Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protoco
ls, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease.
Cancer development is a multistep process often starting with a single cell in which a number of epigenetic and genetic alterations have accumulated thus transforming it into a tumor cell. The progeny of such a single benign tumor cell expands in the
tissue and can at some point progress to malignant tumor cells until a detectable tumor is formed. The dynamics from the early phase of a single cell to a detectable tumor with billions of tumor cells are complex and still not fully resolved, not even for the well-known prototype of multistage carcinogenesis, the adenoma-adenocarcinoma sequence of colorectal cancer. Mathematical models of such carcinogenesis are frequently tested and calibrated based on reported age-specific incidence rates of cancer, but they usually require calibration of four or more parameters due to the wide range of processes these models aim to reflect. We present a cell-based model, which focuses on the competition between wild-type and tumor cells in colonic crypts, with which we are able reproduce epidemilogical incidence rates of colon cancer. Additionally, the fraction of cancerous tumors with precancerous lesions predicted by the model agrees with clinical estimates. The match between model and reported data suggests that the fate of tumor development is dominated by the early phase of tumor growth and progression long before a tumor becomes detectable. Due to the focus on the early phase of tumor development, the model has only a single fit parameter, the replacement rate of stem cells in the crypt. We find this rate to be consistent with recent experimental estimates.
Abstract The transport of nutrients or signal constituents that stimulate growth of bone tissue is supposed to be affected by a static mechanical load. It follows from basic thermodynamical principles that constituents causing volumetric change are d
ragged along the gradients of hydrostatic stress. The present preliminary study examines the behaviour of iodine present in the medullary cavity of a bovine long bone exposed to mechanical load. A section of the bone is x-ray scanned with the static load present, with and without the iodine. The resulting distribution in a selected 2D plane is numerically evaluated using a discrete Radons inverse transform. The result suggests that iodine is a useful constituent with a good attenuation effect on the x-ray beam and clearly reveals the temporal distribution of its transport through the bone. It further result shows some indication that stress does affect the iodine distribution.
Excitable media are prevalent models for describing physical, chemical, and biological systems which support wave propagation. In this letter, we show that the time evolution of the medium state at the wave fronts can be determined by complicated cha
otic attractors. Wave front dynamics can be controlled by initial data choice. Building on this groundwork, we show that there is a mechano-chemical analog of the Universal Turing machine for morphogenesis problems. Namely, a fixed mechano-chemical system can produce any prescribed cell pattern depending on its input (initial data). This universal mechanism uses fundamental physical effects: spontaneous symmetry breaking with formation of many interfaces (kinks), which interact non-locally via a fast diffusing reagent. This interaction creates chaos. We present algorithms allowing us to obtain a prescribed target cell pattern.
Identifying the mechanism of intercellular feedback regulation is critical for the basic understanding of tissue growth control in organisms. In this paper, we analyze a tissue growth model consisting of a single lineage of two cell types regulated b
y negative feedback signalling molecules that undergo spatial diffusion. By deriving the fixed points for the uniform steady states and carrying out linear stability analysis, phase diagrams are obtained analytically for arbitrary parameters of the model. Two different generic growth modes are found: blow-up growth and final-state controlled growth which are governed by the non-trivial fixed point and the trivial fixed point respectively, and can be sensitively switched by varying the negative feedback regulation on the proliferation of the stem cells. Analytic expressions for the characteristic time scales for these two growth modes are also derived. Remarkably, the trivial and non-trivial uniform steady states can coexist and a sharp transition occurs in the bistable regime as the relevant parameters are varied. Furthermore, the bi-stable growth properties allows for the external control to switch between these two growth modes. In addition, the condition for an early accelerated growth followed by a retarded growth can be derived. These analytical results are further verified by numerical simulations and provide insights on the growth behavior of the tissue. Our results are also discussed in the light of possible realistic biological experiments and tissue growth control strategy. Furthermore, by external feedback control of the concentration of regulatory molecules, it is possible to achieve a desired growth mode, as demonstrated with an analysis of boosted growth, catch-up growth and the design for the target of a linear growth dynamic.
While electromyography (EMG) and magnetomyography (MMG) are both methods to measure the electrical activity of skeletal muscles, no systematic comparison between both signals exists. Within this work, we propose a systemic in silico model for EMG and
MMG and test the hypothesis that MMG surpasses EMG in terms of spatial selectivity. The results show that MMG provides a slightly better spatial selectivity than EMG when recorded directly on the muscle surface. However, there is a remarkable difference in spatial selectivity for non-invasive surface measurements. The spatial selectivity of the MMG components aligned with the muscle fibres and normal to the body surface outperforms the spatial selectivity of surface EMG. Particularly, for the MMGs normal-to-the-surface component the influence of subcutaneous fat is minimal. Further, for the first time, we analyse the contribution of different structural components, i.e., muscle fibres from different motor units and the extracellular space, to the measurable biomagnetic field. Notably, the simulations show that the normal-to-the-surface MMG component, the contribution from volume currents in the extracellular space and in surrounding inactive tissues is negligible. Further, our model predicts a surprisingly high contribution of the passive muscle fibres to the observable magnetic field.
Chest physiotherapy is a set of techniques, mostly empirical, used to help the draining of the mucus from the lung in pathological situations. The choice of the techniques, and their adjustment to the patients or to the pathologies, remains as of tod
ay largely empirical. High Frequency Chest Wall Oscillation (HFCWO) is one of these techniques, performed with a device that applies oscillating pressures on the chest. However, there is no clear understanding of how HFCWO devices interact with the lung biomechanics. Hence, we study idealised HFCWO manipulations applied to a mathematical and numerical model of the biomechanics of the lung. The lung is represented by an airway tree connected to an homogeneous elastic medium. We highlight that the biophysics of the idealised HFCWO is driven by two dimensionless numbers. We show that the stress applied to the mucus plays the role of a buffer for the mucus yield stress, hence reducing the amount of stress needed to mobilize the mucus. The stress is the addition of two stresses with different physical origin and of the same order of magnitude: a stress due to the airway wall deformation and a stress due to the air-mucus interactions. Our model predicts the existence of an optimal range of HFCWO working frequencies that is in agreement with the frequencies actually used during HFCWO oscillations. Moreover, our model suggests that analyzing the mouth airflow during HFCWO could allow to estimate the compliance and the hydrodynamic resistance of the lung of a patient.
Novel diagnostic and therapeutic radiopharmaceuticals are increasingly becoming a central part of personalized medicine. Continued innovation in the development of new radiopharmaceuticals is key to sustained growth and advancement of precision medic
ine. Artificial intelligence (AI) has been used in multiple fields of medicine to develop and validate better tools for patient diagnosis and therapy, including in radiopharmaceutical design. In this review, we first discuss common in silico approaches and focus on their utility and challenges in radiopharmaceutical development. Next, we discuss the practical applications of in silico modeling in design of radiopharmaceuticals in various diseases.
The performance of machine learning algorithms used for the segmentation of 3D biomedical images lags behind that of the algorithms employed in the classification of 2D photos. This may be explained by the comparative lack of high-volume, high-qualit
y training datasets, which require state-of-the art imaging facilities, domain experts for annotation and large computational and personal resources to create. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which represents a 1-2 orders of magnitude increase over currently available biomedical datasets. The dataset further contains the underlying raw data, classical segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. By removing limits currently imposed by small training datasets, the provided data open up the possibility for disruptions in machine learning for biomedical image analysis.
Self-organization has become a watchword in developmental biology, characterizing observations in which embryonic or induced stem cells replicate morphological steps and outcomes seen in intact embryos. While the term was introduced in the 18th centu
ry by the philosopher Immanuel Kant to describe the goal-directed properties of living systems, it came into modern use for non-living materials in which complex forms and patterns emerge through dynamical, energy-expending physical processes. What are the relationships among these uses of the term? While multicellular forms arose dozens of times from single-celled organisms, only some of these undergo development, and not all developmental processes are self-organizing. The evolution of the animals (metazoans) from unicellular holozoans was accompanied by the addition of novel gene products which mediated the constitution of the resulting cell clusters as liquid-, liquid crystal-, and solid-like materials with protean morphogenetic propensities. Such materials variously exhibited multilayering, lumen formation and elongation, echoing the self-organizing properties of nonliving matter, generic based on such parallels, though with biologically based subunit properties and modes of interaction. These effects provided evolutionary templates for embryonic forms and morphological motifs of diverse metazoan lineages. Embryos and organ primordia of present-day animal species continue to generate forms that resemble the outcomes of these physical effects. Their development, however, employs overdetermined, highly evolved mechanisms that are often disconnected from their originating processes. Using the examples of gastrulation, somitogenesis, and limb skeletal development, this chapter provides instances of, and a conceptual framework for understanding, the relationships between physical and evolved types of developmental self-organization.
