Evidence map›Paper›PMID 41171566›Full record

ReviewAdvances in experimental medicine and biology2025

Biochemical Diagnosis in Substance and Non-substance Addiction.

Wenwen Shen, Wenjin Xu, Qingxiao Hong, Huifeng Liu, Xiaohu Xie, Haixiong Liu, Wenhua Zhou

Abstract readReview
PubMed Publisher
In one paragraph

Review in Advances in experimental medicine and biology, 2025. 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

7 authors.

Wenwen ShenAffiliated Kangning Hospital of Ningbo University, Ningbo University Health Science Center, Ningbo, Zhejiang Province, China.
Wenjin XuAffiliated Kangning Hospital of Ningbo University, Ningbo University Health Science Center, Ningbo, Zhejiang Province, China.
Qingxiao HongAffiliated Kangning Hospital of Ningbo University, Ningbo University Health Science Center, Ningbo, Zhejiang Province, China.
Huifeng LiuAffiliated Kangning Hospital of Ningbo University, Ningbo University Health Science Center, Ningbo, Zhejiang Province, China.
Xiaohu XieAffiliated Kangning Hospital of Ningbo University, Ningbo University Health Science Center, Ningbo, Zhejiang Province, China.
Haixiong LiuAffiliated Kangning Hospital of Ningbo University, Ningbo University Health Science Center, Ningbo, Zhejiang Province, China.
Wenhua ZhouAffiliated Kangning Hospital of Ningbo University, Ningbo University Health Science Center, Ningbo, Zhejiang Province, China. zhouwenhua@nbu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An optimal biochemical marker for addiction would be some easily traced molecules in body specimens, which indicate indulgent addictive behaviors or susceptibility to certain addictive stimuli. In this chapter, we discussed existing literature about possible biomarkers, and classified them into three categories: the substance molecules and their metabolites, markers from biochemical responses to certain addiction, and genetic markers suggesting susceptibility to addiction. In every category, we examined studies concerning a certain type of addiction one by one, with focus mainly on opiates, psychostimulants, and pathological gambling. Several promising molecules were highlighted, including those of neurotrophic factors, inflammatory factors, and indicators of vascular injury.

Indexed as

Behavior, AddictiveBiomarkersSubstance-Related DisordersAnimalsGamblingHumansBiomarkersBiochemical diagnosisNon-substance addictionSubstance addiction

Identifiers

PMID41171566

What OpenQuestion holds

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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.