The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

Background/Objectives: Chimeric Antigen Receptor (CAR)-T cell therapy has demonstrated impressive clinical results against hematological malignancies. However, currently commercialized CAR-T therapies are designed for autologous use, which entails some disadvantages, including high costs, manufacturing delays, complex standardization, and frequent production failures due to patient T cell dysfunction. Moreover, their CARs target one specific antigen, increasing the probability of antigen-negative tumor relapses. To overcome these limitations, we developed a novel NKG2D CAR-T cell therapy for allogeneic use with broad target specificity, as this CAR targets eight different ligands commonly upregulated in both solid and hematological tumors. Additionally, the manufacturing process was optimized to improve the phenotypic characteristics of the final product. Methods: Multiplex CRISPR/Cas9 technology was applied to eliminate the expression of TCR and HLA class I complexes in healthy donor T cells to reduce the risk of graft-versus-host disease and immune rejection, respectively, as well as lentiviral transduction for introducing the second-generation NKG2D-CAR. Moreover, we sought to optimize this manufacturing process by comparing the effect of different culture interleukin supplementations (IL-2, IL-7/IL-15 or IL-7/IL-15/IL-21) on the phenotypic and functional characteristics of the product obtained. Results: Our results showed that the novel CAR-T cells effectively targeted cervicouterine and colorectal cancer cells, and that those manufactured with IL-7/IL-15/IL-21 supplementation showed the most suitable characteristics among the conditions tested, considering genetic modification efficiency, cell proliferation, antitumor activity and proportion of the stem cell memory T cell subset, which is associated with enhanced in vivo CAR-T cell survival, expansion and long-term persistence. Conclusions: In summary, this new prototype of NKG2D CAR-T cell therapy for allogeneic use represents a promising universal treatment for a wide range of tumor types.

Background/Objectives: The spread of the COVID-19 pandemic has spurred the development of advanced healthcare tools to effectively manage patient outcomes. This study aims to identify key predictors of mortality in hospitalized patients with some level of natural immunity, but not yet vaccinated, using machine learning techniques. Methods: A total of 363 patients with COVID-19 admitted to Río Hortega University Hospital in Spain between the second and fourth waves of the pandemic were included in this study. Key characteristics related to both the patient’s previous status and hospital stay were screened using the Random Forest (RF) machine learning technique. Results: Of the 19 variables identified as having the greatest influence on predicting mortality, the most powerful ones could be identified at the time of hospital admission. These included the assessment of severity in community-acquired pneumonia (CURB-65) scale, age, the Glasgow Coma Scale (GCS), and comorbidities, as well as laboratory results. Some variables associated with hospitalization and intensive care unit (ICU) admission (acute renal failure, shock, PRONO sessions and the Acute Physiology and Chronic Health Evaluation [APACHE-II] scale) showed a certain degree of significance. The Random Forest (RF) method showed high accuracy, with a precision of >95%. Conclusions: This study shows that natural immunity generates significant changes in the evolution of the disease. As has been shown, machine learning models are an effective tool to improve personalized patient care in different periods.