Purpose: This study aimed to determine the optimal cutoff points for age and tumor size of patients with intrahepatic cholangiocarcinoma (ICC) and to establish and verify a predictive nomogram of overall survival at 1, 3, and 5 years. Methods: From the SEER (Surveillance, Epidemiology, and End Results) database, 1,325 ICC patients were selected and randomly divided into training and testing cohorts at a 7:3 ratio. Using the X-tile software, age and tumor size were classified into 3 subgroups: ≤61, 62–74, and ≥75 years and ≤35, 36–55, and ≥56 mm. Subsequently, univariate and multivariate Cox regression analyses were performed using the R software in the training cohort to determine independent risk factors, compile the prediction nomogram, and verify it with the testing cohort findings. Results: The C-indexes of the new prediction nomograms in the training and testing cohorts were 0.738 (95% confidence interval [CI], 0.718–0.758) and 0.750 (95% CI, 0.72–0.78), respectively. Furthermore, the areas under the 1-, 3-, and 5-year receiver operating characteristic (ROC) curves based on the nomogram were 0.792, 0.853, and 0.838, respectively, higher than the ROC based on the 7th and 8th editions of the American Joint Cancer Commission (AJCC) staging system. Conclusion This study established and verified a prognostic nomogram that improved the accuracy of the 1-, 3-, and 5-year survival predictions for ICC patients, compared with that based on the 7th and 8th editions of the AJCC staging system, and can help clinicians make personalized survival predictions.

Objective:To aid in the detection of thyroid cancer by using deep learning to differentiate the unique bioimpedance parameter patterns of different thyroid tissues.Methods:An electrical impedance system was designed to measure 331 ex-vivo thyroid specimens from 321 patients during surgery. The impedance data was then analyzed with one dimensional convolution neural (1D-CNN) combining with long short-term memory (LSTM) network models of deep learning. In the process of analysis, we assigned 80% of the data to training set (1072/1340) and the remaining 20% data to the test set (268/1340). The performance of final model was assessed using receiver operating characteristic (ROC) curves. In addition, sensitivity, specificity, positive predictive value, negative predictive value, Youden index were applied to compare impedance model with ultrasound results.Results:The ROC curve of the two-classification (malignant /non-malignant tissue) model showed a good performance (area-under-the-curve AUC=0.94), with an overall accuracy of 91.4%. To better fit clinical practice, we further performed a three-classification (malignant/ benign/ normal tissue) model, of which the areas under ROC curve were 0.91, 0.85, 0.92 for normal, benign, and malignant group, respectively. The results indicated that the area under micro-average ROC curve and the macro-average ROC curve were 0.91 and 0.90, respectively. Moreover, compared with ultrasound, the impedance model exhibited higher specificity.Conclusions:A deep learning model (CNN-LSTM) trained by thyroid electrical impedance spectroscopy (EIS) parameters shows an excellent performance in distinguishing among different in-vitro thyroid tissues, which is promising for applications. In future clinical utility, our study does not replace existing tests, but rather complements others, thus contributing to therapeutic decision-making and management of thyroid disease.

OBJECTIVE To study the absorption characteristics of phenylpropanoids of Mongolian medicine Tabson-2 decoction(TBD) in Caco-2 cells and to preliminarily clarify the oral absorption mechanism of TBD. METHODS Caco-2 cell monolayer model was used to analyze the uptake components of TBD in Caco-2 cells by UPLC-MS/MS, and UPLC-MS/MS analysis method was established to determine the nine best absorbed components of TBD, protocatechuic acid, neochlorogenic acid, chlorogenic acid, cryptogenic acid, 1,5-dicaffeinate quinic acid, isochlorogenic acid C, caffeic acid, dihydrocaffeic acid, chlorogenic acid. The effects of time, concentration and P-glycoprotein inhibitor on the absorption of each component were investigated. RESULTS The overall intake of caffeic acid and dihydrocaffeic acid showed an upward trend in 0-180 min, and did not show saturation. The absorption of 3-hydroxycinnamic acid was constant at about 90 min and tended to saturation. The intakes of cryptochlorogenic acid, 1,5-dicaffeinate, quinic acid, isochlorogenic acid C, neochlorogenic acid, chlorogenic acid and protocatechuic acid first decreased and then increased with time from about 90 min. The addition of P-glycoprotein inhibitor verapamil and cyclosporin A had an effect on the absorption of dihydrocaffeic acid compared with the phenylpropanoid components, indicated that dihydrocaffeic acid was the substrate of P-glycoprotein. CONCLUSION The main phenylpropanoids of TBD enter Caco-2 mainly by passive diffusion, supplemented by active transport, and the absorption process of the other eight components is not affected by the efflux of P-glycoprotein except dihydrocaffeic acid.