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Purpose: While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.
Methods: We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).
Results: The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.
Conclusion: We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.
Purpose: Few individuals that are latently infected with M. tuberculosis latent tuberculosis infection(LTBI) progress to active disease. We investigated risk factors for LTBI and active pulmonary tuberculosis (PTB) in Germany.
Methods: Healthy household contacts (HHCs), health care workers (HCWs) exposed to M. tuberculosis and PTB patients were recruited at 18 German centres. Interferon-γ release assay (IGRA) testing was performed. LTBI risk factors were evaluated by comparing IGRA-positive with IGRA-negative contacts. Risk factors for tuberculosis were evaluated by comparing PTB patients with HHCs.
Results: From 2008–2014, 603 HHCs, 295 HCWs and 856 PTBs were recruited. LTBI was found in 34.5% of HHCs and in 38.9% of HCWs. In HCWs, care for coughing patients (p = 0.02) and longstanding nursing occupation (p = 0.04) were associated with LTBI. In HHCs, predictors for LTBI were a diseased partner (odds ratio 4.39), sexual contact to a diseased partner and substance dependency (all p < 0.001). PTB was associated with male sex, low body weight (p < 0.0001), alcoholism (15.0 vs 5.9%; p < 0.0001), glucocorticoid therapy (7.2 vs 2.0%; p = 0.004) and diabetes (7.8 vs. 4.0%; p = 0.04). No contact developed active tuberculosis within 2 years follow-up.
Conclusions: Positive IGRA responses are frequent among exposed HHCs and HCWs in Germany and are poor predictors for the development of active tuberculosis.
RITA, the RBP‐J interacting and tubulin‐associated protein, has been reported to be related to tumor development, but the underlying mechanisms are not understood. Since RITA interacts with tubulin and coats microtubules of the cytoskeleton, we hypothesized that it is involved in cell motility. We show here that depletion of RITA reduces cell migration and invasion of diverse cancer cell lines and mouse embryonic fibroblasts. Cells depleted of RITA display stable focal adhesions (FA) with elevated active integrin, phosphorylated focal adhesion kinase, and paxillin. This is accompanied by enlarged size and disturbed turnover of FA. These cells also demonstrate increased polymerized tubulin. Interestingly, RITA is precipitated with the lipoma‐preferred partner (LPP), which is critical in actin cytoskeleton remodeling and cell migration. Suppression of RITA results in reduced LPP and α‐actinin at FA leading to compromised focal adhesion turnover and actin dynamics. This study identifies RITA as a novel crucial player in cell migration and invasion by affecting the turnover of FA through its interference with the dynamics of actin filaments and microtubules. Its deregulation may contribute to malignant progression.
Ribosome heterogeneity is of increasing biological significance and several examples have been described for multicellular and single cells organisms. In here we show for the first time a variation in ribose methylation within the 18S rRNA of Saccharomyces cerevisiae. Using RNA-cleaving DNAzymes, we could specifically demonstrate that a significant amount of S. cerevisiae ribosomes are not methylated at 2′-O-ribose of A100 residue in the 18S rRNA. Furthermore, using LC-UV-MS/MS of a respective 18S rRNA fragment, we could not only corroborate the partial methylation at A100, but could also quantify the methylated versus non-methylated A100 residue. Here, we exhibit that only 68% of A100 in the 18S rRNA of S.cerevisiae are methylated at 2′-O ribose sugar. Polysomes also contain a similar heterogeneity for methylated Am100, which shows that 40S ribosome subunits with and without Am100 participate in translation. Introduction of a multicopy plasmid containing the corresponding methylation guide snoRNA gene SNR51 led to an increased A100 methylation, suggesting the cellular snR51 level to limit the extent of this modification. Partial rRNA modification demonstrates a new level of ribosome heterogeneity in eukaryotic cells that might have substantial impact on regulation and fine-tuning of the translation process.