ArticleIndian journal of microbiology2025
Current Trends and Advances in Nanoplatforms-Based Imaging for Cancer Diagnosis.
Article in Indian journal of microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- From Dynamics to Diagnosis and Therapy: A Multiscale Computational Framework for MALT1-Targeted Cancer Theranostics.Journal of chemical information and modeling · 2026Article
- Revolutionizing Breast Cancer Treatment: Harnessing Ehrlich Ascites Carcinoma Model, Cancer Metabolism, and Nanotechnology-Enhanced Chemotherapy for Improved Patient Outcomes.Cell biochemistry and biophysics · 2026Review
- Advances in nanotechnology for early cancer diagnosis: emerging nanoplatforms and multimodal imaging approaches.American journal of cancer research · 2026Review
- Validation of diagnostic screening test for pharmacogenomic targets for thiopurine drugs in indian pediatric acute lymphoblastic leukemia patients.Frontiers in pharmacology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The intersection of nanotechnology and biomedical imaging has ushered in a new era in the early detection and diagnosis of cancer which has revolutionized biomedical imaging by enhancing sensitivity, resolution, and targeting capability. This review presents a comprehensive overview of the latest developments and innovations in nanoplatforms-based imaging for cancer diagnosis, a burgeoning field that holds significant potential in improving cancer detection and treatment. Recently multimodal imaging techniques utilizing the unique properties of different types of nanoparticles are providing comprehensive diagnostic information. This multi-pronged approach allows for more precise tumor localization, size estimation, and growth rate calculation, offering a holistic view of the tumor and its environment. The primary focus of this review is on the recent progress in various types of nanoparticle-based imaging modalities, including optical, magnetic resonance, ultrasound, and nuclear imaging. Specific advancements in nanomaterial design for targeted imaging are highlighted, showing the improvement of precision targeting as an impact on the detection of cancer cells, even in early-stage tumors. A keen examination on the integration of diagnostic and therapeutic capabilities into single nano-based platforms for theranostics, underscoring their potential in personalized medicine is provided. The current challenges in the field, such as issues related to toxicity, biodistribution, and clearance of nanoparticles, and it explores ongoing research aimed at overcoming these hurdles. The growing body of research in this field highlights the promising future of nanoplatforms in improving the early detection and treatment of cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.