Humoral immune response to two doses of COVID-19 mRNA-based vaccines in people living with HIV: A systematic review and meta-analysis.

People living with HIV (PLWH) are susceptible to severe COVID-19 infection and hence this fragile population has prioritised vaccination. This systematic review and meta-analysis aimed to assess the humoral immune response after receiving two doses schedule of COVID-19 mRNA vaccinations in this high-risk population. A systematic electronic search on the PubMed database and manual searches were performed for relevant articles until 30 Sep 2022. Two outcomes of interest were seroconversion rates and anti-spike receptor binding domain (anti-S-RBD) antibody titres at the median time of 14-35 days following two-dose vaccination among PLWH. Nineteen cohorts and one cross-sectional study were eligible for inclusion in this study. The pooled estimate of seroconversion rate after receiving two doses of mRNA vaccination schedule were 98.4% and 75.2% among PLWH with CD4>500 cells/mm3 and CD4<200 cells/mm3 , respectively. Compared with controls, PLWH with CD4>500 cells/mm3 had a 51% likelihood of having positive anti-Spike-RBD immunoglobulin G (IgG) (OR: 0.509, 95% CI: 0.228, 1.133, p = 0.098) post-vaccination and this value was only 1.4% (OR: 0.014, 95% CI: 0.002, 0.078, p = 0.000) for PLWH with CD4<200 cells/mm3 . There was no significant difference in titres of antibodies on 14-35 days post-vaccination between PLWH with CD4>500 cells/mm3 and healthy controls (p = 0.06). The pooled median of anti-S-RBD IgG values were 1461.93 binding antibody units (BAU)/ml and 457.41 BAU/ml in PLWH with CD4>500 cells/mm3 and CD4<200 cells/mm3 , respectively. According to these findings, vaccination with both Pfizer-BioNTech and Moderna vaccines induced a robust humoral response in ART-treated HIV patients with preserved CD4 cell count. A diminished humoral immune response to vaccination against COVID-19 in PLWH with unrestored CD4 count implied the need of specific vaccination schemes.

Sotrovimab therapy in solid organ transplant recipients with mild to moderate COVID-19: a systematic review and meta-analysis.

Purpose: Solid organ transplant recipients (SOTR) have a high risk for severe COVID-19 infection; hence it is necessary to find alternative treatment strategies to protect these patients from the complications caused by the severe progression of the disease. This study aimed to determine the effectiveness of sotrovimab among SOTR with COVID-19.Materials and methods: A systematic literature search was conducted with relevant keywords to find studies that reported clinical outcomes regarding sotrovimab administration in SOTR outpatients with confirmed COVID-19 infection, who had mild-to-moderate symptoms.Results: Of 796 records found by a systematic search, only 14 met the inclusion criteria for reporting in a systematic review and only 6 enrolled in a meta-analysis. This meta-analysis indicated that SOTR outpatients with mild to moderate COVID-19 who received sotrovimab had lower likelihood of all-cause hospitalization (OR: 0.29, CI: 0.16, 0.52, p < 0.001), ICU admission (OR: 0.17, CI: 0.05, 0.64, p = 0.009) and mortality (OR: 0.15, CI: 0.03, 0.64, p = 0.010) within 30 days of drug infusion compared to controls.Conclusions: Our findings confirm that monoclonal antibody therapy with sotrovimab in SOTR is associated with better outcomes and consequently a reduced risk of disease progression in this high-risk population.

Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran.

BACKGROUND: The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients. METHODS: This cohort study included 2181 COVID-19 patients admitted from February 2020 to July 2021 in Sina and Besat hospitals in Hamadan, west of Iran. A total of 22 baseline features including patients' demographic information and clinical biomarkers were collected. Four strategies including removing missing values, mean, K-Nearest Neighbor (KNN), and Multivariate Imputation by Chained Equations (MICE) imputation methods were used to deal with missing data. Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. Next, considering the selected features, the predictive performance of GNAM for predicting mortality outcome was compared with logistic regression, RF, generalized additive model (GAMs), gradient boosting decision tree (GBDT), and deep neural networks (DNNs) classification models. Each model trained on fifty different subsets of a train-test dataset to ensure a model performance. The average accuracy, F1-score and area under the curve (AUC) evaluation indices were used for comparison of the predictive performance of the models. RESULTS: Out of the 2181 COVID-19 patients, 624 died during hospitalization and 1557 recovered. The missing rate was 3 percent for each patient. The mean age of dead patients (71.17 ± 14.44 years) was statistically significant higher than recovered patients (58.25 ± 16.52 years). Based on RF, 10 features with the highest relative importance were selected as the best influential features; including blood urea nitrogen (BUN), lymphocytes (Lym), age, blood sugar (BS), serum glutamic-oxaloacetic transaminase (SGOT), monocytes (Mono), blood creatinine (CR), neutrophils (NUT), alkaline phosphatase (ALP) and hematocrit (HCT). The results of predictive performance comparisons showed GNAM with the mean accuracy, F1-score, and mean AUC in the test dataset of 0.847, 0.691, and 0.774, respectively, had the best performance. The smooth function graphs learned from the GNAM were descending for the Lym and ascending for the other important features. CONCLUSIONS: Interpretable GNAM can perform well in predicting the mortality of COVID-19 patients. Therefore, the use of such a reliable model can help physicians to prioritize some important demographic and clinical biomarkers by identifying the effective features and the type of predictive trend in disease progression.

The Influence of Face Masks on Verbal Communication in Persian in the Presence of Background Noise in Healthcare Staff.

Wearing face masks has resulted in verbal communication being more challenging during the COVID-19 pandemic. This study aimed to investigate the effect of face masks on the speech comprehensibility of Persian nurses in healthcare settings. Twenty female nurses from the governmental hospitals randomly participated in an experiment on seven typical commercial face masks at two background noise levels. Nurses' speech intelligibility from a human talker when wearing each face mask was determined based on the speech discrimination score. The vocal effort of nurses wearing each face mask was determined based on the Borg CR10 scale. Based on the linear mixed model, the speech intelligibility of nurses from a human speaker wearing surgical masks, N95 masks, and a shield with face masks were approximately 10%, 20%, and 40-50% lower, respectively, than no-mask conditions (p < 0.01). The background noise decreased the speech intelligibility of nurses by approximately 22% (p < 0.01). The use of a face shield further decreased speech intelligibility up to 30% compared to using a face mask alone (p < 0.01). The vocal efforts of nurses when wearing surgical masks were not significant compared with the baseline vocal efforts (p > 0.05); however, vocal efforts of nurses when wearing N95 and N99 respirators were at an unacceptable level. The face masks had no considerable effect on the speech spectrum below 2.5 kHz; however, they reduced high frequencies by different values. Wearing face masks has a considerable impact on the verbal communication of nurses in Persian. The level of background noise in the healthcare setting can aggravate the effect sizes of face masks on speech comprehensibility.