Biomedicine (Journal)
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Presentations and management of different causes of chylothorax in children: one medical center’s experience
No full textContribution of matrix metalloproteinases-1 genotypes to gastric cancer susceptibility in Taiwan
No full text\u3ci\u3eTorenia concolor\u3c/i\u3eLindley var.\u3ci\u3eformosana\u3c/i\u3eYamazaki extracts improve inflammatory response and lipid accumulation\u3ci\u3evia\u3c/i\u3ePPARs activation
No full textWeight loss might be an early clinical feature of undiagnosed human immunodeficiency virus infection in Taiwan
No full textCase report of C5 palsy after C3-C6 posterior decompression and instrumented fusion in a patient undergoing inpatient rehabilitation
No full textJuxtaposition of Bone Age and Sexual Maturity Rating of the Taiwanese Population
No full textBackground Bone age (BA) and sexual maturity rating (SMR) are the imperative measures in assessing adolescent growth and development. However, studies indigenous to the Taiwanese population with regard to the association of SMR and BA in adolescence is scarce. This study attempts to utilize the AI-assessed BA results to establish a relationship between BA and SMR of the Taiwanese population in regard to the initiation of puberty. Materials and methods The electronic medical records with examinations of bone age assessed between January 1st 2019 and December 31st 2019 were reviewed retrospectively. Records of the same individuals were retained only the latest entry within this period. Records without valid SMR were removed. Records with apparent medical concerns were eliminated. Males of chronological age between 7 and 17 years and females of chronological age between 6 and 16 years were included. Results The pubertal onset was found to present at the median bone age of 11.50 years [11.42, 11.83] for males and the median bone age of 9.33 years [9.25, 9.50] for females. Conclusion Consistency between BA and SMR could be an alternative approach in appraising pubertal status to peripubertal children seeking a less intrusive evaluation disregarding the chronological age (CA)
Advanced whole transcriptome sequencing and artificial intelligence/machine learning (AI/ML) in imiquimod-induced psoriasis-like inflammation of human keratinocytes
No full textIntroduction: Although the HaCaT keratinocyte model has been used in previous research to study the effects of anti-psoriatic agents, there is still a lack of comprehensive understanding of the mechanism of imiquimod (IMQ)-induced proliferation and signal transduction in psoriasis-like keratinocytes.
Objectives: This study aimed to investigate the molecular mechanisms and pathways associated with psoriasis-like inflammation caused by IMQ in human keratinocytes.
Materials and Methods: HaCaT cells were exposed to different concentrations of IMQ to induce inflammation similar to that observed in psoriasis. Cell viability was evaluated using the MTT assay and cell morphology was examined using phase-contrast microscopy. Gene expression profiles were analyzed through whole transcriptome sequencing, followed by bio-informatics network analysis using IPA software. The GSEA was conducted with the aim of identifying enriched pathways. The expression of key cytokines IL-6 and TNF-α was confirmed by QPCR. Artificial intelligence/machine learning (AI/ML) algorithms were used to predict potential diseases and phenotypes associated with the observed gene profiles.
Results: IMQ treatment demonstrated a substantial positive impact on cell survival without any detectable alterations in the morphology of HaCaT cells. A comprehensive analysis of the entire set of transcribed genes identified 513 genes that exhibited differential expression. Bioinformatics analysis revealed key pathways associated with immune response, cellular proliferation, and cytokine signaling. GSEA identified significant enrichment in the IFN-γ response and JAK-STAT signaling pathways. QPCR analysis confirmed the increased mRNA expression levels of IL-6 and TNF-α in cells treated with IMQ. AI/ML algorithms have identified potential correlations with diseases, such as multiple sclerosis, lympho-proliferative malignancy, and autoimmune disorders.
Conclusion: Our results highlight the importance of specific genes and pathways, particularly those associated with IFN-γ pathway and IL-6/JAK-STAT signaling. AI/ML predictions indicate potential associations with various diseases and provide valuable insights for the development of novel therapeutic approaches for psoriasis and related disorders