Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting.

Xinli Li, Suyi Li, Qingyi Wu

Open access · goldAbstract read

In one paragraph

Article in Molecules (Basel, Switzerland), 2023. 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

0.7field-weighted citation impact, top 17% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

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 at 2 institutions in 1 country.

Xinli LiCollege of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China.

Suyi LiCollege of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China.

Qingyi WuChangchun Institute of Optics, Fine Mechanics and Physics, Changchun 130033, China.ORCID 0000-0002-9655-7676

Jilin University · CNChangchun Institute of Optics, Fine Mechanics and Physics · CN

Funding

National Key Research and Development Program of China 2022YFC2807904

6 · The paper itself

Abstract

Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within

Indexed as

AlgorithmsEscherichia coliCell CycleCell ProliferationFermentationSaccharomyces cerevisiaebiomolecular abundance detectionfermentative microorganismsmultimodel fittingRaman spectratarget extraction

Identifiers

PMID38202740

DOI10.3390/molecules29010157

PMCPMC10780171

OpenAlexW4390269965

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.