Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

INVESTIGATING T CELLS IN THE CONTEXT OF NEUROIMMUNOLOGY: MOLECULAR AND CELLULAR MECHANISMS DURING A STRESS RESPONSE AND A PATIENT-BASED STUDY IN PARKINSON'S DISEASE

For a long time, the nervous system and the immune system have been studied as isolated entities, but a growing body of evidence shows that there is an extensive crosstalk between both systems. In fact, neurons and immune cells share certain functional features and reside in close proximity within the tissues, enabling them to effectively communicate. T cells are crucial for mounting and controlling almost any kind of immune response. However, when dysregulated, T cells fail to protect the host from invading pathogens or can cause damage to surrounding tissues, leading to autoimmunity-related pathology. In the first part of this cumulative thesis, we aimed at identifying novel genes regulating CD4 T cell responses and identified VIMP, one of the 25 human proteins containing the 21st amino acid selenocysteine, as a gene having anti-inflammatory functions. Furthermore, T cells express various neurotransmitter receptors allowing the integration of neuronal signal for an appropriate response. In the second part, we showed a CD4-T-cell-intrinsic mechanism through which stress hormones mediate their control over the immune system. We identified a previously unrecognized pathway regulating CD4 T cell differentiation that involves the circadian clock gene Per1 and mTORC1 signalling. Finaly, T cells involvement in different neuropathologies has been reported in the past few decades. Emerging evidence indicates the involvement of the immune system and in particular T cells in the pathogenesis of Parkinson’s disease (PD), the 2nd most common neurodegenerative disease. In the 3rd part of the thesis we systematically characterized the immunological status of early-to-mid stage PD patients and matched healthy controls, and identified a distinct peripheral immunological fingerprint in PD patients, especially in the CD8 T-cell compartment. The findings of the studies described in this cumulative thesis advance our understanding of the regulatory nodes of CD4 T cells during a stress response and fill the knowledge gap on the early involvement of CD8 T cells and other immune subsets in neurodegenerative diseases in the case of PD

Cardiopulmonary Long-Term Sequelae in Patients after Severe COVID-19 Disease

We aimed to identify cardiopulmonary long-term effects after severe COVID-19 disease as well as predictors of Long-COVID in a prospective registry. A total of 150 consecutive, hospitalized patients (February 2020 and April 2021) were included six months post hospital discharge for a clinical follow-up. Among them, 49% experienced fatigue, 38% exertional dyspnea and 75% fulfilled criteria for Long-COVID. Echocardiography detected reduced global longitudinal strain (GLS) in 11% and diastolic dysfunction in 4%. Magnetic resonance imaging revealed traces of pericardial effusion in 18% and signs of former pericarditis or myocarditis in 4%. Pulmonary function was impaired in 11%. Chest computed tomography identified post-infectious residues in 22%. Whereas fatigue did not correlate with cardiopulmonary abnormalities, exertional dyspnea was associated with impaired pulmonary function (OR 3.6 [95% CI: 1.2–11], p = 0.026), reduced GLS (OR 5.2 [95% CI: 1.6–16.7], p = 0.003) and/or left ventricular diastolic dysfunction (OR 4.2 [95% CI: 1.03–17], p = 0.04). Predictors of Long-COVID included length of in-hospital stay (OR: 1.15 [95% CI: 1.05–1.26], p = 0.004), admission to intensive care unit (OR cannot be computed, p = 0.001) and higher NT-proBNP (OR: 1.5 [95% CI: 1.05–2.14], p = 0.026). Even 6 months after discharge, a majority fulfilled criteria for Long-COVID. While no associations between fatigue and cardiopulmonary abnormalities were found, exertional dyspnea correlated with impaired pulmonary function, reduced GLS and/or diastolic dysfunction

Non-Standard Errors

URL des documents de travail : https://centredeconomiesorbonne.cnrs.fr/publications/Documents de travail du Centre d'Economie de la Sorbonne 2021.33 - ISSN : 1955-611XVoir aussi ce document de travail sur SSRN: https://ssrn.com/abstract=3981597In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants