Search

PIK3CA-related overgrowth spectrum (PROS) conditions of the head and neck are treatment challenges. Traditionally, these conditions require multiple invasive interventions, with incomplete malformation removal, disfigurement, and possible dysfunction. Use of the PI3K inhibitor alpelisib, previously shown to be effective in PROS, has not been reported in PIK3CA-associated head and neck lymphatic malformations (HNLMs) or facial infiltrating lipomatosis (FIL). We describe prospective treatment of 5 children with PIK3CA-associated HNLMs or head and neck FIL with alpelisib monotherapy.A total of 5 children with PIK3CA-associated HNLMs (n = 4) or FIL (n = 1) received alpelisib monotherapy (aged 2-12 years). Treatment response was determined by parental report, clinical evaluation, diary/questionnaire, and standardized clinical photography, measuring facial volume through 3-dimensional photos and magnetic resonance imaging.All participants had reduction in the size of lesion, and all had improvement or resolution of malformation inflammation/pain/bleeding. Common invasive therapy was avoided (ie, tracheotomy). After 6 or more months of alpelisib therapy, facial volume was reduced (range 1%-20%) and magnetic resonance imaging anomaly volume (range 0%-23%) were reduced, and there was improvement in swallowing, upper airway patency, and speech clarity.Individuals with head and neck PROS treated with alpelisib had decreased malformation size and locoregional overgrowth, improved function and symptoms, and fewer invasive procedures.

OBJECTIVES: To evaluate the performance of 4-dimensional computed tomography (4D-CT) in assessing upper airway obstruction (UAO) in patients with Robin Sequence (RS) and compare the accuracy and reliability of 4D-CT and flexible fiberoptic laryngoscopy (FFL). STUDY DESIGN: Prospective survey of retrospective clinical data. SETTING: Single, tertiary care pediatric hospital. METHODS: At initial and 30-day timepoints, a multidisciplinary group of 11 clinicians who treat RS rated UAO severity in 36 sets of 4D-CT visualizations and FFL videos (dynamic modalities), and static CT images. Raters assessed UAO at the velopharynx and oropharynx (1-none to 5-complete) and noted confidence levels (LOC) of each rating. Intraclass correlation and Krippendorff’s Alpha were used to assess intra- and inter-rater reliability, respectively. Accuracy was assessed by comparing clinician ratings with Quantitative Percent Constriction (QPC) Ratings, calculated based on 4D-CT airway cross-sectional area. Results were compared using Wilcoxon rank-sum and signed-rank tests. RESULTS: 4D-CT and FFL demonstrated similar intra-rater agreement (moderate to substantial) and both demonstrated fair inter-rater agreement. Both modalities underestimated UAO severity, though 4D-CT ratings were significantly more accurate, as determined by QPC similarity, than FFL (−1.06 and −1.46 vs QPC Ratings, p=0.004). Overall LOC was similar for 4D-CT and FFL, but other specialists were significantly less confident in FFL ratings than were otolaryngologists (2.25 and 3.92, p

Background Facioscapulohumeral muscular dystrophy (FSHD) is a patchy and slowly progressive disease of skeletal muscle. MRI short tau inversion recovery (STIR) sequences of patient muscles often show increased hyperintensity that is hypothesized to be associated with inflammation. This is supported by the presence of inflammatory changes on biopsies of STIR-positive muscles. We hypothesized that the STIR positivity would normalize with targeted immunosuppressive therapy. Case presentation 45-year-old male with FSHD type 1 was treated with 12 weeks of immunosuppressive therapy, tacrolimus and prednisone. Tacrolimus was treated to a goal serum trough of > 5 ng/mL and prednisone was tapered every month. Quantitative strength exam, functional outcome measures, and muscle MRI were performed at baseline, week 6, and week 12. The patient reported subjective worsening as reflected in quantitative strength exam. The MRI STIR signal was slightly increased from 0.02 to 0.03 of total muscle; while the T1 fat fraction was stable. Functional outcome measures also were stable. Conclusions Immunosuppressive therapy in refractive autoimmune myopathy in other contexts has been shown to reverse STIR signal hyperintensity, however this treatment did not reverse STIR signal in this patient with FSHD. In fact, STIR signal slightly increased throughout the treatment period. This is the first study of using MRI STIR and T1 fat fraction to follow treatment effect in FSHD. We find that STIR might not be a dynamic marker for suppressing inflammation in FSHD.

Abnormal cerebrospinal fluid (CSF) flow is associated with a variety of poorly understood neurological disorders such as Alzheimer's Disease and hydrocephalus. The lack of comprehensive understanding of the fluid and solid mechanics of CSF flow remains a critical barrier in the development of diagnostic assessment and potential treatment options for these diseases. We have developed a whole brain, patient-specific computational fluid dynamics (CFD) simulation of CSF flow in the cranial cavity as a step towards comprehensive understanding of CSF dynamics and how they relate to neurodegenerative diseases.A patient-specific 3D geometry of the CSF filled spaces was segmented from structural MRI. Patient-specific boundary conditions were measured using phase contrast MRI. A rigid wall three-dimensional CFD simulation was conducted using only patient-specific waveforms as boundary conditions. Deformation of brain tissue is accounted for using volumetric flowrate boundary conditions calculated via the conservation of mass. Phase contrast MRI measurement of maximum velocity at the cerebral aqueduct was used to validate the simulation with excellent agreement.The CSF dynamics across the cardiac cycle are presented, illustrating the relationship between arterial flow and CSF flow. Flow in and out of the ventricles was shown to have a slight phase delay (∼20 % of the cardiac cycle) from flow in the subarachnoid space. Intracranial pressure dynamics are presented, with pressure in the Lateral Ventricles demonstrating less significant transient effects than pressure in the subarachnoid space.This work presents a quantitatively validated whole-brain simulation of CSF flow for a single healthy subject. The computational methodology improves over the state of the art by eliminating non-physiological boundary conditions and unnecessary assumptions about the mechanical properties of brain tissue, providing an essential step towards clinically useful tools for assessing the development of neurodegenerative disorders.

Thorough assessment of dynamic upper airway obstruction (UAO) in Robin sequence (RS) is critical, but traditional evaluation modalities have significant limitations. Four-dimensional computed tomography (4D-CT) is promising in that it enables objective and quantitative evaluation throughout all phases of respiration. However, there exist few protocols or analysis tools to assist in obtaining and interpreting the vast amounts of obtained data. A protocol and set of data analysis tools were developed to enable quantification and visualization of dynamic 4D-CT data. This methodology was applied to a sample case at 2 time points. In the patient with RS, overall increases in normalized airway caliber were observed from 5 weeks to 1 year. There was, however, continued dynamic obstruction at all airway levels, though objective measures of UAO did improve at the nasopharynx and oropharynx. Use of 4D-CT and novel analyses provide additional quantitative information to evaluate UAO in patients with RS.

Chest radiographs are a common diagnostic tool in pediatric care, and several computer-augmented decision tasks for radiographs would benefit from knowledge of the anatomic locations within the thorax. For example, a pre-segmented chest radiograph could provide context for algorithms designed for automatic grading of catheters and tubes. This work develops a deep learning approach to automatically segment chest radiographs into multiple regions to provide anatomic context for future automatic methods. This type of segmentation offers challenging aspects in its goal of multi-class segmentation with extreme class imbalance between regions. In an IRB-approved study, pediatric chest radiographs were collected and annotated with custom software in which users drew boundaries around seven regions of the chest: left and right lung, left and right subdiaphragm, spine, mediastinum, and carina. We trained a U-Net-style architecture on 328 annotated radiographs, comparing model performance with various combinations of loss functions, weighting schemes, and data augmentation. On a test set of 70 radiographs, our best-performing model achieved 93.8% mean pixel accuracy and a mean Dice coefficient of 0.83. We find that (1) cross-entropy consistently outperforms generalized Dice loss, (2) light augmentation, including random rotations, improves overall performance, and (3) pre-computed pixel weights that account for class frequency provide small performance boosts. Overall, our approach produces realistic eight-class chest segmentations that can provide anatomic context for line placement and potentially other medical applications.

Background: The accuracy of absolute myocardial blood flow (MBF) from dynamic contrast-enhanced cardiac computed tomography acquisitions has not been fully characterized. We evaluate computed tomography (CT) compared with rubidium-82 positron emission tomography (PET) MBF estimates in a high-risk population. Methods: In a prospective trial, patients receiving clinically indicated rubidium-82 PET exams were recruited to receive a dynamic contrast-enhanced cardiac computed tomography exam. The CT protocol included a rest and stress dynamic portion each acquiring 12 to 18 cardiac-gated frames. The global MBF was estimated from the PET and CT exam. Results: Thirty-four patients referred for cardiac rest-stress PET were recruited. Of the 68 dynamic contrast-enhanced cardiac computed tomography scans, 5 were excluded because of injection errors or mismatched hemodynamics. The CT-derived global MBF was highly correlated with the PET MBF (r=0.92; P P Conclusions: In a high-risk clinical population, a clinically practical dynamic contrast-enhanced cardiac computed tomography provided unbiased MBF estimates within 20% of rubidium-82 PET. Although unbiased, the CT estimates contain substantial variance with an standard error of the estimate of 0.44 mL/min per gram. Myocardial flow reserve estimation was not as accurate as individual MBF estimates.

Quantitative myocardial blood flow (MBF) estimation by dynamic contrast enhanced cardiac computed tomography (CT) requires multi-frame acquisition of contrast transit through the blood pool and myocardium to inform the arterial input and tissue response functions. Both the input and the tissue response functions for the entire myocardium are sampled with each acquisition. However, the long breath holds and frequent sampling can result in significant motion artifacts and relatively high radiation dose. To address these limitations, we propose and evaluate a new static cardiac and dynamic arterial (SCDA) quantitative MBF approach where (1) the input function is well sampled using either prediction from pre-scan timing bolus data or measured from dynamic thin slice 'bolus tracking' acquisitions, and (2) the whole-heart tissue response data is limited to one contrast enhanced CT acquisition. A perfusion model uses the dynamic arterial input function to generate a family of possible myocardial contrast enhancement curves corresponding to a range of MBF values. Combined with the timing of the single whole-heart acquisition, these curves generate a lookup table relating myocardial contrast enhancement to quantitative MBF. We tested the SCDA approach in 28 patients that underwent a full dynamic CT protocol both at rest and vasodilator stress conditions. Using measured input function plus single (enhanced CT only) or plus double (enhanced and contrast free baseline CT's) myocardial acquisitions yielded MBF estimates with root mean square (RMS) error of 1.2 ml/min/g and 0.35 ml/min/g, and radiation dose reductions of 90% and 83%, respectively. The prediction of the input function based on timing bolus data and the static acquisition had an RMS error compared to the measured input function of 26.0% which led to MBF estimation errors greater than threefold higher than using the measured input function. SCDA presents a new, simplified approach for quantitative perfusion imaging with an acquisition strategy offering substantial radiation dose and computational complexity savings over dynamic CT.

Dynamic contrast enhanced cardiac CT acquisitions can quantify myocardial blood flow (MBF) in absolute units (ml/min/g), but repeated scans increase X-ray radiation dose to the patient. We propose a novel approach using high temporal sampling of the input function with reduced temporal sampling of the myocardial tissue response. This type of data could be acquired with current bolus tracking acquisitions or with new acquisition sequences offering reduced radiation dose and potentially easier data processing and flow estimation. To evaluate this type of data, we prospectively acquired a full dynamic series [12 -18 frames (mean 14.5±1.4) over 23 to 44 seconds (mean 31.3±5.0 sec)] on 28 patients at rest and stress (N=56 studies) and examined the relative performance of myocardial perfusion estimation when the myocardial data is subsampled down to 8, 4, 2 or 1 frame(s). Unlike previous studies, for all frame rates, we consider a well-sampled input function. As expected, subsampling linearly reduces radiation dose while progressively decreasing estimation accuracy, with the typical absolute error in MBF (as compared to the full-length series) increasing from 0.22 to 0.30 to 0.35 to 1.12 ml/min/g as the number of frames used for estimation decreases from 8 to 4 to 2 to 1, respectively. These results suggest that high temporal sampling of the input function with low temporal sampling of the myocardial response can provide much of the benefit of dynamic CT for MBF quantification with dramatic reductions in the required number of myocardial acquisitions and the associated radiation dose (e.g. 77% dose reduction for 2-frame case).

Quantification of myocardial blood flow (MBF) can aid in the diagnosis and treatment of coronary artery disease. However, there are no widely accepted clinical methods for estimating MBF. Dynamic cardiac perfusion computed tomography (CT) holds the promise of providing a quick and easy method to measure MBF quantitatively. However, the need for repeated scans can potentially result in a high patient radiation dose, limiting the clinical acceptance of this approach. In our previous work, we explored techniques to reduce the patient dose by either uniformly reducing the tube current or by uniformly reducing the number of temporal frames in the dynamic CT sequence. These dose reduction techniques result in noisy time-attenuation curves (TACs), which can give rise to significant errors in MBF estimation. We seek to investigate whether nonuniformly varying the tube current and/or sampling intervals can yield more accurate MBF estimates for a given dose. Specifically, we try to minimize the dose and obtain the most accurate MBF estimate by addressing the following questions: when in the TAC should the CT data be collected and at what tube current(s)? We hypothesize that increasing the sampling rate and/or tube current during the time frames when the myocardial CT number is most sensitive to the flow rate, while reducing them elsewhere, can achieve better estimation accuracy for the same dose. We perform simulations of contrast agent kinetics and CT acquisitions to evaluate the relative MBF estimation performance of several clinically viable variable acquisition methods. We find that variable temporal and tube current sequences can be performed that impart an effective dose of 5.5 mSv and allow for reductions in MBF estimation root-mean-square error on the order of 20% compared to uniform acquisition sequences with comparable or higher radiation doses.

Quantification of myocardial blood flow (MBF) can aid in the diagnosis and treatment of coronary artery disease (CAD). However, there are no widely accepted clinical methods for estimating MBF. Dynamic CT holds the promise of providing a quick and easy method to measure MBF quantitatively, however the need for repeated scans has raised concerns about the potential for high radiation dose. In our previous work, we explored techniques to reduce the patient dose by either uniformly reducing the tube current or by uniformly reducing the number of temporal frames in the dynamic CT sequence. These dose reduction techniques result in very noisy data, which can give rise to large errors in MBF estimation. In this work, we seek to investigate whether nonuniformly varying the tube current or sampling intervals can yield more accurate MBF estimates. Specifically, we try to minimize the dose and obtain the most accurate MBF estimate through addressing the following questions: when in the time attenuation curve (TAC) should the CT data be collected and at what tube current(s). We hypothesize that increasing the sampling rate and/or tube current during the time frames when the myocardial CT number is most sensitive to the flow rate, while reducing them elsewhere, can achieve better estimation accuracy for the same dose. We perform simulations of contrast agent kinetics and CT acquisitions to evaluate the relative MBF estimation performance of several clinically viable adaptive acquisition methods. We found that adaptive temporal and tube current sequences can be performed that impart an effective dose of about 5 mSv and allow for reductions in MBF estimation RMSE on the order of 11% compared to uniform acquisition sequences with comparable or higher radiation doses.

Cardiac CT acquisitions for perfusion assessment can be performed in a dynamic or static mode. In this simulation study, we evaluate the relative classification and quantification performance of these modes for assessing myocardial blood flow (MBF). In the dynamic method, a series of low dose cardiac CT acquisitions yields data on contrast bolus dynamics over time; these data are fit with a model to give a quantitative MBF estimate. In the static method, a single CT acquisition is obtained, and the relative CT numbers in the myocardium are used to infer perfusion states. The static method does not directly yield a quantitative estimate of MBF, but these estimates can be roughly approximated by introducing assumed linear relationships between CT number and MBF, consistent with the ways such images are typically visually interpreted. Data obtained by either method may be used for a variety of clinical tasks, including 1) stratifying patients into differing categories of ischemia and 2) using the quantitative MBF estimate directly to evaluate ischemic disease severity. Through simulations, we evaluate the performance on each of these tasks. The dynamic method has very low bias in MBF estimates, making it particularly suitable for quantitative estimation. At matched radiation dose levels, ROC analysis demonstrated that the static method, with its high bias but generally lower variance, has superior performance in stratifying patients, especially for larger patients.

We have derived a broad, deterministic model of the steady-state actin cycle that includes its major regulatory mechanisms. Ours is the first model to solve the complete nucleotide profile within filaments, a feature that determines the dynamics and geometry of actin networks at the leading edges of motile cells, and one that has challenged investigators developing models to interpret steady-state experiments. We arrived at the nucleotide profile through analytic and numerical approaches that completely agree. Our model reproduces behaviors seen in numerous experiments with purified proteins, but allows a detailed inspection of the concentrations and fluxes that might exist in these experiments. These inspections provide new insight into the mechanisms that determine the rate of actin filament treadmilling. Specifically, we find that mechanisms for enhancing Pi release from the ADP·Pi intermediate on filaments, for increasing the off rate of ADP-bound subunits at pointed ends, and the multiple, simultaneous functions of profilin, make unique and essential contributions to increased treadmilling. In combination, these mechanisms have a theoretical capacity to increase treadmilling to levels limited only by the amount of available actin. This limitation arises because as the cycle becomes more dynamic, it tends toward the unpolymerized state.

Dynamic CT could provide a relatively inexpensive and widely available technique to measure myocardial blood flow (MBF) for evaluating coronary artery disease (CAD). The main limitation to clinical acceptance is the substantial radiation dose. The dose can be reduced by decreasing the photon flux or the number of temporal frames, both resulting in noisy 4D data from which accurate MBF information needs to be extracted. In this work, we investigate two novel image domain denoising techniques based on total variation (TV). These methods are possible generalizations of the Rudin Osher Fatemi denoising model to multiple frames of dynamic CT data. Our first method, scalar total variation (STV), considers the sum of total variation of multiple temporal images as the penalty. Our second method, vectorial total variation (VTV) leverages the fact that images reconstructed from different dynamic frames have a common edge structure. We performed simulation studies of low-dose, dynamic cardiac CT acquisitions (25 mAs, 30 frames) of several flow states (flow = 0.5, 1.0, 2.0, 3.0 ml/g/min) and compared the accuracy of MBF estimates obtained from the proposed STV and VTV methods to a sinogram-domain 4D smoothing technique. Through simulation studies, we found that the proposed VTV algorithm gave us the best MBF estimate for 25 mAs tube current. The VTV method is fast and versatile, offering a pragmatic filtering strategy to improve the quality of dynamic 4D CT images. The STV method did not perform as well as the other two denoising methods.

There is a strong need for an accurate and easily available technique for myocardial blood flow (MBF) estimation to aid in the diagnosis and treatment of coronary artery disease (CAD). Dynamic CT would provide a quick and widely available technique to do so. However, its biggest limitation is the dose imparted to the patient. We are exploring techniques to reduce the patient dose by either reducing the tube current or by reducing the number of temporal frames in the dynamic CT sequence. Both of these dose reduction techniques result in very noisy data. In order to extract the myocardial blood flow information from the noisy sinograms, we have been looking at several data-domain smoothing techniques. In our previous work, 1 we explored the sinogram restoration technique in both the spatial and temporal domain. In this work, we explore the use of Karhunen-Loeve (KL) transform to provide temporal smoothing in the sinogram domain. This technique has been applied previously to dynamic image sequences in PET. 2, 3 We find that the cluster-based KL transform method yields noticeable improvement in the smoothness of time attenuation curves (TAC). We make use of a quantitative blood flow model to estimate MBF from these TACs and determine which smoothing method provides the most accurate MBF estimates.

Contrast enhancement on cardiac CT provides valuable information about myocardial perfusion and methods have been proposed to assess perfusion with static and dynamic acquisitions. There is a lack of knowledge and consensus on the appropriate approach to ensure 1) sufficient diagnostic accuracy for clinical decisions and 2) low radiation doses for patient safety. This work developed a thorough dynamic CT simulation and several accepted blood flow estimation techniques to evaluate the performance of perfusion assessment across a range of acquisition and estimation scenarios. Cardiac CT acquisitions were simulated for a range of flow states (Flow = 0.5, 1, 2, 3 ml/g/min, cardiac output = 3,5,8 L/min). CT acquisitions were simulated with a validated CT simulator incorporating polyenergetic data acquisition and realistic x-ray flux levels for dynamic acquisitions with a range of scenarios including 1, 2, 3 sec sampling for 30 sec with 25, 70, 140 mAs. Images were generated using conventional image reconstruction with additional image-based beam hardening correction to account for iodine content. Time attenuation curves were extracted for multiple regions around the myocardium and used to estimate flow. In total, 2,700 independent realizations of dynamic sequences were generated and multiple MBF estimation methods were applied to each of these. Evaluation of quantitative kinetic modeling yielded blood flow estimates with an root mean square error (RMSE) of ∼0.6 ml/g/min averaged across multiple scenarios. Semi-quantitative modeling and qualitative static imaging resulted in significantly more error (RMSE = ∼1.2 and ∼1.2 ml/min/g respectively). For quantitative methods, dose reduction through reduced temporal sampling or reduced tube current had comparable impact on the MBF estimate fidelity. On average, half dose acquisitions increased the RMSE of estimates by only 18% suggesting that substantial dose reductions can be employed in the context of quantitative myocardial blood flow estimation. In conclusion, quantitative model-based dynamic cardiac CT perfusion assessment is capable of accurately estimating MBF across a range of cardiac outputs and tissue perfusion states, outperforms comparable static perfusion estimates, and is relatively robust to noise and temporal subsampling.

Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions, leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml (min g)(-1), cardiac output = 3, 5, 8 L min(-1)). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiological features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 s sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11 000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This suggests that there is no particular advantage between quantitative estimation methods nor to performing dose reduction via tube current reduction compared to temporal sampling reduction. These data are important for optimizing implementation of cardiac dynamic CT in clinical practice and in prospective CT MBF trials.

Actin monomers assemble into filaments that structurally support cells as well as drive membrane protrusion for cell movement. Within cells, some actin structures are very dynamic and turn over rapidly, while others are very stable. Even purified actin filament dynamics are complex, and researchers have often turned to mathematical models in order to interpret data, test hypotheses, make predictions, and deepen understanding. Models of actin dynamics can be broadly divided into time-dependent models and time-independent models. Most commonly, time-independent models use numerical solutions of sets of differential equations to explore the effects of key parameters on the actin cycle at steady state. Recent examples have been used to predict the nucleotide profile of steady-state filaments and to illuminate the mechanisms behind profilin's effects on actin dynamics. Time-dependent models of actin dynamics have been either Monte Carlo simulations, which track individual filaments at various levels of detail or less commonly stochastic models, which have been explored and solved analytically. These Monte Carlo and stochastic models have recently been used to investigate filament length diffusion, filament length distributions, annealing and fragmentation, and pyrene fluorescence overshoots. We do not review force production/protrusion models as they tend to reduce the complexity of actin dynamics to a single 'elongation rate' and because these models have been recently well reviewed.1. Copyright © 2009 John Wiley & Sons, Inc. For further resources related to this article, please visit the WIREs website.

Multi-cell migration is important for tissue development and repair. An experimentally accessible example of multi-cell migration is provided by the classic scratch-wound assay. In this assay, a confluent monolayer is `injured' by forcibly removing a strip of cells, and the remaining monolayer `heals' through some combination of cell migration, spreading and proliferation. The scratch wound has been used for decades as a model of wound healing and an assay of cell migration, however the mechanisms that underlie the coherent expansion of cells in the surviving monolayer are still debated. Here we develop an agent-based computational model that predicts the most robust characteristics of healing in scratch wounds. The cells in our model are simple mechanical agents that respond to cell contact by redirecting migration and slowing division. We imbued model cells with crawling and growth dynamics and measured for individual L1 fibroblasts and found that simulated recovery occurs in a steady, sheet-like and division-independent fashion to mimic healing by L1s. The lack of cohesion and biochemical cell-cell communication in the model suggests that these factors are not strictly necessary for cells to migrate as a group. Instead, our analysis suggests that steady sheet migration can be explained by cell spreading in the monolayer.

In this report we extend our recent mathematical formulation of the actin cycle model [Bindschadler et al. Biophys. J. 86 (2004) 2720] to predict the influence of key regulatory mechanisms on network-scale state variables estimable in live cell experiments. Specifically, we examine the influence of regulation by cofilin, profilin, capping protein and proteins that adjust filament number through nucleation and/or filament severing, on the higher order variables of average filament length, polymer fraction, and filament turnover rate. Importantly, we find that severing/nucleation, the acceleration of ADP-subunit disassembly by cofilin, and the catalytic and shuttle functions of profilin have 'signature' effects on the higher order state variables. In this way, measurement of the state variables in live cells can allow inference of regulatory mechanism(s) underlying changes in cell state. Our results compare favorably to published data for endothelial cells undergoing a transition from non-motile confluent cells to highly motile subconfluent cells. The extension of our model to higher order state variables allows us to investigate other important issues such as the distinction between basic and higher order measures of filament dynamics, the influence of thymosin beta4 on network state variables, the interplay between thymosin beta4 and profilin, and the synergystic effects of cofilin and profilin.

In many cell types, asynchronous or synchronous oscillations in the concentration of intracellular free calcium occur in adjacent cells that are coupled by gap junctions. Such oscillations are believed to underlie oscillatory intercellular calcium waves in some cell types, and thus it is important to understand how they occur and are modified by intercellular coupling. Using a previous model of intracellular calcium oscillations in pancreatic acinar cells, this article explores the effects of coupling two cells with a simple linear diffusion term. Depending on the concentration of a signal molecule, inositol (1,4,5)-trisphosphate, coupling two identical cells by diffusion can give rise to synchronized in-phase oscillations, as well as different-amplitude in-phase oscillations and same-amplitude antiphase oscillations. Coupling two nonidentical cells leads to more complex behaviors such as cascades of period doubling and multiply periodic solutions. This study is a first step towards understanding the role and significance of the diffusion of calcium through gap junctions in the coordination of oscillatory calcium waves in a variety of cell types. (c) 2001 American Institute of Physics.

Dynamic contrast-enhanced computed tomography (CT) could provide an accurate and widely available technique for myocardial blood flow (MBF) estimation to aid in the diagnosis and treatment of coronary artery disease. However, one of its primary limitations is the radiation dose imparted to the patient. We are exploring techniques to reduce the patient dose by either reducing the tube current or by reducing the number of temporal frames in the dynamic CT sequence. Both of these dose reduction techniques result in noisy data. In order to extract the MBF information from the noisy acquisitions, we have explored several data-domain smoothing techniques. In this work, we investigate two specific smoothing techniques: the sinogram restoration technique in both the spatial and temporal domains and the use of the Karhunen–Loeve (KL) transform to provide temporal smoothing in the sinogram domain. The KL transform smoothing technique has been previously applied to dynamic image sequences in positron emission tomography. We apply a quantitative two-compartment blood flow model to estimate MBF from the time-attenuation curves and determine which smoothing method provides the most accurate MBF estimates in a series of simulations of different dose levels, dynamic contrast-enhanced cardiac CT acquisitions. As measured by root mean square percentage error (% RMSE) in MBF estimates, sinogram smoothing generally provides the best MBF estimates except for the cases of the lowest simulated dose levels (tube current=25 mAs, 2 or 3 s temporal spacing), where the KL transform method provides the best MBF estimates. The KL transform technique provides improved MBF estimates compared to conventional processing only at very low doses (

While cellular conditions strongly favor the growth of actin filaments, growth occurs only when actin monomers have access to a filament's fast-growing barbed end. This is the key to understanding how the only known de novo generators of actin filaments in cells, the Arp2/3 complex and members of the formin family, can give rise to such structurally different networks (Fig. 1). The Arp2/3 complex binds to the sides of an actin filament where it nucleates a daughter filament with a free barbed end at an acute angle to the mother (1). The new filament grows for only a short period before being blocked by barbed-end capping proteins, and so networks generated by the actions of many Arp2/3 complexes consist of short filaments arranged in a dendritic brush-like structure. By contrast, some formins generate filaments that can grow at their barbed ends while protected from the activity of capping proteins (2). Assemblies of these formins create elongated parallel bundles of filaments like those comprising the actin cables of budding yeast (2). Fig. 1. Actin network geometry depends on how long the barbed end is free during filament nucleation. Starting with the same building blocks, the formins and Arp2/3 complex will create very different networks because of their opposite treatment of barbed ends. ... Armed with crystal structures and numerous inferences from actin assembly kinetics in the presence of formins and capping protein, investigators have imagined that formins stay affixed to the barbed ends of growing filaments even while they permit actin monomers to assemble onto the filament barbed end (3). In this issue of PNAS, Kovar and Pollard (4) confirm this mechanism by directly visualizing single filaments growing from isolated and immobilized domains of two yeast formins. An exciting by-product of this effort is the first measurement of the pushing force generated by a growing actin filament.