Purpose: We investigated whether the use of a phosphodiesterase-5 inhibitor (PDE5i) after robot assited radical prostatectomy has a survival benefit over non-use patients because there are controversial results on the association between PDE5i use and survival outcomes for prostate cancer patients in literature. Materials and Methods: We designed a retrospective, matched, large-sample cohort study of 5,545 patients who underwent robot assisted radical prostatectomy (RARP) during 2013–2021 in a single institute. The exclusion criteria was patients who were aged >70 years at surgery, American Society of Anesthesiologists (ASA) physical status classification grade 4 or 5, history of other malignancies, patients who started PDE5i 6 months after survery and patients with follow up period less than 24 months after surgery. Among the 1,843 included patients, 1,298 were PDE5i users, and 545 were PDE5i non-users. We performed propensity score matching (PSM) of PDE5i users (n=529) with non-users (n=529) by adjusting for the variables of age, Gleason grade group, pathological T stage, preoperative ASA physical status grade, and International Index of Erectile Function score. Results: There were no significant difference in patient characteristics according to PSM. Kaplan–Meier curve revealed the difference of overall survival for PDE5i users and non-users (clustered log-rank test p<0.05). In a stratified Cox regression analysis, PDE5i use after RARP was associated with improved overall survival and reduced risk of death (hazard ratio 0.43; confidence interval 0.24–0.79; p=0.007). The limitation of this study was that the indication for the prescription of PDE5i was not given. Conclusions PDE5i administration after RARP were associated with overall survival of patients with prostate cancer. A further randomized control trial may reveal whether routine use of PDE5i after prostatectomy can improve survival of prostate cancer patient.

Background: It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. Methods: This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups.Each lesion patch cluster was described by a characteristic imaging term for comparison.For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. Results: The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. Conclusion Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showedcorrelations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.

Background: It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. Methods: This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups.Each lesion patch cluster was described by a characteristic imaging term for comparison.For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. Results: The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. Conclusion Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showedcorrelations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.

Background: and Purpose Endovascular recanalization therapy (ERT) is becoming increasingly important in the management of acute ischemic stroke (AIS). However, the hospital volume threshold for optimal ERT remains unknown. We investigated the relationship between hospital volume of ERT and risk-adjusted patient outcomes. Methods: From the National Health Insurance claims data in Korea, 11,745 patients with AIS who underwent ERT from July 2011 to June 2016 in 111 hospitals were selected. We measured the hospital’s ERT volume and patient outcomes, including the 30-day mortality, readmission, and postprocedural intracranial hemorrhage (ICH) rates. For each outcome measure, we constructed risk-adjusted prediction models incorporating demographic variables, the modified Charlson comorbidity index, and the stroke severity index (SSI), and validated them. Risk-adjusted outcomes of AIS cases were compared across hospital quartiles to confirm the volume-outcome relationship (VOR) in ERT. Spline regression was performed to determine the volume threshold. Results: The mean AIS volume was 14.8 cases per hospital/year and the unadjusted means of mortality, readmission, and ICH rates were 11.6%, 4.6%, and 8.6%, respectively. The VOR was observed in the risk-adjusted 30-day mortality rate across all quartile groups, and in the ICH rate between the first and fourth quartiles (P<0.05). The volume threshold was 24 cases per year. Conclusions There was an association between hospital volume and outcomes, and the volume threshold in ERT was identified. Policies should be developed to ensure the implementation of the AIS volume threshold for hospitals performing ERT.

OBJECTIVES: The objective of this study is to investigate differences in clinical characteristics between female panic disorder (PD) patients with abortion history (PD+A) and without abortion history (PD−A).METHODS: We examined data from 341 female patients diagnosed with PD. We divided the patients with PD into PD+A (82 patients) and PD−A (259 patients) to compare demographic and clinical characteristics. The following instruments were applied : stress coping strategies, NEO-neuroticism, the Anxiety Sensitivity Index-Revised (ASI-R), the Albany Panic and Phobia Questionnaire (APPQ), the Beck Depression Inventory, the Beck Anxiety Inventory (BAI) and the Sheehan Disability Scale.RESULTS: Compared to the PD−A, the PD+A group showed no significant difference in coping strategies. However, significantly higher scores in neuroticism, the ASI-R, the APPQ and the BAI were observed. In terms of health-related disability, the PD+A group did not show significant difference.CONCLUSIONS: Our results suggest that the PD+A group may differ from the PD−A group in trait markers such as neuroticism and anxiety sensitivity, and abortion history may be associated with panic-related symptom severity. Our study suggests that further consideration is needed on such clinical characteristics in PD patients with abortion history.
Abortion, Induced ; Anxiety ; Depression ; Female ; Humans ; Panic Disorder ; Panic ; Phobic Disorders

OBJECTIVE: Uric acid is a non-enzymatic antioxidant associated with depression. Despite its known protective role in other brain disorders, little is known about its influence on the structural characteristics of brains of patients with major depressive disorder (MDD). This study explored the association between uric acid and characteristics of white matter (WM) in patients with MDD. METHODS: A total of 32 patients with MDD and 23 healthy controls (HCs) were examined. All participants were scored based on the Beck Depression Inventory and Beck Anxiety Inventory at baseline. All patients were also rated with the Hamilton Depression Rating Scale. We collected blood samples from all participants immediately after their enrollment and before the initiation of antidepressants in case of patients. Tract-based spatial statistics were used for all imaging analyses. RESULTS: Lower fractional anisotropy (FA) and higher radial diffusivity (RD) values were found in the MDD group than in the HC group. Voxelwise correlation analysis revealed that the serum uric acid levels positively correlated with the FA and negatively with the RD in WM regions that previously showed significant group differences in the MDD group. The correlated areas were located in the left anterior corona radiata, left frontal lobe WM, and left anterior cingulate cortex WM. CONCLUSION: The present study suggests a significant association between altered WM connectivity and serum uric acid levels in patients with MDD, possibly through demyelination.
Anisotropy ; Antidepressive Agents ; Antioxidants ; Anxiety ; Brain ; Brain Diseases ; Demyelinating Diseases ; Depression* ; Depressive Disorder ; Depressive Disorder, Major ; Frontal Lobe ; Gyrus Cinguli ; Humans ; Neuroimaging ; Oxidative Stress ; Uric Acid* ; White Matter*

No abstract available.
Humans ; Reperfusion ; Stroke

Transient isoniazid-induced brain lesions have rarely been reported. The lesions were in the dentate nucleus of cerebellum and thalamus. Meanwhile, the neurotoxicity of rifampin has not been reported evidently. We observed bilateral lesions in the internal capsule in a young woman after taking a combination of isoniazid and rifampin. She transiently suffered numbness in both hands, dysarthria, and left side motor weakness while taking the medication. Isoniazid may induce structural lesions in various brain areas including the internal capsule.
Brain ; Cerebellar Nuclei ; Cerebellum ; Dysarthria ; Extremities ; Female ; Hand ; Humans ; Hypesthesia ; Internal Capsule ; Isoniazid ; Neurotoxicity Syndromes ; Rifampin ; Thalamus