Developmental and reproductive toxicity of a recombinant protein subunit COVID-19 vaccine (ZF2001) in rats.

ZF2001, a protein subunit vaccine against coronavirus disease 2019 (COVID-19), contains recombinant tandem repeat of dimeric receptor-binding domain (RBD) protein of the SARS-CoV-2 spike protein with an aluminium-based adjuvant. During the development of this vaccine, two nonclinical studies were conducted to evaluate female fertility, embryo-fetal development, and postnatal developmental toxicity in Sprague‒Dawley rats according to the ICH S5 (R3) guideline. In Study 1 (embryo-fetal developmental toxicity, EFD), 144 virgin female rats were randomly assigned into four groups and received three doses of vaccine (25 µg or 50 µg RBD protein/dose, containing the aluminium-based adjuvant), the aluminium-based adjuvant or a sodium chloride injection administered intramuscularly on days 21 and 7 prior to mating and on gestation day (GD) 6. In Study 2 (pre- and postnatal developmental toxicity, PPND), ZF2001 at a dose of 25 µg RBD protein/dose or sodium chloride injection was administered intramuscularly to female rats (n = 28 per group) 7 days prior to mating and on GD 6, GD 20 and postnatal day (PND) 10. There were no obvious adverse effects in dams, except for local injection site reactions related to the aluminium-based adjuvant (yellow nodular deposits in the interstitial muscle fibres). There were also no effects of ZF2001 on the mating performance, fertility or reproductive performance of parental females, embryo-fetal development, postnatal survival, growth, physical development, reflex ontogeny, behavioural and neurofunctional development, or reproductive performance of the offspring. The strong immune responses associated with binding and neutralising antibodies were both confirmed in dams and fetuses or offspring in these two studies. These results would support clinical trials or the use of ZF2001 in maternal immunisation campaigns, including those involving women with childbearing potential, regardless of pregnancy status.

Incidence of workplace violence against nurses among Chinese hospitals: A meta-analysis.

AIMS: To explore the incidence of workplace violence against nurses in Chinese hospitals. BACKGROUND: Previous systematic reviews on the incidence of workplace violence against Chinese health care workers did not include many articles published in Chinese. Although several studies have investigated cases of violence against health care providers in China, no meta-analysis has been conducted to assess the incidence of violence against Chinese nurses. EVALUATION: In this study, relevant data were retrieved from studies published up to July 2020. A meta-analysis was conducted using R software (Version 4.0). KEY FINDINGS: The 12-month incidence of workplace violence among Chinese nurses was 71% (95% CI 67%-75%), and verbal violence was the most common sub-type of violence (63%, 95% CI 58%-67%). CONCLUSION: Chinese nurses are at a high risk of violence at workplace. Hospital managers should explore ways to reduce violence against their employees, especially the younger nurses who work in secondary hospitals. IMPLICATIONS FOR NURSING MANAGEMENT: The findings of this study highlight the need to enhance the legal system in terms of laws meant to effectively mitigate violence against nurses in Chinese hospitals. Measures should be particularly taken to protect younger nurses who work in secondary hospitals.

Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network.

A three-dimensional (3D) deep learning method is proposed, which enables the rapid diagnosis of coronavirus disease 2019 (COVID-19) and thus significantly reduces the burden on radiologists and physicians. Inspired by the fact that the current chest computed tomography (CT) datasets are diversified in equipment types, we propose a COVID-19 graph in a graph convolutional network (GCN) to incorporate multiple datasets that differentiate the COVID-19 infected cases from normal controls. Specifically, we first apply a 3D convolutional neural network (3D-CNN) to extract image features from the initial 3D-CT images. In this part, a transfer learning method is proposed to improve the performance, which uses the task of predicting equipment type to initialize the parameters of the 3D-CNN structure. Second, we design a COVID-19 graph in GCN based on the extracted features. The graph divides all samples into several clusters, and samples with the same equipment type compose a cluster. Then we establish edge connections between samples in the same cluster. To compute accurate edge weights, we propose to combine the correlation distance of the extracted features and the score differences of subjects from the 3D-CNN structure. Lastly, by inputting the COVID-19 graph into GCN, we obtain the final diagnosis results. In experiments, the dataset contains 399 COVID-19 infected cases, and 400 normal controls from six equipment types. Experimental results show that the accuracy, sensitivity, and specificity of our method reach 98.5%, 99.9%, and 97%, respectively.