Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis

Introduction:

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections (COVID 19) is a progressive viral infection that has been investigated extensively. However, genetic features and molecular pathogenesis underlying remdesivir treatment for SARS-CoV-2 infection remain unclear. Here, we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of remdesivir-treated SARS-CoV-2-infected patients.

Methods:

Expression profiling by high-throughput sequencing dataset (GSE149273) was downloaded from the Gene Expression Omnibus, and the differentially expressed genes (DEGs) in remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples with an adjusted P value of <.05 and a |log fold change| > 1.3 were first identified by limma in R software package. Next, pathway and gene ontology (GO) enrichment analysis of these DEGs was performed. Then, the hub genes were identified by the NetworkAnalyzer plugin and the other bioinformatics approaches including protein-protein interaction network analysis, module analysis, target gene—miRNA regulatory network, and target gene—TF regulatory network. Finally, a receiver-operating characteristic analysis was performed for diagnostic values associated with hub genes.

Results:

A total of 909 DEGs were identified, including 453 upregulated genes and 457 downregulated genes. As for the pathway and GO enrichment analysis, the upregulated genes were mainly linked with influenza A and defense response, whereas downregulated genes were mainly linked with drug metabolism—cytochrome P450 and reproductive process. In addition, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1, and APBB1) were identified. Receiver-operating characteristic analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5, and PDE1A) had good diagnostic values.

Conclusion:

This study provided insights into the molecular mechanism of remdesivir-treated SARS-CoV-2 infection that might be useful in further investigations.

Evaluation of reader performance during interpretation of breast cancer screening: the Recall and detection Of breast Cancer in Screening (ROCS) trial study design

The magnitude of the tradeoff between recall rate (RR) and cancer detection rate (CDR) in breast-cancer screening is not clear, and it is expected to depend on target population and screening program characteristics. Multi-reader multi-case research studies, which may be used to estimate this tradeoff, rely on enriched datasets with artificially high prevalence rates, which may bias the results. Furthermore, readers participating in research studies are subject to "laboratory" effects, which can alter their performance relative to actual practice. The Recall and detection Of breast Cancer in Screening (ROCS) trial uses a novel data acquisition system that minimizes these limitations while obtaining an estimate of the RR-CDR curve during actual practice in the Dutch National Breast Cancer Screening Program. ROCS involves collection of at least 40,000 probability-of-malignancy ratings from at least 20 radiologists during interpretation of approximately 2,000 digital mammography screening cases each. With the use of custom-built software on a tablet, and a webcam, this data was obtained in the usual reading environment with minimal workflow disruption and without electronic access to the review workstation software. Comparison of the results to short- and medium-term follow-up allows for estimation of the RR-CDR and receiver operating characteristics curves, respectively. The anticipated result of the study is that performance-based evidence from practice will be available to determine the optimal operating point for breast-cancer screening. In addition, this data will be useful as a benchmark when evaluating the impact of potential new screening technologies, such as digital breast tomosynthesis or artificial intelligence. KEY POINTS: • The ROCS trial aims to estimate the recall rate-cancer detection rate curve during actual screening practice in the Dutch National Breast Cancer Screening Program. • The study design is aimed at avoiding the influence of the "laboratory effect" in usual observer performance studies. • The use of a tablet and a webcam allows for the acquisition of probability of malignancy ratings without access to the review workstation software.

Reliability and Validity of the Korean Version of Children's Depression Inventory 2 Short Version as a Screening Tool: A Comparison With the Standard 28-Item Version

OBJECTIVE

To investigate the reliability and validity of the Korean version of Children's Depression Inventory 2 Short Version (CDI 2:S) in comparison with its full-length version (CDI 2) as a screening tool for depressive youth.

METHODS

A total of 714 children from the community and 62 psychiatric patients were enrolled in this study. The Korean version of the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version (K-SADS-PL-K) served as the reference standard for computing receiver operating characteristic (ROC) curves. To evaluate the ability of the CDI 2 and CDI 2:S to discriminate major depressive disorders, areas under the curves (AUCs) were compared. To investigate psychometric properties of the CDI 2:S, internal consistency was calculated and confirmatory factor analysis was conducted.

RESULTS

For the CDI 2, the cutoff at 20 yielded the best balance between sensitivity (83%) and specificity (91%). For the CDI 2:S, the cutoff point of 10 resulted in high sensitivity (82%) and high specificity (93%). The short form was proven to be as sensitive and specific as the CDI 2. Further analyses confirmed that the CDI 2:S also had good reliability and validity.

CONCLUSION

The CDI 2:S, a sensitive and brief form of the CDI 2, may serve as a better option in time-constrained psychiatric settings.

Role of mitochondrial sirtuins in rheumatoid arthritis

AIM

Current study is intended to evaluate the expression and epigenetic variations of mitochondrial situins in 306 rheumatoid arthritis (RA) cases and compared with age/gender matched controls.

MATERIALS AND METHODS

The expression level was measured using the quantitative Real time PCR (qPCR) and epigenetic analysis was performed by measuring deacetylation activity. Oxidative stress was also measured in present study using the enzyme linked immunoassay (ELISA). The obtained results were evaluated by means of the student t-test, spearman correlation and ROC curve analysis.

RESULTS

Expression analysis showed the significant downregulation of SIRT3 (p < 0.0001), SIRT4 (p < 0.0001) and SIRT5 (p < 0.0001) in RA cases when compared with controls. Downregulation of mitochondrial sirtuins was significantly associated with positive anti-CCP status, increased ESR level and with increased CRP levels. Epigenetic analysis showed significant increased histone deacetylation in RA patients compared to controls. Co-expression analysis showed the significant negative association between expression level of mitochondrial sirtuins and deacytylation level (SIRT3 r = -0.438, p < 0.0001; SIRT4 r = -0.424, p < 0.0001; SIRT5 r = -0.282, p < 0.0001). ROC curve analysis exhibited that downregulation of mitochondrial sirtuins (SIRT3 AUC = 0.91, p < 0.001; SIRT4 AUC = 0.92, p < 0.001; SIRT5 AUC = 0.85, p < 0.001) was act as the good diagnostic marker for detection/diagnosis of arthritis.

CONCLUSIONS

The results show that significant deregulation of mitochondrial sirtuins was associated with increased arthritis risk and can be act as an indicator of advance clinical outcome.

Discovery of metabolomic biomarkers for discriminating platinum-sensitive and platinum-resistant ovarian cancer by using GC-MS

This study aims to determine ovarian cancer (OC) patients with platinum resistance for alternative treatment protocols by using metabolomic methodologies. Urine and serum samples of platinum-resistant and platinum-sensitive OC were analyzed using GC-MS. After data processing of GC-MS raw data, multivariate analyses were performed to interpret complex data for biologically meaningful information and to identify the biomarkers that cause differences between two groups. The biomarkers were verified after univariate, multivariate, and ROC analysis. Finally, metabolomic pathways related to group separations were specified. The results of biomarker analysis showed that 3,4-dihydroxyphenylacetic acid, 4-hydroxybutyric acid, L-threonine, D- mannose, and sorbitol metabolites were potential biomarkers in urine samples. In serum samples, L-arginine, linoleic acid, L-glutamine, and hypoxanthine were identified as important biomarkers. R2Y, Q2, AUC, sensitivity and specificity values of platinum-resistant and sensitive OC patients' urine and serum samples were 0.85, 0.545, 0.844, 91.30%, 81.08 and 0.570, 0.206, 0.743, 77.78%, 74.28%, respectively. In metabolic pathway analysis of urine samples, tyrosine metabolism and fructose and mannose metabolism were found to be statistically significant (p < 0.05) for the discrimination of the two groups. While 3,4-dihydroxyphenylacetic acid, L-tyrosine, and fumaric acid metabolites were effective in tyrosine metabolism. D-sorbitol and D-mannose metabolites were significantly important in fructose and mannose metabolism. However, seven metabolomic pathways were significant (p < 0.05) in serum samples. In terms of p-value, L-glutamine in the nitrogen metabolic pathway from the first three pathways; L-glutamine and pyroglutamic acid metabolites in D-glutamine and D-glutamate metabolism. In the arginine and proline metabolic pathway, L-arginine, L-proline, and L-ornithine metabolites differed significantly between the two groups.

Misinformation effects in an online sample: results of an experimental study with a five day retention interval

Traditional face-to-face laboratory studies have contributed greatly to our understanding of how misinformation effects develop. However, an area of emerging concern that has been relatively under-researched is the impact of misinformation following exposure to traumatic events that are viewed online. Here we describe a novel method for investigating misinformation effects in an online context. Participants (N = 99) completed the study online. They first watched a 10-min video of a fictional school shooting. Between 5 and 10 days later, they were randomly assigned to receive misinformation or no misinformation about the video before completing a recognition test. Misinformed participants were less accurate at discriminating between misinformation and true statements than control participants. This effect was most strongly supported by ROC analyses (Cohen's d = 0.59, BF10 = 8.34). Misinformation effects can be established in an online experiment using candid violent viral-style video stimuli.

Correlation of Acetabular Anteversion and Thoracic Kyphosis Postoperatively with Proximal Junctional Failure in Adult Spinal Deformity Fused to Pelvis

Objectives

To investigate whether the immediate thoracic kyphosis (TK) and acetabular anteversion (AA) postoperatively are correlated with proximal junctional failure (PJF) in adult spinal deformity (ASD) patients underwent surgical treatment.

Methods

This is a retrospective study. Following institutional ethics approval, a total of 57 patients (49 Female, eight Male) with ASD underwent surgery fused to sacroiliac bone (S1, S2, or ilium) from March 2014 to January 2019 were included. All of those patients were followed up for at least 2 years. Demographic, radiographic and surgical data were recorded. The maximum range of flexion motion (F‐ROM) and extension motion (E‐ROM) actively of hip joints was measured and recorded at pre‐ and postoperation. The sum of F‐ROM and E‐ROM was defined as the range of hip motion (H‐ROM). Receiver operating characteristic (ROC) curve analysis was used to obtain the cut off value of parameters for PJF. A Kaplan–Meier curve and log‐rank test were used to analyze the differences in PJF‐free survival.

Results

In all, 14 patients developed PJF during follow‐up. Comparisons between patients with and without PJF showed significant differences in immediate TK (P < 0.001) and AA (P = 0.027) postoperatively. ROC curve analysis determined an optimal threshold of 13° for immediate AA postoperatively (sensitivity = 74.3%, specificity = 85.7%, area under the ROC curve [AUC] = 0.806, 95% CI [0.686–0.926]). Nineteen patients with post‐AA ≤13° were assigned into the observational group, and 38 patients with post‐AA >13° were being as the control group. Patients in the observational group had smaller H‐ROM (P = 0.016) and F‐ROM (P < 0.001), but much larger E‐ROM (P < 0.001). There were 10 patients showing PJF in the observational group and four in the control group (10/9 vs 4/34, P < 0.001). PJF‐free survival time significantly decreased in the observational group (P = 0.001, log‐rank test). Furthermore, patients in the observational group had much larger TK (post‐TK, P = 0.015). The optimal threshold for post‐TK (sensitivity = 85.7%, specificity = 76.7%; AUC = 0.823, 95% CI [0.672–0.974]) was 28.1° after the ROC curve was analyzed. In the observational group, those patients with post‐TK ≥28.1° had significantly higher incidence of PJF (9/2 vs 1/7, P < 0.001) than those with post‐TK < 28.1°. Moreover, PJF‐free survival time in those patients significantly decreased (P = 0.001, log‐rank test).

Conclusions

ASD patients with acetabular anteversion of ≤13° at early postoperation may suffer significantly restricted hip motion and much higher incidence of PJF during follow‐up, moreover, in those patients, postoperative TK ≥28.1° would be a significant risk factor for PJF developing.

Bioactive substances in the stratum corneum of the epidermis found as indicators of skin damage due to sun exposure

Background/Purpose

Although an inflammatory response upon acute injury caused by ultraviolet radiation (UV) can be observed immediately, the influence of long‐term, repetitive low‐dose UV exposure on the skin cannot be precisely perceived, making early detection of chronic damage difficult. This study investigated bioactive substances in the stratum corneum as a potential early and sensitive indicator of the influence of sun exposure on the skin using receiver operating characteristic (ROC) analysis.

Methods

Receiver operating characteristic analysis was performed to assess the responsiveness of cytokines [interleukin (IL)‐1α, IL‐1 receptor antagonist (IL‐1ra), IL‐10, tumor necrosis factor (TNF)‐α], BCL2‐associated protein X (Bax), Toll‐like receptor (TLR)3, and TLR4 in the stratum corneum of healthy people exposed (dorsum of the hand) and unexposed (inner arm) to UV. Sunscreen was applied to patients with photodermatosis for 4 weeks to evaluate changes in IL‐1ra/IL‐1α, TNF‐α, Bax, and TLR3 levels after sunscreen application, as these molecules exhibited high responsiveness to sun exposure according to ROC analysis. In addition, IL‐1ra, IL‐1α, and IL‐10 levels were quantified by enzyme‐linked immunosorbent assay, and TNF‐α, Bax, TLR3, and TLR4 levels were semi‐quantitatively assessed by immunocytochemistry.

Results

Receiver operating characteristic analysis identified IL‐1ra/IL‐1α, TNF‐α, Bax, and TLR3 in the stratum corneum as highly responsive to sun exposure. Moreover, in participants, including patients with photodermatosis, IL‐1ra/IL‐1α, TNF‐α, and Bax levels decreased significantly after sunscreen application.

Conclusion

The results revealed that IL‐1ra/IL‐1α, TNF‐α, and Bax in the stratum corneum represent sensitive indicators of the influence of sun exposure on the skin.

Serum IgA Antibodies Specific to M. leprae Antigens as Biomarkers for Leprosy Detection and Household Contact Tracking

Leprosy remains endemic in several developing countries, such as India and Brazil, in part due to delayed diagnosis that facilitates ongoing transmission. Although immunoglobulins against several Mycobacterium leprae antigens have been indicated for the early diagnosis, and IgA participates in the early stages of leprosy and in subclinical infection, relatively little research has examined anti-M. leprae IgA responses. Here, we investigated serum IgA reactivity against NDO-HSA, LID-1 and NDO-LID, in paucibacillary (PB) and multibacillary (MB) patients and their household contacts, using enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy of each ELISA was evaluated by receiver operating characteristic (ROC) curve analysis. Our data reveal elevated IgA serum levels against the three M. leprae specific antigens in MB patients, whereas IgA reactivity in PB patients was increased only to NDO-HSA. Further, MB and PB household contacts displayed higher IgA reactivity to NDO-HSA than non-endemic controls. Our data suggest measurement of serum IgA against NDO-HSA as an additional tool in the diagnosis and classification of the disease, with potential utility for household contact follow-up.

Flexible diagnostic measures and new cut-point selection methods under multiple ordered classes

Medical diagnosis is essentially a classification problem and usually it is done with multiple ordered classes. For example, cancer diagnosis might be "non-malignant," "early stage," or "late stage." Therefore, appropriate measures are needed to assess the accuracy of diagnostic markers under multiple ordered classes. However, all existing measures fail to differentiate among some distinctly different biomarkers. This paper presents a multi-step procedure for evaluating biomarker accuracy under multiple ordered classes. This procedure leads to two new flexible overall measures as well as three new cut-point selection methods with great computational ease. The performance of proposed measures and cut-point selection methods are numerically explored via a simulation study. In the end, an ovarian cancer dataset from the Prostate, Lung, Colorectal, and Ovarian cancer study is analyzed. The proposed accuracy measures were estimated for markers CA125 and HE4, and cut-points were estimated for the risk of ovarian malignancy algorithm score.

A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson's Disease Investigation

A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson's disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time-frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time-frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time-frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson's disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson's disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams.

Identification of serum microRNA signatures associated with autism spectrum disorder as promising candidate biomarkers

Background

MicroRNAs (miRNAs) are short non-coding RNA molecules with a well-recognized role in gene expression mostly at the post-transcriptional level. Recently, dysregulation of miRNAs and miRNA-mRNA interactions has been associated with CNS diseases, including numerous psychiatric disorders. Dynamic changes in the expression profiles of circulating miRNA are nowadays regarded as promising non-invasive biomarkers that may facilitate the accurate and timely diagnosis of complex conditions.

Methods

In this study, we investigated the gene expression patterns of four miRNAs, which were previously reported to be dysregulated in pooled serum samples taken from Autism Spectrum Disorder (ASD) patients and typically developing children. The performance of a diagnostic model for ASD based on these four miRNAs was assessed by a receiver operating characteristic (ROC) curve analysis, which evaluates the diagnostic accuracy of the investigated miRNA biomarkers for ASD. Finally, to examine the potential modulation of CNS-related biological pathways, we carried out target identification and pathway analyses of the selected miRNAs.

Results

Significant differential expression for all the four studied miRNAs: miR-500a-5p, miR-197-5p, miR-424-5p, and miR-664a-3p, was consistently measured in the samples from ASD patients. The ROC curve analysis demonstrated high sensitivity and specificity for miR-500a-5p, miR-197-5p, and miR-424-5p. With all miRNA expression data integrated into an additive ROC curve, the combination of miR-500a-5p and miR-197-5p provided the most powerful diagnostic model. On the other hand, the mRNA target mining showed that miR-424-5p and miR-500-5p regulate pools of target mRNA molecules which are enriched in a number of biological pathways associated with the development and differentiation of the nervous system.

Conclusions

The steady expression patterns of miR-500a-5p, miR-197-5p, miR-424-5p, and miR-664a-3p in ASD children suggest that these miRNAs can be considered good candidates for non-invasive molecular biomarkers in the study of ASD patients. The highest diagnostic potential is manifested by miR-500a-5p and miR-197-5p, whose combined ROC curve demonstrates very strong predictive accuracy.

Novel Magnetic Resonance Imaging-Based Method for Accurate Diagnosis of Meniere's Disease

Background: Pathologically, Meniere's disease symptoms are considered to be associated with endolymphatic hydrops. Examinations revealing endolymphatic hydrops can be useful for accurate Meniere's disease diagnosis. We previously reported a quantitative method for evaluating endolymphatic hydrops, i.e., by measuring the volume of the endolymphatic space using three-dimensional magnetic resonance imaging (MRI) of the inner ear. This study aimed to confirm the usefulness of our methods for diagnosing Meniere's disease. Here, we extracted new explanatory factors for diagnosing Meniere's disease by comparing the volume of the endolymphatic space between healthy volunteers and patients with Meniere's disease. Additionally, we validated our method by comparing its diagnostic accuracy with that of the conventional method.

Methods and Findings: This is a prospective diagnostic accuracy study performed at vertigo/dizziness centre of our university hospital, a tertiary hospital. Eighty-six patients with definite unilateral Meniere's disease and 47 healthy volunteers (25 and 33 males, and 22 and 53 females in the control and patient groups, respectively) were enrolled. All participants underwent 3-Tesla MRI 4 h after intravenous injection of gadolinium to reveal the endolymphatic space. The volume of the endolymphatic space was measured and a model for Meniere's disease diagnosis was constructed and compared with models using conventional criteria to confirm the effectiveness of the methods used. The area under the receiver operating characteristic curve of the method proposed in this study was excellent (0.924), and significantly higher than that derived using the conventional criteria (0.877). The four indices, sensitivity, specificity, positive predictive value, and negative predictive value, were given at the threshold; all of these indices achieved higher scores for the 3D model compared to the 2D model. Cross-validation of the models revealed that the improvement was due to the incorporation of the semi-circular canals.

Conclusions: Our method showed high diagnostic accuracy for Meniere's disease. Additionally, we revealed the importance of observing the semi-circular canals for Meniere's disease diagnosis. The proposed method can contribute toward providing effective symptomatic relief in Meniere's disease.

Exploration and time-serial validation of logistic regression models composed of multiple laboratory tests for early detection of HCV-associated hepatocellular carcinoma

BACKGROUND

We developed a laboratory test-based regression model for early detection of hepatocellular carcinoma (HCC) associated with HCV in its surveillance.

METHODS

This matched case-control study was conducted by enrolling 452 patients with chronic hepatitis and/or cirrhosis, including 129 patients complicated with HCC. One-to-one propensity score matching was performed by referring to sex, age, and fibrosis-4 index, which resulted in 102 patients each in HCC and non-HCC groups. Logistic regression models (LRM) for distinguishing the two groups were explored from variable combinations of laboratory tests. The model was validated by our new scheme of applying it retroactively to trimonthly previous datasets.

RESULTS

Models with a practical level of diagnostic accuracy (C-statistic) were α-fetoprotein (AFP) alone (0.810), LRM3 comprising AFP, AST, and ALT (0.850), and LRM4 comprising AFP, AFP/(AST × ALT), and AST (0.862). After retroactive application of each model, LRM4 showed the highest distinction of the two groups at -12M, -6M, -3M with C-statistics of 0.654, 0.786, 0.834, respectively. LRM4 was accurate even after limiting cases to early-stage HCC.

CONCLUSIONS

LRM4 was proved useful in prompting clinicians to perform timely image study in the surveillance. The retroactive validation scheme is applicable to assess diagnostic models of other neoplastic diseases.

Alix: A Candidate Serum Biomarker of Alzheimer's Disease

Alzheimer’s disease (AD) is the most common fatal neurodegenerative disease of the elderly worldwide. The identification of AD biomarkers will allow for earlier diagnosis and thus earlier intervention. The aim of this study was to find such biomarkers. It was observed that the expression of Alix was significantly decreased in brain tissues and serum samples from AD patients compared to the controls. A significant correlation between Alix levels and cognitive decline was observed (r = 0.80; p < 0.001) as well as a significant negative correlation between Alix and Aβ₄₀ in serum levels (r =−0.60, p < 0.001). The receiver operating characteristic curve (ROC) analysis showed the area under the curve (AUC) of Alix was 0.80, and the optimal cut-off point of 199.5 pg/ml was selected with the highest sum of sensitivity and specificity. The diagnostic accuracy for serum Alix was 74%, with 76% sensitivity and 71% specificity respectively, which could differentiate AD from controls. In addition, the expression of Alix was found to be significantly decreased in AD compared to vascular dementia (VaD). ROC analysis between AD and VaD showed that the AUC was 0.777, which could be indicative of the role of serum Alix as a biomarker in the differential diagnosis between AD and VaD. Most surprisingly, the decreased expression of Alix was attenuated after the treatment of Memantine in different AD animal models. In conclusion, our results indicate the possibility of serum Alix as a novel and non-invasive biomarker for AD for the first time.

Pregnancy complications in G20210A mutation carriers associated with high prothrombin activity

OBJECTIVE

To study the association between high activity of Factor II (prothrombin) in blood plasma with G20210A mutation and the development of great obstetrical syndromes.

MATERIAL AND METHODS

A prospective clinical cohort study was conducted on 290 pregnant women (average age 31.7 ± 4.7 years old). The main group was made up of 140 G20210A patients, while the control group comprised 150 women with the wild G20210G type. The aim was to evaluate the activity of Factor II in the venous blood plasma during the stages of pregnancy with regard to trophoblast invasion waves. As per results, association analysis of Factor II activity value and gestational complications was carried out.

RESULTS

In the control group, the median (Me) of Factor II activity ranged from 108% (preconception period) to 144% (pregnancy) [95% CI 130-150]. In patients with the GA type, the value was significantly higher in related periods, ranging from 149 to 181% [95% CI 142-195], p < 0.0001. With Factor II activity ranging from 148.5 to 180.6%, pregnancies in the main group had no complications. Higher levels of Factor II activity were associated with the development of early and/or severe preeclampsia (PE) and fetal growth retardation (FGR).

CONCLUSION

The data obtained regarding Factor II activity in blood plasma, juxtaposed with the development of great obstetrical syndromes, allow to assume that manifestation of G20210A in early and/or severe PE and FGR is associated with this coagulation factor's level of activity. Threshold value of the Factor II activity with G20210A mutation, allowing to predict the development of PE, comprised 171.0% at the preconception stage (AUC - 0.86; p < 0.0001) and within 7-8 weeks of gestation it was 181.3% (AUC - 0.84; p < 0.0001).

Tutorial on Biostatistics: Receiver-Operating Characteristic (ROC) Analysis for Correlated Eye Data

Purpose: To demonstrate methods for receiver-operating characteristic (ROC) analysis of correlated eye data.Methods: We applied the Obuchowski's nonparametric approach and cluster bootstrap for estimating and comparing the area under ROC curve (AUC) between different sets of predictors to three datasets with varying inter-eye correlation.Results: In an optic neuritis (ON) study of 152 eyes (80 patients), the AUC of optical coherence tomography retinal nerve fiber layer thickness for diagnosing ON (inter-eye kappa = 0.13) was 0.71 [95% confidence interval (95% CI): 0.622, 0.792] from the naïve approach without accounting for inter-eye correlation was narrower than from nonparametric (95% CI: 0.613, 0.801) or cluster bootstrap (95% CI: 0.614, 0.797) approaches. In an analysis of 198 eyes (135 patients), the baseline Age-related Eye disease Study scale predicted 5-year incidence of advanced age-related macular degeneration (inter-eye kappa = 0.23) with AUC of 0.72. The 95% CI from the naïve approach was slightly narrower (0.645, 0.794) than from the nonparametric (0.641, 0.797) or cluster bootstrap (0.641, 0.793) approaches. In an analysis of 1542 eyes (771 infants), birthweight and gestational age predicted treatment-requiring retinopathy of prematurity (inter-eye kappa = 0.98) with AUC of 0.80. Furthermore, the 95% CI from the naïve approach was narrower (0.769, 0.835) than from the nonparametric (0.755, 0.848) or cluster bootstrap (0.755, 0.845) approaches. 95% CIs for AUC differences between different models were narrower in the naïve approach than the nonparametric or cluster bootstrap approaches.Conclusion: In ROC analysis of correlated eye data, ignoring inter-eye correlation leads to narrower 95% CI with underestimation dependent on magnitude of inter-eye correlation. Nonparametric and cluster bootstrap approaches properly account for inter-eye correlation.

The distinction between discriminability and reliability in forensic science

Forensic science plays an increasingly important role in the criminal justice system; yet, many forensic procedures have not been subject to the empirical scrutiny that is expected in other scientific disciplines. Over the past two decades, the scientific community has done well to bridge the gap, but have likely only scratched the tip of the iceberg. We offer the discriminability-reliability distinction as a critical framework to guide future research on diagnostic-testing procedures in the forensic science domain. We argue that the primary concern of the scientist ought to be maximizing discriminability and that the primary concern of the criminal justice system ought to be assessing the reliability of evidence. We argue that Receiver Operating Characteristic (ROC) analysis is uniquely equipped for determining which of two procedures or conditions has better discriminability and we also demonstrate how estimates of reliability can be extracted from this Signal Detection framework.

Evaluation of the Sysmex UF-4000i urine analyzer as a screening test to rule out urinary tract infection and reduce urine cultures

INTRODUCTION

Urinary tract infection (UTI) diagnosis by urine culture is time- and labor- consuming. In the Ibn Rochd microbiology laboratory, up to 70% of urine culture samples yield no growth or insignificant growth.

OBJECTIVE

To evaluate the new generation of Sysmex UF-4000i fluorescence flow cytometry analyzer with a blue semiconducting laser as a method to rule out negative urine samples for UTI, in comparison of urine culture.

MATERIAL AND METHODS

Flow cytometry and microbiological analysis were performed on 502 urine samples included in the study. We used ROC analysis to determine cutoff points at which optimal sensitivity and specificity are achieved for clinical use.

RESULTS

Our results showed that bacteria count at a cut-off of 100/μL, and/or the leucocytes count ≥45/μL are the optimal indicator for positive culture results. At these cut off, bacteria sensitivity (SE), specificity (SP), Positive predictive value (PPV) and negative predictive value (NPV) were 97,3%, 95%, 87,8% and 98,8% respectively. For leucocytes, SE, SP, PPV and NPV were 99,1%, 95,8%, 88,6% and 99,7% respectively.

DISCUSSION AND CONCLUSION

The bacterial and leucocytes counts generated by UF-4000i analysis may be useful in our context as a rapid screening to exclude UTI by reducing about 70% of urines cultures and then workload. Nevertheless, further validation is needed for different patient groups especially with urological disease or immunocompromised patients.

Is the Performance of a Periodontal Prediction Model for Identification of Diabetes affected by Participants' Characteristics?

OBJECTIVE

To evaluate whether the diagnostic accuracy of a novel periodontal prediction model (PPM) for identification of adults with diabetes varies according to participants' characteristics.

BASIC RESEARCH DESIGN

The study was carried out among 250 adults attending primary care clinics in Riyadh (Saudi Arabia). The study adopted a case-control approach, where diabetes status was first ascertained, and data collection carried out afterwards using questionnaires and periodontal examinations. Variations in the performance of the PPM by demographic (sex and age), socioeconomic (education) and behavioural factors (smoking status and last dental visit) were evaluated using receiver-operating characteristic (ROC) regression.

RESULTS

The PPM including 3 periodontal parameters (missing teeth, percentage of sites with pocket depth ≥6mm and mean pocket depth) had an area under the ROC curve (AUC) of 0.69 (95% Confidence Interval: 0.61-0.78), which dropped to 0.64 (95% CI: 0.53-0.75) after adjustment for covariates. Larger variations in performance were found by participants' sex, age and education, but not by smoking status or last dental visit. The PPM performed better among male (adjusted AUC: 0.76; 95% CI: 0.53 to 0.99), younger (0.67; 95% CI: 0.50 to 0.84) and less educated participants (0.76; 95% CI: 0.60, 0.92).

CONCLUSIONS

The diagnostic accuracy of a novel periodontal prediction model to identify individuals with diabetes varied according to participants' characteristics. This study highlights the importance of adjusting for covariates on studies of diagnostic accuracy.

Prediction of Paroxysmal Atrial Fibrillation From Complexity Analysis of the Sinus Rhythm ECG: A Retrospective Case/Control Pilot Study

Paroxysmal atrial fibrillation (PAF) is the most common cardiac arrhythmia, conveying a stroke risk comparable to persistent AF. It poses a significant diagnostic challenge given its intermittency and potential brevity, and absence of symptoms in most patients. This pilot study introduces a novel biomarker for early PAF detection, based upon analysis of sinus rhythm ECG waveform complexity. Sinus rhythm ECG recordings were made from 52 patients with (n = 28) or without (n = 24) a subsequent diagnosis of PAF. Subjects used a handheld ECG monitor to record 28-second periods, twice-daily for at least 3 weeks. Two independent ECG complexity indices were calculated using a Lempel-Ziv algorithm: R-wave interval variability (beat detection, BD) and complexity of the entire ECG waveform (threshold crossing, TC). TC, but not BD, complexity scores were significantly greater in PAF patients, but TC complexity alone did not identify satisfactorily individual PAF cases. However, a composite complexity score (h-score) based on within-patient BD and TC variability scores was devised. The h-score allowed correct identification of PAF patients with 85% sensitivity and 83% specificity. This powerful but simple approach to identify PAF sufferers from analysis of brief periods of sinus-rhythm ECGs using hand-held monitors should enable easy and low-cost screening for PAF with the potential to reduce stroke occurrence.

Combining multiple biomarkers to linearly maximize the diagnostic accuracy under ordered multi-class setting

Either in clinical study or biomedical research, it is a common practice to combine multiple biomarkers to improve the overall diagnostic performance. Despite the fact there exist a large number of statistical methods for biomarker combination under binary classification, research on this topic under multi-class setting is sparse. The overall diagnostic accuracy, i.e. the sum of correct classification rates, directly measures the classification accuracy of the combined biomarkers. Hence the overall accuracy can serve as an important objective function for biomarker combination, especially when the combined biomarkers are used for the purpose of making medical diagnosis. In this paper, we address the problem of combining multiple biomarkers to directly maximize the overall diagnostic accuracy by presenting several grid search methods and derivation-based methods. A comprehensive simulation study was conducted to compare the performances of these methods. An ovarian cancer data set is analyzed in the end.

Viability of Preictal High-Frequency Oscillation Rates as a Biomarker for Seizure Prediction

Motivation: There is an ongoing search for definitive and reliable biomarkers to forecast or predict imminent seizure onset, but to date most research has been limited to EEG with sampling rates <1,000 Hz. High-frequency oscillations (HFOs) have gained acceptance as an indicator of epileptic tissue, but few have investigated the temporal properties of HFOs or their potential role as a predictor in seizure prediction. Here we evaluate time-varying trends in preictal HFO rates as a potential biomarker of seizure prediction.

Methods: HFOs were identified for all interictal and preictal periods with a validated automated detector in 27 patients who underwent intracranial EEG monitoring. We used LASSO logistic regression with several features of the HFO rate to distinguish preictal from interictal periods in each individual. We then tested these models with held-out data and evaluated their performance with the area-under-the-curve (AUC) of their receiver-operating curve (ROC). Finally, we assessed the significance of these results using non-parametric statistical tests.

Results: There was variability in the ability of HFOs to discern preictal from interictal states across our cohort. We identified a subset of 10 patients in whom the presence of the preictal state could be successfully predicted better than chance. For some of these individuals, average AUC in the held-out data reached higher than 0.80, which suggests that HFO rates can significantly differentiate preictal and interictal periods for certain patients.

Significance: These findings show that temporal trends in HFO rate can predict the preictal state better than random chance in some individuals. Such promising results indicate that future prediction efforts would benefit from the inclusion of high-frequency information in their predictive models and technological architecture.

Cut-off points between pain intensities of the postoperative pain using receiver operating characteristic (ROC) curves

Background

Pain assessment and management are important in postoperative circumstances as overdosing of opioids can induce respiratory depression and critical consequences. We aimed this study to check the reliability of commonly used pain scales in a postoperative setting among Korean adults. We also intended to determine cut-off points of pain scores between mild and moderate pain and between moderate and severe pain by which can help to decide to use pain medication.

Methods

A total of 180 adult patients undergoing elective non-cardiac surgery were included. Postoperative pain intensity was rated with a visual analog scale (VAS), numeric rating scale (NRS), faces pain scale revised (FPS-R), and verbal rating scale (VRS). The VRS rated pain according to four grades: none, mild, moderate, and severe. Pain assessments were performed twice: when the patients were alert enough to communicate after arrival at the postoperative care unit (PACU) and 30 min after arrival at the PACU. The levels of agreement among the scores were evaluated using intraclass correlation coefficients (ICCs). The cut-off points were determined by receiver operating characteristic curves.

Results

The ICCs among the VAS, NRS, and FPS-R were consistently high (0.839–0.945). The pain categories were as follow: mild ≦ 5.3 / moderate 5.4 ~ 7.1 /severe ≧ 7.2 in VAS, mild ≦ 5 / moderate 6 ~ 7 / severe ≧ 8 in NRS, mild ≦ 4 / moderate 6 / severe 8 and 10 in FPS-R. The cut-off points for analgesics request were VAS ≧ 5.5, NRS ≧ 6, FPS-R ≧ 6, and VRS ≧ 2 (moderate or severe pain).

Conclusions

During the immediate postoperative period, VAS, NRS, and FPS-R were well correlated. The boundary between mild and moderate pain was around five on 10-point scales, and it corresponded to the cut-off point of analgesic request. Healthcare providers should consider VRS and other patient-specific signs to avoid undertreatment of pain or overdosing of pain medication.

A comparison of the value of two machine learning predictive models to support bovine tuberculosis disease control in England

Nearly a decade into Defra's current eradication strategy, bovine tuberculosis (bTB) remains a serious animal health problem in England, with c.30,000 cattle slaughtered annually in the fight against this insidious disease. There is an urgent need to improve our understanding of bTB risk in order to enhance the current disease control policy. Machine learning approaches applied to big datasets offer a potential way to do this. Regularized regression and random forest machine learning methodologies were implemented using 2016 herd-level data to generate the best possible predictive models for a bTB incident in England and its three surveillance risk areas (High-risk area [HRA], Edge area [EA] and Low-risk area [LRA]). Their predictive performance was compared and the best models in each area were used to characterize herds according to risk. While all models provided excellent discrimination, random forest models achieved the highest balanced accuracy (i.e. average of sensitivity and specificity) in England, HRA and LRA, whereas the regularized regression LASSO model did so in the EA. The time since the last confirmed incident was resolved was the only variable in the top-ten ranking in all areas according to both types of models, which highlights the importance of bTB history as a predictor of a new incident. Risk categorisation based on Receiver Operating Characteristic (ROC) analysis was carried out using the best predictive models in each area setting a 99 % threshold value for sensitivity and specificity (97 % in the LRA). Thirteen percent of herds in the whole of England as well as in its HRA, 14 % in its EA and 31 % in its LRA were classified as high-risk. These could be selected for the deployment of additional disease control measures at national or area level. In this way, low-risk herds within the area considered would not be penalised unnecessarily by blanket control measures and limited resources be used more efficiently. The methodology presented in this paper demonstrates a way to accurately identify high-risk farms to inform a targeted disease control and prevention strategy in England that supplements existing population strategies.