Cochlear wavenumber and impedance are mechanistic variables that encode information regarding how the cochlea works - specifically wave propagation and Organ of Corti dynamics. These mechanistic variables underlie interesting features of cochlear signal processing such as its place-based wavelet analyzers, dispersivity and high-gain. Consequently, it is of interest to estimate these mechanistic variables in various species (particularly humans) and across various locations along the length of the cochlea. In this paper, we develop methods to estimate the mechanistic variables (wavenumber and impedance) from noninvasive response characteristics (such as the quality factors of psychophysical tuning curves) using an existing analytic shortwave single-partition model of the mammalian cochlea. We then apply these methods to estimate human mechanistic variables using reported values for quality factors from psychophysical tuning curves and a location-invariant ratio extrapolated from chinchilla. Our resultant estimates for human wavenumbers and impedances show that the minimum wavelength (which occurs at the peak of the traveling wave) is smaller in base than the apex. The Organ of Corti is stiffness dominated rather than mass dominated, and there is negative effective damping prior to the peak followed by positive effective damping. The effective stiffness, and positive and negative effective damping are greater in the base than the apex. The methods introduced here for estimating mechanistic variables from characteristics of invasive or noninvasive responses enable us to derive such estimates across various species and locations where the responses are describable by sharp filters. In addition to studying cochlear wave propagation and dynamics, the estimation methods developed here are also useful for auditory filter design.
Antibodies and humoral memory are key components of the adaptive immune system. We consider and computationally model mechanisms by which humoral memory present at baseline might instead increase infection load; we refer to this effect as EI-HM (enhancement of infection by humoral memory). We first consider antibody dependent enhancement (ADE) in which antibody enhances the growth of the pathogen, typically a virus, and typically at intermediate "Goldilocks" levels of antibody. Our ADE model reproduces ADE in vitro and enhancement of infection in vivo from passive antibody transfer. But notably the simplest implementation of our ADE model never results in EI-HM. Adding complexity, by making the cross-reactive antibody much less neutralizing than the de novo generated antibody or by including a sufficiently strong non-antibody immune response, allows for ADE-mediated EI-HM. We next consider the possibility that cross-reactive memory causes EI-HM by crowding out a possibly superior de novo immune response. We show that, even without ADE, EI-HM can occur when the cross-reactive response is both less potent and "directly" (i.e. independently of infection load) suppressive with regard to the de novo response. In this case adding a non-antibody immune response to our computational model greatly reduces or completely eliminates EI-HM, which suggests that "crowding out" is unlikely to cause substantial EI-HM. Hence, our results provide examples in which simple models give qualitatively opposite results compared to models with plausible complexity. Our results may be helpful in interpreting and reconciling disparate experimental findings, especially from dengue, and for vaccination.
Kidney disease of unknown aetiology (CKDu) has been identified in many countries extending from MesoAmerica and Egypt, to South-east Asia and China. Although CKDu has been linked by various authors to farming, it is an artifact of treating multi-modal disease distributions as unimodal. There is NO correlation of CKDu with agriculture since affected farming villages are often surrounded by other farming villages free of CKDu. Initial studies looked for a correlation of CKDu with toxic heavy metal residues of arsenic, cadmium etc., or herbicides like glyphosate that may be present in the environment, as the causative factors. There is now considerable consensus that their concentrations are below danger thresholds, be it in Mesoamerica or south-east Asia. The conceptual basis of a search for etiology within a systems approach is discussed, and attempts to name the disease to bias the identification of its etiology are reviewed. Current research has narrowed down the etiology to geochemical electrolytic contaminants like fluorides and ionic components in hard water, nanosilica (found in water as well as in the air), as well as renal toxins similar to indoxyl sulphates that may arise from interactions of ions with humic acids contained in aqueous organic matter. However, while agrochemical toxins are increasingly considered less relevant to the etiology of CKDu, it has become a firm public belief. In Sri Lanka this has spawned ideology-based agricultural policies for partial and complete banning of agrochemicals (2014-2021), followed by some back-tracking, disrupting the economy and the food supply. A farmer's uprising in 2022 was spawned by poor harvests. It triggered a larger popular uprising that led to the collapse of a government wedded to romanticized eco-extremist agricultural policies in a country already facing difficulties in the wake of Covid and Ukraine.
Cell spheroids are in vitro multicellular model systems that mimic the crowded micro-environment of biological tissues. Their mechanical characterization can provide valuable insights in how single-cell mechanics and cell-cell interactions control tissue mechanics and self-organization. However, most measurement techniques are limited to probing one spheroid at a time, require specialized equipment and are difficult to handle. Here, we developed a microfluidic chip that follows the concept of glass capillary micropipette aspiration in order to quantify the viscoelastic behavior of spheroids in an easy- to-handle, high-throughput manner. Spheroids are loaded in parallel pockets via a gentle flow, after which spheroid tongues are aspirated into adjacent aspiration channels using hydrostatic pressure. After each experiment, the spheroids are easily removed from the chip by reversing the pressure and new spheroids can be injected. The presence of multiple pockets with a uniform aspiration pressure, combined with the ease to conduct successive experiments, allows for a high throughput of tens of spheroids per day. We demonstrate that the chip provides accurate deformation data when working at different aspiration pressures. Lastly, we measure the viscoelastic properties of spheroids made of different cell lines and show how these are consistent with previous studies using established experimental techniques. In summary, our chip provides a high-throughput way to measure the viscoelastic deformation behavior of cell spheroids, in order to mechanophenotype different tissue types and examine the link between cell-intrinsic properties and overall tissue behavior.
Introduction: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can substantially shorten the scan time or enhance the spatial resolution without increasing scan time; however, this may lead to significant DGM susceptibility underestimation. Method: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages. Pre-processed magnetic field maps are extended symmetrically from the centre of globus pallidus in the coronal plane to simulate QSM acquisitions of difference spatial coverages.Results: The proposed xQSM network led to the lowest DGM contrast lost with the smallest susceptibility variation range across all spatial coverages. For the digital brain phantom simulation, xQSM improved the DGM susceptibility underestimation more than 20% in small spatial coverages. For the in vivo acquisition, less than 5% DGM susceptibility error was achieved in 48 mm axial slabs using the xQSM network, while a minimum of 112 mm coverage was required for conventional methods. It is also shown that the background field removal process performed worse in reduced brain coverages, which further deteriorated the subsequent dipole inversion. Conclusion: The recently proposed deep learning-based xQSM method significantly improves the accuracy of DGM QSM from small spatial coverages as compared with conventional QSM algorithms, which can shorten DGM QSM acquisition time substantially.
Thanks to advancements in diagnosis and treatment, prostate cancer patients have high long-term survival rates. Currently, an important goal is to preserve quality-of-life during and after treatment. The relationship between the radiation a patient receives and the subsequent side effects he experiences is complex and difficult to model or predict. Here, we use machine learning algorithms and statistical models to explore the connection between radiation treatment and post-treatment gastro-urinary function. Since only a limited number of patient datasets are currently available, we used image flipping and curvature-based interpolation methods to generate more data in order to leverage transfer learning. Using interpolated and augmented data, we trained a convolutional autoencoder network to obtain near-optimal starting points for the weights. A convolutional neural network then analyzed the relationship between patient-reported quality-of-life and radiation. We also used analysis of variance and logistic regression to explore organ sensitivity to radiation and develop dosage thresholds for each organ region. Our findings show no connection between the bladder and quality-of-life scores. However, we found a connection between radiation applied to posterior and anterior rectal regions to changes in quality-of-life. Finally, we estimated radiation therapy dosage thresholds for each organ. Our analysis connects machine learning methods with organ sensitivity, thus providing a framework for informing cancer patient care using patient reported quality-of-life metrics.
Alvason Zhenhua Li, Karsten Eichholz, Anton Sholukh
et al.
Multiplexed immuno-fluorescence tissue imaging, allowing simultaneous detection of molecular properties of cells, is an essential tool for characterizing the complex cellular mechanisms in translational research and clinical practice. New image analysis approaches are needed because tissue section stained with a mixture of protein, DNA and RNA biomarkers are introducing various complexities, including spurious edges due to fluorescent staining artifacts between touching or overlapping cells. We have developed the RRScell method harnessing the stochastic random-reaction-seed (RRS) algorithm and deep neural learning U-net to extract single-cell resolution profiling-map of gene expression over a million cells tissue section accurately and automatically. Furthermore, with the use of manifold learning technique UMAP for cell phenotype cluster analysis, the AI-driven RRScell has equipped with a marker-based image cytometry analysis tool (markerUMAP) in quantifying spatial distribution of cell phenotypes from tissue images with a mixture of biomarkers. The results achieved in this study suggest that RRScell provides a robust enough way for extracting cytometric single cell morphology as well as biomarker content in various tissue types, while the build-in markerUMAP tool secures the efficiency of dimension reduction, making it viable as a general tool in the spatial analysis of high dimensional tissue image.
Nivethika Sivakumaran, Imesha Rashmini Rathnayaka, Rashida Shabbir
et al.
Umbilical cord blood (UBC) can be viewed as the most promising source of stem cells, in which collection cost is minimal and its benefits are immense. The cord blood is used to treat malignant and nonmalignant diseases; this is due to its progenitor characteristics know as stem cells.Its properties of being, immunologically immature and high plasticity has made it superior to other sources of stem cells. The stem cells collected from cord blood have neutral differentiation capabilities which allow medical professionals to produce functional neural cells from these stem cells.Cord Blood Banking (CBB) is the storing of the umbilical cord blood which is collected immediately after the delivery of the baby. Great care and concern are needed for proper storage of these progenitor cells, hence cord blood banks come into the play, they are of 3 types which are: public, private and direct donation banks.Clinical trials are still at its very early stages having abundances to still be uncovered but results were obtained have demonstrated high potential and more scope towards effective development therapies and treatments for rare disorders.
There is no method to quantify the spatial complexity within colon polyps. This paper describes a spatial transformation that translates the tissue architecture within a polyp, or a normal colon lining, into a complex sinusoid wave composed of discrete points. This sinusoid wave can then undergo the Fast Fourier Transform to obtain a spectrum of frequencies that represents the sinusoid wave. This spectrum can then serve as a signature of the spatial complexity [an index] within the polyp. By overlaying vertical lines that radiate from the bottom middle [like a fold-out fan] of an image of a polyp stained by hematoxylin and eosin, the image is segmented into sectors. Each vertical line also forms an angle with the horizontal axis of the image, ranging from 0 degrees to 180 degrees rising counter clockwise. Each vertical line will intersect with various features of the polyp [border of lumens, border of epithelial lining]. Each of these intersections is a point that can be characterized by its distance from the origin [this distance is also a magnitude of that point]. Thus, each intersection between radial line and polyp feature can be mapped by polar coordinates [radius length, angle measure]. By summing the distance of all points along the same radial line, each radial line that divides the image becomes one value. Plotting these values [y variable] against the angle of each radial line from the horizontal axis [x variable] results in a sinusoid wave consisting of discrete points. This method is referred to as the Linearized Compressed Polar Coordinates [LCPC] Transform. The LCPC transform, in conjunction with the Fast Fourier Transform, can reduce the complexity of visually hidden histological grades in colon polyps into categories of similar wave frequencies [each histological grade has a signature consisting of a handful of frequencies].
Felix Meister, Tiziano Passerini, Viorel Mihalef
et al.
To date, the simulation of organ deformations for applications like therapy planning or image-guided interventions is calculated by solving the elastodynamics equations. While efficient solvers have been proposed for fast simulations, methods that are both real-time and accurate are still an open challenge. An ideal, interactive solver would be able to provide physically and numerically accurate results at high frame rate, which requires efficient force computation and time integration. Towards this goal, we explore in this paper for the first time the use of neural networks to directly learn the underlying biomechanics. Given a 3D mesh of a soft tissue segmented from medical images, we train a neural network to predict vertex-wise accelerations for a large time step based on the current state of the system. The model is trained using the deformation of a bar under torsion, and evaluated on different motions, geometries, and hyperelastic material models. For predictions of ten times the original time step we observed a mean error of 0.017mm $\pm$ 0.014 (0.032) at a mesh size of 50mm x 50mm x 100mm. Predictions at 20dt yield an error of 2.10mm $\pm$ 1.73 (4.37) and by further increasing the prediction time step the maximum error rises to 38.3mm due to an artificial stiffening. In all experiments our proposed method stayed stable, while the reference solver fails to converge. Our experiments suggest that it is possible to directly learn the mechanical simulation and open further investigations for the direct application of machine learning to speed-up biophysics solvers.
Michael G. Watson, Helen M. Byrne, Charlie Macaskill
et al.
Atherosclerotic plaque growth is characterised by chronic inflammation that promotes accumulation of cellular debris and extracellular fat in the inner artery wall. This material is highly thrombogenic, and plaque rupture can lead to the formation of blood clots that occlude major arteries and cause myocardial infarction or stroke. In advanced plaques, vascular smooth muscle cells (SMCs) migrate from deeper in the artery wall to synthesise a cap of fibrous tissue that stabilises the plaque and sequesters the thrombogenic plaque content from the bloodstream. The fibrous cap provides crucial protection against the clinical consequences of atherosclerosis, but the mechanisms of cap formation are poorly understood. In particular, it is unclear why certain plaques become stable and robust while others become fragile and vulnerable to rupture. We develop a multiphase model with non-standard boundary conditions to investigate early fibrous cap formation in the atherosclerotic plaque. The model is parameterised using a range of in vitro and in vivo data, and includes highly nonlinear mechanisms of SMC proliferation and migration in response to an endothelium-derived chemical signal. We demonstrate that the model SMC population naturally evolves towards a steady-state, and predict a rate of cap formation and a final plaque SMC content consistent with experimental observations in mice. Parameter sensitivity simulations show that SMC proliferation makes a limited contribution to cap formation, and highlight that stable cap formation relies on a critical balance between SMC recruitment to the plaque, SMC migration within the plaque and SMC loss by apoptosis. The model represents the first detailed in silico study of fibrous cap formation in atherosclerosis, and establishes a multiphase modelling framework that can be readily extended to investigate many other aspects of plaque development.
Haley D. Clark, Stefan A. Reinsberg, Vitali Moiseenko
et al.
Introduction: Intra-organ radiation dose sensitivity is becoming increasingly relevant in clinical radiotherapy. One method for assessment involves partitioning delineated regions of interest and comparing the relative contributions or importance to clinical outcomes. We show that an intuitive method for dividing organ contours, compound (sub-)segmentation, can unintentionally lead to sub-segments with inconsistent volumes, which will bias relative importance assessment. An improved technique, nested segmentation, is introduced and compared. Methods: Clinical radiotherapy planning parotid contours from 510 patients were segmented. Counts of radiotherapy dose matrix voxels interior to sub-segments were used to determine the equivalency of sub-segment volumes. The distribution of voxel counts within sub-segments were compared using Kolmogorov-Smirnov tests and characterized by their dispersion. Analytical solutions for 2D/3D analogues were derived and sub-segment area/volume were compared directly. Results: Both parotid and 2D/3D region of interest analogue segmentation confirmed compound segmentation intrinsically produces sub-segments with volumes that depend on the region of interest shape and selection location. Significant volume differences were observed when sub-segmenting parotid contours into 18ths, and vanishingly small sub-segments were observed when sub-segmenting into 96ths. Central sub-segments were considerably smaller than sub-segments on the periphery. Nested segmentation did not exhibit these shortcomings and produced sub-segments with equivalent volumes when dose grid and contour collinearity was addressed, even when dividing the parotid into 96ths. Nested segmentation was always faster or equivalent in runtime to compound segmentation. Conclusions: Nested segmentation is more suited than compound segmentation for analyses requiring equal weighting of sub-segments.