OBJECTIVES: Benzydamine is a nonsteroidal anti-inflammatory agents agent with anti-inflammatory and local anesthesia properties that is available in the entire world as an oral spray for oral mucositis patients who are suffering from radiation effects. The effect of benzydamine on oral mucositis in vivo is well known; however, the effect of the drug on tracheal smooth muscle has rarely been explored. During administration of the benzydamine for oral symptoms, it might affect the trachea via oral intake or inhalation. METHODS: We examined the effectiveness of benzydamine on isolated rat tracheal smooth muscle. The following assessments of benzydamine were performed: effect on tracheal smooth muscle resting tension; effect on contraction caused by 10(-6)M methacholine as a parasympathetic mimetic; and effect of the drug on electrically induced tracheal smooth muscle contractions. RESULTS: Addition of methacholine to the incubation medium caused the trachea to contract in a dose-dependent manner. Addition of benzydamine at doses of 10(-5)M or above elicited a significant relaxation response to 10(-6)M methacholine-induced contraction. Benzydamine could inhibit electrical field stimulation-induced spike contraction. It alone had a minimal effect on the basal tension of trachea as the concentration increased. CONCLUSION: This study indicated that high concentrations of benzydamine might actually inhibit parasympathetic function of the trachea. Benzydamine might reduce asthma attacks in oral mucositis patients because it could inhibit parasympathetic function and reduce methacholine-induced contraction of tracheal smooth muscle.
Anesthesia, Local ; Animals ; Anti-Inflammatory Agents, Non-Steroidal ; Asthma ; Benzydamine* ; Humans ; Inhalation ; Methacholine Chloride ; Muscle, Smooth* ; Radiation Effects ; Rats ; Relaxation ; Stomatitis ; Trachea

OBJECTIVES: Thioperamide is used as an antagonist to the histamine H3 receptor. During administration of the drug, the trachea may be affected via nasal or oral inhalation. This study was to determine the effects of thioperamide on the trachea of rats in vitro. METHODS: We tested the effectiveness of thioperamide on isolated rat trachea submersed in Kreb's solution in a muscle bath. Changes in tracheal contractility in response to the application of parasympathetic mimetic agents were measured. The following assessments of thioperamide were performed: 1) effect on tracheal smooth muscle resting tension; 2) effect on contraction caused by 10(-6) M methacholine as a parasympathetic mimetic; 3) effect of the drug on electrically-induced tracheal smooth muscle contractions. RESULTS: Thioperamide induced a significant relaxation response at a preparation concentration up to 10(-4) M. The drug also inhibited the electrical field stimulation induced spike contraction. However, thioperamide alone had a minimal effect on the basal tension of the trachea at increasing concentrations. CONCLUSION: The study indicated that high concentrations of thioperamide might actually antagonize cholinergic receptors and block parasympathetic function of the trachea.
Animals ; Asthma ; Baths ; Contracts ; Inhalation ; Methacholine Chloride ; Muscle, Smooth ; Muscles ; Piperidines ; Rats ; Receptors, Cholinergic ; Receptors, Histamine H3 ; Relaxation ; Trachea

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.