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.

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.

Objective: Endovascular coil embolization is the primary treatment modality for intracranial aneurysms. However, its long-term durability remains of concern, with a considerable proportion of cases requiring aneurysm reopening and retreatment. Therefore, establishing optimal follow-up imaging protocols is necessary to ensure a durable occlusion. This study aimed to develop guidelines for follow-up imaging strategies after endovascular treatment of intracranial aneurysms. Methods: A committee comprising members of the Korean Neuroendovascular Society and other relevant societies was formed. A literature review and analyses of the major published guidelines were conducted to gather evidence. A panel of 40 experts convened to achieve a consensus on the recommendations using the modified Delphi method. Results: The panel members reached the following consensus: 1. Schedule the initial follow-up imaging within 3-6 months of treatment. 2. Noninvasive imaging modalities, such as three-dimensional time-of-flight magnetic resonance angiography (MRA) or contrast-enhanced MRA, are alternatives to digital subtraction angiography (DSA) during the first follow-up. 3. Schedule mid-term follow-up imaging at 1, 2, 4, and 6 years after the initial treatment. 4. If noninvasive imaging reveals unstable changes in the treated aneurysms, DSA should be considered. 5. Consider late-term follow-up imaging every 3–5 years for lifelong monitoring of patients with unstable changes or at high risk of recurrence. Conclusions The guidelines aim to provide physicians with the information to make informed decisions and provide patients with high-quality care. However, owing to a lack of specific recommendations and scientific data, these guidelines are based on expert consensus and should be considered in conjunction with individual patient characteristics and circumstances.

Background: Limited data are available on the mortality rates of patients receiving extracorporeal membrane oxygenation (ECMO) support for coronavirus disease 2019 (COVID-19). We aimed to analyze the relationship between COVID-19 and clinical outcomes for patients receiving ECMO. Methods: We retrospectively investigated patients with COVID-19 pneumonia requiring ECMO in 19 hospitals across Korea from January 1, 2020 to August 31, 2021. The primary outcome was the 90-day mortality after ECMO initiation. We performed multivariate analysis using a logistic regression model to estimate the odds ratio (OR) of 90-day mortality. Survival differences were analyzed using the Kaplan–Meier (KM) method. Results: Of 127 patients with COVID-19 pneumonia who received ECMO, 70 patients (55.1%) died within 90 days of ECMO initiation. The median age was 64 years, and 63% of patients were male. The incidence of ECMO was increased with age but was decreased after 70 years of age. However, the survival rate was decreased linearly with age. In multivariate analysis, age (OR, 1.048; 95% confidence interval [CI], 1.010–1.089; P = 0.014) and receipt of continuous renal replacement therapy (CRRT) (OR, 3.069; 95% CI, 1.312–7.180; P = 0.010) were significantly associated with an increased risk of 90-day mortality. KM curves showed significant differences in survival between groups according to age (65 years) (log-rank P = 0.021) and receipt of CRRT (log-rank P = 0.004). Conclusion Older age and receipt of CRRT were associated with higher mortality rates among patients with COVID-19 who received ECMO.