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Mesoporous silica nanoparticles (MSNs) are highly porous carriers used in drug and gene delivery research for biomedical applications due to their high surface area, narrow particle size distribution, and low toxicity. Incorporating disulfide (SS) bonds into the walls of MSNs (MSN-SSs) offers a dual pathway for drug release due to the pore delivery and collapsing porous structure after cellular engulfment. This study explores the effect of embedding disulfide bonds into MSNs through various structural and biological characterization methods. Raman spectroscopy is employed to detect the SS bonds, SEM and TEM for morphology analyses, and a BET analysis to determine the required amount of SSs for achieving the largest surface area. The MSN-SSs are further loaded with doxorubicin, an anticancer drug, to assess drug release behavior under various pH conditions. The MSN-SS system demonstrated an efficient pH-responsive drug release, with over 65% of doxorubicin released under acidic conditions and over 15% released under neutral conditions. Cleaving the SS bonds using dithiothreitol increased the release to 94% in acidic conditions and 46% in neutral conditions. Biocompatibility studies were conducted using cancer cells to validate the engulfment of the nanoparticle. These results demonstrate that MSN-SS is a feasible nanocarrier for controlled-release drug delivery.
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Mesoporous Silica Nanoparticles: Properties and Strategies for Enhancing Clinical Effect
Due to the theragnostic potential of mesoporous silica nanoparticles (MSNs), these were extensively investigated as a novel approach to improve clinical outcomes. Boasting an impressive array of formulations and modifications, MSNs demonstrate significant in vivo efficacy when used to identify or treat myriad malignant diseases in preclinical models. As MSNs continue transitioning into clinical trials, a thorough understanding of the characteristics of effective MSNs is necessary. This review highlights recent discoveries and advances in MSN understanding and technology. Specific focus is given to cancer theragnostic approaches using MSNs. Characteristics of MSNs such as size, shape, and surface properties are discussed in relation to effective nanomedicine practice and projected clinical efficacy. Additionally, tumor-targeting options used with MSNs are presented with extensive discussion on active-targeting molecules. Methods for decreasing MSN toxicity, improving site-specific delivery, and controlling release of loaded molecules are further explained. Challenges facing the field and translation to clinical environments are presented alongside potential avenues for continuing investigations.
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- Award ID(s):
- 1911370
- PAR ID:
- 10332564
- Publisher / Repository:
- MSPI
- Date Published:
- Journal Name:
- Pharmaceutics
- Volume:
- 13
- Issue:
- 4
- ISSN:
- 1999-4923
- Page Range / eLocation ID:
- 570
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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There is a need to develop new and sustainable agricultural technologies to help provide global food security, and nanoscale materials show promising results in this area. In this study, mesoporous silica nanoparticles (MSNs) and chitosan-coated mesoporous silica nanoparticles (CTS-MSNs) were synthesized and applied to soybeans (Glycine max) by two different strategies in greenhouse and field studies to study the role of dissolved silicic acid and chitosan in enhancing plant growth and suppressing disease damage caused by Fusarium virguliforme. Plant growth and health were assessed by measuring the soybean biomass and chlorophyll content in both healthy and Fusarium-infected plants at harvest. In the greenhouse study, foliar and seed applications with 250 mg/L nanoparticle treatments were compared. A single seed treatment of MSNs reduced disease severity by 30% and increased chlorophyll content in both healthy and infected plants by 12%. Based on greenhouse results, seed application was used in the follow-up field study and MSNs and CTS-MSNs reduced disease progression by 12 and 15%, respectively. A significant 32% increase was observed for chlorophyll content for plants treated with CTS-MSNs. Perhaps most importantly, nanoscale silica seed treatment significantly increased (23–68%) the micronutrient (Zn, Mn, Mg, K, B) content of soybean pods, suggesting a potential sustainable strategy for nano-enabled biofortification to address nutrition insecurity. Overall, these findings indicate that MSN and CTS-MSN seed treatments in soybeans enable disease suppression and increase plant health as part of a nano-enabled strategy for sustainable agriculture.more » « less
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Abstract Manufacturing Supply Networks (MSNs) involve group decisions to achieve group goals and network decisions to achieve network goals. These decisions are made across multiple levels of a decision hierarchy. Given the frequent disruptions in MSNs, ‘resilience’ — the ability to maintain satisfactory network functionality despite disruptions, is a vital network goal. When designing MSNs for resilience, the resilience and group goals often conflict, requiring simultaneous consideration of network and group decisions. Limited information in the early stages of MSN design necessitates focusing on design exploration. Hence, facilitating ‘co-design exploration’ — a simultaneous exploration of network and group solution spaces is crucial. Current approaches for designing MSNs for resilience do not support simultaneous consideration of network and group decisions. To bridge this gap, we present the Co-Design Exploration of MSNs for Resilience (CoDE-MR) framework to facilitate co-design exploration of the network and the groups. CoDE-MR framework allows designers to model multilevel network and group decisions and their interactions, manage disruptions, and visualize and simultaneously explore the multilevel network and group solution spaces. In the framework, we integrate a combination of Preemptive and Archimedean formulations of the coupled-compromise Decision Support Problem construct with Resilience Index metric and interpretable Self-Organizing Map (iSOM)-based visualization to facilitate co-design exploration of MSNs for resilience. The framework’s efficacy is demonstrated using a steel MSN test problem, considering network and group decisions across two levels. The use of information flow and generic constructs makes the framework generic and well-suited for co-design exploration of multilevel systems to ensure resilience.more » « less
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Abstract Manufacturing supply networks (MSNs) involve group decisions to achieve group goals and network decisions to achieve network goals. These decisions are made across multiple levels of a decision hierarchy. Resilience—the ability to maintain the satisfactory network functionality despite disruptions, is a vital network goal. When designing MSNs for resilience, the resilience and group goals often conflict, requiring simultaneous consideration of network and group decisions. Limited information in the early stages of MSN design necessitates focusing on design exploration. Hence, facilitating “co-design exploration”—a simultaneous exploration of network and group solution spaces, is crucial. Current approaches for designing MSNs for resilience do not support simultaneous consideration of network and group decisions. To bridge this gap, we present the co-design exploration of MSNs for resilience (CoDE-MR) framework to facilitate co-design exploration of the network and the groups. The CoDE-MR framework allows designers to model multilevel network and group decisions and their interactions, manage disruptions, and visualize and simultaneously explore the multilevel network and group solution spaces. In the framework, we integrate a combination of Preemptive and Archimedean formulations of the coupled-compromise decision support problem construct with resilience index metric and interpretable self-organizing map (iSOM)-based visualization to facilitate co-design exploration of MSNs for resilience. The framework's efficacy is demonstrated using a steel MSN problem, considering network and group decisions across two levels. The decision-centric framework is generic and well suited for the co-design exploration of multilevel systems to ensure resilience.more » « less
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We propose a geometric reinforcement learning algorithm for real-time path planning for mobile sensor networks (MSNs) in the problem of reconstructing a spatial-temporal varying field described by the advection-diffusion partial differential equation. A Luenberger state estimator is provided to reconstruct the concentration field, which uses the collected measurements from a MSN along its trajectory. Since the path of the MSN is critical in reconstructing the field, a novel geometric reinforcement learning (GRL) algorithm is developed for real-time path planning. The basic idea of GRL is to divide the whole area into a series of lattice to employ a specific time-varying reward matrix, which contains the information of the length of the path and the mapping error. Thus, the proposed GRL can balance the performance of the field reconstruction and the efficiency of the path. By updating the reward matrix, the real-time path planning problem can be converted to the shortest path problem in a weighted graph, which can be solved efficiently using dynamic programming. The convergence of calculating the reward matrix is theoretically proven. Simulation results serve to demonstrate the effectiveness and feasibility of the proposed GRL for an MSN.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/pharmaceutics13040570
