by Keyword: lung-cancer
Husser, Clara, Kwon, Hyesoo, Andersson, Klara, Appelberg, Sofia, Montserrat, Nuria, Mirazimi, Ali, Monteil, Vanessa M, Monteil, Vanessa M, (2024). P53-Independent G1-Cell Cycle Arrest Increases SARS-CoV-2 RNA Replication Microorganisms 12, 443 
While having already killed more than 7 million of people worldwide in 4 years, SARS-CoV-2, the etiological agent of COVID-19, is still circulating and evolving. Understanding the pathogenesis of the virus is of capital importance. It was shown that in vitro and in vivo infection with SARS-CoV-2 can lead to cell cycle arrest but the effect of the cell cycle arrest on the virus infection and the associated mechanisms are still unclear. By stopping cells in the G1 phase as well as targeting several pathways involved using inhibitors and small interfering RNAs, we were able to determine that the cell cycle arrest in the late G1 is beneficial for SARS-CoV-2 replication. This cell cycle arrest is independent of p53 but is dependent on the CDC25A-CDK2/cyclin E pathway. These data give a new understanding in SARS-CoV-2 pathogenesis and highlight some possible targets for the development of novel therapeutic approaches.
JTD Keywords: Cell lung-cancer,exchanger nhe,g1 phase,proliferation,inhibitio, Covid-19,coronavirus,pathogenicity,replication,cdk2,cyclin e,cdc25a,treatment
de Oliveira, LF, Mallafré-Muro, C, Giner, J, Perea, L, Sibila, O, Pardo, A, Marco, S, (2022). Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis Clinica Chimica Acta 526, 6-13
Background and aims: In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients. Materials and methods: A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples. Results: Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI95%: 84–100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test. Conclusions: Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples. © 2021 The Author(s)
JTD Keywords: biomarkers, breath analysis, bronchiectasis, diagnosis, e-nose, fingerprints, gc-ms, identification, lung-cancer, partial least-squares, pseudomonas-aeruginosa, signal processing, validation, volatile organic-compounds, Airway bacterial-colonization, Breath analysis, Bronchiectasis, E-nose, Gc-ms, Signal processing
