Objective: To study the chemical constituents of Pouzolzia zeylanica var. microphylla. Methods 95% ethanol was used for reflux extraction. The solvent extraction method was used to extract the alcohol extracts. The ethyl acetate extracts were separated by normal phase and reversed phase column chromatography and semi-preparative high performance liquid chromatography. The compounds were determined by spectral analysis. Results Ten compounds were isolated and identified from the ethyl acetate part of P. zeylanicaas, which were (2R,3S,4R,5S)-2,5-bis (4-hydroxy-3-methoxyphenyl)-3,4-bis (3,5-dihydroxybenzene) terahydrofyran (1), pouzolignan K (2), saropeptide (3), dehydrocuciferol (4), p-hydroxybenzoic acid (5), isovanetic acid (6), protocatechuic acid (7), syringic acid (8), protocatechuic aldehyde (9), and ethyl protocatechuate (10), respectively. Conclusion Compound 1 is a new compound named isopouzolignan K, and compounds 3—10 are isolated from this genus for the first time.

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.

OBJECTIVE: To develop an improved version of the Quality-of-Life Assessment instrument for Lung Cancer Patients Based on Traditional Chinese Medicine (QLASTCM-Lu) and to evaluate its psychometric property. METHODS: The structured group method and the theory in developing rating scale were employed to revise the preliminary scale. The psychometric property (reliability, validity, and responsiveness) of the established QLASTCM-Lu (modified) were evaluated by quality of life data measured in 100 lung cancer patients. Statistical analyses were made accordingly by way of correlation analysis, factor analysis and paired t-test. RESULTS: The internal consistency reliability of the overall scale and all domains was from 0.80 to 0.94. Correlation and factor analyses demonstrated that the scale was good in construct validity. The criterion validity was formed with European Organization for Research and Treatment of Cancer-Quality of Life Questionnaire-Lung Cancer (EORTC QLQ-LC43) as the criterion. Statistically significant changes were found apart from such domain as "mental condition" and "social function", with the standardized response means being close to those of QLQ-LC43. CONCLUSION QLASTCM-Lu (modified) could be used to measure the quality of life of lung cancer patients with good reliability, validity and a certain degree of responsiveness.