The attachment, proliferation, morphology, and differentiation of two cell types-skeletal muscle cells and chondrocytes-were investigated on different compositions of poly(ethylene glycol) and poly(butylene terephthalate) segmented block copolymers. Four weight percentages (40, 55, 60, and 70%) and two different molecular weights (300 and 1000 Da) of poly(ethylene glycol) were tested. Varying the weight percentage and molecular weight of poly(ethylene glycol) resulted in different behaviors for skeletal muscle cells and chondrocytes. The attachment of skeletal muscle was the highest (similar to tissue culture polystyrene) when copolymers containing 55 wt % of poly(ethylene glycol) were used, regardless of the poly(ethylene glycol) molecular weight. Maximum proliferation and differentiation of skeletal muscle cells was achieved when copolymers containing 55 wt % and 300 Da molecular weight of poly(ethylene glycol) were used. In contrast, the weight percentage and molecular weight of poly(ethylene glycol) had no significant effect on chondrocyte attachment and proliferation; the attached chondrocytes retained a differentiated phenotype only when a 70 wt % of poly(ethylene glycol) was used. Cell behavior was correlated with the surface properties of the copolymer films, as indicated by contact-angle measurements. These results suggest that an optimized wt % and molecular weight of poly(ethylene glycol) will be useful depending on the specific cell type.
Nanosensor-based detection of biomarkers can improve medical diagnosis; however, a critical factor in nanosensor development is deciding which biomarker to target, as most diseases present several biomarkers. Biomarker-targeting decisions can be informed via an understanding of biomarker expression. Currently, immunohistochemistry (IHC) is the accepted standard for profiling biomarker expression. While IHC provides a relative mapping of biomarker expression, it does not provide cell-by-cell readouts of biomarker expression or absolute biomarker quantification. Flow cytometry overcomes both these IHC challenges by offering biomarker expression on a cell-by-cell basis, and when combined with calibration standards, providing quantitation of biomarker concentrations: this is known as qFlow cytometry. Here, we outline the key components for applying qFlow cytometry to detect biomarkers within the angiogenic vascular endothelial growth factor receptor family. The key aspects of the qFlow cytometry methodology include: antibody specificity testing, immunofluorescent cell labeling, saturation analysis, fluorescent microsphere calibration, and quantitative analysis of both ensemble and cell-by-cell data. Together, these methods enable high-throughput quantification of biomarker expression.
Australia has strong European traditions, rooted in its history of the past 200 years. On the other hand, Australia differs from many European countries in a number of important respects, including geography, population density and aspects of government economic policy. This paper uses data from the International Manufacturing Strategy Survey (1996‐98) to examine how these similarities and differences may have impacted on the manufacturing strategies adopted by firms in the two regions. Whilst Australian and European manufacturers seem similar in many respects, in that they are listening to their customers, adopting quality strategies and utilizing technology, there are important differences in the speed of adoption of some aspects of these approaches. In particular, European manufacturers introduced a number of manufacturing technologies earlier and are using them more extensively than their Australian counterparts, whilst health, environmental and safety activities seem to be more prominent among Australian firms.
Objective
Pediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasound views rapidly performed by clinicians to diagnose hemorrhage. A technical limitation of FAST is the lack of expertise to consistently acquire all required views. We sought to develop an accurate deep learning view classifier using a large heterogeneous dataset of clinician‐performed pediatric FAST.
Methods
We developed and conducted a retrospective cohort analysis of a deep learning view classifier on real‐world FAST studies performed on injured children less than 18 years old in two pediatric emergency departments by 30 different clinicians. FAST was randomly distributed to training, validation, and test datasets, 70:20:10; each child was represented in only one dataset. The primary outcome was view classifier accuracy for video clips and still frames.
Results
There were 699 FAST studies, representing 4925 video clips and 1,062,612 still frames, performed by 30 different clinicians. The overall classification accuracy was 97.8% (95% confidence interval [CI]: 96.0–99.0) for video clips and 93.4% (95% CI: 93.3–93.6) for still frames. Per view still frames were classified with an accuracy: 96.0% (95% CI: 95.9–96.1) cardiac, 99.8% (95% CI: 99.8–99.8) pleural, 95.2% (95% CI: 95.0–95.3) abdominal upper quadrants, and 95.9% (95% CI: 95.8–96.0) suprapubic.
Conclusion
A deep learning classifier can accurately predict pediatric FAST views. Accurate view classification is important for quality assurance and feasibility of a multi‐stage deep learning FAST model to enhance the evaluation of injured children.
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