Evidence map›Paper›PMID 40822423›Full record

Article3 Biotech2025

In silico and in vitro evaluation of mucus-binding proteins from probiotics against

Ghazaleh Sheikhi, Soheil Shajari, Sepehr Nouri, Hassan Mohabatkar, Mandana Behbahani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in 3 Biotech, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Ghazaleh SheikhiDepartment of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, 81746-73441 Iran.
Soheil ShajariDepartment of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, 81746-73441 Iran.
Sepehr NouriDepartment of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, 81746-73441 Iran.
Hassan MohabatkarDepartment of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, 81746-73441 Iran.ORCID 0000-0002-5151-3768
Mandana BehbahaniDepartment of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, 81746-73441 Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to develop a predictive model for mucus-binding proteins using machine learning and to experimentally evaluate the anti-cariogenic effects of selected probiotic strains. In silico, a computational method was established utilizing Support Vector Machine (SVM) and AdaBoost algorithms with pseudo amino acid composition (PseAAC) for protein sequence representation. The predictive model achieved high accuracy. Specifically, the SVM model demonstrated 94% accuracy, 96% sensitivity, 91% specificity, and an 88% Matthews correlation coefficient (MCC) on a labeled test dataset. In vitro experiments assessed the antimicrobial activity and anti-biofilm formation effects of various probiotic strains against

Indexed as

Machine learningMucus-binding proteinsProbioticsStreptococcus mutansSupport vector machine (SVM)

Identifiers

PMID40822423
PMCPMC12354941

What OpenQuestion holds

Textmetadata
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.