Objective To produce a novel tool for breaking off the ampoule bottles,which can protect the nurses from wounding their fingers by the glass fragment so as to guarantee the safety for the nurses. Methods We design a mould with the self curing plastic and leave some space in the end to insert the ampoule bottle,then fix the bistrique and spring to break off the bottles. Results This tool can break off several kinds of ampoule bottles with any bottles cracked and has achieved good clinical effect. Conclusion This mulfunctional tool is scientificly designed,easy to make,and it is very helpful for the medical staff in protection and enhancing working efficiency,so it is very valuable to be popularized in hospitals.

Objective To construct a prediction model for post-neoadjuvant therapy recurrent laryngeal nerve lymph node(RLN LN)status via clinical and CT image data in esophageal cancer patients pre-neoadjuvant therapy.Methods A retrospective analysis was conducted on 403 patients with locally advanced esophageal cancer who received neoadjuvant therapy and radical resection for esophageal cancer.All patients were divided into a training cohort(n=270)and a validation cohort(n=133)randomly according to a 2:1 ratio.Clinical and imaging features associated with positive RLN LN pathology were selected by univariate analysis.Multivariate logistic stepwise regression model was used to construct the prediction model.The prediction ability of the model was evaluated by receiver operating characteristic(ROC)curve.Results The basic model included neoadjuvant therapy and RLN LN short diameter,with an area under the curve(AUC)of 0.7(training cohort)and 0.65(validation cohort).The final prediction model included neoadjuvant therapy,human albumin,platelet count,largest lymph node enhancement characteristics,whether the largest lymph node was in the recurrent laryngeal region,and RLN LN short diameter,with AUC of 0.83[95％confidence interval(CI)0.768-0.899]and 0.76(95％CI 0.645-0.887)for the training and validation cohorts,respectively.Conclusion The model based on clinical data and imaging features pre-neoadjuvant therapy for esophageal cancer can assist in clinically predicting the post-neoadjuvant therapy RLN LN status.