severe condition in COVID-19 patients.

Excessive monocyte activation with the development of excessive or uncontrolled release of proinflammatory cytokines often results in host tissue injury and even death in patients with pneumonia caused by the 2019 novel coronavirus. However, the changes of cytokine profiles of coronavirus disease 2019 (COVID-19) patients, as well as the underlying mechanisms that are involved, remain unknown. Using a cytokine array containing 174 inflammation-related cytokines, we found significantly altered cytokine profiles in severe COVID-19 patients compared with those in mild patients or healthy controls, and identified leptin, CXCL-10, IL-6, IL-10, IL-12, and TNF-α as the top differentially expressed cytokines. Notably, leptin showed high consistency with CXCL-10 and TNF-α in predicting disease severity, and correlated with body mass index, decreased lymphocyte counts, and disease progression. Further analysis demonstrated that monocytes in severe patients with higher leptin levels were inclined toward M1 polarization. Mechanistic studies revealed that leptin synergistically up-regulated expression levels of inflammatory cytokines and surface markers with IL-6 in monocytes through STAT3 and NF-κB signaling pathways. Collectively, our results suggest that overweight COVID-19 patients were prone to have higher leptin levels, which further activated monocytes, resulting in amplified or dysregulated immune responses. Taken together, our findings argue that leptin correlates severity of COVID-19 and may indicate a possible mechanism by which overweight patients have a greater tendency to develop severe conditions.

Analysis of 3 death cases of critical COVID-19 in Sichuan

Objective: To analyze the clinical characteristics of critical COVID-19, and we improve the understanding of the diagnosis and treatment of severe cases.