The primary node molecules in the cell signaling network in cancer tissues are maladjusted and mutated in comparison to normal tissues, which promotes the occurrence and progression of cancer. Pancreatic cancer (PC) is a highly fatal cancer with increasing incidence and low five-year survival rates. Currently, there are several therapies that target cell signaling networks in PC. However, PC is a "cold tumor" with a unique immunosuppressive tumor microenvironment (poor effector T cell infiltration, low antigen specificity), and targeting a single gene or pathway is basically ineffective in clinical practice. Targeted matrix therapy, targeted metabolic therapy, targeted mutant gene therapy, immunosuppressive therapy, cancer vaccines, and other emerging therapies have shown great therapeutic potential, but results have been disappointing. Therefore, we summarize the identified and potential drug-resistant cell signaling networks aimed at overcoming barriers to existing PC therapies.
Review question / Objective: P=patients who had undergone gastric cancer; I=AI for diagnosing gastric cancer; C=pathology for diagnosing gastric cancer; O=sensitivity, specificity, positive and negative likelihood ratio, diagnostic odds ratio, and the area under the curve of the summary receiver operating characteristic; S=clinical cohort or case control studies.Condition being studied: Gastric cancer is one of the most common malignancies across the globe.With the deepening research of its efficient computing power and learning capacities, there has been INPLASY 1 International Platform of Registered Systematic Review and Meta-analysis Protocols INPLASY PROTOCOL Performance of artificial intelligence in gastric cancer detection: A protocol for systematic review and meta-analysis
Endoscopy is an important method for diagnosing gastrointestinal (GI) diseases. In this study, we provide an overview of the advances in artificial intelligence (AI) technology in the field of GI endoscopy over recent years, including esophagus, stomach, large intestine, and capsule endoscopy (small intestine). AI-assisted endoscopy shows high accuracy, sensitivity, and specificity in the detection and diagnosis of GI diseases at all levels. Hence, AI will make a breakthrough in the field of GI endoscopy in the near future. However, AI technology currently has some limitations and is still in the preclinical stages.
National holidays are associated with high mortality in some diseases, but little is known about patients undergoing peritoneal dialysis (PD). The research aimed to investigate the impact of national holidays on the health outcomes of PD patients.Over ten years, all episodes of unplanned hospitalization, death, and peritonitis in PD patients were collected in our center. Seven national holidays in China were chosen, and non-holiday days were selected as the control period. The effect of national holidays was observed by comparing the hospitalization, death, and peritonitis rates between holiday and non-holiday groups.There were 297 events in all holiday periods and 1247 in non-holiday periods. There is no significant difference in hospitalization rate between holiday and non-holiday groups (32.4% ± 6.4% vs. 29.2% ± 3.4%, p = 0.175). So is the death rate [6.3% (4.8-12.3%) vs.5.0% (4.2-8.9%), p = 0.324] and peritonitis rate [0.19 (0.13-0.53) vs. 0.22 (0.18-0.27), p = 0.445] between the two groups. Significant differences were observed in the distribution of peritonitis causes between the two groups (p = 0.017). The rate of secondary to other infections in the holiday group was significantly higher than in the non-holiday group (25.0 vs. 10.3%, p = 0.015).Our study suggested no national holiday effect on health outcomes of PD patients based on ten-year data in our center.
The tumor microenvironment (TME) has emerged as a pivotal determinant in the progression of cancer and the development of resistance to therapeutic interventions. The heterogeneous cellular composition of the TME not only facilitates tumor proliferation but also poses formidable obstacles to the efficacy of conventional treatments. This chapter delves into an examination of the distinctive attributes of the TME, exploring both established and innovative approaches designed to target the TME. Through a thorough analysis of the intricate involvement of the TME in cancer biology, we underscore the imperative for a comprehensive understanding and specific modulation of the TME to enhance the efficacy of cancer treatments. This elucidation provides novel insights for further research endeavors and clinical applications.
Targeted therapies in cancer treatment can improve in vivo efficacy and reduce adverse effects by altering the tissue exposure of specific biomolecules.However, there are still large number of target proteins in cancer are still undruggable, owing to the following factors including (1) lack of ligand-binding pockets, (2) function based on protein-protein interactions (PPIs), (3) the highly specific conserved active sites among protein family members, and (4) the variability of tertiary docking structures.The current status of undruggable targets proteins such as KRAS, TP53, C-MYC, PTP, are carefully introduced in this review.Some novel techniques and drug designing strategies have been applicated for overcoming these undruggable proteins, and the most classic and well-known technology is proteolysis targeting chimeras (PROTACs).In this review, the novel drug development strategies including targeting protein degradation, targeting PPI, targeting intrinsically disordered regions, as well as targeting protein-DNA binding are described, and we also discuss the potential of these strategies for overcoming the undruggable targets.Besides, intelligence-assisted technologies like Alpha-Fold help us a lot to predict the protein structure, which is beneficial for drug development.The discovery of new targets and the development of drugs targeting them, especially those undruggable targets, remain a huge challenge.New drug development strategies, better extraction processes that do not disrupt protein-protein interactions, and more precise artificial intelligence technologies may provide significant assistance in overcoming these undruggable targets.
Abstract With the increasing incidence of kidney diseases, there is an urgent need to develop therapeutic strategies to combat post‐injury fibrosis. Immune cells, including platelets, play a pivotal role in this repair process, primarily through their released cytokines. However, the specific role of platelets in kidney injury and subsequent repair remains underexplored. Here, the detrimental role of platelets in renal recovery following ischemia/reperfusion injury and its contribution to acute kidney injury to chronic kidney disease transition is aimed to investigated. In this study, it is shown that depleting platelets accelerates injury resolution and significantly reduces fibrosis. Employing advanced single‐cell and spatial transcriptomic techniques, macrophages as the primary mediators modulated by platelet signals is identified. A novel subset of macrophages, termed “cycling M2”, which exhibit an M2 phenotype combined with enhanced proliferative activity is uncovered. This subset emerges in the injured kidney during the resolution phase and is modulated by platelet‐derived thrombospondin 1 (THBS1) signaling, acquiring profibrotic characteristics. Conversely, targeted inhibition of THBS1 markedly downregulates the cycling M2 macrophage, thereby mitigating fibrotic progression. Overall, this findings highlight the adverse role of platelet THBS1‐boosted cycling M2 macrophages in renal injury repair and suggest platelet THBS1 as a promising therapeutic target for alleviating inflammation and kidney fibrosis.
Abstract Long non‐coding RNAs (lncRNAs) and dendritic cells (DC) play crucial roles in the development of acute coronary syndrome (ACS); however, the mechanisms remain unclear. To investigate this, we analysed the differentially expressed lncRNAs in monocyte‐derived DCs (moDCs) from patients with ACS. Peripheral blood mononuclear cells were transformed into moDCs. Cellular morphology and expression levels of moDC‐specific markers (CD80, CD86, CD11c, CD14 and HLA‐DR) were analysed using electron microscopy (EM) and flow cytometry (FCM), respectively. Differentially expressed lncRNAs and their functions were predicted using gene sequencing, gene ontology and the Kyoto Encyclopedia of Genes and Genomes. The expression levels of markers, signalling pathway molecules (p‐PI3K and p‐AKT), inflammatory cytokines (IL‐6 and IL‐12p70) and target gene (C‐C motif chemokine ligand ( CCL ) 15 and CCL14 ) were analysed by overexpression or silencing of candidate lncRNAs. EM revealed the cells to be suspended in dendritic pseudopodia. CD11c and HLA‐DR were upregulated, while CD80 and CD86 were downregulated. Comparison between the UA versus ST group showed the highest number of differentially expressed lncRNAs ( n = 113), followed by UA versus NST ( n = 115), CON versus NST ( n = 49) and CON versus ST ( n = 35); however, the number was low for CON versus UA and ST versus NST groups. moDC‐specific marker expression, signalling pathway molecules, inflammatory cytokines and CCL14 were upregulated following lentiviral overexpression of smart silencer‐ CCL15‐CCL14 ; however, expression levels decreased following transfection with siRNA. The morphology, function and lncRNA expression of moDCs differ depending on the type of ACS. The differentially expressed lncRNAs, particularly CCL15‐CCL14, regulate the function of moDCs. Thus, our study provides new insights regarding the role of lncRNAs in ACS and indicates the potential use of CCL15‐CCL14 as a novel diagnostic marker and therapeutic target.
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