Evidence map›Paper›PMID 33642609›Full record

ArticleApplied physics letters2021

High-speed label-free two-photon fluorescence microscopy of metabolic transients during neuronal activity.

Andrew J Bower, Carlos Renteria, Joanne Li, Marina Marjanovic, Ronit Barkalifa, Stephen A Boppart

Open access · greenAbstract read
In one paragraph

Article in Applied physics letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

12 citing papers in PubMed, 14 citations in OpenAlex.

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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.

Marina Marjanovic
Ronit BarkalifaBeckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
University of Illinois Urbana-Champaign · US

Funding

Intraoperative Polarization-Sensitive OCT for Assessing Breast Tumor MarginsR01CA213149 · NCI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI BOPPART, STEPHEN A · 2017 to 2021
$2.4M
Tissue microenvironment (TIMe) training programT32EB019944 · NIBIB · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI BHARGAVA, ROHIT, GASKINS, REX · 2016 to 2025
$1.9M
Optical Amplification Microscopy of Weak Back-Scattered LightR01EB023232 · NIBIB · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI BOPPART, STEPHEN A · 2016 to 2019
$1.8M
NCI NIH HHS R01 CA213149NIBIB NIH HHS R01 EB023232NIBIB NIH HHS T32 EB019944
6 · The paper itself

Abstract

The brain is an especially active metabolic system, requiring a large supply of energy following neuronal activation. However, direct observation of cellular metabolic dynamics associated with neuronal activation is challenging with currently available imaging tools. In this study, an optical imaging approach combining imaging of calcium transients and the metabolic co-enzyme nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) is utilized to track the metabolic dynamics in hippocampal neuron cultures. Results show distinct cellular components for the NAD(P)H response following neuronal activity, where notable differences in the NAD(P)H dynamics between neurons and astrocytes can be directly observed. Additionally, tracking of these responses across a large field of view is demonstrated for metabolic profiling of neuronal activation. Observation of neuronal dynamics using these methods allows for closer examination of the complex metabolic machinery of the brain, and may lead to a better understanding of the cellular metabolism of neuronal activation.

Identifiers

PMID33642609
PMCPMC7904318
OpenAlexW3130599887

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.