A run-in with some artefact-laden AI-generated analyses convinced Lei Zhu that machine learning wasn’t making his role irrelevant, but more important than ever.

Background The advancement of technology and continuous glucose monitoring (CGM) systems has introduced several computational and technical challenges for clinicians and researchers. The growing volume of CGM data necessitates the development of efficient computational tools capable of handling and processing this information effectively. This paper introduces GlucoStats, an open-source and multi-processing Python library designed for efficient computation and visualization of a comprehensive set of glucose metrics derived from CGM. It simplifies the traditionally time-consuming and error-prone process of manual CGM metrics calculation, making it a valuable tool for both clinical and research applications. Results Its modular design ensures easy integration into predefined workflows, while its user-friendly interface and extensive documentation make it accessible to a broad audience, including clinicians and researchers. GlucoStats offers several key features: (i) window-based time series analysis, enabling time series division into smaller ‘windows’ for detailed temporal analysis, particularly beneficial for CGM data; (ii) advanced visualization tools, providing intuitive, high-quality visualizations that facilitate pattern recognition, trend analysis, and anomaly detection in CGM data; (iii) parallelization, leveraging parallel computing to efficiently handle large CGM datasets by distributing computations across multiple processors; and (iv) scikit-learn compatibility, adhering to the standardized interface of scikit-learn to allow an easy integration into machine learning pipelines for end-to-end analysis. Conclusions GlucoStats demonstrates high efficiency in processing large-scale medical datasets in minimal time. Its modular design enables easy customization and extension, making it adaptable to diverse research and clinical needs. By offering precise CGM data analysis and user-friendly visualization tools, it serves both technical researchers and non-technical users, such as physicians and patients, with practical and research-driven applications.

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The layoffs jeopardize this resource, which has served more than 4,000 labs for about three decades.

Clustering and nesting (C&N) arise in many preclinical studies, such as when animals are group-housed or share litters, or in cell culture. Ignoring C&N undermines the validity of analyses. He...

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Rarefaction in microbiome studies - Is it a good idea or just a bad practice? What are the alternatives?

A lack of standardized benchmarks is hindering progress and patient benefits

Instamos os políticos a reconhecerem que a ciência não é um interruptor que pode ser ligado e desligado sem consequências profundas e duradouras.