Objective To study the pharmacokinetic and pharmacodynamic characteristics of compound levodopa/carbidopa(250 mg/25 mg)and levodopa/benserazide(200 mg/50 mg)in patients with Parkinson's disease(PD).Methods This experiment used a levodopa challenge test with a randomized crossover design.In the first week,20 PD patients orally received either 275 mg of compound levodopa/carbidopa or 250 mg of levodopa/benserazide on an empty stomach,and in the second week,they received the other treatment.The levodopa blood concentration was measured using high-performance liquid chromatography-tandem mass spectrometry,and motor symptoms were evaluated using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale Ⅲ.Results Data from 17 patients in the compound levodopa/carbidopa group and 18 patients in the levodopa/benserazide group was included in the analysis.After administration,the Cmax values of compound levodopa/carbidopa and levodopa/benserazide groups were(3 563.76±1 003.06)and(3 642.44±1 192.70)ng·mL-1;the tmax values were(1.10±0.44)and(1.03±0.55)h;the t1/2 values were(1.52±0.15)and(1.68±0.27)h;the AUC0-t values were(7 625.19±1 706.85)and(5 846.07±1 191.16)ng·mL-1·h;the mean residence time(MRT)values were(2.39±0.361)and(2.14±0.37)h,respectively.There were no statistically significant differences in the Cmax,tmax,and t1/2 values between the two groups(all P＞0.05).Compared with the levodopa/benserazide group,the compound levodopa/carbidopa group increased levodopa AUC and prolonged MRT(all P＜0.05).The improvement in motor symptoms and levodopa blood concentration showed consistent trends at various time points in both groups.The compound levodopa/carbidopa group showed significantly better improvement in motor function at 6 and 8 hours after medication compared to the levodopa/benserazide group[(-10.82±8.91)points vs(-5.17±6.78)points,(-7.88±10.05)points vs(-2.11±4.84)points;both P＜0.05].Conclusion The pharmacokinetic and pharmacodynamic characteristics of compound levodopa/carbidopa are similar to those of levodopa/benserazide.

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.

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.

Celastrol, extracted from Tripterygium wilfordii, is a natural pentacyclic triterpene compound, which has an anti-pulmonary fibrosis effect. However, its effect, binding targets and regulatory mechanism in pulmonary fibroblasts remain unclear. In this study, we found that celastrol could prevent fibroblast-myofibroblast transformation (FMT) by significantly inhibiting transforming growth factor β1 (TGFβ1)-induced α-smooth muscle actin and type I collagen expression. Previous studies suggested that heat shock protein 60 (HSP60) may be the target of celastrol. This study confirmed the direct interaction between celastrol and HSP60 through cellular thermal shift assay and surface plasmon resonance experiment, and demonstrated that the K_D value of celastrol binding to HSP60 was 8.59 μmol·L^-1. Further studies showed that knockdown of HSP60 promoted TGFβ1-induced FMT, especially in the medium and low dose TGFβ1 treatment group, and that the anti-FMT effect of celastrol was significantly weakened after HSP60 knockdown. These results indicated that HSP60 was involved in maintaining the resting state of fibroblasts, and the anti-FMT effect of celastrol was dependent on HSP60. Furthermore, the autophagy promotion and antioxidant effects of celastrol were also weakened after HSP60 knockdown. In conclusion, celastrol inhibits FMT by targeting HSP60, thus exerting anti-pulmonary fibrosis function.

Humans ; Consensus ; Hypersensitivity ; Desensitization, Immunologic/adverse effects* ; Immunotherapy/adverse effects* ; Injections, Subcutaneous ; Allergens