Scanning electron microscopy (SEM) methods are widely used in the geosciences to determine grain shape and surface characteristics using SEM–secondary electron and backscatter imagery (SEM-SE/BSE) and elemental composition of minerals using SEM–energy dispersive X-ray spectroscopy (SEM-EDS). We discuss applications and best practices for utilizing widely available SEM methods for luminescence dating, including (1) checking sample purity following mineral separation, (2) imaging grain shape and surface characteristics related to weathering and transport, (3) quantifying feldspar-mineral phases in feldspar separates, and (4) determining internal potassium concentration (wt% K) in feldspars for use in estimating internal beta contribution to the dose rate for a sample.

Quartz and feldspar purification checks of mineral separates require the least sample preparation and instrument time. These methods utilize the “environmental” or “low-vacuum” conditions of SEM. These conditions are less conducive to acquiring high-quality compositional data but can be used to quickly determine sample purity.

Conversely, to acquire higher-quality compositional data, SEM working conditions require high vacuum and accelerating voltages. The resulting semiquantitative SEM-EDS results can be used to determine the phase composition of feldspar separates and more accurately determine the internal potassium content for dose-rate and age calculations.

Emerging evidence suggests that metabolic and hormonal disturbances in polycystic ovary syndrome (PCOS) may increase vulnerability to neurodegenerative disorders. However, the link between PCOS and Alzheimer’s disease (AD)-related pathology remains unclear.

In this cross-sectional study, plasma levels of β-amyloid (Aβ40, Aβ42), phosphorylated tau (p-tau181), neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) were quantified in women with PCOS and age-matched controls. Homeostasis model assessment of insulin resistance (HOMA-IR), inflammatory cytokines (IL-6, TNF-α) and hormonal parameters were assessed. Mediation and moderation analyses were conducted to explore metabolic and hormonal pathways underlying biomarker alterations.

Among 400 women (200 PCOS, 200 controls), age and BMI were comparable (P > 0.05). Compared with controls, PCOS participants had increased Aβ40, p-tau181, NfL and GFAP, a slightly higher Aβ42, and a lower Aβ42/40 ratio (all P < 0.05). p-tau181 correlated positively with HOMA-IR (r = 0.41) and IL-6 (r = 0.36), while Aβ42/40 ratio correlated negatively with HOMA-IR (r = –0.27). In multivariable analysis, p-tau181 (aOR = 1.34, 95% CI 1.05–1.71), IL-6 (aOR = 1.19) and TNF-α (aOR = 1.14) were independent predictors of insulin resistance. Mediation analysis indicated that HOMA-IR, IL-6 and TNF-α jointly mediated ∼ 71% of the PCOS–p-tau181 association, suggesting a metabolic–inflammatory pathway linking PCOS to AD-related tau pathology.

PCOS is linked to peripheral markers of early Alzheimer’s pathology, largely mediated by insulin resistance and inflammation. PCOS may provide a clinical context to explore metabolic–inflammatory contributors to early neurodegenerative changes.

The bidirectional Glenn surgery is an important staging procedure for patients with single ventricle physiology. Approximately 1000 children are born each year in the United States with this subset of CHD. There is limited data regarding optimal post-operative management for these children. We surveyed paediatric cardiac intensive care providers surrounding their management strategies after the bidirectional Glenn surgery.

An anonymous survey was distributed via email to paediatric cardiac intensive care providers. The survey included anonymised demographic data and focused on post-operative physiologic targets for patients recovering after the bidirectional Glenn surgery.

Thirty-five paediatric cardiac intensive care providers responded to an anonymous 12-question survey. Subjects were mostly comprised of paediatric cardiac intensive care attendings (80%), with an average of 7.86 years of training. The respondents primarily practised in settings with medical trainees, and all practised in settings with extracorporeal membrane oxygenation capabilities.

Respondents were asked to complete a web-based survey. Five of the survey questions were devoted to background demographic data, and seven questions were aimed at identifying physiologic targets. Two of the seven questions were in relation to a provided clinical vignette.

This survey demonstrated that there is a lack of consensus in the management of patients after the bidirectional Glenn surgery. Specifically, granular SpO2, mean arterial pressure, and pH Goals were all less than 75% consensus. This survey highlights the variable practice patterns in providers taking care of patients after the bidirectional Glenn surgery, and further demonstrates the need for physiologic and outcome-driven targets to optimise the post-operative care.

Surveillance activities are emerging as exemplar use cases for large language models (LLMs) in health care. The aim of this study was to evaluate the potential for LLMs to support the expansion of surveillance activities to include cardiovascular implantable electronic device (CIED) procedures.

A validated machine learning-based infection flagging tool was applied to a cohort of VA CIED procedures from 7/1/2021 to 9/30/2023; cases with ≥10% probability of CIED infection underwent manual review. Then, a weighted random sample of 50 infected and 50 uninfected cases was reviewed with generative artificial intelligence (GenAI) assistance. GenAI prompts were iteratively refined to extract and classify all components of infection-related variables from clinical notes. Data extracted by GenAI were compared with manual chart reviews to assess infection status and extraction consistency.

Among 12,927 CIED procedures, 334 (2.58%) had ≥10% probability of CIED infection. Among 100 sampled cases, 50 of 50 uninfected cases were correctly categorized. Among 50 infection cases, GenAI identified all CIED infections, but the timing of events and the attribution to a preceding procedure were incorrect in 7 of 50 cases. The overall specificity of the GenAI-assisted process was 100% and the sensitivity for accurately classifying timing and attribution of CIED infection events was 82%. Errors in timing improved with iterative prompt updates. Manual chart reviews averaged 25 minutes per chart; the GenAI-assisted process averaged 5–7 minutes per chart.

LLMs can help streamline the review process for healthcare-associated infection surveillance, but manual adjudication of output is needed to ensure the correct timeline of events and attribution.