Article in RSC advances, 2023. 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
0.9field-weighted citation impact, top 31% 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, 8 citations in OpenAlex.
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 3 institutions in 2 countries.
Jiaojiao QinState Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China jinsong.han@cpu.edu.cn.
Hao WangState Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China jinsong.han@cpu.edu.cn.
Yu XuState Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China jinsong.han@cpu.edu.cn.
Fangfang ShiState Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China jinsong.han@cpu.edu.cn.
Shijie YangState Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China jinsong.han@cpu.edu.cn.
Hui HuangMing Wai Lau Centre for Reparative Medicine, Karolinska Institutet Sweden huihuang.cn@hotmail.com.
Jun LiuShandong Yuwang Ecological Food Industry Co., Ltd De Zhou 251200 China.
Linxian LiMing Wai Lau Centre for Reparative Medicine, Karolinska Institutet Sweden huihuang.cn@hotmail.com.
Fei LiState Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China jinsong.han@cpu.edu.cn.ORCID https://orcid.org/0000-0002-7930-5485
Jinsong HanState Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, Department of Food Quality and Safety, College of Engineering, China Pharmaceutical University 211109 China jinsong.han@cpu.edu.cn.ORCID https://orcid.org/0000-0003-1832-2641
Wenwen WuDepartment of Pharmacy, Children's Hospital of Nanjing Medical University Nanjing Jiangsu Province 211109 China wuwenwen_506@163.com.
China Pharmaceutical University · CNKarolinska Institutet · SESecond Affiliated Hospital of Nanjing Medical University · CN
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
Bioactive flavonoids, the major ingredients of red wines, have been proven to prevent atherosclerosis and cardiovascular disease due to their anti-inflammatory and anti-oxidant activity. However, flavonoids have proven challenging to identify, even when multiple approaches are combined. Hereby, a simple array was constructed to detect flavonoids by employing phenylboronic acid modified perylene diimide derivatives (PDIs). Through multiple non-specific interactions (hydrophilic, hydrophobic, charged, aromatic, hydrogen-bonded and reversible covalent interactions) with flavonoids, the fluorescence of PDIs can be modulated, and variations in intensity can be used to create fingerprints of flavonoids. This array successfully discriminated 14 flavonoids of diverse structures and concentrations with 100% accuracy, based on patterns in fluorescence intensity modulation,
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.
A simple array integrating machine learning for identification of flavonoids in red wines. · full record | OpenQuestion