The Long COVID Symptoms and Severity Score: Development, Validation, and Application.

OBJECTIVES: The primary focus of this research is the proposition of a methodological framework for the clinical application of the long COVID symptoms and severity score (LC-SSS). This tool is not just a self-reported assessment instrument developed and validated but serves as a standardized, quantifiable means to monitor the diverse and persistent symptoms frequently observed in individuals with long COVID. METHODS: A 3-stage process was used to develop, validate, and establish scoring standards for the LC-SSS. Validation measures included correlations with other patient-reported measures, confirmatory factor analysis, Cronbach's α for internal consistency, and test-retest reliability. Scoring standards were determined using K-means clustering, with comparative assessments made against hierarchical clustering and the Gaussian Mixture Model. RESULTS: The LC-SSS showed correlations with EuroQol 5-Dimension 5-Level (rs = -0.55), EuroQol visual analog scale (rs = -0.368), Patient Health Questionnaire-9 (rs = 0.538), Beck Anxiety Inventory (rs = 0.689), and Insomnia Severity Index (rs = 0.516), confirming its construct validity. Structural validity was good with a comparative fit index of 0.969, with Cronbach's α of 0.93 indicating excellent internal consistency. Test-retest reliability was also satisfactory (intraclass correlation coefficient 0.732). K-means clustering identified 3 distinct severity categories in individuals living with long COVID, providing a basis for personalized treatment strategies. CONCLUSIONS: The LC-SSS provides a robust and valid tool for assessing long COVID. The severity categories established via K-means clustering demonstrate significant variation in symptom severity, informing personalized treatment and improving care quality for patients with long COVID.

[The effects of complement C3f segment on expression and secretion of collagen I, III and transforming growth factor-beta1 in human embryonic lung fibroblast].

OBJECTIVE: To observe the effects of complement fragment C3f on expression and secretion of collagen I, III and transforming growth factor( TGF)-beta1 in human embryonic lung fibroblast (MRC-5) cells. METHODS: MRC-5 cells were cultured with C3f (the synthetic 17 peptides fragments of complement C3). The extracellular and intracellular expression levels of type I, III collagens and TGF-beta1 in MRC-5 cultures were detected by ELISA and immunohistochemistry, respectively. RESULTS: The expression levels of type I, III collagen and TGF-beta1 in the supernatant of MRC-5 cultures decreased significantly with the concentrations of C3f as compared with controls (P < 0.05). Also the expression level of TGF-beta1 in MRC-5 cytoplasm reduced significantly as compared with controls (P < 0.05). CONCLUSION: The results of present in vitro study showed that the complement fragment C3f could reduce the formation of TGF-beta1 and type I, III collagens in MRC-5 cells, and inhibit the lung tissue fibrosis.

[Correlation analysis between the exposure levels and the serum protein fingerprints in population exposure to silica].

OBJECTIVE: To explore the correlation between the exposure levels and serum protein fingerprints in population exposed to silica. METHODS: Liquid chip time-of-flight mass spectrometry technology was used to investigate the serum profiles in control group (30 cases), group exposed to silica (30 cases), silicosis group (I stage, 25 cases) and suspected silicosis group (30 cases), and screen the differential expression proteins. The correlation between the levels of the differential expression proteins and the exposure levels was performed. RESULTS: Five differential expression proteins were found among 4 groups, the expression of 5081 and 5066 proteins was upregulated, and the expression of 3954, 2021 and 1777 proteins was downregulated. There was no the correlation between the exposure levels and the peak with M/Z among those proteins. CONCLUSION: the results of present investigation indicated there was no correlation between the exposure levels and protein/peptide peak.

[Diagnostic prediction of early silicosis patients using neural network and MALDI-TOF-MS in serum].

Serum of 79 workers exposed to silica and 25 healthy controls cases were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). 7 protein peaks were selected and used by artificial neural network (ANN) to establish a diagnostic model. A blinded test showed that accuracy, sensitivity and specificity were 91.35%, 93.69%, and 84.52%, respectively. The diagnostic pattern was also established to distinguish each stage of silica-exposed population. The diagnostic pattern worked excellently with 89.23%, 94.20% and 92.37% of accurate rate for classifying phase 0, phase 0+, and phase I of silicosis, respectively.