Dimensionality Reduction Analysis Research Articles

Integrated multi-level omics profiling of disulfidptosis identifis SPAG4 as an innovative immunotherapeutic target in glioblastoma

ObjectiveTo investigate the association between disulfidptosis-related genes (DFRGs) and patient prognosis, while concurrently identifying potential therapeutic targets in glioblastoma (GBM).MethodsWe retrieved RNA sequencing data and clinical characteristics of GBM patients from the TCGA database. We found there was a total of 6 distinct clusters in GBM, which was identified by the t-SNE and UMAP dimension reduction analysis. Prognostically significant genes in GBM were identified using the limma package, coupled with univariate Cox regression analysis. Machine learning algorithms were then applied to identify central genes. The CIBERSORT algorithm was utilized to assess the immunological landscape across different GBM subtypes. In vitro and in vivo experiments were conducted to investigate the role of SPAG4 in regulating the proliferation, invasion of GBM, and its effects within the immune microenvironment.Results23 genes, termed DFRGs, were successfully identified, demonstrating substantial potential for establishing a prognostic model for GBM. Single cell analysis revealed a significant correlation between DFRGs and the progression of GBM. Utilizing individual risk scores derived from this model enabled the stratification of patients into two distinct risk groups, revealing significant variations in immune infiltration patterns and responses to immunotherapy. Utilizing the random survival forests algorithm, SPAG4 was identified as the gene with the highest prognostic significance within our model. In vitro studies have elucidated SPAG4’s significant role in GBM pathogenesis, potentially through the regulation of fatty acid metabolism pathways. Our in vivo investigations using a subcutaneous xenograft model have confirmed SPAG4’s influence on tumor growth and its capacity to modulate the immune microenvironment. Advanced research hints that SPAG4 might achieve immune evasion by increasing CD47 expression, consequently reducing phagocytosis.ConclusionsThese findings highlight SPAG4 as a potential GBM therapeutic target and emphasize the complexity of the immune microenvironment in GBM progression.

A Novel Non-Contact Multi-User Online Indoor Positioning Strategy Based on Channel State Information

The IEEE 802.11bf-based wireless fidelity (WiFi) indoor positioning system has gained significant attention recently. It is important to recognize that multi-user online positioning occurs in real wireless environments. This paper proposes an indoor positioning sensing strategy that includes an optimized preprocessing process and a new machine learning (ML) method called NKCK. The NKCK method can be broken down into three components: neighborhood component analysis (NCA) for dimensionality reduction, K-means clustering, and K-nearest neighbor (KNN) classification with cross-validation (CV). The KNN algorithm is particularly suitable for our dataset since it effectively classifies data based on proximity, relying on the spatial relationships between points. Experimental results indicate that the NKCK method outperforms traditional methods, achieving reductions in error rates of 82.4% compared to naive Bayes (NB), 85.0% compared to random forest (RF), 72.1% compared to support vector machine (SVM), 64.7% compared to multilayer perceptron (MLP), 50.0% compared to density-based spatial clustering of applications with noise (DBSCAN)-based methods, 42.0% compared to linear discriminant analysis (LDA)-based channel state information (CSI) amplitude fingerprinting, and 33.0% compared to principal component analysis (PCA)-based approaches. Due to the sensitivity of CSI, our multi-user online positioning system faces challenges in detecting dynamic human activities, such as human tracking, which requires further investigation in the future.

Gene polymorphisms of miR-323b and miR-1343 that regulate kininogen L are associated with schizophrenia susceptibility: A preliminary population‑based study.

miR-SNP is a type of functional single nucleotide polymorphism (SNP) that affects the regulatory functions of miRNA genes, miRNA binding sites, or components of miRNA biogenesis. This study aimed to explore the relationship between miRNA gene polymorphisms that regulate the kininogen L protein and schizophrenia (SCZ). Bioinformatics methods predicted miRNA gene polymorphism sites regulating the kininogen L protein. The polymorphisms of rs56103835, rs6513496, rs651349, and rs2986407 were detected using imLDR multiple SNP typing technologies in 513 SCZ patients and 509 controls. The association of miR-SNP variations with SCZ susceptibility and symptoms was evaluated using SNPstat to determine the optimal inheritance model. Generalized multifactor dimensionality reduction (GMDR) analysis and logistic regression were used to calculate miR-SNP interactions. The association between hsa-miR-323b-rs56103835 and SCZ was statistically significant under the dominant model. The result of gene-gene interaction showed that the three-factor model (rs56103835 / rs2986407 / rs2155248) was the best, but it could not be considered significantly related to SCZ. Additionally, SCZ patients with the CC or CT genotype on rs2986407 were more likely to experience auditory hallucinations than those with the TT genotype. Our data revealed that the mutation of hsa-miR-323b-rs56103835 from C to T was associated with susceptibility to SCZ. The mutation of hsa-miR-1343-rs2986407 from T to C increases the risk of auditory hallucinations in SCZ patients.

Development of a Spectral Flow Cytometry Analysis Pipeline for High-dimensional Immune Cell Characterization.

Flow cytometry is used for immune cell analysis for cell composition and function. Spectral flow cytometry allows for high-dimensional analysis of immune cells, overcoming limitations of conventional flow cytometry. However, analyzing data from large Ab panels is challenging using traditional biaxial gating strategies. We present, to our knowledge, a novel analysis pipeline to improve analysis of spectral flow cytometry. We employ this method to identify rare T cell populations in aging. We isolated splenocytes from young (2-3 mo old) and aged (18-19 mo old) female C57BL/6N mice and then stained these with a panel of 20 fluorescently labeled Abs. We performed spectral flow cytometry and then data processing and analysis using Python within a Jupyter Notebook environment to perform dimensionality reduction, batch correction, unsupervised clustering, and differential expression analysis. Our analysis of 3,776,804 T cells from 11 spleens revealed 35 distinct T cell clusters identified by surface marker expression. We observed significant differences between young and aged mice, with clusters enriched in one age group over the other. Naive, effector memory, and central memory CD8+ and CD4+ T cell subsets exhibited age-associated changes in abundance and marker expression. We also demonstrate the utility of our pipeline in a human PBMC dataset that used a 50-fluorescent color panel. By leveraging high-dimensional analysis methods, we provide insights into the immune aging process. This approach offers a robust and easily implemented analysis pipeline for spectral flow cytometry data that may facilitate the discovery of novel therapeutic targets for age-related immune dysfunction.

Credit spread drivers and cross-country connectedness: a study of emerging economies in Asia

PurposeWhile the existing literature lacks a holistic approach to determining credit spreads and is limited to mostly developed countries, this study investigates credit spread determinants and their cross-country connectedness in the context of four emerging economies in Asia by incorporating bonds, market risk, macroeconomic and global factors.Design/methodology/approachThis study utilizes principal component analysis for dimensionality reduction and variable representation. Furthermore, we employ the dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity model to capture the cross-country credit spread connectedness between the variables.FindingsThe findings indicate that market volatilities are the most significant drivers of credit spreads, while global factors play a moderating role. Furthermore, the results provide compelling evidence of cross-country credit spread connectedness, with China as the primary transmitter and Malaysia as the primary receiver among the selected emerging economies.Originality/valueThis study addresses the limitations of previous research by extending the analysis beyond the commonly studied developed economies and focusing on emerging economies in Asia. It also employs a comprehensive approach to determine credit spread and explores cross-country credit spread connectedness in developing economies, thereby shedding light on financial risks and vulnerabilities within interconnected global financial systems.

Integrating different fidelity models for process optimization: A case of equilibrium and rate-based extractive distillation using ionic liquids

We integrate equilibrium and rate-based models to formulate a hybrid optimization scheme for designing an ionic liquid-based extractive distillation process for mixed-refrigerant separation. The equilibrium model assumes vapor–liquid equilibrium at each stage but challenges arise with low-volatility, high-viscosity solvents, which drive the system away from equilibrium. The rate-based approach considers mass and heat transfer rates, giving more accurate representation. We compare the two models for separating R-410A, an azeotropic mixture of R-32 and R-125, using [EMIM][SCN] ionic liquid as entrainer. Analyzing over 4300 simulations with various dimensionality reduction and topological analysis techniques, we find that predictions from the two models exhibit similar trends, but the overestimation in equilibrium-based purities sometimes leads to infeasible process designs. The proposed optimization algorithm thus combines the strengths of the two models to locate feasible and optimal designs.

The application of improved DTW algorithm in sports posture recognition

Sports posture recognition plays a crucial role in modern sports science and training. Posture recognition and analysis plays a positive role in improving sports quality and ensuring sports safety. However, existing recognition technologies still have poor recognition and accuracy in large amounts of posture data. Therefore, to further improve the performance of the existing posture recognition techniques, this study assumes that postures during movement can be effectively represented through the time series of skeletal key points, and the local similarity of these postures can be captured through the Dynamic Time Warping (DTW) algorithm. Based on this assumption, the existing DTW algorithm is improved by introducing the K-Nearest Neighbor (KNN) algorithm and combining it with Principal Component Analysis (PCA) for feature dimensionality reduction. A novel algorithmic model for postures recognition is proposed. The experimental results showed that the improved algorithm performed well in postures recognition rate and accuracy. Especially, when the k value was 5, the recognition rate reached up to 89%, and the accuracy reached 87%. Compared with the existing algorithm, the improved KNN-DTW algorithm has significant improvement in accuracy and computational efficiency. In summary, the new algorithm shows significant advantages in terms of accuracy and stability, providing a powerful tool for the analysis of athletic postures in the field of sports. Meanwhile, this research result has important application prospects in fields such as sports training, sports medicine, and virtual reality.

Design of an Energy-Saving System for Smart Home Decoration Based on Situational Awareness

This article analyzes the specific energy-saving requirements for smart home decoration in the current market to determine the overall plan of the entire smart home decoration energy-saving system. Based on situational analysis combined with grounded theory, organize and summarize the original data, and use factor analysis for dimensionality reduction analysis. Combining the multi-level analysis method, experts evaluated and scored the user experience evaluation indicators, obtained the weight assignment of each influencing factor, and constructed a user experience evaluation model for smart home products based on situational theory. After extensive testing, it was found that the ZigBee based smart home network implemented in this article has low energy consumption, overcomes many shortcomings of smart home networks, and has high application value.

A taxonomy for human social perception: Data-driven modeling with cinematic stimuli.

Every day, humans encounter complex social situations that need to be encoded effectively to allow interaction with others. Yet, principles for organizing the perception of social features from the external world remain poorly characterized. In this large-scale study, we investigated the principles of social perception in dynamic scenes. In the primary data set, we presented 234 movie clips (41 min) containing various social situations to 1,140 participants and asked them to evaluate the presence of 138 social features in each clip. Analyses of the social feature ratings revealed that some features are perceived categorically (present or absent) and others continuously (intensity) and simple social features requiring immediate response are perceived most consistently across participants. To establish the low-dimensional perceptual organization for social features based on movies, we used principal coordinate analysis and consensus clustering for the feature ratings. These dimension reduction analyses revealed that the social perceptual structure can be modeled with eight main dimensions and that behaviorally relevant perceptual categories emerge from these main dimensions. This social perceptual structure generalized from the perception of unrelated Hollywood movie clips to the perception of a full Finnish movie (70 min) and to the perception of static images (n = 468) and across three independent sets of participants (n = 2,254). Based on the results, we propose eight basic dimensions of social perception as a model for rapid social perception where social situations are perceived along eight orthogonal perceptual dimensions (most importantly emotional valence, empathy vs. dominance, and cognitive vs. physical behavior). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Statistical batch-aware embedded integration, dimension reduction, and alignment for spatial transcriptomics.

Spatial transcriptomics (ST) technologies provide richer insights into the molecular characteristics of cells by simultaneously measuring gene expression profiles and their relative locations. However, each slice can only contain limited biological variation, and since there are almost always non-negligible batch effects across different slices, integrating numerous slices to account for batch effects and locations is not straightforward. Performing multi-slice integration, dimensionality reduction, and other downstream analyses separately often results in suboptimal embeddings for technical artifacts and biological variations. Joint modeling integrating these steps can enhance our understanding of the complex interplay between technical artifacts and biological signals, leading to more accurate and insightful results. In this context, we propose a hierarchical hidden Markov random field model STADIA to reduce batch effects, extract common biological patterns across multiple ST slices, and simultaneously identify spatial domains. We demonstrate the effectiveness of STADIA using five datasets from different species (human and mouse), various organs (brain, skin, and liver), and diverse platforms (10x Visium, ST, and Slice-seqV2). STADIA can capture common tissue structures across multiple slices and preserve slice-specific biological signals. In addition, STADIA outperforms the other three competing methods (PRECAST, fastMNN, and Harmony) in terms of the balance between batch mixing and spatial domain identification, and it demonstrates the advantage of joint modeling when compared to STAGATE and GraphST. The source code implemented by R is available at https://github.com/zhanglabtools/STADIA and archived with version 1.01 on Zenodo https://zenodo.org/records/13637744.

Global sensitivity analysis of nuclear district heating reactor primary heat exchanger and pressure vessel optimization

Recently, small modular reactors (SMRs) have received greater interest as a source for clean and affordable district heating (DH). Compared to power plants, the low-pressure, low-temperature design and nearly 100 % efficiency reduce the cost of produced energy considerably. However, few practical implementations exist yet, and cost estimates and design principles are subject to uncertainties whose interactions remain largely unknown. In this work, we present a techno-economic optimization and sensitivity analysis of a natural circulation DH SMR primary heat exchanger. A Cuckoo Search variant augmented with a modified Hooke-Jeeves search was used as the optimizer, with SimDec (simulation decomposition) subsequently employed for global sensitivity analysis. The reactor pressure vessel and containment vessel specific costs exhibited the greatest impact on the cost of heat and the optimized configurations. While low-pressure, low-temperature design is central to heating reactor cost-effectiveness, optimized primary circuit temperatures clearly exceeded previous assumptions. In a 5260 full-load hours mid-load application, a 34–41 €/MWh cost range was found for produced heat at 8 % interest and 20-year lifetime. For heat exchanger optimization, the results indicate the potential for considerable performance improvement from using deterministic local search for terminal convergence and sensitivity analysis for dimensionality reduction.

Association of superoxide dismutase and catalase genetic variants and their gene-gene interactions with the severity of COVID-19

BACKGROUND: Since the outbreak of COVID-19 infection, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), oxidative stress has been proposed as an important player in its severity. This increased the interest in studying antioxidant systems to evaluate their possible role in counteracting disease progression. AIM: The aim of the current study was to investigate the association of single nucleotide polymorphism (SNP) of superoxide dismutase (SOD) and catalase (CAT) genes with the severity of COVID-19. MATERIALS AND METHODS: Study subjects were divided into two groups based on the severity of their symptoms. Allele-specific PCR was used for genotyping, and multifactor dimensionality reduction (MDR) analysis was performed to investigate the SNP–SNP interaction models. RESULTS: The results showed a significant association of SOD2 rs4880 with the severity of COVID-19 (p = 0.002). SOD2 47TT genotype was significantly more frequent among patients with severe COVID-19 (OR 4.34; 95% CI 1.72–10.96). The three-locus SNP–SNP interaction model, resulted from MDR analysis, was statistically significant (0.55 × 10–4, OR 3.81; 95% CI 1.96–7.42). Carriers of SOD1 7958G * SOD2 47T * CAT 262C allele combination had a higher risk of severe COVID-19 (p = 0.0045, OR 2.84, 95% CI 1.40–5.78). CONCLUSIONS: The obtained results contribute to better understanding of COVID-19 pathogenesis and suggest novel potential prognostic biomarkers of the infection.

Interactions of Potential Endocrine-Disrupting Chemicals with Whole Human Proteome Predicted by AlphaFold2 Using an In Silico Approach.

Binding with proteins is a critical molecular initiating event through which environmental pollutants exert toxic effects in humans. Previous studies have been limited by the availability of three-dimensional (3D) protein structures and have focused on only a small set of environmental contaminants. Using the highly accurate 3D protein structure predicted by AlphaFold2, this study explored over 60 million interactions obtained through molecular docking between 20,503 human proteins and 1251 potential endocrine-disrupting chemicals. A total of 66,613,773 docking results were obtained, 1.2% of which were considered to be high binding, as their docking scores were lower than -7. Monocyte to macrophage differentiation factor 2 (MMD2) was predicted to interact with the highest number of environmental pollutants (526), with polychlorinated biphenyls and polychlorinated dibenzofurans accounting for a significant proportion. Dimension reduction and clustering analysis revealed distinct protein profiles characterized by high binding affinities for perfluoroalkyl and polyfluoroalkyl substances (PFAS), phthalate-like chemicals, and other pollutants, consistent with their uniquely enriched pathways. Further structural analysis indicated that binding pockets with a high proportion of charged amino acid residues, relatively low α-helix content, and high β-sheet content were more likely to bind to PFAS than others. This study provides insights into the toxicity pathways of various pollutants impacting human health and offers novel perspectives for the establishment and expansion of adverse outcome pathway-based models.

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.

Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host-reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A leave-one-covariate-out inference approach identified specific HIV reservoir and clinical-demographic parameters, such as age and biological sex, that were particularly important in predicting immunophenotypes. Overall, immune parameters were more strongly associated with total HIV proviral frequencies than intact proviral frequencies. Uniquely, however, expression of the IL-7 receptor alpha chain (CD127) on CD4 T cells was more strongly correlated with the intact reservoir. Unsupervised dimension reduction analysis identified two main clusters of PWH with distinct immune and reservoir characteristics. Using reservoir correlates identified in these initial analyses, decision tree methods were employed to visualize relationships among multiple immune and clinical-demographic parameters and the HIV reservoir. Finally, using random splits of our data as training-test sets, ML algorithms predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA . The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy

Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host–reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A leave-one-covariate-out inference approach identified specific HIV reservoir and clinical–demographic parameters, such as age and biological sex, that were particularly important in predicting immunophenotypes. Overall, immune parameters were more strongly associated with total HIV proviral frequencies than intact proviral frequencies. Uniquely, however, expression of the IL-7 receptor alpha chain (CD127) on CD4 T cells was more strongly correlated with the intact reservoir. Unsupervised dimension reduction analysis identified two main clusters of PWH with distinct immune and reservoir characteristics. Using reservoir correlates identified in these initial analyses, decision tree methods were employed to visualize relationships among multiple immune and clinical–demographic parameters and the HIV reservoir. Finally, using random splits of our data as training-test sets, ML algorithms predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA . The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.

The rs3918188 and rs1799983 loci of eNOS gene are associated with susceptibility in patients with systemic lupus erythematosus in Northeast China

To investigate the association between single nucleotide polymorphism (SNP) at the rs3918188, rs1799983 and rs1007311 loci of the endothelial nitric oxide synthase (eNOS) gene and genetic susceptibility to systemic lupus erythematosus (SLE) in northeastern China. The base distribution of eNOS gene rs3918188, rs1799983 and rs1007311 in 1712 human peripheral blood samples from Northeast China was detected by SNaPshot sequencing technology. The correlation between genotype, allele and gene model of these loci of the eNOS gene and the genetic susceptibility to SLE was investigated by logistic regression analysis. The results of the differences in the frequency distribution of their gene models were visualised using R 4.3.2 software. Finally, HaploView 4.2 software was used to analyse the relationship between the haplotypes of the three loci mentioned above and the genetic susceptibility to SLE. A multifactor dimensionality reduction (MDR) analysis was used to determine the best SNP-SNP interaction model. The CC genotype and C allele at the rs3918188 locus may be a risk factor for SLE (CC vs AA: OR = 1.827, P < 0.05; C vs A: OR = 1.558, P < 0.001), and this locus increased the risk of SLE in the dominant model and the recessive model (AC + CC vs AA: OR = 1.542, P < 0.05; CC vs AA + AC: OR = 1.707, P < 0.001), while the risk of SLE was reduced in the overdominant model (AC vs AA + CC: OR = 0.628, P < 0.001). The GT genotype and T allele at locus rs1799983 may be a protective factor for SLE (GT vs GG: OR = 0.328, P < 0.001; T vs G: OR = 0.438, P < 0.001) and this locus reduced the risk of SLE in the overdominant model (GT vs GG + TT: OR = 0.385, P < 0.001). There is a strong linkage disequilibrium between the rs1007311 and rs1799983 loci of the eNOS gene. Among them, the formed haplotype AG increased the risk of SLE compared to GG. AT and GT decreased the risk of SLE compared to GG. In this study, the eNOS gene rs3918188 and rs1799983 loci were found to be associated with susceptibility to SLE. This helps to deeply explore the mechanism of eNOS gene and genetic susceptibility to SLE. It provides a certain research basis for the subsequent exploration of the molecular mechanism of these loci and SLE, as well as the early diagnosis, treatment and prognosis of SLE.

Integrative analysis of multi-omics data reveals importance of collagen and the PI3K AKT signalling pathway in CAKUT

Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of childhood chronic kidney failure and a significant cause of chronic kidney disease in adults. Genetic and environmental factors are known to influence CAKUT development, but the currently known disease mechanism remains incomplete. Our goal is to identify affected pathways and networks in CAKUT, and thereby aid in getting a better understanding of its pathophysiology. With this goal, the miRNome, peptidome, and proteome of over 30 amniotic fluid samples of patients with non-severe CAKUT was compared to patients with severe CAKUT. These omics data sets were made findable, accessible, interoperable, and reusable (FAIR) to facilitate their integration with external data resources. Furthermore, we analysed and integrated the omics data sets using three different bioinformatics strategies: integrative analysis with mixOmics, joint dimensionality reduction and pathway analysis. The three bioinformatics analyses provided complementary features, but all pointed towards an important role for collagen in CAKUT development and the PI3K-AKT signalling pathway. Additionally, several key genes (CSF1, IGF2, ITGB1, and RAC1) and microRNAs were identified. We published the three analysis strategies as containerized workflows. These workflows can be applied to other FAIR data sets and help gaining knowledge on other rare diseases.

A rigorous workflow and comparative analysis for accurate determination of vitrinite reflectance using data-driven approaches in the Persian Gulf region

Vitrinite reflectance (VR) is a critical measure of source rock maturity in geochemistry. Although VR is a widely accepted measure of maturity, its accurate measurement often proves challenging and costly. Rock–Eval pyrolysis offers the advantages of being cost-effective, fast, and providing accurate data. Previous studies have employed empirical equations and traditional machine learning methods using T-max data for VR prediction, but these approaches often yielded subpar results. Therefore, the quest to develop a precise method for predicting vitrinite reflectance based on Rock–Eval data becomes particularly valuable. This study presents a novel approach to predicting VR using advanced machine learning models, namely ExtraTree and XGBoost, along with new ways to prepare the data, such as winsorization for outlier treatment and principal component analysis (PCA) for dimensionality reduction. The depth and three Rock–Eval parameters (T-max, S1/TOC, and HI) were used as input variables. Three model sets were examined: Set 1, which involved both Winsorization and PCA; Set 2, which only included Winsorization; and Set 3, which did not include either. The results indicate that the ExtraTree model in Set 1 demonstrated the highest level of predictive accuracy, whereas Set 3 exhibited the lowest level of accuracy, confirming the methodology's effectiveness. The ExtraTree model obtained an overall R2 score of 0.997, surpassing traditional methods by a significant margin. This approach improves the accuracy and dependability of virtual reality predictions, showing significant advancements compared to conventional empirical equations and traditional machine learning methods.

Renin and CYP P450 gene variations significantly associated with essential hypertension: A prospective pharmacogenomics study

ObjectiveThis study aims to investigate the association between Renin-angiotensin aldosterone system (RAAS) and Cytochrome P450 gene polymorphisms in hypertension patients of the South Indian population using pharmacogenomics profile. MethodsHypertension cases (N = 147) and control subjects (n = 150) were collected from Tamil Nadu, India. A case-control association study was conducted to assess the involvement of RAAS gene polymorphisms (REN rs397514691, REN rs544315427, REN rs567667202) and CYP gene polymorphisms (CYP2D6 rs754164689, CYP2D6 rs1058172, CYP3A4 rs765598920) in essential hypertensive patients. Genotyping was performed using the PCR-RFLP method, and significant results were validated through RT-PCR analysis. ResultsThe genotype and allele distribution of REN rs397514691 and rs544315427 variants significantly associated with hypertensive patients (Variant Allele Frequency (VAF) = 0.11; VAF = 0.27, respectively). CYP2D6 polymorphisms rs754164689 and rs1058172 variant alleles were significantly associated with female hypertensive patients, suggesting a potential risk allele for essential hypertension in the South Indian population. REN and CYP3A4 variants, highly connected in pharmacology action, were validated through RT-PCR amplification studies, providing new insights into their role in the development of hypertension. Association confirmation was achieved through multifactor dimensionality reduction analysis. ConclusionThe genes associated with specific variants, particularly REN and CYP2D6, may serve as potential markers for the early diagnosis of hypertension and as new drug targets, particularly in the female population.

Assessment of LINC-PINT genetic polymorphisms and esophageal squamous cell carcinoma risk in the Hainan Han population

Objectives Esophageal squamous cell carcinoma (ESCC) is a malignant tumor with high incidence and mortality rates worldwide. This study aimed to investigate the correlation between LINC-PINT polymorphisms and ESCC risk in the Hainan Han population. Methods A total of 391 patients with ESCC and 452 healthy controls were enrolled to evaluate the effect of LINC-PINT SNPs (single nucleotide polymorphisms) on ESCC susceptibility. Associations were evaluated by calculating odds ratios (OR) and 95% confidence intervals (CIs). Multifactor dimensionality reduction analysis was performed to explore the association between SNP-SNP interactions and ESCC susceptibility. We further determined the correlation between clinical indicators and SNP in patients with ESCC. Results Our study showed that rs157916 (OR 0.63, p = 0.011) and rs157928 (OR 0.80, p = 0.021) were associated with a decreased risk of ESCC. Stratified analysis indicated that rs157916 could decrease the risk of ESCC in people aged >64 years, in males, and non-drinkers (OR 0.58, p = 0.042; OR 0.58, p = 0.010; OR 0.62, p = 0.025, respectively). Rs16873842 was related to a decreased risk of ESCC in males (OR 0.70, p = 0.015). Rs7801029 was associated with ESCC risk in females (OR 0.39, p = 0.033) and non-drinkers (OR 0.68, p = 0.040). Rs7781295 decreased the ESCC risk in smokers (OR 0.58, p = 0.046) and drinkers (OR 0.58, p = 0.046). In addition, rs157928 played a protective role in ESCC risk in females (OR 0.39, p = 0.033) and non-smokers (OR 0.32, p = 0.006). Additionally, the best predictive model for ESCC was a combination of rs157916, rs16873842, rs7801029, rs7781295, rs28662387, and rs157928. Conclusion Our study revealed that LINC-PINT polymorphisms were associated with ESCC risk.