Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Mais filtros











Base de dados
Intervalo de ano de publicação
1.
Respir Res ; 24(1): 196, 2023 Aug 09.
Artigo em Inglês | MEDLINE | ID: mdl-37559053

RESUMO

BACKGROUND: Coronavirus disease 2019 (COVID-19) patients can develop pulmonary fibrosis (PF), which is associated with impaired outcome. We assessed specific leukocytic transcriptome profiles associated with PF and the influence of early dexamethasone (DEXA) treatment on the clinical course of PF in critically ill COVID-19 patients. METHODS: We performed a pre-post design study in 191 COVID-19 patients admitted to the Intensive Care Unit (ICU) spanning two treatment cohorts: the pre-DEXA- (n = 67) and the DEXA-cohort (n = 124). PF was identified based on radiological findings, worsening of ventilatory parameters and elevated circulating PIIINP levels. Longitudinal transcriptome profiles of 52 pre-DEXA patients were determined using RNA sequencing. Effects of prednisone treatment on clinical fibrosis parameters and outcomes were analyzed between PF- and no-PF-patients within both cohorts. RESULTS: Transcriptome analyses revealed upregulation of inflammatory, coagulation and neutrophil extracellular trap-related pathways in PF-patients compared to no-PF patients. Key genes involved included PADI4, PDE4D, MMP8, CRISP3, and BCL2L15. Enrichment of several identified pathways was associated with impaired survival in a external cohort of patients with idiopathic pulmonary fibrosis. Following prednisone treatment, PF-related profiles reverted towards those observed in the no-PF-group. Likewise, PIIINP levels decreased significantly following prednisone treatment. PF incidence was 28% and 25% in the pre-DEXA- and DEXA-cohort, respectively (p = 0.61). ICU length-of-stay (pre-DEXA: 42 [29-49] vs. 18 [13-27] days, p < 0.001; DEXA: 42 [28-57] vs. 13 [7-24] days, p < 0.001) and mortality (pre-DEXA: 47% vs. 15%, p = 0.009; DEXA: 61% vs. 19%, p < 0.001) were higher in the PF-groups compared to the no-PF-groups within both cohorts. Early dexamethasone therapy did not influence these outcomes. CONCLUSIONS: ICU patients with COVID-19 who develop PF exhibit upregulated coagulation, inflammation, and neutrophil extracellular trap-related pathways as well as prolonged ICU length-of-stay and mortality. This study indicates that early dexamethasone treatment neither influences the incidence or clinical course of PF, nor clinical outcomes.


Assuntos
COVID-19 , Fibrose Pulmonar Idiopática , Humanos , SARS-CoV-2 , Prednisona , Respiração Artificial , Dexametasona , Progressão da Doença
2.
Nature ; 594(7862): 265-270, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-34040261

RESUMO

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.


Assuntos
Blockchain , Tomada de Decisão Clínica/métodos , Confidencialidade , Conjuntos de Dados como Assunto , Aprendizado de Máquina , Medicina de Precisão/métodos , COVID-19/diagnóstico , COVID-19/epidemiologia , Surtos de Doenças , Feminino , Humanos , Leucemia/diagnóstico , Leucemia/patologia , Leucócitos/patologia , Pneumopatias/diagnóstico , Aprendizado de Máquina/tendências , Masculino , Software , Tuberculose/diagnóstico
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA