Study of multistep Dense U-Net-based automatic segmentation for head MRI scans.

BACKGROUND: Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used. PURPOSE: The goal of this study is to evaluate the effectiveness of an automatic segmentation method for head MRI scans using a multistep Dense U-Net (MDU-Net) architecture. METHODS: The MDU-Net-based method comprises two steps. The first step is to segment the scalp, skull, and whole brain from head MRI scans using a convolutional neural network (CNN). In the first step, a hybrid network is used to combine 2.5D Dense U-Net and 3D Dense U-Net structure. This hybrid network acquires logits in three orthogonal planes (axial, coronal, and sagittal) using 2.5D Dense U-Nets and fuses them by averaging. The resultant fused probability map with head MRI scans then serves as the input to a 3D Dense U-Net. In this process, different ratios of active contour loss and focal loss are applied. The second step is to segment the cerebrospinal fluid (CSF), white matter, and gray matter from extracted brain MRI scans using CNNs. In the second step, the histogram of the extracted brain MRI scans is standardized and then a 2.5D Dense U-Net is used to further segment the brain's specific tissues using the focal loss. A dataset of 100 head MRI scans from an OASIS-3 dataset was used for training, internal validation, and testing, with ratios of 80%, 10%, and 10%, respectively. Using the proposed approach, we segmented the head MRI scans into five areas (scalp, skull, CSF, white matter, and gray matter) and evaluated the segmentation results using the Dice similarity coefficient (DSC) score, Hausdorff distance (HD), and the average symmetric surface distance (ASSD) as evaluation metrics. We compared these results with those obtained using the Res-U-Net, Dense U-Net, U-Net++, Swin-Unet, and H-Dense U-Net models. RESULTS: The MDU-Net model showed DSC values of 0.933, 0.830, 0.833, 0.953, and 0.917 in the scalp, skull, CSF, white matter, and gray matter, respectively. The corresponding HD values were 2.37, 2.89, 2.13, 1.52, and 1.53 mm, respectively. The ASSD values were 0.50, 1.63, 1.28, 0.26, and 0.27 mm, respectively. Comparing these results with other models revealed that the MDU-Net model demonstrated the best performance in terms of the DSC values for the scalp, CSF, white matter, and gray matter. When compared with the H-Dense U-Net model, which showed the highest performance among the other models, the MDU-Net model showed substantial improvements in the HD view, particularly in the gray matter region, with a difference of approximately 9%. In addition, in terms of the ASSD, the MDU-Net model outperformed the H-Dense U-Net model, showing an approximately 7% improvements in the white matter and approximately 9% improvements in the gray matter. CONCLUSION: Compared with existing models in terms of DSC, HD, and ASSD, the proposed MDU-Net model demonstrated the best performance on average and showed its potential to enhance the accuracy of automatic segmentation for head MRI scans.

Synergistic effect of TTF and 5-FU combination treatment on pancreatic cancer cells.

The present study investigated the therapeutic potential of combining tumor-treating fields (TTF), a novel cancer treatment modality that employs low-intensity, alternating electric fields, with 5-fluorouracil (5-FU), a standard chemotherapy drug used for treating pancreatic cancer. The HPAF-II and Mia-Paca II pancreatic cancer cell lines were treated with TTF, 5-FU, or their combination. Combination treatment produced a significantly greater inhibitory effect on cancer cell proliferation than each single modality. Furthermore, combination therapy induced a substantially higher rate of pancreatic cancer cell apoptosis and exhibited a synergistic effect in clonogenic assays. Additionally, combination treatment showed a greater inhibition of cancer cell migration and invasion than either TTF or 5-FU alone. In conclusion, these findings suggest that the synergistic properties of TTF and 5-FU result in greater therapeutic efficacy against pancreatic cancer cells than either modality alone and may improve survival rates in patients with pancreatic cancer.

The combination of tumor treating fields and hyperthermia has synergistic therapeutic effects in glioblastoma cells by downregulating STAT3.

Glioblastoma multiforme (GBM), the most common type of brain tumor, is a very aggressive and treatment-refractory cancer, with a 5-year survival rate of approximately 5%. Hyperthermia (HT) and tumor treating fields (TTF) therapy have been used to treat cancer, either alone or in combination with other treatment methods. Both treatments have been reported to increase the efficacy of other treatment techniques and to improve patient prognosis. The present study evaluated the therapeutic effects of combining HT and TTF on GBM cell lines. Cells were subjected to HT, TTF, HT+TTF, or neither treatment, followed by comparisons of cell proliferation, apoptosis, migration and invasiveness. Clonogenic assays showed that the two treatments had a synergistic effect. The levels of cleaved PARP and cleaved caspase-3 were higher and apoptosis was increased in cells treated with HT+TTF than in cells treated with HT or TTF alone. In addition, HT+TTF showed greater inhibition of GBM cell migration and invasiveness and greater downregulation of STAT3 than either HT or TTF alone. The stronger anticancer effect of HT+TTF suggested that this combination treatment can increase the survival rate of patients with difficult-to-treat cancers such as GBM.

The Mediating Effects of Work-Life Balance (WLB) and Ease of Using WLB Programs in the Relationship between WLB Organizational Culture and Turnover Intention.

Work-life balance (WLB) is an important concern for all workers irrespective of their age, sex, education level, family structure, or occupation. This study analyzes WLB's mediating effects and the ease of using WLB programs in the relationship between WLB organizational culture of hotels and turnover intention of its culinary staff. We conducted a survey featuring 320 culinary staff members at hotels in Incheon from 10 to 30 August 2020 and performed statistical analysis using 290 responses. We find that the company's willingness for WLB, empathetic communication with colleagues, material support of colleagues for WLB, and the ease of using WLB programs in organizational culture had a positive impact on WLB. The company's willingness for WLB, boss's consideration for WLB, empathetic communication with colleagues, and material support of colleagues for WLB in organizational culture had a negative impact on turnover intention. The ease of using WLB programs had no indirect effect on the relationship between organizational culture and turnover intention. However, WLB had an indirect effect on the relationship between the four components except for the boss's consideration for WLB and turnover intention. Hotel management should create an organizational culture that supports the WLB of culinary staff.

Osteoblastic/cementoblastic and neural differentiation of dental stem cells and their applications to tissue engineering and regenerative medicine.

Recently, dental stem and progenitor cells have been harvested from periodontal tissues such as dental pulp, periodontal ligament, follicle, and papilla. These cells have received extensive attention in the field of tissue engineering and regenerative medicine due to their accessibility and multilineage differentiation capacity. These dental stem and progenitor cells are known to be derived from ectomesenchymal origin formed during tooth development. A great deal of research has been accomplished for directing osteoblastic/cementoblastic differentiation and neural differentiation from dental stem cells. To differentiate dental stem cells for use in tissue engineering and regenerative medicine, there needs to be efficient in vitro differentiation toward the osteoblastic/cementoblastic and neural lineage with well-defined and proficient protocols. This would reduce the likelihood of spontaneous differentiation into divergent lineages and increase the available cell source. This review focuses on the multilineage differentiation capacity, especially into osteoblastic/cementoblastic lineage and neural lineages, of dental stem cells such as dental pulp stem cells (DPSC), dental follicle stem cells (DFSC), periodontal ligament stem cells (PDLSC), and dental papilla stem cells (DPPSC). It also covers various experimental strategies that could be used to direct lineage-specific differentiation, and their potential applications in tissue engineering and regenerative medicine.