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Polymers Jun 2024Biofouling is a great challenge for engineering material in medical-, marine-, and pharmaceutical-related applications. In this study, a novel trimethylamine -oxide...
Biofouling is a great challenge for engineering material in medical-, marine-, and pharmaceutical-related applications. In this study, a novel trimethylamine -oxide (TMAO)-analog monomer, 3-(2-methylacrylamido)-,-dimethylpropylamine -oxide (MADMPAO), was synthesized and applied for the grafting of poly(MADMPAO) (MPAO) brushes on quartz crystal microbalance (QCM) chips by the combination of bio-inspired poly-dopamine (DA) and surface-initiated atom transfer radical polymerization technology. The result of ion adsorption exhibited that a sequential DA and MPAO arrangement from the chip surface had different characteristics from a simple DA layer. Ion adsorption on MPAO-grafted chips was greatly inhibited at low salt concentrations of 1 and 10 mmol/L due to strong surface hydration in the presence of charged N and O of zwitterionic MPAO brushes on the outer layer on the chip surface, well known as the "anti-polyelectrolyte" effect. During BSA adsorption, MPAO grafting also led to a marked decrease in frequency shift, indicating great inhibition of protein adsorption. It was attributed to weaker BSA-MPAO interaction. In this study, the Au@DA-4-MPAO chip with the highest coating concentration of DA kept stable dissipation in BSA adsorption, signifying that the chip had a good antifouling property. The research provided a novel monomer for zwitterionic polymer and demonstrated the potential of MPAO brushes in the development and modification of antifouling materials.
PubMed: 38931984
DOI: 10.3390/polym16121634 -
Pharmaceutics Jun 2024In the past several decades, polymeric microparticles (MPs) have emerged as viable solutions to address the limitations of standard pharmaceuticals and their...
In the past several decades, polymeric microparticles (MPs) have emerged as viable solutions to address the limitations of standard pharmaceuticals and their corresponding delivery methods. While there are many preclinical studies that utilize polymeric MPs as a delivery vehicle, there are limited FDA-approved products. One potential barrier to the clinical translation of these technologies is a lack of understanding with regard to the manufacturing process, hindering batch scale-up. To address this knowledge gap, we sought to first identify critical processing parameters in the manufacturing process of blank (no therapeutic drug) and protein-loaded double-emulsion poly(lactic-co-glycolic) acid MPs through a quality by design approach. We then utilized the design of experiments as a tool to systematically investigate the impact of these parameters on critical quality attributes (e.g., size, surface morphology, release kinetics, inner occlusion size, etc.) of blank and protein-loaded MPs. Our results elucidate that some of the most significant CPPs impacting many CQAs of double-emulsion MPs are those within the primary or single-emulsion process (e.g., inner aqueous phase volume, solvent volume, etc.) and their interactions. Furthermore, our results indicate that microparticle internal structure (e.g., inner occlusion size, interconnectivity, etc.) can heavily influence protein release kinetics from double-emulsion MPs, suggesting it is a crucial CQA to understand. Altogether, this study identifies several important considerations in the manufacturing and characterization of double-emulsion MPs, potentially enhancing their translation.
PubMed: 38931917
DOI: 10.3390/pharmaceutics16060796 -
Pharmaceutics Jun 2024A classical emulsion formulation based on petrolatum and mineral oil as the internal phase with emulsifier wax as a typical topical emulsion cream was investigated for...
A classical emulsion formulation based on petrolatum and mineral oil as the internal phase with emulsifier wax as a typical topical emulsion cream was investigated for the effect of process parameters on drug product quality and performance attributes. The Initial Design of Experiment (DoE) suggested that an oil phase above 15%, coupled with less than 10% emulsifying wax, resulted in less stable emulsions. Different processing parameters such as homogenization speed, duration, cooling rate, and final temperature showed minimal influence on properties and failed to improve stability. The final DoE suggested that the optimal emulsion stability was achieved by introducing a holding period midway through the cooling stage after solvent addition. Within the studied holding temperature range (25-35 °C), a higher holding temperature correlated with increased emulsion stability. However, the application of shear during the holding period, using a paddle mixer, adversely affected stability by disrupting the emulsion microstructure. IVRT studies revealed that the release of lidocaine was higher in the most stable emulsion produced at a holding temperature of 35 °C compared to the least stable emulsion produced at a holding temperature of 25 °C. This suggests that a holding temperature of 35 °C improves both the stability and active release performance. It appears that a slightly higher holding temperature, 35 °C, allows a more flexible and stable emulsifying agent film around the droplets facilitating stabilization of the emulsion. This study offers valuable insights into the relationship between process parameters at various stages of manufacture, microstructure, and various quality attributes of emulsion cream systems. The knowledge gained will facilitate improved design and optimization of robust manufacturing processes, ensuring the production of the formulations with the desired critical quality attributes.
PubMed: 38931894
DOI: 10.3390/pharmaceutics16060773 -
Pharmaceutics Jun 2024Drug absorption via chylomicrons holds significant implications for both pharmacokinetics and pharmacodynamics. However, a mechanistic understanding of predicting in...
Drug absorption via chylomicrons holds significant implications for both pharmacokinetics and pharmacodynamics. However, a mechanistic understanding of predicting in vivo intestinal lymphatic uptake remains largely unexplored. This study aimed to delve into the intestinal lymphatic uptake of drugs, investigating both enhancement and inhibition using various excipients through our previously established in vitro model. It also examined the applicability of the model by assessing the lymphatic uptake enhancement of a lymphotropic formulation with linoleoyl polyoxyl-6 glycerides using the same model. The model successfully differentiated among olive, sesame, and peanut oils in terms of lymphatic uptake. However, it did not distinguish between oils containing long-chain fatty acids and coconut oil. Coconut oil, known for its abundance of medium-chain fatty acids, outperformed other oils. This heightened uptake was attributed to the superior emulsification of this oil in artificial chylomicron media due to its high content of medium-chain fatty acids. Additionally, the enhanced uptake of the tested formulation with linoleoyl polyoxyl-6 glycerides underscored the practical applicability of this model in formulation optimization. Moreover, data suggested that increasing the zeta potential of Intralipid using sodium lauryl sulfate (SLS) and decreasing it using (+/-) chloroquine led to enhanced and reduced uptake in the in vitro model, respectively. These findings indicate the potential influence of the zeta potential on intestinal lymphatic uptake in this model, though further research is needed to explore the possible translation of this mechanism in vivo.
PubMed: 38931889
DOI: 10.3390/pharmaceutics16060768 -
Pharmaceutics May 2024Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of...
Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes.
Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUC ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.
PubMed: 38931859
DOI: 10.3390/pharmaceutics16060737 -
Sensors (Basel, Switzerland) Jun 2024The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal...
The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models.
PubMed: 38931803
DOI: 10.3390/s24124022 -
Sensors (Basel, Switzerland) Jun 2024Remote sensing products are typically assessed using a single accuracy estimate for the entire map, despite significant variations in accuracy across different map areas...
Remote sensing products are typically assessed using a single accuracy estimate for the entire map, despite significant variations in accuracy across different map areas or classes. Estimating per-pixel uncertainty is a major challenge for enhancing the usability and potential of remote sensing products. This paper introduces the open access tool, a novel statistical design-based approach that specifically addresses this issue by estimating per-pixel uncertainty through a bootstrap resampling procedure. Leveraging Sentinel-2 remote sensing data as auxiliary information, the capabilities of the Google Earth Engine cloud computing platform, and the R programming language, can be applied in any world region and variables of interest. In this study, the tool was tested in the Rincine forest estate study area-eastern Tuscany, Italy-focusing on volume density as the variable of interest. The average volume density was 0.042, corresponding to 420 m per hectare. The estimated pixel errors ranged between 93 m and 979 m per hectare and were 285 m per hectare on average. The ability to produce error estimates for each pixel in the map is a novel aspect in the context of the current advances in remote sensing and forest monitoring and assessment. It constitutes a significant support in forest management applications and also a powerful communication tool since it informs users about areas where map estimates are unreliable, at the same time highlighting the areas where the information provided via the map is more trustworthy. In light of this, the tool aims to support researchers and practitioners in the spatially exhaustive use of remote sensing-derived products and map validation.
PubMed: 38931731
DOI: 10.3390/s24123947 -
Sensors (Basel, Switzerland) Jun 2024This paper addresses the critical need for advanced real-time vehicle detection methodologies in Vehicle Intelligence Systems (VIS), especially in the context of using...
This paper addresses the critical need for advanced real-time vehicle detection methodologies in Vehicle Intelligence Systems (VIS), especially in the context of using Unmanned Aerial Vehicles (UAVs) for data acquisition in severe weather conditions, such as heavy snowfall typical of the Nordic region. Traditional vehicle detection techniques, which often rely on custom-engineered features and deterministic algorithms, fall short in adapting to diverse environmental challenges, leading to a demand for more precise and sophisticated methods. The limitations of current architectures, particularly when deployed in real-time on edge devices with restricted computational capabilities, are highlighted as significant hurdles in the development of efficient vehicle detection systems. To bridge this gap, our research focuses on the formulation of an innovative approach that combines the fractional B-spline wavelet transform with a tailored U-Net architecture, operational on a Raspberry Pi 4. This method aims to enhance vehicle detection and localization by leveraging the unique attributes of the NVD dataset, which comprises drone-captured imagery under the harsh winter conditions of northern Sweden. The dataset, featuring 8450 annotated frames with 26,313 vehicles, serves as the foundation for evaluating the proposed technique. The comparative analysis of the proposed method against state-of-the-art detectors, such as YOLO and Faster RCNN, in both accuracy and efficiency on constrained devices, emphasizes the capability of our method to balance the trade-off between speed and accuracy, thereby broadening its utility across various domains.
PubMed: 38931720
DOI: 10.3390/s24123938 -
Sensors (Basel, Switzerland) Jun 2024Remote sensing image classification plays a crucial role in the field of remote sensing interpretation. With the exponential growth of multi-source remote sensing data,...
Remote sensing image classification plays a crucial role in the field of remote sensing interpretation. With the exponential growth of multi-source remote sensing data, accurately extracting target features and comprehending target attributes from complex images significantly impacts classification accuracy. To address these challenges, we propose a Canny edge-enhanced multi-level attention feature fusion network (CAF) for remote sensing image classification. The original image is specifically inputted into a convolutional network for the extraction of global features, while increasing the depth of the convolutional layer facilitates feature extraction at various levels. Additionally, to emphasize detailed target features, we employ the Canny operator for edge information extraction and utilize a convolution layer to capture deep edge features. Finally, by leveraging the Attentional Feature Fusion (AFF) network, we fuse global and detailed features to obtain more discriminative representations for scene classification tasks. The performance of our proposed method (CAF) is evaluated through experiments conducted across three openly accessible datasets for classifying scenes in remote sensing images: NWPU-RESISC45, UCM, and MSTAR. The experimental findings indicate that our approach based on incorporating edge detail information outperforms methods relying solely on global feature-based classifications.
PubMed: 38931695
DOI: 10.3390/s24123912 -
Sensors (Basel, Switzerland) Jun 2024This article describes a novel fusion of a generative formal model for three-dimensional (3D) shapes with deep learning (DL) methods to understand the geometric...
This article describes a novel fusion of a generative formal model for three-dimensional (3D) shapes with deep learning (DL) methods to understand the geometric structure of 3D objects and the relationships between their components, given a collection of unorganized point cloud measurements. Formal 3D shape models are implemented as shape grammar programs written in Procedural Shape Modeling Language (PSML). Users write PSML programs to describe complex objects, and DL networks estimate the configured free parameters of the program to generate 3D shapes. Users write PSML programs to enforce fundamental rules that define an object class and encode object attributes, including shapes, components, size, position, etc., into a parametric representation of objects. This fusion of the generative model with DL offers artificial intelligence (AI) models an opportunity to better understand the geometric organization of objects in terms of their components and their relationships to other objects. This approach allows human-in-the-loop control over DL estimates by specifying lists of candidate objects, the shape variations that each object can exhibit, and the level of detail or, equivalently, dimension of the latent representation of the shape. The results demonstrate the advantages of the proposed method over competing approaches.
PubMed: 38931658
DOI: 10.3390/s24123874