Publications by authors named "A Hirose"
Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction.
View Article and Find Full Text PDFAim: This study examined the relationship between postpartum hair loss and psychological symptoms.
Methods: This questionnaire-based, cross-sectional study included postpartum women who had delivered at two facilities and completed the questionnaire 10-18 months after delivery. Study protocols were sent by mail in two parts.
Background In our hospital, anastomotic leakage (AL) is observed in approximately 2% of functional end-to-end anastomosis (FEEA) cases annually. It is also usually observed at the staple line of the entry hole closure in several reoperation cases. This study aimed to investigate whether AL would occur in FEEA using a new staple line reinforcement tool, ECHELON ENDOPATH Staple Line Reinforcement (SLR) (Ethicon, Raritan, NJ, USA).
View Article and Find Full Text PDFArticle Synopsis
- - Fullerene whiskers (FLWs) and fullerenes (FLs) are rod-like structures made of carbon, and their shape raises concerns about potential health risks similar to those posed by asbestos and carbon nanotubes, but no long-term studies on their carcinogenic effects have been conducted.
- - In a study, male rats were given FL, FLW, and different types of multi-walled carbon nanotubes (MWCNT) to assess lung and pleural carcinogenicity, leading to analyses at 1 and 104 weeks post-exposure.
- - Results showed that while MWCNT exposure led to significant increases in lung tumors and oxidative DNA damage, FL and FLW did not induce carcinogenic effects in the
Article Synopsis
- Traditional relapse prediction models for post-transplant patients don't adapt based on new information collected after the transplant, making them less effective for treatment adjustments.
- This study focused on creating a dynamic relapse prediction model specifically for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) patients after allogeneic hematopoietic cell transplantation (allo-HCT), utilizing WT1mRNA levels from blood tests.
- The new model demonstrated significantly better predictive performance compared to older models, allowing for real-time predictions of relapse risk and is accessible through a user-friendly web application.