Signal Detection Research Articles

Unveiling Weak Signals of Emergence in Underwater Sensing Research Trends

Detecting emerging research trends is crucial as it allows for the proactive identification and monitoring of novel and influential topics in the scientific community. Monitoring research trends aids researchers, institutions, and policymakers in allocating resources, fostering innovation, and staying competitive in rapidly changing scientific landscapes. The growing significance of underwater sensing technologies in various domains has propelled research endeavours aimed at understanding the characteristics of academic discourse in this field. In this work, we comprehensively analyzed the academic research topics related to underwater sensing technologies using advanced computational methodologies. Leveraging natural language processing, topic modelling, and weak signal detection techniques, and focusing on underwater sensing as the case technology, we dissect a large corpus of scholarly articles published between 2007 and 2021 to unveil underlying thematic patterns and emergent trends within this domain while shedding light on signals of emerging technologies. Among the eighty extracted topics, six research topics were identified and recognized as emerging weak signals, and validated by experts. Notably, deep learning for underwater imaging was the only topic that transitioned from being weak to a strong signal in the final period.

Real‐Time Nuclear Magnetic Resonance Detection Using Maximum Likelihood Estimation with Single‐Shallow‐Nitrogen‐Vacancy Centers in Quantum Heterodyne Measurements

Single‐nitrogen‐vacancy (NV) centers in diamond are highly promising quantum nuclear magnetic resonance (NMR) sensors. However, their exposure to many sources of noise, such as surface impurities, shot noise from avalanche photodiode overlaps, and spin‐state projection errors inherent in quantum systems, limits their usefulness. Often, long measurement durations are required to accumulate sufficient data for NMR signal detection via fast Fourier transform spectrometry. For practical reasons, methods to shorten the necessary accumulation time for NMR signal detection are greatly desired. In this article, an on‐line formulation of maximum likelihood estimation (MLE) signal processing for quantum heterodyne NMR measurements is presented as a step toward this goal. This MLE method reduces the required data accumulation time by orders of magnitude and provides good estimates of target frequency locales in real time. These results are significant to practitioners of NMR detection with single‐NV centers in diamond who require a quick litmus test for potential signals when probing a wide area.

A Retrospective Observational Study of Adverse Drug Reactions (ADR) Reported to ADR Monitoring Centre from 2010 to 2020.

Adverse Drug Reactions (ADR) are one of the essential causes of hospital admissions and pose a significant clinical and economic burden on the healthcare system. The Adverse Drug Reaction Monitoring Centre (AMC) in JIPMER functioning under the Pharmacovigilance Programme of India (PvPI) plays a vital role in ensuring medication safety by routinely detecting and monitoring ADRs. Hence, this study aimed to assess the characteristics of ADR reported from 2010 to 2020 in AMC JIPMER and to detect signals, if any. To study the characteristics of Adverse Drug Reactions (ADR) reported to a regional ADR monitoring center from 2010 to 2020 and to detect signals of disproportionate reporting (SDRs) if any from the reported ADRs. A total of 6007 ADR reports with a single suspect drug were included for analysis from 2010 to 2020. The characteristics of these reports, including patient's age and gender, Number and percentage of ADRs, the causality of ADR using WHO UMC (World Health Organization-Uppsala Monitoring Scale), the seriousness of the ADR, and outcome were collected from the ADR reports. MedDRA (Medical Dictionary for Regulatory Activities) Preferred Terms (PT) were used to classify adverse drug reactions. Causality analysis using the Naranjo Algorithm and Preventability using Modified Schumock and Thornton criteria were performed for the ADRs. The number and percentage of severe ADRs were analyzed. The System Organ class of all the ADRs was enumerated. ADRs not mentioned in the US FDA (United States Food and Drug Administration) product label (unlabelled reactions) were documented. Unlabeled reactions with ≥3 ADR reports were included for signal detection by disproportionality analysis. Antineoplastic drugs, followed by antimicrobials, anticonvulsants, Anti snake venom, and NSAID were the most common drugs implicated in ADRs. Skin and subcutaneous tissue disorders were the most common System Organ Class (SOC) involved in the ADRs. Among the 6007 reports, 19.2% were serious ADRs. Most of the ADR reports were of possible causality followed by probable and certain as per WHO UMC and Naranjo causality scales. Only ten ADRs were preventable and one reaction (Tamoxifen-induced neuropathy) was eligible for signal detection. Disproportionality analysis using a 2x2 contingency table showed insignificant signal detection using the Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR). Analysis of ADRs from an ADR Monitoring center functioning in a tertiary care hospital shows antineoplastic drugs to be the most common drugs associated with adverse drug reactions, with rash being the most common adverse effect. The majority of the ADRs were not preventable. No Signals of Disproportionate Reporting (SDR) were detected in our study.

Self‐Powered Wearable Pressure Sensors for Detection and Separation of Signals for Various Human Movements

A detailed study on the dynamic response of mountable pressure sensors is presented, with a focus on foot pressure sensors integrated with carbon nanotube (CNT)‐coated cotton fibers. The research explores the sensor's sensitivity to pressure changes, repeatability, hysteresis, and durability through rigorous modeling and experimental validation. Computational simulations using Python and experimental data demonstrate the sensor's nonlinear conductance response to applied force, attributed to the varying contact area and number of contact points among the fibers. Long‐term outdoor exposure tests confirm the material's resilience, maintaining electrical conductivity and structural integrity. The study also demonstrates the sensor's ability to monitor human activities like walking, running, stair climbing, and jumping, as well as detecting muscle movements during swallowing, coughing, and speech, highlighting potential applications in health monitoring and artificial voice synthesis. The MRMR algorithm is used for feature selection to distinguish activities, demonstrating the sensor's potential for activity recognition. Additionally, a piezoelectric sensor on the pressure sensors estimates harvested electric power to power wearable devices. This work contributes to the advancement of self‐powered wearable pressure sensors to monitor real‐time human activity, with implications for healthcare, sports performance, and assistive technologies.

High-Performance Flexible Pressure Sensor with Micropyramid Structure for Human Motion Signal Detection and Electromagnetic Interference Shielding.

Flexible pressure sensors have a wide range of applications in the field of human motion signal detection, but the electromagnetic radiation generated during the monitoring process has become an unavoidable problem. Nowadays, it is still a challenge to develop high-performance pressure sensors with excellent electromagnetic interference (EMI) shielding performance. Herein, the Ti3C2TX MXene/carbon fiber/multiwalled carbon nanotube/polydimethylsiloxane (MXene/CMP) films with a micropyramidal structure were developed by the technology of vacuum high-temperature hot pressing and spray deposition. Benefiting from the dielectric loss of the conductive fillers and the presence of the microstructure, the MXene/CMP film exhibits excellent EMI shielding performance, and the EMI shielding efficiency (SE) can reach 49.37 dB. Besides, the introduction of CF effectively improves the mechanical properties of the MXene/CMP films, and the MXene nanosheets deposited on the surface of the film can reduce stress concentration, which in turn delays the expansion of cracks. Furthermore, the MXene/CMP sensor exhibits high sensitivity (89.76 kPa-1) and fast response/recovery time (61/60 ms). The deformation of microstructure and the construction of conductive networks enable the sensor to realize the monitoring of human motion signals (such as finger bending, knee bending, wrist bending, etc.). Meanwhile, the sensor maintains sensing stability even after 2000 pressure cycles, which can be attributed to the improvement in mechanical properties. In summary, the developed high-performance flexible pressure sensor with micropyramid structure has great application prospects in wearable devices and healthcare.

Adverse events associated with carbamazepine: a pharmacovigilance study using the FDA Adverse Event Reporting System

ABSTRACT Introduction Carbamazepine (CBZ) is a commonly used antiseizures medications (ASM), but its adverse drug reactions (ADRs) can impact the successful management of epilepsy. There are currently limited systematic studies on ADRs related to CBZ, necessitating further investigation. Areas covered Using the FDA Adverse Event Reporting System (FAERS) database , we extracted reports where CBZ was the primary suspect, conducting subgroup analyses stratified by sex and age. Four risk signal detection methods ROR, PRR, BCPNN, and EGBM were employed to systematically analyze the ADRs associated with CBZ. Expert opinion In the epilepsy population, ADRs related to CBZ often involve examinations, hereditary disorders, and infections. Subgroup analysis showed differences in ADR signals among male, female, elderly, and young patients. Human Herpesvirus 6 Infection and Dermatitis Exfoliative were consistent CBZ-induced ADRs, unaffected by age or sex. The study also identified previously overlooked ADRs such as bone metabolism abnormalities, ocular toxicity, and ototoxicity. Many ADRs linked to CBZ remain underreported. It is crucial to enhance monitoring and information dissemination about CBZ use in epileptic patients. Adjusting medication regimens for high-risk individuals, potentially incorporating acupuncture, traditional Chinese medicine, or alternative anti-seizure medications, should be considered when necessary.

A human cadaveric model for venous air embolism detection tool development.

A human cadaveric model combining standard lung protective mechanical ventilation and modified cardiac bypass techniques was developed to allow investigation into automated modes of detection of venous air emboli (VAE) prior to in vivo human or animal investigations. In this study, in order to create an artificial cardiopulmonary circuit in a cadaver that could mimic VAE physiology, the direction of flow was reversed from conventional cardiac bypass. Normal saline was circulated in isolation through the heart and lungs as opposed to the peripheral organs by placing the venous cannula into the aorta and the arterial cannula into the inferior vena cava with selective ligation of other vessels. Mechanical ventilation and this reversed cardiac bypass scheme allowed preliminary detection of VAE independently but not in concert in our current simulation scheme due to pulmonary edema in the cadaver. A limited dissection approach was used initially followed by a radical exposure of the great vessels, and both proved feasible in terms of air signal detection. We used electrical impendence as a preliminary tool to validate detection in this cadaveric model however we theorize that it would work for echocardiographic, intravenous ultrasound or other novel modalities as well. A cadaveric model allows monitoring technology development with reduced use of animal and conventional human testing.

A Clustering Ensemble Method for Drug Safety Signal Detection in Post-Marketing Surveillance.

Post-marketing surveillance refers to the process of monitoring the safety of drugs once they reach the market, after the successful completion of clinical trials. In this work, we investigate a computational approach using data mining tools to detect safety signals from post-market safety databases, or in other words, to identify adverse events (AEs) with disproportionately high reporting rates compared to other AEs, associated with a particular drug or a drug class. Essentially, we view this as a problem of cluster analysis-based anomaly detection on post-market safety data, where the goal is to 'unsupervisedly' detect the anomalous or the signal AEs. Our findings demonstrate the potential of using a clustering ensemble method to detect drug safety signals. It employs multiple clustering or anomaly detection algorithms, followed by a performance comparison of the candidate algorithms based on a collection of appropriate measures of goodness of clustering results. The method is general enough to include any number of clustering or anomaly detection algorithms and goodness measures, and performs better than any of the candidate algorithms in identifying the signal AEs. Extensive simulation studies illustrate that the ensemble method detects the AE signals from synthetic post-market safety datasets pretty accurately across the different scenarios explored. Based on the cases reported to the FDA Adverse Event Reporting System (FAERS) between 2013 and 2022, we further demonstrate that the ensemble method successfully identifies and confirms most of the adverse events that are known to occur most frequently in reaction to antiepileptic drugs and -lactam antibiotics.

Advanced Anomaly Detection in ECG Signals Through Convolutional Autoencoders

This article aims to present a comprehensive study on convolutional autoencoders for advanced anomaly detection in ECG signals. Anomaly detection in complex datasets has become increasingly critical due to the rising need for systems that can effectively identify irregularities that may indicate fraud, system failures, or significant deviations from normal operations. Traditional methods often need help capturing nuanced patterns in high-dimensional data, necessitating more sophisticated approaches. This research uses an autoencoder-based model as a robust solution for anomaly detection, utilizing its capability to learn high-level representations in an unsupervised manner. The proposed model uses a convolutional autoencoder architecture to compress and decompress input data, thus highlighting anomalies through reconstruction errors. We outline detailed experiment strategies, including model training on average data to minimize reconstruction loss, setting an optimal threshold for anomaly sensitivity based on validation loss, and evaluating the model using precision, recall, F1-score, and AUC-ROC metrics. These experiments were conducted using a dataset with labeled normal and abnormal instances, allowing precise tuning and assessment of model performance. The results indicate that the autoencoder discriminates between normal and abnormal data, achieving high precision and recall at 99.22% and 98.98%, respectively. The confusion matrix and loss distribution analysis further validate the model's efficacy, clearly distinguishing between normal and abnormal data loss values concerning the defined threshold. This research shows the autoencoder model demonstrates high accuracy in anomaly detection and offers insights into the types of anomalies it can detect, supporting its application across various domains requiring reliable anomaly identification.

Bearing fault signal detection and prediction based on an improved particle filter method

Awareness and predicting bearing health are challenging because of uncertainties in the operating environment and noise in fault-signal measurements. Considering the sudden occurrence of bearing faults and their relatively brief duration over the bearing lifespan, this paper presents a fusion framework that combines an eighth-order polynomial model with a cuckoo search thought particle filter (CST-PF) method to estimate the failures occurrences. The fault characteristic signals are extracted from the amplitude spectrum within a specific frequency range and modeled by an eighth-order polynomial model. Cuckoo search (CS) is introduced to address the particle dilution in particle filter (PF). Furthermore, the proposed CST-PF refines the step size update equations and discovery probability for balancing the search speed and accuracy, thus enhancing the adaptability to bearing fault diagnosis and prediction. It ensures the parameter convergences in the prediction model by learning the fault characteristic signal during regular bearing operations, facilitating accurate predictions of subsequent failures. Validation results show that the eighth-order model accurately captures changes in fault characteristic signals throughout the bearing lifespan, with MAE and RMSE metrics surpassing those of traditional models. The CST-PF method demonstrates superior predictive ability compared to the Cuckoo search particle filter (CS-PF), traditional PF, and other PF variants.

Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach

Massive MIMO technology is recognized as a key enabler for beyond 5G (B5G) and next-generation wireless networks. By utilizing large-scale antenna arrays at the base station (BS), it significantly improves both system capacity and energy efficiency. Despite these advantages, the deployment of a high number of antennas at the BS presents considerable challenges, particularly in the design of signal detectors that can operate with low computational complexity. While the minimum mean square error (MMSE) detector offers optimal performance in these large-scale systems, it suffers from the computational burden that makes its practical implementation challenging. To mitigate this, various iterative methods and their improved versions have been introduced. However, these iterative methods often converge slowly and are less accurate. To address these challenges, this study introduces an improved variant of traditional accelerated over-relaxation (AOR), called optimized AOR (OAOR). AOR is an over-relaxation method, and its performance is highly dependent on its relaxation parameters. To find the optimal parameters, we have developed an innovative approach that integrates a nature-inspired meta-heuristic algorithm known as Particle Swarm Optimization (PSO). Specifically, we introduce a novel variant of PSO that improves upon basic PSO by enhancing the cognitive coefficients to optimize the relaxation parameters for OAOR. These key modifications to the standard PSO improve its ability to explore various solutions efficiently and help to find the optimal parameters more quickly for signal detection. It facilitates the OAOR with faster convergence towards the optimal solution by reducing the error rate, resulting in high detection accuracy and simultaneously decreasing computational complexity from O(K3) to O(K2) making it suitable for modern wireless communication systems. We conduct extensive simulations across various configurations of massive MIMO systems. The results indicate that our proposed method achieves better performance compared to existing techniques. This improvement is particularly evident in terms of both computational complexity and error rate.

Signal detection analysis of affective prosody recognition in forensic inpatients who have committed sexual offenses

ABSTRACT Socio-affective functioning, or the way we interact and relate to others, is one of the four dynamic sexual recidivism risk domains. Accurately recognizing emotions enables the inference of mental and affective states supporting social adaptation. As little attention has been paid to affective prosody recognition in forensic inpatients who have committed sexual offenses (FICSOs), this study assessed the accuracy and sensitivity scores of 111 male participants assigned into three groups: FICSOs (n = 35), forensic inpatients who have committed non-sexual offenses (FICNSOs, n = 26) and community members (CoM, n = 50). Collected data also include response bias, emotion labeling reflection time, task easiness and task easiness reflection time. Using non-parametric group comparisons (Kruskal–Wallis H and Mann–Whitney U), results highlight, overall, a pervasive impairment of affective prosody recognition in FICSOs and FICNSOs compared to CoM. However, there was no difference in disgust sensitivity scores between FICSOs and CoM. FICSOs and FICNSOs took significantly longer than CoM to select an emotional label, especially for happiness. In addition, a metacognitive impairment was found in FICSOs and FICNSOs as they found the task significantly easier than CoM while being less sensitive.

Real-World Data Mining for Signal Detection of Antipsychotics-Associated Adverse Events Using the Korea Adverse Event Reporting System (KAERS) Database.

Background and Objectives: Recent studies suggest that the binary categorization of first-generation antipsychotics (FGAs) as being primarily responsible for extrapyramidal symptoms (EPSs) and second-generation antipsychotics (SGAs) for cardiometabolic abnormalities is an oversimplification. SGAs also demonstrate antagonistic affinity for D2 receptors, indicating their potential to induce EPSs. This study utilized the Korea Adverse Event Reporting System (KAERS) database to explore adverse drug event (ADE) signals related to both FGAs and SGAs. Materials and Methods: Relevant ADE reports from January 2013 to December 2022 were extracted from the KAERS database and analyzed using disproportionality analysis, employing the proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC) with its 95% lower confidence interval (LCI) indices. Results: Of the initial dataset of 2,890,702 ADE reports, those with insufficient data and duplicates were removed, resulting in a final dataset of 5249 reports for analysis. Aripiprazole, an SGA, showed signals for movement disorders, including EPSs (PRR 4.7, ROR 4.8, IC 2.2), tremors (PRR 5.3, ROR 5.4, IC 2.4), and akathisia (PRR 18.6, ROR 19.3, IC 3.5). Notably, for quetiapine, cardiovascular signals were detected, including increased blood pressure (PRR 2.1, ROR 2.3, IC 0.5), and tachyarrhythmia (PRR 13.9, ROR 14.1, IC 1.8), along with peripheral edema (PRR 2.5, ROR 2.5, IC 0.2). Metabolic abnormalities, such as weight gain and increased appetite, were identified for four SGAs: aripiprazole, olanzapine, quetiapine, and risperidone. Safety signals related to movement disorders were not detectable for FGAs, likely due to the limited number of ADE reports available for analysis. Conclusions: Our study findings support that the distribution of ADEs between FGAs and SGAs is not strictly binary. Aripiprazole, despite being an SGA, showed signals for extrapyramidal movement disorders. Four SGAs (aripiprazole, olanzapine, quetiapine, and risperidone) were linked to metabolic side effects, while quetiapine was associated with cardiovascular safety signals.

Silk fibroin-based bioelectronic devices for high-sensitivity, stable, and prolonged in vivo recording

Silk fibroin, recognized for its biocompatibility and modifiable properties, has significant potential in bioelectronics. Traditional silk bioelectronic devices, however, face rapid functional losses in aqueous or in vivo environments due to high water absorption of silk fibroin, which leads to expansion, structural damage, and conductive failure. In this study, we developed a novel approach by creating oriented crystallization (OC) silk fibroin through physical modification of the silk protein. This advancement enabled the fabrication of electronic interfaces for chronic biopotential recording. A pre-stretching treatment of the silk membrane allowed for tunable molecular orientation and crystallization, markedly enhancing its aqueous stability, biocompatibility, and electronic shielding capabilities. The OC devices demonstrated robust performance in sensitive detection and motion tracking of cutaneous electrical signals, long-term (over seven days) electromyographic signal acquisition in live mice with high signal-to-noise ratio (SNR >20), and accurate detection of high-frequency oscillations (HFO) in epileptic models (200–500 Hz). This work not only improves the structural and functional integrity of silk fibroin but also extends its application in durable bioelectronics and interfaces suited for long-term physiological environments.

A Universal Plug-and-Play Approach to In Situ Multinuclear Magnetic Resonance Analysis of Electrochemical Phenomena in Commercial Battery Cells.

Advancing electrochemical energy storage devices relies on versatile analytical tools capable of revealing the molecular mechanisms behind the function and degradation of battery materials in situ. The nuclear magnetic resonance phenomenon plays a pivotal role in fundamental studies of energy materials and devices because of its exceptional sensitivity to local environments and the dynamics of many electrochemically relevant elements. The jelly roll architecture is one of the most energy-dense and, therefore, most popular concepts implemented in pouch, prismatic, and cylindrical Li- and Na-ion cells. Such widely commercialized designs, however, represent a significant obstacle for a range of powerful in situ magnetic resonance-based methodologies due to negligible radio frequency electromagnetic field penetration through conductive metal casings and current collectors. In this work, we introduce an experimental setup that enables direct RF wave transmission through the cell terminals and current collectors, and provides efficient excitation and detection of NMR signals. An RF adapter designed as a plug-and-play device effectively turns the battery cell into an NMR probe that can be tuned to a broad range of Larmor frequencies. Due to its exceptional sensitivity, versatility, and multinuclear capability, the proposed methodology is suitable for fundamental research and industrial high-throughput screening applications. In situ NMR lineshapes provide a direct quantitative description of the chemical environments of electrochemically active elements and enable new metrics for the accurate assessment of state-of-charge (SoC) and state-of-health (SoH). Specifically, in commercial pouch cells based on lithium cobalt oxide, lithium nickel manganese cobalt oxide, and sodium nickel iron manganese oxide chemistries, 7Li and 23Na NMR data unambiguously show electrochemical transformations between intercalated and metallic forms of charge carrier ions, and reveal anisotropic magnetic properties of the electrode coating.

A novel memristor chaotic circuit and its application in weak signal detection of wind turbine fault

A novel memristor chaotic circuit and its application in weak signal detection of wind turbine fault

High Performance Self‐Powered p+‐GaN/n−‐ZnO/Au UV Heterojunction Phototransistor with 3D Interconnected ZnO Nanowire Network for Solar‐Blind Imaging and Optical Communication

AbstractHeterojunction phototransistor (HPT) is considered the most promising detector technology in weak ultraviolet (UV) signal detection due to its inherent internal gain through transistor action. However, achieving high‐performance self‐powered UV HPTs remains a significant challenge. In this study, a high‐performance self‐powered p+‐GaN/n−‐ZnO/Au UV HPT with ZnO serving as the floating base is cleverly designed. The device features a unique back‐to‐back configuration of the p+‐GaN/n−‐ZnO heterojunction and n−‐ZnO/Au Schottky junction, along with an ultrathin ZnO base layer of a 3D nanowire network structure. This design enables the fabricated p+‐GaN/n−‐ZnO/Au UV HPT to operate in a self‐powered mode with exceptional performance metrics. Under zero bias, the device exhibits outstanding characteristics including ultralow dark current of 1.7 × 10−13 A, remarkable Iphoto/Idark ratio of 106, fast response speed (tr = 150 µs / tf = 250 µs), high responsivity of 9.74 A W−1, and detectivity of 1.58 × 1014 Jones, representing the best result achieved for UV HPTs thus far at zero bias. Moreover, the fabricated UV HPT is successfully demonstrated in solar‐blind imaging and UV communication systems without the need for an external power supply. This work presents effective and practical design strategies for achieving high‐performance self‐powered UV HPTs.

ZGSO:Cr3+,Ni2+ Persistent Phosphors with Dual Emission in NIR-I and SWIR Ranges for Bio-Imaging Applications.

Persistent luminescence (PersL) is widely used for near infrared (NIR-I, 650-950nm) imaging as they allow getting images without background. Bio-imaging in the second shortwave-infrared region SWIR-II (NIR-II, 1000-1400nm) is less widespread but is growing as it offers the advantages of low photon scattering, increased in vivo penetration depth, and improved imaging clarity. In this work, the preparation and the complete optical properties of a new material is reported, Zn1.33Ga1.33Ni0.005Cr0.005Sn0.33O3.995 (ZGSO:Cr3+, Ni2+) able of emitting in both deep-red/NIR-I and SWIR (NIR-II) and shows its potential in bioimaging. ZGSO:Cr3+, Ni2+ can be excited using different sources such as X-rays, UV, and visible light to emit persistent signals in dual biological windows (dual-BW). By integrating an energy transfer process from Cr3+ to Ni2+ within this newly synthesized material, the influence of co-dopants on signal intensity and emission wavelengths is sought to explore. PersL at ≈700nm (NIR-I) and ≈1300nm (NIR-II) have been tested in preliminary bioimaging experiments using different protocols, allowing signal detection with good spatial resolution and depth sensitivity. The dual-BW PersL imaging strategy expands the toolbox for highly accurate analysis and has, for the first time, allowed access to accurately high-resolution sensing, and tracing.

Evaluation of Automatic Signal Detection of In Situ Hybridization for Detecting HPV DNA in Cervical Tissue Derived from Patients with Cervical Intraepithelial Neoplasia

Background: Cervical cancer is fourth the most common cancer in women worldwide. Due to the prevalence of human papillomavirus (HPV) in the population (80–90%), scientists are likely to discover even more associations of this pathogen with other diseases in the future. In recent years, In Situ Hybridization (ISH) assays that use automated signal-detecting methods in formalin-fixed, paraffin-embedded (FFPE) cervical tissue, such as the enzyme-categorized signal-detecting system, have shown a higher sensitivity. Objectives and Methods: To evaluate automatic signal detection of ISH assay for detecting HPV DNA, we compared the ability of an ISH probe, Inform HPV II and III (Ventana Medical Systems, Tucson, AZ), to that of PCR assays to detect HPV DNA in cervical tissue specimens with cervical intraepithelial neoplasia (CIN; CIN 1, 28 cases; CIN 2, 22 cases; and CIN 3, 20 cases) and normal cervix (2 cases). Results: Our findings showed a significant relation was confirmed between ISH III level and HPV outcome (positive/negative). Patients with positive HPV outcomes had significantly lower ISH III levels, MD = −7961.82 CI95 [−17,230.00; −199.21], p = 0.005. Conclusions: Automatic signal detection of ISH assay is not particularly applicable to cervical tissue material. A more useful method of confirming the presence of HPV in the cervix is the HPV test with genotyping, as it allows for collecting a larger amount of material from the cervical disc and canal. The interpretation of a positive or negative ISH test must be guided in the context of clinical history and morphology.

Research on underwater acoustic detection technology based on optical waveguide resonator cavity

Purpose In acoustic detection technology, optical microcavities offer higher detection bandwidth and sensitivity than traditional acoustic sensors. However, research on acoustic detection technologies involving optical microcavities has not yet been reported. Therefore, this paper aims to design and construct an underwater acoustic detection system based on optical microcavities and study its acoustic detection technology to improve its performance. Design/methodology/approach Based on the principles of optical microcavity acoustic sensors, a signal-detection circuit was designed to form a detection system in conjunction with a laser, an optical waveguide resonator and an oscilloscope. This circuit consists of two modules: a photodetection module and a filter amplification module. Findings The photodetection module features a baseline noise of −106.499 dBm and can detect device spectral line depths of up to 2410 mV. The gain stability of the filter amplification module was 58 dB ± 1 dB with a noise gain of −107.626 dBm. This design allows the acoustic detection system to detect signals with high sensitivity within the 10 Hz−1.2 MHz frequency band, achieving a maximum sensitivity of −126 dB re 1 V/µPa at 800 Hz and a minimum detectable pressure (MDP) of 0.37 mPa/Hz1/2, corresponding to a noise equivalent pressure (NEP) of 51.36 dB re 1 V/µPa. Originality/value This study designs and constructs a broadband underwater acoustic detection system specifically for optical waveguide resonators based on the sensing principles of silicon dioxide optical waveguide resonators. Experiments demonstrated that the signal detection module improves the sensitivity of underwater acoustic detection based on optical waveguides.