Use of artificial neural networks to examine parameters affecting the immobilization of streptokinase in chitosan.

Seyed Mohamad Sadegh Modaresi, Mohammad Ali Faramarzi, Arash Soltani, Hadi Baharifar, Amir Amani

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In one paragraph

Article in Iranian journal of pharmaceutical research : IJPR, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

0.6field-weighted citation impact, top 33% 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

1 citing paper in PubMed, 6 citations in OpenAlex.

Predicting of Effective Dose as Biomarker for Cytotoxicity Using Partial Least Square-Fourier Transform Infrared Spectroscopy (PLS_FTIR).Iranian journal of pharmaceutical research : IJPR · 2015
Article

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

5 authors at 1 institution in 1 country.

Seyed Mohamad Sadegh ModaresiDepartemant of Biology, Faculty of Basic Sciences, Kharazmi University, Tehran, Iran.

Mohammad Ali FaramarziBiotechnology Research Center, School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran. ; Department of Pharmaceutical Biotechnology, School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran.

Arash SoltaniDepartemant of Biology, Faculty of Basic Sciences, Kharazmi University, Tehran, Iran.

Hadi BaharifarDepartment of Medical Nanotechnology, School of Advanced Technologies in Medicine , Tehran University of Medical Sciences, Tehran, Iran.

Amir AmaniDepartment of Medical Nanotechnology, School of Advanced Technologies in Medicine , Tehran University of Medical Sciences, Tehran, Iran. ; Medical Biomaterials Research Center, Tehran University of Medical Sciences, Tehran, Iran.

Tehran University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Streptokinase is a potent fibrinolytic agent which is widely used in treatment of deep vein thrombosis (DVT), pulmonary embolism (PE) and acute myocardial infarction (MI). Major limitation of this enzyme is its short biological half-life in the blood stream. Our previous report showed that complexing streptokinase with chitosan could be a solution to overcome this limitation. The aim of this research was to establish an artificial neural networks (ANNs) model for identifying main factors influencing the loading efficiency of streptokinase, as an essential parameter determining efficacy of the enzyme. Three variables, namely, chitosan concentration, buffer pH and enzyme concentration were considered as input values and the loading efficiency was used as output. Subsequently, the experimental data were modeled and the model was validated against a set of unseen data. The developed model indicated chitosan concentration as probably the most important factor, having reverse effect on the loading efficiency.

Indexed as

Artificial neural networksChitosanElectrostatic interactionsHalf-lifeStreptokinase

Identifiers

PMID25587327

PMCPMC4232804

OpenAlexW2164216844

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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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.