Physio-metabolic and clinical consequences of wearing face masks-Systematic review with meta-analysis and comprehensive evaluation.

Background: As face masks became mandatory in most countries during the COVID-19 pandemic, adverse effects require substantiated investigation. Methods: A systematic review of 2,168 studies on adverse medical mask effects yielded 54 publications for synthesis and 37 studies for meta-analysis (on n = 8,641, m = 2,482, f = 6,159, age = 34.8 ± 12.5). The median trial duration was only 18 min (IQR = 50) for our comprehensive evaluation of mask induced physio-metabolic and clinical outcomes. Results: We found significant effects in both medical surgical and N95 masks, with a greater impact of the second. These effects included decreased SpO2 (overall Standard Mean Difference, SMD = -0.24, 95% CI = -0.38 to -0.11, p < 0.001) and minute ventilation (SMD = -0.72, 95% CI = -0.99 to -0.46, p < 0.001), simultaneous increased in blood-CO2 (SMD = +0.64, 95% CI = 0.31-0.96, p < 0.001), heart rate (N95: SMD = +0.22, 95% CI = 0.03-0.41, p = 0.02), systolic blood pressure (surgical: SMD = +0.21, 95% CI = 0.03-0.39, p = 0.02), skin temperature (overall SMD = +0.80 95% CI = 0.23-1.38, p = 0.006) and humidity (SMD +2.24, 95% CI = 1.32-3.17, p < 0.001). Effects on exertion (overall SMD = +0.9, surgical = +0.63, N95 = +1.19), discomfort (SMD = +1.16), dyspnoea (SMD = +1.46), heat (SMD = +0.70), and humidity (SMD = +0.9) were significant in n = 373 with a robust relationship to mask wearing (p < 0.006 to p < 0.001). Pooled symptom prevalence (n = 8,128) was significant for: headache (62%, p < 0.001), acne (38%, p < 0.001), skin irritation (36%, p < 0.001), dyspnoea (33%, p < 0.001), heat (26%, p < 0.001), itching (26%, p < 0.001), voice disorder (23%, p < 0.03), and dizziness (5%, p = 0.01). Discussion: Masks interfered with O2-uptake and CO2-release and compromised respiratory compensation. Though evaluated wearing durations are shorter than daily/prolonged use, outcomes independently validate mask-induced exhaustion-syndrome (MIES) and down-stream physio-metabolic disfunctions. MIES can have long-term clinical consequences, especially for vulnerable groups. So far, several mask related symptoms may have been misinterpreted as long COVID-19 symptoms. In any case, the possible MIES contrasts with the WHO definition of health. Conclusion: Face mask side-effects must be assessed (risk-benefit) against the available evidence of their effectiveness against viral transmissions. In the absence of strong empirical evidence of effectiveness, mask wearing should not be mandated let alone enforced by law. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021256694, identifier: PROSPERO 2021 CRD42021256694.

Personalized predictions of adverse side effects of the COVID-19 vaccines.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

Covid Vaccine Adverse Side-Effects Prediction with Sequence-to-Sequence Model

COVID-19 caused more than 5 million deaths in the world. After lot of efforts and hard work of many scientists, few vaccines are discovered and are approved for use. It is necessary to understand and to evaluate systematically with the potential side effects due to the vaccine itself. This work proposed a sequence-to-sequence learning (Seq2Seq) model to predict the adverse effects due to COVID-19 vaccine. Seq2Seq model is used to convert sequences of one domain to another domain. In this work, a structured data such as Vaccine Adverse Event Reposting System (VAERS) data are used to predict the adverse side effects of COVID-19 vaccination. The data formulated for Seq2Seq model architecture and trying to predict the adverse side effects of vaccination with age and gender attribute as input and obtained the result of 88% as average accuracy using long short-term memory-based (LSTM) deep learning model in adverse effect prediction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Safety of third dose of COVID-19 vaccination in frail patients: Results from the prospective Italian VAX4FRAIL study.

Importance: Despite people with impaired immune competence due to an underlying disease or ongoing therapy, hereinafter frail patients, are (likely to be) the first to be vaccinated, they were usually excluded from clinical trials. Objective: To report adverse reactions of frail patients after receipt of the third dose (booster) administered after completion of a two-dose mRNA vaccination and to compare with those reported after the receipt of the first two doses. Design: A multicenter, observational, prospective study aimed at evaluating both the safety profile and the immune response of Pfizer-BioNTech or Moderna vaccines in frail patients. Setting: National Project on Vaccines, COVID-19 and Frail Patients (VAX4FRAIL). Participants: People consenting and included in the VAX4FRAIL trial. Exposure: A series of three doses of COVID-19 mRNA vaccination from the same manufacturer. Main outcomes and measures: Evaluation of a self-assessment questionnaire addressing a predefined list of eight symptoms on a five-item Likert scale. Symptoms were classified as severe if the patient rated them as severe or overwhelming. Results: Among 320 VAX4FRAIL participants diagnosed/treated for hematological malignancies (N=105; 32.8%), solid tumors (N=48; 15.0%), immune-rheumatological diseases (N=60; 18.8%), neurological diseases (N=107; 33.4%), and receiving the booster dose, 70.3% reported at least one loco-regional or systemic reactions. Adverse events were mostly mild or moderate, none being life-threatening. Only six of the 320 (1.9%) patients had their treatment postponed due to the vaccine. The safety profile of the booster compared to previously administered two doses showed a stable prevalence of patients with one or more adverse events (73.5%, 79.7% and 73.9% respectively), and a slightly increment of patients with one or more severe adverse events (13.4%, 13.9% and 19.2% respectively). Conclusions and relevance: The booster of the mRNA COVID-19 vaccine was safely administered in the largest prospective cohort of frail patients reported so far. VAX4FRAIL will continue to monitor the safety of additional vaccine doses, especially systemic adverse events that can be easily prevented to avoid interruption of continuity of care. Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT04848493, identifier NCT04848493.

De novo podocytopathy following moderna COVID-19 vaccine: A case report and racial disproportionality in adverse effect reports.

In this study, we report a case of de novo minimal change disease shortly after the second dose of the Moderna COVID-19 vaccine. A previously healthy 51-year-old Asian man presented with lower-limb edema and foamy urine 3 days after receiving the second dose of the vaccine. Laboratory data revealed the following: serum creatinine, 0.65 mg/dl; serum albumin, 1.3 g/dl; urine protein-to-creatinine ratio, 15.3 g. A renal biopsy was performed, and minimal change in the disease was confirmed. The patient achieved complete remission in the tenth week after starting treatment with prednisolone (1 mg/kg/day). Ethnic differences may influence the adverse effects of drugs and vaccines. However, there is very limited data to address the influence of ethnic diversity on disease prevalence, clinical presentation, and treatment outcomes in COVID-19 vaccine-associated glomerulonephritis.

Vaccine Side Effects Following COVID-19 Vaccination Among the Residents of the UAE-An Observational Study.

COVID-19 vaccines have proven to be very safe in the clinical trials, however, there is less evidence comparing the safety of these vaccines in real-world settings. Therefore, we aim to investigate the nature and severity of the adverse effects reported and the differences based on the type of vaccine received. A survey was conducted among 1,878 adult (≥18 years) COVID-19 vaccine recipients through online survey platforms and telephonic interviews during March to September 2021. The factors potentially associated with the reported side effects like age, gender, ethnicity, comorbidities, and previous COVID-19 infection were analyzed based on the type of vaccine received. Differences in adverse events and the severity were compared between inactivated and mRNA vaccine recipients. The major adverse effects reported by the COVID-19 vaccine recipients were pain at the site of injection, fatigue and drowsiness, and headache followed by joint/muscle pain. The adverse effects were more common among recipients of mRNA Pfizer-BioNTech vaccine than among recipients of inactive Sinopharm vaccine with the odds ratio of 1.39 (95% CI 1.14-1.68). The average number of adverse effects reported between individuals who had received Sinopharm and Pfizer-BioNTech vaccines was 1.61 ± 2.08 and 2.20 ± 2.58, respectively, and the difference was statistically significant (p <0.001). Severe adverse effects after COVID-19 vaccinations were rare and 95% of the adverse effects reported after either an inactivated or mRNA vaccine were mild requiring no or home-based treatment. The study found that individuals less than 55 years of age, female gender, with history of one or more comorbid conditions, who had received mRNA Pfizer- BioNTech vaccine, and with history of COVID-19 infections are at higher odds of developing an adverse effect post COVID-19 vaccination compared to the others.

Incidence of Suspected Serious Adverse Drug Reactions in Corona Virus Disease-19 Patients Detected by a Pharmacovigilance Program by Laboratory Signals in a Tertiary Hospital in Spain: Cautionary Data.

BACKGROUND: From March to April 2020, Spain was the center of the SARS-CoV-2 pandemic, particularly Madrid with approximately 30% of the cases in Spain. The aim of this study is to report the suspected serious adverse drug reactions (SADRs) in COVID-19 patients vs. non-COVID-19 patients detected by the prospective pharmacovigilance program based on automatic laboratory signals (ALSs) in the hospital (PPLSH) during that period. We also compared the results with the suspected SADRs detected during the same period for 2019. METHODS: All ALSs that reflected potential SADRs including neutropenia, pancytopenia, thrombocytopenia, anemia, eosinophilia, leukocytes in cerebrospinal fluid, hepatitis, pancreatitis, acute kidney injury, rhabdomyolysis, and hyponatremia were prospectively monitored in hospitalized patients during the study periods. We analyzed the incidence and the distribution of causative drugs for the COVID-19 patients. RESULTS: The incidence rate of SADRs detected in the COVID-19 patients was 760.63 (95% CI 707.89-816.01) per 10,000 patients, 4.75-fold higher than the SADR rate for non-COVID-19 patients (160.15 per 10,000 patients, 95% CI 137.09-186.80), and 5.84-fold higher than the SADR rate detected for the same period in 2019 (130.19 per 10,000 patients, 95% CI 109.53-154.36). The most frequently related drugs were tocilizumab (59.84%), dexketoprofen (13.93%), azithromycin (8.43%), lopinavir-ritonavir (7.35%), dexamethasone (7.62%), and chloroquine/hydroxychloroquine (6.91%). CONCLUSIONS: The incidence rate of SADRs detected by the PPSLH in patients with COVID-19 was 4.75-fold higher than that of the non-COVID-19 patients. Caution is recommended when using medications for COVID-19 patients, especially drugs that are hepatotoxic, myotoxic, and those that induce thromboembolic events.