Evidence map›Paper›PMID 38172356›Full record

ArticleJournal of natural medicines2024

Improved quantitative analysis of tenuifolin using hydrolytic continuous-flow system to build prediction models for its content based on near-infrared spectroscopy.

Tatsuki Kitazoe, Chisato Usui, Eiichi Kodaira, Takuro Maruyama, Noriaki Kawano, Hiroyuki Fuchino, Kazuhiko Yamamoto, Yasushi Kitano, Nobuo Kawahara, Kayo Yoshimatsu and 2 more

Abstract read
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In one paragraph

Article in Journal of natural medicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 47% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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

12 authors at 5 institutions in 1 country.

Tatsuki KitazoeSchool of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8641, Japan.
Chisato UsuiSchool of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8641, Japan.
Eiichi KodairaSchool of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8641, Japan.
Takuro MaruyamaDivision of Pharmacognosy, Phytochemistry and Narcotics, National Institute of Health Sciences, 3-25-26, Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa, 210-9501, Japan.
Noriaki KawanoNational Institutes of Biomedical Innovation, Health and Nutrition, 1-2 Hachimandai, Tsukuba, Ibaraki, 305-0843, Japan.
Hiroyuki FuchinoNational Institutes of Biomedical Innovation, Health and Nutrition, 1-2 Hachimandai, Tsukuba, Ibaraki, 305-0843, Japan.
Kazuhiko YamamotoNational Institutes of Biomedical Innovation, Health and Nutrition, 1-2 Hachimandai, Tsukuba, Ibaraki, 305-0843, Japan.
Yasushi KitanoNippon Funmatsu Yakuhin Co., Ltd, 2-5-11, Doshomachi, Chuo-ku, Osaka, 541-0045, Japan.
Nobuo KawaharaNational Institutes of Biomedical Innovation, Health and Nutrition, 1-2 Hachimandai, Tsukuba, Ibaraki, 305-0843, Japan.
Kayo YoshimatsuNational Institutes of Biomedical Innovation, Health and Nutrition, 1-2 Hachimandai, Tsukuba, Ibaraki, 305-0843, Japan.
Tatsuya ShirahataSchool of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8641, Japan.
Yoshinori KobayashiSchool of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8641, Japan. kobayashiy@pharm.kitasato-u.ac.jp.ORCID http://orcid.org/0000-0002-4289-7392
Kitasato University · JPNational Institute of Biomedical Innovation, Health and Nutrition · JPKitasato Institute Hospital · JPMakino Botanical Garden · JPNational Institute of Health Sciences · JP

Funding

Japan Agency for Medical Research and Development JP18ak0101104The Japan Science and Technology Agency's Center of Innovation Program (JST COI) JPMJCE1301
6 · The paper itself

Abstract

This study used two types of analyses and statistical calculations on powdered samples of Polygala root (PR) and Senega root (SR): (1) determination of saponin content by an independently developed quantitative analysis of tenuifolin content using a flow reactor, and (2) near-infrared spectroscopy (NIR) using crude drug powders as direct samples for metabolic profiling. Furthermore, a prediction model for tenuifolin content was developed and validated using multivariate analysis based on the results of (1) and (2). The goal of this study was to develop a rapid analytical method utilizing the saponin content and explore the possibility of quality control through a wide-area survey of crude drugs using NIR spectroscopy. Consequently, various parameters and appropriate wavelengths were examined in the regression analysis, and a model with a reasonable contribution rate and prediction accuracy was successfully developed. In this case, the wavenumber contributing to the model was consistent with that of tenuifolin, confirming that this model was based on saponin content. In this series of analyses, we have succeeded in developing a model that can quickly estimate saponin content without post-processing and have demonstrated a brief way to perform quality control of crude drugs in the clinical field and on the market.

Indexed as

SaponinsSpectroscopy, Near-InfraredLeast-Squares AnalysisQuality ControlSaponinsHigh-performance liquid chromatographyHydrolysisMedicinal plantMetabolic profilingNear-infrared spectroscopy

Identifiers

PMID38172356
OpenAlexW4390589939

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.