High Positive Rate Research Articles

Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks

Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain experts to select an optimal feature, obtains a limited accuracy, and has a high false positive rate due to handcrafting features extraction. The deep learning model overcomes these limitations, but these models require large amounts of training data and computation resources, and further improvement in the model performance is needed. To do this, we employ a novel framework called the Ensemble-based Channel and Spatial Attention Network (ECS-A-Net) to automatically classify infected regions within BC images. The proposed framework consists of two phases: in the first phase, we apply different augmentation techniques to enhance the size of the input data, while the second phase includes an ensemble technique that parallelly leverages modified SE-ResNet50 and InceptionV3 as a backbone for feature extraction, followed by Channel Attention (CA) and Spatial Attention (SA) modules in a series manner for more dominant feature selection. To further validate the ECS-A-Net, we conducted extensive experiments between several competitive state-of-the-art (SOTA) techniques over two benchmarks, including DDSM and MIAS, where the proposed model achieved 96.50% accuracy for the DDSM and 95.33% accuracy for the MIAS datasets. Additionally, the experimental results demonstrated that our network achieved a better performance using various evaluation indicators, including accuracy, sensitivity, and specificity among other methods.

Securing web applications against XSS and SQLi attacks using a novel deep learning approach

Modern web application development involves handling enormous amounts of sensitive and consequential data. Security is, therefore, a crucial component of developing web applications. A web application's security is concerned with safeguarding the data it processes. The web application framework must have safeguards to stop and find application vulnerabilities. Among all web application attacks, SQL injection and XSS attacks are common, which may lead to severe damage to Web application data or web functionalities. Currently, there are many solutions provided by various study for SQLi and XSS attack detection, but most of the work shown have used either SQL/XSS payload-based detection or HTTP request-based detection. Few solutions available can detect SQLi and XSS attacks, but these methods provide very high false positive rates, and the accuracy of these models can further be improved. We proposed a novel approach for securing web applications from both cross-site scripting attacks and SQL injection attacks using decoding and standardization of SQL and XSS payloads and HTTP requests and trained our model using hybrid deep learning networks in this paper. The proposed hybrid DL model combines the strengths of CNNs in extracting features from input data and LSTMs in capturing temporal dependencies in sequential data. The soundness of our approach lies in the use of deep learning techniques that can identify subtle patterns in the data that traditional machine learning-based methods might miss. We have created a testbed dataset of Normal and SQLi/XSS HTTP requests and evaluated the performance of our model on this dataset. We have also trained and evaluated the proposed model on the Benchmark dataset HTTP CSIC 2010 and another SQL/XSS payload dataset. The experimental findings show that our proposed approach effectively identifies these attacks with high accuracy and a low percentage of false positives. Additionally, our model performed better than traditional machine learning-based methods. This soundness approach can be applied to various network security applications such as intrusion detection systems and web application firewalls. Using our model, we achieved an accuracy of 99.84%, 99.23% and 99.77% on the SQL-XSS Payload dataset, Testbed dataset and HTTP CSIC 2010 dataset, respectively.

Abstract B006: Exosomal protein Eps8 as a new biomarker for pancreatic cancer

Abstract Exosomes are extracellular vesicles containing nucleic acids, proteins, and lipids of approximately 100 nm, which are released from cancer cells to create a microenvironment favorable for their survival. Epidermal growth factor receptor pathway substrate 8 (Eps8), an exosomal protein, is reportedly associated with metastasis. We investigated the relationship between Eps8 in the serum exosomes of patients with pancreatic cancer and clinicopathological factors and examined the usefulness of exosomal Eps8 as a biomarker for pancreatic cancer. Methods: Serum exosomes of 93 patients with pancreatic disease (64 cases of pancreatic cancer, 12 cases of chronic pancreatitis, and 17 cases of IPMN) were extracted via ultracentrifugation, and Eps8 in exosomes were evaluated by western blotting. We analyzed 1) the clinicopathological factors of patients with pancreatic cancer, 2) the Eps8 positive rate in each disease, 3) the sensitivity, specificity, and positive predictive value of Eps8 for the diagnosis of pancreatic cancer, 4) the relationship between Eps8 and clinicopathological factors, and 5) compared and discussed between exosomal Eps8 and serum CA19-9. Results: The median patient age was 72 years (47–85), with 35 male and 29 female patients included in the study. The median body mass index was 20.8 kg/m2 (12–26.7 kg/m2). 1) Site Ph33/Pbt31, cStageIV/III/II/I: 28/11/14/11; 2) pancreatic cancer 59.4%, chronic pancreatitis 33%, IPMN 12%; 3) sensitivity 59.4. 4) In patients with pancreatic cancer, Eps8-positive patients had significantly higher ALT (p = 0.03), significantly less cancer (p = 0.04), lower IPMN complication rates, and more chronic pancreatitis and autoimmune diseases than the exosomal Eps8-negative patients. Stage I 64% (7/11), Stage II 36% (5/14), Stage III 64% (7/11) Stage IV 68% (19/28) 5) Exosomal Eps8 positivity (64%) was higher than serum CA19-9 positivity (45%) in Stage I. We also found some patients were negative for serum CA19-9 but positive for exosomal Eps8. Conclusion: Exosomal Eps8 has a higher positive rate in Stage I than serum CA19-9. Diagnosis with exosomal Eps8 may improve diagnostic performance for pancreatic cancer. Citation Format: Kazunao Hayashi, Atsunori Tsuchiya, Shuji Terai. Exosomal protein Eps8 as a new biomarker for pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B006.

Using an automated tail movement sensor device to predict calving time in dairy cows

This study aimed to evaluate the effectiveness of an automated tail movement sensor device (Moocall; Bluebell, Dublin, Ireland) to predict time of calving in dairy cows. At a commercial dairy farm in southern Ontario, Moocall (MC) devices were attached with the device's strap, and an additional elastic wrap, to the tail of cows approximately 3 d before their expected calving date. The MC has 2 types of alarm, a high activity alarm in the previous hour (1HA), and a high activity alarm in the previous 2 h (2HA); these alarms were sent and registered to the MC software. The calving and close-up pens were video monitored to determine the exact time of the onset of stage II of calving (amniotic sac visible at the vulva) and the end of stage II of calving (total expulsion of the calf). A total of 49 cows were enrolled, but we excluded 13 animals from the analysis as they had 3 or more MC drops from the tail (n = 6), a swollen tail (n = 3), or the MC device was lost (n = 4); this left 36 cows. In total, the device dropped off 21 (42%) cows. The average number of alarms (1HA and 2HA) per cow before stage II of calving was 2.7 ± 2.3 (± standard error). The first alarm after fitting the device on the tail was used to determine the device's sensitivity and specificity. Depending on the interval before the onset of parturition (i.e., 2, 4, 8, 12 h) in which the alarm was triggered, sensitivity varied from 5% to 72% and specificity from 50% to 93%. The false positive rate varied between 6% and 50% depending on the interval from the alarm to the onset of parturition. The high false positive and device drop rates (despite the addition of the elastic wrap) may compromise the applicability of this sensor device in a commercial setting.

Development and evaluation of a test strip for the rapid detection of antibody against equine infectious anemia virus

Equine infectious anemia (EIA) is a contagious disease of horses caused by the equine infectious anemia virus (EIAV). The clinical signs at the acute phase include intermittent high fever, thrombocytopenia, hemorrhage, edema, and anemia. The clinical signs at chronic and relapsing subclinical levels include emaciation and progressive weakness. Surviving horses become lifelong carriers because of the integration of the viral genome into that of the host, and these horses can produce and transmit the virus to other animals. This increases the difficulty of imposing practical control measures to prevent epidemics of this disease. Serological tests measuring the antibodies in equine sera are considered to be a reliable tool for the long-term monitoring of EIA. However, the standard serological tests for EIV either have low sensitivity (e.g., agar gel immunodiffusion test, AGID) or are time consuming to perform (e.g., ELISA and western blotting). The development of a rapid and simple method for detecting the disease is therefore critical to control the spread of EIA. In this study, we designed and developed a colloidal gold immunochromatographic (GICG) test strip to detect antibodies against EIAV based on the double-antigen sandwich. Both the p26 and gp45 proteins were used as the capture antigens, which may help to improve the positive detection rate of the strip. We found that the sensitivity of the test strip was 8 to 16 times higher than those of two commercially available ELISA tests and 128 to 256 times higher than AGID, but 8 to 16 times lower than that of western blotting. The strip has good specificity and stability. When serum samples from experimental horses immunized with the attenuated EIAV vaccine (n = 31) were tested, the results of the test strip showed 100% coincidence with those from NECVB-cELISA and 70.97% with AGID. When testing clinical serum samples (n = 1014), the test strip surprisingly provided greater sensitivity and a higher number of “true positive” results than other techniques. Therefore, we believe that the GICG test strip has demonstrated great potential in the field trials as a simple and effective tool for the detection of antibodies against EIAV.Key points• A colloidal gold immunochromatographic (GICG) fast test strip was developed with good specificity, sensitivity, stability, and repeatability• The test strip can be used in point-of-care testing for the primary screening of EIAV antibodies• Both the p26 and gp45 proteins were used as the capture antigens, giving a high positive detection rate in the testing of experimentally infected animal and field samples

Measles Outbreaks in the Republic of Congo: Epidemiology of Laboratory-Confirmed Cases Between 2019 and 2022.

In Africa, measles epidemics are frequently reported, despite numerous preventive measures, such as vaccination, which targets children under 5 years of age. Unfortunately, the Republic of the Congo is not an exception to this major health concern. Indeed, many cases are reported annually. Here, we provide an overview of the epidemiological characteristics of laboratory-confirmed measles cases from January 2019 to October 2022 as well as the risk factors associated with the occurrence of measles outbreak. Samples from suspected measles cases were collected across the country and sent to the National Laboratory of Public Health for confirmation. Specific IgM was tested using the enzyme-linked immunosorbent assay (ELISA). Data were analyzed using descriptive and analytic statistics (p < 0.05 was statistically significant). A total of 1330 samples were collected and analyzed. Over those 4 years, 537 samples were confirmed to be positive (40.3%) but with important disparities between years. A relatively low frequency of cases was reported in 2020. Overall, a progressive and significant evolution of positive cases was observed between 2019 and 2022, increasing from 16.8% in 2019 to 65.9% in 2022 (p < 0.0001). We report a low vaccination rate among children (44.8%) and a significantly high positivity rate in this group (46.6%) (p < 0.0008). No difference was reported according to the completeness of the vaccination scheme (p=0.094). Females were slightly more exposed to this infection than males (p=0.04; adjusted odds ratio [aOR]: 1.25 [1.01-1.6]), with an increased risk of exposure in rural areas (p=0.0001; aOR: 0.41 [0.32-0.53]). The department of Pointe-Noire had the highest positivity rate, while three other departments were considered high-risk areas: Likouala (p = 0.0001; aOR: 3.18 [1.80-5.61]), Pool (p=0.0001; aOR: 2.90 [1.70-4.95]), and Brazzaville (p=0.0005; aOR: 0.52 [0.36-0.75]). This study calls for strengthening the epidemiological surveillance system and vaccination strategy in the country. It remains important to research factors that induce a high positive rate among vaccinated children by biological verification of the immunization.

Epidemiological and molecular surveillance of norovirus in the Brazilian Amazon: description of recombinant genotypes and improvement of evolutionary analysis.

Noroviruses are highly infectious, genetically diverse viruses. Global outbreaks occur frequently, making molecular surveillance important for infection monitoring. This cross-sectional descriptive study aimed to monitor cases of norovirus gastroenteritis in the Brazilian Amazon. Fecal samples were tested by immunoenzymatic assay, RT-PCR and genetic sequencing for the ORF1/ORF2 and protease regions. Bayesian inference with a molecular clock was employed to construct the phylogeny. The norovirus prevalence was 25.8%, with a higher positivity rate among children aged 0-24 months. Genogroup GII accounted for 98.1% of the sequenced samples, while GI accounted for 1.9% of them. The GII.P16/GII.4 genotype was the most prevalent, with an evolution rate of 2.87x10-3 and TMRCA estimated in 2012. This study demonstrates that norovirus is a primary causative agent of gastroenteritis and provides data on viral genetic diversity that may facilitate infection surveillance and vaccine development.

Virome analysis provides new insights into the pathogenesis mechanism and treatment of SLE disease.

This study aimed to investigate the virome diversity of the SLE disease and the association between viral infections and the disease. SLE-related RNA-Seq data were retrieved from public databases. A rigorous computational workflow was employed to identify the human viruses. Differential expression analysis and functional enrichment analysis were conducted in R. We identified ten human virus species from 826 RNA-Seq samples of human blood, comprising 688 SLE patients and 138 healthy controls. Eight of the ten virus species exhibited higher positive rates in SLE patients compared to healthy controls, with Human betaherpesvirus 5 (HHV5) having the highest positive rate (4.1%) and being exclusively detected in SLE samples. The virus abundances were low and comparable in both SLE patients and healthy controls. Analysis of the antiviral interferon-stimulated genes (ISGs) in samples showed higher ISG expression levels in HHV4 and HHV5-positive samples compared to virus-negative samples. Several genes that were up-regulated in SLE patients were further up-regulated after HHV5 infection, and they were mainly enriched in immune response-related biological processes. Additionally, the expression levels of several marker genes of SLE severity were compared between HHV5-positive and virus-negative SLE patients, suggesting that HHV5 infection may be associated with aggravated SLE disease. We found that SLE patients are more susceptible to viral infections than healthy individuals. Viral infections, such as HHV5, may be associated with aggravated SLE disease. This study deepens our understanding of the association between viruses and SLE and provides new insights into prevention and control of the disease.

Utility of Metagenomic Next-Generation Sequencing for Diagnosis of Infectious Diseases in Critically Ill Immunocompromised Pediatric Patients.

Infections cause high rates of illness and death in children worldwide. However, studies on the clinical value of metagenomic next-generation sequencing (mNGS) for immunocompromised children are still limited. From June 2021 to December 2023, 119 samples were collected at Pediatric Intensive Care Unit (PICU) of a single-center pediatric hospital and classified into two groups based on their immune states. We compared the diagnostic performance of mNGS and conventional microbiological test (CMT) for pathogen identification, and assessed the clinical impacts of mNGS. Among the 119 samples, 48 (40.34%) belonged to the immunocompromised children. mNGS had a higher positivity rate than CMT (76.47% vs 55.46%, P = 0.0006). The positive percent agreement (PPA) of mNGS for immunocompromised children was higher compared to immunocompetent children (95.24% vs 77.78%). The most common pathogens for immunocompromised patients were gram-negative bacteria and herpesvirus. However, immunocompetent children showed a higher detection rate for gram-positive bacteria and respiratory viruses. Furthermore, the proportions of the positive impact of mNGS results were significantly higher in immunocompromised patients compared to immunocompetent patients for both diagnosis (91.67% vs 57.75%) and treatment (95.83% vs 64.79%) (P < 0.0001). Immunocompromised state, length of hospital stays, times stay in ICU, Pediatric Risk of Mortality (PRISM) score, neutrophil percentage (NEUT%) and the ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2) were considered independent factors for poor prognosis in critically ill pediatric patients. In patients from PICU, mNGS had a greater clinical significance in immunocompromised children compared to immunocompetent children. mNGS technology is an important auxiliary method for achieving accurate diagnosis and treatment of critically ill pediatric patients.

Detection and seroprevalence of Q fever infection in dairy goats in Besut district, Malaysia.

This study aimed to investigate the seroprevalence of Q fever and its association with age and gender among Saanen dairy goats in Malaysia. One hundred dairy goats (n = 100) aged 6 months to 6 years were randomly selected, and blood samples were collected for serological analysis using the enzyme-linked immunosorbent assay technique. The results revealed a seropositive rate of 70% among the goats, with medium-positive titers being the most common. The prevalence of Q fever varied among different age groups, with higher rates observed in adult goats aged between 5 and 6 years. Gender analysis showed that males had a higher positive rate (p < 0.05) of Q fever compared to females. These findings strongly indicate the presence of Coxiella burnetii in the dairy goat population and highlight the importance of implementing biosecurity measures and control strategies to prevent further transmission of this disease. This research has contributed to a better understanding of Q fever epidemiology and provides insights for effective control and prevention strategies in dairy goat populations.

Epidemiological characteristics of mycoplasma pneumoniae in hospitalized children before, during, and after COVID-19 pandemic restrictions in Chongqing, China.

Mycoplasma pneumoniae (MP) is a significant cause of community-acquired pneumonia with high macrolide resistance rates. Various COVID-19 pandemic restrictions have impacted the prevalence of MP. To assess the changes in the pattern of MP infections among children before, during, and after the COVID-19 pandemic. A total of 36685 enrolled patients, aged 0-18 years, diagnosed with pneumonia and admitted to Children's Hospital of Chongqing Medical University from January 2019 to December 2023, were retrospectively reviewed in this study. The epidemiological characteristics of pediatric MP infection were analyzed. Among 36685 patients, 7610 (20.74%) tested positive for MP. The highest positive rate was observed among children aged over 6 years (55.06%). There was no gender disparity in MP infection across the three phases of the COVID-19 pandemic. Hospital stays were longest for children during the COVID-19 pandemic (P <0.001). MP infection was most prevalent in the summer (29.64%). The lowest positive rate was observed during the pandemic, with the highest rate found after easing the measures across all age groups (P <0.001). There was a surge in the positive rate of MP in the third year after the COVID-19 pandemic. Regression analyses demonstrated a shift in the age range susceptible to MP infection, with children aged 3.8 to 13.5 years post-pandemic compared to the pre-pandemic range of 5.3 to 15.5 years old. Additionally, the average macrolide resistance rate was 79.84%. We observed a higher resistance rate during the pandemic than in the pre- and post-pandemic phases (P <0.001). The restrictive measures implemented during the COVID-19 pandemic have influenced the spread of MP to some extent and altered demographic and clinical characteristics, such as age, age group, season, length of stay, and macrolide resistance. We recommend continuous surveillance of the evolving epidemiological characteristics of MP infection in the post-pandemic period when restrictions are no longer necessary.

Rethinking False Positive Exercise Electrocardiographic Stress Tests by Assessing Coronary Microvascular Function

BackgroundExercise electrocardiographic stress testing (EST) has historically been validated against the demonstration of obstructive coronary artery disease. However, myocardial ischemia can occur because of coronary microvascular dysfunction (CMD) in the absence of obstructive coronary artery disease. ObjectivesThe aim of this study was to assess the specificity of EST to detect an ischemic substrate against the reference standard of coronary endothelium-independent and endothelium-dependent microvascular function in patients with angina with nonobstructive coronary arteries (ANOCA). MethodsPatients with ANOCA underwent invasive coronary physiological assessment using adenosine and acetylcholine. CMD was defined as impaired endothelium-independent and/or endothelium-dependent function. EST was performed using a standard Bruce treadmill protocol, with ischemia defined as the appearance of ≥0.1-mV ST-segment depression 80 ms from the J-point on electrocardiography. The study was powered to detect specificity of ≥91%. ResultsA total of 102 patients were enrolled (65% women, mean age 60 ± 8 years). Thirty-two patients developed ischemia (ischemic group) during EST, whereas 70 patients did not (nonischemic group); both groups were phenotypically similar. Ischemia during EST was 100% specific for CMD. Acetylcholine flow reserve was the strongest predictor of ischemia during exercise. Using endothelium-independent and endothelium-dependent microvascular dysfunction as the reference standard, the false positive rate of EST dropped to 0%. ConclusionsIn patients with ANOCA, ischemia on EST was highly specific of an underlying ischemic substrate. These findings challenge the traditional belief that EST has a high false positive rate.

An Efficient Cyber Assault Detection System using Feature Optimization for IoT-based Cyberspace

With the exponential growth in connected smart devices that interchange sensitive, crucial, and personal data over the Internet of Things (IoT)-based cyberspace, IoT becomes undefended to cyber assaults. Intrusion Detection System (IDS) is a vital segment of security tool to detect cyber assaults. However, IDS produce high False Positive Rate (FPR) because IDS improves accuracy at the same time as it increases FPR. Therefore, a more effective cyber assaults detection system is needed. Based on the aforementioned issues, this paper proposes an approach to recognizing cyber assaults in the IoT environment. This approach first performs various data pre-processing steps. Next, important features are optimized using modified Binary Pigeon Inspired Optimizer. After that, optimized feature subset is fed separately to the monolithic classifiers such as k-Nearest Neighbours (kNN), Support Vector Machine (SVM), Decision Tree (DT) and bagging-based ensemble model such as Random Forest (RF) for detection of cyber assaults. In this study, UNSW-NB15 is used to test the success of the system, and related existing methods are compared with these results. Based on the results of the experiment, the proposed system with RF is the most effective in terms of accuracy (99.41%), F1-score (99.33%), detection rate (99.09%), and precision (99.92%) with a minimum FPR (0.03%) for a subset of optimized features (4 independent features out of 42).

VMD: A Visualizable Malware Detection Scheme Based on Multi-dimensional Dynamic Behavior Information

Enormous traditional malware detection methods have been proposed and they are efficient in detecting known malware, however, these methods usually either are ineffective or have high false positive rate in detecting unknown malware. To improve the performance in unknown malware detection, many novel methods are presented by introducing and employing software behavior visualization, Artificial Intelligence, and other popular technologies. Unfortunately, they are still facing the inadequate data utilization and benchmark dataset lack problems. Therefore, in this paper, we propose an effective malware detection method by utilizing multi-dimensional software dynamic behavior information. To construct the concrete scheme, we combine a mapping mechanism from key parameters of software operation to grayscale images with a malware prediction model. In addition, to our knowledge, we constructed the first publicly accessible software dynamic behavior dataset containing over 800 malicious and 600 non-malicious software behavior images. Finally, the experiment results show that our method can effectively detect unknow malware and outperforms the existing comparative baseline schemes in terms of accuracy, precision, and recall.

Extrapulmonary tuberculosis - A cross-sectional study comparing the utility of Ziehl-Neelsen stain and immunohistochemistry

Background: Tuberculosis (TB) continues to be a significant global health issue despite known causes and effective treatments. Factors such as virulent strains, drug resistance, and the human immunodeficiency virus pandemic, especially in India, contribute to its prevalence. Mycobacterium tuberculosis, the TB agent, can evade immune responses, persisting within host cells. A distinct feature of TB, granuloma formation, can also appear in diseases such as leprosy, histoplasmosis, and non-infectious conditions like Crohn’s disease. Proper diagnosis is vital, as in resource-limited countries, TB treatment often relies on presumptive diagnoses based on imaging and symptoms, leading to high false positive rates. Aims and Objectives: This study aims to compare the utility of Ziehl-Neelsen (ZN) staining and immunohistochemistry (IHC) staining in suspected extrapulmonary TB lesions. Materials and Methods: This is a retrospective cross-sectional study conducted at the Institute of Pathology, Rajiv Gandhi Government General Hospital, Chennai, over a 2-year period from August 2015 to July 2017. Fifty cases out of the 198 suspected extrapulmonary TB lesions were selected for the study. Results: About 12% of cases were positive for acid-fast bacilli by ZN staining, whereas 82% of cases were positive for acid-fast bacilli by IHC staining. P value was found to be &lt;0.001 by McNemar test which was statistically significant. Conclusion: Diagnosing extrapulmonary TB can be challenging because the disease presents with various clinical manifestations and the bacterial load may be low. Immunohistochemistry is superior to ZN staining for localization of the tubercle bacilli and thus can serve as an effective ancillary technique in diagnoses of extrapulmonary TB in formalin-fixed paraffin embedded tissue sections.

Bitcoin Address Clustering Based on Change Address Improvement

Change address identification is one of the difficulties in bitcoin address clustering as an emerging social computing problem. Most of the current-related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multiconditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3% more one-time change addresses than other heuristics. On top of the multi-input heuristic clustering method, the proposed method also improves the address clustering performance by 5.7%, achieves optimal recognition results compared with similar methods, and significantly reduces the false positive rate of recognition results. This work provides the technical basis for antimoney laundering efforts based on entity identification. Code and data could be accessed from https://github.com/ECNU-Cross-Innovation-Lab/BitcoinAddressClustering.

High-Frequency Discrete-Interval Binary Sequence in Asynchronous C-VEP-Based BCI for Visual Fatigue Reduction.

In code-modulated visual evoked potential (c-VEP) based BCI systems, flickering visual stimuli may result in visual fatigue. Thus, we introduced a discrete-interval binary sequence (DIBS) as visual stimulus modulation, with its power spectrum optimized to emphasize high-frequency components (40 Hz-60 Hz). 8 and 17 subjects participated, respectively, in offline and online experiments on a 4-target asynchronous c-VEP-based BCI system designed to realize a high positive predictive value (PPV), a low false positive rate (FPR) during idle states, and a high true positive rate (TPR) in control states, while minimizing visual fatigue level. Two visual stimuli modulations were introduced and compared: a maximum length sequence (m-sequence) and the high-frequency discrete-interval binary sequence (DIBS). The decoding algorithm was compared among the canonical correlation analysis (CCA), the task-related component analysis (TRCA), and two approaches of sub-band component weight calculation (the traditional method and the proportional method) for FBCCA and FBTRCA. In the online experiments, the average PPV, FPR and TPR achieved, respectively [Formula: see text], [Formula: see text], [Formula: see text] with m-sequence, while [Formula: see text], [Formula: see text] and [Formula: see text] with DIBS. Estimated by objective eye-related metrics and a subjective questionnaire, the visual fatigue in DIBS cases is significantly smaller than that in m-sequence cases. In this study, the feasibility of a novel modulation approach for visual fatigue reduction was proved in an asynchronous c-VEP system, while maintaining comparable performance to existing methods, which provides further insights towards enhancing this field's long-term viability and user-friendliness.

Direct SARS-CoV-2 Detection System Utilizing Simple-to-Use Capillary Gel Electrophoresis Sample-to-Result.

We present a Direct SARS-CoV-2 Detection System that achieves sample-to-results in less than two hours in three simple steps. The Detection System includes Direct one-step Reverse Transcription PCR (RT-PCR) reagents (Qexp-MDx kit), a portable thermal cycler (Qampmini) with a preprogrammed chip and a simple-to-use Capillary Gel Electrophoresis system (Qsep Series Bio-Fragment Analyzer) with high fluorescence detection sensitivity to solve the problems associated with traditional real-time PCR (qPCR) systems which produces high false positive results. The proposed simple-to-use detection platform can provide high detection sensitivity (identify less than 20 copies) and fast results (less than 120 minutes), which would be suitable for field testing applications. Our high detection sensitivity platform provides fast and accurate results in 120 minutes without doing DNA/RNA extraction.

Final Outcome Analysis of Hospitalized Patients with Suspected NPSLE: A Retrospective Single-Center Study

Background: Neuropsychiatric systemic lupus erythematosus (NPSLE) is a serious complication in SLE, and distinguishing these manifestations from other neuropsychiatric disorders is challenging. Method: This was a retrospective single-center study. NP manifestations were attributed to SLE according to clinical experts’ opinions. Demographic and clinical data were obtained in patients with and without NP manifestations attributed to SLE. And antibodies profiles were included for hierarchical cluster analysis, analyze the correlation between different antibody clusters and clinical manifestations of SLE. Results: The final analysis group included 176 patients, of which 151 (85.8%) had NP manifestations attributed by SLE. 1, The positive rates of anti-nuclear antibodies (ANA) and anti-SSB of NPSLE patients and the levels of immunoglobulin G (IgG) were significantly higher in comparison with those NP symptoms not attributed to SLE (non-NPSLE) patients [145 (96.0%) vs 18 (72.0%), P [Formula: see text] 0.001; 23 (15.2%) vs 0 (0.0%), P=0.048; median 23.5 vs 9.6 points, IQR 10.00, 48.25 vs 7.58,15.83 points, P [Formula: see text] 0.01; respectively]. The levels of C4 and the proportion of patients with concomitant hemolytic anemia were lower than non-NPSLE [median 0.09 vs 0.15 points, IQR 0.05, 0.14 vs 0.07, 0.17 points, P [Formula: see text] 0.01; 9 (6.0%) vs 7 (28.0%), P [Formula: see text] 0.005; respectively]; 2, NP manifestations in the central nervous system were the most frequent (86.9%). In patients with NPSLE, cerebrovascular disease, seizures, headche occurred most often; Among non-NPSLE, common causes included infection, non-specific cerebrovascular disease, hematological disease; 3, Four clusters among 151 NPSLE patients were identified. Cluster 1 comprised patients with triple aPLs (antiphospholipid antibodies) positivity and demonstrates a high incidence of cerebrovascular disease, seizure, Cluster 2 was characterized by high positive rates of anti-SSA and anti-SSB, with high risk of system damage such as mucocutaneous, renal. Cluster 3 was characterized by a high positive rate of anti Sm, mainly present with aseptic meningitis and polyneuropathy, often accompanied by renal and hematological involvement. Cluster 4 was characterized by high positive rates of anti Ro52, anti RNP (ribosome P protein), includes seizure and demyelinating syndrome. Conclusion: Four characterized antibody clusters are identified in NPSLE patients in this study. Different clusters are associated with certain clinical features and complications of NPSLE. However, the correlations found in this study need to be investigated further in larger populations.

Efficacy of targeted next generation sequencing for pathogen detection in lower respiratory tract infections.

To investigate the diagnostic utility of targeted next-generation sequencing (tNGS) in the diagnosis of lower respiratory tract infections. Patients with lower respiratory tract infection in East Area of Yantai Yantaishan Hospital from December 2021 to September 2023 were retrospectively analyzed. Sputum samples were tested using both tNGS technology and conventional microbiological examination. Data were collected on general clinical features and test outcomes. The study evaluated the efficacy of tNGS by comparing its positive detection rate against traditional methods and analyzing detection differences among patients with varying clinical characteristics. Receiver operating characteristic (ROC) analysis was used to determine the diagnostic accuracy of both testing methods. A total of 281 patients were included, with corresponding sputum specimens. The tNGS method showed a higher positivity rate of 90.0%, significantly outperforming the conventional method's rate of 70.82% (P<0.05). Among 199 patients with concordant positive results, 38.22% fully agreed, while 53.40% completely disagreed between the two methods. Mycobacterium tuberculosis, Candida albicans, and Pseudomonas aeruginosa were the most frequently detected pathogens, respectively. tNGS significantly reduced the time required for pathogen detection (P<0.001) and identified a higher rate of mixed infections compared to conventional methods (49.11% vs 2.85%, P<0.001). Positive tNGS detection rates significantly differed between patients with abnormal vs normal C-reactive protein or procalcitonin levels. The AUC for tNGS was 0.867, indicating superior diagnostic accuracy over the conventional method (P<0.05). tNGS technology demonstrates a high positivity rate and rapid pathogen detection in lower respiratory tract infections, with notable advantages in identifying mixed infections. This method shows potential for enhancing diagnostic accuracy and treatment decisions in clinical settings.