Evidence map›Paper›PMID 42376474›Full record

ReviewJournal of pharmaceutical analysis2026

Raman spectroscopy combined with multiple technologies for label-free identification of immune cells: An overview.

Chengshun Jiang, Jie Deng, Wanwan Gan, Jiaqi Zou, Tongkai Cai, Hao Yin, Yongbing Cao

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of pharmaceutical analysis, 2026. 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.

Chengshun JiangInstitute of Vascular Diseases, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200086, China.
Jie DengInstitute of Vascular Diseases, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200086, China.
Wanwan GanDepartment of Physiology and Pharmacology, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
Jiaqi ZouDepartment of Physiology and Pharmacology, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
Tongkai CaiShanghai Diacart Biomedical Science and Technology Limited Company, Shanghai, 201203, China.
Hao YinInstitute of Vascular Diseases, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200086, China.
Yongbing CaoInstitute of Vascular Diseases, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200086, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional immune cell identification and sorting methods rely on antibodies and fluorophores, which may compromise cell viability and functionality. Raman spectroscopy, a label-free and highly sensitive technique, enables precise differentiation of immune cell subtypes based on their intrinsic biochemical composition. When integrated with chemometrics, microfluidics, and machine learning (ML)/deep learning (DL) approaches, Raman spectroscopy significantly enhances the accuracy, throughput, and efficiency of immune cell sorting. This review systematically analyzes the principles and advantages of these integrated strategies and explores their potential applications in immunological research, clinical diagnostics, and precision medicine, paving the way for non-invasive and high-efficiency immune cell analysis.

Indexed as

ChemometricsImmune cell identificationLabel-free detectionMachine learningMicrofluidicsRaman spectroscopy

Identifiers

PMID42376474
PMCPMC13312135

What Socratic 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 Socratic graph.