The present study indicates that the inherent capacity of hPDLSCs could be maintained until a later passage, P8 in MBM, and MBM appears to be an optimal choice for manipulating the finest and most stable hPDLSCs.

No abstract

No abstract

Background Androgenic alopecia (AGA) is the most common type of hair loss. It is likely inherited genetically and is promoted by dihydrotestosterone. 5α-reductase has been proven a good target through finasteride use. However, the pathogenesis of AGA cannot be fully explained based only on dihydrotestosterone levels. Objective To identify similar hairloss inhibition activity of RE-ORGA with mode of action other than finasteride. Methods We prepared RE-ORGA from Korean herb mixtures. We performed MTT assays for cytotoxicity, Cell Counting Kit-8 assays for cell proliferation, and western blot to identify expression levels of 5α-reductase and Bax. RNA-sequencing was performed for the expression patterns of genes in dihydrotestosterone-activated pathways. Anti-inflammatory activity was also assessed by the expression levels of tumor necrosis factor-alpha (TNF-α) and interleukin 6. Results REORGA could promote the proliferation of human dermal papilla cells and showed low cytotoxicity. It also inhibited the expression of 5α-reductases and Bax in the cells. RNA-sequencing results verified that the mRNA expressions of SRD5A1 , Bax, transforming growth factor-beta 1 (TGF-β1), and TGF-β1 induced transcript 1 (TGFβ1I1) were decreased, whereas expression of protein tyrosine kinase 2 beta (PTK2β) was more elevated. REORGA also showed anti-inflammatory activity through decreased mRNA levels of TNF-α. Conclusion Transcriptionally, up-regulation of PTK2β and concomitant down-regulation of TGFβ1I1 imply that RE-ORGA can modulate androgen receptor sensitivity, decreasing the expression of 5α-reductase type II and Bax together with TGF-β1 transcripts; RE-ORGA also showed partial anti-inflammatory activity. Overall, RE-ORGA is expected to alleviate hair loss by regulating 5α-reductase activity and the receptor's androgen sensitivity.

Background Dog-associated infections are related to more than 70 human diseases. Given that the health diagnosis of a dog requires expertise of the veterinarian, an artificial intelligence model for detecting dog diseases could significantly reduce time and cost required for a diagnosis and efficiently maintain animal health. Objective We collected normal and multispectral images to develop classification model of each three dog skin diseases (bacterial dermatosis, fungal infection, and hypersensitivity allergic dermatosis). The single models (normal image- and multispectral image-based) and consensus models were developed used to four CNN model architecture (InceptionNet, ResNet, DenseNet, MobileNet) and select well-performed model. Results For single models, such as normal image- or multispectral image-based model, the best accuracies and Matthew’s correlation coefficients (MCCs) for validation data set were 0.80 and 0.64 for bacterial dermatosis, 0.70 and 0.36 for fungal infection, and 0.82 and 0.47 for hypersensitivity allergic dermatosis. For the consensus models, the best accuracies and MCCs for the validation set were 0.89 and 0.76 for the bacterial dermatosis data set, 0.87 and 0.63 for the fungal infection data set, and 0.87 and 0.63 for the hypersensitivity allergic dermatosis data set, respectively, which supported that the consensus models of each disease were more balanced and well-performed. Conclusions We developed consensus models for each skin disease for dogs by combining each best model developed with the normal and multispectral images, respectively. Since the normal images could be used to determine areas suspected of lesion of skin disease and additionally the multispectral images could help confirming skin redness of the area, the models achieved higher prediction accuracy with balanced performance between sensitivity and specificity.