Performance In Terms Of Specificity Research Articles

Comparing the association of two metabolic syndrome definitions, NCEP ATP III and IDF, with the risk of developing atherosclerotic cardiovascular disease: An analytical cross-sectional study.

Atherosclerotic cardiovascular diseases (ASCVD) are significant sources of mortality and morbidity with substantial economic implications and preventive measures play key roles in this regard. Metabolic syndrome (MetS) is a common condition, and its association with ASCVD and mortality has made it clinically important. However, controversies persist regarding the best definition for MetS. Here in, we investigated the ability of the International Diabetes Federation (IDF) and the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) in the prediction of ASCVD incidence. We conducted an investigation on individuals diagnosed with MetS as part of the "Kerman Coronary Artery Diseases Risk Factor Study" (KERCADRS). This study was a cohort study conducted on a population aged 15-75 years residing in Kerman, Iran to assess the risk of ASCVD. We employed ACC/AHA ASCVD Risk Estimator for predicting ASCVD occurrence in the future and then compared the results with different definitions of MetS including IDF and NCEP ATP III. Patients with MetS consistent with NCEP ATP III had higher ASCVD risk scores than those with IDF (10.63 ± 10.989 vs. 9.50 ± 9.357). NCEP ATP III had better overall performance in terms of specificity, accuracy, sensitivity and positive and negative predictive values especially in higher ASCVD risk score categories. The agreement between IDF and NCEP ATP III was none to slight (Cohen's Kappa <0.2) except for IDF in the group of ASCVD >30%, which revealed no agreement (Cohen's Kappa = 0). NCEP ATP III has better overall performance compared to IDF. The ability of NCEP ATP III increases as the ASCVD risk score goes higher. IDF may be useful in primary screening and patients with lower ASCVD risk scores.

Prediction of freezing of gait based on self-supervised pretraining via contrastive learning

Freezing of gait (FoG) is a kind of serious motor symptom happened to most patients with Parkinson's disease (PD). Predicting the onset of FoG is of great value since it can enable the delivery of external cues to prevent the FoG event. Most previous methods rely on hand-crafted features to predict the occurrence of FoG events. Despite the merits of high efficiency and satisfactory accuracy for these methods, the generalization of these models is problematic. To this end, this study proposed a deep-learning-based framework for predicting FoG. In this framework, the latent gait representations are first learned by self-supervised contrastive learning, and then further tuned with the labeled instances. Different augmented views of unlabeled segments of acceleration data are utilized to learn invariant gait representations in a self-supervised manner. Two different contrasting modules are designed to enforce the similarity of the gait representations between different augmented views of the same segments. The prediction of FoG is implemented by classifying the data segment into one of the three classes: ‘pre-FoG’, ‘Walk’, and ‘FoG’. Our framework for FoG prediction is evaluated on the famous DAPHnet dataset and another multi-modal FoG dataset collected at Beijing Xuanwu Hospital, China (BXHC), the best overall classification performance in terms of specificity, sensitivity, and F1 score is (94.74 %, 84.61 %, and 86.19 %) on DAPHnet, and (93.08 %, 80.00 %, and 81.07 %) on BXHC when evaluated in a subject-dependent scheme. These promising results indicate the effectiveness and potential of our framework in providing an accurate and robust performance of FoG prediction.

Enhancing Cardiac Disease Prediction Through Data Recovery and Deep Learning Analysis of Electronic Sensor Data

Remote health monitoring plays a pivotal role in tracking the health of patients outside traditional clinical settings. It facilitates early disease detection, preventive interventions, and cost-effective healthcare, relying on electronic sensors to collect essential data. The accuracy of medical data analysis is paramount for early disease identification, patient treatment, and optimizing social services, particularly as data utilization expands within the biomedical and healthcare sectors. However, the presence of incomplete or inconsistent data hampers the accuracy of analysis. This paper introduces a novel approach, employing Grey Wolf Optimization-based Convolutional Neural Networks (GW-CNN), to recover missing data and enhance cardiac disease identification. The proposed method combines data imputation techniques for identifying and predicting missing values in electronic sensor data, followed by feature extraction to capture relevant information. The CNN model leverages Grey Wolf Optimization to improve its predictive capabilities for cardiac disease. Comparative evaluation against existing models assesses the new model’s performance in terms of specificity, accuracy, precision, recall, and F1 score.

Algerian Workers' Exposure to Mycotoxins-A Biomonitoring Study.

Mycotoxins, produced by fungi as secondary metabolites, have the potential to induce both short-term and long-term toxic consequences in animals and humans. The present study aimed to determine multi-mycotoxin levels in Algerian workers using urine as the target. A method based on a QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction procedure followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) was optimized and validated for the determination of eleven mycotoxins in 96 urine samples. Different sorbents were tested to be used in the dispersive solid-phase extraction (d-SPE) cleanup step of QuEChERS. The final method was fit-for-purpose and showed good analytical performance in terms of specificity, linearity, and precision. All samples contained at least two mycotoxins, and toxin-2 (T-2) was the most common, being found in 92.7% of the samples, followed by zearalenone (ZEN) in 90.6% of positive samples, and ochratoxin A (OTA) in 86.4%. T-2 levels ranged from 0.3 μg/L to 36.3 μg/L, while OTA ranged from 0.3 μg/L to 3.5 μg/L, and ZEN ranged from 7.6 μg/L to 126.8 μg/L. This was the first mycotoxin biomonitoring study carried out in the Algerian population. The findings highlight the need for accurate data for better risk assessment and for the development of better regulation to manage mycotoxin contamination in this country.

Congenital Toxoplasmosis Diagnosis: Current Approaches and New Insights.

The aim of this study is to describe and discuss current disadvantages in congenital toxoplasmosis (CT) diagnosis, and what can be improved or changed through new perspectives and technological advances. We used Pubmed, Cochrane, and EBSCO databases to research publications from 10years to date describing current diagnostic methods for CT. The keywords used for this Mini-Review were Toxoplasma gondii, congenital toxoplasmosis, diagnosis, and prospects using Boolean operators such as AND, OR, identifying scientific publications highlighting the importance of implementing new diagnostic methods. Current diagnosis methods have several disadvantages, i.e., time-consuming, low sensitivity or specificity, and non-cost effective, that bring up the necessity of improving or developing new approaches. Recombinant proteins can help improve specificity by generating tests that use circulating strains in a specific geographical region, SAG1 and BAG1, as they are expressed during a particular stage of the disease (acute or chronic, respectively), for its use in serological diagnoses, such as capture ELISA and immunochromatography. Point of Care (POC) tests are methods performed at the patient care site, which leads to rapid patient treatment; despite the advantages, several improvements and perspectives are necessary to be implemented globally. Although already established diagnosis methods for CT may be sufficient in some regions, there is still a persistent demand to develop tests with higher throughput, cost, and time reduction in developing countries, where prevalence is high. New approaches in CT diagnosis, such as recombinant proteins, capture ELISA, immunochromatography, and POC tests methods, can increase performance in terms of specificity and sensitivity simplifying diagnostic tests' requirements.

Hollow-structured molecularly imprinted polymers enabled specific enrichment and highly sensitive determination of aflatoxin B1 and sterigmatocystin against complex sample matrix

The biotoxins with high toxicity have the potential to be manufactured into biochemical weapons, seriously threatening international public security. Developing robust and applicable sample pretreatment platforms and reliable quantification methods has been recognized as the most promising and practical approach to solving these problems. Through the integration of the hollow-structured microporous organic networks (HMONs) as the imprinting carriers, we proposed a molecular imprinting platform (HMON@MIP) with enhanced adsorption performance in terms of specificity, imprinting cavity density as well as adsorption capacity. The HMONs core of MIPs provided a hydrophobic surface that enhanced the adsorption of biotoxin template molecules during the imprinting process, resulting in an increased imprinting cavity density. The HMON@MIP adsorption platform could produce a series of MIP adsorbents by changing the biotoxin template, such as aflatoxin and sterigmatocystin, and showed promising generalizability. The limits of detection (LOD) of the HMON@MIP-based preconcentration method for AFT B1 and ST were 4.4 and 6.7 ng L−1, respectively, and the method was applicable to food sample with satisfied recoveries of 81.2–95.1%. And the specific recognition and adsorption sites left on HMON@MIP by the imprinting process can achieve outstanding selectivity for AFT B1 and ST. The developed imprinting platforms hold great potential for application in the identification and determination of various food hazards in complex food sample matrices and contribute to precise food safety inspection.

Application of machine learning methods to guide patient management by predicting the risk of malignancy of Bethesda III-V thyroid nodules.

Indeterminate thyroid nodules (ITN) are common and often lead to (sometimes unnecessary) diagnostic surgery. We aimed to evaluate the performance of two machine learning methods (ML), based on routinely available features to predict the risk of malignancy (RM) of ITN. Multi-centric diagnostic retrospective cohort study conducted between 2010 and 2020. Adult patients who underwent surgery for at least one Bethesda III-V thyroid nodule (TN) with fully available medical records were included. Of the 7917 records reviewed, eligibility criteria were met in 1288 patients with 1335 TN. Patients were divided into training (940 TN) and validation cohort (395 TN). The diagnostic performance of a multivariate logistic regression model (LR) and its nomogram, and a random forest model (RF) in predicting the nature and RM of a TN were evaluated. All available clinical, biological, ultrasound, and cytological data of the patients were collected and used to construct the two algorithms. There were 253 (19%), 693 (52%), and 389 (29%) TN classified as Bethesda III, IV, and V, respectively, with an overall RM of 35%. Both cohorts were well-balanced for baseline characteristics. Both models were validated on the validation cohort, with performances in terms of specificity, sensitivity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve of 90%, 57.3%, 73.4%, 81.4%, 84% (CI95%: 78.5%-89.5%) for the LR model, and 87.6%, 54.7%, 68.1%, 80%, 82.6% (CI95%: 77.4%-87.9%) for the RF model, respectively. Our ML models performed well in predicting the nature of Bethesda III-V TN. In addition, our freely available online nomogram helped to refine the RM, identifying low-risk TN that may benefit from surveillance in up to a third of ITN, and thus may reduce the number of unnecessary surgeries.

Silica-coated gold nanorods biofunctionalization for localized surface plasmon resonance (LSPR) biosensing

We introduce here the engineering of nanobiosensors designed from gold nanorods coated with an ultrathin layer of silica (AuNR@SiO2) and biofunctionalized with antibodies for the Localized Surface Plasmon Resonance (LSPR) biosensing of proteins. Despite the outstanding properties of AuNRs, their use for LSPR biosensing is limited due to the presence of the surfactant cetyltrimethylammonium bromide (CTAB) – mandatory for their synthesis - which forms a strongly-bounded and positively-charged bilayer at their surface and significantly complicates their bio-functionalization. When coated with a thin layer of silica, these nanomaterials exhibit an improved sensitivity to refractive index change which augurs for better analytical performances. Here, we undertook an in-depth investigation of the biofunctionalization of AuNR@SiO2via three different routes to design and test a label-free LSPR biosensor operating in solution. In the first route, we took advantage of the negatively charged external silica shell to immobilize anti-rabbit IgG antibody by electrostatic physisorption. In the second and third routes, the silica surface was reacted with thiol or aldehyde terminated silanes, subsequently utilized to covalently attach anti-rabbit IgG antibody to the surface. The resulting nanoprobes were characterized by a wide range of physical methods (TEM, XPS, DLS, ELS and UV-Visible spectroscopy) then tested for the biosensing of rabbit-IgG. The three nanobiosensors maintain an excellent colloidal stability after analyte recognition and exhibit extremely high analytical performances in terms of specificity and dynamic range, with an LoD down to 12 ng/mL.

Evaluation of commercially available fully automated and ELISA-based assays for detecting anti-SARS-CoV-2 neutralizing antibodies

Rapid and accurate measurement of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2)-specific neutralizing antibodies (nAbs) is paramount for monitoring immunity in infected and vaccinated subjects. The current gold standard relies on pseudovirus neutralization tests which require sophisticated skills and facilities. Alternatively, recent competitive immunoassays measuring anti-SARS-CoV-2 nAbs are proposed as a quick and commercially available surrogate virus neutralization test (sVNT). Here, we report the performance evaluation of three sVNTs, including two ELISA-based assays and an automated bead-based immunoassay for detecting nAbs against SARS-CoV-2. The performance of three sVNTs, including GenScript cPass, Dynamiker, and Mindray NTAb was assessed in samples collected from SARS-CoV-2 infected patients (n = 160), COVID-19 vaccinated individuals (n = 163), and pre-pandemic controls (n = 70). Samples were collected from infected patients and vaccinated individuals 2–24 weeks after symptoms onset or second dose administration. Correlation analysis with pseudovirus neutralization test (pVNT) and immunoassays detecting anti-SARS-CoV-2 binding antibodies was performed. Receiver operating characteristic (ROC) curve analysis was generated to assess the optimal threshold for detecting nAbs by each assay. All three sVNTs showed an excellent performance in terms of specificity (100%) and sensitivity (100%, 97.0%, and 97.1% for GenScript, Dynamiker, and Mindray, respectively) in samples collected from vaccinated subjects. GenScript demonstrated the strongest correlation with pVNT (r = 0.743, R2 = 0.552), followed by Mindray (r = 0.718, R2 = 0.515) and Dynamiker (r = 0.608, R2 = 0.369). Correlation with anti-SARS-CoV-2 binding antibodies was variable, but the strongest correlations were observed between anti-RBD IgG antibodies and Mindray (r = 0.952, R2 = 0.907). ROC curve analyses demonstrated excellent performance for all three sVNT assays in both groups, with an AUC ranging between 0.99 and 1.0 (p < 0.0001). Also, it was shown that the manufacturer's recommended cutoff values could be modified based on the tested cohort without significantly affecting the sVNT performance. The sVNT provides a rapid, low-cost, and scalable alternative to conventional neutralization assays for measuring and expanding nAbs testing across various research and clinical settings. Also, it could aid in evaluating actual protective immunity at the population level and assessing vaccine effectiveness to lay a foundation for boosters' requirements.

Multiplex Detection of 24 Staphylococcal Enterotoxins in Culture Supernatant Using Liquid Chromatography Coupled to High-Resolution Mass Spectrometry

Staphylococcal food poisoning outbreaks are caused by the ingestion of food contaminated with staphylococcal enterotoxins (SEs). Among the 27 SEs described in the literature to date, only a few can be detected using immuno-enzymatic-based methods that are strongly dependent on the availability of antibodies. Liquid chromatography, coupled to high-resolution mass spectrometry (LC-HRMS), has, therefore, been put forward as a relevant complementary method, but only for the detection of a limited number of enterotoxins. In this work, LC-HRMS was developed for the detection and quantification of 24 SEs. A database of 93 specific signature peptides and LC-HRMS parameters was optimized using sequences from 24 SEs, including their 162 variants. A label-free quantification protocol was established to overcome the absence of calibration standards. The LC-HRMS method showed high performance in terms of specificity, sensitivity, and accuracy when applied to 49 enterotoxin-producing strains. SE concentrations measured depended on both SE type and the coagulase-positive staphylococci (CPS) strain. This study indicates that LC-MS is a relevant alternative and complementary tool to ELISA methods. The advantages of LC-MS clearly lie in both the multiplex analysis of a large number of SEs, and the automated analysis of a high number of samples.

CNN supported automated recognition of Covid-19 infection in chest X-ray images

Automatic recognition of lung system is use to identify normal and covid infected lungs from chest X-ray images of the people. In the year 2020, the coronavirus forcefully pushed the entire world into a freakish situation, the foremost challenge is to diagnosis the coronavirus. We have got standard diagnosis test called PCR test which is complex and costlier to check the patient’s sample at initial stage. Keeping this in mind, we developed a work to recognize the chest X-ray image automatically and label it as Covid or normal lungs. For this work, we collected the dataset from open-source data repository and then pre-process each X-ray images from each category such as covid X-ray images and non-covid X-ray images using various techniques such as filtering, edge detection, segmentation, etc., and then the pre-processed X-ray images are trained using CNN-Resnet18 network. Using PyTorch python package, the resnet-18 network layer is created which gives more accuracy than any other algorithm. From the acquired knowledge the model is correctly classifies the testing X-ray images. Then the performance of the model is calculated and analyzed with various algorithms and hence gives that the resnet-18 network improves our model performance in terms of specificity and sensitivity with more than 90%.

Characterization and evaluation of a recombinant multiepitope peptide antigen MAG in the serological diagnosis of Toxoplasma gondii infection in pigs

BackgroundToxoplasmosis caused by Toxoplasma gondii is a serious disease threatening human and animal health. People can be infected with T. gondii by ingesting raw pork contaminated with cysts or oocysts. Serological test is a sensitive and specific method usually used for large-scale diagnosis of T. gondii infection in humans and animals (such as pigs). Commercial pig Toxoplasma antibody ELISA diagnostic kits are expensive, which limits their use; moreover, the wide antigen composition used in these diagnostic kits is still unclear and difficult to standardize. The multiepitope peptide antigen is a novel diagnostic marker, and it has potential to be developed into more accurate and inexpensive diagnostic kits.MethodsThe synthetic multiepitope antigen (MAG) cDNA encoding a protein with epitopes from five T. gondii-dominant antigens (SAG1, GRA1, ROP2, GRA4, and MIC3) was designed, synthesized, and expressed in Escherichia coli BL21 (DE3) strain. The recombinant protein was detected through western blot with pig anti-T. gondii-positive and -negative serum, and then IgG enzyme-linked immunosorbent assay (ELISA) named MAG-ELISA was designed. The MAG-ELISA was evaluated in terms of specificity, sensitivity, and stability. The MAG-ELISA was also compared with a commercial PrioCHECK®Toxoplasma Ab porcine ELISA (PrioCHECK ELISA). Finally, the trend of pig anti-T. gondii IgG levels after artificial infection with RH tachyzoites was evaluated using MAG-ELISA and two other ELISA methods (rMIC3-ELISA and PrioCHECK ELISA).ResultsMAG antigen could be specifically recognized by pig anti-T. gondii-positive but not -negative serum. MAG-ELISA showed high diagnostic performance in terms of specificity (88.6%) and sensitivity (79.1%). MAG-ELISA could be used for detecting anti-T. gondii IgG in the early stage of T. gondii infection in pigs (at least 7 days after artificial infection).ConclusionsOur results suggest that MAG antigen can be applied to specifically recognize anti-T. gondii IgG in pig, and MAG-ELISA has the potential for large-scale screening tests of T. gondii infection in pig farms and intensive industries.Graphical abstract

Intra-Laboratory Validation of Alpha-Galactosidase Activity Measurement in Dietary Supplements.

Introduction: Alpha-galactosidase (α-Gal) is an enzyme responsible for the hydrolyzation of glycolipids and glycoprotein commonly found in dietary sources. More than 20% of the general population suffers from abdominal pain or discomfort caused by intestinal gas and by indigested or partially digested food residuals. Therefore, α-Gal is used in dietary supplements to reduce intestinal gases and help complex food digestion. Marketed enzyme-containing dietary supplements must be produced in accordance with the Food and Drug Administration (FDA) regulations for Current Good Manufacturing Practice (cGMPs). Aim: in this work we illustrated the process used to develop and validate a spectrophotometric enzymatic assay for α-Gal activity quantification in dietary supplements. Methods: The validation workflow included an initial statistical-phase optimization of materials, reagents, and conditions, and subsequently a comparative study with another fluorimetric assay. A final validation of method performance in terms of specificity, linearity, accuracy, intermediate-precision repeatability, and system precision was then executed. Results and conclusions: The proven method achieved good performance in the quantitative determination of α-Gal activity in commercial food supplements in accordance with the International Council for Harmonisation of Technical Requirements for Pharmaceuticals (ICH) guidelines and is suitable as a rapid in-house quality control test.

Modified Atom Search Optimization-based Deep Recurrent Neural Network for epileptic seizure prediction using electroencephalogram signals

Context and backgroundEpilepsy is considered as the common neurological disease in the world. Early prediction of epileptic seizure gained great influence on the epileptic patient’s life. Epileptic patients suffer from unpredictable conditions that may occur at any moment. MotivationVarious epileptic seizure prediction methods are introduced for accurately predicting the pre-ictal state of human brain, but to determine the discriminative features poses a major challenge in the medical sector. HypothesisDevelop a technique for epileptic seizure prediction using electroencephalogram signals that detects the epileptic seizure automatically. MethodIn this research, an effective optimization algorithm, named Modified Atom Search Optimization-based Deep Recurrent Neural Network is proposed to perform accurate seizure prediction with less computation time. Here, the Deep Recurrent Neural Network classifier performs the seizure prediction using various hidden layers associated in the hierarchy layer based on the optimally selected features. The proposed Modified Atom Search Optimization algorithm is designed using the Squirrel Search Algorithm and Atom Search Optimization. It is worth interesting to note that the proposed Modified Atom Search Optimization-based Deep Recurrent Neural Network performed early and accurate seizure prediction using electroencephalogram signals. ResultThe analysis of the proposed SASO-based Deep RNN is carried out using CHB-MIT Scalp EEG dataset using the metrics, namely accuracy, sensitivity, and specificity. The proposed algorithm obtained better performance in terms of specificity, accuracy, and sensitivity with the values of 97.536%, 96.545%, and 96.520% by varying training percentage, and 93.736%, 94.128%, and 96.520% by varying K-fold value. ConclusionThe proposed method has significant benefits like, faster convergence rate, easy to implement, low complexity, high speed, and robustness. The weights of the classifier are optimally trained using the proposed algorithm in order to reveal the effectiveness of prediction performance.

Development and validation of an ion-pair HPLC chromatography for simultaneous determination of lactone and carboxylate forms of SN-38 in nanoparticles

A simple and reproducible reversed-phase ion-pair high performance liquid chromatography (HPLC) method using isocratic elution with UV absorbance detection was developed and validated for the simultaneous determination of carboxylate (C) and lactone (L) forms of SN-38 (active metabolite of irinotecan) in nanoparticles (NPs). Reversed phase (RP) chromatography was performed on a C18 column with an ion-pair solution, KH2PO4 buffer solution and acetonitrile, under a mobile phase 30:30:40 (v/v) and at a flow rate of 1 mL/min. Detection was performed at 265 nm and sharp peaks were obtained for C and L forms of SN-38 at retention times of 2.9 ± 0.01 and 5.1 ± 0.01 min respectively. Linear regression analysis data for the calibration plot showed a good linear relationship between response and concentration in the range of 4 to 100 μg/mL; the regression coefficient was 0.9999 for L form and 0.9998 for C form. The optimized method showed good performance in terms of specificity, linearity, detection and quantitation limits, precision and accuracy in accordance with the International Conference on Harmonization (ICH) Q2 (R1) guidelines. This assay was demonstrated to be applicable for routine quantitation of SN-38 in PLGA NPs.

Guidelines for Validation of Qualitative Real-Time PCR Methods for Molecular Diagnostic Identification of Probiotics

Abstract Backgroud: Probiotics have been shown to benefit human health through several mechanisms, including their role in improving the health of our gastrointestinal tracts. The health benefits of probiotics are strain specific, and therefore it is critical to include the correct strains in probiotic products when claiming specific health benefits. Several studies have reported issues concerning the accuracy of labeling of commercial probiotic products, including inaccurate taxonomy, missing species, or undeclared species. Consequently, there is a growing need to develop and validate assays to reliably verify strain identity in commercial probiotic products. PCR-based methods are the most commonly used methods for food species ingredient diagnostics because they are simple, fast, sensitive, and can be validated. Objective: The aim of this paper is to set the guidelines for validating targeted qualitative real-time PCR assays to verify the presence of specific strains in a probiotic supplement. Methods and Results: Qualitative real-time PCR assays are validated to evaluate the assay performance in terms of specificity, sensitivity, repeatability, and reproducibility in detecting target strains. Conclusions and Highlights: Setting these guidelines will facilitate and streamline the validation process for qualitative real-time PCR-based assays for probiotic identity authentication in support of quality assurance systems.

Can High-Sensitivity Troponins Predict Future Hypertension? A Systematic Literature Review

Hypertension (HT) is associated with cardiovascular events and increased mortality, and identification of persons at risk in due time is therefore important. Finding a biomarker to identify those at risk will enable early preventive treatment, and high-sensitivity cardiac troponin (hs-cTn) seems to be a highly relevant candidate. To gather the existing knowledge on the association between hs-cTn and future HT, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) reporting guidelines and using the PICOS system. Medline was searched until August 2018. Of 45 extracted papers, eleven papers were eligible for the study. None were randomized controlled trials. Three studies assessed hs-cTnI, eight studies assessed hs-cTnT. All studies found statistically significant associations between hs-cTn concentrations and future HT, but the studies included different types of blood pressure (diastolic, systolic, diurnal). Due to differences in troponin assay construction and test capability, a direct comparison of test performance in terms of specificity, sensitivity and predictive values was not possible, and a specific hs-cTn cutoff value for HT prediction could therefore not be defined. Furthermore, a number of conditions known to affect troponin concentrations (e.g. gender, renal function, and co-morbidities) were not sufficiently studied. All retrieved studies found significant associations between hs-cTn concentrations and future HT; although the findings are promising, the studies were too heterogeneous, and many conditions affecting troponin concentrations needs investigation at these low concentrations before hs-cTn can be considered a useful HT predictor.

Quantitation of dietary dihydrochalcones in Indian crabapple (Malus sikkimensis) using validated high-performance liquid chromatography.

In the present study, a systematic validated method was developed for the determination of two key dietary dihydrochalcones (DHC) viz. phloridzin (PZ) and phloretin (PT) in the leaves of Sikkim crabapple (Malus sikkimensis) using HPLC-Photo Diode Array (PDA). Chromatographic separation was optimized on a C18 column using a gradient elution of water/acetonitrile with the flow rate of 1.0mL/min at 25°C at 280nm. Sample preparation approach is rapid and energy efficient, and it requires no pre-concentration before analysis. Validation showed a good analytical performance in terms of specificity, linearity (r2>0.999), precision (% RSD<1.08), recovery (97-100.4%) and sensitivities (limits of detection: 12.48 and 14.95ng/mL; limit of quantification: 41.61 and 49.85ng/mL) of PZ and PT, respectively. Developed approach was employed for targeted phytochemical analysis in the bark and fruits of M. sikkimensis. The PZ content in the bark and leaves was highest (12-13mg/100mg), about 90-fold higher than fruits. PT was only present in the leaves (0.57mg/100mg). The comparative data on PZ and PT content in various wild apple species/cultivar from different countries have also been discussed. The reliability of the validated method was established by analyzing global and expanded uncertainties in two DHC determinations in wild apple. The present method fulfills the technical requirement of ISO 17025:2017 for quality control of M. sikkimensis.

Two dimensional CCA via penalized matrix decomposition for structure preserved fMRI data analysis

Two dimensional canonical correlation analysis (2DCCA) is a data driven method that has been used to preserve the local spatial structure of functional magnetic resonance (fMR) images and to detect brain activation patterns. 2DCCA finds pairs of left and right linear transforms by directly operating on two dimensional data (i.e., image data) such that the correlation between their projections is maximized without neglecting the local spatial structure of the data. However, in the context of high dimensional data, the performance of 2DCCA suffers from interpretability of learned projection variables. In this study, to improve the interpretability of projection variables while preserving the local spatial structure of fMR images, we propose two new 2DCCA approaches, sparse 2DCCA and regularized 2DCCA. The proposed algorithms aim at improving the activation detection performance in terms of specificity of activated voxels by directly operating on image data without rearranging fMRI slices in 1D-vectors. The validity of the proposed algorithms has been evaluated on synthetic and real fMRI datasets and it has been shown that the proposed algorithms produce activation maps with higher specificity of activated voxels compared with CCA, 2DCCA, and existing sparse 2DCCA (S2DCCA).

Guidelines for Validation of Qualitative Real-Time PCR Methods for Molecular Diagnostic Identification of Probiotics.

Backgroud: Probiotics have been shown to benefit human health through several mechanisms, including their role in improving the health of our gastrointestinal tracts. The health benefits of probiotics are strain specific, and therefore it is critical to include the correct strains in probiotic products when claiming specific health benefits. Several studies have reported issues concerning the accuracy of labeling of commercial probiotic products, including inaccurate taxonomy, missing species, or undeclared species. Consequently, there is a growing need to develop and validate assays to reliably verify strain identity in commercial probiotic products. PCR-based methods are the most commonly used methods for food species ingredient diagnostics because they are simple, fast, sensitive, and can be validated. Objective: The aim of this paper is to set the guidelines for validating targeted qualitative real-time PCR assays to verify the presence of specific strains in a probiotic supplement. Methods and Results: Qualitative real-time PCR assays are validated to evaluate the assay performance in terms of specificity, sensitivity, repeatability, and reproducibility in detecting target strains. Conclusions and Highlights: Setting these guidelines will facilitate and streamline the validation process for qualitative real-time PCR-based assays for probiotic identity authentication in support of quality assurance systems.