Single Biomarker Research Articles

Predictive Utility of Biochemical Markers for the Diagnosis and Prognosis of Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a common complication during pregnancy with an increasing prevalence worldwide. Early prediction of GDM and its associated adverse outcomes is crucial for timely intervention and improved maternal and fetal health. The objective of this review is to provide a comprehensive summary of contemporary evidence on biomarkers, focusing on their potential to predict the development of GDM and serve as predictors of maternal, fetal, and neonatal outcomes in women with GDM. A literature search was conducted in the PubMed database using relevant terms. Original research articles published in English between 1 January 2015, and 30 June 2024, were included. A two-stage screening process was employed to identify studies on biomarkers for GDM diagnosis and prognosis and to evaluate the evidence for each biomarker’s diagnostic performance and its potential prognostic correlation with GDM. Various biochemical markers, including adipokines, inflammatory markers, insulin resistance markers, glycemic markers, lipid profile markers, placenta-derived markers, and other related markers, have shown promise in identifying women at risk of developing GDM and predicting adverse pregnancy outcomes. Several promising markers with high predictive performance were identified. However, no single biomarker has demonstrated sufficient accuracy to replace the current diagnostic criteria for GDM. The complexity of multiple pathways in GDM pathogenesis highlights the need for a multi-marker approach to improve risk stratification and guide personalized management strategies. While significant progress has been made in GDM biomarker research, further studies are required to refine and validate these markers for clinical use and to develop a comprehensive, evidence-based approach to GDM prediction and management that can improve maternal and child health outcomes.

The Molecular Classification of Pheochromocytomas and Paragangliomas: Discovering the Genomic and Immune Landscape of Metastatic Disease.

Pheochromocytomas (PCCs) and paragangliomas (PGLs, together PPGLs) are the most hereditary tumors known. PPGLs were considered benign, but the fourth edition of the World Health Organisation (WHO) classification redefined all PPGLs as malignant neoplasms with variable metastatic potential. The metastatic rate differs based on histopathology, genetic background, size, and location of the tumor. The challenge in predicting metastatic disease lies in the absence of a clear genotype-phenotype correlation among the more than 20 identified genetic driver variants. Recent advances in molecular clustering based on underlying genetic alterations have paved the way for improved cluster-specific personalized treatments. However, despite some clusters demonstrating a higher propensity for metastatic disease, cluster-specific therapies have not yet been widely adopted in clinical practice. Comprehensive genomic profiling and transcriptomic analyses of large PPGL cohorts have identified potential new biomarkers that may influence metastatic potential. It appears that no single biomarker alone can reliably predict metastatic risk; instead, a combination of these biomarkers may be necessary to develop an effective prediction model for metastatic disease. This review evaluates current guidelines and recent genomic and transcriptomic findings, with the aim of accurately identifying novel biomarkers that could contribute to a predictive model for mPPGLs, thereby enhancing patient care and outcomes.

Deciphering lipoprotein(a) atherogenicity with use of high-throughput proteomics

Abstract Background Accumulating observational and genetic evidence suggests that lipoprotein(a) [Lp(a)], is significantly more atherogenic than other apolipoprotein-B containing particles. Systemic inflammation is thought to mediate the atherogenic effects of Lp(a). Previous studies have predominantly focused on single inflammatory biomarkers, an approach that fails to capture the broader molecular response to elevated Lp(a) levels. Purpose The aim of this study was to derive a proteome-wide signature of plasma Lp(a) concentrations and subsequently identify the pathways through which Lp(a) influences the risk of coronary artery disease (CAD). Methods This study included 41,123 UK Biobank participants with baseline Lp(a) measurements who also underwent a proteomic assessment. Relative concentration of 2,920 proteins was quantified in EDTA plasma samples using a high-throughput proximity extension assay. Lp(a)-associated proteins were identified in linear regression models, adjusting for age, sex, body mass index, race, smoking status, estimated glomerular filtration rate, medication use, and apolipoprotein B. Proteins linked with Lp(a) concentration were then evaluated for their association with incident CAD risk using Cox proportional hazards models. Significant traits were further included in pathway enrichment analysis. Results Out of 2,920 analyzed proteins (Figure 1), 140 showed a positive association with Lp(a) concentration, while 336 exhibited a negative association after adjustment for multiple testing (False discovery rate < 0.05). The strongest positive associations were observed for apolipoprotein F, group 10 secretory phospholipase A, and C4b-binding protein beta chain. The strongest negative associations were observed for asporin, aminoacylase-1, and retinol-binding protein 5. Among 140 proteins positively associated with Lp(a), 49 were associated with an increased risk of CAD. Conversely, among the 336 proteins negatively associated with Lp(a), 10 were associated with a decreased risk of CAD. Pathway enrichment analysis (Figure 2) indicated that proteins linked with both Lp(a) levels and CAD risk predominantly upregulated pathways related to peptide-ligand receptor signalling, platelet degranulation, neutrophil degranulation, and cytosolic calcium response. Conversely, pathways related to HDL remodeling, PI3K/AKT, KIT, and nuclear receptor signalling were downregulated. Conclusions The greater atherogenic potential of Lp(a) may be attributable to the modulation of several molecular pathways, particularly those involving platelet and neutrophil responses, cell signalling, and lipoprotein metabolism.

Comparison of the clinical chemistry score to other biomarker algorithms for rapid rule-out of acute myocardial infarction and risk stratification

Abstract Background The clinical chemistry score (CCS) comprising high-sensitivity (hs) cardiac troponins (cTn), glucose and estimated glomerular filtration rate has been previously validated with superior accuracy for detection and risk stratification of acute myocardial infarction (AMI) compared to hs-cTn alone. Methods The CCS was directly compared to other biomarker-based algorithms for rapid rule-out and prognostication of AMI including the hs-cTnT limit-of-blank (LOB, <3 ng/L) or limit-of-detection (LOD, <5 ng/L) and the dual marker strategy (DMS) (copeptin <10 pmol/L and hs-cTnT ≤14 ng/L) in 1506 patients presenting to the emergency department (ED) with symptoms suggestive of acute coronary syndrome. Negative predictive values (NPV) and sensitivities for rule-out of AMI were assessed and outcomes included rates of the combined end-point of all-cause mortality, myocardial re-infarction and stroke within 12 months. Results NPVs of 100% (98.3-100%) could be found for a CCS=0, hs-cTnT LoB and hs-cTnT LoD with rule-out efficacies of 11.1%, 7.6% and 18.3% as well as specificities of 13.0% (9.9-16.6%), 8.8% (7.3-10.5%) and 21.4% (19.2-23.8%), respectively. A CCS≤ 1 achieved a rule-out in 32.2% of all patients with a NPV of 99.6% (98.4-99.9%) and specificity of 37.4% (34.2-40.5%) compared to a rule-out efficacy of 51.2%, NPV of 99.0 (98.0-99.5) and specificity of 59.7% (57.0-62.4%) for the DMS. Rates of the combined end-point of death/AMI within 30 days ranged between 0.0% and 0.5% for all fast-rule-out protocols. Conclusions The CCS enables a reliable rule-out of AMI with low outcome rates in short and long-term follow-up for a specific population of ED patients. However, compared to a single or dual biomarker strategy, the CCS rule-out is attenuated by a loss of specificity and lower efficacy. Thus, the clinical benefit of the CCS in clinical practice seems to be negligible.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Enrichment for clinical trials of early AD: Combining genetic risk factors and plasma p-tau as screening instruments.

Identifying low-cost, minimally-invasive screening instruments for Alzheimer's disease (AD) trial enrichment will improve the efficiency of AD trials. A total of 685 cognitively normal (CN) individuals and individuals with mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were grouped according to cutoffs of genetic risk factor (G) polygenic hazard score (PHS) and tau pathology (T) plasma phosphorylated tau-181 (p-tau181) into four groups: G+T+, G-T-, G+T-, and G-T+. We assessed the associations between group level and longitudinal cognitive decline and AD conversion. Power analyses compared the estimated sample size required to detect differences in cognitive decline. The G+T+ group was associated with faster cognitive decline and higher AD risk. Clinical trials enrolling G+T+ participants would benefit from significantly reduced sample sizes compared with similar trials using only single makers as an inclusion criterion. The combination of two low-cost, minimally-invasive measures-genetics and plasma biomarkers-would be a promising screening procedure for clinical trial enrollment. Participants with unimpaired or mildly impaired cognition were grouped based on cutoffs on genetic risk factors (G: polygenic hazardous score [PHS]) and Alzheimer's pathology (T: baseline plasma phosphorylated tau-181 [p-tau181]). Participants with high PHSs and plasma p-tau181 levels (G+T+) were at risk of faster cognitive decline and AD progression. The combination of PHS and plasma p-tau181 could enhance clinical trial enrichment more effectively than using single biomarkers.

From a solitary blood-derived biomarker to combined biomarkers of sarcopenia: Experiences from the Korean Frailty and Aging Cohort Study.

Sarcopenia is recognized as a complex and multifactorial disorder that includes nutritional deficiency, inactivity, proinflammatory status, hormonal changes, neurological degeneration, and metabolic disturbances. Its' pathogenesis is not fully understood. Therefore, identifying specific biomarkers of sarcopenia will help us understand its pathophysiology. The most frequently reported blood-derived biomarkers of sarcopenia are growth factors, neuromuscular junctions, endocrine systems, mitochondrial dysfunction, inflammation-mediated and redox processes, muscle protein turnover, blood metabolomics, and behavior-mediated biomarkers. Here, we address the implications of sarcopenia biomarkers based on our research experience with KFACS cohort data. It includes free testosterone, myostatin, fibroblast growth factor 21 (FGF-21), growth differentiation factor 15 (GDF-15), procollagen type III N-terminal peptide (P3NP), creatinine-based biomarkers, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), brain-derived neurotrophic factor (BDNF), metabolites (proline, alanine, tryptophan), and multi-biomarker risk score. We attempted to explain the paradoxical findings of myostatin and FGF-21 levels in relation to sarcopenia. GDF-15 levels were associated with sarcopenia prevalence but not its incidence. Plasma P3NP and BDNF levels may be biomarkers of muscle quality rather than quantity. Lower erythrocyte EPA and DHA levels were associated with slow gait speed, and erythrocyte EPA levels were associated with low handgrip strength. We developed a multi-biomarker risk score for sarcopenia and found that its accuracy in diagnosing sarcopenia was higher than that of any single biomarker.

The Prospect of Improving Pancreatic Cancer Diagnostic Capabilities by Implementing Blood Biomarkers: A Study of Evaluating Properties of a Single IL-8 and in Conjunction with CA19-9, CEA, and CEACAM6.

Background/Objectives: This study aims to evaluate the possible clinical application of interleukin 8 (IL-8) as a single biomarker and its capabilities in combination with carbohydrate antigen (CA19-9), carcinoembryonic antigen (CEA), and carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) as diagnostic and prognostic tools for pancreatic ductal adenocarcinoma (PDAC). Methods: A total of 170 serum samples from patients with PDAC (n = 100), chronic pancreatitis (CP) (n = 39), and healthy individuals (n = 31) were analysed. IL-8 and CEACAM6 were measured by an enzyme-linked immunosorbent assay (ELISA). CA19-9 and CEA were determined by chemiluminescent microparticle immunoassay, and bilirubin was quantified using a diazonium salt reaction. Receiver operating characteristic curve analysis, logistic regression, and Kaplan-Meier analyses were performed to evaluate the properties of a single IL-8 and in combination with other biomarkers. Results: The concentrations of IL-8 were statistically significantly higher in the PDAC group compared to the CP and control groups. Heterogeneous levels of IL-8 correlated with PDAC stages (p = 0.007). IL-8 had good and satisfactory diagnostic efficacy in differentiating PDAC from controls (0.858; p < 0.001) and patients with CP (0.696; p < 0.001), respectively. High and low expressions of IL-8 were not significantly associated with overall survival (OS) or disease-free survival (DFS). A combination of IL-8, CEACAM6, and CA19-9 reached the highest AUC values for differentiating PDAC from the control group. The best classification score between PDAC and the control group with CP patients was obtained by merging IL-8 and CA19-9 (0.894; p < 0.001). Conclusions: These results provide compelling evidence of IL-8 as a promising diagnostic biomarker. Nonetheless, due to the high complexity of PDAC, only the conjunction of IL-8, CA19-9, and CEACAM6 integrates sufficient diagnostic capabilities.

Role of Circulating microRNAs in Liver Disease and HCC: Focus on miR-122.

miR-122 is the most abundant microRNA (miRNA) in the liver; it regulates several genes mainly involved in cell metabolism and inflammation. Host factors, diet, metabolic disorders and viral infection promote the development of liver diseases, including hepatocellular carcinoma (HCC). The downregulation of miR-122 in tissue is a common feature of the progression of liver injury. In addition, the release of miR-122 in the bloodstream seems to be very promising for the early diagnosis of both viral and non-viral liver disease. Although controversial data are available on the role of circulating miR-122 as a single biomarker, high diagnostic accuracy has been observed using miR-122 in combination with other circulating miRNAs and/or proteins. This review is focused on comprehensively summarizing the most recent literature on the potential role of circulating miR-122, and related molecules, as biomarker(s) of metabolic liver diseases, hepatitis and HCC.

Machine Learning-Aided 3D Dynamic SERS Strategy for Physiological Mapping: Biotoxicity of Environmentally Dimensional Aged Nanoplastics and Corresponding Protein Corona Complexes.

Nanoplastics (NPs) are emerging pollutants that undergo inevitable aging in the environment, raising concerns about human exposure and health hazards. Research on the cytotoxicity of various polymer types of NPs, aged nanoplastics (aNPs), and their interactions with proteins (aNPs-protein corona) is still nascent. Traditional cytotoxicity detection methods often rely on end point assays with restricted temporal resolution and analysis of single or multiple biomarkers. Here, we propose a novel approach integrating the 3D dynamic SERS strategy (DSS) with machine learning to rapidly analyze the cell fate and death modes induced by NPs, aNPs, and aNPs-protein corona complexes at the molecular level. PS, PVC, PMMA, and PC products from the water environment were used to prepare the corresponding NPs, and the impact of UV irradiation on their physicochemical properties was examined. DSS systematically maps the molecular changes in the cellular secretome caused by these NPs. Machine learning effectively extracts information from complex spectra, differentiating between biological samples. Results show prolonged UV exposure increases cell sensitivity to ferroptosis and cytotoxicity in various aNPs, while the protein corona on aNPs significantly mitigates toxicity associated with surface oxygen-containing functional groups, resulting in a reduced similarity to ferroptosis signatures. 3D DSS with machine learning technique analyzes the overall metabolite profile at the molecular level rather than individual biomarkers. This study is the first attempt to compare the biotoxicity of diverse polymer NPs, aNPs, and aNPs-protein coronas at cellular and molecular levels in human hepatocytes, enhancing our understanding of the complex biological impacts of NPs.

The application and interpretation of laboratory biomarkers for the evaluation of vitamin B12 status.

Vitamin B12 (cobalamin; B12) is an essential micronutrient, but deficiency is common. The prompt diagnosis and treatment of B12 deficiency protects against megaloblastic anaemia, neuropathy and neuropsychiatric changes. Biomarkers of B12 status include the measurement of serum B12 (also known as total B12 or serum cobalamin), holotranscobalamin (holoTC or 'active B12'), methylmalonic acid (MMA) and total plasma homocysteine (Hcy). There is no 'gold standard' test for deficiency and the sensitivity and specificity of each biomarker for the evaluation of B12 status is affected by analytical and biological factors that may confer a high degree of diagnostic uncertainty. Limited access to technical and clinical expertise can lead to an over-reliance on the serum B12 test, which is readily available and highly automated. In some cases, the sequential use of different B12 status biomarkers or the calculation of a composite B12 status score, derived from a panel of B12 biomarkers and adjusted for folate status and age, can be used to detect deficient states that may otherwise be overlooked when using a single biomarker approach. This review summarises the utility of B12-related biomarkers and describes approaches to their application and interpretation.

B-240 Multi-omic blood test for early detection of cancer and pre-cancer

Abstract Background The earlier diagnosis of cancer is vital to improve patient survival. Many liquid biopsy technologies are focused on single tumor-derived biomarkers, which limits test sensitivity, especially for early-stage cancers that do not shed sufficient genetic material into the bloodstream. The Dxcover liquid biopsy employs Multi-Omic Spectral Analysis (MOSA) that interrogates a blood sample with Infrared (IR) radiation and produces a distinctive signature that represents the whole biomolecular profile of the sample. This multi-omic blood test analyzes the full complement of tumor and immune-derived markers present within blood derivatives and could facilitate the earlier detection of cancer. The technology can be tuned for high sensitivity (rule out) or high specificity (rule in) depending on the desired application, clinical priorities and international healthcare markets. Methods Three clinical studies have been completed, assessing the technology for the detection of: (1) brain cancer, (2) multiple cancers, and (3) colorectal cancer (CRC) and pre-cancerous lesions. In the first study, we recruited 988 patients prospectively with non-specific symptoms associated with a brain tumor. Secondly, we carried out a large-scale multi-cancer evaluation (n = 2092 patients), targeting eight different cancers. Moreover, the CREATE (ColoREctal cancer &amp; Adenoma Test Evaluation) feasibility study (3) examined 100 CRC, 92 adenoma samples and 104 colonoscopy screening control patients. Results In study 1, the brain cancer algorithm detected 96% of the patients with brain cancer, and 100% of glioblastomas, the most aggressive primary brain tumor. In the multi-cancer study (2), area under the receiver operating characteristic curve values were calculated for the eight cancer types versus symptomatic non-cancer controls: brain (0.90), breast (0.76), colorectal (0.91), kidney (0.91), lung (0.91), ovarian (0.86), pancreatic (0.84) and prostate (0.86). We also assessed the test performance when all eight cancer types were pooled to classify ‘any cancer’ against non-cancer patients. The cancer versus asymptomatic non-cancer classification detected 64% of stage I cancers when specificity was 99% (overall sensitivity 57%). When tuned for higher sensitivity, this model identified 99% of Stage I cancers (with specificity 59%). In the CREATE study, the test reported promising results for pre-cancer detection; at 90% specificity, the test reported sensitivities of 80% for CRC and 59% for advanced adenomas. Conclusions A rapid liquid biopsy that is sensitive to pre-cancerous lesions and early-stage cancer could substantially improve patient outcomes. There is a low barrier to integrating this blood test into existing diagnostic pathways as the technology is rapid, simple to use, and sample preparation is minimal. In addition, the spectroscopic liquid biopsy has the potential to be combined with other orthogonal tests, such as cell-free DNA, which could provide an efficient route to diagnosis. Cancer treatment can be more effective when given earlier, and this low-cost strategy has the potential to improve patient prognosis.

Biomarkers for systemic lupus erythematosus: A scoping review.

In recent years, newly discovered potential biomarkers have great research potential in the diagnosis, disease activity prediction, and treatment of systemic lupus erythematosus (SLE). In this study, a scoping review of potential biomarkers for SLE over several years has identified the extent to which studies on biomarkers for SLE have been conducted, the specificity, sensitivity, and diagnostic value of potential biomarkers of SLE, the research potential of these biomarkers in disease diagnosis, and activity detection is discussed. In PubMed and Google Scholar databases, "SLE," "biomarkers," "predictor," "autoimmune diseases," "lupus nephritis," "neuropsychiatric SLE," "diagnosis," "monitoring," and "disease activity" were used as keywords to systematically search for SLE molecular biomarkers published from 2020 to 2024. Analyze and summarize the literature that can guide the article. Recent findings suggest that some potential biomarkers may have clinical application prospects. However, to date, many of these biomarkers have not been subjected to repeated clinical validation. And no single biomarker has sufficient sensitivity and specificity for SLE. It is not scientific to choose only one or several biomarkers to judge the complex disease of SLE. It may be a good direction to carry out a meta-analysis of various biomarkers to find SLE biomarkers suitable for clinical use, or to evaluate SLE by combining multiple biomarkers through mathematical models. At the same time, advanced computational methods are needed to analyze large data sets and discover new biomarkers, and strive to find biomarkers that are sensitive and specific enough to SLE and can be used in clinical practice, rather than only staying in experimental research and data analysis.

Urinary phthalate metabolites associated with bone mineral density in adults: Data from the NHANES 2011–2018

Phthalates (PAEs) are common environmental endocrine disruptors and environmental bone poisons that can reduce bone mineral density (BMD). The purpose of this study is to investigate whether the concentration of PAE metabolites in urine is related to BMD in many parts of adult bones. We examined a series of cross-sectional data of male (n = 1835) and female (n = 1756) participants aged 18 to 59 years old in the National Health and Nutrition Examination Survey from 2011 to 2018 and measured urine PAE metabolites and dual-energy X-ray absorption to determine BMD (total body, lumbar spine, and pelvis). We used linear regression to test the correlation between a single phthalate biomarker and BMD. After adjusting all confounding variables, MEHP was positively correlated with BMD of total body, lumbar spine and pelvis, and BMD levels of the total body, lumbar spine and pelvis decreased with the increase of MECPP concentration. We used the restricted cubic spline function to test the nonlinear correlation between PAE biomarkers and BMD. The results show that urinary PAE metabolites have a nonlinear relationship with total body BMD, lumbar spine BMD, and pelvic BMD. With the increase in the PAE concentration, the BMD level first increased and then decreased, showing an inverted U-shaped trend (P < 0.05). Gender stratification also shows the same related trend. PAEs may be related to the BMD of adults. When the concentration of PAEs increases to a certain threshold, it will lead to a significant decrease in BMD.

Machine learning applications in breast cancer survival and therapeutic outcome prediction based on multi-omic analysis.

The high heterogeneity within and between breast cancer patients complicates treatment determination and prognosis assessment. Treatment decision-making is influenced by various factors, such as tumor subtype, histological grade, and genotype, necessitating personalized treatment strategies. Prognostic outcomes vary significantly depending on patient-specific conditions. As a critical branch of artificial intelligence, machine learning efficiently handles large datasets and automates decision-making processes. The introduction of machine learning offers new solutions for breast cancer treatment selection and prognosis assessment. In the field of cancer therapy, traditional methods for predicting treatment and survival outcomes often rely on single or few biomarkers, limiting their ability to capture the complexity of biological processes comprehensively. Machine learning analyzes patients' multi-omic data and the intricate patterns of variations during cancer initiation and progression to predict patients' survival and treatment outcomes. Consequently, it facilitates the selection of appropriate therapeutic interventions to implement early intervention and improve treatment efficacy for patients. Here, we first introduce common machine learning methods, and then elaborate on the application of machine learning in the field of survival prediction and prognosis from two aspects: evaluating survival and predicting treatment outcomes for breast cancer patients. The aim is to provide breast cancer patients with precise treatment strategies to improve therapeutic outcomes and quality of life.

Identifying biomarkers for treatment of uveal melanoma by T cell engager using a QSP model.

Uveal melanoma (UM), the primary intraocular tumor in adults, arises from eye melanocytes and poses a significant threat to vision and health. Despite its rarity, UM is concerning due to its high potential for liver metastasis, resulting in a median survival of about a year after detection. Unlike cutaneous melanoma, UM responds poorly to immune checkpoint inhibition (ICI) due to its low tumor mutational burden and PD-1/PD-L1 expression. Tebentafusp, a bispecific T cell engager (TCE) approved for metastatic UM, showed potential in clinical trials, but the objective response rate remains modest. To enhance TCE efficacy, we explored quantitative systems pharmacology (QSP) modeling in this study. By integrating a TCE module into an existing QSP model and using clinical data on UM and tebentafusp, we aimed to identify and rank potential predictive biomarkers for patient selection. We selected 30 important predictive biomarkers, including model parameters and cell concentrations in tumor and blood compartments. We investigated biomarkers using different methods, including comparison of median levels in responders and non-responders, and a cutoff-based biomarker testing algorithm. CD8+ T cell density in the tumor and blood, CD8+ T cell to regulatory T cell ratio in the tumor, and naïve CD4+ density in the blood are examples of key biomarkers identified. Quantification of predictive power suggested a limited predictive power for single pre-treatment biomarkers, which was improved by early on-treatment biomarkers and combination of predictive biomarkers. Ultimately, this QSP model could facilitate biomarker-guided patient selection, improving clinical trial efficiency and UM treatment outcomes.

Protein biomarkers in assessing kidney quality before transplantation‑current status and future perspectives (Review).

To meet the demand for kidney transplants (KTx), organs are frequently retrieved not only from standard criteria donors (SCD; a donor who is aged <50 years and suffered brain death from any number of causes, such as traumatic injuries or a stroke) but also from expanded criteria donors (any donor aged >60 years or donors aged >50 years with two of the following: A history of high blood pressure, a creatinine serum level ≥1.5 mg/dl or death resulting from a stroke). This comes at the cost of a higher risk of primary non‑function (the permanent hyperkalemia, hyperuremia and fluid overload that result in the need for continuous dialysis after KTx), delayed graft function (the need for dialysis session at least once during the first week after KTx), earlier graft loss and urinary complications (vesico‑ureteral reflux, obstruction of the vesico‑ureteral anastomosis, urine leakage). At present, there are no commercially available diagnostic tools for assessing kidney quality prior to KTx. Currently available predictive models based on clinical data, such as the Kidney Donor Profile Index, are insufficient. One promising option is the application of perfusion solutions for protein biomarkers of kidney quality and predictors of short‑ and long‑term outcomes. However, to date, protein markers that can be detected with ELISA, western blotting and cytotoxic assays have not been identified to be a beneficial predictors of kidney quality. These include lactate dehydrogenases, glutathione S‑transferases, fatty acid binding proteins, extracellular histones, IL‑18, neutrophil gelatinase‑associated lipocalin, MMPs and kidney injury molecule‑1. However, novel methods, including liquid chromatography‑mass spectrometry (LC‑MS) and microarrays, allow the analysis of all renal proteins suspended/dissolved in the acellular preservation solution used for kidney storage before KTx (including hypothermic machine perfusion as one of kidney storage methods) e.g. Belzer University of Wisconsin. Recent proteomic studies utilizing LC‑MS have identified complement pathway elements (C3, C1QB, C4BPA, C1S, C1R and C1RL), desmoplakin, blood coagulation pathway elements and immunoglobulin heavy variable 2‑26 to be novel predictors of kidney quality before transplantation. This was because they were found to correlate with estimated glomerular filtration rate at 3 and 12 months after kidney transplantation. However, further proteomic studies focusing on distinct markers obtained from hypothermic and normothermic machine perfusion are needed to confirm their predictive value and to improve kidney storage methods. Therefore, the present literature review from PubMed, Scopus, Embase and Web of Science was performed with the aims of summarizing the current knowledge on the most frequently studied single protein biomarkers. In addition, novel analytical methods and insights into organ injury during preservation were documented, where future directions in assessing organ quality before kidney transplantation were also discussed.

Electrochemical immunoplatform for the determination of multiple salivary biomarkers of oral diseases related to microbiome dysbiosis

Several diseases of the oral cavity are related to compositional and functional shifts in the oral microbiome. The analysis of saliva is an attractive alternative for the diagnosis and prognosis of these diseases. Samples can be obtained by no invasive procedures and processing is relatively simple. However, sensitive and selective analytical methods are needed to make the diagnosis as specific as possible. In this work, four salivary biomarkers of oral diseases: interleukin-6 (IL-6), receptor activator of NF-kB ligand (RANKL), protein arginine deiminase 4 (PAD4) and the corresponding antibody (anti-PAD-4) were selected as targets for their simultaneous determination using an electrochemical immunosensing platform. Sandwich-type amperometric immunoassays were implemented using horseradish peroxidase (HRP)/H2O2/hydroquinone (HQ) for application to the analysis of saliva of six volunteers. The developed method provides excellent sensitivity, selectivity, and wide linear ranges with LOD values of 0.09 pg mL−1 (IL-6), 0.10 pg mL−1 (RANKL); 0.09 ng mL−1(PAD4) and 14.5 ng mL−1 (anti-PAD4) and allows the accurate analysis of saliva without matrix effects, using 25 μL of raw sample. The developed methodology is competitive with commercial ELISA kits available only for a single biomarker determination, while the assay for the four biomarkers can be completed in less than two hours.

Nonictal electroencephalographic measures for the diagnosis of functional seizures.

Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epilepsy clinics are diagnosed with this condition. FS are diagnosed by recording a seizure using video-electroencephalography (EEG), from which an expert inspects the semiology and the EEG. However, this method can be expensive and inaccessible and can present significant patient burden. No single biomarker has been found to diagnose FS. However, the current limitations in FS diagnosis could be improved with machine learning to classify signal features extracted from EEG, thus providing a potentially very useful aid to clinicians. The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with epilepsy. The dataset included interictal and preictal EEG recordings from 48 subjects with FS (mean age = 34.76 ± 10.55 years, 14 males) and 29 subjects with epilepsy (mean age = 38.95 ± 13.93 years, 18 males) from which various statistical, temporal, and spectral features from the five EEG frequency bands were extracted then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches. The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a support vector machine classifier. Machine learning was much more effective than using individual features and could be a powerful aid in FS diagnosis. Furthermore, combining the frequency bands improved the accuracy of the classifiers in most cases, and the lowest performing EEG bands were consistently delta and gamma.

Influence of Different Diagnostic Criteria on Alzheimer Disease Clinical Research.

Updates in Alzheimer disease (AD) diagnostic guidelines by the National Institute on Aging-Alzheimer's Association (NIA-AA) and the International Working Group (IWG) over the past 11 years may affect clinical diagnoses. We assessed how these guidelines affect clinical AD diagnosis in a cohort of cognitively unimpaired (CU) and cognitively impaired (CI) individuals. We applied clinical and biomarker data in algorithms to classify individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort according to the following diagnostic guidelines for AD: 2011 NIA-AA, 2016 IWG-2, 2018 NIA-AA, and 2021 IWG-3, assigning the following generic diagnostic labels: (1) not AD (nAD), (2) increased risk of developing AD (irAD), and (3) AD. Diagnostic labels were compared according to their frequency, convergence across guidelines, biomarker profiles, and prognostic value. We also evaluated the diagnostic discordance among the criteria. A total of 1,195 individuals (mean age 73.2 ± 7.2 years, mean education 16.1 ± 2.7, 44.0% female) presented different repartitions of diagnostic labels according to the 2011 NIA-AA (nAD = 37.8%, irAD = 23.0%, AD = 39.2%), 2016 IWG-2 (nAD = 37.7%, irAD = 28.7%, AD = 33.6%), 2018 NIA-AA (nAD = 40.7%, irAD = 9.3%, AD = 50.0%), and 2021 IWG-3 (nAD = 51.2%, irAD = 8.4%, AD = 48.3%) frameworks. Discordant diagnoses across all guidelines were found in 512 participants (42.8%) (138 [91.4%] occurring in only β-amyloid [CU 65.4%, CI 34.6%] and 191 [78.6%] in only tau-positive [CU 71.7%, CI 28.3%] individuals). Differences in predicting cognitive impairment between nAD and irAD groups were observed with the 2011 NIA-AA (hazard ratio [HR] 2.21, 95% CI 1.34-3.65, p = 0.002), 2016 IWG-2 (HR 2.81, 95% CI 1.59-4.96, p < 0.000), and 2021 IWG-3 (HR 3.61, 95% CI 2.09-6.23, p < 0.000), but not with 2018 NIA-AA (HR 1.69, 95% CI 0.87-3.28, p = 0.115). Over 42% of the studied population presented discordant diagnoses when using the different examined AD criteria, mostly in individuals with a single positive biomarker. Except for 2018 NIA-AA, all guidelines identified asymptomatic individuals at risk of cognitive impairment. Our findings highlight important differences between the guidelines, emphasizing the necessity for updated criteria with enhanced staging metrics, considering clinical, research, therapeutic, and trial design aspects.