Detection of Circulating VZV-Glycoprotein E-Specific Antibodies by Chemiluminescent Immunoassay (CLIA) for Varicella–Zoster Diagnosis

Varicella and herpes zoster are mild symptoms-associated diseases caused by varicella–zoster virus (VZV). They often cause severe complications (disseminated zoster), leading to death when diagnoses and treatment are delayed. However, most commercial VZV diagnostic tests have low sensitivity, and the most sensitive tests are unevenly available worldwide. Here, we developed and validated a highly sensitive VZV diagnostic kit based on the chemiluminescent immunoassay (CLIA) approach. VZV-glycoprotein E (gE) was used to develop a CLIA diagnostic approach for detecting VZV-specific IgA, IgG, and IgM. The kit was tested with 62 blood samples from 29 VZV-patients classified by standard ELISA into true-positive and equivocal groups and 453 blood samples from VZV-negative individuals. The diagnostic accuracy of the CLIA kit was evaluated by receiver-operating characteristic (ROC) analysis. The relationships of immunoglobulin-isotype levels between the two groups and with patient age ranges were analyzed. Overall, the developed CLIA-based diagnostic kit demonstrated the detection of VZV-specific immunoglobulin titers depending on sample dilution. From the ELISA-based true-positive patient samples, the diagnostic approach showed sensitivities of 95.2%, 95.2%, and 97.6% and specificities of 98.0%, 100%, and 98.9% for the detection of VZV-gE-specific IgA, IgG, and IgM, respectively. Combining IgM to IgG and IgA detection improved diagnostic accuracy. Comparative analyses on diagnosing patients with equivocal results displaying very low immunoglobulin titers revealed that the CLIA-based diagnostic approach is overall more sensitive than ELISA. In the presence of typical VZV symptoms, CLIA-based detection of high titer of IgM and low titer of IgA/IgG suggested the equivocal patients experienced primary VZV infection. Furthermore, while no difference in IgA/IgG level was found regarding patient age, IgM level was significantly higher in young adults. The CLIA approach-based detection kit for diagnosing VZV-gE-specific IgA, IgG, and IgM is simple, suitable for high-throughput routine analysis situations, and provides enhanced specificity compared to ELISA.

Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study

ObjectivesTo develop predictive models for blood culture (BC) outcomes in an emergency department (ED) setting.DesignRetrospective observational study.SettingED of a large teaching hospital in the Netherlands between 1 September 2018 and 24 June 2020.ParticipantsAdult patients from whom BCs were collected in the ED. Data of demographic information, vital signs, administered medications in the ED and laboratory and radiology results were extracted from the electronic health record, if available at the end of the ED visits.Main outcome measuresThe primary outcome was the performance of two models (logistic regression and gradient boosted trees) to predict bacteraemia in ED patients, defined as at least one true positive BC collected at the ED.ResultsIn 4885 out of 51 399 ED visits (9.5%), BCs were collected. In 598/4885 (12.2%) visits, at least one of the BCs was true positive. Both a gradient boosted tree model and a logistic regression model showed good performance in predicting BC results with area under curve of the receiver operating characteristics of 0.77 (95% CI 0.73 to 0.82) and 0.78 (95% CI 0.73 to 0.82) in the test sets, respectively. In the gradient boosted tree model, the optimal threshold would predict 69% of BCs in the test set to be negative, with a negative predictive value of over 94%.ConclusionsBoth models can accurately identify patients with low risk of bacteraemia at the ED in this single-centre setting and may be useful to reduce unnecessary BCs and associated healthcare costs. Further studies are necessary for validation and to investigate the potential clinical benefits and possible risks after implementation.

Classification of Melanoma and Nevus for Diagnosis of Skin Cancer by Using Optimized Convolution Neural Network

Melanoma is taken into account a fatal sort of carcinoma .Differentiating melanoma from nevus is difficult task. Nevus is a common pigmented skin lesion, usually developing during adulthood, which is harmless. Since they look similar it has to be identified and reduce the risk of cancer. The death rate thanks to this disease is in particular other skin-related consolidated malignancies. In this work, we have used convolution neural networks to classify the image into melanoma and nevus. The images are pre-processed using median filter, top-bottom hat filter and are passed through layers of CNN. We have achieved an accuracy of 97.56%, sensitivity of 95.23%.The F1_socre is 97.56. Index terms: Melanoma, Nevus, True Positive, True Negative, False Negative, False Positive, Confusion Matrix, Epoch, Convolution Neural Network.

Preoperative Diagnosis of Hepatocellular Carcinoma Patients with Bile Duct Tumor Thrombus using Deep Learning Method

Preoperative diagnosis of bile duct tumor thrombus (BDTT) is clinically important as the surgical prognosis of hepatocellular carcinoma (HCC) patients with BDTT is significantly different from that of patients without BDTT. The current diagnosis of BDTT is usually based on identifying dilated bile ducts (DBDs) on medical images (eg., CT and MRI images). However, it is easy for doctors to ignore DBDs when reporting imaging scan results, leading to a high misdiagnosis rate in practice. The aim of the present study was to develop an artificial intelligence (AI) pipeline for diagnosing HCC patients with BDTT using medical images. The proposed AI pipeline includes two stages. First, the object detection neural network Faster R-CNN is adopted to identify DBDs; then, an HCC patient is diagnosed to have BDTT if the proportion of images with at least one identified DBD exceeds some threshold value. The proposed AI pipeline was applied to a real dataset consisting of 2,611 CT images collected from 34 HCC patients (16 with BDTT and 18 without BDTT). The average true positive rate for identifying DBDs per patient was 0.92, while the patient-level true positive rate for diagnosing BDTT was 0.94. The area under ROC curve for patient-level diagnosis of BDTT was 0.92 (95% CI: 0.83, 1.00), compared with 0.71 (95% CI: 0.52, 0.89) by random forest based on preoperative clinical variables. These results demonstrated that the proposed AI pipeline is successful in the diagnosis of BDTT. The automatic detection of DBDs is a key step in early diagnosis of HCC patients with BDTT, and is helpful in the treatment and prognosis of these patients.

Banks Credit Risk Prediction with Optimized ANN Based on Improved Owl Search Algorithm

Credit is one of the most significant elements in banks and financial institutions. It can also be described as unpredicted events, which mainly occur in the form of either assets or liabilities. The risk occurrence is that the facility recipients have no willingness and ability to repay their debt to the bank, which is a default that is synonymous with credit risk. Credit ratings are a way to decrease and measure credit risk and, therefore, manage it appropriately. Credit rating is an approach for estimating the features and recipients of facilities’ performance based on quantitative criteria, including the company’s financial information. The anticipated future performance allows the applicants to obtain facilities with the exact specifications. In this study, due to the need and significance of calculating the credit risk concept, a novel method based on the hybrid method of artificial neural networks and an improved version of Owl search algorithm (IOSA) and forecasting of C5 risk of decision tree credit is done. This algorithm has two major parts. The decision tree runs based on an IOSA to provide the best weighting of the neural network. The weights created along with the problem data are then given as the input to the main network, and the data are classified. The algorithm has the highest level of accuracy, 96% that is much higher than other algorithms. The results also show a precision of 0.885 and a recall of 0.83 for 618 true positive samples. The proposed method has the highest accuracy and reliability toward the other comparative methods. The study is based on actual data noticed in one of the branches of the Bank Melli, Iran.

Influence of sequencing depth on the fidelity and sensitivity of 1%-5% low-frequency mutation detection and recommendation for standardization of sequencing depth

Sequencing depth has always played an important role in the accurate detection of low-frequency mutations. The increase of sequencing depth and the reasonable setting of threshold can maximize the probability of true positive mutation, or sensitivity. Here, we found that when the threshold was set as a fixed number of positive mutated reads, the probability of both true and false-positive mutations increased with depth. However, When the number of positive mutated reads increased in an equal proportion with depth (the threshold was transformed from a fixed number to a fixed percentage of mutated reads), the true positive probability still increased while false positive probability decreased. Through binomial distribution simulation and experimental test, it is found that the "fidelity" of detected-VAFs is the cause of this phenomenon. Firstly, we used the binomial distribution to construct a model that can easily calculate the relationship between sequencing depth and probability of true positive (or false positive), which can standardize the minimum sequencing depth for different low-frequency mutation detection. Then, the effect of sequencing depth on the fidelity of NA12878 with 3% mutation frequency and circulating tumor DNA (ctDNA of 1%, 3% and 5%) showed that the increase of sequencing depth reduced the fluctuation range of detected-VAFs around the expected VAFs, that is, the fidelity was improved. Finally, based on our experiment result, the consistency of single-nucleotide variants (SNVs) between paired FF and FFPE samples of mice increased with increasing depth, suggesting that increasing depth can improve the precision and sensitivity of low-frequency mutations.

Evaluation of the BioFire Gastrointestinal Panel to Detect Diarrheal Pathogens in Pediatric Patients

Infectious diarrhea is a global pediatric health concern; therefore, rapid and accurate detection of enteropathogens is vital. We evaluated the BioFire® FilmArray® Gastrointestinal (GI) Panel with that of comparator laboratory tests. Stool samples of pediatric patients with diarrhea were prospectively collected and tested. As a comparator method for bacteria, culture, conventional PCR for diarrheagenic E. coli, and Allplex GI-Bacteria(I) Assay were tested. For discrepancy analysis, BD MAX Enteric Bacterial Panel was used. As a comparator method for virus, BD MAX Enteric Virus Panel and immunochromatography was used and Allplex GI-Virus Assay was used for discrepancy analysis. The “true positive” was defined as culture-positive and/or positive results from more than two molecular tests. Of the 184 stool samples tested, 93 (50.5%) were true positive for 128 pathogens, and 31 (16.9%) were positive for multiple pathogens. The BioFire GI Panel detected 123 pathogens in 90 of samples. The BioFire GI Panel demonstrated a sensitivity of 100% for 12 targets and a specificity of >95% for 16 targets. The overall positive rate and multiple pathogen rate among patients in the group without underlying diseases were significantly higher than those in the group with hematologic disease (57.0% vs. 28.6% (p = 0.001) and 20.4% vs. 4.8% (p = 0.02), respectively). The BioFire GI Panel provides comprehensive results within 2 h and may be useful for the rapid identification of enteropathogens.

Considerations on the region of interest in the ROC space

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

Development of an Automatic Solar-driven Hand Sanitizing System (AHSS) using Proximity Sensors

Inception of COVID ’19 has brought new normal globally. Contagious nature of various infectious diseases necessitated frequent hand washing in order to reduce rate of contamination and community transmission. The need to contain the spread of COVID-19 necessitated the development of an Automatic Hand Sanitizing System (AHSS). The AHSS employed proximity sensor (IR) to sense the hand and actuate the 5V DC submersible pumps in charge of both water and sanitizer units of the AHSS. The DC voltage that powered the system was harvested from the Sun with the help of 5v Photovoltaic cell connected to a controlled charging circuit. The system responded to presence of user object within the active zone of the IR proximity sensors. This presence sends signal to the pumps to release either the Sanitizer/water. Evaluation based on Delay Time (DT), Average DT (ADT), True Positive (TP), False Positive (FP), Unable to Detect (UTD) and Accuracy (A) was conducted. The system was tested 180 times among students of School of Engineering, Federal Polytechnic, Ile-Oluji (FEDPOLEL). Results of evaluation indicate 12s, 180, 0.00, 0.00 and 100% for ADT, TP, FP, UTD and Accuracy, respectively. Accuracy of the designed AHSS was encouraging. An AHSS that can notify user about level of water and sanitizer, also test for presence of COVID-19 infection can also be designed and constructed.

An Artificial Intelligence Pipeline for Diagnosing Hepatocellular Carcinoma Patients with Bile Duct Tumor Thrombus

Background and purpose: Preoperative diagnosis of bile duct tumor thrombus (BDTT) is clinically important as the surgical prognosis of hepatocellular carcinoma (HCC) patients with BDTT is significantly different from that of patients without BDTT. The preoperative diagnosis of BDTT is usually based on identification of dilated bile ducts (DBDs) on medical images (eg., CT and MRI images). However, it is easy for doctors to ignore DBDs when reporting the imaging scan result, leading to a high misdiagnosis rate in practice. The aim of the present study was to develop an artificial intelligence (AI) pipeline for diagnosing HCC patients with BDTT using medical images. Methods: The proposed AI pipeline included two stages. First, the object detection neural network Faster R-CNN was adopted to identify DBDs; then, an HCC patient was diagnosed to have BDTT if the proportion of images with at least one identified DBD exceeds some threshold value. Four-fold cross validation was used to evaluate the performance of the proposed AI pipeline. Results: The proposed AI pipeline was applied on a real dataset consisting of CT images collected from 34 HCC patients (16 with BDTT and 18 without BDTT). The average true positive rate for identifying DBDs per patient was 0.92, while the patient-level true positive rate for diagnosing BDTT was 0.94. The AUC value of patient-level diagnosis of BDTT was 0.92 (95% CI: 0.83, 1.00), compared with 0.71 (95% CI: 0.52, 0.89) by random forest. Conclusions: This study first proposes an AI pipeline to identify DBDs and diagnose BDTT, and the high accuracies demonstrate that it is successful in the diagnosis of BDTT.