Eye rubbing-induced changes in intraocular pressure and corneal thickness measured at five locations, in subjects with ocular allergy

AIM To assess the effects of eye rubbing on corneal thickness (CT) and intraocular pressure (IOP) measurements obtained 0-30min after habitual eye rubbing in symptomatic patients. METHODS Measurements of IOP and CT were obtained at five locations (central, temporal, superior, nasal and inferior) before, and every 5min for 30min interval after 30s of eye rubbing, for 25 randomly selected eyes of 14 subjects with ocular allergy and 11 age-matched normals. Differences in measurements were calculated in each group [Baseline measurements minus measurements recorded at each time interval after eye rubbing (for IOP), and for each corneal location (for CT)] and comparison were then made between groups (allergic versus control) for differences in any observed effects. RESULTS Within groups, baseline mean IOPs in the allergic patient-group (14.2±3.0 mm Hg) and in the control group (13.1±1.9 mm Hg) were similar at all times, after eye rubbing (P >0.05, for all). The maximum reduction in IOP was 0.8 mm Hg in the control subjects and the maximum increase was also 0.8 mm Hg in the allergic subjects. Between groups (allergic versus control), the changes in IOP remained under 1 mm Hg at all times (P=0.2) after 30min of eye rubbing. Between 0 and 30min of CT measurements after eye rubbing, the mean central CT (CCT), inferior CT (ICT), superior CT (SCT), temporal CT (TCT) and nasal CT (NCT) did not vary significantly from baseline values in the control and allergic-subject groups (P>0.05, for both). Between both groups, changes in CT were similar at all locations (P>0.05) except for the TC which was minimally thinner by about 4.4 µm (P=0.001) in the allergic subjects than in the control subjects, 30min following 30s of eye rubbing. CONCLUSION IOP measured in allergic subjects after 30s of habitual eye rubbing was comparable with that obtained in normal subjects at all times between 0 and 30min. Although, CT in the allergic subjects were similar to those of the control subjects at all times, it varied between +10 and -7.5 µm following eye rubbing, with the temporal cornea showing consistent reductions in thickness in the subjects with allergy. However, this reduction was minimal and was considered to not be clinically relevant.

Cross-scale biophysics modelling of F-actin cytoskeleton in cell

This thesis is a comparative study of the modelling of mechanical behaviours of F-actin cytoskeleton which is an important structural component in living cells. A new granular model was developed for F-actin cytoskeleton based on the concept of multiscale modelling. This framework overcomes difficulties encountered in physical modelling of cytoskeleton in conventional continuum mechanics modelling, and the computational challenges in all-atom molecular dynamics simulation. The thermostat algorithm was further modified to better predict the thermodynamic properties of F-actin cytoskeleton in modelling. This multiscale modelling framework was applied in explaining the physical mechanisms of cytoskeleton responses to external mechanical loads.

Double star chopper cell converter for battery electric vehicles with inter-module SoC balancing and fault tolerant control

A modularized battery system with Double Star Chopper Cell (DSCC) based modular multilevel converter is proposed for a battery operated electric vehicle (EV). A design concept for the modularized battery micro-packs for DSCC is described. Multidimensional pulse width modulation (MD-PWM) with integrated inter-module SoC balancing and fault tolerant control is proposed and explained. The DSCC can be operated either as an inverter to drive the EV motor or as a synchronous rectifier connected to external three phase power supply equipment for charging the battery micro-packs. The methods of operation as inverter and synchronous rectifier with integrated inter-module SoC balancing and fault tolerant control are discussed. The proposed system operation as inverter and synchronous rectifier are verified through simulations and the results are presented.

Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation

Wound healing and tumour growth involve collective cell spreading, which is driven by individual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that these parameters are constants and often ignore any uncertainty in the estimated values. We use approximate Bayesian computation (ABC) to estimate the cell diffusivity, D, and the cell proliferation rate, λ, from a discrete model of collective cell spreading, and we quantify the uncertainty associated with these estimates using Bayesian inference. We use a detailed experimental data set describing the collective cell spreading of 3T3 fibroblast cells. The ABC analysis is conducted for different combinations of initial cell densities and experimental times in two separate scenarios: (i) where collective cell spreading is driven by cell motility alone, and (ii) where collective cell spreading is driven by combined cell motility and cell proliferation. We find that D can be estimated precisely, with a small coefficient of variation (CV) of 2–6%. Our results indicate that D appears to depend on the experimental time, which is a feature that has been previously overlooked. Assuming that the values of D are the same in both experimental scenarios, we use the information about D from the first experimental scenario to obtain reasonably precise estimates of λ, with a CV between 4 and 12%. Our estimates of D and λ are consistent with previously reported values; however, our method is based on a straightforward measurement of the position of the leading edge whereas previous approaches have involved expensive cell counting techniques. Additional insights gained using a fully Bayesian approach justify the computational cost, especially since it allows us to accommodate information from different experiments in a principled way.

Cell type-dependent, infection-induced, aberrant DNA methylation in gastric cancer

Epigenetic changes correspond to heritable modifications of the chromatin structure, which do not involve any alteration of the DNA sequence but nonetheless affect gene expression. These mechanisms play an important role in cell differentiation, but aberrant occurrences are also associated with a number of diseases, including cancer and neural development disorders. In particular, aberrant DNA methylation induced by H. Pylori has been found to be a significant risk factor in gastric cancer. To investigate the sensitivity of different genes and cell types to this infection, a computational model of methylation in gastric crypts is developed. In this article, we review existing results from physical experiments and outline their limitations, before presenting the computational model and investigating the influence of its parameters.

Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis

Systems-level identification and analysis of cellular circuits in the brain will require the development of whole-brain imaging with single-cell resolution. To this end, we performed comprehensive chemical screening to develop a whole-brain clearing and imaging method, termed CUBIC (clear, unobstructed brain imaging cocktails and computational analysis). CUBIC is a simple and efficient method involving the immersion of brain samples in chemical mixtures containing aminoalcohols, which enables rapid whole-brain imaging with single-photon excitation microscopy. CUBIC is applicable to multicolor imaging of fluorescent proteins or immunostained samples in adult brains and is scalable from a primate brain to subcellular structures. We also developed a whole-brain cell-nuclear counterstaining protocol and a computational image analysis pipeline that, together with CUBIC reagents, enable the visualization and quantification of neural activities induced by environmental stimulation. CUBIC enables time-course expression profiling of whole adult brains with single-cell resolution.

Whole-Body Imaging with Single-Cell Resolution by Tissue Decolorization

The development of whole-body imaging at single-cell resolution enables system-level approaches to studying cellular circuits in organisms. Previous clearing methods focused on homogenizing mismatched refractive indices of individual tissues, enabling reductions in opacity but falling short of achieving transparency. Here, we show that an aminoalcohol decolorizes blood by efficiently eluting the heme chromophore from hemoglobin. Direct transcardial perfusion of an aminoalcohol-containing cocktail that we previously termed CUBIC coupled with a 10 day to 2 week clearing protocol decolorized and rendered nearly transparent almost all organs of adult mice as well as the entire body of infant and adult mice. This CUBIC-perfusion protocol enables rapid whole-body and whole-organ imaging at single-cell resolution by using light-sheet fluorescent microscopy. The CUBIC protocol is also applicable to 3D pathology, anatomy, and immunohistochemistry of various organs. These results suggest that whole-body imaging of colorless tissues at high resolution will contribute to organism-level systems biology.

Experimental and numerical investigation of strain-rate dependent mechanical properties of single living cells

The objective of this project is to investigate the strain-rate dependent mechanical behaviour of single living cells using both experimental and numerical techniques. The results revealed that living cells behave as porohyperlastic materials and that both solid and fluid phases within the cells play important roles in their mechanical responses. The research reported in this thesis provides a better understanding of the mechanisms underlying the cellular responses to external mechanical loadings and of the process of mechanical signal transduction in living cells. It would help us to enhance knowledge of and insight into the role of mechanical forces in supporting tissue regeneration or degeneration.

Numerical investigation of case hardening of plant tissue during dying and its influence on the cellular level shrinkage

Dried plant food materials are one of the major contributors to the global food industry. Widening the fundamental understanding on different mechanisms of food material alterations during drying assists the development of novel dried food products and processing techniques. In this regard, case hardening is an important phenomenon, commonly observed during the drying processes of plant food materials, which significantly influences the product quality and process performance. In this work, a recent meshfree-based numerical model of the authors is further improved and used to simulate the influence of case hardening on shrinkage characteristics of plant tissues during drying. In order to model fluid and wall mechanisms in each cell, Smoothed Particle Hydrodynamics (SPH) and the Discrete Element Method (DEM) are used. The model is fundamentally more capable of simulating large deformation of multiphase materials, when compared with conventional grid-based modelling techniques such as Finite Element Methods (FEM) or Finite Difference Methods (FDM). Case hardening is implemented by maintaining distinct moisture levels in the different cell layers of a given tissue. In order to compare and investigate different factors influencing tissue deformations under case hardening, four different plant tissue varieties (apple, potato, carrot and grape) are studied. The simulation results indicate that the inner cells of any given tissue undergo limited shrinkage and cell wall wrinkling compared to the case hardened outer cell layers of the tissues. When comparing unique deformation characteristics of the different tissues, irrespective of the normalised moisture content, the cell size, cell fluid turgor pressure and cell wall characteristics influence the tissue response to case hardening.

Reduced intensity conditioning for allogeneic hematopoietic stem cell transplant determines the kinetics of acute Graft-Versus-Host Disease

Background Preparative myeloablative conditioning regimens for allogeneic hematopoietic stem-cell transplantation (HSCT) may control malignancy and facilitate engraftment but also contribute to transplant related mortality, cytokine release, and acute graft-versus-host disease (GVHD). Reduced intensity conditioning (RIC) regimens have decreased transplant related mortality but the incidence of acute GVHD, while delayed, remains unchanged. There are currently no in vivo allogeneic models of RIC HSCT, limiting studies into the mechanism behind RIC-associated GVHD. Methods We developed two RIC HSCT models that result in delayed onset GVHD (major histocompatibility complex mismatched (UBI-GFP/BL6 [H-2b]→BALB/c [H-2d]) and major histocompatibility complex matched, minor histocompatibility mismatched (UBI-GFP/BL6 [H-2b]→BALB.B [H-2b])) enabling the effect of RIC on chimerism, dendritic cell (DC) chimerism, and GVHD to be investigated. Results In contrast with myeloablative conditioning, we observed that RIC-associated delayed-onset GVHD is characterized by low production of tumor necrosis factor-α, maintenance of host DC, phenotypic DC activation, increased T-regulatory cell numbers, and a delayed emergence of activated donor DC. Furthermore, changes to the peritransplant milieu in the recipient after RIC lead to the altered activation of DC and the induction of T-regulatory responses. Reduced intensity conditioning recipients suffer less early damage to GVHD target organs. However, as donor cells engraft, activated donor DC and rising levels of tumor necrosis factor-α are associated with a later onset of severe GVHD. Conclusions Delineating the mechanisms underlying delayed onset GVHD in RIC HSCT recipients is vital to improve the prediction of disease onset and allow more targeted interventions for acute GVHD.

Developing a therapeutic anti-dendritic cell antibody to prevent graft versus host disease

Host and donor dendritic cells (DC) stimulate alloreactive donor T lymphocytes, and initiate GVHD. We have shown that polyclonal antibody to the DC surface activation marker human CD83 (anti hCD83), which depletes activated DC, can prevent human DC and T cell induced lethal xenogeneic GVHD in SCID mice without impairing T cell mediated anti-leukaemic and anti-viral (CMV and influenza) immunity (J Exp Med 2009; 206: 387). Therefore, we made and tested a polyclonal anti mouse CD83 (RAM83) antibody in murine HSCT models and developed a human mAb against hCD83 as a potential new therapeutic immunosuppressive agent.

Drug accumulation into single drug-sensitive and drug-resistant prostate cancer cells conducted on the single cell bioanalyzer

Multidrug resistance (MDR) occurs in prostate cancer, and this happens when the cancer cells resist chemotherapeutic drugs by pumping them out of the cells. MDR inhibitors such as cyclosporin A (CsA) can stop the pumping and enhance the drugs accumulated in the cells. The cellular drug accumulation is monitored using a microfluidic chip mounted on a single cell bioanalyzer. This equipment has been developed to measure accumulation of drugs such as doxorubicin (DOX) and fluorescently labeled paclitaxel (PTX) in single prostate cancer cells. The inhibition of drug efflux on the same prostate cell was examined in drug-sensitive and drug-resistant cells. Accumulation of these drug molecules was not found in the MDR cells, PC-3 RX-DT2R cells. Enhanced drug accumulation was observed only after treating the MDR cell in the presence of 5 μM of CsA as the MDR inhibitor. We envision this monitoring of the accumulation of fluorescent molecules (drug or fluorescent molecules), if conducted on single patient cancer cells, can provide information for clinical monitoring of patients undergoing chemotherapy in the future.

Melanoma cell colony expansion parameters revealed by approximate Bayesian computation

In vitro studies and mathematical models are now being widely used to study the underlying mechanisms driving the expansion of cell colonies. This can improve our understanding of cancer formation and progression. Although much progress has been made in terms of developing and analysing mathematical models, far less progress has been made in terms of understanding how to estimate model parameters using experimental in vitro image-based data. To address this issue, a new approximate Bayesian computation (ABC) algorithm is proposed to estimate key parameters governing the expansion of melanoma cell (MM127) colonies, including cell diffusivity, D, cell proliferation rate, λ, and cell-to-cell adhesion, q, in two experimental scenarios, namely with and without a chemical treatment to suppress cell proliferation. Even when little prior biological knowledge about the parameters is assumed, all parameters are precisely inferred with a small posterior coefficient of variation, approximately 2–12%. The ABC analyses reveal that the posterior distributions of D and q depend on the experimental elapsed time, whereas the posterior distribution of λ does not. The posterior mean values of D and q are in the ranges 226–268 µm2h−1, 311–351 µm2h−1 and 0.23–0.39, 0.32–0.61 for the experimental periods of 0–24 h and 24–48 h, respectively. Furthermore, we found that the posterior distribution of q also depends on the initial cell density, whereas the posterior distributions of D and λ do not. The ABC approach also enables information from the two experiments to be combined, resulting in greater precision for all estimates of D and λ.

A cellular automata model to investigate immune cell–tumor cell interactions in growing tumors in two spatial dimensions

We develop a hybrid cellular automata model to describe the effect of the immune system and chemokines on a growing tumor. The hybrid cellular automata model consists of partial differential equations to model chemokine concentrations, and discrete cellular automata to model cell–cell interactions and changes. The computational implementation overlays these two components on the same spatial region. We present representative simulations of the model and show that increasing the number of immature dendritic cells (DCs) in the domain causes a decrease in the number of tumor cells. This result strongly supports the hypothesis that DCs can be used as a cancer treatment. Furthermore, we also use the hybrid cellular automata model to investigate the growth of a tumor in a number of computational “cancer patients.” Using these virtual patients, the model can explain that increasing the number of DCs in the domain causes longer “survival.” Not surprisingly, the model also reflects the fact that the parameter related to tumor division rate plays an important role in tumor metastasis.