Evidence map›Paper›PMID 28166845›Full record

ReviewParasitology2018

Let's get fISSical: fast in silico synchronization as a new tool for cell division cycle analysis.

Brooke Morriswood, Markus Engstler

Abstract readReview
In one paragraph

Review in Parasitology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

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

2 authors.

Brooke MorriswoodDepartment of Cell & Developmental Biology,University of Würzburg,Biocentre, Am Hubland, 97074 Würzburg,Germany.
Markus EngstlerDepartment of Cell & Developmental Biology,University of Würzburg,Biocentre, Am Hubland, 97074 Würzburg,Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell cycle progression is a question of fundamental biological interest. The coordinated duplication and segregation of all cellular structures and organelles is however an extremely complex process, and one which remains only partially understood even in the most intensively researched model organisms. Trypanosomes are in an unusual position in this respect - they are both outstanding model systems for fundamental questions in eukaryotic cell biology, and pathogens that are the causative agents of three of the neglected tropical diseases. As a failure to successfully complete cell division will be deleterious or lethal, analysis of the cell division cycle is of relevance both to basic biology and drug design efforts. Cell division cycle analysis is however experimentally challenging, as the analysis of phenotypes associated with it remains hypothesis-driven and therefore biased. Current methods of analysis are extremely labour-intensive, and cell synchronization remains difficult and unreliable. Consequently, there exists a need - both in basic and applied trypanosome biology - for a global, unbiased, standardized and high-throughput analysis of cell division cycle progression. In this review, the requirements - both practical and computational - for such a system are considered and compared with existing techniques for cell cycle analysis.

Indexed as

Computer SimulationAnimalsAutomationCell CycleDrug DesignHumansOrganellesTrypanosoma brucei bruceiTrypanosomiasis, Africanautomationcell division cycleorganellessynchronizationTrypanosoma brucei

Identifiers

PMID28166845
PMCPMC5964468

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.