Potential Disease Biomarkers Research Articles

The plasma miRNAome in ADNI: Signatures to aid the detection of at-risk individuals.

MicroRNAs are short non-coding RNAs that control proteostasis at the systems level and are emerging as potential prognostic and diagnostic biomarkers for Alzheimer's disease (AD). We performed small RNA sequencing on plasma samples from 847 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. We identified microRNA signatures that correlate with AD diagnoses and help predict the conversion from mild cognitive impairment (MCI) to AD. Our data demonstrate that plasma microRNA signatures can be used to not only diagnose MCI, but also, critically, predict the conversion from MCI to AD. Moreover, combined with neuropsychological testing, plasma microRNAome evaluation helps predict MCI to AD conversion. These findings are of considerable public interest because they provide a path toward reducing indiscriminate utilization of costly and invasive testing by defining the at-risk segment of the aging population. We provide the first analysis of the plasma microRNAome for the ADNI study. The levels of several microRNAs can be used as biomarkers for the prediction of conversion from MCI to AD. Adding the evaluation of plasma microRNA levels to neuropsychological testing in a clinical setting increases the accuracy of MCI to AD conversion prediction.

Angiopoietin-like 4 is a potential biomarker for diabetic kidney disease in type 2 diabetes patients.

The association between serum angiopoietin-like 4 (ANGPTL4) levels and the severity of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus remains unclear. A total of 1,115 type 2 diabetes mellitus patients were analyzed in this cross-sectional study. DKD index included DKD stages defined by estimated glomerular filtration rate, the albuminuria grades and DKD risk management grades. Serum levels of ANGPTL4 and other biomarkers were detected. Multivariable-adjusted linear and logistic analyses were used to study the association between ANGPTL4 and DKD. The protein levels of ANGPTL4 were assessed in the kidney. Renal tubular cells were stimulated with glucose to study ANGPTL4 expression. Compared with the participants in the third or fourth quantile of ANGPTL4, those in the first or second quantile of ANGPTL4 were younger, with lower glycated hemoglobin, triglycerides and urinary albumin creatinine ratio (all P < 0.05). There was a negative nonlinear relationship between ANGPTL4 and estimated glomerular filtration rate in type 2 diabetes mellitus patients. One standard deviation increased serum ANGPTL4 levels, the odds ratio of having DKD was 1.40 (95% confidence interval 1.08-1.80). The mediation analysis showed that triglycerides did not mediate the association between ANGPTL4 and DKD. Furthermore, ANGPTL4 could be the strongest among multiple panels of biomarkers in its association of DKD. Compared with mice at 8 weeks-of-age, db/db mice at 18 weeks-of-age had increased ANGPTL4 expression in glomeruli and tubular segments. In vitro, glucose could stimulate ANGPTL4 expression in tubular cells in a dose-dependent manner. ANGPTL4 could be a potential marker and therapeutic target for DKD treatment.

Proteomics and Metabolic Characteristics of Boar Seminal Plasma Extracellular Vesicles Reveal Biomarker Candidates Related to Sperm Motility.

Although seminal plasma extracellular vesicles (SPEVs) play important roles in sperm function, little is known about their metabolite compositions and roles in sperm motility. Here, we performed metabolomics and proteomics analysis of boar SPEVs with high or low sperm motility to investigate specific biomarkers affecting sperm motility. In total, 140 proteins and 32 metabolites were obtained through differentially expressed analysis and weighted gene coexpression network analysis (WGCNA). Seven differentially expressed proteins (DEPs) (ADIRF, EPS8L1, PRCP, CD81, PTPRD, CSK, LOC100736569) and six differentially expressed metabolites (DEMs) (adenosine, beclomethasone, 1,2-benzenedicarboxylic acid, urea, 1-methyl-l-histidine, and palmitic acid) were also identified in WGCNA significant modules. Joint pathway analysis revealed that three DEPs (GART, ADCY7, and NTPCR) and two DEMs (urea and adenosine) were involved in purine metabolism. Our results suggested that there was significant correlation between proteins and metabolites, such as IL4I1 and urea (r = 0.86). Furthermore, we detected the expression level of GART, ADCY7, and CDC42 in sperm of two groups, which further verified the experimental results. This study revealed that several proteins and metabolites in SPEVs play important roles in sperm motility. Our results offered new insights into the complex mechanism of sperm motility and identified potential biomarkers for male reproductive diseases.

A potential biomarker for age-related macular degeneration disease: iris freckles

BackgraundTo determine the potential relationship between age-related macular degeneration and iris freckles.MethodIn this case-control study, iris photographs of 300 eyes of 300 patients diagnosed with age-related macular degeneration and 300 eyes of 300 healthy volunteers were obtained with the help of a high-resolution mobile phone camera. The evaluated iris photographs were classified according to the Descriptive Iris Color Classification Scale.ResultsThe average age of the AMD group is 73.05 ± 6.93, and the average age of the control group is 73.43 ± 5.72. (p = 0.124) While freckles were present in 200 (66.7%) of the patients in the AMD group, freckles were not observed in 100 patients (33.3%) of AMD group. While freckles were present in 142 (47.3%) of the patients in the control group, freckles were not observed in 158 of control group(52.7%). There was a significant difference in the presence of freckles between the two groups. (p < 0.001) The average number of freckles in the AMD group was 3.97 ± 3.07, and the number of freckles in the control group was 3.06 ± 2.55. (p = 0.001)ConclusionWe think that evaluation of iris details, especially the presence of iris freckles, should be used routinely in age-related macular degeneration screening. The risk of age-related macular degeneration can be predicted by evaluating iris details, which is an easy and inexpensive method.

Postmortem-Derived Exosomal MicroRNA 486-5p as Potential Biomarkers for Ischemic Heart Disease Diagnosis.

Exosomes are nanovesicles 30-150 nm in diameter released extracellularly. Those isolated from human body fluids reflect the characteristics of their cells or tissues of origin. Exosomes carry extensive biological information from their parent cells and have significant potential as biomarkers for disease diagnosis and prognosis. However, there are limited studies utilizing exosomes in postmortem diagnostics. In this study, we extended our initial research which identified the presence and established detection methodologies for exosomes in postmortem fluids. We analyzed exosomal miRNA extracted from plasma and pericardial fluid samples of a control group (n = 13) and subjects with acute myocardial infarction (AMI; n = 24). We employed next-generation sequencing (NGS) to investigate whether this miRNA could serve as biomarkers for coronary atherosclerosis leading to acute myocardial infarction. Our analysis revealed 29 miRNAs that were differentially expressed in the AMI group compared to the control group. Among these, five miRNAs exhibited more than a twofold increase in expression across all samples from the AMI group. Specifically, miR-486-5p levels were significantly elevated in patients with high-grade (type VI or above) atherosclerotic plaques, as per the American Heart Association criteria, highlighting its potential as a predictive biomarker for coronary atherosclerosis progression. Our results indicate that postmortem-derived exosomal microRNAs can serve as potential biomarkers for various human diseases, including cardiovascular disorders. This finding has profound implications for forensic diagnostics, a field critically lacking diagnostic markers.

Foundation model-driven distributed learning for enhanced retinal age prediction.

The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction. The proposed framework employs a memory-efficient 8-bit quantized version of RETFound, a cutting-edge foundation model for retinal image analysis, to extract features from fundus images. These features are then used to train an efficient linear regression head model for predicting retinal age. The framework explores federated learning (FL) as well as traveling model (TM) approaches for distributed training of the linear regression head. To evaluate this framework, we simulate a client network using fundus image data from the UK Biobank. Additionally, data from patients with type 1 diabetes from the UK Biobank and the Brazilian Multilabel Ophthalmological Dataset (BRSET) were utilized to explore the clinical utility of the developed methods. Our findings reveal that the developed distributed learning framework achieves retinal age prediction performance on par with centralized methods, with FL and TM providing similar performance (mean absolute error of 3.57 ± 0.18 years for centralized learning, 3.60 ± 0.16 years for TM, and 3.63 ± 0.19 years for FL). Notably, the TM was found to converge with fewer local updates than FL. Moreover, patients with type 1 diabetes exhibited significantly higher RAG values than healthy controls in all models, for both the UK Biobank and BRSET datasets (P < .001). The high computational and memory efficiency of the developed distributed learning framework makes it well suited for resource-constrained environments. The capacity of this framework to integrate data from underrepresented populations for training of retinal age prediction models could significantly enhance the accessibility of the RAG as an important disease biomarker.

LKLPDA: A Low-Rank Fast Kernel Learning Approach for Predicting piRNA-Disease Associations.

Piwi-interacting RNAs (piRNAs) are increasingly recognized as potential biomarkers for various diseases. Investig-ating the complex relationship between piRNAs and diseases through computational methods can reduce the costs and risks associated with biological experiments. Fast kernel learning (FKL) is a classical method for multi-source data fusion that is widely employed in association prediction research. However, biological networks are noisy due to the limitations of measurement technology and inherent natural variation, which can hamper the effectiveness of the network-based ideal kernel. The conventional FKL method does not address this issue. In this study, we propose a low-rank fast kernel learning (LRFKL) algorithm, which consists of low-rank representation (LRR) and the FKL algorithm. The LRFKL algorithm is designed to mitigate the effects of noise on the network-based ideal kernel. Using LRFKL, we propose a novel approach for predicting piRNA-disease associations called LKLPDA. Specifically, we first compute the similarity matrices for piRNAs and diseases. Then we use the LRFKL to fuse the similarity matrices for piRNAs and diseases separately. Finally, the LKLPDA employs AutoGluon-Tabular for predictive analysis. Computational results show that LKLPDA effectively predicts piRNA-disease associations with higher accuracy compared to previous methods. In addition, case studies confirm the reliability of the model in predicting piRNA-disease associations. Availability and implementation: The LKLPDA software and data are freely available at https://github.com/Shiqzz/LKLPDA-master.git.

Skin Tau Quantification as a Novel Biomarker in Huntington's Disease.

Emerging research implicates tau protein dysregulation in the pathophysiology of Huntington's disease. This study investigated skin tau quantification as a potential biomarker for Huntington's disease and its correlation with disease burden outcomes. In this cross-sectional study, we measured skin tau levels using enzyme-linked immunosorbent assay in 23 Huntington's disease mutations carriers and eight control subjects, examining group discrimination, correlations with genetic markers, clinical assessments, and neuroimaging data. Brain atrophy was quantified by both volumetric measurements from brain segmentation and a voxel-based morphometry approach. Our findings showed elevated skin tau levels in manifest Huntington's disease compared with premanifest and healthy controls. These levels correlated with CAG repeat length, CAG-Age-Product score, composite Unified Huntington's Disease Rating Scale Total Motor Score, cognitive assessments, and disease-related cortical and subcortical volumes, all independent of age and gender. Using skin tau levels in cluster analysis along with genetic and clinical measures led to improved subject stratification, providing enhanced distinction and validity of clusters. This study not only confirms the feasibility of skin tau quantification in Huntington's disease but also establishes its potential as a biomarker for enhancing group classification and assessing disease severity across the Huntington's disease spectrum, opening new directions in biomarker research. © 2024 International Parkinson and Movement Disorder Society.

Current development of Fc gamma receptors (FcγRs) in diagnostics: a review.

The ability of the immune system to fight against pathogens relies on the intricate collaboration between antibodies and Fc gamma receptors (FcγRs). These receptors are a group of transmembrane glycoprotein molecules, which can specifically detect and bind to the Fc portion of immunoglobulin G (IgG) molecules. They are distributed on a diverse array of immune cells, forming a strong defence system to eliminate invading threats. FcγRs have gained increasing attention as potential biomarkers for various diseases in recent years due to their ability to reflect immune dysregulation and disease pathogenesis. Increasing lines of evidence have shed new light on the remarkable association of FcγRs polymorphisms with the susceptibility of autoimmune diseases such as systemic lupus erythematosus (SLE) and lupus nephritis. Several studies have also reported the application of FcγR as a novel biomarker for the diagnosis of infection and cancer. Due to the surge in interest and concern regarding the potential of FcγRs as promising diagnostic biomarkers, this review, thereby, serves to provide a comprehensive overview of the structural characteristics, functional roles, and expression patterns of FcγRs, with a particular focus on their evolving role as diagnostic and prognostic biomarkers.

Integrated analyses of 5 mC, 5hmC methylation and gene expression reveal pathology-associated AKT3 gene and potential biomarkers for Alzheimer's disease

Aims5 mC methylation and hydroxymethylation (5hmC) are associated with Alzheimer's disease (AD). However, previous studies were limited by the absence of a 5hmC calculation. This study aims to find AD associated predictors and potential therapeutic chemicals using bioinformatics approach integrating 5 mC, 5hmC, and expression changes, and an AD mouse model. MethodsGene expression microarray and 5 mC and 5hmC sequencing datasets were downloaded from GEO repository. 142 AD and 52 normal entorhinal cortex specimens were enrolled. Data from oxidative bisulfite sequencing (oxBS)-treated samples, which represent only 5 mC, were used to calculate 5hmC level. Functional analyses, random forest supervised classification and methylation validation were applied. Potential chemicals were predicted by CMap. Morris water maze, Y maze and novel object recognition behavior tests were performed using FAD4T AD mice model. Cortex and hippocampus tissues were isolated for immunohistochemical staining. ResultsC1QTNF5, UBD, ZFP106, NEDD1, AKT3, and MBP genes involving 13 promoter CpG sites with 5mc, 5hmC methylation and expression difference were identified. AKT3 and MBP were down-regulated in both patients and mouse model. Three CpG sites in AKT3 and MBP showed significant methylation difference on validation. FAD4T AD mice showed recession in brain functions and lower AKT3 expression in both cortex and hippocampus. Ten chemicals were predicted as potential treatments for AD. ConclusionsAKT3 and MBP may be associated with AD pathology and could serve as biomarkers. The ten predicted chemicals might offer new therapeutic approaches. Our findings could contribute to identifying novel markers and advancing the understanding of AD mechanisms.

High mobility group box1 (HMGB1) is a potential disease biomarker in cell and mouse models of Duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is a progressive muscle wasting disorder affecting 1:3500 male births and is associated with myofiber degeneration, regeneration, and inflammation. Glucocorticoid treatments have been the standard of care due to immunomodulatory/immunosuppressive properties but novel genetic approaches, including exon skipping and gene replacement therapy, are currently being developed. The identification of additional biomarkers to assess DMD-related inflammatory responses and the potential efficacy of these therapeutic approaches are thus of critical importance. The current study uses RNA sequencing of skeletal muscle from two mdx mouse models to identify high mobility group box1 (HMGB1) as a candidate biomarker potentially contributing to DMD-related inflammation. HMGB1 protein content was increased in a human iPSC-derived skeletal myocyte model of DMD and microdystrophin treatment decreased HMGB1 back to control levels. In vivo, HMGB1 protein levels were increased in vehicle treated B10-mdx skeletal muscle compared to B10-WT and significantly decreased in B10-mdx animals treated with adeno-associated virus (AAV)-microdystrophin. However, HMGB1 protein levels were not increased in D2-mdx skeletal muscle compared to D2-WT, demonstrating a strain-specific difference in DMD-related immunopathology.

Leucine-rich alpha-2 glycoprotein is useful in predicting clinical relapse in patients with Crohn's disease during biological remission.

Serum leucine-rich alpha-2 glycoprotein (LRG) is a potential biomarker of Crohn's disease (CD). This study aimed to evaluate the usefulness of LRG in predicting clinical relapse in patients in remission with CD. This retrospective observational study assessed the relationships among patient-reported outcome (PRO2), LRG, and other blood markers. The influence of LRG on clinical relapse was assessed in patients in remission with CD. Data of 94 patients tested for LRG between January 2021 and May 2023 were collected. LRG level did not correlate with PRO2 score (ρ = 0.06); however, it strongly correlated with C-reactive protein (CRP) level (r=0.79) and serum albumin level (r=-0.70). Among 69 patients in clinical remission, relapse occurred in 22 patients (31.9%). In the context of predicting relapse, LRG showed the highest area under the curve, followed by CRP level, platelet count, and albumin level. Multivariate analysis revealed that only LRG (P= 0.02) was an independent factor for predicting clinical remission. The cumulative non-relapse rate was significantly higher in patients with LRG < 13.8 μg/mL than in patients in remission with LRG ≥ 13.8 μg/mL and normal CRP level (P= 0.002) or normal albumin level (P= 0.001). Cumulative non-relapse rate was also higher in patients with LRG < 13.8 μg/mL compared to those with LRG ≥ 13.8 μg/mL in patients with L3 or B2+B3 of Montreal calcification. LRG is useful in predicting clinical relapse in patients with CD during biological remission. LRG is a useful biomarker for predicting prognosis, even in patients with intestinal stenosis, or previous/present fistulas.

Leucine-rich alpha-2 glycoprotein is useful in predicting clinical relapse in patients with Crohn's disease during biological remission.

Serum leucine-rich alpha-2 glycoprotein (LRG) is a potential biomarker of Crohn's disease (CD). This study aimed to evaluate the usefulness of LRG in predicting clinical relapse in patients in remission with CD. This retrospective observational study assessed the relationships among patient-reported outcome (PRO2), LRG, and other blood markers. The influence of LRG on clinical relapse was assessed in patients in remission with CD. Data of 94 patients tested for LRG between January 2021 and May 2023 were collected. LRG level did not correlate with PRO2 score (ρ = 0.06); however, it strongly correlated with C-reactive protein (CRP) level (r=0.79) and serum albumin level (r=-0.70). Among 69 patients in clinical remission, relapse occurred in 22 patients (31.9%). In the context of predicting relapse, LRG showed the highest area under the curve, followed by CRP level, platelet count, and albumin level. Multivariate analysis revealed that only LRG (P= 0.02) was an independent factor for predicting clinical remission. The cumulative non-relapse rate was significantly higher in patients with LRG < 13.8 μg/mL than in patients in remission with LRG ≥ 13.8 μg/mL and normal CRP level (P= 0.002) or normal albumin level (P= 0.001). Cumulative non-relapse rate was also higher in patients with LRG < 13.8 μg/mL compared to those with LRG ≥ 13.8 μg/mL in patients with L3 or B2+B3 of Montreal calcification. LRG is useful in predicting clinical relapse in patients with CD during biological remission. LRG is a useful biomarker for predicting prognosis, even in patients with intestinal stenosis, or previous/present fistulas.

Hollow-like three-dimensional structure of methyl orange-delaminated Ti3C2 MXene nanocomposite for high-performance electrochemical sensing of tryptophan.

Tryptophan(Trp) is being explored as a potential biomarker for various diseases associated with decreased tryptophan levels; however, metabolomic methods are expensive and time-consuming and require extensive sample analysis, making them urgently needed for trace detection. Toexploit the properties of Ti3C2 MXenes a rational porous methyl orange (MO)-delaminated Ti3C2 MXene was preparedvia a facile mixing process for the electrocatalytic oxidation of Trp. The hollow-like 3D structure with a more open structure and the synergistic effect of MO and conductive Ti3C2 MXene enhanced its electrochemical catalytic capability toward Trp biosensing. More importantly, MO can stabilize Ti3C2 MXene nanosheets through noncovalent π-π interactions and hydrogen bonding. Compared with covalent attachment, these non-covalent interactions preserve the electronic conductivity of the Ti3C2 MXene nanosheets. Finally, the addition of MO-derived nitrogen (N) and sulfur (S) atoms to Ti3C2 MXene enhanced the electronegativity and improved its affinity for specific molecules, resulting in high-performance electrocatalytic activity. The proposed biosensor exhibited a wide linear response in concentration ranges of 0.01-0.3 µM and 0.5-120 µM, with a low detection limit of 15 nM for tryptophan detection, and high anti-interference ability in complex media of human urine and egg white matrices. The exceptional abilities of the MO/Ti3C2 nanocatalyst make it a promising electrode material for the detection of important biomolecules.

The Framework of Quantifying Biomarkers of OCT and OCTA Images in Retinal Diseases.

Despite the significant advancements facilitated by previous research in introducing a plethora of retinal biomarkers, there is a lack of research addressing the clinical need for quantifying different biomarkers and prioritizing their importance for guiding clinical decision making in the context of retinal diseases. To address this issue, our study introduces a novel framework for quantifying biomarkers derived from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images in retinal diseases. We extract 452 feature parameters from five feature types, including local binary patterns (LBP) features of OCT and OCTA, capillary and large vessel features, and the foveal avascular zone (FAZ) feature. Leveraging this extensive feature set, we construct a classification model using a statistically relevant p value for feature selection to predict retinal diseases. We obtain a high accuracy of 0.912 and F1-score of 0.906 in the task of disease classification using this framework. We find that OCT and OCTA's LBP features provide a significant contribution of 77.12% to the significance of biomarkers in predicting retinal diseases, suggesting their potential as latent indicators for clinical diagnosis. This study employs a quantitative analysis framework to identify potential biomarkers for retinal diseases in OCT and OCTA images. Our findings suggest that LBP parameters, skewness and kurtosis values of capillary, the maximum, mean, median, and standard deviation of large vessel, as well as the eccentricity, compactness, flatness, and anisotropy index of FAZ, may serve as significant indicators of retinal conditions.

Circulating microRNAs as biomarkers for stratifying different phases of liver cancer progression and response to therapy

Hepatocellular carcinoma (HCC) is the most common liver cancer and is among the leading causes of cancer-related death worldwide. There is no reliable biomarker for the early diagnosis of HCC. Circulating microRNAs (miRNAs) have attracted attention as potential biomarkers of disease. By small-RNA next-generation sequencing, the analysis of serum miRNAs led to the identification of molecular signatures able to discriminate advanced HCC from early HCC (n = 246); advanced HCC from CIRRHOSIS (n = 299); advanced HCC from HEALTHY (n = 320); HEALTHY from early HCC (n = 343); and HEALTHY from CIRRHOSIS (n = 414). Cirrhotic patients and early HCC patients exhibited similar serum miRNA profiles, yet a small number of miRNAs (n = 57) were able to distinguish these two classes of patients. A second objective of the study was to identify serum miRNAs capable of predicting the response to therapy in patients with advanced HCC. All patients were treated with sorafenib as first-line therapy: 24 were nonresponsive and 24 responsive. Analysis of circulating miRNAs revealed a 54 miRNAs signature able to separate the two subgroups. This study suggested that circulating miRNAs could be useful biomarkers for monitoring patients with liver diseases ranging from cirrhosis to advanced HCC and possibly predicting susceptibility to first-line treatment based on sorafenib.

Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms

BackgroundPatients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common biomarkers.MethodsGene expression profiles for AF and stable CAD were downloaded from the Gene Expression Omnibus database. Overlapping genes related to both diseases were identified using weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Hub genes were then identified using the machine learning algorithm. Immune cell infiltration and correlations with hub genes were explored, followed by drug predictions. Hub gene expression in AF and CAD patients was validated by real-time qPCR.ResultsWe obtained 28 common overlapping genes in AF and stable CAD, mainly enriched in the PI3K-Akt, ECM-receptor interaction, and relaxin signaling pathway. Two hub genes, COL6A3 and FKBP10, were positively correlated with the abundance of MDSC, plasmacytoid dendritic cells, and regulatory T cells in AF and negatively correlated with the abundance of CD56dim natural killer cells in CAD. The AUCs of COL6A3 and FKBP10 were all above or close to 0.7. Drug prediction suggested that collagenase clostridium histolyticum and ocriplasmin, which target COL6A3, may be potential drugs for AF and stable CAD. Additionally, COL6A3 and FKBP10 were upregulated in patients with AF and CAD.ConclusionCOL6A3 and FKBP10 may be key biomarkers for AF and CAD, providing new insights into the diagnosis and treatment of this disease.

Investigation of Extracted Plasma Cell-Free DNA as a Biomarker in Foals with Sepsis.

Cell-free DNA (cfDNA) is fragmented extracellular DNA that is released from cells into various body fluids. Previously published data from adult horses supports cfDNA as a potential disease biomarker, but also shows that direct measurement in plasma is inaccurate due to matrix effect. It is currently unknown whether a similar matrix effect exists in foal plasma. Given this, the objectives of the current study were to investigate foal plasma for potential matrix effect during fluorescence measurement of cfDNA using a Qubit fluorometer, and to determine whether neat and/or extracted plasma cfDNA concentrations are significantly different in healthy, sick non-septic (SNS) or septic foals. We hypothesized that matrix effect would interfere with accurate fluorescent measurement of cfDNA in foal plasma. Further, we hypothesized that mean extracted cfDNA concentrations, and/or extracted cfDNA:neutrophil ratio, would be elevated in plasma of septic foals compared to healthy or SNS foals. Cell-free DNA was measured in neat plasma, and following DNA extraction with a commercial kit, from 60 foals. Foal plasma exhibited both autofluorescence and non-specific dye binding, confirming matrix effect. However, even with extraction, no significant difference was found in cfDNA concentrations, or cfDNA:neutrophil ratios, between healthy (sepsis score ≤ 5), SNS (sepsis score 6-11 and negative blood culture), or septic (sepsis score ≥ 12 ± positive blood culture) foals. Our data show that matrix effect interferes with accurate Qubit measurement of cfDNA in foal plasma and supports previous findings that plasma cfDNA concentrations are not associated with sepsis diagnosis in foals. Further research is needed to better understand neutrophil function and dysfunction in foal sepsis.

Optical Coherence Tomography Angiography Retinal Imaging Associations With Burden of Small Vessel Disease and Amyloid Positivity in the Brain.

Alzheimer disease (AD) and other dementias are associated with vascular changes and amyloid deposition, which may be reflected as density changes in the retinal capillaries. These changes may can be directly visualized and quantified with optical coherence tomography angiography (OCTA), making OCTA a potential noninvasive preclinical biomarker of small vessel disease and amyloid positivity. Our objective was to investigate the feasibility of retinal imaging metrics as noninvasive biomarkers of small vessel disease and amyloid positivity in the brain. We investigated associations between OCTA and neuroimaging and cognitive metrics in 41 participants without dementia from the Mayo Clinic Study of Aging and Alzheimer's Disease Research Center. OCTA metrics included superficial, deep, and full retina capillary density of the fovea, parafovea, and macula as well as the area of the foveal avascular zone (FAZ). Neuroimaging metrics included a high burden of white matter hyperintensity (WMH), presence of cerebral microbleeds (CMB), lacunar infarcts, and amyloid positivity as evidenced on positron emission tomography (PET), whereas cognitive metrics included mini-mental status examination (MMSE) score. We performed generalized estimating equations to account for measurements in each eye while controlling for age and sex to estimate associations between OCTA metrics and neuroimaging and cognitive scores. Associations between OCTA and neuroimaging metrics were restricted to the fovea. OCTA showed decreased capillary density with high burden of WMH in both the superficial (P = 0.003), deep (P = 0.004), and full retina (P = 0.01) in the fovea but not the parafovea or whole macula. Similarly, participants with amyloid PET positivity had significantly decreased capillary density in the superficial fovea (P = 0.027) and deep fovea (P = 0.03) but higher density in the superficial parafovea (P = 0.038). Participants with amyloid PET positivity also had a significantly larger FAZ (P = 0.031), whereas in those with high WMH burden the difference did not reach statistical significance (P = 0.075). There was also a positive association between MMSE and capillary density of the full retina within the fovea (P = 0.037) and in the superficial parafovea (P = 0.046). No associations were found between OCTA metrics and presence of CMB or presence of lacunar infarcts. The associations of lower foveal capillary density with cerebral WMH and amyloid positivity suggest that further research is warranted to evaluate for shared mechanisms of disease between small vessel disease and AD pathologies.

Proteomic Profiling of Tears in Blau Syndrome Patients in Identification of Potential Disease Biomarkers.

Blau syndrome (BS) is a rare autoinflammatory granulomatosis characterized by granulomatous arthritis, uveitis, and dermatitis. Ocular complications are particularly severe in BS, significantly contributing to morbidity. This study aims to identify potential biomarkers for BS ocular degeneration through proteomic profiling of tear samples from affected patients. Seven subjects from the same family, including four carriers of the BS-associated NOD2 mutation (p.E383K), were recruited alongside healthy controls. Tear samples were collected using Schirmer strips and analyzed via mass spectrometry. A total of 387 proteins were identified, with significant differences in protein expression between BS patients, healthy familial subjects, and healthy controls. Key findings include the overexpression of alpha-2-macroglobulin (A2M) and immunoglobulin heavy constant gamma 4 (IGHG4) in BS patients. Bioinformatic analysis revealed that differentially expressed proteins are involved in acute-phase response, extracellular exosome formation, and protein binding. Notably, neutrophils' azurophilic granule components, as azurocidin (AZU1), myeloperoxidases (MPO), and defensins (DEFA3), were highly expressed in the most severely affected subject, suggesting a potential role of neutrophils in BS ocular severity. These proteins might be promising biomarkers for ocular involvement in BS, facilitating early detection and tailored treatment strategies.