The Analgesic Activity of Bestatin as a Potent APN Inhibitor

Bestatin, a small molecular weight dipeptide, is a potent inhibitor of various aminopeptidases as well as LTA4 hydrolase. Various physiological functions of Bestatin have been identified, viz.: (1) an immunomodifier for enhancing the proliferation of normal human bone marrow granulocyte–macrophage progenitor cells to form CFU-GM colonies; Bestatin exerts a direct stimulating effect on lymphocytes via its fixation on the cell surface and an indirect effect on monocytes via aminopeptidase B inhibition of tuftsin catabolism; (2) an immunorestorator and curative or preventive agent for spontaneous tumor; Bestatin alone or its combination with chemicals can prolongate the disease-free interval and survival period in adult acute or chronic leukemia, therefore, it was primarily marketed in 1987 in Japan as an anticancer drug and servers as the only marketed inhibitor of Aminopeptidase N (APN/CD13) to cure leukemia to date; (3) a pan-hematopoietic stimulator and restorator; Bestatin promotes granulocytopoiesis and thrombocytopoiesis in vitro and restores them in myelo-hypoplastic men; (4) an inhibitor of several natural opioid peptides. Based on the knowledge that APN can cleave several bioactive neuropeptides such as Met-enkaphalins, Leu-enkaphalins, β-Endorphin, and so on, the anti-aminopeptidase action of Bestatin also allows it to protect endopeptides against their catabolism, exhibiting analgesic activity. Although many scientific studies and great accomplishments have been achieved in this field, a large amount of problems are unsolved. This article reviews the promising results obtained for future development of the analgesic activity of Bestatin that can be of vital interest in a number of severe and chronic pain syndromes

Coupled effects of local movement and global interaction on contagion

By incorporating segregated spatial domain and individual-based linkage into the SIS (susceptible-infected-susceptible) model, we investigate the coupled effects of random walk and intragroup interaction on contagion. Compared with the situation where only local movement or individual-based linkage exists, the coexistence of them leads to a wider spread of infectious disease. The roles of narrowing segregated spatial domain and reducing mobility in epidemic control are checked, these two measures are found to be conducive to curbing the spread of infectious disease. Considering heterogeneous time scales between local movement and global interaction, a log-log relation between the change in the number of infected individuals and the timescale $\tau$ is found. A theoretical analysis indicates that the evolutionary dynamics in the present model is related to the encounter probability and the encounter time. A functional relation between the epidemic threshold and the ratio of shortcuts, and a functional relation between the encounter time and the timescale $\tau$ are found

Law Article-Enhanced Legal Case Matching: a Causal Learning Approach

Legal case matching, which automatically constructs a model to estimate the similarities between the source and target cases, has played an essential role in intelligent legal systems. Semantic text matching models have been applied to the task where the source and target legal cases are considered as long-form text documents. These general-purpose matching models make the predictions solely based on the texts in the legal cases, overlooking the essential role of the law articles in legal case matching. In the real world, the matching results (e.g., relevance labels) are dramatically affected by the law articles because the contents and the judgments of a legal case are radically formed on the basis of law. From the causal sense, a matching decision is affected by the mediation effect from the cited law articles by the legal cases, and the direct effect of the key circumstances (e.g., detailed fact descriptions) in the legal cases. In light of the observation, this paper proposes a model-agnostic causal learning framework called Law-Match, under which the legal case matching models are learned by respecting the corresponding law articles. Given a pair of legal cases and the related law articles, Law-Match considers the embeddings of the law articles as instrumental variables (IVs), and the embeddings of legal cases as treatments. Using IV regression, the treatments can be decomposed into law-related and law-unrelated parts, respectively reflecting the mediation and direct effects. These two parts are then combined with different weights to collectively support the final matching prediction. We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models. Comprehensive experiments show that Law-Match can outperform state-of-the-art baselines on three public datasets.Comment: 10 pages accepted by SIGIR202

Composition and characteristics of Libyan flora

The composition, life forms and the distribution of plants in Libya were studied. The results show that in Libya there are 2103 species that belong to 856 genera and 155 families. The distribution among Libyan seed plants was characterized by a high proportion of herbs (annual to perennial), unlike the low number of woody (tree and shrub) species; these have an important influence on the structure of floral composition. The geographic element of the flora was predominantly tropical and Mediterranean. The local plants belong to representative tropical desert flora. The presence and distribution characteristics of flora in Libya show that climate, environmental condition, ecological amplitude and adaptive capacity of the plants have a determinative influence on the floristic stock in the area studies