Automatic quantitative analysis of morphology of apoptotic HL-60 cells.

Yahui Liu, Wang Lin, Xu Yang, Weizi Liang, Jun Zhang, Maobin Meng, John R Rice, Yu Sa, Yuanming Feng

Abstract read

In one paragraph

Article in EXCLI journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis.PloS one · 2014
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yahui LiuDepartment of Biomedical Engineering, Tianjin University, Tianjin, China.

Wang LinDepartment of Biomedical Engineering, Tianjin University, Tianjin, China.

Xu YangDepartment of Biomedical Engineering, Tianjin University, Tianjin, China.

Weizi LiangDepartment of Biomedical Engineering, Tianjin University, Tianjin, China.

Jun ZhangDepartment of Biomedical Engineering, Tianjin University, Tianjin, China.

Maobin MengDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin China.

John R RiceDepartment of Radiation Oncology, East Carolina University, Greenville, NC/USA.

Yu SaDepartment of Biomedical Engineering, Tianjin University, Tianjin, China.

Yuanming FengDepartment of Biomedical Engineering, Tianjin University, Tianjin, China ; Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin China ; Department of Radiation Oncology, East Carolina University, Greenville, NC/USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Morphological identification is a widespread procedure to assess the presence of apoptosis by visual inspection of the morphological characteristics or the fluorescence images. The procedure is lengthy and results are observer dependent. A quantitative automatic analysis is objective and would greatly help the routine work. We developed an image processing and segmentation method which combined the Otsu thresholding and morphological operators for apoptosis study. An automatic determination method of apoptotic stages of HL-60 cells with fluorescence images was developed. Comparison was made between normal cells, early apoptotic cells and late apoptotic cells about their geometric parameters which were defined to describe the features of cell morphology. The results demonstrated that the parameters we chose are very representative of the morphological characteristics of apoptotic cells. Significant differences exist between the cells in different stages, and automatic quantification of the differences can be achieved.

Indexed as

apoptosiscell morphologyquantitative analysis

Identifiers

PMID26417240

PMCPMC4464453

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.