Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation.

Background: Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed.

Format-tuning of in vivo-launched bispecific T cell engager enhances efficacy against renal cell carcinoma.

Background: Advanced clear cell renal cell carcinoma (ccRCC) is a prevalent kidney cancer for which long-term survival rates are abysmal, though immunotherapies are showing potential. Not yet clinically vetted are bispecific T cell engagers (BTEs) that activate T cell-mediated cancer killing through intercellular synapsing. Multiple BTE formats exist, however, with limited cross-characterizations to help optimize new drug design.

Patterns of use of symptomatic treatments for Alzheimer's disease dementia (AD).

Background: Symptomatic treatment for Alzheimer's disease (AD) dementia could temporarily slow symptom worsening and improve the quality of life for both AD dementia patients and their caregivers. A comprehensive evaluation of symptomatic treatment patterns using recent data for newly diagnosed AD dementia has not been performed and compared across different countries.

Methods: The drug name, time to the first therapy, duration, discontinuation or switches were described in newly diagnosed AD dementia patients in two databases (a major U.

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract.