Self-assembled membrane-polymer nanoparticles of top-notch tissue tolerance for the treatment of gastroesophageal reflux disease

Enwereuzo, Onyekachi Oluchi; Akakuru, Obinna Chigoziem; Uwaoma, R.C.; Elemike, Elias E.; Akakuru, Ozioma Udochukwu

doi:10.1007/s40097-021-00394-w

Cited by 7 publications

(3 citation statements)

References 73 publications

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“…Controlled release technology revolutionizes the delivery of active substances, offering precise targeting and sustained release, thereby maximizing treatment efficacy while minimizing potential side effects. By harnessing the potential of controlled release technology, healthcare professionals can optimize therapeutic interventions, providing a promising avenue for improving patient care and achieving superior treatment outcomes [ 80 ]. NPs can also serve as carriers for drug delivery purposes [ 29 ].…”

Section: Gastroesophageal Reflux Disease (Gerd)mentioning

confidence: 99%

“…In the case of GERD, this could involve delivering drugs that neutralize stomach acid or strengthen the LES directly to the affected site. In this study, researchers synthesized nanoparticles loaded with drugs specifically for treating GERD, taking advantage of the advantages offered by a controlled release approach [ 80 ]. Some studies showed that these NPs can slow down the release of these drugs in acidic environments such as the stomach [ 82 ].…”

Section: Gastroesophageal Reflux Disease (Gerd)mentioning

confidence: 99%

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Chitosan Nanoparticles for Gastroesophageal Reflux Disease Treatment

Herdiana

2023

Polymers

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Gastroesophageal Reflux Disease (GERD) is a chronic ailment that results from the backward flow of stomach acid into the esophagus, causing heartburn and acid regurgitation. This review explores nanotechnology as a novel treatment approach for GERD. Chitosan nanoparticles (CSNPs) offer several advantages, including biocompatibility, biodegradability, and targeted drug delivery capabilities. CSNPs have been extensively studied due to their ability to encapsulate and release medications in a controlled manner. Different nanoparticle (NP) delivery systems, including gels, microspheres, and coatings, have been developed to enhance drug retention, drug targeting, and controlled release in the esophagus. These nanoparticles can target specific molecular pathways associated with acid regulation, esophageal tissue protection, and inflammation modulation. However, the optimization of nanoparticle formulations faces challenges, including ensuring stability, scalability, and regulatory compliance. The future may see CSNPs combined with other treatments like proton pump inhibitors (PPIs) or mucosal protectants for a synergistic therapeutic approach. Thus, CSNPs provide exciting opportunities for novel GERD treatment strategies.

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Section: Gastroesophageal Reflux Disease (Gerd)mentioning

confidence: 99%

Section: Gastroesophageal Reflux Disease (Gerd)mentioning

confidence: 99%

Chitosan Nanoparticles for Gastroesophageal Reflux Disease Treatment

Herdiana

2023

Polymers

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“…The past decade has witnessed significant advances in nanomaterial-based drug delivery, especially for tumor treatment 10 , 11 , 12 , 13 . As regards tumor chemotherapy in general, research in our laboratories on tumor-targeted chemotherapeutic nanomaterials have also contributed a great deal to recent advances on tumor chemotherapy.…”

Section: Chemotherapeutic Nanomaterialsmentioning

confidence: 99%

Chemotherapeutic nanomaterials in tumor boundary delineation: Prospects for effective tumor treatment

Akakuru

Zhang

Iqbal

et al. 2022

Acta Pharmaceutica Sinica B

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Non-carcinogenic health risk assessment and predicting of pollution indexing of groundwater around Osisioma, Nigeria, using artificial neural networks and multi-linear modeling principles

Akakuru

Njoku²,

Obinna-Akakuru³

et al. 2023

Stoch Environ Res Risk Assess

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Non-carcinogenic health risk assessment and predicting of organic and heavy metal pollution of groundwater around Osisioma, Nigeria, using Artificial Neural Networks and Multi-Linear Modeling Principles has been done. 30 groundwater samples were collected systematically and analyzed for organic and heavy metal pollutants. The results of the analysis showed that the heavy metals and organic pollutants within the study area contributed to the pollution of groundwater resources in the locality. However, copper, ethylbenzene, xylene and toluene were within the recommended standard, whereas arsenic, iron, chromium, lead, and benzene were above the recommended standard for drinking water. Correlation matrix and principal component analysis assessment indicated weak correlation and that organic pollutants were major contributors to the loadings. The Contamination factor, Pollution load index, Metal pollution index, Geoaccumulation index, Potential ecological risk index, Elemental Contamination Index, and overall Metal Contamination Index showed no significant pollution, whereas the Heavy Metal Evaluation Index, Pollution Index of Groundwater results showed worrisome impact of the anthropogenic activities on the groundwater quality. Health risk assessment showed that children are more at risk than adults as it related to taking polluted water. MLR models performed better than the ANN. Seven (7) mathematical models were generated for the prediction of pollution indices. Based on the results, this study recommends regular monitoring of groundwater resources and the integration of ANN and MLR modeling approaches for the prediction of pollution indices.

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Self-assembled membrane-polymer nanoparticles of top-notch tissue tolerance for the treatment of gastroesophageal reflux disease

Cited by 7 publications

References 73 publications

Chitosan Nanoparticles for Gastroesophageal Reflux Disease Treatment

Chitosan Nanoparticles for Gastroesophageal Reflux Disease Treatment

Chemotherapeutic nanomaterials in tumor boundary delineation: Prospects for effective tumor treatment

Non-carcinogenic health risk assessment and predicting of pollution indexing of groundwater around Osisioma, Nigeria, using artificial neural networks and multi-linear modeling principles

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