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Dandelion (Taraxacum officinale), a common plant worldwide, is used as both a medicine and food. Fermentation is a food processing technology that has many advantages, such as low energy cost, changes in product characteristics, and enhanced product quality. The purpose of this study was to investigate the effect of solid-state fermentation (SSF) on the content, composition and antioxidant activity of dandelion flavonoids. Response surface methodology was used to optimize dandelion fermentation conditions. Under optimized conditions, the maximum flavone concentration was 66.05 ± 1.89 mg/g. The flavonoid content of the crude extract from fermented dandelion (FDF) was 183.72 ± 2.24 mg/g. The flavonoid compounds in the crude extracts were further identified by UPLC-ESI-MS/MS. A total of 229 flavonoid compounds were identified, and 57 differential flavonoids (including 27 upregulated and 30 downregulated flavonoids) between FDF and the crude extract of unfermented dandelion (DF) were observed, of which 25 were annotated to metabolic pathways. FDF displayed superior antioxidant activity to that of DF in in vitro DPPH radical-scavenging and reducing power assays. The favorable results of our investigation could provide a new way for the exploitation and utilization of dandelion, which could be promising for its application as an antioxidant and functional food additive with flavonoids as ingredients.

This paper investigates the dynamical behavior of a quintom dark energy model, which combines quintessence and phantom scalar fields, across four distinct interacting scenarios: (I) quintessence-matter interaction, (II) phantom-matter interaction, (III) coupled quintessence-phantom-matter interaction, and (IV) intra-dark energy interaction (quintessence-phantom energy exchange). By constructing autonomous dynamical systems for each case, we analyze the stability of critical points and evaluate the cosmological evolution using the statefinder diagnostic pair { r , s } . The exponential potentials for both scalar fields and interaction terms proportional to the matter density ( Q 1 , Q 2 ) are adopted to derive fixed points, revealing that all scenarios admit late-time phantom-dominated attractors, consistent with accelerated expansion. Notably, interactions between dark sectors significantly alter transient regimes: energy transfer from dark matter to dark energy prolongs matter-quintessence coexistence phases, while reverse transfer accelerates phantom dominance. The statefinder diagnostic, however, fails to distinguish interactions due to overlapping s - r trajectories across cases. Numerical simulations further demonstrate epochs of negative phantom energy density in scenarios involving phantom coupling, linked to non-physical fixed points. These results highlight quintom’s flexibility in mimicking observed dark energy behavior while emphasizing the limited discriminatory power of { r , s } for coupling-specific dynamics. The study underscores the role of interactions in alleviating cosmic coincidence problems and shaping multi-phase cosmic histories.

Clustering of magnetic nanoparticles (MNPs) is perhaps the most effective, yet intriguing strategy to enhance T 2 relaxivity in magnetic resonance imaging (MRI). However, the underlying mechanism is still not fully understood and the attempts to generalize the classic outersphere theory from single particles to clusters have been found to be inadequate. Here we show that clustering of MNPs enhances local field inhomogeneity due to reduced field symmetry, which can be further elevated by artificially involving iron oxide NPs with heterogeneous geometries in terms of size and shape. The r 2 values of iron oxide clusters and Landau–Lifshitz–Gilbert simulations confirmed our hypothesis, indicating that solving magnetic field inhomogeneity may become a powerful way to build correlation between magnetization and T 2 relaxivity of MNPs, especially magnetic clusters. This study provides a simple yet distinct mechanism to interpret T 2 relaxivity of MNPs, which is crucial to the design of high-performance MRI contrast agents. The signal detected in magnetic resonance imaging comes from the relaxation of proton nuclear magnetization. Here, Zhou et al . introduce magnetic field inhomogeneity as a parameter to design iron oxide nanoparticle clusters to enhance the relaxation rate of nearby protons, thereby increasing image contrast.

Resonance interaction between a molecular transition and a confined electromagnetic field can lead to weak or strong light-matter coupling. Considering the substantial exciton–phonon coupling in thermally activated delayed fluorescence (TADF) materials, it is thus interesting to explore whether weak light-matter coupling can be used to redistribute optical density of states and to change the rate of radiative decay. Here, we demonstrate that the emission distribution of TADF emitters can be reshaped and narrowed in a top-emitting organic light-emitting device (OLED) with a weakly coupled microcavity. The Purcell effect of weak microcavity is found to be different for TADF emitters with different molecular orientations. We demonstrate that radiative rates of the TADF emitters with vertical orientation can be substantial increased in weakly coupled organic microcavity. These observations can enhance external quantum efficiencies, reduce efficiency roll-off, and improve color-purities of TADF OLEDs, especially for emitters without highly horizontal orientation.This work not only presents photophysical processes of thermally activated delayed fluorescent emitters in a weak Fabry-Pérot cavity, but also establishes a connection between emitter orientation and Purcell effect.

Background For women who have experienced recurrent pregnancy loss (RPL), it is crucial not only to treat them but also to evaluate the risk of recurrence. The study aimed to develop a risk predictive model to predict the subsequent early pregnancy loss (EPL) in women with RPL based on preconception data. Methods A prospective, dynamic population cohort study was carried out at the Second Hospital of Lanzhou University. From September 2019 to December 2022, a total of 1050 non-pregnant women with RPL were participated. By December 2023, 605 women had subsequent pregnancy outcomes and were randomly divided into training and validation group by 3:1 ratio. In the training group, univariable screening was performed on RPL patients with subsequent EPL outcome. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to select variables, respectively. Subsequent EPL prediction model was constructed using generalize linear model (GLM), gradient boosting machine (GBM), random forest (RF), and deep learning (DP). The variables selected by LASSO regression and multivariate logistic regression were then established and compared using the best prediction model. The AUC, calibration curve, and decision curve (DCA) were performed to assess the prediction performances of the best model. The best model was validated using the validation group. Finally, a nomogram was established based on the best predictive features. Results In the training group, the GBM model achieved the best performance with the highest AUC (0.805). The AUC between the variables screened by the LASSO regression (16-variables) and logistic regression (9-variables) models showed no significant difference (AUC: 0.805 vs. 0.777, P  = 0.1498). Meanwhile, the 9-variable model displayed a well discrimination performance in the validation group, with an AUC value of 0.781 (95%CI 0.702, 0.843). The DCA showed the model performed well and was feasible for making beneficial clinical decisions. Calibration curves revealed the goodness of fit between the predicted values by the model and the actual values, the Hosmer–Lemeshow test was 7.427, and P  = 0.505. Conclusions Predicting subsequent EPL in RPL patients using the GBM model has important clinical implications. Future prospective studies are needed to verify the clinical applicability. Trial registration This study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2000039414 (27/10/2020).

Pregnancy loss is a prevalent issue among women of childbearing age and can have significant physical and psychological consequences for patients and their families. However, the association between the number of previous pregnancy loss and the risk of adverse pregnancy outcomes (APOs) in subsequent pregnancies remains uncertain. This study aimed to provide clinical data to determine whether the number of previous pregnancy loss increases the risk of APOs in women with a history of pregnancy loss. We conducted a cohort-based, nested case-control study involving 1074 women with a history of pregnancy loss. Detailed demographic and medical history information was collected at baseline, and biological specimens were retained for laboratory testing. APOs were selected as the primary outcome, with cases defined as individuals who experienced any APO events. Cases were matched with event-free control subjects at a 1:2 ratio. Using conditional logistic regression, we examined the relationship between the number of previous pregnancy loss and APO events, using age at first pregnancy as the reference exposure. Among the study participants, we identified 358 cases with APO events and 716 controls. The occurrence of APOs was found to be associated with an increased number of previous pregnancy loss exposures compared to controls without APOs, with an odds ratio (OR) of 1.36 and a 95% confidence interval (CI) of 1.14 to 1.61. This association persisted even after adjusting for patient demographics, the total number of previous pregnancies, and other exposures, with an adjusted odds ratio (aOR) of 1.75 and a 95% CI of 1.28 to 2.4. Furthermore, our study also indicated that age at first pregnancy within a certain range may be a risk factor for APOs. In conclusion, our findings suggest that an increased number of previous pregnancy loss is associated with a higher risk of APOs in subsequent pregnancies among women with a history of pregnancy loss. These results provide valuable clinical data and underscore the importance of considering the number of previous pregnancy loss when assessing the risk of APOs in this population. Additionally, age at first pregnancy may also play a role in APO risk. Further research is warranted to better understand the mechanisms underlying these associations and to develop appropriate interventions to mitigate the risks associated with pregnancy loss.

The slag-metal-gas multiphase system in converters is crucial for smelting effectiveness and process stability during steelmaking. However, as a key course of the multiphase interaction, slag foaming was not the focus in previous studies due to its complexity. Therefore, we designed a modeling method of slag foaming. Using this method, this article investigated the effects of top blowing impingement and bottom blowing agitation on slag foaming in a combined top and bottom blowing converter. The experimental results indicated that foamed slag, primarily caused by the slag-metal reaction releasing gas, is a significant part of the multiphase system. The maximum height of foamed slag was reduced with increasing bottom blowing gas flowrate as the result of the acceleration of gas escaping. The top blowing gas presented a suppression effect on the foamed slag, which was strengthened by a decrease in top blowing lance height and increase in top blowing gas flowrate.

N 6 -methyladenosine (m 6 A) modification on viral RNAs has a profound impact on infectivity. m 6 A is also a highly pervasive modification for influenza viral RNAs. However, its role in virus mRNA splicing is largely unknown. Here, we identify the m 6 A reader protein YTHDC1 as a host factor that associates with influenza A virus NS1 protein and modulates viral mRNA splicing. YTHDC1 levels are enhanced by IAV infection. We demonstrate that YTHDC1 inhibits NS splicing by binding to an NS 3′ splicing site and promotes IAV replication and pathogenicity in vitro and in vivo . Our results provide a mechanistic understanding of IAV-host interactions, a potential therapeutic target for blocking influenza virus infection, and a new avenue for the development of attenuated vaccines.

Identification of stable major quantitative trait loci (QTLs) for yield-related traits is important for yield potential improvement in wheat breeding. In the present study, we genotyped a recombinant inbred line (RIL) population using the Wheat 660K SNP array and constructed a high-density genetic map. The genetic map showed high collinearity with the wheat genome assembly. Fourteen yield-related traits were evaluated in six environments for QTL analysis. A total of 12 environmentally stable QTLs were identified in at least three environments, explaining up to 34.7% of the phenotypic variation. Of these, for thousand kernel weight (TKW), ( ) for plant height (PH), spike length (SL) and spikelet compactness (SCN), for PH, and for total spikelet number per spike (TSS) were detected in at least five environments. A set of Kompetitive Allele Specific PCR (KASP) markers were converted based on the above QTLs and used to genotype a diversity panel comprising of 190 wheat accessions across four growing seasons. ( ), and were successfully validated. Compared with previous studies, and should be novel QTLs. These results provided a solid foundation for further positional cloning and marker-assisted selection of the targeted QTLs in wheat breeding programs.

Liver hepatocellular carcinoma (LIHC) is one of the major causes of cancer-related death worldwide with increasing incidences, however there are very few studies about the underlying mechanisms and pathways in the development of LIHC. We obtained LIHC samples from The Cancer Genome Atlas (TCGA) to screen differentially expressed mRNAs, lncRNAs, miRNAs and driver mutations. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses and protein–protein interaction (PPI) network were performed. Moreover, we constructed a competing endogenous lncRNAs-miRNAs-mRNAs network. Finally, cox proportional hazards regression analysis was used to identify important prognostic differentially expressed genes. Total of 1284 mRNAs, 123 lncRNAs, 47 miRNAs were identified within different tissues of LIHC patients. GO analysis indicated that upregulated and downregulated differentially expressed mRNAs (DEmRNAs) were mainly associated with cell division, DNA replication, mitotic sister chromatid segregation and complement activation respectively. Meanwhile, KEGG terms revealed that upregulated and downregulated DEmRNAs were primarily involved in DNA replication, Metabolic pathways, cell cycle and Metabolic pathways, chemical carcinogenesis, retinol metabolism pathway respectively. Among the DERNAs, 542 lncRNAs-miRNAs-mRNAs pairs were predicted to construct a ceRNA regulatory network including 35 DElncRNAs, 26 DEmiRNAs and 112 DEmRNAs. In the Kaplan‐Meier analysis, total of 43 mRNAs, 14 lncRNAs and 3 miRNAs were screened out to be significantly correlated with overall survival of LIHC. The mutation signatures were analyzed and its correlation with immune infiltrates were evaluated using the TIMER in LIHC. Among the mutation genes, TTN mutation is often associated with poor immune infiltration and a worse prognosis in LIHC. This work conducted a novel lncRNAs-miRNAs-mRNAs network and mutation signatures for finding potential molecular mechanisms underlying the development of LIHC. The biomarkers also can be used for predicting prognosis of LIHC.