Functional Activation of Autologous Human Diabetic Stem Cells for Cell Therapy

Diabetic retinopathy (DR) is a common cause of vision loss and blindness. Healthy CD34+ stem cells are capable of homing to vascular lesions and facilitating vascular repair. However, many diabetic patients have dysfunctional CD34+ stem cells with no reparative potential. CD34+ dysfunction is corrected by transiently inhibiting endogenous transforming growth factor-β1 (TGF-β1) within the patient’s own dysfunctional CD34+ stem cells using phosphorodiamidate morpholino oligomers (PMOs). Antisense TGF-β1-treated dysfunctional CD34+ stem cells are now functional, no longer require growth factor stimulation to evade apoptosis, and are stable at 37°C ex vivo for >5 days. We identified three markers of restored stem cell function: (1) upregulation of CXCR4 expression necessary for stem cell homing and adhesion, (2) SDF-1-mediated nitric oxide (NO) production required for cell mobility, and (3) restoration of the ability of CD34+ cells to migrate and repair vascular lesions. The antisense targets autocrine TGF-β expression, whereas neutralizing antibodies do not. The PMO antisense triggers a cascade of hematopoietic proliferation and differentiation that paracrine TGF-β cannot alter. We describe optimal PMO manipulation of CD34+ stem cells ex vivo for transplantation, screening multiple gene targets leading to the identification of TGF-β1, and a lead TGF-β1 inhibitor evaluated in clinical studies

The interpretation of particle size, shape, and carbon flux of marine particle images is strongly affected by the choice of particle detection algorithm

In situ imaging of particles in the ocean are rapidly establishing themselves as powerful tools to investigate the ocean carbon cycle, including the role of sinking particles for carbon sequestration via the biological carbon pump. A big challenge when analysing particles in camera images is determining the size of the particle, which is required to calculate carbon content, sinking velocity and flux. A key image processing decision is the algorithm used to decide which part of the image forms the particle and which is the background. However, this critical analysis step is often unmentioned and its effect rarely explored. Here we show that final flux estimates can easily vary by an order of magnitude when selecting different algorithms for a single dataset. We applied a range of static threshold values and 11 different algorithms (seven threshold and four edge detection algorithms) to particle profiles collected by the LISST-Holo system in two contrasting environments. Our results demonstrate that the particle detection method does not only affect estimated particle size but also particle shape. Uncertainties are likely exacerbated when different particle detection methods are mixed, e.g., when datasets from different studies or devices are merged. We conclude that there is a clear need for more transparent method descriptions and justification for particle detection algorithms, as well as for a calibration standard that allows intercomparison between different devices

Waiting time distribution in public health care: empirics and theory

Excessive waiting times for elective surgery have been a long-standing concern in many national healthcare systems in the OECD. How do the hospital admission patterns that generate waiting lists affect different patients? What are the hospitals characteristics that determine waiting times? By developing a model of healthcare provision and analysing empirically the entire waiting time distribution we attempt to shed some light on those issues. We first build a theoretical model that describes the optimal waiting time distribution for capacity constraint hospitals. Secondly, employing duration analysis, we obtain empirical representations of that distribution across hospitals in the UK from 1997–2005. We observe important differences on the ‘scale’ and on the ‘shape’ of admission rates. Scale refers to how quickly patients are treated and shape represents trade-offs across duration-treatment profiles. By fitting the theoretical to the empirical distributions we estimate the main structural parameters of the model and are able to closely identify the main drivers of these empirical differences. We find that the level of resources allocated to elective surgery (budget and physical capacity), which determines how constrained the hospital is, explains differences in scale. Changes in benefits and costs structures of healthcare provision, which relate, respectively, to the desire to prioritise patients by duration and the reduction in costs due to delayed treatment, determine the shape, affecting short and long duration patients differently