Modelling the impact of non-pharmaceutical interventions on workplace transmission of SARS-CoV-2 in the home-delivery sector.

OBJECTIVE: We aimed to use mathematical models of SARS-COV-2 to assess the potential efficacy of non-pharmaceutical interventions on transmission in the parcel delivery and logistics sector. METHODS: We devloped a network-based model of workplace contacts based on data and consultations from companies in the parcel delivery and logistics sectors. We used these in stochastic simulations of disease transmission to predict the probability of workplace outbreaks in this settings. Individuals in the model have different viral load trajectories based on SARS-CoV-2 in-host dynamics, which couple to their infectiousness and test positive probability over time, in order to determine the impact of testing and isolation measures. RESULTS: The baseline model (without any interventions) showed different workplace infection rates for staff in different job roles. Based on our assumptions of contact patterns in the parcel delivery work setting we found that when a delivery driver was the index case, on average they infect only 0.14 other employees, while for warehouse and office workers this went up to 0.65 and 2.24 respectively. In the LIDD setting this was predicted to be 1.40, 0.98, and 1.34 respectively. Nonetheless, the vast majority of simulations resulted in 0 secondary cases among customers (even without contact-free delivery). Our results showed that a combination of social distancing, office staff working from home, and fixed driver pairings (all interventions carried out by the companies we consulted) reduce the risk of workplace outbreaks by 3-4 times. CONCLUSION: This work suggests that, without interventions, significant transmission could have occured in these workplaces, but that these posed minimal risk to customers. We found that identifying and isolating regular close-contacts of infectious individuals (i.e. house-share, carpools, or delivery pairs) is an efficient measure for stopping workplace outbreaks. Regular testing can make these isolation measures even more effective but also increases the number of staff isolating at one time. It is therefore more efficient to use these isolation measures in addition to social distancing and contact reduction interventions, rather than instead of, as these reduce both transmission and the number of people needing to isolate at one time.

Contact patterns of UK home delivery drivers and their use of protective measures during the COVID-19 pandemic: a cross-sectional study.

OBJECTIVES: To quantify contact patterns of UK home delivery drivers and identify protective measures adopted during the pandemic. METHODS: We conducted a cross-sectional online survey to measure the interactions of 170 UK delivery drivers during a working shift between 7 December 2020 and 31 March 2021. RESULTS: Delivery drivers had a mean number of 71.6 (95% CI 61.0 to 84.1) customer contacts per shift and 15.0 (95% CI 11.2 to 19.2) depot contacts per shift. Maintaining physical distancing with customers was more common than at delivery depots. Prolonged contact (more than 5 min) with customers was reported by 5.4% of drivers on their last shift. We found 3.0% of drivers had tested positive for SARS-CoV-2 since the start of the pandemic and 16.8% of drivers had self-isolated due to a suspected or confirmed case of COVID-19. In addition, 5.3% (95% CI 2.3% to 10.2%) of participants reported having worked while ill with COVID-19 symptoms, or with a member of their household having a suspected or confirmed case of COVID-19. CONCLUSION: Delivery drivers had a large number of face-to-face customer and depot contacts per shift compared with other working adults during this time. However, transmission risk may be curtailed as contact with customers was of short duration. Most drivers were unable to maintain physical distance with customers and at depots at all times. Usage of protective items such as face masks and hand sanitiser was widespread.

Adjuvant-Free COVID-19 Vaccine with Glycoprotein Antigen Oxidized by Periodate Rapidly Elicits Potent Immune Responses.

Modification of antigens to improve their immunogenicity represents a promising direction for the development of protein vaccine. Here, we designed facilely prepared adjuvant-free vaccines in which the N-glycan of SARS-CoV-2 receptor-binding domain (RBD) glycoprotein was oxidized by sodium periodate. This strategy only minimally modifies the glycans and does not interfere with the epitope peptides. The RBD glycoprotein oxidized by high concentrations of periodate (RBDHO) significantly enhanced antigen uptake mediated by scavenger receptors and promoted the activation of antigen-presenting cells. Without any external adjuvant, two doses of RBDHO elicited 324- and 27-fold increases in IgG antibody titers and neutralizing antibody titers, respectively, compared to the unmodified RBD antigen. Meanwhile, the RBDHO vaccine could cross-neutralize all of the SARS-CoV-2 variants of concern. In addition, RBDHO effectively enhanced cellular immune responses. This study provides a new insight for the development of adjuvant-free protein vaccines.

Are platelet volume indices of clinical use in COVID-19? A systematic review.

Background: The incidence of thrombotic complications is high in COVID-19 patients with severe disease. As key regulators of thrombus formation, platelets likely play a crucial role as mediators of severe acute respiratory syndrome coronavirus 2 associated pathogenesis. Studies have reported that parameters reflecting platelet size, known as platelet volume indices (PVI), are raised in patients with thrombosis and can predict poor outcomes. This systematic review evaluates the potential for PVI to be used as a predictor of COVID-19 morbidity and mortality. Methods: English and Chinese databases were searched electronically to identify studies reporting data on mean platelet volume, platelet distribution width or platelet-large cell ratio in COVID-19 patients. Included articles underwent a quality rating and descriptive narrative analysis. Results: Thirty-two studies were included in the systematic review. The results show a general trend for PVI to be raised in severe COVID-19 patients and non-survivors, with 14 studies reporting significant differences of baseline PVI between severe and mild disease. Nonetheless, longitudinal studies showed varying PVI trends over the course of the disease and evidence for PVI to be associated with disease progression was limited. The quality rating of 12 studies was poor, 16 were rated fair and four were good. Most studies were retrospective in design, used small study populations and did not consider confounding factors that influence platelet volume. Studies also contained technical flaws in PVI measurement, limiting the reliability of the results. Conclusion: The evidence on the clinical usefulness of PVI is greatly limited by the lack of prospective evaluation, together with technical problems in measuring PVI. Carefully designed prospective studies are warranted. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=304305, identifier CRD42022304305.

Novel sialoglycan linkage for constructing adjuvant-protein conjugate as potent vaccine for COVID-19.

Self-adjuvanting protein vaccines have been proved to be highly immunogenic with efficient codelivery of adjuvant and antigen. Current protein vaccines with built-in adjuvants are all modified at the peptide backbone of antigen protein, which could not achieve minor epitope interference and adjuvant multivalency at the same time. Herein, we developed a new conjugate strategy to construct effective adjuvant-protein vaccine with adjuvant cluster effect and minimal epitope interference. The toll-like receptor 7 agonist (TLR7a) is covalently conjugated on the terminal sialoglycans of SARS-CoV-2-S1 protein, leading to intracellular release of the small-molecule stimulators with greatly reduced risks of systemic toxicity. The resulting TLR7a-S1 conjugate elicited strong activation of immune cells in vitro, and potent antibody and cellular responses with a significantly enhanced Th1-bias in vivo. TLR7a-S1-induced antibody also effectively cross-neutralized all variants of concern. This sialoglycoconjugation approach to construct protein conjugate vaccines will have more applications to combat SARS-CoV-2 and other diseases.

Self-Adjuvanting Protein Vaccine Conjugated with a Novel Synthetic TLR4 Agonist on Virus-Like Liposome Induces Potent Immunity against SARS-CoV-2.

Exploring potent adjuvants and new vaccine strategies is crucial for the development of protein vaccines. In this work, we synthesized a new TLR4 agonist, structurally simplified lipid A analogue GAP112, as a potent built-in adjuvant to improve the immunogenicity of SARS-CoV-2 spike RBD protein. The new TLR4 agonist GAP112 was site-selectively conjugated on the N-terminus of RBD to construct an adjuvant-protein conjugate vaccine in a liposomal formulation. It is the first time that a TLR4 agonist is site-specifically and quantitatively conjugated to a protein antigen. Compared with an unconjugated mixture of GAP112/RBD, a two-dose immunization of the GAP112-RBD conjugate vaccine strongly activated innate immune cells, elicited a 223-fold increase in RBD-specific antibodies, and markedly enhanced T-cell responses. Antibodies induced by GAP112-RBD also effectively cross-neutralized SARS-CoV-2 variants (Delta/B.1.617.2 and Omicron/B.1.1.529). This conjugate strategy provides an effective method to greatly enhance the immunogenicity of antigen in protein vaccines against SARS-CoV-2 and other diseases.

Are platelet volume indices of clinical use in COVID-19? A systematic review

Background The incidence of thrombotic complications is high in COVID-19 patients with severe disease. As key regulators of thrombus formation, platelets likely play a crucial role as mediators of severe acute respiratory syndrome coronavirus 2 associated pathogenesis. Studies have reported that parameters reflecting platelet size, known as platelet volume indices (PVI), are raised in patients with thrombosis and can predict poor outcomes. This systematic review evaluates the potential for PVI to be used as a predictor of COVID-19 morbidity and mortality. Methods English and Chinese databases were searched electronically to identify studies reporting data on mean platelet volume, platelet distribution width or platelet-large cell ratio in COVID-19 patients. Included articles underwent a quality rating and descriptive narrative analysis. Results Thirty-two studies were included in the systematic review. The results show a general trend for PVI to be raised in severe COVID-19 patients and non-survivors, with 14 studies reporting significant differences of baseline PVI between severe and mild disease. Nonetheless, longitudinal studies showed varying PVI trends over the course of the disease and evidence for PVI to be associated with disease progression was limited. The quality rating of 12 studies was poor, 16 were rated fair and four were good. Most studies were retrospective in design, used small study populations and did not consider confounding factors that influence platelet volume. Studies also contained technical flaws in PVI measurement, limiting the reliability of the results. Conclusion The evidence on the clinical usefulness of PVI is greatly limited by the lack of prospective evaluation, together with technical problems in measuring PVI. Carefully designed prospective studies are warranted. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=304305, identifier CRD42022304305.

A Chess and Card Room-Induced COVID-19 Outbreak and Its Agent-Based Simulation in Yangzhou, China.

Objective: To evaluate epidemiological characteristics of the COVID-19 outbreak that resurged in Yangzhou and to simulate the impact of different control measures at different regional scales. Methods: We collected personal information from 570 laboratory-confirmed cases in Yangzhou from 28 July to 26 August 2021, and built a modified susceptible-exposed-infected-removed (SEIR) model and an agent-based model. Results: The SEIR model showed that for passengers from medium-high risk areas, pre-travel nucleic acid testing within 3 days could limit the total number of infected people in Yangzhou to 50; among elderly persons, a 60% increase in vaccination rates could reduce the estimated infections by 253. The agent-based model showed that when the population density of the chess and card room dropped by 40%, the number of infected people would decrease by 54 within 7 days. A ventilation increase in the chess and card room from 25 to 50% could reduce the total number of infections by 33 within 7 days; increasing the ventilation from 25 to 75% could reduce the total number of infections by 63 within 7 days. Conclusions: The SEIR model and agent-based model were used to simulate the impact of different control measures at different regional scales successfully. It is possible to provide references for epidemic prevention and control work.

Agility and Sustainability: A Qualitative Evaluation of COVID-19 Non-pharmaceutical Interventions in the UK Logistics Sector.

Background: The emergence of SARS-CoV-2 triggered a chain of public health responses that radically changed our way of living and working. Non-healthcare sectors, such as the logistics sector, play a key role in such responses. This research aims to qualitatively evaluate the non-pharmaceutical interventions (NPIs) implemented in the UK logistics sector during the COVID-19 pandemic. Methods: We conducted nine semi-structured interviews in July-August 2020 and May-June 2021. In total 11 interviewees represented six companies occupying a range of positions in the UK's logistics sector, including takeaway food delivery, large and small goods delivery and home appliance installation, and logistics technology providers. Thematic analysis was completed using NVivo12. Codes relevant to NPIs were grouped into themes and mapped deductively onto an adapted Hierarchy of Control (HoC) framework, focusing on delivery workers. Codes relevant to the implementation process of NPIs were grouped into themes/subthemes to identify key characteristics of rapid responses, and barriers and facilitators. Results: HoC analysis suggests the sector has implemented a wide range of risk mitigation measures, with each company developing their own portfolio of measures. Contact-free delivery was the most commonly implemented measure and perceived effective. The other implemented measures included social distancing, internal contact tracing, communication and collaboration with other key stakeholders of the sector. Process evaluation identified facilitators of rapid responses including capacity to develop interventions internally, localized government support, strong external mandates, effective communication, leadership support and financial support for self-isolation, while barriers included unclear government guidance, shortage of testing capacity and supply, high costs and diversified language and cultural backgrounds. Main sustainability issues included compliance fatigue, and the possible mental health impacts of a prolonged rapid response. Conclusions: This research identified drivers and obstacles of rapid implementation of NPIs in response to a respiratory infection pandemic. Existing implementation process models do not consider speed to respond and the absence or lack of guidance in emergency situations such as the COVID-19. We recommend the development of a rapid response model to inform the design of effective and sustainable infection prevention and control policies and to focus future research priorities.

Role of Neuroimmune Interactions in COVID-19-related Cardiovascular Damage.

Coronavirus disease 2019 (COVID-19) has caused a global pandemic impacting over 200 countries/regions and more than 200 million patients worldwide. Among the infected patients, there is a high prevalence of COVID-19-related cardiovascular injuries. However, the specific mechanisms linking cardiovascular damage and COVID-19 remain unclear. The COVID-19 pandemic also has exacerbated the mental health burden of humans. Considering the close association between neuroimmune interactions and cardiovascular disease, this review assessed the complex pathophysiological mechanisms connecting neuroimmune interactions and cardiovascular disease. It was revealed that the mental health burden might be a pivotal accomplice causing COVID-19-associated cardiovascular damage. Specifically, the proinflammatory status of patients with a terrible mood state is closely related to overdrive of the hypothalamus-pituitary-adrenal (HPA) axis, sympathovagal imbalance, and endothelial dysfunction, which lead to an increased risk of developing cardiovascular injury during COVID-19. Therefore, during the prevention and treatment of cardiovascular complications in COVID-19 patients, particular attention should be given to relieve the mental health burden of these patients.

Hydrophilic rhodamine B-loaded / boronic acid-modified graphene oxide nanocomposite as a substitute of enzyme-labeled second antibody for ultrasensitive detection of antibodies.

Enzyme-labeled secondary antibody is often used to amplify the output signal in the process of antibody detection. However, its preparation process is complex and time-consuming. Herein, we fabricated an innovative hydrophilic rhodamine B-loaded / boronic acid-modified graphene oxide (HRBGO) nanocomposite, used as a substitute of enzyme-labeled second antibody. The synthetic HRBGO was loaded with generous rhodamine B and modified with boronic acid. Therefore, the HRBGO could selectively label the carbohydrate chains of Fc fragment of primary antibody through specific boronate affinity recognition, and then perform signal output and amplification by releasing rhodamine B. To verify the practicability of HRBGO, trastuzumab as a humanized monoclonal antibody targeting human epidermal growth factor receptor-2 (HER2) was selected as model antibody. A glycosylation site-blocked / HER2-immobilized magnetic nanoparticles (GHMN) was also prepared for selectively capturing trastuzumab from complex samples via specific immunoaffinity. Because the glycosylation sites of HER2 can also be labeled with the HRBGO by boronate affinity recognition, these sites were blocked by a masking agent to minimize the background signal. For specific and ultrasensitive detection of trastuzumab, the integration of GHMN and HRBGO was proposed and optimized in detail. Trastuzumab detection based on HRBGO consisted of three steps: specific capture, selective labeling, and output signal. The proposed strategy provided ultrahigh sensitivity with limit of detection of 0.35 fg mL-1 and was successfully applied in the detection of trastuzumab in spiked serum sample with recovery and relative standard deviation in the range of 98.7-103.8% and 3.8-6.0%, respectively. To assess universal applicability, the HRBGO was also successfully used for the determination of anti-SARS-COV2 RBD antibody in human serum sample.

A protein vaccine with Alum/c-GAMP/poly(I:C) rapidly boosts robust immunity against SARS-CoV-2 and variants of concern.

Adjuvants are important components in vaccines to increase the immunogenicity of proteins and induce optimal immunity. In this study, we designed a novel ternary adjuvant system Alum + c-GAMP + poly(I:C) with STING agonist 3,3'-c-GAMP (c-GAMP) and TLR3 agonist poly(I:C) co-adsorbed on the conventional adjuvant aluminum gel (Alum), and further constructed an S1 protein vaccine. Two doses of vaccination with the ternary adjuvant vaccine were sufficient to induce a balanced Th1/Th2 immune response and robust humoral and cellular immunity. Additionally, the ternary adjuvant group had effective neutralizing activity against live virus SARS-CoV-2 and pseudovirus of all variants of concern (alpha, beta, gamma, delta and omicron). These results indicate that the ternary adjuvants have a significant synergistic effect and can rapidly trigger potent immune responses; the combination of the ternary adjuvant system with S1 protein is a promising COVID-19 vaccine candidate.

MPLA-Adjuvanted Liposomes Encapsulating S-Trimer or RBD or S1, but Not S-ECD, Elicit Robust Neutralization Against SARS-CoV-2 and Variants of Concern.

Safe and effective vaccines are the best method to defeat worldwide SARS-CoV-2 and its circulating variants. The SARS-CoV-2 S protein and its subunits are the most attractive targets for the development of protein-based vaccines. In this study, we evaluated three lipophilic adjuvants, monophosphoryl lipid A (MPLA), Toll-like receptor (TLR) 1/2 ligand Pam3CSK4, and α-galactosylceramide (α-GalCer), in liposomal and nonliposomal vaccines. The immunological results showed that the MPLA-adjuvanted liposomal vaccine induced the strongest humoral and cellular immunity. Therefore, we further performed a systematic comparison of S-trimer, S-ECD, S1, and RBD as antigens in MPLA-adjuvanted liposomes and found that, although these four vaccines all induced robust specific antibody responses, only S-trimer, S1, and RBD liposomes, but not S-ECD, elicited potent neutralizing antibody responses. Moreover, RBD, S-trimer, and S1 liposomes effectively neutralized variants (B.1.1.7/alpha, B.1.351/beta, P.1/gamma, B.1.617.2/delta, and B.1.1.529/omicron). These results provide important information for the subunit vaccine design against SARS-CoV-2 and its variants.

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.

Perspective of HLA-G Induced Immunosuppression in SARS-CoV-2 Infection.

COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has threatened public health worldwide. Host antiviral immune responses are essential for viral clearance and disease control, however, remarkably decreased immune cell numbers and exhaustion of host cellular immune responses are commonly observed in patients with COVID-19. This is of concern as it is closely associated with disease severity and poor outcomes. Human leukocyte antigen-G (HLA-G) is a ligand for multiple immune inhibitory receptors, whose expression can be upregulated by viral infections. HLA-G/receptor signalling, such as engagement with immunoglobulin-like transcript 2 (ILT-2) or ILT-4, not only inhibit T and natural killer (NK) cell immune responses, dendritic cell (DC) maturation, and B cell antibody production. It also induces regulatory cells such as myeloid-derived suppressive cells (MDSCs), or M2 type macrophages. Moreover, HLA-G interaction with CD8 and killer inhibitory receptor (KIR) 2DL4 can provoke T cell apoptosis and NK cell senescence. In this context, HLA-G can induce profound immune suppression, which favours the escape of SARS-CoV-2 from immune attack. Although detailed knowledge on the clinical relevance of HLA-G in SARS-CoV-2 infection is limited, we herein review the immunopathological aspects of HLA-G/receptor signalling in SARS-CoV-2 infection, which could provide a better understanding of COVID-19 disease progression and identify potential immunointerventions to counteract SARS-CoV-2 infection.

Risk factors associated with respiratory infectious disease-related presenteeism: a rapid review.

BACKGROUND: Workplace transmission is a significant contributor to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks. Previous studies have found that infectious illness presenteeism could contribute to outbreaks in occupational settings and identified multiple occupational and organisational risk factors. Amid the COVID-19 pandemic, it is imperative to investigate presenteeism particularly in relation to respiratory infectious disease (RID). Hence, this rapid review aims to determine the prevalence of RID-related presenteeism, including COVID-19, and examines the reported reasons and associated risk factors. METHODS: The review followed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search approach and focused on studies published in English and Chinese. Database searches included MEDLINE, EMBASE, Web of Science, China Knowledge Resource Integrated Database (CNKI) and preprint databases MedRxiv and BioRxiv. RESULTS: The search yielded 54 studies, of which four investigated COVID-19-related presenteeism. Prevalence of work presenteeism ranged from 14.1 to 55% for confirmed RID, and 6.6 to 100% for those working with suspected or subclinical RID. The included studies demonstrated that RID-related presenteeism is associated with occupation, sick pay policy, age, gender, health behaviour and perception, vaccination, peer pressure and organisational factors such as presenteeism culture. CONCLUSIONS: This review demonstrates that presenteeism or non-adherence to isolation guidance is a real concern and can contribute to workplace transmissions and outbreaks. Policies which would support workers financially and improve productivity, should include a range of effective non-pharmaceutical inventions such as workplace testing, promoting occupational health services, reviewing pay and bonus schemes and clear messaging to encourage workers to stay at home when ill. Future research should focus on the more vulnerable and precarious occupational groups, and their inter-relationships, to develop comprehensive intervention programs to reduce RID-related presenteeism.

Diabetes patients with comorbidities had unfavorable outcomes following COVID-19: A retrospective study.

BACKGROUND: Previous studies have shown that diabetes mellitus is a common comorbidity of coronavirus disease 2019 (COVID-19), but the effects of diabetes or anti-diabetic medication on the mortality of COVID-19 have not been well described. AIM: To investigate the outcome of different statuses (with or without comorbidity) and anti-diabetic medication use before admission of diabetic after COVID-19. METHODS: In this multicenter and retrospective study, we enrolled 1422 consecutive hospitalized patients from January 21, 2020, to March 25, 2020, at six hospitals in Hubei Province, China. The primary endpoint was in-hospital mortality. Epidemiological material, demographic information, clinical data, laboratory parameters, radiographic characteristics, treatment and outcome were extracted from electronic medical records using a standardized data collection form. Most of the laboratory data except fasting plasma glucose (FPG) were obtained in first hospitalization, and FPG was collected in the next day morning. Major clinical symptoms, vital signs at admission and comorbidities were collected. The treatment data included not only COVID-19 but also diabetes mellitus. The duration from the onset of symptoms to admission, illness severity, intensive care unit (ICU) admission, and length of hospital stay were also recorded. All data were checked by a team of sophisticated physicians. RESULTS: Patients with diabetes were 10 years older than non-diabetic patients [(39 - 64) vs (56 - 70), P < 0.001] and had a higher prevalence of comorbidities such as hypertension (55.5% vs 21.4%, P < 0.001), coronary heart disease (CHD) (9.9% vs 3.5%, P < 0.001), cerebrovascular disease (CVD) (3% vs 2.2%, P < 0.001), and chronic kidney disease (CKD) (4.7% vs 1.5%, P = 0.007). Mortality (13.6% vs 7.2%, P = 0.003) was more prevalent among the diabetes group. Further analysis revealed that patients with diabetes who took acarbose had a lower mortality rate (2.2% vs 26.1, P < 0.01). Multivariable Cox regression showed that male sex [hazard ratio (HR) 2.59 (1.68 - 3.99), P < 0.001], hypertension [HR 1.75 (1.18 - 2.60), P = 0.006), CKD [HR 4.55 (2.52-8.20), P < 0.001], CVD [HR 2.35 (1.27 - 4.33), P = 0.006], and age were risk factors for the COVID-19 mortality. Higher HRs were noted in those aged ≥ 65 (HR 11.8 [4.6 - 30.2], P < 0.001) vs 50-64 years (HR 5.86 [2.27 - 15.12], P < 0.001). The survival curve revealed that, compared with the diabetes only group, the mortality was increased in the diabetes with comorbidities group (P = 0.009) but was not significantly different from the non-comorbidity group (P = 0.59). CONCLUSION: Patients with diabetes had worse outcomes when suffering from COVID-19; however, the outcome was not associated with diabetes itself but with comorbidities. Furthermore, acarbose could reduce the mortality in diabetic.

Evaluation and Comparison of Serological Methods for COVID-19 Diagnosis.

The worldwide pandemic of COVID-19 has become a global public health crisis. Various clinical diagnosis methods have been developed to distinguish COVID-19-infected patients from healthy people. The nucleic acid test is the golden standard for virus detection as it is suitable for early diagnosis. However, due to the low amount of viral nucleic acid in the respiratory tract, the sensitivity of nucleic acid detection is unsatisfactory. As a result, serological screening began to be widely used with the merits of simple procedures, lower cost, and shorter detection time. Serological tests currently include the enzyme-linked immunosorbent assay (ELISA), lateral flow immunoassay (LFIA), and chemiluminescence immunoassay (CLIA). This review describes various serological methods, discusses the performance and diagnostic effects of different methods, and points out the problems and the direction of optimization, to improve the efficiency of clinical diagnosis. These increasingly sophisticated and diverse serological diagnostic technologies will help human beings to control the spread of COVID-19.

Pulling-Force Spinning Top for Serum Separation Combined with Paper-Based Microfluidic Devices in COVID-19 ELISA Diagnosis.

The spread of Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), resulting in a global pandemic with around four million deaths. Although there are a variety of nucleic acid-based tests for detecting SARS-CoV-2, these methods have a relatively high cost and require expensive supporting equipment. To overcome these limitations and improve the efficiency of SARS-CoV-2 diagnosis, we developed a microfluidic platform that collected serum by a pulling-force spinning top and paper-based microfluidic enzyme-linked immunosorbent assay (ELISA) for quantitative IgA/IgM/IgG measurements in an instrument-free way. We further validated the paper-based microfluidic ELISA analysis of SARS-CoV-2 receptor-binding domain (RBD)-specific IgA/IgM/IgG antibodies from human blood samples as a good measurement with higher sensitivity compared with traditional IgM/IgG detection (99.7% vs 95.6%) for early illness onset patients. In conclusion, we provide an alternative solution for the diagnosis of SARS-CoV-2 in a portable manner by this smart integration of pulling-force spinning top and paper-based microfluidic immunoassay.

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.