Association of glial fibrillary acid protein, Alzheimer's disease pathology and cognitive decline.

Increasing evidence shows that neuroinflammation is a possible modulator of tau spread effects on cognitive impairment in Alzheimer's disease. In this context, plasma levels of the glial fibrillary acidic protein (GFAP) have been suggested to have a robust association with Alzheimer's disease pathophysiology. This study aims to assess the correlation between plasma GFAP and Alzheimer's disease pathology, and their synergistic effect on cognitive performance and decline. A cohort of 122 memory clinic subjects with amyloid and tau positron emission tomography, MRI scans, plasma GFAP, and Mini-Mental State Examination (MMSE) was included in the study. A subsample of 94 subjects had a follow-up MMSE score at least one year after baseline. Regional and voxel-based correlations between Alzheimer's disease biomarkers and plasma GFAP were assessed. Mediation analyses were performed to evaluate the effects of plasma GFAP on the association between amyloid and tau PET, and tau PET and cognitive impairment and decline. GFAP was associated with increased tau PET ligand uptake in the lateral temporal and inferior temporal lobes in a strong left-sided pattern independently of age, gender, education, amyloid, and APOE status (ß=0.001, p < 0.01). The annual rate of MMSE change was significantly and independently correlated with both GFAP (ß=0.006, p < 0.01) and global tau SUVR (ß=4.33, p < 0.01), but not with amyloid burden. Partial mediation effects of GFAP were found on the association between amyloid and tau pathology (13.7%), and between tau pathology and cognitive decline (17.4%), but not on global cognition at baseline. Neuroinflammation measured by circulating GFAP is independently associated with tau Alzheimer's disease pathology and with cognitive decline, suggesting neuroinflammation as a potential target for future disease-modifying trials targeting tau pathology. Peretti et al. show that a circulatory marker of neuroinflammation-glial fibrillary acidic protein-is associated with tau pathology in lateral temporal and frontal regions in patients with Alzheimer's disease, independent of amyloid load. Neuroinflammation appears to modulate the association between amyloid and tau biomarkers.

Menopausal vasomotor symptoms and plasma Alzheimer disease biomarkers.

BACKGROUND: Identifying risk factors for Alzheimer disease in women is important as women compose two-thirds of individuals with Alzheimer disease. Previous work links vasomotor symptoms, the cardinal menopausal symptom, with poor memory performance and alterations in brain structure, function, and connectivity. These associations are evident when vasomotor symptoms are monitored objectively with ambulatory skin conductance monitors. OBJECTIVE: This study aimed to determine whether vasomotor symptoms are associated with Alzheimer disease biomarkers. STUDY DESIGN: Between 2017 and 2020, the MsBrain study enrolled 274 community-dwelling women aged 45 to 67 years who had a uterus and at least 1 ovary and were late perimenopausal or postmenopausal status. The key exclusion criteria included neurologic disorder, surgical menopause, and recent use of hormonal or nonhormonal vasomotor symptom treatment. Women underwent 24 hours of ambulatory skin conductance monitoring to assess vasomotor symptoms. Plasma concentrations of Alzheimer disease biomarkers, including amyloid ß 42-to-amyloid ß 40 ratio, phosphorylated tau (181 and 231), glial fibrillary acidic protein, and neurofilament light, were measured using a single-molecule array (Simoa) technology. Associations between vasomotor symptoms and Alzheimer disease biomarkers were assessed via linear regression models adjusted for age, race and ethnicity, education, body mass index, and apolipoprotein E4 status. Additional models adjusted for estradiol and sleep. RESULTS: A total of 248 (mean age, 59.06 years; 81% White; 99% postmenopausal status) of enrolled MsBrain participants contributed data. Objectively assessed vasomotor symptoms occurring during sleep were associated with significantly lower amyloid ß 42/amyloid ß 40, (beta, -.0010 [standard error, .0004]; P=.018; multivariable), suggestive of greater brain amyloid ß pathology. The findings remained significant after additional adjustments for estradiol and sleep. CONCLUSION: Nighttime vasomotor symptoms may be a marker of women at risk of Alzheimer disease. It is yet unknown if these associations are causal.

Fast, sensitive LC-MS resolution of α -hydroxy acid biomarkers via SPP-teicoplanin and an alternative UV detection approach.

Enantioseparation of α -hydroxy acids is essential since specific enantiomers of these compounds can be used as disease biomarkers for diagnosis and prognosis of cancer, brain diseases, kidney diseases, diabetes, etc., as well as in the food industry to ensure quality. HPLC methods were developed for the enantioselective separation of 11 α -hydroxy acids using a superficially porous particle-based teicoplanin (TeicoShell) chiral stationary phase. The retention behaviors observed for the hydroxy acids were HILIC, reversed phase, and ion-exclusion. While both mass spectrometry and UV spectroscopy detection methods could be used, specific mobile phases containing ammonium formate and potassium dihydrogen phosphate, respectively, were necessary with each approach. The LC-MS mode was approximately two orders of magnitude more sensitive than UV detection. Mobile phase acidity and ionic strength significantly affected enantioresolution and enantioselectivity. Interestingly, higher ionic strength resulted in increased retention and enantioresolution. It was noticed that for formate-containing mobile phases, using acetonitrile as the organic modifier usually resulted in greater enantioresolution compared to methanol. However, sometimes using acetonitrile with high ammonium formate concentrations led to lengthy retention times which could be avoided by using methanol as the organic modifier. Additionally, the enantiomeric purities of single enantiomer standards were determined and it was shown that almost all standards contained some levels of enantiomeric impurities.

The impact of tau deposition and hypometabolism on cognitive impairment and longitudinal cognitive decline.

INTRODUCTION: Tau and neurodegeneration strongly correlate with cognitive impairment, as compared to amyloid. However, their contribution in explaining cognition and predicting cognitive decline in memory clinics remains unclarified. METHODS: We included 94 participants with Mini-Mental State Examination (MMSE), tau positron emission tomography (PET), amyloid PET, fluorodeoxyglucose (FDG) PET, and MRI scans from Geneva Memory Center. Linear regression and mediation analyses tested the independent and combined association between biomarkers, cognitive performance, and decline. Linear mixed-effects and Cox proportional hazards models assessed biomarkers' prognostic values. RESULTS: Metabolism had the strongest association with cognition (r = 0.712; p < 0.001), followed by tau (r = -0.682; p < 0.001). Neocortical tau showed the strongest association with cognitive decline (r = -0.677; p < 0.001). Metabolism mediated the association between tau and cognition and marginally mediated the one with decline. Tau positivity represented the strongest risk factor for decline (hazard ratio = 32). DISCUSSION: Tau and neurodegeneration synergistically contribute to global cognitive impairment while tau drives decline. The tau PET superior prognostic value supports its implementation in memory clinics. HIGHLIGHTS: Hypometabolism has the strongest association with concurrent cognitive impairment. Neocortical tau pathology is the main determinant of cognitive decline over time. FDG-PET has a superior value compared to MRI as a measure of neurodegeneration. The prognostic value of tau-PET exceeded all other neuroimaging modalities.

The role of the Alzheimer's Disease Neuroimaging Initiative in establishing the Dominantly Inherited Alzheimer Network.

The Dominantly Inherited Alzheimer Network (DIAN) initially was funded by the National Institute on Aging (NIA) in 2008 and thus was able to adopt and incorporate the protocols developed by the Alzheimer's Disease Neuroimaging Initiative (ADNI) that had been established by the NIA in 2004. The use of ADNI protocols for DIAN neuroimaging studies and assays of biological fluids for Alzheimer disease (AD) biomarkers permitted examination of the hypothesis that autosomal dominant AD (ADAD), studied by DIAN, and "sporadic" late-onset AD (LOAD), studied by ADNI, shared the same pathobiological construct. In a collaborative effort, the longitudinal DIAN and ADNI databases were compared and the findings supported the conclusion that ADAD and LOAD share a similar pathophysiology. The importance of the DIAN study thus is amplified by its relevance to LOAD, as characterized by the "parent" ADNI program.

ESMSec: Prediction of Secreted Proteins in Human Body Fluids Using Protein Language Models and Attention.

The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation. Here, we propose a deep learning framework called ESM Predict Secreted Proteins (ESMSec) to predict three types of proteins secreted in human body fluid. The ESMSec is based on the ESM2 model and attention architecture. Specifically, the protein sequence data are firstly put into the ESM2 model to extract the feature information from the last hidden layer, and all the input proteins are encoded into a fixed 1000 × 480 matrix. Secondly, multi-head attention with a fully connected neural network is employed as the classifier to perform binary classification according to whether they are secreted into each body fluid. Our experiment utilized three human body fluids that are important and ubiquitous markers. Experimental results show that ESMSec achieved average accuracy of 0.8486, 0.8358, and 0.8325 on the testing datasets for plasma, cerebrospinal fluid (CSF), and seminal fluid, which on average outperform the state-of-the-art (SOTA) methods. The outstanding performance results of ESMSec demonstrate that the ESM can improve the prediction performance of the model and has great potential to screen the secretion information of human body fluid proteins.

Developments of Recent Applications for Early Diagnosis of Diseases Using Electronic-Nose and Other VOC-Detection Devices.

This Editorial provides summaries and an overview of research and review articles published in the Sensors journal, volumes 21 (2021), 22 (2022), and 23 (2023), within the biomedical Special Issue "Portable Electronic-Nose Devices for Noninvasive Early Disease Detection", which focused on recent sensors, biosensors, and clinical instruments developed for noninvasive early detection and diagnosis of human and animal diseases. The ten articles published in this Special Issue provide new information associated with recent electronic-nose (e-nose) and related volatile organic compound (VOC)-detection technologies developed to improve the effectiveness and efficiency of diagnostic methodologies for early disease detection prior to symptom development. For review purposes, the summarized articles were placed into three broad groupings or topic areas, including veterinary-wildlife pathology, human clinical pathology, and the detection of dietary effects on VOC emissions. These specified categories were used to define sectional headings devoted to related research studies with a commonality based on a particular disease being investigated or type of analytical instrument used in analyses.

Molecularly Imprinted Polymers for the Determination of Cancer Biomarkers.

Biomarkers can provide critical information about cancer and many other diseases; therefore, developing analytical systems for recognising biomarkers is an essential direction in bioanalytical chemistry. Recently molecularly imprinted polymers (MIPs) have been applied in analytical systems to determine biomarkers. This article aims to an overview of MIPs used for the detection of cancer biomarkers, namely: prostate cancer (PSA), breast cancer (CA15-3, HER-2), epithelial ovarian cancer (CA-125), hepatocellular carcinoma (AFP), and small molecule cancer biomarkers (5-HIAA and neopterin). These cancer biomarkers may be found in tumours, blood, urine, faeces, or other body fluids or tissues. The determination of low concentrations of biomarkers in these complex matrices is technically challenging. The overviewed studies used MIP-based biosensors to assess natural or artificial samples such as blood, serum, plasma, or urine. Molecular imprinting technology and MIP-based sensor creation principles are outlined. Analytical signal determination methods and the nature and chemical structure of the imprinted polymers are discussed. Based on the reviewed biosensors, the results are compared, and the most suitable materials for each biomarker are discussed.

DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning.

BACKGROUND: A limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we wanted to expand the state-of-the-art in disease biomarker prediction from RNA-seq data using DL. However, application of DL to RNA-seq data is challenging due to absence of appropriate labels and smaller sample size as compared to number of genes. Deep learning coupled with transfer learning can improve prediction performance on novel data by incorporating patterns learned from other related data. With the emergence of new disease datasets, biomarker prediction would be facilitated by having a generalized model that can transfer the knowledge of trained feature maps to the new dataset. To the best of our knowledge, there is no Convolutional Neural Network (CNN)-based model coupled with transfer learning to predict the significant upregulating (UR) and downregulating (DR) genes from both trained and untrained datasets. RESULTS: We implemented a CNN model, DEGnext, to predict UR and DR genes from gene expression data obtained from The Cancer Genome Atlas database. DEGnext uses biologically validated data along with logarithmic fold change values to classify differentially expressed genes (DEGs) as UR and DR genes. We applied transfer learning to our model to leverage the knowledge of trained feature maps to untrained cancer datasets. DEGnext's results were competitive (ROC scores between 88 and 99[Formula: see text]) with those of five traditional machine learning methods: Decision Tree, K-Nearest Neighbors, Random Forest, Support Vector Machine, and XGBoost. DEGnext was robust and effective in terms of transferring learned feature maps to facilitate classification of unseen datasets. Additionally, we validated that the predicted DEGs from DEGnext were mapped to significant Gene Ontology terms and pathways related to cancer. CONCLUSIONS: DEGnext can classify DEGs into UR and DR genes from RNA-seq cancer datasets with high performance. This type of analysis, using biologically relevant fine-tuning data, may aid in the exploration of potential biomarkers and can be adapted for other disease datasets.

Systemic and central nervous system neuroinflammatory signatures of neuropsychiatric symptoms and related cognitive decline in older people.

BACKGROUND: Neuroinflammation may contribute to psychiatric symptoms in older people, in particular in the context of Alzheimer's disease (AD). We sought to identify systemic and central nervous system (CNS) inflammatory alterations associated with neuropsychiatric symptoms (NPS); and to investigate their relationships with AD pathology and clinical disease progression. METHODS: We quantified a panel of 38 neuroinflammation and vascular injury markers in paired serum and cerebrospinal fluid (CSF) samples in a cohort of cognitively normal and impaired older subjects. We performed neuropsychiatric and cognitive evaluations and measured CSF biomarkers of AD pathology. Multivariate analysis determined serum and CSF neuroinflammatory alterations associated with NPS, considering cognitive status, AD pathology, and cognitive decline at follow-up visits. RESULTS: NPS were associated with distinct inflammatory profiles in serum, involving eotaxin-3, interleukin (IL)-6 and C-reactive protein (CRP); and in CSF, including soluble intracellular cell adhesion molecule-1 (sICAM-1), IL-8, 10-kDa interferon-γ-induced protein, and CRP. AD pathology interacted with CSF sICAM-1 in association with NPS. Presenting NPS was associated with subsequent cognitive decline which was mediated by CSF sICAM-1. CONCLUSIONS: Distinct systemic and CNS inflammatory processes are involved in the pathophysiology of NPS in older people. Neuroinflammation may explain the link between NPS and more rapid clinical disease progression.

Role of extracellular vesicles in mitochondrial eye diseases.

Extracellular vesicles (EVs) are small packages that are released by almost all types of cells. While the role of EVs in pathogenesis of certain diseases such as cancer is well established, EVs role in ocular health and disease is still at early stages of investigation. Given the significant role of EVs in pathological development and progression of diseases such as cancer, EVs present a similar opportunity for investigation in ocular pathophysiology. Studies have shown the presence of EVs in fluids from the ocular environment have close links with ocular health and disease. Hence, the cargo carried in EVs from ocular fluids can be used for monitoring disease phenotypes or therapeutic outcomes in eye-related disorders. Furthermore, in recent times EVs have increasingly gained attention as therapeutics and drug-delivery vehicles for treatment of eye diseases. There is a close relationship between EVs and mitochondria functioning with mitochondria dysfunction leading to a significant number of ophthalmic disorders. This review discusses the current knowledge of EVs in visual systems with a special focus on eye diseases resulting from dysfunctional mitochondria.

Ammonium quantification in human plasma by proton nuclear magnetic resonance for staging of liver fibrosis in alcohol-related liver disease and nonalcoholic fatty liver disease.

Liver fibrosis staging is a key element driving the prognosis of patients with chronic liver disease. Currently, biopsy is the only technique capable of diagnosing liver fibrosis in patients with alcohol-related liver disease (ArLD) and nonalcoholic fatty liver disease (NAFLD) unequivocally. Noninvasive (e.g. plasma-based) biomarker assays are attractive tools to diagnose and stage disease, yet must prove that they are reliable and sensitive to be used clinically. Here, we demonstrate proton nuclear magnetic resonance as a method to rapidly quantify the endogenous concentration of ammonium ions from human plasma extracts and show their ability to report upon early and advanced stages of ArLD and NAFLD. We show that, irrespective of the disease etiology, ammonium concentration is a more robust and informative marker of fibrosis stage than current clinically assessed blood hepatic biomarkers. Subject to validation in larger cohorts, the study indicates that the method can provide accurate and rapid staging of ArLD and NAFLD without the need for an invasive biopsy.

Dried blood spots in clinical lipidomics: optimization and recent findings.

Dried blood spots (DBS) are being considered as an alternative sampling method of blood collection that can be used in combination with lipidomic and other omic analysis. DBS are successfully used in the clinical context to collect samples for newborn screening for the measurement of specific fatty acid derivatives, such as acylcarnitines, and lipids from whole blood for diagnostic purposes. However, DBS are scarcely used for lipidomic analysis and investigations. Lipidomic studies using DBS are starting to emerge as a powerful method for sampling and storage in clinical lipidomic analysis, but the major research work is being done in the pre- and analytical steps and procedures, and few in clinical applications. This review presents a description of the impact factors and variables that can affect DBS lipidomic analysis, such as the type of DBS card, haematocrit, homogeneity of the blood drop, matrix/chromatographic effects, and the chemical and physical properties of the analyte. Additionally, a brief overview of lipidomic studies using DBS to unveil their application in clinical scenarios is also presented, considering the studies of method development and validation and, to a less extent, for clinical diagnosis using clinical lipidomics. DBS combined with lipidomic approaches proved to be as effective as whole blood samples, achieving high levels of sensitivity and specificity during MS and MS/MS analysis, which could be a useful tool for biomarker identification. Lipidomic profiling using MS/MS platforms enables significant insights into physiological changes, which could be useful in precision medicine.

Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression.

The incidence of systemic and metabolic co-morbidities increases with aging. The purpose was to investigate a novel paradigm for modeling the orchestrated changes in many disease-related biomarkers that occur during aging. A hybrid strategy that integrates machine learning and stochastic modeling was evaluated for modeling the long-term dynamics of biomarker systems. Bayesian networks (BN) were used to identify quantitative systems pharmacology (QSP)-like models for the inter-dependencies for three disease-related datasets of metabolic (MB), metabolic with leptin (MB-L), and cardiovascular (CVB) biomarkers from the NHANES database. Biomarker dynamics were modeled using discrete stochastic vector autoregression (VAR) equations. BN were used to derive the topological order and connectivity of a data driven QSP model structure for inter-dependence of biomarkers across the lifespan. The strength and directionality of the connections in the QSP models were evaluated using bootstrapping. VAR models based on QSP model structures from BN were assessed for modeling biomarker system dynamics. BN-restricted VAR models of order 1 were identified as parsimonious and effective for characterizing biomarker system dynamics in the MB, MB-L and CVB datasets. Simulation of annual and triennial data for each biomarker provided good fits and predictions of the training and test datasets, respectively. The novel strategy harnesses machine learning to construct QSP model structures for inter-dependence of biomarkers. Stochastic modeling with the QSP models was effective for predicting the age-varying dynamics of disease-relevant biomarkers over the lifespan.

Biomarker Metabolites Discriminate between Physiological States of Field, Cave and White-nose Syndrome Diseased Bats.

Analysis of volatile organic compound (VOC) emissions using electronic-nose (e-nose) devices has shown promise for early detection of white-nose syndrome (WNS) in bats. Tricolored bats, Perimyotis subflavus, from three separate sampling groups defined by environmental conditions, levels of physical activity, and WNS-disease status were captured temporarily for collection of VOC emissions to determine relationships between these combinations of factors and physiological states, Pseudogymnoascus destructans (Pd)-infection status, and metabolic conditions. Physiologically active (non-torpid) healthy individuals were captured outside of caves in Arkansas and Louisiana. In addition, healthy and WNS-diseased torpid bats were sampled within caves in Arkansas. Whole-body VOC emissions from bats were collected using portable air-collection and sampling-chamber devices in tandem. Electronic aroma-detection data using three-dimensional Principal Component Analysis provided strong evidence that the three groups of bats had significantly different e-nose aroma signatures, indicative of different VOC profiles. This was confirmed by differences in peak numbers, peak areas, and tentative chemical identities indicated by chromatograms from dual-column GC-analyses. The numbers and quantities of VOCs present in whole-body emissions from physiologically active healthy field bats were significantly greater than those of torpid healthy and diseased cave bats. Specific VOCs were identified as chemical biomarkers of healthy and diseased states, environmental conditions (outside and inside of caves), and levels of physiological activity. These results suggest that GC/E-nose dual-technologies based on VOC-detection and analyses of physiological states, provide noninvasive alternative means for early assessments of Pd-infection, WNS-disease status, and other physiological states.

Alzheimer's disease biomarkers in Black and non-Hispanic White cohorts: A contextualized review of the evidence.

Black Americans are disproportionately affected by dementia. To expand our understanding of mechanisms of this disparity, we look to Alzheimer's disease (AD) biomarkers. In this review, we summarize current data, comparing the few studies presenting these findings. Further, we contextualize the data using two influential frameworks: the National Institute on Aging-Alzheimer's Association (NIA-AA) Research Framework and NIA's Health Disparities Research Framework. The NIA-AA Research Framework provides a biological definition of AD that can be measured in vivo. However, current cut-points for determining pathological versus non-pathological status were developed using predominantly White cohorts-a serious limitation. The NIA's Health Disparities Research Framework is used to contextualize findings from studies identifying racial differences in biomarker levels, because studying biomakers in isolation cannot explain or reduce inequities. We offer recommendations to expand study beyond initial reports of racial differences. Specifically, life course experiences associated with racialization and commonly used study enrollment practices may better account for observations than exclusively biological explanations.

Circulating MicroRNAs and Extracellular Vesicle-Derived MicroRNAs as Predictors of Functional Recovery in Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.

Stroke accounts for the second leading cause of death and a major cause of disability, with limited therapeutic strategy in both the acute and chronic phases. Blood-based biomarkers are intensively researched and widely recognized as useful tools to predict the prognoses of patients confronted with therapeutically limited diseases. We performed a systematic review of the circulating biomarkers in IS patients with prognostic value, with a focus on microRNAs and exosomes as predictive biomarkers of motor and cognitive recovery. We identified 63 studies, totalizing 72 circulating biomarkers with prognostic value in stroke recovery, as follows: 68 miRNAs and exosomal-miRNAs being identified as predictive for motor recovery after stroke, and seven biomarkers being predictive for cognitive recovery. Twelve meta-analyses were performed using effect sizes (random-effects and fixed-effects model). The most significant correlation findings obtained after pooling were with miR-21, miR-29b, miR-125b-5p, miR-126, and miR-335. We identified several miRNAs that were correlated with clinical outcomes of stroke severity and recovery after ischemic stroke, providing predictive information on motor and cognitive recovery. Based on the current state of research, we identified serum miR-9 and neutrophil miR-29b as the most promising biomarkers for in-depth follow-up studies, followed by serum miR-124 and plasma miR-125b.

Advanced Glycations End Products in the Skin as Biomarkers of Cardiovascular Risk in Type 2 Diabetes.

The incidence and prevalence of diabetes are increasing worldwide, and cardiovascular disease (CVD) is the leading cause of death among subjects with type 2 diabetes (T2D). The assessment and stratification of cardiovascular risk in subjects with T2D is a challenge. Advanced glycation end products are heterogeneous molecules produced by non-enzymatic glycation of proteins, lipids, or nucleic acids. Accumulation of advanced glycation end products is increased in subjects with T2D and is considered to be one of the major pathogenic mechanism in developing complications in diabetes. Skin AGEs could be assessed by skin autofluorescence. This method has been validated and related to the presence of micro and macroangiopathy in individuals with type 2 diabetes. In this context, the aim of this review is to critically summarize current knowledge and scientific evidence on the relationship between skin AGEs and CVD in subjects with type 2 diabetes, with a brief reference to other diabetes-related complications.

Composition of the immunoglobulin G glycome associates with the severity of COVID-19.

A large variation in the severity of disease symptoms is one of the key open questions in coronavirus disease 2019 (COVID-19) pandemics. The fact that only a small subset of people infected with severe acute respiratory syndrome coronavirus 2 develops severe disease suggests that there have to be some predisposing factors, but biomarkers that reliably predict disease severity have not been found so far. Since overactivation of the immune system is implicated in a severe form of COVID-19 and the immunoglobulin G (IgG) glycosylation is known to be involved in the regulation of different immune processes, we evaluated the association of interindividual variation in IgG N-glycome composition with the severity of COVID-19. The analysis of 166 severe and 167 mild cases from hospitals in Spain, Italy and Portugal revealed statistically significant differences in the composition of the IgG N-glycome. The most notable difference was the decrease in bisecting N-acetylglucosamine in severe patients from all three cohorts. IgG galactosylation was also lower in severe cases in all cohorts, but the difference in galactosylation was not statistically significant after correction for multiple testing.

Mass spectrometry-based methods for robust measurement of Alzheimer's disease biomarkers in biological fluids.

Alzheimer's disease (AD) is the most common form of dementia affecting 60%-70% of people afflicted with this disease. Accurate antemortem diagnosis is urgently needed for early detection of AD to enable reliable estimation of prognosis, intervention, and monitoring of the disease. The National Institute on Aging/Alzheimer's Association sponsored the 'Research Framework: towards a biological definition of AD', which recommends using different biomarkers in living persons for a biomarker-based definition of AD regardless of clinical status. Fluid biomarkers represent one of key groups of them. Since cerebrospinal fluid (CSF) is in direct contact with brain and many proteins present in the brain can be detected in CSF, this fluid has been regarded as the best biofluid in which to measure AD biomarkers. Recently, technological advancements in protein detection made possible the effective study of plasma AD biomarkers despite their significantly lower concentrations versus to that in CSF. This and other challenges that face plasma-based biomarker measurements can be overcome by using mass spectrometry. In this review, we discuss AD biomarkers which can be reliably measured in CSF and plasma using targeted mass spectrometry coupled to liquid chromatography (LC/MS/MS). We describe progress in LC/MS/MS methods' development, emphasize the challenges, and summarize major findings. We also highlight the role of mass spectrometry and progress made in the process of global standardization of the measurement of Aß42/Aß40. Finally, we briefly describe exploratory proteomics which seek to identify new biomarkers that can contribute to detection of co-pathological processes that are common in sporadic AD.