Entrevista con las creadoras del Proyecto Diarios de la Pandemia

El Proyecto Diarios de la Pandemia (PJP) es una plataforma de diarios virtuales e investigación que registra experiencias de las personas durante covid-19. En esta entrevista, las cofundadoras de PJP, las doctoras Willen y Mason, hablan sobre el papel de los derechos humanos en la concepción, diseño, e implementación de PJP. Describen cómo PJP contribuye a un esfuerzo general para hacer avanzar la justicia social por medio de la preservación de las cuentas registradas de comunidades históricamente subrepresentadas. Willen y Mason también comparten aprendizajes sobre los derechos humanos a partir de la narrativa de los participantes de PJP. Concluyen con una discusión sobre cómo planean divulgar los resultados de la investigación, así como los próximos pasos de PJP

Genetic Determinants of EGFR-Driven Lung Cancer Growth and Therapeutic Response In Vivo.

In lung adenocarcinoma, oncogenic EGFR mutations co-occur with many tumor suppressor gene alterations; however, the extent to which these contribute to tumor growth and response to therapy in vivo remains largely unknown. By quantifying the effects of inactivating 10 putative tumor suppressor genes in a mouse model of EGFR-driven Trp53-deficient lung adenocarcinoma, we found that Apc, Rb1, or Rbm10 inactivation strongly promoted tumor growth. Unexpectedly, inactivation of Lkb1 or Setd2-the strongest drivers of growth in a KRAS-driven model-reduced EGFR-driven tumor growth. These results are consistent with mutational frequencies in human EGFR- and KRAS-driven lung adenocarcinomas. Furthermore, KEAP1 inactivation reduced the sensitivity of EGFR-driven tumors to the EGFR inhibitor osimertinib, and mutations in genes in the KEAP1 pathway were associated with decreased time on tyrosine kinase inhibitor treatment in patients. Our study highlights how the impact of genetic alterations differs across oncogenic contexts and that the fitness landscape shifts upon treatment. SIGNIFICANCE: By modeling complex genotypes in vivo, this study reveals key tumor suppressors that constrain the growth of EGFR-mutant tumors. Furthermore, we uncovered that KEAP1 inactivation reduces the sensitivity of these tumors to tyrosine kinase inhibitors. Thus, our approach identifies genotypes of biological and therapeutic importance in this disease.This article is highlighted in the In This Issue feature, p. 1601

NMR metabolomics of human blood and urine in disease research

This paper reviews the main applications of NMR metabolomics of blood and urine in disease research, over the last 5 years. The broad range of disease types addressed attests the increasing interest within the academic and medical communities to explore the recognised potential of metabolomics to (1) provide insight into underlying disease pathogenesis and (2) unveil new metabolic markers for disease diagnosis and follow up. Importantly, most recent studies reveal an increasing awareness of possible limitations and pitfalls of the metabolomics approach, together with efforts for improved study design and statistical validation, which are crucial requisites for the sound development of NMR metabolomics and its progress into the clinical setting. (C) 2013 Elsevier B.V. All rights reserved

A blood-based signature of cerebrospinal fluid A beta(1-42) status

It is increasingly recognized that Alzheimer's disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid beta(1-42) (A beta(1-42)) may be an earlier indicator of Alzheimer's disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual's CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF A beta(1-42) levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOE epsilon 4 carrier status and four plasma analytes (CGA, A beta(1-42), Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF A beta(1-42) levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF A beta(1-42) levels and that the resulting model also validates reasonably across PET A beta(1-42) status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOE epsilon 4 carrier status, is able to predict CSF A beta(1-42) status, the earliest risk indicator for AD, with high accuracy