Necroptosis pathway emerged as potential diagnosis markers in spinal cord injury.

The present research focused on identifying necroptosis-related differentially expressed genes (NRDEGs) in spinal cord injury (SCI) to highlight potential therapeutic and prognostic target genes in clinical SCI. Three SCI-related datasets were downloaded, including GSE151371, GSE5296 and GSE47681. MSigDB and KEGG datasets were searched for necroptosis-related genes (NRGs). Differentially expressed genes (DEGs) and NRGs were intersected to obtain NRDEGs. The MCC algorithm was employed to select the first 10 genes as hub genes. A protein-protein interaction (PPI) network related to NRDEGs was developed utilizing STRING. Several databases were searched to predict interactions between hub genes and miRNAs, transcription factors, potential drugs, and small molecules. Immunoassays were performed to identify DEGs using CIBERSORTx. Additionally, qRT-PCR was carried out to verify NRDEGs in an animal model of SCI. Combined analysis of all datasets identified 15 co-expressed DEGs and NRGs. GO and KEGG pathway analyses highlighted DEGs mostly belonged to pathways associated with necroptosis and apoptosis. Hub gene expression analysis showed high accuracy in SCI diagnosis was associated with the expression of CHMP7 and FADD. A total of two hub genes, i.e. CHMP7, FADD, were considered potential targets for SCI therapy.

A Treg-related riskscore model may improve the prognosis evaluation of colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)-related gene signature in CRC. METHODS: We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co-expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg-related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored. RESULTS: Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg-related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients' survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients' survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples. CONCLUSION: We established a useful riskscore model in light of seven Treg-related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients.

Role of dysregulated macrophage subpopulation ratios and functional changes in the development of coronary atherosclerosis.

Coronary heart disease is one of the most significant risk factors affecting human health worldwide. Its pathogenesis is intricate, with atherosclerosis being widely regarded as the leading cause. Aberrant lipid metabolism in macrophages is recognized as one of the triggering factors in atherosclerosis development. To investigate the role of macrophages in the formation of coronary artery atherosclerosis, we utilized single-cell data from wild-type mice obtained from the aortic roots and ascending aortas after long-term high-fat diet feeding, as deposited in GSE131776. Seurat software was employed to refine the single-cell data in terms of scale and cell types, facilitating the identification of differentially expressed genes. Through the application of differential expression genes, we conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses at 0, 8 and 16 weeks, aiming to uncover pathways with the most pronounced functional alterations as the high-fat diet progressed. The AddModuleScore function was employed to score the expression of these pathways across different cell types. Subsequently, macrophages were isolated and further subdivided into subtypes, followed by an investigation into intercellular communication within these subtypes. Subsequent to this, we induced THP-1 cells to generate foam cells, validating critical genes identified in prior studies. The results revealed that macrophages underwent the most substantial functional changes as the high-fat diet progressed. Furthermore, two clusters were identified as potentially playing pivotal roles in macrophage functional regulation during high-fat diet progression. Additionally, macrophage subtypes displayed intricate functionalities, with mutual functional counterbalances observed among these subtypes. The proportions of macrophage subtypes and the modulation of anti-inflammatory and pro-inflammatory functions played significant roles in the development of coronary artery atherosclerosis.

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.

Mechanism of Xing 9 ling tablet candy for alcoholic liver disease based on network pharmacology.

Xing 9 Ling tablet candy (X9LTC) effectively treats alcoholic liver disease (ALD), but its potential mechanism and molecular targets remain unstudied. We aimed to address this gap using network pharmacology. Furthermore, high-performance liquid chromatography (HPLC) and database analysis revealed a total of 35 active ingredients and 311 corresponding potential targets of X9LTC. Protein interaction analysis revealed PTGS2, JUN, and FOS as its core targets. Enrichment analysis indicated that chemical carcinogenesis-receptor activation, IL-17 and TNF signaling pathway were enriched by multiple core targets, which might be the main pathway of action. Further molecular docking validation showed that the core targets had good binding activities with the identified compounds. Animal experiments showed that X9LTC could reduce the high expression of ALT, AST and TG in the serum of ALD mice, alleviate the lesions in liver tissues, and reverse the high expression of PTGS2, JUN, and FOS proteins in the liver tissues. In this study, we established a method for the determination of X9LTC content for the first time, and predicted its active ingredient and mechanism of action in treating ALD, providing theoretical basis for further research.

Genome-Scale Investigation of the Regulation of azoR Expression in Escherichia coli Using Computational Analysis and Transposon Mutagenesis.

Bacterial azoreductases are enzymes that catalyze the reduction of ingested or industrial azo dyes. Although azoreductase genes have been well identified and characterized, the regulation of their expression has not been systematically investigated. To determine how different factors affect the expression of azoR, we extracted and analyzed transcriptional data from the Gene Expression Omnibus (GEO) resource, then confirmed computational predictions by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results showed that azoR expression was lower with higher glucose concentration, agitation speed, and incubation temperature, but higher at higher culture densities. Co-expression and clustering analysis indicated ten genes with similar expression patterns to azoR: melA, tpx, yhbW, yciK, fdnG, fpr, nfsA, nfsB, rutF, and chrR (yieF). In parallel, constructing a random transposon library in E. coli K-12 and screening 4320 of its colonies for altered methyl red (MR)-decolorizing activity identified another set of seven genes potentially involved in azoR regulation. Among these genes, arsC, relA, plsY, and trmM were confirmed as potential azoR regulators based on the phenotypic decolorization activity of their transposon mutants, and the expression of arsC and relA was confirmed, by qRT-PCR, to significantly increase in E. coli K-12 in response to different MR concentrations. Finally, the significant decrease in azoR transcription upon transposon insertion in arsC and relA (as compared to its expression in wild-type E. coli) suggests their probable involvement in azoR regulation. In conclusion, combining in silico analysis and random transposon mutagenesis suggested a set of potential regulators of azoR in E. coli.

Identification of circRNA-mediated competing endogenous RNA network involved in the development of cervical cancer.

BACKGROUND: The abnormal expression of circRNA may contribute to the progression of cervical cancer by influencing the biological processes. AIM: This study aimed to identify the differentially expressed circRNAs in cervical cancer and validate the circ_0008193 ceRNA network in cervical cancer cells. METHODS: Using the absolute log2 value of fold change >1 and p-value of <0.05, the differentially expressed circRNAs were obtained from GSE102686 and GSE113696 from cervical cancer tissues and cervical cancer cells with the help of the GEO2R tool. Downstream miRNAs and mRNAs were predicted using relevant informatics databases. The circRNA-miRNA-mRNA interaction network was conducted with the assistance of Cytoscape. Circ_0008193-miR-182-5p-PTEN axis was validated with expression level and cell function using RT-qPCR, a dual-luciferase reporter assay, and cellular experiments. RESULTS: GSE102686 and GSE113696 databases overlapped 7 differentially expressed circRNAs and five circRNAs have the same expression pattern. Based on the literature and expression pattern, a circRNA-miRNA-mRNA network was conducted. The circ_0008193, miR-182-5p, and PTEN expression patterns were downregulation, upregulation, and downregulation, respectively. Overexpressed circ_0008193 suppressed proliferation, migration, and invasion of cervical cancer cells. MiR-182-5p diminished the inhibitory influence of circ_0008193 on cellular behaviors, while PTEN counteracted the effect of miR-182-5p. CONCLUSION: This investigation revealed the existence of a circRNA-miRNA-mRNA network in cervical cancer, and preliminary verified the function of circ_0008193-miR-182-5p-PTEN axis in cervical cancer cells, which offers additional guidance on investigating the molecular mechanisms of cervical cancer.

Are maximum magnitudes of induced earthquakes controlled by pressure diffusion?

There is an ongoing discussion about how to forecast the maximum magnitudes of induced earthquakes based on operational parameters, subsurface conditions and physical process understanding. Although the occurrence of damage caused by induced earthquakes is rare, some cases have caused significant economic loss, injuries and even loss of life. We analysed a global compilation of earthquakes induced by hydraulic fracturing, geothermal reservoir stimulation, water disposal, gas storage and reservoir impoundment. Our analysis showed that maximum magnitudes scale with the characteristic length of pressure diffusion in the brittle Earth's crust. We observed an increase in the nucleation potential of larger-magnitude earthquakes with time and explained it by diffusion-controlled growth of the pressure-perturbed part of faults. Numerical and analytical fault size modelling supported our findings. Finally, we derived magnitude scaling laws to manage induced seismic hazard of upcoming energy projects prior to operation. This article is part of the theme issue 'Induced seismicity in coupled subsurface systems'.

Quantitative analysis of soil quality around brick kilns using pollution indices and ANOVA in Jammu district of Jammu and Kashmir, India.

The study investigated soil quality around brick kilns in the Jammu district of Jammu and Kashmir, analyzing 200 samples from 50 sites for selected parameters such as pH, electrical conductiv1ity, soil temperature, organic carbon content, organic matter, macronutrients, and heavy metals. The findings revealed that soil electrical conductivity ranged from 0.33 to 0.63 dS/m, with significant differences observed at varying distances from the kilns. Copper concentrations were highest at 5.32 mg/kg near the kilns, while iron and lead levels also varied significantly, indicating potential contamination. The mean soil temperature was recorded to be 27.69°C.The pH values ranged from 6.5 to 7.8, and the average pH of 8.22 indicated the slightly alkaline nature of the soil around the brick kilns. The organic carbon ranged from 0.34% to 1.02%.Soil temperature and electrical conductivity decreased with increasing distance from the kilns, with temperature showing positive correlations with organic carbon, organic matter, nitrogen, potassium, manganese, and iron and negative correlations with pH, phosphorus, zinc, copper, lead, and cadmium. A perfect positive correlation was noted among nitrogen, organic carbon, and organic matter. Heavy metals, except for zinc and manganese, showed positive correlations with each other. The average Zn, Cu, Mn, Fe, Pb and Cd concentration was recorded as 1.07, 1.03, 6.71, 10.30, 37.04 and 1.91 ppm, respectively. The contamination factor indicated moderate contamination with lead and cadmium, while the geo-accumulation index also suggested moderate contamination. The pollution load index reflected unpolluted soil and enrichment factor values for heavy metals ranked as Cd > Pb > Cu > Zn > Mn > Fe.ANOVA results revealed significant variations in electrical conductivity, copper, iron, and lead, underscoring the potential environmental impacts at different distances from the kilns. However, no significant differences were found between agricultural and non-agricultural sites in other physicochemical parameters. These variations highlight the considerable impact of brick kilns on soil health, emphasizing the need for enhanced environmental management and further research to mitigate these effects.

Construction of a 12-gene prognostic model for colorectal cancer based on heat shock protein-related genes.

Some heat shock proteins (HSPs) have been shown to influence tumor prognosis, but their prognostic significance in colorectal cancer (CRC) remains unclear. This study explored the prognostic significance of HSP-related genes in CRC. Transcriptional data and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database, and a literature search was conducted to identify HSP-related genes. Using Least Absolute Selection and Shrinkage Operator (LASSO) regression and univariate/multivariate Cox regression analyses, 12 HSP-related genes demonstrating significant associations with CRC survival were successfully identified and employed to formulate a predictive risk score model. The efficacy and precision of this model were validated utilizing TCGA and Gene Expression Omnibus (GEO) datasets, demonstrating its reliability in CRC prognosis prediction. gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed significant disparities between high- and low-risk groups in chromatin remodeling biological functions and neutrophil extracellular trap formation pathways. Single sample gene set enrichment analysis (ssGSEA) further revealed differences in immune cell types and immune functional status between the two risk groups. Differential analysis showed higher expression of immune checkpoints within the low-risk group, while the high-risk group exhibited notably higher Tumor Immune Dysfunction and Exclusion (TIDE) scores. Additionally, we predicted the sensitivity of different prognosis risk patients to various drugs, providing potential drug choices for tailored treatment. Combined, our study successfully crafted a novel CRC prognostic model that can effectively predict patient survival, immune landscape, and treatment response, providing important support and guidance for CRC patient prognosis.

Effect of Calebin-A on Critical Genes Related to NAFLD: A Protein-Protein Interaction Network and Molecular Docking Study.

Background: Calebin-A is a minor phytoconstituent of turmeric known for its activity against inflammation, oxidative stress, cancerous, and metabolic disorders like Non-alcoholic fatty liver disease(NAFLD). Based on bioinformatic tools. Subsequently, the details of the interaction of critical proteins with Calebin-A were investigated using the molecular docking technique. Methods: We first probed the intersection of genes/ proteins between NAFLD and Calebin-A through online databases. Besides, we performed an enrichment analysis using the ClueGO plugin to investigate signaling pathways and gene ontology. Next, we evaluate the possible interaction of Calebin-A with significant hub proteins involved in NAFLD through a molecular docking study. Results: We identified 87 intersection genes Calebin-A targets associated with NAFLD. PPI network analysis introduced 10 hub genes (TP53, TNF, STAT3, HSP90AA1, PTGS2, HDAC6, ABCB1, CCT2, NR1I2, and GUSB). In KEGG enrichment, most were associated with Sphingolipid, vascular endothelial growth factor A (VEGFA), C-type lectin receptor, and mitogen-activated protein kinase (MAPK) signaling pathways. The biological processes described in 87 intersection genes are mostly concerned with regulating the apoptotic process, cytokine production, and intracellular signal transduction. Molecular docking results also directed that Calebin-A had a high affinity to bind hub proteins linked to NAFLD. Conclusion: Here, we showed that Calebin-A, through its effect on several critical genes/ proteins and pathways, might repress the progression of NAFLD.

Ego- and Geo-Centered References: A Functional Neuroimagery Study.

INTRODUCTION: The integration of vestibular, visual, and somatosensory cues allows the perception of space through the orientation of our body and surrounding objects with respect to gravity. The main goal of this study was to identify the cortical networks recruited during the representation of body midline and the representation of verticality. METHODS: Thirty right-handed healthy participants were evaluated using fMRI. Brain networks activated during a subjective straight-ahead (SSA) task were compared to those recruited during a subjective vertical (SV) task. RESULTS: Different patterns of cortical activation were observed, with differential increases in the angular gyrus and left cerebellum posterior lobe during the SSA task, in right rolandic operculum and cerebellum anterior lobe during the SV task. DISCUSSION: The activation of these areas involved in visuo-spatial functions suggests that bodily processes of great complexity are engaged in body representation and vertical perception. Interestingly, the common brain networks involved in SSA and SV tasks were comprised of areas of vestibular projection that receive multisensory information (parieto-occipital areas) and the cerebellum, and reveal a predominance of the right cerebral and cerebellar hemispheres. The outcomes of this first fMRI study designed to unmask common and specific neural mechanisms at work in gravity- or body-referenced tasks pave a new way for the exploration of spatial cognitive impairment in patients with vestibular or cortical disorders.

Identification of common mechanisms and biomarkers for dermatomyositis and atherosclerosis based on bioinformatics analysis.

BACKGROUND: Dermatomyositis (DM) manifests as an autoimmune and inflammatory condition, clinically characterized by subacute progressive proximal muscle weakness, rashes or both along with extramuscular manifestations. Literature indicates that DM shares common risk factors with atherosclerosis (AS), and they often co-occur, yet the etiology and pathogenesis remain to be fully elucidated. This investigation aims to utilize bioinformatics methods to clarify the crucial genes and pathways that influence the pathophysiology of both DM and AS. METHOD: Microarray datasets for DM (GSE128470, GSE1551, GSE143323) and AS (GSE100927, GSE28829, GSE43292) were retrieved from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was used to reveal their co-expressed modules. Differentially expression genes (DEGs) were identified using the "limma" package in R software, and the functions of common DEGs were determined by functional enrichment analysis. A protein-protein interaction (PPI) network was established using the STRING database, with central genes evaluated by the cytoHubba plugin, and validated through external datasets. Immune infiltration analysis of the hub genes was conducted using the CIBERSORT method, along with Gene Set Enrichment Analysis (GSEA). Finally, the NetworkAnalyst platform was employed to examine the transcription factors (TFs) responsible for regulating pivotal crosstalk genes. RESULTS: Utilizing WGCNA analysis, a total of 271 overlapping genes were pinpointed. Subsequent DEG analysis revealed 34 genes that are commonly found in both DM and AS, including 31 upregulated genes and 3 downregulated genes. The Degree Centrality algorithm was applied separately to the WGCNA and DEG collections to select the 15 genes with the highest connectivity, and crossing the two gene sets yielded 3 hub genes (PTPRC, TYROBP, CXCR4). Validation with external datasets showed their diagnostic value for DM and AS. Analysis of immune infiltration indicates that lymphocytes and macrophages are significantly associated with the pathogenesis of DM and AS. Moreover, GSEA analysis suggested that the shared genes are enriched in various receptor interactions and multiple cytokines and receptor signaling pathways. We coupled the 3 hub genes with their respective predicted genes, identifying a potential key TF, CBFB, which interacts with all 3 hub genes. CONCLUSION: This research utilized comprehensive bioinformatics techniques to explore the shared pathogenesis of DM and AS. The three key genes, including PTPRC, TYROBP, and CXCR4, are related to the pathogenesis of DM and AS. The central genes and their correlations with immune cells may serve as potential diagnostic and therapeutic targets.

Geographical coverage of SARS-CoV-2 screening and care centers in Haiti: what do national surveillance data tell us?

BACKGROUND: In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years. METHODS: We analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection. RESULTS: Among the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection. CONCLUSIONS: This study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated.

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.

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.

Identification of Differentially Expressed Genes in Human Colorectal Cancer Using RNASeq Data Validated on the Molecular Level with Real-Time PCR.

Colorectal cancer (CRC) is a prevalent cancer with high morbidity and mortality rates worldwide. Late diagnosis is a significant contributor to low survival rates in a minority of cases. The study aimed to perform a robust pipeline using integrated bioinformatics tools that will enable us to identify potential diagnostic and prognostic biomarkers for early detection of CRC by exploring differentially expressed genes (DEGs). In addition to, testing the capability of replacing chemotherapy with plant extract in CRC treatment by validating it using real-time PCR. RNA-seq data from cancerous and adjacent normal tissues were pre-processed and analyzed using various tools such as FastQC, Kallisto, DESeq@ R package, g:Profiler, GNEMANIA-CytoScape and CytoHubba, resulting in the identification of 1641 DEGs enriched in various signaling routes. MMP7, TCF21, and VEGFD were found to be promising diagnostic biomarkers for CRC. An in vitro experiment was conducted to examine the potential anticancer properties of 5-fluorouracile, Withania somnifera extract, and their combination. The extract was found to exhibit a positive trend in gene expression and potential therapeutic value by targeting the three genes; however, further trials are required to regulate the methylation promoter. Molecular docking tests supported the findings by revealing a stable ligand-receptor complex. In conclusion, the study's analysis workflow is precise and robust in identifying DEGs in CRC that may serve as biomarkers for diagnosis and treatment. Additionally, the identified DEGs can be used in future research with larger sample sizes to analyze CRC survival.

Identification of a Hypoxia-Related Signature as Candidate Detector for Schizophrenia Based on Genome-Wide Gene Expression.

INTRODUCTION: Schizophrenia (SCZ), a severe neuropsychiatric disorder with high genetic susceptibility, has high rates of misdiagnosis due to the unavoidably subjective factors and heterogeneous clinical presentations. Hypoxia has been identified as an importantly risk factor that participates in the development of SCZ. Therefore, development of a hypoxia-related biomarker for SCZ diagnosis is promising. Therefore, we dedicated to develop a biomarker that could contribute to distinguishing healthy controls and SCZ patients. METHODS: GSE17612, GSE21935, and GSE53987 datasets, consisting of 97 control samples and 99 SCZ samples, were involved in our study. The hypoxia score was calculated based on the single-sample gene-set enrichment analysis using the hypoxia-related differentially expressed genes to quantify the expression levels of these genes for each SCZ patient. Patients in high-score groups were defined if their hypoxia score was in the upper half of all hypoxia scores and patients in low-score groups if their hypoxia score was in the lower half. GSEA was applied to detect the functional pathway of these differently expressed genes. CIBERSORT algorithm was utilized to evaluate the tumor-infiltrating immune cells of SCZ patients. RESULTS: In this study, we developed and validated a biomarker consisting of 12 hypoxia-related genes that could distinguish healthy controls and SCZ patients robustly. We found that the metabolism reprogramming might be activated in the patient with high hypoxia score. Finally, CIBERSORT analysis illustrated that lower composition of naive B cells and higher composition of memory B cells might be observed in low-score groups of SCZ patients. CONCLUSION: These findings revealed that the hypoxia-related signature was acceptable as a detector for SCZ, providing further insight into effective diagnosis and treatment strategies for SCZ.

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.

Deciphering programmed cell death mechanisms in osteosarcoma for prognostic modeling.

Osteosarcoma (OS), known for its high recurrence and metastasis rates, poses a significant challenge in oncology. Our research investigates the role of programmed cell death (PCD) genes in OS and develops a prognostic model using advanced bioinformatics. We analyzed single-cell sequencing data from the Gene Expression Omnibus (GEO) database to identify subpopulations, distinguish malignant from non-malignant cells, assess cell cycle phases, and map PCD gene distribution. Additionally, we applied consistency clustering to bulk sequencing data from GEO and TARGET (Therapeutically Applicable Research to Generate Effective Treatments) databases, facilitating survival analysis across clusters with the Kaplan-Meier method. We calculated PCD scores for each cluster using the Single-sample Gene Set Enrichment Analysis (ssGSEA), which enabled a detailed examination of PCD-related gene expression and pathway scores. Our study also explored drug sensitivity differences and conducted comprehensive immune cell infiltration analyses using various algorithms. We identified differentially expressed genes, leading to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses that provided insights into relevant biological processes and pathways. The prognostic model, based on five pivotal genes (BAMBI, TMCC2, NOX4, DKK1, and CBS), was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and validated in the TARGET-OS and GSE16091 datasets, showing significant predictive accuracy. This research enhances our understanding of PCD in OS and supports the development of effective treatments.