Glycolipid metabolic disorders (GLMDs) are various metabolic disorders resulting from dysregulation in glycolipid levels, consequently leading to an increased risk of obesity, diabetes, liver dysfunction, neuromuscular complications, and cardiorenal vascular diseases (CRVDs). In patients with GLMDs, excess caloric intake and a lack of physical activity may contribute to oxidative stress (OxS) and systemic inflammation. This study aimed to review the connection between GLMD, OxS, metainflammation, and the onset of CRVD. GLMD is due to various metabolic disorders causing dysfunction in the synthesis, breakdown, and absorption of glucose and lipids in the body, resulting in excessive ectopic accumulation of these molecules. This is mainly due to neuroendocrine dysregulation, insulin resistance, OxS, and metainflammation. In GLMD, many inflammatory markers and defense cells play a vital role in related tissues and organs, such as blood vessels, pancreatic islets, the liver, muscle, the kidneys, and adipocytes, promoting inflammatory lesions that affect various interconnected organs through their signaling pathways. Advanced glycation end products, ATP-binding cassette transporter 1, Glucagon-like peptide-1, Toll-like receptor-4, and sphingosine-1-phosphate (S1P) play a crucial role in GLMD since they are related to glucolipid metabolism. The consequences of this is system organ damage and increased morbidity and mortality. 

Glycolipid Metabolic Disorders, Metainflammation, Oxidative Stress, and Cardiovascular Diseases: Unraveling Pathways

Acute lung injury is a devastating illness characterized by severe inflammation mediated by aberrant activation of macrophages, resulting in significant morbidity and mortality, highlighting the urgent need for novel pharmacological targets and drug candidates. In this study, we identified a novel target for regulating inflammation in macrophages and acute lung injury via chemical proteomics and genetics based on a marine alkaloid, naamidine J (NJ). The structures of NJ-related naamidine alkaloids were first confirmed or revised by a combination of quantum chemical calculations and X-ray diffraction analysis. NJ was found as a potential anti-inflammatory agent by screening our compound library, and CSE1L was identified by chemoproteomics as a main cellular target of NJ to inhibit inflammation in macrophages and protect against acute lung injury. Mechanistically, we demonstrated that NJ directly interacted with CSE1L on the sites of His745 and Phe903 and then inhibited the nuclear translocation and transcriptional activity of transcription factor SP1, thereby suppressing inflammation in macrophages and ameliorating acute lung injury. Taken together, these findings have uncovered a novel pharmacological target for the treatment of acute lung injury and have also provided a potential druggable pocket of CSE1L and a lead compound or an available chemical tool from marine sources for investigating CSE1L function and developing novel drug candidates against acute lung injury. 

Chemoproteomics of Marine Natural Product Naamidine J Unveils CSE1L as a Therapeutic Target in Acute Lung Injury

Advanced machine learning approaches are qualified for recognizing the too composite patterns in the massive datasets. We provide a perspective technical survey analysis in machine learning (ML), and deep learning (DL) approaches for genome analysis. It's quickly rising applications related to cancer diseases such as cancer diagnosis or subtypes of cancer through omics input data. It discusses effective approaches in the fields of genomics regulatory, pathogenicity, and variant calling. Moreover, the representation of ML's potential benefits due to the several technological platforms involved in its diagnosis, prognosis, and treatment. We concentrate on the most up-to-date knowledge of cancer classification models, targeted therapy, and define how genetic mutations inspire targeted therapy's responsiveness and highlight the different related issues in this era of precision medicine. Finally, we disuse limitations of the different approaches and hopeful ways of upcoming research in targeted therapy.

http://www.journal.atmph-specialissues.org/uploads/179/9095_pdf.pdf

A Survey on Classification Analysis for Cancer Genomics: Limitations and Novel Opportunity in the Era of Cancer Classification and Target Therapies

Abstract Background Sparganosis is a worldwide food-borne parasitic disease caused by spargana infection, which infects the muscle of frogs and snakes as well as many tissues and organs in humans. There are currently no viable treatments for sparganosis. Understanding spargana’s nutrition source and carbohydrate metabolism may be crucial for identifying its energy supply and establishing methods of treatment for sparganosis. Methods Using an amino acid analyzer and nutrient concentration detection kits, we assessed nutrient concentrations in the muscles of Fejervarya limnocharis and Pelophylax plancyi infected or not infected with spargana. Quantitative polymerase chain reaction (PCR) was used to quantify the major enzymes involved in five glucose metabolism pathways of spargana developing in vivo. We also used quantitative PCR to assess key enzymes and transcriptome sequencing to explore the regulation of carbohydrate metabolic pathways in vitro in response to different 24-h food treatments. Results Infected muscle tissues had considerably higher concentrations of glucogenic and/or ketogenic amino acids, glucose, and glycogen than non-infected muscle tissues. We discovered that the number of differentially expressed genes in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was larger in low-glucose than in other dietary groups. We examined differences in the expression of genes producing amino acid transporters, glucose transporters, and cathepsins in spargana grown in various nutritional environments. In the normal saline group, only the major enzymes in the tricarboxylic acid cycle (TCA), glycogenesis, and glycogenolysis pathways were expressed. The l -glutamine group had the greatest transcriptional levels of critical rate-limiting enzymes of gluconeogenesis and glycogenesis. Furthermore, the low-glucose group had the highest transcriptional levels of critical rate-limiting enzymes involved in the TCA, glycolytic, and glycogenolysis pathways. Surprisingly, when compared to the in vitro culturing groups, spargana developing in vivo exhibited higher expression of these critical rate-limiting enzymes in these pathways, with the exception of the pentose phosphate pathway. Conclusions Spargana have a variety of nutritional sources, and there is a close relationship between nutrients and the carbohydrate metabolism pathways. It takes a multi-site approach to block nutrient absorption and carbohydrate metabolism pathways to provide energy to kill them. Graphical Abstract 

Important nutrient sources and carbohydrate metabolism patterns in the growth and development of spargana

Cross-lingual summarization (CLS) is essential for enhancing global communication by facilitating efficient information exchange across different languages. However, owing to the scarcity of CLS data, recent studies have employed multi-task frameworks to combine parallel monolingual summaries. These methods often use independent decoders or models with non-shared parameters because of the mismatch in output languages, which limits the transfer of knowledge between CLS and its parallel data. To address this issue, we propose a unified training method for CLS that combines parallel machine translation (MT) pairs with CLS pairs, jointly training them within a single model. This design ensures consistent input and output languages and promotes knowledge sharing between the two tasks. To further enhance the model’s capability to focus on key information, we introduce two additional loss terms to align the hidden representations and probability distributions between the parallel MT and CLS pairs. Experimental results demonstrate that our method outperforms competitive methods in both full-dataset and low-resource scenarios on two benchmark datasets, Zh2EnSum and En2ZhSum. 

Unified Training for Cross-Lingual Abstractive Summarization by Aligning Parallel Machine Translation Pairs

Abstract Background Atherosclerosis (AS), the main pathological basis of life‐threatening cardiovascular disease, is essentially caused by chronic macrophage inflammation. Overexpression of proline/serine‐rich coiled‐coil protein 1 (PSRC1) reduces macrophage inflammatory responses and delays AS development. However, the exact mechanism of PSRC1 is unclear. Methods Proteins interacting with PSRC1 were screened by proteomics in RAW264.7 cells, followed by RT‒qPCR, immunoprecipitation and immunofluorescence to explore the specific mechanistic pathways affecting inflammation. CRISPR‒Cas9 constructs for PSRC1−/−ApoE−/−(DKO) mice and high‐fat diet‐fed ApoE−/− and DKO mice were used for AS models for in vivo experiments. Upstream transcription factors of PSRC1 were predicted by ATAC‐seq, ChIP‐seq and UCSC, and the regulatory mechanism was verified by ChIP‒qPCR and dual luciferase assays. Peripheral blood serum and monocytes were collected from coronary artery disease (CAD) patients and non‐CAD patients. Results Increased binding of ANXA2 to PSRC1 in macrophages under oxidized low‐density lipoprotein stimulation and decreased release of ANXA2 to the extracellular compartment were observed. Knockdown of ANXA2 in AS model mice delayed AS progression. Knockdown of ANXA2 in DKO mice reversed the AS‐promoting effect of PSRC1 knockdown. Mechanistically, ANXA2 promotes STAT3 phosphorylation, which in turn promotes inflammatory responses. In addition, SP1 is a PSRC1 upstream repressive transcription factor, and the SP1 inhibitor mithramycin (Mith) elevated PSRC1 expression and exerted anti‐AS effects in AS model mice. Patients with CAD had considerably greater serum levels of ANXA2 than those without CAD, and Mith reduced the secretion of ANXA2 in peripheral blood monocytes of CAD patients. Conclusion In macrophages, PSRC1 can interact with ANXA2 to inhibit its extracellular release and delay AS development. SP1 is an upstream transcription factor of PSRC1 and inhibits the transcription of PSRC1. The SP1 inhibitor Mith can elevate PSRC1 levels and slow AS progression while reducing ANXA2 release from monocytes in CAD patients. Mith is expected to be a new agent for AS treatment.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ctm2.1220

Proline/serine‐rich coiled‐coil protein 1 inhibits macrophage inflammation and delays atherosclerotic progression by binding to Annexin A2

Existing cross-lingual summarization (CLS) datasets consist of inconsistent sample quality and low scale. To address these problems, we propose a method that jointly supervises quality and scale to build CLS datasets. In terms of quality supervision, the method adopts a multi-strategy filtering algorithm to remove low-quality samples of monolingual summarization (MS) from the perspectives of character and semantics, thereby improving the quality of the MS dataset. In terms of scale supervision, the method adopts a text augmentation algorithm based on the pretrained model to increase the size of CLS datasets with quality assurance. This method was used to build an English-Chinese CLS dataset and evaluate it with a reasonable data quality evaluation framework. The evaluation results show that the dataset is of good quality and large size. These outcomes show that the proposed method may comprehensively improve quality and scale, thereby resulting in a high-quality and large-scale CLS dataset at a lower cost.

Dataset construction method of cross-lingual summarization based on filtering and text augmentation

Colon cancer (Cc) is the third high-malignancy cancer in the world, and there are abundant publications about colon cancer. A Support Vector Machine classifier is used to extract the relation about mutated genes, microRNAs (miRNA) in Cc cells by mining scientific literature containing experimentally validated data. Three topology networks are constructed based on the extracted genes and miRNAs. The feed-back loops and self-adaption relations are found within these networks, and used for identifying the mechanism of colon cancer by comparing and analyzing the difference and relatedness among the networks. This study provides a fundamental hypothesis for further analysis of colon tumorigenesis.

Mutation Relation Extraction and Genes Network Analysis in Colon Cancer

Knowledge graph representation learning aims to provide accurate entity and relation representations for tasks such as intelligent question answering and recommendation systems. Existing representation learning methods, which only consider triples, are not sufficiently accurate, so some methods use external auxiliary information such as text, type, and time to improve performance. However, they often encode this information independently, which makes it challenging to fully integrate this information with the knowledge graph at a semantic level. In this study, we propose a method called SP-TAG, which realizes the semantic propagation on text-augmented knowledge graphs. Specifically, SP-TAG constructs a text-augmented knowledge graph by extracting named entities from text descriptions and connecting them with the corresponding entities. Then, SP-TAG uses a graph convolutional network to propagate semantic information between the entities and new named entities so that the text and triple structure are fully integrated. The results of experiments on multiple benchmark datasets show that SP-TAG attains competitive performance. When the number of training samples is limited, SP-TAG maintains its high performance, verifying the importance of text augmentation and semantic propagation.

https://downloads.hindawi.com/journals/cin/2022/1438047.pdf

Representation Learning Method with Semantic Propagation on Text-Augmented Knowledge Graphs

Long noncoding RNAs (lncRNAs) play an important role in the entire progression of atherosclerosis. In this study, we identified an uncharacterized lncRNA, Liver Expressions by PSRC1 Induce Specifically (LEPIS). The expression of LEPIS and its potential target tropomodulin 4 (TMOD4) in the liver of ApoE-/- mice fed a high-fat diet was increased. An ApoE-/- mouse model with the overexpression of LEPIS or TMOD4 in liver was established, and we found that both LEPIS and TMOD4 increased the burden of atherosclerosis and reduced hepatic cholesterol levels. Further study revealed that LEPIS and TMOD4 affect the expression of genes related to hepatic cholesterol homeostasis, including proprotein convertase subtilisin/kexin type9 (PCSK9) and low-density lipoprotein receptor (LDLR), which are closely related to hypercholesterolemia. Mechanistically, human antigen R (HuR), an RNA-binding protein, was shown to be critical for the regulation of TMOD4 by LEPIS. Further, we found that overexpression of LEPIS promoted the shuttling of HuR from the nucleus to the cytoplasm, enhanced the stability of TMOD4 mRNA, and in turn promoted the expression of TMOD4. In addition, TMOD4 was found to affect intracellular cholesterol levels through PCSK9. These results suggest that the LEPIS-HuR-TMOD4 axis is a potential intervention target for hepatic cholesterol homeostasis and atherosclerosis. Graphical abstract

The LEPIS-HuR-TMOD4 axis regulates hepatic cholesterol homeostasis and accelerates atherosclerosis

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