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1.
Sci Rep ; 12(1): 14772, 2022 08 30.
Article in English | MEDLINE | ID: covidwho-2016839

ABSTRACT

Limited data exists on SARS-CoV-2 sustained-response to vaccine in patients with rheumatic diseases. This study aims to evaluate neutralizing antibodies (nAB) induced by SARS-CoV-2 vaccine after 3 to 6 months from administration in Systemic Lupus Erythematosus (SLE) patients, as a surrogate of sustained-immunological response. This cross-sectional study compared nAB titre of 39 SLE patients and 37 Healthy individuals with no previous SARS-CoV-2 infection, who had all received a complete regimen of a mRNA SARS-CoV-2 vaccine within the last 3 to 6 months. We included four lines of SLE treatment including Not-treated, Hydroxychloroquine, immunosuppressive drugs and biological therapy. Glucocorticoids were allowed in all groups. Healthy and Not-treated individuals showed the highest levels of nAB. Treated patients presented lower nAB titres compared to Healthy: a 73% decrease for First-Line patients, 56% for Second-Line treatment and 72% for Third-Line. A multivariate analysis pointed to Glucocorticoids as the most associated factor with declining nAB levels (75% decrease) in treated SLE. Furthermore, a significant reduction in nAB titres was observed for Rituximab-users compared to Healthy subjects (89% decrease). Medium-term response of SLE patients to SARS-CoV-2 mRNA vaccines is negatively impacted in Glucocorticoids and Rituximab users. These findings might help to inform recommendations in vaccination protocols for SLE patients.


Subject(s)
COVID-19 , Lupus Erythematosus, Systemic , Antibodies, Neutralizing/therapeutic use , Antibodies, Viral/therapeutic use , COVID-19/prevention & control , COVID-19 Vaccines , Cross-Sectional Studies , Glucocorticoids/therapeutic use , Humans , Rituximab/therapeutic use , SARS-CoV-2 , Vaccines, Synthetic , mRNA Vaccines
2.
Sci Rep ; 12(1): 640, 2022 01 12.
Article in English | MEDLINE | ID: covidwho-1900548

ABSTRACT

COVID-19 pathophysiology is currently not fully understood, reliable prognostic factors remain elusive, and few specific therapeutic strategies have been proposed. In this scenario, availability of biomarkers is a priority. MS-based Proteomics techniques were used to profile the proteome of 81 plasma samples extracted in four consecutive days from 23 hospitalized COVID-19 associated pneumonia patients. Samples from 10 subjects that reached a critical condition during their hospital stay and 10 matched non-severe controls were drawn before the administration of any COVID-19 specific treatment and used to identify potential biomarkers of COVID-19 prognosis. Additionally, we compared the proteome of five patients before and after glucocorticoids and tocilizumab treatment, to assess the changes induced by the therapy on our selected candidates. Forty-two proteins were differentially expressed between patients' evolution groups at 10% FDR. Twelve proteins showed lower levels in critical patients (fold-changes 1.20-3.58), of which OAS3 and COG5 found their expression increased after COVID-19 specific therapy. Most of the 30 proteins over-expressed in critical patients (fold-changes 1.17-4.43) were linked to inflammation, coagulation, lipids metabolism, complement or immunoglobulins, and a third of them decreased their expression after treatment. We propose a set of candidate proteins for biomarkers of COVID-19 prognosis at the time of hospital admission. The study design employed is distinctive from previous works and aimed to optimize the chances of the candidates to be validated in confirmatory studies and, eventually, to play a useful role in the clinical practice.


Subject(s)
Blood Proteins , COVID-19/blood , COVID-19/diagnosis , Hospitalization , Aged , Aged, 80 and over , Biomarkers/blood , Disease Progression , Female , Humans , Male , Mass Spectrometry , Middle Aged , Prospective Studies , Proteome
3.
Viruses ; 12(11)2020 11 09.
Article in English | MEDLINE | ID: covidwho-918256

ABSTRACT

BACKGROUND: COVID-19 pathophysiology and the predictive factors involved are not fully understood, but lymphocytes dysregulation appears to play a role. This paper aims to evaluate lymphocyte subsets in the pathophysiology of COVID-19 and as predictive factors for severe disease. PATIENT AND METHODS: A prospective cohort study of patients with SARS-CoV-2 bilateral pneumonia recruited at hospital admission. Demographics, medical history, and data regarding SARS-CoV-2 infection were recorded. Patients systematically underwent complete laboratory tests, including parameters related to COVID-19 as well as lymphocyte subsets study at the time of admission. Severe disease criteria were established at admission, and patients were classified on remote follow-up according to disease evolution. Linear regression models were used to assess associations with disease evolution, and Receiver Operating Characteristic (ROC) and the corresponding Area Under the Curve (AUC) were used to evaluate predictive values. RESULTS: Patients with critical COVID-19 showed a decrease in CD3+CD4+ T cells count compared to non-critical (278 (485 IQR) vs. 545 (322 IQR)), a decrease in median CD4+/CD8+ ratio (1.7, (1.7 IQR) vs. 3.1 (2.4 IQR)), and a decrease in median CD4+MFI (21,820 (4491 IQR) vs. 26,259 (3256 IQR)), which persisted after adjustment. CD3+CD8+ T cells count had a high correlation with time to hospital discharge (PC = -0.700 (-0.931, -0.066)). ROC curves for predictive value showed lymphocyte subsets achieving the best performances, specifically CD3+CD4+ T cells (AUC = 0.756), CD4+/CD8+ ratio (AUC = 0.767), and CD4+MFI (AUC = 0.848). CONCLUSIONS: A predictive value and treatment considerations for lymphocyte subsets are suggested, especially for CD3CD4+ T cells. Lymphocyte subsets determination at hospital admission is recommended.


Subject(s)
CD4-Positive T-Lymphocytes/pathology , CD8-Positive T-Lymphocytes/pathology , COVID-19/diagnosis , Lymphocyte Subsets/pathology , SARS-CoV-2/pathogenicity , Aged , Area Under Curve , Biomarkers/analysis , CD4-CD8 Ratio/statistics & numerical data , CD4-Positive T-Lymphocytes/immunology , CD4-Positive T-Lymphocytes/virology , CD8-Positive T-Lymphocytes/immunology , CD8-Positive T-Lymphocytes/virology , COVID-19/immunology , COVID-19/pathology , COVID-19/virology , Disease Progression , Female , Humans , Lung , Lymphocyte Count , Lymphocyte Subsets/immunology , Lymphocyte Subsets/virology , Male , Middle Aged , Patient Discharge/statistics & numerical data , Prognosis , Prospective Studies , ROC Curve , SARS-CoV-2/immunology , Severity of Illness Index
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