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

Numerous studies have indicated that organic fertilizers (OFer) might contain heavy metals (HMs) that present health risks to organic farmers (OFar). This study compared the concentrations of six HMs (Zn, Ni, Cd, Cu, Pb, Cr) in the blood of two distinct groups of farmers: 30 OFar from a designated organic area in eastern Taiwan, and 74 conventional farmers (CFar) from neighboring non-organic designated regions. The findings revealed that the OFar exhibited higher levels of Zn (1202.70 ± 188.74 μg/L), Cr (0.20 ± 0.09 μg/L), and Ni (2.14 ± 1.48 μg/L) in their blood compared to the CFar (988.40 ± 163.16 μg/L, 0.18 ± 0.15 μg/L, and 0.77 ± 1.23 μg/L), respectively. The disparities in Zn, Cr, and Ni levels were measured at 214.3 μg/L, 0.02 μg/L, and 1.37 μg/L, respectively. Furthermore, among the OFar, those who utilized green manures (GM) displayed significantly elevated blood levels of Zn (1279.93 ± 156.30 μg/L), Cr (0.24 ± 0.11 μg/L), and Ni (1.94 ± 1.38 μg/L) compared to individuals who exclusively employed chemical fertilizers (CFer) (975.42 ± 165.35 μg/L, 0.19 ± 0.16 μg/L, and 0.74 ± 1.20 μg/L), respectively. The differences in Zn, Cr, and Ni levels were measured at 304.51 μg/L, 0.05 μg/L, and 1.20 μg/L, respectively. As a result, OFar should be careful in choosing OFer and avoid those that may have heavy metal contamination.

PURPOSE: The main objective of this study was to investigate the relationship among the clinical characteristics and the frequency of T790M mutation in advanced epidermal growth factor receptor (EGFR)–mutant lung adenocarcinoma patients with acquired resistance after firstline EGFR–tyrosine kinase inhibitor (TKI) treatment. MATERIALS AND METHODS: We enrolled EGFR-mutant stage IIIB-IV lung adenocarcinoma patients, who had progressed to prior EGFR-TKI therapy, and evaluated their rebiopsy EGFR mutation status. RESULTS: A total of 205 patients were enrolled for analysis. The overall T790M mutation rate of rebiopsy was 46.3%. The T790M mutation rates among patients with exon 19 deletion mutation, exon 21 L858R point mutation, and other mutations were 55.0%, 37.3%, and 27.3%, respectively. Baseline exon 19 deletion was associated with a significantly higher frequency of T790M mutation (adjusted odds ratio, 2.14; 95% confidence interval [CI], 1.20 to 3.83; p=0.010). In the exon 19 deletion subgroup, there was a greater prevalence of T790M mutation than other exon 19 deletion subtypes in patients with the Del E746-A750 mutation (61.6% vs. 40.6%; odds ratio, 2.35; 95% CI, 1.01 to 5.49; p=0.049). The progression-free survival (PFS) of first-line TKI treatment > 11 months was also associated with a higher T790M mutation rate (54.1% vs. 39.3%; adjusted odds ratio, 1.82; 95% CI, 1.02 to 3.25; p=0.044). Patients who underwent rebiopsy at metastatic sites had more chance to harbor T790M mutation (52.6% vs. 33.8%; adjusted odds ratio, 1.97; 95% CI, 1.06 to 3.67; p=0.032). CONCLUSION: PFS of first-line EGFR-TKI, rebiopsy site, EGFR exon 19 deletion and its subtype Del E746-A750 mutation are associated with the frequency of T790M mutation.
Adenocarcinoma* ; Disease-Free Survival ; Epidermal Growth Factor* ; Exons ; Humans ; Lung Neoplasms ; Lung* ; Mutation Rate ; Odds Ratio ; Phosphotransferases ; Point Mutation ; Prevalence ; Receptor, Epidermal Growth Factor* ; Sequence Deletion

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.