Background:The clinical benefits of recognizing errors from dosimetric quality assurance (DQA) can be realized by improving the dose delivery accuracy. However, an efficient error detection method for data with multiple types of errors is still needed. This study sought to develop an algorithm for quantitatively analyzing multiple errors in DQA data by leveraging Bayesian optimization (BO) and statistical methods. Methods:The analysis included 79 treatment plans, randomly divided into a training subset (comprising 60 plans) and a testing subset (comprising 19 plans), delivered using an Infinity linear accelerator (LINAC). The analysis examined errors stemming from bilateral multi-leaf collimator (MLC) leaf-banks, jaws, and collimator rotation. A Gaussian process (GP) model functioned as the surrogate for BO, which aimed to adjust the error matrix to minimize failure rates in the DQA. The algorithm's performance was evaluated using simulated and real-world data. To evaluate the efficacy of the algorithm in detecting errors, error matrices of two magnitudes were introduced into the simulations: [-0.5 mm, 0.5 mm, 0.5 mm, 0.5 mm, -0.5 degrees], and [-1 mm, 1 mm, 1 mm, -1 mm, -1 degrees]. In the analysis of the real-world data, inherent systematic errors in the training subset were identified by statistically analyzing the coefficient of variation in the solution sets produced through BO, and corrections were subsequently applied to the original plans. The precision of the error identification was measured by comparing the adjustments to the failure rates for both the training and testing subsets. Results:Systemic biases were identified, and the detected error matrices of [-0.46±0.466 mm, 0.47±0.477 mm, 0.23±1.589 mm, -0.01±1.786 mm, -0.54±0.408 degrees], and [-0.92±0.553 mm, 0.83±0.453 mm, 0.95±1.924 mm, -0.55±1.719 mm, -0.91±0.435 degrees] closely mirrored the expected magnitudes. The analysis of inherent errors revealed substantial improvements in the failure rates following correction, including reductions from 6.06%±4.783% to 1.78%±1.033% in the training subset and from 4.15%±2.643% to 2.02%±1.261% in the testing subset. Conclusions:The error pattern recognition algorithm can quantitatively detect errors in data with multiple types of errors and analyze the inherent systematic errors in plans that have already passed gamma analysis. The method can enhance the overall performance of plan implementation on specific equipment. Additionally, the algorithm can analyze inherent systematic deviations in clinical DQA data and provide well-labeled datasets for deep-learning methods.

The convergence and interplay of pyroptosis, apoptosis, and necroptosis have led to the conceptualization of PANoptosis, an innovative paradigm of inflammatory programmed cell death. Characterized by the hierarchical assembly and activation of the PANoptosome, PANoptosis operates through tightly orchestrated signaling hubs and is intricately linked to organelle functionality. Accumulating evidence underscores its pivotal role in diverse oncogenic processes, positioning PANoptosis as a compelling frontier for antitumor therapeutic exploration. This review delineates the mechanistic underpinnings of PANoptosis, synthesizes its established contributions to tumor progression, and examines its dynamic crosstalk with the tumor immune microenvironment (TIME). Notably, we highlight recent breakthroughs in PANoptosis-driven immunotherapeutic strategies. We further propose that targeting PANoptosis to reprogram TIME represents a transformative approach in oncology, shifting the research paradigm from unimodal cell death regulation to multidimensional intervention. This perspective not only advances fundamental understanding but also holds significant promise for clinical translation, heralding a new era in cancer therapeutics.

Purpose. Gamma analysis serves as a critical safety assurance tool in radiotherapy, yet its broader clinical implementation remains constrained by insufficient error cause determination. To address this limitation, this study proposes a gamma passing rate (GPR) prediction method with error classification capabilities by integrating dosimetric feature engineering with a dose prediction model.Method. The study cohort comprised 26 clinical cases, with 6 cases (1,515 static segments generated from volumetric modulated arc therapy (VMAT) plans) allocated for model training and 20 cases (10 step-and-shoot plans with 415 segments and 10 VMAT plans) allocated for testing. Measurements were performed using a MatriXX chamber array at a gantry angle of 0 degrees. Data was acquired segment-by-segment for step-and-shoot plans and via integration for VMAT plans, respectively. A dosimetric feature engineering protocol was used to partition each static segment into five distinct regions (Region 1-5) on the basis of physical characteristics and error susceptibility patterns. These regional dose distributions served as both model inputs and independent variables for error analysis. A GAN-based model was trained to predict segment doses, which were subsequently aggregated for plan-level GPR calculations. Model accuracy was first validated by statistically analyzing GPR differences between measurements and predictions across various plan types in the test set, followed by assessing dose discrepancies at failure and passing points for both measured and predicted values.Results. The predicted GPR was 70.26% ± 13.07% for segments, 93.53% ± 2.06% for step-and-shoot plans, and 92.61% ± 4.67% for VMAT plans, with corresponding measured values of 74.47% ± 10.06%, 96.35% ± 1.82%, and 91.60% ± 4.05%, respectively. Regional dose analysis revealed statistically significant differences (p < 0.05) in the measured values for Regions 2-5, with classification AUC values of 0.69, 0.64, 0.65, and 0.63, respectively. The predicted values showed comparable performance for Region 2 with AUC of 0.66, whereas Regions 3-5 demonstrated AUCs of 0.50, 0.59, and 0.57 respectively.Conclusions. The integrated approach enables accurate GPR prediction while providing actionable error localization at the control point level. The quantitative error source analysis offers valuable guidance for modifying high-risk treatment plans and demonstrates significant potential for enhancing clinical radiotherapy quality assurance protocols.

Purpose: During the radiation treatment planning process, one of the time-consuming procedures is the final high-resolution dose calculation, which obstacles the wide application of the emerging online adaptive radiotherapy techniques (OLART). There is an urgent desire for highly accurate and efficient dose calculation methods. This study aims to develop a dose super resolution -based deep learning model for fast and accurate dose prediction in clinical practice. Method: A Multi -stage Dose Super -Resolution Network (MDSR Net) architecture with sparse masks module and multistage progressive dose distribution restoration method were developed to predict high -resolution dose distribution using low -resolution data. A total of 340 VMAT plans from different disease sites were used, among which 240 randomly selected nasopharyngeal, lung, and cervix cases were used for model training, and the remaining 60 cases from the same sites for model benchmark testing, and additional 40 cases from the unseen site (breast and rectum) was used for model generalizability evaluation. The clinical calculated dose with a grid size of 2 mm was used as baseline dose distribution. The input included the dose distribution with 4 mm grid size and CT images. The model performance was compared with HD U -Net and cubic interpolation methods using Dose -volume histograms (DVH) metrics and global gamma analysis with 1%/1 mm and 10% low dose threshold. The correlation between the prediction error and the dose, dose gradient, and CT values was also evaluated. Results: The prediction errors of MDSR were 0.06 - 0.84% of D- mean indices, and the gamma passing rate was 83.1 - 91.0% on the benchmark testing dataset, and 0.02 - 1.03% and 71.3 - 90.3% for the generalization dataset respectively. The model performance was significantly higher than the HD U -Net and interpolation methods (p < 0.05). The mean errors of the MDSR model decreased (monotonously by 0.03 - 0.004%) with dose and increased (by 0.01 - 0.73%) with the dose gradient. There was no correlation between prediction errors and the CT values. Conclusion: The proposed MDSR model achieved good agreement with the baseline high -resolution dose distribution, with small prediction errors for DVH indices and high gamma passing rate for both seen and unseen sites, indicating a robust and generalizable dose prediction model. The model can provide fast and accurate high -resolution dose distribution for clinical dose calculation, particularly for the routine practice of OLART.

Background: Cancer pain is one of the most intolerable and frightening symptoms of cancer patients. However, the clinical effect of the three-step analgesic ladder method (TSAL) is not satisfactory. The combination of external treatment of traditional Chinese medicine (TCM) can improve the clinical effect. Objective: This study used network meta-analysis to compare the effects of different external treatment methods of TCM combined with TSAL on cancer pain. Methods: Databases searched by our team included Google Scholar, Web of Science, Scopus, Embase, PubMed, and Cochrane Library. Randomized controlled trials related to the external treatment of TCM combined with TSAL for cancer pain were screened from the establishment of the database till now. The above literature extracted clinical efficacy, NRS score, KPS score, analgesic onset time, and duration as the main results after the screening. The 95% confidence interval (95% CI) of OR value and SMD value was used as the effect index to compare the difference in efficacy of different interventions, and the ranking was conducted. STATA 17.0 software was used for the statistical analysis of the above data. Results: A total of 78 studies were included, including 8 interventions and 5742 participants. Based on ranking probability, the clinical effective rate of manual acupuncture combined with TSAL was the best when the intervention time was set at 4 weeks [OR = 5.42, 95% CI (1.99,14.81)], and the improvement effect on KPS score was also the best [SMD = 0.97, 95% CI (0.61, 1.33)]. Acupoint external application was the best intervention in reducing NRS score [SMD = −1.14, 95% CI (−1.90, −0.93)]. Acupoint moxibustion combined with TSAL was considered to be the most effective intervention to prolong the duration of analgesia [SMD = 1.69, 95% CI (0.84, 2.54)] and shortening the onset time of analgesia [SMD = −3.00, 95% CI (−4.54, −1.47)]. Conclusions: TSAL combined with manual acupuncture is the best in terms of clinical efficacy and improvement of patients’ functional activity status. With the extension of treatment time, the intervention of this kind of treatment on the clinical effect is more pronounced. Acupoint external application also has a unique advantage in reducing the pain level of patients. From the point of view of analgesic duration and duration of analgesia, combined acupoint moxibustion has the best effect.

Objective:To analyze and explore the possible mechanism of anti-tumor metastasis of Notoginseng Radix et Rhizoma using Internet pharmacology. Methods:The active components and targets of Notoginseng Radix et Rhizoma were screened by retrieving Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP). GeneCards database was used to screen the anti-tumor metastasis-related targets, and compounds and disease targets were under mapping analysis. Key targets of Notoginseng Radix et Rhizoma for anti-tumor metastasis were screened through Venn map. With the help of Cytoscape 3.7.2 software, a compound-disease network diagram was constructed. String platform was used to build a PPI network. Bioconductor was used to enrich the target genes for KEGG signaling pathway and GO biological process analysis. Results:Totally 119 active components were selected from Notoginseng Radix et Rhizoma. There were 8 eligible active components, corresponding to 162 related targets, 121 targets related to anti-tumor metastasis, and 30 key targets screened by PPI network, including AKT1, MAPK1, JUN, RELA, IL6, etc. GO enrichment analysis mainly involved biological processes such as cytokine receptor binding, heme binding, RNA polymerase Ⅱ transcription factor binding, ubiquitin protein ligase binding, and steroid hormone receptor activity. 149 signal pathways related to Notoginseng Radix et Rhizoma anti-tumor metastasis were obtained by KEGG enrichment analysis, mainly involving multiple signal pathways, such as AGE-RAGE and PI3K-Akt, and hepatitis B, Kaposi's sarcoma-associated herpes virus infection, human cytomegalovirus infection and other viral infections and various tumors. Conclusion:Notoginseng Radix et Rhizoma can pass multiple active components, such as ginsenoside f2, ginsenoside rh2 β-, sitosterol, stigmasterol and quercetin, and multiple targets, such as AKT1, MAPK1, JUN, RELA and IL6, acting on multiple pathways such as PI3K-Akt, thereby playing the role of anti-tumor metastasis.

排卵障碍是多囊卵巢综合征(PCOS)的特征之一,也是导致PCOS不孕症的主要原因.因此,PCOS排卵障碍的发病机制受到临床广泛关注.近期研究证实PCOS患者存在卵巢血管生成异常现象,这一现象与排卵障碍密切相关并加速PCOS发展进程.络病理论是中医学解释血管异常相关疾病的理论桥梁,临床上常从络病病机入手防治血管相关疾病.基于络病理论探讨卵巢血管生成异常与PCOS排卵障碍之间的相关性,并分析PCOS排卵障碍的络病病机,既可丰富PCOS中医理论内涵,又可为PCOS的诊治提供新思路.

溶瘤病毒(OV)是一类能特异性地在肿瘤细胞中复制,靶向杀死肿瘤细胞且不损伤正常细胞的病毒,可通过免疫介导机制发挥抗肿瘤作用.目前多种OV正被应用于肝细胞癌(HCC)的临床前研究,已有OV被认定为HCC治疗药物.OV治疗已成为肿瘤免疫疗法之一,在晚期HCC治疗中的临床应用前景广阔,但面临许多挑战,如病毒的传递、抗病毒免疫等,其安全性和有效性也需进一步研究,包括探索联合应用药物组合、调整给药方式和抑制肿瘤微环境等.该文就OV在HCC治疗中应用的研究进展作一综述.

近年来,阿替丽珠单抗联合贝伐珠单抗已经取代索拉菲尼成为肝细胞癌一线治疗方案,让我们意识到免疫疗法特别是免疫检查点抑制剂疗法在肝细胞癌治疗中具有广阔的前景.肿瘤微环境是肝细胞癌进展迅速的场所,缺氧、血管生成、酸化、炎症及免疫抑制状态是其特征,从而促进肿瘤发生、发展及转移.而免疫检查点抑制剂疗法可以通过调节肿瘤微环境,抑制促进肿瘤进展的因素,重新唤醒免疫细胞,控制肿瘤进展,延长晚期肝细胞癌患者生存期.本综述致力于阐明肝细胞癌肿瘤微环境的特征,并由此分析免疫检查点抑制剂疗法单用或联合其他疗法通过靶向肿瘤微环境提高肝细胞癌治疗疗效的有效性和可行性.

目的:基于分子对接技术探讨还涎方抑制溃疡性结肠炎(UC)癌前病变的作用机制.方法:选取60只BALB/C小鼠,随机分为空白组、模型组、还涎方组、美沙拉嗪组、康复新液组,每组12只.使用氧化偶氮甲烷和葡聚糖硫酸钠诱导建立UC癌前病变模型,采用保留灌肠法治疗UC.观察小鼠便隐血情况,计算疾病活动指数(DAI)评分、结肠炎症反应评分;利用系统药理学分子高通量技术筛选获得关键靶点,进行病理HE染色及Western blot验证,利用分子对接技术分析还涎方中主要成分与关键靶点的结合能力.结果:与模型组比较,还涎方可显著缓解UC癌前病变小鼠便隐血症状,降低DAI评分及结肠黏膜损伤(P＜0.01).高通量筛选得出关键靶点p53、cox-2、bcl-2;与空白组比较,模型组p53、cox-2和bcl-2蛋白表达水平显著升高(P＜0.01);与模型组比较,还涎方组p53、cox-2和bcl-2蛋白表达水平显著降低(P＜0.01).p53、cox-2和bcl-2与还涎方中主要成分进行分子对接,亲和力水平cox-2＞p53＞bcl-2.结论:还涎方可通过干预cox-2、p53、bcl-2靶点抑制UC癌前病变的发生.

Cervical cancer (CC) is the second most common malignancy among women. GEPIA demonstrated that MEF2C-AS1 and its nearby gene MEF2C present downregulation in CC tissues. We attempted to clarify molecular mechanism between MEF2C-AS1 and MEF2C underlying CC progression. RT-qPCR was used to measure expression levels and subcellular distribution of MEF2C-AS1 and MEF2C in CC cell lines. Gain-of-function assays were conducted to reveal roles of MEF2C-AS1 and MEF2C in CC cell behaviors. Bioinformatics, RNA pull down, and RIP assays were performed to assess association of MEF2C-AS1 or MEF2C with miR-20 b-5p in CC cells. Rescue assays were done to assess regulatory function of the MEF2C-AS1-miR-20 b-5p-MEF2C axis in CC cellular processes. MEF2C-AS1 and its nearby gene MEF2C showed downregulation and had a positive expression correlation in CC tissues. MEF2C-AS1 and MEF2C presented downregulation in CC cells, and they majorly distributed in CC cell cytoplasm. MEF2C-AS1 and MEF2C upregulation repressed CC cell proliferative, migratory, and angiogenic abilities. MEF2C-AS1 competitively bound with miR-20 b-5p to upregulate MEF2C in CC cells. The impacts of MEF2C-AS1 elevation on CC cell proliferative, migratory, and angiogenic capabilities were countervailed by miR-20 b-5p overexpression. The impacts of miR-20 b-5p inhibitor on CC cell proliferative, migratory and angiogenic capabilities were countervailed by MEF2C depletion. To sum up, MEF2C-AS1 and its nearby gene MEF2C present downregulation and serve as tumor suppressors in CC cells. MEF2C-AS1 suppresses CC cell malignancy in vitro through sponging miR-20 b-5p to upregulate MEF2C, which may provide a potential new direction for seeking therapeutic plans of CC.

目的:通过网络药理学探讨玉屏风散治疗肝细胞癌(HCC)的作用机制.方法:利用中药系统药理学数据库与分析平台(TCMSP)对玉屏风散中所含药物进行检索,得到玉屏风散活性成分所对应的靶基因,再利用GeneCards数据库筛选肝细胞癌所对应的基因,利用Cytoscape 3.7.2软件绘制"药物-疾病-成分-靶点"网络图.使用STRING数据库将所得数据进行导入,构建玉屏风散治疗肝细胞癌的核心靶点的蛋白质-蛋白质相互作用(PPI)网络,然后将以上所得数据导入至Cy-toscape 3.7.2中进行网络拓扑参数分析,并以Degree值大于平均值进行筛选,最终得到玉屏风散治疗HCC的核心靶点的相关信息,将核心靶点在DAVID数据库中进行导入,以进行基因本体(GO)富集分析和京都基因和基因组百科全书(KEGG)富集分析.结果:玉屏风散的活性成分对应靶点共92个,其中有效成分包括细胞肿瘤抗原P53、凋亡调节剂Bcl-2、血管内皮生长因子受体2、白细胞介素-6(IL-6)等,在其治疗肝细胞癌中共筛选出IL-6、TP53、TNF、NOS3、ESR1、JUN、PTGS2等26个的核心靶点.结论:玉屏风散治疗肝细胞癌主要靶点与细胞核、RNA聚合酶Ⅱ启动子转录的正调控、MAPK级联反应的正调控、类固醇激素受体活性等有关.

Objective: To explore the effect of Sini Decoction plus Ginseng on COVID-19 based on network pharmacological analysis. Methods: TCMSP platform was used to search the compounds of Sini Decoction plus Ginseng with oral bioavailability(OB)≥30％ and drug-likeness(DL)≥0.18,and the results were input into the UniProt database and converted into standard target names;Genecards and OMIM database were used to find COVID-19 target, and the intersection targets were obtained and Venn diagram was drawn. Cytoscape 3.7.2 was applied to make the network diagram of composition-disease-target;The interaction database platform-String was used to analyze the target protein interaction, and the data were input into the software of Cytoscape 3.7.2 to make the network diagram;David database was used to analyze the accumulation of Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathways of drug-disease intersection targets. The component target pathway network was constructed by using Cytoscape 3.7.2. Results: Altogether, 112 active components of Sini Decoction plus Ginseng were screened, corresponding to 234 targets. Besides, 261 COVID-19 targets were obtained after screening, and the Venn map showed that 50 targets were intersected. Quercetin, kaempferol,naringenin,and β-sitosterol were found to have higher moderate values shown by the component-disease-target network. The target proteins with high PPI network analysis value were IL-6,MAPK8,MAPK3, MAPK1,TP53,TNF,and CASP3. GO enrichment function analysis showed that 341 biological processes, 33 cell components,and 50 molecular functions were involved,which showed inflammatory reaction,cell response to lipopolysaccharide,exogenous apoptosis signal pathway,positive regulation of RNA polymerase Ⅱ promoter transcription,cytokine activity,chemokine activity,heme binding,and other biological processes. KEGG signaling pathway enrichment function analysisshowed that there were 112 signaling pathways. It included TNF signaling pathway,tuberculosis,influenza A,PI3K Akt signaling pathway,HIF-1 signaling pathway,and Toll-like receptor signaling pathway. According to the component-target-pathway network diagram,quercetin,kaempferol,naringenin, and β -sitosterol in Sini Decoction plus Ginseng may act on IL-6,MAPK8, MAPK3, MAPK1,TP53,TNF,CASP3,and other targets through TNF signaling pathway,MAPK signaling pathway,Tolllike receptor signaling pathway,PI3K-Akt signaling pathway,and HIF-1 signaling pathway. Conclusion:Sini Decoction plus Ginseng can affect COVID-19 through multiple active components,multiple targets,and multiple pathways.

目的:基于网络药理学探讨四逆加人参汤治疗新冠肺炎的作用机制.方法:运用中医药系统药理数据库(TCMSP)平台查询四逆加人参汤中化合物成分,设置口服利用度(OB)≥30％,类药性(DL)≥0.18,得到有效化合物.将结果输入Uniprot数据库,统一转换为规范靶点名称.运用GeneCards和OMIM数据库查询COVID-19靶点,得到二者交集靶点,绘制Venn图.运用Cytoscape 3.7.2软件,绘制成分-疾病-靶点网络图.运用相互作用数据库平台String进行靶蛋白互作分析,将数据输入Cytoscape 3.7.2软件进行网络图绘制.运用DAVID数据库对药物-疾病交集靶点进行基因本体(GO)富集分析和京都基因与基因组百科全书(KEGG)信号通路富集分析.运用Cytoscape 3.7.2软件构建成分-靶点-通路网络图.结果:经筛选得到四逆加人参汤有效活性成分112个,对应234个靶点.经查询筛选得到COVID-19靶点261个,Venn图显示二者交集靶点50个.成分-疾病-靶点网络图中度值较高的化合物为槲皮素、山奈酚、柚皮素、β-谷甾醇.PPI网络分析度值较高的靶蛋白为白细胞介素-6(IL-6)、丝裂原活化蛋白激酶8(MAPK8)、丝裂原活化蛋白激酶3(MAPK3)、丝裂原活化蛋白激酶1(MAPK1)、TP53、肿瘤坏死因子(TNF)、胱天蛋白酶3(CASP3).GO富集功能分析显示涉及生物过程341个,细胞组分33个,分子功能50个,涉及炎症反应、细胞对脂多糖的反应、外源性凋亡信号通路、RNA聚合酶Ⅱ启动子转录的正调控、细胞因子活性、趋化因子活性,血红素结合等生物过程.KEGG信号通路富集功能分析显示共有112条信号通路.包括TNF信号通路、结核、甲型流感、PI3K-Akt信号通路、HIF-1信号通路、Toll样受体信号通路等.成分-靶点-通路网络图显示,四逆加人参汤治疗COVID-19可能主要由槲皮素、山奈酚、柚皮素、β-谷甾醇等活性成分,通过TNF信号通路、MAPK信号通路、Toll样受体信号通路、PI3K-Akt信号通路、HIF-1信号通路等通路,作用于IL-6、MAPK8、MAPK3、MAPK1、TP53、TNF,CASP3等靶点.结论:四逆加人参汤通过多种活性成分,多靶点、多通路来发挥治疗COVID-19的作用.

乌梅丸自古以来作为厥阴病主方,主要用于治疗蛔厥之证.随着中医理论的发展,乌梅丸应用范围逐渐扩大,现已广泛运用于临床各科杂病的治疗.近年来,乌梅丸在治疗恶性肿瘤方面疗效确切,本文现对乌梅丸的实验研究和临床应用研究进行综述,以期为恶性肿瘤的治疗和研究提供新的思路.

减毒增效、改善患者生存质量是中医药治疗恶性肿瘤的特色和优势所在,运用中医药来减轻肿瘤患者放化疗毒副反应、提高患者对放化疗的耐受性、增强免疫力、延长患者生存时间的临床效果常让人满意.刘松江教授运用中药治疗恶性肿瘤已有30余年,临证经验丰富,其在运用芪桂消癥方治疗卵巢癌方面,也积累了宝贵的心得体会.笔者通过跟诊学习以及对门诊典型医案的收集、整理,初步总结、概括了刘松江教授运用芪桂消癥方治疗卵巢癌的学术经验.刘松江教授认为东北地区的卵巢癌患者普遍具有虚、毒、瘀的病理特点,而由桂枝茯苓丸加减化裁而来的芪桂消癥方的立论组方恰好契合该病理特点.该方剂在临证应用时,可以根据患者的不同证型来进行加减化裁或与其他方剂联合运用.本文总结了该方剂在卵巢癌常见5种证型治疗中的具体化裁、运用情况,并详细介绍运用芪桂消癥方加减治疗的卵巢癌术后患者1例,以期为临床工作者提供辨证施治经验与思路.

通过临床实习,学生有机会学习理论知识和实践,并巩固和加深对理论知识的理解,但要掌握专业技能,提高综合能力.临床教学水平的高低直接关系到能否培养合格人才的社会需求,这是深化和继续教育的基础,是搞活工作的任务.提出了中医药疾病文献数据语义网络模型构想的思想优势,实现了中医药文献数据疾病的整合共享,进一步揭示了中医药疾病信息内容的优势.本文探讨了基于经验的临床教学模式,改革型临床教学模式,以期为临床实践提供参考.

Purpose Gastric cancer (GC) is a malignant disease of digestive tract. Clinically, radiation therapy is widely applied in treating GC, while with undesirable outcome due to tumor re-proliferation and recurrence and metastasis after radiation. Therefore, it is crucial to explore potential molecular mechanisms to develop therapeutic strategies. The present study found that miR-26a-5p has low expression in GC patients and could regulate Wnt5a to inhibit tumor growth, which was a potential therapeutic target for GC. To explore the expression and related mechanism of miR-26a-5p and Wnt5a in GC. Patients and Methods MiR-26a-5p and Wnt5a were extracted from the transcriptome data of GC downloaded from TCGA database for analysis. The expression levels of miR-26a-5p and Wnt5a in patients’ tissues and serum were detected by qRT-PCR, and their correlation with patients’ pathological data and survival was analyzed. In addition, miR-26a-5p and Wnt5a overexpression and inhibition vectors were transfected into cells to observe the effects on the proliferation, invasion and apoptosis of GC cells. The relationship between miR-26a-5p and Wnt5a was analyzed by dual luciferase report. Results The database and clinical samples showed that miR-26a-5p level was low while Wnt5a was high in GC. MiR-26a-5p level decreased in patients with stage III+IV, lymphatic metastasis and tumor ≥3cm, and Wnt5a was contrary to that of the miR-26a-5p, with diagnostic value. Overexpressed miR-26a-5p and inhibited Wnt5a enhanced apoptosis, decreased proliferation and invasion, reduced Bcl-2 and β-catenin proteins, and elevated Caspase 3, E-cadherin and Bax proteins, while inhibited miR-26a-5p and over-expressed Wnt5a showed the opposite results. Dual luciferase report confirmed that miR-26a-5p targeted to regulate Wnt5a, and rescue experiments found that these effects could be counteracted by reducing miR-26a-5p level. Conclusion Overexpressed miR-26a-5p can inhibit Wnt5a expression, promote cell apoptosis, and suppress cell proliferation and invasion in GC.

目的:分析伴癌因性疲乏(CRF)肺癌患者中医证候分布规律特点及其影响因素.方法:选取2017年1月-2018年12月期间我院肿瘤科门诊与住院的120例CRF肺癌患者为研究对象,收集患者的相关资料,采用Piper疲乏量表评估CRF程度,并采用单证及单证组合方式,总结肺癌CRF的中医证型.结果:120例患者中,CRF程度包括轻度18例(15.00％)、中度76例(63.33％)、重度26例(21.67％),CRF严重程度与患者TNM分期、卡氏评分(KPS)、治疗方法密切相关(P＜0.05).CRF肺癌患者的一般症状主要包括乏力(70.83％)、肢体困重或浮肿(55.00％)、口苦口干(52.50％)、久咳不愈(48.33％)、形体消瘦(46.67％)等;胸腹部症状以腹部隐痛(53.33％)、腹胀(48.33％)、胸脘痞闷(48.33％)为主,多可见两便异常;舌象以舌淡、白苔、腻苔、舌黯紫居多.单证统计中,CRF肺癌患者单证出现构成比超过50％发热证型包括肺气虚证、脾气虚证、痰湿证、血瘀证、肾虚证;组合证型共55例,未见单证组合,其中以4证相兼最常见,4证相兼32例(58.18％),其次分别为3证相兼15例(27.27％)、2证相兼6例(10.91％)、5证相兼2例(3.64％).结论:肺癌患者CRF程度受TNM分期、KPS评分及治疗方法影响,中医证型分布广,多为2个以上证型合并,且以虚实夹杂证为主.

半夏泻心汤是中医学经典方剂之一,出自《伤寒杂病论》,在临床中应用广泛.结合经典病案介绍刘松江灵活运用半夏泻心汤加减治疗恶性肿瘤及放疗、化疗后副反应的临床经验,深入剖析该方治疗不同疾病的病机特点,探索中医方药的特色及优势.