Effects of three plant extracts (Hordeum vulgare L., Apocynum Venetum L., Brasenia schreberi J.F.G mel.) on human gingival fibroblasts were examined. As a result, we observed the promoting effect of the extract of Hordeum vulgare L. and the extract of Apocynum Venetum L. respectively on FGF2 and FGF7 production. Moreover, the mixture of the three plant extracts showed the effect of improving the changes in type I collagen gene expression and matrix metalloproteinase 1 gene expression by LPS addition. Next, a dentifrice containing the three plant extracts was subjected to human efficacy trials. We measured periodontal pocket depth, attachment level, bleeding on probing and saliva TNFα as an indicator of periodontal disease. The results suggest that the dentifrice formulated with the three plant extracts were effective for the improvement of periodontal disease.

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.

Objective: Therapeutic antibodies have few varieties of side effects due to their high specificity; however, many therapeutic antibodies have serious side effects. A thorough understanding of the side effects is crucial for early recognition and optimal management. To facilitate the understanding of the side effects of therapeutic antibodies, this study attempted to classify therapeutic antibodies based on their side effects using principal component analysis (PCA) and cluster analysis. Method: We collected data on the serious side effects of therapeutic antibodies from package inserts and created a therapeutic antibody-side effect matrix, with therapeutic antibodies as indices and side effects as columns. PCA was performed on the therapeutic antibody-side effect matrix, and hierarchical cluster analysis was performed using principal components. Results: The therapeutic antibodies were classified into four clusters. Cluster 1 included immune checkpoint inhibitors, and featured type 1 diabetes, thyroid disorder, and myasthenia gravis. Cluster 2 included antibodies that inhibit the vascular endothelial growth factor pathway, and featured impaired wound healing, nephrotic syndrome, and thrombosis. Cluster 3 included anti-epidermal growth factor receptor antibodies, and featured diarrhea, hypomagnesemia, and skin disorders. Cluster 4 included other therapeutic antibodies, and featured infection, bone marrow suppression, and hypersensitivity. Conclusion: Therapeutic antibodies can be classified based on their side effects. The results of this study make it easier to understand the side effects of therapeutic antibodies with complex profiles. A better understanding facilitates early detection of side effects and enables high-quality management of side effects.