Construction of Prognostic Prediction Models for Colorectal Cancer Based on Ferroptosis-Related Genes: A Multi-Dataset and Multi-Model Analysis.

Background: Colorectal cancer (CRC) remains a significant health burden globally, necessitating a deeper understanding of its molecular landscape and prognostic markers. This study characterized ferroptosis-related genes (FRGs) to construct models for predicting overall survival (OS) across various CRC datasets. Methods: In TCGA-COAD dataset, differentially expressed genes (DEGs) were identified between tumor and normal tissues using DESeq2 package. Prognostic genes were identified associated with OS, disease-specific survival, and progression-free interval using survival package. Additionally, FRGs were downloaded from FerrDb website, categorized into unclassified, marker, and driver genes. Finally, multiple models (Coxboost, Elastic Net, Gradient Boosting Machine, LASSO Regression, Partial Least Squares Regression for Cox Regression, Ridge Regression, Random Survival Forest [RSF], stepwise Cox Regression, Supervised Principal Components analysis, and Support Vector Machines) were employed to predict OS across multiple datasets (TCGA-COAD, GSE103479, GSE106584, GSE17536, GSE17537, GSE29621, GSE39084, GSE39582, and GSE72970) using intersection genes across DEGs, OS, disease-specific survival, and progression-free interval, and FRG categories. Results: Six intersection genes (ASNS, TIMP1, H19, CDKN2A, HOTAIR, and ASMTL-AS1) were identified, upregulated in tumor tissues, and associated with poor survival outcomes. In the TCGA-COAD dataset, the RSF model demonstrated the highest concordance index. Kaplan-Meier analysis revealed significantly lower OS probabilities in high-risk groups identified by the RSF model. The RSF model exhibited high accuracy with AUC values of 0.978, 0.985, and 0.965 for 1-, 3-, and 5-year survival predictions, respectively. Calibration curves demonstrated excellent agreement between predicted and observed survival probabilities. Decision curve analysis confirmed the clinical utility of the RSF model. Additionally, the model's performances were validated in GSE29621 dataset. Conclusions: The study underscores the prognostic relevance of 6 intersection genes in CRC, providing insights into potential therapeutic targets and biomarkers for patient stratification. The RSF model demonstrates robust predictive performance, suggesting its utility in clinical risk assessment and personalized treatment strategies.

Luminescence properties of Sr2Ga2GeO7:Bi3+ phosphors and temperature sensing characteristics of co-doped Sm3.

As one of the fundamental physical quantities, temperature is extremely important in various fields. In order to study the temperature sensing characteristics of dual-emitting center phosphors, Bi3+-doped and Bi3+/Sm3+-doped Sr2Ga2GeO7 phosphors were synthesized by high-temperature solid-phase method. Under 312 nm excitation, the Sr2Ga2GeO7:Bi3+ phosphor exhibits a blue broadband emission corresponding to the 3P1 → 1S0 transition of Bi3+ ions. By testing the temperature change spectrum of phosphors, it was found that Bi3+ exhibited strong thermal sensitivity. However, due to the fact that single ion doped phosphors are easily affected by other factors when applied to the field of temperature sensing, based on the thermal sensitivity of Bi3+, Sm3+ with low temperature sensitivity was selected as the co-doped ion, and it was found that the two ions had different thermal quenching characteristics when the temperature change spectrum was tested. Using the temperature detection method based on the fluorescence intensity ratio (FIR) of the dual emission centers, it was found that the best absolute sensitivity Sa was 3.125% K-1 and the maximum relative sensitivity Sr was 1.275% K-1 in the range of 303-423 K. These results show that Sr2Ga2GeO7:Bi3+/Sm3+ phosphors have broad application prospects in the field of optical temperature sensing.

Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach.

Background: The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC. Method: This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis. Results: Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways. Conclusions: This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.

Identification of potential therapeutic targets for systemic lupus erythematosus based on GEO database analysis and Mendelian randomization analysis.

Background: Systemic lupus erythematosus (SLE) is a complex autoimmune disease. Current treatments mainly rely on immunosuppressants, which lack specificity and pose challenges during treatment. This study aims to deeply explore the molecular pathogenic mechanism of SLE through gene expression databases (GEO) and bioinformatics analysis methods, combined with Mendelian randomization analysis, to provide key clues for new therapeutic targets. Methods: In this study, the SLE-related gene chip dataset GSE65391 was selected from the GEO database, and the data were preprocessed and statistically analyzed using R language and bioinformatics tools. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), GO, and KEGG enrichment analysis were used to screen differentially expressed genes (DEGs) for functional annotation and pathway localization. Furthermore, Mendelian randomization analysis was conducted to identify core genes closely related to SLE risk, and immune cell infiltration analysis and compound molecular docking studies were performed on the core gene ISG15. Results: The study successfully screened 3,456 DEGs and identified core gene modules highly related to SLE through WGCNA analysis, including key genes closely related to the pathogenesis of SLE, such as STAT1, DDX58, ISG15, IRF7, and IFIH1. In particular, this study found a significant positive correlation between the ISG15 gene and SLE, suggesting that it may be a potential risk factor for SLE. Additionally, through molecular docking technology, it was discovered that the ISG15 gene can effectively bind to two compounds, genistein, and flavopiridol, which have anti-inflammatory and immunosuppressive effects, respectively. This provides new potential drug targets for SLE treatment. Discussion: As an immunomodulatory cytokine, ISG15 plays a crucial role in the pathogenesis of SLE. This study found that variations in the ISG15 gene may increase the risk of SLE and exacerbate inflammatory responses and tissue damage through multiple mechanisms. Furthermore, molecular docking revealed that genistein and flavopiridol can effectively bind to ISG15, offering a new approach for SLE treatment. These two compounds, with their anti-inflammatory and immunosuppressive properties, have the potential to slow the progression of SLE by influencing the expression and function of ISG15. Conclusion: Through comprehensive bioinformatics analysis and Mendelian randomization analysis, this study deeply explored the molecular pathogenic mechanism of SLE and successfully identified ISG15 as a potential therapeutic target for SLE. Simultaneously, molecular docking technology revealed that two compounds, genistein and flavopiridol, have potential therapeutic effects with ISG15, providing new potential drugs for SLE treatment. These discoveries not only enhance our understanding of the pathogenesis of SLE but also provide important clues for developing new treatment strategies.

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches.

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 µg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.

Exploring the predictive values of SERP4 and FRZB in dilated cardiomyopathy based on an integrated analysis.

BACKGROUND AND OBJECTIVE: The aim of this study was to investigate potential hub genes for dilated cardiomyopathy (DCM). METHODS: Five DCM-related microarray datasets were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were used for identification. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, disease ontology, gene ontology annotation and protein-protein interaction (PPI) network analysis were then performed, while a random forest was constructed to explore central genes. Artificial neural networks were used to compare with known genes and to develop new diagnostic models. 240 population blood samples were collected and expression of hub genes was verified in these samples using RT-PCR and demonstrated by Nomogram. RESULTS: After differential analysis, 33 genes were statistically significant (adjusted P < 0.05). Functional enrichment of these differential genes resulted in 85 Gene Ontology (GO) functions identified and 6 pathways enriched for the KEGG pathway. PPI networks and molecular complex assays identified 10 hub genes (adjusted P < 0.05). Random forest identified SMOC2 and SFRP4 as the most important, followed by FCER1G and FRZB. NeuraHF models (SMOC2, SFRP4, FCER1G and FRZB) were selected by artificial neural network model and had better diagnostic efficacy for the onset of DCM, compared with the traditional KG-DCM models (MYH7, ACTC1, TTN and LMNA). Finally, SFRP4 and FRZB were expressed higher in DCM verified by RT-PCR and as a factor for DCM identified by Nomogram. CONCLUSIONS: We performed an integrated analysis and identified SFRP4 and FRZB as a new factor for DCM. But the exact mechanism still needs further experimental verification.

Effects of dietary curcumin on gene expression: An analysis of transcriptomic data in mice.

BACKGROUND: Curcumin, a ubiquitous polyphenol in turmeric, possesses many anti-cancer and anti-inflammatory properties. These therapeutic effects are largely resultant of curcumin's ability to modulate global gene expression. Bioinformatics-based approaches for analyzing differential gene expression are effective tools in gaining a more profound understanding of the underlying mechanisms of action. AIM: In this study, we aimed to identify key genes that were differentially regulated by curcumin treatment of mice. METHODS: We downloaded GSE10684 and GSE13705 microarray profiles from the GEO database. Differentially expressed genes were identified and compared in both data sets. Twenty-seven genes that are significantly differentially regulated in both datasets were considered as key genes. RESULTS: Gene ontology (GO) enrichment indicates these key genes were mostly enriched in GO Process of regulation of immune response and immune system process. The KEGG pathways of Cytokine-cytokine receptor interaction and TISSUES of Immune system were the top enriched terms of key genes base on strength and false discovery rate. The protein-protein interactions were analyzed by the STRING. PPI clustering showed that cluster 1 with Csf1, Cxcl16, Cxcr3, Fas, Il7r, Rassf2, and Rp2h was the most significant cluster. GO enrichment analysis for this cluster also showed the roles of these genes in immune system regulation. CONCLUSIONS: Overall, the microarray datasets to identify the key genes and the related pathways which were affected by curcumin treatments show that curcumin has a significant impact on immune system regulation through the modulation of gene expression.

Paleoenvironmental reconstruction and metal assessment in the Prodelta of a gulf in southern Caribbean through last millennium.

Estuarine sediments are major repositories for terrigenous materials and can record the changes of regional human activities as well as natural processes. This study seeks to evaluate correlations among the sedimentary content of silicon (Si), titanium (Ti), aluminum (Al), iron (Fe), manganese (Mn), magnesium (Mg), arsenic (As), cobalt (Co), chromium (Cr), nickel (Ni), lead (Pb), vanadium (V), and mercury (Hg) with changes in precipitation patterns, land use in tributary basins, chemical weathering, erosion, oxygenation, redox potential or oceanographic dynamics in a Caribbean gulf during the late Holocene. The geochemical analysis was performed on a sedimentary profile 210 cm long and 6.35 cm in diameter recovered in the northeastern sector of the prodelta in the Gulf of Urabá. The geochemistry used Si, Al, and Ti as normalizing elements. The temporal variation of the 13 metals in the profile extended ca. 1000 calendar years, based on 14C results. The sediments were mainly muddy with few interbedded sandy facies. The metal content ranged 45.12-50.48 % for Si; 0.73-0.78 % for Ti; 16.40-17.11 % for Al; 9.72-10.17 % for Fe; 0.20-0.30 % for Mn; 3.81-4.02 % for Mg; 15.00-23.00 mg/kg for As; 20.00-24.00 mg/kg for Co; 142.00-154.00 mg/kg for Cr; 54.00-64.00 mg/kg for Ni; 7.00-13.00 mg/kg for Pb; 229.00-252.00 mg/kg for V; and 70.15-113.55 µg/kg for Hg. The enrichment factor (EF) and geo-accumulation index (Igeo) revealed moderate but significant enrichment of polymetals, except for Pb. The Si, Ti, Al, Mn, Mg, Co, Ni, and Hg correlated with the granulometry and the organic matter, expressed as loss on ignition (LOI). The correlation between Al and Fe was high and significant. The granulometry of the sedimentary profile recovered in the Prodelta showed an increasing long-term trend, indicating a slight increase in the transport energy towards present. Decreased content of Al, Si, and Ti atop core indicated reduction in contemporary centennial precipitation rate. The increased transport energy might have not originated from river flow, but rather from positive feedback of sea level rise, increased surface temperature and sedimentation of tributary channels.

Exploring a novel four-gene system as a diagnostic and prognostic biomarker for triple-negative breast cancer, using clinical variables.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis. This research aims to find real hub genes for prognostic biomarkers of TNBC therapy. The GEO datasets GSE27447 and GSE233242 were analyzed using R package limma to explore DEGs. The PPI was generated using the STRING database. Cytoscape software plug-ins were used to screen the hub genes. Using the DAVID database, GO functional enrichment and KEGG pathway enrichment analysis were performed. Different online expression databases were employed to investigate the functions of real hub genes in tumor driving, diagnosis, and prognosis in TNBC patients with various clinicopathologic characteristics. A total of one hundred DEGs were identified between both datasets. The seven hub genes were identified after the topological parameter analysis of the PPI network. The KEGG pathway and GO analysis suggest that four genes (PSMB1, PSMC1, PSMF1, and PSMD8) are highly enriched in proteasome and were finally considered as real hub genes. Additionally, the expression analysis demonstrated that hub genes were notably up-regulated in TNBC patients compared to controls. Furthermore, correlational analyses revealed the positive and negative correlations among the expression of the real hub genes and various ancillary data, including tumor purity, promoter methylation status, overall survival (OS), genetic alterations, infiltration of CD8+ T and CD4+ immune cells, and a few more, across TNBC samples. Finally, our analysis identified a couple of significant chemotherapeutic drugs, miRNAs and transcription factors (TFS) with intriguing curative potential. In conclusion, we identified four real hub genes as novel biomarkers to overcome heterogenetic-particular challenges in diagnosis, prognosis, and therapy for TNBC patients.

Identification of KLHL17 as a prognostic marker for prostate cancer based on single-cell sequencing and in vitro/in vivo experiments.

BACKGROUND: Prostate cancer (PCa) is a leading cause of cancer-related mortality among men, characterized by significant heterogeneity that complicates diagnosis and treatment. METHODS AND RESULTS: To enhance our understanding of PCa, we utilized single-cell RNA sequencing (scRNA-seq) data to identify distinct malignant epithelial cell subpopulations and their molecular characteristics. By integrating scRNA-seq data with bulk RNA-seq data, we constructed a prognostic risk score model. The influence of key genes identified in the risk score on PCa was validated through both in vitro and in vivo experiments. Our study revealed eight unique malignant epithelial cell clusters, each exhibiting distinct molecular characteristics and biological functions. KEGG and GO enrichment analyses highlighted their involvement in various pathways. The prognostic risk score model demonstrated strong predictive power for patient outcomes, particularly in predicting progression-free survival (PFS). Notably, KLHL17, identified as a high-risk gene, was found to significantly impact PCa cell proliferation, migration, invasion, and apoptosis upon knockdown. This finding was further validated in vivo using a subcutaneous xenograft tumor model, where reduced KLHL17 expression led to decreased tumor growth. CONCLUSION: Our research provides a comprehensive analysis of PCa heterogeneity and highlights KLHL17 as a potential therapeutic target.

Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation.

Rejection seriously affects the success of kidney transplantations. However, the molecular mechanisms underlying this rejection remain unclear. The GSE21374 and GSE36059 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to infer the proportions of 22 immune cells. Moreover, infiltrating immune cell-related genes were identified using weighted gene co-expression network analysis (WGCNA), and enrichment analysis was conducted to observe their biological functions. Extreme Gradient Boosting (XGBoost) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithms were used to screen hub genes. Quantitative real-time PCR was conducted to verify the number of immune cells and hub gene expression levels. The rejection and non-rejection groups showed significantly different distributions (P < 0.05) of eight immune cells (B cell memory, Plasma cells, mast cells, follicular helper T cells, T CD8 cells, Macrophages M1, T Cells CD4 memory activated, and gamma delta T cells). Subsequently, CD8A, CRTAM, GBP2, WARS, and VAMP5 were screened as hub genes using the XGBoost and LASSO algorithms and could be used as diagnostic biomarkers. Finally, differential analysis and quantitative real-time PCR suggested that CD8A, CRTAM, GBP2, WARS, and VAMP5 were upregulated in rejection samples compared to non-rejection samples. The present study identified five key infiltrating immune cell-related genes (CD8A, CRTAM, GBP2,WARS, and VAMP5) involved in kidney transplant rejection, which may explain the molecular mechanism of rejection in kidney transplantation development.

Construction and validation of a histone-related gene signature for the diagnosis of endometriosis.

OBJECTIVES: Endometriosis is a common chronic disease in childbearing women and a major cause of infertility. Our study aimed to identify and validate a novel gene signature for diagnosing endometriosis based on histone-related genes (HRGs), and to investigate their biological functions in endometriosis. MATERIAL AND METHODS: RNA sequence data were downloaded from the Gene Expression Omnibus database, and HRGs were retrieved from the GeneCards database. We identified differentially expressed genes using the limma package, and constructed a diagnostic model using the rms package. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed for visualization, annotation, and integrated discovery. Subsequently, we validated the model using the recall and decision curve analysis (DCA). Additionally, we analyzed the immune microenvironment features using CIBERSORT. RESULTS: A total of 18 differentially expressed HRGs were identified in patients with endometriosis compared with controls. GO and KEGG enrichment was mainly in spindle organization, positive regulation of the cell cycle process, progesterone-mediated oocyte maturation, and cellular senescence and cell cycle. We obtained a signature of four HRGs (JUNB, FRY, LMNB1, and SPAG1). DCA revealed that the diagnostic model benefits patients with endometriosis, regardless of the incidence. CIBERSORT analysis showed that the number of plasma cells increased significantly in endometriosis samples from all four datasets. CONCLUSIONS: Our findings provide novel insights into the function of HRGs in the development of endometriosis and identify a new signature of four HRGs that may serve as valuable diagnostic markers and therapeutic targets for this disease.

Bioinformatics analysis of immune infiltration in human diabetic retinopathy and identification of immune-related hub genes and their ceRNA networks.

Diabetic retinopathy (DR) is the most common microvascular complication in diabetic patients, and recent studies have shown that immune regulatory mechanisms are closely associated with retinal damage in DR. Therefore, this study focused on exploring immune cells and immune-related genes (IRGs) in DR and gaining insight into the ceRNA mechanisms by which IRGs regulate DR progression. Four datasets from human DR model retinal tissues were obtained from the Gene Expression Omnibus (GEO) database. R software was first used to identify differentially expressed mRNAs (DE-mRNAs) in the dataset GSE160306-mRNAs, then the distribution of immune cells in the gene matrix was analyzed by xCell and ImmuCellAI, ImmPort and InnateDB database were used to obtain immune-related hub genes (IRHGs) in the DR, and finally the STRING online tool and Cytoscape to construct the immune-related ceRNA network. The datasets GSE102485, GSE160308 and GSE160306-lncRNAs were used to validate the results of the ceRNA network further. The results of immune cell infiltration analysis showed that macrophages are important immune cells in DR; immune-related gene screening showed that FCGR2B is an IRHG in DR, and 2 immune-related ceRNA networks of IRHG were obtained: DDN-AS1/miR-10a-5p/FCGR2B and LINC01515/miR-10a-5p/FCGR2B. Our study suggests that infiltration of immune cells, especially the immune role of macrophages, is an important component of DR progression; the immune-related hub gene FCGR2B and its ceRNA network may be a key regulatory network for DR progression. The discovery of key immune cells, IRHG and ceRNA networks in this study may provide new prospects for early intervention and targeted treatment of DR.

Performance analysis of DC-DC Buck converter with innovative multi-stage PIDn(1+PD) controller using GEO algorithm.

Power electronic converters are widely used in various fields of electrical equipment. Due to their fast dynamics and non-linear nature, controlling them requires dealing with various complexities. Therefore, having a well-designed, high-speed, and robust controller is critical to ensure the effective operation of these devices. In a DC-DC converter, steady-state performance with minimum error and fast dynamic response relies on controller design. This paper presents the design of a multi-stage PID controller with an N-filter combined with a one plus proportional derivative (1+PD) controller. This controller illustrates fast tracking reference voltage; additionally, it shows incredible results when the DC-DC converter operates in different modes. The parameters of the proposed controller are effectively determined using the golden eagle optimization (GEO) algorithm. Furthermore, a comprehensive comparison between the proposed controller, proportional-integral-derivative (PID), and fractional order PID (FOPID) controllers, as well as different metaheuristic optimization methods in various conditions, has been conducted to demonstrate the effectiveness of the proposed controller. The behavior of the closed-loop system under different conditions has been thoroughly investigated. The superior time and frequency domain characteristics of the closed-loop system with the PIDn(1+PD) controller highlight its superiority over other controllers. The demonstrated enhancements in settling time, voltage regulation accuracy, and transient response emphasize the potential applicability of the proposed control strategy in real-world power electronics systems, particularly in scenarios requiring high efficiency, stability, and dynamic performance.

Geo-Sensing-Based Analysis of Urban Heat Island in the Metropolitan Area of Merida, Mexico.

Urban Heat Islands are a major environmental and public health concern, causing temperature increase in urban areas. This study used satellite imagery and machine learning to analyze the spatial and temporal patterns of land surface temperature distribution in the Metropolitan Area of Merida (MAM), Mexico, from 2001 to 2021. The results show that land surface temperature has increased in the MAM over the study period, while the urban footprint has expanded. The study also found a high correlation (r> 0.8) between changes in land surface temperature and land cover classes (urbanization/deforestation). If the current urbanization trend continues, the difference between the land surface temperature of the MAM and its surroundings is expected to reach 3.12 °C ± 1.11 °C by the year 2030. Hence, the findings of this study suggest that the Urban Heat Island effect is a growing problem in the MAM and highlight the importance of satellite imagery and machine learning for monitoring and developing mitigation strategies.

Spatiotemporal variation and driving factors of oases in the arid region of Northwest China.

Based on Landsat images and digital elevation model data during 2000-2020, we investigated the spatio-temporal variations and driving forces of oases in the arid region of Northwest China, using an object-oriented method for oasis classification, and employing trends analysis, centroid migration, and geographic detectors methods. The results showed that from 2000 to 2020, the oasis area in the arid region of Northwest China exhibited a linear increasing trend, with a rate of 1079.66 km2·a-1. The growth rate of oasis area, from highest to lowest, was Alxa, Southern Xinjiang, Hexi Corridor and Northern Xinjiang, respectively. Oases in the arid region of Northwest China were mainly distributed in bands or dots along the northern and southern foothills of Tianshan Mountain, Kunlun Mountain, the northern foothills of Qilian Mountain, and the Alxa Plateau. The oasis area in Northern Xinjiang increased while that in the south decreased. Oases in Southern Xinjiang mainly expanded along rivers, with some edges experiencing recession. Expansion and recession of oases in the Hexi Corridor occurred along the rivers in the northwest. Alxa oasis expanded in a scattered pattern with no significant recession areas. The centroids of oases in Northern and Southern Xinjiang generally shifted northeastward, while that in the Hexi Corridor moved northwestward. The centroid of Alxa oasis fluctuated in a north-south direction. The interpretations of agricultural production potential for spatial differentiation of oases in Northern Xinjiang and the Hexi Corridor were the most significant, at 43.6% and 45.3% respectively. Precipitation was the strongest environmental factor affecting Alxa oasis distribution, with an interpretation of 27.6%. Soil types were the strongest factor affecting the distribution of oases in Sou-thern Xinjiang, with an interpretation of 44.9%. The interaction among human activities in oases in the arid region of Northwest China was mainly enhanced by two factors, while the interaction among natural factors was enhanced by both two factors and nonlinear enhancement.

Ecological risk assessment of heavy metals contaminated mining sites of eastern india using soil and moss.

The blooming industrialization and urbanization is leading to increased mining operations. These intensified mining activities emit heavy metals into the environment, posing serious threats to ecosystems. Hence, this study focused on assessing heavy metal pollution in mining soil, utilizing mosses as bioindicators. The ecological risk, geo-accumulation factor, and contamination factor have been calculated to know the harmful effect of heavy metals on ecosystem. The study covered three distinct mining sites of eastern India within Odisha: Jajpur's Sukinda Valley (SP1, Cr), Keonjhar's Joda-Barbil (SP2, Fe and Mn), and Sundargarh's Koira-Joda (SP3, Fe). The collection of 48 soil samples through random sampling revealed significant variations in heavy metal concentrations. SP1 recorded Cr concentration of 6572 ± 445 mg/kg and Ni of 8042.47 ± 501.38 mg/kg, surpassing eco-toxicological levels. The storage site in SP2 exhibited the highest Fe concentration at 9872 ± 502 mg/kg, and Mn levels in SP3 were at 7884 ± 432 mg/kg. Storage areas in all three regions held the highest concentrations of heavy metals. Mosses in studied area demonstrated as potential bioindicators for monitoring heavy metal pollution. EF and Igeo assessments showed Cd, Pb, Hg, and other heavy metal contamination compared to earlier investigations. This study indicated higher ecological risks for Pb, As, Cu, Ni, and Zn. The Hyophila involuta accumulates Mn, Cr, Cd, Pb, Fe, and Hg, while Barbula arcuata accumulates Mn, As, and Cu in SP1. Hyophila involuta and Trematodon longicollis accumulate Mn, Cr, Cd, Pb, Fe, Hg, and Zn in SP2. Trematodon ambiguous accumulates Cd, Fe, and Ni, while Fissidens diversifolius accumulates Mn, Cr, Hg, As, Cu, and Zn in SP3. These findings emphasize the necessity of monitoring heavy metal pollution in contaminated zones using moss as a potential bioindicator.

Urban green space, perceived everyday discrimination and mental health among rural-to-urban migrants: a multilevel analysis in Wuhan, China.

BACKGROUND: While the mental health benefits of urban green spaces (UGS) are increasingly recognized, less is known about how these relationships vary for socially marginalized groups. This study investigates the association between UGS and mental health among rural-to-urban migrants in Wuhan, China, examining the roles of the quality and quantity of UGS and the intermediary function of perceived everyday discrimination. METHODS: We used Multilevel Structural Equation Modeling to analyze data from a social survey, integrating with park-related social media ratings, street view imagery, and geospatial datasets to characterize UGS features and contextual factors, therefore verifying our hypotheses. RESULTS: Both the quality and quantity of UGS significantly influence migrants' mental health, with quantity demonstrating a stronger overall correlation, challenging common assumptions. Notably, social media scores of parks, reflecting positive user experiences, were found to improve mental health. However, the relationship with UGS quantity was nuanced: higher park density and green view index were positively associated with mental health, while increased park area proportion demonstrated the opposite effect. Furthermore, perceived discrimination emerged as a critical socio-psychological factor and operated spatial heterogeneity. In inner-city areas, neighborhoods characterized by plaza-type parks and high park density were associated with reduced perceived discrimination among migrants, showing active social functions of UGS. However, larger park areas are paradoxically correlated with increased discrimination experiences and poorer mental health. Interestingly, this mediatory effect of perceived discrimination was less pronounced in inner-suburban areas. These findings suggest a nuanced role of UGS in the lives of migrants. While certain aspects of UGS quantity, such as plentiful smaller parks, can facilitate social inclusion and improve mental health, others, like overlarge parks, may unintentionally contribute to feelings of marginalization and negatively impact mental health. CONCLUSION: Our findings highlight the crucial need for context-sensitive green space planning that balances quality and quantity while mitigating discriminatory experiences to improve the mental health of rural-to-urban migrants.

Construction and validation of a prognostic model of angiogenesis-related genes in multiple myeloma.

BACKGROUND: Angiogenesis is associated with tumour growth, infiltration, and metastasis. This study aimed to detect the mechanisms of angiogenesis-related genes (ARGs) in multiple myeloma (MM) and to construct a new prognostic model. METHODS: MM research foundation (MMRF)-CoMMpass cohort, GSE47552, GSE57317, and ARGs were sourced from public databases. Differentially expressed genes (DEGs) in the tumour and control cohorts in GSE47552 were determined through differential expression analysis and were enriched with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Weighted gene coexpression network analysis (WGCNA) was applied to derive modules linked to the ARG scores and obtain module genes in GSE47552. Differentially expressed ARGs (DE-ARGs) were selected for subsequent analyses by overlapping DEGs and module genes. Furthermore, prognostic genes were selected using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Depending on the prognostic genes, a risk model was constructed, and risk scores were determined. Moreover, MM samples from MMRF-CoMMpass were sorted into high- and low-risk teams on account of the median risk score. Additionally, correlations among clinical characteristics, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), immune analysis, immunotherapy predictions and the mRNA‒miRNA‒lncRNA network were carried out. RESULTS: A total of 898 DEGs, 211 module genes, 24 DE-ARGs and three prognostic genes (AKAP12, C11orf80 and EMP1) were selected for this study. Enrichment analysis revealed that the DEGs were related to 86 GO terms, such as 'cytoplasmic translation', and 41 KEGG pathways, such as 'small cell lung cancer'. A prognostic gene-based risk model was created in MMRF-CoMMpass and confirmed with the GSE57317 dataset. Moreover, a nomogram was established on the basis of independent prognostic factors that have proven to be good predictors. In addition, the immune cell infiltration results suggested that memory B cells were enriched in the high-risk group and that immature B cells were enriched in the low-risk group. Finally, the mRNA‒miRNA‒lncRNA network demonstrated that hsa-miR-508-5p was tightly associated with EMP1 and AKAP12. RT‒qPCR was used to validate the expression of the genes associated with prognosis. CONCLUSION: A new prognostic model of MM associated with ARGs was created and validated, providing a new perspective for exploring the connection between ARGs and MM.

Predictors of adolescent childbearing among Ethiopian women with spatial effect adjustment.

BACKGROUND: Childbearing under the age of 20 is referred to as teenage childbearing. Compared to high-income countries, it is significantly higher in low-income countries. Adolescent childbearing is influenced by a number of variables, including economic, demographic, and social factors, and these vary geographically. Thus, this study aimed to determine the predictors of adolescent childbearing among Ethiopian women with spatial effect adjustment. METHODS: A total weighted sample of 4712 women aged 15 to 49 were included. The data were obtained from the 2019 Ethiopia Demographic and Health Survey. A generalized Geoadditive model which accounts for spatial effect and the non-linear effect of continuous variables was adopted to determine the associated factors of adolescent childbearing among Ethiopian women. RESULTS: The spatial pattern of adolescent childbearing was non-random in Ethiopia with Moran's index statistics 1.731999 (P-value < 0.001). Based on the evidence of spatial variation in a model, the highest risk of adolescent childbearing was observed in Jijiga, Shinilie, Welwel and Walder, Afar (Zone1 and Zone 5), Assosa, Metekel, and Gambela (Zone1). We also noted that women not intending to use a contraceptive method, Muslim religion, living in a rural area, and large household family size were significantly associated with a high risk of adolescent childbearing. Furthermore, our model results also confirmed that higher educational levels, older household age, and good economic status significantly reduced the risk of adolescent childbearing. CONCLUSIONS: This study revealed that adolescent childbearing distribution was significantly clustered in the Eastern and Southwestern parts of Ethiopia. Intervention programs aimed at the prevention of early marriage and raising awareness of sexual activity are essential to reducing adolescent childbearing.