Lineage-mosaic and mutation-patched spike proteins for broad-spectrum COVID-19 vaccine.

SARS-CoV-2 spread in humans results in continuous emergence of new variants, highlighting the need for vaccines with broad-spectrum antigenic coverage. Using inter-lineage chimera and mutation-patch strategies, we engineered a recombinant monomeric spike variant (STFK1628x) that contains key regions and residues across multiple SAR-CoV-2 variants. STFK1628x demonstrated high immunogenicity and mutually complementary antigenicity to its prototypic form (STFK). In hamsters, a bivalent vaccine composed of STFK and STFK1628x elicited high titers of broad-spectrum neutralizing antibodies to 19 circulating SARS-CoV-2 variants, including Omicron sublineages BA.1, BA.1.1, BA.2, BA.2.12.1, BA.2.75, and BA.4/5. Furthermore, this vaccine conferred robust protection against intranasal challenges by either SARS-CoV-2 ancestral strain or immune-evasive Beta and Omicron BA.1. Strikingly, vaccination with the bivalent vaccine in hamsters effectively blocked within-cage virus transmission of ancestral SARS-CoV-2, Beta variant, and Omicron BA.1 to unvaccinated sentinels. Thus, our study provided insight and antigen candidates for the development of next-generation COVID-19 vaccines.

Enhanced antibacterial behavior of a novel Cu-bearing high-entropy alloy.

Contact infection of bacteria and viruses has been a critical threat to human health. The worldwide outbreak of COVID-19 put forward urgent requirements for the research and development of the self-antibacterial materials, especially the antibacterial alloys. Based on the concept of high-entropy alloys, the present work designed and prepared a novel Co0.4FeCr0.9Cu0.3 antibacterial high-entropy alloy with superior antibacterial properties without intricate or rigorous annealing processes, which outperform the antibacterial stainless steels. The antibacterial tests presented a 99.97% antibacterial rate against Escherichia coli and a 99.96% antibacterial rate against Staphylococcus aureus after 24 h. In contrast, the classic antibacterial copper-bearing stainless steel only performed the 71.50% and 80.84% antibacterial rate, respectively. The results of the reactive oxygen species analysis indicated that the copper ion release and the immediate contact with copper-rich phase had a synergistic effect in enhancing antibacterial properties. Moreover, this alloy exhibited excellent corrosion resistance when compared with the classic antibacterial stainless steels, and the compression test indicated the yield strength of the alloy was 1015 MPa. These findings generate fresh insights into guiding the designs of structure-function-integrated antibacterial alloys.

The Effect of Social Media Use on Depressive Symptoms in Older Adults with Self-Reported Hearing Impairment: An Empirical Study.

Older people with hearing impairment are more likely to develop depressive symptoms due to physical disability and loss of social communication. This study investigated the effects of social media on social relations, subjective aging, and depressive symptoms in these older adults based on the stimulus-organism-response (S-O-R) framework. It provides new empirical evidence to support improving the mental health and rebuilding the social relations of older people. A formal questionnaire was designed using the Wenjuanxing platform and distributed online through WeChat; 643 valid questionnaires were received from older people with self-reported hearing impairments, and SmartPLS 3.28 was used to analyze the data. The results show that (1) social media significantly impacts the social relations of older people with hearing impairment (social networks, ß = 0.132, T = 3.444; social support, ß = 0.129, T = 2.95; social isolation, ß = 0.107, T = 2.505). (2) For these older people, social isolation has the biggest impact on their psychosocial loss (ß = 0.456, T = 10.458), followed by the impact of social support (ß = 0.103, T = 2.014); a hypothesis about social network size was not confirmed (ß = 0.007, T = 0.182). Both social media (ß = 0.096, T = 2.249) and social support (ß = 0.174, T = 4.434) significantly affect the self-efficacy of hearing-impaired older people. (3) Both subjective aging (psychosocial loss, ß = 0.260, T = 6.036; self-efficacy, ß = 0.106, T = 3.15) and social isolation (ß = 0.268, T = 6.307) significantly affect depressive symptoms in older people with hearing impairment. This study expands the theories of social media aging cognition, social support, and social networks and can provide practical contributions to the social media use and mental health of special persons 60 years and older.

Variation in Obsessive-Compulsive Disorder Symptoms and Treatments: A Side Effect of COVID-19.

The Coronavirus Disease 2019 (COVID-19) exerts variable impact on patients with obsessive compulsive disorders (OCD). There remains a challenge to determine the extent to which OCD is exacerbated due to the pandemic. Therefore, our aim is to explicate the latest researching progress of OCD under COVID-19 based on a review of 15 existing articles. Our review confirms the prevalence of OCD exacerbation in different age groups and particular symptoms. However, it also reveals nonconformity among research, lack of investigation in OCD treatment, and imbalance in OCD symptoms research. Further, we discuss the probable reasons of the exacerbation and current situation of OCD treatments. Finally, based on our discussion, we offer suggestions on how to manage OCD under the new circumstance, including the introduction of new policies, the use of communications technology, the improvement of researching methods, and possible angles for further research.

Machine learning discovery of distinguishing laboratory features for severity classification of COVID-19 patients

Abstract The exponential spread of COVID-19 worldwide is evident, with devastating outbreaks primarily in the United States, Spain, Italy, the United Kingdom, France, Germany, Turkey and Russia. As of 1 May 2020, a total of 3,308,386 confirmed cases have been reported worldwide, with an accumulative mortality of 233,093. Due to the complexity and uncertainty of the pathology of COVID-19, it is not easy for front-line doctors to categorise severity levels of clinical COVID-19 that are general and severe/critical cases, with consistency. The more than 300 laboratory features, coupled with underlying disease, all combine to complicate proper and rapid patient diagnosis. However, such screening is necessary for early triage, diagnosis, assignment of appropriate level of care facility, and institution of timely intervention. A machine learning analysis was carried out with confirmed COVID-19 patient data from 10 January to 18 February 2020, who were admitted to Tongji Hospital, in Wuhan, China. A softmax neural network-based machine learning model was established to categorise patient severity levels. According to the analysis of 2662 cases using clinical and laboratory data, the present model can be used to reveal the top 30 of more than 300 laboratory features, yielding 86.30% blind test accuracy, 0.8195 F1-score, and 100% consistency using a two-way patient classification of severe/critical to general. For severe/critical cases, F1-score is 0.9081 (i.e. recall is 0.9050, and precision is 0.9113). This model for classification can be accomplished at a mini-second-level computational cost (in contrast to minute-level manual). Based on available COVID-19 patient diagnosis and therapy, an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100% consistency to significantly improve diagnostic and classification efficiency. The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines, thus creating a more comprehensive assessment of COVID-19 cases during the early stages of infection. Such early differentiation will help the assignment of the appropriate level of care for individual patients.

Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China.

Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. METHODS: A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 ± 14.0 years (age range, 11-93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans. RESULTS: CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung's volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (≥ 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia. CONCLUSION: Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients.

Evaluation of the Adjuvant Efficacy of Natural Herbal Medicine on COVID-19: A Retrospective Matched Case-Control Study

Since the outbreak of Corona Virus Disease 2019 (COVID-19) in Hubei province, the epidemic scale has increased rapidly, and no effective antiviral drug therapy has been identified yet. This study aimed to evaluate the adjuvant efficacy of Natural Herbal Medicine (NHM) combined with Western medicine in the treatment of COVID-19. We performed a retrospective, 1:1 matched, case-control study of the first cohort of hospitalized COVID-19-confirmed cases (January 17, 2020 to January 28, 2020). A total of 22 of the 36 confirmed patients were included in this study, split into two groups of 11: the NHM group (NHM combined standard Western medicine treatment) and control group (standard Western medicine treatment alone). All patients received appropriate supportive care and regular clinical and laboratory monitoring. Main evaluation indicators included improvement of clinical symptoms such as fever, cough and diarrhea after hospitalization;pathogen nucleic acid test result of respiratory tract and fecal specimens of the patient after hospitalization, and change of chest CT examination after hospitalization. The duration of fever in the NHM group (3.4+/-2.4 days) was significantly shorter than that in the control group (5.6+/-2.2 days) (p=0.03). During the whole hospitalization period, the number of cases with diarrhea in the NHM group (two cases) was less than that in the control group (eight cases) (p=0.03). Compared with the control group (7.5+/-1.6), the duration for improvement (DI) of chest CT in the NHM group (5.6+/-2.3) was significantly shorter (p=0.04). Our results suggest that NHM could improve the clinical symptoms of COVID-19 patients and may be effective in treating COVID-19;thus, a larger, prospective, randomized, controlled clinical trial should be conducted to further evaluate the adjuvant efficacy of NHM in the treatment of COVID-19.