AIMC Topic: Polyethylene Glycols

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Combining crystal engineering and surface engineering to estimate the structure -functions relationship of Tafamidis solid state forms with the aid of machine learning.

International journal of pharmaceutics
The mutual benefits of surface engineering and crystal engineering led to the discovery of a pharmaceutical cocrystal with balanced biopharmaceutical properties. The surface engineering of a pharmaceutical API (Active Pharmaceutical Ingredient), name...

Machine Learning-Guided microfluidic optimization of clinically inspired liposomes for nanomedicine applications.

International journal of pharmaceutics
Liposomes have transformed drug delivery by enhancing the solubility, stability, and bioavailability of therapeutic agents, driving widespread clinical adoption and contributing to a rapidly expanding multi-billion-dollar market. However, despite the...

Artificial Intelligence-Assisted Low-Dose High Atomic Number Contrast Agent for Ultrahigh-Resolution Computed Tomography Angiography.

ACS nano
Achieving high resolution while minimizing contrast agent dosage remains a key goal, yet a major challenge in contrast-enhanced computed tomography (CT) imaging. Herein, we propose an artificial intelligence-assisted low-dose high atomic number contr...

Label-free classification of nanoscale drug delivery systems using hyperspectral imaging and convolutional neural networks.

International journal of pharmaceutics
Label-free characterization of nanoscale drug delivery systems remains a critical challenge in pharmaceutical research. Traditional analytical methods, such as cryo-electron microscopy, are labor-intensive, low-throughput, and often require labeling,...

Identification of cariogenic bacteria by click chemistry mediated polyethylene glycolized graphyne nanozymes.

Mikrochimica acta
Dental caries, one of the most common oral diseases, is mainly induced by multiple cariogenic bacteria in the oral microenvironment, so it is important to construct a method that can identify oral multiply cariogenic bacteria. Herein, a machine learn...

Interpretable machine learning model for prediction functional cure in chronic hepatitis B patients receiving Peg-IFN therapy: A multi-center study.

International journal of medical informatics
BACKGROUND: Functional cure is the ideal treatment goal for chronic hepatitis B (CHB) treatment. We developed and validated machine learning (ML) models to predict functional cure in CHB patients.

Optimizing gelation time for cell shape control through active learning.

Soft matter
Hydrogels are popular platforms for cell encapsulation in biomedicine and tissue engineering due to their soft, porous structures, high water content, and excellent tunability. Recent studies highlight that the timing of network formation can be just...

The application of design of experiments and artificial neural networks in the evaluation of the impact of acidic conditions on cloud point extraction.

Journal of chromatography. A
This study aimed to analyze the impact of acidic conditions on the recovery of ciprofloxacin and levofloxacin for cloud point extraction with the Design of Experiments and Artificial Neural Networks. The design included 27 experiments featuring three...

Machine learning models to further identify advantaged populations that can achieve functional cure of chronic hepatitis B virus infection after receiving Peg-IFN alpha treatment.

International journal of medical informatics
OBJECTIVE: Functional cure is currently the highest goal of hepatitis B virus(HBV) treatment.Pegylated interferon(Peg-IFN) alpha is an important drug for this purpose,but even in the hepatitis B e antigen(HBeAg)-negative population,there is still a p...