Blue LED light promoting the growth, accumulation of high-value isoflavonoids and astragalosides, antioxidant response, and biosynthesis gene expression in Astragalus membranaceus (Fisch.) Bunge hairy root cultures

The root of Astragalus membranaceus (Fisch.) Bunge is one of the most frequently used herbs in traditional Chinese medicine (TCM) formulae for fighting COVID-19 infections, due to the presence of isoflavonoids and astragalosides associated with antiviral and immune-enhancing activities. For the first time, the exposure of A. membranaceus hairy root cultures (AMHRCs) to different colors of LED lights i.e., red, green, blue, red/green/blue (1/1/1, RGB), and white, was conducted to promote the root growth and accumulation of isoflavonoids and astragalosides. LED light treatment regardless of colors was found beneficial for root growth, which might be a result of the formation of more root hairs upon light stimulation. Blue LED light was found most effective for enhancing phytochemical accumulation. Results showed that the productivity of root biomass in blue-light grown AMHRCs with an initial inoculum size of 0.6% for 55 days was 1.40-fold higher than that in dark (control), and yields of high-value isoflavonoids and astragalosides including calycosin, formononetin, astragaloside IV, and astragaloside I increased by 3.17-fold, 2.66-fold, 1.78-fold, and 1.52-fold relative to control, respectively. Moreover, the photooxidative stress together with transcriptional activation of biosynthesis genes might contribute to the enhanced accumulation of isoflavonoids and astragalosides in blue-light grown AMHRCs. Overall, this work offered a feasible approach for obtaining higher yields of root biomass and medicinally important compounds in AMHRCs via the simple supplementation of blue LED light, which made blue-light grown AMHRCs industrially attractive as plant factory in controlled growing systems. Graphical Supplementary Information The online version contains supplementary material available at 10.1007/s11240-023-02486-7. Key Message Blue LED light was found to simultaneously promote the root growth and accumulation of medicinally important compounds (calycosin, formononetin, astragaloside IV, and astragaloside I) in Astragalus membranaceus (Fisch.) Bunge hairy root cultures. Supplementary Information The online version contains supplementary material available at 10.1007/s11240-023-02486-7.

Blue LED light promoting the growth, accumulation of high-value isoflavonoids and astragalosides, antioxidant response, and biosynthesis gene expression in Astragalus membranaceus (Fisch.) Bunge hairy root cultures.

The root of Astragalus membranaceus (Fisch.) Bunge is one of the most frequently used herbs in traditional Chinese medicine (TCM) formulae for fighting COVID-19 infections, due to the presence of isoflavonoids and astragalosides associated with antiviral and immune-enhancing activities. For the first time, the exposure of A. membranaceus hairy root cultures (AMHRCs) to different colors of LED lights i.e., red, green, blue, red/green/blue (1/1/1, RGB), and white, was conducted to promote the root growth and accumulation of isoflavonoids and astragalosides. LED light treatment regardless of colors was found beneficial for root growth, which might be a result of the formation of more root hairs upon light stimulation. Blue LED light was found most effective for enhancing phytochemical accumulation. Results showed that the productivity of root biomass in blue-light grown AMHRCs with an initial inoculum size of 0.6% for 55 days was 1.40-fold higher than that in dark (control), and yields of high-value isoflavonoids and astragalosides including calycosin, formononetin, astragaloside IV, and astragaloside I increased by 3.17-fold, 2.66-fold, 1.78-fold, and 1.52-fold relative to control, respectively. Moreover, the photooxidative stress together with transcriptional activation of biosynthesis genes might contribute to the enhanced accumulation of isoflavonoids and astragalosides in blue-light grown AMHRCs. Overall, this work offered a feasible approach for obtaining higher yields of root biomass and medicinally important compounds in AMHRCs via the simple supplementation of blue LED light, which made blue-light grown AMHRCs industrially attractive as plant factory in controlled growing systems. Supplementary Information: The online version contains supplementary material available at 10.1007/s11240-023-02486-7.

Electronic skin as wireless human-machine interfaces for robotic VR.

The coronavirus pandemic has highlighted the importance of developing intelligent robotics to prevent infectious disease spread. Human-machine interfaces (HMIs) give a chance of interactions between users and robotics, which play a significant role in teleoperating robotics. Conventional HMIs are based on bulky, rigid, and expensive machines, which mainly focus on robots/machines control, but lack of adequate feedbacks to users, which limit their applications in conducting complicated tasks. Therefore, developing closed-loop HMIs with both accurate sensing and feedback functions is extremely important. Here, we present a closed-loop HMI system based on skin-integrated electronics, whose electronics compliantly interface with the whole body for wireless motion capturing and haptic feedback via Bluetooth, Wireless Fidelity (Wi-Fi), and Internet. The integration of visual and haptic VR via skin-integrated electronics together into a closed-loop HMI for robotic VR demonstrates great potentials in noncontact collection of bio samples, nursing infectious disease patients and many others.

Cost analysis of severe burn victims in Southwest China: A 7-year retrospective study.

Background: Severe burn injury can be a life-threatening experience and can also lead to financial issues for suffers. The purpose of the current study was to analyze the direct hospitalization costs of severe burn inpatients in Southwest China. Methods: Data related to all inpatients admitted with severe burns [total body surface area (TBSA) ≥30%] pooled from 2015 to 2021 were reviewed retrospectively at the Institute of Burn Research of Army Medical University. Demographic parameters, medical economics, and clinical data were obtained from medical records. Results: A total of 668 cases were identified. The average age was 37.49 ± 21.00 years, and 72.3% were men. The average TBSA was 51.35 ± 19.49%. The median length of stay of inpatients in the burn intensive care unit was 14 [interquartile range (IQR): 5.0-34.8] days, and the median length of stay (LOS) was 41 (IQR: 22.0-73.8) days. The mortality rate was 1.6%. The median total cost was 212,755.45 CNY (IQR: 83,908.80-551,621.57 CNY) per patient varying from 3,521.30 to 4,822,357.19 CNY. The direct cost of scald burns was dramatically lower compared with that of other types of burns, with 11,213.43 to 2,819,019.14 CNY. Medical consumables presented the largest portion of total costs, with a median cost of 65,942.64 CNY (IQR: 18,771.86-171,197.97 CNY). The crucial risk factors for medical cost in our study were TBSA, surgical frequency, LOS, depth of burn, and outcome. Conclusion: We conclude that an effective burn prevention program, shorter hospital stays, and facilitating the healing of wounds should be focused on with tailored precautionary protocols to reduce the medical costs of inpatients with severe burns.

Triclosan and related compounds in the environment: Recent updates on sources, fates, distribution, analytical extraction, analysis, and removal techniques.

Triclosan (TCS) has been widely used in daily life because of its broad-spectrum antibacterial activities. The residue of TCS and related compounds in the environment is one of the critical environmental safety problems, and the pandemic of COVID-19 aggravates the accumulation of TCS and related compounds in the environment. Therefore, detecting TCS and related compound residues in the environment is of great significance to human health and environmental safety. The distribution of TCS and related compounds are slightly different worldwide, and the removal methods also have advantages and disadvantages. This paper summarized the research progress on the source, distribution, degradation, analytical extraction, detection, and removal techniques of TCS and related compounds in different environmental samples. The commonly used analytical extraction methods for TCS and related compounds include solid-phase extraction, liquid-liquid extraction, solid-phase microextraction, liquid-phase microextraction, and so on. The determination methods include liquid chromatography coupled with different detectors, gas chromatography and related methods, sensors, electrochemical method, capillary electrophoresis. The removal techniques in various environmental samples mainly include biodegradation, advanced oxidation, and adsorption methods. Besides, both the pros and cons of different techniques have been compared and summarized, and the development and prospect of each technique have been given.

Safety and Effectiveness of SA58 Nasal Spray against COVID-19 Infection in Medical Personnel：An Open-label, Blank-controlled Study (preprint)

Approved COVID-19 vaccines to date have limited effectiveness in protecting infection and blocking transmission. A nasal spray of broad-spectrum antibody against COVID-19 (SA58 Nasal Spray) has recently been developed by Sinovac Life Sciences Co., Ltd.. From October 31 to November 30, 2022, an open-label, blank controlled study on the SA58 Nasal Spray against COVID-19 infection was conducted with the medical personnel working in the designated COVID-19 hospitals and Fangcang shelter hospitals (alternate care sites) of COVID-19 cases in Hohhot city, the Inner Mongolia Autonomous Region. A total of 6662 medical personnel were involved in this study: 3368 used SA58 Nasal Spray from the drug group, and 3294 not used from blank control group. The medication was self-administered intranasally 1~2 times per day with an interval of 6 hours for 30 days.. The safety results indicated that the SA58 Nasal Spray was well tolerant. The incidence of adverse events (AEs) was 28.6% (497/1736), and the majority of the AEs were mild and from administrative site. 135 COVID-19 cases were identified for SARS-CoV-2 by RT-PCR during the 30-day observation. The cumulative incidence of COVID-19 in the drug group and the control group were 0.026% and 0.116%, respectively. The effectiveness of the SA58 Nasal Spray for preventing COVID-19 infection among medical personnel was evaluated as 77.7% (95% CI: 52.2% - 89.6%). In conclusion, the SA58 Nasal Spray is well-tolerant and highly effective against COVID-19 infection.

SARS-CoV-2: Review of Conditions Associated With Severe Disease and Mortality.

The 2019 Coronavirus Virus Disease (COVID-19) represents a global public health challenge in the twenty-first century. As of June 2020, the virus had spread across 216 countries across the globe. This paper aims to analyze and identify those existing comorbidities among COVID-19 patients that represent potential risk factors for COVID-19 complications, severe illness, and death. Multiple database resources were searched. The resources include the University of Saskatchewan library USearch, Google Scholar, PubMed, Medline, and the Google search engine. Thirty-seven articles, which included 15 different types of chronic diseases, were selected. Among the reviewed diseases and conditions, cancer, diabetes, lymphopenia, hypertension, kidney disease, smoking, chronic obstructive pulmonary disease (COPD), and organ transplant were found to represent potential risk factors for COVID-19 complications, severe illness, and death. Other conditions that require further research as to whether they predispose subjects to severe illness and death include coronary artery disease, cerebrovascular disease, valvular heart disease, gastrointestinal diseases, HIV/AIDS, asthma, and liver disease. In conclusion, this article explains the association between diseases mentioned above and the severity of COVID-19 and clearly shows the population at risk. This paper will help government bodies and decision-makers prioritize resources for these populations to reduce mortality rates and overall quality of life.

How perceived threat of COVID-19 related to aggressive tendencies during the pandemic in Hubei Province and other regions of China: Mediators and moderators.

During the COVID-19 pandemic in early 2020, domestic violence, interpersonal conflicts, and cyberbullying have risen sharply in China. We speculate that the perceived threat of COVID-19 is related to a general, non-target-specific aggressive tendency during the pandemic. We surveyed 1556 Chinese people in April 2020 (757 people in Hubei Province, the pandemic epicenter in China, and 799 in other regions of China where the pandemic is relatively not severe). A multiple-group structural equation modeling analysis found significant total effects between perceived threat of COVID-19 and aggressive tendencies during the pandemic in both regional groups, and the effect between them was mainly achieved through the mediating roles of sense of control and powerlessness during the pandemic. For all participants, negative coping strategies significantly aggravated the association between perceived threat of COVID-19 and aggressive tendencies during the pandemic, but the buffers were different across regions of outbreak severity. For participants in other regions where the pandemic is relatively not severe, positive coping strategies could mitigate the association between perceived threat of COVID-19 and aggressions. However, for participants in Hubei Province, the epicenter of China's pandemic, higher life satisfaction was more effective in buffering. These findings extend the possible consequences of the perceived COVID-19 threat and suggest that improving the life satisfaction of residents in areas with severe outbreaks is more effective in mitigating the adverse effects of COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-021-01792-7.

An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data.

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.

A novel methodology for the synchronous collection and multimodal visualisation of continuous neurocardiovascular and neuromuscular physiological data in adults with long COVID (preprint)

Reports suggest that adults with post-COVID-19 syndrome or long COVID may be affected by orthostatic intolerance syndromes, with autonomic nervous system dysfunction as a possible causal factor of neurocardiovascular instability (NCVI). Long COVID can also manifest as prolonged fatigue, which may be linked to neuromuscular function impairment (NMFI). The current clinical assessment for NCVI monitors neurocardiovascular performance upon the application of orthostatic stressors such as an active (i.e. self-induced) stand or a passive (tilt table) standing test. Lower limb muscle contractions may be important in orthostatic recovery via the skeletal muscle pump. In this study, adults with long COVID were assessed with a protocol that, in addition to the standard NCVI tests, incorporated simultaneous lower limb muscle monitoring for NMFI assessment. To accomplish such an investigation, a wide range of continuous non-invasive biomedical technologies were employed, including digital artery photoplethysmography for the extraction of cardiovascular signals, near-infrared spectroscopy for the extraction of regional tissue oxygenation in brain and muscle, and electromyography for assessment of timed muscle contractions in the lower limbs. With the novel technique described and exemplified in this paper, we were able to integrate signals from all instruments used in the assessment in a precisely synchronized fashion. We demonstrate that it is possible to visualize the interactions between all different physiological signals during the combined NCVI/NMFI assessment. Multiple counts of evidence were collected, which can capture the dynamics between skeletal muscle contractions and neurocardiovascular responses. The proposed multimodal data visualization can offer an overview of the functioning of the muscle pump during both supine rest and orthostatic recovery and can conduct comparison studies with signals from multiple participants at any given time in the assessment. This could help researchers and clinicians generate and test hypotheses based on the multimodal inspection of raw data, in long COVID and other clinical cohorts.

Orthostatic intolerance in adults with long COVID was not associated with postural orthostatic tachycardia syndrome (preprint)

In this observational cross-sectional study, we investigated predictors of orthostatic intolerance (OI) in adults with long COVID. Participants underwent a 3-minute active stand (AS) with Finapres® NOVA, followed by a 10-minute unmedicated 70-degree head-up tilt test. 85 participants were included (mean age 46 years, range 25-78; 74% women), of which 56 (66%) reported OI during AS (OI AS ). OI AS seemed associated with female sex, more fatigue and depressive symptoms, and greater inability to perform activities of daily living (ADL), as well as a higher heart rate (HR) at the lowest systolic blood pressure (SBP) point before the 1 st minute post-stand (mean HR nadir 88 vs 75 bpm, P=0.004). In a regression model also including age, sex, fatigue, depression, ADL inability, and peak HR after the nadir SBP, HR nadir was the only OI AS predictor (OR=1.09, 95% CI: 1.01-1.18, P=0.027). 22 participants had initial (iOH) and 5 classical (cOH) orthostatic hypotension, but neither correlated with OI AS . 71 participants proceeded to tilt, of which 28 had OI during tilt (OI tilt ). Of the 53 who had a 10-minute tilt, 7 (13%) fulfilled hemodynamic postural orthostatic tachycardia syndrome (POTS) criteria, but 6 did not report OI tilt . OI AS was associated with a higher initial HR on AS, which after 1 minute equalized with the non-OI AS group. Despite these initial orthostatic HR differences, POTS was infrequent and largely asymptomatic. ClinicalTrials.gov Identifier: NCT05027724 (retrospectively registered on August 30, 2021).

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

A systematic review of persistent symptoms and residual abnormal functioning following acute COVID-19: Ongoing symptomatic phase vs. post-COVID-19 syndrome (preprint)

Objective | To compare the two phases of long COVID, namely ongoing symptomatic COVID-19 (OSC; signs and symptoms from 4 to 12 weeks from initial infection) and post-COVID-19 syndrome (PCS; signs and symptoms beyond 12 weeks) with respect to symptomatology, abnormal functioning, psychological burden, and quality of life. Design | Systematic review. Data Sources | Electronic search of EMBASE, MEDLINE, ProQuest Coronavirus Research Database, LitCOVID, and Google Scholar between January and April 2021, and manual search for relevant citations from review articles. Eligibility Criteria | Cross-sectional studies, cohort studies, randomised control trials, and case-control studies with participant data concerning long COVID symptomatology or abnormal functioning. Data Extraction | Studies were screened and assessed for risk of bias by two independent reviewers, with conflicts resolved with a third reviewer. The AXIS tool was utilised to appraise the quality of the evidence. Data were extracted and collated using a data extraction tool in Microsoft Excel. Results | Of the 1,145 studies screened, 39 were included, all describing adult cohorts with long COVID and sample sizes ranging from 32 to 1,733. Studies included data pertaining to symptomatology, pulmonary functioning, chest imaging, cognitive functioning, psychological disorder, and/or quality of life. Fatigue presented as the most prevalent symptom during both OSC and PCS at 43% and 44%, respectively. Sleep disorder (36%; 33%), dyspnoea (31%; 40%), and cough (26%; 22%) followed in prevalence. Abnormal spirometry (FEV1 <80% predicted) was observed in 15% and 11%, and abnormal chest imaging observed in 34% and 28%, respectively. Cognitive impairments were also evident (20%; 15%), as well as anxiety (28%; 34%) and depression (25%; 32%). Decreased quality of life was reported by 40% of patients with OSC and 57% by those with PCS. Conclusions | The prevalences of OSC and PCS were highly variable. Reported symptoms covered a wide range of body systems, with general overlap in frequencies between the two phases. However, abnormalities in lung function and imaging seemed to be more common in OSC, whilst anxiety, depression, and poor quality of life seemed more frequent in PCS. In general, the quality of the evidence was moderate and further research is needed to better understand the complex interplay of somatic versus psychosocial drivers in long COVID. Systematic Review Registration | Registered with PROSPERO with ID #CRD42021247846.

Factors influencing improvements in air quality in Guanzhong cities of China, and variations therein for 2014–2020

It is interesting and important to know what factors cause improvements in regional air quality. This study analyzed the factors that improved the air quality in cities in the Guanzhong region of China–in terms of meteorology and controlling emissions–following the implementation of the “Action Plan for Air Pollution Prevention and Control” in 2013. The average air quality index (AQI) values, PM2.5, PM10, SO2, CO, and NO2 in these cities in 2020 decreased by 45.1%, 43.8%, 82.9%, 57.3%, and 31.6%, respectively, compared to the values in 2014, while the O3 concentration increased by 16.7%. During the COVID-19 pandemic of February to May 2020, lockdown measures in cities in Guanzhong resulted in reductions of approximately 18.4%, 24.2%, and 17.9% in the AQI, PM2.5, and PM10 concentrations compared to the same period in 2019. Principal component analysis showed that the yearly reduction in AQI in cities in Guanzhong was attributed mainly to reductions in industrial emissions, followed by reductions in emissions from homes and motor vehicle exhausts. We propose the strengthening of measures to control particulate matter, O3 and greenhouse gas to see the improvement of air quality among this region in the future.

Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study.

BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.

OBJECTIVE: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. MATERIALS AND METHODS: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. RESULTS: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. CONCLUSION: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

If the link missed：Could inflammatory skin disorders with barrier dysfunction increase the risk of COVID-19? (preprint)

Since the end of 2019, COVID-19 pandemic caused by the SARS-CoV-2 emerged globally. The angiotensin-converting enzyme 2 (ACE2) on the cell surface is crucial for SARS-COV-2 entering into the cells. The expression of ACE2 in skin suggested that skin might be a way of transmitting SARS-CoV-2. We found the elevated ACE2 level of patients with psoriasis but downregulated after IL-17 antibody treatment. Further results showed that ACE2 expression increased either in psoriasis or in atopic dermatitis, which were typical inflammatory skin disorders with barrier dysfunction. And elevated ACE2 level was also detected in mouse models of dermatitis induced by imiquimod, calcipotriol, repeated tape-stripping or 1-Fluoro-2,4-dinitrobenzene (DNFB) respectively. Moreover, alleviation of cutaneous inflammation with skin recovery moisture also lowered expression of ACE2 in mouse models with barrier deteriorated inflammatory skin disorders. Furthermore, inflammatory skin disorders with barrier dysfunction increased the penetration of topical FITC conjugated spike protein into the skin. Conversely, improvement in skin permeability barrier could prevent this penetration. Thus, indicating the special link between inflammatory skin disorders with skin barrier dysfunction and increasing risk of COVID-19.