A multi-biomarker panel for predicting Tocilizumab response in Rheumatoid arthritis patients.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammation in the synovial lining of the joints. Key inflammatory cytokines such as interleukin-6 (IL-6), TNF-α, and others play a critical role in the activation of local synovial leukocytes and the induction of chronic inflammation. Tocilizumab (TCZ), a humanized anti-IL-6 receptor monoclonal antibody, has demonstrated significant clinical efficacy in treating RA patients. However, similar to other inflammatory cytokine blockers, such as TNF-alpha inhibitors, Interleukin-1 inhibitors, or CD20 inhibitors, some patients do not respond to treatment. To address this challenge, our study employed a high-precision proteomics approach to identify protein biomarkers capable of predicting clinical responses to Tocilizumab in RA patients. Through the use of data-independent acquisition (DIA) mass spectrometry, we analyzed serum samples from both TCZ responders and non-responders to discover potential biomarker candidates. These candidates were subsequently validated using individual serum samples from two independent cohorts: a training set (N = 70) and a test set (N = 18), allowing for the development of a robust multi-biomarker panel. The constructed multi-biomarker panel demonstrated an average discriminative power of 86 % between response and non-response groups, with a high area under the curve (AUC) value of 0.84. Additionally, the panel exhibited 100 % sensitivity and 60 % specificity. Collectively, our multi-biomarker panel holds promise as a diagnostic tool to predict non-responders to TCZ treatment in RA patients.

Alterations to sphingolipid metabolism from antipsychotic administration in healthy volunteers are restored following the use of cannabidiol.

Randomized clinical trials substantiate cannabidiol (CBD) as a next-generation antipsychotic, effective in alleviating positive and negative symptoms associated with psychosis, while minimising the adverse effects seen with established treatments. Although the mechanisms remain debated, CBD is known to induce drug-responsive changes in lipid-based retrograde neurotransmitters. Lipid aberrations are also frequently observed with antipsychotics, which may contribute to their efficacy or increase the risk of undesirables, including metabolic dysfunction, obesity and dyslipidaemia. Our study investigated CBD's impact following lipid responses triggered by interaction with second-generation antipsychotics (SGA) in a randomized phase I safety study. Untargeted mass spectrometry assessed the lipidomic profiles of human sera, collected from 38 healthy volunteers. Serum samples were obtained prior to commencement of any medication (t = 0), 3 days after consecutive administration of one of the five, placebo-controlled, treatment arms designed to achieve steady-state concentrations of each SGA (amisulpride, 150 mg/day; quetiapine, 300 mg/day; olanzapine 10 mg/day; risperidone, 3 mg/day), and after six successive days of SGA treatment combined with CBD (800 mg/day). Receiver operating characteristics (ROC) refined 3712 features to a putative list of 15 lipids significantly altered (AUC > 0.7), classified into sphingolipids (53 %), glycerolipids (27 %) and glycerophospholipids (20 %). Targeted mass spectrometry confirmed reduced sphingomyelin and ceramide levels with antipsychotics, which mapped along their catabolic pathway and were restored by CBD. These sphingolipids inversely correlated with body weight after olanzapine, quetiapine, and risperidone treatment, where CBD appears to have arrested or attenuated these effects. Herein, we propose CBD may alleviate aberrant sphingolipid metabolism and that further investigation into sphingolipids as markers for monitoring side effects of SGAs and efficacy of CBD is warranted.

Biomarker panels for fruit intake assessment: a metabolomics analysis in the ELSA-Brasil study.

INTRODUCTION: Food intake biomarkers are used to estimate dietary exposure; however, selecting a single biomarker to evaluate a specific dietary component is difficult due to the overlap of diverse compounds from different foods. Therefore, combining two or more biomarkers can increase the sensitivity and specificity of food intake estimates. OBJECTIVE: This study aimed to evaluate the ability of metabolite panels to distinguish between self-reported fruit consumers and non-consumers among participants in the Longitudinal Study of Adult Health. MATERIALS AND METHODS: A total of 93 healthy adults of both sexes were selected from the Longitudinal Study of Adult Health. A 24-h dietary recall was obtained using the computer-assisted 24-h food recall GloboDiet software, and 24-h urine samples were collected from each participant. Metabolites were identified in urine using liquid chromatography coupled with high-resolution mass spectrometry by comparing their exact mass and fragmentation patterns using free-access databases. Multivariate receiver operating characteristic curve (ROC) analysis and partial least squares discriminant analysis were used to verify the ability of the metabolite combination to classify daily and non-daily fruit consumers. Fruit intake was identified using a 24 h dietary recall (24 h-DR). RESULTS: Bananas, grapes, and oranges are included in the summary. The panel of biomarkers exhibited an area under the curve (AUC) > 0.6 (Orange AUC = 0.665; Grape AUC = 0.622; Bananas AUC = 0.602; All fruits AUC = 0.679; Citrus AUC = 0.693) and variable importance projection score > 1.0, and these were useful for assessing the sensitivity and predictability of food intake in our population. CONCLUSION: A panel of metabolites was able to classify self-reported fruit consumers with strong predictive power and high specificity and sensitivity values except for banana and total fruit intake.

Blood-based inflammatory protein biomarker panel for the prediction of relapse and severity in patients with neuromyelitis optica spectrum disorder: A prospective cohort study.

BACKGROUND: To date, most existing models for predicting neuromyelitis optica spectrum disorder (NMOSD) are based primarily on clinical characteristics. Blood-based NMOSD severity and prognostic predictive immune- and inflammation-related biomarkers are needed. We aimed to investigate the associations between plasma inflammatory biomarkers and relapse and attack severity in NMOSD. METHODS: This two-step, single-center prospective cohort study included discovery and validation cohorts. We quantified 92 plasma inflammatory proteins by using Olink's proximity extension assay and identified differentially expressed proteins in the relapse group (relapse within 1 year of follow-up) and severe attack group. To define a new molecular prognostic model, we calculated the risk score of each patient based on the key protein signatures and validated the results in the validation cohort. RESULTS: The relapse prediction model, including FGF-23, DNER, GDNF, and SLAMF1, predicted the 1-year relapse risk. The severe attack prediction model, including PD-L1 and MCP-2, predicted the severe clinical attack risk. Both the relapse and severe attack prediction models demonstrated good discriminative ability and high accuracy in the validation cohort. CONCLUSIONS: Our discovered biomarker signature and prediction models may complement current clinical risk stratification approaches. These inflammatory biomarkers could contribute to the discovery of therapeutic interventions and prevent NMOSD progression.

Predictive, preventive and personalised approach as a conceptual and technological innovation in primary and secondary care of inflammatory bowel disease benefiting affected individuals and populations.

Inflammatory bowel disease (IBD) is a global health burden which carries lifelong morbidity affecting all age groups in populations with the disease-specific peak of the age groups ranging between 15 and 35 years, which are of great economic importance for the society. An accelerating incidence of IBD is reported for newly industrialised countries, whereas stabilising incidence but increasing prevalence is typical for countries with a Westernised lifestyle, such as the European area and the USA. Although the aetiology of IBD is largely unknown, the interplay between the genetic, environmental, immunological, and microbial components is decisive for the disease manifestation, course, severity and individual outcomes. Contextually, the creation of an individualised patient profile is crucial for the cost-effective disease management in primary and secondary care of IBD. The proposed pathomechanisms include intestinal pathoflora and dysbiosis, chronic inflammation and mitochondrial impairments, amongst others, which collectively may reveal individual molecular signatures defining IBD subtypes and leading to clinical phenotypes, patient stratification and cost-effective protection against health-to-disease transition and treatments tailored to individualised patient profiles-all the pillars of an advanced 3PM approach. The paradigm change from reactive medical services to predictive diagnostics, cost-effective targeted prevention and treatments tailored to individualised patient profiles in overall IBD management holds a promise to meet patient needs in primary and secondary care, to increase the life-quality of affected individuals and to improve health economy in the area of IBD management. This article analyses current achievements and provides the roadmap for future developments in the area in the context of 3P medicine benefiting society at large.

Development and validation of a novel biomarker panel for Crohn's disease and rheumatoid arthritis diagnosis and treatment.

BACKGROUND: Crohn's disease (CD) and rheumatoid arthritis (RA) are immune-mediated inflammatory diseases. However, the molecular mechanisms linking these two diseases remain unclear. METHODS: To identify shared core genes between CD and RA, we employed differential gene analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Functional annotation of these core biomarkers was performed using consensus clustering and gene set enrichment analysis. We also constructed a protein-protein network and a miRNA-mRNA network using multiple databases, and potential therapeutic agents targeting the core biomarkers were predicted. Finally, we confirmed the expression of the genes in the biomarker panel in both CD and RA using quantitative PCR. RESULTS: A total of five shared core genes, namely C-X-C motif chemokine ligand 10 (CXCL10), C-X-C motif chemokine ligand 9 (CXCL9), aquaporin 9 (AQP9), secreted phosphoprotein 1 (SPP1), and metallothionein 1M (MT1M), were identified as core biomarkers. These biomarkers activate classical pro-inflammatory and immune signaling pathways, influencing immune cell aggregation. Additionally, testosterone was identified as a potential therapeutic agent targeting the biomarkers identified in this study. The expression of genes in the biomarker panel in CD and RA was confirmed through quantitative PCR. CONCLUSION: Our study revealed some core genes shared between CD and RA and established a novel biomarker panel with potential implications for the diagnosis and treatment of these diseases.

Using saliva epigenetic data to develop and validate a multivariable predictor of esophageal cancer status.

Background: Salivary epigenetic biomarkers may detect esophageal cancer. Methods: A total of 256 saliva samples from esophageal adenocarcinoma patients and matched volunteers were analyzed with Illumina EPIC methylation arrays. Three datasets were created, using 64% for discovery, 16% for testing and 20% for validation. Modules of gene-based methylation probes were created using weighted gene coexpression network analysis. Module significance to disease and gene importance to module were determined and a random forest classifier generated using best-scoring gene-related epigenetic probes. A cost-sensitive wrapper algorithm maximized cancer diagnosis. Results: Using age, sex and seven probes, esophageal adenocarcinoma was detected with area under the curve of 0.72 in discovery, 0.73 in testing and 0.75 in validation datasets. Cancer sensitivity was 88% with specificity of 31%. Conclusion: We have demonstrated a potentially clinically viable classifier of esophageal cancer based on saliva methylation.

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables.

BACKGROUND: Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies. METHODS: In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO2 (titanium dioxide) strategy. RESULTS: Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69). CONCLUSIONS: To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.

[Serum brain damage biomarkers as a diagnostic and prognostic tool in ischemic stroke]. / Syvorotochnye biomarkery porazheniya mozga kak diagnosticheskii i prognosticheskii instrument pri ishemicheskom insul'te.

OBJECTIVE: To study serum quantities of neuron specific enolase (NSE), glial fibrillary acidic protein (GFAP) and NR2-antibodies (NR2-ab) in various cerebrovascular pathology and assess their value as a panel used as a diagnostic and predictive tool for stroke. MATERIAL AND METHODS: NSE, GFAP and NR2-ab serum levels were measured twice for 84 patients with ischemic stroke (IS) and 8 patients with hemorrhagic stroke (HI), once for 8 patients with transient ischemic attack (TIA), 26 patients with chronic brain ischemia (CBI), 27 healthy volunteers (HV). RESULTS: NSE and GFAP levels were significantly higher in IS than in CBI and HV patients, and NR2-ab levels in IS were higher than in TIA and lower than in HV. In patients with more pronounced neurological deficiency and less favorable functional outcome by day 10-14 of IS, the levels of NSE, GFAP and NR2-ab were higher. Sensitivity and specificity of biomarker panel was higher than with their separate application. CONCLUSION: The NSE, GFAP and NR2-ab biomarkers have a diagnostic and predictive value for IS.

Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies.

Background: Idiopathic pulmonary fibrosis (IPF) is a rare interstitial lung disease with a poor prognosis that currently lacks effective treatment methods. Preventing the acute exacerbation of IPF, identifying the molecular subtypes of patients, providing personalized treatment, and developing individualized drugs are guidelines for predictive, preventive, and personalized medicine (PPPM / 3PM) to promote the development of IPF. Oxidative stress (OS) is an important pathological process of IPF. However, the relationship between the expression levels of oxidative stress-related genes (OSRGs) and clinical indices in patients with IPF is unclear; therefore, it is still a challenge to identify potential beneficiaries of antioxidant therapy. Because PPPM aims to recognize and manage diseases by integrating multiple methods, patient stratification and analysis based on OSRGs and identifying biomarkers can help achieve the above goals. Methods: Transcriptome data from 250 IPF patients were divided into training and validation sets. Core OSRGs were identified in the training set and subsequently clustered to identify oxidative stress-related subtypes. The oxidative stress scores, clinical characteristics, and expression levels of senescence-associated secretory phenotypes (SASPs) of different subtypes were compared to identify patients who were sensitive to antioxidant therapy to conduct differential gene functional enrichment analysis and predict potential therapeutic drugs. Diagnostic markers between subtypes were obtained by integrating multiple machine learning methods, their expression levels were tested in rat models with different degrees of pulmonary fibrosis and validation sets, and nomogram models were constructed. CIBERSORT, single-cell RNA sequencing, and immunofluorescence staining were used to explore the effects of OSRGs on the immune microenvironment. Results: Core OSRGs classified IPF into two subtypes. Patients classified into subtypes with low oxidative stress levels had better clinical scores, less severe fibrosis, and lower expression of SASP-related molecules. A reliable nomogram model based on five diagnostic markers was constructed, and these markers' expression stability was verified in animal experiments. The number of neutrophils in the immune microenvironment was significantly different between the two subtypes and was closely related to the degree of fibrosis. Conclusion: Within the framework of PPPM, this work comprehensively explored the role of OSRGs and their mediated cellular senescence and immune processes in the progress of IPF and assessed their capabilities aspredictors of high oxidative stress and disease progression,targets of the vicious loop between regulated pulmonary fibrosis and OS for targeted secondary and tertiary prevention, andreferences for personalized antioxidant and antifibrotic therapies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00334-4.

Metabolomics analysis of visceral leishmaniasis based on urine of golden hamsters.

BACKGROUND: Leishmaniasis is one of the most neglected tropical diseases and is spread mainly in impoverished regions of the world. Although many studies have focused on the host's response to Leishmania invasion, relatively less is known about the complex processes at the metabolic level, especially the metabolic alterations in the infected hosts. METHODS: In this study, we conducted metabolomics analysis on the urine of golden hamsters in the presence or absence of visceral leishmaniasis (VL) using the ultra-performance liquid chromatography (UPLC) system tandem high-resolution mass spectrometer (HRMS). The metabolic characteristics of urine samples, along with the histopathological change and the parasite burden of liver and spleen tissues, were detected at 4 and 12 weeks post infection (WPI), respectively. RESULTS: Amino acid metabolism was extensively affected at both stages of VL progression. Meanwhile, there were also distinct metabolic features at different stages. At 4 WPI, the significantly affected metabolic pathways involved alanine, aspartate and glutamate metabolism, the pentose phosphate pathway (PPP), histidine metabolism, tryptophan metabolism and tyrosine metabolism. At 12 WPI, the markedly enriched metabolic pathways were almost concentrated on amino acid metabolism, including tyrosine metabolism, taurine and hypotaurine metabolism and tryptophan metabolism. The dysregulated metabolites and metabolic pathways at 12 WPI were obviously less than those at 4 WPI. In addition, seven metabolites that were dysregulated at both stages through partial least squares-discriminant analysis (PLS-DA) and receiver-operating characteristic (ROC) tests were screened to be of diagnostic potential. The combination of these metabolites as a potential biomarker panel showed satisfactory performance in distinguishing infection groups from control groups as well as among different stages of infection. CONCLUSION: Our findings could provide valuable information for further understanding of the host response to Leishmania infection from the aspect of the urine metabolome. The proposed urine biomarker panel could help in the development of a novel approach for the diagnosis and prognosis of VL.

Network analysis identifies a gene biomarker panel for sepsis-induced acute respiratory distress syndrome.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is characterized by non-cardiogenic pulmonary edema caused by inflammation, which can lead to serious respiratory complications. Due to the high mortality of ARDS caused by sepsis, biological markers that enable early diagnosis are urgently needed for clinical treatment. METHODS: In the present study, we used the public microarray data of whole blood from patients with sepsis-induced ARDS, patients with sepsis-alone and healthy controls to perform an integrated analysis based on differential expressed genes (DEGs) and co-expression network to identify the key genes and pathways related to the development of sepsis into ARDS that may be key targets for diagnosis and treatment. RESULTS: Compared with controls, we identified 180 DEGs in the sepsis-alone group and 152 DEGs in the sepsis-induced ARDS group. About 70% of these genes were unique to the two groups. Functional analysis of DEGs showed that neutrophil-mediated inflammation and mitochondrial dysfunction are the main features of ARDS induced by sepsis. Gene network analysis identified key modules and screened out key regulatory genes related to ARDS. The key genes and their upstream regulators comprised a gene panel, including EOMES, LTF, CSF1R, HLA-DRA, IRF8 and MPEG1. Compared with the healthy controls, the panel had an area under the curve (AUC) of 0.900 and 0.914 for sepsis-alone group and sepsis-induced ARDS group, respectively. The AUC was 0.746 between the sepsis-alone group and sepsis-induced ARDS group. Moreover, the panel of another independent blood transcriptional expression profile dataset showed the AUC was 0.769 in diagnosing sepsis-alone group and sepsis-induced ARDS group. CONCLUSIONS: Taken together, our method contributes to the diagnosis of sepsis and sepsis-induced ARDS. The biological pathway involved in this gene biomarker panel may also be a critical target in combating ARDS caused by sepsis.

Exosomal miRNA Biomarker Panel for Pancreatic Ductal Adenocarcinoma Detection in Patient Plasma: A Pilot Study.

Pancreatic ductal adenocarcinoma (PDAC) is rapidly becoming one of the leading causes of cancer-related deaths in the United States, and with its high mortality rate, there is a pressing need to develop sensitive and robust methods for detection. Exosomal biomarker panels provide a promising avenue for PDAC screening since exosomes are highly stable and easily harvested from body fluids. PDAC-associated miRNAs packaged within these exosomes could be used as diagnostic markers. We analyzed a series of 18 candidate miRNAs via RT-qPCR to identify the differentially expressed miRNAs (p < 0.05, t-test) between plasma exosomes harvested from PDAC patients and control patients. From this analysis, we propose a four-marker panel consisting of miR-93-5p, miR-339-3p, miR-425-5p, and miR-425-3p with an area under the curve (AUC) of the receiver operator characteristic curve (ROC) of 0.885 with a sensitivity of 80% and a specificity of 94.7%, which is comparable to the CA19-9 standard PDAC marker diagnostic.

Short communication: unique metabolic signature of proliferative retinopathy in the tear fluid of diabetic patients with comorbidities - preliminary data for PPPM validation.

Type 2 diabetes (T2DM) defined as the adult-onset type that is primarily not insulin-dependent, comprises over 95% of all diabetes mellitus (DM) cases. According to global records, 537 million adults aged 20-79 years are affected by DM that means at least 1 out of 15 persons. This number is projected to grow by 51% by the year 2045. One of the most common complications of T2DM is diabetic retinopathy (DR) with an overall prevalence over 30%. The total number of the DR-related visual impairments is on the rise, due to the growing T2DM population. Proliferative diabetic retinopathy (PDR) is the progressing DR and leading cause of preventable blindness in working-age adults. Moreover, PDR with characteristic systemic attributes including mitochondrial impairment, increased cell death and chronic inflammation, is an independent predictor of the cascading DM-complications such as ischemic stroke. Therefore, early DR is a reliable predictor appearing upstream of this "domino effect". Global screening, leading to timely identification of DM-related complications, is insufficiently implemented by currently applied reactive medicine. A personalised predictive approach and cost-effective targeted prevention shortly - predictive, preventive and personalised medicine (PPPM / 3PM) could make a good use of the accumulated knowledge, preventing blindness and other severe DM complications. In order to reach this goal, reliable stage- and disease-specific biomarker panels are needed characterised by an easy way of the sample collection, high sensitivity and specificity of analyses. In the current study, we tested the hypothesis that non-invasively collected tear fluid is a robust source for the analysis of ocular and systemic (DM-related complications) biomarker patterns suitable for differential diagnosis of stable DR versus PDR. Here, we report the first results of the comprehensive ongoing study, in which we correlate individualised patient profiles (healthy controls versus patients with stable D as well as patients with PDR with and without co-morbidities) with their metabolic profiles in the tear fluid. Comparative mass spectrometric analysis performed has identified following metabolic clusters which are differentially expressed in the groups of comparison: acylcarnitines, amino acid & related compounds, bile acids, ceramides, lysophosphatidyl-choline, nucleobases & related compounds, phosphatidyl-cholines, triglycerides, cholesterol esters, and fatty acids. Our preliminary data strongly support potential clinical utility of metabolic patterns in the tear fluid indicating a unique metabolic signature characteristic for the DR stages and PDR progression. This pilot study creates a platform for validating the tear fluid biomarker patterns to stratify T2DM-patients predisposed to the PDR. Moreover, since PDR is an independent predictor of severe T2DM-related complications such as ischemic stroke, our international project aims to create an analytical prototype for the "diagnostic tree" (yes/no) applicable to healthrisk assessment in diabetes care.

Serum biomarker panel for disease severity and prognosis in patients with COVID-19.

BACKGROUND: Coronavirus disease-2019 (COVID-19) has become a worldwide emergency and has had a severe impact on human health. Inflammatory factors have the potential to either enhance the efficiency of host immune responses or damage the host organs with immune overreaction in COVID-19. Therefore, there is an urgent need to investigate the functions of inflammatory factors and serum markers that participate in disease progression. METHODS: In total, 54 COVID-19 patients were enrolled in this study. Disease severity was evaluated by clinical evaluation, laboratory tests, and computed tomography (CT) scans. Data were collected at: admission, 3-5 days after admission, when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA detection became negative, and composite endpoint. RESULTS: We found that the positive rate in sputum was three times higher than that in throat swabs. Higher levels of C-reactive protein (CRP), lactate dehydrogenase (LDH), D-dimer (D-D), interleukin-6 (IL-6) and neutrophil-to-lymphocyte ratio (NLR) or lower lymphocyte counts suggested more severe disease, and the levels of cytokines and serum markers were intrinsically correlated with disease progression. When SARS-CoV-2 RNA detection became negative, the receiver operating characteristic (ROC) curve demonstrated that LDH had the highest sensitivity independently, and four indicators (NLR, CRP, LDH, and D-D) when combined had the highest sensitivity in distinguishing critically ill patients from mild ones. CONCLUSIONS: Monitoring dynamic changes in NLR, CRP, LDH, IL-6, and D-D levels, combined with CT imaging and viral RNA detection in sputum, could aid in severity evaluation and prognosis prediction and facilitate COVID-19 treatment.

Plasma IGFBP-1, Fas, kallistatin, and P-selectin as predictive biomarkers of histologic chorioamnionitis and associated intra-amniotic infection in women with preterm labor.

PROBLEM: To determine whether altered levels of 13 plasma biomarkers, alone or in combination, could be independently associated with histologic chorioamnionitis (HCA) and microbial-associated HCA (defined as the presence of HCA along with microbial invasion) in women with preterm labor (PTL). METHODS OF STUDY: This was a retrospective cohort study involving 77 singleton pregnant women with PTL (23-34 gestational weeks) who delivered within 96 h of plasma and amniotic fluid (AF) sampling. DKK-3, E-selectin, Fas, haptoglobin, IGFBP-1, kallistatin, MMP-2, MMP-8, pentraxin 3, progranulin, P-selectin, SAA4, and TGFBI levels were assayed in plasma samples by ELISA. AF obtained via amniocentesis was used for microorganism identification. RESULTS: Multiple logistic regression analyses revealed significant associations between low plasma IGFBP-1 levels and acute HCA, and between low plasma Fas and kallistatin levels, and elevated plasma P-selectin levels and microbial-associated HCA (all p < .05), after adjusting for gestational age. Using a stepwise regression procedure, a multi-biomarker panel for microbial-associated HCA was developed, which included plasma MMP-2, kallistatin, and P-selectin levels (area under the curve [AUC], .867). The AUC for this three-marker panel was significantly or borderline significantly greater than that of any single variable included in the panel. However, a predictive model for acute HCA could not be developed because only one variable (MMP-2) was selected. CONCLUSIONS: These findings demonstrate that IGFBP-1, Fas, kallistatin, and P-selectin are associated with acute HCA and microbial-associated HCA in women with PTL. Their combined use can significantly improve the diagnostic ability for the detection of microbial-associated HCA.

Multi-biomarker panel prediction model for diagnosis of pancreatic cancer.

BACKGROUND/PURPOSE: The current study aimed to develop a prediction model using a multi-marker panel as a diagnostic screening tool for pancreatic ductal adenocarcinoma. METHODS: Multi-center cohort of 1991 blood samples were collected from January 2011 to September 2019, of which 609 were normal, 145 were other cancer (colorectal, thyroid, and breast cancer), 314 were pancreatic benign disease, and 923 were pancreatic ductal adenocarcinoma. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers: LRG1, TTR, and CA 19-9. Using a logistic regression model on a training data set, the predicted values for pancreatic ductal adenocarcinoma were obtained, and the result was classification into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and three biomarkers. RESULTS: Participants were categorized into four groups as normal (n = 609), other cancer (n = 145), pancreatic benign disease (n = 314), and pancreatic ductal adenocarcinoma (n = 923). The normal, other cancer, and pancreatic benign disease groups were clubbed into the non-pancreatic ductal adenocarcinoma group (n = 1068). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively. CONCLUSIONS: This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing pancreatic ductal adenocarcinoma from normal and benign pancreatic disease states, as well as patients with other cancers.

Comparison between models for detecting hepatocellular carcinoma in patients with chronic liver diseases of various etiologies: ASAP score versus GALAD score.

BACKGROUND: Diagnostic panels based on multiple biomarkers and clinical characteristics are considered more favorable than individual biomarker to diagnose hepatocellular carcinoma (HCC). Based on age, sex, alpha-fetoprotein (AFP), and protein induced by vitamin K absence II (PIVKA-II) with/without AFP-L3, ASAP and GALAD models are potential diagnostic panels. The diagnostic performances of these two panels were compared relative to HCC detection among patients with various etiologies of chronic liver diseases (CLDs). METHODS: A multicenter case-control study recruited CLDs patients with and without HCC from 14 Chinese hospitals. The etiologies of CLDs included hepatitis B virus (HBV), hepatitis C virus (HCV), alcoholic liver disease (ALD), and nonalcoholic fatty liver disease (NAFLD). Using area under the receiver operating characteristic curve (AUC) values, the diagnostic performances of ASAP and GALAD models were compared to detect HCC among patients with various etiologies of CLDs. RESULTS: Among 248 HCC patients and 722 CLD controls, the ASAP model demonstrated the highest AUC (0.886) to detect HCC at any stage, outperforming the GALAD model (0.853, P = 0.001), as well as any individual biomarker (0.687-0.799, all P < 0.001). In the subgroup analysis of various CLDs etiologies, the ASAP model outperformed the GALAD model to HCC independent of CLDs etiology. In addition, the ASAP model performed better in detecting early-stage (BCLC stage 0/A) HCC versus the GALAD model. CONCLUSIONS: Despite using one less laboratory variable (AFP-L3), the ASAP model demonstrated better diagnostic performance than the GALAD model to detect all-stage HCC among patients with various etiologies of CLDs-related HCC.

A liquid biopsy signature of circulating exosome-derived mRNAs, miRNAs and lncRNAs predict therapeutic efficacy to neoadjuvant chemotherapy in patients with advanced gastric cancer.

At present, there is no validated marker to identify the subpopulation of patients with advanced gastric cancer (AGC) who might benefit from neoadjuvant chemotherapy (NACT). In view of this clinical challenge, the identification of non-invasive biomarkers for efficacy prediction of NACT in patients with AGC is imperative. Herein, we aimed to develop a non-invasive, liquid-biopsy-based assay by using an exosome-derived RNAs model based on multi-omics characteristics of RNAs. We firstly used a multi-omics strategy to characterize the mRNAs, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) profiles of circulating exosome enriched fractions in responders to NACT paired with non-responders, using RNA sequencing. Finally, numerous miRNAs, mRNAs and lncRNAs were identified to be associated with the response to NACT in patients with AGC, and it was validated in an independent cohort with promising AUC values. Furthermore, we established a 6-exosome-RNA panel that could robustly identified responders from non-responders treated with fluorouracil-based neoadjuvant chemotherapy.

A metabolic biomarker panel of restless legs syndrome in peritoneal dialysis patients.

BACKGROUND: Restless legs syndrome (RLS) is a neuromotor disorder, and dialysis patients are more likely to develop RLS. RLS often causes sleep disorders, anxiety and depression in patients. It will increase the risk of death and severely affect the life of patients. At present, RLS has not received enough recognition and attention, and the misdiagnosis rate can reach more than 10%. METHODS: The discovery set selected 30 peritoneal dialysis (PD) patients and 27 peritoneal dialysis patients with RLS (PD-RLS). A metabolomics method based on ultra performance liquid chromatography tandem quadrupole time-of-flight mass spectrometric method (UPLC-Q-TOF/MS) was used to analyze the differential metabolites of the two groups. 51 PD patients and 51 PD-RLS patients were included in the validation set. The receiver operating characteristic (ROC) analysis was used to evaluate the early diagnostic biomarkers, and the correlation between the differential metabolites and laboratory test indexes was analyzed to explore the biological function of the differential metabolites. RESULTS: Through the integrated analysis, four metabolites can be used as markers for the diagnosis of PD-RLS, including Hippuric acid, Phenylacetylglutamine, N,N,N-Trimethyl-L-alanyl-L-proline betaine and Threonic acid. Through ROC analysis, it is found that they can be used as a metabolic biomarker panel, and the area under the curve of this combination is more than 0.9, indicating that the panel has good diagnostic and predictive ability. CONCLUSION: Metabolomics based on UPLC-Q-TOF/MS technology can effectively identify the potential biomarkers, and provide a theoretical basis for the early diagnosis, prevention and treatment on PD-RLS.