Detection Applications Research Articles

Nanotechnology in retinal diseases: From disease diagnosis to therapeutic applications

Nanotechnology has demonstrated tremendous promise in the realm of ocular illnesses, with applications for disease detection and therapeutic interventions. The nanoscale features of nanoparticles enable their precise interactions with retinal tissues, allowing for more efficient and effective treatments. Because biological organs are compatible with diverse nanomaterials, such as nanoparticles, nanowires, nanoscaffolds, and hybrid nanostructures, their usage in biomedical applications, particularly in retinal illnesses, has increased. The use of nanotechnology in medicine is advancing rapidly, and recent advances in nanomedicine-based diagnosis and therapy techniques may provide considerable benefits in addressing the primary causes of blindness related to retinal illnesses. The current state, prospects, and challenges of nanotechnology in monitoring nanostructures or cells in the eye and their application to regenerative ophthalmology have been discussed and thoroughly reviewed. In this review, we build on our previously published review article in 2021, where we discussed the impact of nano-biomaterials in retinal regeneration. However, in this review, we extended our focus to incorporate and discuss the application of nano-biomaterials on all retinal diseases, with a highlight on nanomedicine-based diagnostic and therapeutic research studies.

Review on peak detect and hold circuits and their applicability in partial discharge detection

Abstract This article reviews various designs of electrical peak detect and hold circuits and tests their applicability for the detection of partial discharge pulses. Since partial discharges are very short pulses in the nanosecond range, the peak detector circuit must be very fast and thus work precisely over a wide bandwidth up to the GHz range. The article begins with a comprehensive literature review and gives an overview of the state of the art of peak detector circuits. As a result of the literature research, four different peak detector designs are identified that appear suitable for measuring partial discharges. These four circuits are then simulatively tested for their performance in measuring the pulse height of various partial discharge pulses. It turns out that two of the identified circuits are able to measure nanosecond pulses of small amplitude. These two circuits must be further optimized for partial discharge detection in the future. In addition, the circuit should be tested prototypically with real partial discharge pulses.

Developing an AI-based application for caries index detection on intraoral photographs.

This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University Hospital, Karachi, Pakistan, this study utilized a dataset comprising 7,465 intraoral images, including both primary and secondary dentitions. These images were meticulously annotated by two experienced dentists and further verified by senior dentists. A YOLOv5s model was trained on this dataset and integrated into a smartphone application, while a Detection Transformer was also fine-tuned for comparative purposes. Explainable AI techniques were employed to assess the AI's decision-making processes. A sample of 70 photographs was used to directly compare the application's performance with that of junior dentists. Results showed that the YOLOv5s-based smartphone application achieved a precision of 90.7%, sensitivity of 85.6%, and an F1 score of 88.0% in detecting dental decay. In contrast, junior dentists achieved 83.3% precision, 64.1% sensitivity, and an F1 score of 72.4%. The study concludes that the YOLOv5s algorithm effectively detects dental decay on intraoral photographs and performs comparably to junior dentists. This application holds potential for aiding in the evaluation of the caries index within populations, thus contributing to efforts aimed at reducing the disease burden at the community level.

Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers

Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation for diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction and large dataset requirements. EDA–ViT overcomes these by using a multi-scale weighted feature aggregation and a feature interaction module, enhancing both local and global feature extraction. The adaptive data augmentation method refines the training process, boosting accuracy and robustness. With a dataset of 8226 images, EDA–ViT achieved a classification accuracy of 96.58%, an F1 score of 96.10%, and a Matthews Correlation Coefficient (MCC) of 92.24%, outperforming other models. The inclusion of the Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced feature capture. Ablation studies revealed that the adaptive augmentation contributed to a 0.56% accuracy improvement and a 0.34% increase in MCC. In summary, EDA–ViT presents an innovative solution for custard apple disease diagnosis, with potential applications in broader agricultural disease detection, ultimately aiding precision agriculture and crop health management.

Ultra Enhancement of the Resolution of the Divergence Beam-Based SPR Sensor Using a Wavelet Filter

Surface Plasmon Resonance (SPR) sensors are highly sensitive to refractive index variations, making them ideal for biosensing and chemical detection applications. However, their performance can be constrained by noise, resolution limits, and signal distortions, particularly in divergence beam-based configurations. This research presents an ultra-enhancement of the performance of divergence beam-based SPR sensors by employing advanced wavelet filtering techniques. Wavelets, with their multi-resolution analysis capability, are applied to denoise and refine the sensor signal, significantly improving key performance metrics, including resolution, mean square error (MSE), root mean square error (RMSE), signal-to-noise ratio (SNR), sensitivity matrix (SM), and combined sensitivity factor (CSF).The wavelet filter effectively decomposes the SPR signal into distinct frequency bands, separating noise while retaining high-frequency components required for accurate sensing. This results in a significant reduction in MSE and RMSE of 30 and 92.26%, respectively, while simultaneously improving SNR by 54.08%, maintaining signal quality. The wavelet filter significantly improved the resolution of the SPR sensor by 99.95% (1.3772×10-7 RIU), allowing for more precise detection of refractive index changes and boosting diverge beam-based SPR sensor performance. In addition, a sensitivity matrix (SM) and combined sensitivity factor (CSF) were improved by 42 and 41.94%, respectively, allowing for a more thorough evaluation of the sensor's performance across multiple operational parameters. Simulation and experimentation show that wavelet filtering surpasses standard filtering approaches in terms of noise suppression and signal clarity. This technology has considerable potential for improving the accuracy and reliability of SPR sensors in high-sensitivity applications

Digital PCR in Virology: Current Applications and Future Perspectives.

Digital PCR (dPCR) has been used in the field of virology since its inception. Technological innovations in microfluidics more than a decade ago caused a sharp increase in its use. There is an emerging consensus that dPCR now outperforms quantitative PCR (qPCR) in the basic parameters such as precision, sensitivity, accuracy, repeatability and resistance to inhibitors. These strengths have led to several current applications in quantification, mutation detection and environmental DNA and RNA samples. In high throughput scenarios, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the cost and throughput still significantly hampered the adaption of dPCR. There is much unexplored potential within the multiplexing capabilities of dPCR. This will allow simultaneous multi-target quantification and can also partially alleviate the throughput and cost drawback. In this review, we discuss the strengths and weaknesses of dPCR with a focus on virology applications and we discuss future applications. Finally, we discuss recent evolutions of the technology in the form of real-time dPCR and digital high-resolution melting.

Securing transactions: a hybrid dependable ensemble machine learning model using IHT-LR and grid search

Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively rare, they can result in substantial financial losses, particularly due to the high monetary value associated with fraudulent transactions. Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the instant hardness threshold (IHT) technique in conjunction with logistic regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications.

AI in Agriculture : Revolutionizing Precision Farming and Sustainable Crop Management

This article explores the transformative impact of Artificial Intelligence (AI) on agriculture, focusing on precision farming and sustainable crop management. It highlights how AI-driven technologies are revolutionizing agricultural practices by optimizing resource utilization, enhancing productivity, and promoting sustainability. The article discusses key applications of AI in agriculture, including crop health monitoring, predictive analytics, resource optimization, smart irrigation systems, variable rate application of chemicals, and pest and disease detection. It presents case studies and statistical data demonstrating the significant improvements in crop yields, resource efficiency, and environmental sustainability achieved through AI adoption. The article also addresses the challenges facing widespread AI implementation in agriculture, such as cost barriers, lack of technical expertise, data quality issues, and infrastructure limitations, while exploring future directions including the integration of AI with robotics and blockchain technology.

Highly Responsive Cryogel Based Sensing Platform by Encapsulating Programmed DNA for Colorimetric Detection of 17β-estradiol

Responsive hydrogels show obvious superiorities when being employed at detection owing to their polymeric networks and optical properties. Albeit important, the slow response of traditional hydrogels against targets limits their further applications, and enhancing the responsiveness of hydrogel is therefore imperative. Herein, a cryogel with hierarchical structures was designed through adjusting the hydrogel structure by the freezing process to improve the responsive properties, and fabricated for rapid analysis of 17β-estradiol (E2). The cryogel encapsulating the DNA hairpins rapidly adsorbed the complementary DNA isolated from the aptamer in the presence of E2 and activated an efficient hybridization chain reaction (HCR). After integration with a smartphone system, a portable colorimetric sensing platform was created for sensitive detection of E2 with a low detection limit of 0.5 nM. Our work provides a promising way for highly responsive hydrogel-based biosensor design, and broadens their applications in rapid and portable target detection.

A quinolinium-based fluorescent probe for ultrafast detection of bisulfite and its applications in food detection, SO2 gas detection, and cell imaging

A quinolinium-based fluorescent probe for ultrafast detection of bisulfite and its applications in food detection, SO2 gas detection, and cell imaging

Leading Edge Erosion Classification in Offshore Wind Turbines Using Feature Extraction and Classical Machine Learning

Leading edge (LE) erosion is a type of damage that inhibits the aerodynamic performance of a wind turbine, resulting in high operation and maintenance (O&M) costs. This paper makes use of a small dataset consisting of 50 images of LE erosion and healthy blades for feature extraction and the training of four types of classifiers, namely, support vector machine (SVM), random forest, K-nearest neighbour (KNN), and multi-layer perceptron (MLP). Six feature extraction methods were used with these classifiers to train 24 models. The dataset has also been used to train a convolutional neural network (CNN) model developed using Keras. The purpose of this work is to determine whether classical machine learning (ML) classifiers trained with extracted features can produce higher-accuracy results, train faster, and classify faster than deep learning (DL) models for the application of LE damage detection of wind turbine blades. The oriented fast and rotated brief (ORB)-trained SVM achieved an accuracy of 90% ± 0.01, took 80.4 s to train, and achieved inference speeds of 63 frames per second (FPS), compared to the CNN model, which achieved an accuracy of 79.4% ± 2.07, took 4667.4 s to train, and achieved an inference speed of 1.3 FPS. These results suggest that classical ML models can be more accurate and efficient than DL models if the appropriate feature extraction method is used.

Synthesis of 2-methyl-6-methoxy-4-quinolinecarboxylic acid N-hydroxysuccinimide ester (MMQC-OSu) for streamlined and effective HPLC-based fluorescence detection of aliphatic amines in environmental samples

Aliphatic amines are widely distributed in the environment and food sources, posing potential health risks through skin and mucosal irritation. Consequently, their quantitative detection is crucial for assessing environmental health. Despite high reactivity and fluorescence properties of succinimidyl ester-based derivatization reagents, their application in the aliphatic amines detection is hampered by challenges such as limited detection sensitivity and fluorescence interference. We established an innovative synthetic approach to produce a series of succinimidyl esters with the desirable substituents. This advancement enabled the creation of efficient and highly sensitive reagents for the detection of aliphatic amines. Among them, 2-methyl-6-methoxy-4-quinolinecarboxylic acid N-hydroxysuccinimide ester (MMQC-OSu) exhibited the best detection performance. MMQC-OSu reacted with aliphatic amines at 40 °C in pH 8.0 buffer for 20 min, which was subsequently separated in a C18 chromatographic column with the fluorescence detection wavelength of 336/432 nm. This detection approach featured a rapid and mild reaction process, minimal interference from corresponding hydrolysis products, and impressive sensitivity (0.05 nM). These characteristics indicate that MMQC-OSu significantly surpassing commercial aliphatic amines detection reagents. Finally, the detection strategy of MMQC-OSu was successfully applied in environmental sample analysis, with recovery rate of 93 %–108 % and RSDs between 1.4 % and 6.5 %.

Colorimetric and fluorescence detection of Pb2+ by fluorescein derivative

A simple and low cost method for rapid detection of Pb2+ was developed in this study using a simple fluorescent probe, biotin-4-fluorescein. The presence of Pb2+ induced the color change of fluorescein from yellow to pink which can be detected visually by naked eyes. The color change of biotin-4-fluorescein was not observed while testing with other metal ions, such as Fe3+, Cu2+, Ca2+, Co2+ and Cd2+, indicating the selectivity of fluorescein derivative with Pb2+. Moreover, the color change was observed at pH above 7.0, and the pink color got more intense when increasing pH. Adding EDTA, a chelating reagent, into the solution of biotin-4-fluorescein/Pb2+ resulted to the rebound of the color to the original color of free biotin-4-fluorescein. The fluorescence spectra of fluorescein decreased with increasing Pb2+concentration, and the quenching was enhanced at higher pH. The association constant (Ka) of 2.00 × 104 M−1 was calculated from the fluorescence spectra with the limit of detection (LOD) and the limit of quantitation (LOQ) of 1.38 × 10−5 M (2.86 ppm) and 4.61 × 10−5 M (9.55 ppm), respectively. Job’s plot analysis indicated 2:1 ratio of binding interaction. Pb2+ in untreated wastewater was quantified for 24.99 ppm using this method. This quick analysis can be beneficial for the application of lead detection in real sample solutions, such as wastewater from industrial factories.

The Use of CellCollector Assay to Detect Free Cancer Cells in the Peritoneal Cavity of Colorectal Cancer Patients: An Experimental Study.

Colorectal cancer (CRC) is associated with high incidence and mortality rates globally. The presence of intraperitoneal free cancer cells (IFCCs) is recognized as an independent prognostic factor for CRC patients. However, a clinical gold standard for IFCCs detection is lacking. The GILUPI CellCollector has demonstrated high sensitivity and specificity in detecting free cancer cells, yet its application for CRC IFCCs detection remains unreported. We selected CRC and normal cell lines to evaluate the CellCollector's ability to detect tumor cells. A total of 70 CRC patients and 17 patients with benign disease undergoing laparoscopic procedures were investigated. Peritoneal lavage fluid was collected pre- and post-operation, and both real-time PCR (CEA mRNA) and CellCollector detection were performed. We compared the sensitivity and specificity of these two methods. CellCollector can distinguish well between CRC and normal cells in cell line experiments. CellCollector detects IFCCs better than real-time PCR (CEA) in CRC patients in different TNM Stages. The sensitivity of CellCollector was higher than that of real-time PCR (84.6% vs. 48.4%), and the specificity of CellCollector was also higher than real-time PCR (79.1% vs. 60.4%). There was no significant difference in the results of IFCCs detected by CellCollector before and after total mesorectal excision (TME) or complete mesocolic excision (CME) radical colorectomy (p > 0.05), but there was a significant difference in real-time PCR detection (p < 0.05). The CellCollector demonstrates superior sensitivity and specificity compared to real-time PCR for detecting IFCCs in CRC patients, suggesting its potential as a clinical tool for IFCCs detection. ClinicalTrials.gov identifier: NCT01978444.

Cr3+-doped Na2CaSn2Ge3O12 garnet showing broadband near-infrared emission at 820 nm with zero thermal quenching and near-unity quantum yield

The continually advancing near-infrared (NIR) phosphor-converted light-emitting diode (pc-LED) hold pivotal significance in optical imaging and NIR spectroscopy. Cr3+-doped garnet broadband NIR phosphors have attracted considerable attention owing to their high optical performance enabled by the rigid structure. Nonetheless, the emission wavelength is severely restricted, and longer wavelength emissions usually result in inferior quantum efficiency and poor luminescence stability. In this work, the concurrent reconstruction of dodecahedral and octahedral sites by introducing [Na+-Sn4+]C unit in the framework of Na2CaSn2Ge3O12 garnet contributes to the Cr3+ broadband NIR emission spanning 700–1200 nm, with a peak at 820 nm. Utilizing a charge compensation strategy via Ta5+ codoping largely boosts the internal quantum efficiency (IQE) of Na2CaSn2Ge3O12:Cr3+ from 93.06 % up to 95.04 %. Trap-mediated energy compensation is proposed to achieve a record-breaking zero-thermal-quenching behavior (99.39 %@423 K) of Cr3+-doped garnet with such a longer wavelength emission. The encapsulated prototype broadband NIR pc–LED gives rise to an overwhelming NIR output power of 590.9 mW@1300 mA and a photoconversion efficiency (PCE) of 32.62 % at 30 mA, rendering exceptional performance in night vision, detection, and NIR imaging applications.

MAxPy: A Framework for Bridging Approximate Computing Circuits to Its Applications

This paper presents MAxPy, a framework for bridging approximate computing (AxC) circuit design to its applications. MAxPy is an application-agnostic framework able to automatically build a cycle-accurate Python model of an approximate hardware design. This model can easily be emulated and integrated as a module in Python-based applications. We are herein proposing this framework aiming to build an AxC toolbox stimulating open research in academia to promote the integration of the existing and future open-source (i) AxC benchmarks, (ii) approximate logic synthesis tools, and (iii) P unit insights. In this paper, we demonstrate the use of MAxPy to explore the AxC design space of a Sobel filter hardware design as a case study exploiting a set of approximate adders combined with the data-driven approximate logic synthesis via probabilistic pruning. Then, we present a Pareto front for circuit area, energy, and delay reduction versus the application-level metrics: edge detection accuracy and structural similarity index (SSIM). The Pareto front results of the multiple AxC techniques herein explored show circuit area savings ranging from 40.4% to 59.1% for a 98.1% to 99.6% accuracy on edge detection application MAxPy code is open source in: github.com/MAxPy-Project

Electron sensitization and chemical sensitization of ZnWO4/WO3 nanorod heterojunctions for high performance triethylamine sensor

Real-time detecting of triethylamine gas is necessary and challenging in environmental protection and industrial production. However, conventional sensing materials still suffer from low response, long response times and high detection limit, mainly due to the insufficient charge transfer capability of the sensing materials. Herein, ZnWO4/WO3 was successfully prepared for TEA detection by a two-step hydrothermal method. Benefiting from the synergy between the heterojunctions, including electron sensitization and chemical sensitization, the sensor based on 0.1-ZnWO4/WO3 composite demonstrates remarkable performance in terms of faster response/recovery time (3.3-fold/2.02-fold), higher response (2.21-fold), lower detection limit (0.5 ppm) and lower power consumption (30℃-decrement) as compared with the pristine WO3 sensor. In addition, the long-term stability, repeatability, 10 s fast response, and satisfactory anti-interference ability of the composite sensor indicate its potential application in TEA detection. This work provides a feasible scheme to design advanced gas sensors through the synergistic effect of electron sensitization and chemical sensitization.

Diagnostic and prognostic value of heparin-binding protein in pediatric community-acquired pneumonia

BackgroundCommunity-acquired pneumonia (CAP) and its associated complications pose a noteworthy public health apprehension in the pediatric population, leading to considerable morbidity and mortality. The objective of this study was to assess the diagnostic and prognostic value of heparin-binding protein (HBP) as a promising biomarker in children hospitalized with CAP.MethodsThis prospective, single-center study included 50 children admitted to the Pediatric Pulmonology Unit between April 2023 and January 2024 with a diagnosis of CAP, as well as age-matched 50 healthy children as a control group. Demographic, clinical, and laboratory data were recorded. The measurement of serum HBP was conducted upon admission utilizing the enzyme-linked immunosorbent assay technique.ResultsSerum HBP was elevated in the CAP group in comparison to the control group (p < 0.001). Out of 50 patients, 27 (54.0%) had non-complicated pneumonia, and 23 (46.0%) had complicated pneumonia. The levels of HBP were significantly elevated in patients compared to the healthy control group and even higher in patients with complicated CAP compared to those with non-complicated CAP (p < 0.001). Analysis of the ROC curve revealed that the HBP level of ≥ 34.97 ng/ml was linked to a significantly higher AUC of 0.837 (95% CI 0.722–0.951, P < 0.001).ConclusionThe level of HBP was observed to be notably elevated in patients as compared to the healthy control, thereby indicating its potential applicability in the early detection of CAP. Moreover, elevated HBP was an independent prognostic factor for complicated CAP in children.

Emerging New-Generation Semiconductor Single Crystals of Metal Halide Perovskites for Radiation Detection

Radiation detection uses semiconductor materials to convert high-energy photons into charge (direct detection) or low-energy photons (indirect detection), and it has a wide range of applications in nuclear physics, medical imaging, astronomical detection, homeland security, and other fields. Metal halide perovskites have the advantages of high frequency number, high carrier mobility, high defect tolerance, low defect density, adjustable band gap, and fast light response, and they have wide application prospects in the field of radiation detection. However, the research is still in its infancy stage, and it is far from meeting the requirements of industrial application. This paper focuses on the advantages of metal halide perovskite single-crystal materials in both semiconductors-based direct conversion detection and scintillator-based indirect detection as well as the latest progress in this promising field. This paper not only introduces the latest application of lead halide perovskite monocrystalline materials in high-energy electromagnetic radiation detection (X-ray and γ-rays), but it also introduces the latest development of α-particle/β-particle/neutron detection. Finally, this paper points out the challenges and future prospects of metal halide perovskite single-crystal materials in radiation detection.

Label-free detection and simultaneous viability determination of CTCs by lens-free imaging cytometry.

The detection of extremely rare circulating tumor cells (CTCs) in peripheral blood and simultaneously identifying their viabilities are significant for cancer diagnosis and prognosis as well as monitoring the efficacy of personalized treatment. A lens-free imaging system features high-resolution images taken over a large field of view (FOV), which has great potential for CTC detection and viability determination. But current still lens-free systems restrict the application for CTC detection in real samples due to the inherent limitations of lens-free technology: (1) the location of cells in the FOV will affect the imaging; (2) the extremely rare CTCs probably did not exist in one observation. In this paper, we realized the detection of CTCs in whole blood and the simultaneous determination of their viabilities by lens-free imaging cytometry. Our in-flow system plus a large FOV range of lens-free imaging highly increased the detection rate of rare CTCs with a high throughput of 150,000 cells per minute and improved the recognition efficiency for blood cells, living/dead CTCs by using a cell tracing-assisted deep learning algorithm. With this method, the average precision of blood cells, living/dead lung cancer cells A549, and living/dead colon cancer cells SW620 reached 98.80%, 97.88%, 97.93%, 97.72%, and 98.60%, respectively. Our system got a highly consistent result with the manual counting method using fluorescent staining (Pearson's r 99.93% for SW620) and can easily detect as few as 10 dead or living CTCs from 100,000 white blood cells (WBCs). Finally, real clinical samples were detected in our system. Both dead and living CTCs were found in all six advanced-stage cancer patients, and the number of living CTCs per million WBCs ranged from 13 to 39, more than that of the dead CTCs (5 to 25), while none of the CTCs were detected in six healthy control subjects. Moreover, we also found that CTCs died very quickly after leaving the human body, indicating that CTCs should be studied as soon as possible after sampling. Although this method is implemented for CTCs, it can also be used for the detection of other rare cells.