Neck dissection of cN0 maxillary oral squamous cell carcinoma: A study based on SEER database.

OBJECTIVE: For patients with clinical nodal-negative (cN0) maxillary oral squamous cell carcinoma (MOSCC), neck dissection (ND) and clinical observation are the main two management strategies for the neck. However, the indications corresponding to these two options remain controversial. This study aimed to elucidate the clinical factors affecting ND treatment and to identify clinical characteristics of the population that may benefit from ND based on a retrospective analysis of cN0 MOSCC patient data from the Surveillance, Epidemiology, and End Results (SEER) database. METHODS: 8846 MOSCC patients were identified in the SEER database from 2000 to 2020. The Kaplan-Meier method was utilized to examine overall survival (OS) and disease-specific survival (DSS), while the hazard ratio (HR) was estimated using the stepwise multivariate Cox regression model. Furthermore, multi-subgroup analyses of DSS and OS were performed to compare ND and No ND. RESULTS: We included 2,512 cN0 MOSCC patients. Basic survival analysis and Cox regression modeling showed that ND was an independent prognostic factor that promoted DSS and OS. Additional subgroup analyses revealed that the primary site and T-stage might influence the efficacy of ND modality. Moreover, patients with T3/T4 stage of upper gingival squamous cell carcinoma (UGSCC) (DSS p = 0.009, OS p = 0.004), hard palate squamous cell carcinoma (HPSCC) (DSS p = 0.001, OS p < 0.001), and soft palate squamous cell carcinoma (SPSCC) (p = 0.029) showed a better survival benefit with ND in OS and DSS. Nonetheless, no differences were observed in OS and DSS between ND and No ND at the T1/T2 stage of the abovementioned primary tumor sites. Additionally, the DSS outcomes for T1/T2 stage upper lip squamous cell carcinoma (ULSCC) patients were significantly worse in the ND group than in the No ND group (p = 0.018). However, no significant differences were noted in OS (p = 0.140) as well as OS (p = 0.248) and DSS (p = 0.627) for T1/T2 and T3/T4 patients, respectively. CONCLUSION: Active surveillance might be a feasible strategy for managing all T-staged ULSCC as well as early-stage (T1/T2) UGSCC, SPSCC, and HPSCC, provided regular and meticulous follow-up is performed. Hence, concurrent ND is recommended for patients with intermediate to advanced (T3/T4) stage UGSCC, SPSCC, and HPSCC.

[Dose-response Relationship of Dexmedetomidine Combined with Sufentail for Postoperative Intravenous Analgesia in Video-assisted Thoracoscopic Surgery].

Objective To investigate the 50% effective dose(ED50)and 95% effective dose(ED95)of dexmedetomidine(DEX)combined with 0.032 µg/(kg·h)sufentanil as well as its analgesic effect for patient-controlled intravenous analgesia(PCIA)after video-assisted thoracoscopic surgery(VATS).Methods Totally 25 patients undergoing elective VATS were enrolled. DEX and 0.032 µg/(kg·h)sufentanil were used for postoperative PCIA. The loading dose of DEX was 0.048 µg/(kg·h),and the dose difference between two adjacent patients was 0.008 µg/(kg·h). The DEX dose of a current patient was determined by whether the previous patient was satisfied with postoperative analgesic effect. If the previous patient was satisfied with postoperative analgesic effect,the DEX dose of the current patient was decreased by 0.008 µg/(kg·h);and if the previous analgestic effect was not satisfactory,DEX dose of the current patient was increased by 0.008 µg/(kg·h). The study endpoint was dexmedetomidine dose was<0.008 µg/(kg· h) within 7 upper and lower cycles in 7 consecutive cases. Finally,the probability unit regression was used to estimate the ED50 and ED95 of DEX and their 95% CI.Results When DEX combined with 0.032 µg/(kg·h) sufentanil was used for postoperative PCIA in young patients undergoing VATS,the ED50 and ED95of DEX were 0.0346 µg/(kg· h)[95%CI:0.0283-0.0408 µg/(kg·h)] and 0.0459 µg/(kg·h)[95%CI:0.0400-0.0880 µg/(kg·h)],respectively. No adverse reaction such as vomiting,respiratory depression,or bradycardia occurred. The average Visual Analogue Scale(VAS)scores at rest(Z=-5.128,P=0.000)and cough(Z=-6.642,P=0.000)and the Ramsay sedation score(Z=-2.335,P=0.020)within 6 hours after surgery were higher than those after 6 hour.Conclusion DEX combined with 0.032 µg/(kg·h) sufentanil are effective for postoperative PCIA in patients undergoing VATS when the ED50 and ED95 are 0.0346 µg/(kg·h)and 0.0459 µg/(kg·h),respectively.

Propofol-sparing effect of different concentrations of dexmedetomidine : Comparison of gender differences.

BACKGROUND: The pharmacodynamics of propofol are closely linked to gender. Dexmedetomidine can decrease propofol needs during propofol anesthesia. The aim of this study was to compare the gender differences on the calculated effect site median effective concentration (EC50) of propofol for loss of consciousness (LOC) after pretreatment with different concentrations of dexmedetomidine. METHODS: In this study 60 male and 60 female patients were randomly allocated to receive dexmedetomidine at target plasma concentrations of 0.0 ng/ml (0.0 group), 0.4 ng/ml (0.4 group), 0.6 ng/ml (0.6 group) and 0.8 ng/ml (0.8 group). Propofol was administered after dexmedetomidine had been intravenously infused for 15 min. The propofol infusion was targeted to provide an initial effect-site concentration of 1.0 µg/ml, followed by increments by 0.2 µg/ml when the effect-site concentration and target concentration of propofol were in equilibrium until LOC was established, where LOC was defined by the observer's assessment of alertness/sedation scale (OAA/S) score < 2. RESULTS: The calculated effect-site EC50 of propofol LOC was higher in males than in females in the 0.0, 0.4, 0.6, and 0.8 groups (2.43 vs. 2.17, 1.99 vs. 1.82, 1.72 vs. 1.56 and 1.50 vs. 1.32 µg/ml, respectively, all p < 0.05). The hypnotic interaction between dexmedetomidine and propofol could be described with an additive model of pharmacodynamic interaction. CONCLUSION: Gender significantly influenced the calculated effect-site EC50 of propofol for LOC after pretreatment with different concentrations of intravenous dexmedetomidine. It was concluded that an additive interaction could describe the results seen. Thus, gender has to be considered when these drugs are co-administered.

Systematic analysis of genes involved in oral cancer metastasis to lymph nodes.

Oral cancer remains a deadly disease worldwide. Lymph node metastasis and invasion is one of the causes of death from oral cancer. Elucidating the mechanism of oral cancer lymph node metastasis and identifying critical regulatory genes are important for the treatment of this disease. This study aimed to identify differentially expressed genes (gene signature) and pathways that contribute to oral cancer metastasis to lymph nodes. The GSE70604-associated study compared gene profiles in lymph nodes with metastasis of oral cancer to those of normal lymph nodes. The GSE2280-associated study compared gene profiles in primary tumor of oral cancer with lymph node metastasis to those in tumors without lymph node metastasis. There are 28 common differentially expressed genes (DEGs) showing consistent changes in both datasets in overlapping analysis. GO biological process and KEGG pathway analysis of these 28 DEGs identified the gene signature CCND1, JUN and SPP1, which are categorized as key regulatory genes involved in the focal adhesion pathway. Silencing expression of CCND1, JUN and SPP1 in the human oral cancer cell line OECM-1 confirmed that those genes play essential roles in oral cancer cell invasion. Analysis of clinical samples of oral cancer found a strong correlation of these genes with short survival, especially JUN expression associated with metastasis. Our study identified a unique gene signature - CCND1, JUN and SPP1 - which may be involved in oral cancer lymph node metastasis.

[Population pharmacokinetic modeling and evaluation of propofol from multiple centers].

In order to successfully develop the effective population pharmacokinetic model to predict the concentration of propofol administrated intravenously, the data including the concentrations across both distribution and elimination phases from five hospitals were analyzed using nonlinear mixed effect model (NONMEM). Three-compartment pharmacokinetic model was applied while the exponential model was used to describe the inter-individual variability and constant coefficient model to the intra-individual variability, accordingly. Covariate effect including the body weight on the parameter CL, V1, Q2, V2, Q3 and V3 were investigated. The performance of final model was assessed by Bootstrapping, goodness-of-fit and visual predictive checking (VPC). The context-sensitive half-times and the infusion rates necessary to maintain the concentration of 1 microg x mL(-1) were simulated to six subpopulations. The results were as follows: the typical value of CL, V1, Q2, V2, Q3 and V3 were 0.965 x (1 + 0.401 x VESS) x (BW/59)(0.578) L x min(-1), 13.4 x (AGE/45)(-0.317) L, 0.659 x (1 + GENDER x 0.385) L x min(-1), 28.8 L, 0.575 x (1 + GENDER x 0.367) x (1 - 0.369 x VESS) L x min(-1) and 196 L respectively. Coefficients of the inter-individual variability of CL, V1, Q2, V2, Q3 and V3 were 29.2%, 46.9%, 35.2%, 40.4%, 67.0% and 49.9% respectively, and the coefficients of residual variability were 24.7%, 16.1% and 22.5%, the final model indicated a positive influence of a body weight on CL, and also that a negative correlation of age with V1. Q2 and Q3 in males were higher than those in females at 38.5% and 36.7%. The CL and Q3 were 40.1% increased and 36.9% decreased in arterial samples compared to those in venous samples. The determination coefficient of observations (DV)-individual predicted value (IPRED) by the final model was 0.91 which could predict the propofol concentration fairly well. The stability and the predictive performance were accepted by Bootstrapping, the goodness-of-fit and VPC. The context-sensitive half-times and infusion rates necessary to maintain the concentration of 1 microg x mL(-1) were different obviously among the 6 sub-populations obviously. The three-compartment model with first-order elimination could describe the pharmacokinetics of propofol fairly well. The involved fixed effects are age, body weight, gender and sampling site. The simulations in 6 subpopulations were available in clinical anesthesia. The propofol anesthesia monitor care could be improved by individualization of pharmacokinetic parameter estimated from the final model.