No Abstract Available.
Arthrobacter* ; Chitinase*

BACKGROUND/AIMS: We applied a back light system (BLS) with a magnifying glass to improve the ability to assess the adequacy of specimen sampling using endosonography. We conducted this study to evaluate the efficacy of the BLS in sampling of specimens by endoscopic ultrasound-guided fine needle aspiration of solid pancreatic masses. METHODS: This was a prospective, randomized, crossover, single-center clinical trial. An endosonographer evaluated adequacy on gross visual inspection and identified whitish specimen sampling sites with and without the BLS according to a randomization sequence in the first and second passes with a 25-G needle. On cytological evaluation, the presence of well-defined pancreatic ductal epithelium was evaluated by a cytopathologist who was blinded to any clinical information. RESULTS: A total of 80 consecutive patients were eligible during the study period. Adequacy was observed for 52 specimens (65%) with the BLS and 54 (68%) without the BLS (p=0.88). In assessment of specimen adequacy on gross examination, only fair agreement was observed both with and without BLS (kappa score 0.40 and 0.29, respectively). CONCLUSIONS: The BLS did not influence the ability to identify specimen sampling sites or reliable assessment of specimen site adequacy using gross visual inspection.
Biopsy, Fine-Needle ; Cross-Over Studies ; Endoscopic Ultrasound-Guided Fine Needle Aspiration ; Endosonography ; Epithelium ; Glass ; Humans ; Needles ; Pancreatic Ducts ; Pancreatic Neoplasms ; Prospective Studies ; Random Allocation

Drug dispensing is a statutory and designated duty of a pharmacist. We aimed to examine the changes in the nature of drug dispensing using a text mining method. Our corpus consisted of text documents from “Chozai Shishin”, the most standard manual for dispensing drugs in Japan, Editions 1 to 13 (Japan Pharmaceutical Association), and we used the KH Coder software for text mining. We constructed networks showing the association between frequent word co-occurrence and edition number, and co-occurrence relations for frequent words in each edition. We found that “patient” superseded “dispensing” as a frequent term over time. “Dispensing” was another frequent term with a highly centralized node in each edition. Accordingly, we targeted the term “dispensing” for network analysis to depict its co-occurrence relations. We found that the range of related words for “dispensing” broadened from “preparation” and “compounding” to include “patient adherence instructions”, “assessment”, “medical treatment”, and “information provision”. Accordingly, we concluded that the content of “dispensing”, which is a pharmacist’s duty, has expanded from the duties of “dispensing drugs” to include “responding to patients” within the definition of “dispensing”, and we were able to present this finding as objective data by using the mechanical method known as text mining.

Drug dispensing is a statutory and designated duty of a pharmacist. We aimed to examine the changes in the nature of drug dispensing using a text mining method. Our corpus consisted of text documents from “Chozai Shishin”, the most standard manual for dispensing drugs in Japan, Editions 1 to 13 (Japan Pharmaceutical Association), and we used the KH Coder software for text mining. We constructed networks showing the association between frequent word co-occurrence and edition number, and co-occurrence relations for frequent words in each edition. We found that “patient” superseded “dispensing” as a frequent term over time. “Dispensing” was another frequent term with a highly centralized node in each edition. Accordingly, we targeted the term “dispensing” for network analysis to depict its co-occurrence relations. We found that the range of related words for “dispensing” broadened from “preparation” and “compounding” to include “patient adherence instructions”, “assessment”, “medical treatment”, and “information provision”. Accordingly, we concluded that the content of “dispensing”, which is a pharmacist’s duty, has expanded from the duties of “dispensing drugs” to include “responding to patients” within the definition of “dispensing”, and we were able to present this finding as objective data by using the mechanical method known as text mining.