【摘要】 目的 剪切修复偶联因子1（ERCC1）是核苷酸外切修复家族中的重要成员，它在核酸损伤修复过程和凋亡过程中起着重要作用；存活蛋白（Survivin）属凋亡抑制蛋白家族，是迄今发现的最强的凋亡抑制因子之一。研究中初步探索晚期非小细胞肺癌（non-small-cell lung cancer，NSCLC）中ERCC1和Survivin与铂类化学疗法敏感性的关系及其相关性。 方法 2001年1月－2002年6月对51例晚期NSCLC（ⅢB或Ⅳ期）标本经免疫组织化学检测ERCC1和Survivin的表达，患者行至少2周期含铂方案化学疗法，2周期化学疗法后评价疗效，采用SPSS 13.0软件就检测指标和化学疗法疗效评价进行相关统计分析。 结果 ERCC1和Survivin在肿瘤组织中阳性表达率分别为58.8 %（30/51）和76.5 %（39/51）。ERCC1阴性组化学疗法有效率高于阳性组（Plt;0.05），5年生存时间高于阳性组（Plt;0.05）；Survivin阴性组化学疗法有效率虽高于阳性组，但无统计学意义（Pgt;0.05），其5年生存时间与阴性组比较无差别（Pgt;0.05）。Spearman相关分析提示ERCC1与Survivin之间无相关性（rs=-0.088，P=0.537）。 结论 ERCC1和Survivin可能与NSCLC的发生相关，ERCC1可能与肿瘤的预后相关，并对化学疗法疗效具有一定预测价值。ERCC1和Survivin之间耐药机制可能各不相同。【Abstract】 Objective Excision repair cross-complementing 1 (ERCC1), an important member of the DNA repair gene family, plays a key role in nucleotide excision repair and apoptosis of tumor cells. Survivin, a member of inhibitor of apoptosis protein (IAP) family, is one of the most powerful factors in inhibiting apoptosis up to now. This study is to explore the value of ERCC1 and Survivin in predicting the sensitivity of non-small cell lung cancer (NSCLC) to platinum-based chemotherapy and the interrelationship between the two markers. Methods From January 2001 to June 2002, expressions of ERCC1 and Survivin of 51 advanced NSCLC patients (Ⅲ B or IV) were tested through immunohistochemistry. The patients were treated with at least 2 cycles of platinum-based chemotherapy. The curative effect was evaluated later, and the relationship among detected data, curative effect of chemotherapy and patients′ clinical parameters were analyzed with SPSS 13.0 software. Results The positive expression rates of ERCC1 and Survivin in NSCLC tissues were 58.8 % (30/51) and 76.5 % (39/51), respectively. The effective rate of chemotherapy and 5-year survival rate for the negative group of ERCC1 were significantly higher than those for the positive group (Plt;0.05). The results for Survivin were similar to those for ERCC1, but there was no statistical significance (Pgt;0.05). We also found there was no relationship between ERCC1 and Survivin by Spearman′s correlation analysis (rs=-0.088, P=0.537). Conclusion ERCC1 and Survivin may be correlated with the development of NSCLC, and ERCC1 may be related to curative effect and prognosis of NSCLC. There was probably no mechanism of coordination or regulation in multi-drug resistance between ERCC1 and Survivin.
ObjectiveTo reveal the scientific output and trends in pulmonary nodules/early stage lung cancer-prediction models. MethodsPublications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science Core Collection database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. ResultsA marked increase in the number of publications related to pulmonary nodules/early stage lung cancer-prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals that published the most pulmonary nodules/early stage lung cancer-prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, separately. Chest is the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early stage lung cancer-prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. ConclusionOver the last two decades, research on risk-prediction models for pulmonary nodules/early stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early stage lung cancer-prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early stage lung cancer-prediction models further and reduce the global burden of lung cancer.
ObjectiveTo preliminarily verify the effectiveness of self-designed artificial condyle-mandibular distraction (AC-MD) complex in the treatment of Pruzansky type ⅡB and Ⅲ hemifacial microsomia (HFM) through model test. MethodsFive children with Pruzansky type ⅡB and Ⅲ HFM who were treated with mandibular distraction osteogenesis (MDO) between December 2016 and December 2021 were selected as the subjects. There were 3 boys and 2 girls wih an average age of 8.4 years (range, 6-10 years). Virtual surgery and model test of AC-MD complex were performed according to preoperative skull CT of children. The model was obtained by three-dimensional (3D) printing according to the children’s CT data at a ratio of 1∶1. The occlusal guide plate was designed and 3D printed according to the children’s toothpaste model. The results of the model test and the virtual surgery were matched in three dimensions to calculate the error of the residual condyle on the affected side, and the model test was matched with the actual skull CT after MDO to measure and compare the inclination rotation of the mandible, the distance between the condylar of the healthy side and the residual condyle of the affected side, and the lengthening length of the mandible. ResultsThe error of residual condyle was (1.07±0.78) mm. The inclination rotation of the mandible, the distance between the condylar of the healthy side and the residual condyle of the affected side, and the lengthening length of the mandible after 3D printing model test were significantly larger than those after MDO (P<0.05). Conclusion In the model test, the implantation of AC-MD complex can immediately rotate the mandible to the horizontal position and improve facial symmetry, and the residual condyle segment can be guided close to the articular fossa or the preset pseudoarticular position of the skull base after operation.
Objective This study aimed to analyze the differences between the distribution of Traditional Chinese Medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without. Additionally, it seeked to explore the potential correlation between the distribution of Traditional Chinese Medicine (TCM) syndrome elements and salivary microbiota in patients with pulmonary nodules. Methods We retrospectively recruited 173 patients with pulmonary nodules (PN) and 40 healthy controls (HC). The four diagnostic information was collected from all participants, and syndrome differentiation method was used to analyze the distribution of Traditional Chinese Medicine (TCM) syndrome elements in both groups. Saliva samples were obtained from the subjects for 16S rRNA high-throughput sequencing to obtain differential microbiota and to explore the correlation between Traditional Chinese Medicine (TCM) syndrome elements and salivary microbiota in the evolution of the pulmonary nodule disease. Results The study found that in the PN group, the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease location were the lung and liver, and the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease location were the lung and spleen, and the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of Traditional Chinese Medicine (TCM) syndrome elements related to disease location (lung, liver, kidney, exterior, heart) and disease nature (yin deficiency, phlegm, qi stagnation, qi deficiency, dampness, blood deficiency, heat, blood stasis) (P<0.05). The species abundance of the salivary microbiota was higher in the PN group than that in the HC group (P<0.05), and there were significant differences in community composition between the two groups (P<0.05). Correlation analysis using multiple methods, including Mantel test network heatmap analysis and Spearman correlation analysis and so on, showed that in the PN group, Prevotella and Porphyromonas were positively correlated with disease location in the lung, and Porphyromonas and Granulicatella were positively correlated with disease nature in yin deficiency (P<0.05). Conclusion The study concludes that there are notable differences in the distribution of Traditional Chinese Medicine (TCM) syndrome elements and the species abundance and composition of salivary microbiota between patients with pulmonary nodules and healthy individuals. The distinct external syndrome manifestations in patients with pulmonary nodules, compared to healthy individuals, may be a cascade event triggered by changes in the salivary microbiota. The dual correlation of Porphyromonas with both disease location and nature suggests that changes in its abundance may serve as an objective indicator for the improvement of symptoms in patients with yin deficiency-type pulmonary nodules.
Purpose To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. MethodsThe study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the department of cardiothoracic surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including Random Forest (RF), k-Nearest Neighbor (KNN), logistic Regression (LR), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results(1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%. Conclusion Electronic nose combined with machine learning not only has the potential to differentiate the benign and malignant pulmonary nodules but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.