AIMC Topic: Particle Size

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Multistep Machine Learning Pipeline For Polymeric Nanoparticle Design.

AAPS PharmSciTech
Integrating machine learning (ML) into nanotechnology represents a promising strategy for rational design and accelerated development of drug delivery systems. However, studies in this field are scarce and face methodological and interpretative probl...

Development of a magnetically driven microrobot covered with a time-dependent film for colon drug delivery.

Journal of materials chemistry. B
Oral administration is an ideal method for drug delivery, but achieving targeted drug delivery to the colon remains a challenge. In this study, a magnetic microrobot incorporating a colon-specific method was developed, featuring both time-dependent a...

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...

Machine learning real-time control of continuous granulation process.

International journal of pharmaceutics
The transition from traditional batch to continuous pharmaceutical manufacturing puts additional demands on the efficient process development and operation. The comprehensive understanding of complex interdependencies between critical process paramet...

Meta-Analysis and Machine Learning Prediction of Protein Corona Composition across Nanoparticle Systems in Biological Media.

ACS nano
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokinetics, biodistribution, and cellular interactions. ...

Machine learning-based prediction of aerodynamic performance in arformoterol-lactose dry powder inhaler formulations using surface roughness features.

International journal of pharmaceutics
Dry powder inhalers (DPIs) are widely used for pulmonary drug delivery, and their aerodynamic performance is highly dependent on particle surface morphology. This paper presents a machine learning-based framework to quantitatively predict DPI aerodyn...

An artificial intelligence-enhanced early ovarian cancer diagnosis biosensor.

Journal of materials chemistry. B
In early cancer diagnosis, extracellular vesicles (EVs) are more advantageous than circulating tumor cells due to their smaller size, greater stability, and enhanced tissue penetration. These qualities lead to higher EV concentrations in body fluids,...

Factors Influencing the Dispersibility of Glycopyrronium Bromide and Indacaterol Maleate - Combined In Vitro and In Silico Study.

AAPS PharmSciTech
The development of dry powder inhalers (DPIs) for pulmonary drug delivery is complex, requiring optimization of variable factors to ensure effective lung deposition. This study investigates the factors influencing the dispersibility of glycopyrronium...

Quantifying Aviation-Related Contributions to Ambient Ultrafine Particle Number Concentrations Using Interpretable Machine Learning.

Environmental science & technology
Ultrafine particles (UFP, < 100 nm) are abundantly emitted by aircraft, but quantifying their contributions to ambient particle number concentrations (PNC) is challenging due to confounding from local traffic and complex interactions between aircraf...