A new multi-species Protein A-ELISA assay for plague diagnosis in humans and other mammal hosts

Background: The Hemagglutination assay (HA) is widely used in plague diagnosis, however, it has a subjective interpretation and demands high amounts of antigen and other immunobiological supplies. Conventional IgG-ELISA is limited by the need of specific conjugates for multiple plague hosts. Methods: Thus, we developed an ELISA Protein A-peroxidase method to detect anti-F1 antibodies across several species, including humans. To determine the cut-off and performance rates, HA results from 288 samples (81 rabbits, 64 humans, 66 rodents and 77 dogs) were used as reference. Results: Optimal conditions were found with 250ng/well of F1 and 1:500 serum dilution. Protein A-ELISA showed high repeatability and reproducibility. The positive/negative OD ratios were higher in Protein A-ELISA and there was no significant cross-reaction with other pathogenic yersiniae. The overall sensitivity/specificity, area under the curve and Kappa rates for Protein A-ELISA were 93.9/98.9%; 0.993 and 0.938, respectively. Similar results were observed in each species separately. There was a strong agreement between Protein A and IgG assays (kappa=0.973) in independent analysis (n=487). Conclusions: Altogether, the Protein A-ELISA showed high performance when compared both to HA and IgG-ELISA, with a polyvalent single protocol that requires reduced amounts of antigen and can be employed to any plague hosts.

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Establishment of a clinical nomogram model to predict the progression of COVID-19 to severe disease

10.21203/rs.3.rs-44136/v1 ◽

2020 ◽

Author(s):

changli tu ◽

Guojie Wang ◽

Cuiyan Tan ◽

Meizhu Chen ◽

Zijun Xiang ◽

...

Keyword(s):

Disease Progression ◽

Selection Process ◽

Severe Disease ◽

Area Under The Curve ◽

Training Dataset ◽

Validation Dataset ◽

Efficient Manner ◽

And Performance ◽

Sensitivity Specificity ◽

D Dimer

Abstract Background Coronavirus disease 2019 (COVID-19) is a worldwide public health pandemic with a high mortality rate, among severe cases. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. It is important to ensure early detection of the virus to curb disease progression to severe COVID-19. This study aimed to establish a clinical-nomogram model to predict the progression to severe COVID-19 in a timely, efficient manner. Methods This retrospective study included 202 patients with COVID-19 who were admitted to the Fifth Affiliated Hospital of Sun Yat-sen University and Shiyan Taihe Hospital from January 17 to April 30, 2020. The patients were randomly assigned to the training dataset (n = 163, with 43 progressing to severe COVID-19) or the validation dataset (n = 39, with 10 progressing to severe COVID-19) at a ratio of 8:2. The optimal subset algorithm was applied to filter for the clinical factors most relevant to the disease progression. Based on these factors, the logistic regression model was fit to distinguish severe (including severe and critical cases) from non-severe (including mild and moderate cases) COVID-19. Sensitivity, specificity, and area under the curve (AUC) were calculated using the R software package to evaluate prediction performance. A clinical nomogram was established and performance assessed with the discrimination curve. Results Risk factors, including demographics data, symptoms, laboratory and image findings were recorded for the 202 patients. Eight of the 52 variables that were entered into the selection process were selected via the best subset algorithm to establish the predictive model; they included gender, age, BMI, CRP, D-dimer, TP, ALB, and involved-lobe. Sensitivity, specificity and AUC were 0.91, 0.84 and 0.86 for the training dataset, and 0.87, 0.66, and 0.80 for the validation dataset. Conclusions We established an efficient and reliable clinical nomogram model which showed that gender, age, and initial indexes including BMI, CRP, D-dimer, involved-lobe, TP, and ALB could predict the risk of progression to severe COVID-19.

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Persian leopard and wild sheep distribution modeling using the Maxent model in the Tang-e-Sayad protected area, Iran

Mammalia ◽

10.1515/mammalia-2016-0155 ◽

2018 ◽

Vol 83 (1) ◽

pp. 84-96 ◽

Cited By ~ 2

Author(s):

Ali Jafari ◽

Rasool Zamani-Ahmadmahmoodi ◽

Rouhollah Mirzaei

Keyword(s):

Protected Area ◽

Area Under The Curve ◽

Predictor Variables ◽

Maxent Model ◽

Threshold Limit ◽

Conservation Actions ◽

Wild Sheep ◽

And Performance ◽

Sensitivity Specificity ◽

Jackknife Procedure

Abstract The maximum entropy (Maxent) model was used to predict the distribution of Persian leopards and wild sheep in the Tang-e-Sayad protected area in Iran. For this purpose, eight variables, as well as 30 occurrence points of leopard and 98 points of wild sheep, were used. Two techniques, density-based occurrence points thinning and performance-based predictor variables selection were used to improve the results of the model. The model results were analyzed based on four threshold limit-based statistics (sensitivity, specificity, kappa and true skill statistics) and area under the curve (AUC), followed by determining the relative importance of variables based on the jackknife procedure. The results of threshold limit-based statistics revealed that the success of the model for distribution prediction of leopard and wild sheep were good and relatively good, respectively. According to the jackknife procedure, for wild sheep and for leopard, slope and distance to road, respectively, were the most important predictor variables. The results also indicated that the efficiency of the model did not improve by reducing the density of occurrence points for the wild sheep (AUC=0.784–0.773). However, the selection of predictor variables slightly improved the performance of the model (AUC=0.794–0.819). The results of the study also showed overlapping habitat for two species due to both human and ecological reasons for which we proposed some conservation actions such as excluding domestic grazing, controlling illegal poaching and restoration of old migratory corridors.

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Serum SP-A and KL-6 levels can predict the improvement and deterioration of patients with interstitial pneumonia with autoimmune features

10.21203/rs.3.rs-35462/v1 ◽

2020 ◽

Author(s):

Jingxian Wang ◽

Peiyan Zheng ◽

Zhifeng Huang ◽

Huimin Huang ◽

Mingshan Xue ◽

...

Keyword(s):

Lung Function ◽

Interstitial Pneumonia ◽

Protein A ◽

Area Under The Curve ◽

High Sensitivity ◽

Serum Levels ◽

Surfactant Protein ◽

Roc Curve Analysis ◽

Group 13 ◽

Sensitivity Specificity

Abstract Background Some patients with Interstitial pneumonia with autoimmune features (IPAF) showed a progressive course despite therapy. Furthermore, it is difficult to predict poor patient outcomes. This study aimed to evaluate whether serial changes in serum levels of surfactant protein-A (SP-A) and Krebs von den Lungen-6 (KL-6) can predict disease progression. Methods 64 patients with IPAF and 50 healthy people served as normal controls. Based on long-term changes in lung function, 36 IPAF patients who were followed up for more than 3 months were divided into a progressive group (9), improvement group (13), and stable group (14). Serum KL-6 and SP-A levels were measured. The sensitivity, specificity, cut off value, and area under the curve (AUC) value for each of the indices were determined using the receiver operating characteristic (ROC) curve analysis. The expression differences of these biomarkers and their correlation with the severity of the disease were analyzed. Results Compared with healthy controls, serum SP-A and KL-6 levels in IPAF patients increased significantly [SP-A: (p < 0.001); KL-6: (p < 0.001)] and were negatively correlated with DLCO (SP-A: rS = -0.323, p = 0.018; KL-6: rS = -0.348, p = 0.0011). In the patients with progressive disease, the posttreatment serum SP-A and KL-6 levels increased significantly compared with pretreatment levels [SP-A: (p = 0.021); KL-6: (p = 0.008)]. In the patients showing improvement, the levels decreased significantly SP-A (p = 0.007) and KL-6 (p = 0.002). Changes in serum biomarkers (Delta SP-A and Delta KL-6) were significantly negatively correlated with changes in lung function (Delta FVC, Delta DLCO and Delta FEV1) (rS = 0.482, p < 0.05). A significant positive correlation was found between Delta SP-A and Delta KL-6 (rS = 0.482, p < 0.001).Conclusions Serum SP-A and KL-6 offers high sensitivity and specificity for the diagnosis of IPAF. The decrease of serum SP-A and / or KL-6 levels in patients with IPAF is related to the improvement of pulmonary function. SP-A and KL-6 may be important biomarkers to evaluate the prognosis of patients with IPAF.

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Validation of an Enzyme-Linked Immunosorbent Assay for Diagnosis of Human Trichinellosis

Clinical and Vaccine Immunology ◽

10.1128/cvi.00257-08 ◽

2008 ◽

Vol 15 (11) ◽

pp. 1723-1729 ◽

Cited By ~ 63

Author(s):

Maria Angeles Gómez-Morales ◽

Alessandra Ludovisi ◽

Marco Amati ◽

Simona Cherchi ◽

Patrizio Pezzotti ◽

...

Keyword(s):

Immunosorbent Assay ◽

Area Under The Curve ◽

Curve Analysis ◽

Enzyme Linked Immunosorbent Assay ◽

Serum Samples ◽

Roc Curve Analysis ◽

Epidemiological Surveys ◽

Human Sera ◽

Repeatability And Reproducibility ◽

High Repeatability

ABSTRACT Trichinellosis is a zoonotic disease caused by the consumption of raw or semiraw meat from different animals harboring Trichinella larvae in their muscles. Since there are no pathognomonic signs, diagnosis can be difficult; for this reason, serology is important. The objective of this study was to validate an enzyme-linked immunosorbent assay (ELISA) using excretory/secretory antigens to detect anti-Trichinella immunoglobulin G antibodies in human sera. A total of 3,505 human serum samples were tested. A receiver-operator characteristic (ROC) curve analysis was performed. The accuracy of the test was determined by calculating the area under the curve, which was equal to 0.999, indicating high accuracy. The coefficient of variation calculated for data from four serum samples in eight working sessions was no higher than 5% for the positive sera or 14% for the negative sera. Moreover, the analysis of the differences in optical density between duplicates indicated a high repeatability for the ELISA. At the ROC optimized cutoff, the sensitivity and specificity of the test were, respectively, 99.2% and 90.6% (specificity of 95.6% when excluding the samples from multiparasitized persons from Tanzania). The validated ELISA showed good performance in terms of sensitivity, repeatability, and reproducibility, whereas the specificity was limited. These results suggest that this test is suitable for detecting anti-Trichinella antibodies in human sera for diagnostic purposes, whereas its use in epidemiological surveys could be questionable.

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Trace nanoanalysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100172437 ◽

1994 ◽

Vol 52 ◽

pp. 940-941

Author(s):

D. E. Newbury ◽

R. D. Leapman

Keyword(s):

High Performance ◽

Secondary Ion Mass Spectrometry ◽

Detection Efficiency ◽

Volume Element ◽

And Performance ◽

Trace Constituents ◽

Secondary Ion ◽

Concentration Levels ◽

Structure Properties

Trace constituents, which can be very loosely defined as those present at concentration levels below 1 percent, often exert influence on structure, properties, and performance far greater than what might be estimated from their proportion alone. Defining the role of trace constituents in the microstructure, or indeed even determining their location, makes great demands on the available array of microanalytical tools. These demands become increasingly more challenging as the dimensions of the volume element to be probed become smaller. For example, a cubic volume element of silicon with an edge dimension of 1 micrometer contains approximately 5×1010 atoms. High performance secondary ion mass spectrometry (SIMS) can be used to measure trace constituents to levels of hundreds of parts per billion from such a volume element (e. g., detection of at least 100 atoms to give 10% reproducibility with an overall detection efficiency of 1%, considering ionization, transmission, and counting).

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Simultaneous Determination of Eight Potential Q-Markers in Zishen Tongguan Capsules Based on UHPLC-MS/MS

Current Pharmaceutical Analysis ◽

10.2174/1573412915666190522081113 ◽

2020 ◽

Vol 17 (1) ◽

pp. 47-56

Author(s):

Shun Liu ◽

Xun Wang ◽

Kaiping Zou ◽

Wei Liu ◽

Cunyu Li ◽

...

Keyword(s):

Quality Control ◽

Chinese Medicine ◽

Simultaneous Determination ◽

High Performance ◽

Multiple Reaction Monitoring ◽

Gradient Elution ◽

Quality Markers ◽

High Repeatability ◽

Comprehensive Study

Background: Zishen Tongguan (ZSTG) capsules were prepared at the Affiliated Hospital of Nanjing University of Chinese Medicine and have been proven to be clinically effective for treating pyelonephritis and benign prostatic hyperplasia. However, the quality standards are not ideal; a comprehensive study of the “quality markers” (Q-markers), the chemicals inherent in traditional Chinese medicine and its preparations, has not been carried out. Experimental Methods: In this paper, a sensitive and specific ultra-high-performance liquid chromatographictandem mass spectrometry (UHPLC-MS/MS) method was developed for the simultaneous determination of eight potential Q-markers of ZSTG, including timosaponin A3, berberine, jatrorrhizine, phellodendrine, palmatine, mangiferin, neomangiferin, and timosaponin BII. A Kromasil 100-3.5 C18 column was used with a mobile phase of 0.2% formic acid with acetonitrile, and gradient elution at a flow rate of 0.2 mL/min was achieved in 13 minutes and used for separation. Detection was performed in positive/negative mode with multiple reaction monitoring (MRM). Results: The analytical method was validated in terms of the sensitivity, linearity, accuracy, precision, repeatability, stability and recovery. The method established here was successfully applied to study the potential Q-markers in 8 batches of commercial samples, which demonstrated its use in improving the quality control of ZSTG. Conclusion: The developed method had high repeatability and accuracy and was suitable for the simultaneous analysis of multiple Q-markers, which may provide a new basis for the comprehensive assessment and overall quality control of ZSTG.

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https://imsciences.edu.pk/files/journals/vol12_2/New%201%20MA.864.pdf

Business & Economic Review ◽

10.22547/ber/12.2.2 ◽

2020 ◽

Vol 12 (2) ◽

pp. 19-50 ◽

Cited By ~ 1

Author(s):

Muhammad Siddique ◽

Shandana Shoaib ◽

Zahoor Jan

Keyword(s):

Organizational Performance ◽

Structural Equation ◽

High Performance ◽

Service Sector ◽

Performance Outcomes ◽

Theory And Practice ◽

Relational Coordination ◽

Multiple Sources ◽

And Performance ◽

High Level

A key aspect of work processes in service sector firms is the interconnection between tasks and performance. Relational coordination can play an important role in addressing the issues of coordinating organizational activities due to high level of interdependence complexity in service sector firms. Research has primarily supported the aspect that well devised high performance work systems (HPWS) can intensify organizational performance. There is a growing debate, however, with regard to understanding the “mechanism” linking HPWS and performance outcomes. Using relational coordination theory, this study examines a model that examine the effects of subsets of HPWS, such as motivation, skills and opportunity enhancing HR practices on relational coordination among employees working in reciprocal interdependent job settings. Data were gathered from multiple sources including managers and employees at individual, functional and unit levels to know their understanding in relation to HPWS and relational coordination (RC) in 218 bank branches in Pakistan. Data analysis via structural equation modelling, results suggest that HPWS predicted RC among officers at the unit level. The findings of the study have contributions to both, theory and practice.

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Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

Otolaryngology ◽

10.1177/0194599820939071 ◽

2020 ◽

Vol 163 (6) ◽

pp. 1156-1165

Author(s):

Juan Xiao ◽

Qiang Xiao ◽

Wei Cong ◽

Ting Li ◽

Shouluan Ding ◽

...

Keyword(s):

Thyroid Nodules ◽

Characteristic Curve ◽

Area Under The Curve ◽

Diagnostic Study ◽

Diagnostic Efficiency ◽

Training Set ◽

Multivariable Logistic Regression Model ◽

Predictive Values ◽

Validation Set ◽

Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

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Sphingolipid Analysis Indicate Lactosylceramide as a Potential Biomarker of Inflammatory Bowel Disease in Children

Biomolecules ◽

10.3390/biom10071083 ◽

2020 ◽

Vol 10 (7) ◽

pp. 1083

Author(s):

Aleksandra Filimoniuk ◽

Agnieszka Blachnio-Zabielska ◽

Monika Imierska ◽

Dariusz Marek Lebensztejn ◽

Urszula Daniluk

Keyword(s):

Inflammatory Bowel Disease ◽

Bowel Disease ◽

High Performance ◽

Area Under The Curve ◽

Study Groups ◽

Diagnostic Value ◽

Serum Samples ◽

Specificity And Sensitivity ◽

Potential Biomarker ◽

Inflammatory Bowel

An altered ceramide composition in patients with inflammatory bowel disease (IBD) has been reported recently. The aim of this study was to evaluate the concentrations of sphingolipids in the serum of treatment-naive children with newly diagnosed IBD and to determine the diagnostic value of the tested lipids in pediatric IBD. The concentrations of sphingolipids in serum samples were evaluated using a quantitative method, an ultra-high-performance liquid chromatography-tandem mass spectrometry in children with Crohn’s disease (CD) (n=34), ulcerative colitis (UC) (n = 39), and controls (Ctr) (n = 24). Among the study groups, the most significant differences in concentrations were noted for C16:0-LacCer, especially in children with CD compared to Ctr or even to UC. Additionally, the relevant increase in C20:0-Cer and C18:1-Cer concentrations were detected in both IBD groups compared to Ctr. The enhanced C24:0-Cer level was observed only in UC, while C18:0-Cer only in the CD group. The highest area under the curve (AUC), specificity, and sensitivity were determined for C16:0-LacCer in CD diagnosis. Our results suggest that the serum LacC16-Cer may be a potential biomarker that distinguishes children with IBD from healthy controls and differentiates IBD subtypes. In addition, C20:0-Cer and C18:0-Cer levels also seem to be closely connected with IBD.

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Platform Independent Analysis of Probabilities on Multithreaded Programs

International Journal of Software Innovation ◽

10.4018/ijsi.2013070104 ◽

2013 ◽

Vol 1 (3) ◽

pp. 48-65

Author(s):

Yuting Chen

Keyword(s):

Operating System ◽

Static Analysis ◽

Main Idea ◽

Experimental Results ◽

Machine To Machine ◽

Concurrent Program ◽

Acceptable Accuracy ◽

Multithreaded Programs ◽

Independent Analysis ◽

And Performance

A concurrent program is intuitively associated with probability: the executions of the program can produce nondeterministic execution program paths due to the interleavings of threads, whereas some paths can always be executed more frequently than the others. An exploration of the probabilities on the execution paths is expected to provide engineers or compilers with support in helping, either at coding phase or at compile time, to optimize some hottest paths. However, it is not easy to take a static analysis of the probabilities on a concurrent program in that the scheduling of threads of a concurrent program usually depends on the operating system and hardware (e.g., processor) on which the program is executed, which may be vary from machine to machine. In this paper the authors propose a platform independent approach, called ProbPP, to analyzing probabilities on the execution paths of the multithreaded programs. The main idea of ProbPP is to calculate the probabilities on the basis of two kinds of probabilities: Primitive Dependent Probabilities (PDPs) representing the control dependent probabilities among the program statements and Thread Execution Probabilities (TEPs) representing the probabilities of threads being scheduled to execute. The authors have also conducted two preliminary experiments to evaluate the effectiveness and performance of ProbPP, and the experimental results show that ProbPP can provide engineers with acceptable accuracy.

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