Pathology - Research and Practice, Journal Year: 2024, Volume and Issue: 266, P. 155784 - 155784
Published: Dec. 20, 2024
Language: Английский
Pathology - Research and Practice, Journal Year: 2024, Volume and Issue: 266, P. 155784 - 155784
Published: Dec. 20, 2024
Language: Английский
Experimental Cell Research, Journal Year: 2024, Volume and Issue: 445(1), P. 114401 - 114401
Published: Dec. 29, 2024
Language: Английский
Citations
2Clinical & Translational Oncology, Journal Year: 2022, Volume and Issue: 25(1), P. 33 - 47
Published: Aug. 24, 2022
Language: Английский
Citations
9Clinical & Translational Oncology, Journal Year: 2022, Volume and Issue: 25(1), P. 48 - 65
Published: Aug. 30, 2022
Language: Английский
Citations
7Pathology - Research and Practice, Journal Year: 2023, Volume and Issue: 253, P. 155014 - 155014
Published: Dec. 12, 2023
Language: Английский
Citations
3PubMed, Journal Year: 2023, Volume and Issue: 17(4), P. 218 - 225
Published: Aug. 7, 2023
Recurrent pregnancy loss (RPL) or recurrent miscarriage is the failure of before 20-24 weeks that influences around 2-5% couples. Several genetic, immunological, environmental and physical factors may influence RPL. Although various traditional methods have been used to treat post-implantation failures, identifying mechanisms underlying RPL improve an effective treatment. Recent evidence suggested gene expression alterations presented essential roles in occurrence It has found long non-coding RNAs (lncRNAs) play functional pathologies, such as miscarriage. lncRNAs can function dynamic scaffolds, modulate chromatin function, guide bind microRNAs (miRNAs) transcription factors. lncRNAs, by targeting miRNAs mRNAs, progression suppression Therefore, their downstream targets might be a suitable strategy for diagnosis treatment In this review, we summarized emerging several stimulation
Language: Английский
Citations
1Journal of Obstetrics Gynecology and Cancer Research, Journal Year: 2023, Volume and Issue: 8(4), P. 308 - 314
Published: July 7, 2023
Polycystic ovary syndrome (PCOS) is a hormonal disorder and common health problem that affects women at the early to late reproductive stage. Several genetic environmental factors such as obesity, liver diseases, imbalance of androgens, menstrual dysfunction have contributed progression PCOS. Research has shown link between diabetes, hypertension, miscarriages, cardiovascular disease with Experimental discoveries begun evaluate mechanisms involved in Although various classical interventions are used treatment PCOS, current medications not able control outcomes PCOS management this still challenging. Accumulating evidence showed dysregulation long non-coding RNAs (lncRNAs) essential pathogenesis. LncRNAs class transcripts mediate process gene expressions level transcription post-transcription. It been found lncRNA metastasis‐associated lung adenocarcinoma transcript‐1 (MALAT1 or nuclear-enriched abundant transcript 2 (NEAT2)) presents vital role regulating MALAT-1 competing endogenous RNA (ceRNA) can suppress microRNAs (miRNAs) decrease granulosa cell proliferation, apoptosis, Abnormal expression MALAT1 one prognostic for autophagy, migration, drug resistance. be potential biomarker However, exact roles cells remain largely unknown further studies required confirm its action. In present article, we summarize functions MALAT-1/miRNA axes
Language: Английский
Citations
1Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(16), P. 6662 - 6675
Published: Aug. 7, 2024
Identifying new relevant long noncoding RNAs (lncRNAs) for various human diseases can facilitate the exploration of causes and progression these diseases. Recently, several graph inference methods have been proposed to predict disease-related lncRNAs by exploiting topological structure node attributes within graphs. However, did not prioritize target lncRNA disease nodes over auxiliary like miRNA nodes, potentially limiting their ability fully utilize features nodes. We propose a method, mask-guided feature learning dynamic detailed enhancement lncRNA-disease association prediction (MDLD), enhance improved prediction. First, we designed heterogeneous masked transformer autoencoder guide learning, focusing more on (disease) The were increasingly as training progressed, which helps develop robust model. Second, developed convolutional network with residuals (GCNDR) learn integrate topology all lncRNA, disease, GCNDR employs an interlayer residual strategy evolution mitigate oversmoothing caused multilayer convolution. estimates importance learned in previous GCN encoding layer current layer. Additionally, since there are dependencies individual (disease, miRNA) across multiple layers, gated recurrent unit-based is encode dependencies. Finally, perspective-level attention mechanism obtain informative pairs from perspectives mask-enhanced dynamic-enhanced features. Cross-validation experimental results demonstrated that MDLD outperformed 10 other state-of-the-art methods. Ablation experiments case studies candidate three further proved technical contributions its capability discover lncRNAs.
Language: Английский
Citations
0Pathology - Research and Practice, Journal Year: 2024, Volume and Issue: 266, P. 155784 - 155784
Published: Dec. 20, 2024
Language: Английский
Citations
0