Citations

Summary Asthma genetic architecture varies according to age at onset and biological sex. Medication‐defined severity showed limited common variant genetic heterogeneity compared with age at onset and sex.

Abstract Background Sarcopenia is common in chronic obstructive pulmonary disease (COPD) and contributes to adverse outcomes. Alpha-1 antitrypsin deficiency (AATD) is the most common genetic cause of COPD, but its impact on skeletal muscle remains to be determined. Methods We performed a retrospective analysis of 487,207 participants in the UK Biobank with available SERPINA1 genotyping. Associations between AAT genotypes and measures of skeletal muscle mass were determined using multivariable linear and logistic regression models. Models adjusted for age, sex, smoking status, genetic principal components, lung function (FEV₁% predicted), and liver fibrosis risk (FIB-4). We determined that the Z-allele was associated with sarcopenia and mortality using regression models and mediation analysis. Results Compared with PI*MM individuals, carriers of the Z allele (PI*MZ, PI*SZ, PI*ZZ) demonstrated significantly lower fat free mass index (FFMI) and appendicular skeletal muscle index (ASMI), as well as higher odds of sarcopenia by binary definition. Associations persisted after adjustment for lung and liver function. A dose-dependent relationship was observed, with the greatest reductions in muscle mass and highest sarcopenia associated with PI*ZZ individuals (adjusted OR for FFMI-defined sarcopenia 2.02, 95% CI 1.16-3.32). Each additional Z allele was associated with increased mortality risk (adjusted OR 1.09, 95% CI 1.02-1.16), with sarcopenia mediating a modest proportion (∼2.6%) of this association. Conclusions. The SERPINA1 Z allele is associated with reduced muscle mass and increased risk for sarcopenia independent of lung and liver disease. Our findings expand the phenotypic spectrum of AATD-related diseases to include skeletal muscle as a modifiable target for intervention.

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.

BACKGROUND: Technetium-based cardiac amyloid radionuclide imaging enables noninvasive diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM), but conventional planar and visual semiquantitative approaches have important limitations. We developed the quantitative 3-dimensional Mayo 3D Score, defined as the ratio of mean left ventricular myocardial radiotracer uptake to right atrial blood-pool uptake using computed tomography (CT)-fused single-photon emission CT (SPECT) volumes of interest, and sought to validate its diagnostic performance against myocardial pathology. METHODS: Consecutive patients who underwent 99m Tc-pyrophosphate SPECT/CT at Mayo Clinic for suspected ATTR-CM between June 2017 and January 2025 with archived myocardial tissue were included. Baseline characteristics, including demographics, echocardiogram, biomarkers, visual semiquantitative, and Mayo 3D Score metrics, were collected. Myocardial specimens underwent histopathologic confirmation of amyloid, with amyloid subtype determination and further semiquantitative grading of amyloid burden and deposition patterns. RESULTS: Among 230 patients (median age, 72 years; 78% male), 107 (47%) had ATTR-CM, of whom 9 had hereditary ATTR-CM. The Mayo 3D Score showed excellent discrimination against pathology (area under the receiver operating characteristic curve, 0.92 [0.88–0.96]), exceeding planar Perugini (0.87 [0.83–0.92]; P =0.006), and heart-to-contralateral lung ratio (0.86 [0.81–0.91]; P =0.004) and comparable to SPECT Perugini (0.90 [0.86–0.94]; P =0.12). A cutoff ≥1.0 yielded 86.0% sensitivity, 92.7% specificity, 91.1% positive predictive value, and 88.4% negative predictive value. Among ATTR-positive cases, the Mayo 3D Score correlated strongly with semiquantitative amyloid burden (Spearman ρ=0.69), was higher in pericellular than nodular deposition patterns ( P <0.001), and was not associated with vascular involvement ( P =0.42). CONCLUSIONS: In patients with suspected ATTR-CM undergoing 99m Tc-pyrophosphate SPECT/CT, the Mayo 3D Score demonstrated superior diagnostic performance compared with planar metrics and comparable to SPECT Perugini, while correlating with myocardial ATTR burden and deposition patterns on pathology. These findings support its integration as a quantitative adjunct within clinical diagnostic ATTR-CM workflows.

Abstract Background Failure to rescue (FTR) is a key driver of postoperative mortality and reflects a hospital’s ability to recognize, escalate, and treat complications. Although FTR varies substantially between hospitals, the hospital characteristics that underpin this variation are inconsistently described. The aim of this study was to synthesize evidence on hospital-level factors associated with FTR after postoperative complications across surgical populations and health systems. Methods A systematic review of observational studies evaluating associations between hospital-level characteristics and FTR in surgical patients was undertaken. MEDLINE, EMBASE, and Scopus were systematically searched on 17 July 2024 and updated to 31 December 2025. Hospital factors were classified into structural, staffing, and system domains. Where three or more non-overlapping studies reported adjusted estimates in comparable formats, random-effects meta-analyses were performed; otherwise, findings were summarized narratively. Methodological quality of included studies was assessed using the ROBINS-I tool (version 2). Results A total of 111 studies encompassing over 137 million patients and 301 hospital factors were included. Meta-analyses demonstrated lower FTR rates in larger hospitals (odds ratio 0.85, 95% confidence interval 0.74 to 0.97), hospitals with greater intensive care unit capacity (odds ratio 0.80, 0.71 to 0.90), and teaching hospitals (odds ratio 0.88, 0.83 to 0.93). Better nursing resources were consistently associated with lower FTR, including patient-to-nurse ratios (odds ratio 1.07, 1.03 to 1.10), higher nurse education (odds ratio 0.95, 0.91 to 0.99), and more favourable work environments (odds ratio 0.93, 0.90 to 0.95). Safety-net hospital status was associated with higher FTR (odds ratio 1.22, 1.03 to 1.45). Substantial heterogeneity was observed across most analyses. Conclusion Hospital-level structural, staffing, and system characteristics are associated with variation in FTR. Postoperative mortality is closely linked to institutional capacity to detect and manage complications, underscoring the importance of adequate resourcing and system organization to improve surgical outcomes.

Summary Background Postoperative nausea and vomiting (PONV) is a common consequence of anaesthesia, affecting up to 30% of postoperative patients. Female sex is one of the strongest risk factors for PONV, yet no dedicated analysis has examined how this association varies across surgical settings and timepoints. This systematic review and meta-analysis aimed to quantify sex differences in PONV incidence across different surgical contexts. Methods A systematic search was conducted using PRISMA guidelines across Medline and Embase from inception to September 1, 2025. Eligible studies were observational cohort studies (n≥500) of adult patients that conducted multivariate regression analyses including sex as a variable. Two reviewers independently screened, extracted data, and assessed risk of bias using ROBINS-E. A random-effects meta-analysis was performed. Subgroup analyses and multiple sensitivity analyses were completed. Results From 4620 identified studies, 23 met the inclusion criteria, including 462,828 patients across various surgical settings and specialties (52% female). The pooled incidence of PONV was 21% (95% CI[16-27%]), with high heterogeneity (I 2 =99.9%). Meta-analysis confirmed females had a higher risk of developing PONV compared to males (pooled OR=2.40, 95% CI[2.06-2.79], I 2 =93.1%, p<0.0001). Sensitivity analyses confirmed robustness of pooled estimates. Subgroup analyses demonstrated consistently elevated risk of PONV for females at all three timepoints (PACU, 24-hour, 48-hour post-operative) and across studies including and excluding female-only surgeries. Studies were generally at high risk of bias. Discussion Female sex is a strong risk factor for PONV across surgical settings. Further research into precise subgroups, underlying mechanisms of sex-differences, and the use of prophylaxis may help improve this inequity.

Abstract Exposure to asbestos is the primary cause of mesothelioma, however the processes underpinning its initiation and evolutionary trajectory remain elusive. To address this knowledge gap, we conducted whole exome sequencing and phylogenetic analysis of 277 mesotheliomas. Using multidimensional Dirichlet process clustering we inferred phylogenies, uncovering mutation order dominated by early 13q,1p, 8p, 6q and 22q somatic copy number alterations. Mesotheliomas exhibit a spectrum of genomic intra-tumor heterogeneity spanning linear (L) to highly branched (B) tree topologies, quantifiable by the Weighted Shannon Entropy (WSE). Somatic copy number alterations emerge earlier than single nucleotide variants (SNVs), and at a much faster relative rate in L-mesotheliomas. BAP1 is the only positively selected driver in L-mesotheliomas, whereas, B-mesotheliomas harbor multiple positively selected drivers and an aggressive phenotype, including a bias of loss of RPL5, and MTAP deletion linked to breakpoint enriched with long interspersed nuclear elements. L-trees had lower WSE, but greater T-cell inflammation and tertiary lymphoid structures, despite having a lower neoantigen burden. WSE is higher in larger volume, immunologically cold mesotheliomas, and is associated with shorter survival. In summary, progressive genomic heterogeneity is a defining feature of more aggressive mesothelioma yet illuminates opportunities for precision therapeutic intervention.

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, arising from both B and T cell lineages (B-ALL and T-ALL). Current therapy exploits ALL cells’ low expression of asparagine synthetase (ASNS) by using L-asparaginase, a bacterial enzyme that depletes circulating asparagine. However, resistance can emerge through induction of ASNS, mediated in part by the amino acid stress sensor GCN2. In this study, we addressed the efficacy of L-asparaginase in combination with genetic or pharmacological inhibition of GCN2 and the ASNS inhibitor ASX-173. Using a Kras G12D -driven mouse model of T-ALL, we found that GCN2 is dispensable for leukemogenesis. However, genetic inactivation or pharmacologic inhibition of GCN2 sensitized ALL cells to asparagine depletion, correlating with impaired ASNS induction. While GCN2 targeting enhanced sensitivity to asparagine depletion, a subset of Gcn2 –/– T-ALL cells retained high ASNS expression and remained resistant to L-asparaginase. Likewise, some human T-ALL cells with elevated ASNS levels were refractory to GCN2 inhibition even under asparagine-depleted conditions. When combined with L-asparaginase, ASX-173 effectively eliminated ASNS hi leukemic cells in vitro and in vivo. These findings suggest that direct targeting of ASNS provides therapeutic benefit in leukemias that express high levels of ASNS and are resistant to GCN2 inhibition under asparagine-depleted conditions.

Background Intermittent fasting (IF) represents a dietary strategy with known metabolic benefits, however its safety and applicability in patients with chronic kidney disease (CKD) remains poorly understood. AI chatbots’ reliability for clinical and dietary guidance in clinically complex areas remains underexplored. No study has examined AI performance on IF in patients with CKD. This study evaluated the accuracy, consistency, readability, and quality of responses generated by ChatGPT-5 and Claude 4.5 Sonnet regarding IF in CKD. Methods Twenty-five patient-centered questions spanning seven clinical domains were developed by two board-certified nephrologists and submitted independently to ChatGPT-5 and Claude 4.5 Sonnet twice per model. Prompts were submitted under standard and unmodified user conditions without customized system instructions to represent a zero-shot evaluation that is illustrative of typical patient use. Responses were evaluated for accuracy, consistency (comparatively between responses), and appropriateness by the evaluators. Response readability and quality were assessed using the Flesch-Kincaid Grade Level (FKGL) formula and the DISCERN instrument respectively. Results Both ChatGPT-5 and Claude 4.5 Sonnet demonstrated identical accuracy scores during the first question prompts with notable improvement upon re-prompting after 24-hours. Both models demonstrated an identical internal consistency at 90%. ChatGPT-5 produced responses at the middle school level, better aligned with the US adult health literacy benchmark, while Claude 4.5 Sonnet generated responses at the high school level. DISCERN scoring was comparable between chatbots indicative of good response quality. Both chatbots had similar performance regarding individual subdomains. Conclusions Both chatbots illustrated clinically accurate and cautious responses to questions regarding IF in CKD. ChatGPT-5 responses were better suited to the average health literacy of the general United States adult population. Despite our findings, we do not recommend either chatbot be used as the primary source for clinical counseling regarding IF in CKD. It is important for clinicians to help to guide patients toward evidence-based clinical information.

Abstract Rationale Alpha-1 antitrypsin deficiency (AATD) continues to be underdiagnosed. The estimated prevalence of the most common severe deficient genotype, PI*ZZ, is ∼1:3500 or 100,000 Americans but only ∼10,000 such individuals have been recognized. Diagnostic delays persist and these delays are associated with worse disease. High-resolution CT (HRCT) imaging may contain latent phenotypic AATD signatures that are not visibly apparent to clinicians. We hypothesized a deep learning model could identify imaging patterns predictive of AATD from inspiratory HRCT scans. Methods Confirmed genotypes and HRCT scans were pooled from COPDGene, SPIROMICS, and GRADS, yielding 350 MM, 102 MZ, and 69 ZZ participants. Only scans reconstructed with smooth kernels (GE STANDARD, Philips B, Siemens B31f/B35f) and slice thickness &lt; =1.25mm were used. The cohort was selected to have similar distributions of quantitative emphysema, sex, and age by matching five MZ and two MM subjects to each ZZ subject (&lt;10-year age difference, same sex, and &lt;3% difference in %LAA). Tobacco smoke-exposure was more prevalent among the MM/MZ groups given these were predominantly from the tobacco smoke-enriched COPDGene and SPIROMICS cohorts. A DenseNet121 convolutional neural network (CNN) was trained using the MONAI platform to predict genotype by outputting probabilities for MM, MZ, and ZZ. Input to the CNN was a three-channel LAA-910, LAA-950, and LAA-970 map downsampled to 2.0mm3 resolution to remove bias due to differences in scanner and image reconstruction parameters. Five-fold cross-validation was performed. Area under the curve (AUC) was calculated for classification of ZZ vs other genotypes (MM/MZ) and MZ vs MM, with statistical significance assessed at p &lt; 0.05. Results The three groups were similar across age (mean±SD years: MM 57.9±8.8, MZ: 57.5±8.9, ZZ: 55.0±10.7), sex (% female: MM: 48.2%, MZ: 46.7%, ZZ: 47.6), and emphysema (LAA-950%: MM: 13.0±12.2, MZ: 12.4±11.8, ZZ: 13.1±11.9). There was a higher prevalence of ever smokers among MM (100%) and MZ (91.7%), compared to ZZ (47.6%). The model achieved an AUC of 0.806 (p &lt; 0.001) for classification of ZZ vs other genotypes, demonstrating robust discriminatory ability for homozygous AATD. Classification of MZ vs other genotypes did not achieve an AUC significantly greater than 0.5. Conclusions This proof-of-concept study demonstrates that deep learning applied to routine inspiratory HRCT can accurately identify homozygous AATD. These findings support the potential role of imaging-based machine learning as a non-invasive screening tool for undiagnosed ZZ individuals. Future work will focus on cohort enrichment, integration with clinical parameters, and explainability of model-derived imaging features. This abstract is funded by: NIH NHLBI K23HL173570