Comparison of myeloma cell contamination of bone marrow and peripheral blood stem cell harvests

It could be speculated for patients with myeloma and other lymphoproliferative disorders that peripheral blood stem cells may be preferable to bone marrow for autologous transplantation because they may be less contaminated by neoplastic cells. To test this possibility, the immunoglobulin heavy chain gene rearrangement and limiting dilution polymerase chain reaction were used to sensitively quantify myeloma cells in bone marrow and peripheral blood stem cell collections, taken at a similar time, from eight patients with multiple myeloma. Levels of residual disease in the peripheral blood stem cell harvests were variable and did not reflect the tumour burden in the marrow. Peripheral blood stem cells contained 1.7 to 23 700-fold fewer myeloma cells compared with the bone marrow and would have resulted in reinfusion of 0.08 to 59 480-fold fewer myeloma cells based on total reinfused CFU-GM and 0.24 to 24 700-fold fewer myeloma cells based on total reinfused nucleated cells. Assuming that the proportion of clonogenic myeloma cells is equivalent, peripheral blood stem cells may be better than bone marrow as a source of haemopoietic stem cells for transplantation in multiple myeloma. The clinical follow-up suggested that patients transplanted with peripheral blood stem cells containing a low number of myeloma cells had better disease control than those transplanted with peripheral blood stem cells containing a high number.     

Genetically engineered SCN5A mutant pig hearts exhibit conduction defects and arrhythmias

SCN5A encodes the α subunit of the major cardiac sodium channel Na_V1.5. Mutations in SCN5A are associated with conduction disease and ventricular fibrillation (VF); however, the mechanisms that link loss of sodium channel function to arrhythmic instability remain unresolved. Here, we generated a large-animal model of a human cardiac sodium channelopathy in pigs, which have cardiac structure and function similar to humans, to better define the arrhythmic substrate. We introduced a nonsense mutation originally identified in a child with Brugada syndrome into the orthologous position (E558X) in the pig SCN5A gene. SCN5A^E558X/+ pigs exhibited conduction abnormalities in the absence of cardiac structural defects. Sudden cardiac death was not observed in young pigs; however, Langendorff-perfused SCN5A^E558X/+ hearts had an increased propensity for pacing-induced or spontaneous VF initiated by short-coupled ventricular premature beats. Optical mapping during VF showed that activity often began as an organized focal source or broad wavefront on the right ventricular (RV) free wall. Together, the results from this study demonstrate that the SCN5A^E558X/+ pig model accurately phenocopies many aspects of human cardiac sodium channelopathy, including conduction slowing and increased susceptibility to ventricular arrhythmias.     

Department of Veterans Affairs Geriatric Research, Education and Clinical Centers: translating aging research into clinical geriatrics

Department of Veterans Affairs (VA) Geriatric Research, Education and Clinical Centers (GRECCs) originated in 1975 in response to the rapidly aging veteran population. Since its inception, the GRECC program has made major contributions to the advancement of aging research, geriatric training, and clinical care within and outside the VA. GRECCs were created to conduct translational research to enhance the clinical care of future aging generations. GRECC training programs also provide leadership in educating healthcare providers about the special needs of older persons. GRECC programs are also instrumental in establishing robust clinical geriatric and aging research programs at their affiliated university schools of medicine. This report identifies how the GRECC program has successfully adapted to changes that have occurred in VA since 1994, when the program's influence on U.S. geriatrics was last reported, focusing on its effect on advancing clinical geriatrics in the last 10?years. This evidence supports the conclusion that, after more than 30?years, the GRECC program remains a vibrant "jewel in the crown of the VA" and is poised to make contributions to aging research and clinical geriatrics well into the future.     

Endoscopic access to fascia lata for use in static facial reanimation—a cadaveric and clinical study

Background

Current methods of autogenous fascia lata harvest for the static surgical treatment of longstanding facial paralysis often result in a high level of donor site morbidity and unsightly visual scarring on the patient’s lateral thigh due to the open technique traditionally used. With endoscopic access already being widely used in other areas of plastic and reconstructive surgery, it was hypothesised that it would be feasible to retrieve sufficient amounts of fascia lata endoscopically to achieve satisfactory static facial reanimation.

Methods

In the first instance, we used an 85-year-old female cadaver selected from the regular stock at the University of Glasgow to establish if retrieval of fascia lata endoscopically was feasible. Through two small incisions on the lateral aspect of the thigh (proximally and distally), we successfully retrieved a strip of fascia lata measuring 9?×?2.5 cm. Due to the ease of access, one of the authors then performed endoscopic retrieval of the fascia lata for five patients requiring static facial reanimation.

Results

It was shown that in all cases it was feasible to retrieve sufficient amounts of fascia lata to perform static facial reanimation with a similar operating time compared to the open technique which is currently used. In addition, there were no complications related to donor site morbidity.

Conclusions

We have shown that endoscopic access to the fascia lata for use in static facial reanimation is perfectly feasible, with no complications, minimal scarring and no significant increase in operating time compared to the traditional open technique currently used. Level of Evidence: Level V, therapeutic study.     

Simplified computational model for generating biological networks

A method to generate and simulate biological networks is discussed. An expanded Wooten–Winer–Weaire bond switching methods is proposed which allows for a distribution of node degrees in the network while conserving the mean average node degree. The networks are characterised in terms of their polygon structure and assortativities (a measure of local ordering). A wide range of experimental images are analysed and the underlying networks quantified in an analogous manner. Limitations in obtaining the network structure are discussed. A “network landscape” of the experimentally observed and simulated networks is constructed from the underlying metrics. The enhanced bond switching algorithm is able to generate networks spanning the full range of experimental observations.

We discuss a Monte Carlo method to simulate biological networks and compare to the underlying networks in experimental images.

Two dimensional random networks are observed in a range of contexts across considerably different length scales in nature: from nanometres, in the form of amorphous graphene; to metres, in the form of the Giant''s causeway; to tens of kilometres, in the form of geopolitical borders.^1–3 A framework for describing these continuous random networks for chemical systems was first introduced by Zachariasen to describe silica-like glasses, and has proved to be extremely versatile in the years since,⁴ being used to describe biological networks, including actin networks and basement membranes made of collagen and laminin.^5–8Despite the physical diversity of continuous random networks, they share some common properties which arise from the fact that all planar 2D networks can be represented as a tiled arrangement of polygons, joined at their edges.⁹ Viewing a network as a collection of polygons explains some restrictions on the nature of the network. For example, the angles at each node must add up to 360° and the mean number of edges connected to each node affects the mean number of edges [or ring size] of each polygon. Traditionally, semi-empirical laws provided a theoretical understanding of the complex behaviour of real networks, most commonly for inorganic networks such as MgO grains, silica, or epithelial cells.^10–12 Some examples of these laws include: Lewis'' law,¹² governing the distribution of polygon areas; Lemaître''s law,¹³ relating the number of hexagons to the width of the polygon distribution; and the Aboav–Weaire law,¹⁴ governing the preference of polygons to be surrounded by similar or dissimilar polygons. Recent work has helped clarify the latter by re-casting the problem in terms of the assortativity, a metric designed for network theorists to describe social networks.^15,16Studying generic 2D networks in biology is a potentially difficult task. There are a variety of different techniques used, including fluorescence light-, atomic force and electron-microscopy.^17–19 The resolution of light microscopy is limited by diffusion,¹⁸ and the power of electron or laser beams have to be limited to avoid damaging the delicate networks.¹⁷ However, even when a high-quality image is obtained, there is further difficulty in analysis. For example, each edge in a network is a complex molecule made up of tens of thousands of atoms which may not lie strictly in a single plane. In addition, the most interesting dynamic behaviour often occurs over very long timescales – potentially decades. To make this complexity scientifically digestible, different simplified models have been developed for biological networks, as well as semi-empirical laws. A simplified model is useful for exploring the complex behaviour of these biological networks, and to provide greater understanding of experimental data by eliminating the effects of finite sample size and providing precise control over the conditions. These models can then be used to understand macroscopic or mechanical properties of the networks, either directly or to parameterise multiscale approaches.^6,20 Examples of existing simplified graph-based models include Erdös–Rényi random graphs in which edges are randomly placed connecting nodes, Mikado networks for many-layered systems in which edges are randomly placed across one another, and bond switching in which edges in an ordered graph are randomly exchanged.^21,22In this work, an extension of a bond switching method, known as the Wooten–Winer–Weaire (WWW) algorithm, is presented to generate 2D networks which align with those observed in biological systems.^23,24 Bond switching methods offer good control over physical properties in the networks generated, and are sufficiently flexible to reproduce a wide range of networks. To rigorously compare networks generated by bond switching to real biological networks, key metrics are extracted from a range of experimental microscopy images of networks made of collagen IV or laminin.Bond switching is a stochastic sampling (often described as a Monte Carlo) method, in which an initially regular network is systematically modified by a series of edge deletion and addition moves (corresponding to bond-breaking and bond-creation in chemical networks). In the WWW method,²⁴ the moves are chosen randomly to “switch” the coordination arrangements around two nodes. These switches preserve the coordination number k, of each node. For a more detailed discussion of the original method and figures, see Wooten et al.²⁴ However, in the extended bond switching method, the mean coordination number 〈k〉 is fixed at an initial value whilst the k of individual nodes may change. A schematic image of this is available as Fig. SI 1 in the ESI.^† The changes in k are affected by the potential model and the moves chosen. The relaxation of the move selection criterion is especially useful for generating biological continuous random networks as it can encompass a range of different behaviours, for example cross-linking along the backbone of biopolymers or lateral interactions of head groups.The steps in our algorithm are:• Initialise using a network with known polygon structure, calculating the energy according to a potential model.• Select one edge between two nodes and make a trial move by deleting it, and adding a new edge between the first node and a neighbour of the second.• Locally optimise the geometry around the switching site according to the potential model and recalculate the energy (giving the energy difference ΔE).• Accept or reject the trial move randomly with probability p, determined by the Metropolis criterion:²⁵p = min[1,exp(−ΔE/k_BT)]1• If the trial move was rejected, return to the previous accepted state. If it was accepted, replace the network with the trial network and repeat the process.This algorithm is quick to compute, because the geometry only needs to be optimised in a small region around the switching site. The algorithm works best for potential models with explicit bonds and well-defined neighbours. To this end, a simplified Keating potential is applied which consists of harmonic bond (of length r) and angular (of angle θ) terms:²⁶2with the equilibrium bond length r_eqm fixed at 1, and the equilibrium angle θ_eqm chosen to be with k the coordination number of the central node. The force constants k_r and K_θ are both free parameters. When the coordination number of two nodes changes in the simulation, the θ_eqm is recalculated for both of them. Fixing θ_eqm (i.e. not changing with k) led to the final networks exhibiting more artefacts and often showing significant demixing of nodes.This simple, but physically motivated, potential model is useful to explore the range of networks that can be generated. Future developments will show how more detailed and biologically accurate potential models, such as the exponential–quadratic form used by Burd,²⁷ may further improve agreement with experimental observation. The simplicity of the potential model could be useful as a framework to be combined with more complex requirements. For example, a strong energetic penalty could be applied to configurations exhibiting undesirable artefacts. In this work this technique is used to reject all configurations in which any node has coordination number k ≥ 8 or a polygon with side count n ≥ 20. Ormrod Morley and Wilson extend this idea further; by using Monte Carlo to minimise a cost function instead of energy directly they demonstrate how key structural metrics (there the fraction of 6-membered rings in amorphous graphene) can be targeted.²³ It is, however, useful to develop an understanding of the structures formed by simplistic models prior to considering how to effectively contrain them to eliminate unphysical properties, rather than pre-judging what is required.A honeycomb network of hexagons with 〈k〉 = 3 is used as the starting lattice, to allow comparison with previous networks.^28,29 The use of a well-defined initial polygon structure, and monitoring the changes at each step, allows the algorithm to ensure that the structure generated is physically acceptable. By constraining the mean node coordination, the mean number of edges per polygon is also constrained because of Euler''s characteristic equation for polyhedra. For a 2D network which is either periodic or infinitely large, 〈k〉 = 3 corresponds to 〈n〉 = 6. This does not hold for finite-size aperiodic networks, such as those typically imaged experimentally. However, the approximation for aperiodic systems is small (for example, for 100 rings, 〈n〉 = 5.94).¹⁶The biological networks considered are visually extremely diverse. To demonstrate that diversity, three networks are shown in Fig. 1, which are accompanied by snapshots of the simulated networks, showing that these can qualitatively replicate the range of observed structures. The more regular networks in the figure are characteristic of those formed by inorganic materials.¹⁶ The figure also highlights associated structural metrics, the second moment of the ring distribution, μ₂(k), and the assortativity, r, both of which are discussed below. The difference in accompanying structural metrics in the experimental and simulated configurations highlights the need for a quantitative comparison instead of qualitative similarity. The structural metrics were extracted by a combination of image analysis tools and a mathematical approach based on graph theory as described below. A microscope image can be converted into an abstract graph by extracting the positions of edges and nodes, here using the Ridge Detection plugin for the image processing package ImageJ.^30,31 The image analysis algorithms are discussed in detail in the ESI.^† Obtaining a set of nodes and edges for each image allows the same analysis methods to be applied for the simulated networks.Open in a separate windowFig. 1Images of biological network from (left to right) Barnard et al.,³² Bos et al.,³³ and Wang et al.³⁴ respectively. The lower panels show networks generated by our method that appear similar, from left to right with {k_l, K_θ} = {0.01, 0.01}, {10, 0.01} and {100, 100}. The simulated networks were chosen on grounds of visual similarity; the difference in accompanying metrics (assortativity r and second moment of the k distribution μ₂(k)) demonstrates why a more rigorous method of comparison is needed. The structural metrics are discussed in more detail in the body of the text. Reproduced from Barnard et al.³² and Bos et al.,³³ Copyright (1992) and (2001), with permission from Elsevier. Reproduced from Wang et al.,³⁴ Copyright (2017) with permission.Images of biological networks produced by Barnard et al., Bos et al., Wang et al., and Yurchenco et al. taken between 1984 and 2017 were analysed.^32–36 A subset of the results are shown in † The experimental networks show a mean node coordination 〈k〉 = 2.78, with a standard deviation of 0.237. The average number of polygon sides 〈n〉 is 6.88, with a standard deviation of 1.42. The data seen in 35 are shown in order to highlight the sensitivity of the obtained metrics. A more detailed table is available in the ESI

Initial Validation of the Toulouse St. Louis University Mini Falls Assessment in Older Adults

Background/Objectives

Falls are one of the most prevalent health issues facing older adults. This study examines the validity of the Toulouse-St. Louis University Mini Falls Assessment (TSLUMFA). Objectives were to validate the TSLUMFA by testing if it differentiates between prior non fallers (n=80) and fallers (n=23), and predicts future falls as well as or better than the gold standard Tinetti Gait and Balance Instrument (TGBI). Examine if the subset of FRAIL Scale items on the TSLUMFA distinguishes between previous non fallers (n=75) and fallers (n=20), and predicts future falls as well as or better than the TGBI. Identify TSLUMFA cut offs scores for fall risk.

Design

Prospective validation study.

Setting

Participants were ambulatory patients presenting to the SLU Geriatrics Clinic.

Participants

103 ambulatory older adults.

Measurements

Fall risk was assessed using the three assessments. Outcome measures were previous falls and follow up falls.

Results

TSLUMFA, FRAIL, and TGBI differentiated between previous fallers and non fallers. A TSLUMFA score <23 stratified patients as moderate risk (Sensitivity=0.806 Specificity=0.776) and a score <21 stratified patients as high risk (Sensitivity=0.929 Specificity=0.897). 78% of patients (n=80) participated in follow up and 20% (n=16) of these patients fell during follow up. TSLUMFA and TGBI absolute scores were lower among patients who fell during the follow up period versus non fallers but the observed differences were not statistically significant (TSLUMFA P=0.123 and TGBI P=0.074).

Conclusion

This study validated the TSLUMFA and FRAIL. No test predicted falls with statistical significance (most likely due to the low follow up participation) but a positive trend was seen. Clinical recommendations from this study are to use the FRAIL as an initial fall screen and patients scoring > 3 should be analyzed by TSLUMFA. The TSLUMFA’s advantage is that it pinpoints areas that will directly benefit from therapy to reduce falls.

     

Trading in your spindles for blebs： the amoeboid tumor cell phenotype in prostate cancer

Prostate cancer （PCa） remains a principal cause ofmortalityin developed countries.Because no clinical interventions overcome resistance to androgen ablation therapy, management of castration resistance and metastatic disease remains largely untreatable. Metastasis is a multistep process in which tumor cells lose cell-cell contacts, egress from the primary tumor, intravasate, survive shear stress within the vasculature and extravasate into tissues to colonize ectopic sites. Tumor ceils reestablish migratory behaviors employed during nonneoplastic processes such as embryonic development, leukocyte trafficking and wound healing. While mesenchymal motility is an established paradigm of dissemination, an alternate, ＇amoeboid＇ phenotype is increasingly appreciated as relevant to human cancer.