Jmp statistical software version 16

The cycle threshold (Ct) values were determined by RT-qPCR in Biomedica de Referencia SA de CV. The SARS-CoV-2 RT-qPCR test provides real-time quantification by first reverse transcribing SARS-CoV-2 RNA into cDNA (RT step) and then performing qPCR, during which a fluorescence signal increases proportionally to the amount of amplified nucleic acid, enabling accurate quantitation of the RNA in the sample (Tom and Mina, 2020 (link)). Many qPCR assays involve a Ct cut-off 34 to consider the test positive, allowing the detection of very few starting RNA molecules (Cruz-Rangel et al., 2022 (link)).
To estimate whether there was a correlation between Ct values and IP for both IgG and IgM, 31 SARS-CoV-2 PCR-positive serum samples were tested. All the data were analyzed with Pearson’s correlation analysis. The correlation analysis was performed using the JMP statistical software, version 16 (SAS Institute Inc, 2000).

Data are expressed as mean ± standard deviation or median (minimum, maximum) for continuous variables and frequencies and percentages for discrete variables. Univariate analyses were conducted using methods such as single regression analysis, contingency tables, analysis of variance, and logistic regression analysis to investigate the data related to the findings of VF for swallowing 3 and 10 mL of water, as well as temporal analyses. In this process, we included the mean values of rigidity scores, tremor scores, PIGD scores, and limb scores, and as mentioned, UPDRS Part 3, in the analyses. Subsequently, we explored the associations between various VF findings and the corresponding UPDRS subscores. Initially, we conducted univariate analyses and extracted factors with p-values < 0.10. These factors were then subjected to a multivariate analysis. Statistical analyses were performed using JMP statistical software version 16 (SAS Institute Inc., Cary, NC, USA). Appropriate statistical tests, such as the χ2 test, Mann–Whitney U test, or unpaired t-test, were employed to assess intergroup variances. Statistical significance was set at p < 0.05.

Quantitative data are presented as mean ± standard deviation or percentage. Differences in categorical variables were analyzed using the chi-square test with the Yates correction or Fisher’s exact test. The risk factors for poor prognosis were analyzed using univariate and multivariate analyses. Continuous and qualitative variables were analyzed using Student’s t-test or the Mann–Whitney U test and Pearson’s chi-squared test, respectively. Statistical significance was set at p < 0.05. Logistic regression analysis was performed to examine the risk factors for poor OS. OS and DSS rates were calculated using the Kaplan–Meier method. All statistical analyses were performed using JMP statistical software version 16.0.0 (SAS Institute, Cary, North Carolina, USA).