Recently, successful predictions using machine learning (ML) algorithms have been reported in various fields. However, in traumatic brain injury (TBI) cohorts, few studies have examined modern ML algorithms. To develop a simple ML model for TBI outcome prediction, we conducted a performance comparison of nine algorithms: ridge regression, LASSO regression, random forest, gradient boosting, extra trees, decision tree, Gaussian naïve Bayes, multinomial naïve Bayes, and support vector machine. Fourteen feasible parameters were introduced in the ML models, including age, Glasgow coma scale, systolic blood pressure, abnormal pupillary response, major extracranial injury, computed tomography findings, and routinely collected laboratory values (glucose, C-reactive protein, and fibrin/fibrinogen degradation products). Data from 232 TBI patients were randomly divided into a training sample (80%) for hyperparameter tuning and validation sample (20%). The bootstrap method was used for validation. Random forest demonstrated the best performance for in-hospital poor outcome prediction and ridge regression for in-hospital mortality prediction: the mean statistical measures were 100% sensitivity, 72.3% specificity, 91.7% accuracy, and 0.895 area under the receiver operating characteristic curve (AUC); and 88.4% sensitivity, 88.2% specificity, 88.6% accuracy, and 0.875 AUC, respectively. Based on the feature selection method using the tree-based ensemble algorithm, age, Glasgow coma scale, fibrin/fibrinogen degradation products, and glucose were identified as the most important prognostic factors for poor outcome and mortality. Our results indicated the relatively good predictive performance of modern ML for TBI outcome. Further external validation is required for more heterogeneous samples to confirm our results.
The SARS-CoV-2 variant Omicron is now under investigation. We evaluated cross-neutralizing activity against Omicron in COVID-19 convalescent patients (n = 23) who had received two doses of an mRNA vaccination (BNT162b2 or mRNA-1273). Intriguingly, after the second vaccination, the neutralizing antibody titers of subjects against SARS-CoV-2 variants, including Omicron, all became seropositive, and significant fold-increases (21.1–52.0) were seen regardless of the disease severity of subjects. Our findings thus demonstrate that two doses of mRNA vaccination to SARS-CoV-2 convalescent patients can induce cross-neutralizing activity against Omicron.
BackgroundSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the virus responsible for the Coronavirus Disease 2019 (COVID-19) pandemic. The emergence of variants of concern (VOCs) has become one of the most pressing issues in public health. To control VOCs, it is important to know which COVID-19 convalescent sera have cross-neutralizing activity against VOCs and how long the sera maintain this protective activity.MethodsSera of patients infected with SARS-CoV-2 from March 2020 to January 2021 and admitted to Hyogo Prefectural Kakogawa Medical Center were selected. Blood was drawn from patients at 1-3, 3-6, and 6-8 months post onset. Then, a virus neutralization assay against SARS-CoV-2 variants (D614G mutation as conventional strain; B.1.1.7, P.1, and B.1.351 as VOCs) was performed using authentic viruses.ResultsWe assessed 97 sera from 42 patients. Sera from 28 patients showed neutralizing activity that was sustained for 3-8 months post onset. The neutralizing antibody titer against D614G significantly decreased in sera of 6-8 months post onset compared to those of 1-3 months post onset. However, the neutralizing antibody titers against the three VOCs were not significantly different among 1-3, 3-6, and 6-8 months post onset.DiscussionOur results indicate that neutralizing antibodies that recognize the common epitope for several variants may be maintained for a long time, while neutralizing antibodies having specific epitopes for a variant, produced in large quantities immediately after infection, may decrease quite rapidly.
Background In March 2021, Japan is facing a 4th wave of SARS-CoV-2 infection. To prevent further spread of infection, sera cross-neutralizing activity of patients previously infected with conventional SARS-CoV-2 against novel variants is important but is not firmly established. Methods We investigated the neutralizing potency of 81 COVID-19 patients' sera from the 1st–4th waves of pandemic against SARS-CoV-2 D614G, B.1.1.7, P.1, and B.1.351 variants using their authentic viruses. Results Most sera had neutralizing activity against all variants, showing similar activity against B.1.1.7 and D614G, but lower activity especially against B.1.351. In the 4th wave, sera-neutralizing activity against B.1.1.7 was significantly higher than that against any other variants, including D614G. The sera-neutralizing activity in less-severe patients was lower than that of more-severe patients for all variants. Conclusions The cross-neutralizing activity of convalescent sera was effective against all variants but was potentially weaker for B.1.351. The high neutralizing activity specific for B.1.1.7 in the 4th wave suggests that the mutations in the virus might cause conformational change of its spike protein, which affects immune recognition for D614G. Our results indicate that individuals who recover from COVID-19 could be protected from the severity caused by infection with newly emerging variants.
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