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ObjectiveTo provide a reference for the establishment of an ideal corneal neovascularization （CNV） animal model by summarizing the modeling characteristics of CNV animal models. MethodWith "CVN" as the theme word， this paper searched the China National Knowledge Infrastructure （CNKI）， Wanfang， Chinese medical journals full-text database， and PubMed database and screened out relevant literature on CNV animal experiments from 2013 to 2023. The database was established by Excel 2021， and the experimental animal strain， gender， modeling method， detection index， and application category were sorted out. The characteristics of the CNV animal model were analyzed. ResultAfter comparative analysis， it was found that the animal strains were Sprague-Dawley rats （87 times， 29.49%） and New Zealand white rabbits （52 times， 17.63%）. Male animals were recommended. Most modeling methods for efficacy verification and mechanism studies were the alkali burn method. Index detection methods included apparent index observation， histopathological detection， immunohistochemistry （IHC）， Western blot， and various polymerase chain reaction （PCR） tests. Detection indexes included apparent indication， corneal histopathology， CNV regulation， etc. ConclusionThe CNV model of SD rats induced by the alkali burn method is recommended for model replication， and the indexes are mainly selected from the growth of CNV， corneal histopathological test， and vascular endothelial growth factor （VEGF）-related test. In addition， according to the demand， the corneal apparent indication and the basic indexes related to the regulation of CNV， such as vascular endothelial growth factor receptor 2 （VEGFR2）， basic fibroblast growth factor （bFGF）， and secretogranin Ⅲ （Scg3） are also selected. Clinical treatment of CNV relies on anti-inflammatory drugs and anti-VEGF drugs， and there is a lack of application of traditional Chinese medicine （TCM）， so the model needs to be improved by adding elements of TCM syndromes.
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Objective To construct a nomogram prediction model for the treatment effect of anlotinib with the participation of traditional Chinese medicine syndrome elements on the patients with extensive-stage small cell lung cancer (ES-SCLC) who previously received multiple lines of chemotherapy. Methods The clinical data of 127 patients with ES-SCLC who received at least two cycles of anlotinib treatment were retrospectively studied. Kaplan-Meier method was used to analyze the relationship between each factor and the overall survival time. Cox regression analysis was applied to screen the independent influencing factors of the prognosis of patients with ES-SCLC. R language was employed to build a nomogram prediction model, C-index was used to evaluate the model, and calibration curve was adopted to verify the accuracy of the model. Results Age, PS score, brain metastases, qi deficiency syndrome, yin deficiency syndrome, and blood stasis syndrome were related risk factors for ES-SCLC treated with anlotinib. PS score, brain metastasis, and blood stasis syndrome were independent prognostic factors. On the basis of these three independent influencing factors, a nomogram model was established to predict the prognosis of patients with ES-SCLC treated with anlotinib. The predicted risk was close to the actual risk, showing a high degree of coincidence. Conclusion The nomogram model established with PS score, blood stasis syndrome elements, and brain metastasis as independent factors can predict the prognosis of patients with ES-SCLC receiving second- and third-line treatment of anlotinib.
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Objective To evaluate predictive factors affecting the short-term efficacy of PD-1 inhibitors in non-small cell lung cancer (NSCLC) and to construct a prediction model. Methods From October 2019 to November 2021, 221 patients with advanced NSCLC who met the inclusion criteria and were treated with PD-1 inhibitors were prospectively enrolled. Patients who were enrolled before May 1st, 2021 were included inthe modeling group (n=149), whereas those who enrolled thereafter were included in the validation group (n=72). The general clinical data of patients, information of the four TCM diagnoses were collected, and TCM syndrome elements were identified. R software version 4.0.4 was used in constructing a nomogram clinical prediction model of objective response rate. The predictive ability and discrimination of the model were evaluated and externally validated by using a validation group. Results After two to four cycles of PD-1 inhibitor therapy in 221 patients, the overall objective response rate was 44.80%. Multivariate logistic regression analysis of the modeling group showed that the TPS score (OR=0.261, P=0.001), number of treatment lines (OR=3.749, P=0.002), treatment mode (OR=2.796, P=0.019), qi deficiency disease syndrome elements (OR=2.296, P=0.043), and syndrome elements of yin deficiency disease (OR=3.228, P=0.005) were the independent predictors of the short-term efficacy of PD-1 inhibitors. Based on the above five independent predictors, a nomogram prediction model for the short-term efficacy of PD-1 inhibitors was constructed. The AUC values of the modeling and validation groups were 0.8317 and 0.7535, respectively. The calibration curves of the two groups showed good agreement between the predicted and true values. The mean absolute errors were 0.053 and 0.039, indicating that the model has good predictive performance. Conclusion The nomogram model constructed on the basis of the syndrome elements of Qi-deficiency disease and Yin-deficiency syndrome of TCM, as well as TPS score, number of treatment lines and treatment mode, is a stable and effective tool for predicting the short-term efficacy of PD-1 inhibitors in advanced non-small cell lung cancer.