COVID-19 could cause long term peripheral nerve demyelination and axonal loss: A One Year Prospective Cohort Study (preprint)

Background: There is a lack of studies on large-sample, medium-, or long-term follow-up data of peripheral neuropathy (PNP) in the COVID-19 survivors. This study evaluated the characteristics and related risk factors of PNP in the medium- and long-term rehabilitation,which provided real-world study data for the complete recovery of COVID-19 patients. Methods: This study was a prospective cohort study of the COVID-19 survivors. We collected data on baseline characteristics, symptoms at onset and after discharge during the 6-month and 12-month follow-up. Peripheral nerves were measured by electromyography and inducible potentiometer. We used multivariable logistic regression to analyze the influencing factors of PNP. Additionally, we compared the difference between the two measurements among the population who completed both measurements. Results: 313 patients were included in the study and all of them underwent nerve conduction study. 67 patients completed two measurements at 6-month and 12-month follow-up. Commonly reported symptoms contained memory loss (86%), hair loss (28%), anxiety (24%), and sleep difficulties (24%). 232 patients (74%) were found with PNP, including 51 (16%) with mononeuropathy and 181 (58%) with generalized PNP. Patients with measurement at 12-month follow-up had a higher prevalence of generalized PNP (p=0.006). For pathological types, 64 (20%) patients had only axonal loss, 67 (21%) had only demyelination, and 101 (32%) had a mixed type. There was no significant difference in the prevalence of accompanying symptoms after discharge between the two groups with or without PNP. After adjustment, age was positively associated with PNP (OR=1.22 per 10-year increase of age, 95% CI, 1.05-1.41). Compared with less than the median amount of IgG at discharge, higher amount of IgG was associated with decreased risk of F-wave abnormality (OR=0.32, 95%CI, 0.11-0.82), but no significant difference in other types of PNP. Conclusions and Relevance: SARS-CoV-2 could cause PNP in hospital survivors with COVID-19, which persisted and was associated with age, education, and IgG antibody at discharge, but had no significant correlation with symptoms after discharge.

Computed tomography in coronavirus disease 2019: diagnosis and clinical significance

Objective To investigate the computed tomography (CT) features of the coronavirus disease 2019 (COVID-19) and the clinical significance, so as to improve our understanding of CT imaging of this disease. Methods The chest CT features of seven COVID-19 patients, who were diagnosed by virus nucleic acid test from Jan. 25 to Feb. 15, 2020 in Changhai Hospital of Naval Medical University (Second Military Medical University), were analyzed retrospectively. There were six males and one female, aged (51.1±18.8) years (range 29-75 years). All the seven patients received chest CT plain scan examimation. The CT images were interpreted by two experienced senior radiologists, and the distribution, location and density of lesions, number of involved lobes, air bronchogram, mediastinal lymphadenopathy and pleural effusion were analyzed. Results The average time from onset of symptoms to CT examination was 3.6 d (range 1-9 d) in the seven COVID-19 patients. The lesions were distributed in single lung in one case and bilateral lungs in six cases. The lesions involved middle and lateral fields of lungs in five cases and the whole field of lungs in two cases. The lesions showed ground-glass opacity in four cases and mixed shadow in three cases. The lesions involved two or less lobes in four cases and five lobes in three cases. One case had air bronchogram. No mediastinal lymphadenopathy or pleural effusion were found. Conclusion COVID-19 patients have characteristic CT findings, which has important clinical significance for the diagnosis and treatment of COVID-19. However, the diagnosis should be confirmed based on the patient's epidemic history, clinical symptoms and laboratory indicators.

Coronavirus disease 2019 (COVID-19): Experiences and protocols from the Department of Prosthodontics at the Wuhan University.

STATEMENT OF PROBLEM: The novel Coronavirus Disease 2019 (COVID-19) is a global pandemic, and many countries and regions are still currently in the midst of the outbreak. This pandemic has caused prosthodontics units to suspend their clinical and educational operations in academia. PURPOSE: The purpose of this article was to review the experiences from the Department of Prosthodontics, Wuhan University School and Hospital of Stomatology (DP-WHUSHS), during the COVID-19 outbreak and the protocols DP-WHUSHS used to resume clinical activities after the outbreak. MATERIAL AND METHODS: The descriptive approach was used in this article to provide a chronological narrative of the experiences and protocols from the DP-WHUSHS during the COVID-19 outbreak and after the outbreak. RESULTS: During the COVID-19 outbreak period, clinical care was provided for patients with dental emergencies by using enhanced grade 2 or grade 3 personal protective equipment (PPE). Teledentistry was used to provide care for patients with nonemergency needs. Online webinars and lectures were conducted for the predoctoral students, residents, and dentists to minimize the interruption in their education and engage the dental community amid the pandemic. Various factors were considered before clinical activities resumed after the outbreak subsided. Additional resources were allocated for facility preparation and management and employee training. New infection control and clinical operation protocols were developed to minimize the health-care-associated infection of airborne transmission diseases. The psychological health and mental wellness of the employees were emphasized. Distance or online education is still under rapid development to provide students and dentists opportunities to advance their knowledge amid the pandemic. CONCLUSIONS: Within the limitation of this descriptive review, the following conclusions were drawn. Patient welfare and emergency needs should be considered amid the pandemic. Enhanced grade 2 or grade 3 PPE should be used during the outbreak. Multifactorial considerations for work resumption after the outbreak included facility preparation and management, training for employees, and clinical operation management. In-person psychological consultation and online mental wellness programs were available to employees to improve their mental wellness. Distance or online education was under rapid development to minimize the interruption in education for the students and to engage the dental community amid a pandemic.

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT (preprint)

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Toilets dominate environmental detection of SARS-CoV-2 virus in a hospital (preprint)

Background: Respiratory and faecal aerosols play a suspected role in transmitting the SARS-CoV-2 virus. We performed extensive environmental sampling in a dedicated hospital building for Covid-19 patients in both toilet and non-toilet environments, and analysed the associated environmental factors. Methods: We collected data of the Covid-19 patients. 107 surface samples, 46 air samples, two exhaled condensate samples, and two expired air samples were collected were collected within and beyond the four three-bed isolation rooms. We reviewed the environmental design of the building and the cleaning routines. We conducted field measurement of airflow and CO2 concentrations. Findings: The 107 surface samples comprised 37 from toilets, 34 from other surfaces in isolation rooms (ventilated at 30-60 L/s), and 36 from other surfaces outside isolation rooms in the hospital. Four of these samples were positive, namely two ward door-handles, one bathroom toilet-seat cover and one bathroom door-handle; and three were weakly positive, namely one bathroom toilet seat, one bathroom washbasin tap lever and one bathroom ceiling-exhaust louvre. One of the 46 air samples was weakly positive, and this was a corridor air sample. The two exhaled condensate samples and the two expired air samples were negative. Interpretation: The faecal-derived aerosols in patients' toilets contained most of the detected SARS-CoV-2 virus in the hospital, highlighting the importance of surface and hand hygiene for intervention.