Prostate-Specific Antigen Variation as a Predictor of Prostate Cancer in Patients With Prostate-Specific Antigen ≤20 ng/mL Who Underwent Magnetic Resonance Imaging-Targeted Prostate Biopsy

Purpose@#We investigated the role of prostate-specific antigen (PSA) variation as a predictor of prostate cancer in patients who underwent prebiopsy multiparametric magnetic resonance imaging (MRI). @*Materials and Methods@#The clinicopathological data of 266 patients with PSA ≤20 ng/mL who underwent prebiopsy MRI and prostate biopsy between September 2019 and February 2021 were included. PSA variation was defined as the difference in PSA values taken when a prostate biopsy was recommended and performed (median 20 days). Receiver operating characteristic (ROC) curves and area under the ROC curves (AUCs) for predicting prostate cancer were analyzed through 4 models that considered conventional clinical variables and PSA variation. @*Results@#Of the 258 patients, 166 (64.3%) were diagnosed with prostate cancer. The prostate cancer (+) group had a lower median PSA variation (-0.09 mg/mL vs. -0.27 ng/mL, p=0.006) and higher proportion of patients with PSA variation within -0.54 to 0.05 ng/mL (40 ng/mL [range, 24.1%] vs. 9 ng/mL [9.8%], p=0.002) than the prostate cancer (-) group. There was no significant difference in the duration between the 2 PSA measurements. When PSA variation and conventional variables, such as age, PSA density, prostate biopsy history, number of target lesions, were considered, the highest AUC value was 0.870. In a subgroup analysis of patients with PSA ≤10 ng/mL, the highest AUC value was 0.860 when PSA variation and conventional variables were considered. @*Conclusions@#A large PSA variation within 1 month was a negative predictor of prostate cancer among patients who underwent prebiopsy MRI.

Permanent Pacemaker Implantations after Catheter Ablation in Patients with Atrial Fibrillation Associated with Underlying Sinus Node Dysfunction

BACKGROUND AND OBJECTIVES: The objective of this study was to evaluate the long-term clinical outcomes and the incidence of permanent pacemaker implantation after catheter ablation in patients with of atrial fibrillation (AF) and sinus node dysfunction (SND).METHODS: Among 3,068 total consecutive patients who underwent AF catheter ablation (AFCA), this study included 222 (9.5%; men 53.2%, 63.7±9.2 years of age, 81.5% paroxysmal AF) with underlying SND and a regular rhythm follow-up. We analyzed the rhythm outcomes, changes in the mean heart rate or heart rate variability, and permanent pacemaker implantation rate.RESULTS: During 47.5±28.8 months of follow-up, 25 (11.3%) patients received pacemaker implantations due to symptomatic SND. More than half (56.0%, 14/25) underwent a pacemaker implantation within 3 months of the AFCA, and the annual pacemaker implantation rate was 2.0% afterwards. Both the early (68.0% vs. 31.0%, p<0.001) and clinical AF recurrence (68.0% vs. 32.5%, p=0.001) rates and continuous antiarrhythmic drug use after 3 months (44.0% vs. 24.4%, p=0.036) were significantly higher in patients requiring pacemaker implantations than those that did not. An anterior linear ablation (odds ratio [OR], 9.37 [3.03–28.9]; p<0.001) and the E/Em (OR, 1.15 [1.02–1.28]; p=0.018) were independently associated with permanent pacemaker implantations after AFCA in patients with AF and SND.CONCLUSIONS: After AFCA in patients with AF and SND, 1 of 9 patients needed a pacemaker implantation and half needed implantations within 3 months. The AF recurrence rate was significantly higher in those who required pacemaker implantations after the AFCA.

Permanent Pacemaker Implantations after Catheter Ablation in Patients with Atrial Fibrillation Associated with Underlying Sinus Node Dysfunction

BACKGROUND AND OBJECTIVES@#The objective of this study was to evaluate the long-term clinical outcomes and the incidence of permanent pacemaker implantation after catheter ablation in patients with of atrial fibrillation (AF) and sinus node dysfunction (SND).@*METHODS@#Among 3,068 total consecutive patients who underwent AF catheter ablation (AFCA), this study included 222 (9.5%; men 53.2%, 63.7±9.2 years of age, 81.5% paroxysmal AF) with underlying SND and a regular rhythm follow-up. We analyzed the rhythm outcomes, changes in the mean heart rate or heart rate variability, and permanent pacemaker implantation rate.@*RESULTS@#During 47.5±28.8 months of follow-up, 25 (11.3%) patients received pacemaker implantations due to symptomatic SND. More than half (56.0%, 14/25) underwent a pacemaker implantation within 3 months of the AFCA, and the annual pacemaker implantation rate was 2.0% afterwards. Both the early (68.0% vs. 31.0%, p<0.001) and clinical AF recurrence (68.0% vs. 32.5%, p=0.001) rates and continuous antiarrhythmic drug use after 3 months (44.0% vs. 24.4%, p=0.036) were significantly higher in patients requiring pacemaker implantations than those that did not. An anterior linear ablation (odds ratio [OR], 9.37 [3.03–28.9]; p<0.001) and the E/Em (OR, 1.15 [1.02–1.28]; p=0.018) were independently associated with permanent pacemaker implantations after AFCA in patients with AF and SND.@*CONCLUSIONS@#After AFCA in patients with AF and SND, 1 of 9 patients needed a pacemaker implantation and half needed implantations within 3 months. The AF recurrence rate was significantly higher in those who required pacemaker implantations after the AFCA.

Review of Biological Network Data and Its Applications

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.