Citations

ABSTRACT Microplastic pollution may influence neurodevelopment, yet its relationship with attention‐deficit/hyperactivity disorder (ADHD) remains unclear. We integrated a pediatric cohort, multi‐omics analyses, network toxicology, and animal experiments to investigate polyethylene (PE) microplastic exposure in ADHD vulnerability. Among 90 children, PE was the predominant polymer detected in blood and was more abundant in children with ADHD. Higher PE burden was associated with greater symptom severity, increased IL‐6, and reduced antioxidant capacity. Multi‐omics and network analyses converged on PI3K/AKT1/Nrf2 signaling. In a perinatal nicotine mouse model, developmental PE co‐exposure exacerbated ADHD‐like behaviors, dopaminergic dysfunction, and oxidative injury. Melatonin partially alleviated these abnormalities by restoring PI3K/AKT1/GSK3β and Nrf2 signaling, whereas AAV‐mediated PI3K knockdown weakened its protective effects. Collectively, the human findings identify circulating PE burden as an environmental correlate of ADHD‐related neurodevelopmental vulnerability, while the animal experiments provide evidence that developmental PE co‐exposure can aggravate ADHD‐like abnormalities in a susceptible background.

ABSTRACT Background and Objectives Among patient with early‐stage breast cancer, breast conservation surgery (BCS) is the most performed surgery, utilized in approximately two‐thirds of patients. While 5‐year overall‐survival rates are strong—typically above 90%—additional re‐excisions can occur in up to 25% of patients who undergo index BCS and some patients will require subsequent surgery for new breast cancer or recurrences. As such, these additional procedures can complicate patients’ pathway to survivorship, negatively impact quality of life, and lead to excess healthcare costs. Methods This retrospective cohort study used linked claims and an electronic healthcare record database to (1) characterize patients at higher risk for additional breast oncologic surgery (aBOS) following BCS, and (2) quantify the long‐term financial impact of aBOS after BCS. In total, 87,190 patients with breast cancer, who had an index BCS between January 1, 2012, to March 31, 2023, were followed for up to 5‐years post‐operatively. Patients with and without aBOS were analyzed, comparing demographics, tumor characteristics, healthcare utilization, and per‐person‐per‐month (PPPM) costs. Results Twenty‐three percent ( n = 20,212) of patients underwent aBOS. These patients were younger, had higher tumor grades and larger tumor sizes, and incurred greater healthcare costs compared to patients who did not undergo a secondary surgery. Conclusion This study highlights significant factors influencing BCS outcomes and healthcare utilization, underscoring the need for tailored therapeutic strategies for patients with breast cancer.

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by the destruction of insulin-producing pancreatic β cells. Both genetic susceptibility and epigenetic dysregulation contribute to T1D risk. Recent studies have reported β-cell dysfunction before disease onset, suggesting that β-cell-intrinsic mechanisms contribute to pathogenesis. However, the mechanistic link between disease-associated variants and β-cell dysfunction remains poorly understood. We hypothesized that a subset of T1D-associated variants directly influences cell type-specific candidate cis-regulatory elements (cCREs) by disrupting transcription factor (TF) binding and altering gene expression. To define cell type-specific cCREs in human islets, we integrated publicly available epigenomic datasets, including bulk histone modification profiles and single-cell multiomic data from human pancreatic islets. We combined these data with T1D genome-wide association study (GWAS) variants to identify disease-associated SNPs located within enhancer regions. Using multiomic datasets from PANC-DB, we characterized cell type-specific chromatin accessibility in nondiabetic and T1D islets and identified regulatory regions that may control gene expression in β cells and other islet cell types. We then applied ChromBPNet, a deep learning model that predicts base-pair-resolution chromatin accessibility from scATAC-seq data, to evaluate how specific variants may alter local regulatory activity. In parallel, we used TF footprinting to nominate TFs likely to bind these variant-containing regions. These analyses identified several T1D-associated SNPs predicted to alter chromatin accessibility at candidate TF binding sites, suggesting mechanisms by which noncoding variants may contribute to β-cell dysfunction and T1D susceptibility. Our findings link T1D-associated variants to cell type-specific enhancer activity and regulatory pathways and provide candidates for future mechanistic studies.

Abstract The development of biocompatible and biologically responsive scaffolds remains a key challenge for bone tissue engineering. Here, we report an enzyme-degradable, metal-free click-cross-linked organic–inorganic nanohybrid (Enz-Deg-Click-ON) scaffold with high biocompatibility for bone repair. The formulation was synthesized using a cyclooctyne-functionalized poly(propylene fumarate) polyphosphoester (PPF-poly(PE-BCN)) and azide-terminated dendritic polycaprolactone (PCL) with a polyhedral oligomeric silsesquioxane core (POSS-PCL-N3) via strain-promoted alkyne-azide cycloaddition (SPAAC) bioorthogonal click chemistry. The incorporation of phosphate-containing linkages enabled alkaline phosphatase (ALP)-responsive degradation. The scaffold exhibited rapid catalyst-free crosslinking, tunable swelling, and suitable mechanical properties. In vitro studies demonstrated excellent cytocompatibility, robust stem cell proliferation, and enhanced osteogenic differentiation with an elevated level of ALP and Runx2 expression. Enzyme-triggered degradation was confirmed in the presence of ALP. In the in vivo rat calvarial defect model, the scaffold significantly improved bone regeneration, as shown by an increased bone volume, mineral density, and defect coverage. Histological and immunofluorescence analyses revealed enhanced collagen deposition, angiogenesis, and osteogenesis. These results indicate that the Enz-Deg-Click-ON system provides a dynamic, bioresponsive platform for bone repair.

ABSTRACT Background Mobility functional status is inconsistently recorded in electronic health records. Recent advances in Large Language Models (LLMs) enable automated extraction of functional information from unstructured clinical notes. Leveraging a validated functional LLM with high performance in mobility extraction, we aimed to study the association between LLM‐derived mobility functional status and clinical outcomes, including emergency department (ED) visits, hospitalizations, and mortality. Methods Individuals aged 60 years and older enrolled in the Mayo Clinic Study of Aging, with documented clinical notes in 2022 were included. Individuals without any identifiable functional information were excluded. The LLM‐derived mobility functional status categorized mobility as impaired or unimpaired, across the five mobility domains as classified by International Classification of Functioning, Disability, and Health. Clinical outcomes in 2023 were extracted from the Rochester Epidemiology Project database. Logistic regression models assessed associations between mobility impairment and outcomes, adjusted for age, sex, Elixhauser comorbidity index, and education. Results Three thousand three hundred thirty‐seven individuals were screened, of whom three hundred forty‐three had no functional information. Two thousand nine hundred ninety‐four individuals were analyzed. 79.4% had at least one mobility impairment. Higher mobility impairment is associated with older age and greater Elixhauser comorbidity. Individuals with 3 or more impairments had an increased adjusted odds of ED visits (Adjusted Odds Ratio (aOR): 2.18 (1.64, 2.90)) and hospitalizations (aOR: 2.35 (1.51, 3.65)) compared to unimpaired individuals ( p < 0.001), while mortality did not demonstrate a significant association with mobility impairment. Conclusion LLM‐derived mobility functional status assessment was associated with ED visits and hospitalization, but not with all‐cause mortality after adjustment. Prospective validation and evaluation of incremental discrimination, calibration, and clinical utility are needed before clinical implementation.

Abstract Orthoebolaviruses, including Ebola virus (EBOV) and Bundibugyo virus (BDBV), cause sporadic outbreaks and devastating hemorrhagic infections in humans. A significant challenge that impedes the study of filoviruses is that the infectious virus can only be handled in biosafety level 4 (BSL4) facilities due to their extreme virulence. Life cycle modeling systems that use minigenomes and transcription/replication-competent virus-like particles (trVLPs) are powerful tools for studying these virus’ biology and to explore antiviral therapies under BSL2 conditions. To date, these powerful tools have only been utilized in vitro in cell culture systems. In this work, we demonstrate the use of EBOV trVLPs in vivo. We report the fine specificity of first infection events in a model of EBOV injection in the skin. Cre recombinase expressing monocistronic EBOV trVLPs were packaged and used to inject Cre reporter mice intradermally (ID). Initial infection events were monitored in living animals by luciferase imaging and then by flow cytometry to track cell-specific expression of Cre-activated GFP. By this approach, EBOV trVLPs were shown to directly infect T cells, monocytes, dendritic cells (DCs) with notably high first infections of skin-resident Langerhans cells (LCs) in the skin barrier and its draining lymph node. EBOV trVLPs were next tested as an in vivo tool to assess antiviral therapies by testing the ability of an EBOV vaccine to reduce trVLP luciferase levels after infection in the skin. This work demonstrates first in vivo use of EBOV trVLPs and provides proof of concept for their use as an in vivo BSL2 model to more rapidly develop antiviral agents and vaccines.

Background Bicuspid aortic valve (BAV) is the most common congenital heart defect, associated with a lifetime morbidity burden exceeding 80% and an estimated 7% probability of transmission to first-degree relatives. Hence, echocardiographic screening of relatives is recommended. The aim of our study is to develop an artificial intelligence-enabled electrocardiogram (AI-ECG) model for the detection of BAV. Methods Between January 1, 1990, and June 30, 2023, the Mayo Clinic healthcare system database was searched for patients aged ≥18 years with echocardiograms performed for family history of BAV, abnormal auscultation, a BAV-related diagnosis, or to rule out the condition, and with an electrocardiogram (ECG) obtained within 6 months. Cases were patients with a confirmed BAV diagnosis, and controls were those with normal aortic valves. ECGs were randomly assigned into training (80%), internal validation (10%), and test (10%) groups. The AI-ECG model was built using Keras under TensorFlow. Results A total of 13,065 valid ECG–echocardiogram pairs were included (mean [SD] age, 58 [17] years; 44% women), with a median time of 1 day (interquartile range, 0–3 days) between studies. With a 45% BAV prevalence in the test group ( n = 1301), the area under the curve was 0.704, positive predictive value (PPV) 60.1%, and negative predictive value (NPV) 69.4%. At a prevalence of 7%, the NPV increased to 96.1%, but the PPV decreased to 12.2%. Conclusions In this retrospective cohort and using the current AI-ECG model, BAV detection showed modest diagnostic performance and limited standalone clinical utility, suggesting that ECG-only screening for BAV may require additional modalities or more refined phenotyping.

Transcranial magnetic stimulation can provide noninvasive measures of cortical excitability and plasticity, but relationships between these measures and clinically observable features of autism are not fully characterized. Nineteen autistic participants aged 15 to 40 years were included in the analysis of a left primary motor cortex continuous theta-burst stimulation (cTBS) biomarker protocol. Motor evoked potentials were measured at baseline and at seven assessments from 5 to 60 min after stimulation. Linear mixed-effects models evaluated whether clinical measures moderated the post-stimulation motor evoked potential log-response ratio over time. Aberrant Behavior Checklist (ABC) and Attenuated Behavior Questionnaire (ABQ) analyses were restricted to the same 15 participants with caregiver-informant assessments. Catatonia severity, social impairment, ABQ Motor Total, ABQ Total, and cognitive ability did not significantly moderate the post-stimulation response. ABC Inappropriate Speech was associated with progressively greater post-stimulation facilitation (standardized Time-by-Inappropriate Speech interaction: beta = +0.688, SE = 0.210, 95% CI +0.276 to +1.099; Holm-adjusted P = .008 across eight informant-rated models). Two individual speech-related items ("Talks excessively" and "Talks to self loudly") survived false-discovery-rate correction. An exploratory eight-item ABC phenotype showed a large descriptive in-sample association (beta = +0.904, SE = 0.200, P less than .001) and directionally positive held-out performance. However, full-pipeline permutation tests were nonsignificant for both Pearson (r = .357, two-sided empirical P = .360) and Spearman correlations (rho = .446, two-sided empirical P = .261). Caregiver-rated inappropriate speech may be associated with altered post-cTBS motor cortical facilitation in autism. The candidate phenotype remains hypothesis-generating and requires independent validation.

Abstract Purpose Large language models (LLMs) have proven performance for certain diagnostic tasks; however, limited studies have evaluated their consistency in recommending appropriate medication regimens for a given diagnosis. Medication management is a complex task that requires synthesis of drug formulation and complete order instructions for safe use. Here, the performance of GPT-4o, an LLM available with OpenAI’s ChatGPT, was tested on 3 medication management tasks. Methods GPT-4o performance was tested on 3 medication tasks: identifying available formulations for a given generic drug name, identifying drug-drug interactions (DDIs) for a given medication regimen, and preparing a medication order for a given generic drug name. For each experiment, the model’s raw text response was captured exactly as returned and evaluated using clinician evaluation in addition to standard LLM metrics, including Term Frequency-Inverse Document Frequency (TF-IDF) vectors, normalized Levenshtein similarity, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE-1/ROUGE-L) F1 score between each response and its reference string. Results For the first task of drug-formulation matching, GPT-4o had 49% accuracy for generic medications being matched to all available formulations, with an average of 1.23 omissions per medication and 1.14 hallucinations per medication. For the second task of drug-drug interaction identification, the accuracy was 54.7% for identifying the DDI pair. For the third task, GPT-4o generated order sentences containing no medication or abbreviation errors in 65.8% of the cases. Conclusion Model performance for basic medication tasks was consistently poor. This evaluation highlights the need for domain-specific training through clinician-annotated datasets and a comprehensive evaluation framework for benchmarking performance.

Abstract Purpose We compared perioperative morbidity after repeat transurethral resection (re-TURP) versus primary TURP, focusing on transfusion and broader bleeding-related complications relevant to counseling and risk stratification. Methods We analyzed the American College of Surgeons National Surgical Quality Improvement Program (2013–2023) to identify men undergoing primary TURP (Current Procedural Terminology [CPT] 52601) or re-TURP (CPT 52630). The primary outcome was 30-day red blood cell transfusion. Secondary outcomes included a prespecified postoperative hematuria endpoint based on ICD-10 codes. Multivariable logistic regression evaluated associations with bleeding endpoints and downstream complications. Results Among 83,539 TURP procedures, 76,983 (92.2%) were primary and 6,556 (7.8%) were repeat. Re-TURP patients were older, had higher ASA class, and more often underwent urgent surgery. Transfusion occurred in 1.9% after re-TURP versus 1.5% after primary TURP; hematuria-coded events occurred in 3.5% versus 1.3%, respectively. After adjustment, re-TURP was associated with postoperative hematuria (adjusted odds ratio [aOR] 2.42, 95% CI 2.03–2.88) but not with transfusion (aOR 1.09, 95% CI 0.87–1.36). Older age, urgent/emergent status, bleeding disorder, and lower hematocrit predicted both bleeding endpoints. Re-TURP was associated with slightly lower odds of readmission (aOR 0.87; 95% CI 0.76–0.99) and was not associated with return to the operating room. Transfusion strongly predicted return to the operating room (aOR 7.21) and readmission (aOR 2.05), whereas hematuria predicted return to the operating room only (aOR 1.50). Conclusions Compared with primary TURP, re-TURP was associated with more hematuria-coded events but not higher adjusted transfusion risk, suggesting a relative shift toward lower-severity bleeding morbidity.