Evidence map›Paper›PMID 35473350›Full record

ArticleJournal of traditional Chinese medicine = Chung i tsa chih ying wen pan2022

Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan.

Yan Shixing, Liu Ziqing, Ren Meng, H E Haiyang, Xiao Li, Guo Feng, Peng Miao, L I Xiaoxia, Wang Yong, X U Xi and 4 more

Abstract read
In one paragraph

Article in Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan, 2022. 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
–field-weighted citation impact
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.

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

14 authors.

Yan ShixingDepartment of TCM Data Intelligence, Shanghai Daosh Medical Technology Co., Ltd., Shanghai 201200, China.
Liu ZiqingElectronical Medical Records and Information Management Center, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai 200437, China.
Ren MengHepatic Disease Institute, Hubei Key Laboratory of Theoretical and Applied Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China.
H E HaiyangDepartment of TCM Data Intelligence, Shanghai Daosh Medical Technology Co., Ltd., Shanghai 201200, China.
Xiao LiCollege of Acupuncture and Massage, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Guo FengDepartment of TCM Data Intelligence, Shanghai Daosh Medical Technology Co., Ltd., Shanghai 201200, China.
Peng MiaoHepatic Disease Institute, Hubei Key Laboratory of Theoretical and Applied Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China.
L I XiaoxiaDepartment of TCM Data Intelligence, Shanghai Daosh Medical Technology Co., Ltd., Shanghai 201200, China.
Wang YongChinese Medicine Development research Center, Shanghai Literature Institute of TCM, Shanghai 200025, China.
X U XiHepatic Disease Institute, Hubei Key Laboratory of Theoretical and Applied Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China.
Yang TaoCollege of Artificial Intelligence and Information Technology, Institute of Information and Technology Nanjing University of Chinese Medicine, Nanjing 210023, China.
Shao ZuoyuHepatic Disease Institute, Hubei Key Laboratory of Theoretical and Applied Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China.
Huang JingjingHepatic Disease Institute, Hubei Key Laboratory of Theoretical and Applied Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China.
Xiao MingzhongHepatic Disease Institute, Hubei Key Laboratory of Theoretical and Applied Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo summarize the potential characteristics of convalescent patients with coronavirus disease 2019 (COVID-19) in China based on emerging clinical tongue data and guide the treatment and recovery of COVID-19 patients from the perspective of Traditional Chinese Medicine tongue diagnosis.

methodsIn this study, we developed and validated radiomics-based and lab-based methods as a novel approach to provide individualized pretreatment evaluation by analyzing different features to mine the orderliness behind tongue data of convalescent patients. In addition, this study analyzed the tongue features of convalescent patients from clinical tongue qualitative values, including thick and thin, fur, peeling, fat and lean, tooth marks and cracked, and greasy and putrid fur.

resultsWe included 2164 tongue images in total (34% from day 0, 35.4% from day 14 and 30.6% from day 28) from convalescent patients. The significance results are shown as follows. Firstly, as the recovery time prolongs, the L average values of tongue and coat decrease from 60.21 to 57.18 and from 60.06 to 57.03 respectively. Secondly, the decrease of abnormality rate of tongue coat, included greasy tongue fur, putrid fur, teeth-mark, thick-thin fur, are of significant statistical difference ( < 0.05). Thirdly, the average value of gray-level co-occurrence matrices increases from 0.173 to 0.194, the average value of entropy increases from 0.606 to 0.665, the average value of inverse difference normalized decrease from 0.981 to 0.979, and the average value of dissimilarity decrease from 0.1576 to 0.1828. The details of other radiomics features are describe in results section.

conclusionsOur experiment shows that patients in different recovery periods have a relationship with quantitative values of tongue images, including L color space of the tongue and coat radiomics features analysis. This relationship can help clinical doctors master the recovery and health of patients as soon as possible and improve their understanding of the potential mechanisms underlying the dynamic changes and mechanisms underlying COVID-19.

Indexed as

COVID-19ChinaHumansMedicine, Chinese TraditionalTongueCOVID-19dynamic change mechanismsquantitative valuesradiomics features analysistongue inspection

Identifiers

PMID35473350
PMCPMC9924780

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.