The multifaceted role of insulin-like growth factor binding protein 7.

Insulin-like growth factor binding protein 7 (IGFBP7) serves as a crucial extracellular matrix protein, exerting pivotal roles in both physiological and pathological processes. This comprehensive review meticulously delineates the structural attributes of IGFBP7, juxtaposing them with other members within the IGFBP families, and delves into the expression patterns across various tissues. Furthermore, the review thoroughly examines the multifaceted functions of IGFBP7, encompassing its regulatory effects on cell proliferation, apoptosis, and migration, elucidating the underlying mechanistic pathways. Moreover, it underscores the compelling roles in tumor progression, acute kidney injury, and reproductive processes. By rigorously elucidating the diverse functionalities and regulatory networks of IGFBP7 across various physiological and pathological contexts, this review aims to furnish a robust theoretical framework and delineate future research trajectories for leveraging IGFBP7 in disease diagnosis, therapeutic interventions, and pharmaceutical innovations.

The TCR ß chain CDR3 insights of bovine liver immune repertoire under heat stress.

Objective: The liver plays a dual role in regulating temperature and immune responses. Examining the influence of Heat stress (HS) on liver T cells contributes significantly to understanding the intricate interplay between the immune system and hepatic tissues under thermal stress. This study focused on investigating the characteristics of the T-cell receptor (TCR) ß chain CDR3 repertoire in bovine liver samples under both HS and pair-fed (PF) environmental conditions. Methods: Sequencing data from six samples sourced from the GEO database underwent annotation. Utilizing immunarch and VDJtool software, the study conducted comprehensive analyses encompassing basic evaluation, clonality assessment, immune repertoire comparison, diversity estimation, gene usage profiling, VJ gene segment pairing scrutiny, clonal tracking, and Kmers analysis. Results: All four TCR chains, namely α, ß, γ, and δ, were detected, with the α chains exhibiting the highest detection frequency, followed closely by the ß chains. The prevalence of αß TCRs in bovine liver samples underscored their crucial role in governing hepatic tissue's physiological functions. The TCR ß CDR3 repertoire showcased substantial inter-individual variability, featuring diverse clonotypes exhibiting distinct amino acid lengths. Intriguingly, HS cattle displayed heightened diversity and clonality, suggesting potential peripheral T cell migration into the liver under environmental conditions. Notably, differential VJ gene pairings were observed in HS cattle compared to the PF, despite individual variations in V and J gene utilization. Additionally, while most high-frequency amino acid 5-mers remained consistent between the HS and PF, GELHF and YDYHF were notably prevalent in the HS group. Across all samples, a prevalent trend of high-frequency 5mers skewed towards polar and hydrophobic amino acids was evident. Conclusion: This study elucidates the characteristics of liver TCR ß chain CDR3 repertoire under HS conditions, enhancing our understanding of HS implications.

Mindfulness mediates the relationship between positive parenting and aggression, depression, and suicidal ideation: A longitudinal study in middle school students.

Previous research has indicated that parenting factors affect the risk of maladaptive psychological outcomes (e.g., aggression, depression, or suicidal ideation), and that positive parenting is a prospective risk factor for maladaptive psychological outcomes. However, the mechanisms underlying the relationships between positive parenting, mindfulness, and maladaptive psychological outcomes remain unknown, as do the processes that mediate the effect of positive parenting on maladaptive psychological outcomes in adolescents. The objective of the present study was to investigate the longitudinal relationship between positive parenting, mindfulness, and maladaptive psychological outcomes in middle school students, as well as the mediating effect of mindfulness in the relationships between positive parenting and depression, aggression, and suicidal ideation. In this study, 386 middle school children (aged 12-16) were tested three times over a period of 6 months. Positive parenting was assessed at Time 1, mindfulness at Time 2, and depression, aggression, and suicidal ideation at Time 3. Using structural equation modeling, positive parenting was revealed to be longitudinally associated with mindfulness and negatively associated with maladaptive psychological outcomes. More crucially, mindfulness mediated the relationship between positive parenting and maladaptive psychological outcomes. This research provides important insights into how to effectively decrease adolescent maladaptive psychological outcomes and highlights the importance of teaching mindfulness to youths.

Relation Extraction in Biomedical Texts Based on Multi-Head Attention Model With Syntactic Dependency Feature: Modeling Study.

BACKGROUND: With the rapid expansion of biomedical literature, biomedical information extraction has attracted increasing attention from researchers. In particular, relation extraction between 2 entities is a long-term research topic. OBJECTIVE: This study aimed to perform 2 multiclass relation extraction tasks of Biomedical Natural Language Processing Workshop 2019 Open Shared Tasks: relation extraction of Bacteria-Biotope (BB-rel) task and binary relation extraction of plant seed development (SeeDev-binary) task. In essence, these 2 tasks are aimed at extracting the relation between annotated entity pairs from biomedical texts, which is a challenging problem. METHODS: Traditional research methods adopted feature- or kernel-based methods and achieved good performance. For these tasks, we propose a deep learning model based on a combination of several distributed features, such as domain-specific word embedding, part-of-speech embedding, entity-type embedding, distance embedding, and position embedding. The multi-head attention mechanism is used to extract the global semantic features of an entire sentence. Meanwhile, we introduced a dependency-type feature and the shortest dependency path connecting 2 candidate entities in the syntactic dependency graph to enrich the feature representation. RESULTS: Experiments show that our proposed model has excellent performance in biomedical relation extraction, achieving F1 scores of 65.56% and 38.04% on the test sets of the BB-rel and SeeDev-binary tasks. Especially in the SeeDev-binary task, the F1 score of our model is superior to that of other existing models and achieves state-of-the-art performance. CONCLUSIONS: We demonstrated that the multi-head attention mechanism can learn relevant syntactic and semantic features in different representation subspaces and different positions to extract comprehensive feature representation. Moreover, syntactic dependency features can improve the performance of the model by learning dependency relation between the entities in biomedical texts.

Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations.

BACKGROUND: Clinical named entity recognition (CNER), whose goal is to automatically identify clinical entities in electronic medical records (EMRs), is an important research direction of clinical text data mining and information extraction. The promotion of CNER can provide support for clinical decision making and medical knowledge base construction, which could then improve overall medical quality. Compared with English CNER, and due to the complexity of Chinese word segmentation and grammar, Chinese CNER was implemented later and is more challenging. OBJECTIVE: With the development of distributed representation and deep learning, a series of models have been applied in Chinese CNER. Different from the English version, Chinese CNER is mainly divided into character-based and word-based methods that cannot make comprehensive use of EMR information and cannot solve the problem of ambiguity in word representation. METHODS: In this paper, we propose a lattice long short-term memory (LSTM) model combined with a variant contextualized character representation and a conditional random field (CRF) layer for Chinese CNER: the Embeddings from Language Models (ELMo)-lattice-LSTM-CRF model. The lattice LSTM model can effectively utilize the information from characters and words in Chinese EMRs; in addition, the variant ELMo model uses Chinese characters as input instead of the character-encoding layer of the ELMo model, so as to learn domain-specific contextualized character embeddings. RESULTS: We evaluated our method using two Chinese CNER datasets from the China Conference on Knowledge Graph and Semantic Computing (CCKS): the CCKS-2017 CNER dataset and the CCKS-2019 CNER dataset. We obtained F1 scores of 90.13% and 85.02% on the test sets of these two datasets, respectively. CONCLUSIONS: Our results show that our proposed method is effective in Chinese CNER. In addition, the results of our experiments show that variant contextualized character representations can significantly improve the performance of the model.