Adult ; Humans ; Laparoscopy ; methods ; Male ; Retrocaval Ureter ; surgery ; Ureterostomy ; methods ; Young Adult

Aged ; Calcinosis ; Diagnosis, Differential ; Female ; Humans ; Leiomyoma ; diagnostic imaging ; pathology ; Radiography ; Uterine Neoplasms ; diagnostic imaging ; pathology

Objective: Rumination is a maladaptive emotional-regulation strategy that is strongly associated with depression. Impaired executive function can lead to difficulties in disengaging from rumination, thus exacerbating depression. In this study, we inspect an electroencephalograph neurofeedback protocol that enhance the target peak alpha frequency (PAF) activation in the prefrontal region. We examine the protocol’s effects on depression and rumination. Methods: We randomly assigned 30 dysphoric participants into either the neurofeedback training group or the control group. We then evaluated their depression, rumination, and executive function at pre- and posttraining so as to examine the effects of the neurofeedback. Results: The results show that this neurofeedback protocol can specifically enhance participants’ target PAF. The participants’ executive function performances significantly improved after undergoing 20 neurofeedback sessions. Compared with those in the control group, those in the neurofeedback group had significantly fewer depressive symptoms and significantly reduced rumination. Moreover, as target PAF and executive function improved, depression and rumination both declined. Conclusion Our data are in line with those of previous studies that indicated a relationship between upper-band alpha activity and executive function. This PAF neurofeedback can effectively enhance participants’ executive function, which can reduce rumination and ameliorate depression. This neurofeedback training is based on basic cognitive neuroscience, so it sheds light on depression’s pathological factors and etiology.

PURPOSE: The Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines for chronic obstructive pulmonary disease (COPD) uses the post-bronchodilator spirometry for diagnosis and severity staging. We evaluated differences in the severity classification of COPD, based on pre- and post-bronchodilator spirometry. MATERIALS AND METHODS: From 2000 to 2004, 207 COPD patients who underwent spirometry before and after inhalation of 400 microg of fenoterol were analyzed. A responder to the bronchodilator test (BDT) was defined by the American Thoracic Society (ATS) as an increase in forced expiratory volume in one second (FEV1) or forced vital capacity > or = 12% and > or = 200 mL, and by the European Respiratory Society (ERS) as an increase in FEV1 > or = 10% of the predicted value. COPD severity was classified according to the 2008 GOLD guidelines. RESULTS: For the entire study population, the FEV1 increased by 11.8 +/- 12.5% of baseline after BDT and 41.1% and 27.1% of subjects were classified as responders using the ATS and ERS criteria, respectively. Based on pre-BDT spirometry, 55, 85, 58, and 9 patients were classified as Stage I-IV COPD, respectively. Sixty-seven (32.4%) patients changed severity staging after BDT, including 20.0%, 28.2%, 44.8%, and 66.7% of pre-BDT patients Stages I through IV, respectively. More ATS or ERS BDT-responders had a change in severity staging than non-responders (52.9% vs. 18.9% and 62.5% vs. 21.2%, both p < 0.001). CONCLUSION: Our data suggest that the severity staging of COPD using pre-BDT spirometry might lead to significant differences as compared to staging, based on post-BDT spirometry, as recommended by the current GOLD guidelines.
Bronchodilator Agents/*diagnostic use ; Fenoterol/diagnostic use ; Forced Expiratory Volume/drug effects ; Humans ; Practice Guidelines as Topic ; Prognosis ; Pulmonary Disease, Chronic Obstructive/*diagnosis ; Spirometry/methods

BACKGROUND AND OBJECTIVES: Recent studies showed that, in addition to parasympathetic nerves, cervical vagal nerves contained significant sympathetic nerves. We hypothesized that cervical vagal nerve stimulation (VNS) may capture the sympathetic nerves within the vagal nerve and activate the stellate ganglion. MATERIALS AND METHODS: We recorded left stellate ganglion nerve activity (SGNA), left thoracic vagal nerve activity (VNA), and subcutaneous electrocardiogram in seven dogs during left cervical VNS with 30 seconds on-time and 30 seconds off time. We then compared the SGNA between VNS on and off times. RESULTS: Cervical VNS at moderate (0.75 mA) output induced large SGNA, elevated heart rate (HR), and reduced HR variability, suggesting sympathetic activation. Further increase of the VNS output to >1.5 mA increased SGNA but did not significantly increase the HR, suggesting simultaneous sympathetic and parasympathetic activation. The differences of integrated SGNA and integrated VNA between VNS on and off times (DeltaSGNA) increased progressively from 5.2 mV-s {95% confidence interval (CI): 1.25-9.06, p=0.018, n=7} at 1.0 mA to 13.7 mV-s (CI: 5.97-21.43, p=0.005, n=7) at 1.5 mA. The difference in HR (DeltaHR, bpm) between on and off times was 5.8 bpm (CI: 0.28-11.29, p=0.042, n=7) at 1.0 mA and 5.3 bpm (CI 1.92 to 12.61, p=0.122, n=7) at 1.5 mA. CONCLUSION: Intermittent cervical VNS may selectively capture the sympathetic components of the vagal nerve and excite the stellate ganglion at moderate output. Increasing the output may result in simultaneously sympathetic and parasympathetic capture.
Animals ; Autonomic Nervous System ; Dogs* ; Electrocardiography ; Heart Rate ; Stellate Ganglion* ; Vagus Nerve Stimulation*

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.