BioMedicine-Taiwan最新文献

Introduction: Our previous research demonstrated that a large language model (LLM) based on the transformer architecture, specifically the MegaMolBART encoder with an XGBoost classifier, effectively predicts the blood-brain barrier (BBB) permeability of compounds. However, the permeability coefficients of compounds that can traverse this barrier remain unclear. Additionally, the absorption, distribution, metabolism, and excretion (ADME) characteristics of substances obtained from the Natural Product Research Laboratory (NPRL) at China Medical University Hospital (CMUH) have not yet been determined.

Objectives: The study aims to investigate the pharmacokinetic ADME properties and BBB permeability coefficients of NPRL compounds.

Materials and methods: A combined model using a transformer-based MegaMolBART encoder and XGBoost classifier was employed to predict BBB permeability. Machine learning (ML) tools from Discovery Studio were used to assess the ADME characteristics of the NPRL compounds. The CCK-8 assay was conducted to evaluate the cytotoxic effects of NPRL compounds on bEnd.3 brain endothelial cells after exposure to 10 μg/mL of the compounds. We assessed the permeability coefficient by subjecting bEnd.3 cell monolayers to the test compounds and measuring the permeability of FITC-dextran.

Results: There were 4956 compounds that could cross the blood-brain barrier (BBB+) and 2851 that could not (BBB-) in the B3DB dataset that was utilized for training. A total of 2461 BBB+ and 2184 BBB- compounds were used in the NPRL-CMUH dataset for testing. The permeability coefficient of temozolomide (TMZ) and 21 other BBB + compounds exceeded 10 × 10^-7 cm/s. Computational analysis revealed that NPRL compounds exhibited a variety of ADME characteristics.

Conclusion: Computer-based predictions for the NPRL of CMUH compounds regarding their capacity to traverse the BBB are verified by the findings. Artificial intelligence (AI) prediction models have effectively identified the potential ADME characteristics of various compounds.

Introduction: Spine surgery is a common source of narcotic prescriptions and carries potential for long-term opioid dependence. As prescription opioids play a role in nearly 25 % of all opioid overdose deaths in the United States, mitigating risk for prolonged postoperative opioid utilization is crucial for spine surgeons.

Purpose: The aim of this study was to employ six ML algorithms to identify clinical variables predictive of increased opioid utilization across spinal surgeries, including anterior cervical discectomy and fusion (ACDF), posterior thoracolumbar fusion (PTLF), and lumbar laminectomy.

Methods: A query of the author's institutional database identified adult patients undergoing ACDF, PTLF, or lumbar laminectomy between 2013 and 2022. Six supervised ML algorithms, including Random Forest, Extreme Gradient Boosting, and LightGBM, were tasked with predicting additional opioid prescriptions at a patient's first postoperative visit based on set variables. Predictive variables were evaluated for missing data and optimized. Model performance was assessed with common analytical metrics, and variable importance was quantified using permutation feature importance. Statistical analysis utilized Pearson's Chi-square tests for categorical and independent sample t-tests for numerical differences.

Results: The author's query identified 3202 patients matching selection criteria, with 841, 1,409, and 952 receiving ACDF, PTLF, and lumbar laminectomy, respectively. The ML algorithms produced an aggregate AUC of 0.743, performing most effectively for lumbar laminectomy. Random Forest and LightGBM classifiers were selected for generation of permutation feature importance (PFI) values. Hospital length of stay was the only highly featured variable carrying statistical significance across all procedures.

Conclusion: Notable risk factors for increased postoperative opioid use were identified, including shorter hospital stays, younger age, and prolonged operative time. These findings can help identify patients at increased risk and guide strategies to mitigate opioid dependence.

Background: Bone age (BA) and sexual maturity rating (SMR) are crucial measures in assessing adolescent growth and development. However, studies specifically focusing on the association between BA and SMR in the Taiwanese adolescent population are limited. This study aims to utilize AI-assessed BA results to establish a relationship between BA and SMR in the Taiwanese adolescent population, particularly regarding the initiation of puberty.

Materials and methods: The electronic medical records of bone age assessments conducted between January 1, 2019, and December 31, 2019, were reviewed retrospectively. For individuals with multiple records, only the latest entry within this period was retained. Records lacking a valid SMR or presenting significant medical conditions were excluded from the analysis. Males aged 7-17 years and females aged 6-16 years were included in the study.

Results: The onset of puberty was observed to occur at a median bone age of 11.50 years (95% CI: 11.42-11.83) for males and 9.33 years (95% CI: 9.25-9.50) for females.

Conclusion: The consistency between BA and SMR could serve as an alternative approach for assessing pubertal status in peripubertal children, providing a less intrusive evaluation regardless of chronological age (CA).

Background: One of the most challenging cancers is triple-negative breast cancer, which is subdivided into many molecular subtypes. Due to the high degree of heterogeneity, the role of precision medicine remains challenging. With the use of machine learning (ML)-guided gene selection, the differential gene expression analysis can be optimized, and eventually, the process of precision medicine can see great advancement through biomarker discovery.

Purpose: Enhancing precision medicine in the oncology field by identification of the most representative differentially-expressed genes to be used as biomarkers or as novel drug targets.

Methods: By utilizing data from the Gene Expression Omnibus (GEO) repository and The Cancer Genome Atlas (TCGA), we identified the differentially expressed genes using the linear model for microarray analysis (LIMMA) and edgeR algorithms, and applied ML-based feature selection using several algorithms.

Results: A total of 27 genes were selected by merging features identified with both LIMMA and ML-based feature selection methods. The models with the highest area under the curve (AUC) are CatBoost, Extreme Gradient Boosting (XGBoost), Random Forest, and Multi-Layer Perceptron classifiers. ESR1, FOXA1, GATA3, XBP1, GREB1, AR, and AGR2 were identified as hub genes in a highly interconnected cluster.

Conclusion: ML-based gene selection shows a great impact on the identification of hub genes. The ML models built can improve precision oncology in diagnosis and prognosis. The identified hub genes can serve as biomarkers and warrant further research for potential drug target development.

Introduction: In recent years, significant progress has been made in regenerative medicine, specifically in using mesenchymal stem cells (MSCs) due to their regenerative and differentiating abilities. An exciting development in this area is the utilization of exosomes derived from MSCs, which have shown promise in tissue restoration, immune system modulation, and cancer treatment.

Objectives: This study aims to analyze global research trends and the academic impact of MSCs-derived exosomes from 2014 to 2023, providing a comprehensive overview of this emerging field.

Materials and methods: The Web of Science database selected 948 relevant publications from 2014 to 2023. Artificial intelligence (AI)-bibliometric tools, including Bibliometrix, CiteSpace, and VOSviewer, were employed to analyze and visualize the data. The focus was on publication quantity, research nations, institutional partnerships, keywords, and research focal points.

Results: The study revealed that China, Japan, Taiwan, and the United States are the leaders in publication volume and impact in MSCs-derived exosome research. China has the highest number of publications, while the United States and Iran excel in research quality and influence. Primary research themes were identified through keyword and clustering analyses, including tissue repair, immune modulation, bone regeneration, and cancer treatment. The study also emphasized the importance of international collaboration, with China and the United States demonstrating the most robust cooperation.

Conclusion: MSCs-derived exosome research rapidly expands worldwide, showing promising prospects in regenerative medicine and cell therapy. With continued research and international collaboration, MSCs-derived exosomes are expected to play a vital role in future therapeutic application.

The Natural Product Research Laboratory (NPRL) of China Medical University Hospital (CMUH) was established in collaboration with CMUH and Professor Kuo-Hsiung Lee from the University of North Carolina at Chapel Hill. The laboratory collection features over 6000 natural products worldwide, including pure compounds and semi-synthetic derivatives. This is the most comprehensive and fully operational natural product database in Taiwan. This review article explores the history and development of the NPRL of CMUH. We then provide an overview of the recent applications and impact of artificial intelligence (AI) in new drug discovery. Finally, we examine advanced powerful AI-tools and related software to explain how these resources can be utilized in research on large-scale drug data libraries. This article presents a drug research and development (R&D) platform that combines AI with the NPRL. We believe that this approach will reduce resource wastage and enhance the research capabilities of Taiwan's academic and industrial sectors in biotechnology and pharmaceuticals.

Introduction: Although the HaCaT keratinocyte model has been used in previous research to study the effects of antipsoriatic 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.

Background: Dahlia (Dahlia variabilis), a widely cultivated ornamental plant in Indonesia, is known to contain 84.08% inulin in its tubers. Numerous studies have demonstrated the antidiabetic potential of inulin from various plant sources. However, most of the research is in the form of a mixture of inulin with other active substances, and no one has analyzed the effects of inulin derived from dahlia tubers. This study examines the effect of inulin from dahlia tuber extract on blood glucose levels, serum insulin expression, pancreatic tissue insulin expression, homeostatic model assessment of insulin resistance (HOMA-IR), and the extent of insulitis in diabetic rats.

Methods: In this experimental study, 20 male Wistar rats were randomly allocated to five groups. Group I served as the control, Group II as the STZ-induced diabetic group, Group III as the STZ-induced diabetic group treated with inulin (0.5 g/kgBW), Group IV as the STZ induced diabetic group treated with inulin (1.0 g/kgBW), and Group V as the STZ-induced diabetic group treated with inulin (1.5 g/kgBW). The inulin was administered for 21 days. The degree of insulitis was evaluated using a scoring system, serum insulin concentration via ELISA, and insulin expression in the pancreas through immunohistochemistry.

Results: Administration of inulin from dahlia tubers significantly reduced serum glucose concentrations in diabetic rats. Notably, only inulin extracts at doses of 1 g/kgBW and 1.5 g/kgBW showed a significant reduction in insulitis and HOMA-IR index in diabetic rats, while the 0.5 g/kgBW inulin extract reduced insulitis without affecting HOMA-IR. Inulin extract administration did not affect insulin expression in serum or pancreatic tissue.

Conclusions: Inulin from dahlia tuber can exert antidiabetic properties by improving insulin resistance and insulitis. These studies suggest the great potential of dahlia tubers as the source of inulin for prebiotic functional foods.

Sustaining the continuity of cells and their homeostasis throughout the lifespan is compulsory for the survival of an organism. Cellular senescence is one of mechanisms involved in cell homeostasis and survival, and plays both important and detrimental roles in the maintenance of malfunctioned and normal cells. However, when exposed to various insults (genetic, metabolic and environmental), the cells undergo oxidative stress which may induce premature senescence, or so-called stress-induced premature senescence. Many age-related diseases are associated with premature senescence. Hence, there is growing interest in the intake of natural sources such as dietary food, which has protective functions on human health and diseases as well as on premature senescence. There are many natural food sources which have beneficial effects on delaying cell senescence, of which bee products are one of them. Bee products (honey, propolis, royal jelly, bee pollen, bee bread, venom and wax) are rich in polyphenols, a compound that exerts powerful antioxidant actions against oxidative stress and is able to delay premature senescence that is linked to ageing. This review describes the factors triggering senescence, the biomarkers involved and the prevention of senescence by the polyphenols present in bee products. Thus, it is hoped that this will provide new insights into the clinical management of age-related diseases.

Lithium, despite being an indispensable agent in the treatment of psychiatric disorders, has a narrow therapeutic index and needs to be carefully administered. Neuroleptic malignant syndrome (NMS) is a rare but potentially fatal complication due to central dopaminergic blockade. This case report illustrates the challenges in lithium therapy particularly related to the development of NMS when further risk factors such as polypharmacy and dehydration are present. We report a case of a 50-year-old man with underlying bipolar affective disorder who was previously able to tolerate olanzapine and lithium well, however developed chronic lithium toxicity due to diminished lithium elimination in acute kidney injury following a two-week history of viral acute gastroenteritis. He also developed NMS which could either be triggered independently by olanzapine; lithium toxicity; or attributed by a synergistic combination from lithium and olanzapine which led to an enhanced neurotoxicity in an already unstable dopaminergic pathway. Fluid therapy and supportive care allowed the patient to recover, and he was discharged well with a lower potency neuroleptic with slow dose titration.