Objective:To explore the medication law of national TCM master Lu Fang in the treatment of primary trigeminal neuralgia (PTN) based on data mining.Methods:With the prescription of the outpatient patients of Harbin Traditional Chinese Medicine Hospital of Professor Lu Fang from September 2014 to September 2022 as the data source, the frequency, property and taste, and meridian tropism of the prescribed drugs were analyzed using Excel 2022 software. R 4.2.1 was used for mining analysis on Chinese materia medica, including correlation, relevance, and clustering,and the medication law in the treatment of PTN was discussed.Results:A total of 300 prescriptions were analyzed, involving 177 kinds of Chinese materia medica, with a frequency of 3 120 times, and 34 kinds of of high-frequency Chinese materia medica. The high frequently Chinese materia medica included Chuanxiong Rhizoma, Angelicae Dahuricae Radix, Puerariae Lobatae Radix, Ligustici Rhizoma et Radix, and Viticis Fructus. The main properties were warm, slightly cold, and neutral, while the main tastes were pungent, bitter, and sweet. The meridian tropism analysis ranked the liver, lung, spleen, and stomach meridians in descending order. Analysis yielded 21 strong association rules, and the association analysis formed a core prescription group based on Chuanxiong Rhizoma, Angelicae Dahuricae Radix, and Ligustici Rhizoma et Radix. The analysis obtained 5 types of clustering combinations.Conclusion:Professor Lu Fang's the medication law to treat primary trigeminal neuralgia is mainly dispelling wind and alleviating pain, which is often combined with the methods, such as searching and dredging collaterals, clearing and dispelling the stagnated heat, calming the liver and subduing yang, soothing the liver and invigorating the spleen.

Purpose: The systemic inflammation response index (SIRI) has been reported to have prognostic ability in various solid tumors but has not been studied in gallbladder cancer (GBC). We aimed to determine its prognostic value in GBC. Materials and Methods: From 2003 to 2017, patients with confirmed GBC were recruited. To determine the SIRI’s optimal cutoff value, a time-dependent receiver operating characteristic curve was applied. Univariate and multivariate Cox analyses were performed for the recognition of significant factors. Then the cohort was randomly divided into the training and the validation set. A nomogram was constructed using the SIRI and other selected indicators in the training set, and compared with the TNM staging system. C-index, calibration plots, and decision curve analysis were performed to assess the nomogram’s clinical utility. Results: One hundred twenty-four patients were included. The SIRI’s optimal cutoff value divided patients into high (≥ 0.89) and low SIRI (< 0.89) groups. Kaplan-Meier curves according to SIRI levels were significantly different (p < 0.001). The high SIRI group tended to stay longer in hospital and lost more blood during surgery. SIRI, body mass index, weight loss, carbohydrate antigen 19-9, radical surgery, and TNM stage were combined to generate a nomogram (C-index, 0.821 in the training cohort, 0.828 in the validation cohort) that was significantly superior to the TNM staging system both in the training (C-index, 0.655) and validation cohort (C-index, 0.649). Conclusion The SIRI is an independent predictor of prognosis in GBC. A nomogram based on the SIRI may help physicians to precisely stratify patients and implement individualized treatment.