RESUMEN
To investigate the clinical value of changes in the subtypes of peripheral blood lymphocytes and levels of inflammatory cytokines in patients with COVID-19, the total numbers of lymphocytes and CD4+ lymphocytes and the ratio of CD4+/CD8+ lymphocytes were calculated and observed in different groups of patients with COVID-19. The results show that the lymphocytopenia in patients with COVID-19 was mainly manifested by decreases in the CD4+ T lymphocyte number and the CD4+/CD8+ ratio. The decreased number of CD4+ T lymphocytes and the elevated levels of TNF-α and IL-6 were correlated with the severity of COVID-19 disease.
Asunto(s)
Linfocitos T CD4-Positivos/patología , Infecciones por Coronavirus/sangre , Infecciones por Coronavirus/inmunología , Citocinas/sangre , Neumonía Viral/sangre , Neumonía Viral/inmunología , Adolescente , Adulto , Anciano , Betacoronavirus , Recuento de Linfocito CD4 , Relación CD4-CD8 , COVID-19 , Niño , Infecciones por Coronavirus/diagnóstico , Femenino , Humanos , Interleucina-6/sangre , Linfopenia/sangre , Linfopenia/patología , Masculino , Persona de Mediana Edad , Pandemias , Neumonía Viral/diagnóstico , SARS-CoV-2 , Índice de Severidad de la Enfermedad , Factor de Necrosis Tumoral alfa/sangreRESUMEN
AIM: To investigate differently expressed genes associated with cardiac fibrosis induced independently by aldosterone. METHODS: Fetal cardiac fibroblasts (FCFs)were isolated and cultured. Total RNA was extracted 8 hours after aldosterone administration. Then gene chips were used to screen these RNA samples. Some of candidate genes were confirmed by RT-PCR and Western blot. RESULTS: Differently expressed 1519 genes were screened. Up-regulated genes were 714 while down-regulated genes were 805. The expression of CCL7, MMP-26 and IL31RA was tested by RT-PCR and western blot, the results is identical with those by gene chips. CONCLUSION: Gene chip can efficiently single out differently expressed genes induced dependently by aldosterone in FCFs. CCL7, MMP-26 and IL31RA may be associated with cardiac fibrosis induced by aldosterone.
Asunto(s)
Aldosterona/farmacología , Fibroblastos/efectos de los fármacos , Perfilación de la Expresión Génica , Expresión Génica/efectos de los fármacos , Western Blotting , Células Cultivadas , Quimiocina CCL7/genética , Quimiocina CCL7/metabolismo , Feto , Fibroblastos/metabolismo , Fibrosis/genética , Humanos , Metaloproteinasas de la Matriz Secretadas/genética , Metaloproteinasas de la Matriz Secretadas/metabolismo , Miocardio , Análisis de Secuencia por Matrices de Oligonucleótidos , Receptores de Interleucina/genética , Receptores de Interleucina/metabolismo , Reacción en Cadena de la Polimerasa de Transcriptasa InversaRESUMEN
BACKGROUND: Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification. RESULTS: We developed a novel discrete wavelet transform (DWT) and a 'Spatial Adaptive Algorithm' to remove noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the Trans-Proteomic Pipeline (TPP), a commonly used open source proteomics analysis pipeline. CONCLUSIONS: We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at http://systemsbiozju.org/data/WaveletQuant.
