El Centro Elementary School District

Machine learning has made a huge impact in day to day life and processes, and is showing potential to continue changing the future.One of the fields that machine learning is showing potential in is healthcare, specifically, diagnosing and predicting chronic diseases, which are diseases that last for one or more years.The goal of this paper is to summarize some ways machine learning is being used for this, such as analyzing medical images, and how machine learning models can be developed more in the future on more specific tasks, like predicting cancer from a single cell.Additionally, this paper talks more specifically about machine learning.Finally, this paper also discusses some limitations in current models, such as models developing a bias and cost of implementing machine learning models.

Comorbid type 2 diabetes mellitus (T2D) and chronic kidney disease (CKD) is associated with poor health outcomes and a high economic burden. Management of these conditions remains a significant challenge for current healthcare systems. The objective of this article is to describe the experiences of patients living with T2D and CKD and their thoughts on how communication between patients and their clinicians could be improved despite the multiple comorbidities that need to be addressed. We present the individual perspectives of three patient authors, followed by relevant discussion around the management of CKD in patients with T2D by clinician authors.Audio abstract available for this article. Audio . In this audio introduction, the authors Patrick Gee (a patient author) and Eugene Wright (a clinician author) provide a brief overview and discuss the key findings of their article titled “Living with Chronic Kidney Disease and Type 2 Diabetes Mellitus: The Patient and Clinician Perspective”.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12325-022-02325-9.

An important goal for transformation of education is undoing the inequities in the system and fully engaging with the entire community being served.

This study summarizes the results of a four-year project in science education conducted in a rural setting with English learners in grades K–6 in the El Centro Elementary School District in southern California. Data were collected to measure student achievement in science, writing, reading, and mathematics for participating students. These data were analyzed relative to the number of years that students participated in kit- and inquiry-based science instruction that included the use of science notebooks. Results indicated that the achievement of English learners increased in relation to the number of years they participated in the project. The longer they were in the program, the higher their scores were in science, writing, reading, and mathematics.