Real-time Testing Research Articles

Optimization of the Parameters for Gravity Heat Pipes in Coal Gangue Hills by Measuring Thermal Power Generation

In order to effectively control high temperatures inside coal gangue hills, gravity heat pipes with specific spacings are vertically installed in coal gangue hills. Heat extracted from these heat pipes can be utilized for power generation through energy conversion. In this study, an equivalent model of gravity heat pipes in coal gangue hills was established and, in a laboratory setting, experimental research and optimization were conducted on power generation per unit area using the temperature difference of gravity heat pipes for electricity generation. To facilitate real-time testing of different heat pipe parameters and to display the experimental results, a multi-parameter measurement system was designed and constructed. This study systematically investigated the effects of various structural parameters such as inclination angle, heating temperature, initial absolute pressure, and working fluid height. Through single-factor experiments, it was determined that the inclination angle had no significant impact. The range of values for heating temperature, initial absolute pressure, and working fluid height were confirmed based on six sets of experiments. To maximize the performance of the thermoelectric generator, a response surface analysis experiment was conducted using the Design-Expert software. The optimal conditions were determined to be a working fluid height of 200.001 mm, an initial absolute pressure of 0.002 MPa, and a heating temperature of 413.15 K. Under these conditions, the power generation per unit area of the thermoelectric generator reached 0.122981 W/(m2·K). The accuracy of the theoretical experiments was verified through on-site industrial experiments. By calculations, it was determined that the maximum temperature difference power generation capacity per gravity heat pipe was 42.39 W. This provides a new solution for the management of coal mine gangue hills and the secondary utilization of waste energy.

In Situ Twistronics: A New Platform Based on Superlubricity.

Twistronics, an emerging field focused on exploring the unique electrical properties induced by twist interface in graphene multilayers, has garnered significant attention in recent years. The general manipulation of twist angle depends on the assembly of van der Waals (vdW) layered materials, which has led to the discovery of unconventional superconductivity, ferroelectricity, and nonlinear optics, thereby expanding the realm of twistronics. Recently, in situ tuning of interlayer conductivity in vdW layered materials has been achieved based on scanning probe microscope. In this Perspective, the advancements in in situ twistronics are focused on by reviewing the state-of-the-art in situ manipulating technology, discussing the underlying mechanism based on the concept of structural superlubricity, and exploiting the real-time twistronic tests under scanning electron microscope (SEM). It is shown that the real-time manipulation under SEM allows for visualizing and monitoring the interface status during in situ twistronic testing. By harnessing the unique tribological properties of vdW layered materials, this novel platform not only enhances the fabrication of twistronic devices but also facilitates the fundamental understanding of interface phenomena in vdW layered materials. Moreover, this platform holds great promise for the application of twistronic-mechanical systems, providing avenues for the integration of twistronics into various mechanical frameworks.

RESEARCH ON THE DETECTION OF HELICOBACTER PYLORI IN SALIVA OF GASTRITIS AND DUODENITIS PATIENTS BY REAL-TIME PCR TECHNIQUE

Background: Helicobacter pylori plays an important role in the etiology and pathogenesis of gastritis and duodenitis. There are two groups of test methods to detect Helicobacter pylori infection: invasive methods and non-invasive methods. Testing for Helicobacter pylori in saliva by real-time PCR technique belongs to group of non-invasive test methods. Objectives: To determine the detection rate of Helicobacter pylori in saliva by real-time PCR technique in gastritis and duodenitis patients and to investigate some factors related to Helicobacter pylori infection detected in saliva using real-time PCR technique in gastritis and duodenitis. Materials and method: A cross-sectional descriptive study on 91 patients after convenient sampling was preliminarily diagnosed with gastritis and duodenitis with indications for endoscopy and biopsy. Biopsy specimens were used for urease and histopathology tests. Saliva is taken directly from the mouth after spitting into a dedicated bottle and conducting a real-time PCR test. Gene amplification will be performed with a PYLORI-PCR mix containing primers and for the expected target product of 203 base pairs length: 5' - AGCGCTCTCACTTCCATAGGC - 3' and 5' - TCTTCGGTTAAAAAAGCGAT - 3'. Install the program "Protocol" for the Real-time PCR machine to operate with the following cycles: 1 cycle: 950C for 5 minutes; 40 cycles: 950C for 15 seconds, 550C for 1 minute and 720C for 20 seconds. Results: The positive rate for Helicobacter pylori in saliva by realtime PCR technique, urease test and histopathology were 20.9%, 19.8% and 46.2% respectively. The detection rate of H. pylori by real-time PCR in saliva and the detection rate on urease test as well as histopathology had a statistically significant correlation. A few important factors related to Helicobacter pylori-positive patients in saliva as follows: personal history suffer from gastritis and duodenitis 40.7%, reflux esophagitis 67%, and congestive inflammation 75.8%. Conclusions: The rate of detecting Helicobacter pylori in saliva by real-time PCR technique in gastritis and duodenitis patients in Can Tho University of Medicine and Pharmacy Hospital is higher than urease test and lower than histopathology.

Advanced load frequency control of microgrid using a bat algorithm supported by a balloon effect identifier in the presence of photovoltaic power source.

Due to the unpredictability of the majority of green energy sources (GESs), particularly in microgrids (μGs), frequency deviations are unavoidable. These factors include solar irradiance, wind disturbances, and parametric uncertainty, all of which have a substantial impact on the system's frequency. An adaptive load frequency control (LFC) method for power systems is suggested in this paper to mitigate the aforementioned issues. For engineering challenges, soft computing methods like the bat algorithm (BA), where it proves its effectiveness in different applications, consistently produce positive outcomes, so it is used to address the LFC issue. For online gain tuning, an integral controller using an artificial BA is utilized, and this control method is supported by a modification known as the balloon effect (BE) identifier. Stability and robustness of analysis of the suggested BA+BE scheme is investigated. The system with the proposed adaptive frequency controller is evaluated in the case of step/random load demand. In addition, high penetrations of photovoltaic (PV) sources are considered. The standard integral controller and Jaya+BE, two more optimization techniques, have been compared with the suggested BA+BE strategy. According to the results of the MATLAB simulation, the suggested technique (BA+BE) has a significant advantage over other techniques in terms of maintaining frequency stability in the presence of step/random disturbances and PV source. The suggested method successfully keeps the frequency steady over I and Jaya+BE by 61.5% and 31.25%, respectively. In order to validate the MATLAB simulation results, real-time simulation tests are given utilizing a PC and a QUARC pid_e data acquisition card.

Stability prediction method for real-time hybrid test system based on the measured dynamics of physical test system

The stability analysis of real-time hybrid test (RTHT) is crucial for the division of numerical and physical substructures, performance evaluation of loading system controllers, and feasibility judgment of RTHT systems. Existing stability analysis methods involve the parameter identification and numerical modeling of physical test systems. In contrast, an RTHT involves conducting a physical test on the hard-to-model part. Therefore, it is difficult to accurately model the physical substructure as well as the loading and acquisition systems. In this study, an RTHT stability prediction method based on the measured dynamics of the physical test system was developed, which avoid the tedious numerical modeling of the physical test system. The effectiveness of the stability prediction method was verified through numerical simulation and the experimental testing of a multi-degree-of-freedom shaking table RTHT. The experimental results showed that the accuracy of the developed stability prediction method was higher than that of the model-based stability prediction method by 35.1 %. The stability prediction method developed in this study simplifies the stability analysis of RTHT and will help determine the feasibility of test in advance.

Froriepia subpinnata Leaf Extract-Induced Apoptosis in the MCF-7 Breast Cancer Cell Line by Increasing Intracellular Oxidative Stress.

Froriepia subpinnata is one of the plants used in the diet of Iranian people. Previous studies have investigated the antioxidant and antibacterial effects of this plant extract, but no study has been conducted on its anticancer properties. In this study, we investigated the effect of F. subpinnata extract on MCF-7 breast cancer cells. The inhibitory effect of F. subpinnata leaf extract was determined on the growth of cancer cells by the MTT test. The ROS (reactive oxygen species) test was used to investigate the impact of the extract on intracellular oxidative stress. Flow cytometry and real-time PCR tests were used to investigate the apoptosis-related molecular processes. The GC-MS analysis was performed to determine the most abundant components. The GC-MS analysis showed that phytol, mono-ethylhexyl phthalate (MEHP), cinnamaldehyde, and neophytadiene constituted 60% of the extracted content. The MTT assay demonstrated that F. subpinnata leaf extract caused 50% lethality at a 400 μg/mL dose in MCF7 cells. The F. subpinnata extract at low doses decreased the ROS level for 24 hours in MCF-7, but by increasing the concentration, the ROS levels increased. At the IC50 dose (inhibitory concentration (IC) associated with 50% impact), the ROS level increased 3.5 times compared to the control group. Examining the effect of N-acetyl cysteine (NAC) showed that this antioxidant agent could prevent the lethal impact of the extract and eliminate the ROS increase in MCF7 cells. Flow cytometry and real-time PCR results showed that the extract specifically induced apoptosis through the internal apoptosis pathway in this cancer cell line. The F. subpinnata extract induced apoptosis by increasing ROS in MCF-7 cancer cells and can be considered for further studies.

Investigating the Security of EV Charging Mobile Applications as an Attack Surface

The adoption rate of EVs has witnessed a significant increase in recent years driven by multiple factors, chief among which is the increased flexibility and ease of access to charging infrastructure. To improve user experience and increase system flexibility, mobile applications have been incorporated into the EV charging ecosystem. EV charging mobile applications allow consumers to remotely trigger actions on charging stations and use functionalities such as start/stop charging sessions, pay for usage, and locate charging stations, to name a few. In this article, we study the security posture of the EV charging ecosystem against a new type of remote that exploits vulnerabilities in the EV charging mobile applications as an attack surface. We leverage a combination of static and dynamic analysis techniques to analyze the security of widely used EV charging mobile applications. Our analysis was performed on 31 of the most widely used mobile applications including their interactions with various components such as cloud management systems. The attack scenarios that exploit these vulnerabilities were verified on a real-time co-simulation test bed. Our discoveries indicate the lack of user/vehicle verification and improper authorization for critical functions, which allow adversaries to remotely hijack charging sessions and launch attacks against the connected critical infrastructure. The attacks were demonstrated using the EVCS mobile applications showing the feasibility and the applicability of our attacks. Indeed, we discuss specific remote attack scenarios and their impact on EV users. More importantly, our analysis results demonstrate the feasibility of leveraging existing vulnerabilities across various EV charging mobile applications to perform wide-scale coordinated remote charging/discharging attacks against the connected critical infrastructure (e.g., power grid), with significant economical and operational implications. Finally, we propose countermeasures to secure the infrastructure and impede adversaries from performing reconnaissance and launching remote attacks using compromised accounts.

COVID-19 Misdiagnosis Due to Atypical Symptoms: A Case Report


 
 
 COVID-19 has recently become a pandemic. Early diagnosis and the transmission chain cutting off are critical keys to controlling this disease and stopping its spread. In this case report, the significance of proper and on-time diagnosis is discussed. Misdiagnosis of COVID-19 may lead to delay in early diagnosis of the disease. A 78-year-old man with the chief complaint of dark stool and constipation was admitted. Based on the preliminary complaints of the patient, the emergency medical specialist transferred him to the internal caregiving service, but according to radiological findings and his positive Real-time PCR test, COVID-19 was the final diagnosis. Healthcare planners and policymakers should prepare precise clinical guidelines that are compatible with domestic conditions of the country to improve the ability of healthcare providers to diagnose patients with COVID-19 to promote controlling this condition.
 
 

Optimal Control Method of Semi-Active Suspension System and Processor-in-the-Loop Verification

This study presents an implementation of a proportional–integral–derivative (PID) controller utilizing particle swarm optimization (PSO) to enhance the compromise on road holding and ride comfort of a quarter car semi-active suspension system (SASS) through simulation and experimental study. The proposed controller is verified with a processor-in-the-loop (PIL) approach before real-time suspension tests. Using experimental data, the magnetorheological damper (MR) is modeled by an artificial neural network (ANN). A series of experiments are applied to the system for three distinct bump disturbances. The algorithm performance is evaluated by various key metrics, such as suspension deflection, sprung mass displacement, and sprung mass acceleration for simulation. The phase plane method is used to prove the stability of the system. The experimental results reveal that the proposed controller for the SASS significantly improves road holding and ride comfort simultaneously.

Bacterial Co-Infection in Patients with Coronavirus: A Rapid Review to Support COVID-19 Antimicrobial Prescription

The goal of this study was to determine the most common microorganisms present in COVID-19 patients with co-infections at the General Hospital of Mexicali. Bacterial co-infections have been reported in two previous global pandemics caused by viruses. In a retrospective observational study, we obtained information from 1979 patients. All had symptoms of respiratory disease, and we performed real-time Polymerase Chain Reaction tests on nasopharyngeal swab samples. Of the 1979 patients, 316 were negative; so, they were excluded. One thousand and sixty-three patients had positive results for COVID-19; one hundred and seventy-two (10.34%) had respiratory co-infections. These data were corroborated by positive growth results in culture media and identified using the MALDI-TOF MS System. Vitek 2® Compact, an automated identification system, determined the antimicrobial susceptibility testing results. We analyzed and determined the microorganisms in co-infected patients. Different microorganisms were found, including bacteria and fungi. The most prevalent of all the organisms was Acinetobacter baumannii, which was present in 64 patients (37.2%). We recommend improving the diagnostic and surveillance protocols for possible cases of co-infections in patients with COVID-19. Unlike co-infections in pandemic influenza, the spectrum of microorganisms that cause COVID-19 is too broad and varied to recommend empiric antibiotic therapy.

An enhanced distributed framework for real-time performance testing of large scale IoT dataset using big data analytic tools

<p>The demand for analyzing enormous IoT datasets is rising in parallel with the popularity of the IoT. There are considerable obstacles to effective processing and analysis due to the amount, velocity, and variety of IoT data. In this research, we present a distributed system that makes use of big data analytic tools like Apache Hive, Spark, and Hadoop to efficiently test the performance of massive IoT datasets. The framework addresses the lack of a comprehensive solution by providing a scalable and fault-tolerant architecture. We discuss the motivation behind real-time performance testing in the context of big data analytics for IoT datasets and highlight the need for a distributed framework. A literature review is conducted to explore existing performance testing frameworks, big data analytic tools, and approaches for performance testing big data analytics. The proposed framework’s key components, including dataset generation, test scenario specification, cluster configuration, performance metrics collection, analysis and visualization modules, and implementation details, including tool choices, are discussed. An experimental evaluation is conducted to validate the framework’s performance, and it is suggested to incorporate blockchain technology. Overall, the proposed framework offers a comprehensive solution for real-time performance testing of large-scale IoT datasets, providing organizations and researchers with a valuable tool to ensure efficient and reliable IoT data processing and analysis.</p>

A real-time hierarchical control method for safe human–robot coexistence

In modern industrial environments, robots are expected to work close to human operators collaborating with them in completing various tasks, e.g., collaborative assembly or delivery of objects. However, it is difficult to take task optimization into account under the premise of ensuring safety in a human-involved dynamic environment. Therefore, a real-time hierarchical control method containing two hierarchical optimal controllers with complementary functions is proposed to tackle the above problems. The upper-layer Model Predictive Controller aims at performing primary tasks, such as end-effector pose tracking, singularity, and joint limit avoidance. It is formulated as a Bayesian Inference problem with a Gaussian process prior and an exponential likelihood function. The resulting maximum a posteriori estimation problem can be solved efficiently using the Matrix-Scaled Stein Variational Gradient Descent and GPU. The upper-layer optimal controller aims at performing primary tasks, such as end-effector pose tracking, singularity, and joint limit avoidance. The lower-layer safety-critical controller, formulated as a constrained quadratic programming problem, is responsible for tracking the output interpolation of the higher-layer controller while respecting the safety constraints constructed in the form of Stochastic Control Barrier Functions. Both of the optimal controllers run repeatedly but with different frequencies (upper-layer controller: 20Hz, lower-layer controller: 40Hz). The proposed method provides a solution to deal with both collision avoidance and task constraints. Lastly, we conducted real-time simulation and experimental tests on a Doosan robot, and the test results validated the effectiveness of the proposed method.

Cultivating Insight: Detecting Autism Spectrum Disorder through Residual Attention Network in Facial Image Analysis

Revolutionizing Autism Spectrum Disorder Identification through Deep Learning: Unveiling Facial Activation Patterns. In this study, our primary objective is to harness the power of deep learning algorithms for the precise identification of individuals with autism spectrum disorder (ASD) solely from facial image datasets. Our investigation centers around the utilization of face activation patterns, aiming to uncover novel insights into the distinctive facial features of ASD patients. To accomplish this, we meticulously examined facial imaging data from a global and multidisciplinary repository known as the Autism Face Imaging Data Exchange. Autism spectrum disorder is characterized by inherent social deficits and manifests in a spectrum of diverse symptomatic scenarios. Recent data from the Centers for Disease Control (CDC) underscores the significance of this disorder, indicating that approximately 1 in 54 children are impacted by ASD, according to estimations from the CDC's Autism and Developmental Disabilities Monitoring Network (ADDM). Our research delved into the intricate functional connectivity patterns that objectively distinguish ASD participants, focusing on their facial imaging data. Through this investigation, we aimed to uncover the latent facial patterns that play a pivotal role in the classification of ASD cases. Our approach introduces a novel module that enhances the discriminative potential of standard convolutional neural networks (CNNs), such as ResNet-50, thus significantly advancing the state-of-the-art. Our model achieved an impressive accuracy rate of 99% in distinguishing between ASD patients and control subjects within the dataset. Our findings illuminate the specific facial expression domains that contribute most significantly to the differentiation of ASD cases from typically developing individuals, as inferred from our deep learning methodology. To validate our approach, we conducted real-time video testing on diverse children, achieving an outstanding accuracy score of 99.90% and an F1 score of 99.67%. Through this pioneering work, we not only offer a cutting-edge approach to ASD identification but also contribute to the understanding of the underlying facial activation patterns that hold potential for transforming the diagnostic landscape of autism spectrum disorder.

Crumpled graphene induced by commercial Heat-Shrinkable material for chemiresistive biosensors toward cancer biomarker detection

Chemiresistive biosensors have revolutionized the field of biomedical diagnosis owing to the novel properties of graphene layered materials that can enhance their sensitivity. We propose a commercial heat-shrinkable material (polystyrene) induced crumpled graphene for cancer biomarker detection. After heating polystyrene, the thermally induced transformation causes the shrinkage and deformation of graphene, resulting in the graphene structure becoming crumpled and thus increasing the surface area. Low-damage plasma treatment is used to oxidize the surface of crumpled graphene with high controllability, forming the bioactive graphene oxide. The DNA probe is then immobilized on graphene oxide via a chemical covalent-amide bond to facilitate hybridization with the biological target miRNA-21. Because the attachment of the miRNA-21 on the oxidized crumpled graphene alters the conductivity, we measure its electrical resistance change rate as a sensor response. The results demonstrate that the sensor response linearity increases with miRNA-21 concentration (10 nM to 1 pM) with the linearity of 0.988 and the limit of detection of 1.74 pM. The results of the real-time applicability test reveal a very high performance with low interference. Therefore, a crumpled graphene-based chemiresistive biosensor has good sensitivity with valuable selectivity, and its biomarker detection raises the possibility of using these sensors for real-time cancer determination and other screening applications in the future.

Application of multiplex realtime PCR detection for hemorrhagic fever syndrome viruses

BackgroundMultiplex real-time PCR is a quick and cost effective method for detection of various gene simultaneously. HFSV (Hemorrhagic Fever Syndrome Virus) is a newly emerging infectious disease because of globalization and climate change. We tried to develop a molecular diagnostic technique for various causative viruses and evaluate its usefulness for improving public health. MethodsMolecular diagnostic test method that qualitatively detects viruses causing viral hemorrhagic fevers hired Taq-Man Real-time RT-PCR technique. The Ct value was experimentally observed three or more times at the RNA concentration before and after the detection limit. After designing a multiplex real-time RT-PCR test for target gene of selected 17 viruses, the detection limit for each target and the presence or absence of cross-reaction and interference reaction were evaluated to determine its availability. ResultsSix kinds of viruses, including Crimean-Congo hemorrhagic fever virus, Omsk hemorrhagic fever virus, Sabia virus, Chapare virus, Yellow fever virus, and Variola virus (A4L gene, B12R gene), were able to confirm the detection limit of 0.5 copies/μl, and other Ebola virus, Marburg virus, Rift Valley fever virus, Kyasanur Forest disease virus, Junin virus, Guanarito virus, Machupo virus, Chikungunya virus, Hantavirus, Dengue virus types 1–4, and Lassa virus (L gene, GPC gene), and 11 kinds of viruses, the detection limit was confirmed at 5 copies/μl. No cross-reaction or interference between detected genes was observed. ConclusionThe virus test method developed through this study using multiplex is expected to be used for public health and quarantine as a test method that can be used when a hemorrhagic fever virus of unknown cause is introduced.

Predicting the Performance of PEM Fuel Cells by Determining Dehydration or Flooding in the Cell Using Machine Learning Models

Modern industries encourages the use of hydrogen as an energy carrier to decarbonize the electricity grid, Polymeric Electrolyte membrane fuel cell which uses hydrogen as a fuel to produce electricity, is an efficient and reliable ‘power to gas’ technology. However, a key issue obstructing the advancement of PEMFCs is the unpredictability of their performance and failure events caused by flooding and dehydration. The accurate prediction of these two events is required to avoid any catastrophic failure in the cell. A typical approach used to predict failure modes relies on modeling failure-induced performance losses and monitoring the voltage of a cell. Data-driven machine learning models must be developed to address these challenges. Herein, we present a machine learning model for the prediction of the failure modes of operating cells. The model predicted the relative humidity of a cell by considering the cell voltage and current density as the input parameters. Advanced regression techniques, such as support vector machine, decision tree regression, random forest regression and artificial neural network, were used to improve the predictions. Features related to the model were derived from cell polarization data. The model’s results were validated with real-time test data obtained from the cell. The statistical machine learning models accurately provided information on the flooding- and dehydration-induced failure events.

Diagnostic challenge of Creutzfeldt-Jakob disease in a patient with multimorbidity: a case-report

BackgroundCreutzfeldt-Jakob disease (CJD) is a rapidly progressive and ultimately fatal neurodegenerative condition caused by prions. The clinical symptoms of CJD vary with its subtype, and may include dementia, visual hallucinations, myoclonus, ataxia, (extra)pyramidal signs and akinetic mutism. In the early course of disease however, several clinical symptoms of CJD may mimic those of co-existing morbidities.Case presentationWe report a male in his 60s with a history of situs inversus totalis and Churg Strauss syndrome, who presented with speech fluency disturbances, neuropsychiatric symptoms and allodynia, a few months after becoming a widower. Initially presumed a bereavement disorder along with a flare-up of Churg Strauss, his symptoms gradually worsened with apraxia, myoclonic jerks and eventually, akinetic mutism. MRI revealed hyperintensities at the caudate nucleus and thalami, while the cerebrospinal fluid was positive for the 14-3-3 protein and the real-time quick test, making the diagnosis of CJD highly probable. This case illustrates the complexities that may arise in diagnosing CJD when pre-existing multimorbidity may cloud the clinical presentation. We also discuss the potential mechanisms underlying the co-occurrence of three rare conditions (situs inversus totalis, Churg Strauss syndrome, CJD) in one patient, taking into consideration the possibility of coincidence as well as common underlying factors.ConclusionsThe diagnosis of CJD may be easily missed when its clinical symptoms are obscured by those of pre-existing (rare) multimorbidity. This case highlights that when the multimorbidity has neurological manifestations, an extensive evaluation remains crucial to establish the diagnosis, minimize the risk of prion-transmission and provide appropriate guidance to patients and their caregivers.

Real-Time Evaluation of Helicobacter pylori Infection by Convolution Neural Network During White-Light Endoscopy: A Prospective, Multicenter Study (With Video).

Convolutional neural network during endoscopy may facilitate evaluation of Helicobacter pylori infection without obtaining gastric biopsies. The aim of the study was to evaluate the diagnosis accuracy of a computer-aided decision support system for H. pylori infection (CADSS-HP) based on convolutional neural network under white-light endoscopy. Archived video recordings of upper endoscopy with white-light examinations performed at Sir Run Run Shaw Hospital (January 2019-September 2020) were used to develop CADSS-HP. Patients receiving endoscopy were prospectively enrolled (August 2021-August 2022) from 3 centers to calculate the diagnostic property. Accuracy of CADSS-HP for H. pylori infection was also compared with endoscopic impression, urea breath test (URT), and histopathology. H. pylori infection was defined by positive test on histopathology and/or URT. Video recordings of 599 patients who received endoscopy were used to develop CADSS-HP. Subsequently, 456 patients participated in the prospective evaluation including 189 (41.4%) with H. pylori infection. With a threshold of 0.5, CADSS-HP achieved an area under the curve of 0.95 (95% confidence interval [CI], 0.93-0.97) with sensitivity and specificity of 91.5% (95% CI 86.4%-94.9%) and 88.8% (95% CI 84.2%-92.2%), respectively. CADSS-HP demonstrated higher sensitivity (91.5% vs 78.3%; mean difference = 13.2%, 95% CI 5.7%-20.7%) and accuracy (89.9% vs 83.8%, mean difference = 6.1%, 95% CI 1.6%-10.7%) compared with endoscopic diagnosis by endoscopists. Sensitivity of CADSS-HP in diagnosing H. pylori was comparable with URT (91.5% vs 95.2%; mean difference = 3.7%, 95% CI -1.8% to 9.4%), better than histopathology (91.5% vs 82.0%; mean difference = 9.5%, 95% CI 2.3%-16.8%). CADSS-HP achieved high sensitivity in the diagnosis of H. pylori infection in the real-time test, outperforming endoscopic diagnosis by endoscopists and comparable with URT. Clinicaltrials.gov ; ChiCTR2000030724.

Breakthrough infection among healthcare personnel following exposure to COVID-19: Experience after one year of the world’s largest vaccination drive

ABSTRACT Context: The COVID-19 vaccination drive globally was supposedly a game-changing event. However, the emerging variants of the virus and waning immunity over time posed new challenges for breakthrough infections. Standing at the frontline of defense against COVID-19, healthcare personnel (HCP) were vulnerable to such infections. Aims: This study estimates i) the vaccine breakthrough infections (VBI) among HCP following exposure to COVID-19 cases, and ii) the mean interval between the second dose of vaccine and laboratory-confirmed SARS-CoV-2 infection Materials and Methods: A cross-sectional study was conducted including 385 HCP with a history of exposure to COVID-19 cases during January and February 2022. Demographic details and clinical and vaccination history were collected from the test forms and the Web-based hospital management system. Laboratory testing of COVID-19 was carried out by real-time RT-PCR test. Results: The majority of the HCP were males (262; 68.05%) and nurses (180; 46.75%) by occupation. Two doses of vaccines were received by 278 (87.7%) HCP. VBI was confirmed in 185 (66.55%) HCP. No significant difference in VBI between the COVAXIN and COVISHIELD recipients (P = 0.69) was observed. The interval between the second dose and confirmed SARS-CoV-2 infection was significantly higher (P < 0.00001) in COVAXIN recipients (median 228 days) than in COVISHIELD recipients (median 95 days). Conclusions: The incidence of VBI was very high among the HCP, but not statistically different among the COVAXIN and COVISHIELD-recipients. Waning immunity over time suggests boosting immunity with a third dose because of emerging variants.

Comparison of Positive/Inconclusive Xpert Xpress SARS-CoV-2 with the Standard M nCoV Real-Time Detection.

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can be diagnosed using rapid real-time PCR, real-time reverse transcription PCR (rRT-PCR), or rapid antigen testing. Among these, rRT-PCR is considered the gold standard assay. The Xpert Xpress SARS-CoV-2 assay is a rapid real-time PCR test, approved by the Korean Disease Control and Prevention Agency in 2020. The overall concordance and positive concordance rates of the Xpert assay with the STANDARD M nCoV Real-Time Detection kit were determined. All samples with positive or inconclusive Xpert test results from July 2021 to February 2023 that underwent confirmatory testing using the reference rRT-PCR assay were included in the analysis. Samples from 224 patients (93 men and 131 women) with a median age of 59 years (range 15 - 90 years) were included. Of 212 samples that tested positive using Xpert, 112 (52.8%) were true positves and 100 (47.2%) were false positives on rRT-PCR testing. The overall concordance and positive concordance rates were 52.8% (112/212) and 54.5% (112/224), respectively. In the Xpert positive group, the samples had a lower Ct value for the E gene than the N2 gene. The Ct values for the E and N2 genes were significantly lower in the positive group than in the inconclusive group. Positive or inconclusive Xpert results should be confirmed by the gold standard rRT-PCR for early control of this disease. Furthermore, Korea's policy should be reconsidered given the high false-positive rate of the rapid real-time PCR Xpert Xpress SARS-CoV-2 assay.