Detection Scheme Research Articles

A simple and cost-effective strategy for electrochemiluminescence spectral determination

BackgroundElectrochemiluminescence (ECL), as a unique and powerful analytical technique, has been widely used in various fields. The determination of ECL spectra plays a crucial role in understanding ECL reaction mechanisms and conducting spectra-resolved ECL analysis. ECL intensity is typically detected using a photomultiplier tube, which offers high sensitivity for detecting extremely weak light signals but does not allow for spectral identification. Due to the time-dependent variation of ECL intensity caused by the applied potential and electrochemical reaction processes, it is challenging to perform ECL spectral detection using conventional wavelength-scanning spectrometers. ResultsIn this study, we present a straightforward and cost-effective ECL spectral detection strategy by incorporating an automatically controlled tunable optical filter device between a commonly used PMT detector and a specially designed ECL reaction cell. The effectiveness of this approach was confirmed through initial validation, where the spectrum of a green LED spotlight was measured and compared with a commercial spectrometer. In a dynamic system with stable ECL signals, the ECL spectrum of the typical Ru(bpy)32+/TPA system was rapidly acquired by adjusting the bandpass filters. To account for time-varying ECL signals in practical measurements, time-based correction algorithms were implemented to rectify variations in ECL intensity. By integrating time-based correction algorithms and an automatically controlled tunable optical filter device into a commonly utilized PMT detector, the rapid and sensitive ECL spectra determination was achieved. Experimental results demonstrated the reliability of the proposed strategy. SignificanceThis strategy is based on the widely used high-sensitivity PMT detection component, enabling the rapid and sensitive measurement of ECL spectra without altering the ECL detection hardware. It is simple, fast, efficient, and cost-effective, with the potential to be widely used for rapid ECL spectral detection and spectra-resolved ECL analysis.

Cost-sensitive stacked long short-term memory with an evolutionary framework for minority class detection

‘Minority attack detection’ is a matter of great concern while designing a secure network and safeguarding it against cyber criminals who attempt to breach its defenses, intrude into its systems, and gain unauthorized access to sensitive information. An adequate intrusion detection system is demanded to counter such threats involving class imbalance and high dimensionality in network traffic. In this context, an artificial intelligence-based multiclass framework MCGAL is presented, which leverages the evolutionary technique of genetic algorithm to achieve the finest feature selection and stacked long short-term memory (LSTM) for traffic classification. The proposed approach utilizes cost-sensitive LSTM by assigning greater significance to minority or rarely occurring classes, thereby addressing the challenge of misclassification in the presence of imbalanced data in a real web attack subset of the CSE-CIC-IDS2018 dataset. A comprehensive performance analysis is performed to evaluate our proposed scheme concerning various class metrics using weighted and macro averages, which reveals a classification accuracy of 100 %. An achieved weighted average of 1.00 is then compared with baseline classifiers like decision trees, AdaBoost, and support vector machines utilizing both linear and radial kernels. The results of MCGAL are also assessed with recently reported techniques on the same dataset demonstrating remarkable improvements in all metrics such as precision, recall, and F-score of minority class detection. A pair-wise comparison and Kruskal-Wallis test at a 95 % significance level further illustrate the statistical significance of the proposed scheme for minority class detection.

Advancements in Early Detection and Screening Strategies for Pancreatic Cancer: From Genetic Susceptibility to Novel Biomarkers.

Pancreatic cancer is a rare but lethal cancer due to its biologically aggressive nature, advanced stage at the time of diagnosis, and poor response to oncologic therapies. The risk of pancreatic cancer is significantly higher to 5% in certain high-risk individuals with inherited genetic susceptibility. Screening for pancreatic cancer in these individuals from high-risk groups can help with the early detection of pancreatic cancer as well as the detection of precursor lesions leading to early surgical resection and improved overall outcomes. The advancements in radiological imaging as well as advanced endoscopic procedures has made a significant impact on the early diagnosis, surveillance, and staging of pancreatic cancer. There is also a significant advancement in the development of biomarkers for the early detection of pancreatic cancer, which has also led to the development of liquid biopsy, allowing for microRNA detection in serum and circulating tumor cells. Various societies and organizations have provided guidelines for pancreatic cancer screening and surveillance in high-risk individuals. In this review, we aim to discuss the hereditary risk factors for developing pancreatic cancer, summarize the screening recommendations by different societies, and discuss the development of novel biomarkers and areas for future research in pancreatic cancer screening for high-risk individuals.

Early anomaly detection of wind turbine gearbox based on SLFormer neural network

Detecting faults in wind turbines at early stage is of great significance in improving the economic efficiency of wind farms. However, the widely used fault detection techniques are mostly based on traditional deep learning frameworks, which are limited in handling long-term dependencies, leading to constraints in global feature extraction. Additionally, current studies often build models based on the operational performance of an individual wind turbine, limiting the generalizability of the models. Therefore, this study introduces an improved model called SLFormer, which integrates Long Short-Term Memory into the Transformer encoder for early fault detection in the gearbox. Simultaneously, a novel fault detection strategy based on SCADA data analysis and transfer learning is proposed. Case studies from three wind farms indicate that the SLFormer model significantly outperforms six other popular prediction models in terms of stability and accuracy in modeling normal behavior and exhibits high robustness to random disturbances in SCADA data. The SLFormer model can predict gearbox anomalies twenty-one days in advance and effectively avoids false fault reports. The proposed strategy can help the model acquire fault knowledge from multiple wind farms, thus creating a framework with generalizability.

(Invited) Hydrogen Detection Strategies to Support H2@Scale--the NREL Sensor Laboratory

Hydrogen represents a major pathway to decarbonize and stabilize the national and international energy industry and select manufacturing markets. To facilitate the development of hydrogen markets, the US Department of Energy initiated H2@Scale to bring together stakeholders to advance affordable hydrogen production, transport, storage, and utilization to increase revenue opportunities in multiple energy sectors. One major impediment to hydrogen implementation is cost. To expedite the use of hydrogen in energy and other markets, the United States announced in 2021 the Hydrogen Shot, which seeks to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). As the cost of hydrogen drops, new applications will emerge that will require unique configurations of existing equipment and infrastructure, and eventually lead to advances in the generation and utilization of hydrogen. As the hydrogen economy expands, sensors and detection methods will need to adapt to changing infrastructure demands to address the primary targets of health & safety, emissions monitoring, and process control. The NREL Sensor Laboratory is playing a pivotal role in advancing the use of hydrogen sensors and detection methodologies in each of these categories to support DOE’s mission for safe and efficient utilization in emerging markets. Health & safety monitors are required to ensure that operators and facilities can react to unintended hydrogen releases, either as GH2, LH2, or as a constituent of blends (e.g., natural gas or ammonia). Current detection methodologies focus on safety applications to detect near its lower flammable limit (4 vol %), and typically include point sensors in applications such as fixed or mobile detectors (e.g., personal gas monitors). Methodologies amenable for area detection include acoustic, emerging optical imaging methods, and flame detectors.Comparable detection strategies can be utilized for emissions monitoring and quantization, however few methods can simultaneously cover both low (emissions) and high (health & safety) levels. Deployment of emission level detectors will be required to 1) reduce product loss through small but potentially significant leaks from an environmental or cost perspective, 2) reduce downtime of high demand systems by early identification of eminent system failures (leaks through pump or compressor seals indicative of impending failure), and 3) address potential emission monitoring requirements that may be set by regulating bodies. The first two points should be adopted by industry to reduce the cost-of-goods-sold.The third main category for hydrogen detection relates to process control and may be advantageous for many existing applications. Two main applications are emerging. For example, the purity requirements for hydrogen that is dispensed from refueling systems for hydrogen fuel cell electric vehicles (FCEV) is rigorously regulated by the Standard SAE J2719, which prescribes maximum allowable levels of multiple impurities in the hydrogen fuel and must be verified by a regulatory body. Hydrogen contaminant detectors (HCD) integrated to the fueling station can assure this compliance. HCDs must be able operate in 100% H2 backgrounds and be able to distinguish between multiple contaminants at low ppm to low ppb levels.Secondly, as a strategy to decarbonize the natural gas grid, there are proposals to blend hydrogen with natural gas. This blending will affect transport applications (pipeline infrastructure), stationary combustion systems (turbines), and consumer and commercial appliances. In the short-term, hydrogen levels up to 20% are proposed. Variations in the hydrogen level can have dramatic impact on the combustion process and on the potential response of safety sensors. These mixtures may be regulated so that the concentration at a delivery point must be monitored with high precision. However, routine maintenance may introduce background gases such as ambient air (with water) or maintenance gases (introduced with welding processes or adhesive outgassing.) Therefore, the detection methodology must be robust enough to recover or respond to various contaminants.Several reviews can be found in literature addressing sensing and detection technologies, including their limitations and applications. However, for most applications, limitations can be alleviated by combining various detection techniques either through system integration or implementation of machine learning methods (artificial intelligence). Many detection methods already implement some sort of senor fusion, i.e., integrated temperature and humidity sensors allow for compensation methods of point sensors to increase the accuracy and precision of detectors through a wide range of environmental conditions. Similarly, sensing arrays (electronic noses) may implement intelligent algorithms to quantify multiple gasses present in a sample, or simply to identify a single gas in a mixture.In this presentation, we will discuss several applications, highlight their current approach for hydrogen detection, and suggest detection strategies to supplement their limitations.

A Reduced-Order Model of the Coupled Venting-Thermal-Electrochemical Behavior to Predict First Venting Under External Short Circuit Conditions

Modeling abusive battery operation before thermal runaway (TR) can reduce the testing requirements and facilitate the development of effective and reliable early failure detection and fast discharge strategies [1] which require multiple types of sensor signals, including current, voltage, temperature, and pressure. We present a multi-physics model that can predict the rapidly evolving discharge behavior of cells undergoing an external short circuit, including hazards like first venting. First venting models account for gas generation due to SEI decomposition and electrolyte vaporization at high temperatures to calculate the build-up of internal cell pressure under constant-displacement conditions [2]. Modeling gas generation requires accurate temperature prediction, which subsequently requires accurate Joule heating calculated from current and voltage predictions. From the cascading chain of model input dependencies, the first challenge is managing error propagation through the submodels with appropriate parameterization. Previous modeling work parameterized a venting model for a cell undergoing an external short circuit (ESC) but did not include a thermal or electrical model [2]. The second challenge is modeling the diffusion-limited discharge behavior with currents approaching 50-80 C with a fast and accurate model that is feasible for detection and control applications. Diffusion-limited behavior occurs when the local Li concentration saturates, suddenly causing large concentration overpotentials that are observable in the terminal voltage and current (see Fig. 1) as well as numerical instability when solving the Butler-Volmer (BV) equation. Previous ESC modeling works either used the Doyle-Fuller-Newman (DFN) model with a limiting current density in the BV equation [3] or an equivalent circuit model (Eq-CM) [1]. However, solving the DFN is computationally intensive for a control application, while Eq-CMs cannot predict the diffusion-limited electrical behavior beyond the data they were parameterized on, requiring apriori experiments to be accurate. To balance the trade-offs, we instead look towards reduced-order modeling.In this work, we present a control-oriented, reduced-order, multi-physics model that captures the electrochemical, thermal, and venting behavior of four 4.6 Ah NMC pouch cells undergoing an external short circuit with different initial state-of-charge (SOC). The multi-physics model couples a first venting model with a lumped thermal model driven by Joule heating calculated through the overpotentials from the Single-Particle Model with electrolyte (SPMe). The combined model was parameterized through four experiments by fitting five key parameters in the submodels related to the SEI decomposition rate, cell thermal behavior, and solid electrode diffusion to capture the first venting timing, peak temperature, and diffusion-limited current-voltage drops. Additionally, two resistances were tuned to match the initial current and voltage with all other parameters were taken from the literature. Applying the same parameter set consisting of the average of the fitted values on all four cells, the combined multi-physics model correctly predicted that the fully-charged cells would vent and conservatively predicted the cell venting timing up to about 40 s before it occurred in the experiment. For high initial SOC cells, the SPMe also accurately predicted the current and voltage drops associated with diffusion limitations and SOC up until the cell vented. This is the first work that systematically parameterizes and couples a reduced-order, physics-based model to capture the electrical, thermal, and venting behavior under battery abuse conditions. Future work will explore applications for the model in early detection and response to critical safety events.REFERENCES[1] Tran, Vivian, Jason Siegel, and Anna Stefanopoulou. "Emergency Li-ion Battery Discharge using Nonlinear Model Predictive Control with Temperature and Venting Pressure Constraints." 2023 American Control Conference (ACC). IEEE, 2023.[2] Cai, Ting, et al. "Modeling li-ion battery first venting events before thermal runaway." IFAC-PapersOnLine 54.20 (2021): 528-533.[3] Rheinfeld, Alexander, et al. "Quasi-isothermal external short circuit tests applied to lithium-ion cells: Part ii. modeling and simulation." Journal of The Electrochemical Society 166.2 (2019): A151. Fig. 1: 15-min simulation of an external short circuit for cells at different initial SOC. Model results and experimental measurements are shown for four cells that were externally shorted. The measured current, voltage, temperature, and expansion force were measured at 10 Hz and plotted versus log time to highlight the dynamic current-voltage behavior in the first few minutes. The diffusion-limited current and voltage behavior are captured by the model. The two cells at 100% SOC, experienced venting and higher peak temperatures, which are well captured by the model. The cell venting timing was predicted to be 43 s and 7 s early for Cell 100A and 100B, respectively. The cells that were shorted at lower SOC did not vent. Figure 1

Secure and Reliable Intrusion Detection Scheme for Software-Defined Networking Using LFTS-Rnn and PC-JTFOA

In recent times, Software Defined Networking (SDN) has gained immense popularity among communication networks due to its high network connectivity and security. But, owing to the lack of sufficient security schemes and dynamic access control, conventional techniques could be more efficient. Hence, this paper proposes a secure intrusion detection scheme for SDN utilizing LogishFTS-based Recurrent Neural Networks (LFTS-RNN) and Poisson Clumping Japanese Tree Frog Optimization Algorithm (PC-JTFOA). Initially, by using a username, password, and Signed Access Control (SAC), the registered users log in to the network. The Entropy Makwa-based Digital Signature Algorithm (EMDSA) is utilized to identify the attackers during the login process. Then, the Intrusion Detection System (IDS) is trained using the historical dataset. The critical features are extracted in IDS; subsequently, by using the PC-JTFOA, the optimal features are selected. Likewise, the selected features are inputted into the LFTS-RNN to recognize the intruders in the application plane. Moreover, the process of data security and load balancing is undergone by the data. Afterward, the balanced data is subjected to IDS to detect the attackers in the control plane. According to experimental outcomes, the proposed technique obtained a higher prominence with an accuracy of 98.35%.

Fabrication of a graphite-paraffin carbon paste electrode and demonstration of its use in electrochemical detection strategies.

Electrochemical detection methods hold many advantages over their optical counterparts, such as operation in complex sample matrices, low-cost and high volume manufacture and possible equipment miniaturisation. Despite these advantages, the use of electrochemical detection is currently limited in the clinical setting. There is a wide range of potential electrode materials, selected for optimal signal-to-noise ratios and reproducibility when detecting target analytes. The use of carbon paste electrodes (CPEs) for electrochemical detection can be limited by their analytical performance, however they remain very attractive due to their low cost and biocompatibility. This paper presents the fabrication of an easy-to-make and use graphite powder/paraffin wax paste combined with a substrate produced via additive manufacturing and confirms its functionality for both direct and indirect electrochemical measurements. The produced CPEs enable the direct voltammetric detection of hexaammineruthenium(III) chloride and dopamine at an experimental limit of detection (ELoD) of 62.5 μM. The key inflammatory biomarker Interleukin-6 through an enzyme-linked immunosorbant assay (ELISA) was also quantified, yielding a clinically-relevant ELoD of 150 pg ml-1 in 10% human serum. The performance of low-cost and easy-to-use CPEs obtained in 0.5 hours is showcased in this study, demonstrating the platform's potential uses for point-of-need electroanalytical applications.

High-Sensitivity Homogeneous Detection of miRNA-155 Governed by DNA Walker-Regulated Surface DNA Density of Magnetic Electrochemiluminescence Nanoparticles.

Homogeneous electrochemiluminescence (ECL) has gained attention for its simplicity and stability. However, false positives due to solution background interference pose a challenge. To address this, magnetic ECL nanoparticles (Fe3O4@Ru@SiO2 NPs) were synthesized, offering easy modification, magnetic separation, and stable luminescence. These were utilized in an ECL sensor for miRNA-155 (miR-155) detection, with locked DNAzyme and substrate chain (mDNA) modified on their surface. The poor conductivity of long-chain DNA significantly impacts the conductivity and electron transfer capability of Fe3O4@Ru@SiO2 NPs, resulting in weaker ECL signals. Upon target presence, unlocked DNAzyme catalyzes mDNA cleavage, leading to shortened DNA chains and reduced density. In contrast, the presence of short-chain DNA has minimal impact on the conductivity and electron transfer capability of Fe3O4@Ru@SiO2 NPs. Simultaneously, the material surface's electronegativity decreases, weakening the electrostatic repulsion with the negatively charged electrode, resulting in the system detecting stronger ECL signals. This sensor enables homogeneous ECL detection while mitigating solution background interference through magnetic separation. Within a range of 100 fM to 10 nM, the sensor exhibits a linear relationship between ECL intensity and target concentration, with a 26.91 fM detection limit. It demonstrates high accuracy in clinical sample detection, holding significant potential for clinical diagnostics. Future integration with innovative detection strategies may further enhance sensitivity and specificity in biosensing applications.

Computational Analyses Reveal Deregulated Clock Genes Associated with Breast Cancer Development in Night Shift Workers.

Breast cancer (BC) is the leading cause of cancer death among women worldwide. Women employed in shift jobs face heightened BC risk due to prolonged exposure to night shift work (NSW), classified as potentially carcinogenic by the International Agency for Research on Cancer (IARC). This risk is linked to disruptions in circadian rhythms governed by clock genes at the cellular level. However, the molecular mechanisms are unclear. This study aimed to assess clock genes as potential BC biomarkers among women exposed to long-term NSW. Clock gene expression was analysed in paired BC and normal breast tissues within Nurses' Health Studies I and II GEO datasets. Validation was performed on additional gene expression datasets from healthy night shift workers and women with varying BC susceptibility, as well as single-cell sequencing datasets. Post-transcriptional regulators of clock genes were identified through miRNA analyses. Significant alterations in clock gene expression in BC compared to normal tissues were found. BHLHE40, CIART, CLOCK, PDPK1, and TIMELESS were over-expressed, while HLF, NFIL3, NPAS3, PER1, PER3, SIM1, and TEF were under-expressed. The downregulation of PER1 and TEF and upregulation of CLOCK correlated with increased BC risk in healthy women. Also, twenty-six miRNAs, including miR-10a, miR-21, miR-107, and miR-34, were identified as potential post-transcriptional regulators influenced by NSW. In conclusion, a panel of clock genes and circadian miRNAs are suggested as BC susceptibility biomarkers among night shift workers, supporting implications for risk stratification and early detection strategies.

Attitude collaborative control strategy for space gravitational wave detection

This paper proposes a high-precision attitude cooperative control strategy for three spacecraft in space gravitational wave detection. Firstly, based on the layout of optical components on the spacecraft and factors such as external interference and model uncertainty, a relative attitude model based on the Euler angle is constructed for the characteristics of small angle motion in differential wavefront sensing mode. Secondly, aiming at the cooperative control problem of multi drag-free attitude control system after the laser link capture between two spacecraft is completed, an attitude control method based on consensus mode cooperative control strategy is proposed. It combines local neighbourhood error from star sensing and differential wavefront sensing with an adaptive controller for equilateral triangle configuration control. Additionally, a cooperative control strategy based on laser measurement information is established to improve control accuracy by avoiding star sensors with low accuracy. Simulation results indicate that the control accuracy of the consistency mode and its sub-mode laser measurement mode is superior to the master-slave and cyclic modes, achieving better than 5×10−6 rad. The laser measurement mode demonstrates the highest accuracy, with relative attitude control accuracy reaching 1.5×10−7 rad, meeting the requirements for space gravitational wave detection.

A novel framework for low-contrast and random multi-scale blade casting defect detection by an adaptive global dynamic detection transformer

The radiographic inspection plays a crucial role in ensuring the casting quality for improving the service life under harsh environments. However, due to the low-contrast between the defects and the image background, the random spatial position distribution, random shapes and aspect ratios of the defects, the development of an accurate defect automatic detection system is still challenging. To address these issues, this paper proposes a novel framework for low-contrast and random multi-scale casting defect detection, which is referred to as adaptive global dynamic detection transformer (AGD-DETR). A novel defect-aware data augmentation method is first proposed to adaptively highlight the feature of the low-contrast defect boundary. A multi-attentional pyramid feature refinement (MPFR) module is then established to refine and fuse the multi-scale defect features of random sizes. Afterwards, a novel global dynamic receptive fusion-transformer (GDRF-Transformer) detection scheme is designed to perform the global perception and feature dynamic extraction of complex internal casting defects. It includes 4D-anchor query and cross-layer box update strategy, query rectification by prior information of defect aspect ratio, and global adaptive-feed forward network (GA-FFN). A dataset comprising turbine blade casting defect radiographic (TBCDR) images, is used to demonstrate the high efficiency of the proposed AGD-DETR. The obtained results show that the proposed method can accurately capture the spatial position distributions and complex defect shapes. Furthermore, it outperforms existing state-of-the-art defect detection methods.

Recent Strategies for MicroRNA Detection: A Comprehensive Review of SERS-Based Nanobiosensors

With advances in technology, diagnostic techniques have become more sophisticated and efficient at detecting biomarkers rapidly. Biomarkers such as microRNA (miRNA), which exhibit exceptional specificity and sensitivity compared with other biomarkers, have garnered particular interest. Composed of 21–24 nucleotides, miRNAs constitute a noncoding RNA group that regulates gene expression, immune system activation, apoptosis, and other cellular processes; hence, they are frequently used as biomarkers for various diseases. This has sparked significant interest regarding the identification of the specific miRNAs implicated in many diseases. Presently, miRNA detection methods include northern blots, reverse transcription-quantitative polymerase chain reaction, and next-generation sequencing. While these methods are all sensitive, they are time-consuming, complex, and expensive, which renders them unsuitable for on-site detection. Surface-enhanced Raman scattering (SERS) can overcome these limitations to enable the sensitive and rapid detection of miRNA. This technique amplifies Raman signals, with signal enhancement levels changing sensitively depending on the distance between the target molecule and substrate. Therefore, this review covers the principle of SERS as a method for detecting miRNAs using nanomaterials, along with examples of nanomaterials and SERS applications. Based on the available literature, SERS is anticipated to enable the convenient, early diagnosis of various diseases, potentially lowering mortality rates. This review could therefore contribute significantly to the advancement of medical and diagnostic technologies.

Challenges associated with follow-up care after implementation of an HPV screen-and-treat program with ablative therapy for cervical cancer prevention

BackgroundCervical cancer is a preventable cancer; however, decreasing its prevalence requires early detection and treatment strategies that reduce rates of loss to follow-up. This study explores factors associated with loss to follow-up among HPV-positive women after implementation of a new HPV-based screen-and-treat approach for cervical cancer prevention in Iquitos, Peru.MethodsWe conducted semi-structured interviews with “obstetras” (i.e., midwives) (n = 15) working in cervical cancer prevention and women (n = 24) who were recorded as lost to follow-up after positive HPV results. We used the Health Care Access Barriers Model to guide analyses. We utilized manifest content analysis to describe barriers to follow-up according to the obstetras and thematic analysis to report themes from the women’s perspectives. We also report the steps and time taken to contact women.ResultsWe found an incomplete and fragmented patient monitoring system. This incomplete system, in conjunction with challenges in contacting some of the women, led to structural barriers for the obstetras when attempting to deliver positive results. Women in this study expressed a desire to receive treatment, however, faced cognitive barriers including a lack of understanding about HPV results and treatment procedures, fear or anxiety about HPV or treatment, and confusion about the follow-up process. Women also reported having important work matters as a barrier and reported frequently using natural medicine. Reported financial barriers were minimal.ConclusionThis study highlights the barriers to follow-up after implementation of a primary-level HPV-based screen-and-treat approach. While some barriers that have previously been associated with loss to follow-up were not as prominently observed in this study (e.g., financial), we emphasize the need for screen-and-treat programs to focus on strategies that can address incomplete registry systems, structural challenges in results delivery, cognitive barriers in understanding results and treatment, and work-related barriers.

Occurrence and case fatality rate of invasive aspergillosis in children with acute leukemia: A systematic review and meta-analysis.

Invasive aspergillosis (IA) is a potentially life-threatening complication of childhood acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). We conducted a systematic review and meta-analyses of studies on acute leukemia in children aged 0-17 years since 2000. Findings were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. We included 24 studies with 3,661 ALL patients and 1,728 AML patients. IA cumulative incidence varied (0%-10% for ALL and 0%-18% for AML) across the studies. Pooled cumulative IA incidences were estimated at 3.2% (95% CI: 1.8% - 5.8%) in ALL and 5.2% (95% CI: 3.1% - 8.6%) in AML, with corresponding case-fatality-rates of 13.3% (95% CI: 6.3% - 25.9%) and 7.8% (95% CI: 0.7% - 51.2%), respectively. Our analysis highlights the impact of IA in childhood leukemia, underscoring the need to address strategies for prevention, early detection, and treatment of IA in pediatric leukemia.

Correlational Insights into Attention-Deficit/Hyperactivity Disorder in Lebanon.

Attention-Deficit/Hyperactivity Disorder (ADHD), a prevalent childhood neurodevelopmental disorder with complex etiology involving genetic and environmental factors, causes impairments across various life domains and substantial social and economic burden. Identifying correlates to prevent its onset and decrease its incidence is crucial. To our knowledge, our study represents the first case-control investigation of Lebanese ADHD patients to explore potential correlations between familial, maternal, and child health variables and ADHD to enhance understanding of its etiology and aid in prevention efforts. We recruited 61 Lebanese ADHD patients and 58 matched controls aged 6-24 years from all districts of Lebanon. The data to analyze were collected using a questionnaire. We employed statistical tests, including the independent samples t-test and the Chi-square test or Fisher's exact test. We conducted a multivariate logistic regression analysis to identify the statistically significant factors explaining ADHD likelihood. We observed male predominance (68.9%) among patients. Maternal anemia during pregnancy (OR = 3.654; 95% CI [1.158-11.529]), maternal self-reported stress during pregnancy (OR = 3.268; 95% CI [1.263-8.456]), neonatal jaundice (OR = 5.020; 95% CI [1.438-17.532]), and familial history of ADHD (OR = 12.033; 95% CI [2.950-49.072]) were significantly associated with increased odds of the disorder. On the other hand, breastfeeding (OR = 0.263; 95% CI [0.092-0.757]) was identified as a protective factor against ADHD. This pilot study shed light on risk and protective factors associated with ADHD in the Lebanese population. The results are relevant, as some identified correlates could be avoidable. Further rigorous investigation is required to expand upon the observed correlations and to assist in early detection, prevention, and intervention strategies targeting ADHD.

Senescence detection using reflected light.

Senescence is an important cellular program occurring in development, tissue repair, cancer, and aging. Increased senescence is also associated with disease states, including obesity and Type 2 diabetes (T2D). Characterizing and quantifying senescent cells at a single cell level has been challenging and particularly difficult in large primary cells, such as human adipocytes. In this study, we present a novel approach that utilizes reflected light for accurate senescence-associated beta-galactosidase (SABG) staining measurements, which can be integrated with immunofluorescence and is compatible with primary mature adipocytes from both human and mouse, as well as with differentiated 3T3-L1 cells. This technique provides a more comprehensive classification of a cell's senescent state by incorporating multiple criteria, including robust sample-specific pH controls. By leveraging the precision of confocal microscopy to detect X-gal crystals using reflected light, we achieved superior sensitivity over traditional brightfield techniques. This strategy allows for the capture of all X-gal precipitates in SABG-stained samples, revealing diverse X-gal staining patterns and improved detection sensitivity. Additionally, we demonstrate that reflected light outperforms western blot analysis for the detection and quantification of senescence in mature human adipocytes, as it offers a more accurate representation of SABG activity. This detection strategy enables a more thorough investigation of senescent cell characteristics and specifically a deeper look at the relationship between adipocyte senescence and obesity associated disorders, such as T2D.

Machine learning-based SERS label-free detection of plasma and exosome binding in early-stage lung cancer

Lung cancer is extremely lethal and early screening can be an effective treatment for lung cancer. Liquid biopsies allow for rapid, non-destructive testing and are promising for early screening. Plasma is rich in biomarkers and has been widely used for cancer diagnosis. Exosomes derived from plasma are considered a more promising biomarker. This paper reports a label-free detection strategy for plasma and exosomes of cancer-free (HC), adenocarcinoma in situ (AIS) and early-stage invasive adenocarcinoma (IAC) using surface-enhanced Raman spectroscopy (SERS). SERS spectra of plasma and exosomes were trained one to each other using four machine learning algorithms. Notably, this work breaks new ground by proposing a SERS “Combined Spectrum” of plasma combined with exosomes. The SERS “Combined Spectrum” synthesizes SERS spectra of plasma and exosomes from the same case sample into a completely new spectrum for training purposes. Calculated by each of the four machine learning algorithms, the plasma and exosome SERS “Combined Spectrum” were calculated with an accuracy higher than 97 % and an AUC value higher than 0.9500. Based on these results, the combined SERS “Combined Spectrum” of plasma and exosome can be used for the early detection of lung cancer, and it also has a promising future for application in the detection of other diseases.

Spectrophotometric-Based Sensor for the Detection of Multiple Fertilizer Solutions

The online detection of fertilizer solution information is a crucial link in the implementation of intelligent and precise variable fertilization techniques. However, achieving simultaneous rapid online detection of multiple fertilizer components is still challenging. Therefore, a rapid detection method based on spectrophotometry for qualitative and quantitative identification of four fertilizers (typical N, P, and K fertilizers: KNO3, (NH4)2SO4, KH2PO4, and K2SO4) was proposed in this work. Full-scan absorption spectra of fertilizer solutions at varying concentrations were obtained using a UV–visible/near-infrared spectrophotometer. By assessing the linear fit between fertilizer concentration and absorbance at each wavelength within the characteristic band, the characteristic wavelengths for KNO3, (NH4)2SO4, KH2PO4, and K2SO4 were identified as 214 nm, 410 nm, 712 nm, and 1708 nm, respectively. The identification method of fertilizer type and the prediction model of concentration were constructed based on characteristic wavelength and the Lambert–Beer law. Based on the above analysis, a four-channel photoelectric sensor was designed with four LEDs emitting wavelengths closely matched to characteristic wavelengths for fertilizer detection. A detection strategy of “qualitative analysis followed by quantitative detection” was proposed to realize the online detection of four fertilizer types and their concentrations. Evaluation of the sensor’s performance showed its high stability, with an accuracy of 81.5% in recognizing fertilizer types. Furthermore, the relative error of the sensor detection was substantially less than ±15% for the fertilizer concentrations not exceeding 80 mg/L. These results confirm the capability of the sensor to meet the practical requirements for online detection of four fertilizer types and concentrations in the field of agricultural engineering.

Simplifying asynchronous optical sampling: an experimental approach toward industrial integration exploiting lock-in acquisition

Time-resolved optical spectroscopies are emerging as a go-to technique for non-destructive testing of nanomaterials. Inspecting the thermal and mechanical properties of a mesoscale device requires achieving delay times beyond the ns timescale in a nanoscopy setup, potentially in a vibration polluted environment. These requirements constitute a major challenge for traditional pump-probe techniques based on moving mechanical delay lines and lock-in detection. Asynchronous optical sampling (ASOPS) and electronically controlled optical sampling (ECOPS), avoiding any moving mechanical parts, are good alternatives. However, their detection scheme is based on fast-balanced photodiodes, which, as a technology, are not as widespread, not as developed, and lack the performance of lock-in based detection. In this study, we introduce what we believe is a novel approach that integrates ASOPS/ECOPS and lock-in detection methodologies, eliminating the necessity for a reference signal and streamlining the optical configuration. By leveraging the strengths of each technique, our approach enhances simplicity and efficiency. The scheme is first validated against standard approaches in the frame of a beam-depletion measurement in a sum frequency experiment. It is then tested in a paradigmatic case study to inspect the mechanics of a single gold nanodisk, with dimensions in the 100 nm range, nanopatterned on a sapphire substrate. These results widen the range of applicability of time-resolved optical techniques as a nano-metrology tool to industrial settings.