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The aim of this study was to investigate the effects of thin-cap fibroatheromas (TCFAs) on stent neointimal coverage at the 9‑month follow-up after EXCEL stent implantation assessed by optical coherence tomography (OCT).A total of 93 patients with non-ST elevation acute coronary syndrome (NSTEACS) who underwent EXCEL stent implantation were prospectively enrolled in the study and divided into a TCFA group (n = 47) and a non-TCFA group (n = 46) according to whether EXCEL stents covered the TCFAs. A TCFA was defined as a plaque with lipid content in more than one quadrant and fibrous cap thickness measuring less than 65 μm. The effect of TCFAs on stent neointimal coverage at the 9‑month follow-up after stent implantation was evaluated by OCT. The primary study endpoints were the incidence of neointimal uncoverage and stent malapposition.At the 9‑month follow-up, the minimal lumen diameter of the TCFA group tended to be smaller (2.8 ± 0.8 vs. 2.1 ± 0.8, p = 0.08) and the diameter of stenosis in the TCFA group tended to be larger (15.1 ± 10.3% vs. 26.3 ± 15.1%, p = 0.08) than those in the non-TCFA group. The mean intimal thickness of the TCFA group was significantly lower than that of the non-TCFA group (67.2 ± 35.5 vs. 145.1 ± 48.7, p 0.001). The uncovered struts (10.1 ± 9.7 vs. 4.8 ± 4.3, p = 0.05) and malapposed struts (2.1 ± 4.7 vs. 0.3 ± 0.5, p = 0.003) in the TCFA group were more significant than those in the non-TCFA group. Multivariate analysis showed that TCFAs and lesion types were independent predictors of incomplete neointimal coverage (p 0.05), and lesion types were independent predictors of stent malapposition (p 0.05).In patients with NSTEACS, TCFAs delayed endothelium coverage at 9 months after stent implantation, and TCFAs were independent predictors of incomplete neointimal coverage of the stent.ZIEL: Mittels optischer Kohärenztomographie (OCT) sollten die Auswirkungen von Fibroatheromen mit dünner Endothelschicht (TCFA) auf die neointimale Abdeckung von Stents bei der 9‑Monats-Nachuntersuchung nach EXCEL-Stentimplantation untersucht werden.Prospektiv wurden 93 Patienten mit akutem Koronarsyndrom ohne ST-Hebung, die eine EXCEL-Stentimplantation erhielten, in die TCFA-Gruppe (n = 47) und die Nicht-TCFA-Gruppe (n = 46) eingeteilt, je nachdem, ob EXCEL-Stents TCFA bedeckten. Ein TCFA wurde als Plaque mit Lipidgehalt in 1 Quadranten und einer Dicke der Faserkappe von weniger als 65 µm definiert. Die Wirkung von TCFA auf die neointimale Abdeckung der Stents bei der Nachuntersuchung nach 9 Monaten nach der Stentimplantation wurde mittels OCT bewertet. Die primären Studienendpunkte waren die Inzidenz der Feststellung einer Neointima und einer Stent-Fehlpositionierung.Bei der Nachuntersuchung nach 9 Monaten war der minimale Lumendurchmesser der TCFA-Gruppe tendenziell kleiner (2,8 ± 0,8 vs. 2,1 ± 0,8; p = 0,08) und der Durchmesser der Stenose in der TCFA-Gruppe tendenziell größer (15,1 ± 10,3 % vs. 26,3 ± 15,1 %; p = 0,08) als in der Nicht-TCFA-Gruppe. Die mittlere Intimadicke der TCFA-Gruppe war signifikant geringer als die der Nicht-TCFA-Gruppe (67,2 ± 35,5 vs. 145,1 ± 48,7; p 0,001). Die unbedeckten Streben (10,1 ± 9,7 vs. 4,8 ± 4,3; p = 0,05) und die fehlangelegten Streben (2,1 ± 4,7 vs. 0,3 ± 0,5; p = 0,003) in der TCFA-Gruppe waren signifikanter als die in der Nicht-TCFA-Gruppe. Die multivariate Analyse zeigte, dass TCFA und Läsionstypen unabhängige Prädiktoren für eine unvollständige neointimale Abdeckung (p 0,05) und Läsionstypen unabhängige Prädiktoren für eine Stent-Fehlpositionierung (p 0,05) waren.Bei Patienten mit akutem Koronarsyndrom ohne ST-Hebung verzögerten TCFA die Endothelabdeckung 9 Monate nach der Stentimplantation, und TCFA waren unabhängige Prädiktoren für eine unvollständige neointimale Abdeckung des Stents.

The highly crystalline S and O co-doped g-C3N4 nanosheets show outstanding performance in photocatalytic hydrogen evolution, which is attributed to the enhanced visible light absorption, high crystallinity and abundant surface active sites.

Our study aims to detect the underlying mechanism of the suppressive effect of Follistatin-like 1 (FSTL1) on lung metastasis of triple negative breast cancer (TNBC). We found that FSTL1 had no effect on the proliferation and metastasis of 4T1 cells in vitro, while in the tumor-bearing Fstl1 heterozygous (Fstl1+/-) mice, the number of anti-tumor T lymphocytes in the lung was significantly reduced with the increase in lung metastasis. Impaired development of T cells can cause dysfunction of adaptive immune system, which promotes cancer metastasis. Therefore the effect of FSTL1 on T cell development was further investigated. Lower population of T cells in periphery and decreased proliferation of CD4- CD8- double negative (DN) thymocytes and impairment development of T cells were found in Fstl1+/- mice. Furthermore, high expression of FSTL1 in medullary thymus epithelial (mTEC) cells and decreased mRNA expression of inducible costimulator on activated T-cell ligand (Icosl) in mTECsh Fstl1 were detected. Combining other studies that the generation of ICOSL by mTEC cells promotes CD4+ single positive (SP) thymocytes to produce IL-2, which promotes T cell development. Our results indicate FSTL1 deficiency in mTEC cells impairs T cell development to promote the lung metastasis of TNBC.

Background The inositol phospholipid signaling system, which is based on the metabolism of phosphoinositide (PI), mediates plant growth, development, and responses to adversity. Diacylglycerol kinase (DGK) is one of the key enzymes in the PI-cycle, which catalyzes the phosphorylation of diacylglycerol (DAG) to form phosphatidic acid (PA). To date, comprehensive genomic and functional analyses of DGK genes have not been reported in wheat. Results In this study, 20 DGK gene family members from the heterologous hexaploid wheat genome (TaDGKs) were identified and analyzed. Each putative protein was found to consist of a DGK catalytic domain and a accessory domain. The analyses of phylogenetic and gene structure revealed that each TaDGK gene could be grouped to clusters I, II, or III. In each phylogenetic subgroup, the TaDGKs demonstrated high conservation in functional domains, for example gene structure and amino acid sequences. By cloning, four coding sequences were ascertained from Chinese spring wheat. Expression analysis of these four genes revealed that each had a unique spatial and developmental expression pattern, indicating their functional diversification in wheat growth and development processes. Additionally, TaDGKs were also prominently up-regulated express under salt and drought stresses, suggesting their possible roles in dealing with adversity environment. Further cis-regulatory elements analysis elucidated transcriptional regulation and potential biological functions. Conclusions These results provide valuable information for understanding the putative functions of DGK genes in wheat, and conduce to ulterior functional analysis of this pivotal gene family. The 20 TaDGKs identified and analyzed in this study provide a strong foundation for further exploration of the biological function and regulatory mechanisms of TaDGKs in response to environmental stimuli.

Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

Experimental studies were conducted by thermogravimetric analyzer and drop tube furnace to clarify the co-combustion and NO formation characteristics of semi-coke and bituminous coal. The same experiments were carried out on the blend of anthracite and bituminous coal for comparison. Results indicated that the ignition and burnout temperatures of the blends decreased as the blending ratio of bituminous coal increased, and there existed significant interaction between semi-coke (or anthracite) and bituminous coal. The ignition index C and comprehensive combustion index S presented negative correlations with fuel ratio, and the relationships of C = 6.31–0.35FR and S = 3.87–0.26FR can be used to estimate the co-combustion characteristics of bituminous coal and semi-coke (or anthracite). The addition of bituminous coal gave rise to two competitive effects on semi-coke burnout and NO formation. When the bituminous coal accounted for 80%, the promotion on burnout was superior and the inhibition on NO formation was significant. Fuel ratio, rather than nitrogen content played more important role in the NO formation, and relationship of XNO = 40.96–4.24 N + 1.60FR was established to predict the NO formation during co-combustion of binary coal-based fuels. Besides, semi-coke had better combustion behavior and lower NO conversion ratio compared to anthracite, which mainly resulted from the more porous structure in semi-coke.