Extensive calcification of the mitral annulus presents a formidable technical challenge to surgeons and increases the risk of serious complications such as intractable hemorrhage, atrioventricular disruption, and ventricular rupture during mitral valve surgery. We present a case of aortic and mitral valve replacements for a patient with extensive calcification of an intervalvular fibrous body. A 76-year-old woman was admitted with dyspnea on effort, leg edema and syncope. Transthoracic echocardiography showed severe aortic stenosis, and mitral stenosis with regurgitation, and extensive mitral annular calcification. Decalcification was performed with CUSA and we selected a trans-aortic-valve approach for decalcification of the intervalvular fibrous body. The calcification was left to a certain extent in order to preserve annular strength. Postoperative echocardiography showed no perivalvular leakage from either prostheses. The patient was transferred to a local hospital for further rehabilitation.

Objective: Distigmine can cause cholinergic crisis as the side effect.  In 2010, the safety information of distigmine was announced and its dosage was changed up to 5 mg per day.  However, the malpractice that a pharmacist dispensed over dose of distigmine caused severe health damages in a community pharmacy.  Therefore, we made the protocol with the urologists for the appropriate use of distigmine, including contents of monitoring the side effects.  The purpose of this study was to measure using the protocol was useful for the propulsion of proper use of distigmine.
Methods: The protocol was introduced in 10 community pharmacies and 1 hospital pharmacy from December 2013 to April 2014, and the patients and pharmacists were filled out the answer to the questions that we have made.  The protocol consisted of five main checks; the dosage, lower urinary tract symptom, presence of renal disease, combined drugs, and signs of the side effects.  Each patient was filled out the checklist given by the pharmacist to monitor the signs of the side effects for 2 weeks.
Results: The 3 prescriptions of distigmine (18.8%) were more than 10 mg per day.  Although 2 patients were confirmed diarrhea and sweating etc., they were mild.  The pharmacists significantly more (p<0.05) answered that the protocol made their motivation to do the pharmaceutical interventions.  All of the patients answered that the pharmaceutical interventions made them relieved.
Conclusion: The use of protocol that we made supported pharmacists to do the pharmaceutical interventions and patients welcome them.

Objective: Currently, limited information is available on the milk transfer properties of drugs when consumed by lactating women. Therefore, we aim to construct a prediction model of milk transfer of drugs using machine learning methods.Methods: We obtained data from Hale’s Medications & Mothers’ Milk (MMM) and SciFinder®, and then constructed the datasets. The physicochemical and pharmacokinetic data were used as feature variables with M/P ratio ≥ 1 and M/P ratio < 1 as the objective variables, classified into two groups as the classification of milk transferability. In this study, analyses were conducted using machine learning methods: logistic regression, linear support vector machine (linear SVM), kernel method support vector machine (kernel SVM), random forest, and k-nearest neighbor classification. The results were compared to those obtained with the linear regression equation of Yamauchi et al. from a previous study. The analysis was performed using scikit-learn (version 0.24.2) with python (version 3.8.10).Results: Model construction and validation were performed on the training data comprising 159 drugs. The results revealed that the random forest had the highest accuracy, area under the receiver operating characteristic curve (AUC), and F value. Additionally, the results with test data A and B (n = 36, 31), which were not used for training, showed that both F value and accuracy for the random forest and the kernel method SVM exceeded those with the linear regression equation of Yamauchi et al. Conclusion: We were able to construct a predictive model of milk transferability with relatively high performance using a machine learning method capable of nonlinear separation. The predictive model in this study can be applied to drugs with unknown M/P ratios for providing a new source of information on milk transfer.