Ferulic acid (FA), a hydroxycinnamic acid derivative, is a key bioactive component in traditional medicinal plants including Angelica sinensis and Asafoetida. Accumulating evidence supports its therapeutic efficacy in inflammatory disorders, such as rheumatoid arthritis (RA) and ulcerative colitis (UC). FA exerts anti-inflammatory effects through (1) the regulation of inflammatory cytokine levels; (2) modulation of signaling pathways such as nuclear factor kappa B (NF-κB), mitogen-activated protein kinase (MAPK), and janus kinase/signal transducer and activator of transcription (JAK/STAT); (3) amelioration of oxidative stress; and (4) regulation of immune cell homeostasis. At the pharmacokinetic level, studies show that FA is rapidly absorbed but exhibits low bioavailability, mainly due to the influence of metabolic pathways and food matrix characteristics. This review systematically summarizes the literature on the anti-inflammatory effects of FA, covering molecular mechanisms, pharmacokinetic characteristics, and application scenarios. Preclinical studies show that FA has low toxicity and good safety, demonstrating potential for development as a novel anti-inflammatory drug. However, its clinical translation is hindered by bottlenecks such as low bioavailability and insufficient human clinical data. Future research should prioritize developing novel drug delivery systems and conducting large-scale clinical trials to facilitate its clinical translation.

Scene flow estimation determines a scene’s 3D motion field, by predicting the motion of points in the scene, especially for aiding tasks in autonomous driving. Many networks with large-scale point clouds as input use voxelization to create a pseudo-image for real-time running. However, the voxelization process often results in the loss of point-specific features. This gives rise to a challenge in recovering those features for scene flow tasks. Our paper introduces DeFlow which enables a transition from voxel-based features to point features using Gated Recurrent Unit (GRU) refinement. To further enhance scene flow estimation performance, we formulate a novel loss function that accounts for the data imbalance between static and dynamic points. Evaluations on the Argoverse 2 scene flow task reveal that DeFlow achieves state-of-the-art results on large-scale point cloud data, demonstrating that our network has better performance and efficiency compared to others. The code is available at https://github.com/KTH-RPL/deflow.

Backgrounds: The plasma colloid osmotic pressure (COP) values for predicting mortality are not well-estimated. A user-friendly nomogram could predict mortality by incorporating clinical factors and scoring systems to facilitate physicians modify decision-making when caring for patients with serious neurological conditions.Methods: Patients were prospectively recruited from March 2017 to September 2018 from a tertiary hospital to establish the development cohort for the internal test of the nomogram, while patients recruited from October 2018 to June 2019 from another tertiary hospital prospectively constituted the validation cohort for the external validation of the nomogram. A multivariate logistic regression analysis was performed in the development cohort using a backward stepwise method to determine the best-fit model for the nomogram. The nomogram was subsequently validated in an independent external validation cohort for discrimination and calibration. A decision-curve analysis was also performed to evaluate the net benefit of the insertion decision using the nomogram.Results: A total of 280 patients were enrolled in the development cohort, of whom 42 (15.0%) died, whereas 237 patients were enrolled in the validation cohort, of which 43 (18.1%) died. COP, neurological pathogenesis and Acute Physiology and Chronic Health Evaluation II (APACHE II) score were predictors in the prediction nomogram. The derived cohort demonstrated good discriminative ability, and the area under the receiver operating characteristic curve (AUC) was 0.895 [95% confidence interval (CI), 0.840–0.951], showing good correction ability. The application of this nomogram to the validation cohort also provided good discrimination, with an AUC of 0.934 (95% CI, 0.892–0.976) and good calibration. The decision-curve analysis of this nomogram showed a better net benefit.Conclusions : A prediction nomogram incorporating COP, neurological pathogenesis and APACHE II score could be convenient in predicting mortality for critically ill neurological patients.

Objective Changes in thyroid function will be accompanied by changes in urinary N-acetyl-β-D-glucosaminidase (uNAG) levels. Therefore, whether thyroid hormones interfere the ability of uNAG in detecting acute kidney injury (AKI) has raised concern in patients with critical illness. Design A prospectively recruited, observational study was performed. Setting Adults admitted to the intensive care unit of a grade A tertiary hospital in China. Participants A total of 1919 critically ill patients were enrolled in the study. Main outcome measures To investigate the variations of the ability of uNAG to detect AKI in patients with critical illness under different thyroid hormones levels (differences in area under the curve (AUC) for uNAG diagnosis and prediction of AKI with different thyroid hormones levels). Results The bivariate correlation analysis revealed that FT3 and TT3 levels were independently associated with uNAG levels (p<0.001). FT3 and uNAG also showed correlation in multivariable linear regression analysis (p<0.001). After stratification according to the levels of FT3 or TT3, significant variation was observed in the uNAG levels with different quartiles (p0.05). Conclusions Even if uNAG levels varied with FT3 and TT3 levels, these hormones did not interfere with uNAG’s ability to detect AKI in patients with critical illness.