The CheckMate 816 trial: a milestone in neoadjuvant chemoimmunotherapy of nonsmall cell lung cancer.

Advancements in immunotherapy in the perioperative setting have revolutionised the treatment of resectable nonsmall cell lung cancer (NSCLC). Here we present the methodology and results of the clinical trial CheckMate 816 demonstrating the benefit of neoadjuvant therapy with nivolumab plus chemotherapy compared with chemotherapy alone. Furthermore, this article discusses the implications for future practice in resectable NSCLC and the need for future research.

Ginkgo biloba extract modulates astrocytic and microglial recruitment in the hippocampus and hypothalamus of menopause-induced ovariectomized rats.

Background: Changes in steroid hormone levels associated with menopause are known to affect body composition, with increased accumulation of visceral fat and impaired actions of appetite-regulating neuropeptides. Anti-obesogenic, antioxidant, anti-inflammatory and neuromodulatory properties have been attributed to Ginkgo biloba extract (GbE) oral supplementation.

Hypothesis/purpose: We investigated in menopause-induced ovariectomized rats the effects of GbE oral supplementation on microglial reactivity and astrocyte recruitment in hippocampal and hypothalamic subregions involved in the regulation of feeding behavior and energy homeostasis.

National Patterns of Remote Patient Monitoring Service Availability at US Hospitals and their Readmission Performance for Cardiovascular Conditions.

Background: Digital remote patient monitoring (RPM) enables longitudinal care outside traditional healthcare settings, especially in the vulnerable period after hospitalizations, with broad coverage of the service by payers. We sought to evaluate patterns of RPM service availability at US hospitals and the association of these services with 30-day readmissions for two key cardiovascular conditions, heart failure (HF) and acute myocardial infarction (AMI).

Methods: We used contemporary national data from the American Hospital Association (AHA) Annual Survey to ascertain US hospitals offering RPM services for post-discharge or chronic care and used census-based county-level data to define the characteristics of the communities they serve.

An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.

Background: Identifying structural heart diseases (SHDs) early can change the course of the disease, but their diagnosis requires cardiac imaging, which is limited in accessibility.

Objective: To leverage images of 12-lead ECGs for automated detection and prediction of multiple SHDs using an ensemble deep learning approach.

Methods: We developed a series of convolutional neural network models for detecting a range of individual SHDs from images of ECGs with SHDs defined by transthoracic echocardiograms (TTEs) performed within 30 days of the ECG at the Yale New Haven Hospital (YNHH).

Automated Transformation of Unstructured Cardiovascular Diagnostic Reports into Structured Datasets Using Sequentially Deployed Large Language Models.

Background: Rich data in cardiovascular diagnostic testing are often sequestered in unstructured reports, with the necessity of manual abstraction limiting their use in real-time applications in patient care and research.

Methods: We developed a two-step process that sequentially deploys generative and interpretative large language models (LLMs; Llama2 70b and Llama2 13b). Using a Llama2 70b model, we generated varying formats of transthoracic echocardiogram (TTE) reports from 3,000 real-world echo reports with paired structured elements, leveraging temporal changes in reporting formats to define the variations.