VCF2PCACluster: a simple, fast and memory-efficient tool for principal component analysis of tens of millions of SNPs.

Principal component analysis (PCA) is an important and widely used unsupervised learning method that determines population structure based on genetic variation. Genome sequencing of thousands of individuals usually generate tens of millions of SNPs, making it challenging for PCA analysis and interpretation. Here we present VCF2PCACluster, a simple, fast and memory-efficient tool for Kinship estimation, PCA and clustering analysis, and visualization based on VCF formatted SNPs. We implemented five Kinship estimation methods and three clustering methods for its users to choose from. Moreover, unlike other PCA tools, VCF2PCACluster possesses a clustering function based on PCA result, which enabling users to automatically and clearly know about population structure. We demonstrated the same accuracy but a higher performance of this tool in performing PCA analysis on tens of millions of SNPs compared to another popular PLINK2 software, especially in peak memory usage that is independent of the number of SNPs in VCF2PCACluster.

Analyzing postprandial metabolomics data using multiway models: a simulation study.

BACKGROUND: Analysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. RESULTS: We introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using principal component analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased versus healthy groups. CONCLUSIONS: Our experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

Raman developmental markers in root cell walls are associated with lodging tendency in tef.

MAIN CONCLUSION: Using Raman micro-spectroscopy on tef roots, we could monitor cell wall maturation in lines with varied genetic lodging tendency. We describe the developing cell wall composition in root endodermis and cylinder tissue. Tef [Eragrostis tef (Zucc.) Trotter] is an important staple crop in Ethiopia and Eritrea, producing gluten-free and protein-rich grains. However, this crop is not adapted to modern farming practices due to high lodging susceptibility, which prevents the application of mechanical harvest. Lodging describes the displacement of roots (root lodging) or fracture of culms (stem lodging), forcing plants to bend or fall from their vertical position, causing significant yield losses. In this study, we aimed to understand the microstructural properties of crown roots, underlining tef tolerance/susceptibility to lodging. We analyzed plants at 5 and 10 weeks after emergence and compared trellised to lodged plants. Root cross sections from different tef genotypes were characterized by scanning electron microscopy, micro-computed tomography, and Raman micro-spectroscopy. Lodging susceptible genotypes exhibited early tissue maturation, including developed aerenchyma, intensive lignification, and lignin with high levels of crosslinks. A comparison between trellised and lodged plants suggested that lodging itself does not affect the histology of root tissue. Furthermore, cell wall composition along plant maturation was typical to each of the tested genotypes independently of trellising. Our results suggest that it is possible to select lines that exhibit slow maturation of crown roots. Such lines are predicted to show reduction in lodging and facilitate mechanical harvest.

Statistical methods for discrimination of STR genotypes using high resolution melt curve data.

Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen's principal component analysis (PCA)-based ScreenClust® HRM® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene® Q qPCR instrument and EvaGreen® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.

IMU Data-Driven and PCA-Based Approach to Establish Quantifiable and Practically Applicable Measures for V2 Technique Elements in Cross-Country Skiing.

Quantifying movement coordination in cross-country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula-informed distinct elements of the V2 ski-skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set-down, leg push-off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element-specific principal component analyses (PCAs)-driven by the variance produced by the technique extremes-resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set-down, and leg push-off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski-skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists.

Key predictors of food security and nutrition in Africa: a spatio-temporal model-based study.

There is voluminous literature on Food Security in Africa. This study explicitly considers the spatio-temporal factors in addition to the usual FAO-based metrics in modeling and understanding the dynamics of food security and nutrition across the African continent. To better understand the complex trajectory and burden of food insecurity and nutrition in Africa, it is crucial to consider space-time factors when modeling and interpreting food security. The spatio-temporal anova model was found to be superior(employing statistical criteria) to the other three models from the spatio-temporal interaction domain models. The results of the study suggest that dietary supply adequacy, food stability, and consumption status are positively associated with severe food security, while average food supply and environmental factors have negative effects on Food Security and Nutrition. The findings also indicate that severe food insecurity and malnutrition are spatially and temporally correlated across the African continent. Spatio-temporal modeling and spatial mapping are essential components of a comprehensive practice to reduce the burden of severe food insecurity. likewise, any planning and intervention to improve the average food supply and environment to promote sustainable development should be regional instead of one size fit all.

UPLC-Q-TOF/MS analysis of chemical components of single decoction and combined decoction of Qige decoction reveals the differences between various preparation processes.

The aim of this work was to explore the differences between various pharmaceutical processes in combined solutions of a single decoction (QGHBY) and a combined decoction (QGHJY) of Qi-Ge decoction from the perspective of chemical composition changes, so as to further guide the clinical application of drugs. A combined solution of a single decoction and a combined decoction of Astragali Radix, Puerariae Lobatae Radix and Citri Reticulatae Chachiensis Pericarpium was prepared with the same technological parameters. The chemical components of the two were detected and identified based on UPLC-Q-TOF/MS, and the different components were determined by principal component analysis. Eighty-eight compounds were identified in the pharmaceutical solution of Qi-Ge decoction. Principal component analysis revealed 11 different components of QGHBY and QGHJY with the conditions of Variable Importance in Projection (VIP) ≥ 1, fold change ≥ 2 and p < 0.05, among which hesperidin, hesperitin, isosinensetin, sinensetin and 5-demethylnobiletin were the components of Citri Reticulatae Chachiensis Pericarpium. The levels of these 11 different components in QGHJY were higher than those of QGHBY. The combined decoction is beneficial for the dissolution of flavonoids and other chemical components, and there is a significant difference in the content of chemical components between modern herbal concentrate granules and traditional decoctions.

Enzyme activities and heavy metal interactions in calcareous soils under different land uses.

This study was carried out to examine the interaction of enzyme activities, microbial biomass carbon, and CO2 respiration with heavy metals under different land uses in terms of quality and sustainability of the soil. There is a statistically significant positive correlation between dehydrogenase enzyme activity and Mn, Pb, Cd, and Co, while it was negative between Cr. There was a positive correlation between catalase enzyme activity and Mn and Pb and between urease and Co. The higher interaction of dehydrogenase activity with heavy metals, which is included in the endo enzyme group, has been explained as a much stronger effect of heavy metals on living microorganisms and endoenzymes than extracellular enzymes stabilized on clay minerals and organic matter. The high clay content of the soil is thought to reduce some of the negative effects of heavy metals on enzymes. The results of this study may be good indicators of enzyme activities, especially dehydrogenase, catalase, and urease, for soil health and quality, chemical degradation and restoration processes, and ecosystem functioning in soils contaminated or to be contaminated with heavy metals. It shows that the activities of these enzymes are very sensitive and can decrease rapidly in case of high concentrations of heavy metals.

Soil health and quality, chemical degradation and restoration processes, and soils contaminated with heavy metals or potentially polluted can be good indicators of ecosystem functioning. This study was carried out with the belief that the interaction of enzymes with heavy metals in this type of soil will be revealed in detail and will shed light on such studies to be done in the future.

Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach.

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm's robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability.

Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data.

The implementation of power line communications (PLC) in smart electricity grids provides us with exciting opportunities for real-time cable monitoring. In particular, effective fault classification and estimation methods employing machine learning (ML) models have been proposed in the recent past. Often, the research works presenting PLC for ML-aided cable diagnostics are based on the study of synthetically generated channel data. In this work, we validate ML-aided diagnostics by integrating measured channels. Specifically, we consider the concatenation of clustering as a data pre-processing procedure and principal component analysis (PCA)-based dimension reduction for cable anomaly detection. Clustering and PCA are trained with measurement data when the PLC network is working under healthy conditions. A possible cable anomaly is then identified from the analysis of the PCA reconstruction error for a test sample. For the numerical evaluation of our scheme, we apply an experimental setup in which we introduce degradations to power cables. Our results show that the proposed anomaly detector is able to identify a cable degradation with high detection accuracy and low false alarm rate.

Quantitative analysis of solid dosage forms of Atenolol by Raman spectroscopy.

OBJECTIVE: To develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol.Significance: For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol. METHODS: Various solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques, such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively. RESULTS: As the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model. CONCLUSIONS: Based on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination.

Hydrogeochemical characteristics of groundwater contamination in Guwahati city, Assam, India: Tracing the elemental Threads.

Groundwater serves as an important resource for drinking and agriculture in many countries, including India. Assessing the quality of groundwater is essential for understanding its chemical characteristics and suitability for consumption. This study aims to explore the factors affecting the hydrogeochemical changes in groundwater within Guwahati City, Assam, India. Groundwater samples were collected and analyzed for major and trace elements, as well as anion concentrations. Concentrations of As, Al, Ba, Cu, F-, Fe, Mn, and Pb exceeded the permissible limits set by both World Health Organization (WHO) and Bureau of Indian Standards (BIS), indicating serious health concerns for the local inhabitants. The distribution pattern of trace elements exceeding the guideline values is intricate, suggesting widespread contamination of groundwater throughout the study area. The Heavy Metal Pollution Index (HPI) and Water Quality Index (WQI) revealed that, except for the central zone, groundwater across the entire study area requires intervention. Piper plot illustrated that the groundwater is predominantly of Ca-HCO3 type, indicating the dominance of alkaline earth and weak acids. Groundwater hydrogeochemistry is mainly controlled by rock-water interaction and evolves through silicate weathering, carbonate weathering, and cation exchange processes. Multivariate statistical analysis identified distinct groups of groundwater based on chemical characteristics, emphasizing the role of both natural processes and anthropogenic activities in influencing groundwater quality. Regular monitoring, management, and intervention of groundwater sources throughout the study area are crucial for long-term use. The findings of this study will assist stakeholders, regulators, and policymakers in formulating strategies for the sustainable use of groundwater.

Managing open loop water resource value chain through IoT focused decision and information integration (DII) modelling using fuzzy MCDM approach.

Water is a limited and invaluable resource that is essential for human survival. Negligence and unregulated water use have brought about a global water crisis. Proper management with a relevant decision and information integration approach can aid water to continue as a renewable resource. The water and wastewater industry must shift from outmoded, inefficient techniques to more sustainable, data-driven solutions to address water concerns and improve public health. The Internet of Things (IoT) has emerged as an innovative strategy for decision and information integration to drive an open-loop Water Value Chain (WVC) efficiently. The IoT-driven network allows objects to connect and communicate, gather data in real-time, analyze data and develop reasonable decision - making insights instantaneously. This study aims to find the enablers of IoT for an open-loop WVC. It examines 25 factors for IoT implementation in the open-loop WVC. The 25 factors are clustered into seven enablers using Principal Component Analysis (PCA). These principal components are analyzed by employing a Multi-Criteria Decision Making (MCDM) approach, i.e., the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), which helps to find the cause-effect relationship to prioritize the enablers. The fuzzy set theory is used to address the uncertainty and vagueness in experts' opinions and data deficiency problems. The study reveals that the Ecosystem of an IoT network, IoT network configuration and adaptation and data mobility in an IoT network are the most prominent enablers to consider for the implementation of IoT in an open loop WVC. The study may be helpful for regulatory agencies and enterprises in water distribution and processing for identifying and prioritizing the potential enablers of IoT in an open-loop WVC.

Identification of New Hepatic Metabolites of Miconazole by Biological and Electrochemical Methods Using Ultra-High-Performance Liquid Chromatography Combined with High-Resolution Mass Spectrometry.

The main objective of this study was to investigate the metabolism of miconazole, an azole antifungal drug. Miconazole was subjected to incubation with human liver microsomes (HLM) to mimic phase I metabolism reactions for the first time. Employing a combination of an HLM assay and UHPLC-HRMS analysis enabled the identification of seven metabolites of miconazole, undescribed so far. Throughout the incubation with HLM, miconazole underwent biotransformation reactions including hydroxylation of the benzene ring and oxidation of the imidazole moiety, along with its subsequent degradation. Additionally, based on the obtained results, screen-printed electrodes (SPEs) were optimized to simulate the same biotransformation reactions, by the use of a simple, fast, and cheap electrochemical method. The potential toxicity of the identified metabolites was assessed using various in silico models.

Chemical Characterization of the Essential Oil Compositions of Mentha spicata and M. longifolia ssp. cyprica from the Mediterranean Basin and Multivariate Statistical Analyses.

This present study aims to characterize the essential oil compositions of the aerial parts of M. spicata L. and endemic M. longifolia ssp. cyprica (Heinr. Braun) Harley by using GC-FID and GC/MS analyses simultaneously. In addition, it aims to perform multivariate statistical analysis by comparing with the existing literature, emphasizing the literature published within the last two decades, conducted on both species growing within the Mediterranean Basin. The major essential oil components of M. spicata were determined as carvone (67.8%) and limonene (10.6%), while the major compounds of M. longifolia ssp. cyprica essential oil were pulegone (64.8%) and 1,8-cineole (10.0%). As a result of statistical analysis, three clades were determined for M. spicata: a carvone-rich chemotype, a carvone/trans-carveol chemotype, and a pulegone/menthone chemotype, with the present study result belonging to the carvone-rich chemotype. Carvone was a primary determinant of chemotype, along with menthone, pulegone, and trans-carveol. In M. longifolia, the primary determinants of chemotype were identified as pulegone and menthone, with three chemotype clades being pulegone-rich, combined menthone/pulegone, and combined menthone/pulegone with caryophyllene enrichment. The primary determinants of chemotype were menthone, pulegone, and caryophyllene. The present study result belongs to pulegone-rich chemotype.

Analysis of the chemical characteristics and causes of high total hardness of groundwater in Jianghan Plain, China.

Familiarity with the chemical characteristics of regional groundwater can provide important guidance and reference for the development of regional groundwater exploitation. Jianghan Plain has been reported to have high groundwater total hardness (TH), resulting in the inability of local groundwater to be directly used as drinking water. In order to explore the causes of high TH, the paper analyzed the hydrochemical characteristics of shallow groundwater in Jianghan Plain combined with software of SPSS, JMP, and PHEEQC. The results showed that the cations in the groundwater in the area were mainly Ca2+, while the anions were mainly HCO3-. 20% of groundwater exceed the China national guideline for TH (i.e., 450 mg/L). The groundwater chemistry in the study area was controlled by three main factors of dissolution of carbonate rocks, human activities, and redox conditions, among which the interaction between water and rock had the greatest impact. The water carbonate rock interaction within Jianghan Plain was affected by various factors such as water flow and aquifers and showed a gradually weakening trend from west to east. This work not only strengthened the understanding of the causes of the high TH of groundwater in the region, but also provided reference value for regional groundwater environmental management.

Estimation of Time-Since-Deposition of bloodstains on different surfaces using ATR-FTIR Spectroscopy and Chemometrics.

Blood is commonly discovered at crime scenes in various forms, including stains, dried residue, pools, and fingerprints on assorted surfaces. Estimating the age of bloodstains is a crucial aspect of reconstructing crime scenes. This research aimed to investigate how the nature of different surfaces affects the estimation of bloodstain age, utilizing a reliable and non-destructive approach. The study employed ATR-FTIR spectroscopy in conjunction with Chemometric techniques such as PCA (Principal Component Analysis) and OPLSR (Orthogonal Signal Correction Partial Least Square Regression Analysis) to analyze spectral data and develop regression models for estimating bloodstain age on cement, metal, and wooden surfaces for up to eleven days. The chemometric models for bloodstains on all three substrates demonstrated strong performance, with predictive Root Mean Square Error (RMSE) values ranging from 1.1 to 1.43 and R2 values from 0.84 to 0.89. Notably, the model developed for metal surfaces was found to be the most accurate with minimal prediction error. The findings of the study showed that the porosity of the substrates upon which bloodstains were found had a discernible influence on the age-related transformations observed in bloodstains; the majority of which occured within the spectral range of 2800 cm- 1 to 3500 cm- 1.

Fluorescence Spectroscopy for the Assessment of Microbial Load in UVC Treated Water.

In this article, Fluorescence spectroscopy has been employed for the assessment of microbial load and it has been compared with the gold standard colony forming unit (CFU) and optical density (OD) methods. In order to develop a correlation between three characterization techniques, water samples of different microbial loads have been prepared by UVC disinfection method through an indigenously developed NUVWater sterilizer, which operates in close cycle flow configuration. A UV dose of 58.9 mJ/cm2 has been determined for 99.99% disinfection for a flow rate of 0.8 l/min. The water samples were excited at 270 nm which results in the tryptophan like fluorescence at 360 nm that decreases gradually with increase of UVC dose, indicating the bacterial degradation and it has been confirmed by OD and CFU methods. In addition, it has been proved that a close cycle water flow around UV lamp is imperative so that an appropriate dose must be delivered to microorganisms for an efficient disinfection. It has been found that due to the sensitive nature of Fluorescence spectroscopy, it yields immediate results, whereas, for CFU and OD methods, water samples needs to be inoculated for 24 h. Fluorescence spectroscopy, therefore, provide a fast, online, reliable and sensitive method for the monitoring of pathogenic quantification in drinking water.

Acute phase proteins and total antioxidant capacity in free-roaming cats infected by pathogenic leptospires.

BACKGROUND: Leptospirosis is a neglected but widespread zoonotic disease throughout the world. Most mammals are hosts of Leptospira spp., including domestic cats, species in which no consensus has been reached on the clinical presentation or diagnosis of the disease. The study of acute-phase proteins (APPs) and biomarkers of oxidative status would contribute to knowledge about the disease in cats. This report evaluated four APPs: Serum amyloid A-SAA, Haptoglobin-Hp, albumin and Paraoxonase 1-PON1 and the antioxidant response through Total Antioxidant Capacity-TAC, in 32 free-roaming cats. Cats were classified as seroreactive for anti-leptospiral antibodies (group 1, n = 8), infected with Leptospira spp (group 2, n = 5) and leptospires-free cats (group 3, n = 19). RESULTS: SAA differences were observed between groups 1 and 2 (p-value = 0.01) and between groups 2 and 3 (p-value = 0.0001). Hp concentration differences were only detected between groups 2 and 3 (p-value = 0.001). Albumin concentrations only differed between groups 1 and 3 (p-value = 0.017) and 2 and 3 (p-value < 0.005). Cats in groups 1 (p-value < 0.005) and 2 (p-value < 0.005) had lower PON1 concentrations than group 3. No statistically significant differences between pairs of groups were detected for TAC concentrations. The principal component analysis (PCA) retained two principal components, (PC1 and PC2), explaining 60.1% of the observed variability of the inflammatory proteins and the antioxidant TAC. CONCLUSIONS: Increases in Serum SAA, Hp, and decreases in PON1 activity may indicate an active inflammatory state in infected cats (currently or recently infected).

Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques.

BACKGROUND: This study used machine learning techniques to evaluate cardiovascular disease risk factors (CVD) and the relationship between sex and these risk factors. The objective was pursued in the context of CVD being a major global cause of death and the need for accurate identification of risk factors for timely diagnosis and improved patient outcomes. The researchers conducted a literature review to address previous studies' limitations in using machine learning to assess CVD risk factors. METHODS: This study analyzed data from 1024 patients to identify the significant CVD risk factors based on sex. The data comprising 13 features, such as demographic, lifestyle, and clinical factors, were obtained from the UCI repository and preprocessed to eliminate missing information. The analysis was performed using principal component analysis (PCA) and latent class analysis (LCA) to determine the major CVD risk factors and to identify any homogeneous subgroups between male and female patients. Data analysis was performed using XLSTAT Software. This software provides a comprehensive suite of tools for Data Analysis, Machine Learning, and Statistical Solutions for MS Excel. RESULTS: This study showed significant sex differences in CVD risk factors. 8 out of 13 risk factors affecting male and female patients found that males and females share 4 of the eight risk factors. Identified latent profiles of CVD patients, suggesting the presence of subgroups among CVD patients. These findings provide valuable insights into the impact of sex differences on CVD risk factors. Moreover, they have important implications for healthcare professionals, who can use this information to develop individualized prevention and treatment plans. The results highlight the need for further research to elucidate these disparities better and develop more effective CVD prevention measures. CONCLUSIONS: The study explored the sex differences in the CVD risk factors and the presence of subgroups among CVD patients using ML techniques. The results revealed sex-specific differences in risk factors and the existence of subgroups among CVD patients, thus providing essential insights for personalized prevention and treatment plans. Hence, further research is necessary to understand these disparities better and improve CVD prevention.