BACKGROUND/AIMS: Both sexual and physical abuse history have been reported to be associated with irritable bowel syndrome (IBS) in Western countries. The impact of abuse history in IBS patients in Asia remains unclear. We aim to determine the prevalence of abuse history, its associated psychological profiles, and sleep problems among IBS patients in Taiwan. METHODS: In total, 194 Rome III-defined IBS patients were invited to participate. Age- and sex-matched healthy carriers of chronic hepatitis B or hepatitis C without chronic abdominal symptoms were identified as disease-controls. We administered a validated questionnaire to evaluate bowel symptoms, physical/sexual abuse history, anxiety/depression (Hospital Anxiety and Depression Scale [HADS]), and sleep quality. RESULTS: IBS patients had a significantly higher prevalence of sexual abuse history than the disease-control group both before (16.5% vs 6.7%, P < 0.05) and after (16.0% vs 6.6%, P < 0.05) adolescence. These significant differences were mainly observed in women (13.4% vs 3.4%, P < 0.05). No difference was noted in history of physical abuse between the 2 groups. IBS patients with a history of sexual abuse had significantly higher HADS scores and higher frequencies of sleep difficulty than those without. CONCLUSIONS: In Taiwan, sexual abuse history was more prevalent in female IBS patients than controls. Sexual abuse history may contribute to higher anxiety/depression levels and sleep difficulties, which are commonly experienced in IBS patients. In Asia, abuse history should be obtained when approaching IBS patients to facilitate better management.
Adolescent ; Anxiety ; Asia ; Depression ; Female ; Hepatitis B, Chronic ; Hepatitis C ; Humans ; Irritable Bowel Syndrome ; Physical Abuse ; Prevalence ; Sex Offenses ; Taiwan

OBJECTIVE: To investigate whether the diffusion tensor imaging-derived metrics are capable of differentiating the ischemic penumbra (IP) from the infarct core (IC), and determining stroke onset within the first 4.5 hours. MATERIALS AND METHODS: All procedures were approved by the local animal care committee. Eight of the eleven rats having permanent middle cerebral artery occlusion were included for analyses. Using a 7 tesla magnetic resonance system, the relative cerebral blood flow and apparent diffusion coefficient maps were generated to define IP and IC, half hour after surgery and then every hour, up to 6.5 hours. Relative fractional anisotropy, pure anisotropy (rq) and diffusion magnitude (rL) maps were obtained. One-way analysis of variance, receiver operating characteristic curve and nonlinear regression analyses were performed. RESULTS: The evolutions of tensor metrics were different in ischemic regions (IC and IP) and topographic subtypes (cortical, subcortical gray matter, and white matter). The rL had a significant drop of 40% at 0.5 hour, and remained stagnant up to 6.5 hours. Significant differences (p < 0.05) in rL values were found between IP, IC, and normal tissue for all topographic subtypes. Optimal rL threshold in discriminating IP from IC was about -29%. The evolution of rq showed an exponential decrease in cortical IC, from -26.9% to -47.6%; an rq reduction smaller than 44.6% can be used to predict an acute stroke onset in less than 4.5 hours. CONCLUSION: Diffusion tensor metrics may potentially help discriminate IP from IC and determine the acute stroke age within the therapeutic time window.
Animal Care Committees ; Animals ; Anisotropy ; Cerebrovascular Circulation ; Diffusion Tensor Imaging ; Diffusion* ; Gray Matter ; Infarction, Middle Cerebral Artery ; Models, Animal* ; Rats* ; ROC Curve ; Stroke

Thyroid radiofrequency ablation and microwave ablation are widely adopted minimally invasive treatments for diverse thyroid conditions worldwide. Fundamental skills such as the trans-isthmic approach and the moving shot technique are crucial for performing thyroid ablation, and advanced techniques, including hydrodissection and vascular ablation, improve safety and efficacy and reduce complications. Given the learning curve associated with ultrasound-guided therapeutic procedures, operators need training and experience. While training models exist, limited attention has been given to ultrasound maneuvers in ablation needle manipulation. This article introduces two essential maneuvers, the zigzag moving technique and the alienate maneuver, while also reviewing the latest ultrasound techniques in thyroid ablation, contributing valuable insights into this evolving field.

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.