Improvement In Sensitivity Research Articles

A Comprehensive Review of the Diagnostics for Pediatric Tuberculosis Based on Assay Time, Ease of Operation, and Performance

Pediatric tuberculosis (TB) is still challenged by several diagnostic bottlenecks, imposing a high TB burden in low- and middle-income countries (LMICs). Diagnostic turnaround time (TAT) and ease of operation to suit resource-limited settings are critical aspects that determine early treatment and influence morbidity and mortality. Based on TAT and ease of operation, this article reviews the evolving landscape of TB diagnostics, from traditional methods like microscopy and culture to cutting-edge molecular techniques and biomarker-based approaches. We examined the benefits of efficient rapid results against potential trade-offs in accuracy and clinical utility. The review highlights emerging molecular methods and artificial intelligence-based detection methods, which offer promising improvements in both speed and sensitivity. The review also addresses the challenges of implementing these technologies in resource-limited settings, where most pediatric TB cases occur. Gaps in the existing diagnostic methods, algorithms, and operational costs were also reviewed. Developing optimal diagnostic strategies that balance speed, performance, cost, and feasibility in diverse healthcare settings can provide valuable insights for clinicians, researchers, and policymakers.

Computationally efficient physics-informed difference-symmetric nonlinear equalizer for C-band DML-DD links

Volterra nonlinear equalizer (VNE) is widely used in intensity modulation and direct detection (IM/DD) systems because it employs multi-order operations to effectively capture the nonlinear characteristics of signals as a generic tool. In the specific directly-modulated laser with direct detection (DML-DD) link, the interaction between the chirp of DML and chromatic dispersion (CD) can be modeled as composite second-order (CSO) distortion. By incorporating the CSO model into the nonlinear equalizer, it is possible to better extract the feature of the end-to-end channel, achieving superior performance with lower complexity. In this work, we propose a computationally efficient physics-informed difference-symmetric nonlinear equalizer (DSNE) based on the analytical formulation of the CSO. Additionally, we provide a thorough comparison of the computational complexity and bit-error-rate (BER) performance of various equalizers. Compared to the conventional VNE, the DSNE provides a 1-dB improvement in receiver sensitivity while reducing computational complexity by 51%. It is shown that the model-assisted DSNE structure enhances the matching to channel nonlinearity by omitting the less cost-effective taps in the conventional VNE and applying difference operations to the symmetric taps. The DSNE incorporates difference-symmetric terms, in contrast to the quadratic nonlinear equalizer (QNE), which uses only diagonal terms. This addition leads to a 56% reduction in BER while incurring only a 12% increase in computational complexity. The proposed DSNE technique demonstrates significant potential for low-cost, high-performance DML-DD optical transmission systems.

Design and Performance Analysis of N+ Pocket-Doped Vertical DMDGDP TFET With T-Shape Channel for Sensitivity Improvement—As a Biosensor

Design and Performance Analysis of N+ Pocket-Doped Vertical DMDGDP TFET With T-Shape Channel for Sensitivity Improvement—As a Biosensor

Hydrogen-Bonded Organic Framework Nanosheets with Rich Free Oxygen Atoms for NO2 Sensing.

Hydrogen-bonded organic frameworks (HOFs) are under fast development in broad applications but have not been well explored for chemiresistive gas sensing yet primarily due to insufficient active sites. Herein, a new porphyrin-based HOF-199 is constructed by OH···O hydrogen bonds featuring layered networks and rich free oxygen (O) atoms, which is further exfoliated into few-layer nonosheets with more dangling O sites through an ultrasound-assisted liquid exfoliation method (namely L-HOF-199). Benefiting from rich electron-donor sites, L-HOF-199 demonstrates exceptional NO2 sensing properties under ambient conditions, achieving a remarkable 3.25-fold improvement in sensitivity (152% toward 5 ppm of NO2) and a faster response speed (52 s), relative to HOF-199. This work provides a promising platform for the rational design of advanced gas sensors via functional HOF chemistry.

Refining PREVENT prediction models for 10-year risk of cardiovascular disease using measures of anxiety and depression

Refining PREVENT prediction models for 10-year risk of cardiovascular disease using measures of anxiety and depression

Sensitivity Improvement of Creatinine Detection Based on Functionalized Carbon Dots and Silver Nanotriangular Localized Surface Plasmon Resonance

Sensitivity Improvement of Creatinine Detection Based on Functionalized Carbon Dots and Silver Nanotriangular Localized Surface Plasmon Resonance

Subthreshold nanosecond laser therapy for chronic central serous chorioretinopathy: A prospective study.

To investigate the morphologic and functional outcomes of nanosecond subthreshold (ST) laser treatment for patients with chronic central serous chorioretinopathy (CSC). In this prospective study, 44 patients were treated with the ST nanosecond laser with a follow-up period of 12 months. All target variables were measured at 1, 3, 6 and 12 months after the first laser treatment. This study showed a significant improvement in macular sensitivity (MS), a significant reduction in central macular thickness (CMT) as well as a significant reduction in subretinal fluid height (SRF) after 3 months of treatment. The subfoveal choroidal thickness (SFCT) was significantly reduced after 12 months of treatment. However, the best-corrected visual acuity (BCVA) (logMAR) did not change significantly at any time during the study. A high proportion of patients (85%) showed complete resolution of SRF after 12 months, indicating a positive response to treatment in the majority of our patients. This study showed statistically significant functional improvement in MS as well as significant anatomical reduction in CMT, SFCT and SRF height in CSC patients treated with ST nanosecond laser therapy. Patients with higher SRF at baseline required repeated laser treatments to achieve complete resolution of the SRF.

Ultrasensitive "Hunter of Target": Rabbit Monoclonal Antibody-Based Competitive Lateral Flow Immunoassay for Saxitoxin.

The rapid, sensitive, and accurate detection of paralytic shellfish toxins (PSTs), such as saxitoxin (STX), is critical for protecting human health due to the frequent occurrence of toxic red tides. In this work, to address the low affinity of traditional mouse monoclonal antibodies (m-mAbs), rabbit monoclonal antibodies (r-mAbs) against STX were produced by a single B-cell sorting culture and a cross-selection strategy. The r-mAbs showed 100-fold improvement in sensitivity (IC50 = 0.018 ng/mL), improved stability under strong acid/alkali resistance (pH = 3-12), and high tolerance to organic solvent (MOH, 30%) in comparison with m-mAbs. The structural analysis of PSTs shows that the r-mAbs primarily interacted with common functional groups in PSTs, including guanidine, amide, and alcoholic hydroxyl groups (-OH). R-mAbs3C3 recognized STX, GTX2/3, C1/C2, NEO, and dcSTX with broad specificity through hydrogen bonding facilitated by Asp93, Gln27, Ser27A, and Asp95 in the complementarity-determining region (CDR) pocket. In contrast, r-mAbs3C7 displayed high specificity for STX, mediated by Ser98, Leu96, Ser30, Tyr97, and Ser53, which formed hydrogen bonds and hydrophobic interactions. The lack of interaction with Asp93 and Asp95 in r-mAbs3C7 likely contributed to its enhanced specificity. The anti-STX r-mAbs3C3 was then used to develop an ultrasensitive lateral flow immunoassay (LFIA) for the detection of STX with a cutoff value of 100 pg/mL. The LFIA demonstrated recovery rates ranging from 86.6 to 106.3% in food samples (mussels, nassariidae, bullacta exarata, and fresh water). Given its high target selectivity and sensitivity, this study provides a sight for rapid, sensitive, and accurate monitoring of food toxins.

Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting.

One challenge using wearable sensors is nonwear time. Without a nonwear (e.g., capacitive) sensor, actigraphy data quality can be biased by subjective determinations confounding sleep/wake classification. We developed and evaluated a machine learning algorithm supplemented by dynamic features to discern wear/nonwear episodes. Actigraphy data from wrist actigraph (Spectrum, Philips-Respironics). The built-in nonwear sensor as "ground truth" to classify nonwear periods using other data, mimicking features of Actiwatch 2. Data were collected over 1week from employed adults (n=853). Extreme gradient boosting (XGBoost), a tree-based classifier algorithm, was used to classify wear/nonwear, supplemented by dynamic features calculated over various time windows. The performance of the proposed algorithm was tested over 30-second epochs. Additional analytics and exploratory analyses: Evaluation of the SHapley Additive exPlanations (SHAP) values to find the effectiveness of the dynamic features. The XGBoost classifier yielded substantial improvements in balanced accuracy, sensitivity, and specificity, including dynamic features and comparison to default actiwatch classification algorithms. The proposed classifier effectively distinguished between valid and invalid days, and the duration of contiguous periods of nonwear correctly identified. Our findings highlight the potential of XGBoost using dynamic features of varying activity levels across the time series to provide insights on wear/nonwear classification using a large dataset. The methodology provides an alternative to laborious manual benchmarking of the data for similar devices that do not have a nonwear sensor.

Highly-multiplex detection of plasma cell-free human papillomavirus-16 DNA in oropharyngeal carcinoma.

Plasma cell-free Human Papillomavirus DNA (cfHPVDNA) is a biomarker for oropharyngeal carcinoma. Existing diagnostics may be limited by inadequate sensitivity or high cost/complexity for longitudinal monitoring. We hypothesized that sensitive and specific plasma cfHPVDNA detection may be achieved via a highly-multiplex qPCR method. We designed and validated a single-tube one-step genotype-specific qPCR assay for detection of cfHPV16DNA in human plasma using >8,000 genomes spanning 18 genotypes. Amplicons were optimized for cfHPVDNA fragment size. The cfHPV16DNA qPCR amplicons spanned 16 % of the HPV16 genome. Amplicons were conserved in a median of 99.0 % of 3,944 genomes in silico. The 95 % lower limit of detection was 0.35 genome copies/reaction and the limit of blank was 0. Multiplexing achieved a tenfold improvement in sensitivity compared with single amplicons using in silico simulations of cfHPVDNA fragmentation, which was in close agreement with experimental observations. An assay was replicated for HPV18 with similar observations. Among 36 patients with head/neck mucosal carcinomas (26 HPV-positive, 12 HPV-negative), there was 100 % concordance with tissue HPV status and with NavDx digital PCR. Pre-treatment specimens with sub-genomic cfHPVDNA concentration were detected. False negatives were observed with single amplicons but not with this multiplexed method. Among 17 patients with post-treatment landmark specimens, there was 100 % PPV and 100 % NPV for recurrence. This assay is specific for plasma cfHPVDNA detection and prognostic for recurrence. Sub-genomic sensitivity was in close agreement with in silico simulations. The format might be more accessible than dPCR or NGS for longitudinal testing.

Revolutionizing classification: A novel gray level co-occurrence matrix and statistical feature-based segmentation approach

The accurate and efficient classification of leukemia images is crucial for early diagnosis and effective treatment planning. Traditional methods often face challenges in handling the complexity and variability of medical images. To address these challenges, we propose a novel approach that leverages the Gray Level Co-occurrence Matrix (GLCM) and statistical feature-based segmentation techniques. In this paper, we present a comprehensive framework for the automated classification of leukemia images using advanced image processing techniques. The methodology involves six key stages: input of leukemia images, preprocessing to enhance image quality, segmentation to isolate relevant features, feature extraction using texture analysis, classification using multiple distance metrics Euclidean, Manhattan, Canberra, and Chebyshev, and performance evaluation. Our results demonstrate significant improvements in classification accuracy, sensitivity, specificity, and error rates across various metrics and feature sets. For instance, using the Chebyshev distance, we achieved an average accuracy of 82.69%, sensitivity of 85.95%, and specificity of 82.77%. The Canberra distance provided optimal performance with 65 features, yielding an accuracy of 85.18%, sensitivity of 86.39%, and specificity of 86.31%. These findings underscore the efficacy of our approach in distinguishing between healthy and leukemic cells, thereby contributing to early diagnosis and effective treatment planning for leukemia.

Sex-specific differences in insulin response and substrate oxidation after repeated, brief whole-body immersion in 45°C water: A prospective, interventional study.

Prolonged heat exposure is suggested to improve glucose metabolism and fat oxidation, but no studies have addressed whether brief heat stimuli represent a viable, time-efficient, alternative approach. Consequently, we examined the ability of brief stimuli evoked by 45°C water to improve glucose tolerance, insulin sensitivity, and fat oxidation in young, non-obese, males and females. Twenty-four participants completed fourteen 5-min sessions involving whole body passive heating in 45°C water. Changes in resting catecholamines, cytokines, substrate oxidation, resting energy expenditure, glucose tolerance, and insulin release in response to an oral glucose tolerance test, were assessed before and 24-h after intervention, and 1 month after the end of the intervention. The results showed that repeated short-duration heat intervention had no significant effects on epinephrine, norepinephrine, interleukin-6, and tumor necrosis factor alpha production in both sexes. Glucose area under the curve (AUC) was not affected. However, females had a lower insulin AUC and improved insulin sensitivity as indicated by a decrease in homeostatic model assessment for insulin resistance, and an increase in the quantitative insulin sensitivity check index and the Matsuda insulin sensitivity index values one month after the end of the heat intervention. No effect was observed in resting energy expenditure, but carbohydrate oxidation per kilogram increased in females, and this substrate oxidation change was maintained after one month. In conclusion, fourteen sessions of brief 5-min whole-body immersion in 45°C water produced an improvement in insulin sensitivity and increased reliance on carbohydrate oxidation in females.

Method for Sensitivity Improvement of MEMS Pressure Sensor: Structural Design and Optimization of Concave Resonant Pressure Sensor

Method for Sensitivity Improvement of MEMS Pressure Sensor: Structural Design and Optimization of Concave Resonant Pressure Sensor

Authenticated Access Control for Vehicle Ignition System and Long Range Vehicle Monitoring System based on LoRa WAN Protocol

Abstract: The development of LPWAN technology is gradually becoming an evolution for its significant improvements of signal sensitivity and noise tolerance. At present, however, many IoT applications, such as Gsm location tracking , are still using the communication technology of traditional mobile network, which consumes considerable power and suffers from high communication cost. In this project a long-range vehicle monitoring system, based on the LoRaWAN protocol[1]. We clarify the system parameters and determine its communication range. This prototype system is implemented to track the vehicle route in real time, to monitor the health of vehicle, biometric authentication for ignition of vehicle . With the data collected from the prototype system, the relationship between the Packet Delivery Rate(PDR) and Signal to Noise Ratio(SNR) is built. Considering the impact of signal contention, a model is theoretically verified to decide the PDR under different node count and duty cycle. Finally, the communication range is concluded and a solution is proposed for setting up a Authenticated Access Control for Vehicle Ignition System and Long range Vehicle monitoring system based LoRaWAN protocol. This system in the real by LoRaWAN, which reduces power consumption and eases gateway deployment[1].

Nitrogen vacancy centers integrated magnetometer based on multi-frequency modulation measurement technique

Purpose This study aims to improve the sensitivity of magnetic detection. In this article, a multi-frequency modulation technique is used to increase the magnetic detection sensitivity of diamond nitrogen vacancy (NV) centers sensors. Design/methodology/approach In the field of magnetic detection, NV centers have corresponding advantages due to their unique long coherence property at room temperature. The important indicators for NV centers magnetometers are the magnetic detection sensitivity of the NV centers and the integration of the magnetometer. To solve this problem, the authors propose a multi-frequency modulated magnetic detection technique, using an integrated probe as well as a lock-in amplifier for the double enhancement of sensitivity as well as integration. Findings The following results can be obtained by processing and calculating the experimental data with an integrated lock-in amplifier circuit with an area of 27.50 cm2 and a probe volume of 3.12 cm3. The multi-frequency modulation technique was used to increase the magnetic detection sensitivity of the NV centers from 8.59 nT/Hz1 / 2–2.42 nT/Hz1 / 2. Research limitations/implications The authors propose a signal modulation technique with an integrated design, which achieves an improvement in the sensitivity of the sensor’s magnetic detection through practical testing. Originality/value The authors propose a signal modulation technique with an integrated design, which achieves an improvement in the sensitivity of the sensor’s magnetic detection through practical testing. This technique provides new research solution for the subsequent improvement of the magnetic detection sensitivity.

Flexible Electronic Skin Based on Graphene and Its Composites

Flexible electronic skin, as an important part of bionic technology, has a wide range of applications in the fields of wearable devices, intelligent robots, and bionic prostheses. Graphene, as a two-dimensional material with excellent electrical conductivity, mechanical flexibility and chemical stability, shows great potential in flexible electronic skin. This paper reviews recent advances in flexible electronic skin based on graphene and its composites, presenting the unique physicochemical properties of graphene and its advantages in flexible sensor materials. This paper discusses in detail the application of graphene and its composites in flexible sensors, including pressure sensors, humidity sensors, and flexible supercapacitors. This paper summarizes the challenges faced in current research, such as the cost and process complexity of material preparation, increased sensor integration, and issues of long-term stability. In addition, this paper proposes future directions, including the large-scale preparation of low-cost and high-performance graphene materials, the improvement of sensor sensitivity and durability, the optimization of graphene composites, and the realization of multifunctional and integrated flexible e-skin systems. Valuable references are provided to further promote the practical applications of graphene-based flexible electronic skin in the fields of medical monitoring, bionic technology and smart wearable.

Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis

This article explores the practical implementation of autoencoders for anomaly detection, emphasizing their latent space manipulation and applicability across various domains. This study highlights the impact of optimizing parameter configurations, lightweight architectures, and training methodologies to enhance anomaly detection performance. A comparative analysis of autoencoders, Variational Autoencoders, and their modified counterparts was conducted within a tailored experimental environment designed to simulate real-world scenarios. The results demonstrate that these models, when fine-tuned, achieve significant improvements in detection accuracy, specificity, and sensitivity while maintaining computational efficiency. The findings underscore the importance of lightweight, practical models and the integration of streamlined training processes in developing effective anomaly detection systems. This study provides valuable insights into advancing machine learning methods for real-world applications and sets the stage for further refinement of autoencoder-based approaches.

Partial microglial depletion and repopulation exert subtle but differential effects on amyloid pathology at different disease stages

Colony-stimulating factor-1-receptor (CSF1R) inhibitors have been widely used to rapidly deplete microglia from the brain, allowing the remaining microglia population to self-renew and repopulate. These new-born microglia are thought to be “rejuvenated” and have been shown to be beneficial in several disease contexts and in normal aging. Their role in Alzheimer’s disease (AD) is thus of great interest as they represent a potential disease-modifying therapy. Here, we explored the differential effects of microglial depletion and repopulation during amyloid pathology progression using 5xFAD mice. We utilized the CSF1R inhibitor PLX3397 to induce microglial self-renewal and tracked microglia-plaque dynamics with in vivo imaging. We observed transient improvement in plaque burden on different timescales depending on the animal’s age. While the improvement in plaque burden did not persist in any age group, renewing microglia during mid- to late-pathology might still be beneficial as we observed a potential improvement in microglial sensitivity to noradrenergic signaling. Altogether, our findings provide further insights into the therapeutic potential of microglial renewal in AD.

Spectral Data Fusion From Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and X-ray Fluorescence (XRF) Analyzers for Improved Detection of Cerium in a Simulated Dispersal Accident.

This work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity. Fusing principal component scores from the two sensors yielded an order of magnitude improvement in precision and sensitivity of predictions made with an artificial neural network, compared to predictions made by models trained on independent sensor data. Lastly, a boosted ensemble trained on the fused spectral features yielded an ideal predictor with root-mean-squared error on the order of 10-6 and calculated limit of detection order 10-5 wt.

DarkSide-20k sensitivity to light dark matter particles

The dual-phase liquid argon time projection chamber is presently one of the leading technologies to search for dark matter particles with masses below 10 GeV c−2. This was demonstrated by the DarkSide-50 experiment with approximately 50 kg of low-radioactivity liquid argon as target material. The next generation experiment DarkSide-20k, currently under construction, will use 1,000 times more argon and is expected to start operation in 2027. Based on the DarkSide-50 experience, here we assess the DarkSide-20k sensitivity to models predicting light dark matter particles, including Weakly Interacting Massive Particles (WIMPs) and sub-GeV c−2 particles interacting with electrons in argon atoms. With one year of data, a sensitivity improvement to dark matter interaction cross-sections by at least one order of magnitude with respect to DarkSide-50 is expected for all these models. A sensitivity to WIMP–nucleon interaction cross-sections below 1 × 10−42 cm2 is achievable for WIMP masses above 800 MeV c−2. With 10 years exposure, the neutrino fog can be reached for WIMP masses around 5 GeV c−2.