Rapid Analysis Research Articles

Precise Genotyping Via Surface‐Enhanced Raman Spectroscopy‐Based Optical Sensing Chip for Guiding Targeted Therapy in Lung Cancer

AbstractThe emergence of “precision medicine” marks a notable shift in cancer treatment, moving from a tumor type–oriented approach to a more targeted, gene‐oriented approach. Detecting low‐abundance mutant genes in blood is challenging but crucial for personalized treatment plans. Herein, a novel platform combining catalytic hairpin self‐assembly (CHA)‐mediated self‐calibrating surface‐enhanced Raman spectroscopy (SERS) with a high‐throughput Raman system (CCSPS) was designed. This platform enables ultrasensitive and rapid genotype analysis of gene mutations. The development of CCSPS specifically targets EGFR mutations, which serve as crucial therapeutic targets for precision therapy in lung cancer. This system shows excellent sensitivity and selectivity, capable of detecting multiple EGFR mutations (Del‐19, L858R, and T790M) with a detection limit as low as attomolar levels. Additionally, precise genotyping analysis was successfully conducted on 42 clinical samples using the CCSPS, yielding results consistent with those obtained through next‐generation sequencing. These results underscore the efficacy of the CCSPS in noninvasively identifying circulating tumor DNA (ctDNA) mutations, facilitating immediate therapeutic decision making at the bedside. In summary, the CCSPS is a fast, accurate, versatile, and compact testing system capable of precisely screening individuals who stand to benefit from targeted therapy, thus promoting personalized and precise healthcare.

Optimization of pretreatment protocol for strontium-90 analysis in marine fish bone samples.

For systematic monitoring of radioactive nuclides in marine products, this study aimed at streamlining and simplifying the analysis method for the prominent radioisotope, strontium-90 (90Sr). The DGA chelate solid-phase extraction technique was employed for enhanced efficiency. The study focused on optimizing the necessary pretreatment procedures while minimizing the steps involving HNO3 leaching. This protocol enabled the quantitative recovery of strontium, and it facilitated a rapid analysis without the need for a time-consuming evaporation step and without waiting for secular equilibrium between 90Sr and its progeny to be reached. The method incorporating the optimized pretreatment protocol was applied to three diverse marine fish species and the accurate quantification of 90Sr at background levels in surface seawater was achieved. The method obtained concentrations in bone samples from these species that ranged from 0.036 to 0.120 mBq per kg-dry, and chemical yield values were notably high, ranging from 87.7% to 92.5%.

Remote Assistance for Bone-Fractured Patients using Deep Learning Models

ABSTRACT Remote diagnosis enables healthcare professionals to evaluate and diagnose patients from a distance using telecommunication technologies, enhancing healthcare delivery by improving accessibility, especially for those in remote or underserved areas. One of the significant sustainability challenges in remote medical diagnostics is offering timely assistance to vulnerable groups like the elderly, disabled, mentally impaired individuals, and wounded military personnel in combat zones. This becomes particularly difficult in emergencies when rapid analysis of medical records is needed, especially if the data is stored on secure blockchain networks. The proposed work addresses these challenges by deploying a comprehensive framework for large-scale analysis, utilizing both document and image classification for dual validation. It integrates advanced techniques such as Inception V3, VGG-16, VGG-19, RESNET-50, and Densenet-201 for bone fracture detection, with Inception V3 achieving the highest accuracy of 95.1%. In addition, a Document Classification Analysis (DCA) method is proposed, which automatically classifies the severity of fractures. Object detection techniques are also introduced for detecting minor fractures using region-based image segmentation, ensuring precise diagnosis even for subtle injuries. This pioneering integration of technologies provides a holistic solution for remote medical diagnostics.

A Perspective of Multi-Reflecting TOF MS.

Time-of-flight mass spectrometry (TOF MS) excels in rapid and high-sensitivity analysis, making it a cornerstone of analytical chemistry. But as sample complexity explodes in omics studies, so does the need for higher resolving power to ensure accurate results. Traditional TOF instruments face a challenge: achieving high resolution often requires a very large instrument. To overcome this limitation, scientists developed alternative designs for TOF analyzers called multi-pass TOF analyzers (MPT). These MPT analyzers come in two main configurations: multi-turn (MTT) and multi-reflecting (MRT). Drawing on the authors' extensive experience, this review describes two decades of MPT advancements. It highlights the critical development of optimized analyzer designs, tracing the evolution towards mirror-based MRT instruments, generally providing superior resolution and spatial acceptance compared to MTT. While the manuscript attempts to overview MTT advances, it primarily focuses on MRT technology. Additionally, the review explores the role of orthogonal accelerators and trap pulse converters, comparing their efficiency and the dynamic range limits imposed by space charge effects. By comparing various MRT configurations and commercially available instruments, the review sets out to inform and empower researchers so they can make informed decisions about MRT mass spectrometers.

A Novel Neural Network Architecture Dedicated for LIBS Spectrum Analysis With Its Application to Steel Pipe Classification

Abstract As a non-destructive rapid composition analysis method, Laser-induced breakdown spectroscopy (LIBS) has received widespread attention in recent years. However, its application in the steel industry has not achieved an effective breakthrough in its detection accuracy due to the existence of matrix effect. This paper introduces a neural network model for LIBS spectral line analysis, which can automatically construct the features and analyze the correlation between them, and this model has been applied in the steel sample classification of seamless steel pipe with good results.

Instagram data for validating Nothofagus pumilio distribution mapping in the Southern Andes: A novel ground truthing approach

The availability of valid, non-biased species occurrence data has always been a major challenge for biodiversity research and modelling studies. Data from open-source databases or remote sensing are promising approaches to increase the availability of species occurrence data. However, these data may contain spatial, temporal, and taxonomic biases or require ground truthing. In recent years, social media has received attention for its contribution to species occurrence data sampling and ground truthing approaches. The wide reach of social media platforms allows for rapid and large-scale analyses. Here we introduce a novel Instagram ground truthing approach to validate the occurrence mapping of Nothofagus pumilio across its entire distribution range. The treeline species of the southern Andes has been extensively studied in small-scale studies, but large-scale modelling approaches are largely missing due to limited accessibility to treeline sites resulting in a lack of occurrence data. The content posted on the social media platform Instagram consists of images and videos in which the species N. pumilio and its location can be identified. By searching for suitable posts using hashtags and location tags, we created 1238 Instagram ground truthing points. We compared the performance of our dataset with open-source data from the Global Biodiversity Information Facility (GBIF) through visual, quantitative, and bias analyses, acknowledging that both social media-based and Citizen Science data are subject to sampling and spatial biases due to collection in human-accessible areas. The Instagram ground truthing points were subsequently used to validate remote sensing occurrence data, generated using Sentinel-2 level 2A data and Supervised Classification. The combined approach – Instagram ground truthing and remote sensing – allows for the collection of occurrence data over the entire latitudinal range of N. pumilio, covering approximately 2000 km. The use of social media content provides potentially important contributions to species occurrence data sampling and ground truthing In our study we introduce a novel ground truthing approach for species occurrence data sampling based on Instagram data Instagram ground truthing points, combined with Supervised Classification generate species occurrence data of Nothofagus pumilio over its entire distribution range in the southern Andes The performance of the Instagram ground truthing points is evaluated by comparison with existing data from the GBIF database. Our Instagram ground truthing approach demonstrates a new way of sampling species occurrence data and can be applied to other suitable species and study areas.

QOL-22. VACILLATING EMOTIONS AND SHIFTING INFORMATION PREFERENCES: A QUALITATIVE ANALYSIS OF CAREGIVER DISTRESS IN FACING A MALIGNANT GLIOMA DIAGNOSIS

Abstract BACKGROUND Caregivers of patients with malignant gliomas (MG) experience considerable psychological distress as they contend with the implications of their loved one’s terminal illness. However, caregivers’ specific reactions to the patient’s diagnosis/prognosis and optimal ways to support caregivers in the months following diagnosis remain poorly understood. METHODS At a single academic medical center, we enrolled English-speaking adults identified by patients with newly diagnosed MG as their primary caregiver. Using a semi-structured interview guide, we conducted in-depth interviews with caregivers at the conclusion of concurrent chemoradiation exploring their experiences learning about the patient’s diagnosis and their prognostic information preferences. We used rapid qualitative analysis to analyze interview data. RESULTS We interviewed 7 caregivers (female: 5/7; spouse/partner: 6/7, sibling: 1/7) of MG patients (glioblastoma: 5/7). Caregivers described the time following diagnosis as tumultuous and difficult as they struggled to cope with their distress related to navigating the practical burdens of cancer treatment while also managing the emotional burden (e.g. fear, sadness) of living amidst uncertainty. Caregivers vacillated between feeling devastated by the patient’s poor prognosis and wanting to remain hopeful (e.g., by seeking out inspiring survivorship stories). Caregivers also reported varying and often evolving preferences regarding the manner, timing, and context in which they received information about the patient’s MG. Many caregivers desired more prognostic information (e.g., to facilitate planning for their future and for the patient’s eventual demise) while some chose to focus on the present (e.g., to avoid “dwelling” on potential negative outcomes), in light of inherent uncertainty about the patient’s trajectory. CONCLUSIONS Caregivers experience significant distress in the context of vacillating emotions and shifting information needs as they adapt to life in the aftermath of a MG diagnosis. These results highlight the need for tailored prognostic information and support for caregivers during this stressful, emotionally fraught time.

PATH-49. INTRA-OPERATIVE GENETIC ANALYSES FOR DECISION-MAKING DURING TUMOR REMOVAL OF ADULT-TYPE DIFFUSE GLIOMA USING RAPID QUANTITATIVE PCR DEVICE

Abstract BACKGROUND Recent comprehensive molecular analyses of central nervous system (CNS) tumors identified several diagnostic or prognostic markers. Especially, for adult-type diffuse glioma, IDH mutation, TERT promoter (pTERT) mutation and CDKN2A homozygous deletion are quite important markers for diagnosis and prognosis of this type of glioma. Intra-operative identification of these molecular alterations could be effective for decision-making of tumor removal strategy. METHODS In this study, we established intra-operative gene analysis method using rapid quantitative PCR device, GeneSoC, by which we could obtain genotype of these important genes for 20 minutes in an operating room. Using GeneSoC, we could perform 50 cycles of quantitative PCR for approximately 12 minutes. Results; We established rapid gene analyses of mutations of IDH1 R132H, pTERT C225T, pTERT C250T and homozygous deletion of CDKN2A for adult-type diffuse gliomas. For elution of DNA, we incubated at least 1mg of tumor tissue at 95°for 5 minutes. Combined this boiling method with GeneSoC, we could obtain those genetic alterations for 20 minutes after collection of tumor tissue. In analyses of 111 adult-type diffuse glioma cases, sensitivity and specificity for detection of IDH1 p.R132H are 98% and 98%, respectively, and those of pTERT (C225T or C250T) are 100% and 100%, respectively. In analyses of 50 cases, sensitivity and specificity for detection of CDKN2A homozygous deletion are 100% and 83%, respectively. Conclusion; In this study, we could obtain the genotype of important molecular markers intra-operatively, not only during tumor removal but even during tumor biopsy, using GeneSoC device. This rapid genetic analysis might be effective not only for intra-operative diagnosis but also for detection of boundary between tumor and normal tissue.

Practice Changes 6 Months Following a Physical Therapist Training Program Regarding Opioid Use and Misuse Screening and Assessment: A Qualitative Study.

The U.S. Preventive Services Task Force recommends that all healthcare providers be trained to screen for misuse and/or opioid use disorder. An opioid misuse training program for physical therapists was developed and implemented. The purposes of this research were to (1) understand whether the participants found the training useful, (2) understand if they instituted practice changes that resulted from the training, and (3) understand barriers to implementing the skills learned in the training. Four separate 30-minute virtual training sessions were provided (1 session every 2 weeks). The topics included an introduction to the opioid crisis, screening, assessing, and communicating with patients and with the healthcare team about opioid misuse. A final training manual was distributed after the final training session at which time participants were asked to implement what they learned in the training. Semi-structured interviews were conducted with all training program participants (n = 13) approximately 6 months after the final training session. Rapid content analysis was used to understand the perceptions of and barriers to implementing what the physical therapists learned in the training. Generally, the participants found the training important and useful and strengthened their perceptions of physical therapists' role in screening for opioid misuse. Compared to the opioid misuse-related content of the training, most found it easier to implement general opioid-related content such as asking whether their patients were taking an opioid. Few participants screened any of their patients for opioid misuse. Barriers to implementation included concerns about how the patient might respond to being asked about their opioid use. These findings provide insight into physical therapists' perceptions of an opioid misuse training program and the challenges with implementing what they learned in the program. Future training could incorporate activities such as role-playing to improve participants' comfort with screening for opioid misuse.

QOL-17. REFLECTIONS ON A PSYCHOEDUCATION INTERVENTION FOR PATIENTS WITH NEWLY DIAGNOSED BRAIN TUMORS (NEUROPATHWAYS): A QUALITATIVE STUDY

Abstract BACKGROUND Patients with newly diagnosed primary malignant brain tumors (PMBT) face unique challenges processing and absorbing information regarding their diagnosis, often while contending with cognitive and communication deficits. Limited research exists regarding optimal information provision and support for this highly distressed population. We used input from clinician, patient, and caregiver stakeholders to design a population-specific, psychoeducation intervention (“NeuroPathways”) to address these needs. METHODS We conducted a single-arm study (N=12) to pilot and refine NeuroPathways in English-speaking patients within six-weeks of a PMBT diagnosis. Neuropathways comprises 1) a comprehensive (105 pages) handbook covering information about PMBT diagnosis, treatment, coping, self-care, and vetted resources, and 2) four brief, individual telehealth sessions with a trained interventionist (e.g., psychologist, physician) over 4-8 weeks to assist and empower patients to obtain information, support, and coping guidance at their preferred depth and pace, using the handbook as a resource. Ten participants completed all four sessions and participated in semi-structured exit interviews. We used rapid qualitative analysis to explore participants’ perceptions of intervention delivery, content, and benefit. RESULTS Participants (83% male, median age=52 years) uniformly described NeuroPathways as accessible, citing the telehealth format as particularly important for facilitating participation given logistical challenges related to cancer treatment. Perceived benefits of participation included: a) access to well-organized information targeting both current and future disease- and treatment-related concerns; b) increased skill in problem solving and communication with loved ones and/or medical providers; c) personalized emotional support from interventionists. While most felt the timing was appropriate, several who enrolled later in the eligibility window stated a desire for earlier intervention. CONCLUSION Participant feedback highlights the accessibility and potential benefits of this psychoeducation intervention for patients with newly diagnosed PMBT. These data will inform refinement of NeuroPathways in preparation for a pilot randomized controlled trial to formally assess feasibility and acceptability.

3D-Printed Electrochemical Sensors: A Comprehensive Review of Clinical Analysis Applications

Three-dimensional printing technology has emerged as a versatile and cost-effective alternative for the fabrication of electrochemical sensors. To enhance sensor sensitivity and biocompatibility, a diverse range of biocompatible and conductive materials can be employed in these devices. This allows these sensors to be modified to detect a wide range of analytes in various fields. 3D-printed electrochemical sensors have the potential to play a pivotal role in personalized medicine by enabling the real-time monitoring of metabolite and biomarker levels. These data can be used to personalize treatment strategies and optimize patient outcomes. The portability and low-cost nature of 3D-printed electrochemical sensors make them suitable for point-of-care (POC) diagnostics. These tests enable rapid and decentralized analyses, aiding in diagnosis and treatment decisions in resource-limited settings. Among the techniques widely reported in the literature for 3D printing, the fused deposition modeling (FDM) technique is the most commonly used for the development of electrochemical devices due to the easy accessibility of equipment and materials. Focusing on the FDM technique, this review explores the critical factors influencing the fabrication of electrochemical sensors and discusses potential applications in clinical analysis, while acknowledging the challenges that need to be overcome for its effective adoption.

Engagement in a virtual group-based walking intervention for persons with schizophrenia: a qualitative study.

Exercise is beneficial for persons with schizophrenia; however, high dropout rates limit the impact of interventions. Virtual exercise programs have the potential to improve engagement; however, few intervention studies of virtual programs have been conducted in this population. This study examined qualitative data from 15 adults with schizophrenia who participated in a pilot randomized controlled trial of Virtual PACE-Life, a live, video-delivered group walking intervention guided by self-determination theory. Interviews elicited feedback on the intervention, barriers and facilitators to engagement, recommendations for intervention refinement, and preferences for exercise programming modality. Rapid qualitative analysis was used to explore similarities and differences between completers (i.e., those that attended ≥ 50% of virtual walking sessions; n = 9) and non-completers (i.e., those that attended < 50% of virtual walking sessions; n = 6). Both groups viewed Virtual PACE-Life positively but found the virtual exercise sessions challenging and inadequate for facilitating social interaction. Work obligations impacted completers' attendance whereas technological issues and forgetting impacted non-completers' attendance at virtual walking sessions. Completers preferred virtual exercise programs and non-completers preferred in-person exercise programs. These findings suggest that future virtual group-based walking programs should prioritize enhancing the social aspect, offering scheduling choices, and regularly assessing the perceived difficulty of exercise sessions. These modifications not only have the potential to improve intervention engagement but they also may increase participant autonomy and relatedness, core components of self-determination theory.

METAIS PESADOS E SEUS EFEITOS NA SAÚDE HUMANA: REVISÃO DE LITERATURA

Heavy metals are elements found in the environment by both natural processes and human activities, some are essential for the body such as iron, copper and zinc, while others, such as mercury, lead, cadmium and arsenic, are toxic and can cause serious damage to health, even at low concentrations or in prolonged exhibitions. Exposure to these metals occurs by ingestion, inhalation or contact, leading to accumulation in organs such as liver, kidneys and brain, with varied toxic effects according to metal and exposure. As for heavy metal detection techniques, AAS is an effective analytical technique for the detection of metals at moderate concentrations, with versions that vary in sensitivity and cost, such as flame and graphite oven AAS. ICP-MS offers high sensitivity and multielement capacity, ideal for detailed analyses in various samples. ICP-OES is robust and allows simultaneous analysis of multiple elements, being useful in environmental monitoring and quality control. Portable sensors, in turn, represent an advance in the monitoring of heavy metals, enabling rapid and accurate analysis in the place of collection. As for exposure biomarkers, that is, the biological parameters that indicate the presence and level of these metals in the body, as well as the chemical form of metal, are obtained through samples such as blood, urine, hair and nails. Thus, growing environmental contamination requires monitoring, decontamination and public policies to reduce exposure. Therefore, this review aims to examine the toxicity and methods of detection of major heavy metals.

Evaluation of reduced-order models for the rapid aerodynamic analysis of supersonic and hypersonic bodies

Abstract This paper presents a comprehensive evaluation of reduced-order models (ROMs) for the determination of pressure coefficient distributions on supersonic and hypersonic bodies. The study investigates the limitations, aerodynamic precision and computational performance associated with various methodologies, ranging from simplistic Newtonian theory-based approaches to more advanced first and second-order shock-expansion theories. Validation is performed by comparing computed results with experimental and computational data for pressure distributions, drag and lift coefficients and centres of pressure for fundamental geometries and authentic vehicle design over a wide range of freestream conditions. The study also includes a comprehensive computational complexity analysis, demonstrating the superiority of finite-element ROM approaches over traditional finite-volume computational fluid dynamics (CFD) simulations. The primary objective of this paper is to scrutinise the extension of these methodological classes to the low supersonic regime. Hence, thermo-chemical reactions within the flow are disregarded, and the ideal gas law is adopted. A value of $\gamma = 1.4$ is chosen for consistency and comparability across the analyses. The proposed ROMs show remarkable potential for reducing high-speed simulation execution times by four orders of magnitude, maintaining accuracy within 20 per cent and as low as 1 per cent. The study unveils three key findings: first, the accuracy degradation of Newtonian-based theories for inclined elements, particularly around 45 degrees, and their reduced dependency on Mach number at large inclination. Secondly, the study presents novel insights into the impact of shock-wave-Mach-wave interactions on pressure distribution calculations, emphasising the Mach number as a crucial metric governing recompression effects. Lastly, the study demonstrates the exceptional accuracy of DeJarnette’s method, providing ${C_P}$ results within 2 per cent for a wide range of conditions, offering an attractive alternative to the Taylor-Maccoll equation.

Smart E-Tongue Based on Polypyrrole Sensor Array as Tool for Rapid Analysis of Coffees from Different Varieties

Due to the lucrative coffee market, this product is often subject to adulteration, as inferior or non-coffee materials or varieties are mixed in, negatively affecting its quality. Traditional sensory evaluations by expert tasters and chemical analysis methods, although effective, are time-consuming, costly, and require skilled personnel. The aim of this work was to evaluate the capacity of a smart electronic tongue (e-tongue) based on a polypyrrole sensor array as a tool for the rapid analysis of coffees elaborated from beans of different varieties. The smart e-tongue device was developed with a polypyrrole-based voltammetric sensor array and portable multi-potentiostat operated via smartphone. The sensor array comprised seven electrodes, each doped with distinct counterions to enhance cross-selectivity. The smart e-tongue was tested on five Arabica coffee varieties (Typica, Bourbon, Maragogype, Tabi, and Caturra). The resulting voltammetric signals were analyzed using principal component analysis assisted by neural networks (PCNN) and cluster analysis (CA), enabling clear discrimination among the coffee samples. The results demonstrate that the polypyrrole sensors can generate distinct electrochemical patterns, serving as “fingerprints” for each coffee variety. This study highlights the potential of polypyrrole-based smart e-tongues as a rapid, cost-effective, and portable alternative for coffee quality assessment and adulteration detection, with broader applications in the food and beverage industry.

Handheld NIR Spectroscopy Combined with a Hybrid LDA-SVM Model for Fast Classification of Retail Milk

The EU market offers different types of milk, distinguished by origin, production method, processing technology, fat content, and other characteristics, which are often detailed on product labels. In this context, ensuring the authenticity of milk is crucial for maintaining standards and preventing fraud. Various food authenticity techniques have been employed to achieve this. Among them, near-infrared (NIR) spectroscopy is valued for its non-destructive and rapid analysis capabilities. This study evaluates the effectiveness of a miniaturized NIR device combined with support vector machine (SVM) algorithms and LDA feature selection to discriminate between four commercial milk types: high-quality fresh milk, milk labeled as mountain product, extended shelf-life milk, and TSG hay milk. The results indicate that NIR spectroscopy can effectively classify milk based on the type of milk, relying on different production systems and heat treatments (pasteurization). This capability was greater in distinguishing high-quality mountain and hay milk from the other types, while resulting in less successful class assignment for extended shelf-life milk. This study demonstrated the potential of portable NIR spectroscopy for real-time and cost-effective milk authentication at the retail level.

Rapid quantitative analysis of semi-volatile organic compounds in indoor surface film using direct analysis in real time mass spectrometry: a case study on phthalates

Abstract. Direct analysis in real time mass spectrometry (DART-MS) has recently emerged as a promising approach for measuring semi-volatile organic compounds (SVOCs) on indoor surface films. However, its broader application in indoor environments is limited by low measurement repeatability and no separation of isomers. Herein we developed a sampling suite of indoor surface films for DART-MS analysis, optimized settings of DART to obtain higher analytical performance, and demonstrated the possibility of separating isomeric compounds using tandem mass spectrometry (MS/MS). Two pairs of isomeric phthalate esters, including di(2-ethylhexyl) phthalate (DEHP) and di-n-octyl phthalate (DnOP) and diisobutyl phthalate (DiBP) and di-n-butyl phthalate (DnBP), were used as examples for method optimization and validation. Under optimized conditions, the instrument responses for all four compounds exhibited good linearity (r&gt;0.992) and acceptable repeatability (intraday relative standard deviation (RSD) &lt; 11.0 %). The limits of quantification for the four phthalate esters ranged from 0.042 to 0.24 ng cm−2. The uncertainty in the separation of isomeric components using MS/MS was &lt; 11.4 %, which is acceptable for real sample analysis. To further assess the developed method, we analyzed 10 film samples collected side by side in an occupied office. DnOP was not detected. The RSD among samples was 6.1 % for DEHP, 4.6 % for DnBP, and 10.4 % for DiBP, indicating the overall good repeatability of the collection and measurement method developed. With improved performance, the developed method increases the feasibility of the DART-MS technique for monitoring the dynamics of chemical composition of indoor surface films.

Rapid Raman spectroscopy analysis assisted with machine learning: a case study on Radix Bupleuri.

Radix Bupleuri has been widely used for its plentiful pharmacological effects. But it is hard to evaluate their safety and efficacy because the concentrations of components are tightly affected by the surrounding environment. Thus, Radix Bupleuri samples from different regions and varieties were collected. Based on the experimental and computational Raman spectrum, machine learning is emphasized for certain obscured characteristics; for example, linear discriminant analysis (LDA), support vector machine (SVM), eXtreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM). After dimension reduction by LDA, models of SVM, XGBoost and LightGBM were trained for classification and regression prediction of Bupleurum production regions. Support vector classifiers achieved the best accuracy of 98% and an F1 score above 0.96 on the test set. Support vector regression has a good fitting performance with an R2 score above 0.90 and a relatively low mean square error. However, complex models were prone to overfitting, resulting in poor generalization ability. Among these machine learning models, the typical LDA-SVM models, consistent with the high-performance liquid chromatography results, demonstrate great performance and stability. We envision that this rapid classification and regression technique can be extended to predictions for other herbs. © 2024 Society of Chemical Industry.

Streamlined Flow Synthesis of Plasmonic Nanoparticles and SERS Detection of Uremic Toxins with Trace-Level Liquid Volumes in a Microchamber.

Rapid detection of uremic toxins is crucial due to their severe health risks, including oxidative stress, inflammation, neurotoxicity, cardiovascular complications, and progression of chronic kidney disease. Surface-enhanced Raman spectroscopy (SERS) may provide sensitive, fast, and clinical-grade real-time monitoring of these toxins, enabling effective management with timely dialysis treatments. This study introduces a 3D-printed microchamber that integrates the fabrication of plasmonic metal nanoparticles for the in-flow detection of biological toxins and pharmaceutical drugs using SERS, making it ideal for on-site diagnostics in clinical settings. The microchamber supports quantitative and highly reproducible detection with liquid volumes under 100 μL, which is crucial for trace-level biomarker detection and minimizing cross-contamination. It employs a tunable solvent exchange method for the in situ synthesis of silver nanoparticles (AgNPs) on flexible PDMS or rigid Si wafer substrates, avoiding costly nanofabrication techniques. Ultralow detection limits were achieved for two model compounds and three pharmaceutical drugs: 10-11 M for rhodamine 6G, 10-7 M for adenine, and 10-6 M for the pharmaceutical drugs. A total of 13 biological toxins, including three neurotransmitters, one neuromodulator, five amino acids, two polyamines, and two urea cycle metabolites, were detected with quantitative limits ranging from 10-3 to 10-6 M, all below permissible levels and aligning with physiological conditions. SERS detection within microchambers facilitates rapid on-site analysis, proving ideal for personalized health monitoring, point-of-care diagnostics, and environmental pollution assessment.

Convolutional-free Malware Image Classification using Self-attention Mechanisms

Malware analysis is a critical aspect of cybersecurity, aiming to identify and differentiate malicious software from benign programmes to protect computer systems from security threats. Despite advancements in cybersecurity measures, malware continues to pose significant risks in cyberspace, necessitating accurate and rapid analysis methods. This paper introduces an innovative approach to malware classification using image analysis, involving three key phases: converting operation codes into RGB image data, employing a Generative Adversarial Network (GAN) for synthetic oversampling, and utilising a simplified Vision Transformer (ViT)-based classifier for image analysis. The method enhances feature richness and explainability through visual imagery data and addresses imbalanced classification using GAN-based oversampling techniques. The proposed framework combines the strengths of convolutional autoencoders, hybrid classifiers, and adapted ViT models to achieve a balance between accuracy and computational efficiency. As shown in the experiments, our convolutional-free approach possesses excellent accuracy and precision compared with convolutional models and outperforms CNN models on two datasets, thanks to the multi-head attention mechanism. On the Big2015 dataset, our model outperforms other CNN models with an accuracy of 0.8369 and an AUC of 0.9791. Specifically, our model reaches an accuracy of 0.9697 and an F1 score of 0.9702 on MALIMG, which is extraordinary.