Evidence map›Paper›PMID 34853934›Full record

ReviewAAPS PharmSciTech2021

3D Printed Personalized Medicine for Cancer: Applications for Betterment of Diagnosis, Prognosis and Treatment.

Harshada Bhuskute, Pravin Shende, Bala Prabhakar

Abstract readReview
PubMed Publisher
In one paragraph

Review in AAPS PharmSciTech, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
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

28 citing papers in PubMed, 65 citations in OpenAlex.

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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 at 1 institution in 1 country.

Harshada BhuskuteShobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM's NMIMS, Vile Parle (West), Mumbai, India.
Pravin ShendeShobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM's NMIMS, Vile Parle (West), Mumbai, India.
Bala PrabhakarShobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM's NMIMS, Vile Parle (West), Mumbai, India. bala.prabhakar@nmims.edu.
Narsee Monjee Institute of Management Studies · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer treatment is challenging due to the tumour heterogeneity that makes personalized medicine a suitable technique for providing better cancer treatment. Personalized medicine analyses patient-related factors like genetic make-up and lifestyle and designs treatments that offer the benefits of reduced side effects and efficient drug delivery. Personalized medicine aims to provide a holistic way for prevention, diagnosis and treatment. The customization desired in personalized medicine is produced accurately by 3D printing which is an established technique known for its precision. Different 3D printing techniques exhibit their capability in producing cancer-specific medications for breast, liver, thyroid and kidney tumours. Three-dimensional printing displays major influence on cancer modelling and studies using cancer models in treatment and diagnosis. Three-dimensional printed personalized tumour models like physical 3D models, bioprinted models and tumour-on-chip models demonstrate better in vitro and in vivo correlation in drug screening, cancer metastasis and prognosis studies. Three-dimensional printing helps in cancer modelling; moreover, it has also changed the facet of cancer treatment. Improved treatment via custom-made 3D printed devices, implants and dosage forms ensures the delivery of anticancer agents efficiently. This review covers recent applications of 3D printed personalized medicine in various cancer types and comments on the possible future directions like application of 4D printing and regularization of 3D printed personalized medicine in healthcare.

Indexed as

NeoplasmsPrecision MedicineDrug Delivery SystemsHumansPrinting, Three-DimensionalPrognosis3D bioprinting3D printingCancer modellingpersonalized medicineTumour-on-chip

Identifiers

PMID34853934
OpenAlexW3217203343

What OpenQuestion holds

Textmetadata
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.