Upon a detailed review of the methodology and findings, I would like to seek clarification from the authors on the following points:
A-Study Design and Sample Size
- The study includes a relatively small sample size (n = 50), with 15 Long COVID patients. Given the heterogeneity of Long COVID symptoms, how do you account for inter-individual variability?
- Were any power calculations performed to determine whether the sample size was sufficient to detect significant differences?
- Is there an independent validation cohort to confirm the reproducibility of your findings?
B-Data Processing and Quality Control
- You mention that 93.2% of proteins had a coefficient of variation (CV) below 30%. Could you provide a threshold for acceptable data quality and explain how outliers were handled?
- Missing proteomic values were imputed using the K-Nearest Neighbors (KNN) method. Given the small sample size, how do you ensure that this imputation did not introduce bias or artificial patterns?
- Were technical replicates or batch effect corrections applied to minimize variability in proteomics and metabolomics data?
C-Statistical Analysis and Interpretation
- The study applies a fold-change threshold of ≥1.5 or ≤0.67 with a p-value < 0.05 (adjusted using Benjamini-Hochberg correction). Given the multiple comparisons involved, how do you address the risk of false positives?
- Were any sensitivity analyses performed to assess the robustness of the differential expression findings?
D-Interpretation of Findings
- Your study suggests that coagulation dysfunction, platelet degranulation, and complement activation contribute to Long COVID pathology. However, how do you differentiate these changes as causal mechanisms rather than secondary effects from prior hospitalization, medication use, or comorbid conditions?
- Have you considered incorporating longitudinal follow-ups to assess whether these alterations persist over time?
- The study highlights dysregulated lipid metabolism, particularly glycerophospholipids and sphingolipids. Were any functional validation experiments (e.g., targeted metabolomics, biochemical assays) conducted to confirm the biological significance of these findings?
E-Future Directions and Clinical Implications
- Given that metabolomic and proteomic findings may be influenced by lifestyle factors such as diet, physical activity, and medication use, how were these potential confounders controlled for?
- Are there plans to integrate transcriptomic or immune profiling data to provide a more comprehensive view of Long COVID pathophysiology?