Evidence map›Paper›PMID 42307051›Full record

ArticleBiotechnology journal2026

Validation of an Automated Fluorescence- and Image-Based Viable Cell Counting Method for Fecal Microbiota Transplantation Drug Products.

Timon Lang, Fedja Farowski, Jozef Al-Gousous, Peter Langguth, Maria J G T Vehreschild

Abstract readValidation Study
In one paragraph

Article in Biotechnology journal, 2026. 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

5 authors.

Timon LangDepartment II of Internal Medicine, Goethe University Frankfurt, University Hospital Frankfurt, Infectious Diseases, Frankfurt am Main, Germany.ORCID https://orcid.org/0009-0006-2120-7344
Fedja FarowskiDepartment II of Internal Medicine, Goethe University Frankfurt, University Hospital Frankfurt, Infectious Diseases, Frankfurt am Main, Germany.
Jozef Al-GousousDepartment of Biopharmaceutics and Pharmaceutical Technology, Johannes Gutenberg-University, Mainz, Germany.ORCID https://orcid.org/0000-0001-7994-4175
Peter LangguthDepartment of Biopharmaceutics and Pharmaceutical Technology, Johannes Gutenberg-University, Mainz, Germany.
Maria J G T VehreschildDepartment II of Internal Medicine, Goethe University Frankfurt, University Hospital Frankfurt, Infectious Diseases, Frankfurt am Main, Germany.

Funding

German Centre for Infection Research
6 · The paper itself

Abstract

Fecal microbiota transplantation (FMT) is an established treatment for recurrent Clostridioides difficile infection. As a drug product, FMT is subject to pharmaceutical quality standards, including accurate quantification of viable bacteria to ensure product consistency. We developed and validated an automated fluorescence- and image-based viable-cell counting method for FMT products; total-cell enumeration was examined secondarily. The QUANTOM Tx system (Logos Biosystems) was validated according to ICH Q2(R2) using Escherichia coli and Bacillus subtilis as reference strains. The Viable Cell Staining Kit was validated with Escherichia coli, whereas the Total Cell Staining Kit was examined in selected experiments using both strains. Applicability to complex FMT matrices was confirmed using authentic fecal samples. Viable-cell counting showed strong linearity across a range of 9.4 × 10

Indexed as

Fecal Microbiota TransplantationFecesBacillus subtilisCell CountEscherichia coliFluorescenceHumansReproducibility of Resultsanalytical method validationfecal microbiota transplantationGMPICH‐Guideline Q2 (R2)viable bacterial quantification

Identifiers

PMID42307051
PMCPMC13273919

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Registered trials

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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.