Standard Biomarkers Research Articles

Tear and Saliva Metabolomics in Evaporative Dry Eye Disease in Females.

Accurate diagnosis of dry eye disease (DED) is challenging, and even today there is no gold standard biomarker of DED. Hypothesis-free global metabolomic studies of tears from DED patients have great potential to discover metabolites and pathways affected in the pathophysiology of DED, and to identify possible future biomarkers. These metabolites and biomarkers could be important for diagnosing and monitoring disease as well as for new therapeutic targets and strategies. As DED is associated with dry mouth, this study aimed to perform metabolomic analyses of tears and saliva from patients with decreased tear film break-up time but normal Schirmer test, and age-matched controls with both tear production and stability within physiological range. We applied strict inclusion criteria to reduce sampling bias in the metabolomic analyses and selected only age-matched females with Schirmer test values between 10-15 mm/5 min. The tear film analysis arm included 19 patients (with tear film break-up time 0-5 s) and 12 controls (with tear film break-up time 10-30 s), while the salivary analysis arm consisted of a subset which included 18 patients and six controls. Metabolomic analyses were performed using liquid chromatography and high-resolution mass spectrometry. Analyses using a global database search detected a total of 56 metabolites in tear samples that were significantly different between the groups. Of these, several have known associations with DED. These metabolites are present in meibum and have anti-oxidative characteristics or associations with the ocular microbiome, and altered concentrations suggest that they may play a significant role in DED associated with decreased tear film stability. In saliva, hypotaurine levels were lower among patients with tear film instability. In this pilot study, we found different levels of several metabolites in patients with decreased tear film break-up time that may have associations with DED. Future studies are required to replicate our findings and clarify the exact roles of these metabolites.

Normal isn't normal: On the medicalization of health

BackgroundThis study investigated the proportion of the U.S. population classified as healthy based on 10 common indicators, examined in two ways: (1) above or below (in the healthy direction) the sample median (termed “normal”), and (2) below diagnostic cut-off points for clinical caseness or high risk (termed “ideal”). MethodsData are from the 2017-March 2020 round of the National Health and Nutrition Examination Survey (NHANES). Sample sizes ranged from 3,956 to 8,961 for respective health indicators, with a total of 3,102 respondents for two weighted multi-item measures described below. Measures included the Alameda 5 health behaviors (smoking, drinking exercising, sleeping, and body mass index) and five standard biomarkers (systolic and diastolic blood pressure, resting heart rate, fasting glucose, and total cholesterol). Besides point prevalences for the normal and ideal categories for each indicator, we also calculated the proportion healthy for all 10 indicators, again calculated both ways, termed “meta-normal” and “meta-ideal.” ResultsThe prevalence of meta-normality was 1.05%, suggesting that hardly any adult Americans are completely healthy according to population norms. Findings for meta-ideality showed that while most Americans are not clinical cases for any respective indicator, only 5.55% met the official criteria for being healthy according to all 10 indicators. ConclusionMost Americans appear healthy according to nearly all key health indicators and biomarkers, according to “normal” or “ideal” criteria. However, the proportion healthy according to all measures is extremely small. Relatively few U.S. adults are completely healthy according to clinical criteria (meta-ideal), and even fewer are completely healthy according to population norms (meta-normal). Results are interpreted through sociological writing on medicalization.

An innovative PCR-free approach for DNA methylation measure: An application for early colorectal cancer detection by means of an organic biosensor

Colorectal cancer (CRC) is one of the most treatable cancers, but high mortality levels have been reported as a result of its asymptomatic nature in the very early stages of the disease. This justifies the importance of mass screening programs to fight colorectal cancer, and thus the interest in the development of minimally invasive, cost-effective tests. In this paper, a novel approach for the detection of CRC biomarkers in DNA stool samples is reported. The strategy is based on the detection of DNA methylation alterations as highly sensitive and selective epigenetic biomarkers by means of an organic transistor-based sensor, particularly suited for the integration in portable, low-cost and easy-to-use sensing system. A clear demonstration of the detection strategy effectiveness is provided using a standard CRC biomarker, namely SEPT9 (encoding for septin9), and further deepened using an innovative biomarker, GRIA4 (encoding for glutamate ionotropic receptor AMPA type subunit 4), in several samples coming from CRC patients. The correct detection of biomarkers, as confirmed by independent optical analysis, is demonstrated both on amplicons and unamplified genomic DNA derived from stool samples, thus making the proposed approach particularly attractive for the future development of non-invasive, PCR-free and low-cost mass screening tests.

H1Innovative approaches to combat anti-cancer drug resistance: Targeting lncRNA and autophagy.

To date, standardizing clinical predictive biomarkers for assessing the response to immunotherapy remains challenging due to variations in personal genetic signatures, tumour microenvironment complexities and epigenetic onco-mechanisms. Early monitoring of key non-coding RNA (ncRNA) biomarkers may help in predicting the clinical efficacy of cancer immunotherapy and come up with standard predictive ncRNA biomarkers. For instance, reduced miR-125b-5p level in the plasma of non-small cell lung cancer patients treated with anti-PD-1 predicts a positive outcome. The level of miR-153 in the plasma of colorectal cancer patients treated with chimeric antigen receptor T lymphocyte (CAR-T) cell therapy may indicate the activation of T-cell killing activity. miR-148a-3p and miR-375 levels may forecast favourable responses to CAR-T-cell therapy in B-cell acute lymphoblastic leukaemia. In cancer patients treated with the GPC3 peptide vaccine, serum levels of miR-1228-5p, miR-193a-5p and miR-375-3p were reported as predictive biomarkers of good response and improved overall survival. Therefore, there is a critical need for further studies to elaborate on the key ncRNA biomarkers that have the potential to predict early clinical responses to immunotherapy. This review summarises important predictive ncRNA biomarkers that were reported in cancer patients treated with different immunotherapeutic modalities including monoclonal antibodies, small molecule inhibitors, cancer vaccines and CAR-T cells. In addition, a concise discussion on forthcoming perspectives is provided, outlining technical approaches for the optimal utilisation of immune-modulatory ncRNA biomarkers as predictive tools and therapeutic targets.

100P Standard serum biomarkers to help predict a cancer diagnosis in patients with non-specific symptoms: Data from Guy´s rapid diagnostic clinic

100P Standard serum biomarkers to help predict a cancer diagnosis in patients with non-specific symptoms: Data from Guy´s rapid diagnostic clinic

Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions

Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer patients. These slides are often not used to define objective biomarkers for patient stratification and treatment selection. Standard biomarkers often pertain to costly and slow genetic tests. However, recent work has shown that relevant biomarkers can be extracted from these images using convolutional neural networks (CNNs). The CNN-based biomarkers predicted colorectal cancer patient outcomes comparably to gold standards. Extracting CNN-biomarkers is fast, automatic, and of minimal cost. CNN-based biomarkers rely on the ability of CNNs to recognize distinct tissue types from microscope whole slide images. The quality of these biomarkers (coined ‘Deep Stroma’) depends on the accuracy of CNNs in decomposing all relevant tissue classes. Improving tissue decomposition accuracy is essential for improving the prognostic potential of CNN-biomarkers. In this study, we implemented a novel training strategy to refine an established CNN model, which then surpassed all previous solutions . We obtained a 95.6% average accuracy in the external test set and 99.5% in the internal test set. Our approach reduced errors in biomarker-relevant classes, such as Lymphocytes, and was the first to include interpretability methods. These methods were used to better apprehend our model’s limitations and capabilities.

DUAL PET IMAGING OF NEOPLASTIC AND INflAMMATORY BIOMARKERS IN HIGH GRADE GLIOMA

Abstract AIMS To characterize spatial distributions of 18kDa translocator protein (TSPO) and amino acid uptake as measured through dynamic [11C] (R)-PK11195 and [11C]-methionine PET in high-grade glioma. METHOD Twelve patients with newly diagnosed high-grade glioma underwent dual PET studies. [11C] (R)-PK11195 binding potential (BPND) maps were generated using simplified reference tissue model with grey-matter cerebellar time- activity-curve as tissue input-function. [11C]-methionine uptake was calculated as tumour-to-background ratio (TBR). Volumes of interest (VOIs) were defined on T1W post-contrast MRI as contrast-enhancing (CE) tumour and a ‘peritumoural’ region 5mm from the CE edge. Voxel-wise comparison of the tracers used Pearson’s correlation coeffcient. VOIs were furthermore subdivided into higher/lower TSPO (BPND > 0.17) and higher/lower methionine (TBR > 1.7). RESULTS Within the CE, voxel-wise comparison showed a strong correlation (r > 0.7) in eight subjects, moderate correlation (r > 0.5) in three subjects and weak correlation (r < 0.5) in one subject. Volume (mm3) with high BPND/high TBR accounted for 62±29% whereas high BPND/low TBR accounted for 13±16%. Within the peritumoral area, there was a small reduction in the proportion of subjects in the strong correlation group (6/12) but the pro- portion of voxels with higher TSPO binding and lower methionine uptake was substantially greater (35±16%). CONCLUSIONS Within high-grade glioma, there is a strong overall correlation between TSPO expression and amino acid uptake. However, in the peritumoural region there are areas of elevated TSPO expression without increased methionine uptake which could represent an inflammatory component or a discrete neoplastic cell population that may not be adequately defined with standard imaging biomarkers.

Abstract PO-031: Integrated bioinformatics and functional spatial analysis platform for the discovery of predictive biomarkers in oral cancer

Abstract Background: Oral cavity squamous cell carcinoma (OCSCC) has an unpredictable response to immunotherapy, and standard biomarkers evaluating the tumor immune microenvironment (TIME) lack widespread translational applicability. Defining the TIME is critical to understanding mechanisms and allows the development of reproducible and accurate prognostic and predictive biomarkers based on immune profiles; however, to date, this remains elusive for clinical use. To further study and deconstruct the TIME within OCSCC, we integrated our previously developed computational spatial analysis (CSA) with in-silico deconvolution analyses to comprehensively study the TIME in OCSCC. Methods: We leveraged access to two independent OCSCC cohorts (UM and TCGA) with paired bulk RNASeq, WES, and CSA-immune score data. TMB, neoantigen load, and deconvolution analysis (CIBERSORTx) were performed to generate in silico immune profiles. A previously described CSA evaluates the density and distribution of tumor-infiltrating lymphocytes (TILs) relative to cancer cells to generate immune scores (GCross function [GFx]). GFx scores were then correlated with WES and RNASeq-derived cell-type deconvolution results, immune cell activation expression signatures, and gene expression-based radiosensitivity index scores. Pearson's correlation coefficient and associated p-value were calculated for each comparison. Cox proportional hazards multivariate regression was performed using the entire cohort for survival and recurrence analysis. Results: Here, we better characterize the immunologic tumor environment by objectively measuring the TIL density in spatial relationships with cancer cells. We found that immunoreactive tumors with high GFx scores are highly prognostic for overall survival, with the lowest tiers associated with poor survival. Combining the spatial relationship with immunogenomics (WES and RNASeq immune profiles) increased the prognostic capability of the integrated bioinformatics analysis model. In addition, we further revealed that lymphocyte-infiltrated regions, specifically CD8+ T-cells, are associated with TMB, neoantigen load, and improved overall survival. Conclusion: This study provides an integrated dataset from which to generate future insights and possible immunomodulatory therapies to counteract the poor immunogenicity of OCSCC. We show that integrating computational spatial analysis and in-silico deconvolution analysis is generalizable to multiple patient populations with different demographic compositions and pathology images collected from distinctive digitization methods. Our machine-learning approaches provide clinically actionable predictions that confirm the relevance of these biomarkers with immune cell infiltration and suggest the role of digital pathology profiling in predicting response to immunotherapy which could inform treatments for oral squamous carcinoma patients. Citation Format: Michael M. Allevato, Lisa Chionis, Joshua Smith, Santhoshi Krishnan, Leanne Henry, Tingting Qin, Janice Farlow, Laura Rozek, Jonathan Mchugh, Arvind Rao, Maureen Sartor, Steven B. Chinn. Integrated bioinformatics and functional spatial analysis platform for the discovery of predictive biomarkers in oral cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-031.

Standardizing HER2 immunohistochemistry assessment: calibration of color and intensity variation in whole slide imaging caused by staining and scanning

In the evaluation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) — one of the standard biomarkers for breast cancer— visual assessment is laborious and subjective. Image analysis using whole slide image (WSI) could produce more consistent results; however, color variability in WSIs due to the choice of stain and scanning processes may impact image analysis. We therefore developed a calibration protocol to diminish the staining and scanning variations of WSI using two calibrator slides. The IHC calibrator slide (IHC-CS) contains peptide-coated microbeads with different concentrations. The color distribution obtained from the WSI of stained IHC-CS reflects the staining process and scanner characteristics. A color chart slide (CCS) is also useful for calibrating the color variation due to the scanner. The results of the automated HER2 assessment were compared to confirm the effectiveness of two calibration slides. The IHC-CS and HER2 breast cancer cases were stained on different days. All stained slides and CCS were digitized by two different WSI scanners. Results revealed 100% concordance between automated evaluation and the pathologist’s assessment with both the scanner and staining calibration. The proposed method may enable consistent evaluation of HER2.

Non-coding RNAs in cancer immunotherapy: Predictive biomarkers and targets.

To date, standardising clinical predictive biomarkers for assessing the response to immunotherapy remains challenging due to variations in personal genetic signatures, tumour microenvironment complexities and epigenetic onco-mechanisms. Early monitoring of key non-coding RNA (ncRNA) biomarkers may help in predicting the clinical efficacy of cancer immunotherapy and come up with standard predictive ncRNA biomarkers. For instance, reduced miR-125b-5p level in the plasma of non-small cell lung cancer patients treated with anti-PD-1 predicts a positive outcome. The level of miR-153 in the plasma of colorectal cancer patients treated with chimeric antigen receptor T lymphocyte (CAR-T) cell therapy may indicate the activation of T-cell killing activity. miR-148a-3p and miR-375 levels may forecast favourable responses to CAR-T-cell therapy in B-cell acute lymphoblastic leukaemia. In cancer patients treated with the GPC3 peptide vaccine, serum levels of miR-1228-5p, miR-193a-5p and miR-375-3p were reported as predictive biomarkers of good response and improved overall survival. Therefore, there is a critical need for further studies to elaborate on the key ncRNA biomarkers that have the potential to predict early clinical responses to immunotherapy. This review summarises important predictive ncRNA biomarkers that were reported in cancer patients treated with different immunotherapeutic modalities, including monoclonal antibodies, small molecule inhibitors, cancer vaccines and CAR-T cells. In addition, a concise discussion on forthcoming perspectives is provided, outlining technical approaches for the optimal utilisation of immunomodulatory ncRNA biomarkers as predictive tools and therapeutic targets.

Breath analysis combined with cardiopulmonary exercise testing and echocardiography for monitoring heart failure patients: the AEOLUS protocol

This paper describes the AEOLUS pilot study which combines breath analysis with cardiopulmonary exercise testing (CPET) and an echocardiographic examination for monitoring heart failure (HF) patients. Ten consecutive patients with a prior clinical diagnosis of HF with reduced left ventricular ejection fraction were prospectively enrolled together with 15 control patients with cardiovascular risk factors, including hypertension, type II diabetes or chronic ischemic heart disease. Breath samples were collected at rest and during CPET coupled with exercise stress echocardiography (CPET-ESE) protocol by means of needle trap micro-extraction and were analyzed through gas-chromatography coupled with mass spectrometry. The protocol also involved using of a selected ion flow tube mass spectrometer for a breath-by-breath isoprene and acetone analysis during exercise. At rest, HF patients showed increased breath levels of acetone and pentane, which are related to altered oxidation of fatty acids and oxidative stress, respectively. A significant positive correlation was observed between acetone and the gold standard biomarker NT-proBNP in plasma (r = 0.646, p < 0.001), both measured at rest. During exercise, some exhaled volatiles (e.g., isoprene) mirrored ventilatory and/or hemodynamic adaptation, whereas others (e.g., sulfide compounds and 3-hydroxy-2-butanone) depended on their origin. At peak effort, acetone levels in HF patients differed significantly from those of the control group, suggesting an altered myocardial and systemic metabolic adaptation to exercise for HF patients. These preliminary data suggest that concomitant acquisition of CPET-ESE and breath analysis is feasible and might provide additional clinical information on the metabolic maladaptation of HF patients to exercise. Such information may refine the identification of patients at higher risk of disease worsening.

Paper-based multiplexed colorimetric biosensing of cardiac and lipid biomarkers integrated with machine learning for accurate acute myocardial infarction early diagnosis and prognosis

This study demonstrates how a colorimetric biosensor based on microfluidic paper can swiftly diagnose a disease and predict its prognosis to triage patients effectively. This was the first biosensor to quantify the gold standard cardiac troponin (cTnI) and lipid biomarkers, including high-density lipoprotein (HDL) and low-density lipoprotein (LDL) simultaneously. Prior research encountered obstacles or limitations, such as measuring a single biomarker or total cholesterol, which cannot distinguish between LDL and HDL. CatBoost, an advanced machine learning (ML) technique used for diagnosis that combines the predictive power of ML algorithms obtained an impressive area under the receiver operating curves (AUROC) of 0.97 ± 0.018 for all possible classification thresholds. CatBoost is a brand-new ensemble framework based on the interaction of multiple health parameters that can generate AUROC values of 0.897 ± 0.047 for the accurate prognosis of recurrent acute myocardial infarction (AMI), demonstrating remarkably accurate AMI diagnosis and prognosis. In addition, this paper-based analytical device (µPAD) biosensor employs an electrophoretic method to overcome the challenges posed by non-specific adsorption. This is accomplished by isolating non-specific biomolecules based on differences in their isoelectric points and removing non-specifically adsorbing colorimetric markers. The limits of detection (LoD) for cTnI in AMI were lower than their respective clinical cutoff values. This study also demonstrated that the proposed ML framework produced significantly better results than conventional statistical analysis. High correlation filtering and (t-SNE) dimensionality reduction were utilized for a limited number of data points. The respectable accuracy and AUROC of this method were also validated using cross-validation.

Engineered calprotectin-sensing probiotics for IBD surveillance in humans

Inflammatory bowel disease (IBD) is a spectrum of autoimmune diseases affecting the gastrointestinal tract characterized by a relapsing and remitting course of gut mucosal inflammation. Disease flares can be difficult to predict, and the current practice of IBD disease activity surveillance through endoscopy is invasive and requires medical expertise. Recent advancements in synthetic biology raise the possibility that symbiotic microbes can be engineered to selectively detect disease biomarkers used in current clinical practice. Here, we introduce an engineered probiotic capable of detecting the clinical gold standard IBD biomarker, calprotectin, with sensitivity and specificity in IBD patients. Specifically, we identified a bacterial promoter in the probiotic strain Escherichia coli Nissle 1917 (EcN) which exhibits a specific expression increase in the presence of calprotectin. Using murine models of colitis, we show that the reporter signal is activated in vivo during transit of the GI tract following oral delivery. Furthermore, our engineered probiotic can successfully discriminate human patients with active IBD from those in remission and without IBD using patient stool samples, where the intensity of reporter signal quantitatively tracks with clinical laboratory-measured levels of calprotectin. Our pilot study sets the stage for probiotics that can be engineered to detect fecal calprotectin for precise noninvasive disease activity monitoring in IBD patients.

Higher MRI lesion load in multiple sclerosis is related to the N-glycosylation changes of cerebrospinal fluid immunoglobulin G

BackgroundIntrathecal clonal expansion of antibody-producing plasma cells in multiple sclerosis (MS) perpetuates central nervous system injury and is associated with active demyelination. Immunoglobulin G (IgG) effector functions are modulated by linked N-glycan structures. The aim of the study was to detect potential differences in N-glycosylation of IgG in serum and cerebrospinal fluid (CSF) and total sera proteins between people with MS and those in whom the diagnosis of MS was excluded. Furthermore, we investigated the association with standard laboratory biomarkers of intrathecal inflammation as well as clinical and neuroradiological disease activity. MethodsThis cross-sectional study included patients with suspected demyelinating disease. MS diagnosis was based on the 2017 McDonald criteria and controls were patients with excluded MS diagnosis. N-glycans were compared with Expanded Disability Status Scale (EDSS), magnetic resonance imaging (MRI) markers of disease activity and biomarkers of intrathecal inflammation (cell count, CSF-IgG concentration, percentage of intrathecal IgG, oligoclonal bands (OCB), virus-specific antibody index (MRZH reaction)). ResultsDifferences between groups were observed only in the CSF-IgG N-glycome. In MS, the presence of bisecting N-acetylglucosamine (Padj=2.63E-05) and monogalactosylation (Padj=1.49E-06) were more abundant and associated with positive OCBs. N-glycans monogalactosylated at the α6 arm FA2[6]G1 (r = 0.56) and FA2[6]BG1 (r = 0.45) correlated with percentage of intrathecal IgG, but not total CSF-IgG. This trait was also more abundant in MRZH positive people with MS who had higher MRI lesion load (P = 0.018) but unrelated to active lesions or EDSS. ConclusionsMore abundant monogalactosylation of intrathecally synthesized IgG is the most prominent trait in MS and is associated with higher MRI lesion load.

Cancer Associated Macrophage-like Cells Are Prognostic for Highly Aggressive Prostate Cancer in Both the Non-Metastatic and Metastatic Settings.

Despite advancements in the early-stage detection and expansion of treatments for prostate cancer (PCa), patient mortality rates remain high in patients with aggressive disease and the overtreatment of indolent disease remains a major issue. Prostate-specific antigen (PSA), a standard PCa blood biomarker, is limited in its ability to differentiate disease subtypes resulting in the overtreatment of non-aggressive indolent disease. Here we assess engorged cancer-associated macrophage-like cells (CAMLs), a ≥50 µm, cancer-specific, polynucleated circulating cell type found in the blood of patients with PCa as a potential companion biomarker to PSA for patient risk stratification. We found that rising PSA is positively correlated with increasing CAML size (r = 0.307, p = 0.004) and number of CAMLs in circulation (r = 0.399, p < 0.001). Over a 2-year period, the presence of a single engorged CAML was associated with 20.9 times increased likelihood of progression (p = 0.016) in non-metastatic PCa, and 2.4 times likelihood of progression (p = 0.031) with 5.4 times likelihood of death (p < 0.001) in metastatic PCa. These preliminary data suggest that CAML cell monitoring, in combination with PSA, may aid in differentiating non-aggressive from aggressive PCas by adding biological information that complements traditional clinical biomarkers, thereby helping guide treatment strategies.

Using metabolomics to predict severe traumatic brain injury outcome (GOSE) at 3 and 12 months

BackgroundPrognostication is very important to clinicians and families during the early management of severe traumatic brain injury (sTBI), however, there are no gold standard biomarkers to determine prognosis in sTBI. As has been demonstrated in several diseases, early measurement of serum metabolomic profiles can be used as sensitive and specific biomarkers to predict outcomes.MethodsWe prospectively enrolled 59 adults with sTBI (Glasgow coma scale, GCS ≤ 8) in a multicenter Canadian TBI (CanTBI) study. Serum samples were drawn for metabolomic profiling on the 1st and 4th days following injury. The Glasgow outcome scale extended (GOSE) was collected at 3- and 12-months post-injury. Targeted direct infusion liquid chromatography-tandem mass spectrometry (DI/LC–MS/MS) and untargeted proton nuclear magnetic resonance spectroscopy (1H-NMR) were used to profile serum metabolites. Multivariate analysis was used to determine the association between serum metabolomics and GOSE, dichotomized into favorable (GOSE 5–8) and unfavorable (GOSE 1–4), outcomes.ResultsSerum metabolic profiles on days 1 and 4 post-injury were highly predictive (Q2 > 0.4–0.5) and highly accurate (AUC > 0.99) to predict GOSE outcome at 3- and 12-months post-injury and mortality at 3 months. The metabolic profiles on day 4 were more predictive (Q2 > 0.55) than those measured on day 1 post-injury. Unfavorable outcomes were associated with considerable metabolite changes from day 1 to day 4 compared to favorable outcomes. Increased lysophosphatidylcholines, acylcarnitines, energy-related metabolites (glucose, lactate), aromatic amino acids, and glutamate were associated with poor outcomes and mortality.DiscussionMetabolomic profiles were strongly associated with the prognosis of GOSE outcome at 3 and 12 months and mortality following sTBI in adults. The metabolic phenotypes on day 4 post-injury were more predictive and significant for predicting the sTBI outcome compared to the day 1 sample. This may reflect the larger contribution of secondary brain injury (day 4) to sTBI outcome. Patients with unfavorable outcomes demonstrated more metabolite changes from day 1 to day 4 post-injury. These findings highlighted increased concentration of neurobiomarkers such as N-acetylaspartate (NAA) and tyrosine, decreased concentrations of ketone bodies, and decreased urea cycle metabolites on day 4 presenting potential metabolites to predict the outcome. The current findings strongly support the use of serum metabolomics, that are shown to be better than clinical data, in determining prognosis in adults with sTBI in the early days post-injury. Our findings, however, require validation in a larger cohort of adults with sTBI to be used for clinical practice.

Biological Recognition-Based Electrochemical Aptasensor for Point-of-Care Detection of cTnI.

As a "gold standard biomarker", cardiac troponin I (cTnI) is widely used to diagnose acute myocardial infarction (AMI). For an early clinical diagnosis of AMI, it is necessary to develop a facile, fast and on-site device for cTnI detection. According to this demand, a point-of-care electrochemical aptasensor was developed for cTnI detection by coupling the advantages of screen-printed carbon electrode (SPCE) with those of an aptamer. Thiol and methylene blue (MB) co-labelled aptamer (MB-Apt-SH) was assembled on the surface of hierarchical flower-like gold nanostructure (HFGNs)-decorated SPCE (SPCE-HFGNs) to recognize and analyze cTnI. In the presence of cTnI, the specific biological recognition reaction between cTnI and aptamer caused the decrease in electrochemical signal. Under the optimal condition, this designed aptasensor showed wide linear range (10 pg/mL-100 ng/mL) and low detection limit for (8.46 pg/mL) for cTnI detection with high selectivity and stability. More importantly, we used a mobile phone coupled with a simple APP to efficiently detect cTnI in 10 μL 100% human serum samples, proving that this aptasensor has a promising potential in point-of-care testing.

Analytical validation, sample stability, and clinical evaluation of a new high-sensitivity cardiac troponin I immunoassay for use in dogs, with comparison to a previous ultrasensitive assay.

Cardiac troponin I (cTnI) is considered the gold standard biomarker for myocardial injury and shows a high degree of homology between humans and dogs. The ADVIA Centaur XP High-Sensitivity Troponin I (AC-cTnI-HS) assay has been validated for use in humans but not dogs. The study objectives were to analytically validate the AC-cTnI-HS assay in dogs, to assess correlation between the AC-cTnI-HS and a previous ADVIA Centaur TnI-Ultra (AC-cTnI-U) assay, to assess cTnI sample storage stability, and to clinically evaluate the AC-cTnI-HS assay in healthy dogs and dogs with cardiac disease. Canine serum samples were used for analytical validation. Intra- and inter-assay variability, dilutional parallelism, and spiking recovery were assessed. Samples from 196 client-owned dogs were evaluated (healthy dogs (n = 39) or dogs with congenital heart disease (n = 54), myxomatous mitral valve disease (n = 68), dilated cardiomyopathy (n = 15), or myocarditis (n = 20)). Inter- and intra-assay coefficient of variation (%CV) was between 2.8-41.4% and 3.8-30.2%, respectively, with pools with concentrations >20 pg/mL all having %CVs <10%. The observed to expected ratios for dilutional parallelism and spiking recovery experiments ranged between 92.3 and 266.7.0% and 84.3 and 108%, respectively. A strong correlation between the AC-cTnI-HS and AC-cTnI-U assays was observed (Spearman's ρ = 0.927), though a proportional bias existed, with AC-cTnI-HS assay concentrations being proportionally lower than AC-cTnI-U assay concentrations. Serum samples stored at -80°C had stable cTnI measurements for up to 2.7 years and after a single freeze-thaw cycle. Healthy dogs and dogs with congenital heart disease had significantly lower cTnI concentrations than dogs in the other three groups. The AC-cTnI-HS assay precisely, reproducibly, and accurately measures cTnI concentrations in dog serum with cTnI concentrations >20 pg/mL.

Molecular biomarkers for objective assessment of symptomatic pulpitis: A systematic review and meta-analysis.

Inflammatory biomarkers are potentially useful targets for pulpal diagnostic tests that can identify pulp status and predict vital pulp treatment (VPT) outcome, however, their accuracy is unknown. (1) Calculate sensitivity, specificity and diagnostic odds ratio (DOR) of previously investigated pulpitic biomarkers; (2) Determine if biomarker levels discriminate between clinical diagnoses of pulpitis based on the presence or absence of spontaneous pain (3) Evaluate if biomarker level can predict VPT outcome. Searches: PubMed/MEDLINE, Ovid SP, Cochrane Central Register of Controlled Trials (CENTRAL), International Clinical Trials Registry Platform (ICTRP), ClinicalTrials.gov, Embase, Web of Science and Scopus in May 2023. prospective and retrospective observational studies and randomized trials. Participants were humans with vital permanent teeth and a well-defined pulpal diagnosis. deciduous teeth, in vitro and animal studies. Risk of bias was assessed with modified-Downs and Black quality assessment checklist. Meta-analysis was performed using bivariate random effect model in Meta-DiSc 2.0 and RevMan and the quality of the evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation. Fifty-six studies were selected, reporting >70 individual biomolecules investigating pulpal health and disease at the gene and protein level. Most studies were of low and fair quality. Among the biomolecules investigated, IL-8 and IL-6 demonstrated a level of diagnostic accuracy with high sensitivity, specificity and DOR to discriminate between healthy pulps and those exhibiting spontaneous pain suggestive of IRP (low-certainty evidence). However, none was shown to have high DOR and the ability to discriminate between pulpitic states (very low certainty evidence). Limited data suggests high levels of matrix metalloproteinase 9 correlate with poorer outcomes of full pulpotomy. The inability of identified molecular inflammatory markers to discriminate between dental pulps with spontaneous and non-spontaneous pain should shift the focus to improved study quality or the pursuit of other molecules potentially associated with healing and repair. Low-quality evidence suggests IL-8 and IL-6 demonstrated level of diagnostic accuracy to discriminate between healthy pulps and those exhibiting spontaneous pain. There is a need for standardized biomarker diagnostic and prognostic studies focusing on solutions that can accurately determine the degree of pulp inflammation. PROSPERO CRD42021259305.

Rapid electrical biosensor consisting of DNA aptamer/carbon nanonetwork on microelectrode array for cardiac troponin I in human serum

Rapid detection of cardiac biomarkers is important for the early diagnosis of acute myocardial infarction (AMI). Currently, cardiac troponin I (cTnI) is the standard biomarker for precise diagnosis of AMI. It is essential for rapid diagnosis of AMI patient. Herein, we presented the rapid electrical biosensor composed of a carbon nanotube network (CNN) and DNA aptamer on a microelectrode array to detect cTnI in human serum sample. The island-shaped Au microgap electrodes array was used to provide a high signal-to-noise ratio response and maximize detection sensitivity. In addition, an alternating current electrothermal flow (ACEF) method was introduced to improve the affinity between the DNA aptamer and the target molecule, and cTnI was detected within 10 min using this method. The current change was analyzed using pulse measurement for 100 μs, and the integrated current method was proposed to improve the performance of the sensor platform. This biosensor can detect cTnI in a wide linear range from 1 pM to 100 nM in 10 % human serum. The limit of detection is 6.59 fM, indicating high selectivity. Thus, the proposed electrical biosensor platform may facilitate rapid and early detection of AMI in the future.