Reduced Analysis Time Research Articles

Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation.

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.

SlumberNet: deep learning classification of sleep stages using residual neural networks

Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of overall performance (accuracy = 97%; F1 score = 96%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~ 50 × faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet further demonstrates the power of deep learning in mouse sleep research.

A Fast and Simple Solid Phase Extraction-Based Method for Glucosinolate Determination: An Alternative to the ISO-9167 Method.

Glucosinolates (GLSs) are a well-studied sulfur-containing compound found in Brassicaceae plants that play critical roles in plant resistance and human health. Correctly identifying and reliably quantifying the total and individual GLS content is of great importance. An improved method as an alternative to the ISO 9167-1 (ISO) method is developed in the present study. An efficient extraction and purification procedure is proposed with a commercially available dimethylaminopropyl (DEA)-based weak anion exchange solid-phase extraction (SPE) cartridge instead of using the self-prepared ion-exchange columns in the ISO method. The GLSs are identified and quantified by ultra high-performance liquid chromatography (UHPLC) high-resolution mass spectrometry (HRMS). The method demonstrates a comparable quantification of total and individual GLSs on certified rapeseeds and other Brassicaceae vegetables when compared to the ISO method. The developed SPE method is simpler and more efficient, thus allowing for applications to a large sample size with reduced analysis time, improved repeatability and accuracy, and possible automation.

Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders.

Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density-phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4-/CD8- double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe.

Mechanical stirring: Novel engineering approach for in situ spectroscopic analysis of melt at high temperature

This paper proposes a novel engineering approach to control molten metals at high temperatures considering the industrial environment of such materials. To reduce analysis time and cost, in-line analysis techniques are more advantageous as they provide real-time information about melt composition. For this reason, recent research works focus on the development of new devices based on LIBS (Laser Induced Breakdown Spectroscopy). These devices allowed for analyzing impurities inside molten metals with great performance. However, improvements related to the immersion probe conception are still required. Indeed, the previous design used bubbling inside the melt, leading to spatial instabilities of the surface analyzed by LIBS. The solution presented here is mechanical stirring by innovative rotary blades which will be a part of an immersion LIBS probe. Their rotation will generate a representative, renewed, and stable surface that will be targeted by spectroscopic techniques in general and particularly by LIBS laser for molten metal monitoring at high temperatures. This solution was validated using experimental tests based on particle imaging velocimetry (PIV) in water at room temperature and then applied to silicon melt at high temperatures. To do so, it was necessary to design a system that allows the introduction of the blade in the melt and controls its rotation.

A Comparative Study on the Structural Response of Multi-Linked Floating Offshore Structure between Digital Model and Physical Model Test for Digital Twin Implementation

A digital twin is a virtual model of a real-world structure (such as a device or equipment) which supports various problems or operations that occur throughout the life cycle of the structure through linkage with the actual structure. Digital twins have limitations as a general simulation method because the characteristic changes (motion, stress, vibration, etc.) that occur in the actual structure must be acquired through installed sensors. Additionally, it takes a huge computing cost to output changes in the structure’s characteristics in real time. In particular, in the case of ships and offshore structures, simulation requires a lot of time and resources due to the size of the analysis model and environmental conditions where the wave load acts irregularly, so the application of a different simulation methodology from existing ones is required. The order reduction method, which accurately represents the system’s characteristics and expresses them in a smaller model, can significantly reduce analysis time and is an effective option. In this study, to analyze the applicability of the order reduction method to the development of digital twins for offshore structures, the structural responses of a multi-connected floating offshore structure were estimated by applying the order reduction method based on distortion base mode. The order reduction method based on the distortion base mode predicts the responses by constructing an order-reduced conversion matrix consisting of the selected distortion base mode, based on the mode vector’s orthogonality and autocorrelation coefficients. The predicted structural responses with the reduced order model (ROM) were compared with numerical analysis results derived using the higher order boundary element method and finite element method with in-house code owned by the Korea Research Institute of Ship & Ocean Engineering and measured responses with a model test. When compared with the numerical analysis results, the structural responses were predicted with high accuracy in the wave direction and wave frequency band of the selected distortion base mode, but there are differences due to changed characteristics of the structure when compared with the results of the model test. In addition, differences were also seen in reduced order model evaluation with different sensor locations, and it was confirmed that the more similar the extracted distortion base modes of input sensor location set is to the distortion base modes of predicted location set, the higher accuracy is in predicting the structural responses. As a result, the performance of the reduced order model is determined by the distortion base mode selection method, the locations of the sensor, and the prediction for the structural response.

Serum lipidomic study of long-chain fatty acids in psoriasis patients prior to and after anti-IL-17A monoclonal antibody treatment by quantitative GC‒MS analysis with in situ extraction

BackgroundLong-chain fatty acids (LCFAs) are involved in regulating multiple physiological processes as signalling molecules. Gas chromatography–mass spectrometry (GC–MS) is widely used to quantify LCFAs. However, current quantitative methods for LCFAs using GC–MS have demonstrated complicated issues. Psoriasis is a chronic inflammatory skin disease, and its pathogenesis may be related to the overproduction of interleukin-17A (IL-17A). Clinical efficacy of anti-IL-17A monoclonal antibody (mAb) treatment in psoriasis patients has been demonstrated. Recent studies suggest that LCFAs play varying roles in the pathogenesis of psoriasis. However, more comprehensive research is needed to illuminate the mechanism of LCFAs in psoriasis.MethodsThe established in situ derivatization method for analysing LCFAs with a GC–MS platform was utilized to conduct serum lipidomics analysis of healthy volunteers and psoriasis patients receiving pretherapy and posttreatment with of anti-IL-17A mAb. Imiquimod (IMQ)-treated wild type (WT) and T-cell receptor delta chain knock-out (Tcrd−/−) mice were used to investigate the correlation between IL-17A and abnormal changes in LCFAs in psoriasis patients.ResultsA rapid and sensitive in situ extraction derivatization method for quantifying LCFAs using GC–MS was established. Serum lipidomic results showed that psoriasis patients had higher levels of saturated fatty acids (SFAs) and ω-6 polyunsaturated fatty acids (PUFAs) but lower levels of monounsaturated fatty acids (MUFAs) and ω-3 PUFAs than healthy individuals, indicating impaired serum LCFA metabolism. Anti-IL-17A mAb treatment affected most of these LCFA changes. Analysis of LCFAs in IMQ-treated mice showed that LCFAs increased in the serum of WT mice, while there were no significant changes in the Tcrd−/− mice. SFAs increased in IMQ-treated WT mice, while MUFAs showed the opposite trend, and PUFAs did not change significantly.ConclusionsThis study presented a dependable method for quantifying LCFAs that enhanced sensitivity and reduced analysis time. The lipidomic analysis results showed that anti-IL-17A mAb not only ameliorated skin lesions in psoriasis patients but also affected abnormal LCFAs metabolism. Furthermore, the study indicated a potential correlation between IL-17A and abnormal LCFA metabolism in psoriasis patients, which was supported by the alterations in serum LCFAs observed in IMQ-treated WT and Tcrd−/− mice.

Rapid Identification of Phenolics in Mixtures by Two‐Dimensional Tandem Mass Spectrometry with Microdroplet Accelerated Derivatization Reactions

AbstractComplex mixtures are typically analyzed by chromatographic separation followed by mass spectrometry. Two‐dimensional tandem mass spectrometry (2D MS/MS) may eliminate the need for separation and therefore significantly reduce analysis time while expanding the information space by recording the intensities of all fragment ions generated from all trapped precursor ions. Additional chemical specificity can be provided by using chemical derivatization. In this study phenolic compounds are identified after triazole click derivatization by the observation of two diagnostic features in the 2D MS/MS data domain, a mass shift of 175 Da on the precursor ion axis and a neutral loss (NL) of 119 Da. Additional NL transitions were highly correlated with particular substituents including carbonyl, ester, amino, and alkyl/phenyl groups. Substituent position information was also easily diagnosed. The derivatization reactions are accelerated in the charged electrosprayed microdroplets. Using this method, members of this single class of compounds can be identified within 1–2 minutes while structural information on the individual compounds is acquired simultaneously.

Solid-phase microextraction - a future technique in pharmacology and coating trends.

Traditional sample preparation techniques based on liquid-liquid extraction (LLE) or solid-phase extraction (SPE) often suffer from a major error due to the matrix effects caused by significant co-extraction of matrix components. The implementation of a modern extraction technique such as solid-phase microextraction (SPME) was aimed at reducing analysis time and the use of organic solvents, as well as eliminating pre-analytical and analytical errors. Solid-phase microextraction (SPME) is an innovative technique for extracting low molecular weight compounds (less than 1500 Da) from highly complex matrices, including biological matrices. It has a wide range of applications in various types of analysis including pharmaceutical, clinical, metabolomics and proteomics. SPME has a number of advantages over other extraction techniques. Among the most important are low environmental impact, the ability to sample and preconcentrate analytes in one step, simple automation, and the ability to extract multiple analytes simultaneously. It is expected to become, in the future, another method for cell cycle research. Numerous available literature sources prove that solid-phase microextraction can be a future technique in many scientific fields, including pharmaceutical sciences. This paper provides a literature review of trends in SPME coatings and pharmacological applications.

Flexible nanoplasmonic sensor for multiplexed and rapid quantitative food safety analysis with a thousand-times sensitivity improvement

The accumulation of trace amounts of certain small molecules in food poses considerable human health challenges, including the potential for carcinogenesis and mutagenesis. Here, an ultrasensitive gold–platinum nanoflower-coupled metasurface plasmon resonance (MetaSPR) (APNMSPR) biosensor, based on a competitive immunoassay, was developed for the multiplexed and rapid quantitative analysis of trace small molecules in eggs, offering timely monitoring of food safety. This one-step biosensor can be integrated into either a newly designed detachable high-throughput MetaSPR chip-strip plate device or a standard 96-well plate for multiplexed small-molecule detection within a single egg. The limits of detection were 0.81, 1.12, and 1.74 ppt for florfenicol, fipronil, and enrofloxacin, respectively, demonstrating up to 1000-fold increased sensitivity and a 15-fold reduction in analysis time compared with those of traditional methods. The results obtained using the APNMSPR biosensor showed a strong correlation with those obtained using liquid chromatography–tandem mass spectrometry. The APNMSPR biosensor holds immense promise for the multiplexed, highly sensitive, and rapid quantitative analysis of small molecules for applications in food safety control, early diagnosis, and environmental monitoring.

Direct and rapid profiling of triacylglycerols in extra virgin olive oil using UHPLC-MS/MS

Extra virgin olive oil (EVOO) prices reached record levels in 2023, due to the decline in global production, mainly linked to drought in the Mediterranean. Without effective controls, the already widespread fraud in this sector will irrevocably worsen. However, the reliability of the current standard method (CSM) for controlling the authenticity of EVOO has been questioned. Besides, authors have recently called for the replacement of certain steps to reduce analysis time and solvent consumption. In fact, CSM based on HPLC-RID analysis of triacylglycerols (TAGs) and GC analysis of fatty acid methyl esters (FAMEs) is tedious and very time consuming. Here, we optimized and validated a UHPLC-APCI-MS/MS method capable of unambiguously identifying 33 TAGs in EVOO in less than 6 min. The limit of detection calculated for the available TAG standards is between 0.3 and 2.5 ng/ml (0.02 ng–0.13 ng of injected quantity). The linearity of the method in real samples is satisfactory (r2 > 0.99, n = 6) for 27 TAGs with an average area greater than 0.04 %. In real samples, the between-run precision (RSD %) for 29 TAGs with mean area greater than 0.02% does not exceed 11.6%, provided that the injected amount is equal to or greater than 4 μg of EVOO sample. The application of this method to eleven EVOO samples, including a control sample from the International Olive Council, and the processing of the data with a PCA model allow their fine discrimination according to their TAG profiles. In addition to eliminating the GC step of FAME analysis, the high productivity of the proposed method significantly reduces the cost of this essential control step compared to CSM.

Improving hospital quality risk-adjustment models using interactions identified by hierarchical group lasso regularisation

BackgroundRisk-adjustment (RA) models are used to account for severity of illness in comparing patient outcomes across hospitals. Researchers specify covariates as main effects, but they often ignore interactions or use stratification to account for effect modification, despite limitations due to rare events and sparse data. Three Agency for Healthcare Research and Quality (AHRQ) hospital-level Quality Indicators currently use stratified models, but their variable performance and limited interpretability motivated the design of better models.MethodsWe analysed patient discharge de-identified data from 14 State Inpatient Databases, AHRQ Healthcare Cost and Utilization Project, California Department of Health Care Access and Information, and New York State Department of Health. We used hierarchical group lasso regularisation (HGLR) to identify first-order interactions in several AHRQ inpatient quality indicators (IQI) - IQI 09 (Pancreatic Resection Mortality Rate), IQI 11 (Abdominal Aortic Aneurysm Repair Mortality Rate), and Patient Safety Indicator 14 (Postoperative Wound Dehiscence Rate). These models were compared with stratum-specific and composite main effects models with covariates selected by least absolute shrinkage and selection operator (LASSO).ResultsHGLR identified clinically meaningful interactions for all models. Synergistic IQI 11 interactions, such as between hypertension and respiratory failure, suggest patients who merit special attention in perioperative care. Antagonistic IQI 11 interactions, such as between shock and chronic comorbidities, illustrate that naïve main effects models overestimate risk in key subpopulations. Interactions for PSI 14 suggest key subpopulations for whom the risk of wound dehiscence is similar between open and laparoscopic approaches, whereas laparoscopic approach is safer for other groups. Model performance was similar or superior for composite models with HGLR-selected features, compared to those with LASSO-selected features.ConclusionsIn this application to high-profile, high-stakes risk-adjustment models, HGLR selected interactions that maintained or improved model performance in populations with heterogeneous risk, while identifying clinically important interactions. The HGLR package is scalable to handle a large number of covariates and their interactions and is customisable to use multiple CPU cores to reduce analysis time. The HGLR method will allow scholars to avoid creating stratified models on sparse data, improve model calibration, and reduce bias. Future work involves testing using other combinations of risk factors, such as vital signs and laboratory values. Our study focuses on a real-world problem of considerable importance to hospitals and policy-makers who must use RA models for statutorily mandated public reporting and payment programmes.

LAG-based schedulability analysis for preemptive global EDF scheduling with dynamic cache allocation

In recent years, the maturation of modern multicore processor technology and its increasing adoption in critical industrial domains have posed significant challenges for real-time systems, primarily due to contention for shared cache resources and the resulting uncertainty. To address this issue, contemporary processors employ cache partitioning techniques, enhancing temporal predictability by isolating cache access among processor cores. However, this isolation technique may lead to real-time tasks missing their deadlines due to an insufficient number of cache partitions. Consequently, this paper investigates the schedulability of preemptive global Earliest Deadline First (EDF) real-time scheduling algorithms that support dynamic cache allocation. We propose an innovative LAG-based schedulability analysis method for these algorithms and present a utilization-based schedulability condition that reduces analysis time complexity while improving analysis accuracy. Building upon this foundation, and incorporating task characteristics into our analysis, this paper further introduces an optimization technique aimed at minimizing the pessimism inherent in the schedulability test. Lastly, the performance and efficiency of the proposed schedulability determination method are validated through simulation experiments with randomly generated tasks.

Discrimination of Human Insulin and its Analogs by Flow Injection Analysis with Mass Spectrometry

Although insulin therapy is recognized as an effective treatment for diabetes, it can lead to serious side effects and medical accidents. Since living organisms contain many foreign components, the separation of these components by liquid chromatography (LC) is essential for the identification of insulin preparations. However, it is difficult to conclude that separation by LC is indispensable because multiple components are unlikely to be mixed in a sample obtained from a vial or syringe needle. Therefore, the introduction of flow injection analysis (FIA), in which samples are analyzed with the column removed, can potentially eliminate the sample separation process and reduce analysis time. In addition, tandem mass spectrometry (MS/MS), which allows detailed analysis, requires expensive equipment, and some facilities only allow analysis by single mass spectrometry (MS). The discriminatory ability of each analysis method should also be clarified. Here, we examined the analysis time and discriminatory ability using a combination of FIA, MS, and MS/MS. The results showed that FIA reduced the analysis time, FIA-MS could not discriminate when the molecular weights of the samples were the same or similar, and FIA-MS/MS could discriminate between different amino acid sequences. FIA-MS has limitations in discrimination, whereas FIA-MS/MS is considered an analytical method that can shorten the analysis time and discriminate formulations.

Development and Validation of Simultaneous Quantitative Dissolution Analysis for the Two Active Pharmaceutical Ingredients in Dapagliflozin Propanediol Monohydrate-sitagliptin Phosphate Monohydrate Multi-layered Tablets

Background: Recently, a combination prescription with the main ingredients sitagliptin and dapagliflozin as dipeptidyl peptidase-4 andsodium–glucose cotransporter-2 inhibitors, respectively, for the treatment of type 2 diabetes has widely been issued in hospitals. However, the development of double-layered tablets requires simultaneous quantitative dissolution tests that are significantly efficient and cost-effective. Objective: Individual analysis of the two active pharmaceutical ingredients (APIs) incurs more than twice the time and cost. Consequently, this study aimed to develop a dissolution analysis method that simultaneously quantifies the APIs dapagliflozin and sitagliptin in multi- layered tablets. This simultaneous quantitative dissolution analysis can dramatically reduce analysis time and cost. Methods: For reversed-phase high-performance liquid chromatography (RP-HPLC) analysis using ultraviolet detection, a Zorbax C18 column (4.6 × 150 mm, 5 μm) was used, and the flow rate was 1.5 mL/min, injection amount 20 μL, and maximum absorption wavelength set to 205 nm. Additionally, the analysis time was set to 1.5 times the retention time of dapagliflozin Results: The retention times of dapagliflozin and sitagliptin were 11.57 and 2.56 min, respectively. Further, their relative standard deviations were 0.11% and 0.05%, respectively. Quantitative analysis using RP-HPLC confirmed no peak interference between the APIs and excipients. Both APIs exhibited linearity at a 20–120% concentration. Conclusion: The dissolution method developed in this study can quantify both APIs simultaneously, thereby reducing analysis time and cost by more than 50% and increasing efficiency in the pharmaceutical industry.in the pharmaceutical industry.

Simple Tool for Rapidly Assessing the Quality of Multiplexed Single Cell Proteomics Data.

Recent advances in the sensitivity and speed of mass spectrometers coupled with improved sample preparation methods have enabled the field of single cell proteomics to proliferate. While heavy development is occurring in the label free space, dramatic improvements in throughput are provided by multiplexing with tandem mass tags. Hundreds or thousands of single cells can be analyzed with this method, yielding large data sets which may contain poor data arising from loss of material during cell sorting or poor digestion, labeling, and lysis. To date, no tools have been described that can assess data quality prior to data processing. We present herein a lightweight python script and accompanying graphic user interface that can rapidly quantify reporter ion peaks within each MS/MS spectrum in a file. With simple summary reports, we can identify single cell samples that fail to pass a set quality threshold, thus reducing analysis time waste. In addition, this tool, Diagnostic Ion Data Analysis Reduction (DIDAR), will create reduced MGF files containing only spectra possessing a user-specified number of single cell reporter ions. By reducing the number of spectra that have excessive zero values, we can speed up sample processing with little loss in data completeness as these spectra are removed in later stages in data processing workflows. DIDAR and the DIDAR GUI are compatible with all modern operating systems and are available at: https://github.com/orsburn/DIDARSCPQC. All files described in this study are available at www.massive.ucsd.edu as accession MSV000088887.

MODL-48. AUTOMATING ANALYSIS OF ORGANOTYPIC BRAIN SLICE CULTURE USING MACHINE LEARNING-POWERED COMPUTER VISION

Abstract The difficulty of image labeling and analysis poses a problem for many areas of scientific research, including the recently developed Organotypic Brain Slice Culture (OBSC) tumor characterization platform. An OBSC experiment generates dozens of images which, up to this point, had to be manually quantified. In order to increase the efficiency, consistency, and accuracy of the OBSC image analysis process, we have created a computer program which automates the analysis of OBSC images. This platform uses a number of computer vision (CV) techniques to identify objects of interest and measure their corresponding signals. For fluorescent images of single tumor spots, a straightforward thresholding algorithm divides the image into foreground and background. This automated approach substantially reduces analysis time, and the thresholding algorithm measures unusually shaped tumor spots up to 9% more accurately than previously used manual methods. Other images capture an entire plate of up to 12 OBSCs, complicating the image analysis task. A machine-learning algorithm trained on the web-based platform Biodock could identify individual OBSCs, even if they were touching each other or the sides of the wells. The ML-powered methods consistently reproduced killing curves for treatment effects on tumor tissue as well as the brain tissue substrate, and these results were obtained in much less time. By combining various CV approaches to image analysis, we have completely automated the image analysis component of our OBSC platform. This approach could be adapted to other tumor co-culture platforms.

Differentiating Between Three Licorice Species Using SFC-TOF/MS Analysis With Principal Component Analysis

Objective Although the discrimination of crude drugs by metabolomics analysis using liquid chromatography–mass spectrometry (LC–MS) has been widely reported, there are few reports on natural products analysis using supercritical fluid chromatography (SFC)–MS. This study aimed to compare the results of metabolomic analysis of licorice obtained using SFC–MS and LC–MS. Methods We analyzed 14 CHCl3 extracts of licorice roots ( Glycyrrhiza glabra, G. uralensis, and G. inflata) using SFC-time-of-flight (TOF)/MS and LC-TOF/MS in conjunction with principal component analysis (PCA). The three species were assessed for discrimination based on the PCA results. The characteristic constituents of each variety were purified, and their chemical structures were elucidated using various spectroscopic analyses. Results The PCA score plots of the results of both SFC and LC classified the licorice samples into three groups. Seven compounds (six known compounds and one new phenolic compound, glyglabone) were isolated from G. glabra, five known compounds were isolated from G. uralensis, and four known compounds were isolated from G. inflata. Conclusions The 16 isolated compounds may be characteristic compounds in the three licorice species. The SFC and LC methods showed comparable results in distinguishing the three species. SFC was highly effective for directly analyzing CHCl3 extracts, reducing analysis time and organic solvent consumption by over 50% compared to the LC analysis. Thus, SFC-TOF/MS in combination with PCA can be used to differentiate between G. glabra, G. uralensis, and G. inflata.

B-023 Optimizing Measurement of Homocysteine in an Emergency Care Pathway

Abstract Background/Context New indications for homocysteine measurement have emerged in recent years, and homocysteine is now being used as a marker of nutritional deficiency as well as a marker of nitrous oxide intoxication and there is a need to return quickly results to adapt the treatment and orientate diagnosis. Current rapid methods are not adapted to extremely high values encountered which are frequently greater than 50 µmol/L that need use of mass spectrometry that is unfortunately not adapt to emergency. Hence, we evaluated here a new immunological method that allows for the reliable and rapid rendering of homocysteine results in order to integrate it into our care pathways. Method We prospectively measured plasma homocysteine using both liquid chromatography-mass spectrometry (UPCL—Sciex AP 3200) and the Chemiluminescence Immunoassay (CLIA) method (Maglumi-Snibe). To limit the bias for each assay, the analyses were performed on the same EDTA plasma sample with the same pre-analytical treatment. Bland-Altman plots and Deming regression were used to assess agreement between the homocysteine LC-MS/MS method and homocysteine CLIA method. Results Our study included 169 paired samples. The intra- and inter-assay imprecision was comprised between 0.73–2.21% and between 2.75–3.73%, respectively. Linearity, without sample dilution, was acceptable in the range of 5–70 mol/L. This CLIA assay correlated with the HCY LC-MS/MS assay with a slope of 0.9334, intercept of 1.750, R2 = 0.9637 and average bias of −0.6867 μmol/L. Discussion The CLIA method produces results comparable to mass spectrometry while significantly reducing analysis time and result delay. This method is thus more appropriate when homocysteine is used not only for emergency diagnosis but also for nutritional monitoring of the patient. This method also allows for the measurement of extremely high concentrations, which are becoming more common in laboratories.

Boosted Additive Angular Margin Loss for breast cancer diagnosis from histopathological images

Pathologists use biopsies and microscopic examination to accurately diagnose breast cancer. This process is time-consuming, labor-intensive, and costly. Convolutional neural networks (CNNs) offer an efficient and highly accurate approach to reduce analysis time and automate the diagnostic workflow in pathology. However, the softmax loss commonly used in existing CNNs leads to noticeable ambiguity in decision boundaries and lacks a clear constraint for minimizing within-class variance. In response to this problem, a solution in the form of softmax losses based on angular margin was developed. These losses were introduced in the context of face recognition, with the goal of integrating an angular margin into the softmax loss. This integration improves discrimination features during CNN training by effectively increasing the distance between different classes while reducing the variance within each class. Despite significant progress, these losses are limited to target classes only when margin penalties are applied, which may not lead to optimal effectiveness. In this paper, we introduce Boosted Additive Angular Margin Loss (BAM) to obtain highly discriminative features for breast cancer diagnosis from histopathological images. BAM not only penalizes the angle between deep features and their target class weights, but also considers angles between deep features and non-target class weights. We performed extensive experiments on the publicly available BreaKHis dataset. BAM achieved remarkable accuracies of 99.79%, 99.86%, 99.96%, and 97.65% for magnification levels of 40X, 100X, 200X, and 400X, respectively. These results show an improvement in accuracy of 0.13%, 0.34%, and 0.21% for 40X, 100X, and 200X magnifications, respectively, compared to the baseline methods. Additional experiments were performed on the BACH dataset for breast cancer classification and on the widely accepted LFW and YTF datasets for face recognition to evaluate the generalization ability of the proposed loss function. The results show that BAM outperforms state-of-the-art methods by increasing the decision space between classes and minimizing intra-class variance, resulting in improved discriminability.