Port-site metastasis of unsuspected gallbladder carcinoma with ossification after laparoscopic cholecystectomy:A case report

BACKGROUND Unsuspected gallbladder carcinoma(UGC)refers to cholecystectomy due to benign gallbladder disease,which is pathologically confirmed as gallbladder cancer during or after surgery.Port-site metastasis(PSM)of UGC following laparoscopic cholecystectomy is rare,especially after several years.CASE SUMMARY A 55-year-old man presenting with acute cholecystitis and gallstones was treated by laparoscopic cholecystectomy in July 2008.Histological analysis revealed unexpected papillary adenocarcinoma of the gallbladder with gallstones,which indicated that the tumor had spread to the muscular space(pT1b).Radical resection of gallbladder carcinoma was performed 10 d later.In January 2018,the patient was admitted to our hospital for a mass in the upper abdominal wall after surgery for gallbladder cancer 10 years ago.Laparoscopic exploration and complete resection of the abdominal wall tumor were successfully performed.Pathological diagnosis showed metastatic or invasive,moderately differentiated adenocarcinoma in fibrous tissue with massive ossification.Immunohistochemistry and medical history were consistent with invasion or metastasis of gallbladder carcinoma.His general condition was well at follow-up of 31 mo.No recurrence was found by ultrasound and epigastric enhanced computed tomography.CONCLUSION PSM of gallbladder cancer is often accompanied by peritoneal metastasis,which indicates poor prognosis.Once PSM occurs after surgery,laparoscopic exploration is recommended to rule out abdominal metastasis to avoid unnecessary surgery.

Solar cycle prediction using a long short-term memory deep learning model

In this paper,we propose a long short-term memory(LSTM)deep learning model to deal with the smoothed monthly sunspot number(SSN),aiming to address the problem whereby the prediction results of the existing sunspot prediction methods are not uniform and have large deviations.Our method optimizes the number of hidden nodes and batch sizes of the LSTM network structures to 19 and 20,respectively.The best length of time series and the value of the timesteps were then determined for the network training,and one-step and multi-step predictions for Cycle 22 to Cycle 24 were made using the well-established network.The results showed that the maximum root-mean-square error(RMSE)of the one-step prediction model was6.12 and the minimum was only 2.45.The maximum amplitude prediction error of the multi-step prediction was 17.2%and the minimum was only 3.0%.Finally,the next solar cycles(Cycle 25)peak amplitude was predicted to occur around 2023,with a peak value of about 114.3.The accuracy of this prediction method is better than that of the other commonly used methods,and the method has high applicability.