Rapid discrimination of Alismatis Rhizoma and quantitative analysis of triterpenoids based on near-infrared spectroscopy.

This study developed a rapid, accurate, objective and economic method to identify and evaluate the quality of Alismatis Rhizoma (AR) commodities. Traditionally, the identification of plant species and geographical origins of AR commodities mainly relied on experienced staff. However, the subjectivity and inaccuracy of human identification negatively impacted the trade of AR. Besides, liquid chromatographic methods such as ultra-high-performance liquid chromatography (UPLC) and high-performance liquid chromatography (HPLC), the major approach for the determination of triterpenoid contents in AR was time-consuming, expensive, and highly demanded in manoeuvre specialists. In this study, the combination of near-infrared (NIR) spectroscopy and chemometrics as the method was developed and utilised to address the two common issues of identifying the quality of AR commodities. Through the discriminant analysis (DA), the raw NIR spectroscopy data on 119 batches samples from two species and four origins in China were processed to the best pre-processed data. Subsequently, orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) as the major chemometrics were used to analyse the best pre-processed data. The accuracy rates by OPLS-DA and RF were respectively 100% and 97.2% for the two species of AR, and respectively100% and 94.4% for the four origins of AR. Meanwhile, a quantitative correction model was established to rapidly and economically predict the seven triterpenoid contents of AR through combining the partial least squares (PLS) method and NIR spectroscopy, and taking the triterpenoid contents measured by UPLC as the reference value, and carry out spectral pre-processing methods and band selection. The final quantitative model correlation coefficients of the seven triterpenoid contents of AR ranged from 0.9000 to 0.9999, indicating that prediction ability of this model had good stability and applicability.

Automated Collective Variable Discovery for MFSD2A transporter from molecular dynamics simulations.

Biomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine learning algorithms, such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)-based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system.

A simplified method for relapsing-remitting multiple sclerosis detection: Insights from resting EEG signals.

BACKGROUND AND OBJECTIVE: Multiple sclerosis (MS) is a neurodegenerative autoimmune disease affecting the central nervous system, leading to various neurological symptoms. Early detection is paramount to prevent enduring damage during MS episodes. Although magnetic resonance imaging (MRI) is a common diagnostic tool, this study aims to explore the feasibility of using electroencephalography (EEG) signals for MS detection, considering their accessibility and ease of application compared to MRI. METHODS: The study involved the analysis of EEG signals during rest from 17 MS patients and 27 healthy volunteers to investigate MS-healthy patterns. Power spectral density features (PSD) were extracted from the 32-channel EEG signals. The study employed Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Classification and Regression Trees (CART), and k-Nearest Neighbor (kNN) classifiers to identify channels with the highest accuracy. Notably, the study achieved 100% accuracy in MS detection using the "Fp1" and "Pz" channels with the LDA classifier. A statistical analysis, utilizing the independent sample t-test, was conducted to explore whether PSD features of these channels differed significantly between healthy individuals and those with MS. RESULTS: The results of the study demonstrate that effective detection of MS can be achieved using PSD features from only two channels of the EEG signal. Specifically, the "Fp1" and "Pz" channels exhibited 100% accuracy in MS detection with the LDA classifier. The statistical analysis further explored and confirmed the significant differences in PSD features between healthy individuals and MS patients. CONCLUSION: The study concludes that the proposed method, utilizing PSD features from specific EEG channels, offers a straightforward and efficient diagnostic approach for the effective detection of MS. The findings suggest the potential utility of EEG signals as a non-invasive and accessible alternative for MS detection, highlighting the importance of further research in this direction.

SERS characterization of biochemical changes associated with biodesulfurization of dibenzothiophene using Gordonia sp. HS126-4N.

In this study, Gordonia sp. HS126-4N was employed for dibenzothiophene (DBT) biodesulfurization, tracked over 9 days using SERS. During the initial lag phase, no significant spectral changes were observed, but after 48 h, elevated metabolic activity was evident. At 72 h, maximal bacterial population correlated with peak spectrum variance, followed by stable spectral patterns. Despite 2-hydroxybiphenyl (2-HBP) induced enzyme suppression, DBT biodesulfurization persisted. PCA and PLS-DA analysis of the SERS spectra revealed distinctive features linked to both bacteria and DBT, showcasing successful desulfurization and bacterial growth stimulation. PLS-DA achieved a specificity of 95.5 %, sensitivity of 94.3 %, and AUC of 74 %, indicating excellent classification of bacteria exposed to DBT. SERS effectively tracked DBT biodesulfurization and bacterial metabolic changes, offering insights into biodesulfurization mechanisms and bacterial development phases. This study highlights SERS' utility in biodesulfurization research, including its use in promising advancements in the field.

Beta-band power classification of Go/No-go arm reaching responses in the human hippocampus.

Introduction Can we decode movement execution and inhibition from hippocampal oscillations during arm-reaching tasks? Traditionally associated with memory encoding, spatial navigation, and motor sequence consolidation, the hippocampus has come under scrutiny for its potential role in movement processing. Stereotactic electroencephalography (SEEG) has provided a unique opportunity to study the neurophysiology of the human hippocampus during motor tasks. Objective In this study, we assess the accuracy of discriminant functions, in combination with principal component analysis (PCA), in classifying between "Go" and "No-go" trials in a Go/No-go arm-reaching task. Our approach centers on capturing the modulation of beta-band (13-30 Hz) power from multiple SEEG contacts in the hippocampus and minimizing the dimensional complexity of channels and frequency bins. Methods This study utilizes SEEG data from the human hippocampus of 10 participants diagnosed with epilepsy. Spectral power was computed during a "center-out" Go/No-go arm-reaching task, where participants reached or withheld their hand based on a colored cue. PCA was used to reduce data dimension and isolate the highest-variance components within the beta band. The Silhouette score was employed to measure the quality of clustering between "Go" and "No-go" trials. The accuracy of five different discriminant functions was evaluated using cross-validation. Results The Diagonal-Quadratic model performed best of the 5 classification models, exhibiting the lowest error rate in all participants (median: 9.91%, average: 14.67%). PCA showed that the first two principal components collectively accounted for 54.83% of the total variance explained on average across all participants, ranging from 36.92% to 81.25% among participants. Conclusion This study shows that PCA paired with a Diagonal-Quadratic model can be an effective method for classifying between Go/No-go trials from beta-band power in the hippocampus during arm-reaching responses. This emphasizes the significance of hippocampal beta-power modulation in motor control, unveiling its potential implications for brain-computer interface (BCI) applications.

Characterization and classification of pathogenic bacteria using native fluorescence and spectral deconvolution.

Identification and classification of pathogenic bacterial strains is of current interest for the early treatment of diseases. In this work, protein fluorescence from eight different pathogenic bacterial strains were characterized using steady state and time resolved fluorescence spectroscopy. The spectral deconvolution method was also employed to decompose the emission contribution from different intrinsic fluorophores and extracted various key parameters, such as intensity, emission maxima, emission line width of the fluorophores, and optical redox ratio. The change in average lifetime values across different bacterial strains exhibits good statistical significance (p ≤ 0.01). The variations in the photophysical characteristics of bacterial strains are due to the different conformational states of the proteins. The stepwise multiple linear discriminate analysis of fluorescence emission spectra at 280 nm excitation across eight different bacterial strains classifies the original groups and cross validated group with 100% and 99.5% accuracy, respectively.

Analysis of characteristic aromas of buckwheat with different germplasm using gas chromatography-mass spectrometry combined with chemometrics and multivariate statistical analysis.

In this study, the volatile components in 40 samples of Tartary buckwheat and common buckwheat from 6 major producing areas in China were analyzed. A total of 77 volatile substances were identified, among which aldehydes and hydrocarbons were the main volatile components. Odor activity value analysis revealed 26 aromatic compounds, with aldehydes making a significant contribution to the aroma of buckwheat. Seven key compounds that could be used to distinguish Tartary buckwheat from common buckwheat were identified. The orthogonal partial least squares-discriminant analysis was effectively used to classify Tartary buckwheat and common buckwheat from different producing areas. This study provides valuable information for evaluating buckwheat quality, breeding high-quality varieties, and enhancing rational resource development.

Colorimetric array sensor based on bimetallic nitrogen-doped carbon-based nanozyme material to detect multiple antioxidants.

Copper-cobalt bimetallic nitrogen-doped carbon-based nanoenzymatic materials (CuCo@NC) were synthesized using a one-step pyrolysis process. A three-channel colorimetric sensor array was constructed for the detection of seven antioxidants, including cysteine (Cys), uric acid (UA), tea polyphenols (TP), lysine (Lys), ascorbic acid (AA), glutathione (GSH), and dopamine (DA). CuCo@NC with peroxidase activity was used to catalyze the oxidation of TMB by H2O2 at three different ratios of metal sites. The ability of various antioxidants to reduce the oxidation products of TMB (ox TMB) varied, leading to distinct absorbance changes. Linear discriminant analysis (LDA) results showed that the sensor array was capable of detecting seven antioxidants in buffer and serum samples. It could successfully discriminate antioxidants with a minimum concentration of 10 nM. Thus, multifunctional sensor arrays based on CuCo@NC bimetallic nanoenzymes not only offer a promising strategy for identifying various antioxidants but also expand their applications in medical diagnostics and environmental analysis of food.

Changes in phenolic compounds of Phyllanthus emblica juice during different storage temperature and pH conditions.

The aim of this experiment was to investigate the effect of storage temperature and pH on phenolic compounds of Phyllanthus emblica juice. Juice was stored at different temperatures and pH for 15 days and sampled on 2-day intervals. The browning index (BI, ABS420 nm), pH, centrifugal precipitation rate (CPR), and phenolic compounds were evaluated. The results showed 4°C and pH 2.5 could effectively inhibit browning and slow down pH drop of P. emblica juice. The result of orthogonal partial least square-discriminant analysis showed P. emblica juice stored at 4°C and pH 2.5 still had a similar phenolic composition, but at 20°C, 37°C, and pH 3.5, the score plots were concentrated only in the first 3 days. Additionally, gallic acid (GA) and ellagic acid (EA) were screened out to be the differential compounds for browning of P. emblica juice. The contents of GA, epigallocatechin (EGC), corilagin (CL), gallocatechin gallate (GCG), chebulagic acid (CA), 1,2,3,4,6-O-galloyl-d-glucose (PGG), and EA were more stable at 4°C and pH 2.5. Overall, during storage at 4°C and pH 2.5, it could inhibit the increase of GA and EA and decrease of CL, GCG, CA, and PGG, whereas EGC did not show significant difference between storage conditions. The CPR was higher at 4°C, while pH 2.5 could reduce the CPR. In conclusion, in order to maintain stability of phenolic compounds and extended storage period, the P. emblica juice could be stored at low temperature and adjust the pH to increase the stability of juice system.

Evaluation of a Voltametric E-Tongue Combined with Data Preprocessing for Fast and Effective Machine Learning-Based Classification of Tomato Purées by Cultivar.

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

A modified sequential wavenumber selection-discriminant analysis with Bayesian optimization strategy for detection and identification of chia seed oil adulteration using Raman spectroscopy.

The detection of oil fraud can be accomplished through the use of Raman spectroscopy, which is a potent analytical technique for identifying the adulteration of edible oils with inferior or less expensive oils. However, appropriate data reduction and classification methods are required to achieve high accuracy and reliability in the analysis of Raman spectra. In this study, data reduction algorithms such as principal component analysis (PCA) and modified sequential wavenumber selection (MSWS) were applied, along with discriminant analysis (DA) as a classifier for detecting oil fraud. The parameters of DA, such as the discriminant type, the amount of regularization, and the linear coefficient threshold, were optimized using Bayesian optimization. The methods were tested on a dataset of chia oil mixed with 5-40 % sunflower oil, which is a common form of fraud in the market. The results showed that MSWS-DA achieved 100 % classification accuracy, while PCA-DA achieved 91.3 % accuracy. Therefore, it was demonstrated that Raman spectroscopy combined with MSWS-DA and Bayesian optimization can effectively detect oil fraud with high accuracy and robustness.

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits.

KEY MESSAGE: Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes. Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.

Differentiation of Listeria monocytogenes serotypes using near infrared hyperspectral imaging.

Among the severe foodborne illnesses, listeriosis resulting from the pathogen Listeria monocytogenes exhibits one of the highest fatality rates. This study investigated the application of near infrared hyperspectral imaging (NIR-HSI) for the classification of three L. monocytogenes serotypes namely serotype 4b, 1/2a and 1/2c. The bacteria were cultured on Brain Heart Infusion agar, and NIR hyperspectral images were captured in the spectral range 900-2500 nm. Different pre-processing methods were applied to the raw spectra and principal component analysis was used for data exploration. Classification was achieved with partial least squares discriminant analysis (PLS-DA). The PLS-DA results revealed classification accuracies exceeding 80 % for all the bacterial serotypes for both training and test set data. Based on validation data, sensitivity values for L. monocytogenes serotype 4b, 1/2a and 1/2c were 0.69, 0.80 and 0.98, respectively when using full wavelength data. The reduced wavelength model had sensitivity values of 0.65, 0.85 and 0.98 for serotype 4b, 1/2a and 1/2c, respectively. The most relevant bands for serotype discrimination were identified to be around 1490 nm and 1580-1690 nm based on both principal component loadings and variable importance in projection scores. The outcomes of this study demonstrate the feasibility of utilizing NIR-HSI for detecting and classifying L. monocytogenes serotypes on growth media.

Classification of healthy and cancerous colon cells by Fourier transform infrared spectroscopy.

Colorectal cancer is one of the most diagnosed types of cancer in developed countries. Current diagnostic methods are partly dependent on pathologist experience and laboratories instrumentation. In this study, we used Fourier Transform Infrared (FTIR) spectroscopy in transflection mode, combined with Principal Components Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares - Discriminant Analysis (PLS-DA), to build a classification algorithm to diagnose colon cancer in cell samples, based on absorption spectra measured in two spectral ranges of the mid-infrared spectrum. In particular, PCA technique highlights small biochemical differences between healthy and cancerous cells: these are related to the larger lipid content in the former compared with the latter and to the larger relative amount of protein and nucleic acid components in the cancerous cells compared with the healthy ones. Comparison of the classification accuracy of PCA-LDA and PLS-DA methods applied to FTIR spectra measured in the 1000-1800 cm-1 (low wavenumber range, LWR) and 2700-3700 cm-1 (high wavenumber range, HWR) remarks that both algorithms are able to classify hidden class FTIR spectra with excellent accuracy (100 %) in both spectral regions. This is a hopeful result for clinical translation of infrared spectroscopy: in fact, it makes reliable the predictions obtained using FTIR measurements carried out only in the HWR, in which the glass slides used in clinical laboratories are transparent to IR radiation.

The combination of Fourier-transform infrared spectroscopy with pattern recognition techniques for classification and discrimination of red snapper fish oils.

Fish oils are good sources for essential fatty acids such as omega-3 and omega-6 fatty acids needed to human growth. Indonesia is rich in fish species and among this, red snapper fish (Lutjanus sp.) can be extracted to get red snapper fish oils (RSFOs). The aim of this study was to classify and discriminate RSFO from different origins using Fourier-transform infrared (FTIR) spectra and pattern recognition techniques. All of the RSFO's FTIR spectra were very similar. The FTIR vibrations showed the presence of triglycerides as the main composition in fish oils. Principal component analysis (PCA) could separate the RSFO according to sample origin. Supervised pattern recognition of partial least square-discriminant analysis (PLS-DA) and sparse PLS-DA (sPLS-DA) successfully discriminated and classified different Lutjanus species of fish oils obtained from different origins. The vibration of functional groups at 1711, 1653, 1745, and 3012 per cm were considered for their important contributions in discriminating of Lutjanus species (variable importance in projection, variable importance in the projection score >1). Fish oils obtained from the same species were classified into the same class indicating similar chemical compositions. Among the three pattern recognition techniques used, sPLS-DA offers the best model for the discrimination and classification of Lutjanus fish oils. It can be concluded that FTIR spectroscopy in combination with the pattern recognition technique is the potential to be used for of fish oil authentication to verify the quality of the fish oils. It can be further developed as a rapid and effective method for fish oil authentication.

Assessment of spatial air quality on the East Coast of Peninsular Malaysia utilizing environmetric techniques.

The presence of harmful substances in the atmosphere poses significant risks to the environment and public health. These pollutants can come from natural sources like dust and wildfires, or from human activities such as industrial, transportation, and agricultural practices. The objective of this study was to assess air quality on the East Coast of Peninsular Malaysia by analyzing historical data from the Department of Environment, Malaysia. Daily measurements of PM10, O3, SO2, NO2, and CO were collected from eight monitoring stations over 11 years (2011-2021) and analyzed using environmetric techniques. Hierarchical agglomerative cluster analysis (HACA) classified two stations as belonging to the high pollution cluster (HPC), three stations as part of the moderate pollution cluster (MPC), and three stations as the low pollution cluster (LPC). Discriminant analysis revealed a correct assignment rate of 90.50%, indicating that all five parameters were able to differentiate pollution levels with high significance (p < 0.0001). Principal component analysis (PCA) was conducted to validate the pattern of air quality variables in relation to the identified clusters (HPC, MPC, and LPC). The results showed that two verifactors (VFs) were extracted in HPC and LPC, while three VFs were identified in MPC. The cumulative variance explained by the PCA for HPC, MPC, and LPC was 69.43%, 82.32%, and 62.16%, respectively. Finally, an artificial neural network (ANN) was used to forecast the air pollutant index (API) levels, using the R2 and RMSE performance metrics. The PCA-MLP Model A yielded an R2 value of 0.8470 and an RMSE of 6.6470, while PCA-MLP Model B achieved an R2 value of 0.8591 and an RMSE of 6.3000, both indicating a significant and strong correlation.

A New Investigation to Discriminate Sexes in Alive Nile Tilapia (Oreochromis niloticus) Using Cyp19a1a and Dmrt1 Gene Expression in Tail Fin Tissues.

The Nile Tilapia (Oreochromis niloticus), a gonochoristic teleost fish with a XX/XY sex-determination system, is an ideal model for investigating gonadal sex differentiation. During gonadal differentiation, the expression of cyp19a1a in XX gonads and dmrt1 in XY gonads are required for undifferentiated tissues to develop into ovary or testis. In this study, quantitative real-time RT-PCR assessed the expression of cyp19a1a and dmrt1 genes in gonads and tail fin tissues. Differences in gene expression mean among sexually differentiated fish were analyzed using two-way analysis of variance (ANOVA) and validation of mixed model using discriminant analysis (DA) for morphometric traits and the gene expression in gonads and tail fin tissues used to validate and utilize them in discriminating sexes in sex-differentiated Nile Tilapia fish. The results revealed that, cyp19a1a gene expression in female ovaries was more significant than dmrt1 in male testis. In the other hand, the dmrt1 gene expression in the tail fin was higher in males than females. Both, cyp19a1a and dmrt1 genes, can discriminate fish sexes by 100% by using their expression in tail fin tissues. In conclusion, the cyp19a1a and dmrt1 genes could be used as a genetic marker to discriminate between the Nile Tilapia sexes, whereas used as an indicator for ovarian or testis differentiation in sexually differentiated Nile Tilapia using tail fin tissues. It is worth mentioning that this is the first investigation for using cyp19a1a and dmrt1 genes from Nile Tilapia tail fin tissues in sex determination.

Comparative Analysis of Biomarkers in Type 2 Diabetes Patients With and Without Comorbidities: Insights Into the Role of Hypertension and Cardiovascular Disease.

Background: Type 2 diabetes mellitus (T2DM) are 90% of diabetes cases, and its prevalence and incidence, including comorbidities, are rising worldwide. Clinically, diabetes and associated comorbidities are identified by biochemical and physical characteristics including glycemia, glycated hemoglobin (HbA1c), and tests for cardiovascular, eye and kidney disease. Objectives: Diabetes may have a common etiology based on inflammation and oxidative stress that may provide additional information about disease progression and treatment options. Thus, identifying high-risk individuals can delay or prevent diabetes and its complications. Design: In patients with or without hypertension and cardiovascular disease, as part of progression from no diabetes to T2DM, this research studied the changes in biomarkers between control and prediabetes, prediabetes to T2DM, and control to T2DM, and classified patients based on first-attendance data. Control patients and patients with hypertension, cardiovascular, and with both hypertension and cardiovascular diseases are 156, 148, 61, and 216, respectively. Methods: Linear discriminant analysis is used for classification method and feature importance, This study examined the relationship between Humanin and mitochondrial protein (MOTSc), mitochondrial peptides associated with oxidative stress, diabetes progression, and associated complications. Results: MOTSc, reduced glutathione and glutathione disulfide ratio (GSH/GSSG), interleukin-1ß (IL-1ß), and 8-isoprostane were significant (P < .05) for the transition from prediabetes to t2dm, highlighting importance of mitochondrial involvement. complement component 5a (c5a) is a biomarker associated with disease progression and comorbidities, gsh gssg, monocyte chemoattractant protein-1 (mcp-1), 8-isoprostane being most important biomarkers. Conclusions: Comorbidities affect the hypothesized biomarkers as diabetes progresses. Mitochondrial oxidative stress indicators, coagulation, and inflammatory markers help assess diabetes disease development and provide appropriate medications. Future studies will examine longitudinal biomarker evolution.

A framework for performance enhancement of classifiers in detection of prostate cancer from microarray gene.

Prostate cancer is a major world health problem for men. This shows how important early detection and accurate diagnosis are for better treatment and patient outcomes. This study compares different ways to find Prostate Cancer (PCa) and label tumors as normal or abnormal, with the goal of speeding up current work in microarray gene data analysis. The study looks at how well several feature extraction methods work with three feature selection strategies: Harmonic Search (HS), Firefly Algorithm (FA), and Elephant Herding Optimization (EHO). The techniques tested are Expectation Maximization (EM), Nonlinear Regression (NLR), K-means, Principal Component Analysis (PCA), and Discrete Cosine Transform (DCT). Eight classifiers are used for the task of classification. These are Random Forest, Decision Tree, Adaboost, XGBoost, and Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. This study looks at how well these classifiers work with and without feature selection methods. It finds that the SVM with radial basis function kernel, using DCT for feature extraction and EHO for feature selection, does the best of all of them, with an accuracy of 94.8 % and an error rate of 5.15 %.

Efficient residual network using hyperspectral images for corn variety identification.

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.