Development of a physiomimetic model of acute respiratory distress syndrome by using ECM hydrogels and organ-on-a-chip devices

Acute Respiratory Distress Syndrome is one of the more common fatal complications in COVID-19, characterized by a highly aberrant inflammatory response. Pre-clinical models to study the effect of cell therapy and anti-inflammatory treatments have not comprehensively reproduced the disease due to its high complexity. This work presents a novel physiomimetic in vitro model for Acute Respiratory Distress Syndrome using lung extracellular matrix-derived hydrogels and organ-on-a-chip devices. Monolayres of primary alveolar epithelial cells were cultured on top of decellullarized lung hydrogels containing primary lung mesenchymal stromal cells. Then, cyclic stretch was applied to mimic breathing, and an inflammatory response was induced by using a bacteriotoxin hit. Having simulated the inflamed breathing lung environment, we assessed the effect of an anti-inflammatory drug (i.e., dexamethasone) by studying the secretion of the most relevant inflammatory cytokines. To better identify key players in our model, the impact of the individual factors (cyclic stretch, decellularized lung hydrogel scaffold, and the presence of mesenchymal stromal cells) was studied separately. Results showed that developed model presented a more reduced inflammatory response than traditional models, which is in line with what is expected from the response commonly observed in patients. Further, from the individual analysis of the different stimuli, it was observed that the use of extracellular matrix hydrogels obtained from decellularized lungs had the most significant impact on the change of the inflammatory response. The developed model then opens the door for further in vitro studies with a better-adjusted response to the inflammatory hit and more robust results in the test of different drugs or cell therapy.

Heritage Conservation Future: Where We Stand, Challenges Ahead, and a Paradigm Shift.

Global cultural heritage is a lucrative asset. It is an important industry generating millions of jobs and billions of euros in revenue yearly. However, despite the tremendous economic and socio-cultural benefits, little attention is usually paid to its conservation and to developing innovative big-picture strategies to modernize its professional field. This perspective aims to compile some of the relevant current global needs to explore alternative ways for shaping future steps associated with the 2030 Agenda for Sustainable Development. From this perspective, it is conceptualized how emerging artificial intelligence (AI) and digital socio-technological models of production based on democratic Peer-2-Peer (P2P) interactions can represent an alternative transformative solution by going beyond the current global communication and technical limitations in the heritage conservation community, while also providing novel digital tools to conservation practitioners, which can truly revolutionize the conservation decision-making process and improve global conservation standards.

Impact of COVID-19 Infection on Patients with Cancer: Experience in a Latin American Country: The ACHOCC-19 Study.

INTRODUCTION: The ACHOCC-19 study was performed to characterize COVID-19 infection in a Colombian oncological population. METHODOLOGY: Analytical cohort study of patients with cancer and COVID-19 infection in Colombia. From April 1 to October 31, 2020. Demographic and clinical variables related to cancer and COVID-19 infection were collected. The primary outcome was 30-day mortality from all causes. The association between the outcome and the prognostic variables was analyzed using logistic regression models and survival analysis with Cox regression. RESULTS: The study included 742 patients; 72% were >51 years. The most prevalent neoplasms were breast (132, 17.77%), colorectal (92, 12.34%), and prostate (81, 10.9%). Two hundred twenty (29.6%) patients were asymptomatic and 96 (26.3%) died. In the bivariate descriptive analysis, higher mortality occurred in patients who were >70 years, patients with lung cancer, ≥2 comorbidities, former smokers, receiving antibiotics, corticosteroids, and anticoagulants, residents of rural areas, low socioeconomic status, and increased acute-phase reactants. In the logistic regression analysis, higher mortality was associated with Eastern Cooperative Oncology Group performance status (ECOG PS) 3 (odds ratio [OR] 28.67; 95% confidence interval [CI], 8.2-99.6); ECOG PS 4 (OR 20.89; 95% CI, 3.36-129.7); two complications from COVID-19 (OR 5.3; 95% CI, 1.50-18.1); and cancer in progression (OR 2.08; 95% CI, 1.01-4.27). In the Cox regression analysis, the statistically significant hazard ratios (HR) were metastatic disease (HR 1.58; 95% CI, 1.16-2.16), cancer in progression (HR 1.08; 95% CI, 1.24-2.61) cancer in partial response (HR 0.31; 95% CI, 0.11-0.88), use of steroids (HR 1.44; 95% CI, 1.01-2.06), and use of antibiotics (HR 2.11; 95% CI, 1.47-2.95). CONCLUSION: In our study, patients with cancer have higher mortality due to COVID-19 infection if they have active cancer, metastatic or progressive cancer, ECOG PS >2, and low socioeconomic status. IMPLICATIONS FOR PRACTICE: This study's findings raise the need to carefully evaluate patients with metastatic cancer, in progression, and with impaired Eastern Cooperative Oncology Group status to define the relevance of cancer treatment during the pandemic, consider the risk/benefit of the interventions, and establish clear and complete communication with the patients and their families about the risk of complications. There is also the importance of offering additional support to patients with low income and residence in rural areas so that they can have more support during cancer treatment.

Mortality and Advanced Support Requirement for Patients With Cancer With COVID-19: A Mathematical Dynamic Model for Latin America.

PURPOSE: In the midst of a global pandemic, evidence suggests that similar to other severe respiratory viral infections, patients with cancer are at higher risk of becoming infected by COVID-19 and have a poorer prognosis. METHODS: We have modeled the mortality and the intensive care unit (ICU) requirement for the care of patients with cancer infected with COVID-19 in Latin America. A dynamic multistate Markov model was constructed. Transition probabilities were estimated on the basis of published reports for cumulative probability of complications. Basic reproductive number (R0) values were modeled with R using the EpiEstim package. Estimations of days of ICU requirement and absolute mortality were calculated by imputing number of cumulative cases in the Markov model. RESULTS: Estimated median time of ICU requirement was 12.7 days, median time to mortality was 16.3 days after infection, and median time to severe event was 8.1 days. Peak ICU occupancy for patients with cancer was calculated at 16 days after infection. Deterministic sensitivity analysis revealed an interval for mortality between 18.5% and 30.4%. With the actual incidence tendency, Latin America would be expected to lose approximately 111,725 patients with cancer to SARS-CoV-2 (range, 87,116-143,154 patients) by the 60th day since the start of the outbreak. Losses calculated vary between < 1% to 17.6% of all patients with cancer in the region. CONCLUSION: Cancer-related cases and deaths attributable to SARS-CoV-2 will put a great strain on health care systems in Latin America. Early implementation of interventions on the basis of data given by disease modeling could mitigate both infections and deaths among patients with cancer.