抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes.

Optical clearing methods facilitate deep biological imaging by mitigating light scattering in situ. Multi-scale high-resolution imaging requires preservation of tissue integrity for accurate signal reconstruction. However, existing clearing reagents contain chemical components that could compromise tissue structure, preventing reproducible anatomical and fluorescence signal stability. We developed ScaleS, a sorbitol-based optical clearing method that provides stable tissue preservation for immunochemical labeling and three-dimensional (3D) signal rendering. ScaleS permitted optical reconstructions of aged and diseased brain in Alzheimer's disease models, including mapping of 3D networks of amyloid plaques, neurons and microglia, and multi-scale tracking of single plaques by successive fluorescence and electron microscopy. Human clinical samples from Alzheimer's disease patients analyzed via reversible optical re-sectioning illuminated plaque pathogenesis in the z axis. Comparative benchmarking of contemporary clearing agents showed superior signal and structure preservation by ScaleS. These findings suggest that ScaleS is a simple and reproducible method for accurate visualization of biological tissue.

ScaleS: an optical clearing palette for biological imaging

It is well established that microglial form and function are inextricably linked. In recent years, the traditional view that microglial form ranges between "ramified resting" and "activated amoeboid" has been emphasized through advancing imaging techniques that point to microglial form being highly dynamic even within the currently accepted morphological categories. Moreover, microglia adopt meaningful intermediate forms between categories, with considerable crossover in function and varying morphologies as they cycle, migrate, wave, phagocytose, and extend and retract fine and gross processes. From a quantitative perspective, it is problematic to measure such variability using traditional methods, but one way of quantitating such detail is through fractal analysis. The techniques of fractal analysis have been used for quantitating microglial morphology, to categorize gross differences but also to differentiate subtle differences (e.g., amongst ramified cells). Multifractal analysis in particular is one technique of fractal analysis that may be useful for identifying intermediate forms. Here we review current trends and methods of fractal analysis, focusing on box counting analysis, including lacunarity and multifractal analysis, as applied to microglial morphology.

/pdf/quantitating-the-subtleties-of-microglial-morphology-with-22cx7voyf5.pdf

Quantitating the subtleties of microglial morphology with fractal analysis.

Despite increasing evidence supporting the neuroinflammatory theory of depression, little is known about cerebral macrophages in individuals suffering from major depression. In the present study, we investigated the morphology and distribution of cells immunostained for the macrophage-specific marker ionized calcium binding adaptor molecule 1 (IBA1) in the dorsal anterior cingulate cortex (dACC) white matter of middle-aged depressed suicides and matched non-psychiatric controls. This region is known for its implication in mood disorders, and its white matter compartment was previously found to display hypertrophic astrocytes in depressed suicides. Distributions of IBA1-immunoreactive (IBA-IR) microglial phenotypes were assessed using stereology and cell morphometry, and blood vessels were characterized as being intimately associated with either a high or a low density of IBA1-IR amoeboid-like cells. Total densities of IBA1-IR microglia did not differ between depressed suicides and controls. However, a finer analysis examining relative proportions of microglial phenotypes revealed that the ratio of primed over ramified ("resting") microglia was significantly increased in depressed suicides. Strikingly, the proportion of blood vessels surrounded by a high density of macrophages was more than twice higher in depressed suicides than in controls, and this difference was strongly significant. Consistent with these observations, gene expression of IBA1 and MCP-1, a chemokine involved in the recruitment of circulating monocytes, was significantly upregulated in depressed suicides. Furthermore, mRNA for CD45, a marker enriched in perivascular macrophages, was also significantly increased in samples from depressed suicides. An increase compared to controls was also observed in the proportion of blood vessels surrounded by a high density of CD45-IR cells, but this difference did not reach significance. These histological and molecular data suggest the recruitment of monocytes in dACC white matter of depressed suicides, although it cannot be excluded that other types of macrophages (including microglia) account for the observed accumulation of macrophages closely associated with blood vessels. Altogether, these findings suggest that the previously reported depression- and suicide-associated increases in circulating pro-inflammatory cytokines may be associated with low-grade cerebral neuroinflammation involving the recruitment of circulating monocytes.

Evidence for increased microglial priming and macrophage recruitment in the dorsal anterior cingulate white matter of depressed suicides

For clinicians grappling with quantifying the complex spatial and temporal patterns of cardiac structure and function (such as myocardial trabeculae, coronary microvascular anatomy, tissue perfusion, myocyte histology, electrical conduction, heart rate, and blood-pressure variability), fractal analysis is a powerful, but still underused, mathematical tool. In this Perspectives article, we explain some fundamental principles of fractal geometry and place it in a familiar medical setting. We summarize studies in the cardiovascular sciences in which fractal methods have successfully been used to investigate disease mechanisms, and suggest potential future clinical roles in cardiac imaging and time series measurements. We believe that clinical researchers can deploy innovative fractal solutions to common cardiac problems that might ultimately translate into advancements for patient care.

/pdf/the-fractal-heart-embracing-mathematics-in-the-cardiology-2dv7x3kk82.pdf

The fractal heart - embracing mathematics in the cardiology clinic.

Timely intervention for diabetic retinopathy (DR) lessens the possibility of blindness and can save considerable costs to health systems. To ensure that interventions are timely and effective requires methods of screening and monitoring pathological changes, including assessing outcomes. Fractal analysis, one method that has been studied for assessing DR, is potentially relevant in today's world of telemedicine because it provides objective indices from digital images of complex patterns such as are seen in retinal vasculature, which is affected in DR. We introduce here a protocol to distinguish between nonproliferative (NPDR) and proliferative (PDR) changes in retinal vasculature using a fractal analysis method known as local connected dimension (D conn ) analysis. The major fi nding is that compared to other fractal analysis methods, D conn analysis better differentiates NPDR from PDR (p = 0.05). In addition, we are the fi rst to show that fractal analysis can be used to differentiate between NPDR and PDR using automated vessel identifi cation. Overall, our results suggest this protocol can complement existing methods by including an automated and objective measure obtainable at a lower level of expertise that experts can then use in screening for and monitoring DR.

/pdf/automated-detection-of-proliferative-retinopathy-in-clinical-1bcu4h28vf.pdf

Automated detection of proliferative retinopathy in clinical practice.

Tissue engineering is currently one of the most exciting fields in biology (Grayson et al., 2009). Fractal analysis is equally exciting (Di Ieva et al., 2013), as is the study of microglia, the brain’s immuno-inflammatory cell, recently shown to be of considerably more importance than previously imagined in both healthy and diseased brain (Tremblay et al., 2011). Each of these fields is developing at a pace far outstripping our capacity to integrate and translate the information gained into clinical use (Karperien et al., 2008b, 2013; Jelinek et al., 2011, 2013), and the excitement more than trebles where these fields intersect. Three elements of fractal analysis – monofractal, multifractal, and lacunarity analysis – applied to microglia may contribute significantly to the next steps forward in engineered tissues and 3D models in neuroscience.

Fractal analysis and lacunarity
To define “fractal analysis” would take a volume, but for this commentary, it is sufficient to understand that fractal analysis in biology assesses the scaling inherent in biological forms or events, and turns out a statistical index of complexity having no units called the “fractal dimension” (DF). This number measures not length, width, height, or density, but scale-invariant detail. For a pattern to have fractal scale-invariant detail means that the pattern repeats itself infinitely as one inspects it at closer and closer resolution (magnifies it), where that detail is not trivial. To elaborate, as one magnifies a simple line, it infinitely repeats itself quite trivially as a simple line, but as one magnifies a fractal line, one finds it never resolves into straight pieces but rather each magnified segment repeats the initial fractal pattern infinitely. A DF measures this infinite scaling, quantifying complex patterns without rendering meaningless the relative numbers of large and small measurements within them. Without getting too technical, fractal analysis of a simple line yields a DF of 1.00, and the higher the “complexity,” the higher the DF (Mandelbrot, 1983; Takayasu, 1990). Building on this so-called monofractal analysis, multifractal analysis, to summarize, is a way of finding for a single pattern a spectrum of DFs, owing to a pattern having characteristically multiple degrees of scaling, such as could be imagined for a cascading fractal phenomenon (Jestczemski and Sernetz, 1996; Falconer, 2014).

The word “lacuna” is derived from the word for lake, and refers to a gap or pool. In fractal analysis, lacunarity translates to measures of gappiness or “visual texture,” such as might be seen in the patchiness of forests, for instance (Plotnick et al., 1993). It has been defined as the degree of inhomogeneity and translational and rotational invariance in an image (Plotnick et al., 1993; Smith et al., 1996), where low lacunarity implies homogeneity and that rotating the image will not change it significantly. Thus, an image having mostly similarly sized gaps and little rotational variance would be expected to have low lacunarity, and one with much heterogeneity, many differently sized gaps, and notable rotational variance, would be expected to have high lacunarity (Karperien et al., 2011a). Lacunarity is frequently assessed during fractal analysis because the data on which it is based are easily collected by the same methods. The details and calculations behind fractal analysis are beyond the scope of this commentary but user-friendly, freely available software for biologists (Karperien, 2001, 2013) and in-depth explanations are available elsewhere (Smith et al., 1996).

/pdf/fractal-multifractal-and-lacunarity-analysis-of-microglia-in-4hexo0daje.pdf

Fractal, Multifractal, and Lacunarity Analysis of Microglia in Tissue Engineering

Introduction 
Cervical intraepithelial neoplasias (CIN) represent precursor lesions of cervical cancer. These neoplastic lesions are traditionally subdivided into three categories CIN 1, CIN 2, and CIN 3, using microscopical criteria. The relation between grades of cervical intraepithelial neoplasia (CIN) and its fractal dimension was investigated to establish a basis for an objective diagnosis using the method proposed.


Methods 
Classical evaluation of the tissue samples was performed by an experienced gynecologic pathologist. Tissue samples were scanned and saved as digital images using Aperio scanner and software. After image segmentation the box counting method as well as multifractal methods were applied to determine the relation between fractal dimension and grades of CIN. A total of 46 images were used to compare the pathologist's neoplasia grades with the predicted groups obtained by fractal methods.


Results 
Significant or highly significant differences between all grades of CIN could be found. The confusion matrix, comparing between pathologist's grading and predicted group by fractal methods showed a match of 87.1%. Multifractal spectra were able to differentiate between normal epithelium and low grade as well as high grade neoplasia.


Conclusion 
Fractal dimension can be considered to be an objective parameter to grade cervical intraepithelial neoplasia.

/pdf/fractal-analysis-of-cervical-intraepithelial-neoplasia-4kd2zwc88w.pdf

Audrey Karperien

Papers

Quantitating the subtleties of microglial morphology with fractal analysis.

The fractal heart - embracing mathematics in the cardiology clinic.

Automated detection of proliferative retinopathy in clinical practice.

Fractal, Multifractal, and Lacunarity Analysis of Microglia in Tissue Engineering

Fractal analysis of cervical intraepithelial neoplasia.