Longevity of immunity following COVID-19 vaccination: a comprehensive review of the currently approved vaccines.

It is unknown how long the immunity following COVID-19 vaccination lasts. The current systematic review provides a perspective on the persistence of various antibodies for available vaccines.Both the BNT162b2 and the mRNA-1273 induce the production of IgA antibodies, reflecting the possible prevention of the asymptomatic spread. The mRNA-1273 vaccine's antibodies were detectable until 6 months, followed by the AZD1222, 3 months, the Ad26.COV2.S and the BNT162b2 vaccines within 2 months.The BNT162b2 produced anti-spike IgGs 11 days after the first dose and peaked at day 21, whereas the AZD1222 induced a neutralizing effect 22 days after the first dose.These vaccines induce T-cell mediated immune responses too. Each one of the AZD1222, Ad26.COV2.S, mRNA-1273 mediates T-cell response immunity at days 14-22, 15, and 43 after the first dose, respectively. Whereas for the BNT162b1 and BNT162b2 vaccines, T-cell immunity is induced 7 days and 12 weeks after the booster dose, respectively.

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU.

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP). Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset. Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.