Background: We evaluated the household secondary attack rate (SAR) of the omicron and delta severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, according to the vaccination status of the index case and household contacts; further, in vaccinated index cases, we evaluated the effect of the antibody levels on household transmission. Methods: A prospective cross-sectional study of 92 index cases and 197 quarantined household contacts was performed. Tests for SARS-CoV-2 variant type and antibody level were conducted in index cases, and results of polymerase chain reaction tests (during the quarantine period) were collected from contacts. Association of antibody levels in vaccinated index cases and SAR was evaluated by multivariate regression analysis. Results: The SAR was higher in households exposed to omicron variant (42%) than in those exposed to delta variant (27%) (P = 0.040). SAR was 35% and 23% for unvaccinated and vaccinated delta variant exposed contacts, respectively. SAR was 44% and 41% for unvaccinated and vaccinated omicron exposed contacts, respectively. Booster dose immunisation of contacts or vaccination of index cases reduced SAR of vaccinated omicron variant exposed contacts. In a model with adjustment, anti-receptor-binding domain antibody levels in vaccinated index cases were inversely correlated with household transmission of both delta and omicron variants.Neutralising antibody levels had a similar relationship. Conclusion Immunisation of household members may help to mitigate the current pandemic.

PURPOSE: The aim of this study was to evaluate the applicability of machine learning methods that combine data on age and prostate-specific antigen (PSA) levels for predicting prostate cancer. MATERIALS AND METHODS: We analyzed 943 patients who underwent transrectal ultrasonography (TRUS)-guided prostate biopsy at Chungnam National University Hospital between 2014 and 2018 because of elevated PSA levels and/or abnormal digital rectal examination and/or TRUS findings. We retrospectively reviewed the patients’ medical records, analyzed the prediction rate of prostate cancer, and identified 20 feature importances that could be compared with biopsy results using 5 different algorithms, viz., logistic regression (LR), support vector machine, random forest (RF), extreme gradient boosting, and light gradient boosting machine. RESULTS: Overall, the cancer detection rate was 41.8%. In patients younger than 75 years and with a PSA level less than 20 ng/mL, the best prediction model for prostate cancer detection was RF among the machine learning methods based on LR analysis. The PSA density was the highest scored feature importances in the same patient group. CONCLUSIONS: These results suggest that the prediction rate of prostate cancer using machine learning methods not inferior to that using LR and that these methods may increase the detection rate for prostate cancer and reduce unnecessary prostate biopsy, as they take into consideration feature importances affecting the prediction rate for prostate cancer.
Biopsy ; Chungcheongnam-do ; Digital Rectal Examination ; Forests ; Humans ; Logistic Models ; Machine Learning ; Medical Records ; Prostate ; Prostate-Specific Antigen ; Prostatic Neoplasms ; Retrospective Studies ; Support Vector Machine ; Ultrasonography