Image_7_A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.jpeg

IntroductionThis study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).MethodsThe research included 881 early-stage HCC patients treated at Beijing You’an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).ResultsThe study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram’s predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.ConclusionsThe established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.</p

Image_3_A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.jpeg

IntroductionThis study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).MethodsThe research included 881 early-stage HCC patients treated at Beijing You’an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).ResultsThe study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram’s predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.ConclusionsThe established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.</p

Image_1_A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.jpeg

IntroductionThis study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).MethodsThe research included 881 early-stage HCC patients treated at Beijing You’an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).ResultsThe study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram’s predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.ConclusionsThe established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.</p

Image_6_A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.jpeg

IntroductionThis study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).MethodsThe research included 881 early-stage HCC patients treated at Beijing You’an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).ResultsThe study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram’s predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.ConclusionsThe established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.</p

Image_5_A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.tiff

IntroductionThis study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).MethodsThe research included 881 early-stage HCC patients treated at Beijing You’an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).ResultsThe study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram’s predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.ConclusionsThe established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.</p

Image_2_A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.tiff

IntroductionThis study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).MethodsThe research included 881 early-stage HCC patients treated at Beijing You’an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).ResultsThe study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram’s predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.ConclusionsThe established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.</p

Image_4_A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.jpeg

IntroductionThis study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).MethodsThe research included 881 early-stage HCC patients treated at Beijing You’an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).ResultsThe study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram’s predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.ConclusionsThe established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.</p

Image_3_Comparing sputum microbiota characteristics between severe and critically ill influenza patients.tif

BackgroundCurrently, limited attention has been directed toward utilizing clinical cohorts as a starting point to elucidate alterations in the lower respiratory tract (LRT) microbiota following influenza A virus (IAV) infection.ObjectivesOur objective was to undertake a comparative analysis of the diversity and composition of sputum microbiota in individuals afflicted by severe and critically ill influenza patients.MethodsSputum specimens were procured from patients diagnosed with IAV infection for the purpose of profiling the microbiota using 16S-rDNA sequencing. To ascertain taxonomic differences between the severe and critically ill influenza cohorts, we leveraged Linear Discriminant Analysis Effect Size (LEfSe). Additionally, Spearman correlation analysis was employed to illuminate associations between sputum microbiota and influenza Ct values alongside laboratory indicators.ResultsOur study encompassed a total cohort of 64 patients, comprising 48 within the severe group and 16 within the critically ill group. Intriguingly, Bacteroidetes exhibited significant depletion in the critically ill cohort (p=0.031). The sputum microbiomes of the severe influenza group were hallmarked by an overrepresentation of Neisseria, Porphyromonas, Actinobacillus, Alloprevotella, TM7x, and Clostridia_UCG-014, yielding ROC-plot AUC values of 0.71, 0.68, 0.60, 0.70, 0.70, and 0.68, respectively. Notably, Alloprevotella exhibited an inverse correlation with influenza Ct values. Moreover, C-reactive protein (CRP) manifested a positive correlation with Haemophilus and Porphyromonas.ConclusionThe outcomes of this investigation lay the groundwork for future studies delving into the connection between the LRT microbiome and respiratory disorders. Further exploration is warranted to elucidate the intricate mechanisms underlying the interaction between IAV and Alloprevotella, particularly in disease progression.</p

Image_4_Comparing sputum microbiota characteristics between severe and critically ill influenza patients.tif

BackgroundCurrently, limited attention has been directed toward utilizing clinical cohorts as a starting point to elucidate alterations in the lower respiratory tract (LRT) microbiota following influenza A virus (IAV) infection.ObjectivesOur objective was to undertake a comparative analysis of the diversity and composition of sputum microbiota in individuals afflicted by severe and critically ill influenza patients.MethodsSputum specimens were procured from patients diagnosed with IAV infection for the purpose of profiling the microbiota using 16S-rDNA sequencing. To ascertain taxonomic differences between the severe and critically ill influenza cohorts, we leveraged Linear Discriminant Analysis Effect Size (LEfSe). Additionally, Spearman correlation analysis was employed to illuminate associations between sputum microbiota and influenza Ct values alongside laboratory indicators.ResultsOur study encompassed a total cohort of 64 patients, comprising 48 within the severe group and 16 within the critically ill group. Intriguingly, Bacteroidetes exhibited significant depletion in the critically ill cohort (p=0.031). The sputum microbiomes of the severe influenza group were hallmarked by an overrepresentation of Neisseria, Porphyromonas, Actinobacillus, Alloprevotella, TM7x, and Clostridia_UCG-014, yielding ROC-plot AUC values of 0.71, 0.68, 0.60, 0.70, 0.70, and 0.68, respectively. Notably, Alloprevotella exhibited an inverse correlation with influenza Ct values. Moreover, C-reactive protein (CRP) manifested a positive correlation with Haemophilus and Porphyromonas.ConclusionThe outcomes of this investigation lay the groundwork for future studies delving into the connection between the LRT microbiome and respiratory disorders. Further exploration is warranted to elucidate the intricate mechanisms underlying the interaction between IAV and Alloprevotella, particularly in disease progression.</p

Image_1_Comparing sputum microbiota characteristics between severe and critically ill influenza patients.tif

BackgroundCurrently, limited attention has been directed toward utilizing clinical cohorts as a starting point to elucidate alterations in the lower respiratory tract (LRT) microbiota following influenza A virus (IAV) infection.ObjectivesOur objective was to undertake a comparative analysis of the diversity and composition of sputum microbiota in individuals afflicted by severe and critically ill influenza patients.MethodsSputum specimens were procured from patients diagnosed with IAV infection for the purpose of profiling the microbiota using 16S-rDNA sequencing. To ascertain taxonomic differences between the severe and critically ill influenza cohorts, we leveraged Linear Discriminant Analysis Effect Size (LEfSe). Additionally, Spearman correlation analysis was employed to illuminate associations between sputum microbiota and influenza Ct values alongside laboratory indicators.ResultsOur study encompassed a total cohort of 64 patients, comprising 48 within the severe group and 16 within the critically ill group. Intriguingly, Bacteroidetes exhibited significant depletion in the critically ill cohort (p=0.031). The sputum microbiomes of the severe influenza group were hallmarked by an overrepresentation of Neisseria, Porphyromonas, Actinobacillus, Alloprevotella, TM7x, and Clostridia_UCG-014, yielding ROC-plot AUC values of 0.71, 0.68, 0.60, 0.70, 0.70, and 0.68, respectively. Notably, Alloprevotella exhibited an inverse correlation with influenza Ct values. Moreover, C-reactive protein (CRP) manifested a positive correlation with Haemophilus and Porphyromonas.ConclusionThe outcomes of this investigation lay the groundwork for future studies delving into the connection between the LRT microbiome and respiratory disorders. Further exploration is warranted to elucidate the intricate mechanisms underlying the interaction between IAV and Alloprevotella, particularly in disease progression.</p