Noncovalent antibody catenation on a target surface greatly increases the antigen-binding avidity.

Immunoglobulin G (IgG) antibodies are widely used for diagnosis and therapy. Given the unique dimeric structure of IgG, we hypothesized that, by genetically fusing a homodimeric protein (catenator) to the C-terminus of IgG, reversible catenation of antibody molecules could be induced on a surface where target antigen molecules are abundant, and that it could be an effective way to greatly enhance the antigen-binding avidity. A thermodynamic simulation showed that quite low homodimerization affinity of a catenator, e.g. dissociation constant of 100 µM, can enhance nanomolar antigen-binding avidity to a picomolar level, and that the fold enhancement sharply depends on the density of the antigen. In a proof-of-concept experiment where antigen molecules are immobilized on a biosensor tip, the C-terminal fusion of a pair of weakly homodimerizing proteins to three different antibodies enhanced the antigen-binding avidity by at least 110 or 304 folds from the intrinsic binding avidity. Compared with the mother antibody, Obinutuzumab(Y101L) which targets CD20, the same antibody with fused catenators exhibited significantly enhanced binding to SU-DHL5 cells. Together, the homodimerization-induced antibody catenation would be a new powerful approach to improve antibody applications, including the detection of scarce biomarkers and targeted anticancer therapies.

Smoking and the Risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection.

INTRODUCTION: It is unclear whether smokers are more vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This study aimed to evaluate the association between smoking and the risk of SARS-CoV-2 infection. METHODS: A matched case-control study was conducted using a large nationwide database. The case group included patients with SARS-CoV-2 infection confirmed by the Korea Centers for Disease Control and Prevention, and the control group was randomly sampled from the general Korean population in the National Health Insurance Service database by matching sex, age, and region of residence. Conditional logistic regression models were used to investigate whether the risk of infection with SARS-CoV-2 was affected by smoking status. RESULTS: A total of 4167 patients with SARS-CoV-2 infection and 20 937 matched controls were enrolled. The proportion of ex-smokers and current smokers was 26.6% of the total participants. In multivariate analysis, smoking was not associated with an increased risk of SARS-CoV-2 infection (odds ratio [OR] = 0.56, confidence interval [CI] = 0.50-0.62). When ex-smokers and current smokers were analyzed separately, similar results were obtained (current smoker OR = 0.33, CI = 0.28-0.38; ex-smoker OR = 0.81, CI = 0.72-0.91). CONCLUSIONS: This study showed that smoking may not be associated with an increased risk of SARS-CoV-2 infection. Smoking tends to lower the risk of SARS-CoV-2 infection; however, these findings should be interpreted with caution. IMPLICATIONS: It is unclear whether smokers are more vulnerable to coronavirus disease 2019. In this large nationwide study in South Korea, smoking tended to lower the risk of infection with severe acute respiratory syndrome coronavirus 2. However, these findings should be interpreted with caution, and further confirmatory studies are required.

Vision-Threatening Ocular Adverse Events after Vaccination against Coronavirus Disease 2019.

A single-center retrospective observational case series was conducted. This case series enrolled patients who showed ophthalmic manifestations within one week after COVID-19 vaccination at Korea University Guro Hospital in Seoul, Korea, from May 2021 to January 2022. The medical records of patients who complained of ocular symptoms and showed ophthalmic adverse events within one week after COVID-19 vaccination were reviewed. Seventeen eyes from 16 patients with a mean age of 63.8 (range 33-83) years were included in the case series, and all symptoms developed within 1-7 days following inoculation. Retinal vein occlusion in nine eyes (52.9%), retinal artery occlusion in one eye (5.9%), newly developed anterior uveitis in one eye (5.9%), exacerbation of previously diagnosed panuveitis in two eyes (11.8%), and angle-closure attack with high intraocular pressure in four eyes (23.5%) were included. Twelve patients (75%) had been vaccinated with the AstraZeneca (AZD1222) and four (25%) with the Pfizer (BNT162b2) vaccines. Of these, 10 patients (62.5%) experienced ocular disease exacerbation after the first dose, 4 (25%) after the second dose, and 2 (12.5%) after the third dose (booster shot). Eleven patients (64.7%) underwent tests for hematological abnormalities, and three of them tested positive for anti-PF4 antibodies, but no abnormal findings were noted. A causal relationship between vaccination and the ocular manifestations could not be determined, which is a limitation of this study. However, clinicians should consider the effect of COVID-19 vaccination on ophthalmic disease. Further studies are required to elucidate the possible effects of COVID-19 vaccination on the eye.

Development and validation of a prognostic model for early triage of patients diagnosed with COVID-19.

We developed a tool to guide decision-making for early triage of COVID-19 patients based on a predicted prognosis, using a Korean national cohort of 5,596 patients, and validated the developed tool with an external cohort of 445 patients treated in a single institution. Predictors chosen for our model were older age, male sex, subjective fever, dyspnea, altered consciousness, temperature ≥ 37.5 °C, heart rate ≥ 100 bpm, systolic blood pressure ≥ 160 mmHg, diabetes mellitus, heart disease, chronic kidney disease, cancer, dementia, anemia, leukocytosis, lymphocytopenia, and thrombocytopenia. In the external validation, when age, sex, symptoms, and underlying disease were used as predictors, the AUC used as an evaluation metric for our model's performance was 0.850 in predicting whether a patient will require at least oxygen therapy and 0.833 in predicting whether a patient will need critical care or die from COVID-19. The AUCs improved to 0.871 and 0.864, respectively, when additional information on vital signs and blood test results were also used. In contrast, the protocols currently recommended in Korea showed AUCs less than 0.75. An application for calculating the prognostic score in COVID-19 patients based on the results of this study is presented on our website ( https://nhimc.shinyapps.io/ih-psc/ ), where the results of the validation ongoing in our institution are periodically updated.

Association between Body Mass Index and Risk of Coronavirus Disease 2019 (COVID-19): A Nationwide Case-control Study in South Korea.

BACKGROUND: Increased body mass index (BMI) has been associated with a higher risk of severe coronavirus disease 2019 (COVID-19) infections. However, whether obesity is a risk factor for contracting COVID-19 has hardly been investigated so far. METHODS: We examined the association between BMI level and the risk of COVID-19 infection in a nationwide case-control study comprised of 3788 case patients confirmed to have COVID-19 between 24 January and 9 April 2020 and 15 152 controls matched by age and sex, who were aged 20 years or more and underwent National Health Insurance Service (NHIS) health examinations between 2015-2017, using data from the Korean NHIS with linkage to the Korea Centers for Disease Control and Prevention data. Our primary exposure of interest was BMI level, categorized into 4 groups: <18.5 (underweight), 18.5-22.9 (normal weight), 23-24.9 (overweight), and ≥25 kg/m2 (obese). RESULTS: Of the entire 18 940 study participants, 11 755 (62.1%) were women, and the mean age of the study participants was 53.7 years (standard deviation, 13.8). In multivariable logistic regression models adjusted for sociodemographic, comorbidity, laboratory, and medication data, there was a graded association between higher BMI levels and higher risk of COVID-19 infection. Compared to normal-weight individuals, the adjusted odds ratios in the overweight and obese individuals were 1.13 (95% confidence interval [CI], 1.03-1.25) and 1.26 (95% CI, 1.15-1.39), respectively. This association was robust across age and sex subgroups. CONCLUSIONS: Higher BMI levels were associated with a higher risk of contracting COVID-19.

Does Diabetes Increase the Risk of Contracting COVID-19? A Population-Based Study in Korea.

BACKGROUND: This study aimed to determine the infection risk of coronavirus disease 2019 (COVID-19) in patients with diabetes (according to treatment method). METHODS: Claimed subjects to the Korean National Health Insurance claims database diagnosed with COVID-19 were included. Ten thousand sixty-nine patients with COVID-19 between January 28 and April 5, 2020, were included. Stratified random sampling of 1:5 was used to select the control group of COVID-19 patients. In total 50,587 subjects were selected as the control group. After deleting the missing values, 60,656 subjects were included. RESULTS: Adjusted odds ratio (OR) indicated that diabetic insulin users had a higher risk of COVID-19 than subjects without diabetes (OR, 1.25; 95% confidence interval [CI], 1.03 to 1.53; P=0.0278). In the subgroup analysis, infection risk was higher among diabetes male insulin users (OR, 1.42; 95% CI, 1.07 to 1.89), those between 40 and 59 years (OR, 1.66; 95% CI, 1.13 to 2.44). The infection risk was higher in diabetic insulin users with 2 to 4 years of morbidity (OR, 1.744; 95% CI, 1.003 to 3.044). CONCLUSION: Some diabetic patients with certain conditions would be associated with a higher risk of acquiring COVID-19, highlighting their need for special attention. Efforts are warranted to ensure that diabetic patients have minimal exposure to the virus. It is important to establish proactive care and screening tests for diabetic patients suspected with COVID-19 for timely disease diagnosis and management.

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study.

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.

Clinical Characteristics of Asymptomatic Patients with COVID-19: A Nationwide Cohort Study in South Korea.

OBJECTIVES: To delineate clinical characteristics of asymptomatic and symptomatic patients confirmed with COVID-19 in South Korea. METHODS: Data were obtained from the Korean National Health Insurance Service database linked to the Korea Centers for Disease Control and Prevention data. RESULTS: Among 10,237 patients (mean [SD] age, 45.0 [19.8] years; 60.1% female) who met the eligibility criteria for the study, 6,350 (62.0%) patients were asymptomatic, and 3,887(38.0%) patients were symptomatic. The mean and median age were similar between asymptomatic and symptomatic patients. Notably, we observed a U-shaped association between age group and the proportion of asymptomatic patients, with the nadir at 57.3% in the 40-49 age group. This U-shaped distribution was largely similar between men and women. The overall prevalence of asymptomatic individuals was higher, regardless of sex, residential area, income levels, and comorbid conditions. CONCLUSIONS: In this national cohort of over 10,000 patients with COVID-19, more than 60% of all cases in South Korea reported no symptoms at the time of diagnosis. Expanding criteria for contact tracing and testing to capture potential transmission before symptom onset should be urgently considered to inform control strategies for COVID-19.