PURPOSE: This pilot study investigated the effect of surface roughness on osseointegration by comparing two types of commercial SLA-treated dental implants with different surface roughness levels: moderately rough (Sa = 1 – 2 µm) and rough surfaces (Sa > 2 µm). MATERIALS AND METHODS: Two implant groups were studied: TS (rough surface) and ADD (moderately rough surface) groups. Surface characteristics were analyzed using optical profilometry and SEM. In vitro studies using BRITER cells assessed cell adhesion, proliferation, and osteogenic differentiation through CCK-8 assay and qRT-PCR for osteopontin (OPN), osteocalcin (OCN), and alkaline phosphatase (ALP) expression. The in vivo study involved 12 implants (six per group) placed in mandibular defects of two beagle dogs. After 8 weeks, histomorphometric analysis evaluated bone to implant contact (BIC) and inter-thread bone density (ITBD). Statistical analysis used Student’s t-test and two-way ANOVA for in vitro data, and Mann-Whitney U test for in vivo data. RESULTS: Surface analysis revealed Sa values of 2.50 ± 0.27 µm for the TS group and 1.80 ± 0.06 µm for the ADD group. In vitro studies showed no significant differences in cell adhesion and proliferation between the groups (P > .05). However, gene expression patterns differed, with ADD group showing higher OPN expression (P < .001) and TS group showing higher ALP expression (P < .01). The in vivo study revealed no statistically significant differences in BIC and ITBD between the two groups (P > .05). CONCLUSION Surface roughness influenced osteoblast differentiation in vitro, but did not significantly affect osseointegration outcomes in vivo. Both moderately rough and rough surfaces appeared to support comparable levels of osseointegration. Larger studies are needed to confirm these findings and determine optimal implant surface characteristics.

PURPOSE: This pilot study investigated the effect of surface roughness on osseointegration by comparing two types of commercial SLA-treated dental implants with different surface roughness levels: moderately rough (Sa = 1 – 2 µm) and rough surfaces (Sa > 2 µm). MATERIALS AND METHODS: Two implant groups were studied: TS (rough surface) and ADD (moderately rough surface) groups. Surface characteristics were analyzed using optical profilometry and SEM. In vitro studies using BRITER cells assessed cell adhesion, proliferation, and osteogenic differentiation through CCK-8 assay and qRT-PCR for osteopontin (OPN), osteocalcin (OCN), and alkaline phosphatase (ALP) expression. The in vivo study involved 12 implants (six per group) placed in mandibular defects of two beagle dogs. After 8 weeks, histomorphometric analysis evaluated bone to implant contact (BIC) and inter-thread bone density (ITBD). Statistical analysis used Student’s t-test and two-way ANOVA for in vitro data, and Mann-Whitney U test for in vivo data. RESULTS: Surface analysis revealed Sa values of 2.50 ± 0.27 µm for the TS group and 1.80 ± 0.06 µm for the ADD group. In vitro studies showed no significant differences in cell adhesion and proliferation between the groups (P > .05). However, gene expression patterns differed, with ADD group showing higher OPN expression (P < .001) and TS group showing higher ALP expression (P < .01). The in vivo study revealed no statistically significant differences in BIC and ITBD between the two groups (P > .05). CONCLUSION Surface roughness influenced osteoblast differentiation in vitro, but did not significantly affect osseointegration outcomes in vivo. Both moderately rough and rough surfaces appeared to support comparable levels of osseointegration. Larger studies are needed to confirm these findings and determine optimal implant surface characteristics.

PURPOSE: This pilot study investigated the effect of surface roughness on osseointegration by comparing two types of commercial SLA-treated dental implants with different surface roughness levels: moderately rough (Sa = 1 – 2 µm) and rough surfaces (Sa > 2 µm). MATERIALS AND METHODS: Two implant groups were studied: TS (rough surface) and ADD (moderately rough surface) groups. Surface characteristics were analyzed using optical profilometry and SEM. In vitro studies using BRITER cells assessed cell adhesion, proliferation, and osteogenic differentiation through CCK-8 assay and qRT-PCR for osteopontin (OPN), osteocalcin (OCN), and alkaline phosphatase (ALP) expression. The in vivo study involved 12 implants (six per group) placed in mandibular defects of two beagle dogs. After 8 weeks, histomorphometric analysis evaluated bone to implant contact (BIC) and inter-thread bone density (ITBD). Statistical analysis used Student’s t-test and two-way ANOVA for in vitro data, and Mann-Whitney U test for in vivo data. RESULTS: Surface analysis revealed Sa values of 2.50 ± 0.27 µm for the TS group and 1.80 ± 0.06 µm for the ADD group. In vitro studies showed no significant differences in cell adhesion and proliferation between the groups (P > .05). However, gene expression patterns differed, with ADD group showing higher OPN expression (P < .001) and TS group showing higher ALP expression (P < .01). The in vivo study revealed no statistically significant differences in BIC and ITBD between the two groups (P > .05). CONCLUSION Surface roughness influenced osteoblast differentiation in vitro, but did not significantly affect osseointegration outcomes in vivo. Both moderately rough and rough surfaces appeared to support comparable levels of osseointegration. Larger studies are needed to confirm these findings and determine optimal implant surface characteristics.

PURPOSE: This pilot study investigated the effect of surface roughness on osseointegration by comparing two types of commercial SLA-treated dental implants with different surface roughness levels: moderately rough (Sa = 1 – 2 µm) and rough surfaces (Sa > 2 µm). MATERIALS AND METHODS: Two implant groups were studied: TS (rough surface) and ADD (moderately rough surface) groups. Surface characteristics were analyzed using optical profilometry and SEM. In vitro studies using BRITER cells assessed cell adhesion, proliferation, and osteogenic differentiation through CCK-8 assay and qRT-PCR for osteopontin (OPN), osteocalcin (OCN), and alkaline phosphatase (ALP) expression. The in vivo study involved 12 implants (six per group) placed in mandibular defects of two beagle dogs. After 8 weeks, histomorphometric analysis evaluated bone to implant contact (BIC) and inter-thread bone density (ITBD). Statistical analysis used Student’s t-test and two-way ANOVA for in vitro data, and Mann-Whitney U test for in vivo data. RESULTS: Surface analysis revealed Sa values of 2.50 ± 0.27 µm for the TS group and 1.80 ± 0.06 µm for the ADD group. In vitro studies showed no significant differences in cell adhesion and proliferation between the groups (P > .05). However, gene expression patterns differed, with ADD group showing higher OPN expression (P < .001) and TS group showing higher ALP expression (P < .01). The in vivo study revealed no statistically significant differences in BIC and ITBD between the two groups (P > .05). CONCLUSION Surface roughness influenced osteoblast differentiation in vitro, but did not significantly affect osseointegration outcomes in vivo. Both moderately rough and rough surfaces appeared to support comparable levels of osseointegration. Larger studies are needed to confirm these findings and determine optimal implant surface characteristics.

PURPOSE: This pilot study investigated the effect of surface roughness on osseointegration by comparing two types of commercial SLA-treated dental implants with different surface roughness levels: moderately rough (Sa = 1 – 2 µm) and rough surfaces (Sa > 2 µm). MATERIALS AND METHODS: Two implant groups were studied: TS (rough surface) and ADD (moderately rough surface) groups. Surface characteristics were analyzed using optical profilometry and SEM. In vitro studies using BRITER cells assessed cell adhesion, proliferation, and osteogenic differentiation through CCK-8 assay and qRT-PCR for osteopontin (OPN), osteocalcin (OCN), and alkaline phosphatase (ALP) expression. The in vivo study involved 12 implants (six per group) placed in mandibular defects of two beagle dogs. After 8 weeks, histomorphometric analysis evaluated bone to implant contact (BIC) and inter-thread bone density (ITBD). Statistical analysis used Student’s t-test and two-way ANOVA for in vitro data, and Mann-Whitney U test for in vivo data. RESULTS: Surface analysis revealed Sa values of 2.50 ± 0.27 µm for the TS group and 1.80 ± 0.06 µm for the ADD group. In vitro studies showed no significant differences in cell adhesion and proliferation between the groups (P > .05). However, gene expression patterns differed, with ADD group showing higher OPN expression (P < .001) and TS group showing higher ALP expression (P < .01). The in vivo study revealed no statistically significant differences in BIC and ITBD between the two groups (P > .05). CONCLUSION Surface roughness influenced osteoblast differentiation in vitro, but did not significantly affect osseointegration outcomes in vivo. Both moderately rough and rough surfaces appeared to support comparable levels of osseointegration. Larger studies are needed to confirm these findings and determine optimal implant surface characteristics.

Purpose: Since the introduction of robotic surgery, robots for colorectal cancer have replaced laparoscopic surgery, and a single-port robot (SPR) platform has been launched and is being used to treat patients. We analyzed the learning curve and initial complications of using an SPR platform in colorectal cancer surgery. Methods: We reviewed 39 patients who underwent SPR colectomy from April to October 2019. All surgeries were performed by the same surgeon using an SPR device. A learning curve was generated using the cumulative sum methodology to assess changes in total operation time, docking time, and surgeon console time. We grouped the patients into 3 groups according to the time period: the first 11 were phase 1, the next 11 were phase 2, and the last 17 were phase 3. Results: The mean age of the patients was 61.28±13.03 years, and they had a mean body mass index of 23.79±2.86 kg/m2. Among the patients, 23 (59.0%) were male, and 16 (41.0%) were female. The average operation time was 186.59±51.30 minutes, the average surgeon console time was 95.49±35.33 minutes, and the average docking time (time from skin incision to robot docking) was 14.87±10.38 minutes. The surgeon console time differed significantly among the different phases (P<0.001). Complications occurred in 8 patients: 2 ileus, 2 postoperation hemoglobin changes, 3 urinary retentions, and 1 complicated fluid collection. Conclusion In our experience, the learning curve for SPR colectomy was achieved after the 18th case.

Purpose: Breast cancer is known to be influenced by genetic and environmental factors, and several susceptibility genes have been discovered. Still, the majority of genetic contributors remain unknown. We aimed to analyze the plasma proteome of breast cancer patients in comparison to healthy individuals to identify differences in protein expression profiles and discover novel biomarkers. Methods: This pilot study was conducted using bioresources from Seoul National University Bundang Hospital’s Human Bioresource Center. Serum samples from 10 breast cancer patients and 10 healthy controls were obtained. Liquid chromatography-mass spectrometry analysis was performed to identify differentially expressed proteins. Results: We identified 891 proteins; 805 were expressed in the breast cancer group and 882 in the control group. Gene set enrichment and differential expression analysis identified 30 upregulated and 100 downregulated proteins in breast cancer. Among these, 10 proteins were selected as potential biomarkers. Three proteins were upregulated in breast cancer patients, including cluster of differentiation 44, eukaryotic translation initiation factor 2-α kinase 3, and fibronectin 1. Seven proteins downregulated in breast cancer patients were also selected: glyceraldehyde-3-phosphate dehydrogenase, α-enolase, heat shock protein member 8, integrin‑linked kinase, tissue inhibitor of metalloproteinases-1, vasodilatorstimulated phosphoprotein, and 14-3-3 protein gamma. All proteins had been previously reported to be related to tumor development and progression. Conclusion The findings suggest that plasma proteome profiling can reveal potential diagnostic biomarkers for breast cancer and may contribute to early detection and personalized treatment strategies. A further validation study with a larger sample cohort of breast cancer patients is planned.