Linear Discriminant Analysis Model Research Articles

Classification and Authentication of Lonicerae Japonicae Flos and Lonicerae Flos by Using 1H-NMR Spectroscopy and Chemical Pattern Recognition Analysis.

Lonicerae japonicae flos and Lonicerae flos are increasingly widely used in food and traditional medicine products around the world. Due to their high demand and similar appearance, they are often used in a confused or adulterated way; therefore, a rapid and comprehensive analytical method is highly required. In this case, the comparative analysis of a total of 100 samples with different species, growth modes, and processing methods was carried out by nuclear magnetic resonance (1H-NMR) spectroscopy and chemical pattern recognition analysis. The obtained 1H-NMR spectrums were employed by principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), orthogonal partial least-squares discriminant analysis (OPLS-DA), and linear discriminant analysis (LDA). Specifically, after the dimensionality reduction of data, linear discriminant analysis (LDA) exhibited good classification abilities for the species, growth modes, and processing methods. It is worth noting that the sample prediction accuracy from the testing set and the cross-validation predictions of the LDA models were higher than 95.65% and 98.1%, respectively. In addition, the results showed that macranthoidin A, macranthoidin B, and dipsacoside B could be considered as the main differential components of Lonicerae japonicae flos and Lonicerae Flos, while secoxyloganin, secologanoside, and sweroside could be responsible for distinguishing cultivated and wild Lonicerae japonicae Flos. Accordingly, 1H-NMR spectroscopy combined with chemical pattern recognition gives a comprehensive overview and provides new insight into the quality control and evaluation of Lonicerae japonicae flos.

Identification of raffinose family oligosaccharides in processed Rehmannia glutinosa Libosch using matrix-assisted laser desorption/ionization mass spectrometry image combined with machine learning.

Currently, research on oligosaccharides primarily focuses on the physiological activity and function, with a few studies elaborating on the spatial distribution characterization and variation in the processing of Rehmannia glutinosa Libosch. Thus, imaging the spatial distributions and dynamic changes in oligosaccharides during the steaming process is significant for characterizing the metabolic networks of R. glutinosa. It will be beneficial to characterize the impact of steaming on the active ingredients and distribution patterns in different parts of the plant. A highly sensitive matrix-assisted laser desorption/ionization mass spectrometry image (MALDI-MSI) method was used to visualize the spatial distribution of oligosaccharides in processed R. glutinosa. Furthermore, machine learning was used to distinguish the processed R. glutinosa samples obtained under different steaming conditions. Imaging results showed that the oligosaccharides in the fresh R. glutinosa were mainly distributed in the cortex and xylem. As steaming progressed, the tetra- and pentasaccharides were hydrolyzed and diffused gradually into the tissue section. MALDI-MS profiling combined with machine learning was used to identify the processed R. glutinosa samples accurately at different steaming intervals. Eight algorithms were used to build classification machine learning models, which were evaluated for accuracy, precision, recall, and F1 score. The linear discriminant analysis and random forest models performed the best, with prediction accuracies of 0.98 and 0.97, respectively, and thus can be considered for identifying the steaming durations of R. glutinosa. MALDI-MSI combined with machine learning can be used to visualize the distribution of oligosaccharides and identify the processed samples after steaming for different durations. This can enhance our understanding of the metabolic changes that occur during the steaming process of R. glutinosa; meanwhile, it is expected to provide a theoretical reference for the standardization and modernization of processing in the field of medicinal plants.

Supervised machine learning to validate a novel scoring system for the prediction of disease remission of functional pituitary adenomas following transsphenoidal surgery

Functional pituitary adenomas (FPAs) are associated with hormonal hypersecretion resulting in systemic endocrinopathies and increased mortality. The heterogenous composition of the FPA population has made modeling predictive factors of postoperative disease remission a challenge. Here, we aim to define a novel scoring system predictive of disease remission following transsphenoidal surgery (TSS) for FPAs and validate our process using supervised machine learning (SML). 392 patients with FPAs treated at one of the three Mayo Clinic campuses were retrospectively reviewed. Variables found significant on multivariate analysis were incorporated into our novel Pit-SCHEME score. The Pit-SCHEME score with a cut-off value ≥ 6 achieved a sensitivity of 86% and positive likelihood ratio of 2.88. In SML models, without the Pit-SCHEME score, the k-nearest neighbor (KNN) model achieved the highest accuracy at 75.6%. An increase in model sensitivity was achieved with inclusion of the Pit-SCHEME score with the linear discriminant analysis (LDA) model achieving an accuracy of 86.9%, which suggests the Pit-SCHEME score is the variable of most importance for prediction of postoperative disease remission. Ultimately, these results support the potential clinical utility of the Pit-SCHEME score and its prospective future for aiding in the perioperative decision making in patients with FPAs.

LAP-MALDI MS Profiling and Identification of Potential Biomarkers for the Detection of Bovine Tuberculosis.

Detecting bovine tuberculosis (bTB) primarily relies on the tuberculin skin test, requiring two separate animal handling events with a period of incubation time (normally 3 days) between them. Here, we present the use of liquid atmospheric pressure (LAP)-MALDI for the identification of bTB infection, employing a three-class prediction model that was obtained by supervised linear discriminant analysis (LDA) and tested with bovine mastitis samples as disease-positive controls. Noninvasive collection of nasal swabs was used to collect samples, which were subsequently subjected to a short (<4 h) sample preparation method. Cross-validation of the three-class LDA model from the processed nasal swabs provided a sensitivity of 75.0% and specificity of 90.1%, with an overall classification accuracy of 85.7%. These values are comparable to those for the skin test, showing that LAP-MALDI MS has the potential to provide an alternative single-visit diagnostic platform that can detect bTB within the same day of sampling.

Clinical severity classes in COVID-19 pneumonia have distinct immunological profiles, facilitating risk stratification by machine learning.

Clinical triage in coronavirus disease 2019 (COVID-19) places a heavy burden on senior clinicians during a pandemic situation. However, risk stratification based on serum biomarker bioprofiling could be implemented by a larger, nonspecialist workforce. Measures of Complement Activation and inflammation in patientS with CoronAvirus DisEase 2019 (CASCADE) patients (n = 72), (clinicaltrials.gov: NCT04453527), classified as mild, moderate, or severe (by support needed to maintain SpO2 > 93%), and healthy controls (HC, n = 20), were bioprofiled using 76 immunological biomarkers and compared using ANOVA. Spearman correlation analysis on biomarker pairs was visualised via heatmaps. Linear Discriminant Analysis (LDA) models were generated to identify patients likely to deteriorate. An X-Gradient-boost (XGB) model trained on CASCADE data to triage patients as mild, moderate, and severe was retrospectively employed to classify COROnavirus Nomacopan Emergency Treatment for covid 19 infected patients with early signs of respiratory distress (CORONET) patients (n = 7) treated with nomacopan. The LDA models distinctly discriminated between deteriorators, nondeteriorators, and HC, with IL-27, IP-10, MDC, ferritin, C5, and sC5b-9 among the key predictor variables during deterioration. C3a and C5 were elevated in all severity classes vs. HC (p < 0.05). sC5b-9 was elevated in the "moderate" and "severe" categories vs. HC (p < 0.001). Heatmap analysis shows a pairwise increase of negatively correlated pairs with IL-27. The XGB model indicated sC5b-9, IL-8, MCP1, and prothrombin F1 and F2 were key discriminators in nomacopan-treated patients (CORONET study). Distinct immunological fingerprints from serum biomarkers exist within different severity classes of COVID-19, and harnessing them using machine learning enabled the development of clinically useful triage and prognostic tools. Complement-mediated lung injury plays a key role in COVID-19 pneumonia, and preliminary results hint at the usefulness of a C5 inhibitor in COVID-19 recovery.

The Visibility of Changes in the Antioxidant Compound Profiles of Strawberry and Raspberry Fruits Subjected to Different Storage Conditions Using ATR-FTIR and Chemometrics.

Strawberry cultivars Portola and Enduro, as well as raspberry cultivars Enrosadira and Kwazi, were evaluated for their antioxidant potential after treatment with gaseous ozone and different refrigeration storage conditions. Their antioxidant capacity was investigated with ABTS and DPPH methods, and the chemical composition was determined by measuring the total phenolic (TPC) and flavonoid (TFC) compounds. The classification of different samples of berry puree was influenced significantly by both the cultivars and the refrigeration storage method. Moreover, FTIR spectroscopy coupled with chemometrics was used as an alternative technique to conventional methods to determine the chemical composition of strawberries and raspberries. The chemometric discrimination of samples was achieved using principal component analysis (PCA), hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) modelling procedures performed on the FTIR preprocessed spectral data for the fingerprint region (1800-500 cm-1). The fingerprint range between 1500 and 500 cm-1, corresponding to deformation vibrations from polysaccharides, pectin and organic acid content, had a significant impact on the grouping of samples. The results obtained by PCA-LDA scores revealed a clear separation between four classes of samples and demonstrated a high overall classification rate of 97.5% in differentiating between the raspberry and strawberry cultivars.

Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study.

Up to 30% of patients with acute coronary syndrome (ACS) die from adverse events, mainly renal failure and myocardial infarction (MI). Accurate prediction of adverse events is therefore essential to improve patient prognosis. Machine learning (ML) methods can accurately identify risk factors and predict adverse events. A total of 5240 patients diagnosed with ACS who underwent PCI were enrolled and followed for 1year. Support vector machine, extreme gradient boosting, adaptive boosting, K-nearest neighbors, random forest, decision tree, categorical boosting, and linear discriminant analysis (LDA) were developed with 10-fold cross-validation to predict acute kidney injury (AKI), MIduring hospitalization, and all-cause mortality within 1year. Features with mean Shapley Additive exPlanationsscore >0.1 were screened by XGBoost method as input for model construction. Accuracy, F1 score, area under curve (AUC), and precision/recallcurve were used to evaluate the performance of the models. Overall, 2.6% of patients died within 1year, 4.2% had AKI, and 4.7% had MI during hospitalization. The LDA model was superior to the other sevenML models, with an AUC of 0.83, F1 score of 0.90, accuracy of 0.85, recall of 0.85, specificity of 0.68, and precision of 0.99 in predicting all-cause mortality. For AKI and MI, the LDA model also showed good discriminating capacity with an AUC of 0.74. The LDA model, using easily accessible variables from in-hospital patients, showed the potential to effectively predict the risk of adverse events and mortality within 1year in ACS patients after PCI.

RamanCMP: A Raman spectral classification acceleration method based on lightweight model and model compression techniques

In recent years, Raman spectroscopy combined with deep learning techniques has been widely used in various fields such as medical, chemical, and geological. However, there is still room for optimization of deep learning techniques and model compression algorithms for processing Raman spectral data. To further optimize deep learning models applied to Raman spectroscopy, in this study time, accuracy, sensitivity, specificity and floating point operations numbers(FLOPs) are used as evaluation metrics to optimize the model, which is named RamanCompact(RamanCMP). The experimental data used in this research are selected from the RRUFF public dataset, which consists of 723 Raman spectroscopy data samples from 10 different mineral categories. In this paper, 1D-EfficientNet adapted to the spectral data as well as 1D-DRSN are proposed to improve the model classification accuracy. To achieve better classification accuracy while optimizing the time parameters, three model compression methods are designed: knowledge distillation using 1D-EfficientNet model as a teacher model to train convolutional neural networks(CNN), proposing a channel conversion method to optimize 1D-DRSN model, and using 1D-DRSN model as a feature extractor in combination with linear discriminant analysis(LDA) model for classification. Compared with the traditional LDA and CNN models, the accuracy of 1D-EfficientNet and 1D-DRSN is improved by more than 20%. The time of the distilled model is reduced by 9680.9s compared with the teacher model 1D-EfficientNet under the condition of losing 2.07% accuracy. The accuracy of the distilled model is improved by 20% compared to the CNN student model while keeping inference efficiency constant. The 1D-DRSN optimized with channel conversion method saves 60% inference time of the original 1D-DRSN model. Feature extraction reduces the inference time of 1D-DRSN model by 93% with 94.48% accuracy. This study innovatively combines lightweight models and model compression algorithms to improve the classification speed of deep learning models in the field of Raman spectroscopy, forming a complete set of analysis methods and laying the foundation for future research.

Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study.

Guidelines for the management of elderly patients with early breast cancer are scarce. Additional adjuvant systemic treatment to surgery for early breast cancer in elderly populations is challenged by increasing comorbidities with age. In non-metastatic settings, treatment decisions are often made under considerable uncertainty; this commonly leads to undertreatment and, consequently, poorer outcomes. This study aimed to develop a decision support tool that can help to identify candidate adjuvant post-surgery treatment schemes for elderly breast cancer patients based on tumor and patient characteristics. Our approach was to generate predictions of patient outcomes for different courses of action; these predictions can, in turn, be used to inform clinical decisions for new patients. We used a cohort of elderly patients (≥ 70 years) who underwent surgery with curative intent for early breast cancer to train the models. We tested seven classification algorithms using 5-fold cross-validation, with 80% of the data being randomly selected for training and the remaining 20% for testing. We assessed model performance using accuracy, precision, recall, F1-score, and AUC score. We used an autoencoder to perform dimensionality reduction prior to classification. We observed consistently better performance using logistic regression and linear discriminant analysis models when compared to the other models we tested. Classification performance generally improved when an autoencoder was used, except for when we predicted the need for adjuvant treatment. We obtained overall best results using a logistic regression model without autoencoding to predict the need for adjuvant treatment (F1-score = 0.869).

Chemometric analysis of fish fin ray multi-elemental signatures with differentiation of wild and farmed fourfinger threadfin fish

Fourfinger threadfin (Eleutheronema tetradactylum) are the commercially important fish along the southern coast of China. The economic value of wild E. tetradactylum was much higher than their farmed counterparts, therefore mislabeling of production methods may lead to premium prices. In the present study, multi-element signatures of fin ray were used to determine the wild caught and pond-cultured E. tetradactylum from southern coast of China. The elemental composition of fish calcified structures was related to the ambient water environment, and could record the water chemistry difference between the aquaculture pond and the natural seawater. Our results demonstrated that multi-element compositions of fish fin rays were significantly different between the wild and cultured fish. Linear discriminant analysis (LDA) and random forest (RF) model correctly discriminated the wild and cultured E. tetradactylum. The classification accuracies of wild and cultured E. tetradactylum were 100% by LDA. The classification accuracies of identifying the source of cultured E. tetradactylum were 95%. However, the fin ray elemental signatures could not differentiate the different wild groups. This strategy provides a promising alternative method to protect against mislabeling of E. tetradactylum.

Combination of the electronic nose with microbiology as a tool for rapid detection of Salmonella

Salmonella is one of the most important foodborne pathogens and its analysis in raw and processed products is mandatory in the food industry. Although microbiological analysis is the standard practice for Salmonella determination, these assays are commonly laborious and time-consuming, thus, alternative techniques based on easy operation, few manipulation steps, low cost, and reduced time are desirable. In this paper, we demonstrate the use of an e-nose based on ionogel composites (ionic liquid + gelatine + Fe3O4 particles) as a complementary tool for the conventional microbiological detection of Salmonella. We used the proposed methodology for differentiating Salmonella from Escherichia coli, Pseudomonas fluorescens, Pseudomonas aeruginosa, and Staphylococcus aureus in nonselective medium: pre-enrichment in brain heart infusion (BHI) (incubation at 35 °C, 24 h) and enrichment in tryptone soy agar (TSA) (incubation at 35 °C, 24 h), whereas Salmonella differentiation from E. coli and P. fluorescens was also evaluated in selective media, bismuth sulfite agar (BSA), xylose lysine deoxycholate agar (XLD), and brilliant green agar (BGA) (incubation at 35 °C, 24 h). The obtained data were compared by principal component analysis (PCA) and different machine learning algorithms: multilayer perceptron (MLP), linear discriminant analysis (LDA), instance-based (IBk), and Logistic Model Trees (LMT). For the nonselective media, under optimized conditions, taking merged data of BHI + TSA (total incubation time of 48 h), an accuracy of 85% was obtained with MLP, LDA, and LMT, while five separated clusters were presented in PCA, each cluster corresponding to a bacterium. In addition, for evaluation of the e-nose for discrimination of Salmonella using selective media, considering the combination of BSA + XLD and total incubation of 72 h, the PCA showed three separated and well-defined clusters corresponding to Salmonella, E. coli, and P. fluorescens, and an accuracy of 100% was obtained for all classifiers.

Discrimination of toxigenic and non-toxigenic Aspergillus flavus in wheat based on nanocomposite colorimetric sensor array

A novel method was developed for the early detection of wheat infected with Aspergillus flavus (A. flavus) using a nanocomposite colorimetric sensors array (CSA). LC-MS analysis revealed the presence of Aflatoxin B1 (AFB1) and Aflatoxin B2 (AFB2) on day seven, demonstrating mycotoxin variabilities in infected wheat. HS-SPME-GC–MS analysis detected 2-methylbutyral, a gas exclusively associated with toxigenic A. flavus. The CSA was modified using three nanoparticles of MOF and successfully used to detect the wheat infected with A. flavus. Discrimination of different types of infected wheat samples was achieved using the RGB difference map and Principal Component Analysis (PCA) model. Additionally, the Linear Discriminant Analysis (LDA) model accurately predicted the presence of toxigenic A. flavus at various stages of infection. These findings highlight the promising capabilities of nanocomposite CSA for early-stage detection of A. flavus infection in wheat.

Application of FT-IR Spectroscopy for Mycobacterium abscessus complex subspecies differentiation

Mycobacterium abscessus complex (MABSC) subspecies differentiation improves patients' therapy and outcome. Fourier-Transform-Infrared Spectroscopy (FT-IRS) was applied for subspecies discrimination of 15 strains on different media: Löwenstein-Jensen showed the best resolution power; Linear Discriminant Analysis model differentiated M. abscessus susbsp. abscessus from M. abscessus subsp. massiliense. FT-IRS has a potential role in rapidly MABSC subspecies identification.

Exploring new dimensions: Single and multi-block analysis of essential oils using DBDI-MS and FT-IR for enhanced authenticity control

BackgroundEssential oils (EOs) are complex mixtures of volatile hydrocarbons with a wide range of applications in the pharmaceutical, fragrance and food industry. The composition of EOs is highly variable and can affect their quality and pharmaceutical efficacy. Moreover, the high economic value of EOs, such as those obtained from Rosa damascena, make falsification and misclassification a lucrative business. Consequently, adulterations can lead to serious health consequences for consumers. While current quality control methods for EOs involve analysing their chromatographic profile or comparing their Fourier transform infrared (FT-IR) spectra, these methods can be time-consuming or lack sensitivity. To address these issues, we compared state-of-the-art quality control methods, including gas chromatography flame ionization detection (GC-FID) quantification and enantiomeric ratio determination, FT-IR spectrometry with dielectric barrier discharge ionization coupled to triple quadrupole mass spectrometer (DBDI-MS), in a chemometric single- and multi-block approach. ResultsOur results show that the best classification accuracy of 94.7% for R. damascena samples was obtained using GC-FID combined with partial least square discriminant analysis (PLS-DA). Comparatively, the enantiomeric ratios did not improve classification accuracy. In contrast, fragmentation data from DBDI-MS (Q3), which was acquired in a fraction of the analysis time and without extensive sample preparation, achieved a classification accuracy of 84.2%. We also found that combining FT-IR with parent ion DBDI-MS (Q1) data in a multi-block sequentially orthogonalized partial least squares linear discriminant analysis (SO-PLS-LDA) model improved classification accuracy, compared to their respective single-block PLS-DA models. SignificanceOverall, our study demonstrates that DBDI, as an ambient ionization method, has significant potential for high-throughput screening. When combined with MS, it can produce comparable classification accuracies to conventional methods, while offering the added benefits of speed and convenience. As such, DBDI-MS is a promising tool for EO quality control.

Development and validation of models for risk of death in patients with systemic lupus erythematosus admitted to the intensive care unit: a retrospective study.

For patients with systemic lupus erythematosus (SLE) in the intensive care unit (ICU), early detection of the mortality risk is a significant factor in improving outcomes. In this study, we developed and validated predictive models for the mortality risk. Using MIMIC-III v1.4 and MIMIC-IV v0.4, we collected data of patients with SLE who were admitted to the ICU. The patients were divided into two groups based on death or survival within 30 days. Two prediction models were built for each group: a traditional logistic regression model and a linear discriminant analysis (LDA) model constructed by the random subspace method (RSM) (RSM-LDA model). The performance of the two models was analyzed using the area under the receiver operating characteristic curve (AUC). MIMIC-III and MIMIC-IV were used to establish and validate the models. This study involved 383 patients with SLE, 65 of whom died. They were divided into two groups according to whether they died within 30 days. The predictive factors were the type of admission to the ICU, SLE-associated interstitial pneumonia, lupus nephritis, immunoglobulin G level, and cardiolipin antibody level. A logistic regression model and an RSM-LDA model were established. The AUCs of the two models were 0.87 (95% confidence interval, 0.86-0.90) and 0.91 (95% confidence interval, 0.88-0.93), respectively. The RSM-LDA model can predict the risk of death in patients with SLE admitted to the ICU at an early stage. Key Points • Compared with traditional prediction models, RSM-LDA model has a better ability to predict the risk of death inpatients with systemic lupus erythematosus. • Compared with traditional prediction models, the more input variables (mortality related risk factors), the better the prediction results of RSM-LDA model.

The spatial differences of the Chinese sauce aroma liquor Daqu and identification by FT-MIR spectroscopy

Chinese sauce aroma liquor (CSL) is one of main types of Baijiu. As the fermentation starter and raw material, CSL Daqu (CSDQ) influences the flavor formation of CSL. Owing to the spatial difference during processing, fermented CSDQ has three types and the quality of a CSDQ is inconsistent. Traditional empirical method has limitations in classification precision and standardization. In this study, the subtype was defined by the layers and types of CSDQ. Differences of CSDQs were systematically evaluated by colorimetric, chemical, and Fourier transform mid-infrared spectroscopy (FT-MIR) analysis. Linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), and k-nearest neighbor method (KNN) algorithms were used to classify the subtypes of CSDQ based on the FT-MIR spectra. Furthermore, the models were optimized, and the wavenumbers with high discrimination were marked by chemical bonds. FT-MIR LDA model achieved 95.61% accuracy after proper preprocessing. An even sampling method was proposed to represent the overall properties of a CSDQ. These findings provided a better understanding of CSDQ and a theoretical basis for promoting industrial standardization.

Complexity analysis of electrical activity during endocardial and epicardial biventricular mapping of ventricular fibrillation.

Ventricular fibrillation (VF) is a lethal cardiac arrhythmia that is a significant cause of sudden cardiac death. Comprehensive studies of spatiotemporal characteristics of VF in situ are difficult to perform with current mapping systems and catheter technology. The goal of this study was to develop a computational approach to characterize VF using a commercially available technology in a large animal model. Prior data suggests that characterization of spatiotemporal organization of electrical activity during VF can be used to provide better mechanistic understanding and potential ablation targets to modify VF and its substrate. We therefore evaluated intracardiac electrograms during biventricular mapping of the endocardium (ENDO) and epicardium (EPI) in acute canine studies. To develop thresholds for organized and disorganized activity, a linear discriminant analysis (LDA)-based approach was performed to the known organized and disorganized activities recorded in ex vivo Langendorff-perfused rat and rabbit hearts using optical mapping experiments. Several frequency- and time-domain approaches were used as individual and paired features to identify the optimal thresholds for the LDA approach. Subsequently, VF was sequentially mapped in 4 canine hearts, using the CARTO mapping system with a multipolar mapping catheter in the ENDO left and right ventricles and EPI to capture the progression of VF at 3 discrete post-induction time intervals: VF period 1 (just after induction of VF to 15 min), VF period 2 (15 to 30 min), and VF period 3 (30 to 45 min). The developed LDA model, cycle lengths (CL), and regularity indices (RI) were applied to all recorded intracardiac electrograms to quantify the spatiotemporal organization of VF in canine hearts. We demonstrated the presence of organized activity in the EPI as VF progresses, in contrary to the ENDO, where the activity stays disorganized. The shortest CL always occurred in the ENDO, especially the RV, indicating a faster VF activity. The highest RI was found in the EPI in all hearts for all VF stages, indicating spatiotemporal consistency of RR intervals. We identified electrical organization and spatiotemporal differences throughout VF in canine hearts from induction to asystole. Notably, the RV ENDO is characterized by a high level of disorganization and faster VF frequency. In contrast, EPI has a high spatiotemporal organization of VF and consistently long RR intervals.

A Paper-Based Biomimetic Sensing Device for the Discrimination of Original and Fraudulent Cigarette Brands Using Mixtures of MoS2 Quantum Dots and Organic Dyes.

In this study, we investigated the combined effects of MoS2 QDs' catalytic properties and the colorimetric responses of organic reagents to create a sniffing device based on the sensor array concept of the mammalian olfactory system. The aim was to differentiate the volatile organic compounds (VOCs) present in cigarette smoke. The designed optical nose device was utilized for the classification of various cigarette VOCs. Unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA) methods were employed for data analysis. The LDA analysis showed promising results, with 100% accuracy in both training and cross-validation. To validate the sensor's performance, we assessed its ability to discriminate between five cigarette brands, achieving 100% accuracy in the training set and 82% in the cross-validation set. Additionally, we focused on studying four popular Iranian cigarette brands (Bahman Kootah, Omega, Montana Gold, and Williams), including fraudulent samples. Impressively, the developed sensor array achieved a perfect 100% accuracy in distinguishing these brands and detecting fraud. We further analyzed a total of 126 cigarette samples, including both original and fraudulent ones, using LDA with a matrix size of (126 × 27). The resulting LDA model demonstrated an accuracy of 98%. Our proposed analytical procedure is characterized by its efficiency, affordability, user-friendliness, and reliability. The selectivity exhibited by the developed sensor array positions it as a valuable tool for differentiating between original and counterfeit cigarettes, thus aiding in border control efforts worldwide.

Simple and Rapid Discrimination of Methicillin-Resistant Staphylococcus aureus Based on Gram Staining and Machine Vision.

Methicillin-resistant Staphylococcus aureus (MRSA) is a clinical threat with high morbidity and mortality. Here, we describe a new simple, rapid identification method for MRSA using oxacillin sodium salt, a cell wall synthesis inhibitor, combined with Gram staining and machine vision (MV) analysis. Gram staining classifies bacteria as positive (purple) or negative (pink) according to the cell wall structure and chemical composition. In the presence of oxacillin, the integrity of the cell wall for methicillin-susceptible S. aureus (MSSA) was destroyed immediately and appeared Gram negative. In contrast, MRSA was relatively stable and appeared Gram positive. This color change can be detected by MV. The feasibility of this method was demonstrated in 150 images of the staining results for 50 clinical S. aureus strains. Based on effective feature extraction and machine learning, the accuracies of the linear linear discriminant analysis (LDA) model and nonlinear artificial neural network (ANN) model for MRSA identification were 96.7% and 97.3%, respectively. Combined with MV analysis, this simple strategy improved the detection efficiency and significantly shortened the time needed to detect antibiotic resistance. The whole process can be completed within 1 h. Unlike the traditional antibiotic susceptibility test, overnight incubation is avoided. This new strategy could be used for other bacteria and represents a new rapid method for detection of clinical antibiotic resistance. IMPORTANCE Oxacillin sodium salt destroys the integrity of the cell wall of MSSA immediately, appearing Gram negative, whereas MRSA is relatively stable and still appears Gram positive. This color change can be detected by microscopic examination and MV analysis. This new strategy has significantly reduced the time to detect resistance. The results show that using oxacillin sodium salt combined with Gram staining and MV analysis is a new, simple and rapid method for identification of MRSA.

Diagnosis and monitoring of hepatocellular carcinoma in Hepatitis C virus patients using attenuated total reflection Fourier transform infrared spectroscopy

BackgroundCurrent diagnostic methods for assessment of hepatitis C virus related hepatocellular carcinoma and subsequent categorization of hepatocellular carcinoma into non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma, to establish appropriate treatment strategies, are costly, invasive and requires multiple screening steps. This demands alternative diagnostic approaches that are cost-effective, time-efficient, and minimally invasive, while maintaining their efficacy for screening of hepatitis c virus related hepatocellular carcinoma. In this study, we propose that attenuated total reflection Fourier transform infrared in conjunction with principal component analysis – linear discriminant analysis and support vector machine multivariate algorithms holds a potential as a sensitive tool for the detection of hepatitis C virus-related hepatocellular carcinoma and the subsequent categorization of hepatocellular carcinoma into non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma. MethodsFreeze-dried sera samples collected from 31 hepatitis c virus related hepatocellular carcinoma patients and 30 healthy individuals, were used to acquire mid-infrared absorbance spectra (3500–900 cm-1) using attenuated total reflection Fourier transform infrared. Chemometric machine learning techniques were utilized to build principal component analysis – linear discriminant analysis and support vector machine discriminant models for the spectral data of hepatocellular carcinoma patients and healthy individuals. Sensitivity, specificity, and external validation on blind samples were calculated. ResultsMajor variations were observed in the two spectral regions i.e., 3500–2800 and 1800–900 cm-1. IR spectral signatures of hepatocellular carcinoma were reliably different from healthy individuals. Principal component analysis – linear discriminant analysis and support vector machine models computed 100% accuracy for diagnosing hepatocellular carcinoma. To classify the non-angio-invasive hepatocellular carcinoma/ angio-invasive hepatocellular carcinoma status, diagnostic accuracy of 86.21% was achieved for principal component analysis – linear discriminant analysis. While the support vector machine showed a training accuracy of 98.28% and a cross-validation accuracy of 82.75%. External validation for support vector machine based classification observed 100% sensitivity and specificity for accurately classifying the freeze-dried sera samples for all categories. ConclusionsWe present the specific spectral signatures for non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma, which were prominently differentiated from healthy individuals. This study provides an initial insight into the potential of attenuated total reflection Fourier transform infrared to diagnose hepatitis C virus related hepatocellular carcinoma but also to further categorize into non-angio-invasive and angio-invasive hepatocellular carcinoma.