Evidence map›Paper›PMID 36968439›Full record

ArticleBio-protocol2023

Multiphoton Microscopy of FITC-labelled

Lishay Parhi, Amjad Shhadeh, Naseem Maalouf, Tamar Alon-Maimon, Viviana Scaiewicz, Gilad Bachrach

Open access · diamondAbstract read
In one paragraph

Article in Bio-protocol, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 38% of its field
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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. Cells · 2024
    Review
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

6 authors at 1 institution in 1 country.

Lishay ParhiThe Institute of Dental Sciences, The Hebrew University-Hadassah School of Dental Medicine, Jerusalem, Israel.
Amjad ShhadehThe Institute of Dental Sciences, The Hebrew University-Hadassah School of Dental Medicine, Jerusalem, Israel.
Naseem MaaloufThe Institute of Dental Sciences, The Hebrew University-Hadassah School of Dental Medicine, Jerusalem, Israel.
Tamar Alon-MaimonThe Institute of Dental Sciences, The Hebrew University-Hadassah School of Dental Medicine, Jerusalem, Israel.
Viviana ScaiewiczThe Institute of Dental Sciences, The Hebrew University-Hadassah School of Dental Medicine, Jerusalem, Israel.
Gilad BachrachThe Institute of Dental Sciences, The Hebrew University-Hadassah School of Dental Medicine, Jerusalem, Israel.
Hebrew University of Jerusalem · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past decades, the main techniques used to visualize bacteria in tissue have improved but are still mainly based on indirect recognition of bacteria. Both microscopy and molecular recognition are being improved, but most procedures for bacteria detection in tissue involve extensive damage. Here, we describe a method to visualize bacteria in tissue slices from an in vivo model of breast cancer. This method allows examining trafficking and colonization of fluorescein-5-isothiocyanate (FITC)-stained bacteria in various tissues. The protocol provides direct visualization of fusobacterial colonization in breast cancer tissue. Rather than processing the tissue or confirming bacterial colonization by PCR or culture, the tissue is directly imaged using multiphoton microscopy. This direct visualization protocol causes no damage to the tissue; therefore, all structures can be identified. This method can be combined with others to co-visualize bacteria, types of cells, or protein expression in cells.

Indexed as

Bacterial colonizationBreast cancerFusobacterium nucleatumMultiphoton microscopy

Identifiers

PMID36968439
PMCPMC10031517
OpenAlexW4327793313

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.