Characteristics of our hypoxemic COVID-19 pneumonia patients receiving corticosteroids and mortality-associated factors.

BACKGROUND: COVID-19 is a disease associated with diffuse lung injury that has no proven effective treatment yet. It is thought that glucocorticoids may reduce inflammation-mediated lung injury, disease progression, and mortality. We aimed to evaluate our patient's characteristics and treatment outcomes who received corticosteroids for COVID-19 pneumonia. METHODS: We conducted a multicenter retrospective study and reviewed 517 patients admitted due to COVID-19 pneumonia who were hypoxemic and administered steroids regarding demographic, laboratory, and radiological characteristics, treatment response, and mortality-associated factors. RESULTS: Of our 517 patients with COVID-19 pneumonia who were hypoxemic and received corticosteroids, the mortality rate was 24.4% (n = 126). The evaluation of mortality-associated factors revealed that age, comorbidities, a CURB-65 score of ≥ 2, higher SOFA scores, presence of MAS, high doses of steroids, type of steroids, COVID-19 treatment, stay in the intensive care unit, high levels of d-dimer, CRP, ferritin, and troponin, and renal dysfunction were associated with mortality. CONCLUSION: Due to high starting and average steroid doses are more associated with mortality, high-dose steroid administration should be avoided. We believe that knowing the factors associated with mortality in these cases is essential for close follow-up. The use of CURB-65 and SOFA scores can predict prognosis in COVID-19 pneumonia.

Assessment of the risk of obstructive sleep apnoea syndrome among healthcare workers.

INTRODUCTION: Sleep deprivation is known to increase the risks for workplace accidents, neuro-behavioural symptoms and reduced quality of life. Shift work leads to sleep related problems, such as sleep deprivation, poor sleep quality and daytime sleepiness. The purpose of our study is to assess snoring prevalence and obstructive sleep apnoea risk among healthcare workers (HCWs) by using the Berlin Questionnaire. MATERIALS AND METHODS: HCWs employed at different centres that agreed to take part in this study were enrolled. Employing the Berlin Questionnaire, demographic characteristics, smoking histories, comorbidities, on-call shifts, number of on-call shifts and sleep durations of the participants were surveyed. RESULT: 604 HCWs including specialist, nurse, junior doctor, medical student, clinical academic, health technician and hospital staff were enrolled in this study. In terms of sleep apnoea, 92 (15.1%) participants were identified as high-risk and 512 (84.8%) as low-risk according to the findings of the questionnaire. When the two groups identified as high and low risk for sleep apnoea according to the Berlin Questionnaire were compared, significant differences were found between the two groups in terms of age, sex, height, weight, BMI value and hypertension (p< 0.001 for all variables). Multivariate logistic regression analysis has shown that on-call shifts (OR= 0.199, CI= 0.053-0.747, p= 0.017) are negative associated with sleep apnoea risk. CONCLUSIONS: With extended working hours and on-call shifts increasing the risk of sleep disorders among HCWs, surveys designed for screening can be used to identify the prospective cases in this population for further examination.

Proteome screening of pleural effusions identifies galectin 1 as a diagnostic biomarker and highlights several prognostic biomarkers for malignant mesothelioma.

Malignant mesothelioma is an aggressive asbestos-induced cancer, and affected patients have a median survival of approximately one year after diagnosis. It is often difficult to reach a conclusive diagnosis, and ancillary measurements of soluble biomarkers could increase diagnostic accuracy. Unfortunately, few soluble mesothelioma biomarkers are suitable for clinical application. Here we screened the effusion proteomes of mesothelioma and lung adenocarcinoma patients to identify novel soluble mesothelioma biomarkers. We performed quantitative mass-spectrometry-based proteomics using isobaric tags for quantification and used narrow-range immobilized pH gradient/high-resolution isoelectric focusing (pH 4-4.25) prior to analysis by means of nano liquid chromatography coupled to MS/MS. More than 1,300 proteins were identified in pleural effusions from patients with malignant mesothelioma (n = 6), lung adenocarcinoma (n = 6), or benign mesotheliosis (n = 7). Data are available via ProteomeXchange with identifier PXD000531. The identified proteins included a set of known mesothelioma markers and proteins that regulate hallmarks of cancer such as invasion, angiogenesis, and immune evasion, plus several new candidate proteins. Seven candidates (aldo-keto reductase 1B10, apolipoprotein C-I, galectin 1, myosin-VIIb, superoxide dismutase 2, tenascin C, and thrombospondin 1) were validated by enzyme-linked immunosorbent assays in a larger group of patients with mesothelioma (n = 37) or metastatic carcinomas (n = 25) and in effusions from patients with benign, reactive conditions (n = 16). Galectin 1 was identified as overexpressed in effusions from lung adenocarcinoma relative to mesothelioma and was validated as an excellent predictor for metastatic carcinomas against malignant mesothelioma. Galectin 1, aldo-keto reductase 1B10, and apolipoprotein C-I were all identified as potential prognostic biomarkers for malignant mesothelioma. This analysis of the effusion proteome furthers our understanding of malignant mesothelioma, identified galectin 1 as a potential diagnostic biomarker, and highlighted several possible prognostic biomarkers of this disease.

Hyaluronan and N-ERC/mesothelin as key biomarkers in a specific two-step model to predict pleural malignant mesothelioma.

PURPOSE: Diagnosis of malignant mesothelioma is challenging. The first available diagnostic material is often an effusion and biochemical analysis of soluble markers may provide additional diagnostic information. This study aimed to establish a predictive model using biomarkers from pleural effusions, to allow early and accurate diagnosis. PATIENTS AND METHODS: Effusions were collected prospectively from 190 consecutive patients at a regional referral centre. Hyaluronan, N-ERC/mesothelin, C-ERC/mesothelin, osteopontin, syndecan-1, syndecan-2, and thioredoxin were measured using ELISA and HPLC. A predictive model was generated and validated using a second prospective set of 375 effusions collected consecutively at a different referral centre. RESULTS: Biochemical markers significantly associated with mesothelioma were hyaluronan (odds ratio, 95% CI: 8.82, 4.82-20.39), N-ERC/mesothelin (4.81, 3.19-7.93), CERC/mesothelin (3.58, 2.43-5.59) and syndecan-1 (1.34, 1.03-1.77). A two-step model using hyaluronan and N-ERC/mesothelin, and combining a threshold decision rule with logistic regression, yielded good discrimination with an area under the ROC curve of 0.99 (95% CI: 0.97-1.00) in the model generation dataset and 0.83 (0.74-0.91) in the validation dataset, respectively. CONCLUSIONS: A two-step model using hyaluronan and N-ERC/mesothelin predicts mesothelioma with high specificity. This method can be performed on the first available effusion and could be a useful adjunct to the morphological diagnosis of mesothelioma.