非典型Spitz痣一例

患者女,45岁,左下肢近踝部出现暗红色孤立肿物5个月,无明显主观症状,近1月余生长迅速。患者既往体检,无系统疾病及恶性肿瘤病史,家族中无恶性肿瘤病史……     

甲氨蝶呤皮下注射治疗应用与展望

【摘要】 研究表明,甲氨蝶呤皮下注射较口服途径可以获得更快、更好的吸收和更高的血药浓度以及较低的生物利用度变异度。在类风湿关节炎患者和银屑病患者,甲氨蝶呤皮下注射的疗效显著优于口服途径,并且胃肠道不良反应发生率更低。对于甲氨蝶呤口服疗效欠佳或者不耐受的患者,改为皮下注射后仍然可以获得较好的治疗反应。甲氨蝶呤皮下注射可以作为生物制剂使用前的治疗选择,可节省大量费用。总之,甲氨蝶呤皮下注射具有较好的临床应用前景。     

Parameter Transfer Deep Neural Network for Single-Modal B-Mode Ultrasound-Based Computer-Aided Diagnosis

Elastography ultrasound (EUS) imaging has shown its effectiveness for diagnosis of tumors by providing additional information about tissue stiffness to the conventional B-mode ultrasound (BUS). However, due to the lack of EUS devices and experienced sonologists, EUS is not widely used, especially in rural areas. It is still a challenging task to improve the performance of the single-modal BUS-based computer-aided diagnosis (CAD) for tumors. In this work, we propose a novel transfer learning (TL)–based deep neural network (DNN) algorithm, named CW-PM-DNN, for the BUS-based CAD by transferring diagnosis knowledge from EUS during model training. CW-PM-DNN integrates both the feature-level and classifier-level knowledge transfer into a unified framework. In the feature-level TL, a bichannel DNN is learned by the cross-weight-based multimodal DL (MDL-CW) algorithm to transfer informative features from EUS to BUS. In the classifier-level TL, a projective model (PM)–based classifier is then embedded to the pretrained bichannel DNN to implement the parameter transfer in the classifier model at the second stage. The back-propagation procedure is then applied to optimize the whole CW-PM-DNN to further improve its performance. Experimental results on two bimodal ultrasound tumor datasets demonstrate that the proposed CW-PM-DNN achieves the best classification accuracy, sensitivity, and specificity of 89.02 ± 1.54%, 88.37 ± 4.72%, and 89.63 ± 4.06%, respectively, for the breast ultrasound dataset, and the corresponding values of 80.57 ± 3.41%, 76.67 ± 3.85%, and 83.94 ± 3.95%, respectively, for the prostate ultrasound dataset. The proposed two-stage TL-based CW-PM-DNN algorithm outperforms all the compared algorithms. It is also proved that the performance of the BUS-based CAD can be significantly improved by transferring the knowledge of EUS. It suggests that CW-PM-DNN has the potential for more applications in the field of medical image–based CAD.

     

下调REV7基因对于人结肠癌HCT116细胞放射敏感性影响及机制研究

目的 研究下调REV7基因表达对人结肠癌HCT116细胞放射敏感性影响及其机制。方法 对HCT116细胞进行培养并运用RNA干扰技术实现REV7基因下调,将细胞分为空白组、转染阴性RNA oligo片段阴性对照组、转染REV7 RNA oligo的REV7基因下调组。克隆形成实验反映细胞增殖水平,蛋白印迹法检测相关基因表达水平、细胞凋亡发生水平和非同源末端连接途径发生水平。结果 6Gy照后REV7 siRNA组细胞克隆形成率降低(P<0.05)。REV7 siRNA组REV7基因下调效率>60%。REV7 siRNA组γH2AX、Caspase9表达升高(P<0.05),Ku80、XRCC4表达降低(P<0.05)。结论 下调REV7基因能提高HCT116细胞放射敏感性,其机制可能与下调REV7后非同源末端连接的发生被削弱有关。