Citations

AbstractThe temporal evolutions and relative orderings of Alzheimer disease biomarkers, including CSF amyloid-β42 (Aβ42), Aβ40, total tau (Tau) and phosphorylated tau181 (pTau181), standardized uptake value ratio (SUVR) from the molecular imaging of cerebral fibrillar amyloid-β with PET using the 11C-Pittsburgh Compound-B (PiB), MRI-based hippocampal volume and cortical thickness and cognition have been hypothesized but not yet fully tested with longitudinal data for all major biomarker modalities among cognitively normal individuals across the adult lifespan starting from 18 years. By leveraging a large harmonized database from 8 biomarker studies with longitudinal data from 2609 participants in cognition, 873 in MRI biomarkers, 519 in PET PiB imaging and 475 in CSF biomarkers for a median follow-up of 5–6 years, we estimated the longitudinal trajectories of all major Alzheimer disease biomarkers as functions of baseline age that spanned from 18 to 103 years, located the baseline age window at which the longitudinal rates of change accelerated and further examined possible modifying effects of apolipoprotein E (APOE) genotype. We observed that participants 18–45 years at baseline exhibited learning effects on cognition and unexpected directions of change on CSF and PiB biomarkers. The earliest acceleration of longitudinal change occurred for CSF Aβ42 and Aβ42/Aβ40 ratio (with an increase) and for Tau, and pTau181 (with a decrease) at the next baseline age interval of 45–50 years, followed by an accelerated increase for PiB SUVR at the baseline age of 50–55 years and an accelerated decrease for hippocampal volume at the baseline age of 55–60 years and finally by an accelerated decline for cortical thickness and cognition at the baseline age of 65–70 years. Another acceleration in the rate of change occurred at the baseline age of 65–70 years for Aβ42/Aβ40 ratio, Tau, pTau181, PiB SUVR and hippocampal volume. Accelerated declines in hippocampal volume and cognition continued after 70 years. For participants 18–45 years at baseline, significant increases in Aβ42 and Aβ42/Aβ40 ratio and decreases in PiB SUVR occurred in APOE ɛ4 non-carriers but not carriers. After age 45 years, APOE ɛ4 carriers had greater magnitudes than non-carriers in the rates of change for all CSF biomarkers, PiB SUVR and cognition. Our results characterize the temporal evolutions and relative orderings of Alzheimer disease biomarkers across the adult lifespan and the modification effect of APOE ɛ4. These findings may better inform the design of prevention trials on Alzheimer disease.

Abstract Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, understanding of the applicability of these model explainers to biological data is still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pre-trained models for predicting tissue types from transcriptomic data and by identifying the top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of multilayer perceptron (MLP) and convolutional neural network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.

Abstract With aging, somatic mutations in hematopoietic stem and progenitor cells (HSPC) can give rise to clonal hematopoiesis of indeterminate potential (CHIP), a premalignant state associated with diverse age-related diseases. Here we report the largest multi-ancestry genome-wide analysis of CHIP to date (N = 1,018,305), including individuals of African (N = 85,978), Admixed American (N = 191,371), East Asian (N = 13,532), European (N = 694,015), and South Asian (N = 13,193) ancestry. Multi-ancestry meta-analyses identified 72 genome-wide significant loci, including 44 novel associations implicating genes such as AFF1 , ATF7IP , ATP8B4 , BCL2 , CEBPA , CYRIA , DNM2 , ELF1 , NKX2-3 , PIK3CB , PRDM16 , RPN1 , TERC , and TRIM4 . Notably, variants at MECOM and PHF20L1 showed opposite allelic effects between DNMT3A - and non- DNMT3A -driven CHIP, highlighting driver-specific germline influences. Implicated loci converge on pathways regulating telomere maintenance, cell-cycle control, hematopoietic transcription, DNA damage response and immune signaling. These findings support a model in which germline variation both expands the HSPC pool and biases clonal selection in a driver-dependent manner, providing a mechanistic basis for inter-individual heterogeneity in CHIP. Phenome-wide association analyses further linked CHIP to hematologic, neoplastic, and circulatory traits, with enrichment across hematopoietic and non-hematopoietic cell types. Together, this work expands the genomic and phenomic landscape of CHIP and reveals germline–somatic interactions that shape clonal evolution during aging.

Introduction The Accreditation Council for Graduate Medical Education (ACGME) offers descriptions of competencies and milestones but does not provide standardized assessments to track trainee competency. Entrustable professional activities (EPAs) and special assessments (SAs) are emerging methods to assess the level of competency obtained by regional anesthesiology and acute pain medicine (RAAPM) fellows. Methods A panel of RAAPM physicians with experience in education and competency assessment and one medical student were recruited to participate in a modified Delphi method with iterative rounds to reach consensus on: a list of EPAs, SAs, and procedural skills; detailed definitions for each EPA and SA; a mapping of the EPAs and SAs to the ACGME milestones; and a target level of entrustment for graduating US RAAPM fellows for each EPA and procedural skill. A gap analysis was performed and a heat map was created to cross-check the EPAs and SAs to the ACGME milestones. Results Participants in EPA and SA development included 19 physicians and 1 medical student from 18 different programs. The Delphi rounds yielded a final list of 23 EPAs, a defined entrustment scale, mapping of the EPAs to ACGME milestones, and graduation targets. A list of 73 procedural skills and 7 SAs were similarly developed. Discussion A list of 23 RAAPM EPAs, 73 procedural skills, and 7 SAs were created using a rigorous methodology to reach consensus. This framework can be utilized to help assess RAAPM fellows in the USA for competency and allow for meaningful performance feedback.

AbstractIntroductionTo address the need for remote assessments of cognitive decline and dementia, we developed and administered electronic versions of the Clinical Dementia Rating (CDR®) and the Financial Capacity Instrument‐Short Form (FCI‐SF) (F‐CAP®), called the eCDR and eFCI, respectively.MethodsThe CDR and FCI‐SF were adapted for remote, unsupervised, online use based on item response analysis of the standard instruments. Participants completed the eCDR and eFCI first in clinic, and then at home within 2 weeks.ResultsOf the 243 enrolled participants, 179 (73%) cognitively unimpaired (CU), 50 (21%) with mild cognitive impairment (MCI) or dementia, and 14 (6%) with an unknown diagnosis, 84% and 85% of them successfully completed the eCDR and eFCI, respectively, at home.DiscussionThese results show initial feasibility in developing and administering online instruments to remotely assess and monitor cognitive decline along the CU to MCI/very mild dementia continuum. Validation is an important next step.

Background: Many insurance companies in the United States rely on the Schnur sliding scale to predict resection weights to determine medical necessity for breast reduction surgery. Accurate methods to predict resection weights are needed to avoid insurance denials. The authors compared the accuracy of formulas such as the Schnur, Appel, Descamps, and Galveston scales in predicting resection weights, and assessed whether they influence insurance coverage decision. Methods: A retrospective review of bilateral reduction mammaplasty procedures from June of 2017 to June of 2019 was performed at the Mayo Clinic, Rochester. Oncoplastic reduction operations were excluded. The accuracy of each formula-based estimate was evaluated with linear regression analysis. Results: One hundred fifty-four patients (308 breasts) were reviewed. The Schnur scale had low correlation with actual resection weight (r 2 = 0.381; b1 = 1.153; p < 0.001). The Appel scale was the most accurate (r 2 = 0.642; b1 = 1.01; p < 0.001), followed by the Descamps (r 2 = 0.572, b1 = 0.934, p < 0.001) and Galveston (r 2 = 0.672; b1 = 0.654; p < 0.001) scales. The Appel, Descamps, and Galveston scales were more accurate for resection weights of 500 g or greater, body mass index greater than 30 kg/m², and patients younger than 50 years. For resection weights of 500 g or greater, the median difference between the estimated and actual resection weight for the Schnur, Appel, Descamps, and Galveston scales was −211.4 ± 272.3, −17.5 ± 272.3, −9.6 ± 229.5, and −99.2 ± 238.5 g, respectively. No scale was accurate for resection weights less than 500 g. Insurance reimbursement was denied in 15.56 percent of patients; of these, 23 percent had resection weights less than 500 g. The Schnur scale overestimated the resection weights in 28.9 percent of patients. Conclusions: The Schnur scale is a poor predictor of breast resection weight. The Appel scale is the most accurate estimator, especially in the young and obese population with larger resections. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, I.

Abstract Glial fibrillary acidic protein (GFAP) is a promising candidate blood-based biomarker for Alzheimer’s disease (AD) diagnosis and prognostication. The timing of its disease-associated changes, its clinical correlates, and biofluid-type dependency will influence its clinical utility. We evaluated plasma, serum, and CSF GFAP in families with autosomal dominant AD (ADAD), leveraging the predictable age at symptom onset to determine changes by stage of disease. Plasma GFAP elevations appear a decade before expected symptom onset, after β-amyloid accumulation and prior to neurodegeneration and cognitive decline. Plasma GFAP distinguished β-amyloid-positive from β-amyloid-negative ADAD participants and showed a stronger relationship with β-amyloid load in asymptomatic than symptomatic ADAD. Higher plasma GFAP was associated with the degree and rate of neurodegeneration and cognitive impairment. Serum GFAP showed similar relationships, but these were less pronounced for CSF GFAP. Our findings support a role for plasma GFAP as a clinical biomarker for β-amyloid-associated cognitive deterioration in AD.

A subset of individuals who recover from coronavirus disease 2019 (COVID-19) develop post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (PASC), but the mechanistic basis of PASC-associated lung abnormalities suffers from a lack of longitudinal tissue samples. The mouse-adapted SARS-CoV-2 strain MA10 produces an acute respiratory distress syndrome in mice similar to humans. To investigate PASC pathogenesis, studies of MA10-infected mice were extended from acute to clinical recovery phases. At 15 to 120 days after virus clearance, pulmonary histologic findings included subpleural lesions composed of collagen, proliferative fibroblasts, and chronic inflammation, including tertiary lymphoid structures. Longitudinal spatial transcriptional profiling identified global reparative and fibrotic pathways dysregulated in diseased regions, similar to human COVID-19. Populations of alveolar intermediate cells, coupled with focal up-regulation of profibrotic markers, were identified in persistently diseased regions. Early intervention with antiviral EIDD-2801 reduced chronic disease, and early antifibrotic agent (nintedanib) intervention modified early disease severity. This murine model provides opportunities to identify pathways associated with persistent SARS-CoV-2 pulmonary disease and test countermeasures to ameliorate PASC.