Novel Isoalantolactone-Based Derivatives as Potent NLRP3 Inflammasome Inhibitors: Design, Synthesis, and Biological Characterization.

The NLRP3 inflammasome has been recognized as a promising therapeutic target in drug discovery for inflammatory diseases. Our initial research identified a natural sesquiterpene isoalantolactone (IAL) as the active scaffold targeting NLRP3 inflammasome. To improve its activity and metabolic stability, a total of 64 IAL derivatives were designed and synthesized. Among them, compound 49 emerged as the optimal lead, displaying the most potent inhibitory efficacy on nigericin-induced IL-1ß release in THP-1 cells, with an IC50 value of 0.29 µM, approximately 27-fold more potent than that of IAL (IC50: 7.86 µM), and exhibiting higher metabolic stability. Importantly, 49 remarkably improved DSS-induced ulcerative colitis in vivo. Mechanistically, we demonstrated that 49 covalently bound to cysteine 279 in the NACHT domain of NLRP3, thereby inhibiting the assembly and activation of NLRP3 inflammasome. These results provided compelling evidence to further advance the development of more potent NLRP3 inhibitors based on this scaffold.

SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs.

piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. In this study, a novel computational model based on the structural perturbation method is proposed to predict potential disease-associated piRNAs, called SPRDA. Notably, SPRDA belongs to positive-unlabeled learning, which is unaffected by negative examples in contrast to previous approaches. In the 5-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.

Line graph attention networks for predicting disease-associated Piwi-interacting RNAs.

PIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlate with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. Line graphs can extend many heterogeneous networks to replace dichotomous networks. In this study, we present a new graph neural network framework, line graph attention networks (LGAT). And we apply it to predict PiRNA disease association (GAPDA). In the experiment, GAPDA performs excellently in 5-fold cross-validation with an AUC of 0.9038. Not only that, it still has superior performance compared with methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.

Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer.

OBJECTIVE: To conduct multiparametric magnetic resonance imaging (MRI)-derived radiomics based on multi-scale tumor region for predicting disease-free survival (DFS) in early-stage squamous cervical cancer (ESSCC). METHODS: A total of 191 ESSCC patients (training cohort, n = 135; validation cohort, n = 56) from March 2016 to September 2019 were retrospectively recruited. Radiomics features were derived from the T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CET1WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) map for each patient. DFS-related radiomics features were selected in 3 target tumor volumes (VOIentire, VOI+5 mm, and VOI-5 mm) to build 3 rad-scores using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Logistic regression was applied to build combined model incorporating rad-scores with clinical risk factors and compared with clinical model alone. Kaplan-Meier analysis was used to further validate prognostic value of selected clinical and radiomics characteristics. RESULTS: Three radiomics scores all showed favorable performances in DFS prediction. Rad-score (VOI+5 mm) performed best with a C-index of 0.750 in the training set and 0.839 in the validation set. Combined model was constructed by incorporating age categorized by 55, Federation of Gynecology and Obstetrics (Figo) stage, and lymphovascular space invasion with rad-score (VOI+5 mm). Combined model performed better than clinical model in DFS prediction in both the training set (C-index 0.815 vs 0.709; p = 0.024) and the validation set (C-index 0.866 vs 0.719; p = 0.001). CONCLUSION: Multiparametric MRI-derived radiomics based on multi-scale tumor region can aid in the prediction of DFS for ESSCC patients, thereby facilitating clinical decision-making. KEY POINTS: • Three radiomics scores based on multi-scale tumor region all showed favorable performances in DFS prediction. Rad-score (VOI+5 mm) performed best with favorable C-index values. • Combined model incorporating multiparametric MRI-based radiomics with clinical risk factors performed significantly better in DFS prediction than the clinical model. • Combined model presented as a nomogram can be easily used to predict survival, thereby facilitating clinical decision-making.

Meta-Analysis of Randomized Controlled Trials Comparing Risk of Major Adverse Cardiac Events and Bleeding in Patients With Prasugrel Versus Clopidogrel.

The use of prasugrel in patients with coronary artery disease (CAD) has been associated with decreased major adverse cardiac events (MACEs) compared with clopidogrel but with an increased risk of bleeding. However, it remains unclear if the risks of bleeding outweigh those of MACEs in patients on prasugrel treatment. We systematically reviewed randomized controlled trials comparing prasugrel with clopidogrel in patients with CAD. We performed a literature search of PubMed, EMBASE, and Cochrane Central Register of Controlled Trial databases from inception to November 25, 2014, and reviewed the reference lists of retrieved articles. A comparative estimate was made for the combined rates of MACEs and bleeding from the same trials in the framework of this meta-analysis and expressed as odds ratios (ORs) and 95% confidence intervals (CIs) in both random- and fixed-effects models. Nine studies involving 25,214 patients were included in our meta-analysis. In both the random- and fixed-effects models, the risks of MACEs outweighed those of major bleeding (OR 7.48, 95% CI 3.75 to 14.94, p <0.0001, random effects) and of minor bleeding (OR 3.77, 95% CI 1.73 to 8.22, p = 0.009, random effects). Results were corroborated in a standard-dose clopidogrel subgroup analysis (OR 7.46, 95% CI 3.54 to 15.68, p <0.0001, and OR 6.44, 95% CI 2.80 to 14.80, p <0.0001, random effects, respectively). In conclusion, despite the increased risk of bleeding associated with prasugrel treatment compared with clopidogrel, the risk of MACEs far outweighed the risk of bleeding.

[Relationship between serum leptin and tumor necrosis factor and endothelin in patients with chronic renal failure].

OBJECTIVE: To assess the serum level of leptin and its relationship between tumor necrosis factor, and endothelin in patients with chronic renal failure. METHODS: Serum level of leptin,tumor necrosis factor and endothelin were measured in 40 patients with chronic renal failure and 20 healthy controls by radioimmunoassay. Correlation analysis was performed between serum level of leptin and tumor necrosis factor and endothelin. RESULTS: Serum leptin in patients with chronic renal failure was significantly higher than that in healthy control subjects((19.25+/-4.89)microg/L vs. (5.57+/-1.69)microg/L, P<0.01). There was significant positive correlation between serum leptin and tumor necrosis factor and endothelin (r(TNF)=0.829, r(ET)=0.605; both P<0.01). CONCLUSION: The abnormal increased serum levels of tumor necrosis factor and endothelin are associated with hyperleptinemia in patients with chronic renal failure.