Current Protocols in Cytometry

Robinson, J. Paul; Darżynkiewicz, Zbigniew; Hoffman, R. A.; Nolan, John P.; Rabinovitch, Peter S.; Watkins, Simon

doi:10.1002/0471142956

Cited by 74 publications

(3 citation statements)

References 49 publications

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“…Figure 3(a), for example, presents the clusters of real time data of phytoplankton cells acquired by the CytoBuoy instrument since the red fluorescence signals are the results of chlorophyll-a response to the laser excitation and the side scatter (SSC) is proportional to cytoplasmic granularity of a cell or internal complexity measurement [60]. Individuals of these groups were better characterized by [39] at a single cell and species level.…”

Section: Resultsmentioning

confidence: 99%

Genetic Optimization of Artificial Neural Networks to Forecast Virioplankton Abundance from Cytometric Data

Pereira¹,

Oliveira²,

Ebecken³

2013

JILSA

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Since viruses are able to influence the trophic status and community structure they should be accessed and accounted in ecosystem functioning and management models. So, this work met a set of biological, chemical and physical time series in order to explore the correlations with marine virioplankton community across different trophic gradients. The case studied is the Arraial do Cabo upwelling system, northeast of Rio de Janeiro State in Southeast coast of Brazil. The main goal is to evolve three type of artificial neural network (ANN) by genetic algorithm (GA) optimization to predict virioplankton abundance and dynamic. The input variables range from the abundance of phytoplankton, bacterioplankton and its ratios acquired by one in situ and another ex situ flow cytometers. These data were collected with weekly frequency from August 2006 to June 2007. Our results show viruses being highly correlated to their host, and that GA provided an efficient method of optimizing ANN architectures to predict the virioplankton abundance. The RBF-NN model presented the best performance to an accuracy of 97% for any period in the year. A discussion and ecological interpretations about the system behavior is also provided.

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Section: Resultsmentioning

confidence: 99%

Genetic Optimization of Artificial Neural Networks to Forecast Virioplankton Abundance from Cytometric Data

Pereira¹,

Oliveira²,

Ebecken³

2013

JILSA

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“…For this, the number of acquired events in a parent or grandparent population needs to be defined in order to reach the needed number of events in the population of interest. The user may define the limits of the assay by predefining the gates during the assay development using negative controls such as isotype and Fluorescence Minus One (FMO) controls [27]. In assays where the signal of interest can be inhibited by the drug, for example the inhibition of a signaling pathway and the resulting inhibition of a phosphosignal, the limit of quantification can be assessed by the lowest signal which gives reliable and reproducible results (measured by replicate precision, detailed in the section below).…”

Section: Summary Of Current Guidelines and Key Challengesmentioning

confidence: 99%

Implementation of Highly Sophisticated Flow Cytometry Assays in Multicenter Clinical Studies: Considerations and Guidance

et al. 2015

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Flow cytometry is increasingly becoming an important technology for biomarkers used in drug discovery and development. Within clinical development flow cytometry is used for the determination of PD biomarkers, disease or efficacy biomarkers or patient stratification biomarkers. Significant differences exist between flow cytometry methodology and other widely used technologies measuring soluble biomarkers including ligand binding and mass spectrometry. These differences include the very heavy reliance on aspects of sample processing techniques as well as sample stabilization to ensure viable samples. These differences also require exploration of new approaches and wider discussion regarding method validation requirements. This paper provides a review of the current challenges, solutions, regulatory environment and recommendations for the application of flow cytometry to measure biomarkers in clinical development.

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“…cell migration, proliferation, apoptosis) can be visualised and quantitatively analyzed through time-lapse video microscopy thus allowing to obtain biochemical and biophysical information about different cell populations at precise time-space windows. [2][3][4] Automated analysis software and dedicated tools were developed after the implementation of time-lapse video microscopy for quantitative analysis, including cell counting, proliferation quantification, wound healing and multiple cell tracking. 5 All these can provide useful methods to characterize and quantify complex biological phenomena.…”

Section: Introductionmentioning

confidence: 99%

Nanoparticles for the delivery of zoledronic acid to prostate cancer cells: A comparative analysis through time lapse video-microscopy technique

Schiraldi

Zappavigna

Agostino

et al. 2014

Cancer Biology & Therapy

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Time-lapse live cell imaging is a powerful tool for studying the responses of cells to drugs. Zoledronic acid (ZOL) is the most potent aminobiphosphonate able to induce cell growth inhibition at very low concentrations. The lack of clear evidence of ZOL-induced anti-cancer effects is likely due to its unfavorable pharmacokinetic profile. The use of nanotechnology-based formulations allows overcoming these limitations in ZOL pharmaco-distribution. Recently, stealth liposomes (LIPOs) and new self-assembly PEGylated nanoparticles (NPs) encapsulating ZOL were developed. Both the delivery systems showed promising anticancer activity in vitro and in vivo.In this work, we investigated the cytostatic effect of these novel formulations (LIPOs and NPs) compared with free ZOL on 2 different prostate cancer cell lines, PC 3 and DU 145 and on prostate epithelial primary cells EPN using time lapse video-microscopy (TLVM). In PC3 cells, free ZOL showed a significant anti-proliferative effect but this effect was lower than that induced by LIPOs and NPs encapsulating ZOL; moreover, LIPO-ZOL was more potent in inducing growth inhibition than NP-ZOL. On the other hand, LIPO-ZOL slightly enhanced the free ZOL activity on growth inhibition of DU 145, while the anti-proliferative effect of NP-ZOL was not statistically relevant. These novel formulations did not induce anti-proliferative effects on EPN cells. Finally, we evaluated cytotoxic effects on DU145 where, LIPO-ZOL induced the highest cytotoxicity compared with NP-ZOL and free ZOL. In conclusion, ZOL can be transformed in a powerful anticancer agent, if administered with nanotechnology-based formulations without damaging the healthy tissues.

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Current Protocols in Cytometry

Cited by 74 publications

References 49 publications

Genetic Optimization of Artificial Neural Networks to Forecast Virioplankton Abundance from Cytometric Data

Genetic Optimization of Artificial Neural Networks to Forecast Virioplankton Abundance from Cytometric Data

Implementation of Highly Sophisticated Flow Cytometry Assays in Multicenter Clinical Studies: Considerations and Guidance

Nanoparticles for the delivery of zoledronic acid to prostate cancer cells: A comparative analysis through time lapse video-microscopy technique

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