Alignment and Composition of Laminin-Polycaprolactone Nanofiber Blends Enhance Peripheral Nerve Regeneration

Peripheral nerve transection occurs commonly in traumatic injury, causing deficits distal to the injury site. Conduits for repair currently on the market are hollow tubes; however, they often fail due to slow regeneration over long gaps. To facilitate increased regeneration speed and functional recovery, the ideal conduit should provide biochemically relevant signals and physical guidance cues, thus playing an active role in regeneration. To that end, laminin and lamininpolycaprolactone (PCL) blend nanofibers were fabricated to mimic peripheral nerve basement membrane. In vitro assays established 10% (wt) laminin content is sufficient to retain neurite-promoting effects of laminin. In addition, modified collector plate design to introduce an insulating gap enabled the fabrication of aligned nanofibers. The effects of laminin content and fiber orientation were evaluated in rat tibial nerve defect model. The lumens of conduits were filled with nanofiber meshes of varying laminin content and alignment to assess changes in motor and sensory recovery. Retrograde nerve conduction speed at 6 weeks was significantly faster in animals receiving aligned nanofiber conduits than in those receiving random nanofiber conduits. Animals receiving nanofiber-filled conduits showed some conduction in both anterograde and retrograde directions, whereas in animals receiving hollow conduits, no impulse conduction was detected. Aligned PCL nanofibers significantly improved motor function; aligned laminin blend nanofibers yielded the best sensory function recovery. In both cases, nanofiber-filled conduits resulted in better functional recovery than hollow conduits. These studies provide a firm foundation for the use of naturalsynthetic blend electrospun nanofibers to enhance existing hollow nerve guidance conduits

Sensor technologies for quality control in engineered tissue manufacturing

The use of engineered cells, tissues, and organs has the opportunity to change the way injuries and diseases are treated. Commercialization of these groundbreaking technologies has been limited in part by the complex and costly nature of their manufacture. Process-related variability and even small changes in the manufacturing process of a living product will impact its quality. Without real-time integrated detection, the magnitude and mechanism of that impact are largely unknown. Real-time and non-destructive sensor technologies are key for in-process insight and ensuring a consistent product throughout commercial scale-up and/or scale-out. The application of a measurement technology into a manufacturing process requires cell and tissue developers to understand the best way to apply a sensor to their process, and for sensor manufacturers to understand the design requirements and end-user needs. Furthermore, sensors to monitor component cells’ health and phenotype need to be compatible with novel integrated and automated manufacturing equipment. This review summarizes commercially relevant sensor technologies that can detect meaningful quality attributes during the manufacturing of regenerative medicine products, the gaps within each technology, and sensor considerations for manufacturing

High aspect ratio nanoimprinted grooves of poly(lactic-co-glycolic acid) control the length and direction of retraction fibers during fibroblast cell division

Retraction fibers (RFs) determine orientation of the cell division axis and guide the spreading of daughter cells. Long and unidirectional RFs, which are especially apparent during mitosis of cells in three-dimensional (3D) environments, enable improved control over cell fate, following division. However, 3D gel environments lack the cues necessary for predetermining the orientation of RFs to direct tissue architecture. While patterning of focal adhesion regions by microcontact printing can determine orientation of the RFs through enhancing focal adhesion numbers along particular directions, the RFs remain short due to the two-dimensional culture environment. Herein, the authors demonstrate that nanoimprinted grooves of polylactic acid glycolic acid (PLGA) with a high aspect ratio (A.R. of 2.0) can provide the cues necessary to control the direction of RFs, as well as enable the maintenance of long and unidirectional RFs as observed within 3D cultures, while the same is not possible with PLGA grooves of lower A.R. (1.0 or lower). Based on enhanced levels of contact guidance of premitotic fibroblast protrusions at high A.R. grooves and deeper levels of focal adhesion due to filopodia extensions into these grooves, it is suggested that submicron (800 nm width) PLGA grooves with A.R. of 2 are capable of supporting mechanical forces from cell protrusions to a greater depth, thereby enabling the maintenance of the protrusions as long and unidirectional RFs during cell division. Given the scalability and versatility of nanoimprint techniques, the authors envision a platform for designing nanostructures to direct tissue regeneration and developmental biology.This is the publisher’s final pdf. The article is copyrighted by the American Vacuum Society and published by Springer. It can be found at: http://link.springer.com/journal/13758See supplementary material at http://dx.doi.org/10.1116/1.4936589 for movies on cell trajectories during division on the grooves, as well as for alignment and elongation of pre-mitotic protrusions on PLGA grooves

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer

The kinetics zinc removal from cobalt electrolytes by Ion exchange

The removal of trace zinc concentrations from the INCO (Port Colborne) cobalt advance electrolyte by solvent impregnated ion exchange, was studied in column and batch tests. The solvent impregnated resins containing the extractants D2EHPA, Cyanex 272 and Cyanex 302, were com pared in terms of zinc loading and selectivity. D2EHPA impregnated OC 1026 resin demonstrated superior zinc loading and selectivity charac teristics, but retained objectionably high amounts of cobalt, which were lost in the zinc elution process. Cobalt loading, was found to be closely related to the electrolyte pH drop across the column and could be reduced by a resin pre-treatment with the advance electrolyte at a pH of 3 or by an increase in the feed electrolyte pH to 5, along with operation at a temperature of 40°C and a flow rate of 10 BV/hr.; all of which act to diminish the pH drop. The kinetics of zinc loading on each of the resins was found to be comparable, and the rate controlling mechanism in batch tests was found to be particle diffusion in the first fifteen minutes, while film diffusion became rate controlling at later time intervals. Pre-treatment enhanced the diffusion coefficient inside the resin phase by nearly an order of magnitude, improved exchange kinetics by allowing a lower pH reduction during the loading process, and improved Co²⁺/Zn²⁺ exchange in a mathx of the cobalt complex. An analysis of the breakthrough curves for the resins was done to determine the mass transfer coefficients inside the column, and a range of other parameters useful in the design of ion exchange columns. The rate controlling regime in the column was a mixture of the particle and film diffusion steps, with the former being the dominant control mechanism at the operating flow rate. Further work is needed in the X-ray microprobe analysis of resin samples from the top, middle and bottom portions of the columns, and of samples from batch tests, to aid in understanding the mechanism of ion exchange. The use of zinc selective electrodes in batch tests could also be undertaken to obtain a more accurate estimate of the diffusion and mass transfer coefficients.Applied Science, Faculty ofMaterials Engineering, Department ofGraduat

Modified Red Blood Cells as Multimodal Standards for Benchmarking Single-Cell Cytometry and Separation Based on Electrical Physiology

Biophysical cellular information at single-cell sensitivity is becoming increasingly important within analytical and separation platforms that associate the cell phenotype with markers of disease, infection, and immunity. Frequency-modulated electrically driven microfluidic measurement and separation systems offer the ability to sensitively identify single cells based on biophysical information, such as their size and shape, as well as their subcellular membrane morphology and cytoplasmic organization. However, there is a lack of reliable and reproducible model particles with well tuned subcellular electrical phenotypes that can be used as standards to benchmark the electrical physiology of unknown cell types or to benchmark dielectrophoretic separation metrics of novel device strategies. Herein, the application of red blood cells (RBCs) as multimodal standard particles with systematically modulated subcellular electrophysiology and associated fluorescence level is presented. Using glutaraldehyde fixation to vary membrane capacitance and by membrane resealing after electrolyte penetration to vary interior cytoplasmic conductivity and fluorescence in a correlated manner, each modified RBC type can be identified at single cell sensitivity based on phenomenological impedance metrics and fitted to dielectric models to compute biophysical information. In this manner, single-cell impedance data from unknown RBC types can be mapped versus these model RBC types for facile determination of subcellular biophysical information and their dielectrophoretic separation conditions, without the need for timeconsuming algorithms that often require unknown fitting parameters. Such internal standards for biophysical cytometry can advance in-line phenotypic recognition strategies

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and cross-sectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting