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.

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.

Human placenta-derived amniotic epithelial cells as a new therapeutic hope for COVID-19-associated acute respiratory distress syndrome (ARDS) and systemic inflammation.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has become in the spotlight regarding the serious early and late complications, including acute respiratory distress syndrome (ARDS), systemic inflammation, multi-organ failure and death. Although many preventive and therapeutic approaches have been suggested for ameliorating complications of COVID-19, emerging new resistant viral variants has called the efficacy of current therapeutic approaches into question. Besides, recent reports on the late and chronic complications of COVID-19, including organ fibrosis, emphasize a need for a multi-aspect therapeutic method that could control various COVID-19 consequences. Human amniotic epithelial cells (hAECs), a group of placenta-derived amniotic membrane resident stem cells, possess considerable therapeutic features that bring them up as a proposed therapeutic option for COVID-19. These cells display immunomodulatory effects in different organs that could reduce the adverse consequences of immune system hyper-reaction against SARS-CoV-2. Besides, hAECs would participate in alveolar fluid clearance, renin-angiotensin-aldosterone system regulation, and regeneration of damaged organs. hAECs could also prevent thrombotic events, which is a serious complication of COVID-19. This review focuses on the proposed early and late therapeutic mechanisms of hAECs and their exosomes to the injured organs. It also discusses the possible application of preconditioned and genetically modified hAECs as well as their promising role as a drug delivery system in COVID-19. Moreover, the recent advances in the pre-clinical and clinical application of hAECs and their exosomes as an optimistic therapeutic hope in COVID-19 have been reviewed.

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.

Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning.

Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however. Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals. Results: The SPM yielded ROC-AUCs of 0.53-0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www.aicovid.net. Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.

Targeted Mitochondrial Therapy With Over-Expressed MAVS Protein From Mesenchymal Stem Cells: A New Therapeutic Approach for COVID-19.

The SARS-CoV-2, the virus that causes COVID-19, has infected millions of people worldwide. The symptoms of this disease are primarily due to pulmonary involvement, uncontrolled tissue inflammation, and inadequate immune response against the invader virus. Impaired interferon (IFN) production is one of the leading causes of the immune system's inability to control the replication of the SARS-CoV-2. Mitochondria play an essential role in developing and maintaining innate cellular immunity and IFN production. Mitochondrial function is impaired during cellular stress, affecting cell bioenergy and innate immune responses. The mitochondrial antiviral-signaling protein (MAVS), located in the outer membrane of mitochondria, is one of the key elements in engaging the innate immune system and interferon production. Transferring healthy mitochondria to the damaged cells by mesenchymal stem cells (MSCs) is a proposed option for regenerative medicine and a viable treatment approach to many diseases. In addition to mitochondrial transport, these cells can regulate inflammation, repair the damaged tissue, and control the pathogenesis of COVID-19. The immune regulatory nature of MSCs dramatically reduces the probability of an immune rejection. In order to induce an appropriate immune response against the SARS-CoV-2, we hypothesize to donate mitochondria to the host cells of the virus. We consider MSCs as an appropriate biological carrier for mitochondria. Besides, enhancing the expression of MAVS protein in MSCs and promoting the expression of SARS-CoV-2 viral spike protein as a specific ligand for ACE2+ cells will improve IFN production and innate immune responses in a targeted manner.

Proposed Mechanisms of Targeting COVID-19 by Delivering Mesenchymal Stem Cells and Their Exosomes to Damaged Organs.

With the outbreak of coronavirus disease (COVID-19) caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the world has been facing an unprecedented challenge. Considering the lack of appropriate therapy for COVID-19, it is crucial to develop effective treatments instead of supportive approaches. Mesenchymal stem cells (MSCs) as multipotent stromal cells have been shown to possess treating potency through inhibiting or modulating the pathological events in COVID-19. MSCs and their exosomes participate in immunomodulation by controlling cell-mediated immunity and cytokine release. Furthermore, they repair the renin-angiotensin-aldosterone system (RAAS) malfunction, increase alveolar fluid clearance, and reduce the chance of hypercoagulation. Besides the lung, which is the primary target of SARS-CoV-2, the heart, kidney, nervous system, and gastrointestinal tract are also affected by COVID-19. Thus, the efficacy of targeting these organs via different delivery routes of MSCs and their exosomes should be evaluated to ensure safe and effective MSCs administration in COVID-19. This review focuses on the proposed therapeutic mechanisms and delivery routes of MSCs and their exosomes to the damaged organs. It also discusses the possible application of primed and genetically modified MSCs as a promising drug delivery system in COVID-19. Moreover, the recent advances in the clinical trials of MSCs and MSCs-derived exosomes as one of the promising therapeutic approaches in COVID-19 have been reviewed.