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

Babajani, Amirhesam; Hosseini-Monfared, Pooya; Abbaspour, Samin; Jamshidi, Elham; Niknejad, Hassan

doi:10.3389/fcell.2021.695362

Cited by 14 publications

(9 citation statements)

References 75 publications

(73 reference statements)

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“…A number of studies have investigated the risk factors affecting COVID-19 infections (34,35). Elevated inflammatory cytokines such as interleukin-6 (IL-6), granulocyte colonystimulating factor (G-CSF), interferon gamma-induced protein 10 (IP-10), and interferon (IFN)-γ have been proposed as poor prognostic factors for COVID-19 patients (36)(37)(38)(39). These markers, however, are not usually used as predictors of the severity of disease in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

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

Jamshidi

Asgary

Tavakoli

et al. 2022

Front. Digit. Health

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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.

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Section: Discussionmentioning

confidence: 99%

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

Jamshidi

Asgary

Tavakoli

et al. 2022

Front. Digit. Health

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“…We have discussed a few of the approaches above and many more are in development [ 271 ]. Direct mitochondrial transplantation from healthy cells was recently suggested as an innovative therapy in COVID-19 [ 272 , 273 ]. Although full of potential, further research in this approach is needed [ 274 , 275 , 276 ].…”

Section: Discussionmentioning

confidence: 99%

A Peek into Pandora’s Box: COVID-19 and Neurodegeneration

Chandra¹,

Johri²

2022

Brain Sciences

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Ever since it was first reported in Wuhan, China, the coronavirus-induced disease of 2019 (COVID-19) has become an enigma of sorts with ever expanding reports of direct and indirect effects of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on almost all the vital organ systems. Along with inciting acute pulmonary complications, the virus attacks the cardiac, renal, hepatic, and gastrointestinal systems as well as the central nervous system (CNS). The person-to-person variability in susceptibility of individuals to disease severity still remains a puzzle, although the comorbidities and the age/gender of a person are believed to play a key role. SARS-CoV-2 needs angiotensin-converting enzyme 2 (ACE2) receptor for its infectivity, and the association between SARS-CoV-2 and ACE2 leads to a decline in ACE2 activity and its neuroprotective effects. Acute respiratory distress may also induce hypoxia, leading to increased oxidative stress and neurodegeneration. Infection of the neurons along with peripheral leukocytes’ activation results in proinflammatory cytokine release, rendering the brain more susceptible to neurodegenerative changes. Due to the advancement in molecular biology techniques and vaccine development programs, the world now has hope to relatively quickly study and combat the deadly virus. On the other side, however, the virus seems to be still evolving with new variants being discovered periodically. In keeping up with the pace of this virus, there has been an avalanche of studies. This review provides an update on the recent progress in adjudicating the CNS-related mechanisms of SARS-CoV-2 infection and its potential to incite or accelerate neurodegeneration in surviving patients. Current as well as emerging therapeutic opportunities and biomarker development are highlighted.

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“…In previous studies, laboratory markers, patient demographics, medical history, and vital signs have been used as effective features in predicting the mortality of patients with COVID-19 (7,13,(17)(18)(19)(20)(21)(22). Some studies used factors including different in ammatory cytokines (23)(24)(25)(26), which are not part of patients' routine admission measurements and cannot be obtained in settings with congested resources in contrast to our predictors. Our model may prompt individualized treatments due to distinguishing patients' prognoses based on their different clinical characteristics.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Mortality Risk Prediction using Clinical and Laboratory Examination: Machine Learning Approach for Implementation

Barough

Safavi-Naini

Siavoshi

et al. 2022

Preprint

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Background and Aim: We aimed to propose a mortality risk prediction tool to facilitate COVID-19 patient management and allocation for the frontline physician on admission day. Methods: We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values on admission were gathered. Different machine learning methods were used to assess the risk of in-hospital mortality, including logistic regression, k-nearest neighbor (KNN), gradient boosting classifier, random forest, support vector machine, and deep neural network (DNN). Least absolute shrinkage and selection operator (LASSO) regression and Boruta feature selection methods were used for feature selection. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from the fourth hospital was used for external validation. Results: 5320 hospitalized COVID-19 patients were enrolled in the study with a mean age of 61.6± 17.6 years and a fatality rate of 17.24% (N=917). All methods showed fair performance with AUC>80%, except for the KNN method. The feature selection method selected ten laboratories and eight clinical features. Our proposed DNN and LASSO feature selection methods showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked fairly when two out of ten laboratory parameters were missing (AUC=81.8%). Conclusion: We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our proposed model showed promising results and confirms the potential of ML methods for use in clinical practice as a decision-support system. Future studies are warranted to investigate barriers to the implementation of ML tools.

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

Cited by 14 publications

References 75 publications

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

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

A Peek into Pandora’s Box: COVID-19 and Neurodegeneration

COVID-19 Mortality Risk Prediction using Clinical and Laboratory Examination: Machine Learning Approach for Implementation

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