scholarly journals The Kynurenine/Tryptophan Ratio Is a Sensitive Biomarker for the Diagnosis of Pediatric Tuberculosis Among Indian Children

2022 ◽  
Vol 12 ◽  
Author(s):  
Jeffrey A. Tornheim ◽  
Mandar Paradkar ◽  
Henry Zhao ◽  
Vandana Kulkarni ◽  
Neeta Pradhan ◽  
...  
Keyword(s):  
Treatment Response ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Group Differences ◽  
Indian Children ◽  
Tb Treatment ◽  
Potential Biomarker ◽  
Pediatric Tb ◽  
Metabolome Profiling ◽  
Pediatric Tuberculosis

ObjectivesPediatric tuberculosis (TB) remains difficult to diagnose. The plasma kynurenine to tryptophan ratio (K/T ratio) is a potential biomarker for TB diagnosis and treatment response but has not been assessed in children.MethodsWe performed a targeted diagnostic accuracy analysis of four biomarkers: kynurenine abundance, tryptophan abundance, the K/T ratio, and IDO-1 gene expression. Data were obtained from transcriptome and metabolome profiling of children with confirmed tuberculosis and age- and sex-matched uninfected household contacts of pulmonary tuberculosis patients. Each biomarker was assessed as a baseline diagnostic and in response to successful TB treatment.ResultsDespite non-significant between-group differences in unbiased analysis, the K/T ratio achieved an area under the receiver operator characteristic curve (AUC) of 0.667 and 81.5% sensitivity for TB diagnosis. Kynurenine, tryptophan, and IDO-1 demonstrated diagnostic AUCs of 0.667, 0.602, and 0.463, respectively. None of these biomarkers demonstrated high AUCs for treatment response. The AUC of the K/T ratio was lower than biomarkers identified in unbiased analysis, but improved sensitivity over existing commercial assays for pediatric TB diagnosis.ConclusionsPlasma kynurenine and the K/T ratio may be useful biomarkers for pediatric TB. Ongoing studies in geographically diverse populations will determine optimal use of these biomarkers worldwide.

scholarly journals Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Noton K. Dutta ◽  
Jeffrey A. Tornheim ◽  
Kiyoshi F. Fukutani ◽  
Mandar Paradkar ◽  
Rafael T. Tiburcio ◽  
...  
Keyword(s):  
Treatment Response ◽  
Characteristic Curve ◽  
Lymphoid Cells ◽  
Mitochondrial Translation ◽  
Indian Children ◽  
Metabolomic Data ◽  
Pediatric Tb ◽  
Novel Biomarkers ◽  
Pediatric Tuberculosis ◽  
Plasma Metabolite

Abstract Pediatric tuberculosis (TB) remains a major global health problem. Improved pediatric diagnostics using readily available biosources are urgently needed. We used liquid chromatography-mass spectrometry to analyze plasma metabolite profiles of Indian children with active TB (n = 16) and age- and sex-matched, Mycobacterium tuberculosis-exposed but uninfected household contacts (n = 32). Metabolomic data were integrated with whole blood transcriptomic data for each participant at diagnosis and throughout treatment for drug-susceptible TB. A decision tree algorithm identified 3 metabolites that correctly identified TB status at distinct times during treatment. N-acetylneuraminate achieved an area under the receiver operating characteristic curve (AUC) of 0.66 at diagnosis. Quinolinate achieved an AUC of 0.77 after 1 month of treatment, and pyridoxate achieved an AUC of 0.87 after successful treatment completion. A set of 4 metabolites (gamma-glutamylalanine, gamma-glutamylglycine, glutamine, and pyridoxate) identified treatment response with an AUC of 0.86. Pathway enrichment analyses of these metabolites and corresponding transcriptional data correlated N-acetylneuraminate with immunoregulatory interactions between lymphoid and non-lymphoid cells, and correlated pyridoxate with p53-regulated metabolic genes and mitochondrial translation. Our findings shed new light on metabolic dysregulation in children with TB and pave the way for new diagnostic and treatment response markers in pediatric TB.

scholarly journals Recurrent Clostridioides difficile infection can be predicted using inflammatory mediator and toxin activity levels

2020 ◽  
Vol 41 (S1) ◽  
pp. s77-s78
Author(s):  
Jonathan Motyka ◽  
Aline Penkevich ◽  
Vincent Young ◽  
Krishna Rao
Keyword(s):  
Machine Learning ◽  
Inflammatory Mediators ◽  
Inflammatory Mediator ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Sensitivity Analyses ◽  
Activity Levels ◽  
Clinical Criteria ◽  
Clostridioides Difficile ◽  
Potential Biomarker

Background:Clostridioides difficile infection (CDI) frequently recurs after initial treatment. Predicting recurrent CDI (rCDI) early in the disease course can assist clinicians in their decision making and improve outcomes. However, predictions based on clinical criteria alone are not accurate and/or do not validate other results. Here, we tested the hypothesis that circulating and stool-derived inflammatory mediators predict rCDI. Methods: Consecutive subjects with available specimens at diagnosis were included if they tested positive for toxigenic C. difficile (+enzyme immunoassay [EIA] for glutamate dehydrogenase and toxins A/B, with reflex to PCR for the tcdB gene for discordants). Stool was thawed on ice, diluted 1:1 in PBS with protease inhibitor, centrifuged, and used immediately. A 17-plex panel of inflammatory mediators was run on a Luminex 200 machine using a custom antibody-linked bead array. Prior to analysis, all measurements were normalized and log-transformed. Stool toxin activity levels were quantified using a custom cell-culture assay. Recurrence was defined as a second episode of CDI within 100 days. Ordination characterized variation in the panel between outcomes, tested with a permutational, multivariate ANOVA. Machine learning via elastic net regression with 100 iterations of 5-fold cross validation selected the optimal model and the area under the receiver operator characteristic curve (AuROC) was computed. Sensitivity analyses excluding those that died and/or lived >100 km away were performed. Results: We included 186 subjects, with 95 women (51.1%) and average age of 55.9 years (±20). More patients were diagnosed by PCR than toxin EIA (170 vs 55, respectively). Death, rCDI, and no rCDI occurred in 32 (17.2%), 36 (19.4%), and 118 (63.4%) subjects, respectively. Ordination revealed that the serum panel was associated with rCDI (P = .007) but the stool panel was not. Serum procalcitonin, IL-8, IL-6, CCL5, and EGF were associated with recurrence. The machine-learning models using the serum panel predicted rCDI with AuROCs between 0.74 and 0.8 (Fig. 1). No stool inflammatory mediators independently predicted rCDI. However, stool IL-8 interacted with toxin activity to predict rCDI (Fig. 2). These results did not change significantly upon sensitivity analysis. Conclusions: A panel of serum inflammatory mediators predicted rCDI with up to 80% accuracy, but the stool panel alone was less successful. Incorporating toxin activity levels alongside inflammatory mediator measurements is a novel, promising approach to studying stool-derived biomarkers of rCDI. This approach revealed that stool IL-8 is a potential biomarker for rCDI. These results need to be confirmed both with a larger dataset and after adjustment for clinical covariates.Funding: NoneDisclosure: Vincent Young is a consultant for Bio-K+ International, Pantheryx, and Vedanta Biosciences.

scholarly journals Uncovering reasons for treatment initiation delays among children with TB in Lima, Peru

2020 ◽  
Vol 24 (12) ◽  
pp. 1254-1260
Author(s):  
J. Coit ◽  
M. Wong ◽  
J. T. Galea ◽  
M. Mendoza ◽  
H. Marin ◽  
...  
Keyword(s):  
Treatment Initiation ◽  
Complex Interventions ◽  
Diagnostic Tools ◽  
Diagnostic Study ◽  
Burden Of Care ◽  
Structured Interviews ◽  
Tb Treatment ◽  
Pediatric Tb ◽  
Field Workers ◽  
Pediatric Tuberculosis

BACKGROUND: Timely diagnosis and treatment of pediatric tuberculosis (TB) is critical to reducing mortality but remains challenging in the absence of adequate diagnostic tools. Even once a TB diagnosis is made, delays in treatment initiation are common, but for reasons that are not well understood.METHODS: To examine reasons for delay post-diagnosis, we conducted semi-structured interviews with Ministry of Health (MoH) physicians and field workers affiliated with a pediatric TB diagnostic study, and caregivers of children aged 0–14 years who were diagnosed with pulmonary TB in Lima, Peru. Interviews were analyzed using systematic comparative and descriptive content analysis.RESULTS: We interviewed five physicians, five field workers and 26 caregivers with children who initiated TB treatment < 7 days after diagnosis (n = 15) or who experienced a delay of ≥7 days (n = 11). Median time in delay from diagnosis to treatment initiation was 26 days (range 7–117). Reasons for delay included: health systems challenges (administrative hurdles, medication stock, clinic hours), burden of care on families and caregiver perceptions of disease severity.CONCLUSION: Reasons for delay in treatment initiation are complex. Interventions to streamline administrative processes and tools to identify and support families at risk for delays in treatment initiation are urgently needed.

scholarly journals 1886. N1,N12-Diacetylspermine as Potential Urinary Biomarker to Monitor Treatment Response and Bacterial Load in Pulmonary Tuberculosis

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S53-S53
Author(s):  
Qianjing (Jenny) Xia ◽  
Myung Hee Lee ◽  
Kyu Rhee ◽  
Flonza Isa
Keyword(s):  
Treatment Response ◽  
Treatment Efficacy ◽  
High Performance ◽  
Bacterial Load ◽  
Treatment Success ◽  
Sample Collection ◽  
Tb Treatment ◽  
Experimental Drug ◽  
Potential Biomarker ◽  
Monitor Treatment Response

Abstract Background Adequate control of the global tuberculosis (TB) burden is limited by a lack of accurate measures of treatment efficacy. Current gold standard relies on sputum cultures, which often lag weeks behind time of sample collection. Previous studies have identified urinary N1,N12-diacetylspermine (DiAcSpm) as a potential biomarker of active pulmonary TB (ATB). We aimed to quantify urinary DiAcSpm in participants being treated for ATB and identify its relationship to treatment response. Methods Longitudinal urine samples were obtained from two separate cohorts of participants treated for ATB. Cohort one consisted of 34 successfully treated ATB cases with urine collected at weeks 0, 2, 4, 8, 17, 26, and 52. Cohort two consisted of 35 ATB cases under two treatment arms: one arm was successfully treated with the standard therapy of rifampin, isoniazid, pyrazinamide, and ethambutol (RIPE), and the other arm received an experimental drug, which was later found to be ineffective against ATB. Urine from cohort two was collected at days 0, 2, 4, and 14. All samples were blinded, randomized, normalized to osmolality and urinary creatinine, and analyzed using high-performance liquid chromatography-mass spectrometry. DiAcSpm concentrations were further tested using ELISA. Results Using linear-mixed modeling that adjusted for age, sex, and participant weight, we found that urinary DiAcSpm significantly decreased by week 2 of successful treatment in both cohorts (P < 0.0001). DiAcSpm levels remained unchanged in the ineffective experimental drug group (cohort two), significantly differentiating treatment success and failure by day 14 (P < 0.0001). Additionally, concentrations of urinary DiAcSpm positively correlate with sputum mycobacterial burden (P = 0.0002).These findings were consistent across both methods of detection. Conclusion Our results suggest that urinary concentrations of DiAcSpm correlate with TB treatment outcome and mycobacterial disease burden, and have the potential to serve as an early biomarker of TB treatment efficacy. Disclosures All Authors: No reported Disclosures.

scholarly journals Predictive Modeling Identifies Total Bleeds at 12-Weeks Postswitch to N8-GP Prophylaxis as a Predictor of Treatment Response

2021 ◽  
Author(s):  
Pratima Chowdary ◽  
Kingsley Hampton ◽  
Victor Jiménez-Yuste ◽  
Guy Young ◽  
Soraya Benchikh el Fegoun ◽  
...  
Keyword(s):  
Predictive Model ◽  
Treatment Response ◽  
Predictive Modeling ◽  
Characteristic Curve ◽  
Model Performance ◽  
Receiver Operator Characteristic Curve ◽  
Main Phase ◽  
Supervised Machine Learning ◽  
Von Willebrand

Abstract Background Predicting annualized bleeding rate (ABR) during factor VIII (FVIII) prophylaxis for severe hemophilia A (SHA) is important for long-term outcomes. This study used supervised machine learning-based predictive modeling to identify predictors of long-term ABR during prophylaxis with an extended half-life FVIII. Methods Data were from 166 SHA patients who received N8-GP prophylaxis (50 IU/kg every 4 days) in the pathfinder 2 study. Predictive models were developed to identify variables associated with an ABR of ≤1 versus >1 during the trial's main phase (median follow-up of 469 days). Model performance was assessed using area under the receiver operator characteristic curve (AUROC). Pre-N8-GP prophylaxis models learned from data collected at baseline; post-N8-GP prophylaxis models learned from data collected up to 12-weeks postswitch to N8-GP, and predicted ABR at the end of the outcome period (final year of treatment in the main phase). Results The predictive model using baseline variables had moderate performance (AUROC = 0.64) for predicting observed ABR. The most performant model used data collected at 12-weeks postswitch (AUROC = 0.79) with cumulative bleed count up to 12 weeks as the most informative variable, followed by baseline von Willebrand factor and mean FVIII at 30 minutes postdose. Univariate cumulative bleed count at 12 weeks performed equally well to the 12-weeks postswitch model (AUROC = 0.75). Pharmacokinetic measures were indicative, but not essential, to predict ABR. Conclusion Cumulative bleed count up to 12-weeks postswitch was as informative as the 12-week post-switch predictive model for predicting long-term ABR, supporting alterations in prophylaxis based on treatment response.

AVALIAÇÃO DE UM INSTRUMENTO DE MENSURAÇÃO DE MORBIDADE PSÍQUICA: ESTUDO DE VALIDAÇÃO DO SELF-REPORTING QUESTIONNAIRE (SRQ-20)

1970 ◽  
Vol 34 (3) ◽  
pp. 544 ◽  
Author(s):  
Kionna Oliveira Bernardes Santos ◽  
Tânia Maria de Araújo ◽  
Paloma de Sousa Pinho ◽  
Ana Cláudia Conceição Silva
Keyword(s):  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Interview ◽  
Receiver Operator Characteristic ◽  
Interview Schedule ◽  
Para Determinar ◽  
Self Reporting Questionnaire

O Self-Reporting Questionnaire (SRQ-20), desenvolvido pela Organização Mundial de Saúde, tem sido utilizado para mensuração de nível de suspeição de transtornos mentais em estudos brasileiros, especialmente em grupos de trabalhadores. O objetivo deste estudo foi avaliar o desempenho do SRQ-20, com base em indicadores de validade (sensibilidade, especificidade, taxa de classificação incorreta e valores preditivos), e determinar o melhor ponto de corte para classificação dos transtornos mentais comuns na população estudada. O estudo incluiu 91 indivíduos selecionados aleatoriamente de um estudo de corte transversal realizado com população residente em áreas urbanas de Feira de Santana (BA). Entrevistas clínicas, realizadas por psicólogas, utilizando o Revised Clinical Interview Schedule (CIS-R), foi adotada como padrão-ouro. Na avaliação do desempenho do SRQ-20 foram estimados indicadores de validade (sensibilidade e especificidade). A curva Receiver Operator Characteristic Curve (ROC) foi utilizada para determinar o melhor ponto de corte para classificação de suspeitos/não suspeitos. O ponto de corte de melhor desempenho foi de 6/7 para a população investigada, revelando desempenho razoável com área sob a curva de 0,789. Os resultados indicam que o SRQ-20 possui característica discriminante regular.

scholarly journals Haemoconcentration as a predictor of severity in acute pancreatitis

2016 ◽  
Vol 4 (1) ◽  
pp. 3-7
Author(s):  
Tanka Prasad Bohara ◽  
Dimindra Karki ◽  
Anuj Parajuli ◽  
Shail Rupakheti ◽  
Mukund Raj Joshi
Keyword(s):  
Acute Pancreatitis ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Cost Effective ◽  
Receiver Operator Characteristic Curve ◽  
First Episode ◽  
P Value ◽  
Receiver Operator Characteristic ◽  
Chi Square

Background: Acute pancreatitis is usually a mild and self-limiting disease. About 25 % of patients have severe episode with mortality up to 30%. Early identification of these patients has potential advantages of aggressive treatment at intensive care unit or transfer to higher centre. Several scoring systems are available to predict severity of acute pancreatitis but are cumbersome, take 24 to 48 hours and are dependent on tests that are not universally available. Haematocrit has been used as a predictor of severity of acute pancreatitis but some have doubted its role.Objectives: To study the significance of haematocrit in prediction of severity of acute pancreatitis.Methods: Patients admitted with first episode of acute pancreatitis from February 2014 to July 2014 were included. Haematocrit at admission and 24 hours of admission were compared with severity of acute pancreatitis. Mean, analysis of variance, chi square, pearson correlation and receiver operator characteristic curve were used for statistical analysis.Results: Thirty one patients were included in the study with 16 (51.61%) male and 15 (48.4%) female. Haematocrit at 24 hours of admission was higher in severe acute pancreatitis (P value 0.003). Both haematocrit at admission and at 24 hours had positive correlation with severity of acute pancreatitis (r: 0.387; P value 0.031 and r: 0.584; P value 0.001) respectively.Area under receiver operator characteristic curve for haematocrit at admission and 24 hours were 0.713 (P value 0.175, 95% CI 0.536 - 0.889) and 0.917 (P value 0.008, 95% CI 0.813 – 1.00) respectively.Conclusion: Haematocrit is a simple, cost effective and widely available test and can predict severity of acute pancreatitis.Journal of Kathmandu Medical College, Vol. 4(1) 2015, 3-7

scholarly journals Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3546
Author(s):  
Katarzyna Sylwia Dobruch-Sobczak ◽  
Hanna Piotrzkowska-Wróblewska ◽  
Piotr Karwat ◽  
Ziemowit Klimonda ◽  
Ewa Markiewicz-Grodzicka ◽  
...  
Keyword(s):  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Quantitative Measure ◽  
Malignant Cell ◽  
Breast Cancer Patients ◽  
The Third ◽  
Leibler Divergence

The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC.

scholarly journals Optimal Utility of H-Reflex RDD as a Biomarker of Spinal Disinhibition in Painful and Painless Diabetic Neuropathy

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1247
Author(s):  
Anne Worthington ◽  
Alise Kalteniece ◽  
Maryam Ferdousi ◽  
Luca Donofrio ◽  
Shaishav Dhage ◽  
...  
Keyword(s):  
Diabetic Neuropathy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Initial Response ◽  
Healthy Controls ◽  
Painful Diabetic Neuropathy ◽  
Stimulus Response ◽  
Rate Dependent ◽  
Potential Biomarker ◽  
Spinal Inhibition

Impaired rate-dependent depression of the Hoffman reflex (HRDD) is a potential biomarker of impaired spinal inhibition in patients with painful diabetic neuropathy. However, the optimum stimulus-response parameters that identify patients with spinal disinhibition are currently unknown. We systematically compared HRDD, performed using trains of 10 stimuli at five stimulation frequencies (0.3, 0.5, 1, 2 and 3 Hz), in 42 subjects with painful and 62 subjects with painless diabetic neuropathy with comparable neuropathy severity, and 34 healthy controls. HRDD was calculated using individual and mean responses compared to the initial response. At stimulation frequencies of 1, 2 and 3 Hz, HRDD was significantly impaired in patients with painful diabetic neuropathy compared to patients with painless diabetic neuropathy for all parameters and for most parameters when compared to healthy controls. HRDD was significantly enhanced in patients with painless diabetic neuropathy compared to controls for responses towards the end of the 1 Hz stimulation train. Receiver operating characteristic curve analysis in patients with and without pain showed that the area under the curve was greatest for response averages of stimuli 2–4 and 2–5 at 1 Hz, AUC = 0.84 (95%CI 0.76–0.92). Trains of 5 stimuli delivered at 1 Hz can segregate patients with painful diabetic neuropathy and spinal disinhibition, whereas longer stimulus trains are required to segregate patients with painless diabetic neuropathy and enhanced spinal inhibition.

scholarly journals Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199398
Author(s):  
Jinwu Peng ◽  
Zhili Duan ◽  
Yamin Guo ◽  
Xiaona Li ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydatid Disease ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Random Forest Model ◽  
Hepatic Hydatid Disease ◽  
Forest Model ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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