Agarwood extract improves psoriatic-like inflammation in HaCaT cells by regulating NF-κB pathway

Objective To investigate the effect and mechanism of agarwood extract (AE) on tumor necrosis factor-α (TNF-α)-induced psoriatic-like inflammation model of keratinocytes (HaCaT). Methods The psoriatic-like inflammation model of HaCaT cells was induced by 40 ng/mL TNF-α, and then the cells were treated with AE at concentrations of 16 μg/mL (low concentration), 24 μg/mL (medium concentration), 32 μg/mL (high concentration). Cell counting kit-8 (CCK-8) assay was used to detect the viability of HaCaT cells. Flow cytometry was applied to detect the apoptosis of HaCaT cells. Reverse transcription-quantitative polymerase chain reaction (RTqPCR) and enzyme-linked immunosorbent assay (ELISA) were used to detect the mRNA expression and secretion levels of various inflammatory mediators, as well as that of inhibitor kappa B alpha (IκBα). Western blotting was used to detect the expression of key proteins in the NF-κB pathway. Results After 40 ng/mL TNF-α stimulation, the viability of HaCaT cells was increased (P < 0.01), the early apoptosis rate and IκBα mRNA level were decreased (P < 0.05), and the mRNA expression levels and release of interleukin-6 (IL-6), interleukin-1β (IL-1β), interleukin17A (IL-17A), TNF-α, chemokine ligand 20 (CCL20) in cells were increased (P < 0.05). The expres‐sion level of phosphorylated nuclear factor-κB p65 (p-p65) protein was increased (P < 0.01). After a certain concentration of AE intervention, the viability of HaCaT cells was decreased (P < 0.01). Treatment with low, medium and high concentrations of AE in TNF-α pretreated HaCaT cells increased the early apoptosis rate and the mRNA-expression level of IκBα (P < 0.01). Meanwhile, it decreased the mRNA expression and release of IL-6, IL-1β, IL-17A, TNF-α and CCL20 (P < 0.05), as well as the p-p65 protein level (P < 0.01). Conclusion AE can inhibit the phosphorylation of p65 protein in the NF-κB pathway, thereby reducing the release of inflammatory factors and chemokines in the psoriatic-like inflammatory model of HaCaT cells, inducing apoptosis, and improving the inflammatory response

Novel emerging nano-assisted anti-cancer strategies based on the STING pathway

Activation of simulator of interferon genes (STING), which induces the production of proinflammatory factors and immune effector cell activation, is considered a promising strategy for enhanced anti-cancer intervention. However, several obstacles prevent STING signaling in solid tumors, such as delivered molecules’ rapid degradation, restriction to tumor sites, insufficient intracellular concentrations, and low responsivity. Well-designed, multifunctional nano-formulations have emerged as optimized platforms for STING activation. Recently, a variety of nano-formulations have been developed and used in STING activation, thus facilitating immunotherapy in preclinical and clinical stages. Herein, we summarize recent advances in nanotechnology-based delivery, activation, and application strategies, which have advanced various aspects of immunotherapy. Novel STING agonists and their mechanisms in STING-activation-mediated tumor interventions are highlighted herein, to provide a comprehensive overview and discuss future directions for boosting immunotherapy via STING regulation

Primary cutaneous nocardiosis in an immunocompetent host

Key Clinical Message Nocardiosis is a rare opportunistic infection that is classically observed in immunocompromised patients but can also affect immunocompetent individuals. It tends to involve the lung, central nervous system, and skin and is often misdiagnosed

Analysis of urinary tobacco-specific nitrosamine 4- (methylnitrosamino)1-(3-pyridyl)-1- butanol (NNAL) and HPV infection in American women: National health and nutrition examination survey.

Tobacco-specific nitrosamines (TSNAs) are a group of toxic substances specific to tobacco. 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) is a tobacco-specific nitrosamine measurable in urine with a much longer half-life than cotinine. We aimed to examine the association between urinary tobacco-specific NNAL and HPV infection among American women. We used cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2014 to collect details on their urinary NNAL, HPV infection status, and other essential variables. The association between dietary urinary NNAL and HPV infection status was analyzed by using a weighted multivariate logistic regression model, and stratified subgroup analysis. In total, 5197 participants aged 18-59 years were identified, with overall prevalence of high-risk and low-risk HPV infection of 22.0% and 19.1%, respectively. The highest quartile of NNAL(Q4) was more positively associated with low-risk HPV infection than the lowest quartile of NNAL(Q1) (OR = 1.83 (1.35,2.50), p<0.001). the highest quartile of NNAL(Q4) was more positively associated with high-risk HPV infection than the lowest quartile of NNAL(Q1) (OR = 2.20 (1.57,3.08), p < 0.001). In subgroup analyses, the positive correlation between urinary NNAL levels and low-risk HPV infection status was inconsistent in marital status and BMI (interaction p < 0.05). The positive association of urinary NNAL levels with high-risk HPV infection status was inconsistent in smoking and BMI. (interaction p < 0.05). Tobacco-specific NNAL levels positively correlate with high- and low-risk HPV. Future well-designed longitudinal studies are still needed to validate the effect of tobacco exposure on HPV infection by NNAL

Water Quality by Spectral Proper Orthogonal Decomposition and Deep Learning Algorithms

Water quality plays a pivotal role in human health and environmental sustainability. However, traditional water quality prediction models are limited by high model complexity and long computation time, whereas AI models often struggle with high-dimensional time series and lack physical interpretability. This paper proposes a two-dimensional water quality surrogate model that couples physical numerical models and AI. The model employs physical simulation results as input, applies spectral proper orthogonal decomposition to reduce the dimensionality of the simulation results, utilizes a long short-term memory neural network for matrix forecasting, and reconstructs the two-dimensional concentration field. The simulation and predictive performance of the surrogate model were systematically evaluated through four design scenarios and three sampling dataset lengths, with a particular focus on the convection&ndash;diffusion zone and high-concentration zone. The results indicated that the model achieves high prediction accuracy for up to 7 h into the future, with sampling dataset lengths ranging from 20 to 80 h. Specifically, the model achieved an average R2 of 0.92, a MAE of 0.38, and a MAPE of 1.77%, demonstrating its suitability for short-term water quality predictions. The methodology and findings of this study demonstrate the significant potential of integrating spectral proper orthogonal decomposition and deep learning for water quality prediction. By overcoming the limitations of traditional models, the proposed surrogate model provides high-accuracy predictions with enhanced physical interpretability, even in complex, dynamic environments. This work offers a practical tool for rapid responses to water pollution incidents and supports improved watershed water quality management by effectively capturing pollutant diffusion dynamics. Furthermore, the model&rsquo;s scalability and adaptability make it a valuable resource for addressing intelligent management in environmental science

Prediction of immune infiltration and prognosis for patients with gastric cancer based on the immune-related genes signature

Objective: The immune microenvironment influenced clinical outcomes and treatment response of gastric cancer (GC) patients. Though thousands of immune-related genes (IRGs) have been identified, their effects on GC are not fully understood. The objective of the study is to analyze the correlations between the expression and effect of IRGs and clinical outcomes. Moreover, we evaluate the efficacy and value of utilizing the immune-related genes signature as a prognosis prediction model for GC patients. Methods: We identified the differentially expressed IRGs and systematically analyzed their functions. We constructed a novel GC prognostic signature and a new nomogram, Moreover, we explored the infiltrated immune cell types in the immune microenvironment and discussed the genetic variation in GC IRGs. Results: Eight IRGs, including CCL15, MSR1, GNAI1, NR3C1, ITGAV, NMB, AEN, and TGFBR1 were identified. Based on the prognostic signature, GC patients were distinguished into two subtype groups. As verified in multiple datasets, the prognostic signature exhibited good performance in predicting the prognosis (AUC = 0.803, P-value <0.001) and revealed the different clinical features and infiltrated immune cell types in the immune microenvironment. Conclusions: In summary, we found that IRGs contributed to GC prognosis prediction and constructed an IRGs-based GC prognostic signature, which could serve as an effective prognostic stratification tool