Evidence map›Paper›PMID 41419708›Full record

ArticleAMB Express2025

Optimizing immunomagnetic separation for efficient E. coli O157:H7 recovery and detection.

Assem Abolmaaty, Abrar AbuHamdia, Dina H Amin

Abstract read
In one paragraph

Article in AMB Express, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Assem AbolmaatyDepartment of Food Science, Faculty of Agriculture, Ain Shams University, Cairo, Egypt.
Abrar AbuHamdiaDivision of Genomics and Precision Medicine, College of Health and Life Sciences, Hamad Bin Khalifa University, 34110, Doha, Qatar.
Dina H AminDepartment of Microbiology, Faculty of Science, Ain Shams University, Cairo, Abbasyia, Egypt. dina.hatem@sci.asu.edu.eg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunomagnetic separation is an invaluable tool in biomedical and food safety applications due to their high specificity and efficiency in capturing target cells. This study is a comprehensive investigation of the Ag-Ab reaction and the effects of several factors such as IgG concentration, incubation temperature, and incubation time on the binding efficiency of target cells to the immunomagnetic beads aiming to maximize captured E. coli O157 cells from ground beef samples. Our findings reveal a concentration-dependent relationship between IgG dilution and binding efficiency, with lower dilutions (1:100) yielding higher binding percentages. Additionally, the optimal incubation temperature for bead-cell interactions was identified as 32 °C, with deviations above or below this temperature impairing binding efficiency. Longer incubation times (up to 120 min) significantly improved antibody coating on the beads, enhancing their ability to capture target cells. The study also demonstrated a strong correlation between bacterial load and binding efficiency, highlighting the sensitivity of the immunomagnetic separation method in detecting E. coli O157 even at low concentrations. These findings underscore the importance of precisely controlling experimental parameters to maximize the efficiency and specificity of immunomagnetic separation techniques, providing a valuable framework for researchers aiming to refine bead-based cell capture protocols for diagnostic or research applications.

Indexed as

Antibody-coated beadsBacterial detectionE. coli O157:H7Ground beefIgG concentrationImmunomagnetic separation

Identifiers

PMID41419708
PMCPMC12834865

What OpenQuestion holds

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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.