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Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly with the use of resting-state functional magnetic resonance imaging (rs-fMRI) data.This study utilized three years of rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low, middle, and high frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics, and the number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. The proposed approach demonstrated superior performance, achieving optimal accuracy with minimal feature utilization. When PSO selected five features, SVM exhibited accuracy, specificity, and sensitivity rates of 77%, 70%, and 83%, respectively. The identified features were as follows: (Mean of clustering coefficient, Mean of strength)/Radius/(Mean Eccentricity, and Modularity) from low/middle/high frequency bands of graph. The study highlights the efficacy of the proposed framework in identifying individuals at risk of AD development using a parsimonious feature set. This approach holds promise for advancing the precision of MCI to AD progression prediction, aiding in early diagnosis and intervention strategies.

Acute lymphoblastic leukemia (ALL) is the most common cancer in children, yet few environmental risk factors have been identified. We previously found an association between early-life tobacco smoke exposure and frequency of somatic deletions of 8 leukemia driver genes among childhood ALL patients in the California Childhood Leukemia Study. To expand analysis genome-wide and examine potential mechanisms, we conducted tumor whole-genome sequencing in 35 ALL patients, including 18 with high prenatal tobacco exposure and 17 with low exposure as determined by established epigenetic biomarkers. High tobacco exposure patients had significantly more structural variants (P < .001) and deletions (P = .001) genome-wide than low exposure patients. Investigation of off-target RAG recombination revealed that 41% of deletions in the high tobacco exposure patients were putatively RAG-mediated (full RAG motif identified at one or both breakpoints) compared with only 21% in the low exposure group (P = .001). In a multilevel model, deletions in high tobacco exposure patients were 2.44-fold (95% CI:1.13-5.38) more likely to be putatively RAG-mediated than deletions in low exposure patients. No point mutational signatures were associated with prenatal tobacco exposure. Our findings suggest that early-life tobacco smoke exposure may promote leukemogenesis by driving development of somatic deletions in pre-leukemic lymphocytes via off-target RAG recombination.

Gastroesophageal adenocarcinoma (GEAC) poses a significant challenge due to its poor prognosis and limited treatment options. Recently, Cancer/testis antigens (CTAs) have emerged as potential therapy targets due to their high expression in tumor cells and their immunogenic nature. We aimed to explore the expression and co-expression of CTAs in GEAC. We analyzed 63 GEAC patients initially and validated our findings in 329 patients from The Cancer Genome Atlas (TCGA) database. CTA expression was measured after RNA sequencing, while clinical information, including survival outcomes and treatment details, was collected from an institutional database. Co-expression patterns among CTAs were determined using Pearson correlation analysis. The majority of the study cohort were male (87%), Caucasian (94%), and had stage IV disease (64%). CTAs were highly prevalent, ranging from 58-19%. The MAGE gene family showed the highest expression, consistent across both cohorts. The correlation matrix revealed a distinct cluster of significantly co-expressed genes, including MAGEA3, NY-ESO-1, and others (0.27 ≤ r ≤ 0.73). Survival analysis revealed that individual CTAs were associated with poorer survival outcomes in patients not receiving immunotherapy while showing potential for improved survival in those undergoing immunotherapy, although these findings lacked robust reliability. Our study provides a comprehensive characterization of CTA expression and co-expression in GEAC. The strong correlation among CTAs like MAGE, NY-ESO-1, and GAGE suggests a potential for therapies targeting multiple CTAs simultaneously. Further research, including prospective trials, is warranted to assess the prognostic value of CTAs and their suitability as therapeutic targets.

Background: Epigenetic Age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional - using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (i) their choice of model; (ii) the primary outcome (EA vs. EAA); and (iii) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA.

Results: Our simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered an accelerated EA rate in males and an advanced EA that decelerates over time in children with higher birthweight.

Conclusion: Our results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked.

Purpose: (2S,4R)-4-[¹⁸F]fluoroglutamine ([¹⁸F]FGln) is a promising metabolic imaging marker in cancer. Based on the fact that major inflammatory cells are heavily dependent on glutamine metabolism like cancer cells, we explored the potential utility of [¹⁸F]FGln as a metabolic imaging marker for inflammation in two rat models: carrageenan-induced paw edema (CIPE) and collagen-induced arthritis (CIA).

Procedures: The CIPE model (n = 4) was generated by injecting 200 μL of 3% carrageenan solution into the left hind paw three hours before the PET. The CIA model (n = 4) was generated by injecting 200 μg of collagen emulsion subcutaneously at the tail base 3-4 weeks before the PET. A qualitative scoring system was used to assess the severity of paw inflammation. After a CT scan, 15.7 ± 4.9 MBq of [¹⁸F]FGln was injected via the tail vein, followed by a dynamic micro-PET scan for 90 minutes under anesthesia with isoflurane. The standard uptake value of [¹⁸F]FGln was measured by placing a volume of interest in each paw. The non-injected right hind paws of the CIPE model rats served as controls for both models. The paws with CIA were pathologically examined after PET.

Results: In CIPE models, uptake in the injected paw was higher compared to the non-injected paw by 52-83%. In CIA models, uptake in the paws with severe inflammation was higher than the averaged controls by 54-173%, while that with mild and no inflammation was slightly higher (33%) and lower (-7%), respectively. Combined overall, the [¹⁸F]FGln uptake in CIA showed a significant positive correlation with inflammation severity (r = 0.88, P = 0.009). The pathological findings confirmed profound inflammation in CIA.

Conclusions: [¹⁸F]FGln uptake was increased in both acute and chronic inflammation, and the uptake level was significantly correlated with the severity, suggesting its potential utility as a novel metabolic imaging marker for inflammation.

Paxlovid has been approved for use in patients who are at high risk for severe acute COVID-19 illness. Evidence regarding whether Paxlovid protects against Post-Acute Sequelae of SARS-CoV-2 infection (PASC), or Long COVID, is mixed in high-risk patients and lacking in low-risk patients. With a target trial emulation framework, we evaluated the association of Paxlovid treatment within 5 days of SARS-CoV-2 infection with incident Long COVID and hospitalization or death from any cause in the post-acute period (30-180 days after infection) using electronic health records from the Patient-Centered Clinical Research Networks (PCORnet) RECOVER repository. The study population included 497,499 SARS-CoV-2 positive patients between March 1, 2022, to February 1, 2023, and among which 165,256 were treated with Paxlovid within 5 days since infection and 307,922 were not treated with Paxlovid or other COVID-19 treatments. Compared with the non-treated group, Paxlovid treatment was associated with reduced risk of Long COVID with a Hazard Ratio (HR) of 0.88 (95% CI, 0.87 to 0.89) and absolute risk reduction of 2.99 events per 100 persons (95% CI, 2.65 to 3.32). Paxlovid treatment was associated with reduced risk of all-cause death (HR, 0.53, 95% CI 0.46 to 0.60; risk reduction 0.23 events per 100 persons, 95% CI 0.19 to 0.28) and hospitalization (HR, 0.70, 95% CI 0.68 to 0.73; risk reduction 2.37 events per 100 persons, 95% CI 2.19 to 2.56) in the post-acute phase. For those without documented risk factors, the associations (HR, 1.03, 95% CI 0.95 to 1.11; risk increase 0.80 events per 100 persons, 95% CI -0.84 to 2.45) were inconclusive. Overall, high-risk, nonhospitalized adult patients with COVID-19 who were treated with Paxlovid within 5 days of SARS-CoV-2 infection had a lower risk of Long COVID and all-cause hospitalization or death in the post-acute period. However, Long COVID risk reduction with Paxlovid was not observed in low-risk patients.

Nicotinamide Adenine Dinucleotide (NAD⁺) is implicated in bioenergetics, DNA repair, and senescence. Depletion of NAD⁺ is associated with aging and neurodegenerative disease, prompting a growing interest in NAD⁺ supplementation. With rising over-the-counter use of NAD, understanding their impact on perioperative recovery becomes essential. This study investigates the effect of NADH, a common NAD⁺ precursor, on anesthesia in rodents. Baseline and post-anesthesia (1.5% isoflurane) open field and Y-maze activity were recorded in adult male and female C57/BL6 mice (n = 8-10/group). NADH (150 mg/kg, intraperitoneal) or vehicle (0.9% normal saline) were given at baseline or during anesthesia. The NADH-treated group exhibited a significant decrease in open-field activity relative to vehicle-treated. This diminished activity was reflected in reduced distance travelled and average velocity after emergence from anesthesia in the NADH-treated group. NADH treatment did not improve Y-maze performance after anesthesia as the number of visits to the novel arm was significantly decreased. This study demonstrates a potentially adverse impact of NADH on recovery from anesthesia. We revealed a depression in open-field activity and Y-maze performance with NADH supplementation, an indicator of cognitive recovery in rodents. The broad implications of NAD⁺ in aging are likely to shape supplementation trends, highlighting the importance of understanding the potential influence of administering NAD⁺ on anesthetic sensitivity and recovery.

Background: Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths.

Methods: We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score.

Results: During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342).

Conclusion: In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.

The basal forebrain cholinergic system (BFCS) participates in functions that are global across the brain, such as sleep-wake cycles, but also participates in capacities that are more behaviorally and anatomically specific, including sensory perception. To better understand the underlying organization principles of the BFCS, more and higher quality anatomical data and analysis is needed. Here, we created a "virtual Basal Forebrain", combining data from numerous rats with cortical retrograde tracer injections into a common 3D reference coordinate space and developed a "spatial density correlation" methodology to analyze patterns in BFCS cortical projection targets, revealing that the BFCS is organized into three principal networks: somatosensory-motor, auditory, and visual. Within each network, clusters of cholinergic cells with increasing complexity innervate cortical targets. These networks represent hierarchically organized building blocks that may enable the BFCS to coordinate spatially selective signaling, including parallel modulation of multiple functionally interconnected yet diverse groups of cortical areas.

Melanoma-associated retinopathy (MAR) is a paraneoplastic syndrome associated with cutaneous metastatic melanoma in which patients develop vision deficits that include reduced night vision, poor contrast sensitivity, and photopsia. MAR is caused by autoantibodies targeting TRPM1, an ion channel found in melanocytes and retinal ON-bipolar cells (ON-BCs). The visual symptoms arise when TRPM1 autoantibodies enter ON-BCs and block the function of TRPM1, thus detection of TRPM1 autoantibodies in patient serum is a key criterion in diagnosing MAR. Electroretinograms are used to measure the impact of TRPM1 autoantibodies on ON-BC function and represent another important diagnostic tool for MAR. To date, MAR case reports have included one or both diagnostic components, but only for a single time point in the course of a patient's disease. Here, we report a case of MAR supported by longitudinal analysis of serum autoantibody detection, visual function, ocular inflammation, vascular integrity, and response to slow-release intraocular corticosteroids. Integrating these data with the patient's oncological and ophthalmological records reveals novel insights regarding MAR pathogenesis, progression, and treatment, which may inform new research and expand our collective understanding of the disease. In brief, we find TRPM1 autoantibodies can disrupt vision even when serum levels are barely detectable by western blot and immunohistochemistry; intraocular dexamethasone treatment alleviates MAR visual symptoms despite high levels of circulating TRPM1 autoantibodies, implicating antibody access to the retina as a key factor in MAR pathogenesis. Elevated inflammatory cytokine levels in the patient's eyes may be responsible for the observed damage to the blood-retinal barrier and subsequent entry of autoantibodies into the retina.