Changes in Exposure to Tobacco-Related Harmful and Potentially Harmful Constituents among Adults who Switched Completely from Smoking Cigarettes to Use of the JUUL2 System for Six Days.

Introduction: Adults who switch from smoking cigarettes to use of electronic nicotine delivery systems (ENDS) may reduce their exposure to harmful and potentially harmful constituents (HPHCs). This study assessed changes in exposure to HPHCs, assessed via biomarkers of exposure (BOEs), among adults who switched to a new ENDS product.

Methods: Adults who smoke cigarettes (N = 89) were randomized to: (1) switch completely to using JUUL2 Virginia Tobacco (N = 24) or Polar Menthol (N = 24); (2) continue smoking usual brand (UB) cigarettes (N = 21); or (3) abstain from all tobacco/nicotine products (N = 20) for six days.

Effects of the COVID-19 pandemic on maternal, newborn, and child health service coverage in Burkina Faso.

Background: While countries' coronavirus disease 2019 (COVID-19) emergency contingency and response plans aimed to prevent and control the spread of the virus, they also caused major disruptions to health services. We assessed the effects of COVID-19 on coverage and inequalities in select maternal, newborn, and child health services in Burkina Faso.

Methods: We analysed data from two cross-sectional household surveys conducted in two provinces, one rural and one urban.

Prioritising domains of glucocorticoid therapy to measure in trials: Results from a modified delphi exercise from the OMERACT glucocorticoid impact working group.

Introduction: There is no consensus amongst patients and healthcare professionals about how to measure important adverse effects of glucocorticoids (GCs) that includes the patient's perspective. The OMERACT GC Impact working group sought to identify the domains of greatest importance to both patients and healthcare professionals for use in a proposed core outcome set.

Methods: Patients and healthcare professionals participated in a Delphi consensus exercise to rate the importance of previously identified candidate domains.

Deep Learning Algorithms for Breast Cancer Detection in a UK Screening Cohort: As Stand-alone Readers and Combined with Human Readers.

Background Deep learning (DL) algorithms have shown promising results in mammographic screening either compared to a single reader or, when deployed in conjunction with a human reader, compared with double reading. Purpose To externally validate the performance of three DL algorithms as mammographic screen readers in an independent UK data set. Materials and Methods Three commercial DL algorithms (DL-1, DL-2, and DL-3) were retrospectively investigated from January 2022 to June 2022 using consecutive full-field digital mammograms collected at two UK sites during 1 year (2017).

Reconciling the contrasting narratives on the environmental impact of large language models.

The recent proliferation of large language models (LLMs) has led to divergent narratives about their environmental impacts. Some studies highlight the substantial carbon footprint of training and using LLMs, while others argue that LLMs can lead to more sustainable alternatives to current practices. We reconcile these narratives by presenting a comparative assessment of the environmental impact of LLMs vs.