Machine Learning-based Macrophage Signature for Predicting Prognosis and Immunotherapy Benefits in Cholangiocarcinoma.

AIMS: We aimed to develop a macrophage signature for predicting clinical outcomes and immunotherapy benefits in cholangiocarcinoma. BACKGROUND: Macrophages are potent immune effector cells that can change phenotype in different environments to exert anti-tumor and anti-tumor functions. The role of macrophages in the prognosis and therapy benefits of cholangiocarcinoma was not fully clarified. OBJECTIVE: The objective of this study is to develop a prognostic model for cholangiocarcinoma. METHODS: The macrophage-related signature (MRS) was developed using 10 machine learning methods with TCGA, GSE89748 and GSE107943 datasets. Several indicators (TIDE score, TMB score and MATH score) and two immunotherapy datasets (IMvigor210 and GSE91061) were used to investigate the performance of MRS in predicting the benefits of immunotherapy. RESULTS: The Lasso + CoxBoost method's MRS was considered a robust and stable model that demonstrated good accuracy in predicting the clinical outcome of patients with cholangiocarcinoma; the AUC of the 2-, 3-, and 4-year ROC curves in the TCGA dataset were 0.965, 0.957, and 1.000. Moreover, MRS acted as an independent risk factor for the clinical outcome of cholangiocarcinoma cases. Cholangiocarcinoma cases with higher MRS scores are correlated with a higher TIDE score, higher tumor escape score, higher MATH score, and lower TMB score. Further analysis suggested high MRS score indicated a higher gene set score correlated with cancer-related hallmarks. CONCLUSION: With regard to cholangiocarcinoma, the current study created a machine learning-based MRS that served as an indication for forecasting the prognosis and therapeutic advantages of individual cases.

An explainable Artificial Intelligence software system for predicting diabetes.

Implementing diabetes surveillance systems is paramount to mitigate the risk of incurring substantial medical expenses. Currently, blood glucose is measured by minimally invasive methods, which involve extracting a small blood sample and transmitting it to a blood glucose meter. This method is deemed discomforting for individuals who are undergoing it. The present study introduces an Explainable Artificial Intelligence (XAI) system, which aims to create an intelligible machine capable of explaining expected outcomes and decision models. To this end, we analyze abnormal glucose levels by utilizing Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN). In this regard, the glucose levels are acquired through the glucose oxidase (GOD) strips placed over the human body. Later, the signal data is converted to the spectrogram images, classified as low glucose, average glucose, and abnormal glucose levels. The labeled spectrogram images are then used to train the individualized monitoring model. The proposed XAI model to track real-time glucose levels uses the XAI-driven architecture in its feature processing. The model's effectiveness is evaluated by analyzing the performance of the proposed model and several evolutionary metrics used in the confusion matrix. The data revealed in the study demonstrate that the proposed model effectively identifies individuals with elevated glucose levels.

Identification of hub glycolysis-related genes in acute myocardial infarction and their correlation with immune infiltration using bioinformatics analysis.

PURPOSE: Glycolysis and immune metabolism play important roles in acute myocardial infarction (AMI). Therefore, this study aimed to identify and experimentally validate the glycolysis-related hub genes in AMI as diagnostic biomarkers, and further explore the association between hub genes and immune infiltration. METHODS: Differentially expressed genes (DEGs) from AMI peripheral blood mononuclear cells (PBMCs) were analyzed using R software. Glycolysis-related DEGs (GRDEGs) were identified and analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for functional enrichment. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape software. Immune infiltration analysis between patients with AMI and stable coronary artery disease (SCAD) controls was performed using CIBERSORT, and correlation analysis between GRDEGs and immune cell infiltration was performed. We also plotted nomograms and receiver operating characteristic (ROC) curves to assess the predictive accuracy of GRDEGs for AMI occurrence. Finally, key genes were experimentally validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting using PBMCs. RESULTS: A total of 132 GRDEGs and 56 GRDEGs were identified on the first day and 4-6 days after AMI, respectively. Enrichment analysis indicated that these GRDEGs were mainly clustered in the glycolysis/gluconeogenesis and metabolic pathways. Five hub genes (HK2, PFKL, PKM, G6PD, and ALDOA) were selected using the cytoHubba plugin. The link between immune cells and hub genes indicated that HK2, PFKL, PKM, and ALDOA were significantly positively correlated with monocytes and neutrophils, whereas G6PD was significantly positively correlated with neutrophils. The calibration curve, decision curve analysis, and ROC curves indicated that the five hub GRDEGs exhibited high predictive value for AMI. Furthermore, the five hub GRDEGs were validated by RT-qPCR and western blotting. CONCLUSION: We concluded that HK2, PFKL, PKM, G6PD, and ALDOA are hub GRDEGs in AMI and play important roles in AMI progression. This study provides a novel potential immunotherapeutic method for the treatment of AMI.

Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning.

Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can lead to dysregulation of immune responses and disease pathogenesis. However, systematic evaluation of NET-related genes (NETRGs) for the diagnosis of pediatric sepsis is still lacking. Three datasets were taken from the Gene Expression Omnibus (GEO) database: GSE13904, GSE26378, and GSE26440. After NETRGs and differentially expressed genes (DEGs) were identified in the GSE26378 dataset, crucial genes were identified by using LASSO regression analysis and random forest analysis on the genes that overlapped in both DEGs and NETRGs. These crucial genes were then employed to build a diagnostic model. The diagnostic model's effectiveness in identifying pediatric sepsis across the three datasets was confirmed through receiver operating characteristic curve (ROC) analysis. In addition, clinical pediatric sepsis samples were collected to measure the expression levels of important genes and evaluate the diagnostic model's performance using qRT-PCR in identifying pediatric sepsis in actual clinical samples. Next, using the CIBERSORT database, the relationship between invading immune cells and diagnostic markers was investigated in more detail. Lastly, to evaluate NET formation, we measured myeloperoxidase (MPO)-DNA complex levels using ELISA. A group of five important genes (MME, BST1, S100A12, FCAR, and ALPL) were found among the 13 DEGs associated with NET formation and used to create a diagnostic model for pediatric sepsis. Across all three cohorts, the sepsis group had consistently elevated expression levels of these five critical genes as compared to the normal group. Area under the curve (AUC) values of 1, 0.932, and 0.966 indicate that the diagnostic model performed exceptionally well in terms of diagnosis. Notably, when applied to the clinical samples, the diagnostic model also showed good diagnostic capacity with an AUC of 0.898, outperforming the effectiveness of traditional inflammatory markers such as PCT, CRP, WBC, and NEU%. Lastly, we discovered that children with high ratings for sepsis also had higher MPO-DNA complex levels. In conclusion, the creation and verification of a five-NETRGs diagnostic model for pediatric sepsis performs better than established markers of inflammation.

Predictive Value of a Diagnostic Five-Gene Biomarker for Pediatric Sepsis.

Background: Pediatric sepsis has a very high morbidity and mortality rate. The purpose of this study was to evaluate diagnostic biomarkers and immune cell infiltration in pediatric sepsis. Methods: Three datasets (GSE13904, GSE26378, and GSE26440) were downloaded from the gene expression omnibus (GEO) database. After identifying overlapping genes in differentially expressed genes (DEGs) and modular sepsis genes selected via a weighted gene co-expression network (WGCNA) in the GSE26378 dataset, pivotal genes were further identified by using LASSO regression and random forest analysis to construct a diagnostic model. Receiver operating characteristic curve (ROC) analysis was used to validate the efficacy of the diagnostic model for pediatric sepsis. Furthermore, we used qRT-PCR to detect the expression levels of pivotal genes and validate the diagnostic model's ability to diagnose pediatric sepsis in 65 actual clinical samples. Results: Among 294 overlapping genes of DEGs and modular sepsis genes, five pivotal genes (STOM, MS4A4A, CD177, MMP8, and MCEMP1) were screened to construct a diagnostic model of pediatric sepsis. The expression of the five pivotal genes was higher in the sepsis group than in the normal group. The diagnostic model showed good diagnostic ability with AUCs of 1, 0.986, and 0.968. More importantly, the diagnostic model showed good diagnostic ability with AUCs of 0.937 in the 65 clinical samples and showed better efficacy compared to conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%). Conclusion: We developed and tested a five-gene diagnostic model that can reliably identify pediatric sepsis and also suggest prospective candidate genes for peripheral blood diagnostic testing in pediatric sepsis patients.

Clinical diagnosis of pheochromocytoma and paraganglioma-induced secondary hypertension through UPLC-MS/MS analysis of plasma catecholamines and their metabolites.

This study aimed to elucidate the clinical diagnostic value of plasma catecholamines and their metabolites for pheochromocytoma and paraganglioma (PPGL)-induced secondary hypertension using ultraperformance liquid chromatography-mass spectrometry (UPLC-MS/MS). The study population included 155 patients with PPGL that were divided into the PPGL with hypertension (n = 79) and a PPGL without hypertension (n = 76) groups, and 90 healthy volunteers and 90 patients with primary hypertension as the control groups. UPLC-MS/MS was performed to detect plasma levels of catecholamines and their metabolites, including dopamine, vanillylmandelic acid (VMA), norepinephrine, metanephrine, and normetanephrine. Receiver operating characteristic curves were generated to analyze the diagnostic value of the plasma levels of catecholamines and their metabolites in PPGL-induced secondary hypertension. Patients in the primary hypertension and PPGL without hypertension groups had higher levels of dopamine, VMA, norepinephrine, metanephrine, and normetanephrine than patients in the normal group (all p < .05). On the other hand, patients in the PPGL with hypertension group had higher levels of dopamine, VMA, norepinephrine, metanephrine, and normetanephrine than patients in the normal, primary hypertension, and PPGL without hypertension groups (all p < .05). Collectively, our findings showed that dopamine, VMA, norepinephrine, metanephrine, and normetanephrine are all effective biomarkers for the diagnosis of PPGL and PPGL-induced secondary hypertension.

Diagnostic Performance of FibroScan in a Liver Disease Center in Bogotá from 2019 to 2022 / Rendimiento diagnóstico del FibroScan en un centro de enfermedades hepáticas en Bogotá durante el 2019 al 2022

Abstract Introduction: The medical record and liver biochemical profile are essential in diagnosing liver diseases. Liver biopsy is the reference parameter for diagnosis, activity evaluation, fibrosis status, or therapeutic response, but it is invasive and carries risks. For fibrosis staging, easily accessible non-invasive tests without resorting to biopsy have been developed. The FIB-4 and APRI indexes are helpful but do not determine the degree of fibrosis in the early and intermediate stages. Fibrosis can be evaluated using elastography, a sensitive technique to differentiate patients without fibrosis from those with advanced fibrosis. Objective: To describe the diagnostic performance of FibroScan in detecting fibrosis compared to the APRI and FIB-4 indexes versus the biopsy in a care center for patients with liver diseases in Bogotá. Methods: A retrospective, cross-sectional cohort study compared the APRI, FIB-4, and Fibroscan with biopsy; diagnostic accuracy measures and an area under the curve (AUROC) analysis were described. Results: The biopsy was positive for fibrosis in 40%. The AUROC was 0.90 (confidence interval [CI]: 0.83-0.97) for FibroScan, 0.52 (CI: 0.35-0.68) for APRI, and 0.52 (CI: 0.37-0.68) for FIB-4. Conclusions: FibroScan helps diagnose and monitor chronic liver disease and should be combined with other tests and the clinical picture. FibroScan was better at detecting advanced stages when discriminating against patients with liver fibrosis than the APRI and FIB-4 indexes.

Resumen Introducción: En el proceso diagnóstico de las enfermedades hepáticas, la historia clínica y el perfil bioquímico hepático son fundamentales. La biopsia hepática es el parámetro de referencia para el diagnóstico, evaluación de la actividad, estado de fibrosis o respuesta terapéutica, pero es invasiva y con riesgos. Para la estadificación de la fibrosis, se han desarrollado pruebas no invasivas de fácil acceso y sin recurrir a la biopsia. Los índices FIB-4 y APRI son útiles, pero no determinan el grado de fibrosis en estadios precoces e intermedios. La fibrosis puede evaluarse mediante elastografía, técnica sensible para diferenciar pacientes sin fibrosis de aquellos con fibrosis avanzada. Objetivo: Describir el desempeño diagnóstico para la detección de fibrosis del FibroScan comparado con los índices APRI y FIB-4 frente a la biopsia de pacientes evaluados en un centro de atención de pacientes con enfermedades hepáticas de Bogotá. Métodos: Estudio de cohorte retrospectivo, transversal, que comparó los índices APRI, FIB-4 y Fibroscan con la biopsia; se describieron las medidas de precisión diagnóstica y un análisis de área bajo la curva (AUROC). Resultados: La biopsia fue positiva para fibrosis en el 40%, FibroScan mostró un AUROC de 0,90 (intervalo de confianza [IC]: 0,83-0,97), índices APRI de 0,52 (IC: 0,35-0,68) y FIB-4 de 0,52 (IC: 0,37-0,68). Conclusiones: FibroScan es útil para el diagnóstico y seguimiento de la enfermedad hepática crónica, y debe utilizarse en combinación con otras pruebas y la clínica. FibroScan mostró un excelente rendimiento en la discriminación de pacientes con fibrosis hepática comparado con los índices APRI y FIB-4, y es mejor para detectar estadios avanzados.

A comparison of single- and double-threshold ROC plots for mixture distributions.

The receiver operating characteristics (ROC) analysis is commonly used in clinical settings to check the performance of a single threshold for distinguishing population-wise bimodal-distributed test results. However, for population-wise three-modal distributed test results, a single threshold ROC (stROC) analysis showed poor discriminative performance. The purpose of this study is to use a double-threshold ROC analysis for the three-modal distributed test results to provide better discriminative performance than the stROC analysis. A double-threshold receiver operating characteristic plot (dtROC) is constructed by replacing the single threshold with a double threshold. The sensitivity and specificity coordinates are chosen to maximize sensitivity for a given specificity value. Besides a simulation study assuming a mixture of lognormal, Poisson, and Weibull distributions, a clinical application is examined by a secondary data analysis of palpation test results of the C7 spinous process using the modified thorax-rib static technique. For the assumed mixture models, the discrimination performance of dtROC analysis outperforms the stROC analysis (area under ROC (AUROC) increased from 0.436 to 0.983 for lognormal distributed test results, 0.676 to 0.752 for the Poisson distribution, and 0.674 to 0.804 for Weibull distribution).

A test for comparing conditional ROC curves with multidimensional covariates.

The comparison of Receiver Operating Characteristic (ROC) curves is frequently used in the literature to compare the discriminatory capability of different classification procedures based on diagnostic variables. The performance of these variables can be sometimes influenced by the presence of other covariates, and thus they should be taken into account when making the comparison. A new non-parametric test is proposed here for testing the equality of two or more dependent ROC curves conditioned to the value of a multidimensional covariate. Projections are used for transforming the problem into a one-dimensional approach easier to handle. Simulations are carried out to study the practical performance of the new methodology. The procedure is then used to analyse a real data set of patients with Pleural Effusion to compare the diagnostic capability of different markers.

Evaluating Risk Prediction with Data Collection Costs: Novel Estimation of Test Tradeoff Curves.

BACKGROUND: The test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions. At a given benefit-cost ratio (the number of false-positive predictions one would trade for a true positive prediction) or risk threshold (the probability of developing disease at indifference between treatment and no treatment), the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction. For example, a test tradeoff of 3,000 invasive tests per true-positive prediction of cancer may suggest that risk prediction is not worthwhile. A test tradeoff curve plots test tradeoff versus benefit-cost ratio or risk threshold. The test tradeoff curve evaluates risk prediction at the optimal risk score cutpoint for treatment, which is the cutpoint of the risk score (the estimated risk of developing disease) that maximizes the expected utility of risk prediction when the receiver-operating characteristic (ROC) curve is concave. METHODS: Previous methods for estimating the test tradeoff required grouping risk scores. Using individual risk scores, the new method estimates a concave ROC curve by constructing a concave envelope of ROC points, taking a slope-based moving average, minimizing a sum of squared errors, and connecting successive ROC points with line segments. RESULTS: The estimated concave ROC curve yields an estimated test tradeoff curve. Analyses of 2 synthetic data sets illustrate the method. CONCLUSION: Estimating the test tradeoff curve based on individual risk scores is straightforward to implement and more appealing than previous estimation methods that required grouping risk scores. HIGHLIGHTS: The test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions.At a given benefit-cost ratio or risk threshold, the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction.Unlike previous estimation methods that grouped risk scores, the method uses individual risk scores to estimate a concave ROC curve, which yields an estimated test tradeoff curve.

Lysosome-Related Diagnostic Biomarkers for Pediatric Sepsis Integrated by Machine Learning.

Background: There is currently no biomarker that can reliably identify sepsis, despite recent scientific advancements. We systematically evaluated the value of lysosomal genes for the diagnosis of pediatric sepsis. Methods: Three datasets (GSE13904, GSE26378, and GSE26440) were obtained from the gene expression omnibus (GEO) database. LASSO regression analysis and random forest analysis were employed for screening pivotal genes to construct a diagnostic model between the differentially expressed genes (DEGs) and lysosomal genes. The efficacy of the diagnostic model for pediatric sepsis identification in the three datasets was validated through receiver operating characteristic curve (ROC) analysis. Furthermore, a total of 30 normal samples and 35 pediatric sepsis samples were gathered to detect the expression levels of crucial genes and assess the diagnostic model's efficacy in diagnosing pediatric sepsis in real clinical samples through real-time quantitative PCR (qRT-PCR). Results: Among the 83 differentially expressed genes (DEGs) related to lysosomes, four key genes (STOM, VNN1, SORT1, and RETN) were identified to develop a diagnostic model for pediatric sepsis. The expression levels of these four key genes were consistently higher in the sepsis group compared to the normal group across all three cohorts. The diagnostic model exhibited excellent diagnostic performance, as evidenced by area under the curve (AUC) values of 1, 0.971, and 0.989. Notably, the diagnostic model also demonstrated strong diagnostic ability with an AUC of 0.917 when applied to the 65 clinical samples, surpassing the efficacy of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%). Conclusion: A four-gene diagnostic model of lysosomal function was devised and validated, aiming to accurately detect pediatric sepsis cases and propose potential target genes for lysosomal intervention in affected children.

Serum matrix metalloproteinase-9 (MMP9) and amyloid-beta protein precursor (APP) as potential biomarkers in children with Fragile-X syndrome: A cross sectional study.

INTRODUCTION: Fragile-X syndrome(FXS) is a neurological disease caused by abnormal repeats in the 5'untranslated region of the FMR1 gene leading to a defective fragile-X-messenger-ribonucleoprotein-1 (FMRP). Although relatively common in children, it is usually under-diagnosed especially in developing countries where genetic screening is not routinely practiced. So far, FXS lacks a laboratory biomarker that can be used for screening, severity scoring or therapeutic monitoring of potential new treatments. METHODS: 110 subjects were recruited; 80 male children with suspected FXS and 30 matched healthy children. We evaluated the clinical utility of serum matrix metalloproteinase-9(MMP9) and amyloid-beta protein precursor(APP) as potential biomarkers for FXS. RESULTS: Out of 80 suspected children, 14 had full mutation, 8 had the premutation and 58 children had normal genotypes. No statistically-significant difference was detected between children with different genotypes concerning age of onset(P = 0.658), main clinical presentation(P = 0.388), clinical severity-score(P = 0.799), patient's disease-course(P = 0.719) and intellectual disability(P = 0.351). Both MMP9 and APP showed a statistically significant difference when comparing different genotype subgroups(P = 0.019 and < 0.001, respectively). Clinically, MMP9 levels were highest in children presenting with language defects, while APP was highest in children with neurodevelopmental delay. In receiver operating curve analysis, comparing full and premutation with the normal genotype group, MMP9 has an area-under-the-curve of 0.701(95 % CI 0.557-0.845), while APP was marginally better at 0.763(95 % CI 0.620-0.906). When combined together, elevated MMP9 or APP had excellent sensitivity > 95 % for picking-up FXS cases in the clinical setting. CONCLUSIONS: Screening for circulating biomarkers in the absence of FXS genetic diagnosis is justified. Our study is the first to evaluate both MMP9 and APP in FXS suspected children in a clinical setting and to assess their correlation with disease presentation and severity.

Clinical Predictors of Monkeypox Diagnosis: A Case-Control Study in a Nonendemic Region during the 2022 Outbreak.

Monkeypox (Mpox) is an emerging zoonotic disease with the potential for severe complications. Early identification and diagnosis are essential to prompt treatment, control its spread, and reduce the risk of human-to-human transmission. This study aimed to develop a clinical diagnostic tool and describe the clinical and sociodemographic features of 19 PCR-confirmed Mpox cases during an outbreak in a nonendemic region of northwestern Mexico. The median age of patients was 35 years, and most were male. Mpox-positive patients commonly reported symptoms such as fever, lumbago, and asthenia, in addition to experiencing painful ulcers and a high frequency of HIV infection among people living with HIV (PLWH). Two diagnostic models using logistic regression were devised, with the best model exhibiting a prediction accuracy of 0.92 (95% CI: 0.8-1), a sensitivity of 0.86, and a specificity of 0.93. The high predictive values and accuracy of the top-performing model highlight its potential to significantly improve early Mpox diagnosis and treatment in clinical settings, aiding in the control of future outbreaks.

Comparison of predictive performance for cage subsidence between CT-based Hounsfield units and MRI-based vertebral bone quality score following oblique lumbar interbody fusion.

OBJECTIVE: To compare the predictive performance between CT-based Hounsfield units (HU) and MRI-based vertebral bone quality (VBQ) for cage subsidence (CS) following oblique lumbar interbody fusion combined with anterolateral single-rod screw fixation (OLIF-AF). METHODS: A retrospective study was performed on consecutive patients who underwent OLIF-AF at our institution from 2018 to 2020. CS was determined by CT according to the change in the midpoint intervertebral space height. The VBQ score and HU value were measured from preoperative MRI and CT, respectively. Then, we evaluated the predictive performance of those two parameters by comparing the receiver operating characteristic (ROC) curves. RESULTS: The mean global and segmental VBQ scores were significantly higher in the CS group, and the mean global and segmental HU values were significantly lower in the CS group. The area under the curve (AUC) of CS prediction was higher in the operative segments' VBQ score and HU value than the measurement in the global lumbar spine. Finally, the combined segmental VBQ score and segmental HU value demonstrated the highest AUC. CONCLUSION: Both MRI-based VBQ score and CT-based HU value can achieve accurate CS prediction. Moreover, the combination of those two measurements indicated the best predictive performance. CLINICAL RELEVANCE STATEMENT: Both MRI-based VBQ score and CT-based HU value can be used for cage subsidence prediction, in order to take preventive measures early enough. KEY POINTS: • Osteoporosis is a risk factor for CS, both MRI-based VBQ score and CT-based HU value are important predictors during vertebral bone quality evaluation. • The VBQ score and HU value measured in the operative segments are better predictors of CS than the measurement in the global lumbar spine. • Combined segmental VBQ score and segmental HU value achieved the best predictive performance for CS.

Evaluating the Diagnostic Performance of Hemoglobin in the Diagnosis of Iron Deficiency Anemia in High-Altitude Populations: A Scoping Review.

We evaluated the available literature on the diagnostic performance of hemoglobin (Hb) in the diagnosis of iron deficiency anemia (IDA) in high-altitude populations. We searched PubMed, Web of Science, Scopus, Embase, Medline by Ovid, the Cochrane Library, and LILCAS until 3 May 2022. We included studies that evaluated the diagnostic performance (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC) curves, and accuracy) of Hb (with and without an altitude correction factor) compared to any iron deficiency (ID) marker (e.g., ferritin, soluble transferrin receptor (sTFR), transferrin saturation, or total body iron (TBI)) in populations residing at altitudes (≥1000 m above sea level). We identified a total of 14 studies (with 4522 participants). We found disagreement in diagnostic performance test values between the studies, both in those comparing hemoglobin with and in those comparing hemoglobin without a correction factor for altitude. Sensitivity ranged from 7% to 100%, whereas specificity ranged from 30% to 100%. Three studies reported higher accuracy of uncorrected versus altitude-corrected hemoglobin. Similarly, two studies found that not correcting hemoglobin for altitude improved the receiver operating characteristic (ROC) curves for the diagnosis of iron deficiency anemia. Available studies on high-altitude populations suggest that the diagnostic accuracy of Hb is higher when altitude correction is not used. In addition, the high prevalence of anemia in altitude regions could be due to diagnostic misclassification.

Classification of pain intensity with the pain beliefs and perceptions inventory (PBPI) and the pain catastrophizing scales (PCS).

PURPOSE: The pain beliefs and perceptions inventory (PBPI) and the pain catastrophizing scales (PCS) characterize beliefs or distress dimensions of the pain experience. It is relatively unknown, however, to what degree the PBPI and the PCS are well suited to classifying pain intensity. METHODS: This study applied a receiver operating characteristic (ROC) approach to these instruments against the criterion of a visual analogue scale (VAS) of pain intensity with fibromyalgia and chronic back pain patients (n = 419). RESULTS: The largest areas under the curve (AUC) were moderate and limited to the constancy subscale (71%) and total score (70%) of the PBPI and to the helplessness subscale (75%) and total score (72%) of the PCS. The best cut-off scores for the PBPI and PCS were better off at detecting true negatives than true positives, with larger specificity than sensitivity values. CONCLUSION: Whereas, the PBPI and PCS are certainly useful instruments to evaluate diverse pain experiences, they may be inappropriate to classify intensity. The PCS performs marginally better than the PBPI for classifying pain intensity.

Screening of coexisting depression or anxiety disorders among Danish patients with alcohol use disorder in outpatient treatment.

BACKGROUND: Depression and anxiety are highly prevalent among patients seeking outpatient treatment for alcohol use disorders (AUD) and if depression and anxiety are addressed the prognosis is improved. Screening instruments for depression and anxiety have been validated in populations suffering from drug use disorders, but not in populations suffering from AUD. The aim of this study was to validate four self-administrated screening instruments (PHQ-9, GAD-7, Kessler-6, and SRQ) and calculate the optimal cut-off value for identifying depression and anxiety. METHODS: The study included 73 patients with self-reported depression or anxiety during AUD treatment. Each patient filled out the above-mentioned instruments and was subsequently interviewed by trained clinicians blinded to the results of the instruments with the Present State Examination to establish a diagnosis of depression or anxiety according to ICD-10. ROC curves were constructed for each instrument and the area under the curve (AUC) was calculated using patients with no depression or anxiety as reference. Youden's index was calculated to assess the optimal cut-off for each instrument. RESULTS: A total of 33 (45.2%) were diagnosed with depression or anxiety. The AUC for PHQ-9, GAD-7, Kessler-6, and SRQ were 0.767, 0.630, 0.793, and 0.698 respectively. Kessler-6, the instruments performing best based on the AUC, identified 27 (82%) of the 33 patients using a cut-off of 10 points. CONCLUSION: Kessler-6 seems to be valid and reliable in identifying patients requiring treatment for depression or anxiety among patients seeking treatment for AUD who are reporting depression or anxiety.

A new window into fish welfare: A proteomic discovery study of stress biomarkers in the skin mucus of gilthead seabream (Sparus aurata).

Fish skin mucus is a dynamic external mucosal layer that acts as the first line of defense in the innate immune system. Skin mucus' exudation and composition change severely under stress, making it a valuable biofluid to search for minimally invasive stress markers. This study focused on the skin mucus proteome response to repetitive handling, overcrowding, and hypoxia, using Sparus aurata, an important species in the Mediterranean aquaculture, as a model. Biomarker discovery analysis was performed using label-free shotgun proteomics coupled with bioinformatics to unveil the most predictive proteins for the stressed phenotype. A mean of 2166 proteins were identified at a < 0.2% false discovery rate, from which the differentially abundant proteins (DAPs) were mainly involved in the immune system and protein metabolism. A sparse partial least squares regression analysis revealed a high correlation between DAPs and plasma physiological stress indicators. Feature selection, performed by recursive feature elimination followed by logistic regression analysis of the selected proteins, disclosed 28 candidate biomarkers with values of area under the curve >0.75. These minimally invasive biomarkers could be used in forthcoming species-specific stress management protocols to improve fish welfare and promote farmed fish safety, positive societal outcomes, and business sustainability. SIGNIFICANCE: The fish skin mucus holds a great promise into fish welfare, as a valuable source of minimally invasive biomarkers for stress assessment. In this shotgun proteomics discovery study, we have identified 28 candidate biomarkers by combining a comprehensive functional analysis of the stress regulated proteome with predictive modeling, supported by a significant correlation (p < 0.01) with physiological stress indicators (cortisol, lactate and glucose). The candidate biomarkers showed a good predictive value in the testing set (AUC > 0.75), paving the way for the next step in their validation by targeted proteomics. An early and timely assessment of fish stressful events, by using minimally invasive biomarkers, as those that can be found in the fish skin mucus, can contribute to promote fish health/welfare in the aquaculture sector and its sustainability. The adoption of preventive and surveillance measures based on proteomics approaches can therefore help to avoid unnecessary adverse outcomes with a negative impact on this primordial food sector.

Evaluation of Serum miR-216a, miR-216b, miR-217, miR-92b, miR-375 and miR-148a as Potential Biomarkers for Acute Pancreatitis and the Role of miR-92b in Attenuating Caerulein-induced Injury and Inflammatory Responses in AR42J Cells.

BACKGROUND: Acute pancreatitis can eventually lead to morbidity and mortality. The present study aimed to identify the differentially expressed microRNAs (miRNAs) that are related to acute pancreatitis and explore the in vitro functional role of miR-92b in acute pancreatitis. METHODS: Bioinformatics analysis was used to identify differentially expressed miRNAs in caerulein- induced acute pancreatitis samples when compared to normal controls. The role of miR-92b in acute pancreatitis was examined by in vitro functional assays. RESULTS: MiRNA-network analysis revealed 12 miRNAs that function as "core regulatory miRNAs". Further validation studies revealed that six miRNAs (miR-216a, miR-216b, miR-217, miR- 92b, miR-375 and miR-148a) were differentially expressed in the serum samples from patients with acute pancreatitis. These six miRNAs have fair diagnostic potential for severe acute pancreatitis. Caerulein induced cell injury and inflammatory response and repressed miR-92b expression in AR42J cells. MiR-92b overexpression attenuated caerulein-induced cell injury and inflammatory responses in AR42J cells. Luciferase reporter assay showed that mitogen-activated protein kinase 4 (MAP2K4) was a direct target of miR-92b. MiR-92b overexpression repressed MAP2K4 expression, while caerulein up-regulated MAP2K4 expression in AR42J cells. The rescue experiments showed that enforced expression of MAP2K4 partially reversed the miR-92b-mediated protective effects on caerulein-induced AR42J cell injury. CONCLUSION: In conclusion, we identified miR-216a, miR-216b, miR217, miR-92b, miR-375 and miR-148a as new candidate biomarkers for acute pancreatitis. Further in vitro functional studies revealed that miR-92b attenuated caerulein-induced cell injury and inflammatory responses in AJ42R cells partially via targeting MAP2K4.

A semi-parametric approach for time-dependent ROC curves with nonignorable missing biomarker.

The main purpose of this paper is to survey the statistical inference for covariate-specific time-dependent receiver operating characteristic (ROC) curves with nonignorable missing continuous biomarker values. To construct time-dependent ROC curves, we consider a joint model which assumes that the failure time depends on the continuous biomarker and the covariates through a Cox proportional hazards model and that the continuous biomarker depends on the covariates through a semiparametric location model. Assuming a purely parametric model on the propensity score, we utilize instrumental variables to deal with the identifiable issue and estimate the unknown parameters of the propensity score by a simple and efficient method. In addition, when the propensity score is estimated, we develop HT and AIPW approaches to estimate our interested quantities. In the presence of nonignorable missing biomarker, our AIPW estimators of the interested quantities are still doubly robust when the true propensity score is a special parametric logistic model. At last, simulation studies are conducted to assess the performance of our proposed approaches, and a real data analysis is also carried out to illustrate its application.