ERS International Congress 2022: highlights from the Basic and Translational Science Assembly.

In this review, the Basic and Translational Science Assembly of the European Respiratory Society provides an overview of the 2022 International Congress highlights. We discuss the consequences of respiratory events from birth until old age regarding climate change related alterations in air quality due to pollution caused by increased ozone, pollen, wildfires and fuel combustion as well as the increasing presence of microplastic and microfibres. Early life events such as the effect of hyperoxia in the context of bronchopulmonary dysplasia and crucial effects of the intrauterine environment in the context of pre-eclampsia were discussed. The Human Lung Cell Atlas (HLCA) was put forward as a new point of reference for healthy human lungs. The combination of single-cell RNA sequencing and spatial data in the HLCA has enabled the discovery of new cell types/states and niches, and served as a platform that facilitates further investigation of mechanistic perturbations. The role of cell death modalities in regulating the onset and progression of chronic lung diseases and its potential as a therapeutic target was also discussed. Translational studies identified novel therapeutic targets and immunoregulatory mechanisms in asthma. Lastly, it was highlighted that the choice of regenerative therapy depends on disease severity, ranging from transplantation to cell therapies and regenerative pharmacology.

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.

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.

Successful Treatment of Covid-19 Associated Cytokine Release Syndrome with Colchicine. A Case Report and Review of Literature.

We describe the case of a 42 year old, healthy patient with Covid-19 who despite improvement in his respiratory symptoms developed a mild to moderate cytokine release syndrome (CRS) and an associated monoarticular gout flare. Since the patient refused admission to the hospital and had stable vital signs, we chose to treat him with a safe anti-inflammatory and non-immunosuppressive therapy. To hit two birds with one stone, we considered colchicine, as it has systemic anti-inflammatory effects and is also effective in gout flare. Unexpectedly, 48 hours after treatment, not only did his ongoing fever and toe pain disappear, he also had significant improvements in his general state of health and all his inflammatory markers including fibrinogen, ferritin, D-dimer, and IL-6 levels normalized. To our knowledge, the use of colchicine in Covid-19 and CRS has not been reported. This observation merits the consideration of colchicine as a safe, inexpensive and oral medication for the treatment of mild to moderate CRS in Covid-19 patients. More importantly, in Covid-19 patients with early lung involvement colchicine may be an appropriate candidate to prevent CRS in adjunction with routine antiviral agents. Indeed, multicenter, randomized controlled studies are required to evaluate the benefits of this therapy.