Combination Of Multiple Biomarkers Research Articles

Predictive biomarkers of rapidly developing insulin deficiency in children with type 1 diabetes

IntroductionThe rate of progression to complete insulin deficiency varies greatly in type 1 diabetes. This constitutes a challenge, especially when randomizing patients in intervention trials aiming to preserve beta cell...

Likelihood ratio combination of multiple biomarkers viasmoothing spline estimated densities.

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.

PREDICTION OF SARCOPENIA USING A MULTIPLE BIOMARKER RISK SCORE OVER TWO-YEAR FOLLOW-UP

Abstract It is necessary to consider multiple biomarkers simultaneously to detect sarcopenia due to its multifactorial etiology. This study is to compare the AUC of multiple biomarker risk score (MBS) that combines multiple biomarkers with that of individual biomarkers and to examine the association of MBS with incidence of sarcopenia in older adults. A total of 1,021 older adults were selected from the Korean Frailty and Aging Cohort Study. Sarcopenia was defined using the Asian Working Group for Sarcopenia 2019 criteria. The MBS was calculated from eight biomarkers (Growth differentiation factor-15, Cystatin-C, Dehydroepiandrosterone, Hemoglobin, Myostatin, Aspartate aminotransferase/Alanine aminotransferase, Estimated glomerular filtration rate, and Blood urea nitrogen) using ridge regression. Receiver operating characteristic (ROC) analysis was conducted to identify an utility of MBS to discriminate sarcopenia. The association of MBS with incidence of sarcopenia were examined by multivariate logistic regression. MBS has an area under the ROC curve (AUC) of 0.71 with an optimal cutoff of 1.76, which is higher than all other individual biomarkers (all, p< 0.01). In prospective study, continuous MBS was positively associated with incidence of sarcopenia after adjusting for confounders (odds ratio[OR]=1.63; 95% CI=1.23–2.17). Participants with high risk (>1.76 score) had a higher odds of sarcopenia than those with low risk (≤ 1.76) (OR=1.82; 95% CI=1.04–3.19). In conclusion, MBS better discriminated the presence of sarcopenia than did individual biomarkers, which can further predict sarcopenia after 2 years in older adults. Thus, combination of multiple biomarkers into single risk score can be helpful for earlier detection of sarcopenia.

Non-Invasive Diagnostics of Male Spermatogenesis from Seminal Plasma: Seminal Proteins.

The compounds of seminal plasma have great potential as biomarkers of male fertility and can be used as a diagnostic tool for types of azoospermia. Azoospermia occurs in approximately 1% of the male population, and for an effective therapy of this form of male infertility, it is important to distinguish between obstructive and non-obstructive azoospermia. Proteins in seminal plasma can serve as biomarkers for diagnosing azoospermia. Considering the various types of obstructions, a combination of multiple proteins is advisable for diagnostic purposes. In this context, testicular and epididymal proteins are particularly significant, as they are specific to these tissues and typically absent in ejaculate during most obstructions. A combination of multiple biomarkers is more effective than the analysis of a single protein. This group of markers contains TEX101 and ECM1 proteins, combined detections of these two bring a diagnostic output with a high sensitivity and specificity. Similar results were observed for combined detection of TEX101 and SPAG1. The effective using of specific biomarkers from seminal plasma can significantly improve the existing approaches to diagnosis of the causes of male infertility.

Combining stable isotopes, trace elements, and distribution models to assess the geographic origins of migratory bats

AbstractThe expansion of industrial‐scale wind‐energy facilities has not only increased the production of low‐carbon emission energy but has also resulted in mortality of wildlife, including migratory bats. Management decisions can be limited by a lack of understanding of the geographic impact of bats killed at wind‐energy facilities. Several studies have leveraged stable hydrogen isotope ratios (δ2H) of bat fur to illuminate this issue but are limited in the precision of conclusion because δ2H values vary primarily across latitudinal and elevational bands. One approach to increase the precision of geographic assignment is to combine independent inferences about spatial location from additional biomarkers and other related information. To test this possibility, we assigned known‐origin individuals of three bat species (Lasiurus borealis, L. cinereus, and Lasionycteris noctivagans) commonly killed at on‐shore wind‐energy facilities in North America to a probable origin using δ2H values, trace element concentrations, and species distribution models. We used cross‐validated calibrated combined model tuning to determine the degree to which assignment probabilities improved when combining datasets. We found that combining markers typically performed better than single approaches. For Lasiurus borealis and L. cinereus, combining all three data sources outperformed any single or other combined approach. With an accuracy set at 80%, an average of 39.7% and 36.0% of each species' total geographic range was considered a potential origin, respectively; stable hydrogen alone included 51.8% and 50.6% of the total geographic area. In contrast, for Lasionycteris noctivagans, including trace elements did not increase precision and adding distribution data to δ2H values only improved precision by 0.6%. Thus, we found that a combination of multiple biomarkers typically, but not always, outperforms single‐marker approaches and optimized combinations of different markers outperform equal weighting of each marker. From a practical perspective, δ2H values performed better than trace elements alone; in cases where cost is a limiting factor, the stable hydrogen should be the single biomarker used in conjunction with species distribution models. Overall, these results highlight the importance of validating methods for each species they are applied to and show that combining information from intrinsic biomarker approaches is a useful tool to document bat movements.

Predictors for histological chorioamnionitis among women with preterm prelabour rupture of membranes after dexamethasone treatment: a retrospective study.

To investigate reliable biomarkers for predicting histological chorioamnionitis (HCA) in women with preterm prelabour rupture of membranes (PPROM). A retrospective study. A maternity care hospital in Shanghai. Women with PPROM before 34+0/7 weeks of gestation. Mean values of biomarkers were compared by two-way analysis of variance (ANOVA). Log-binomial regression models were used to assess the association between biomarkers and risk of HCA. A stepwise logistic regression model was used to develop a multi-biomarker prediction model and identify the independent predictors. The area under the receiver operating characteristic curve (AUC) was used to assess prediction performance. The ability of the individual biomarker and the combination of multiple biomarkers to predict HCA. In 157 mothers with PPROM, 98 (62.42%) women had HCA and 59 (37.58%) women did not have HCA. No significant differences were observed between the two groups in white blood cell, neutrophil or lymphocyte counts, whereas both high-sensitivity C-reactive protein (hsCRP) and procalcitonin (PCT) were significantly higher in the HCA group. HsCRP and PCT were found to be independently associated with the risk of HCA, and PCT had a larger AUC value than hsCRP (p< 0.05). The optimal multi-biomarker prediction model for HCA (AUC=93.61%) included hsCRP at 72 hours and PCT at 48 and 72 hours, and PCT had a stronger prediction capacity than hsCRP. PCT could be a reliable biomarker for the early prediction of HCA in women with PPROM within 72 hours of dexamethasone treatment.

Biomarkers for prognostic functional recovery poststroke: A narrative review.

Background and objective: Prediction of poststroke recovery can be expressed by prognostic biomarkers that are related to the pathophysiology of stroke at the cellular and molecular level as well as to the brain structural and functional reserve after stroke at the systems neuroscience level. This study aimed to review potential biomarkers that can predict poststroke functional recovery. Methods: A narrative review was conducted to qualitatively summarize the current evidence on biomarkers used to predict poststroke functional recovery. Results: Neurophysiological measurements and neuroimaging of the brain and a wide diversity of molecules had been used as prognostic biomarkers to predict stroke recovery. Neurophysiological studies using resting-state electroencephalography (EEG) revealed an interhemispheric asymmetry, driven by an increase in low-frequency oscillation and a decrease in high-frequency oscillation in the ipsilesional hemisphere relative to the contralesional side, which was indicative of individual recovery potential. The magnitude of somatosensory evoked potentials and event-related desynchronization elicited by movement in task-related EEG was positively associated with the quantity of recovery. Besides, transcranial magnetic stimulation (TMS) studies revealed the potential values of using motor-evoked potentials (MEP) and TMS-evoked EEG potentials from the ipsilesional motor cortex as prognostic biomarkers. Brain structures measured using magnetic resonance imaging (MRI) have been implicated in stroke outcome prediction. Specifically, the damage to the corticospinal tract (CST) and anatomical motor connections disrupted by stroke lesion predicted motor recovery. In addition, a wide variety of molecular, genetic, and epigenetic biomarkers, including hemostasis, inflammation, tissue remodeling, apoptosis, oxidative stress, infection, metabolism, brain-derived, neuroendocrine, and cardiac biomarkers, etc., were associated with poor functional outcomes after stroke. However, challenges such as mixed evidence and analytical concerns such as specificity and sensitivity have to be addressed before including molecular biomarkers in routine clinical practice. Conclusion: Potential biomarkers with prognostic values for the prediction of functional recovery after stroke have been identified; however, a multimodal approach of biomarkers for prognostic prediction has rarely been studied in the literature. Future studies may incorporate a combination of multiple biomarkers from big data and develop algorithms using data mining methods to predict the recovery potential of patients after stroke in a more precise way.

A distribution-free smoothed combination method to improve discrimination accuracy in multi-category classification.

Results from multiple diagnostic tests are combined in many ways to improve the overall diagnostic accuracy. For binary classification, maximization of the empirical estimate of the area under the receiver operating characteristic curve has widely been used to produce an optimal linear combination of multiple biomarkers. However, in the presence of a large number of biomarkers, this method proves to be computationally expensive and difficult to implement since it involves maximization of a discontinuous, non-smooth function for which gradient-based methods cannot be used directly. The complexity of this problem further increases when the classification problem becomes multi-category. In this article, we develop a linear combination method that maximizes a smooth approximation of the empirical Hyper-volume Under Manifolds for the multi-category outcome. We approximate HUM by replacing the indicator function with the sigmoid function and normal cumulative distribution function. With such smooth approximations, efficient gradient-based algorithms are employed to obtain better solutions with less computing time. We show that under some regularity conditions, the proposed method yields consistent estimates of the coefficient parameters. We derive the asymptotic normality of the coefficient estimates. A simulation study is performed to study the effectiveness of our proposed method as compared to other existing methods. The method is illustrated using two real medical data sets.

Classification accuracy of blood-based and neurophysiological markers in the differential diagnosis of Alzheimer’s disease and frontotemporal lobar degeneration

BackgroundIn the last decade, non-invasive blood-based and neurophysiological biomarkers have shown great potential for the discrimination of several neurodegenerative disorders. However, in the clinical workup of patients with cognitive impairment, it will be highly unlikely that any biomarker will achieve the highest potential predictive accuracy on its own, owing to the multifactorial nature of Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD).MethodsIn this retrospective study, performed on 202 participants, we analysed plasma neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and tau phosphorylated at amino acid 181 (p-Tau181) concentrations, as well as amyloid β42 to 40 ratio (Aβ1–42/1–40) ratio, using the ultrasensitive single-molecule array (Simoa) technique, and neurophysiological measures obtained by transcranial magnetic stimulation (TMS), including short-interval intracortical inhibition (SICI), intracortical facilitation (ICF), long-interval intracortical inhibition (LICI), and short-latency afferent inhibition (SAI). We assessed the diagnostic accuracy of combinations of both plasma and neurophysiological biomarkers in the differential diagnosis between healthy ageing, AD, and FTLD.ResultsWe observed significant differences in plasma NfL, GFAP, and p-Tau181 levels between the groups, but not for the Aβ1–42/Aβ1–40 ratio. For the evaluation of diagnostic accuracy, we adopted a two-step process which reflects the clinical judgement on clinical grounds. In the first step, the best single biomarker to classify “cases” vs “controls” was NfL (AUC 0.94, p < 0.001), whilst in the second step, the best single biomarker to classify AD vs FTLD was SAI (AUC 0.96, p < 0.001). The combination of multiple biomarkers significantly increased diagnostic accuracy. The best model for classifying “cases” vs “controls” included the predictors p-Tau181, GFAP, NfL, SICI, ICF, and SAI, resulting in an AUC of 0.99 (p < 0.001). For the second step, classifying AD from FTD, the best model included the combination of Aβ1–42/Aβ1–40 ratio, p-Tau181, SICI, ICF, and SAI, resulting in an AUC of 0.98 (p < 0.001).ConclusionsThe combined assessment of plasma and neurophysiological measures may greatly improve the differential diagnosis of AD and FTLD.

PD40-07 CLINICAL DECISION ALGORITHM USING RESTRICTION SPECTRUM IMAGING AND BIOMARKERS TO PREDICT UPGRADING IN ACTIVE SURVEILLANCE

You have accessJournal of UrologyCME1 May 2022PD40-07 CLINICAL DECISION ALGORITHM USING RESTRICTION SPECTRUM IMAGING AND BIOMARKERS TO PREDICT UPGRADING IN ACTIVE SURVEILLANCE Stefan Eng, Benjamin Basasie, Alfonso Lam, John Semmes, Dean Troyer, Geoffrey Clarke, Abhijit Sunnapwar, Robin Leach, Teresa Johnson-Pais, Lori Sokoll, Daniel Chan, Jeffrey Tosoian, Javed Siddiqui, Arul Chinnaiyan, Paul Boutros, and Michael Liss Stefan EngStefan Eng More articles by this author , Benjamin BasasieBenjamin Basasie More articles by this author , Alfonso LamAlfonso Lam More articles by this author , John SemmesJohn Semmes More articles by this author , Dean TroyerDean Troyer More articles by this author , Geoffrey ClarkeGeoffrey Clarke More articles by this author , Abhijit SunnapwarAbhijit Sunnapwar More articles by this author , Robin LeachRobin Leach More articles by this author , Teresa Johnson-PaisTeresa Johnson-Pais More articles by this author , Lori SokollLori Sokoll More articles by this author , Daniel ChanDaniel Chan More articles by this author , Jeffrey TosoianJeffrey Tosoian More articles by this author , Javed SiddiquiJaved Siddiqui More articles by this author , Arul ChinnaiyanArul Chinnaiyan More articles by this author , Paul BoutrosPaul Boutros More articles by this author , and Michael LissMichael Liss More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002601.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To determine the relative value and optimal combination of radiologic and molecular biomarkers to predict prostate cancer progression in men on active surveillance. METHODS: We prospectively enrolled 123 men with prostate cancer on active surveillance who were undergoing repeat biopsy to obtain an MRI and non-invasive biomarkers that predict the risk of upgrading. Subjects underwent a prostate multiparametric MRI and a short duration, diffusion-weighted enhanced MRI called restriction spectrum imaging (RSI) to generate a restricted signal map (RSM) along with evaluation of 30 features (14 clinico-epidemiologic features, 9 molecular biomarkers and 7 radiologic-associated features). Our primary outcome was upgrading to higher grade disease on subsequent active surveillance prostate biopsy. Statistical analysis evaluated AUROC and AUPRC, and improvements in these. Using the data, we then devised a clinical decision algorithm to best predict upgrading using the tested biomarkers. RESULTS: RSM (RSI-MRI biomarker) was the most accurate single biomarker in predicting upgrading at the subsequent active surveillance biopsy (AUROC=0.84; 95%CI: 0.71-0.96). RSI improved a baseline clinical model that included MRI PIRADSv2 scores from an AUROC of 0.74 (95%CI 0.69-0.80) to 0.84 (95% CI 0.79–0.89). The best non-imaging biomarker was the prostate volume corrected Prostate Health Index (PHI) density (AUROC=0.68; 95%CI: 0.53-0.82). Combining these with age as a clinical decision tree to perform or not perform a biopsy using cross validated statistics we reach an AUC of the model at 0.8 (SD 0.18). CONCLUSIONS: Enhancement of diffusion-weighted imaging using RSI in addition to standard PIRADSv2 scoring, PHI, and age provides improvement to the predictive ability of MRI to detect upgrading in active surveillance. A combination of multiple biomarkers in a clinical decision tree improves prediction of groups at high risk for future upgrading. Source of Funding: Department of Defense © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e686 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Stefan Eng More articles by this author Benjamin Basasie More articles by this author Alfonso Lam More articles by this author John Semmes More articles by this author Dean Troyer More articles by this author Geoffrey Clarke More articles by this author Abhijit Sunnapwar More articles by this author Robin Leach More articles by this author Teresa Johnson-Pais More articles by this author Lori Sokoll More articles by this author Daniel Chan More articles by this author Jeffrey Tosoian More articles by this author Javed Siddiqui More articles by this author Arul Chinnaiyan More articles by this author Paul Boutros More articles by this author Michael Liss More articles by this author Expand All Advertisement PDF DownloadLoading ...

Clinical utility of serum fucosylated fraction of alpha-fetoprotein in the diagnostic of hepatocellular carcinoma: a comprehensive analysis with large sample size.

We conducted a comprehensive meta-analysis of the utility of AFP-L3 for the diagnosis of hepatocellular carcinoma, to provide a more accurate estimation for the clinical utility of AFP-L3. We performed online searches in five databases (PubMed, China National Knowledge Infrastructure, Wanfang, Web of Science, and Embase), from inception to December 31, 2021. Pooled sensitivity, specificity, and area under the curve (AUC) with the matching 95% confidence intervals (95% CIs) were calculated to estimate the diagnostic value of AFP-L3. Thirty-four studies were included in the meta-analysis. The pooled sensitivity was 0.70 [95% confidence interval (CI): 0.63–0.77], and the specificity was 0.91 (95% CI: 0.88–0.94). The estimated area under the curve (AUC) was 0.90 (95% CI: 0.87–0.92). The positive likelihood ratio and negative likelihood ratio were 7.78 (95% CI: 5.7–10.7) and 0.33 (95% CI: 0.26–0.41), respectively. The diagnostic odds ratio was 24 (95% CI: 16–37). The subgroup analysis indicated moderate sensitivity (0.79) and high specificity (0.89) for the Asian population (AUC = 0.89), and similar specificity (0.95) but lower sensitivity (0.35) for Caucasians (AUC = 0.80). Deeks’ funnel plot asymmetry test detected no publication bias (P = 0.460). The sensitivity analysis showed that the pooled results were stable. Taken together, our results indicated that AFP-L3 demonstrates high diagnostic ability for HCC, especially among Asian populations. AFP-L3 is a useful means for high-volume screening, which can help doctors optimize diagnosis workflow, reduce workload, and improve detection sensitivity. The combination of multiple biomarkers may provide more accurate diagnostic tools for HCC in the future.

Finding the combination of multiple biomarkers to diagnose oral squamous cell carcinoma – A data mining approach

Data mining has proven to be a reliable method to analyze and discover useful knowledge about various diseases, including cancer research. In particular, data mining and machine learning algorithms to study oral squamous cell carcinoma (OSCC), the most common form of oral cancer, is a new area of research. This malignant neoplasm can be studied using saliva samples. Saliva is an important biofluid that must be used to verify potential biomarkers associated with oral cancer. In this study, first, we provide an overview of OSSC diagnoses based on machine learning and salivary metabolites. To our knowledge, this is the first study to apply advanced data mining techniques to diagnose OSCC. Then, we give new results of classification and feature selection algorithms used to identify potential salivary biomarkers of OSCC. To accomplish this task, we used the filter feature selection random forest importance algorithm and a wrapper methodology to evaluate the importance of metabolites obtained from gas chromatography mass-spectrometry (GC-MS) in the context of differentiation of OSCC and the control group. Salivary samples (n = 68) were collected for the control group, and the OSCC group were from patients matched for gender, age, and smoking habit. The classification process occurred based on Random Forest (RF) classification algorithm along with 10-cross validation. The results showed that glucuronic acid, maleic acid, and batyl alcohol can classify the samples with an area under the curve (AUC) of 0.91 versus an AUC of 0.76 using all 51 metabolites analyzed. The methodology used in this study can assist healthcare professionals and be adopted to discover diagnostic biomarkers for other diseases.

Biomarkers for heart failure and prognostic prediction in patients with Fontan circulation.

A wide variety of pathologies are involved in heart failure in patients with congenital heart disease (CHD). Specific causes of heart failure after the Fontan procedure include not only single-ventricle circulation, but also the function of the right ventricle as the systemic ventricle, atrioventricular or semilunar valve stenosis or regurgitation, pulmonary hypertension, and left ventricular dysfunction secondary to right ventricular enlargement or dysfunction. As heart failure can occur post-Fontan, for a variety of reasons, clarification of the pathophysiology is the first step in management and treatment. At the same time, it is important to understand each patient's current condition and treatment plan to make an accurate prognosis. Because of the wide variety of pathophysiologies in post-Fontan CHD patients, however, no single biomarker is useful in all situations. Relevant biomarkers must be selected according to each patient's disease state, and combinations of multiple biomarkers should also be considered. In this review, the author describes the clinical importance of various biomarkers for patients who have undergone a Fontan procedure.

A Multimodal Approach to Discover Biomarkers for Taxane-Induced Peripheral Neuropathy (TIPN): A Study Protocol.

Introduction: Taxanes are a class of chemotherapeutics commonly used to treat various solid tumors, including breast and ovarian cancers. Taxane-induced peripheral neuropathy (TIPN) occurs in up to 70% of patients, impacting quality of life both during and after treatment. TIPN typically manifests as tingling and numbness in the hands and feet and can cause irreversible loss of function of peripheral nerves. TIPN can be dose-limiting, potentially impacting clinical outcomes. The mechanisms underlying TIPN are poorly understood. As such, there are limited treatment options and no tools to provide early detection of those who will develop TIPN. Although some patients may have a genetic predisposition, genetic biomarkers have been inconsistent in predicting chemotherapy-induced peripheral neuropathy (CIPN). Moreover, other molecular markers (eg, metabolites, mRNA, miRNA, proteins) may be informative for predicting CIPN, but remain largely unexplored. We anticipate that combinations of multiple biomarkers will be required to consistently predict those who will develop TIPN. Methods: To address this clinical gap of identifying patients at risk of TIPN, we initiated the Genetics and Inflammatory Markers for CIPN (GENIE) study. This longitudinal multicenter observational study uses a novel, multimodal approach to evaluate genomic variation, metabolites, DNA methylation, gene expression, and circulating cytokines/chemokines prior to, during, and after taxane treatment in 400 patients with breast cancer. Molecular and patient reported data will be collected prior to, during, and after taxane therapy. Multi-modal data will be used to develop a set of comprehensive predictive biomarker signatures of TIPN. Conclusion: The goal of this study is to enable early detection of patients at risk of developing TIPN, provide a tool to modify taxane treatment to minimize morbidity from TIPN, and improved patient quality of life. Here we provide a brief review of the current state of research into CIPN and TIPN and introduce the GENIE study design.

Predicting mortality in adult patients with sepsis in the emergency department by using combinations of biomarkers and clinical scoring systems: a systematic review

BackgroundSepsis can be detected in an early stage in the emergency department (ED) by biomarkers and clinical scoring systems. A combination of multiple biomarkers or biomarker with clinical scoring system might result in a higher predictive value on mortality. The goal of this systematic review is to evaluate the available literature on combinations of biomarkers and clinical scoring systems on 1-month mortality in patients with sepsis in the ED.MethodsWe performed a systematic search using MEDLINE, EMBASE and Google Scholar. Articles were included if they evaluated at least one biomarker combined with another biomarker or clinical scoring system and reported the prognostic accuracy on 28 or 30 day mortality by area under the curve (AUC) in patients with sepsis. We did not define biomarker cut-off values in advance.ResultsWe included 18 articles in which a total of 35 combinations of biomarkers and clinical scoring systems were studied, of which 33 unique combinations. In total, seven different clinical scoring systems and 21 different biomarkers were investigated. The combination of procalcitonin (PCT), lactate, interleukin-6 (IL-6) and Simplified Acute Physiology Score-2 (SAPS-2) resulted in the highest AUC on 1-month mortality.ConclusionThe studies we found in this systematic review were too heterogeneous to conclude that a certain combination it should be used in the ED to predict 1-month mortality in patients with sepsis. Future studies should focus on clinical scoring systems which require a limited amount of clinical parameters, such as the qSOFA score in combination with a biomarker that is already routinely available in the ED.

Prediction of Hemorrhagic Transformation After Ischemic Stroke: Development and Validation Study of a Novel Multi-biomarker Model.

Objectives: We aimed to develop and validate a novel multi-biomarker model for predicting hemorrhagic transformation (HT) risk after acute ischemic stroke (AIS).Methods: We prospectively included patients with AIS admitted within 24 h of stroke from January 1st 2016 to January 31st 2019. A panel of 17 circulating biomarkers was measured and analyzed in this cohort. We assessed the ability of individual circulating biomarkers and the combination of multiple biomarkers to predict any HT, symptomatic HT (sHT) and parenchymal hematoma (PH) after AIS. The strategy of multiple biomarkers in combination was then externally validated in an independent cohort of 288 Chinese patients.Results: A total of 1207 patients with AIS (727 males; mean age, 67.2 ± 13.9 years) were included as a derivation cohort, of whom 179 patients (14.8%) developed HT. The final multi-biomarker model included three biomarkers [platelets, neutrophil-to-lymphocyte ratios (NLR), and high-density lipoprotein (HDL)] from different pathways, showing a good performance for predicting HT in both the derivation cohort (c statistic = 0·64, 95% CI 0·60–0·69), and validation cohort (c statistic = 0·70, 95% CI 0·58–0·82). Adding these three biomarkers simultaneously to the basic model with conventional risk factors improved the ability of HT reclassification [net reclassification improvement (NRI) 65.6%, P < 0.001], PH (NRI 64.7%, P < 0.001), and sHT (NRI 71.3%, P < 0.001).Conclusion: This easily applied multi-biomarker model had a good performance for predicting HT in both the derivation and external validation cohorts. Incorporation of biomarkers into clinical decision making may help to identify patients at high risk of HT after AIS and warrants further consideration.

Panel biomarkers associated with cancer invasion and prognostic prediction for head-neck cancer.

Aim: Cell invasion leading to metastasis is a major cause of treatment failure in head-neck cancers (HNCs). Identifying prognostic molecules associated with invasiveness is imperative for clinical applications. Materials & methods: A systemic approach was used to globally survey invasion-related genes, including transcriptomic profiling, pathway analysis, data mining and prognostic assessment using TCGA-HNSC dataset. Results: Six functional pathways and six hub molecules(LAMA3, LAMC2, THBS1, IGF1R, PDGFB andTGFβ1) were identified that significantly contributed to cell invasion, leading to poor survival in HNC patients. Combinations of multiple biomarkers substantially increased the probability of accuratelypredicting prognosis. Conclusion: Our six defined invasion-related molecules may be used as a panel signature in precision medicine for prognostic indicators or molecular therapeutic targets for HNC.

A comparison of approaches for combining predictive markers for personalised treatment recommendations

BackgroundIn the presence of heterogeneous treatment effects, it is desirable to divide patients into subgroups based on their expected response to treatment. This is formalised via a personalised treatment recommendation: an algorithm that uses biomarker measurements to select treatments. It could be that multiple, rather than single, biomarkers better predict these subgroups. However, finding the optimal combination of multiple biomarkers can be a difficult prediction problem.MethodsWe described three parametric methods for finding the optimal combination of biomarkers in a personalised treatment recommendation, using randomised trial data: a regression approach that models outcome using treatment by biomarker interactions; an approach proposed by Kraemer that forms a combined measure from individual biomarker weights, calculated on all treated and control pairs; and a novel modification of Kraemer’s approach that utilises a prognostic score to sample matched treated and control subjects. Using Monte Carlo simulations under multiple data-generating models, we compare these approaches and draw conclusions based on a measure of improvement under a personalised treatment recommendation compared to a standard treatment. The three methods are applied to data from a randomised trial of home-delivered pragmatic rehabilitation versus treatment as usual for patients with chronic fatigue syndrome (the FINE trial). Prior analysis of this data indicated some treatment effect heterogeneity from multiple, correlated biomarkers.ResultsThe regression approach outperformed Kraemer’s approach across all data-generating scenarios. The modification of Kraemer’s approach leads to improved treatment recommendations, except in the case where there was a strong unobserved prognostic biomarker. In the FINE example, the regression method indicated a weak improvement under its personalised treatment recommendation algorithm.ConclusionsThe method proposed by Kraemer does not perform better than a regression approach for combining multiple biomarkers. All methods are sensitive to misspecification of the parametric models.

Expression of stem cell markers as useful complementary factors in the early detection of urinary bladder carcinogens by immunohistochemistry for γ-H2AX.

We previously demonstrated that immunohistochemistry for γ-H2AX, a biomarker of DNA damage, is useful for early detection of urinary bladder carcinogens in rats. In a 28-day repeated-dose study, γ-H2AX was shown to have high sensitivity for detection of bladder carcinogens. However, no reports have evaluated whether a combination of multiple biomarkers may further improve sensitivity. Accordingly, in this study, we aimed to evaluate the applicability of bladder tissue and cancer stem cell markers, including cytokeratin 14 (KRT14), aldehyde dehydrogenase 1A1 (ALDH1A1), and cluster of differentiation 44 (CD44), as complementary markers for early detection of bladder carcinogens. Bladder samples obtained from male F344 rats orally treated with 14 bladder carcinogens and five nonbladder carcinogens for 28days were used for immunohistochemical analysis of stem cell markers. In the bladder carcinogen-treated rats, increases in KRT14, ALDH1A1, and CD44 expression were observed in 9, 10, and 10 out of 14 groups, respectively, whereas the five nonbladder carcinogens did not cause upregulation of these markers. Although most epithelial cells with KRT14 or ALDH1A1 expression were also positive for CD44, KRT14 and ALDH1A1 expression were mutually exclusive. Twelve bladder carcinogens showed increases in at least one of the three markers, indicating that the combined evaluation showed higher sensitivity than the use of individual markers alone. Importantly, two of three bladder carcinogens that did not induce γ-H2AX immunostaining showed stem cell marker expression. Our results demonstrated that these stem cell markers may be useful as complementary markers for γ-H2AX in evaluation of bladder carcinogens.

Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning.

Molecular diagnostics have traditionally relied on discrete biological substances as diagnostic markers. In recent years however, advances in on-chip biomarker screening technologies and data analytics have enabled signature-based diagnostics. Such diagnostics aim to utilize unique combinations of multiple biomarkers or diagnostic 'fingerprints' rather than discrete analyte measurements. This approach has shown to improve both diagnostic accuracy and diagnostic specificity. In this review, signature-based diagnostics enabled by microfluidic and micro-/nano- technologies will be reviewed with a focus on device design and data analysis pipelines and methodologies. With increasing amounts of data available from microfluidic biomarker screening, isolation, and detection platforms, advanced data handling and analytics approaches can be employed. Thus, current data analysis approaches including machine learning and recent advances with image processing, along with potential future directions will be explored. Lastly, the needs and gaps in current literature will be elucidated to inform future efforts towards development of molecular diagnostics and biomarker screening technologies.