ABSTRACT
The German Society of Pneumology initiated 2021 the AWMF S1 guideline Long COVID/Post-COVID. In a broad interdisciplinary approach, this S1 guideline was designed based on the current state of knowledge.The clinical recommendations describe current Long COVID/Post-COVID symptoms, diagnostic approaches, and therapies.In addition to the general and consensus introduction, a subject-specific approach was taken to summarize the current state of knowledge.The guideline has an explicit practical claim and will be developed and adapted by the author team based on the current increase in knowledge.
Subject(s)
COVID-19 , Post-Acute COVID-19 Syndrome , HumansABSTRACT
Idiopathic pulmonary fibrosis (IPF), the most common subtype of idiopathic interstitial pneumonia, is a chronic progressive lung disease with a very high mortality. Usually diagnosis is established in adults older than 50 years and most cases are considered to be sporadic. Individuals with a familial form of IPF have at least one affected member in the same primary biological family and account for less than 5% of total patients. Sporadic and familial IPF are clinically and histologically indistinguishable from one another, although some familial forms appear to develop at an earlier age and exhibit different patterns of gene transcription. This case study describes the early clinical course of a patient diagnosed with a familial form of IPF.
Subject(s)
Idiopathic Pulmonary Fibrosis , Pulmonary Fibrosis , Chronic Disease , Humans , Idiopathic Interstitial Pneumonias , Pulmonary Fibrosis/diagnosisABSTRACT
Stochastic subgrid models have been proposed to capture the missing variability and correct systematic medium-term errors in general circulation models. In particular, the poor representation of subgrid-scale deep convection is a persistent problem that stochastic parametrizations are attempting to correct. In this paper, we construct such a subgrid model using data derived from large-eddy simulations (LESs) of deep convection. We use a data-driven stochastic parametrization methodology to construct a stochastic model describing a finite number of cloud states. Our model emulates, in a computationally inexpensive manner, the deep convection-resolving LES. Transitions between the cloud states are modelled with Markov chains. By conditioning the Markov chains on large-scale variables, we obtain a conditional Markov chain, which reproduces the time evolution of the cloud fractions. Furthermore, we show that the variability and spatial distribution of cloud types produced by the Markov chains become more faithful to the LES data when local spatial coupling is introduced in the subgrid Markov chains. Such spatially coupled Markov chains are equivalent to stochastic cellular automata.
ABSTRACT
Non-small cell lung cancer (NSCLC) is a leading cause of death and a substantial fraction of patients with surgically resected disease ultimately dies due to distant metastasis. To identify gene expression differences in early stage adenocarcinoma that either did or did not metastasize within a 5-year period, we employed a subtractive hybridization strategy of pooled RNA from primary adenocarcinomas (stage I) of the lung. Individual clones (n=225) of the subtracted cDNA library were sequenced. Further analyses of mRNA expression levels in a cohort of 70 NSCLC patients (stage I to IIIA) showed that the metastasis association of the identified genes was stage and histology specific. Cox regression analyses identified two genes (EIF4A1, MALA1) to be independent prognostic parameters for patients' survival in stage I and II disease. These findings could help to identify early-stage NSCLC patients at high risk for the development of distant metastasis.
