medRxiv preprint 6 datasets. The predictive performance was further evaluated in test dataset on lung lobe-and patients-level. Main outcomesShort-term hospital stay (≤10 days) and long-term hospital stay (>10 days). ResultsThe CT radiomics models based on 6 second-order features were effective in discriminating short-and long-term hospital stay in patients with pneumonia associated with SARS-CoV-2 infection, with areas under the curves of 0.97 (95%CI 0.83-1.0) and 0.92 (95%CI 0.67-1.0) by LR and RF, respectively, in the test dataset. The LR model showed a sensitivity and specificity of 1.0 and 0.89, and the RF model showed similar performance with sensitivity and specificity of 0.75 and 1.0 in test dataset. ConclusionsThe machine learning-based CT radiomics models showed feasibility and accuracy for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection.All rights reserved. No reuse allowed without permission.author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Results Patient characteristicsA total of 52 patients with laboratory-confirmed SARS-CoV-2 infection and initial CT images were enrolled from 5 designated hospitals in Ankang, Lishui, Zhenjiang, Lanzhou, and Linxia, China. As of February 20, 14 patients were still hospitalized, and 7 patients had non-findings in CT images. Therefore, 31 patients with 72 lesion segments were included in the final analysis. The training and inter-validation cohort comprised 26 patients (12 from Ankang, 8 from Lishui, 4 from Lanzhou, and 2 from Linxia) with 59 lesion segments, and test cohort comprised 5 patients from Zhenjiang with 13 lesion segments. The median age was 38.00 (interquartile range, 26.00-47.00) years and 17 (57%) were male. Comorbidities, symptoms and laboratory findings at admission were summarized in Table 1. Performance of CT radiomics modelThe CT radiomics model, based on 6 features (supplementary Table1), showed the highest AUC on the training and inter-validation dataset. The performance of modeling using LR and RF methods was shown in Figure 2. On lung lobe-level, models using LR method significantly distinguished short-and long-term hospital stay (In training and inter-validation datasets, cut-off value 0.31, AUC 0.94 (95%CI 0.92-0.97), sensitivity 1.0, specificity 0.87, NPV 1.0, and PPV 0.88; In test dataset, AUC 0.97 (95%CI 0.83-1.0), sensitivity 1.0, specificity 0.89, NPV 1.0, and PPV 0.8). Besides, models using RF method obtained satisfied results (In training and inter-validation datasets, cut-off value 0.68, AUC 1.0 (95%CI 1.0-1.0), All rights reserved. No reuse allowed without permission.author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

consultation; and (iii) establish a short-term web-based followup to define drug efficacy and adapt treatment accordingly. Thus, in this particular situation the diagnosis of AIH may be given without histology, if typical biochemical and serological results are followed by a convincing treatment response. Prove of the diagnosis can be undertaken later, either by a relapse upon therapy reduction, or a follow-up liver biopsy when conditions are safer. As already reported in China, 8 advanced liver cirrhosis and decompensated patients can be monitored with a webbased system and all non-urgent medical visits should be postponed until the emergency is over. Urgent procedures (i.e. paracentesis) should be organised using a COVID-19-free path in the hospital, another COVID-19-free facility or home care. Finally, we recommend strict adherence to standard social distancing protocols and social isolation and emphasise, in cirrhotic patients, the importance of vaccination for Streptococcus pneumoniae and seasonal flu and of reinforcing social distancing measures. Further data are needed in order to demonstrate the real impact of COVID-19 infection in immunocompromised patients. Until then, and while vaccination is not available, we suggest continuing a cautious approach during low-level seasonal persistence of COVID-19 in the years to come.Although we cannot currently evaluate the efficacy of our management protocol, we believe this framework might be a useful tool for management of AILD for the time being.

Background: The coronavirus disease 2019 (COVID-19) has become a global challenge since the December 2019. The hospital stay is one of the prognostic indicators, and its predicting model based on CT radiomics features is important for assessing the patients' clinical outcome. The study aimed to develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with COVID-19 pneumonia.Methods: This retrospective, multicenter study enrolled patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images from 5 designated hospitals in Ankang,

Background: The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has outbreak in the world. Little is known about the clinical characteristics of patients with SARS-CoV-2 infection in the high-altitude region of China. We reported the clinical characteristics of patients with coronavirus disease 2019 in Gansu province, China.Methods: In this retrospective study, patients with laboratory-confirmed SARS-CoV-2 infection were consecutively enrolled from January 21, 2020 to February 11, 2020. The information on the epidemiological, clinical characteristics, laboratory tests, radiological features on admission, treatment and outcome were obtained with the final follow-up of March 13, 2020. On the basis of the median length of hospital stay, patients were further analyzed in two groups (long-vs. short-hospital stay).Results: Of the 86 patients of COVID-19 in 11 cities of Gansu Province, the median hospital stay was 14.0 days (interquartile rang, 11.0-19.0 days). In the overall cohort, the median age was 41.0 years (interquartile rang, 31.0-54.3 years), and 48 (55.8%) patients were female. Forty (46.5%) had a history of exposure to epidemic regions, but none exposed to the Huanan seafood market in Wuhan. Common symptoms included fever (41, 47.7%), and cough (38, 44.2%). On admission, 30 (34.9%) and 58 (67.4%) patients had leukopenia and lymphopenia. According to chest CT scans, 53 (66.3%) of 80 patients showed bilateral pneumonia, and 19 (23.8%) of 80 patients showed unilateral pneumonia. Of the 15 asymptomatic cases, 10 (66.6%) cases were found CT findings of pneumonia. Besides, there were 65 (75.6%) patients with mild and moderate type of COVID-19. All 86 patients received antiviral and traditional Chinese medicine therapy, 53 (61.6%) received antibacterial therapy, and 3 (3.5%) patients received invasive ventilator mechanical ventilation. The proportion of patients received antibiotic treatment in long-hospital stay group was significantly higher than that in the short-hospital stay group (P=0.045). As of March 13, 2020, 84 (97.7%) patients were discharged, and two (2.3%) cases died.Conclusions: In the Gansu province cohort of 86 patients of COVID-19, most patients were with mild or moderate type, and most asymptomatic cases showed CT imaging findings of SARS-CoV-2 related pneumonia.

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Obstructive sleep apnea syndrome (OSAS) is a common breathing disorder in sleep in which the airways narrow or collapse during sleep, causing obstructive sleep apnea. The prevalence of OSAS continues to rise worldwide, particularly in middle-aged and elderly individuals. The mechanism of upper airway collapse is incompletely understood but is associated with several factors, including obesity, craniofacial changes, altered muscle function in the upper airway, pharyngeal neuropathy, and fluid shifts to the neck. The main characteristics of OSAS are recurrent pauses in respiration, which lead to intermittent hypoxia (IH) and hypercapnia, accompanied by blood oxygen desaturation and arousal during sleep, which sharply increases the risk of several diseases. This paper first briefly describes the epidemiology, incidence, and pathophysiological mechanisms of OSAS. Next, the alterations in relevant signaling pathways induced by IH are systematically reviewed and discussed. For example, IH can induce gut microbiota (GM) dysbiosis, impair the intestinal barrier, and alter intestinal metabolites. These mechanisms ultimately lead to secondary oxidative stress, systemic inflammation, and sympathetic activation. We then summarize the effects of IH on disease pathogenesis, including cardiocerebrovascular disorders, neurological disorders, metabolic diseases, cancer, reproductive disorders, and COVID-19. Finally, different therapeutic strategies for OSAS caused by different causes are proposed. Multidisciplinary approaches and shared decision-making are necessary for the successful treatment of OSAS in the future, but more randomized controlled trials are needed for further evaluation to define what treatments are best for specific OSAS patients.

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Objectives. The purpose of this study is to investigate the relationship between plasma endocannabinoids and insulin resistance (IR) in patients with obstructive sleep apnea (OSA). Methods. A population of 64 with OSA and 24 control subjects was recruited. Body mass index (BMI), waist circumference, lipids, blood glucose and insulin, homeostasis model of assessment for insulin resistance index (HOMA-IR), anandamide (AEA), 1/2-arachidonoylglycerol (1/2-AG), and apnea-hypopnea index (AHI) were analyzed. Results. Fasting blood insulin (22.9 ± 7.8 mIU/L versus 18.5 ± 7.2 mIU/L, P < 0.05), HOMA-IR (2.9 ± 1.0 versus 2.4 ± 0.9, P < 0.01), AEA (3.2 ± 0.7 nmol/L versus 2.5 ± 0.6 nmol/L, P < 0.01), and 1/2-AG (40.8 ± 5.7 nmol/L versus 34.3 ± 7.7 nmol/L, P < 0.01) were higher in OSA group than those in control group. In OSA group, AEA, 1/2-AG, and HOMA-IR increase with the OSA severity. The correlation analysis showed significant positive correlation between HOMA-IR and AHI (r = 0.44, P < 0.01), AEA and AHI (r = 0.52, P < 0.01), AEA and HOMA-IR (r = 0.62, P < 0.01), and 1/2-AG and HOMA-IR (r = 0.33, P < 0.01). Further analysis showed that only AEA was significantly correlated with AHI and HOMA-IR after adjusting for confounding factors. Conclusions. The present study indicated that plasma endocannabinoids levels, especially AEA, were associated with IR and AHI in patients with OSA.