Citations

ABSTRACT Introduction Postoperative mortality commonly follows complications that are not prevented, recognised or managed effectively. Ward‐based nursing care is central to surveillance, escalation and rescue, but population‐level evidence using shift‐level staffing data is limited. This national population‐based cohort study examined the association between surgical ward nurse staffing and postoperative outcomes. Methods Adults aged 18 years or older undergoing inpatient surgery under general or neuraxial anaesthesia in public hospitals in Aotearoa New Zealand between 2022 and 2024 were included. Prospectively recorded TrendCare staffing data were linked to administrative health datasets. The exposure was shift‐level staffing variance, defined as the difference between required and available nursing care hours on surgical wards in the treating hospital during admission. The primary outcome was 90‐day postoperative mortality. Secondary outcomes were postoperative complications and failure to rescue. Results The cohort included 202 428 patients undergoing 223 415 surgical admissions. Within 90 days, 6296 admissions resulted in death (2.8%). Complications occurred in 41 722 admissions (18.7%), with failure to rescue in 15.1%. Of 281 147 surgical ward shifts, 122 186 (43.5%) were understaffed. After adjustment, each one‐nurse reduction in staffing per shift was associated with higher odds of mortality (aOR: 1.13, 95% CI: 1.07–1.18), complications (aOR: 1.07, 95% CI: 1.05–1.10) and failure to rescue (aOR: 1.07, 95% CI: 1.00–1.14). Modelling estimated that correcting observed staffing deficits was associated with 182 fewer deaths annually. Conclusion Lower surgical ward nurse staffing was associated with increased postoperative mortality, complications and failure to rescue. Nurse staffing is a modifiable system factor associated with postoperative safety and should be prioritised to improve surgical outcomes.

Introduction: Understanding mid-term test-retest reliability and within-subject variability is important for interpreting changes observed in longitudinal and intervention studies. The reliability of resting-state functional magnetic resonance imaging (rs-fMRI) is known to vary across measures and brain regions. However, how reliability differs across functional networks and connectivity- and amplitude-based measures, and whether multi-echo acquisition and processing modify these patterns, remain incompletely characterized. Methods: Twenty-two healthy volunteers underwent two rs-fMRI sessions 15.7 ± 4.0 days apart on a Compact 3T scanner. Multi-echo, middle-echo, and independently acquired single-echo datasets were compared, with multi-echo independent component analysis additionally evaluated as a denoising approach. Functional connectivity (FC) and three amplitude-based measures were evaluated using the Schaefer 400 parcellation. Reliability was systematically assessed using intraclass correlation coefficient (ICC), within-subject standard deviation (wSD), and systematic bias at edge or regional, and network levels. Results: Acquisition-dependent differences in reliability were generally modest. Multi-echo acquisition and processing increased functional connectivity strength and the magnitude of amplitude-based measures and improved inferior cortical coverage, but these enhancements did not consistently translate into substantially higher ICC or lower wSD. In contrast, reliability showed clear network-dependent differences. FC reliability varied markedly across network pairs and was not explained by connectivity strength alone; pairs involving the default mode and control networks generally showed more favorable profiles than several somatomotor and visual network pairs. Fractional amplitude of low-frequency fluctuations (fALFF) also showed network-dependent reliability, with the most favorable regional reproducibility observed in the default mode and control networks and lower reproducibility in the somatomotor and visual networks. Conclusion: These findings provide practical mid-term reliability benchmarks for rs-fMRI on a Compact 3T scanner and show that measurement stability varies more clearly across measures and functional networks than across acquisition approaches.

Background Despite advances in cervical cancer screening, cervical cancer rates have been increasing in the last decade. Hispanic women have higher cervical cancer incidence and mortality rates compared to non-Hispanic women in the United States. These disparities are primarily due to language barriers experienced in this Spanish-preferred population. Our study aims to improve cervical cancer screening rates among Spanish-preferred patients through a Spanish-language multimedia presentation on cervical cancer screening. Methods In patients not adherent to U.S. cervical cancer screening guidelines, we conducted a non-randomized study comparing standard care in Family Medicine (FM) to standard care plus a multimedia presentation with Spanish voiceover intervention in Internal Medicine (IM). We evaluated whether baseline characteristics differed between the two study arms using the Wilcoxon rank sum test and either the Chi-Square test or Fisher’s exact test. For the primary goal, we examined whether there was a difference in screening scheduling rates between the intervention and standard care groups using the Chi-Square test. Using descriptive statistics, we investigated whether there was a difference in characteristics for those who scheduled screening and those who did not. Results We had 52 patients in IM and 58 in FM, differing in age and insurance status, with median ages of 45.5 and 40.0, and percent insured of 65.4% and 82.8%. Post-study, 25.0% (13/52) in IM and 15.5% (9/58) in FM scheduled cervical screening appointments, which was not significantly different, p=0.21. Patients who scheduled screening had higher rates of insurance coverage, at 95.5% compared to 69.3%. Conclusion No statistically significant difference in scheduling rates among Spanish-preferred patients was observed in this study. Further efforts should include larger samples and randomization.

Despite advances in pharmacogenomics that have led to innovative therapeutic strategies, substantial interindividual variability in drug efficacy and toxicity persists. Both endogenous and exogenous factors contribute to this unexplained variation, resulting in suboptimal treatment effectiveness, unnecessary healthcare costs, preventable mortality, and significant human suffering. The emerging field of exposomics is aimed at addressing the complex exposures that impact human health and offers an opportunity to interrogate how these exposures influence drug actions and outcomes. Here, we introduce pharmacoexposomics as a complementary framework that systematically quantifies the body's metabolic response to drug and environmental exposures using high-resolution mass spectrometry. We then present IndiPHARM (Individualized Pharmacology), a multi-institution, Advanced Research Projects Agency for Health–supported platform designed to integrate population-scale pharmacoexposomics with pharmacogenomics and clinical data to elucidate interindividual variability in drug response, identify adverse reactions, and inform tailored, effective treatment strategies that advance precision medicine.

Platelets are central to hemostasis and thrombosis. Excessive platelet activation contributes to arterial thrombotic events, including myocardial infarction, ischemic stroke, and complications of peripheral artery disease, whereas excessive platelet inhibition increases bleeding risk. Antiplatelet therapy remains a cornerstone of secondary prevention in atherosclerotic and thrombotic cardiovascular diseases, yet current treatment paradigms do not fully account for biological heterogeneity in platelet function, including differences related to sex, age, hormonal status, and disease context. This state-of-the-art review examines current antiplatelet strategies, fundamental mechanisms of signal transduction, clinical indications, and limitations of preclinical and clinical studies, with a focus on sex-specific considerations and opportunities for personalized antiplatelet therapy. Aspirin and P2Y 12 receptor antagonists are widely used for secondary prevention. However, women have been underrepresented in many pivotal clinical trials, limiting the precision of sex-specific estimates of efficacy and bleeding risk. In parallel, commonly used preclinical models often fail to recapitulate the physiological conditions in which platelets interact with the vasculature, leukocytes, and soluble factors. Emerging therapeutic approaches seek to refine platelet inhibition by targeting pathways that reduce thrombotic risk while preserving hemostasis. Advancing antiplatelet therapy will require integration of mechanistic platelet biology with diverse clinical trial populations, standardized platelet phenotyping, and disease-specific approaches that account for how platelet function is altered across health and vascular disease.

Breast biopsy markers have evolved from simple metallic devices to sophisticated multimaterial tools that have become essential for breast cancer care, demonstrating their clinical benefits in surgical planning, radiation targeting, and cost reduction.

Rotating magnetic fields (RMFs) are widely used to actuate untethered microrobots, yet delivering clinically relevant RMFs at human scale remains challenging. Here, we create a quantitative framework for human‐scale RMF delivery using robot‐arm‐mounted permanent magnets. It links anatomical separation, surface‐clearance constraints, magnet geometry, rotational inertia, and robot‐arm limitations to define a feasible RMF operating envelope. Magnet geometry is most important when target separation is comparable to magnet size; in this regime, diametrically magnetized cylinders provide the most efficient field delivery at constant magnet volume. At larger separations, field delivery converges toward dipole‐like scaling and becomes primarily governed by total magnet volume. Across the dominant microrobot operating envelope (5–30 mT, 1–30 Hz), required magnet mass ranges from sub‐kilogram values for limb and intracranial targets to more than 30 kg for deep thoracic access. As magnet size increases, rotational inertia rapidly emerges as the dominant mechanical constraint, with limb and intracranial targets remaining feasible whereas some thoracic scenarios exceed practical mechanical limits. We demonstrate a representative actuator designed within the proposed framework tested on a human‐scale vascular phantom under flow. This work provides a foundation for codesigning translational microrobots and their magnetic drive platforms tailored for relevant anatomical targets.

Abstract Anthracycline-induced cardiotoxicity (AIC) remains a dose-limiting complication of cancer treatment, often leading to irreversible heart failure, yet effective cardioprotective therapies remain limited. Graph-driven drug repurposing offers a rapid pathway to identify cardioprotective therapies, but different computational models often generate divergent candidate lists that exceed experimental validation capacity. We applied a novel "Union-then-Filter" framework to prioritize therapeutics for AIC using AI-based approaches and evaluated candidates in zebrafish models. Three state-of-the-art graph learning models (TxGNN, CompGCN, and RLR) were trained on the PrimeKG to generate an initial candidate pool. A Large Language Model (LLM) agent automated evidence synthesis across 4,286 PubMed abstracts for the top 50 drugs from each model. Candidates were stratified using dual decision criteria and filtered against existing clinical trials. The pipeline narrowed 150 initial candidates to four novel candidates: Cysteine, Dasatinib, Tranilast, and Tretinoin. Prospective in vivo validation was subsequently conducted using an adult AIC (aAIC) zebrafish model. Phenotypic screening via echocardiography revealed that Cysteine, Tranilast, and Tretinoin significantly restored ejection fraction (EF) (overall one-way ANOVA: \(\:F\left(\text{4,95}\right)=47.66,\:\:p<0.0001\); Tukey's post hoc: \(\:\:\:p<0.01\) for all three treatments vs. DOX+ control) and normalized cardiac remodeling markers in doxorubicin-treated subjects (Kruskal-Wallis: \(\:{\chi\:}^{2}\left(4\right)=17.21,\:\:p=0.0018\); Dunn’s post hoc: \(\:\:\:p<0.01\) for all three comparisons vs. DOX control). This study demonstrates the translational utility of an LLM-integrated AI pipeline for drug repurposing and highlights zebrafish AIC as an efficient bridge between in silico prediction and in vivo phenotypic validation in cardio-oncology.