Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models.     

烟雾病临床分型研究进展

烟雾病（moyamoya disease，MMD）是一种颈内动脉末端及其分支起始处进行性狭窄伴颅底异常血管网形成的脑血管疾病，临床上常将其分为缺血型、出血型和无症状型。随着诊疗技术的进步，对MMD临床分型的流行病学特征认识也在不断发展。在成年患者中，缺血型MMD占有重要地位，无症状型亦呈逐渐增多的趋势。不同临床分型MMD的发病年龄、遗传背景、病理生理机制、侧支血管代偿、受累血管分布及其预后存在显著差异。本文总结了对MMD临床分型流行病学特征认识的历史演变，不同临床分型可能的遗传背景、病理生理机制和影像学表现上的差异，为基于不同临床分型的MMD治疗决策提供新的思路。     

中日友好医院分型股骨头坏死腓骨植入治疗的三维有限元分析

文题释义：股骨头坏死中日友好医院分型的有限元分析：根据李子荣等提出的中日友好医院分型，建立股骨头坏死三维模型，分为 M型(内侧型)、C型(中央型)和 L型(外侧型)，其中 L型包括L1型(次外侧型)、L2型(极外侧型)和 L3型(全头型)。通过对建立的模型进行有限元分析，为该分型的保髋治疗提供了一定力学依据，显示外侧柱的存留是精准预防塌陷的重要因素，为进一步实现个体化治疗提供力学基础。 腓骨支撑坏死股骨头保髋手术：是对于早中期股骨头坏死需要保留股骨头患者进行的一种手术方式。首先需对股骨头进行髓芯减压，清除一定坏死骨，空腔填塞松质骨(髂骨为主)，打压结实后植入腓骨(异体或自体)支撑，给坏死区的提供力学支撑及生物学修复，预防股骨头进一步坏死及塌陷。 背景：研究报道股骨头坏死的保髋疗效与外侧柱存留密切相关，中日友好医院分型是根据三柱结构确立的，对股骨头塌陷的预测准确性高。 目的：建立股骨头坏死中日友好医院分型各分型仿真的三维有限元模型，通过有限元分析各分型腓骨植入的力学变化，探讨外侧柱存留对保髋疗效的意义，为该分型的塌陷精准预测提供基础。 方法：建立正常股骨头、中日友好医院分型(M型、C型、L1型、L2型、L3型)股骨头坏死及其腓骨植入3组11种三维有限元模型，运用ANSYS软件进行有限元分析计算，观察各组模型的最大应力值、最大位移值及股骨头内部载荷传递模式。 结果与结论：①坏死组位移最大，应变最大，且因坏死分型不同而位移不同，位移变化如下：M型相似文献   

支持向量机法及其在中药研究中的应用

在中医药现代化进程中，有效地结合现代科学技术手段对传统中医药进行更为合理的分析是一个重要研究方向。计算机科学技术与人工智能算法在中医药研究领域已取得长足发展，尤其是分类算法所具有的快速鉴别和分析能力，使其在复杂的中医药研究体系中发挥举足轻重的作用。对支持向量机分类算法及其结合其他算法在解决中医药相关领域问题方面及成果进行综述，以相关智能算法为基础探讨中医药研究过程中的诸多难题，为推动中医药现代化进程提供支撑。     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

Tongue cancer treatment and oncological outcomes: The role of glossectomy classification

BackgroundNowadays surgery remains the gold standard of treatment for tongue cancer. Via a more clear and precise terminology, the glossectomy classification by Ansarin et al. facilitates shared communication between surgeons, allowing comparison between published research and improving surgical practice and patient care. To establish the association of glossectomies, according to their classification by Ansarin et al. with overall survival (OS), disease-free survival (DSF), and cause-specific survival (CSS) in tongue cancer, we conducted a systemic retrospective study on 300 consecutive patients affected by primary oral tongue cancer and treated with surgery at the European Institute of Oncology, IRCCS (IEO).MethodsThree hundred patients with tongue squamous cell carcinoma and treated at the Division of Otorhinolaryngology and Head and Neck Surgery of the European Institute of Oncology, IRCCS were cataloged according to the glossectomy classification. OS, DFS, and CSS were compared by surgical treatments.ResultsOS-5yrs was 80% for the type I glossectomy group, 75% for type II, 65% for type III, and 35% for type IV-V. DFS-5yrs was 74%, 60%, 55%, and 27%, respectively for I, II, III, and IV-V glossectomy group; CSS-5yrs was 82%, 80%, 72%, and 48%, respectively for I, II, III, and IV-V glossectomy group (p < 0.01).ConclusionsThis study confirmed that the application of the glossectomy classification was statistically correlated with patients' oncological outcomes.