Evidence mapPaperPMID 41717260Full record

ReviewRSC advances2026

Carbon nanodots as theranostics agents in cancer: advances in design, targeting, and real-time monitoring.

Mayank Kumar, Manini Bhatt, Bodhisatwa Das

Abstract readReview
In one paragraph

Review in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Mayank KumarDepartment of Biomedical Engineering, Indian Institute of Technology Ropar Punjab India bodhisatwa.das@iitrpr.ac.in.
Manini BhattDepartment of Biomedical Engineering, Indian Institute of Technology Ropar Punjab India bodhisatwa.das@iitrpr.ac.in.
Bodhisatwa DasDepartment of Biomedical Engineering, Indian Institute of Technology Ropar Punjab India bodhisatwa.das@iitrpr.ac.in.ORCID https://orcid.org/0000-0002-1447-9089

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Carbon nanodots (CNDs) have emerged as a promising class of carbon-based nanomaterials for cancer theranostics, uniquely integrating diagnosis and therapy on a single platform. Their ultrasmall size, high aqueous dispersibility, tunable photoluminescence extending into the near-infrared (NIR) window, and compatibility with green, scalable synthesis enable deep-tissue imaging and targeted intervention with reduced systemic toxicity compared with many conventional nanomaterials. This review summarises recent advances in the top-down and bottom-up fabrication of CNDs, including heteroatom doping and surface functionalisation with ligands or stimuli-responsive linkers, and relates these design parameters to optical performance, tumour selectivity, and responsiveness to the tumour microenvironment. Particular emphasis is placed on CND-based platforms for multimodal imaging (fluorescence, MRI, and photoacoustic), controlled-release drug delivery, gene silencing, and light-activated photodynamic and photothermal therapies, as well as emerging synergistic systems that combine these functions for real-time, image-guided treatment. Remaining challenges, such as batch-to-batch variability, incomplete understanding of long-term biosafety (especially for metal-doped CNDs), and limited clinical-scale manufacturing and regulatory readiness, are critically discussed alongside future opportunities, including NIR-II optimisation, protein-corona-resistant surface engineering, and AI-assisted CND design for personalised cancer theranostics.

Identifiers

PMID41717260
PMCPMC12915097

What Socratic holds

Textmetadata
LicenceCC BY
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.