Evidence map›Paper›PMID 34372062›Full record

ArticlePolymers2021

Preparation and Physicochemical Characterization of a Diclofenac Sodium-Dual Layer Polyvinyl Alcohol Patch.

Shafizah Sa'adon, Mohamed Nainar Mohamed Ansari, Saiful Izwan Abd Razak, Joseph Sahaya Anand, Nadirul Hasraf Mat Nayan, Al Emran Ismail, Muhammad Umar Aslam Khan, Adnan Haider

Open access · goldAbstract read
In one paragraph

Article in Polymers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.8field-weighted citation impact, top 9% 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

18 citing papers in PubMed, 47 citations in OpenAlex.

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  6. Characterization andHeliyon · 2023
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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

8 authors at 6 institutions in 3 countries.

Shafizah Sa'adonBioInspired Device and Tissue Engineering Research Group, Faculty of Engineering, School of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Skudai 81300, Johor, Malaysia.
Mohamed Nainar Mohamed AnsariInstitute of Power Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
Saiful Izwan Abd RazakBioInspired Device and Tissue Engineering Research Group, Faculty of Engineering, School of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Skudai 81300, Johor, Malaysia.ORCID 0000-0001-7477-0284
Joseph Sahaya AnandSustainable and Responsive Manufacturing Group, Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Malacca 76100, Malacca, Malaysia.ORCID 0000-0003-1918-5589
Nadirul Hasraf Mat NayanFaculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Johor, Malaysia.
Al Emran IsmailFaculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Johor, Malaysia.ORCID 0000-0003-2562-4509
Muhammad Umar Aslam KhanBioInspired Device and Tissue Engineering Research Group, Faculty of Engineering, School of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Skudai 81300, Johor, Malaysia.
Adnan HaiderDepartment of Biological Sciences, National University of Medical Sciences, Rawalpindi 46000, Pakistan.ORCID 0000-0002-2544-4754
Tun Hussein Onn University of Malaysia · MYUniversity of Technology Malaysia · MYNational University of Medical Sciences · PKShanghai Jiao Tong University · CNTechnical University of Malaysia Malacca · MYUniversiti Tenaga Nasional · MY

Funding

Universiti Teknologi Malaysia 02M44Universiti Tenaga Nasional RJO10517919
6 · The paper itself

Abstract

The aim of this study is to prepare a dual layer polyvinyl (PVA) patch using a combination of electrospinning techniques and cryogelation (freeze-thaw process) then subsequently to investigate the effect of freeze-thaw cycles, nanofiber thickness, and diclofenac sodium (DS) loading on the physicochemical and mechanical properties and formulation of dual layer PVA patches composed of electrospun PVA nanofibers and PVA cryogel. After the successful preparation of the dual layer PVA patch, the prepared patch was subjected to investigation to assess the effect of freeze-thaw cycles, nanofiber thickness and percentages of DS loading on the morphology, physiochemical and mechanical properties. Various spectroscopic techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR), water contact angle, and tensile tests were used to evaluate the physicochemical and mechanical properties of prepared dual layer PVA patches. The morphological structures of the dual layer PVA patch demonstrated the effectiveness of both techniques. The effect of freeze-thaw cycles, nanofiber thickness, and DS percentage loading on the crystallinity of a dual layer PVA patch was investigated using XRD analysis. The presence of a distinct DS peak in the FTIR spectrum indicates the compatibility of DS in a dual layer PVA patch through in-situ loading. All prepared patches were considered highly hydrophilic because the data obtained was less than 90°. The increasing saturation of DS within the PVA matrix increases the tensile strength of prepared patches, however decreased its elasticity. Evidently, the increasing of electrospun PVA nanofibers thickness, freeze-thaw cycles, and the DS saturation has improved the physicochemical and mechanical properties of the DS medicated dual layer PVA patches, making them a promising biomaterial for transdermal drug delivery applications.

Indexed as

cryogeldiclofenac sodiumelectrospinningfreeze-thaw processnanofiberpolyvinyl alcohol

Identifiers

PMID34372062
PMCPMC8347342
OpenAlexW3184424929

What OpenQuestion 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 OpenQuestion graph.