RESUMO
Night work is frequently associated with sleep deprivation and is associated with greater surgical and medical complications. Lung transplantation (LT) is carried out both at night and during the day and involves many medical healthcare workers. The goal of the study was to compare morbidity and mortality between LT recipients according to LT operative time. We performed a retrospective, observational, single-center study. When the procedure started between 6 AM and 6 PM, the patient was allocated to the Daytime group. If the procedure started between 6 PM and 6 AM, the patient was allocated to the Nighttime group. Between January 2015 and December 2020, 253 patients were included. A total of 168 (66%) patients were classified into the Day group, and 85 (34%) patients were classified into the Night group. Lung Donors' general characteristics were similar between the groups. The 90-day and one-year mortality rates were similar between the groups (90-days: n = 13 (15%) vs. n = 26 (15%), p = 0.970; 1 year: n = 18 (21%) vs. n = 42 (25%), p = 0.499). Daytime LT was associated with more one-year airway dehiscence (n = 36 (21%) vs. n = 6 (7.1%), p = 0.004). In conclusion, among patients who underwent LT, there was no significant association between operative time and survival.
Assuntos
Transplante de Pulmão , Duração da Cirurgia , Humanos , Transplante de Pulmão/efeitos adversos , Transplante de Pulmão/mortalidade , Masculino , Feminino , Estudos Retrospectivos , Pessoa de Meia-Idade , Adulto , Complicações Pós-Operatórias/epidemiologia , Complicações Pós-Operatórias/etiologia , Privação do Sono/complicações , IdosoRESUMO
Introduction: Although many studies have underscored the importance of T cells, phenotypically and functionally, fewer have studied the functions of myeloid cells in COVID disease. In particular, the potential role of myeloid cells such as monocytes and low-density neutrophils (LDNs) in innate responses and particular in the defense against secondary bacterial infections has been much less documented. Methods: Here, we compared, in a longitudinal study, healthy subjects, idiopathic fibrosis patients, COVID patients who were either hospitalized/moderate (M-) or admitted to ICU (COV-ICU) and patients in ICU hospitalized for other reasons (non-COV-ICU). Results: We show that COVID patients have an increased proportion of low-density neutrophils (LDNs), which produce high levels of proteases (particularly, NE, MMP-8 and MMP-9) (unlike non-COV-ICU patients), which are partly responsible for causing type II alveolar cell damage in co-culture experiments. In addition, we showed that M- and ICU-COVID monocytes had reduced responsiveness towards further live Pseudomonas aeruginosa (PAO1 strain) infection, an important pathogen colonizing COVID patients in ICU, as assessed by an impaired secretion of myeloid cytokines (IL-1, TNF, IL-8, ). By contrast, lymphoid cytokines (in particular type 2/type 3) levels remained high, both basally and post PAO1 infection, as reflected by the unimpaired capacity of T cells to proliferate, when stimulated with anti-CD3/CD28 beads. Discussion: Overall, our results demonstrate that COVID circulatory T cells have a biased type 2/3 phenotype, unconducive to proper anti-viral responses and that myeloid cells have a dual deleterious phenotype, through their LDN-mediated damaging effect on alveolar cells and their impaired responsiveness (monocyte-mediated) towards bacterial pathogens such as P. aeruginosa.
Assuntos
COVID-19 , Monócitos , Neutrófilos , Infecções por Pseudomonas , Pseudomonas aeruginosa , SARS-CoV-2 , Humanos , COVID-19/imunologia , Pseudomonas aeruginosa/imunologia , Monócitos/imunologia , Masculino , Feminino , Pessoa de Meia-Idade , SARS-CoV-2/imunologia , SARS-CoV-2/fisiologia , Infecções por Pseudomonas/imunologia , Neutrófilos/imunologia , Idoso , Citocinas/metabolismo , Citocinas/imunologia , Adulto , Estudos Longitudinais , Leucócitos Mononucleares/imunologia , Pulmão/imunologia , Pulmão/patologia , Pulmão/microbiologiaRESUMO
PURPOSE: To assess the Consultation And Relational Empathy (CARE) measure as a tool for examiners to assess medical students' empathy during Objective and Structured Clinical Examinations (OSCEs), as the best tool for assessing empathy during OSCEs remains unknown. METHODS: We first assessed the psychometric properties of the CARE measure, completed simultaneously by examiners and standardized patients (SP, either teachers - SPteacher - or civil society members - SPcivil society), for each student, at the end of an OSCE station. We then assessed the qualitative/quantitative agreement between examiners and SP. RESULTS: We included 129 students, distributed in eight groups, four groups for each SP type. The CARE measure showed satisfactory psychometric properties in the context of the study but moderate, and even poor inter-rater reliability for some items. Considering paired observations, examiners scored lower than SPs (p < 0.001) regardless of the SP type. However, the difference in score was greater when the SP was a SPteacher rather than a SPcivil society (p < 0.01). CONCLUSION: Despite acceptable psychometric properties, inter-rater reliability of the CARE measure between examiners and SP was unsatisfactory. The choice of examiner as well as the type of SP seems critical to ensure a fair measure of empathy during OSCEs.
RESUMO
In this work, we present a new approach to predict the risk of acute cellular rejection (ACR) after lung transplantation by using machine learning algorithms, such as Multilayer Perceptron (MLP) or Autoencoder (AE), and combining them with topological data analysis (TDA) tools. Our proposed method, named topological autoencoder with best linear combination for optimal reduction of embeddings (Taelcore), effectively reduces the dimensionality of high-dimensional datasets and yields better results compared to other models. We validate the effectiveness of Taelcore in reducing the prediction error rate on four datasets. Furthermore, we demonstrate that Taelcore's topological improvements have a positive effect on the majority of the machine learning algorithms used. By providing a new way to diagnose patients and detect complications early, this work contributes to improved clinical outcomes in lung transplantation.