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Objective: To determine the inhibitory effects of pachymic acid on lung adenocarcinoma (LUAD) cells and elucidate its underlying mechanism. Methods: CCK-8, wound healing, Transwell, Western blot, tube formation, and immunofluorescence assays were carried out to measure the effects of various concentrations of pachymic acid on LUAD cell proliferation, metastasis, angiogenesis as well as autophagy. Subsequently, molecular docking technology was used to detect the potential targeted binding association between pachymic acid and protein tyrosine phosphatase 1B (PTP1B). Moreover, PTP1B was overexpressed in A549 cells to detect the specific mechanisms of pachymic acid. Results: Pachymic acid suppressed LUAD cell viability, metastasis as well as angiogenesis while inducing cell autophagy. It also targeted PTP1B and lowered PTP1B expression. However, PTP1B overexpression reversed the effects of pachymic acid on metastasis, angiogenesis, and autophagy as well as the expression of Wnt3a and β-catenin in LUAD cells. Conclusions: Pachymic acid inhibits metastasis and angiogenesis, and promotes autophagy in LUAD cells by modulating the Wnt/β-catenin signaling pathway via targeting PTP1B.

The East Kunlun Orogen (EKO) is located in the western part of the Central Asian Orogenic Belt, and it records the Neoproterozoic-Early Devonian tectonic evolution involving the subduction and extinction of the Proto-Tethys Ocean. The Heihaibei granite is located in the South Kunlun belt within EKO. In this study, geochronology, geochemistry, and Hf isotope analysis of Heihaibei monzogranite and granodiorite were carried out. Their LA-ICP-MS zircon U-Pb results yielded ages of 414.1 [+ ou -] 2.0 and 417.8 [+ ou -] 2.0 Ma. The Heihaibei granite is peraluminous (A/CNK = 0.99-1.30), with high alkali contents (7.76-9.14 wt.%) and 10 000 Ga/Al (2.60-3.38), Fe[O.sup.T]/MgO (27.6-49.5), and Y/Nb ratios (1.93-2.93), suggesting that the Heihaibei granite is an [A.sub.2]-type granite. The Heihaibei granite samples have zircon [epsilon]Hf(t) values ranging from-6.3 to-0.3, which suggests that the Early Devonian granite was derived from partial melting of Mesoproterozoic ([T.sub.DM2] = 1797-1415 Ma) crust. When considering data on contemporaneous granites, geochronological and geochemical data for the Heihaibei granite indicate that it was emplaced in a post-collisional extension environment following the closure of the Proto-Tethys Ocean. The Heihaibei granite was formed by extension of the southern subduction accretionary complex as part of the post-collisional setting during the closure of the Proto-Tethys Ocean. Key words: Heihaibei granite, geochemistry, geochronology, zircon Hf isotope, [A.sub.2]-type granite, East Kunlun Orogen L'orogène du Kunlun oriental (OKO) est situé dans la partie occidentale de la ceinture orogénique d'Asie centrale et préserve un registre de l'évolution tectonique du Néoprotérozoïque au Dévonien précoce comprenant la subduction et la disparition de l'océan proto-Téthys. Le granite de Heihaibei est situé dans la ceinture du Kunlun méridionale, dans l'OKO. Une étude géochronologique, géochimique et des isotopes de Hf de monzogranite et de granodiorite de Heihaibei a été réalisée. La datation U-Pb sur zircons par LA-ICP-MS donne des âges de 414,1 [+ ou -] 2,0 Ma et 417,8 [+ ou -] 2,0 Ma. Le granite de Heihaibei est hypera-lumineux (A/CNK = 0,99-1,30), avec de fortes teneurs d'alcalis (7,76-9,14 % en poids) et des rapports 10 000 Ga/Al (2,60-3,38), Fe[O.sup.T]/MgO (27,6-49,5) et Y/Nb (1,93-2,93) élevés, ce qui indiquerait qu'il s'agit d'un granite de type [A.sub.2]. Les échantillons du granite de Heihaibei présentent des valeurs de [epsilon]Hf(t) sur zircons de-6,3 à-0,3 qui indiqueraient que le granite dévonien précoce est dérivé de la fusion partielle de croûte mésoprotérozoïque ([T.sub.DM2] = 1797-1415 Ma). Au vu de données sur des granites du même âge, les données géochronologiques et géochimiques pour le granite de Heihaibei indiquent qu'il a été mis en place dans un milieu d'extension post-collision suivant la fermeture de l'océan proto-Téthys. Le granite de Heihaibei s'est formé par l'extension du complexe accrétionnaire de subduction méridional au sein du milieu post-collision durant la fermeture de l'océan proto-Téthys. [Traduit par la Rédaction] Mots-clés: granite de Heihaibei, géochimie, géochronologie, isotopes de Hf de zircons, granite de type [A.sub.2], orogène du Kunlun oriental, 1. Introduction The East Kunlun Orogen (EKO) is the major component of the Central China Orogenic System and has a complex Precambrian to Cenozoic structural and magmatic history(Jiang et al. [...]

Low‐energy light, endowing with excellent characteristics of lower energy, fewer side reactions, more abundance in solar light. The scenario has been developed for the fabrication of indenone through a low‐energy green light driven radical annulation reaction between α‐keto acids and internal alkynes using Rhodamine 6G as photocatalyst. Various kinds of substrates are suitable for this methodology, affording the corresponding indenones in a greener and efficient manner. [ABSTRACT FROM AUTHOR]

Herein, chiral metal–organic frameworks (MOFs), DCF-20 and LCF-20, were utilized as matrices for both chirality transfer and energy transfer. HBT1@MOFs and HBT2@MOFs emit excitation-dependent circularly polarized luminescence (CPL) due to excited-state intramolecular proton transfer (ESIPT). HBT1/C152/NIR@MOFs exhibit full-color and white CPL. The luminescence dissymmetry factors (glum) were significantly increased, benefiting from the efficient chirality space transfer and high luminescence efficiency. [ABSTRACT FROM AUTHOR]

[C24H38N6S2], triclinic, P1‾$P\overline{1}$ (no. 2), a = 5.9953(4) Å, b = 7.6132(5) Å, c = 15.3709(10) Å, α = 82.089(5)°, β = 88.307(6)°, γ = 75.992(6)°, V = 674.24(8) Å3, Z = 1, Rgt(F) = 0.0587, wRref(F2) = 0.1516, T = 296(2) K.

The aim of this paper is to explore an effective model for predicting the compressive strength of concrete using machine learning technology, as well as to interpret the model using an interpretable method, which overcomes the limitation of the unknowable prediction processes of previous machine learning models. An experimental database containing 228 samples of the compressive strength of standard cubic specimens was built in this study, and six algorithms were applied to build the predictive model. The results show that the XGBoost model has the highest prediction accuracy among all models, as the R2 of the training set and testing set are 0.982 and 0.966, respectively. Further analysis was conducted on the XGBoost model to discuss its applicability. The main steps include the following: (i) obtaining key features, (ii) obtaining trends in the evolution of features, (iii) single-sample analysis, and (iv) conducting a correlation analysis to explore methods of visualizing the variations in the factors that exert influence. The interpretability analyses on the XGBoost model show that the contribution to the compressive strength by each factor is highly in line with the conventional theory. In summary, the XGBoost model proved to be effective in predicting concrete's compressive strength. [ABSTRACT FROM AUTHOR]