Comparison of endostatin(endostar)and avastin's inhibition effect on mice choroidal neovascularization

AIM:To observe the inhibition effect of endostatin(endostar)on mice choroidal neovascularization model(CNV)and compare with the Avastin.<p>METHODS: Using 532nm laser photocoagulation to establish a mouse model of CNV. We observed the formation of CNV by histopathological examination after 2wk later. Forty successful models of mice were randomly divided into control group(group 1, 10 rats), normal saline group(group 2, 10 rats), endostatin group(group 3, 10 rats)and avastin group(group 4, 10 rats). The drugs were injected into the mice' vitreous after photocoagulation 2wk later. Then 1wk later, we took the mice eyeballs to perform the HE and immunohistochemical staining to observe. The statistical analysis of ANOVA was done by SPSS 16.0 and the LSD-<i>t</i> test was used for multiple samples, taking <i>P</i><0.05 as the test standards.<p>RESULTS: Two weeks later, HE histopathological examination was done, light microscope showed large amount of new vessels' formation, the positive rate for CNV was 72.8%. The blank control group compared with the normal saline group <i>P</i>>0.05, had no inhibitory effect on CNV; endostatin treated group compared with control group, <i>P</i><0.05, had a certain inhibitory effect; avastin group compared with the control group, <i>P</i><0.05, had an inhibitory effect on CNV; the LSD-<i>t</i> was performed on Avastin group and endostatin group, <i>P</i><0.05, which were statistically significant. We thought that the two drugs have different inhibitory effect on mice' CNV, because <i>(-overx)</i>_Avastin=26.90,<i>(-overx)</i>_endostatin=29.13,<i>(-overx)</i>_Avastin<<i>(-overx)</i>_endostatin, we can infer that endostar had lower inhibitory effect on mice CNV than Avastin.<p>CONCLUSION: Laser-induced CNV animal models of colored mice C57BL/6J is of short time and high rate establishment and it is an ideal model for CNV study. Endostar has certain inhibitory effect on CNV, and it is likely to become one of the important drugs for CNV-related diseases in the future

(2-Anilino-4-methyl­thia­zol-5-yl)(4-chloro­phen­yl)methanone

The title compound, C17H13ClN2OS, crystallizes with three independent mol­ecules (A, B and C) in the asymmetric unit which differ slightly in their conformations. In mol­ecule A, the thiazole ring makes dihedral angles of 27.44 (14) and 66.05 (6)° with the phenyl and chloro­benzene rings. In mol­ecule B, the respective angles are 29.09 (10) and 47.63 (9)°, while values of 25.67 (11) and 51.01 (7)° are observed in mol­ecule C. In the crystal, N—H⋯N and N—H⋯O hydrogen bonds generate a three-dimensional network structure

Tris(2-ethyl-1H-imidazole-κN 3)(terephthalato-κO)zinc(II)

The title compound, [Zn(C8H4O4)(C5H8N2)3], has a neutral monomeric structure in which one terephthalate dianion and three 2-ethyl-1H-imidazole ligands coordinate to the ZnII ion in a distorted tetra­hedral geometry. The methyl group of one of the ethyl groups is disordered over two positions with occupancies of 0.66 (2) and 0.34 (2). In the crystal structure, mol­ecules are linked into a three-dimensional hydrogen-bonded network by inter­molecular N—H⋯O interactions involving the uncoordinated carboxyl­ate O atoms

Paternal Origin of Mongolic-Speaking Populations: A Review of Studies from Recent Decades (1999–2019) and their Implications for Multidisciplinary Research in the Future

The activities of Mongolic-speaking populations, a large group of people in eastern Eurasia, have important impact on the history of East Asia and other parts of Eurasia. Most previous genetic research of East Asian populations, including ancient DNA studies, have involved samples from Mongolic-speaking populations or their ancient relatives. Here, we summarized frequency data of paternal Y-chromosome haplogroups from all available literature about Mongolic-speaking populations from 1999 to 2019. Fourteen paternal components were identified and six of them were proposed as major and common components in ancestor groups of Mongolic-speaking populations. We thoroughly discussed the possible origin, migration patterns, and the roles of these six components in the evolution history of Mongolic-speaking populations. Meanwhile, we discussed the implications of the present achievements of human genetics for multidisciplinary research in ethnology, history, archaeology and linguistics in the future

6-Chloro-3-nitro-N-(propan-2-yl)pyridin-2-amine

There are two mol­ecules in the asymmetric unit mol­ecule of the title compound, C8H10ClN3O2. Intra­molecular N—H⋯O hydrogen bonds stabilize the mol­ecular structure. There are no classical inter­molecular hydrogen bonds in the crystal structure

Certain Class of Analytic Functions Based on qq-difference operator

In this paper, we considered a generalized class of starlike functions defined by Kanas and R\u{a}ducanu\cite{10} to obtain integral means inequalities and subordination results. Further, we obtain the for various subclasses of starlike functions.Comment:

4-(4-Nitro­benz­yl)morpholine

In the title compound, C11H14N2O3, an inter­molecular inter­action between a nitro group O atom and a neighboring benzene ring helps to stabilize the crystal structure [N⋯centroid = 3.933 (2) Å]. No classical hydrogen bonds are observed in the crystal packing

Genome-wide gene responses in a transgenic rice line carrying the maize resistance gene Rxo1 to the rice bacterial streak pathogen, Xanthomonas oryzae pv. oryzicola

Abstract Background Non-host resistance in rice to its bacterial pathogen, Xanthomonas oryzae pv. oryzicola (Xoc), mediated by a maize NBS-LRR type R gene, Rxo1 shows a typical hypersensitive reaction (HR) phenotype, but the molecular mechanism(s) underlying this type of non-host resistance remain largely unknown. Results A microarray experiment was performed to reveal the molecular mechanisms underlying HR of rice to Xoc mediated by Rxo1 using a pair of transgenic and non-transgenic rice lines. Our results indicated that Rxo1 appeared to function in the very early step of the interaction between rice and Xoc, and could specifically activate large numbers of genes involved in signaling pathways leading to HR and some basal defensive pathways such as SA and ET pathways. In the former case, Rxo1 appeared to differ from the typical host R genes in that it could lead to HR without activating NDR1. In the latter cases, Rxo1 was able to induce a unique group of WRKY TF genes and a large set of genes encoding PPR and RRM proteins that share the same G-box in their promoter regions with possible functions in post-transcriptional regulation. Conclusions In conclusion, Rxo1, like most host R genes, was able to trigger HR against Xoc in the heterologous rice plants by activating multiple defensive pathways related to HR, providing useful information on the evolution of plant resistance genes. Maize non-host resistance gene Rxo1 could trigger the pathogen-specific HR in heterologous rice, and ultimately leading to a localized programmed cell death which exhibits the characteristics consistent with those mediated by host resistance genes, but a number of genes encoding pentatricopeptide repeat and RNA recognition motif protein were found specifically up-regulated in the Rxo1 mediated disease resistance. These results add to our understanding the evolution of plant resistance genes.</p

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

In maritime logistics optimization, considerable research efforts are focused on the extraction of deep behavioral characteristics from comprehensive shipping data to discern patterns in maritime vessel behavior. The effective linkage of these characteristics with maritime infrastructure, such as berths, is critical for the enhancement of ship navigation systems. This endeavor is paramount not only as a research focus within maritime information science but also for the progression of intelligent maritime systems. Traditional methodologies have primarily emphasized the analysis of navigational paths of vessels without an extensive consideration of the geographical dynamics between ships and port infrastructure. However, the introduction of knowledge graphs has enabled the integration of disparate data sources, facilitating new insights that propel the development of intelligent maritime systems. This manuscript presents a novel framework using knowledge graph technology for profound analysis of maritime data. Utilizing automatic identification system (AIS) data alongside spatial information from port facilities, the framework forms semantic triplet connections among ships, anchorages, berths, and waterways. This enables the semantic modeling of maritime behaviors, offering precise identification of ships through their diverse semantic information. Moreover, by exploiting the semantic relations between ships and berths, a reverse semantic knowledge graph for berths is constructed, which is specifically tailored to ship type, size, and category. The manuscript critically evaluates a range of graph embedding techniques, dimensionality reduction methods, and classification strategies through experimental frameworks to determine the most efficacious methodologies. The findings reveal that the maritime knowledge graph significantly enhances the semantic understanding of unmanned maritime equipment, thereby improving decision-making capabilities. Additionally, it establishes a semantic foundation for the development of expansive maritime models, illustrating the potential of knowledge graph technology in advancing intelligent maritime systems