BACKGROUND: This study evaluated the efficacy of an epidural single dose of neostigmine combined with fentanyl to provide postoperative analgesia after radical subtotal gastrectomy. METHODS: Fifty two adults patients with ASA physical status 1 and 2 are randomly allocated to receive a single injection of either epidural fentanyl 100 microgramor combination of fentanyl 100microgram with neostigmine 250, 500 and 750microgramin a total volume of 10 ml. Pain scores were recorded after 0, 5, 10, 15, 20, 30 mins to determine the onset of analgesia. Patients' vital signs as well as side effects were monitored at regular intervals. RESULTS: Patients' demographic data were not different from each other. Epidural neostigmine 750 microgram with fentanyl 100 microgram produced effective analgesia (visual analog scale at 10 min ; 35 +/- 10.6 mm). The time to first rescue analgesics administration was significantly longer in the neostigmine group (250 microgram: 84.2 +/- 9.4, 500 microgram: 90.9 +/- 7.1, 750 microgram: 92.5 +/- 14.4 min) than the control group (53.0 +/- 20.0 min). CONCLUSIONS: Combination of fentanyl with neostigmine was proven to be more effective for treating postoperative pain after subtotal gastrectomy than fentanyl alone. Additionally, the most effective dose of epidural neostigmine was 750microgram.
Adult ; Analgesia ; Analgesics ; Fentanyl ; Gastrectomy* ; Humans ; Neostigmine ; Pain, Postoperative ; Vital Signs

Objectives: . In subset of patients, acinic cell carcinoma (AcCC) exhibits aggressive features such as recurrence, distant metastasis, and mortality. This study aimed to investigate clinicopathologic factors influencing patients’ prognosis and to identify adverse features predictive of an unfavorable prognosis. Methods: . Between January 2000 and December 2016, 59 patients with AcCC were enrolled in this study. Results: . The patients’ 5-year overall survival rate was 93.3%, and their 5-year recurrence-free survival rate was 80.5%. During the study period, recurrence occurred in 10 patients. The mean time to recurrence after surgery was 26 months (range, 5–60 months). During the study period, three patients died from the disease. Univariate analysis showed that sex, surgical extent, extranodal extension, T classification, and TNM stage were significantly associated with disease recurrence. Multivariate analysis showed that, among the clinicopathologic factors included in the analysis, only TNM stage displayed a statistically significant correlation with disease recurrence. Conclusion . Surgical treatment alone yielded good results for AcCC, and additional treatment did not affect the recurrence-free survival rate or the overall survival rate, even when the resection margin was less than 1 mm. Other pathologic factors did not show prognostic significance for disease recurrence or death.

PURPOSE: This study was performed to investigate the ability of recombinant human-bone morphogenic protein-2 immobilized on a heparin-grafted bone substrate to enhance the osteoblastic functions. MATERIALS AND METHODS: The Bio-Oss(R), not coated with any material, was used as a control group. In rhBMP-2-Bio-Oss(R) group, rhBMP-2 was coated with Bio-Oss(R) using only deep and dry methods (50 ng/mL, 24 h). In heparinized rhBMP-2-Bio-Oss(R) group, dopamine was anchored to the surface of Bio-Oss(R), and coated with heparin. rhBMP-2 was immobilized onto the heparinized- Bio-Oss(R) surface. The release kinetics of the rhBMP-2-Bio-Oss(R) and heparinized rhBMP-2-Bio-Oss(R) were analyzed using an enzyme-linked immunosorbent assay. The biological activities of the MG63 cells on the three groups were investigated via cytotoxicity assay, cell proliferation assay, alkaline phosphatase (ALP) measurement, and calcium deposition determination. Statistical comparisons were carried out by one-way ANOVA test. Differences were considered statistically significant at *P<.05 and **P<.001. RESULTS: The heparinized rhBMP-2-Bio-Oss(R) showed more sustained release compared to the rhBMP-2-Bio-Oss(R) over an extended time. In the measurement of the ALP activity, the heparinized group showed a significantly higher ALP activity when compared with the non-heparinized groups (P<.05). The MG63 cells cultivated in the group with rhBMP-2 showed increased calcium deposition, and the MG63 cells from the heparinized group increased more than those that were cultivated in the non-heparinized groups. CONCLUSION: Heparin increased the rhBMP-2 release amount and made sustained release possible, and heparinized Bio-Oss(R) with rhBMP-2 successfully improved the osteoblastic functions.
Alkaline Phosphatase ; Bone Substitutes ; Calcium ; Cell Proliferation ; Dopamine ; Enzyme-Linked Immunosorbent Assay ; Heparin ; Immobilization ; Kinetics ; Osteoblasts

BACKGROUND: Hepatocyte growth factor (HGF) has been shown to facilitate vocal fold (VF) wound healing. This study was undertaken to determine whether the therapeutic efficacy of HGF could be enhanced by applying it in hyaluronic acid and alginate (HA/ALG) composite hydrogels into VFs after injury in a rabbit model. METHODS: HGF was loaded into HA/ALG composite hydrogel (HGF–HA/ALG) and its in vitro release profile was evaluated. In addition, HGF–HA/ALG was injected into the VFs of rabbits immediately after direct injury and HGF or PBS was injected in the same manner into control groups. Macroscopic features were observed by endoscopy at 3 months postinjury. Functional analyses including mucosal waves of VFs and viscoelastic properties were performed by kymography following high-speed digital imaging and rheometer. Histopathological and immunohistochemical evaluations were also conducted on VFs. RESULTS: HGF release from HGF–HA/ALG was sustained for up to 3 weeks. Rabbits treated with HGF–HA/ALG showed improved mucosal vibrations and VF viscoelastic properties as compared with the PBS and HGF controls.Histopathological staining revealed HGF–HA/ALG treated VFs showed less fibrosis than PBS and HGF controls, and immunohistochemical analysis demonstrated amounts of type I collagen and fibronectin were lower in HGF–HA/ALG treated animals than in PBS and HGF controls at 3 months post-injury. CONCLUSION HGF containing HA/ALG hydrogel enhanced healing in our rabbit model of VF injury.

BACKGROUND: Hepatocyte growth factor (HGF) has been shown to facilitate vocal fold (VF) wound healing. This study was undertaken to determine whether the therapeutic efficacy of HGF could be enhanced by applying it in hyaluronic acid and alginate (HA/ALG) composite hydrogels into VFs after injury in a rabbit model. METHODS: HGF was loaded into HA/ALG composite hydrogel (HGF–HA/ALG) and its in vitro release profile was evaluated. In addition, HGF–HA/ALG was injected into the VFs of rabbits immediately after direct injury and HGF or PBS was injected in the same manner into control groups. Macroscopic features were observed by endoscopy at 3 months postinjury. Functional analyses including mucosal waves of VFs and viscoelastic properties were performed by kymography following high-speed digital imaging and rheometer. Histopathological and immunohistochemical evaluations were also conducted on VFs. RESULTS: HGF release from HGF–HA/ALG was sustained for up to 3 weeks. Rabbits treated with HGF–HA/ALG showed improved mucosal vibrations and VF viscoelastic properties as compared with the PBS and HGF controls.Histopathological staining revealed HGF–HA/ALG treated VFs showed less fibrosis than PBS and HGF controls, and immunohistochemical analysis demonstrated amounts of type I collagen and fibronectin were lower in HGF–HA/ALG treated animals than in PBS and HGF controls at 3 months post-injury. CONCLUSION HGF containing HA/ALG hydrogel enhanced healing in our rabbit model of VF injury.

Background/Objectives: This study analyzed the prognostic significance of clinico-pathologic factors including comprehensive nodal factors in parotid gland cancers (PGCs) patients and constructed a survival prediction model for PGCs patients using machine learning techniques.Materials & Methods: A total of 131 PGCs patients were enrolled in the study. Results: There were 19 cases (14.5%) of lymph nodes (LNs) at the lower neck level and 43 cases (32.8%) involved multiple level LNs metastases. There were 2 cases (1.5%) of metastases to the contralateral LNs. Intraparotid LNs metastasis was observed in 6 cases (4.6%) and extranodal extension (ENE) findings were observed in 35 cases (26.7%). Lymphovascular invasion (LVI) and perineural invasion findings were observed in 42 cases (32.1%) and 49 cases (37.4%), respectively. Machine learning prediction models were constructed using clinico-pathologic factors including comprehensive nodal factors and Decision Tree and Stacking model showed the highest accuracy at 74% and 70% for predicting patient’s survival. Conclusion Lower level LNs metastasis and LNR have important prognostic significance for predicting disease recurrence and survival in PGCs patients. These two factors were used as important features for constructing machine learning prediction model. Our machine learning model could predict PGCs patient’s survival with a considerable level of accuracy.

Background and Objectives: The purpose of this study was to analyze the survival data of salivary gland cancer (SGCs) patients to construct machine learning and deep learning models that can predict survival and use them to stratify SGC patients according to risk estimate.Subjects and Method We retrospectively analyzed the clinicopathologic data from 460 patients with SGCs from 2006 to 2018. Results: In Cox proportional hazard (CPH) model, pM, stage, lymphovascular invasion, lymph node ratio, and age exhibited significant correlation with patient’s survival. In the CPH model, the c-index value for the training set was 0.85, and that for the test set was 0.81. In the Random Survival Forest model, the c-index value for the training set was 0.86, and that for the test set was 0.82. Stage and age exhibited high importance in both the Random Survival Forest and CPH models. In the deep learning-based model, the c-index value was 0.72 for the training set and 0.72 for the test set. Among the three models mentioned above, the Random Survival Forest model exhibited the highest performance in predicting the survival of SGC patients. Conclusion A survival prediction model using machine learning techniques showed acceptable performance in predicting the survival of SGC patients. Although large-scale clinical and multicenter studies should be conducted to establish more powerful predictive model, we expect that individualized treatment can be realized according to risk stratification made by the machine learning model.