Acute exercise alters brain glucose metabolism in aging and Alzheimer's disease.

There is evidence that aerobic exercise improves brain health. Benefits may be modulated by acute physiological responses to exercise, but this has not been well characterized in older or cognitively impaired adults. The randomized controlled trial 'AEROBIC' (NCT04299308) enrolled 60 older adults who were cognitively healthy (n = 30) or cognitively impaired (n = 30) to characterize the acute brain responses to moderate [45-55% heart rate reserve (HRR)] and higher (65-75% HRR) intensity acute exercise. Each participant received two fluorodeoxyglucose positron emission tomography (FDG-PET) scans, one at rest and one following acute exercise. Change in cerebral glucose metabolism from rest to exercise was the primary outcome. Blood biomarker responses were also characterized as secondary outcomes. Whole grey matter FDG-PET standardized uptake value ratio (SUVR) differed between exercise (1.045 ± 0.082) and rest (0.985 ± 0.077) across subjects [Diff = -0.060, t(58) = 13.8, P < 0.001] regardless of diagnosis. Exercise increased lactate area under the curve (AUC) [F(1,56) = 161.99, P < 0.001] more in the higher intensity group [mean difference (MD) = 97.0 ± 50.8] than the moderate intensity group (MD = 40.3 ± 27.5; t = -5.252, P < 0.001). Change in lactate AUC and FDG-PET SUVR correlated significantly (R2 = 0.179, P < 0.001). Acute exercise decreased whole grey matter cerebral glucose metabolism. This effect tracked with the systemic lactate response, suggesting that lactate may serve as a key brain fuel during exercise. Direct measurements of brain lactate metabolism in response to exercise are warranted. KEY POINTS: Acute exercise is associated with a drop in global brain glucose metabolism in both cognitively healthy older adults and those with Alzheimer's disease. Blood lactate levels increase following acute exercise. Change in brain metabolism tracks with blood lactate, suggesting it may be an important brain fuel. Acute exercise stimulates changes in brain-derived neurotrophic factor and other blood biomarkers.

An efficient, not-only-linear correlation coefficient based on clustering.

Identifying meaningful patterns in data is crucial for understanding complex biological processes, particularly in transcriptomics, where genes with correlated expression often share functions or contribute to disease mechanisms. Traditional correlation coefficients, which primarily capture linear relationships, may overlook important nonlinear patterns. We introduce the clustermatch correlation coefficient (CCC), a not-only-linear coefficient that utilizes clustering to efficiently detect both linear and nonlinear associations. CCC outperforms standard methods by revealing biologically meaningful patterns that linear-only coefficients miss and is faster than state-of-the-art coefficients such as the maximal information coefficient. When applied to human gene expression data from genotype-tissue expression (GTEx), CCC identified robust linear relationships and nonlinear patterns, such as sex-specific differences, that are undetectable by standard methods. Highly ranked gene pairs were enriched for interactions in integrated networks built from protein-protein interactions, transcription factor regulation, and chemical and genetic perturbations, suggesting that CCC can detect functional relationships missed by linear-only approaches. CCC is a highly efficient, next-generation, not-only-linear correlation coefficient for genome-scale data. A record of this paper's transparent peer review process is included in the supplemental information.

Methods to characterize lactate turnover in aging and Alzheimer's disease; The LEAN study.

BACKGROUND: There is evidence that chronic exercise can benefit the brain, but the effects vary markedly between studies. One potential mechanism for exercise-related benefit is the increase in systemic lactate concentration that is well-characterized to occur during exercise. Lactate is known to cross the blood brain barrier and can be used readily as a fuel for neurons. This may be particularly important in Alzheimer's Disease, which is characterized by cerebral hypometabolism. However, little is known about how whole-body lactate metabolism differs between older adults and individuals with cognitive impairment. This information is critical when considering potential differences in responses to exercise in various cognitive diagnosis groups. METHODS: Here we describe the use of a "lactate clamp" procedure to adjust blood lactate levels to approximate those achieved during exercise, but while at rest. This trial will compare lactate oxidation between cognitively healthy older adults and cognitively impaired participants. We will further evaluate the effect of acute lactate infusion on cognitive performance. DISCUSSION: The findings of the study described here, the Lactate for Energy and Neurocognition trial (clinicaltrials.gov # NCT05207397) will add to our understanding of systemic lactate mechanics in cognitively healthy older adults and individuals with Alzheimer's Disease. These findings will be applicable to ongoing exercise trials and to future studies aimed at modulating systemic bioenergetic function in aging and Alzheimer's Disease.

Fate induction in CD8 CAR T cells through asymmetric cell division.

Early expansion and long-term persistence predict efficacy of chimeric antigen receptor T cells (CARTs)1-7, but mechanisms governing effector versus memory CART differentiation and whether asymmetric cell division induces differential fates in human CARTs remain unclear. Here we show that target-induced proximity labelling enables isolation of first-division proximal-daughter and distal-daughter CD8 CARTs that asymmetrically distribute their surface proteome and transcriptome, resulting in divergent fates. Target-engaged CARs remain on proximal daughters, which inherit a surface proteome resembling activated-undivided CARTs, whereas the endogenous T cell receptor and CD8 enrich on distal daughters, whose surface proteome resembles resting CARTs, correlating with glycolytic and oxidative metabolism, respectively. Despite memory-precursor phenotype and in vivo longevity, distal daughters demonstrate transient potent cytolytic activity similar to proximal daughters, uncovering an effector-like state in distal daughters destined to become memory CARTs. Both partitioning of pre-existing transcripts and changes in RNA velocity contribute to asymmetry of fate-determining factors, resulting in diametrically opposed transcriptional trajectories. Independent of naive, memory or effector surface immunophenotype, proximal-daughter CARTs use core sets of transcription factors known to support proliferation and effector function. Conversely, transcription factors enriched in distal daughters restrain differentiation and promote longevity, evidenced by diminished long-term in vivo persistence and function of distal-daughter CARTs after IKZF1 disruption. These studies establish asymmetric cell division as a framework for understanding mechanisms of CART differentiation and improving therapeutic outcomes.

Converting From Laparoscopic Cholecystectomy to Open Cholecystectomy: A Systematic Review of Its Advantages and Reasoning.

Cholecystectomy is the standard treatment for symptomatic cholelithiasis and asymptomatic impending biliary obstruction, which is typically carried out laparoscopically. However, difficult gallbladders, due to distorted anatomy or increased risk of bleeding, can necessitate conversion to open surgery. This systematic review evaluates the advantages, disadvantages, complications, and outcomes of laparoscopic versus converted open cholecystectomy. We screened articles published from 2011 to 2024 by utilizing advanced filters of PubMed, Cochrane, and Scholar databases. Exclusion criteria included non-English language articles, duplicates, and animal studies. After analyzing relevant articles, 31 articles were included in this study. The total number of participants who underwent laparoscopic procedures was 28,054, of which 5,847 were converted from laparoscopic to open procedures. Conversions were primarily due to bleeding, adhesions, and obscured anatomy, with bile leakage being the most common short-term complication. Converted cases showed higher rates of long-term complications, increased hospital stays, and higher morbidity and mortality. Laparoscopic cholecystectomy remains safe and effective, but identifying high-risk patients for conversion is important. Preoperative identification of high-risk patients and recognizing predictive factors for conversion can enhance surgical outcomes and cost-effectiveness. While laparoscopic cholecystectomy is generally preferred, timely conversion to open surgery is essential for patient safety.

A publishing infrastructure for Artificial Intelligence (AI)-assisted academic authoring.

OBJECTIVE: Investigate the use of advanced natural language processing models to streamline the time-consuming process of writing and revising scholarly manuscripts. MATERIALS AND METHODS: For this purpose, we integrate large language models into the Manubot publishing ecosystem to suggest revisions for scholarly texts. Our AI-based revision workflow employs a prompt generator that incorporates manuscript metadata into templates, generating section-specific instructions for the language model. The model then generates revised versions of each paragraph for human authors to review. We evaluated this methodology through 5 case studies of existing manuscripts, including the revision of this manuscript. RESULTS: Our results indicate that these models, despite some limitations, can grasp complex academic concepts and enhance text quality. All changes to the manuscript are tracked using a version control system, ensuring transparency in distinguishing between human- and machine-generated text. CONCLUSIONS: Given the significant time researchers invest in crafting prose, incorporating large language models into the scholarly writing process can significantly improve the type of knowledge work performed by academics. Our approach also enables scholars to concentrate on critical aspects of their work, such as the novelty of their ideas, while automating tedious tasks like adhering to specific writing styles. Although the use of AI-assisted tools in scientific authoring is controversial, our approach, which focuses on revising human-written text and provides change-tracking transparency, can mitigate concerns regarding AI's role in scientific writing.

Menstrual education programs for girls and young women with intellectual and developmental disabilities: A systematic review.

BACKGROUND: Providing menstrual education and guidance for menstrual management for girls and young women with intellectual disabilities is recommended to ensure smooth pubertal transitions and to support menstrual self-agency. METHOD: The purpose of this systematic review is to explore menstrual education interventions for girls and young women with intellectual disabilities. RESULTS: Nine studies were included. Interventions were provided in small groups (n = 4) and individually (n = 5). Most studies used dolls (n = 7) and task analysis (n = 7) to teach pad-replacement skills. All reported significant improvements in participant skills and/or knowledge following the intervention. Only one study addressed self-agency and self-esteem as an outcome of the intervention. Menstrual education for girls and young women with intellectual disabilities is largely focused on pad-replacement skills. CONCLUSION: Further research is needed to understand the impact of menstrual health and hygiene education on variables apart from skill improvement such as self-agency and long-term health outcomes related to menstrual health.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High-throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a Web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together, this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.

Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms.

BACKGROUND: High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by self-identified race may contribute to poorer HGSC survival among Black versus White individuals. METHODS: We included newly generated RNA sequencing data from Black and White individuals and array-based genotyping data from four existing studies of White and Japanese individuals. We used K-means clustering, a method with no predefined number of clusters or dataset-specific features, to assign subtypes. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. After mapping to The Cancer Genome Atlas-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset. RESULTS: Cluster-specific gene expression was similar across gene expression platforms and racial groups. Comparing the Black population with the White and Japanese populations, the immunoreactive subtype was more common (39% vs. 23%-28%) and the differentiated subtype was less common (7% vs. 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA sequencing data; compared with mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases [Black population HR = 0.79 (0.55, 1.13); White population HR = 0.86 (0.62, 1.19)]. CONCLUSIONS: Although the prevalence of HGSC subtypes varied by race, subtype-specific survival was similar. IMPACT: HGSC subtypes can be consistently assigned across platforms and self-identified racial groups.

Analysis of science journalism reveals gender and regional disparities in coverage.

Science journalism is a critical way for the public to learn about and benefit from scientific findings. Such journalism shapes the public's view of the current state of science and legitimizes experts. Journalists can only cite and quote a limited number of sources, who they may discover in their research, including recommendations by other scientists. Biases in either process may influence who is identified and ultimately included as a source. To examine potential biases in science journalism, we analyzed 22,001 non-research articles published by Nature and compared these with Nature-published research articles with respect to predicted gender and name origin. We extracted cited authors' names and those of quoted speakers. While citations and quotations within a piece do not reflect the entire information-gathering process, they can provide insight into the demographics of visible sources. We then predicted gender and name origin of the cited authors and speakers. We compared articles with a comparator set made up of first and last authors within primary research articles in Nature and a subset of Springer Nature articles in the same time period. In our analysis, we found a skew toward quoting men in Nature science journalism. However, quotation is trending toward equal representation at a faster rate than authorship rates in academic publishing. Gender disparity in Nature quotes was dependent on the article type. We found a significant over-representation of names with predicted Celtic/English origin and under-representation of names with a predicted East Asian origin in both in extracted quotes and journal citations but dampened in citations.

RNA sequences that direct selective ADAR editing from a SELEX library bearing 8-azanebularine.

Adenosine Deaminases Acting on RNA (ADARs) catalyze the deamination of adenosine to inosine in double-stranded RNA (dsRNA). ADARs' ability to recognize and edit dsRNA is dependent on local sequence context surrounding the edited adenosine and the length of the duplex. A deeper understanding of how editing efficiency is affected by mismatches, loops, and bulges around the editing site would aid in the development of therapeutic gRNAs for ADAR-mediated site-directed RNA editing (SDRE). Here, a SELEX (systematic evolution of ligands by exponential enrichment) approach was employed to identify dsRNA substrates that bind to the deaminase domain of human ADAR2 (hADAR2d) with high affinity. A library of single-stranded RNAs was hybridized with a fixed-sequence target strand containing the nucleoside analog 8-azanebularine that mimics the adenosine deamination transition state. The presence of this nucleoside analog in the library biased the screen to identify hit sequences compatible with adenosine deamination at the site of 8-azanebularine modification. SELEX also identified non-duplex structural elements that supported editing at the target site while inhibiting editing at bystander sites.

Pseudomonas aeruginosa transcriptome analysis of metal restriction in ex vivo cystic fibrosis sputum.

Chronic Pseudomonas aeruginosa lung infections are a feature of cystic fibrosis (CF) that many patients experience even with the advent of highly effective modulator therapies. Identifying factors that impact P. aeruginosa in the CF lung could yield novel strategies to eradicate infection or otherwise improve outcomes. To complement published P. aeruginosa studies using laboratory models or RNA isolated from sputum, we analyzed transcripts of strain PAO1 after incubation in sputum from different CF donors prior to RNA extraction. We compared PAO1 gene expression in this "spike-in" sputum model to that for P. aeruginosa grown in synthetic cystic fibrosis sputum medium to determine key genes, which are among the most differentially expressed or most highly expressed. Using the key genes, gene sets with correlated expression were determined using the gene expression analysis tool eADAGE. Gene sets were used to analyze the activity of specific pathways in P. aeruginosa grown in sputum from different individuals. Gene sets that we found to be more active in sputum showed similar activation in published data that included P. aeruginosa RNA isolated from sputum relative to corresponding in vitro reference cultures. In the ex vivo samples, P. aeruginosa had increased levels of genes related to zinc and iron acquisition which were suppressed by metal amendment of sputum. We also found a significant correlation between expression of the H1-type VI secretion system and CFTR corrector use by the sputum donor. An ex vivo sputum model or synthetic sputum medium formulation that imposes metal restriction may enhance future CF-related studies.IMPORTANCEIdentifying the gene expression programs used by Pseudomonas aeruginosa to colonize the lungs of people with cystic fibrosis (CF) will illuminate new therapeutic strategies. To capture these transcriptional programs, we cultured the common P. aeruginosa laboratory strain PAO1 in expectorated sputum from CF patient donors. Through bioinformatic analysis, we defined sets of genes that are more transcriptionally active in real CF sputum compared to a synthetic cystic fibrosis sputum medium. Many of the most differentially active gene sets contained genes related to metal acquisition, suggesting that these gene sets play an active role in scavenging for metals in the CF lung environment which may be inadequately represented in some models. Future studies of P. aeruginosa transcript abundance in CF may benefit from the use of an expectorated sputum model or media supplemented with factors that induce metal restriction.

The probability of edge existence due to node degree: a baseline for network-based predictions.

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

Many direct-to-consumer canine genetic tests can identify the breed of purebred dogs.

OBJECTIVE: To compare pedigree documentation and genetic test results to evaluate whether user-provided photographs influence the breed ancestry predictions of direct-to-consumer (DTC) genetic tests for dogs. ANIMALS: 12 registered purebred pet dogs representing 12 different breeds. METHODS: Each dog owner submitted 6 buccal swabs, 1 to each of 6 DTC genetic testing companies. Experimenters registered each sample per manufacturer instructions. For half of the dogs, the registration included a photograph of the DNA donor. For the other half of the dogs, photographs were swapped between dogs. DNA analysis and breed ancestry prediction were conducted by each company. The effect of condition (ie, matching vs shuffled photograph) was evaluated for each company's breed predictions. As a positive control, a convolutional neural network was also used to predict breed based solely on the photograph. RESULTS: Results from 5 of the 6 tests always included the dog's registered breed. One test and the convolutional neural network were unlikely to identify the registered breed and frequently returned results that were more similar to the photograph than the DNA. Additionally, differences in the predictions made across all tests underscored the challenge of identifying breed ancestry, even in purebred dogs. CLINICAL RELEVANCE: Veterinarians are likely to encounter patients who have conducted DTC genetic testing and may be asked to explain the results of genetic tests they did not order. This systematic comparison of commercially available tests provides context for interpreting results from consumer-grade DTC genetic testing kits.

Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine.

Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has more than 200,000 participants with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped individuals across multiple disease domains. Finally, we describe our current efforts toward return of clinical genetic test results, including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA- and CLIA-certified environment.

Optimizer's dilemma: optimization strongly influences model selection in transcriptomic prediction.

Motivation: Most models can be fit to data using various optimization approaches. While model choice is frequently reported in machine-learning-based research, optimizers are not often noted. We applied two different implementations of LASSO logistic regression implemented in Python's scikit-learn package, using two different optimization approaches (coordinate descent, implemented in the liblinear library, and stochastic gradient descent, or SGD), to predict mutation status and gene essentiality from gene expression across a variety of pan-cancer driver genes. For varying levels of regularization, we compared performance and model sparsity between optimizers. Results: After model selection and tuning, we found that liblinear and SGD tended to perform comparably. liblinear models required more extensive tuning of regularization strength, performing best for high model sparsities (more nonzero coefficients), but did not require selection of a learning rate parameter. SGD models required tuning of the learning rate to perform well, but generally performed more robustly across different model sparsities as regularization strength decreased. Given these tradeoffs, we believe that the choice of optimizers should be clearly reported as a part of the model selection and validation process, to allow readers and reviewers to better understand the context in which results have been generated. Availability and implementation: The code used to carry out the analyses in this study is available at https://github.com/greenelab/pancancer-evaluation/tree/master/01_stratified_classification. Performance/regularization strength curves for all genes in the Vogelstein et al. (2013) dataset are available at https://doi.org/10.6084/m9.figshare.22728644.

BuDDI: BulkDeconvolution withDomainInvariance to predict cell-type-specific perturbations from bulk.

While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution.

Biomarker Responses to Acute Exercise and Relationship with Brain Blood Flow.

BACKGROUND: There is evidence that aerobic exercise is beneficial for brain health, but these effects are variable between individuals and the underlying mechanisms that modulate these benefits remain unclear. OBJECTIVE: We sought to characterize the acute physiological response of bioenergetic and neurotrophic blood biomarkers to exercise in cognitively healthy older adults, as well as relationships with brain blood flow. METHODS: We measured exercise-induced changes in lactate, which has been linked to brain blood flow, as well brain-derived neurotrophic factor (BDNF), a neurotrophin related to brain health. We further quantified changes in brain blood flow using arterial spin labeling. RESULTS: As expected, lactate and BDNF both changed with time post exercise. Intriguingly, there was a negative relationship between lactate response (area under the curve) and brain blood flow measured acutely following exercise. Finally, the BDNF response tracked strongly with change in platelet activation, providing evidence that platelet activation is an important mechanism for trophic-related exercise responses. CONCLUSIONS: Lactate and BDNF respond acutely to exercise, and the lactate response tracks with changes in brain blood flow. Further investigation into how these factors relate to brain health-related outcomes in exercise trials is warranted.

Blood Biomarkers Discriminate Cerebral Amyloid Status and Cognitive Diagnosis when Collected with ACD-A Anticoagulant.

BACKGROUND: The development of biomarkers that are easy to collect, process, and store is a major goal of research on current Alzheimer's Disease (AD) and underlies the growing interest in plasma biomarkers. Biomarkers with these qualities will improve diagnosis and allow for better monitoring of therapeutic interventions. However, blood collection strategies have historically differed between studies. We examined the ability of various ultrasensitive plasma biomarkers to predict cerebral amyloid status in cognitively unimpaired individuals when collected using acid citrate dextrose (ACD). We then examined the ability of these biomarkers to predict cognitive impairment independent of amyloid status. METHODS: Using a cross-sectional study design, we measured amyloid beta 42/40 ratio, pTau-181, neurofilament-light, and glial fibrillary acidic protein using the Quanterix Simoa® HD-X platform. To evaluate the discriminative accuracy of these biomarkers in determining cerebral amyloid status, we used both banked plasma and 18F-AV45 PET cerebral amyloid neuroimaging data from 140 cognitively unimpaired participants. We further examined their ability to discriminate cognitive status by leveraging data from 42 cognitively impaired older adults. This study is the first, as per our knowledge, to examine these specific tests using plasma collected using acid citrate dextrose (ACD), as well as the relationship with amyloid PET status. RESULTS: Plasma AB42/40 had the highest AUC (0.833, 95% C.I. 0.767-0.899) at a cut-point of 0.0706 for discriminating between the two cerebral amyloid groups (sensitivity 76%, specificity 78.5%). Plasma NFL at a cut-point of 20.58pg/mL had the highest AUC (0.908, 95% CI 0.851- 0.966) for discriminating cognitive impairment (sensitivity 84.8%, specificity 89.9%). The addition of age and apolipoprotein e4 status did not improve the discriminative accuracy of these biomarkers. CONCLUSION: Our results suggest that the Aß42/40 ratio is useful in discriminating clinician-rated elevated cerebral amyloid status and that NFL is useful for discriminating cognitive impairment status. These findings reinforce the growing body of evidence regarding the general utility of these biomarkers and extend their utility to plasma collected in a non-traditional anticoagulant.

Is Response to a Pre-implant Diagnostic Peripheral Nerve Block Associated With Efficacy After Peripheral Nerve Stimulation Implantation? A Ten-Year Enterprise-Wide Analysis.

OBJECTIVES: This study aimed to assess whether patient response to targeted diagnostic peripheral nerve block before peripheral nerve stimulator (PNS) device implantation is associated with efficacy after PNS implantation. MATERIALS AND METHODS: The electronic medical records from the Mayo Clinic Enterprise (three quarternary care medical centers and additional satellite medical centers) were reviewed to identify patients who underwent PNS implantation between January 2014 and January 2023. A primary outcome of interest was to assess whether administration of a preimplant diagnostic peripheral nerve block predicted pain relief at three months and six months after temporary and permanent PNS implantation. Another primary outcome was to investigate whether there was an association between the pain relief from a preimplant diagnostic peripheral nerve block and pain relief after three and six months after temporary or permanent PNS therapy. Linear regression analysis was conducted for outcomes of interest. RESULTS: Of 193 eligible patients who underwent PNS therapy, a total of 173 patients were included in the final analysis and were stratified into either the temporary PNS cohort (n = 112) or the permanent PNS cohort (n = 61). Overall, 77.5% of all patients (134/173) underwent a preimplant diagnostic peripheral nerve block and reported a mean percentage relief of 70.1 ± 27.0 from the diagnostic block. Of patients in the temporary PNS cohort, there was no difference in postimplant percentage pain relief between patients who received a diagnostic block (n = 93) and control patients (n = 19) at three months (35.4 ± 36.0 vs 49.8 ± 36.1, respectively; ß -14.45, 95% CI -32.98 to 4.07, p = 0.125) or at six months (23.3 ± 30.8 vs 45.7 ± 40.0, respectively; ß -22.39, 95% CI -46.86 to 2.08, p = 0.072). Of patients in the permanent PNS cohort, there was no difference in postimplant percentage pain relief between patients who received a diagnostic block (n = 41) and control patients (n = 20) at three months (42.4 ± 34.3 vs 43.2 ± 42.4, respectively; ß -0.79, 95% CI -23.56 to 21.99, p = 0.945) or at six months (44.3 ± 35.0 vs 38.8 ± 40.9, respectively; ß 5.42, 95% CI -20.04 to 30.88, p = 0.669). Pain relief from preimplant diagnostic blocks was associated with postimplant pain relief from temporary PNS at three months (ß 0.33, 95% CI 0.04-0.61, p = 0.025). However, pain relief from preimplant diagnostic blocks did not predict postimplant pain relief from temporary PNS at six months, or permanent PNS at three months and six months. CONCLUSIONS: Administration of a diagnostic block is not associated with superior pain relief at three or six months after PNS implantation to that of an approach without diagnostic block. Pain relief from a diagnostic block may potentially predict short-term pain relief after temporary PNS therapy, although future prospective studies are warranted to evaluate the prognostic utility of diagnostic blocks.