Background: Goslings in several Taiwanese farms experienced gosling feather loss disease (GFL) at 21–35 days and goose broke feather disease (GBF) at 42–60 days. The prevalence ranges from a few birds to 500 cases per field. It is estimated that about 12,000 geese have been infected, the morbidity is 70–80% and the mortality is 20–30%. Objectives: This study aims to investigate the pathogens that cause GFL and GBF. Focus on the study of the correlation between goose circovirus (GoCV) and goose parvovirus (GPV) with the goose feather loss in southern Taiwan. Furthermore, a phylogenetic tree was established to align the differences between southern and northern Taiwan and compare with virus strains from China and Europe. Methods: Samples were collected from animal hospitals. Molecular and microscopy diagnostics were used to examine 92 geese. Specific quantitative polymerase chain reaction (Q-PCR) assays are performed to evaluate GPV and GoCV viral loads and simultaneously evaluated the feather loss conditions in geese with the scoring method. Results: High prevalence of GoCV and GPV infection in geese showing signs of GFL and GBF. Inclusion body was detected in the feather follicles and Lieberkühn crypt epithelial cells. The Q-PCR showed the high correlation between feather loss and viruses during 3rd– 5th week. However, the infection was not detected using the same test in 60 healthy geese. Conclusions Thus, GFL and GBF appear to be significantly closely related to GoCV and GPV. The geese feathers showed increasing recovery after being quarantined and disinfected.

PURPOSE: Epidermal growth factor receptor (EGFR) T790M mutation serves as an important predictor of osimertinib efficacy. However, little is known about how it works among patients with various timings of T790M emergence and treatment. MATERIALS AND METHODS: Advanced EGFR-mutant lung adenocarcinoma patients with positive T790M mutation in tumor were retrospectively enrolled and observed to determine the outcomes of osimertinib treatment. We evaluated the association between patients’ characteristics and the efficacy of osimertinib treatment, particularly with respect to the timing of T790M emergence and osimertinib prescription. RESULTS: A total of 91 patients were enrolled, including 14 (15.4%) with primary and 77 (84.6%) with acquired T790M mutation. The objective response rate and disease controlratewere 60.9% and 85.1%, respectively. The median progression-free survival (PFS) and overall survival were 11.5 months (95% confidence interval [CI], 9.0 to 14.0) and 30.4 months (95% CI, 11.3 to 49.5), respectively. There was no significant difference in response rate and PFS between primary and acquired T790M populations. In the acquired T790M subgroup, patientswho received osimertinib after T790M had been confirmed by rebiopsy had a longer PFS than those with intercalated treatments between rebiopsy and osimertinib prescription (14.0 months [95% CI, 9.0 to 18.9] vs. 7.2 months [95% CI, 3.7 to 10.8]; adjusted hazard ratio, 0.48 [95% CI, 0.24 to 0.98; p=0.043]). Rebiopsy timing did not influence the outcome. CONCLUSION: Osimertinib prescription with intercalated treatment following rebiopsy but not the timing of T790M emergence influenced the treatment outcome. We suggest that it is better to start osimertinib treatment once T790M mutation has been confirmed by biopsy.
Adenocarcinoma ; Biopsy ; Disease-Free Survival* ; Humans ; Lung ; Prescriptions ; Receptor, Epidermal Growth Factor ; Retrospective Studies ; Treatment Outcome

Bipolar disorder (BD) is a major psychiatric disorder that is easily misdiagnosed. Patient adherence to a treatment regimen is of utmost importance for successful outcomes in BD. Several trials of antipsychotics suggested that depot antipsychotics, including long-acting first- and second-generation agents, are effective in preventing non-adherence, partial adherence, and in reducing relapse in BD. Various long-acting injectable (LAI) antipsychotics are available, including fluphenazine decanoate, haloperidol decanoate, olanzapine pamoate, risperidone microspheres, paliperidone palmitate, and aripiprazole monohydrate. Due to the increasing number of BD patients receiving LAI antipsychotics, treatment guidelines have been developed. However, the clinical applicability of LAI antipsychotics remains a global cause for concern, particularly in Asian countries. Expert physicians from Taiwan participated in a consensus meeting, which was held to review key areas based on both current literature and clinical practice. The purpose of this meeting was to generate a practical and implementable set of recommendations for LAI antipsychotic use to treat BD; target patient groups, dosage, administration, and adverse effects were considered. Experts recommended using LAI antipsychotics in patients with schizophrenia, rapid cycling BD, BD I, and bipolar-type schizoaffective disorder. LAI antipsychotic use was recommended in BD patients with the following characteristics: multiple episodes and low adherence; seldom yet serious episodes; low adherence potential per a physician's clinical judgment; preference for injectable agents over oral agents; and multiple oral agent users still experiencing residual symptoms.
Antipsychotic Agents* ; Asian Continental Ancestry Group ; Bipolar Disorder* ; Consensus* ; Fluphenazine ; Haloperidol ; Humans ; Judgment ; Microspheres ; Patient Compliance ; Psychotic Disorders ; Recurrence ; Risperidone ; Schizophrenia ; Taiwan ; Aripiprazole ; Paliperidone Palmitate

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

Purpose: The aim of this study was to investigate the efficacy of various epidermal growth factor receptor (EGFR)–tyrosine kinase inhibitors (TKIs) plus bevacizumab in advanced EGFR-mutant lung adenocarcinoma patients. Materials and Methods: From August 2016 to October 2020, we enrolled advanced lung adenocarcinoma patients harboring exon 19 deletion or L858R receiving gefitinib, erlotinib and afatinib plus bevacizumab as the first-line treatment for the purposes of analysis. Results: A total of 36 patients were included in the final analysis. Three patients received gefitinib, 17 received erlotinib, and 16 received afatinib combined with bevacizumab as the first-line treatment. The objective response rate was 77.8%, and disease control rate was 94.4%. The overall median progression-free survival (PFS) was 16.4 months, while the median PFS was 17.1 months in patients with exon 19 deletion, and 16.2 months in patients with L858R mutation (p=0.311). Regarding the use of different EGFR-TKIs, the median PFS was 17.1 months in the erlotinib group and 21.6 months in the afatinib group (p=0.617). In patients with brain metastasis at baseline, the median PFS was 18.9 months in the erlotinib group and 16.4 months in the afatinib group (p=0.747). Amongst patients harboring exon 19 deletion, the median PFS was 16.2 months in the erlotinib group and not-reached in the afatinib group (p=0.141). In patients with L858R mutation, the median PFS was 18.9 months in the erlotinib group and 16.2 months in the afatinib group (p=0.481). Conclusion Our research demonstrates that not only erlotinib combined with bevacizumab, but also afatinib plus bevacizumab as first-line treatment, provides solid clinical efficacy in advanced EGFR-mutant lung adenocarcinoma patients.

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.