Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,179
result(s) for
"Li, Yuming"
Sort by:
Lactylation of METTL16 promotes cuproptosis via m6A-modification on FDX1 mRNA in gastric cancer
Cuproptosis, caused by excessively high copper concentrations, is urgently exploited as a potential cancer therapeutic. However, the mechanisms underlying the initiation, propagation, and ultimate execution of cuproptosis in tumors remain unknown. Here, we show that copper content is significantly elevated in gastric cancer (GC), especially in malignant tumors. Screening reveals that METTL16, an atypical methyltransferase, is a critical mediator of cuproptosis through the m
6
A modification on
FDX1
mRNA. Furthermore, copper stress promotes METTL16 lactylation at site K229 followed by cuproptosis. The process of METTL16 lactylation is inhibited by SIRT2. Elevated METTL16 lactylation significantly improves the therapeutic efficacy of the copper ionophore– elesclomol. Combining elesclomol with AGK2, a SIRT2-specific inhibitor, induce cuproptosis in gastric tumors in vitro and in vivo. These results reveal the significance of non-histone protein METTL16 lactylation on cuproptosis in tumors. Given the high copper and lactate concentrations in GC, cuproptosis induction becomes a promising therapeutic strategy for GC.
Cuproptosis regulation in tumors is unclear. Here the authors find that copper promotes METTL16 lactylation, inducing cuproptosis via stabilizing FDX1 in gastric cancer. Targeting lactyl-METTL16 and cuproptosis offers a potential feasible strategy for cancer therapy.
Journal Article
An efficient fire detection algorithm based on Mamba space state linear attention
As an emerging State Space Model (SSM), the Mamba model draws inspiration from the architecture of Recurrent Neural Networks (RNNs), significantly enhancing the global receptive field and feature extraction capabilities of object detection models. Compared to traditional Convolutional Neural Networks (CNNs) and Transformers, Mamba demonstrates superior performance in handling complex scale variations and multi-view interference, making it particularly suitable for object detection tasks in dynamic environments such as in fire detection scenarios. To enhance the performance of visual fire detection technologies and provide a novel approach, this paper proposes an efficient fire detection algorithm based on the YOLOv9 architecture and introduces multiple key techniques to design a high-performance fire detection model leveraging the Mamba attention mechanism. First, this paper presents an efficient attention mechanism, the Efficient Mamba Attention (EMA) module. Unlike existing self-attention mechanisms, EMA integrates adaptive average pooling with an SSM module, eliminating the need for full-scale association computations across all positions. Instead, it performs dimensionality reduction on input features through adaptive average pooling and utilizes the state update mechanism of the SSM module to significantly enhance feature representation and optimize information flow. Second, to address the limitations of SSM models in local feature modeling, this study incorporates the ConvNeXtV2 module to optimize the backbone network, improving the model’s ability to capture fine-grained local details and thereby strengthening its overall representation capability. Additionally, a dynamic non-monotonic focusing mechanism and distance penalty strategy are employed to refine the loss function, leading to a substantial improvement in bounding box accuracy. Experimental results demonstrate the superior performance of the proposed method in fire detection tasks. The model achieves an FPS of 71, with an
of 91.0% on the large-scale fire dataset and 87.2% on the small-scale fire dataset. Compared to existing methods, the proposed approach maintains high detection performance while exhibiting significant computational efficiency advantages.
Journal Article
Circulating citrate as a mediator in the relationship between HMGCR inhibitors and chronic hepatitis B: a Mendelian randomization study
Observational studies have found that HMGCR inhibitors can be used to treat chronic viral hepatitis. In this study, to explore the potential mechanism of HMGCR inhibitors in treating Chronic hepatitis B (CHB), two-sample and two-step Mendelian randomization (MR) were used to investigate the causal relationship between HMGCR inhibitors and the mediating role of circulating metabolites. GWAS data of expression quantitative trait loci eQTLs of HMGCR inhibitors, 168 circulating metabolites, CHB, and myocardial infarction were obtained from the IEUOpenGWAS project. Random effects inverse-variance weighted (IVW) was the main causal analysis method, and the MR–Egger regression method was used as a supplementary analysis method. Cochran’s Q test and I
2
statistic were used to determine the heterogeneity of SNPs. The intercept terms of the MR–Egger method and MR-PRESSO were used for pleiotropy analysis, and leave-one-out was used for sensitivity analysis. Mediation effect analysis was used to evaluate the mediating role of the circulating metabolites. Genetic variations in the drug target genes of HMGCR inhibitors were associated with a reduced risk of chronic hepatitis B and myocardial infarction (
P
< 0.05). Eight circulating metabolites had a significant causal relationship with HMGCR inhibitors and CHB. After further calculation of the mediation effect, citrate was used as a mediating variable between HMGCR inhibitors and CHB, with a mediation effect of − 0.015 and a mediation ratio of 9.769%. HMGCR inhibitors can significantly reduce the risk of CHB, and the circulating metabolite citrate may mediate this association. However, this study has certain limitations. The short-term effects of HMGCR inhibitors on CHB could not be assessed, and partial overlap between the GWAS data for HMGCR inhibitors and circulating metabolites may introduce bias in estimating causal effects.
Journal Article
Development and validation of a nomogram for predicting survival in gastric signet ring cell carcinoma patients treated with radiotherapy
There is no effective clinical prediction model to predict the prognosis of gastric signet ring cell carcinoma (GSRC) patients treated with radiotherapy. This study retrospectively analyzed the clinical data of 20–80-year-old patients diagnosed with GSRC between 2004 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database. Using Cox regression analyses revealed independent prognostic factors, and a nomogram was constructed. The C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) of the nomogram were greater than those of the TNM staging system for predicting OS, indicating that the nomogram predicted prognosis with greater accuracy. The area under the curve (AUC) values were 0.725, 0.753 and 0.745 for the training group; 0.725, 0.763 and 0.752 for the internal validation group; and 0.795, 0.764 and 0.765 for the external validation group, respectively. Calibration plots demonstrated high agreement between the nomogram’s prediction and the actual observations. The risk stratification system was able to accurately stratify patients who underwent radiotherapy for GSRC into high- and low-risk subgroups, with significant differences in prognosis. The Kaplan‒Meier survival analysis according to different treatments indicated that surgery combined with chemoradiotherapy is a more effective treatment strategy for improving OS in for GSRC patients. The nomogram is sufficiently accurate to predict the prognostic factors of GSRC receiving radiotherapy, allowing for clinicians to predict the 1-, 3-, and 5-year OS.
Journal Article
Isolation of infectious SARS-CoV-2 from urine of a COVID-19 patient
SARS-CoV-2 caused a major outbreak of severe pneumonia (COVID-19) in humans. Viral RNA was detected in multiple organs in COVID-19 patients. However, infectious SARS-CoV-2 was only isolated from respiratory specimens. Here, infectious SARS-CoV-2 was successfully isolated from urine of a COVID-19 patient. The virus isolated could infect new susceptible cells and was recognized by its' own patient sera. Appropriate precautions should be taken to avoid transmission from urine.
Journal Article
Gut-dependent microbial translocation induces inflammation and cardiovascular events after ST-elevation myocardial infarction
Background
Post-infarction cardiovascular remodeling and heart failure are the leading cause of myocardial infarction (MI)-driven death during the past decades. Experimental observations have involved intestinal microbiota in the susceptibility to MI in mice; however, in humans, identifying whether translocation of gut bacteria to systemic circulation contributes to cardiovascular events post-MI remains a major challenge.
Results
Here, we carried out a metagenomic analysis to characterize the systemic bacteria in a cohort of 49 healthy control individuals, 50 stable coronary heart disease (CHD) subjects, and 100 ST-segment elevation myocardial infarction (STEMI) patients. We report for the first time higher microbial richness and diversity in the systemic microbiome of STEMI patients. More than 12% of post-STEMI blood bacteria were dominated by intestinal microbiota (
Lactobacillus
,
Bacteroides
, and
Streptococcus
). The significantly increased product of gut bacterial translocation (LPS and
d
-lactate) was correlated with systemic inflammation and predicted adverse cardiovascular events. Following experimental MI, compromised left ventricle (LV) function and intestinal hypoperfusion drove gut permeability elevation through tight junction protein suppression and intestinal mucosal injury. Upon abrogation of gut bacterial translocation by antibiotic treatment, both systemic inflammation and cardiomyocyte injury in MI mice were alleviated.
Conclusions
Our results provide the first evidence that cardiovascular outcomes post-MI are driven by intestinal microbiota translocation into systemic circulation. New therapeutic strategies targeting to protect the gut barrier and eliminate gut bacteria translocation may reduce or even prevent cardiovascular events post-MI.
Journal Article
Single-cell and bulk RNA sequencing reveals Anoikis related genes to guide prognosis and immunotherapy in osteosarcoma
Anoikis resistance, a notable factor in osteosarcoma, plays a significant role in tumor invasion and metastasis. This study seeks to identify a distinct gene signature that is specifically associated with the anoikis subcluster in osteosarcoma. Clinical, single-cell, and transcriptional data from TARGET and GEO datasets were used to develop a gene signature for osteosarcoma based on the anoikis subcluster. Univariate Cox and LASSO regression analyses were employed. The signature's predictive value was evaluated using time-dependent ROC and Kaplan–Meier analyses. Functional enrichment analyses and drug sensitivity analyses were conducted. Validation of three modular genes was performed using RT-qPCR and Western blotting. Signature (ZNF583, CGNL1, CXCL13) was developed to predict overall survival in osteosarcoma patients, targeting the anoikis subcluster. The signature demonstrated good performance in external validation. Stratification based on the signature revealed significantly different prognoses. The signature was an independent prognostic factor. The low-risk group showed enhanced immune cell infiltration and improved immune function. Drug sensitivity analysis indicated efficacy of chemotherapy agents. Prognostic nomograms incorporating the signature provided greater predictive accuracy and clinical utility. Signatures related to the anoikis subcluster play a significant role in osteosarcoma progression. Incorporating these findings into clinical decision-making can improve osteosarcoma treatment and patient outcomes.
Journal Article
The intratumoral microbiota: a new horizon in cancer immunology
Over the past decade, advancements in high-throughput sequencing technologies have led to a qualitative leap in our understanding of the role of the microbiota in human diseases, particularly in oncology. Despite the low biomass of the intratumoral microbiota, it remains a crucial component of the tumor immune microenvironment, displaying significant heterogeneity across different tumor tissues and individual patients. Although immunotherapy has emerged a major strategy for treating tumors, patient responses to these treatments vary widely. Increasing evidence suggests that interactions between the intratumoral microbiota and the immune system can modulate host tumor immune responses, thereby influencing the effectiveness of immunotherapy. Therefore, it is critical to gain a deep understanding of how the intratumoral microbiota shapes and regulates the tumor immune microenvironment. Here, we summarize the latest advancements on the role of the intratumoral microbiota in cancer immunity, exploring the potential mechanisms through which immune functions are influenced by intratumoral microbiota within and outside the gut barrier. We also discuss the impact of the intratumoral microbiota on the response to cancer immunotherapy and its clinical applications, highlighting future research directions and challenges in this field. We anticipate that the valuable insights into the interactions between cancer immunity and the intratumoral microbiota provided in this review will foster the development of microbiota-based tumor therapies.
Journal Article