Evidence map›Paper›PMID 40258960›Full record

ArticleScientific reports2025

Exploration and future prospects of intelligent supervision technology and system development in biological laboratories.

Yanchao Gao, Hua Gu, Yanfeng Ma

Abstract read
In one paragraph

Article in Scientific reports, 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

3 authors.

Yanchao GaoZhejiang Cancer Hospital, Hangzhou, 310002, Zhejiang, China. kjzxgyc@163.com.
Hua GuZhejiang Medical Service Management Evaluation Center, Hangzhou, 310002, Zhejiang, China. kjzxhgu@163.com.
Yanfeng MaFuyang Laboratory Equipment Research Institute, Hangzhou, 310002, Zhejiang, China.

Funding

the Major Social Welfare Project of Zhejiang Province 2022C03124
6 · The paper itself

Abstract

Biological laboratories, which are crucial for research and medical trials, often face challenges such as disconnected data, fragmented systems, excessive manual processes, and insufficient smart technology. This study presents a comprehensive intelligent supervision system designed to ensure complete element coverage, full-process supervision, full-chain traceability, and dynamic real-time monitoring within these laboratories. The system incorporates algorithms for personnel behavior identification, secure network transmission, native data storage, aggregation, processing terminals, and intelligent decision-making assistance platforms. Implemented across over 2000 laboratories in Zhejiang Province, the system has identified and mitigated over 20,000 risk points, processed over 3000 early warning instances, and achieved an accuracy rate of exceeding 93% in identifying personnel gestures and behavior. The platform uses cutting-edge technology to address information security and risk classification, supporting comprehensive incident management from pre-incident reviews to closed-loop disposal post-inciden. Additionally, it facilitates tiered management and tracks risk treatment, significantly enhancing the intelligent governance capacity for laboratory safety. Future developments will focus on a people-oriented approach, iterative algorithmic technology, and standardized governance of data substrates.

Indexed as

LaboratoriesAlgorithmsComputer SecurityHumansBiological laboratoryFuture outlookIntelligent supervision technology and systemResearch and development exploration

Identifiers

PMID40258960
PMCPMC12012121

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.