Image 2_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif by Ying Ye (72583)

“…</p>Methods<p>In this study, we applied the HuProt array and the bioinformatics analysis to assess the diagnostic values of the decreased autoantibodies in lung cancers.…”

Image 4_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif by Ying Ye (72583)

“…</p>Methods<p>In this study, we applied the HuProt array and the bioinformatics analysis to assess the diagnostic values of the decreased autoantibodies in lung cancers.…”

Image 5_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif by Ying Ye (72583)

“…</p>Methods<p>In this study, we applied the HuProt array and the bioinformatics analysis to assess the diagnostic values of the decreased autoantibodies in lung cancers.…”

Image 8_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif by Ying Ye (72583)

“…</p>Methods<p>In this study, we applied the HuProt array and the bioinformatics analysis to assess the diagnostic values of the decreased autoantibodies in lung cancers.…”

Data Sheet 1_Prognostic impact of dynamic changes of type I melanoma antigen gene proteins CT7 (MAGE-C1/CT7) transcripts in multiple myeloma.docx by Xuelin Dou (10938549)

“…Our data showed the predictive value of peri-ASCT frontline treatment. A 2-log decrease of MAGE-C1/CT7 post-induction cycle 2 compared to baseline correlated with a negative peri-ASCT MAGE-C1/CT7 status, providing an earlier prognostic marker of treatment response.…”

AUC statistics as calculated from simulated time series. Each statistical metric was calculated within sliding windows, throughout the pre-critical interval. We considered five-, fifteen-, and thirty-day sliding windows. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre</p>…”

Percent difference in Precision-Recall AUC, sensitivity, and specificity for pooled models relative to eBird-only models. by Reid Rumelt (21680956)

“…<p>Precision-Recall AUC and sensitivity were higher in all species (positive values); decreases in specificity, present in all species, were more minor (negative values, note differences in x-axis scales between panels).…”

Table 2_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.xlsx by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Image 3_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.tiff by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Table 1_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.xlsx by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Image 2_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.tiff by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Table 3_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.xlsx by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Image 1_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.tiff by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Image 8_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.tif by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Image 6_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.tiff by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Image 4_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.tif by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Image 7_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.tiff by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Image 5_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.tiff by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Table 4_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.xlsx by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”

Table 5_Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.xlsx by Chao-Chao Chen (21090215)

“…In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. …”