Table_1_Genetic markers associated with ferroptosis in Alzheimer’s disease.DOCX

ObjectiveFerroptosis is implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, and vascular dementia, implying that it may have a regulatory effect on the progression of these diseases. However, the specific role of ferroptosis-related genes (FRGs) in Alzheimer’s disease (AD) is not yet fully understood. The aim of the study was to detect ferroptosis related genes with regulatory functions in the disease and explore potential mechanisms in AD.MethodsHub FRGs were obtained through multiple algorithms based on the GSE5281 dataset. The screening process was implemented by R packages including limma, WGCNA, glm and SVM-RFE. Gene Ontology classification and pathway enrichment analysis were performed based on FRGs. Biological processes involved with hub FRGs were investigated through GSVA and GSEA methods. Immune infiltration analysis was performed by the R package CIBERSORT. Receiver operating characteristic curve (ROC) was utilized to validate the accuracy of hub FRGs. The CeRNA network attempted to find non-coding RNA transcripts which may play a role in disease progression.ResultsDDIT4, MUC1, KLHL24, CD44, and RB1 were identified as hub FRGs. As later revealed by enrichment analysis, the hub FRGs had important effects on AD through involvement in diverse AD pathogenesis-related pathways such as autophagy and glutathione metabolism. The immune microenvironment in AD shows increased numbers of resting NK cells, macrophages, and mast cells, with decreased levels of CD8 T cells when compared to healthy samples. Regulatory T cells were positively correlated with MUC1, KLHL24, and DDIT4 expression, while RB1 showed negative correlations with eosinophils and CD8 T cells, suggesting potential roles in modulating the immune environment in AD.ConclusionOur research has identified five hub FRGs in AD. We concluded that ferroptosis may be involved in the disease.</p

Data_Sheet_1_Case report: Methicillin-resistant Staphylococcus aureus with penicillin susceptible (PS-MRSA): first clinical report from a psychiatric hospital in China.CSV

This case report documents the first instance of Penicillin-Susceptible Methicillin-Resistant Staphylococcus aureus (PS-MRSA) in a Chinese psychiatric hospital. The strain was isolated from a patient with Alzheimer’s disease who had a lower respiratory tract infection. Clinical and laboratory analyses, including mass spectrometry, antibiotic susceptibility testing, and whole-genome sequencing, confirmed the PS-MRSA strain. In this case, we systematically introduce the clinical symptoms, laboratory findings, and treatment responses associated with this PS-MRSA strain. This discovery offers a new perspective on our understanding of resistance mechanisms and expands our considerations for existing antibiotic treatments. It may fill a gap in the classification of MRSA strains, enhance the spectrum of MRSA resistance, and complete the therapeutic strategies for MRSA.</p

Table_3_Genetic markers associated with ferroptosis in Alzheimer’s disease.XLSX

ObjectiveFerroptosis is implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, and vascular dementia, implying that it may have a regulatory effect on the progression of these diseases. However, the specific role of ferroptosis-related genes (FRGs) in Alzheimer’s disease (AD) is not yet fully understood. The aim of the study was to detect ferroptosis related genes with regulatory functions in the disease and explore potential mechanisms in AD.MethodsHub FRGs were obtained through multiple algorithms based on the GSE5281 dataset. The screening process was implemented by R packages including limma, WGCNA, glm and SVM-RFE. Gene Ontology classification and pathway enrichment analysis were performed based on FRGs. Biological processes involved with hub FRGs were investigated through GSVA and GSEA methods. Immune infiltration analysis was performed by the R package CIBERSORT. Receiver operating characteristic curve (ROC) was utilized to validate the accuracy of hub FRGs. The CeRNA network attempted to find non-coding RNA transcripts which may play a role in disease progression.ResultsDDIT4, MUC1, KLHL24, CD44, and RB1 were identified as hub FRGs. As later revealed by enrichment analysis, the hub FRGs had important effects on AD through involvement in diverse AD pathogenesis-related pathways such as autophagy and glutathione metabolism. The immune microenvironment in AD shows increased numbers of resting NK cells, macrophages, and mast cells, with decreased levels of CD8 T cells when compared to healthy samples. Regulatory T cells were positively correlated with MUC1, KLHL24, and DDIT4 expression, while RB1 showed negative correlations with eosinophils and CD8 T cells, suggesting potential roles in modulating the immune environment in AD.ConclusionOur research has identified five hub FRGs in AD. We concluded that ferroptosis may be involved in the disease.</p

Table_2_Genetic markers associated with ferroptosis in Alzheimer’s disease.XLSX

ObjectiveFerroptosis is implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, and vascular dementia, implying that it may have a regulatory effect on the progression of these diseases. However, the specific role of ferroptosis-related genes (FRGs) in Alzheimer’s disease (AD) is not yet fully understood. The aim of the study was to detect ferroptosis related genes with regulatory functions in the disease and explore potential mechanisms in AD.MethodsHub FRGs were obtained through multiple algorithms based on the GSE5281 dataset. The screening process was implemented by R packages including limma, WGCNA, glm and SVM-RFE. Gene Ontology classification and pathway enrichment analysis were performed based on FRGs. Biological processes involved with hub FRGs were investigated through GSVA and GSEA methods. Immune infiltration analysis was performed by the R package CIBERSORT. Receiver operating characteristic curve (ROC) was utilized to validate the accuracy of hub FRGs. The CeRNA network attempted to find non-coding RNA transcripts which may play a role in disease progression.ResultsDDIT4, MUC1, KLHL24, CD44, and RB1 were identified as hub FRGs. As later revealed by enrichment analysis, the hub FRGs had important effects on AD through involvement in diverse AD pathogenesis-related pathways such as autophagy and glutathione metabolism. The immune microenvironment in AD shows increased numbers of resting NK cells, macrophages, and mast cells, with decreased levels of CD8 T cells when compared to healthy samples. Regulatory T cells were positively correlated with MUC1, KLHL24, and DDIT4 expression, while RB1 showed negative correlations with eosinophils and CD8 T cells, suggesting potential roles in modulating the immune environment in AD.ConclusionOur research has identified five hub FRGs in AD. We concluded that ferroptosis may be involved in the disease.</p

Table_4_Genetic markers associated with ferroptosis in Alzheimer’s disease.XLSX

ObjectiveFerroptosis is implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, and vascular dementia, implying that it may have a regulatory effect on the progression of these diseases. However, the specific role of ferroptosis-related genes (FRGs) in Alzheimer’s disease (AD) is not yet fully understood. The aim of the study was to detect ferroptosis related genes with regulatory functions in the disease and explore potential mechanisms in AD.MethodsHub FRGs were obtained through multiple algorithms based on the GSE5281 dataset. The screening process was implemented by R packages including limma, WGCNA, glm and SVM-RFE. Gene Ontology classification and pathway enrichment analysis were performed based on FRGs. Biological processes involved with hub FRGs were investigated through GSVA and GSEA methods. Immune infiltration analysis was performed by the R package CIBERSORT. Receiver operating characteristic curve (ROC) was utilized to validate the accuracy of hub FRGs. The CeRNA network attempted to find non-coding RNA transcripts which may play a role in disease progression.ResultsDDIT4, MUC1, KLHL24, CD44, and RB1 were identified as hub FRGs. As later revealed by enrichment analysis, the hub FRGs had important effects on AD through involvement in diverse AD pathogenesis-related pathways such as autophagy and glutathione metabolism. The immune microenvironment in AD shows increased numbers of resting NK cells, macrophages, and mast cells, with decreased levels of CD8 T cells when compared to healthy samples. Regulatory T cells were positively correlated with MUC1, KLHL24, and DDIT4 expression, while RB1 showed negative correlations with eosinophils and CD8 T cells, suggesting potential roles in modulating the immune environment in AD.ConclusionOur research has identified five hub FRGs in AD. We concluded that ferroptosis may be involved in the disease.</p

Hydrogen-Bonded Dihydrotetraazapentacenes

Three new members of <i>N</i>-heteropentacenes explored herein have adjacent pyrazine and dihydropyrazine rings at one end of the pentacene backbone. Interesting findings from this study include self-complementary N–H···N H-bonds in the solid state, solvent-dependent UV–vis absorption caused by H-bonding, and new <i>p</i>-type organic semiconductors with field effect mobility up to 0.7 cm² V^–1 s^–1

Hydrogen-Bonded Dihydrotetraazapentacenes

Three new members of <i>N</i>-heteropentacenes explored herein have adjacent pyrazine and dihydropyrazine rings at one end of the pentacene backbone. Interesting findings from this study include self-complementary N–H···N H-bonds in the solid state, solvent-dependent UV–vis absorption caused by H-bonding, and new <i>p</i>-type organic semiconductors with field effect mobility up to 0.7 cm² V^–1 s^–1

Antibacterial Effect of Silver-Incorporated Flake-Shell Nanoparticles under Dual-Modality

Silver has been recognized as a broad-spectrum antimicrobial agent and extensively used in biomedical applications. Through a sequential one-pot synthesis strategy, we have successfully incorporated silver into flake-shell nanoparticles. Due to the simultaneous growth of networked nanostructures of silica and in situ reduction of silver ions, homogeneously distributed silver into the shell of the nanocapsule was formed. The antibacterial test indicated that the silver-incorporated silica nanocapsule exhibits effective antibacterial activity, inhibiting the bacterial growth by 75%. In addition, with the encapsulation of other antibiotic agent into the structure, an enhanced antibacterial effect under dual-modality could also be achieved

InCl₃ Catalyzed Highly Diastereoselective [3 + 2] Cycloaddition of 1,2-Cyclopropanated Sugars with Aldehydes: A Straightforward Synthesis of Persubstituted <i>Bis</i>-Tetrahydrofurans and Perhydrofuro[2,3‑<i>b</i>]pyrans

A mild and efficient strategy for the construction of persubstituted <i>bis</i>-tetrahydrofuran and perhydrofuro[2,3-<i>b</i>]pyran derivatives has been developed. Persubstituted cyclization products were obtained in good to excellent yields. The [3 + 2] cycloaddition of 1,2-cyclopropanated sugars with aldehydes in the presence of InCl₃ is highly diastereoselective

Additional file 1: Figure S1. of Gene expression, regulation of DEN and HBx induced HCC mice models and comparisons of tumor, para-tumor and normal tissues

Histopathologic examinations of liver tissues under microscope. All the pictures were captured at magnification of 100×. The first two lines are tissue slices of control and DEN treatments at different time point in DEN model. The third line indicates that the Kupffer cells increased over time. Kupffer cell is a kind of specialized macrophage which plays a major anti-inflamination role in liver, its increasing can reflect the injury level of liver. In this study, the injury significantly increased as time goes on. The pictures in the last line show histological changes from control to para-tumor and tumor tissues of liver at the 30th week. Figure S2. Different pathways in the same tissue in two models. a Tumor tissues. b Para-tumor tissues. The terms on vertical axis beginning with ‘DEN’ or ‘HBx’ represent the enrichment terms of genes in DEN model or HBx model. Figure S3. Quantification of the proteins of two genes, DROSHA and ADAR. (a) Pictures of immunohistochemistry results. (b) Gene expression levels (FPKM) and protein expression levels (Integrated optical density, calculated by Image pro plus 6.0) of these two genes. Figure S4. The subnetworks of the TFs Egr1, Atf3 and Klf4. Gold diamond: TFs. Purple oval: genes. (DOCX 7118 kb