Comparison of Endotoxin Activity Assay and Various Biomarkers for Severity Assessment in Colorectal Perforation Patients.

Colorectal perforation is a serious disease with high mortality requiring emergency surgery. This study aimed to evaluate the role of the endotoxin activity assay (EAA) to assess the severity in patients admitted to the intensive care unit after emergency surgeries for colorectal perforations. Patients were divided into high (EAA ≥.4) and low (EAA <.4) groups based on the EAA levels, and the correlation between the EAA values and clinical variables related to the severity was evaluated. The SOFA scores were significantly higher in the high group than those in the low group. The high EAA value persisted even after 48 hours and extended the ICU length of stay. These results suggest that EAA may be a potential biomarker to assess severity and useful as one of the instrumental in predicting the outcomes for colorectal perforation patients.

Similar Drug Proposals Based on Package Inserts Using Latent Semantic Analysis / 医薬品情報学

Objective:The topic model is a well-known method used in the field of natural language processing (NLP)that defines adocument as constructed of topics that combine specific t erms. This method is used to model topic co-occurrencemathematically. In this study,we extracted topics from featu re vectors of explicit documents called medical package insertsby using cluster analysis. Methods:We counted the terms(nouns)recognized by the morphological analysis engine MeCab and created a documentterm matrix. A value of“tf・idf”was calculated in this matrix for term weighting to avoid the effect of term frequency. We reduced the dimensionality of the matrix using singular v alue decomposition,which removed unnecessary data,and weextracted feature vectors attributed to each medical package insert. The distance between feature vectors was calculatedusing cosine distance,and cluster analysis was performed based on the distance between the vectors.Results:Cluster analysis on our document-term matrix show ed that medical package inserts of drugs that have the sameefficacy or active ingredient were included in the same cl uster. Moreover, using term weighting and dimensionalityreduction,we could extract topics from medical package inserts.Conclusion:We obtained a foothold to apply our findings t o the recommendation of similar drugs. Cluster analysis ofmedical package inserts using NLP can contribute to the pro per application of drugs. In addition,our study revealed thesimilarities of drugs and suggested possibilities for new applications from several points of view.