Gamma-glutamyl transferase 5 overexpression in cerebrovascular endothelial cells improves brain pathology, cognition, and behavior in APP/PS1 mice.

JOURNAL/nrgr/04.03/01300535-202502000-00030/figure1/v/2024-05-28T214302Z/r/image-tiff In patients with Alzheimer's disease, gamma-glutamyl transferase 5 (GGT5) expression has been observed to be downregulated in cerebrovascular endothelial cells. However, the functional role of GGT5 in the development of Alzheimer's disease remains unclear. This study aimed to explore the effect of GGT5 on cognitive function and brain pathology in an APP/PS1 mouse model of Alzheimer's disease, as well as the underlying mechanism. We observed a significant reduction in GGT5 expression in two in vitro models of Alzheimer's disease (Aß1-42-treated hCMEC/D3 and bEnd.3 cells), as well as in the APP/PS1 mouse model. Additionally, injection of APP/PS1 mice with an adeno-associated virus encoding GGT5 enhanced hippocampal synaptic plasticity and mitigated cognitive deficits. Interestingly, increasing GGT5 expression in cerebrovascular endothelial cells reduced levels of both soluble and insoluble amyloid-ß in the brains of APP/PS1 mice. This effect may be attributable to inhibition of the expression of ß-site APP cleaving enzyme 1, which is mediated by nuclear factor-kappa B. Our findings demonstrate that GGT5 expression in cerebrovascular endothelial cells is inversely associated with Alzheimer's disease pathogenesis, and that GGT5 upregulation mitigates cognitive deficits in APP/PS1 mice. These findings suggest that GGT5 expression in cerebrovascular endothelial cells is a potential therapeutic target and biomarker for Alzheimer's disease.

Respiratory adverse effects in patients treated with immune checkpoint inhibitors in combination with radiotherapy: a systematic review and meta-analysis.

BACKGROUND: We conducted a systematic review and meta-analysis to assess the risk of respiratory adverse effects in patients with solid tumors treated with immune checkpoint inhibitors (PD-1, PD-L1 and CTLA-4 inhibitors) in combination with radiation therapy. METHODS: We selected eligible studies through the following databases: PubMed, Embase, Cochrane Library, and Clinicaltrials ( https://clinicaltrials.gov/ ). The data was analyzed by using Rstudio. RESULTS: Among 3737 studies, 26 clinical trials, including 2670 patients, were qualified for the meta-analysis. We evaluated the incidence rates of adverse respiratory events, including cough, pneumonia, upper respiratory tract infections, and others: grades 1-5 cough, 0.176 (95%CI: 0.113-0.274, I2 = 92.36%); grades 1-5 pneumonitis, 0.118 (95%CI: 0.067-0.198, I2 = 88.64%); grades 1-5 upper respiratory tract infection, 0.064 (95%CI: 0.049-0.080, I2 = 0.98%); grades 3-5 cough, 0.050 (95%CI: 0.012-0.204, I2 = 8.90%); grades 3-5 pneumonitis, 0.052 (95%CI: 0.031-0.078, I2 = 83.86%); grades 3-5 upper respiratory tract infection, 0.040 (95%CI: 0.007-0.249, I2 = 45.31%). CONCLUSIONS: Our meta-analysis demonstrated that ICI combined with radiotherapy for solid tumors can produce respiratory adverse effects. ICIs combination treatment, a tumor located in the chest, is more likely to cause adverse reactions, and SBRT treatment and synchronous treatment will bring less incidence of adverse reactions. This study provide insights for clinicians to balance the risks of radiotherapy in the course of treating oncology patients.

Molecular mechanism of IgE-mediated FcεRI activation.

Allergic diseases, affecting over a quarter of individuals in industrialized countries, have become significant public health concerns1,2. The high-affinity Fc receptor for IgE (FcεRI), mainly present on mast cells and basophils, plays a crucial role in allergic diseases3-5. Monomeric IgE binding to FcεRI regulates mast cell survival, differentiation, and maturation6-8. However, the underlying molecular mechanism remains unclear. Here we demonstrate that, prior to IgE binding, FcεRI mostly exists as a homo-dimer on human mast cell membrane. The structure of human FcεRI confirms the dimeric organization, with each promoter comprising one α subunit, one ß subunit, and two γ subunits. The transmembrane helices of the α subunits form a layered arrangement with those of the γ and ß subunits. The dimeric interface is mediated by a four-helix bundle of the α and γ subunits at the intracellular juxtamembrane region. Cholesterol-like molecules embedded within the transmembrane domain may stabilize the dimeric assembly. Upon IgE binding, the dimeric FcεRI dissociates into two protomers, each binding to an IgE molecule. Importantly, this process elicits transcriptional activation of Egr1/3 and Ccl2 in rat basophils, which can be attenuated by inhibiting the FcεRI dimer-to-monomer transition. Collectively, our study unveils the mechanism of antigen-independent, IgE-mediated FcεRI activation.

Enhancing RNA-seq analysis by addressing all co-existing biases using a self-benchmarking approach with 2D structural insights.

We introduce a groundbreaking approach: the minimum free energy-based Gaussian Self-Benchmarking (MFE-GSB) framework, designed to combat the myriad of biases inherent in RNA-seq data. Central to our methodology is the MFE concept, facilitating the adoption of a Gaussian distribution model tailored to effectively mitigate all co-existing biases within a k-mer counting scheme. The MFE-GSB framework operates on a sophisticated dual-model system, juxtaposing modeling data of uniform k-mer distribution against the real, observed sequencing data characterized by nonuniform k-mer distributions. The framework applies a Gaussian function, guided by the predetermined parameters-mean and SD-derived from modeling data, to fit unknown sequencing data. This dual comparison allows for the accurate prediction of k-mer abundances across MFE categories, enabling simultaneous correction of biases at the single k-mer level. Through validation with both engineered RNA constructs and human tissue RNA samples, its wide-ranging efficacy and applicability are demonstrated.

Enhancing RNA-seq bias mitigation with the Gaussian self-benchmarking framework: towards unbiased sequencing data.

BACKGROUND: RNA sequencing is a vital technique for analyzing RNA behavior in cells, but it often suffers from various biases that distort the data. Traditional methods to address these biases are typically empirical and handle them individually, limiting their effectiveness. Our study introduces the Gaussian Self-Benchmarking (GSB) framework, a novel approach that leverages the natural distribution patterns of guanine (G) and cytosine (C) content in RNA to mitigate multiple biases simultaneously. This method is grounded in a theoretical model, organizing k-mers based on their GC content and applying a Gaussian model for alignment to ensure empirical sequencing data closely match their theoretical distribution. RESULTS: The GSB framework demonstrated superior performance in mitigating sequencing biases compared to existing methods. Testing with synthetic RNA constructs and real human samples showed that the GSB approach not only addresses individual biases more effectively but also manages co-existing biases jointly. The framework's reliance on accurately pre-determined parameters like mean and standard deviation of GC content distribution allows for a more precise representation of RNA samples. This results in improved accuracy and reliability of RNA sequencing data, enhancing our understanding of RNA behavior in health and disease. CONCLUSIONS: The GSB framework presents a significant advancement in RNA sequencing analysis by providing a well-validated, multi-bias mitigation strategy. It functions independently from previously identified dataset flaws and sets a new standard for unbiased RNA sequencing results. This development enhances the reliability of RNA studies, broadening the potential for scientific breakthroughs in medicine and biology, particularly in genetic disease research and the development of targeted treatments.

Clinical Effect of Norepinephrine Combined with Esmolol Treatment in Patients with Septic Shock and Its Impact on Prognosis.

Objective: To unveil the influence of norepinephrine (NE) combined with esmolol treatment on cardiac function, hemodynamics, inflammatory factor levels, and prognosis in patients with septic shock. Methods: Ninety-six patients with septic shock admitted to our hospital from January 2021 to June 2023 were retrospectively analyzed and divided into the control and observation groups according to the different treatment methods. The control group was treated with standard anti-infection and fluid resuscitation, followed by NE administration [with an infusion rate of 0.1-0.5 µg/(kg-min)]. The observation group was treated with esmolol [starting pumping rate of 50 µg/(kg-min) and adjusting the pumping rate according to the target heart rate] in combination with the control group. Changes in hemodynamic parameters, including heart rate, mean arterial pressure, central venous pressure, cardiac index, stroke volume index, and systemic vascular resistance index, were monitored by pulse-indicating continuous cardiac output monitors before treatment (T0), 24h after treatment (T1), and 72h after treatment (T2); changes in cardiac function before and after 72h of treatment, indicators of inflammatory factors before and after treatment, and indicators of oxygenation metabolism were assessed; and adverse drug reactions during treatment were recorded in both groups. Results: NE combined with esmolol treatment improved the efficacy of patients with septic shock; was beneficial for the enhancement of blood perfusion in patients; improved the patient's cardiac function, reduced myocardial injury, and suppressed the inflammatory response in patients; improved the oxygenation metabolism and the prognosis of patients; did not significantly increase the adverse drug reactions of patients and had a better safety profile. Conclusion: NE combined with esmolol treatment can improve the efficacy of patients with septic shock, improve their cardiac function and hemodynamic indices, reduce myocardial injury and inflammatory response, and have a better safety profile, which is conducive to improving patient prognosis and reducing mortality.

Exploring refined dual visual features cross-combination for image captioning.

For current image caption tasks used to encode region features and grid features Transformer-based encoders have become commonplace, because of their multi-head self-attention mechanism, the encoder can better capture the relationship between different regions in the image and contextual information. However, stacking Transformer blocks necessitates quadratic computation through self-attention to visual features, not only resulting in the computation of numerous redundant features but also significantly increasing computational overhead. This paper presents a novel Distilled Cross-Combination Transformer (DCCT) network. Technically, we first introduce a distillation cascade fusion encoder (DCFE), where a probabilistic sparse self-attention layer is used to filter out some redundant and distracting features that affect attention focus, aiming to obtain more refined visual features and enhance encoding efficiency. Next, we develop a parallel cross-fusion attention module (PCFA) that fully exploits the complementarity and correlation between grid and region features to better fuse the encoded dual visual features. Extensive experiments conducted on the MSCOCO dataset demonstrate that our proposed DCCT method achieves outstanding performance, rivaling current state-of-the-art approaches.

Tracing the electron transport behavior in quantum-dot light-emitting diodes via single photon counting technique.

The electron injection and transport behavior are of vital importance to the performance of quantum-dot light-emitting diodes. By simultaneously measuring the electroluminescence-photoluminescence of the quantum-dot light-emitting diodes, we identify the presence of leakage electrons which leads to the discrepancy of the electroluminescence and the photoluminescence roll-off. To trace the transport paths of the leakage electrons, a single photon counting technique is developed. This technique enables us to detect the weak photon signals and thus provides a means to visualize the electron transport paths at different voltages. The results show that, the electrons, except those recombining within the quantum-dots, leak to the hole transport layer or recombine at the hole transport layer/quantum-dot interface, thus leading to the reduction of efficiency. By reducing the amount of leakage electrons, quantum-dot light-emitting diode with an internal power conversion efficiency of over 98% can be achieved.

Development and validation of a nomogram to predict impacted ureteral stones via machine learning.

BACKGROUND: To develop and evaluate a nomogram for predicting impacted ureteral stones using some simple and easily available clinical features. METHODS: From June 2019 to July 2022, 480 patients who underwent ureteroscopic lithotripsy (URSL) for ureteral calculi were enrolled in the study. From the eligible study population between June 2019 and December 2020, a training and validation set was randomly generated in a 7:3 ratio. To further evaluate the generalization performance of the nomogram, we performed an additional validation using the data from January 2021 to July 2022. Lasso regression analysis was used to identify the most useful predictive features. Subsequently, a multivariate logistic regression algorithm was applied to select independent predictive features. The predictive performance of the nomogram was assessed using Receiver Operating Characteristic (ROC) curves, calibration curves and decision Curve Analysis (DCA). The Hosmer-Lemeshow Test was utilized to evaluate the overall goodness of fit of the nomogram. RESULTS: Multivariate logistic regression analysis showed that flank pain, hydronephrosis, stone length/width, HU below (Hounsfield unit density of the ureter center below the stone), HU above/below (HU above divided by HU below) and UWT (ureteral wall thickness) were ascertained as independent predictors of impacted ureteral stones. The nomogram showed outstanding performance within the training dataset, with the area under the curve (AUC) of 0.907. Moreover, the AUC was 0.874 in the validation dataset. The ROC curve, calibration curve, DCA curve and Hosmer-Lemeshow Test suggested that the nomogram maintains excellent clinical applicability and demonstrates commendable performance. Similar results were achieved in the test dataset as well. CONCLUSIONS: We established a nomogram that can be effectively used for preoperative diagnosis of impacted ureteral stones, which is of great significance for the treatment of this disease.

Anorexia and bulimia in relation to ulcerative colitis: a Mendelian randomization study.

Background: Evidence for anorexia and bulimia in relation to the risk of ulcerative colitis (UC) is limited and inconsistent. The objective of this research was to utilize bi-directional, two-sample Mendelian randomization (MR) analysis to predict the causal association between anorexia nervosa and bulimia nervosa with UC. Methods: The genome-wide association studies (GWAS) provided data for anorexia and bulimia from the UK Biobank, utilizing single-nucleotide polymorphisms (SNP) as instrumental variables. Additionally, genetic associations with UC were collected from various sources including the FinnGen Biobank, the UK Biobank and the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC). The main analytical approach utilized in this study was the inverse-variance-weighted (IVW) method. To evaluate horizontal pleiotropy, the researchers conducted MR-Egger regression and MR-PRESSO global test analyses. Additionally, heterogeneity was assessed using the Cochran's Q test. Results: This study found a negative association between genetically predicted bulimia (OR = 0.943, 95% CI: 0.893-0.996; p = 0.034) and the risk of UC in the IIBDGC dataset, indicating that individuals with bulimia have approximately a 5.7% lower risk of developing UC. No association was observed in the other two datasets. Conversely, genetically predicted anorexia was not found to be causally associated with UC. In bi-directional Mendelian randomization, UC from the IIBDGC dataset was negatively associated with the risk of anorexia (OR = 0.877, 95% CI: 0.797-0.965; p = 0.007), suggesting that UC patients have approximately a 12.3% lower risk of developing anorexia, but not causally associated with bulimia. Conclusion: Genetically predicted bulimia may have a negative association with the onset of UC, while genetically predicted anorexia does not show a causal relationship with the development of UC. Conversely, genetically predicted UC may have a negative association with the development of anorexia.

Valtrate Suppresses TNFSF14-Mediated Arrhythmia After Myocardial Ischemia-Reperfusion by Inducing N-linked Glycosylation of LTßR to Regulate MGA/MAX/c-Myc/Cx43.

ABSTRACT: Myocardial ischemia-reperfusion (MIR)-induced arrhythmia remains a major cause of death in patients with cardiovascular diseases. The reduction of Cx43 has been known as a major inducer of arrhythmias after MIR, but the reason for the reduction of Cx43 remains largely unknown. The aim of this study was to find the key mechanism underlying the reduction of Cx43 after MIR and to screen out an herbal extract to attenuate arrhythmia after MIR. The differentially expressed genes in the peripheral blood mononuclear cell (PBMCs) after MIR were analyzed using the data from several gene expression omnibus data sets, followed by the identification in PBMCs and the serum of patients with myocardial infarction. Tumor necrosis factor superfamily protein 14 (TNFSF14) was increased in PBMCs and the serum of patients, which might be associated with the injury after MIR. The toxic effects of TNFSF14 on cardiomyocytes were investigated in vitro . Valtrate was screened out from several herbal extracts. Its protection against TNFSF14-induced injury was evaluated in cardiomyocytes and animal models with MIR. Recombinant TNFSF14 protein not only suppressed the viability of cardiomyocytes but also decreased Cx43 by stimulating the receptor LTßR. LTßR induces the competitive binding of MAX to MGA rather than the transcriptional factor c-Myc, thereby suppressing c-Myc-mediated transcription of Cx43. Valtrate promoted the N-linked glycosylation modification of LTßR, which reversed TNFSF14-induced reduction of Cx43 and attenuated arrhythmia after MIR. In all, valtrate suppresses TNFSF14-induced reduction of Cx43, thereby attenuating arrhythmia after MIR.

Multi-Parametric Retinal Microvascular Functional Perfusion Imaging Based on Dynamic Fundus Fluorescence Angiography.

Retinal microvascular disease has caused serious visual impairment widely in the world, which can be hopefully prevented via early and precision microvascular hemodynamic diagnosis. Due to artifacts from choroidal microvessels and tiny movements, current fundus microvascular imaging techniques including fundus fluorescein angiography (FFA) precisely identify retinal microvascular microstructural damage and abnormal hemodynamic changes difficulty, especially in the early stage. Therefore, this study proposes an FFA-based multi-parametric retinal microvascular functional perfusion imaging (RM-FPI) scheme to assess the microstructural damage and quantify its hemodynamic distribution precisely. Herein, a spatiotemporal filter based on singular value decomposition combined with a lognormal fitting model was used to remove the above artifacts. Dynamic FFAs of patients (n = 7) were collected first. The retinal time fluorescence intensity curves were extracted and the corresponding perfusion parameters were estimated after decomposition filtering and model fitting. Compared with in vivo results without filtering and fitting, the signal-to-clutter ratio of retinal perfusion curves, average contrast, and resolution of RM-FPI were up to 7.32 ± 0.43 dB, 14.34 ± 0.24 dB, and 11.0 ± 2.0 µm, respectively. RM-FPI imaged retinal microvascular distribution and quantified its spatial hemodynamic changes, which further characterized the parabolic distribution of local blood flow within diameters ranging from 9 to 400 µm. Finally, RM-FPI was used to quantify, visualize, and diagnose the retinal hemodynamics of retinal vein occlusion from mild to severe. Therefore, this study provided a scheme for early and precision diagnosis of retinal microvascular disease, which might be beneficial in preventing its development.

Construction of exosome-related genes risk model in kidney cell carcinoma predicts prognosis and immune therapy response.

Renal cell carcinoma (RCC) is one of the most prevalent types of urological cancer. Exosomes are vesicles derived from cells and have been found to promote the development of RCC, but the potential biomarker and molecular mechanism of exosomes on RCC remain ambiguous. Here, we first screened differentially expressed exosome-related genes (ERGs) by analyzing The Cancer Genome Atlas (TCGA) database and exoRBase 2.0 database. We then determined prognosis-related ERGs (PRERGs) by univariate Cox regression analysis. Gene Dependency Score (gDS), target development level, and pathway correlation analysis were utilized to examine the importance of PRERGs. Machine learning and lasso-cox regression were utilized to screen and construct a 5-gene risk model. The risk model showed high predictive accuracy for the prognosis of patients and proved to be an independent prognostic factor in three RCC datasets, including TCGA-KIRC, E-MTAB-1980, and TCGA-KIRP datasets. Patients with high-risk scores showed worse outcomes in different clinical subgroups, revealing that the risk score is robust. In addition, we found that immune-related pathways are highly enriched in the high-risk group. Activities of immune cells were distinct in high-/low-risk groups. In independent immune therapeutic cohorts, high-risk patients show worse immune therapy responses. In summary, we identified several exosome-derived genes that might play essential roles in RCC and constructed a 5-gene risk signature to predict the prognosis of RCC and immune therapy response.

N6-methyladenosine modification of KLF2 may contribute to endothelial-to-mesenchymal transition in pulmonary hypertension.

BACKGROUND: Pulmonary hypertension (PH) is a progressive disease characterized by pulmonary vascular remodeling. Increasing evidence indicates that endothelial-to-mesenchymal transition (EndMT) in pulmonary artery endothelial cells (PAECs) is a pivotal trigger initiating this remodeling. However, the regulatory mechanisms underlying EndMT in PH are still not fully understood. METHODS: Cytokine-induced hPAECs were assessed using RNA methylation quantification, qRT-PCR, and western blotting to determine the involvement of N6-methyladenosine (m6A) methylation in EndMT. Lentivirus-mediated silencing, overexpression, tube formation, and wound healing assays were utilized to investigate the function of METTL3 in EndMT. Endothelial-specific gene knockout, hemodynamic measurement, and immunostaining were performed to explore the roles of METTL3 in pulmonary vascular remodeling and PH. RNA-seq, RNA Immunoprecipitation-based qPCR, mRNA stability assay, m6A mutation, and dual-luciferase assays were employed to elucidate the mechanisms of RNA methylation in EndMT. RESULTS: The global levels of m6A and METTL3 expression were found to decrease in TNF-α- and TGF-ß1-induced EndMT in human PAECs (hPAECs). METTL3 inhibition led to reduced endothelial markers (CD31 and VE-cadherin) and increased mesenchymal markers (SM22 and N-cadherin) as well as EndMT-related transcription factors (Snail, Zeb1, Zeb2, and Slug). The endothelial-specific knockout of Mettl3 promoted EndMT and exacerbated pulmonary vascular remodeling and hypoxia-induced PH (HPH) in mice. Mechanistically, METTL3-mediated m6A modification of kruppel-like factor 2 (KLF2) plays a crucial role in the EndMT process. KLF2 overexpression increased CD31 and VE-cadherin levels while decreasing SM22, N-cadherin, and EndMT-related transcription factors, thereby mitigating EndMT in PH. Mutations in the m6A site of KLF2 mRNA compromise KLF2 expression, subsequently diminishing its protective effect against EndMT. Furthermore, KLF2 modulates SM22 expression through direct binding to its promoter. CONCLUSIONS: Our findings unveil a novel METTL3/KLF2 pathway critical for protecting hPAECs against EndMT, highlighting a promising avenue for therapeutic investigation in PH.

Single-cell and bulk RNA-sequence identified fibroblasts signature and CD8 + T-cell - fibroblast subtype predicting prognosis and immune therapeutic response of bladder cancer, based on machine learning: bioinformatics multi-omics study.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are found in primary and advanced tumours. They are primarily involved in tumour progression through complex mechanisms with other types of cells in the tumour microenvironment. However, essential fibroblasts-related genes (FRG) in bladder cancer still need to be explored, and there is a shortage of an ideal predictive model or molecular subtype for the progression and immune therapeutic assessment for bladder cancer, especially muscular-invasive bladder cancer based on the FRG. MATERIALS AND METHODS: CAF-related genes of bladder cancer were identified by analysing single-cell RNA sequence datasets, and bulk transcriptome datasets and gene signatures were used to characterize them. Then, 10 types of machine learning algorithms were utilised to determine the hallmark FRG and construct the FRG index (FRGI) and subtypes. Further molecular subtypes combined with CD8+ T-cells were established to predict the prognosis and immune therapy response. RESULTS: Fifty-four BLCA-related FRG were screened by large-scale scRNA-sequence datasets. The machine learning algorithm established a 3-genes FRGI. High FRGI represented a worse outcome. Then, FRGI combined clinical variables to construct a nomogram, which shows high predictive performance for the prognosis of bladder cancer. Furthermore, the BLCA datasets were separated into two subtypes - fibroblast hot and cold types. In five independent BLCA cohorts, the fibroblast hot type showed worse outcomes than the cold type. Multiple cancer-related hallmark pathways are distinctively enriched in these two types. In addition, high FRGI or fibroblast hot type shows a worse immune therapeutic response. Then, four subtypes called CD8-FRG subtypes were established under the combination of FRG signature and activity of CD8+ T-cells, which turned out to be effective in predicting the prognosis and immune therapeutic response of bladder cancer in multiple independent datasets. Pathway enrichment analysis, multiple gene signatures, and epigenetic alteration characterize the CD8-FRG subtypes and provide a potential combination strategy method against bladder cancer. CONCLUSIONS: In summary, the authors established a novel FRGI and CD8-FRG subtype by large-scale datasets and organised analyses, which could accurately predict clinical outcomes and immune therapeutic response of BLCA after surgery.

Tumor Supplying Artery Injection of Liposome@Sunitinib Could Effectively Inhibit the Progression of Kidney Tumor.

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system and is not sensitive to chemotherapy or radiotherapy in its advanced stages. Sunitinib is recommended as a first-line target drug for unresectable and metastatic RCC by targeting tyrosine kinase-related signaling pathways, but its therapeutic effect is unsatisfactory. Recently, nanomaterials have shown great prospects in the medical field because of their unique physicochemical properties. Particularly, liposomes are considered as ideal drug delivery systems due to their biodegradability, biocompatibility, and ideal drug-loading efficiency. Considering that tumor supplying artery injection can directly distribute drugs into tumor tissues, in this study, liposomes were employed to encapsulate water-insoluble sunitinib to construct the liposome@sunitinib (Lipo@Suni) complex, so that the drug could directly target and distribute into tumor tissue, and effectively trapped in tumor tissues after tumor supplying artery injection for the advantage of the physicochemical properties of liposomes, thereby achieving a better therapeutic effect on advanced RCC. Here, we found that compared with the peripheral intravenous administration, trans-renal arterial administration increases the content and prolongs the retention time of liposomes in tumor tissues; accordingly, more sunitinib is dispersed and retained in tumor tissues. Ultimately, trans-renal arterial administration of Lipo@Suni exerts a better suppressive effect on RCC progression than peripheral intravenous administration, even better than the conventional oral administration of sunitinib.

Single-cell insights: pioneering an integrated atlas of chromatin accessibility and transcriptomic landscapes in diabetic cardiomyopathy.

BACKGROUND: Diabetic cardiomyopathy (DCM) poses a growing health threat, elevating heart failure risk in diabetic individuals. Understanding DCM is crucial, with fibroblasts and endothelial cells playing pivotal roles in driving myocardial fibrosis and contributing to cardiac dysfunction. Advances in Multimodal single-cell profiling, such as scRNA-seq and scATAC-seq, provide deeper insights into DCM's unique cell states and molecular landscape for targeted therapeutic interventions. METHODS: Single-cell RNA and ATAC data from 10x Multiome libraries were processed using Cell Ranger ARC v2.0.1. Gene expression and ATAC data underwent Seurat and Signac filtration. Differential gene expression and accessible chromatin regions were identified. Transcription factor activity was estimated with chromVAR, and Cis-coaccessibility networks were calculated using Cicero. Coaccessibility connections were compared to the GeneHancer database. Gene Ontology analysis, biological process scoring, cell-cell communication analysis, and gene-motif correlation was performed to reveal intricate molecular changes. Immunofluorescent staining utilized various antibodies on paraffin-embedded tissues to verify the findings. RESULTS: This study integrated scRNA-seq and scATAC-seq data obtained from hearts of WT and DCM mice, elucidating molecular changes at the single-cell level throughout the diabetic cardiomyopathy progression. Robust and accurate clustering analysis of the integrated data revealed altered cell proportions, showcasing decreased endothelial cells and macrophages, coupled with increased fibroblasts and myocardial cells in the DCM group, indicating enhanced fibrosis and endothelial damage. Chromatin accessibility analysis unveiled unique patterns in cell types, with heightened transcriptional activity in myocardial cells. Subpopulation analysis highlighted distinct changes in cardiomyocytes and fibroblasts, emphasizing pathways related to fatty acid metabolism and cardiac contraction. Fibroblast-centered communication analysis identified interactions with endothelial cells, implicating VEGF receptors. Endothelial cell subpopulations exhibited altered gene expressions, emphasizing contraction and growth-related pathways. Candidate regulators, including Tcf21, Arnt, Stat5a, and Stat5b, were identified, suggesting their pivotal roles in DCM development. Immunofluorescence staining validated marker genes of cell subpopulations, confirming PDK4, PPARγ and Tpm1 as markers for metabolic pattern-altered cardiomyocytes, activated fibroblasts and endothelial cells with compromised proliferation. CONCLUSION: Our integrated scRNA-seq and scATAC-seq analysis unveils intricate cell states and molecular alterations in diabetic cardiomyopathy. Identified cell type-specific changes, transcription factors, and marker genes offer valuable insights. The study sheds light on potential therapeutic targets for DCM.