放射生物学数学模型的研究进展

目的：放射生物学数学模型随着放射治疗的发展不断被提出,最早提出了L-Q模型和靶模型,由于靶模型形式较为复杂、参数较多,L-Q模型得到了广泛的应用。但是随着SRS、SBRT大剂量放疗的出现,越来越多的证据表明,L-Q模型在大剂量范围计算精度不高,尤其是从常规低剂量到大剂量。研究者提出了新的放射生物学数学模型。方法：本文按照模型被提出的时间顺序对L-Q模型、SH-MT模型、ML-Q模型、USC模型、gL-Q模型进行阐述。结果：ML-Q模型、USC模型、gL-Q模型与实验数据符合较好。结论 ：相比较而言,对于目前SRS、SBRT大剂量照射,USC模型和gL-Q模型可能更为适用,但USC模型和gL-Q模型的适用性和模型中的参数仍需进一步的确认。     

癌的遗传易感性、基因组学和表基因组学研究进展

基因组不稳定性与癌的遗传易感性相关,涉及基因组结构改变和表遗传效应,来源于胚系突变和体细胞突变。本文简要介绍癌基因组学、癌表基因组学与癌遗传易感性等研究领域的进展,包括癌易感基因的筛选和鉴定,与临床表型相关的基因表达谱的鉴定,癌细胞DNA甲基化作用的特点以及表遗传学的应用等内容。     

氨磷汀联合精准护理治疗口腔癌放疗的临床效果

目的 探讨氨磷汀联合精准护理治疗口腔癌放疗的效果。方法 选择2015年1月~2017年12月我院收治的口腔癌根治性放化疗患者104例,随机分为观察组（n=43）和对照组（n=52）,两组患者均实施精准护理,观察组配合使用氨磷汀,对照组未使用。比较两组患者放射性口腔黏膜炎发生情况及氨磷汀的消化道反应情况。结果 观察组Ⅳ度放射性黏膜炎5例,愈合时间中位数为28 d,对照组Ⅳ度放射性黏膜炎12例,愈合时间中位数为35 d。观察组出现Ⅳ度黏膜炎比例更低,且愈合时间更快,差异均有统计学意义（P＜0.05）;观察组Ⅰ度消化道反应19例（44.19%）、Ⅱ度消化道反应10例（23.26%）,对照组Ⅰ度消化道反应20例（38.46%）、Ⅱ度消化道反应11例（21.15%）,两组均无Ⅳ度消化道反应出现,观察组Ⅰ~Ⅱ度消化道反应发生率高于对照组,但差异无统计学意义（P＞0.05）。结论 采用氨磷汀治疗口腔癌放疗患者的同时配合精准护理,降低了放射性口腔黏膜炎的发生率,缩短愈合时间,同时氨磷汀的消化道反应未影响患者的生活质量。     

基因组学在血管病研究中的应用

血管病对人类健康危害极大 ,但已确定的致病基因很少。利用最新的基因组学方法 ,如基因组序列比较、单核苷酸多态性 (SNP)、表达谱可以有效的从多个方面研究血管病 ,确定致病基因 ,了解血管病的发生机制和遗传基础。     

血脑屏障的基因组学和蛋白质组学研究进展

血脑屏障是分隔外周血和大脑的屏障,选择性地允许或阻止物质在两者间的通透。近年来陆续有研究指出,血脑屏障存在大量转运体参与重要生理物质选择性进入大脑,并且将代谢产物转运入血以维持脑内环境稳定。血脑屏障功能失调与很多脑疾病密切相关。基因组学与蛋白质组学等研究方法的介入可高通量、大规模地研究血脑屏障中的转运体,极大地加快了人们对血脑屏障在疾病发生发展中的作用以及临床治疗研究中的认识。     

寄生线虫功能基因组学的研究进展

寄生线虫病严重危害人和动物的健康,但目前防治寄生线虫病尚存在困难。随着分子生物学技术的迅速发展,运用生物技术手段来控制寄生线虫病将成为可能,由此而来,研究寄生线虫的基因功能具有非常重要的意义。本文简要概述了研究寄生虫功能基因组学的一些方法,如研究差异表达基因的方法、RNA干涉技术、寄生虫的转基因技术和生物信息学等,并综述了猪蛔虫、圆线科线虫、旋盘尾丝虫、旋毛虫、马来布鲁线虫和有齿食道口线虫等一些重要寄生线虫在功能基因组学上的研究进展。     

比较基因组学在细菌分子进化领域的研究进展

在后基因组时代，比较基因组学已经成为诠释遗传信息生物学意义的重要方法。运用生物信息学的手段，比较分析不同细菌的基因组，我们逐步揭示了细菌染色体物理结构的保守性和基因内容多样性的产生机制。进化生物学家们正试图从基因组数据库中挖掘能够代表物种进化关系的具有普遍适用性的分子线索，重建生命进化树。     

放疗联合抗血管疗法在肿瘤治疗中的研究进展

随着对肿瘤病理机制的深入了解和放射增敏研究的发展，抗血管治疗联合放疗已成为当前肿瘤治疗研究的热点。Teicher等首先报道抗血管治疗能增加患者对放疗的反应，而后有大量的临床前研究证明抗血管治疗能增加放疗的敏感性。由于新的血管靶向药物的不断问世，许多新的联合治疗方法和结果不断见诸报道。     

病毒宏基因组学在HPV感染相关性疾病中的研究进展

人乳头瘤病毒(human papillomavirus,HPV)是乳多空病毒科的一种具有圆形二十面体结构的双链DNA病毒.近年来,基于第二代测序技术的病毒宏基因组学逐渐兴起,它可以直接获得特定环境中的全部病毒核酸信息,并对其进行序列进化分析.可用于研究环境中病毒的多样性、未知病毒的鉴定、实时监测病毒的变异情况以及发现可...     

Insights into the theranostic value of precision medicine on advanced radiotherapy to breast cancer

Breast cancer is the most common cancer in women worldwide. “Breast cancer” encompasses a broad spectrum of diseases (i.e., subtypes) with significant epidemiological, clinical, and biological heterogeneity. Each of these subtypes has a different natural history and prognostic profile. Although tumour staging (TNM classification) still provides valuable information in the overall management of breast cancer, the current reality is that clinicians must consider other biological and molecular factors that directly influence treatment decision-making, including extent of surgery, indication for chemotherapy, hormonal therapy, and even radiotherapy (and treatment volumes). The management of breast cancer has changed radically in the last 15 years due to significant advances in our understanding of these tumours. While these changes have been extremely positive in terms of surgical and systemic management, they have also created significant uncertainties concerning integration of local and locoregional radiotherapy into the therapeutic scheme. In parallel, radiotherapy itself has also experienced major advances. Beyond the evident technological advances, new radiobiological concepts have emerged, and genomic data and other patient-specific factors must now be integrated into individualized treatment approaches. In this context, “precision medicine” seeks to provide an answer to these open questions and uncertainties. Although precision medicine has been much discussed in the last five years or so, the concept remains somewhat ambiguous, and it often appear to be used as a “catch-all” term. The present review aims to clarify the meaning of this term and, more importantly, to critically evaluate the role and impact of precision medicine on breast cancer radiotherapy. Finally, we will discuss the current and future of precision medicine in radiotherapy.     

Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology

Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict molecular test results from digitized images of tissue slides. Furthermore, treatment response and prognosis can be predicted from tissue slides using DL. In this review, we summarized DL-based studies regarding the prediction of genetic mutation, microsatellite instability, tumor mutational burden, molecular subtypes, gene expression, treatment response, and prognosis directly from hematoxylin- and eosin-stained tissue slides. Although performance needs to be improved, these studies clearly demonstrated the feasibility of DL-based prediction of key molecular features in cancer tissues. With the accumulation of data and technical advances, the performance of the DL system could be improved in the near future. Therefore, we expect that DL could provide cost- and time-effective alternative tools for patient stratification in the era of precision oncology.     

磁共振图像(MRI)空间几何失真对立体放射治疗中靶点位置精确度影响的分析

目的：分析模拟靶点MRI空间几何失真的因素,以及其对立体放射治疗中靶点位置精确度的影响程度。材料和方法：将5×5矩阵小孔有机玻璃靶点模拟装置和Leksell-G型立体定位头架进行磁共振扫描,分别测量MRI二维横断面和不同扫描层厚情况下选取层面在纵向的几何失真程度。结果：MRI二维横断面上模拟把点X、Y方向精确度分别为（0．03±0．12）mm、（0.44±0．31）mm,平面距离精确度为（0．46±0．31）mm,选取层面在纵向（Z方向）的精确度为（0．46±0．41）mm。结论：梯度场非线性和主磁场的非均匀性以及不同扫描展尽是MRI几何失真的因素。磁共振作为立体放射治疗靶点定位是可行的。     

放射治疗中医学图像自动分割的研究进展

回顾放射治疗中传统（非深度学习）自动分割算法和新近出现的深度学习（卷积神经网络和全卷积网络）算法的临床 应用和研究进展情况。对以强度阈值算法为代表的第一代自动分割技术,以聚类算法为代表的第二代自动分割技术,以基 于图谱库分割算法为代表的第三代自动分割技术进行了简要概述。指出基于深度学习的第四代自动分割技术已在鲁棒性、 一致性、效率等方面取得了较大提升,但仍存在一些偏差原因无法辨识、影像采集协议不一致、高质量数据集缺乏等局限性, 这些不足需要在日后的工作中不断加以完善。最后探讨了如何针对自动分割软件进行临床调试和质量保证的问题。     

Difficulties and challenges in the development of precision medicine

The rapid development of precision medicine is introducing a new era of significance in medicine. However, attaining precision medicine is an ambitious goal that is bound to encounter some challenges. Here, we have put forward some difficulties or questions that should be addressed by the progress in this field. The proposed issues include the long road to precision medicine for all types of diseases as the unknown domains of the human genome hinder the development of precision medicine. The challenges in the acquisition and analysis of large amounts of omics data, including difficulties in the establishment of a library of biological samples and large-scale data analysis, as well as the challenges of informed consent and medical ethics in precision medicine, must be overcome to attain the goals of precision medicine. To date, precision medicine programs have accomplished many preliminary achievements and will help to drive a dramatic revolution in clinical practices for the medical community. Through these advances, the diagnosis and treatment of many diseases will achieve many breakthroughs. This project is just beginning and requires a great deal of time and money. Precision medicine also requires extensive collaboration. Ultimately, these difficulties can be overcome. We should realize that precision medicine is good for patients, but there is still a long way to go.     

人工智能在胰腺疾病新型诊疗模式中的应用及进展

基于临床资料、医学影像学、基因组学的计算机诊疗辅助系统在鉴别胰腺囊性疾病、诊断胰腺癌等方面显现出优于传统诊疗方法的潜力。本文综述人工智能在胰腺各类疾病的诊断、预后、预测治疗反应和指导治疗方面的作用,并为胰腺疾病精准医疗、改良目前的临床诊疗模式提供新的思路及方法。     

Genetic counseling among minority populations in the era of precision medicine

Precision medicine includes the use of genetic variation to determine the prevention, pathology, management, and treatment of disease, which has the potential to significantly change the practice of healthcare. As such, its success depends on (a) having reliable information about the effects of genetic variation on disease processes; and (b) the patient's understanding of their own genetic makeup such that they can use that information to affect their lifestyle and diet. Given the history of low engagement of under-represented minority populations in both clinical genetic services and genetic research, both of these aspects will be challenged and must be addressed before the benefits of precision medicine will be fully realized. Reflecting on lessons learned in the field of cancer genetic counseling, we present key issues to consider as we look forward to providing genetic counseling to minority communities in the context of precision medicine.     

“互联网+精准医疗”推动医学课程整合与信息化

在医学高等教育与信息深度融合的大背景下,依托毕博网络教学平台开展医学课程混合式教学。以"精准医疗"理念为导向,按照学生专业特点不同个体化教学设计;同时定位于培养学生整体性思维模式,通过混合式学习引导学生通过分析临床病例资料来整合医学各学科相关知识;并制定学生考核方案,运用大数据综合分析学生考核结果。以此推动以学生个体为中心的"精准教学"信息化发展进程。