Blood Protein Markers Research Articles

Identifying clinical and proteomic markers for early diagnosis and prognosis prediction of major psychiatric disorders

BackgroundTo clarify if blood proteins can predict disease progression among individuals at clinical high-risk of severe mental illness (CHR-SMI), we developed a statistical model incorporating clinical and blood protein markers to distinguish the transition group (who developed severe mental illness) (CHR-SMI-T) and from non-transition group (CHR-SMI-NT) at baseline. MethodsNinety individuals (74 at CHR-SMI: 16 patients) were monitored for ≤4 years and were the focus of predictive models. Three predictive models (1 [100 clinical variables], 2 [158 peptides], and 3 [100 clinical variables +158 peptides]) were evaluated using area under the receiver operating characteristic (AUROC) values. Clinical and protein feature patterns were evaluated by linear mixed-effect analysis within the model at 12 and 24 months among patients who did (CHR-SMI-T) and did not transition (CHR-SMI-NT) and the entire group. ResultEighteen CHR-SMI individuals with major psychiatric disorders (first episode psychosis: 2; bipolar II disorder: 13; major depressive disorder; 3) developed disorders over an average of 17.7 months. The combined model showed the highest discriminatory performance (AUROC = 0.73). Cytosolic malate dehydrogenase and transgelin-2 levels were lower in the CHR-SMI-T than the CHR-SMI-NT group. Complement component C9, inter-alpha-trypsin inhibitor heavy chain H4, von Willebrand factor, and C-reactive protein were lower in the patient than the CHR-SMI-NT group. These differences were non-significant after FDR adjustment. LimitationsSmall sample, no control for medication use. ConclusionThis exploratory study identified clinical and proteomic markers that might predict severe mental illness early onset, which could aid in early detection and intervention. Future studies with larger samples and controlled variables are needed to validate these findings.

Proteomic Analysis of Idiopathic Nephrotic Syndrome Triggered by Primary Podocytopathies in Adults: Regulatory Mechanisms and Diagnostic Implications.

Idiopathic nephrotic syndrome (NS) is a heterogeneous group of glomerular disorders which includes two major phenotypes: minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS). MCD and FSGS are classic types of primary podocytopathies. We aimed to explore the molecular mechanisms in NS triggered by primary podocytopathies and evaluate diagnostic value of the selected proteomic signatures by analyzing blood proteome profiling. Totally, we recruited 90 participants in two cohorts. The first cohort was analyzed using label-free quantitative (LFQ) proteomics to discover differential expressed proteins and identify enriched biological process in NS which were further studied in relation to clinical markers of kidney injury. The second cohort was analyzed using parallel reaction monitoring-based quantitative proteomics to verify the data of LFQ proteomics and assess the diagnostic performance of the selected proteins using receiver-operating characteristic curve analysis. Several biological processes (such as immune response, cell adhesion, and response to hypoxia) were found to be associated with kidney injury during MCD and FSGS. Moreover, three proteins (CSF1, APOC3, and LDLR) had over 90% sensitivity and specificity in detecting adult NS triggered by primary podocytopathies. The identified biological processes may play a crucial role in MCD and FSGS pathogenesis. The three blood protein markers are promising for diagnosing adult NS triggered by primary podocytopathies.

A Snapshot of Biomarkers in Psoriasis.

A persistent long-standing, inflammatory skin condition that is brought on by a variety of factors is psoriasis. It is distinguished by itchy, scaly, reddish plaques, particularly on areas of the body that are frequently chafed, including the extensor sites of the limbs. Recent developments in molecular-targeted therapy that use biologics or small-molecule inhibitors can effectively cure even the worst psoriatic indications. The outstanding clinical outcomes of treatment help to clarify the disease's detrimental consequences on quality of life. Biomarkers that identify deep remission are essential for developing uniform treatment plans. Blood protein markers such as AMPs that are consistently quantifiable can be very helpful in routine clinical practice. The metabolic pathways involve biomarkers that can not only help diagnose psoriasis in a clinical setting but also indicate its severity based on the levels present in the body. Machine learning and AI have made a diagnosis of the expression of genes as biomarkers more accessible. In this article, biomarkers, as well as their key role in psoriasis, are discussed.

Capillary blood protein markers of posttraumatic headache in children after concussion.

Posttraumatic headache (PTH) represents the most common acute and persistent symptom in children after concussion, yet there is no blood protein signature to stratify the risk of PTH after concussion to facilitate early intervention. This discovery study aimed to identify capillary blood protein markers, at emergency department (ED) presentation within 48 hours of concussion, to predict children at risk of persisting PTH at 2 weeks postinjury. Capillary blood was collected using the Mitra Clamshell device from children aged 8-17 years who presented to the ED of the Royal Children's Hospital, Melbourne, Australia, within 48 hours of sustaining a concussion. Participants were followed up at 2 weeks postinjury to determine PTH status. PTH was defined per clinical guidelines as a new or worsened headache compared with preinjury. An untargeted proteomics analysis using data-independent acquisition (DIA) was performed. Principal component analysis and hierarchical clustering were used to reduce the dimensionality of the protein dataset. A total of 907 proteins were reproducibly identified from 82 children within 48 hours of concussion. The mean participant age was 12.78 years (SD 2.54 years, range 8-17 years); 70% of patients were male. Eighty percent met criteria for acute PTH in the ED, while one-third of participants with follow-up experienced PTH at 2 weeks postinjury (range 8-16 days). Hemoglobin subunit zeta (HBZ), cystatin B (CSTB), beta-ala-his dipeptidase (CNDP1), hemoglobin subunit gamma-1 (HBG1), and zyxin (ZYX) were weakly associated with PTH at 2 weeks postinjury based on up to a 7% increase in the PTH group despite nonsignificant Benjamini-Hochberg adjusted p values. This discovery study determined that no capillary blood protein markers, measured at ED presentation within 48 hours of concussion, can predict children at risk of persisting PTH at 2 weeks postinjury. While HBZ, CSTB, CNDP1, HBG1, and ZYX were weakly associated with PTH at 2 weeks postinjury, there was no specific blood protein signature predictor of PTH in children after concussion. There is an urgent need to discover new blood biomarkers associated with PTH to facilitate risk stratification and improve clinical management of pediatric concussion.

Blood biomarkers for the differentiation between central and peripheral vertigo in the emergency department: a systematic review and meta-analysis.

In patients with acute vestibular syndrome (AVS), differentiating between stroke and nonstroke causes is challenging in the emergency department (ED). Correct diagnosis of vertigo etiology is essential for early optimum treatment and disposition. The aim of this systematic review and meta-analysis was to summarize the published evidence on the potential of blood biomarkers in the diagnosis and differentiation of peripheral from central causes of AVS. A literature search was conducted for studies published until January 1, 2023, in PubMed, Ovid Medline, and EMBASE databases analyzing biomarkers for the differentiation between central and peripheral AVS. The Quality Assessment of Diagnostic Accuracy Studies questionnaire 2 was used for quality assessment. Pooled standardized mean difference and 95% confidence intervals were calculated if a biomarker was reported in two or more studies. Heterogeneity among included studies was investigated using the I2 metric. A total of 17 studies with 859 central and 4844 peripheral causes of acute dizziness or vertigo, and analysis of 61 biomarkers were included. The general laboratory markers creatinine, blood urea nitrogen, albumin, C-reactive protein, glucose, HbA1c, leukocyte counts, and neutrophil counts and the brain-derived biomarkers copeptin, S100 calcium-binding protein β (S100β), and neuron-specific enolase (NSE) significantly differentiated central from peripheral causes of AVS. This systematic review and meta-analysis highlights the potential of generalized inflammatory markers and brain-specific blood protein markers of NSE and S100β as diagnostic biomarkers for central from peripheral differentiation in AVS. These results, as a complement to clinical characteristics, provide guidance for future large-scale diagnostic research, in this challenging ED patient population.

Blood protein markers predict 15-year risk of dementia.

Blood protein markers predict 15-year risk of dementia.

Decision-Making on the Diagnosis of Oncological Diseases Using Cost-Sensitive SVM Classifiers Based on Datasets with a Variety of Features of Different Natures

This paper discusses the problem of detecting cancer using such biomarkers as blood protein markers. The purpose of this research is to propose an approach for making decisions in the diagnosis of cancer through the creation of cost-sensitive SVM classifiers on the basis of datasets with a variety of features of different nature. Such datasets may include compositions of known features corresponding to blood protein markers and new features constructed using methods for calculating entropy and fractal dimensions, as well as using the UMAP algorithm. Based on these datasets, multiclass SVM classifiers were developed. They use cost-sensitive learning principles to overcome the class imbalance problem, which is typical for medical datasets. When implementing the UMAP algorithm, various variants of the loss function were considered. This was performed in order to select those that provide the formation of such new features that ultimately allow us to develop the best cost-sensitive SVM classifiers in terms of maximizing the mean value of the metric MacroF1−score. The experimental results proved the possibility of applying the UMAP algorithm, approximate entropy and, in addition, Higuchi and Katz fractal dimensions to construct new features using blood protein markers. It turned out that when working with the UMAP algorithm, the most promising is the application of a loss function on the basis of fuzzy cross-entropy, and the least promising is the application of a loss function on the basis of intuitionistic fuzzy cross-entropy. Augmentation of the original dataset with either features on the basis of the UMAP algorithm, features on the basis of the UMAP algorithm and approximate entropy, or features on the basis of approximate entropy provided the creation of the three best cost-sensitive SVM classifiers with mean values of the metric MacroF1−score increased by 5.359%, 5.245% and 4.675%, respectively, compared to the mean values of this metric in the case when only the original dataset was utilized for creating the base SVM classifier (without performing any manipulations to overcome the class imbalance problem, and also without introducing new features).

Study on blood protein markers of Alzheimer's disease

Study on blood protein markers of Alzheimer's disease

Astrogliosis, neuritic microstructure, and sex effects: GFAP is an indicator of neuritic orientation in women

BackgroundData from human studies suggest that immune dysregulation is associated with Alzheimer’s disease (AD) pathology and cognitive decline and that neurites may be affected early in the disease trajectory. Data from animal studies further indicate that dysfunction in astrocytes and inflammation may have a pivotal role in facilitating dendritic damage, which has been linked with negative cognitive outcomes. To elucidate these relationships further, we have examined the relationship between astrocyte and immune dysregulation, AD-related pathology, and neuritic microstructure in AD-vulnerable regions in late life. MethodsWe evaluated panels of immune, vascular, and AD-related protein markers in blood and conducted in vivo multi-shell neuroimaging using Neurite Orientation Dispersion and Density Imaging (NODDI) to assess indices of neuritic density (NDI) and dispersion (ODI) in brain regions vulnerable to AD in a cohort of older adults (n = 109). ResultsWhen examining all markers in tandem, higher plasma GFAP levels were strongly related to lower neurite dispersion (ODI) in grey matter. No biomarker associations were found with higher neuritic density. Associations between GFAP and neuritic microstructure were not significantly impacted by symptom status, APOE status, or plasma Aβ42/40 ratio; however, there was a large sex effect observed for neurite dispersion, wherein negative associations between GFAP and ODI were only observed in females. DiscussionThis study provides a comprehensive, concurrent appraisal of immune, vascular, and AD-related biomarkers in the context of advanced grey matter neurite orientation and dispersion methodology. Sex may be an important modifier of the complex associations between astrogliosis, immune dysregulation, and brain microstructure in older adults.

Paper-based biosensors as point-of-care diagnostic devices for the detection of cancers: a review of innovative techniques and clinical applications.

The development and rapid progression of cancer are major social problems. Medical diagnostic techniques and smooth clinical care of cancer are new necessities that must be supported by innovative diagnostic methods and technologies. Current molecular diagnostic tools based on the detection of blood protein markers are the most common tools for cancer diagnosis. Biosensors have already proven to be a cost-effective and accessible diagnostic tool that can be used where conventional laboratory methods are not readily available. Paper-based biosensors offer a new look at the world of analytical techniques by overcoming limitations through the creation of a simple device with significant advantages such as adaptability, biocompatibility, biodegradability, ease of use, large surface-to-volume ratio, and cost-effectiveness. In this review, we covered the characteristics of exosomes and their role in tumor growth and clinical diagnosis, followed by a discussion of various paper-based biosensors for exosome detection, such as dipsticks, lateral flow assays (LFA), and microfluidic paper-based devices (µPADs). We also discussed the various clinical studies on paper-based biosensors for exosome detection.

A Novel Approach to Decision-Making on Diagnosing Oncological Diseases Using Machine Learning Classifiers Based on Datasets Combining Known and/or New Generated Features of a Different Nature

This paper deals with the problem of diagnosing oncological diseases based on blood protein markers. The goal of the study is to develop a novel approach in decision-making on diagnosing oncological diseases based on blood protein markers by generating datasets that include various combinations of features: both known features corresponding to blood protein markers and new features generated with the help of mathematical tools, particularly with the involvement of the non-linear dimensionality reduction algorithm UMAP, formulas for various entropies and fractal dimensions. These datasets were used to develop a group of multiclass kNN and SVM classifiers using oversampling algorithms to solve the problem of class imbalance in the dataset, which is typical for medical diagnostics problems. The results of the experimental studies confirmed the feasibility of using the UMAP algorithm and approximation entropy, as well as Katz and Higuchi fractal dimensions to generate new features based on blood protein markers. Various combinations of these features can be used to expand the set of features from the original dataset in order to improve the quality of the received classification solutions for diagnosing oncological diseases. The best kNN and SVM classifiers were developed based on the original dataset augmented respectively with a feature based on the approximation entropy and features based on the UMAP algorithm and the approximation entropy. At the same time, the average values of the metric MacroF1-score used to assess the quality of classifiers during cross-validation increased by 16.138% and 4.219%, respectively, compared to the average values of this metric in the case when the original dataset was used in the development of classifiers of the same name.

In-silico selection of cancer blood plasma proteins by integrating genomic and proteomic databases.

Blood protein markers have been studied for the clinical management of cancer. Due to the large number of the proteins existing in blood, it is often necessary to pre-select potential protein markers before experimental studies. However, to date there is a lack of automated method for in-silico selection of cancer blood proteins that integrates the information from both genetic and proteomic studies in a cancer-specific manner. In this work, we synthesized both genomic and proteomic information from several open access databases and established a bioinformatic pipeline for in-silico selection of blood plasma proteins overexpressed in specific type of cancer. We demonstrated the workflow of this pipeline with an example of breast cancer, while the methodology was applicable for other cancer types. With this pipeline we obtained 10 candidate biomarkers for breast cancer. The proposed pipeline provides a useful and convenient tool for in-silico selection of candidate blood protein biomarkers for a variety of cancer research.

Molecular Pathogenesis of Psoriasis and Biomarkers Reflecting Disease Activity.

Psoriasis is a chronic inflammatory skin disease induced by multifactorial causes and is characterized by bothersome, scaly reddish plaques, especially on frequently chafed body parts, such as extensor sites of the extremities. The latest advances in molecular-targeted therapies using biologics or small-molecule inhibitors help to sufficiently treat even the most severe psoriatic symptoms and the extra cutaneous comorbidities of psoriatic arthritis. The excellent clinical effects of these therapies provide a deeper understanding of the impaired quality of life caused by this disease and the detailed molecular mechanism in which the interleukin (IL)-23/IL-17 axis plays an essential role. To establish standardized therapeutic strategies, biomarkers that define deep remission are indispensable. Several molecules, such as cytokines, chemokines, antimicrobial peptides, and proteinase inhibitors, have been recognized as potent biomarker candidates. In particular, blood protein markers that are repeatedly measurable can be extremely useful in daily clinical practice. Herein, we summarize the molecular mechanism of psoriasis, and we describe the functions and induction mechanisms of these biomarker candidates.

Clinical Molecular Marker Testing Data Capture to Promote Precision Medicine Research Within the Cancer Research Network.

To evaluate health care systems for the availability of population-level data on the frequency of use and results of clinical molecular marker tests to inform precision cancer care. We assessed cancer-related molecular marker test data availability across 12 US health care systems in the Cancer Research Network. Overall, these systems provide care to a diverse population of more than 12 million people in the United States. We performed qualitative analyses of test data availability for five blood-based protein, nine germline, and 14 tissue-based tumor marker tests in each health care system's electronic health record and tumor registry using key informants, test code lists, and manual review of data types and output. We then performed quantitative analyses to estimate the proportion of patients with cancer with test utilization data and results for specific molecular marker tests. Health systems were able to systematically capture population-level data on all five blood protein markers, six of 14 tissue-based tumor markers, and none of the nine germline markers. Successful, systematic data capture was achievable for tests with electronic data feeds for test results (blood protein markers) or through prior manual abstraction by tumor registrars (select tumor-based markers). For test results stored in scanned image files (particularly germline and tumor marker tests), information on which test was performed and test results was not readily accessible in an electronic format. Even in health care systems with sophisticated electronic health records, there were few codified data elements available for evaluating precision cancer medicine test use and results at the population level. Health care organizations should establish standards for electronic reporting of precision medicine tests to expedite cancer research and facilitate the implementation of precision medicine approaches.

Small Non-coding RNAs: New Class of Biomarkers and Potential Therapeutic Targets in Neurodegenerative Disease.

Neurodegenerative diseases (NDs) are becoming increasingly prevalent in the world, with an aging population. In the last few decades, due to the devastating nature of these diseases, the research of biomarkers has become crucial to enable adequate treatments and to monitor the progress of disease. Currently, gene mutations, CSF and blood protein markers together with the neuroimaging techniques are the most used diagnostic approaches. However, despite the efforts in the research, conflicting data still exist, highlighting the need to explore new classes of biomarkers, particularly at early stages. Small non-coding RNAs (MicroRNA, Small nuclear RNA, Small nucleolar RNA, tRNA derived small RNA and Piwi-interacting RNA) can be considered a “relatively” new class of molecule that have already proved to be differentially regulated in many NDs, hence they represent a new potential class of biomarkers to be explored. In addition, understanding their involvement in disease development could depict the underlying pathogenesis of particular NDs, so novel treatment methods that act earlier in disease progression can be developed. This review aims to describe the involvement of small non-coding RNAs as biomarkers of NDs and their potential role in future clinical applications.

Genetic diversity at four Nigerian sheep breeds assessed by variation of albumin and carbonic anhydrase in cellulose acetate electrophoretic systems

Abstract. The aim of the study was to evaluate the occurrence and distribution of variations in blood protein markers in sheep breeds in Nigeria and to evaluate the relationships that exist among them. A total of 100 sheep comprising of twenty-five each of Balami, Uda, Yankassa and West African Dwarf (WAD) breeds were sampled for biochemical studies. Blood was collected to determine variations at the Albumin and Carbonic Anhydrase structural protein loci using cellulose acetate electrophoresis. All tested loci were polymorphic yielding four allelic variants (CAF, CAS, AlbA, and AlbB) at the two protein loci. Genetic variability in the studied population was accessed using heterozygosity (observed – Ho and expected – He), effective number of alleles (Ne), fixation index (F) and gene flow (Nm). Genotypic frequencies ranged from 0.01 to 0.62 for Ca and 0.25 to 0.49 for Alb loci. Estimated heterozygosity values ranged from 0.32±0.28 at Balami sheep to 0.70±0.22 in WAD with a global average estimated at 0.43±0.09 for all the breeds studied. The gene flow values for each of the loci studied were 0.82 and 1.24 for Alb and Ca, respectively, with an average value of 1.03. The results showed that Yankassa and Balami sheep populations are more genetically (Nei's genetic identify value – 0.99) alike compared to Yankassa and WAD (0.73). Based on the values of heterozygosity (mean He =0.70 and Ho =0.45) assessed by variation of albumin and carbonic anhydrase the most genetic diverse is WAD sheep breed among the studied populations.

Sputum neutrophils are elevated in smelter workers, and systemic neutrophils are associated with rapid decline in FEV1

ObjectivesIn a previous study on smelter workers we, found significant relationship between exposure to dust and accelerated annual decline in forced expiratory volume in 1 s (FEV1). In this cross-sectional...

Cytometry by time-of-flight immunophenotyping identifies a blood Sjögren's signature correlating with disease activity and glandular inflammation

Mass cytometry has recently emerged as a promising tool for clinical research. However, few studies have demonstrated its benefit for patient stratification and biomarker identification. Primary Sjögren's syndrome (pSS) is a prototype of chronic autoimmune disease, the pathogenesis of which remains unclear and for which treatment does not exist. This observational case-control study was designed to discover new cellular biomarkers and therapeutic targets in patients with pSS. Forty-nine patients with pSS and 45 control subjects were enrolled for clinical evaluation and mass cytometry quantification of 34 protein markers in blood. For a third of these subjects, matched labial salivary gland biopsy specimens were also analyzed by mass cytometry and immunohistochemistry. In salivary gland biopsy specimens from patients with pSS, we identified a high number of activated CD8(+) Tcells, terminally differentiated plasma cells, and activated epithelial cells, pointing to new pathogenic mechanisms for future clinical intervention. In blood, we identified a 6-cell disease signature defined by decreased numbers of CD4 and memory B lymphocytes, decreased plasmacytoid dendritic cell numbers, and increased representation of activated CD4 and CD8 Tcells and plasmablasts. These blood cellular components correlated with clinical parameters and, when taken together, clustered patients into subsets with distinct disease activity and glandular inflammation. This first application of mass cytometry to a well-stratified clinical cohort and small biopsy tissues establishes the benefits of such an approach for the discovery of new biomarkers and therapeutic targets. Similar high-dimensional immunophenotyping strategies could be implemented in longitudinal and interventional clinical settings in this and other disease areas.

Identifying cost-effective predictive rules of amyloid-β level by integrating neuropsychological tests and plasma-based markers.

Detecting participants who are positive for amyloid-β (Aβ) pathology is germane in designing prevention trials by enriching for those cases that are more likely to be amyloid positive. Existing brain amyloid measurement techniques, such as the Pittsburgh Compound B-positron emission tomography and cerebrospinal fluid, are not reasonable first-line approaches limited by either feasibility or cost. We aimed to identify simple and cost-effective rules that can predict brain Aβ level by integrating both neuropsychological measurements and blood-based markers. Several decision tree models were built for extracting the predictive rules based on the Alzheimer's Disease Neuroimaging Initiative cohort. We successfully extracted predictive rules of Aβ level. For cognitive function variables, cases above the 45th percentile in total cognitive score (TOTALMOD), above the 52nd percentile of delayed word recall, and above the 70th percentile in orientation resulted in a group that was highly enriched for amyloid negative cases. Conversely scoring below the 15th percentile of TOTALMOD resulted in a group highly enriched for amyloid positive cases. For blood protein markers, scoring below the 57th percentile for apolipoprotein E (ApoE) levels (irrespective of genotype) enriched two fold for the risk of being amyloid positive. In the high ApoE cases, scoring above the 60th percentile for transthyretin resulted in a group that was >90% amyloid negative. A third decision tree using both cognitive and blood-marker data slightly improved the classification of cases. Our study demonstrated that the integration of the neuropsychological measurements and blood-based markers significantly improved prediction accuracy. The prediction model has led to several simple rules, which have a great potential of being naturally translated into clinical settings such as enrichment screening for AD prevention trials of anti-amyloid treatments.

Cord Blood Proteins and Multichannel-Electroencephalography in Hypoxic-Ischemic Encephalopathy*

To explore the association between multiple umbilical cord blood proteins and severity of hypoxic-ischemic encephalopathy as defined by continuous multichannel electroencephalography. A prospective case-control cohort study, which was divided into separate exploratory and validation cohorts. A single tertiary neonatal intensive care facility. The study recruited full-term infants with perinatal asphyxia and controls. Identical procedures were used to recruit a representative exploratory sample (n = 30) and a subsequent validation cohort (n = 100). All had umbilical cord blood drawn and biobanked at delivery, continuous multichannel electroencephalography commenced in the first 24 hours, and a modified Sarnat score assigned. Analysis of 37 potential cord blood protein markers of hypoxic-ischemic encephalopathy was performed using Luminex multiplex assays. Cord blood from 130 infants was analyzed. Interleukin-16 and interleukin-6 significantly differentiated between a moderate-severely abnormal and normal-mildly abnormal electroencephalography background in both exploratory (p = 0.005 and p = 0.016, respectively) and validation cohorts (p = 0.039 and p = 0.024, respectively). To develop a predictive model for a moderate-severely abnormal electroencephalography, stepwise regression analysis was used to combine these analytes with current standard clinical markers of asphyxia (pH, base deficit, and 10-min Apgar). Only Apgar score and interleukin-16 remained in the model, which was highly predictive of an abnormal electroencephalography (area under the curve [AUC] = 0.956, p < 0.001, positive predictive value = 89%, and negative predictive value = 94%). Cord blood interleukin-6 and interleukin-16 were associated with electrographic grade of hypoxic-ischemic encephalopathy. To predict an abnormal electroencephalography, interleukin-16 and 10-minute Apgar used in combination performed better than current markers.