High Positive Rate Research Articles

Epidemic risks of measles and rubella in China: a systematic review and meta-analysis.

Seroprevalence studies across various countries can contribute to achieving the elimination target for measles and rubella. However, in the Mainland of China, the concept of herd immunity remains unclear due to the lack of a nationwide serosurvey. This systematic review and meta-analysis was conducted by retrieving literature reporting the seroprevalence of measles and rubella published between 2012 and 2023. The pooled positive rates and estimated geometric mean concentrations (GMCs) of measles and rubella immunoglobulin G antibodies were calculated. This study analysed 135 studies on measles and 77 on rubella, including data from 368,023 and 177,422 healthy individuals, respectively. Between 2010 and 2022, the overall pooled positive rates for measles and rubella antibodies were 88.8% and 79.91%, respectively. The age-specific susceptibility analysis showed that infants aged < 1year had the lowest pooled positive rates. Other age groups had a roughly U-shaped distribution, with relatively higher positive rates and GMC of measles and rubella antibodies in young children and older age groups. However, the positive rates for both measles and rubella antibodies fell below the elimination threshold in almost all age groups other than young children aged 1-4years, especially in recent years. In addition, antibody positivity rates varied by geographical region and decreased with economic level. Our findings provide preliminary insights into herd immunity for measles and rubella, highlighting the challenges to achieving their elimination in China.

Flexible 3D nanofiber-based SERS biosensor for detection of miRNA-223-3p in early Laryngeal Cancer diagnosis

MicroRNAs (miRNAs) are small non-coding RNAs (18–22 nucleotides) that regulate gene expression and are associated with various diseases, including Laryngeal Cancer (LCa), which has a high mortality rate due to late diagnosis. Traditional methods for miRNA detection present several drawbacks (time-consuming steps, high cost and high false positive rate). Early-stage diagnosis and selective detection of miRNAs remain challenging.This study proposes a 3D flexible biosensor that combines nanofibers (NFs), gold nanoparticles (AuNPs), and an inverse molecular sentinel (iMS) for enzyme-free, SERS-based detection of miRNA-223-3p, evaluated as a potential LCa biomarker. The electrospun flexible nanofibers decorated with AuNPs enhance Raman signal. Selective detection of miRNA-223-3p is achieved by immobilizing an iMS-DNA probe labeled with a Raman reporter (Cyanine 3) on the AuNPs. The iMS distinctive stem-and-loop structure undergoes a conformational change upon interaction with the miRNA-223-3p, producing an “on to off” SERS signal. The proposed sensor demonstrated a linear detection range from 10 to 250 fM, with a limit of detection (LOD) of 19.50 ± 0.05 fM. The sensor selectivity was confirmed by analyzing the SERS signal behaviour in the presence of both Non-complementary miRNA and miRNA with three mismatched base pairs. This easily fabricable sensor requires no amplification and offers key advantages, including sensitivity, flexibility, and cost-effectiveness.

HawkEye: An end-host method to detect the Low-rate Denial-of-Service attack of cross-traffic over bottleneck links

The adaptive mechanisms of the Transmission Control Protocol (TCP) address network congestion and other unpredictable network conditions. They ensure the reliability of data transmission and the stability of the network. Unfortunately, the vulnerabilities in these adaptive mechanisms are targeted explicitly by low-rate denial-of-service (LDoS) attacks, which severely degrade network service quality. Only by modifying these protocols and addressing their vulnerabilities can one entirely prevent LDoS attacks. Although various improved TCP algorithms exist, they often fail to identify LDoS attacks accurately and, in some cases, may even reduce TCP performance. Furthermore, traditional LDoS attack detection methods rely on intermediate devices, which do not meet TCP’s end-to-end performance optimization needs. We introduce HawkEye, which moves the detection mechanism to the end hosts to address this issue. HawkEye uses an improved genetic algorithm to fine-tune the parameters of the LightGBM on the sending host, integrating multiple network traffic features to detect LDoS attacks. Experimental results show that our proposed method achieves the high accuracy, high true positive rate, and low false positive rate, successfully addressing the limitations of end-host detection of LDoS attacks and providing an innovative and effective solution for enhancing LDoS attack detection.

Role of IFN-γ, IL-33, and IL-35 concentrations in tuberculous pleural effusion in diagnosis and analysis of the effectiveness of combined tests

This study examines the diagnostic utility of the combined interleukin-33 (IL-33), interferon-γ (IFN-γ), and interleukin-35 (IL-35) test in tuberculous pleural effusion. Forty patients with pleural effusion of unknown etiology admitted to the hospital between December 2020 and December 2023 were selected as the study group. The patients were further categorized into tuberculous (TB) (n = 20) and malignant (n = 20) groups on the basis of their relevant data, while sera from 20 healthy medical checkups were used as control group. Thoracentesis was used to collect pleural fluid, and after its collection, enzyme-linked immunosorbent assay was utilized to detect the concentration content of IL-35, IL-33, and IFN-γ in pleural fluid of patients with different types of diseases. Data processing and analysis were executed through SPSS23.0 software to plot the receiver operating characteristic curve and compare the area of the area under the line of each index. Adenosine deaminase level was statistically higher in TB group patients (59.91 U/L) than in malignant group (14.31 U/L) (P &lt; .05). Pleural fluid lactate dehydrogenase levels did not differ significantly between the 2 groups (P &gt; .05). T-SPOT test had a higher positive rate in the TB group (85%) and a lower positive rate in the malignant group (45%). The TB group exhibited significantly higher levels of IFN-γ expression than the malignant group (P &lt; .05). The TB group had higher levels of IL-33 and IL-35 compared to the malignant group, with a statistically significant difference (P &lt; .05). The TB group had a greater level of IL-33 (81.05 ± 10.11 pg/mL) than the cytology-positive group (39.37 ± 9.63 pg/mL), while the difference was not statistically significant (P &gt; .05). The level of IL-35 (70.11 ± 10.37 pg/mL) in the TB group was lower than the level of IL-35 (72.13 ± 9.58 pg/mL) in the cytology-positive group. The specificity of the series of combined tests reached 95.81%, which was statistically superior to the single-factor test (P &lt; .05). In the TB group, IFN-γ and IL-33, IFN-γ and IL-35, and IL-33 and IL-35 showed positive correlation. Combined determination of the concentration levels of IL-35, IL-33, and IFN-γ is of value in the diagnosis of tuberculous pleurisy.

68Ga-FAPI and 18F-NaF PET/CT in psoriatic arthritis: a comparative study.

As fibroblast-like synoviocyte activation and bone formation are associated with psoriatic arthritis (PsA), positron emission tomography (PET) using the tracers of 68Ga-fibroblast activation protein inhibitor (FAPI) and 18F-sodium fluoride (NaF) may sensitively detect the disease. In this prospective study, we aimed to evaluate the performance of 68Ga-FAPI PET/CT in PsA and to compare it with 18F-NaF PET/CT. Sixteen participants (female 7/16, age 42.31 ± 10.66 years) with PsA were prospectively enrolled and underwent dual-tracer PET/CT, clinical assessment and ultrasonography. PET/CT images were scored for PET-positive lesions at the peripheral joints, entheses, and axial joints. The positivity rate of 68Ga-FAPI in peripheral joints is higher than that in entheses and axial joints (21.84% vs 12.15% vs 0%), whereas high positivity rates of 18F-NaF in peripheral joints, entheses, and axial joints were observed (85.23%, 78.13%, and 75%, respectively). The disease activity score 28 was higher in the PET-positive than in the PET-negative group with 68Ga-FAPI (5.25 ± 1.84 vs 2.55 ± 0.94, p= 0.037), but not with 18F-NaF. Besides, the PET joint count at 68Ga-FAPI PET/CT was positively correlated with the TJC (r = 0.604, p= 0.017), SJC (r = 0.773, p= 0.001), DAS28-CRP (r = 0.556, p= 0.032), PASDAS (r = 0.540, p= 0.038) and PsASon13 (r = 0.701, p= 0.005), while no correlation was observed in 18F-NaF PET/CT. The positivity rates of 68Ga-FAPI- and 18F-NaF PET/CT were different in patients with PsA in peripheral joints, entheses, and axial joints. The extent of joint involvement at 68Ga-FAPI PET/CT correlated with clinical and ultrasound variables as well as disease activity.

Mid-Trimester Uterine Artery Doppler for Aspirin Discontinuation in Pregnancies at High Risk for Preterm Preeclampsia: Post Hoc Analysis of StopPRE Trial

(BJOG. 2024;131(3):334–342. doi: 10.1111/1471-0528.17631) Preeclampsia (PEC) is characterized by hypertension and proteinuria or organ dysfunction after 20 weeks of gestation. The cause of the condition has not been fully elucidated; however, PEC is associated with a defect in the development of the placenta in early-onset patients. In unaffected individuals, uterine artery flow impedance (UtA) decreases during early gestation through week 24. However, if pulsatility index (PI) in UtA remains high, it is a sign of maladaptation and is commonly found in patients with preterm PEC. An additional consequence of impaired placentation is uteroplacental ischemia, during which the levels of placental growth factor (PIGF) are decreased and soluble fms-like tyrosine kinase-1 (sFlt-1) increases. In screening first trimester patients for risk of PEC, UtAPI and PIGF may be used. Clinicians prescribe aspirin daily as a preventative measure in patients who screen at risk for PEC. Although the incidence of PEC greatly decreases with this treatment, it is also noteworthy that screening for risk of PEC during the first trimester results in a high false positive rate. Thus, most women pre-emptively receiving aspirin for PEC are false positives. The authors previously performed a study where aspirin was discontinued in patients between 24 and 28 weeks of gestation with a normal sFlt-1/PlGF ratio of ≤38, identifying false positives cases. Although this study showed promising results, the ratio may not be available for use in every clinic. Thus, in this study, the authors examine whether UtAPI can be a correlate for these angiogenic factors and act as an alternative screening measure at 24 to 28 weeks of gestation with the same purpose to discontinue aspirin and assess incidence of preterm PEC.

Digital image processing to detect adaptive evolution.

In recent years, advances in image processing and machine learning have fueled a paradigm shift in detecting genomic regions under natural selection. Early machine learning techniques employed population-genetic summary statistics as features, which focus on specific genomic patterns expected by adaptive and neutral processes. Though such engineered features are important when training data is limited, the ease at which simulated data can now be generated has led to the recent development of approaches that take in image representations of haplotype alignments and automatically extract important features using convolutional neural networks. Digital image processing methods termed α-molecules are a class of techniques for multi-scale representation of objects that can extract a diverse set of features from images. One such α-molecule method, termed wavelet decomposition, lends greater control over high-frequency components of images. Another α-molecule method, termed curvelet decomposition, is an extension of the wavelet concept that considers events occurring along curves within images. We show that application of these α-molecule techniques to extract features from image representations of haplotype alignments yield high true positive rate and accuracy to detect hard and soft selective sweep signatures from genomic data with both linear and nonlinear machine learning classifiers. Moreover, we find that such models are easy to visualize and interpret, with performance rivaling those of contemporary deep learning approaches for detecting sweeps.

Diagnostic utility of Cyttel-Tech in identifying meningeal metastases from malignant solid tumors: An observational study.

To investigate the value of circulating tumor cell (CTC) in cerebrospinal fluid (CSF) by Cyttel-Tech detection in patients with suspected leptomeningeal metastasis (LM) from malignant solid tumors. We collected CSF from 12 patients with suspected LM from malignant solid tumors at our hospital. Twelve milliliters (mL) of CSF were collected from each patient through the first lumbar puncture: 5 mL was for CTC detection by Cyttel-Tech method, 5 mL for CSF cytopathology examination, and 2 mL for cytology and biochemical analysis of the CSF. Additionally, all patients underwent cranial brain magnetic resonance imaging with both plain and contrast-enhanced scans. Among the 12 patients with suspected LM, CTC detection yielded a positivity rate of 11/12 (91.7%). The positivity rate for CSF cytopathology examination was 4/12 (33.3%). However, none of the patients showed positive findings on cranial brain magnetic resonance imaging with both plain and contrast-enhanced scans (0/12, 0%). The difference between CTC detection and cytopathology detection was statistically significant (P < .05). CTC detection may have a high positivity rate in diagnosing LM from malignant solid tumors. It potentially serves as a supplementary diagnostic method for patients with negative pathology results and be used for rapid and accurate adjunctive diagnosis of LM.

Abstract B071: Identifying the clinical utility of CSF as a source of liquid biopsy in pediatric brain tumors

Abstract Pediatric tumors of the central nervous system (CNS) are a leading cause of cancer-related childhood mortality. Along with imaging, it is essential to molecularly characterize tumor tissue for the diagnosis, treatment, and prognosis of these cancers. Biopsies of deeply-seeded lesions may not be possible or may carry significant risks. On the other hand, liquid biopsy, using cerebral spinal fluid (CSF), provides a minimally invasive alternative to identify and monitor tumor DNA at crucial time points, from diagnosis to follow-up. This allows for molecular diagnosis, identification of therapeutic targets, monitoring response to therapy, and early detection of recurrence or progression. The aim of our study was to assess the practicality and usefulness of liquid biopsies in a diverse group of CNS tumor patients. We used a combination of methods, including a targeted hybridization capture panel and low-pass whole genome sequencing (LP-WGS) based on the sample characteristics. We analyzed a total of 243 CSF samples from patients with CNS tumors including: low-grade glioma (LGG; N=57), high-grade glioma (HGG; N=53), medulloblastoma (MB; N=40), germinoma (CNS-GCT; N=29), and no prior biopsy (N=42). We found somatic mutations in 88% of CSF samples from HGG and 67% from LGG with known tumor DNA variants and sufficient sequencing quality. It's important to note that in LGG patients, samples collected from lumbar puncture were positive only in disseminated cases, while a higher positivity rate was found in cases where CSF was collected from ventricular spaces. Moreover, we observed positive copy number alterations (CNA) in CSF samples collected from HGG, embryonal tumors, and CNS-GCT patients at the time of diagnosis and follow-up with a sensitivity of 64%. Within our sample cohort that had an unknown or query tumor due to no prior biopsy, we were able to detect diagnostic, targetable mutations and/or a positive CNA profile in 30% of cases. This supports the utility of liquid biopsy as a diagnostic tool in in CNS lesions. This tool may also assist in monitoring for relapse in high-grade tumors. For example, in a case study, we were able to detect recurrent MB by indication of a positive CNA profile in keeping with the primary tumor CNV, six years post-surgical resection. In CNS- GCT patients, we found that LP-WGS was highly sensitive at diagnosis as we have observed similar CNV in both CSF and tumor DNA. In summary, the utility of our liquid biopsy platform in the context of CNS cancers is an impactful tool that can aid clinicians in diagnosis, treatment and monitoring disease in a minimally invasive manner. Citation Format: Liana Nobre, Mansuba Rana, Yoshiko Nakano, Ian Burns, Robert Siddaway, Richard Yuditskiy, Cyril Li, Andrew Bondoc, Anthony Liu, Uri Taboori, Cynthia Hawkins. Identifying the clinical utility of CSF as a source of liquid biopsy in pediatric brain tumors [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B071.

HPB SO36 - Diagnostic Accuracy in Detecting Gall Bladder Polyps: A Retrospective Study

Abstract Background Gallbladder polyps are outgrowths of the gallbladder mucosal wall. They are usually found incidentally on ultrasonography or after cholecystectomy, but can lead to symptoms similar to those caused by gallbladder stones1. It is important to differentiate pseudo-polyps (cholesterol, adenomyomatosis, inflammatory, hyperplastic) from true-polyps (adenomas, adenocarcinomas2,3). The vast majority of polyps detected by ultrasound are pseudo-polyps, with no risk of malignancy. Ultrasound has been considered the gold standard for diagnosis,. However, it is limited by several factors4,5. The primary objective of this study is to assess the accuracy of ultrasound in diagnosing true polyps in patients undergoing surgery for this condition. Method Retrospective review carried out on all US scans reporting Gallbladder polyps for patients who underwent cholecystectomy, with additional data from MRI and CT scans. Sample size 44 patients Time period: From 2019 to present. Analysis was done on Excel Inclusion Criteria: Patients who had cholecystectomies with USS results confirming GB polyps, and have histology reports.Patients who had cholecystectomies with USS results confirming GB stones, and polyps as a co-existing pathology, and have histology reports. Results We included 44 patients our study, all had polyps on USS, and elective laparoscopic cholecystectomy, the mean age was 52 Years, and 63.6 % were women. Out of 44 USS patients, 24 (54.5%) showed features of polyps on histopathology. However, only 6 (13.6%) specimens were identified as adenomas. 18 USS showed polyps measuring 10mm or more. The majority (55.6%) were benign pathologies (pseudo-polyps). Interestingly, only 3 showed features of adenomas (16.7%). 22 Patients had multiple USS for surveillance, Histology showed polyps in 12 specimens (54.5%), out of which only 4 were adenomas (18.2%). Conclusion The accuracy of transabdominal ultrasound in diagnosing true polyps is limited. However, the sensitivity to distinguish adenomas slightly increases in larger polyps This low diagnostic accuracy underscores the need for caution among surgeons when making clinical decisions based on ultrasound-detected polyps. The high false positive rate suggests that more diagnostic criteria are necessary to improve the reliability of ultrasound in the diagnosis and screening of polyps, which is crucial to help reduce unnecessary procedures in asymptomatic patients who had negative histology reports postoperatively.

Abstract A061: A cost-effective two-step approach for multi-cancer early detection in general population

Abstract In population-wide cancer screening, three key issues need to be focused on: number of cancer cases identified, the issue of false positives, and cost. A high false positive rate can result in a significant waste of healthcare resources. Large-scale screening inevitably leads to significant financial burdens on the healthcare system which is a key factor constraining nationwide screening. OncoSeek is a multi-cancer early detection (MCED) test using a panel of seven plasma protein tumor markers (PTMs) and artificial intelligence (AI). SeekInCare is a MCED test that integrates the seven PTMs and four cancer genomic features (aneuploidy, fragment size, end motifs, and oncogenic viruses) from cell-free DNA (cfDNA) by shallow whole-genome sequencing (sWGS). In a two-step approach, the initial screening is conducted using OncoSeek and SeekInCare is then used as the secondary test for those individuals who tested positive by OncoSeek. In a case-control study (n = 1197) including both OncoSeek and SeekInCare testing results, OncoSeek showed 49.9% sensitivity at 91.0% specificity, while SeekInCare demonstrated 60.0% sensitivity at 98.3% specificity. In the two-step process, specificity significantly increased from 91.0% to 99.3%, while sensitivity decreased from 49.9% to 39.9%. We modeled a screening in five million adults aged ≥ 50 years with a cancer incidence rate of 1.9%. The simulated real-world sensitivities of OncoSeek and two-step were adjusted downward proportionally to 28.0% and 22.4% based on the sensitivity differences observed between GRAIL’s CCGA study (case-control, 51.5%) and PATHFINDER study (prospective, 28.9%). While at 91.0% specificity OncoSeek had 441,450 cases of false positives, using the two-step approach significantly reduced false positives to 34,335 (0.7%). Although Galleri ($949 per test) identified more cancer cases than the two-step MCED, with 27,455 and 21,280 cases respectively, its total cost for the former reached $4,745 million. As the positive predictive value (PPV) of two-step (38.3%) was the same as Galleri (38.3%), it reduced cost 6.6-fold, amounting to a total cost of $713.6 million and a cost of $143 per individual screened. The cost of per cancer case detected using Galleri is $172,828 compared to $33,534 using the two-step MCED, which represents a 5.2-fold difference. In conclusion, the two-step approach not only significantly reduces false positives, but also cuts the screening cost down substantially, making it a cost-effective strategy for population-wide cancer screening. Citation Format: Mao Mao, Geng shuai peng, Li shi yong, Wu Wei, Chang Yin Yin. A cost-effective two-step approach for multi-cancer early detection in general population [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A061.

Abstract 4136781: Stethoscopes to Sequencers: How Next Generation Sequencing Aids Pediatric Cardiac Intensive Care Diagnosis

Background: Despite a high burden of rare genetic diseases among children admitted to cardiac intensive care units (CICUs), comprehensive guidelines for genetic testing in children with heart disease, particularly those that are critically ill, are lacking. Traditional testing methods, such as karyotype, chromosomal microarray (CMA) and candidate gene panels, are often employed based on suspected diagnoses. Rapid advancements in next-generation sequencing (NGS) technologies allow for more comprehensive genetic evaluation which is already recommended for patients with ventricular dysfunction, primary arrhythmias, and pulmonary hypertension (PH). Broad application of NGS in intensive care settings for pediatric heart disease has not been extensively investigated. Aims: Evaluate diagnostic efficacy of NGS in critically ill pediatric patients with cardiac disease. Methods: Retrospective cohort of patients who underwent exome or genome sequencing in a pediatric CICU between January 2020 and August 2023. Results: Exome or genome sequencing was performed on 49 patients with median testing age of 46 days (IQR 7-609). Primary cardiac phenotypes included congenital heart disease, ventricular dysfunction, arrhythmia, and PH. Forty-two pathogenic or likely pathogenic variants were identified and 23 (46.9%) resulted in a genetic diagnosis, of those, 16 (69.6%) were associated with a cardiac phenotype. Positivity rate did not differ between primary cardiac phenotypes but was significantly higher when the patient had associated multiple congenital anomalies (Fig. 1). Genome sequencing trended towards a higher positivity rate (54.5%) than exome (31.3%), though did not meet statistical significance (Fig. 1). Notably, 84.6% and 38.5% of the identified pathogenic variants would have remained undetectable by CMA and many commercially available gene panels, respectively. Conclusion: Exome and genome sequencing demonstrate high diagnostic yield across isolated and non-isolated cardiac disease and various cardiac phenotypes with diagnostic efficacy surpassing CMA and many commercial gene panels. As NGS technologies evolve, they promise to streamline diagnoses and enhance therapeutic decision-making for this patient population.

Epstein-Barr Virus in the RIVERA Case-Control Study of Acute Febrile Illness: Acute Mononucleosis Nearly Absent as an Etiology in the Peruvian Amazon.

Large diagnostic panels allow for pathogens with high or low likelihood of causing attributable illness to be tested simultaneously. Infectious mononucleosis (IM) due to primary infection with Epstein-Barr virus (EBV) is a common cause of acute febrile illness (AFI) in case series from high-income countries, though its contribution to AFI in tropical low-income settings is unclear. As part of a case-control study using multiplex quantitative polymerase chain reaction (qPCR) diagnostics, we set out to determine if primary EBV infection was an underrecognized cause of AFI in the Peruvian Amazon. Presence of EBV DNA in whole-blood samples was equally prevalent among febrile cases and afebrile controls (34.6% [247/714] versus 35.7% [248/695]) and was not correlated with classic IM symptoms. Given the clear lack of clinical significance of the whole-blood PCR results, additional testing was pursued to ascertain the true prevalence of IM among cases of AFI in this population. The presence of EBV DNA in plasma, a marker of active EBV-related processes, was detected in 7% (5/68). Anti-EBNA-1 IgG, a late marker of prior infection, was tested via ELISA and detected in 4/5 of the plasma-positive patients, thereby excluding an acute primary EBV infection in all but one patient. Infectious mononucleosis due to primary infection with EBV was not an important etiology of AFI in the Peruvian Amazon, despite high rates of initial test positivity.

Abstract 4145826: Atrial Fibrillation Screening During Sinus Rhythm Periods by Interrelated Systems Dynamics Analysis

Background: Previous studies have shown that AF screening in at-risk populations can reduce stroke incidence. However, non-targeted screening approaches often result in high false positive rates, placing an unnecessary burden on the healthcare system. In contrast, artificial intelligence-guided screening has been demonstrated to increase diagnostic yield in large prospective clinical trials. This approach, however, requires recording an ECG and a large-scale dataset for model training. Heart rate variability (HRV) analysis has proven effective in deciphering key heart dynamics. By analyzing HRV as interrelated dynamic systems, it may be possible to facilitate targeted AF screening using wearable devices that measure heart rate. The Koopman operator, used for data-driven modeling of interrelated dynamic systems, has been shown to accurately predict complex phenomena in chaotic systems such as climate forecasting and drug adverse reaction prediction. This is achieved by utilizing common characteristics of the systems for most model parameters, with only a small fraction of the parameters being specific to a certain system. Methods: Long (&gt;10 hour) records from 361 individuals (AFDB, LTAFDB) and healthy individuals' datasets from PhysioNet and THEW were analyzed for inter-beat intervals. The unified dataset was then split into 94 training, 17 validation, and 250 test set patients. Recordings from the training set were used to train both the common and specific parts of the interrelated dynamic systems model for each patient, along with a shared small neural network classifying patients into low and high risk for AF based on the unique (not shared between patients) singular values of the dynamic system model. Patient-specific dynamic system models were then fitted for the validation and test sets to calculate the patient dynamic singular values, which were used to classify patients into low and high risk for AF groups. Results: Atrial fibrillation occurred in 48 of 202 (23%) patients classified as low risk and 35 of 48 (72.9%) patients classified as high risk (odds ratio 8.63, 95% CI 4.23-17.64), yielding 72.9% sensitivity with 76.2% specificity. Conclusion: In this retrospective analysis, classification of the dynamic system model singular values identified patients at high risk for atrial fibrillation from sinus rhythm period.

An automatic detection method for cervical liquid‐based cells based on improved Yolov5s network

AbstractTo address the significant challenges of high false positive and false negative rates in existing algorithms for detecting cervical fluid‐based cells, an enhanced Yolov5s network is introduced. This paper details a novel approach that dynamically adjusts the weights of channels and the spatial attention in modules, substantially improving feature extraction from small objects and boosting the detection capabilities of the network. Furthermore, Mixup data augmentation technology is incorporated to counter the issue of imbalanced data categories in the custom dataset. The Complete Intersection over Union loss function is also employed to refine coordinate localization accuracy during training. Tested on the proprietary cervical cytology dataset, the modified Yolov5s achieves a mean Average Precision of 92.1%, surpassing the previous state‐of‐the‐art by 5.6%. This enhancement substantiates the efficacy of the proposed model. Code and models are accessible at https://github.com/youyi888/yolov5_CPCA.

Protein-Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools.

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. For example, the study of PPIs involved in the human:SARS-CoV-2 viral infection mechanism aided in the development of SARS-CoV-2 vaccines. Though several databases exist for the manual curation of PPI networks, text mining methods have been routinely demonstrated as useful alternatives for newly studied or understudied species, where databases are incomplete. Here, the relationship extraction performance of several open-source classical text processing, machine learning (ML)-based natural language processing (NLP), and large language model (LLM)-based NLP tools was compared. Overall, our results indicated that networks derived from classical methods tend to have high true positive rates at the expense of having overconnected networks, ML-based NLP methods have lower true positive rates but networks with the closest structures to the target network, and LLM-based NLP methods tend to exist between the two other approaches, with variable performances. The selection of a specific NLP approach should be tied to the needs of a study and text availability, as models varied in performance due to the amount of text provided.

An Efficient Network Intrusion Detection and Classification System using Machine Learning

In today's digital landscape, network security is of paramount importance, with intrusion detection systems (IDS) playing a crucial role in protecting sensitive data from malicious attacks. Traditional IDS, often reliant on signature-based methods, struggle with high false positive rates, difficulty in adapting to novel threats, and significant computational demands. This paper explores the development of an efficient network intrusion detection and classification system utilizing machine learning techniques to address these challenges. By leveraging datasets such as NSL-KDD and UNSW-NB15, our study employs a combination of supervised learning algorithms, including Support Vector Machines (SVM), Random Forests, and Neural Networks, alongside comprehensive data preprocessing and feature engineering strategies. The evaluation of our models through metrics like accuracy, precision, recall, and ROC-AUC demonstrates a marked improvement in detection capabilities and computational efficiency. Our findings suggest that machine learning-based IDS can significantly enhance network security by reducing false positives and adapting to emerging threats more effectively than traditional systems. This research not only underscores the potential of advanced machine learning techniques in IDS but also provides a robust framework for future developments in the field. In the rapidly evolving landscape of cybersecurity, effective network intrusion detection and classification systems are critical for safeguarding sensitive data and maintaining operational integrity. This paper presents a novel approach utilizing machine learning techniques to enhance the efficiency and accuracy of intrusion detection systems (IDS). By employing a combination of supervised and unsupervised learning algorithms, our system can identify and classify both known and unknown threats in real-time. We leverage advanced feature selection methods to optimize the performance of our models, ensuring high detection rates with minimal false positives. Our experimental results, validated on benchmark datasets, demonstrate significant improvements in detection accuracy and processing speed compared to traditional IDS solutions. The proposed system not only strengthens network defenses but also provides a scalable and adaptive framework for future cybersecurity challenges..

A robust distance-based approach for detecting multidimensional outliers

ABSTRACT Identifying outliers in data analysis is a critical task, as outliers can significantly influence the results and conclusions drawn from a dataset. This study explores the use of the Mahalanobis distance metric for detecting outliers in multivariate data, focusing on a novel approach inspired by the work of M. Falk, [On mad and comedians, Ann. Inst. Stat. Math. 49 (1997), pp. 615–644]. The proposed method is rigorously tested through extensive simulation analysis, where it demonstrates high True Positive Rates (TPR) and low False Positive Rates (FPR) when compared to other existing outlier detection techniques. Through extensive simulation analysis, we empirically evaluate the affine equivariance and breakdown properties of our proposed distance measure and it is evident from the outputs that our robust distance measure demonstrates effective results with respect to the measures FPR and TPR. The proposed method was applied to seven different datasets, showing promising true positive rates (TPR) and false positive rates (FPR), and it outperformed several well-known outlier identification approaches. We can effectively use our proposed distance measure in fields demanding outlier detection.

Epidemiological and molecular characteristics of carbapenem-resistant Klebsiella pneumoniae from pediatric patients in Henan, China

PurposeCarbapenem-resistant Klebsiella pneumoniae (CRKP) is an emerging global threat, whereas its epidemiological characteristics in children are rarely reported. This study aims to analyze clinical and epidemiological characteristics of CRKP from children in Henan, China.MethodsCRKP strains were isolated from pediatric patients, and the antimicrobial susceptibility of CRKP was determined using broth microdilution methods. The epidemiological characteristics of CRKP, including specimen sources, clinical data, carbapenemase types, virulence factors, MLST and PBRT typing were analyzed.ResultsIn total, 108 CRKP isolates were isolated from specimens including sputum, blood and urine, mainly from preterm pediatric department and internal medical intensive care unit (ICU). Newborns and staying in the ICU were risk factors for crude mortality. 107 isolates exhibited a multi-drug resistant (MDR) phenotype, and one isolate was extensively drug-resistant (XDR). Bacterial susceptibility to colistin, tigecycline and trimethoprim/sulfamethoxazole was 98.10%, 78.50% and 91.43%, respectively. Carbapenemase blaKPC (86.11%) was predominant, followed by blaNDM (5.56%) and blaIMP (2.78%). Two strains co-harbored blaKPC-blaNDM, one had blaKPC-blaIMP, whereas three isolates did not carry any of the analyzed carbapenemase genes. All strains possessed fimH, and 98% of the isolates possessed mrkD. Hypervirulent factors rmpA2 and iucA showed high positive rates (71.30% and 49.07%), with 48.15% of strains containing both genes. MLST analysis identified nine distinct sequence types (STs), with ST11 (82.41%) being the most common, followed by ST2154 (4.63%) and ST307 (3.70%). PBRT analysis revealed IncFII (85.19%) as the most prevalent plasmid.ConclusionIn summary, this study reported the epidemiological features of CRKP in pediatric patients in Henan, China, highlighting the high prevalence of multi-drug-resistant and hypervirulent strains, and underscoring the significance of continuous surveillance.

An Application Programming Interface (API) Sensitive Data Identification Method Based on the Federated Large Language Model

The traditional methods for identifying sensitive data in APIs mainly encompass rule-based and machine learning-based approaches. However, these methods suffer from inadequacies in terms of security and robustness, exhibit high false positive rates, and struggle to cope with evolving threat landscapes. This paper proposes a method for detecting sensitive data in APIs based on the Federated Large Language Model (FedAPILLM). This method applies the large language model Qwen2.5 and the LoRA instruction tuning technique within the framework of federated learning (FL) to the field of data security. Under the premise of protecting data privacy, a domain-specific corpus and knowledge base are constructed for pre-training and fine-tuning, resulting in a large language model specifically designed for identifying sensitive data in APIs. This paper conducts comparative experiments involving Llama3 8B, Llama3.1 8B, and Qwen2.5 14B. The results demonstrate that Qwen2.5 14B can achieve similar or better performance levels compared to the Llama3.1 8B model with fewer training iterations.