Differential Analysis Research Articles

Elucidating the Role of Autophagy-related Genes in Polycystic Ovary Syndrome: Implications for Diagnostic Models and Immune Response Regulation.

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder that negatively affects female reproductive capacity. Although the association between autophagy and PCOS is known, there are few detailed studies on the association between autophagy-related genes and PCOS. Publicly available gene expression datasets (GSE102293, GSE138518, GSE34526, GSE114419, GSE137684, GSE155489) were used in a comprehensive analysis to identify a role for autophagy in PCOS. Batch effects were mitigated using the sva package, followed by WGCNA (weighted gene correlation network analysis) and ss- GSEA (single sample gene set enrichment analysis) to identify autophagy-related genes. Recursive feature elimination (RFE) and LASSO COX methods were used to identify important hub genes, and their correlation with immune cell activity was assessed using ssGSEA and Pearson correlation analysis. High autophagy scores were observed in PCOS samples, and the dark green gene module with the highest autophagy correlation was identified. The differential analysis identified a total of 169 up-regulated genes versus 2 down-regulated genes in the PCOS samples, which were intersected by taking the intersection with the deep green module genes and resulted in 121 key genes. Subsequently, 6 hub genes (MMP25, CSF3R, SLPI, MMP9, CLEC4E, and SIGLEC10) were further identified based on RFE and LASSO algorithms. Diagnostic efficacy based on ROC curves showed six autophagy- associated hub genes with AUC values as high as 0.959 and 0.896 in the training and validation sets, respectively. Finally, we observed that these hub genes are strongly associated with immune function, especially chronic inflammation and aberrant immune activation pathways. In this study, we identified autophagy genes closely related to PCOS and constructed a gene model with high diagnostic accuracy. These findings not only provided potential new biomarkers for the diagnosis of PCOS but also revealed the key role of autophagy in the pathogenesis of PCOS.

Natural variation in the chickpea metabolome under drought stress.

Chickpea is the world's fourth largest grown legume crop, which significantly contributes to food security by providing calories and dietary protein globally. However, the increased frequency of drought stress has significantly reduced chickpea production in recent years. Here, we have performed a field experiment with 36 diverse chickpea genotypes to evaluate grain yield, photosynthetic activities and molecular traits related to drought stress. For metabolomics analysis, leaf tissue was collected at three time points representing different pod-filling stages. We identified L-threonic acid, fructose and sugar alcohols involved in chickpea adaptive drought response within the mid-pod-filling stage. A stress susceptibility index for each genotype was calculated to identify tolerance capacity under drought, distributing the 36 genotypes into four categories from best to worst performance. To understand how biochemical mechanisms control different traits for genetic improvement, we performed a differential Jacobian analysis, which unveiled the interplay between various metabolic pathways across three time points, including higher flux towards inositol interconversions, glycolysis for high-performing genotypes, fumarate to malate conversion, and carbon and nitrogen metabolism perturbations. Metabolic GWAS (mGWAS) analysis uncovered gene candidates involved in glycolysis and MEP pathway corroborating with the differential biochemical Jacobian results. Accordingly, this proposed data analysis strategy bridges the gap from pure statistical association to causal biochemical relations by exploiting natural variation. Our study offers new perspectives on the genetic and metabolic understanding of drought tolerance-associated diversity in the chickpea metabolome and led to the identification of metabolic control points that can be also tested in other legume crops.

SaseR: Juggling offsets unlocks RNA-seq tools for fast and Scalable differential usage, Aberrant Splicing and Expression Retrieval.

RNA-seq data analysis relies on many different tools, each tailored to specific applications and coming with unique assumptions and restrictions. Indeed, tools for differential transcript usage, or diagnosing patients with rare diseases through splicing and expression outliers, either lack in performance, discard information, or do not scale to massive data compendia. Here, we show that replacing the normalisation offsets unlocks bulk RNA-seq workflows for scalable differential usage, aberrant splicing and expression analyses. Our method, saseR, is much faster than state-of-the-art methods, dramatically outperforms these to detect aberrant splicing, and provides a single workflow for various short- and long-read RNA-seq applications.

Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning

Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.

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

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. 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. 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. 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. 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.

The immunological and prognostic significance of the diabetes mellitus-related gene WFS1 in endometrial cancer.

Diabetes is associated with the incidence and prognosis of various malignancies, most notably endometrial cancer (EC). This study investigated the connection between diabetes and EC, with a specific focus on elucidating the biological implications of the diabetes mellitus (DM)-related gene WFS1. Using the CTD, GeneCards, and GSEA databases, we identified WFS1 as a diabetes-related gene and then conducted an extensive investigation focusing on WFS1 in the context of EC. First, we identified WFS1 as the target gene and obtained EC data from the TCGA database. Then, comprehensive analyses and verification experiments, including differential expression analysis, prognostic modeling, functional enrichment analysis, gene mutation profiling, assessment of immune cell infiltration, immunophenoscore (IPS), tumor stemness index scoring, drug sensitivity analysis, single-cell transcriptomic analysis, glycolytic pathway analysis, and clinical verification, were performed to comprehensively evaluate the clinical value of WFS1 in EC. The EC group had significantly lower WFS1 expression, with an AUC of 0.857 for the ROC diagnostic curve. Overall survival analysis revealed that WFS1 was an independent risk factor for EC; low WFS1 expression was correlated with a poor prognosis. Stemness index analysis revealed that decreased WFS1 expression was associated with increased tumor grade and enhanced tumor stemness, suggesting increased malignancy of EC. In addition, WFS1 expression was correlated with tumor microenvironment features such as immune cell infiltration. WFS1 was also associated with tumor drug resistance. EC patients with low WFS1 expression have a worse prognosis. WFS1 can be used as diagnostic and prognostic marker for EC.

Epigenetic trajectory predicts development of clinical rheumatoid arthritis in ACPA+ individuals: Targeting Immune Responses for Prevention of Rheumatoid Arthritis (TIP-RA).

The presence of autoantibodies to citrullinated protein antigens (ACPAs) in the absence of clinically-apparent inflammatory arthritis (IA) identifies individuals "at-risk" for developing future clinical rheumatoid arthritis (RA). However, it is unclear why some ACPA+ individuals convert to clinical RA while others do not. We explored the possibility in the Targeting Immune Responses for Prevention of Rheumatoid Arthritis (TIP-RA) study that epigenetic remodeling is part of the trajectory from an at-risk state to clinical disease and identifies novel biomarkers associated with conversion to clinical RA. ACPA-Controls, ACPA+ At-Risk, and Early RA individuals were followed for up to 5 years, including obtaining blood samples annually and at RA diagnosis. Peripheral blood mononuclear cells (PBMCs) were separated into CD19+ B cells, memory CD4+ T cells, and naïve CD4+ T cells using antibodies and magnetic beads. Genome-wide methylation within each cell lineage was assayed using the Illumina MethylationEPIC v1.0 beadchip. ACPA+ At-Risk participants who did or did not develop RA were designated "Pre-RA" or "Non-converters", respectively.Differentially methylated loci (DML) were selected using the Limma software package. Using the Caret package, we constructed machine learning models in test and validation cohorts and identified the most predictive loci of clinical RA conversion. Cross-sectional differential methylation analysis at baseline revealed DMLs that distinguish the Pre-RA methylome from ACPA+ Non-converters, the latter which closely resembled ACPA-Controls. Genes overlapping these DMLs correspond to aberrant NOTCH signaling and DNA repair pathways in B cells. Longitudinal analysis showed that ACPA-Control and ACPA+ Non-converter methylomes are relatively constant. In contrast, the Pre-RA methylome remodeled along a dynamic "RA methylome trajectory" characterized by epigenetic changes in active regulatory elements. Clinical conversion to RA, defined based on diagnosis, marked an epigenetic inflection point for cell cycle pathways in B cells and adaptive immunity pathways in naïve T cells. Machine learning revealed individual loci associated with RA conversion. This model significantly outperformed autoantibodies plus acute phase reactants as predictors of RA conversion. DNA methylation is a dynamic process in ACPA+ individuals at-risk for developing RA that eventually transition to clinical disease. In contrast, non-converters and controls have stable methylomes. The accumulation of epigenetic marks over time prior to conversion to clinical RA conforms to pathways that are associated with immunity and can be used to identify potential pathogenic pathways for therapeutic targeting and/or use as prognostic biomarkers.

Assay for Characterizing Adsorption-Properties of Surfaces (APS).

Analytes, from sample preparation, until entering an analytical instrument, are prone to adsorb to surfaces, driven by the chemical properties of the surface and the liquids they are dissolved in. This problem can be addressed with internal standards when a single or few known analytes are quantified that are usually not available in omics. However, minimal to no loss of analytes is the aim. Here, we present a novel assay for qualifying and quantifying interactions responsible for adsorption of molecules to surfaces (APS) by using LC-MS/MS-based differential quantitative analysis. To reflect a broad range of chemical interactions with surfaces, a reference mixture of thousands of tryptic peptides, with known compositions was selected, representing a variety of different chemical characteristics. The assay was tested by investigating the adsorption properties of several different vials with different surface chemistries. A significant number of hydrophobic peptides adsorbed to conventional polypropylene vials. In contrast, only few peptides adsorbed to polypropylene vials, assigned as low-protein-binding. The highest number of peptides adsorbed to glass vials driven by electrostatic interactions. In summary, the new assay is suitable to characterize adsorption properties of different surfaces and to approximate the loss of analytes during sample preparation.

Identifying HIF1A and HGF as two hub genes in aortic dissection and function analysis by integrating RNA sequencing and single-cell RNA sequencing data.

Aortic dissection (AD) is a severe aortic disease with high mortality, and its pathogenesis remains elusive. To explore the regulatory mechanisms of AD, we integrated public RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets to screen the hub genes of AD and further analyzed their functions, which may provide references to the diagnosis and treatment of AD. Four AD-related datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis and differential expression analysis were applied to identify overlapping genes in dataset GSE153434. Protein-protein interaction (PPI) network was constructed based on overlapping genes. Five methods (closeness, degree, EPC, MCC, and MNN) were used to pick hub genes. The receiver operating characteristic curve was used to evaluate the diagnostic efficiency of the hub genes in extra datasets GSE98770 and GSE52093. scRNA-seq dataset GSE213740 was used to explore the expression and function of the hub genes at the single-cell level. Quantitative real-time polymerase chain reaction was used to verify the expression of hub genes in beta-aminopropionitrile (BAPN)-induced mouse thoracic aortic aneurysm and dissection (TAAD) model. A total of 71 overlapping genes were screened by intersecting the significant genes in the pink module and the differentially expressed genes. A PPI network with 45 nodes and 74 edges was generated, and five top hub genes (HIF1A, HGF, HMOX1, ITGA5, and ITGB3) were identified. All the hub genes had area under the curve values above 0.55. scRNA-seq data analysis showed that HIF1A was significantly upregulated in macrophages and HGF was significantly upregulated in vascular smooth muscle cells (SMCs) of the ascending aortas in AD patients. HIF1A may transcriptionally regulate multiple downstream target genes involving inflammation (TLR2, ALOX5AP, and MIF), glycolysis (ENO1, LDHA, and GAPDH), tissue remodeling (PLAU), and angiogenesis (SERPIN and VEGFA). HGF may participate in the signaling among SMCs, fibroblasts, and endothelial cells through binding to different receptors (MET, EGFR, IGF1R, and KDR). The mRNA expression of Hif1a, Hgf, and their target genes, including Alox5ap, Serpine1, Tlr2, Plau, Egfr, and Igf1r, was significantly upregulated in aortic tissues of BAPN-treated mice. By integrating RNA-seq and scRNA-seq data, we identified HIF1A and HGF as two hub genes with good diagnostic efficiency for AD. HIF1A in macrophages may promote AD formation by promoting inflammation, glycolysis, tissue remodeling, and angiogenesis, and HGF may mediate signaling among SMCs, fibroblasts, and endothelial cells in the development of AD.

Differential analysis of sorting nexin 10 and sterol regulatory element-binding protein 2 expression in inflammatory bowel disease.

Sorting nexin 10 (SNX10) expression induces intestinal barrier dysfunction and inflammatory responses; in contrast, its inhibition promotes intestinal mucosal healing through sterol regulatory element-binding protein 2 (SREBP2)-mediated cholesterol synthesis. However, its regulatory mechanism for the pathogenesis of inflammatory bowel disease (IBD) remains unclear. In this study, we examined SNX10 and SREBP2 expression in ulcerative colitis (UC) and Crohn's disease (CD). A total of 30 and 28 patients with UC and CD, respectively, were recruited. The expression of SNX10 and SREBP2 in the colonic mucosa was measured by immunohistochemistry (IHC). We discovered that patients with CD had significantly higher expression levels of SNX10 and SREBP2 than patients with UC and healthy controls. In addition, the expression of SREBP2 in patients with UC was significantly higher than that in healthy controls. In our study, we indicated that SNX10 and SREBP2 may serve as biomarkers for identifying patients with UC and CD, thereby providing a clinical therapeutic strategy for the treatment of IBD by inhibiting SNX10.

Integrative biomarker discovery and immune profiling for ulcerative colitis: a multi-methodological approach

Background We aimed to pinpoint biomarkers, create a diagnostic model for ulcerative colitis (UC), and delve into its immune features to better understand this autoimmune condition. Methods The sequencing data for both the UC and the control groups were obtained from GEO, including both bulk and single-cell data. Using GSE87466 as training group, we applied differential analysis, WGCNA, PPI, LASSO, RF, and SVM-RFE for biomarker selection. A neural network shaped our diagnostic model, corroborated by GSE92415 as the validation cohort with ROC assessment. Immune cell profiling was conducted using CIBERSORT. Results 53 disease-associated genes were screened. Enrichment analysis highlighted roles in complement cascades and cell adhesion. Eight biomarkers were finally identified through multiple machine learning and PPI: B4GALNT2, PDZK1IP1, FAM195A, REG4, MTMR11, FLJ35024, CD55, and CD44. The diagnostic model had AUCs of 0.984 (training group) and 0.957 (validation group). UC tissues revealed heightened plasma cells, CD8 T cells, and other immune cells. Two unique UC immune patterns emerged, with certain T and NK cells central to immune modulation. Conclusion We identified eight biomarkers of UC by various methods, constructed a diagnostic model through neural networks, and explored the immune complexity of the disease, which contributes to the diagnosis and treatment of UC.

Geographical variation and genetic diversity of Parashorea chinensis germplasm resources.

Parashorea chinensis is a rare monodominant species in southwest China known for its production of high-quality timber, is facing decline due to its narrow distribution, human interference and habitat destruction. However, there are no reports on genetic diversity and geographical variation of phenotypic traits of P. chinensis. In this study, phenotypic characters and genetic diversity of 15 germplasms resources from five provenances in southwest China were investigated, and their relationships with geographical and environmental factors was discussed. Our results revealed a rich phenotypic diversity among the germplasms, with variation coefficients ranging from 3.63% to 45.49%. Among the studied germplasms, NP03 from Napo and ML02 from Mengla region exhibited superior phenotypic traits. Notably, NP03 also demonstrated the highest genetic diversity. Genetic differentiation analyses including genetic differentiation coefficient (0.6264) and gene flow (0.3736) illustrated that genetic variation was most prevalent among populations. Furthermore, redundancy analysis showed that temperature related factors (maximum air temperature, annual mean temperature and minimum air temperature) significantly affected phenotypic variation. Similarly, altitude, longitude, latitude, annual mean precipitation and the minimum air temperature significantly impacted the level of genetic diversity. The molecular variation of the natural population of P. chinensis followed a certain geographical pattern. Our finding indicated abundant phenotypic variation among P. chinensis germplasms. However, populations exhibited low levels of genetic diversity alongside high genetic differentiation, potentially contributing to the species' rarity. Based on our results, NP03 and ML02 germplasm could be used as the parents for breeding superior germplasm of P. chinensis. Overall, this study provides valuable insights into germplasm diversity and conservation, genetic improvement, and utilization of P. chinensis.

Expression and its clinical significance of cell-cycle dependent kinase 1 in malignant peripheral nerve sheath tumors

To explore the role and clinical significance of cell-cycle dependent kinase 1 (CDK1) and its upstream and downstream molecules in the development of malignant peripheral nerve sheath tumor (MPNST) through the analysis of clinical tissue samples. A total of 56 tumor samples from MPNST patients ("Tianjin" dataset) who underwent surgical resection, confirmed by histology and pathology between September 2011 and March 2020, along with 17 normal tissue samples, were selected as the research subjects. MPNST-related hub genes were identified through transcriptome sequencing, bioinformatics analysis, immunohistochemistry staining, and survival analysis, and their expression levels and prognostic associations were analyzed. Transcriptome sequencing and bioinformatics analysis revealed that upregulated genes in MPNST were predominantly enriched in cell cycle-related pathways, with CDK1 occupying a central position among all differentially expressed genes. Further differential analysis demonstrated that CDK1 mRNA expression in sarcoma tissues was significantly higher than in normal tissues [based on searching the cancer genome atlas (TCGA) dataset, P<0.05]. In MPNST tissues, CDK1 mRNA expression was not only significantly higher than in normal tissues (based on Tianjin, GSE141438 datasets, P<0.05), but also significantly higher than in neurofibromatosis (NF) and plexiform neurofibromas (PNF) (based on GSE66743 and GSE145064 datasets, P<0.05). Immunohistochemical staining results indicated that the expression rate of CDK1 protein in MPNST tissues was 40.31%. Survival analysis results demonstrated that CDK1 expression was associated with poor prognosis. The survival time of MPNST patients with high CDK1 mRNA expression was significantly lower than that of the low expression group ( P<0.05), and the overall survival trend of patients with positive CDK1 protein expression was worse than that of patients with negative CDK1 expression. Additionally, differential analysis of CDK family genes (CDK1-8) revealed that only CDK1 was significantly upregulated in MPNST, NF, and PNF. Increased expression of CDK1 is associated with poor prognosis in MPNST patients. Compared to other CDK family members, CDK1 exhibits a unique expression pattern, suggesting its potential as a therapeutic target for MPNST.

Delayed treatment of diabetic foot ulcer in patients with type 2 diabetes and its prediction model

BACKGROUND Diabetic foot (DF) is a serious complication of type 2 diabetes. This study aimed to investigate the factors associated with DF occurrence and the role of delayed medical care in a cohort of patients with type 2 diabetes. AIM To reveal the impact of delayed medical treatment on the development of DF in patients with type 2 diabetes and to establish a predictive model for DF. METHODS In this retrospective cohort study, 292 patients with type 2 diabetes who underwent examination at our hospital from January 2023 to December 2023 were selected and divided into the DF group (n = 82, DF) and nondiabetic foot group (n = 210, NDF). Differential and correlation analyses of demographic indicators, laboratory parameters, and delayed medical treatment were conducted for the two groups. Logistic regression was applied to determine influencing factors. Receiver operating characteristic (ROC) analysis was performed, and indicators with good predictive value were selected to establish a combined predictive model. RESULTS The DF group had significantly higher body mass index (BMI) (P &lt; 0.001), disease duration (P = 0.012), plasma glucose levels (P &lt; 0.001), and HbA1c (P &lt; 0.001) than the NDF group. The NDF group had significantly higher Acute Thrombosis and Myocardial Infarction Health Service System (ATMHSS) scores (P &lt; 0.001) and a significantly lower delayed medical treatment rate (72.38% vs 13.41%, P &lt; 0.001). BMI, duration of diabetes, plasma glucose levels, HbA1c, diabetic peripheral neuropathy, and nephropathy were all positively correlated with DF occurrence. ATMHSS scores were negatively correlated with delayed time to seek medical treatment. The logistic regression model revealed that BMI, duration of diabetes, plasma glucose levels, HbA1c, presence of diabetic peripheral neuropathy and nephropathy, ATMHSS scores, and delayed time to seek medical treatment were influencing factors for DF. ROC analysis indicated that plasma glucose levels, HbA1c, and delayed medical treatment had good predictive value with an area under the curve of 0.933 for the combined predictive model. CONCLUSION Delayed medical treatment significantly affects the probability of DF occurrence in patients with diabetes. Plasma glucose levels, HbA1c levels, and the combined predictive model of delayed medical treatment demonstrate good predictive value.

Utilizing the Discrete Heisenberg Group and Laser Systems in RGB Image Encryption

This study signifies the endpoint of thorough cryptographic experimentation, leading to the creation of an innovative color image encryption scheme. It embodies a fusion of mathematical concepts rooted in both group theory and chaos theory.The novel encryption procedure entails the creation of cube faces, to depict the relative positions of pixels within a given stream, thereby generating six distinct channels. Within our algorithm, each monochromatic layer of an image is independently encrypted using digraph encryption. This involves a technique of rotating the four faces, followed by another rotation to encrypt the second digraph.Subsequently, matrices derived from Heisenberg theory are integrated with the monochromatic layer from the preceding step to fine-tune the image's parameters and introduce blur. Impressively, our approach has yielded promising outcomes across various images and evaluation criteria, demonstrating resilience against differential attacks and statistical analyses. Furthermore, comparative evaluations have highlighted the superiority of our method over existing algorithms.

Synergistic effect of diatomaceous earth as a catalyst and terephthalic acid as a co-catalyst on chemical recycling of polyethylene terephthalate via hydrolysis

Polyethylene terephthalate (PET) is one of the most useful commodity polymers that have been used extensively in packaging and textile industry and hence it has become a concerning waste. In this work, diatomaceous earth (DE), terephthalic acid (TPA) and distilled water were used to hydrolyze PET waste from drinking water bottles into TPA and ethylene glycol (EG). TPA can be used as one of the monomers for the synthesis of PET or other industrial applications such as for the synthesis of alkyd resins. Here DE was used as a solid catalyst which can be separated by a simple filtration after the TPA removal, in contrast to the majority of earlier efforts, which used aqueous acid or base catalysts that are difficult to separate. Additionally, commercial TPA, which is used as co-catalyst, can easily be substituted by produced TPA from the PET hydrolysis. The conversion of PET and the yield of TPA, using 5.0 wt % of DE and 3.0 wt % of commercial TPA with a PET:H2O mass ratio of 1.0:3.0, at 185 °C and 11.2 bar was 100 % and 95.40 % respectively, obtained in 2.5 h. TPA product was isolated from the reaction mixture by basic wash and subsequent neutralization and it was characterized by infrared spectroscopy, nuclear magnetic resonance, differential scanning calorimetry, X-ray diffraction and thermogravimetric analysis. The effects of reaction parameters such as reaction temperature, reaction time, mass ratio of PET:H2O and the catalysts loading on the yield of TPA were studied. The optimized reaction was repeated over seven consecutive reaction cycles using recovered DE to further demonstrate the effectiveness of reused DE and it was found that the outcomes were unaltered. Due to DE's affordability and accessibility, this reaction is a green and convenient way to handle the staggering accumulation of PET waste.

Mitophagy related diagnostic biomarkers for coronary in-stent restenosis identified using machine learning and bioinformatics

Percutaneous coronary intervention (PCI) combined with stent implantation is currently one of the most effective treatments for coronary artery disease (CAD). However, in-stent restenosis (ISR) significantly compromises its long-term efficacy. Mitophagy plays a crucial role in vascular homeostasis, yet its role in ISR remains unclear. This study aims to identify mitophagy-related biomarkers for ISR and explore their underlying molecular mechanisms. Through differential gene expression analysis between ISR and Control samples in the combined dataset, 169 differentially expressed genes (DEGs) were identified. Twenty-three differentially expressed mitophagy-related genes (DEMRGs) were identified by intersecting with mitophagy-related genes (MRGs) from the GeneCards, and functional enrichment analysis indicated their significant involvement in mitophagy-related biological processes. Using Weighted Gene Co-expression Network Analysis (WGCNA) and three machine learning algorithms (Logistic-LASSO, RF, and SVM-RFE), LRRK2, and ANKRD13A were identified as mitophagy-related biomarkers for ISR. The nomogram based on these two genes also exhibited promising diagnostic performance for ISR. Gene Set Enrichment Analysis (GSEA) as well as immune infiltration analyses showed that these two genes were closely associated with immune and inflammatory responses in ISR. Furthermore, potential small molecule compounds with therapeutic implications for ISR were predicted using the connectivity Map (cMAP) database. This study systematically investigated mitophagy-related biomarkers for ISR and their potential biological functions, providing new insights into early diagnosis and precision treatment strategies for ISR.

Screening and expression validation of key proteins for secondary hair follicle growth in cashmere goats based on iTRAQ quantitative proteomics technology.

The growth of secondary hair follicles (SHFs) in cashmere goats has periodic changes, including telogen, anagen, and catagen, during which proteins play important roles as the executor of life activities. In this study, the skin tissues of cashmere goats at three different growth stages of SHFs were collected for proteome sequencing and validation experiments. Through protein differential expression analysis and time series analysis, FKBP prolyl isomerase 10 (FKBP10) and fibrillin 2 (FBN2) were screened as the key proteins for SHF cycle growth of cashmere goats, and albumin (ALB), collagen type I alpha 1 chain (COL1A1) and elastin (ELN) were predicted to be their interacting proteins. The results of quantitative real-time PCR (qRT-PCR), western blot, and immunohistochemistry experiments showed that the mRNA and protein expression levels of FKBP10, FBN2, COL1A1, ELN and ALB were higher in anagen and lower in telogen. They were all highly expressed in the outer root sheath of SHFs in anagen. FKBP10, FBN2, COL1A1, ELN, and ALB can promote the growth of SHFs in cashmere goats. This study lays the foundation for analyzing the growth cycle regulatory mechanism of SHFs in cashmere goats, and provides new ideas for further improving cashmere yield and quality.

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer.

Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non-cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co-expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR-1290, miR-1246, and miR-451a among the top up-regulated, and miR-6875-5p, miR-6784-5p, miR-1228-5p, and miR-6765-5p among the top down-regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA-target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR-187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR-187 expression. In summary, the integration of differential gene expression, co-expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

Land use drives prokaryotic community composition of directly adjacent grasslands

AbstractUnderstanding the impact of agricultural land use on the soil prokaryotic communities in connected downslope sites is crucial for developing sustainable strategies to preserve ecosystem properties and mitigate agriculture’s environmental impacts. In this study, we investigated topsoil samples collected at three time points in 2022 (March, June, and November) from two adjacent catenas, reaching from hillslope to floodplain. The catenas differed in land use (extensive grassland vs. extensive cropland) at the top and middle parts, while the floodplain remained an extensive grassland due to legal restrictions. Using quantitative real-time PCRs and metabarcoding, we assessed prokaryotic abundance and prokaryotic community composition. Results show higher bacterial abundance in the cropland-influenced floodplain part across all time points compared to the grassland-influenced floodplain part. Temporal dynamics revealed a progressive decrease in the shared prokaryotic communities of the floodplain parts, peaking at the summer sampling time point, indicating a significant influence of the respective management type of the agricultural sites over the bacterial and archaeal communities of the floodplain parts. Differential abundance analyses identified several nitrifying taxa as more abundant in the cropland-influenced floodplain. Upstream land use also influenced the prokaryotic network of the cropland-floodplain, with some cropland taxa becoming keystone taxa and altering network morphology, an effect not observed in the grassland-influenced floodplain. These findings suggest that upstream agricultural land use practices have exerted a long-term influence on the floodplain prokaryotic communities over the past three decades. Moreover, there is evidence suggesting that these prokaryotic communities may undergo a potential reset during winter, which requires further investigation.