Mesenchymal stem cells (MSCs), as an undifferentiated group of adult multipotent cells, have remarkable antitumor features that bring them up as a novel choice to treat cancers. MSCs are capable of altering the behavior of cells in the tumor microenvironment, inducing an anti-inflammatory effect in tumor cells, inhibiting tumor angiogenesis, and preventing metastasis. Besides, MSCs can induce apoptosis and inhibit the proliferation of tumor cells. The ability of MSCs to be loaded with chemotherapeutic drugs and release them in the site of primary and metastatic neoplasms makes them a preferable choice as targeted drug delivery procedure. Targeted drug delivery minimizes unexpected side effects of chemotherapeutic drugs and improves clinical outcomes. This review focuses on recent advances on innate antineoplastic features of MSCs and the effect of chemotherapeutic drugs on viability, proliferation, and the regenerative capacity of various kinds of MSCs. It also discusses the efficacy and mechanisms of drug loading and releasing procedures along with in vivo and in vitro preclinical outcomes of antineoplastic effects of primed MSCs for clinical prospection.

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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.