Anteroposterior spondyloschisis of the atlas. Two case reports and literature review

Congenital bony defects of the atlas are uncommon; with isolated posterior clefts being the most frequent anomaly and combined anterior and posterior defects the least common, occurring in only 0.3–0.7% of the population. These anomalies can cause confusion, particularly in the setting of trauma when appearances on plain radiography may be misinterpreted as representing a fracture. Two cases of anteroposterior spondyloschisis are presented and the pathology, diagnosis and the clinical relevance of such defects discussed with reference to previous published literature.     

HSPH1在肝细胞癌中的表达及临床意义

目的?基于癌症基因组图谱数据库（The Cancer Genome Atlas, TCGA）、高通量基因表达数据库（Gene Expression Omnibus, GEO）及人类蛋白质图谱数据库（Human Protein Atls, HPA）等生物信息学数据库探讨热休克蛋白H1（heat shock protein H1, HSPH1）在肝细胞癌中的表达及临床意义。方法?下载TCGA和GEO数据库肝细胞癌相关mRNA微阵列表达谱,比较肝细胞癌和癌旁组织的HSPH1 mRNA表达差异,并利用HPA数据库信息验证其蛋白水平的表达。分析HSPH1表达与肝细胞癌患者预后的相关性。建立ROC曲线和列线图模型,比较不同HSPH1表达组患者的生存差异。采用R软件分析HSPH1表达与肝细胞癌组织中免疫细胞浸润、免疫细胞生物标志物和免疫检查点的相关性。采用STRING数据库分析HSPH1相关蛋白质-蛋白质相互作用网络信息。并利用基因本体分析 （Gene Ontology, GO）、京都基因与基因组数据库（Kyoto Encyclopedia of Genes and Genomes, KEGG）和基因集富集分析（Gene Set Enrichment Analysis, GSEA）等数据库分析HSPH1在肝细胞癌中的功能及信号通路。结果?HSPH1在肝细胞癌组织中的表达水平高于癌旁组织（P=0.000）,Kaplan-Meier图显示HSPH1高表达肝细胞癌患者的总体生存期（P=0.000）、疾病特异性生存期（P=0.000）和无进展间隔期（P=0.010）均较HSPH1低表达者更短,HSPH1对肝细胞癌具有较好的诊断价值（AUC=0.895,95%CI：0.862～0.928,P=0.000）。多因素Cox回归分析显示,HSPH1是肝细胞癌患者预后的预测因素（HR=2.226,95%CI：1.393～3.557,P=0.000）。时间依赖性ROC曲线图显示1年、3年和5年AUC分别为0.699、0.581、0.588。在肝细胞癌组织中,HSPH1蛋白表达水平与树突状细胞和细胞毒性细胞浸润呈负相关,与巨噬细胞、CD56高表达的NK细胞等免疫细胞浸润及免疫检查点程序性死亡受体1和细胞毒性T淋巴细胞相关蛋白4表达呈弱正相关。蛋白质-蛋白质相互作用、GO、KEGG和GSEA通路富集分析显示HSPH1与HSPA2、HSPA4、HSPA8和HSPA9等分子相互作用,并与HSPA4、HSPA8表达呈正相关,且具有调节凋亡、抗原加工和提呈、调节免疫等功能。结论?HSPH1在肝细胞癌组织中高表达,是肝细胞癌潜在的诊断和预后标志物。HSPH1参与了肝细胞癌的免疫调节过程,可能成为其治疗靶点之一。     

THE CONTRAST ANALYSIS BETWEEN BA AND REG IN DIAGNOSIS OF PATIENTS WITH VASCULAR HEADACHE

目的:探讨脑电地形图与脑血流图在血管性头痛患者诊断中的应用价值.方法:脑电地形图与脑血流图被检查并对患者阳性率进行了对比.结果:脑电地形图的阳性率是100%,脑血流图的阳性率是43.1%.两者对比P值<0.05.结论:脑电地形图在血管性头痛的诊断中有重要价值.

Abstract：

Objective:To explore value applied of BA and REG in diagnosis of patients with vascular headache.Method:BA and REG were examened in patients with vascular headache and cornpared in positive rate between BA and REG.Result:The positive rate of REG was 43.1% and BA was 100%(P<0.05).Conclusion:The diagnostic value of BA was important in vascular headache.     

四季鹅(雌)脑立体定位图谱的建立

实验利用12个母鹅脑切制了12套厚度为1.0mm的切片.切片经Lemaevier脑厚片染色法染色,并摄制成照片.从脑正中矢状缝开始.以1.0mm为间距分别读取各脑片外形以及各脑区外形数据.求出所有脑相应脑片相应部位数据的均值.用此值绘制出各脑区图谱座标.另外,根据此脑区图谱.利用慢性徽电极技术,在6只成年母鹅的下丘脑前部共观察记录了54个单位的自发放电活动.     

寰椎枕骨化畸形对寰枢关节运动影响的实验研究

目的用生物力学测试人类寰椎枕骨化模型,阐明此模型对寰枢关节三维运动的影响.方法采用新鲜人体枕颈部标本制作寰椎枕骨化畸形实验模型,用生物力学测试,确定寰枢关节的运动参数,作统计学分析.结果与标本比较,模型的寰枢关节屈曲运动的中性区变化不明显,弹性区和运动范围明显增大(P＜0.05),旋转运动的中性区和运动范围明显增大(P＜0.05),后伸和侧曲运动各运动参数和旋转运动的弹性区相差不显著.结论(1)寰椎枕骨化畸形造成枕寰关节运动丧失,寰枢关节的负荷和旋转不稳定趋势增加,易导致寰椎横韧带退变、松弛和寰枢关节不稳.(2)建议对寰椎枕骨化畸形的患者密切观察,证实寰枢关节临床不稳,尽早手术融合.     

Virtual spatial registration of stand-alone fNIRS data to MNI space

The registration of functional brain data to common stereotaxic brain space facilitates data sharing and integration across different subjects, studies, and even imaging modalities. Thus, we previously described a method for the probabilistic registration of functional near-infrared spectroscopy (fNIRS) data onto Montreal Neurological Institute (MNI) coordinate space that can be used even when magnetic resonance images of the subjects are not available. This method, however, requires the careful measurement of scalp landmarks and fNIRS optode positions using a 3D-digitizer. Here we present a novel registration method, based on simulations in place of physical measurements for optode positioning. First, we constructed a holder deformation algorithm and examined its validity by comparing virtual and actual deformation of holders on spherical phantoms and real head surfaces. The discrepancies were negligible. Next, we registered virtual holders on synthetic heads and brains that represent size and shape variations among the population. The registered positions were normalized to MNI space. By repeating this process across synthetic heads and brains, we statistically estimated the most probable MNI coordinate values, and clarified errors, which were in the order of several millimeters across the scalp, associated with this estimation. In essence, the current method allowed the spatial registration of completely stand-alone fNIRS data onto MNI space without the use of supplementary measurements. This method will not only provide a practical solution to the spatial registration issues in fNIRS studies, but will also enhance cross-modal communications within the neuroimaging community.     

DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer

We propose a Deep learning-based weak label learning method for analyzing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue not requiring pixel-level or tile-level annotations using Self-supervised pre-training and heterogeneity-aware deep Multiple Instance LEarning (DeepSMILE). We apply DeepSMILE to the task of Homologous recombination deficiency (HRD) and microsatellite instability (MSI) prediction. We utilize contrastive self-supervised learning to pre-train a feature extractor on histopathology tiles of cancer tissue. Additionally, we use variability-aware deep multiple instance learning to learn the tile feature aggregation function while modeling tumor heterogeneity. For MSI prediction in a tumor-annotated and color normalized subset of TCGA-CRC (n=360 patients), contrastive self-supervised learning improves the tile supervision baseline from 0.77 to 0.87 AUROC, on par with our proposed DeepSMILE method. On TCGA-BC (n=1041 patients) without any manual annotations, DeepSMILE improves HRD classification performance from 0.77 to 0.81 AUROC compared to tile supervision with either a self-supervised or ImageNet pre-trained feature extractor. Our proposed methods reach the baseline performance using only 40% of the labeled data on both datasets. These improvements suggest we can use standard self-supervised learning techniques combined with multiple instance learning in the histopathology domain to improve genomic label classification performance with fewer labeled data.     

Detection, visualization and animation of abnormal anatomic structure with a deformable probabilistic brain atlas based on random vector field transformations

This paper describes the design, implementation and preliminary results of a technique for creating a comprehensive probabilistic atlas of the human brain based on high-dimensional vector field transformations. The goal of the atlas is to detect and quantify distributed patterns of deviation from normal anatomy, in a 3-D brain image from any given subject. The algorithm analyzes a reference population of normal scans and automatically generates color-coded probability maps of the anatomy of new subjects. Given a 3-D brain image of a new subject, the algorithm calculates a set of high-dimensional volumetric maps (with typically 384² × 256 × 3 ≈ 10⁸ degrees of freedom) elastically deforming this scan into structural correspondence with other scans, selected one by one from an anatomic image database. The family of volumetric warps thus constructed encodes statistical properties and directional biases of local anatomical variation throughout the architecture of the brain. A probability space of random transformations, based on the theory of anisotropic Gaussian random fields, is then developed to reflect the observed variability in stereotaxic space of the points whose correspondences are found by the warping algorithm. A complete system of 384² × 256 probability density functions is computed, yielding confidence limits in stereotaxic space for the location of every point represented in the 3-D image lattice of the new subject's brain. Color-coded probability maps are generated, densely defined throughout the anatomy of the new subject. These indicate locally the probability of each anatomic point being unusually situated, given the distributions of corresponding points in the scans of normal subjects. 3-D MRI and high-resolution cryosection volumes are analyzed from subjects with metastatic tumors and Alzheimer's disease. Gradual variations and continuous deformations of the underlying anatomy are simulated and their dynamic effects on regional probability maps are animated in video format (on the accompanying CD-ROM). Applications of the deformable probabilistic atlas include the transfer of multi-subject 3-D functional, vascular and histologic maps onto a single anatomic template, the mapping of 3-D atlases onto the scans of new subjects, and the rapid detection, quantification and mapping of local shape changes in 3-D medical images in disease and during normal or abnormal growth and development.     

Coexpression analysis of CD133 and CD44 identifies Proneural and Mesenchymal subtypes of glioblastoma multiforme

Accumulating evidence suggests that the stem cell markers CD133 and CD44 indicate molecular subtype in Glioblastoma Multiforme (GBM). Gene coexpression analysis of The Cancer Genome Atlas GBM dataset was undertaken to compare markers of the Glioblastoma Stem-Progenitor Cell (GSPC) phenotype. Pearson correlation identified genes coexpressed with stem cell markers, which were then used to build a gene signature that classifies patients based on a CD133 coexpression module signature (CD133-M) or CD44-M subtype. CD133-M tumors were enriched for the Proneural (PN) GBM subtype compared to Mesenchymal (MES) subtype for CD44-M tumors. Gene set enrichment identified DNA replication/cell cycle genes in the CD133-M and invasion/migration in CD44-M, while functional experiments showed enhanced cellular growth in CD133 expressing cells and enhanced invasion in cells expressing CD44. As with the 4 major molecular subtypes of GBM, there was no long-term survival difference between CD44-M and CD133-M patients, although CD44-M patients responded better to temozolomide while CD133-M patients benefited from radiotherapy. The use of a targeted coexpression approach to predict functional properties of surface marker expressing cells is novel, and in the context of GBM, supports accumulating evidence that CD133 and CD44 protein marker expression correlates with molecular subtype.