Evidence map›Paper›PMID 38218999›Full record

ReviewDrug delivery and translational research2024

Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology.

Anuj A Biswas, Madhukiran R Dhondale, Ashish K Agrawal, Dolores R Serrano, Brahmeshwar Mishra, Dinesh Kumar

Abstract readReview
PubMed Publisher
In one paragraph

Review in Drug delivery and translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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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

6 authors.

Anuj A BiswasDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi, India.ORCID 0009-0009-6451-0951
Madhukiran R DhondaleDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi, India.ORCID 0000-0003-3291-5443
Ashish K AgrawalDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi, India.ORCID 0000-0002-7302-3647
Dolores R SerranoSchool of Pharmacy, Complutense University, Madrid, Spain.ORCID 0000-0002-0475-8420
Brahmeshwar MishraDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi, India. bmishra.phe@itbhu.ac.in.ORCID 0000-0002-8608-2611
Dinesh KumarDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi, India. dinesh.phe@itbhu.ac.in.ORCID 0000-0001-9785-0500

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing. 3D printing enables the production of quality MNs of tuneable characteristics using a variety of materials. Further, the possible integration of artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL) with 3D printing makes it an indispensable tool for fabricating microneedles. Provided that these AI tools can be trained and act with minimal human intervention to control the quality of products produced, there is also a possibility of mass production of MNs using these tools in the future. This work reviews the specific role of AI in the 3D printing of MN-based devices discussing the use of AI in predicting drug release patterns, its role as a quality control tool, and in predicting the biomarker levels. Additionally, the autonomous 3D printing of microneedles using an integrated system of the internet of things (IoT) and machine learning (ML) is discussed in brief. Different categories of machine learning including supervised learning, semi-supervised learning, unsupervised learning, and reinforced learning have been discussed in brief. Lastly, a brief section is dedicated to the biosensing applications of MN-based devices.

Indexed as

Artificial IntelligenceDrug Delivery SystemsNeedlesPrinting, Three-DimensionalAnimalsHumansMicroinjections3D printingAdvanced manufacturingBiosensorsContinuous manufacturingMachine learning

Identifiers

PMID38218999

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.