Validation Of Biomarkers Research Articles

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. To explore the pathways and biological functions these genes are involved in, KEGG and Gene Ontology (GO) enrichment analyses on the disclosed gene sets were performed, highlighting the significance of the genes selected across several relevent pathways and GO terms. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed.

Early Childhood Exposures to Fluorides and Cognitive Neurodevelopment: A Population-Based Longitudinal Study.

It is important to maintain confidence in the risk and benefit balance of major caries-preventive programs using fluoride. The ongoing debate about potential effects of early-life exposures to fluoride on cognitive neurodevelopment requires high-quality scientific evidence. This study aimed to investigate the potential effects of fluoride exposure on cognitive neurodevelopment assessed with the Wechsler Adult Intelligence Scale 4th edition (WAIS-IV) in an Australian population-based sample. The sample was selected from the National Child Oral Health Study (NCOHS) 2012-2014. NCOHS collected data on socioeconomic factors, oral health behaviors, and residential history to estimate percentage lifetime exposure to fluoridated water during the first 5 y of life (%LEFW). NCOHS children were also examined by trained and calibrated examiners to assess dental fluorosis (a reliable and valid individual biomarker of total fluoride intake during early childhood). The sample was followed up in 2022-2023 to collect data on cognitive neurodevelopment (intelligence quotient [IQ]) using the WAIS-IV, which was administered by trained and calibrated qualified psychologists. Multivariable regression models were generated to investigate associations between the 2 exposure measurements (%LEFW and dental fluorosis) with full-scale IQ (FSIQ) scores, controlling for important confounding effects. Hypotheses of noninferiority were also tested, contrasting different levels of exposure to fluoride. Some 357 participants aged 16 to 26 y completed the WAIS-IV, with a mean FSIQ score of 109.2 (95% confidence interval [CI]: 107.8-110.5). The estimates of the multivariable regression models demonstrated slightly higher FSIQ scores among the exposed than the nonexposed. The adjusted β of 100%LEFW versus 0%LEFW was 1.07 (95% CI: -2.86, 5.01) and of having dental fluorosis versus no fluorosis was 0.28 (95% CI: -3.00, 3.57). The hypothesis of noninferiority tests found that FSIQ scores of those exposed and nonexposed to fluoride were equivalent. The study provided consistent evidence that early childhood exposure to fluoride does not have effects on cognitive neurodevelopment.

Navigating Hemorrhagic Shock: Biomarkers, Therapies, and Challenges in Clinical Care

Hemorrhagic shock remains a leading cause of preventable death worldwide, with mortality patterns varying significantly based on injury mechanisms and severity. This comprehensive review examines the complex pathophysiology of hemorrhagic shock, focusing on the temporal evolution of inflammatory responses, biomarker utility, and evidence-based therapeutic interventions. The inflammatory cascade progresses through distinct phases, beginning with tissue injury and endothelial activation, followed by a systemic inflammatory response that can transition to devastating immunosuppression. Recent advances have revealed pattern-specific responses between penetrating and blunt trauma, necessitating tailored therapeutic approaches. While damage control resuscitation principles and balanced blood product administration have improved outcomes, many molecular targeted therapies remain investigational. Current evidence supports early hemorrhage control, appropriate blood product ratios, and time-sensitive interventions like tranexamic acid administration. However, challenges persist in biomarker validation, therapeutic timing, and implementation of personalized treatment strategies. Future directions include developing precision medicine approaches, real-time monitoring systems, and novel therapeutic modalities while addressing practical implementation barriers across different healthcare settings. Success in hemorrhagic shock management increasingly depends on integrating multiple interventions across different time points while maintaining focus on patient-centered outcomes.

Lumipulse-Measured Cerebrospinal Fluid Biomarkers for the Early Detection of Alzheimer Disease.

CSF biomarkers of Aβ42 and phosphorylated tau (p-tau181) are used clinically for the detection of Alzheimer disease (AD) pathology during life. CSF biomarker validation studies have largely used clinical diagnoses and/or amyloid PET imaging as the reference standard. The few existing CSF-to-autopsy studies have been restricted to late-stage AD. This CSF-to-autopsy study investigated associations between CSF biomarkers of AD and AD neuropathologic changes among brain donors who had normal cognition at the time of lumbar puncture (LP). This was a retrospective study of brain donors from the National Alzheimer's Coordinating Center who had normal cognition at the time of LP and who had measurements of CSF Aβ42 and p-tau181 performed with Lumipulse assays. All brain donors were from Washington University Knight ADRC. Staging of AD neuropathologic change (ADNC) was made based on National Institute on Aging-Alzheimer's Association criteria. For this study, participants were divided into 2 categories: "AD-" (no AD/low ADNC) and "AD+" (intermediate/high ADNC). Accuracy of each biomarker for discriminating AD status was evaluated using area under the curve (AUC) statistics generated using predicted probabilities from binary logistic regressions that controlled for age, sex, APOE ε4, and interval between LP and death. The average age at LP was 79.3 years (SD = 5.6), and the average age at death was 87.1 years (SD = 6.5). Of the 49 brain donors, 24 (49%) were male and 47 (95.9%) were White. 20 (40.8%) had autopsy-confirmed AD. The average interval from LP until death was 7.76 years (SD = 4.31). CSF p-tau181/Aβ42 was the optimal predictor of AD, having excellent discrimination accuracy (AUC = 0.97, 95% CI 0.94-1.00, p = 0.003). CSF p-tau181 alone had the second-best discrimination accuracy (AUC = 0.92, 95% CI 0.84-1.00, p = 0.001), followed by CSF Aβ42 alone (AUC = 0.92, 95% CI 0.85-1.00, p = 0.007), while CSF t-tau had the numerically lowest discrimination accuracy (AUC = 0.87, 95% CI 0.76-0.97, p = 0.005). Effects remained after controlling for prevalent comorbid neuropathologies. CSF p-tau181/Aβ42 was strongly associated with CERAD ratings of neuritic amyloid plaque scores and Braak staging of NFTs. This study supports Lumipulse-measured CSF Aβ42 and p-tau181 and, particularly, the ratio of p-tau181 to Aβ42, for the early detection of AD pathophysiologic processes. This study provides Class II evidence that Lumipulse measures of p-tau181/Aβ42 in the CSF accurately discriminated cognitively normal participants with and without Alzheimer disease neuropathologic change.

Validation of salivary proteomic biomarkers for early detection of oral cancer in the Egyptian population.

The present study evaluated the sensitivity and specificity of important proteomic salivary biomarkers; IL-6, IL-8, and sCD44 in the early detection of oral cancer, and any possible associations with risk factors of oral cancer in an Egyptian population. The present investigation was conducted on 100 individuals; 25 healthy controls, 25 patients having oral potentially malignant disorders (OPMDs) with dysplasia; 25 patients having OPMDs without dysplasia, and 25 oral cancer patients. Demographic data modified gingival index, oral hygiene level, and salivary levels of the biomarkers were assessed. Salivary levels of IL-6, IL-8, and sCD44 progressively increased with increased disease severity. Salivary IL-8 and IL-6 levels possess a discriminating potential from normal tissue through different degrees of dysplasia to oral cancer, sCD44 levels had a discriminating power between normal and dysplastic tissues with high sensitivity and specificity. A positive correlation was found between the three biomarkers and the grade of oral squamous cell carcinoma (OSCC) and with different risk factors. This is the first study that evaluated multiple salivary proteomic biomarkers in the Egyptian population, and the results validate the ability of IL-6, IL-8, and sCD44 to be used as sensitive diagnostic and prognostic biomarkers for screening and early detection of oral cancer.

Enhanced neurobiological biomarker differentiation for attention-deficit/hyperactivity disorder through a risk-informed design.

Translation of biomarkers to clinical practice is hindered by the significant overlap in neurobiological measures between ADHD cases and controls. A risk-informed design can enhance the utility and validation of ADHD biomarkers by highlighting differences between individuals with ADHD and those without at differential risk. Participants were 2511 children and adolescents (aged 6 to 14 years) from the Brazilian High Risk Cohort for Mental Conditions. We calculated risk for ADHD among unaffected individuals using a multivariable clinical and sociodemographic risk model. We compared measures of three proposed ADHD biomarkers (polygenic scores, subcortical volumes, and executive function) between participants with vs. without ADHD, and ADHD vs. without ADHD with a high- vs. low-risk loading for ADHD. Compared to the unaffected group, children and adolescents with ADHD had higher ADHD polygenic scores (cohen's d = 0.17), smaller subcortical volumes (d = - 0.25), and poorer executive function (d = - 0.22). Separating the unaffected group into low- and high-risk subgroups revealed more pronounced differences (Cohen's d = 0.20 to 0.60) and nearly doubled the overlap-free area for these three neurobiological measures between the low-risk group and the other two groups. Upon adjustment for the number of ADHD symptoms, simple ADHD vs. without ADHD differences vanished, while the risk-informed analyses remained significant. Here, we demonstrate that a risk-based design increases effect sizes when comparing candidate biomarkers for ADHD. Our study provides a model that may hold promise for evaluating similar contrasts in other mental disorders and samples.

Identification and Validation of Serum Biomarkers to Improve Colorectal Cancer Diagnosis.

The pressing need for reliable biomarkers in colorectal cancer (CRC) diagnosis and prognosis is a major global health concern. Current diagnostic methods rely heavily on invasive procedures like colonoscopy, and existing biomarkers such as Carbohydrate Antigen 19-9 (CA19-9) and Carcinoembryonic Antigen (CEA) exhibit limitations in accuracy and specificity. This study aims to identify and validate novel biomarkers that can enhance the early detection and diagnostic precision of CRC while overcoming the shortcomings of conventional biomarkers. Leveraging advancements in genomic and proteomic technologies, gene expression datasets were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). We identified differentially expressed genes (DEGs) and conducted further analyses, including Gene Ontology (GO) enrichment and Protein-Protein Interaction (PPI) network construction. Five promising biomarkers-INHBA, MMP7, PSAT1, SLC7A5, and TGFBI-were selected based on their robust performance in Receiver Operating Characteristic (ROC) curve analysis. Serum concentrations of these biomarkers were measured in 200 CRC patients and 100 healthy controls. The study revealed significantly elevated expression levels of the selected biomarkers in CRC tissues compared to normal tissues. Additionally, serum concentrations of INHBA, MMP7, PSAT1, SLC7A5, and TGFBI were notably higher in CRC patients than in healthy individuals, with Area Under the Curve (AUC) values ranging from 0.8361 to 0.9869 indicating high diagnostic accuracy. Optimal cutoff values for diagnosis ranged from 38.9 pg/mL to 280.7 pg/mL, yielding sensitivity and specificity values between 74.5% and 92.9%. The findings underscore the potential of INHBA, MMP7, PSAT1, SLC7A5, and TGFBI as effective non-invasive biomarkers for CRC detection. Their elevated serum concentrations and robust discriminatory abilities highlight their promise in improving diagnostic accuracy and patient outcomes compared to traditional biomarkers. The identification and validation of these novel biomarkers represent a significant advancement in CRC diagnosis and management. Further studies are required to validate their clinical applicability in larger cohorts and to elucidate their functional roles in CRC pathogenesis, ultimately enhancing diagnostic strategies and personalized treatment approaches.

Evaluation of Collagen Triple Helix Repeat Containing-1 protein in Postmenopausal Women with Osteoporosis

Background: The collagen triple helix repeat containing 1 (CTHRC1) protein has been connected to decreased levels of vitamin D and calcium, as well as obesity. This study aimed to investigate the relationship between CTHRC1 and osteoporosis in post-menopausal women and compare with healthy subjects. Subjects and Methods: A cross-sectional study consisted of 86 women were enrolled in this study and divided into three groups based on the results of dual-energy densitometry (DXA): 30 women with osteoporosis, 30 women with osteopenia, and 26 healthy women. Data on demographic and clinical features and laboratory values of Calcium (Ca) and Vitamin D3 (V.D3) were recorded. Results: Women with osteoporosis had significantly increased levels of CTHRC1 (P = 0.0001) compare to HS groups. The CTHRC1 showed negative correlation with Ca and V.D3 value at (r= -0.453, P=0.001 and r= -0.415, P= 0.022) respectively in osteopenia patients. CTHRC1 alone show excellent discrimination power (AUC= 0.9) in identifying women with osteoporosis. Conclusions: Serum CTHRC1 is a valid biomarker that can distinguish women with osteoporosis from HS group with high accuracy. Low calcium and vitamin D levels in this age group may be linked to an increase in the CTHRC1 levels and this could make it a Therapeutic target in future studies.

Fluid Biomarkers in Dementia Diagnosis.

This article familiarizes neurologists with the currently available CSF and plasma biomarkers for the diagnosis of dementia and diagnosis-dependent treatment decisions. For Alzheimer disease, the recent US Food and Drug Administration (FDA) approval of monoclonal antibody therapy has increased the urgency of confirming the pathologic diagnosis with biomarkers before initiating therapy. The new availability of disease-modifying therapies also highlights the need for biomarkers to monitor efficacy over time. Both of these needs have been partially addressed by the emergence of improved blood-based biomarkers for Alzheimer disease. Regarding other forms of dementia, the latest development is a CSF assay for aggregated α-synuclein, which permits the biomarker confirmation of synuclein pathology in Lewy body dementia. CSF biomarkers for the diagnosis of Alzheimer disease, Lewy body dementia, and Creutzfeldt-Jakob disease are well established. Blood-based biomarkers for dementia diagnosis are emerging and rapidly evolving. Sensitivity and specificity for diagnosis continue to improve, and they are being incorporated into diagnostic decisions. Fluid biomarkers for monitoring the efficacy of therapy are not yet established. Because serial CSF examinations are impractical, the validation of blood-based biomarkers of disease activity will be critical for addressing this unmet need.

Prognostic signature detects homologous recombination deficient in glioblastoma.

Glioblastoma (GBM) is a frequent malignant tumor in neurosurgery characterized by a high degree of heterogeneity and genetic instability. DNA double-strand breaks generated by homologous recombination deficiency (HRD) are a well-known contributor to genomic instability, which can encourage tumor development. It is unknown, however, whether the molecular characteristics linked with HRD have a predictive role in GBM. The study aims to assess the extent of genomic instability in GBM using HRD score and investigate the prognostic significance of HRD-related molecular features in GBM. The discovery cohort comprised 567 GBM patients from The Cancer Genome Atlas (TCGA) database. We established HRD scores using the single nucleotide polymorphism (SNP) array data and analyzed transcriptomic data from patients with different HRD scores to identify biomarkers associated with HRD. A prognostic model was built by using HRD-related differentially expressed genes (DEGs) and validated in a distinct cohort from the Chinese Glioma Genome Atlas (CGGA) database. Based on the SNP array data, the gene expression profile data, and the clinical characteristics of GBM patients, we found that patients with a high HRD score had a better prognosis than those with a low HRD score. The DNA damage repair (DDR) signaling pathways were notably enriched in the HRD-positive subgroup. The prognostic model was developed by including HRD-related DEGs that could evaluate the clinical prognosis of patients more efficiently than the HRD score. In addition, patients with a low-risk score had a considerably augmented signature of γδT cells. Finally, through univariate and multivariate Cox regression analyses, it was demonstrated that the prognostic model was superior to other prognostic markers. In conclusion, our research has not only demonstrated that a high HRD score is a valid prognostic biomarker in GBM patients but also built a stable prognosis model [odds ratio (OR) 0.18, 95% confidence interval (CI): 0.11-0.23, P<0.001] that is more accurate than conventional prognostic markers such as O6-methylguanine-DNA methyltransferase (MGMT) methylation (OR 0.55, 95% CI: 0.33-0.91, P=0.02).

Minimally Invasive Versus Invasive Proteomics: Urine and Blood Biomarkers in Coronary Artery Disease.

Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide. This underlines the urgent need for effective biomarkers for early diagnosis, risk stratification, and therapeutic counseling. Proteomic signatures from plasma and urine have emerged as promising tools for these efforts, each offering unique advantages and challenges. This review provides a detailed comparison of urine and blood proteomic analyzes in the context of CAD and explores their respective advantages and limitations. Urine proteomics offers a minimally invasive, easily repeatable, and temporally stable sampling method, but faces challenges such as lower protein concentrations and potential contamination. Despite its invasive nature, blood proteomics captures high protein concentration and directly reflects systemic physiological changes, making it valuable for acute assessments. Advances in artificial intelligence (AI) have significantly improved the analysis and interpretation of proteomic data, enabling greater accuracy in diagnosis and predictive modeling. AI algorithms, particularly in pattern recognition and data integration, are helping to uncover subtle relationships between biomarkers and disease progression and supporting the discovery of plasma- and urine-based CAD biomarkers. This review demonstrates the potential of combining urine and blood proteomic data using AI to advance personalized approaches in CAD diagnosis and treatment. Future research should focus on standardization of collection protocols, validation of biomarkers in different populations, and the complexity of integrating data from different sources to maximize the potential of proteomics in the treatment of CAD.

Serum proteome profiling reveals HGFA as a candidate biomarker for pulmonary arterial hypertension

BackgroundIdentification and validation of potential biomarkers could facilitate the identification of pulmonary arterial hypertension (PAH) and thus aid to study their roles in the disease process.MethodsWe used the isobaric tag for relative and absolute quantitation approaches to compare the protein profiles between the serum of PAH patients and the controls. Bioinformatics analyses and enzyme-linked immunosorbent assay (ELISA) identification of PAH patients and the controls were performed to identify the potential biomarkers. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these potential biomarkers. Mendelian randomization (MR) analysis further clarified the relationship between the potential biomarkers and PAH. Additionally, the expression levels of the potential biomarkers were further validated in two PAH animal models (monocrotaline-PH and Sugen5416 plus hypoxia-PH) using ELISA and reverse transcription-quantitative PCR (RT-qPCR).ResultsWe identified significant changes in three proteins including heparanase (HPSE), gelsolin (GSN), and hepatocyte growth factor activator (HGFA) in PAH patients. The ROC analysis showed that the areas under the curve of HPSE, GSN, and HGFA in differentiating PAH patients from controls were 0.769, 0.777, and 0.964, respectively. HGFA was correlated with multiple parameters of right ventricular functions in PAH patients. Besides proteomic analysis, we also used MR method to demonstrate the causal link between genetically reduced HGFA levels and an increased risk of PAH. In subsequent validation study in PAH animal models, the mRNA expression levels of HGFA in the lung tissues were significantly lower in PAH rat models than in controls. In the rat models, serum levels of HGFA were lower compared to the control group and showed a negative correlation with right ventricular systolic pressure.ConclusionThe study demonstrated that HGFA might be a promising biomarker for noninvasive detection of PAH.

Identification and validation of diagnostic biomarkers and immune cell abundance characteristics in Staphylococcus aureus bloodstream infection by integrative bioinformatics analysis.

Staphylococcus aureus (S. aureus) is an opportunistic pathogen that could cause life-threatening bloodstream infections. The objective of this study was to identify potential diagnostic biomarkers of S. aureus bloodstream infection. Gene expression dataset GSE33341 was optimized as the discovery dataset, which contained samples from human and mice. GSE65088 dataset was utilized as a validation dataset. First, after overlapping the differentially expressed genes (DEGs) in S. aureus infection samples from GSE33341-human and GSE33341-mice samples, we detected 63 overlapping genes. Subsequently, the hub genes including DRAM1, PSTPIP2, and UPP1 were identified via three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. Additionally, the receiver operating characteristic curve was leveraged to verify the efficacy of the hub genes. DRAM1 (AUC=1), PSTPIP2 (AUC=1), and UPP1 (AUC=1) were investigated and demonstrated significant expression differences (all P < 0.05) and diagnostic efficacy in the training and validation datasets. Furthermore, the relationship between the diagnostic markers and the abundance of immune cells was assessed using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). These three diagnostic indicators also correlated with multiple immune cells to varying degrees. The expression of DRAM1 was significantly positively correlated with B cell naive and mast cell activation, and negatively correlated with NK cells and CD4/CD8+ T cells. The expression of PSTPIP2 was significantly positively correlated with macrophage M0, macrophage M1, B cell naive, and dendritic cell activation, while the expression of PSTPIP2 was negatively correlated with NK cells and CD4/CD8+ T cells. Significant negative correlations between UPP1 expression and T cell CD4 memory rest and neutrophils were also observed. Finally, we established a mouse model of S. aureus bloodstream infection and collected the blood samples for RNA-Seq analysis and RT-qPCR experiments. The analysis results in RNA-Seq and RT-qPCR experiments further confirmed the significant expression differences (all P < 0.05) of these three genes. Overall, three candidate hub genes (DRAM1, PSTPIP2, and UPP1) were identified initially for S.aureus bloodstream infection diagnosis. Our study could provide potential diagnostic biomarkers for S. aureus bloodstream infection patients.

Urinary Proteomics and Systems Biology Link Eight Proteins to the Higher Risk of Hypertension and Related Complications in Blacks Versus Whites.

Blacks are more prone to salt-sensitive hypertension than Whites. This cross-sectional analysis of a multi-ethnic cohort aimed to search for proteins potentially involved in the susceptibility to salt sensitivity, hypertension, and hypertension-related complications. The study included individuals enrolled in African Prospective Study on the Early Detection and Identification of Cardiovascular Disease and Hypertension (African-PREDICT), Flemish Study of the Environment, Genes and Health Outcomes (FLEMENGHO), Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers (PROVALID)-Austria, and Urinary Proteomics Combined with Home Blood Pressure Telemonitoring for Health Care Reform Trial (UPRIGHT-HTM). Sequenced urinary peptides detectable in 70% of participants allowed the identification of parental proteins and were compared between Blacks and Whites. Of 513 urinary peptides, 300 had significantly different levels among healthy Black (n=476) and White (n=483) South Africans sharing the same environment. Analyses contrasting 582 Blacks versus 1731 Whites, and Sub-Saharan Blacks versus European Whites replicated the findings. COL4A1, COL4A2, FGA, PROC, MGP, MYOCD, FYXD2, and UMOD were identified as the most likely candidates underlying the racially different susceptibility to salt sensitivity, hypertension, and related complications. Enriched pathways included hemostasis, platelet activity, collagens, biology of the extracellular matrix, and protein digestion and absorption. Our study suggests that MGP and MYOCD being involved in cardiovascular function, FGA and PROC in coagulation, FYXD2 and UMOD in salt homeostasis, and COL4A1 and COL4A2 as major components of the glomerular basement membrane are among the many proteins potentially incriminated in the higher susceptibility of Blacks compared to Whites to salt sensitivity, hypertension, and its complication. Nevertheless, these eight proteins and their associated pathways deserve further exploration in molecular and human studies as potential targets for intervention to reduce the excess risk of hypertension and cardiovascular complications in Blacks versus Whites.

Development of a metabolome-based respiratory infection prognostic during COVID-19 arrival.

In a new respiratory virus pandemic, optimizing allocation of scarce medical resources becomes an urgent challenge. Infection prognosis takes on particular importance when allocating scarce antiviral antibodies and drugs, which are most effective when administered before the onset of severe disease. During arrival of the COVID-19 pandemic to the United States in 2020, we conducted a prognostic biomarker discovery and validation effort based upon metabolomic profiling with a liquid-chromatography-mass spectrometer (LC-MS) type used clinically for rapid toxicology. We obtained urine specimens from 163 patients presenting for evaluation. We obtained LC-MS profiles in the initial cohort and used machine learning methods to define a simplified urine metabolomic signature associated with respiratory failure or death by 90 days. This signature was composed of three metabotypes linked to intestinal microbiome metabolism and anticonvulsant use, with a receiver-operator characteristic area under the curve (ROC AUC) of 89.4%. Blinded application of this signature to the subsequent validation cohort yielded a ROC AUC of 81.2%. A model trained on the two baseline metabotypes present before intubation exhibited similar performance in the validation cohort. This study demonstrates the plausibility and promise of rapid metabolome-based prognostic discovery and validation in the opening wave of a pandemic. The approach used here could be used to inform therapeutic and resource allocation decisions early in a future epidemic.IMPORTANCEIn a new respiratory virus pandemic, the ability to identify patients at greatest risk for severe disease is essential to direct scarce medical resources to those most likely to benefit from them. Tools to predict disease severity are best developed early in a pandemic, but laboratory-based resources to develop these may be limited by available technology and by infection precautions. Here, we show that an accessible metabolic profiling approach could identify a prognostic signature of severe disease in the initial wave of COVID-19, when patients presenting for care often exceeded the available doses of convalescent plasma and remdesivir. In a future pandemic, this approach, alongside efforts to identify clinical disease severity predictors, could improve patient outcomes and facilitate therapeutic trials by identifying individuals at high risk for severe disease.

Novel Biomarkers and Imaging Tests for AKI Diagnosis in Patients with Cancer.

The lack of non-invasive urine and blood-based biomarkers for the diagnosis of acute kidney injury (AKI) in patients with cancer is an area of significant unmet clinical need. Traditional non-invasive diagnostic tools that are currently utilized in the clinic, such as creatinine and cystatin C-based eGFR measurements, urinalysis, urine sediment exam, urine protein quantification, and urine electrolyte measurement, lack the sensitivity and specificity to distinguish between the various underlying etiologies of AKI in patients with cancer. Imaging-based diagnostics can be helpful to rule out urinary obstruction, but also lack sensitivity and specificity to diagnose the etiology of AKI. Kidney biopsy is often required for definitive diagnosis. As our scientific understanding of the biological pathways that are dysregulated in AKI has advanced, there has been considerable interest in developing new biomarkers for AKI. For example, the diagnosis of acute interstitial nephritis (AIN), which can occur in patients treated with immune checkpoint inhibitors (ICIs), promises to be revolutionized by the incorporation of urinary testing for inflammatory biomarkers such as C-X-C motif ligand 9 (CXCL9), tumor necrosis factor alpha (TNF-α), and interleukin 9 (IL-9). In the case of cisplatin administration, biomarkers such as neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule 1 (KIM-1) may improve prognostication, differentiating between persistent AKI resulting from acute tubular injury versus pre-renal azotemia. The development and validation of blood, urine and imaging biomarkers into widely utilized diagnostic tests will require a concerted effort, but could improve diagnosis, management and prognostication for a growing group of patients who are at high risk of developing AKI during the course of their illness.

Uromonitor: Clinical Validation and Performance Assessment of a Urinary Biomarker Within the Surveillance of Patients With Non-Muscle-Invasive Bladder Cancer.

Alternative, noninvasive, cost-effective methods to complement or serve as substitutes to current standard-of-care (SOC) procedures in NMIBC follow-up are needed. Uromonitor is a urine biomarker test detecting bladder cancer recurrence through the screening of TERT, FGFR3, and KRAS hotspot mutations. The aim of this study was to assess Uromonitor performance by comparing it with the current SOC methods. Four hundred thirty-nine patients with 528 NMIBC surveillances were enrolled in this study. All patients underwent SOC methods and provided a urine sample for Uromonitor analysis before undergoing cystoscopy. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for recurrence and compared with the gold-standard cystoscopy plus transurethral resection of bladder tumor histopathology. Uromonitor displayed a sensitivity of 87% (95% CI, 74-95), with only 6 of 47 recurrences failing to be detected; specificity of 99% (98-100); PPV of 93% (82-98); and a NPV of 99% (97-99). Cystoscopy showed a total of 22 false positives (32%) not confirmed by transurethral resection of bladder tumor, whereas Uromonitor presented only 3 positive tests where no lesions were found. Overall recurrence rate was 8.9% (n = 47) among 528 total screenings. Sensitivity, specificity, PPV, and NPV values for Uromonitor remained high across all NMIBC grades and stages. Uromonitor represents a reliable tool in the detection of NMIBC recurrence in patients undergoing routine surveillance, regardless of stage and grade. To our knowledge, this is the largest single-center study assessing Uromonitor's performance, thus validating its usefulness in clinical practice.

Targeted multiplex proteomics for the development and validation of biomarkers in primary aldosteronism subtyping.

Primary aldosteronism (PA), a significant cause of secondary hypertension affecting ∼10% of patients with severe hypertension, exacerbates cardiovascular, and cerebrovascular complications even after blood pressure control. PA is categorized into two main subtypes: unilateral aldosterone-producing adenomas (APA) and bilateral hyperaldosteronism (BHA), each requiring distinct treatment approaches. Accurate subtype classification is crucial for selecting the most effective treatment. The goal of this study was to develop novel blood-based proteomic biomarkers to differentiate between APA and BHA subtypes in patients with PA. Five subtyping differential protein biomarker candidates (APOC3, CD56, CHGA, KRT5, and AZGP1) were identified through targeted proteomic profiling of plasma. The subtyping efficiency of these biomarkers was assessed at both the tissue gene expression and blood protein expression levels. To explore the underlying biology of APA and BHA, significant differential pathways were investigated. The five-protein panel proved highly effective in distinguishing APA from BHA in both tissue and blood samples. By integrating these five protein biomarkers with aldosterone and renin, our blood-based predictive methods achieved remarkable receiver operating characteristic (ROC) area under the ROC curves of 0.986 (95% CI: 0.963-1.000) for differentiating essential hypertension from PA, and 0.922 (95% CI: 0.846-0.998) for subtyping APA versus BHA. These outcomes surpass the performance of the existing Kobayashi score subtyping system. Furthermore, the study validated differential pathways associated with the pathophysiology of PA, aligning with current scientific knowledge and opening new avenues for advancing PA care. The new blood-based biomarkers for PA subtyping hold the potential to significantly enhance clinical utility and advance the practice of PA care.

Parkinson's Disease: Biomarkers for Diagnosis and Disease Progression.

Parkinson's disease (PD) is a progressive neurological disease that causes both motor and nonmotor symptoms. While our understanding of putative mechanisms has advanced significantly, it remains challenging to verify biomarkers with sufficient evidence for regular clinical use. Clinical symptoms are the primary basis for diagnosing the disease, which can be mild in the early stages and overlap with other neurological disorders. As a result, clinical testing and medical records are mostly relied upon for diagnosis, posing substantial challenges during both the initial diagnosis and the continuous disease monitoring. Recent biochemical, neuroimaging, and genetic biomarkers have helped us understand the pathophysiology of Parkinson's disease. This comprehensive study focuses on these biomarkers, which were chosen based on their relevance, methodological excellence, and contribution to the field. Biochemical biomarkers, including α-synuclein and glial fibrillary acidic protein (GFAP), can predict disease severity and progression. The dopaminergic system is widely used as a neuroimaging biomarker to diagnose PD. Numerous genes and genome wide association study (GWAS) sites have been related to the development of PD. Recent research on the SNCA gene and leucine-rich repeat protein kinase 2 (LRRK2) has shown promising results. By evaluating current studies, this review intends to uncover gaps in biomarker validation and use, while also highlighting promising improvements. It emphasizes the need for dependable and reproducible indicators in improving PD diagnosis and prognosis. These biomarkers may open up new avenues for early diagnosis, disease progression tracking, and the development of personalized treatment programs.

The use of 4D data-independent acquisition-based proteomic analysis and machine learning to reveal potential biomarkers for stress levels.

Research suggests that individuals who experience prolonged exposure to stress may be at higher risk for developing psychological stress disorders. Currently, psychological stress is primarily evaluated by professional physicians using rating scales, which may be prone to subjective biases and limitations of the scales. Therefore, it is imperative to explore more objective, accurate, and efficient biomarkers for evaluating the level of psychological stress in an individual. In this study, we utilized 4D data-independent acquisition (4D-DIA) proteomics for quantitative protein analysis, and then employed support vector machine (SVM) combined with SHAP interpretation algorithm to identify potential biomarkers for psychological stress levels. Biomarkers validation was subsequently achieved through machine learning classification and a substantial amount of a priori knowledge derived from the knowledge graph. We performed cross-validation of the biomarkers using two batches of data, and the results showed that the combination of Glyceraldehyde-3-phosphate dehydrogenase and Fibronectin yielded an average area under the curve (AUC) of 92%, an average accuracy of 86%, an average F1 score of 79%, and an average sensitivity of 83%. Therefore, this combination may represent a potential approach for detecting stress levels to prevent psychological stress disorders.