Disease Biomarkers Research Articles

Comparison of the neuropsychological-actuarial and clinical-consensus approaches to diagnosis of mild cognitive impairment in an ethnically diverse sample.

Objective: Some studies show using neuropsychological-actuarial mild cognitive impairment (MCI) criteria may produce lower reversion rates (i.e. reverting from a cognitive diagnosis to no diagnosis) and higher associations with Alzheimer's disease biomarkers compared to conventional methods, but this has not been evaluated in a Hispanic sample. This study evaluated neuropsychological-actuarial MCI criteria performance in the Texas Alzheimer's Research and Care Consortium (TARCC) cohort compared to clinical-consensus diagnosis. Method: Data from 2,110 TARCC participants (47% Hispanic) were utilized. McNemar tests evaluated the performance of neuropsychological-actuarial versus clinical-consensus criteria in those who remained stable or converted to dementia over 3 annual visits, stratified by ethnicity (Hispanic vs. non-Hispanic). Chi-square analysis was used to compare frequencies of APOE ε4 allele positivity by diagnostic method. Results: Significantly more Hispanic participants were diagnosed with MCI using neuropsychological-actuarial criteria than were non-Hispanics, χ2(1) = 195.3, p < .001, ϕ = 0.32. Hispanic participants meeting neuropsychological-actuarial MCI criteria at baseline were more likely to revert at follow-up, χ2(1) = 10.04, p < .01, ϕ = 0.10. No differences in reversion rate were found between Hispanic and non-Hispanic individuals with clinical-consensus MCI diagnoses, χ2(1) = 0.38, p = .60. There was no association between APOE ε4 allele positivity and neuropsychological-actuarial diagnosis, while there was an association for clinical-consensus diagnoses χ2(1) = 15.1, p < .001, ϕ = 0.09. Conclusions: In TARCC, the clinical-consensus MCI diagnostic method produced fewer cases of reversion compared to the neuropsychological-actuarial method, particularly in Hispanic participants. This is consistent with recent research investigating the use of the neuropsychological-actuarial method in African American/Black individuals. Caution is warranted when using neuropsychological-actuarial criteria among individuals with diverse backgrounds.

Sex-Specific Markers of Neuroinflammation and Neurodegeneration in the Spinal Cord Proteome of the SOD1G93A Mouse Model of Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that has no cure. The underlying mechanistic details of sex differences in the ALS spinal cord, the site of disease onset, are not understood to an extent that could guide novel drug development. To address this, the spinal cords of 120-day-old wild-type (WT) and SOD1G93A (familial mouse model of ALS with mutant superoxide dismutase 1) mice were subjected to untargeted, quantitative proteomics using tandem mass tag acquisition on high-resolution mass spectrometric instrumentation. Compared to WT, both male and female SOD1G93A spinal cords exhibited an upregulation of neuroinflammatory cascades of both peripheral and central origins, as well as a downregulation of proteins reflective of death and dysfunction of cells within the spinal cord. However, female and male SOD1G93A mouse spinal cords exhibited sex-specific differences in proteins compared to respective WT that related to immune response, as well as cellular structure, function, and homeostasis. The proteomic datasets presented provide entire cohort and sex-specific spinal cord drug targets and disease biomarkers in the SOD1G93A mouse model of ALS that may guide future drug development and sex selection in preclinical study designs utilizing the SOD1G93A model.

TMA-93 (binding by images): Cutoffs optimization based on Alzheimer's disease biomarkers.

BackgroundWith the arrival of new disease-modifying treatments for Alzheimer's disease (AD), feasible cognitive tests, also for illiterate patients, are needed to screen those requiring deeper evaluation among individuals presenting with memory complaints. The TMA-93, a brief binding memory test, has proven useful for diagnosing early AD, and is supported by normative data that accounts for age and cognitive reserve.ObjectiveTo compare the sensitivity of different TMA-93 cutoffs in detecting AD pathology.MethodsA retrospective analysis was performed on a biobank sample of patients with confirmed AD pathology via amyloid PET or cerebrospinal fluid (CSF) biomarkers. The sensitivity of six TMA-93 cutoffs was evaluated: the 10th, 15th, and 20th percentiles based on traditional norming (TN) and regression-based norming (RBN). False negatives (FN) characteristics were also analyzed.ResultsA total of 270 AD-positive patients (96 by amyloid-PET, 174 by CSF biomarkers) were included, comprising 224 with mild cognitive impairment and 46 with mild dementia. The 15th percentile using RBN demonstrated substantial sensitivity (80.4%), higher than that of the 10th percentile, and also provided a more uniform distribution across normative groups compared to the TN approach. Higher global cognition (Mini-Mental State Examination score) and, in patients over 70, lower cognitive reserve (Cognitive Reserve Questionnaire), were linked to a greater likelihood of FN results.ConclusionsThe 15th percentile cutoff based on RBN, accounting for age and cognitive reserve, improves sensitivity for detecting AD pathology, making it a valuable screening tool for memory complaints. Future normative data from biomarker-negative subjects may enhance the sensitivity of cognitive tests.

Association of Alzheimer's disease biomarkers with low premorbid intellectual functioning in a multi-ethnic community-dwelling cohort: A cross-sectional study of HABS-HD.

Background: Individuals with intellectual disability (ID) may have a five-fold increased risk for developing Alzheimer's disease (AD). However, studies investigating brain aging among individuals with ID without Down syndrome (DS) are lacking. To begin addressing this gap, our study utilized word reading, a widely recognized indicator of an individual's premorbid intellectual ability (pIQ), to examine the effects of ID without DS on plasma AD biomarker outcomes. Objective: To investigate the relationship between premorbid intellectual ability (pIQ) and plasma AD biomarkers in individuals with ID without DS, while considering ethnic differences in these associations. Methods: Participants from the Health & Aging Brain Study - Health Disparities (HABS-HD) were categorized into low (z ≤ -2.00) or average (z = 0.00 ± 1.00) pIQ groups based on word reading scores. Plasma biomarkers including Aβ40, Aβ42, Aβ42/40, phosphorylated tau 181 (p-Tau181), neurofilament light chain (NfL), and total tau (t-tau) were assayed using Simoa technology. Results: Individuals with low pIQ exhibited significantly higher levels of p-Tau181 (p < 0.05), NfL (p < 0.05), and t-tau (p < 0.05) compared to those with average pIQ. Stratified analysis by ethnicity revealed differential associations, with Hispanic and non-Hispanic White (NHW) participants showing distinct biomarker profiles relative to non-Hispanic Black (NHB) individuals. Conclusions: The findings demonstrate that low pIQ is a reliable factor associated with plasma AD biomarker outcomes. Ethnicity appears to modulate these associations, suggesting complex interactions between factors driving AD susceptibility across diverse populations. This study highlights the importance of considering both pIQ and ethnicity in neurodegenerative processes, particularly in individuals with non-DS intellectual developmental disability.

Association between home dwelling situations, neighborhood social interactions, and subjective cognitive decline plus among older adults: A cross-sectional study.

BackgroundHome-dwelling situations (HDS) and neighborhood social interactions (NSI) significantly impact older adults' post-retirement lives. However, their relationship with subjective cognitive decline plus (SCD-plus), a potential biomarker of preclinical Alzheimer's disease (AD), remains uncertain.ObjectiveTo explore the association between NSI, HDS and SCD-plus among older adults.MethodsThis cross-sectional study utilized data from the Hubei Memory and Aging Cohort Study (HMACS, 2018-2022), examining the relationship between HDS, NSI, and SCD-plus status and feature scores, using logistic and linear regression models.ResultsAmong 3514 adults (age: ≥ 65 y; female: 52.44%), 1329 had SCD-plus status (37.82%). After adjusting for covariates, living with spouse only was associated with lower odds of SCD-plus (odds ratio [OR] = 0.72, 95% confidence interval [CI] [0.55, 0.95]) compared to living alone. Frequent NSI was linked to lower odds of SCD-plus (OR = 0.65, 95% CI [0.54, 0.80]) and reduced feature scores (regression coefficient [β] = -0.16, 95% CI [-0.25, -0.07]). Compared to living alone - no NSI, all other combinations showed better SCD-plus status, especially living with spouse only with frequent NSI, which demonstrated a 55% reduction in likelihood (OR = 0.45, 95% CI [0.28, 0.73]).ConclusionsLiving with spouse only and frequent NSI significantly lowers the odds of SCD-plus, providing a basis for further exploration of the impact of social interactions on cognitive health.

Clinical Values of Combined Detection of Serum Cystatin C, β2-Microglobulin, and Urine Transferrin in Diagnosing Early Primary Glomerulonephritis.

Despite primary glomerulonephritis (PGN) being a leading cause of chronic kidney disease and end-stage renal disease, specific and sensitive biomarkers for the early detection and monitoring of this condition are lacking. We evaluated the value of the combined detection of serum cystatin C (CYSC), β2-microglobulin (β2-MG), and urine transferrin (TRF) for diagnosing early-stage PGN. From May 2021 to May 2023, we enrolled 105 patients in our hospital as the observation group and 50 healthy volunteers as the control group. Their serum expression levels of CYSC, β2-MG, and TRF were evaluated. We plotted separate ROC curves and calculated the area under the curve (AUC) values of CYSC, β2-MG, and TRF to assess their diagnostic performance in PGN. The levels of CYSC, β2-MG, and TRF were significantly higher (P <0.05) in the observation group than in the healthy control group. CYSC, β2-MG, and TRF were expressed at significantly higher levels in G2, G3a, and G3b of PGN than in G1. The combined use of CYSC, β2-MG, and TRF as biomarkers could significantly improve the early diagnosis and monitoring of PGN and may lead to better patient outcomes by facilitating earlier intervention and treatment strategies.

Associations of KLOTHO-VS heterozygosity and α-Klotho protein with cerebrospinal fluid Alzheimer's disease biomarkers.

BackgroundKLOTHO-VS heterozygosity (KL-VSHET) and soluble α-Klotho (sαKl) protein interfere with Alzheimer's disease (AD) pathophysiology, but the specific relationships remain unclear. This study explored these associations across the AD continuum, focusing on core AD biomarkers and markers of neurodegeneration, neuroinflammation, and synaptic dysfunction.ObjectiveWe investigated whether 1) KL-VSHET is associated with lower AD biomarker burden (Aβ42, Aβ42/40 ratio, P-tau181, T-tau) and neurodegeneration (NfL); 2) sαKl relates to AD biomarkers, neurodegeneration (NfL), neuroinflammation (GFAP), and synaptic dysfunction (Ng); 3) associations vary by APOE ε4 status and clinical subgroup.MethodsParticipants (n = 223) were categorized as cognitively healthy (n = 38), aMCI-AD (n = 94), and AD dementia (n = 91). KLOTHO genotyping was available for 128 participants; 138 had cerebrospinal fluid (CSF) and serum sαKl measurements; and 42 had both. Multiple linear regression evaluated associations between KL-VSHET, sαKl levels, and biomarkers, stratified by APOE ε4 status and clinical subgroup.ResultsOverall, the associations between KL-VSHET and higher CSF Aβ42 and Aβ42/40 ratio were non-significant (ps ≥ 0.059) except when restricted to APOE ε4 carriers only (β = 0.11, p = 0.008 and β = 0.16, p = 0.033, respectively). Within clinical subgroups, KL-VSHET was positively associated with Aβ42/40 ratio only in aMCI-AD (β = 0.23, p = 0.034). No significant associations were observed between KL-VSHET and tau biomarkers or NfL. For sαKl, associations with biomarkers were non-significant except for a negative association of serum sαKl with P-tau181 in aMCI-AD (β = -0.25, p = 0.036) and a positive association with Aβ42/40 ratio in APOE ε4 non-carriers (β = 0.24 p = 0.047).ConclusionsKL-VSHET may help protect against amyloid pathology, particularly in the presence of APOE ε4, and regardless of APOE status in aMCI-AD.

Insulin-like Growth Factor Binding Protein-3 (IGFBP-3): a biomarker of coronary artery disease (CAD) induced myocardial ischemia

Insulin-like Growth Factor Binding Protein-3 (IGFBP-3): a biomarker of coronary artery disease (CAD) induced myocardial ischemia

CD206 and dust particles are prognostic biomarkers of progressive fibrosing interstitial lung disease associated with air pollutant exposure

Current management strategies for progressive fibrosing interstitial lung disease (PF-ILD) and non-PF-ILD differ significantly, underscoring the need for early identification of PF-ILD patients. We analyzed the expression of macrophage markers and the number of dust particles (DP) in lung tissue, as well as complete blood count and blood chemistry tests to identify biomarkers of PF-ILD, and examined the effect of certain pollutants on these biomarkers. Lung biopsies were collected from 73 non-PF-ILD patients and 36 PF-ILD patients. DP were quantified in alveolar wall cells (DP-aw) and desquamated epithelial cells (DP-desq) using polarizing light microscopy. Expression of CD206, transforming growth factor β1 (TGF-β1), connective tissue growth factor (CTGF), C-X-C motif ligand 13 (CXCL13), fibroblast growth factor 2 (FGF-2), tumor necrosis factor α (TNFα), and interleukin 1β (IL-1β) was assessed in lung tissue by immunohistochemistry. The numbers of DP-desq, pulmonary expression of CXCL13, IL-1β and CD206 were higher in ILD patients resided for ≥ 15 days per year in places with 24-hour ambient PM10 level of ≥ 50 µg/m3 compared with ILD patients exposed for < 15 days per year to the similar PM10 concentration. Additionally, CXCL13 expression in lung tissue was higher in smoking ILD patients than in non-smoking ILD patients. Compared with non-PF-ILD patients, PF-ILD patients exhibited higher numbers of DP-aw and DP-desq, as well as increased expression of CD206, CXCL13, IL-1β, TGF-β1, and CTGF in lung tissue. Elevated blood neutrophil-to-lymphocyte (NLR) and platelet-to-lymphocyte ratios were also observed in PF-ILD patients. These biomarkers were found to be independent predictors of PF-ILD. A regression logistic model incorporating NLR, CD206, and DP-desq predicted PF-ILD with an AUC of 0.847, sensitivity of 84.6%, and specificity of 83.3%. Our findings may be useful in predicting PF-ILD and highlight the need for reducing pollutant emission.

Early neutrophil responses are potential biomarkers to predict severe COVID-19 in adults.

In the early COVID-19 pandemic, the strain on healthcare facilities highlighted the need for reliable biomarkers to predict progression to severe COVID-19. Neutrophils, the most abundant leukocytes in circulation, are early defenders against pathogens. In a Singaporean adult cohort, early neutrophil mediators were assessed for their suitability as prognostic biomarkers of COVID-19 complications. Plasma levels of myeloperoxidase, elastase, soluble urokinase plasminogen activator receptor (suPAR) and soluble suppressor of tumorigenicity 2 (sST2) in 35 non-severe and 14 severe cases were measured twice, 2-7 days apart after hospitalisation. Nineteen controls were included. The levels of MPO, elastase, suPAR and sST2 were significantly higher in patients with severe COVID-19 compared to those with mild and healthy controls. At baseline sampling, MPO and suPAR predicted severe COVID-19 and had AUROCs of 0.76 and 0.87, respectively. MPO and suPAR at cut-off values of 26.41 ng/ml and 3.19 ng/ml, respectively showed approximately 71% sensitivity and 81 - 84% specificity to differentiate severe COVID-19. In contrast, elastase and neutrophil counts were less predictive of severe disease. In adult COVID-19, MPO and suPAR may be reliable prognostic biomarkers of severe disease during acute COVID-19. Further validation of these markers in a larger cohort and in other infectious diseases is warranted.

Accurate Diagnosis of Alzheimer's Disease Using Specific Breath Volatile Organic Compounds.

Whether volatile organic compounds (VOCs) from exhaled breath can be used as a novel biomarker for Alzheimer's disease (AD) diagnosis is unclear. To determine the significantly distinctive VOCs for AD, a total of 970 participants were enrolled, including 60 individuals in data set 1 (AD, 30; controls, 30), 164 individuals in data set 2 (AD, 82; controls, 82), 637 individuals in data set 3 (AD, 31; controls, 606), and 109 individuals in data set 4 (frontotemporal dementia, 19; vascular dementia, 21; Parkinson's disease, 69). The participants in data sets 1, 2, and 4 were from Xiangya Hospital, Central South University. Participants in data set 3 were from a two-year follow-up cohort. VOCs in breath and plasma, neuropsychological scores, plasma p-tau181 levels, metabolites in plasma, and brain functional connectivity were detected. We found that six VOCs were significantly different between the two groups in data set 1 and were verified in data set 2 and data set 3. Ethanol (m/z = 46) and pyrrole (m/z = 67) presented AUC values of 0.907 and 0.895 in data sets 1 and 2 (clinical data sets) and 0.849 and 0.974 in data set 3 (community data set), respectively. The six VOCs were associated with cognitive decline as reflected by neuropsychological tests; five of them were correlated with plasma p-tau181, and these five plasma VOCs were consistently altered as breath VOCs. Correlation between metabolites and five VOCs in plasma was noted, and the five VOCs may originate from blood metabolites. Moreover, four breath VOCs were associated with altered brain connectivity. In conclusion, specific breath VOCs may be used as biomarkers for AD detection.

Monitoring Acidification Preceding Aβ Deposition in Alzheimer's Disease.

Amyloid beta (Aβ) is the primary early biomarker of Alzheimer's disease (AD), and since an acidic environment promotes Aβ aggregation, acidification plays a crucial role in AD progression. In this study, a novel acid-responsive near-infrared (NIR) fluorescent probe alongside multiple molecular biology techniques to investigate the temporal relationship between acidification and Aβ deposition, as well as the underlying mechanisms of acidification is employed. By monitoring 2- to 11-month-old APP/PS1 mice and wild-type (WT) mice, it is detected significant fluorescence signal in APP/PS1 mice beginning at 3 months preceding Aβ deposition at 5 months, and peaking at 5 months, followed by cognitive deficits at 8 months. Additionally, elevated monocarboxylate transporter 4 (MCT4) protein expression in 3-month-old APP/PS1 mice indicated disruption of astrocyte-neuron lactate shuttle (ANLS) homeostasis. Overall, this findings first demonstrate that acidification precedes Aβ deposition, peaks at the onset of Aβ deposition, and diminishes thereafter, with early acidification likely driven by the disruption of ANLS.

SCD40 and sCD40L as candidate biomarkers of rheumatic diseases: a systematic review and meta-analysis with meta-regression

sCD40 and sCD40L as candidate biomarkers of rheumatic diseases: a systematic review and meta-analysis with meta-regression

Proteomic Profiling and Therapeutic Targeting of Oxidative Stress in Autoimmune Encephalitis

Autoimmune encephalitis (AE) is an immune-mediated non-infectious disease, and novel and robust biomarkers are needed to improve the diagnosis and prognostic outcomes of AE. Oxidative stress is a ubiquitous cellular process causing damage to various biological molecules. The aim of our study was to understand the clinical implication and mechanism underlying oxidative stress in AE. Liquid chromatography-mass spectrometry analysis was conducted on the serum of eight patients with AE and seven healthy controls, and oxidative stress was characterized. Experimental autoimmune encephalitis (EAE) models were established in C57BL/6 and SJL mice for investigation of the therapeutic effect and mechanism of anti-oxidative stress N-acetylcysteine (NAC). We provided proteomic landscape in the serum of AE and identified antioxidant ALB, APOE, GPX3, and SOD3 as serum diagnostic markers of AE. The antioxidant markers were lowly expressed both in the serum of AE patients and central nervous system (CNS) of EAE mice. NAC administration improved clinical signs and motor function and alleviated nerve injury of EAE mice as well as lowered oxidative stress (decreased MDA content and ROS accumulation and elevated SOD activity and GSH content). ALB, APOE, GPX3, and SOD3 expressions were elevated by NAC in the CNS of EAE mice. Moreover, NAC reduced tissue-resident CD4+ and CD8+ T cells and GFAP-marked astrocytes and Iba-1-marked microglia in EAE mice, thus alleviating autoimmunity-mediated damage and neuroinflammation. Our findings facilitate the discovery of novel oxidative stress-related biomarkers for AE and reveal the promise of anti-oxidative stress for AE management.

Unveiling amyotrophic lateral sclerosis complexity: insights from proteomics, metabolomics, and microbiomics

Abstract Amyotrophic lateral sclerosis is the most common motor neuron disease and manifests as a clinically and genetically heterogeneous neurodegenerative disorder mainly affecting the motor systems. To date, despite promising results and accumulating knowledge on the pathomechanisms of amyotrophic lateral sclerosis, a specific disease-modifying treatment is still not available. In vitro and in vivo disease models coupled with multiomics techniques have helped elucidate the pathomechanisms underlying this disease. In particular, omics approaches are powerful tools for identifying new potential disease biomarkers that may be particularly useful for diagnosis, prognosis, and assessment of treatment response. In turn, these findings could support physicians in stratifying patients into clinically relevant subgroups for the identification of the best therapeutic targets. Here, we provide a comprehensive review of the most relevant literature highlighting the importance of proteomics approaches in determining the role of pathogenic misfolded/aggregated proteins and the molecular mechanisms involved in the pathogenesis and progression of amyotrophic lateral sclerosis. In addition, we explored new findings arising from metabolomic and lipidomic studies, which can aid to elucidate the intricate metabolic alterations underlying amyotrophic lateral sclerosis pathology. Moreover, we integrated these insights with microbiomics data, providing a thorough understanding of the interplay between metabolic dysregulation and microbial dynamics in disease progression. Indeed, a greater integration of these multiomics data could lead to a deeper understanding of disease mechanisms, supporting the development of specific therapies for amyotrophic lateral sclerosis.

Biomarkers in glioblastoma and degenerative CNS diseases: defining new advances in clinical usefulness and therapeutic molecular target

BackgroundDiscovering biomarkers is central to the research and treatment of degenerative central nervous system (CNS) diseases, playing a crucial role in early diagnosis, disease monitoring, and the development of new treatments, particularly for challenging conditions like degenerative CNS diseases and glioblastoma (GBM).MethodsThis study analyzed gene expression data from a public database, employing differential expression analyses and Gene Co-expression Network Analysis (WGCNA) to identify gene modules associated with degenerative CNS diseases and GBM. Machine learning methods, including Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine - Recursive Feature Elimination (SVM-RFE), were used for case-control differentiation, complemented by functional enrichment analysis and external validation of key genes.ResultsNinety-five commonly altered genes related to degenerative CNS diseases and GBM were identified, with RELN and GSTO2 emerging as significant through machine learning screening. Receiver operating characteristic (ROC) analysis confirmed their diagnostic value, which was further validated externally, indicating their elevated expression in controls.ConclusionThe study’s integration of WGCNA and machine learning uncovered RELN and GSTO2 as potential biomarkers for degenerative CNS diseases and GBM, suggesting their utility in diagnostics and as therapeutic targets. This contributes new perspectives on the pathogenesis and treatment of these complex conditions.

Exploring exhaled volatile organic compounds as potential biomarkers in anti-MDA5 antibody-positive interstitial lung disease.

Interstitial lung diseases (ILDs) are a group of pulmonary disorders characterized by fibrosis, inflammation, and lung tissue deterioration. Anti-melanoma differentiation-associated gene 5-positive dermatomyositis-associated interstitial lung disease (MDA5-ILD), a subtype, is associated with high mortality due to rapid progression and severe lung damage. Volatile organic compounds (VOCs) in exhaled breath, reflecting metabolic changes, have been identified as potential non-invasive biomarkers for various diseases, including respiratory conditions. However, their role in MDA5-ILD has not been extensively studied. This retrospective cohort study included 45 exhaled breath samples from 19 ILD patients, with 31 samples from 9 patients with MDA5-ILD and 10 samples from 7 patients with non-MDA5-ILD. VOCs were collected using thermal desorption tubes and analyzed via gas chromatography-mass spectrometry (GC-MS). Clinical data, including the APACHE II score, were integrated with VOC profiles. Two logistic regression models were developed: Model 1 based on 11 clinical indicators, and Model 2 integrating 11 clinical indicators with 5 VOC features. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, sensitivity, specificity, and accuracy metrics. Five VOCs-N-(2-Aziridinyl)ethanamine, Cyclohexanone, Nonanal, Dodecamethylcyclohexasiloxane, and 4-Methyltetradecane-were identified as significant biomarkers distinguishing MDA5-ILD from non-MDA5-ILD. Model 2, which integrated VOC data, outperformed Model 1, achieving an area under the curve (AUC) of 0.93 compared to 0.70. Model 2 also demonstrated enhanced accuracy (84.6% vs. 76.9%), specificity (66.7% vs. 33.3%), precision (90.0% vs. 81.8%), and F1-score (90.0% vs. 85.7%). Additionally, 1,3-Pentadiene and 3-Methylundecane were identified as potential markers of disease severity, with 1,3-Pentadiene negatively correlating and 3-Methylundecane positively correlating with both APACHE II scores and creatinine levels. VOCs in exhaled breath significantly enhance the diagnostic sensitivity and accuracy for detecting MDA5-ILD. In addition, VOCs show promise as disease severity markers, potentially aiding in the assessment of disease severity and progression. While the integration of VOCs holds great potential for improving diagnostic performance, further validation through larger, multicenter studies is necessary.

Taxonomic and functional profile of the anorectal microbiota in HIV-positive and HIV-negative men who have sex with men, using a metagenomic approach.

The study of bacterial diversity in human samples is crucial for developing biomarkers of health and disease. This research characterized the taxonomic and functional diversity of the anorectal bacterial microbiota in men who hae sex with men (MSM) with HIV compared to men from this group without HIV. In July and August 2023, self-collected anorectal swabs were obtained. DNA was extracted from each sample, and metagenomic sequencing was performed. With the obtained data, alpha and beta diversity, bacterial abundance, differential operational taxonomic units, and functional diversity were determined. Initially, 90 samples were collected, with 20 discarded due to having less than 200 ng of DNA and 15 due to incomplete sequencing, leaving 55 samples analysed (15 HIV-positive and 40 HIV-negative). No significant differences were found between groups in terms of alpha diversity (Shannon index p = 0.45) and beta diversity (PERMANOVA R = -0.03). Prevotella was identified as the most abundant genus in both groups. Twelve genes were found to be more abundant in the anorectal microbiota of the HIV group, which promote bacterial growth, colonization and survival. Alterations in the anorectal microbiota could influence the pathogenesis of HIV and its complications in this population, underscoring the need to investigate these mechanisms and explore interventions to improve health. Longitudinal studies are needed to analyse changes in the anorectal microbiota during HIV infection and its response to treatment, integrating metagenomic, clinical, and immunological data to better understand the interactions between HIV, the microbiota and host health.

Immunoelectrochemical assessment of human IgE in non-invasive samples of allergic individuals using PdNCs-labelled antibodies

The escalating global prevalence of allergies presents a substantial public health challenge. Immunoglobulin E (IgE) serves as a key biomarker for allergic diseases, often measured in blood serum by ELISA immunoassays. Despite recent interest in minimally invasive sampling of biological fluids, the low sample volumes and IgE concentrations demand highly sensitive methodologies, typically confined to centralized laboratories. In this article, a decentralizable approach based on competitive immunoassays using Pd nanocluster (PdNCs)-labelled antibodies for electrochemical detection is proposed. With this aim, PdNCs were successfully bioconjugated with an anti-hIgE antibody to perform competitive immunoassays. To improve the analytical capabilities of the methodology, disposable screen-printed carbon electrodes with dual working electrodes were used for enhancing precision. Prior electrodeposition of PdNCs at − 0.6 V for 90 s significantly improved sensitivity (7.1 µA g ng⁻1) and lowered the limit of detection (LoD) to 0.3 ng g⁻1 for PdNCs determination. The use of PdNCs as labels resulted in an improvement in the LoD for IgE determination. Calibration curves performed using competitive immunoassays for IgE determination, ranging from 10−5 to 102 ng g−1, demonstrated sensitivity comparable to high-tech methods, with a LoD of 0.008 ng g−1 for electrochemical measurements. Bimodal detection of Pd (linear sweep voltammetry and inductively coupled plasma–mass spectrometry) in various biological fluids (saliva, tears, nasal exudate, capillary blood, and blood serum) revealed significant differences in IgE levels between allergic and non-allergic individuals. Notably, capillary blood correlated strongly with serum blood, and a certain correlation has also been found with nasal exudate. The electrochemical approach, combining sensitivity and precision with non-invasive sampling, offers a simplified alternative for IgE determination in allergic disease.Graphical

EEG biomarkers for Alzheimer's disease: A novel automated pipeline for detecting and monitoring disease progression.

BackgroundAlzheimer's disease (AD) is a neurodegenerative disorder that profoundly alters brain function and organization. Currently, there is a lack of validated functional biomarkers to aid in diagnosing and classifying AD. Therefore, there is a pressing need for early, accurate, non-invasive, and accessible methods to detect and characterize disease progression. Electroencephalography (EEG) has emerged as a minimally invasive technique to quantify functional changes in neural activity associated with AD. However, challenges such as poor signal-to-noise ratio-particularly for resting-state (rsEEG) recordings-and issues with standardization have hindered its broader application.ObjectiveTo conduct a pilot analysis of our custom automated preprocessing and feature extraction pipeline to identify indicators of AD and correlates of disease progression.MethodsWe analyzed data from 36 individuals with AD and 29 healthy participants recorded using a standard 19-channel EEG and features were processed using our custom end-t-end pipeline. Various features encompassing amplitude, power, connectivity, complexity, and microstates were extracted. Unsupervised machine learning (uniform manifold approximation and projection) and supervised learning (random forest classifiers with nested cross-validation) were used to characterize the dataset and identify differences between AD and healthy groups.ResultsOur pipeline successfully detected several new and previously established EEG-based measures indicative of AD status and progression, demonstrating strong external validity.ConclusionsOur findings suggest that this automated approach provides a promising initial framework for implementing EEG biomarkers in the AD patient population, paving the way for improved diagnostic and monitoring strategies.