Computer-aided design of dry powder inhalers using computational fluid dynamics to assess performance

Dry powder inhalers (DPIs) are gaining popularity for the delivery of drugs. A cost effective and efficient delivery device is necessary. Developing new DPIs by modifying an existing device may be the simplest way to improve the performance of the devices. The aim of this research was to produce a new DPIs using computational fluid dynamics (CFD). The new DPIs took advantages of the Cyclohaler® and the Rotahaler®. We chose a combination of the capsule chamber of the Cyclohaler® and the mouthpiece and grid of the Rotahaler®. Computer-aided design models of the devices were created and evaluated using CFD. Prototype models were created and tested with the DPI dispersion experiments. The proposed model 3 device had a high turbulence with a good degree of deagglomeration in the CFD and the experiment data. The %fine particle fraction (FPF) was around 50% at 60?L/min. The mass median aerodynamic diameter was around 2.8–4?μm. The FPF were strongly correlated to the CFD-predicted turbulence and the mechanical impaction parameters. The drug retention in the capsule was only 5–7%. In summary, a simple modification of the Cyclohaler® and Rotahaler® could produce a better performing inhaler using the CFD-assisted design.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.