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doi:10.1111/his.2006.49.issue-4

Cited by 10 publications

(4 citation statements)

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“…information on both the presence and location of target molecules. Once stained and imaged, quantification is also possible based on image analysis software [195].…”

Section: Adaption Of Conventional Techniques For Micro-tissues 411 Im...mentioning

confidence: 99%

High throughput physiological micro-models for in vitro pre-clinical drug testing: a review of engineering systems approaches

et al. 2020

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To address the low success rate of new drug discovery, there has been significant growth of in vitro physiological micro-models based on human cells. These may be in the form of cell spheroids, organs-on-a-chip, or multi-cellular tissue cultures, and it is expected that the more biomimetic environment they create will be more accurate than standard cell culture in drug screening prior to clinical testing. However, commercial use of complex co-cultures is still limited. This is due to a lack of validation, low throughput rates, and a lack of compatibility with standard assessment techniques. This review paper focusses specifically on the different engineering approaches used to create, mature and analyse these micro-models, with the aim of exploring which approaches have the potential for high throughput. Active and passive pumping and nozzle based dispensing techniques are considered for fluid handling, with transwells, cell patterning, spheroid cultures and microfluidics considered for establishing and maintaining co-cultures, together with conventional analysis techniques (proteomic and genomic approaches, and immunohistochemistry) and novel sensor systems for downstream analysis are considered. It is concluded that (i) throughput is essential for validation as well as exploitation of the models, and (ii) an integrated approach to model re-design for high throughput is key, with the limitations on throughput at each stage considered in order to develop a system which can deliver and analyse at high throughput rates at all stages of the process.

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“…information on both the presence and location of target molecules. Once stained and imaged, quantification is also possible based on image analysis software [195].…”

Section: Adaption Of Conventional Techniques For Micro-tissues 411 Im...mentioning

confidence: 99%

High throughput physiological micro-models for in vitro pre-clinical drug testing: a review of engineering systems approaches

et al. 2020

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“…An immunohistochemical dye is usually analogous to an enzyme-linked immunosorbent assay (ELISA) test performed in the clinical lab, and ELISA tests are widely recognized as being truly quantitative. 250 Although immunohistochemical analysis has some practical advantages over immunofluorescence, the optical properties of the chromogens will affect the linearity and dynamic range of the assay. For instance, the popularly used brown diaminobenzidine (DAB) stain exhibits scattering behavior similar to that of melanosomes and does not obey the Beer-Lambert law (BLL), which underlies that the unmixing approach applies only to pure absorbers.…”

Section: Histological Analysismentioning

confidence: 99%

“…In fact, it is hard to separate DAB from melanin pigmentation spectrally since their spectra arise from the same optical properties. 250 Other chromogens used mostly make precipitating products, such as Vector Red, and also have a similar problem as it may not obey the BLL. However, this does not mean that quantitative immunohistochemistry is out of the question if the entire process, from sample acquisition to analysis and interpretation, is properly integrated, standardized, and combined with spectral imaging technology.…”

Section: Histological Analysismentioning

confidence: 99%

Review of spectral imaging technology in biomedical engineering: achievements and challenges

et al. 2013

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Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

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“…An automated microvessel detection and segmentation algorithm for histological mouse hind limb tissue has been previously reported, 7 and other semiautomated approaches have been developed for this task. 8 However, to the best of our knowledge, no automated method has yet been developed to classify arterioles and venules to allow for the separate analysis of arteriolar and venular structure. Such a system would eliminate the need for the laborious and observer-dependent manual classification performed in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of arterioles from venules in mouse histology images using machine learning

Elkerton

Xu²,

Pickering

et al. 2017

J. Med. Imag

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Analysis and morphological comparison of the arteriolar and venular components of a microvascular network are essential to our understanding of multiple diseases affecting every organ system. We have developed and evaluated the first fully automatic software system for differentiation of arterioles from venules on high-resolution digital histology images of the mouse hind limb immunostained with smooth muscle [Formula: see text]-actin. Classifiers trained on statistical and morphological features by supervised machine learning provided useful classification accuracy for differentiation of arterioles from venules, achieving an area under the receiver operating characteristic curve of 0.89. Feature selection was consistent across cross validation iterations, and a small set of two features was required to achieve the reported performance, suggesting the generalizability of the system. This system eliminates the need for laborious manual classification of the hundreds of microvessels occurring in a typical sample and paves the way for high-throughput analysis of the arteriolar and venular networks in the mouse.

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Untitled

Cited by 10 publications

References 0 publications

High throughput physiological micro-models for in vitro pre-clinical drug testing: a review of engineering systems approaches

High throughput physiological micro-models for in vitro pre-clinical drug testing: a review of engineering systems approaches

Review of spectral imaging technology in biomedical engineering: achievements and challenges

Differentiation of arterioles from venules in mouse histology images using machine learning

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