Assessment of Medication Adherence Using Mobile Applications in Chronic Obstructive Pulmonary Disease: A Scoping Review.

Chronic obstructive pulmonary disease (COPD) is a condition that significantly impacts both patients and healthcare systems. The management of COPD involves various pharmacological intervention strategies, and addressing the issue of low adherence to these strategies has become a subject of significant interest. In response to this concern, there has been a shift toward utilizing telemedicine and mobile applications.

Towards a standard benchmark for variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework.

Background: Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs - ultimately hindering the development of effective prioritisation tools.

The effects of humidity on thermoregulatory physiology of a small songbird.

Scholander-Irving curves describe the relationship between ambient temperature and metabolic rate and are fundamental to understanding the energetic demands of homeothermy. However, Scholander-Irving curves are typically measured in dry air, which is not representative of the humidity many organisms experience in nature. Consequently, it is unclear (1) whether Scholander-Irving curves (especially below thermoneutrality) are altered by humidity, given the effects of humidity on thermal properties of air, and (2) whether physiological responses associated with Scholander-Irving curves in the lab reflect organismal performance in humid field conditions.

Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.

Background: 3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multiple applicator types. The variability and scarcity of data for any given applicator type poses challenges for deep learning.

Tuning the Biodegradation Rate of Silk Materials via Embedded Enzymes.

Conventional thinking when designing biodegradable materials and devices is to tune the intrinsic properties and morphological features of the material to regulate their degradation rate, modulating traditional factors such as molecular weight and crystallinity. Since regenerated silk protein can be directly thermoplastically molded to generate robust dense silk plastic-like materials, this approach afforded a new tool to control silk degradation by enabling the mixing of a silk-degrading protease into bulk silk material prior to thermoplastic processing. Here we demonstrate the preparation of these silk-based devices with embedded silk-degrading protease to modulate the degradation based on the internal presence of the enzyme to support silk degradation, as opposed to the traditional surface degradation for silk materials.