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s u m m a r y Results: Of the 249 patients enrolled, the median age was 51 years old, and 126 (50.6%) were male. The duration from onset of symptoms to hospitalization was 4(2-7) days in symptomatic patients. Fever was occurred in 235(94.3%) patients. A total of 215 (86.3%) patients had been discharged after 16(12-20) days hospitalization. The estimated median duration of fever in all the patients with fever was 10 days (95 confidential intervals [CIs]: 8-11 days) after onset of symptoms. Patients who were transferred to intensive care units (ICU) had significantly longer duration of fever as compared to those not in ICU (31 days v.s. 9 days after onset of symptoms, respectively, P < 0.0 0 01). Radiological aggravation of initial image was observed in 163 (65.7%) patients on day 7 after onset of symptoms. 154(94.5%) of these patients showed radiological improvement on day 14. The median duration to negative reverse-transcriptase PCR tests of upper respiratory tract samples was 11 days (95 CIs: 10-12 days). Viral clearance was more likely to be delayed in patients in ICU than those not in ICU ( P < 0.0 0 01). In multivariate logistical analysis, age (Odds ratio [OR] = 1.06) and CD4 T cell count (OR = 0.55 per 100 cells/ul increase) were independently associated with ICU admission. Conclusions: The majority of COVID-19 cases are mild. The clinical progression pattern suggests that early control of viral replication and application of host-directed therapy in later stage is essential to improve the prognosis of CVOID-19.

Rationale: Some patients with coronavirus disease 2019 (COVID-19) rapidly develop respiratory failure or even die, underscoring the need for early identification of patients at elevated risk of severe illness. This study aims to quantify pneumonia lesions by computed tomography (CT) in the early days to predict progression to severe illness in a cohort of COVID-19 patients. Methods: This retrospective cohort study included confirmed COVID-19 patients. Three quantitative CT features of pneumonia lesions were automatically calculated using artificial intelligence algorithms, representing the percentages of ground-glass opacity volume (PGV), semi-consolidation volume (PSV), and consolidation volume (PCV) in both lungs. CT features, acute physiology and chronic health evaluation II (APACHE-II) score, neutrophil-to-lymphocyte ratio (NLR), and d-dimer, on day 0 (hospital admission) and day 4, were collected to predict the occurrence of severe illness within a 28-day follow-up using both logistic regression and Cox proportional hazard models. Results: We included 134 patients, of whom 19 (14.2%) developed any severe illness. CT features on day 0 and day 4, as well as their changes from day 0 to day 4, showed predictive capability. Changes in CT features from day 0 to day 4 performed the best in the prediction (area under the receiver operating characteristic curve = 0.93, 95% confidence interval [CI] 0.87~0.99; C-index=0.88, 95% CI 0.81~0.95). The hazard ratios of PGV and PCV were 1.39 (95% CI 1.05~1.84, P=0.023) and 1.67 (95% CI 1.17~2.38, P=0.005), respectively. CT features, adjusted for age and gender, on day 4 and in terms of changes from day 0 to day 4 outperformed APACHE-II, NLR, and d-dimer. Conclusions: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.

Background The nutritional status of COVID‐19 patients is unknown. This study evaluates the clinical and nutritional characteristics of severe and critically ill patients infected with SARS‐CoV‐2, and investigates the relationship between nutritional risk and clinical outcomes. Methods A retrospective, observational study was conducted at West Campus of Union Hospital in Wuhan. Patients confirmed with SARS‐CoV‐2 infection by a nucleic acid‐positive test and identified as severe or critically ill, were enrolled in this study. Clinical data and outcomes information was collected and nutritional risk was assessed by using Nutritional Risk Screening 2002 (NRS). Results Totally, 413 patients were enrolled in this study, including 346 severe patients and 67 critically ill patients. Most patients, especially critically ill patients, had significant changes in nutrition‐related parameters and inflammatory markers. As for nutritional risk, the critically ill patients had significantly higher proportion of high NRS scores ( P <0.001), which were correlated with inflammatory and nutrition‐related markers. Among 342 patients with NRS score ≥3, only 84 (25%) received nutritional support. The critically ill patients and the patients with higher NRS score had a higher risk of mortality and longer stay in hospital. In logistic regression models, one unit increased in NRS score was associated with the risk of mortality increased by 1.23 times (adjusted OR = 2.23, 95% CI : 1.10, 4.51, P = 0.026). Conclusions Most severe and critically ill patients infected with SARS‐CoV‐2 are at nutritional risk. The patients with higher nutrition risk have worse outcome, and require nutritional therapy. This article is protected by copyright. All rights reserved

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Objective: CT provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study is to develop a deep learning (DL) based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans. Methods: The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each 8 9

Photodynamic therapy (PDT) is an effective noninvasive therapeutic method that employs photosensitizers (PSs) converting oxygen to highly cytotoxic singlet oxygen ( 1 O 2 ) under light irradiation. The conventional PDT efficacy is, however, compromised by the nonspecific delivery of PSs to tumor tissue, the hypoxic tumor microenvironment, and the reduction of generated 1 O 2 by the intracellular antioxidant glutathione (GSH). Herein, an intelligent multifunctional synergistic nanoplatform (CMGCC) for T 1 -weighted magnetic resonance (MR) imaging-guided enhanced PDT is presented, which consists of nanoparticles composed of catalase (CAT) and manganese dioxide (MnO 2 ) that are integrated within chlorin-e6-modified glycol chitosan (GC) polymeric micelles. In this system, (1) GC polymers with pH-sensitive surface charge switchability from neutral to positive could improve the PS accumulation within the tumor region, (2) CAT could effectively reoxygenate the hypoxic tumor via catalyzing endogenous hydrogen peroxide to O 2 , and (3) MnO 2 could consume the intracellular GSH while simultaneously producing Mn 2+ as a contrast agent for T 1 -weighted MR imaging. The CMGCC particles possess uniform size distribution, well-defined structure, favorable enzyme activity, and superior 1 O 2 generation ability. Both in vitro and in vivo experiments demonstrate that the CMGCC exhibit significantly enhanced PDT efficacy toward HeLa cells and subcutaneous HeLa tumors. Our study thereby demonstrates this to be a promising synergistic theranostic nanoplatform with highly efficient PDT performance for cancer therapy.

Rapidly progressive GGOs and consolidations with air bronchograms and interlobular septal thickening, with right lower lobe predominance, are the main imaging findings in H7N9 pneumonia. The severity of these findings is associated with the severity of the clinical presentation.