Search

A novel screening platform based on an Fe3O4@C@PDA-Ni2+@COX-2 ligand fishing combination with high-performance liquid chromatography–mass spectrometry was first designed, synthesized, and employed to screen and identify COX-2 inhibitors from Panax notoginseng leaves. The obtained magnetic nanoparticles exhibit outstanding preconcentration ability that allows for controlling the enzyme orientation to avoid enzyme active site blocking, conformational changes, or denaturing during immobilization. The as-prepared Fe3O4@C@PDA-Ni2+@COX-2 composite was carefully characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectrometry (FT-IR), Xray powder diffraction (XRD), thermal gravimetric analyzer (TGA), vibrating sample magnetometer (VSM), and Zeta potential analysis. The analytical parameters influencing the magnetic solid-phase fishing efficiency were optimized by univariate and multivariate methods (Box–Behnken design) by testing a positive control and celecoxib with active and inactive COX-2. Under the optimized ligand fishing conditions, twelve potential COX-2 inhibitors were screened and characterized in Panax notoginseng leaves. The results indicate that the proposed method provides a simple, feasible, selective, and effective platform for the efficient screening and identification of active compounds from Chinese herbal medicine. It has guiding significance for the synthesis and development of novel anti-inflammatory drugs, and provides a reference for the efficient discovery of anti-inflammatory drugs or lead compounds from the complex system of Chinese herbal medicine.

Background Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI).Methods This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. Model performance was evaluated using metrics including the area under the curve (AUC), sensitivity, and specificity. Additionally, SHAP (SHapley Additive exPlanations) analysis was used to explain the model’s predicted values as the sum of the attribution values for each input feature. Pathogenomics analysis was employed to explain the molecular mechanisms underlying the model’s predictions.Results Our pathomics-driven ensemble model effectively stratified the response to ICIs in training cohort (AUC 0.985 (95% CI 0.971 to 0.999)), which was further validated in internal validation cohort (AUC 0.921 (95% CI 0.839 to 0.999)), as well as in external validation cohort 1 (AUC 0.914 (95% CI 0.837 to 0.990)), and external validation cohort 2 (0.927 (95% CI 0.802 to 0.999)). The univariate Cox regression analysis revealed that the prediction signature of pathomics-driven ensemble model was a prognostic factor for progression-free survival in patients with gastric cancer who underwent immunotherapy (p

Abstract A novel recursive cascaded full‐resolution residual network (RCFRR‐Net) for abdominal four‐dimensional computed tomography (4D‐CT) image registration was proposed. The entire network was end‐to‐end and trained in the unsupervised approach, which meant that the deformation vector field, which presented the ground truth, was not needed during training. The network was designed by cascading three full‐resolution residual subnetworks with different architectures. The training loss consisted of the image similarity loss and the deformation vector field regularization loss, which were calculated based on the final warped image and the fixed image, allowing all cascades to be trained jointly and perform the progressive registration cooperatively. Extensive network testing was conducted using diverse datasets, including an internal 4D‐CT dataset, a public DIRLAB 4D‐CT dataset, and a 4D cone‐beam CT (4D‐CBCT) dataset. Compared with the iteration‐based demon method and two deep learning‐based methods (VoxelMorph and recursive cascaded network), the RCFRR‐Net achieved consistent and significant gains, which demonstrated that the proposed method had superior performance and generalization capability in medical image registration. The proposed RCFRR‐Net was a promising tool for various clinical applications.

Background Despite remarkable benefits have been provided by immune checkpoint inhibitors in gastric cancer (GC), predictions of treatment response and prognosis remain unsatisfactory, making identifying biomarkers desirable. The aim of this study was to develop and validate a CT imaging biomarker to predict the immunotherapy response in patients with GC and investigate the associated immune infiltration patterns.Methods This retrospective study included 294 GC patients who received anti-PD-1/PD-L1 immunotherapy from three independent medical centers between January 2017 and April 2022. A radiomics score (RS) was developed from the intratumoral and peritumoral features on pretreatment CT images to predict immunotherapy-related progression-free survival (irPFS). The performance of the RS was evaluated by the area under the time-dependent receiver operating characteristic curve (AUC). Multivariable Cox regression analysis was performed to construct predictive nomogram of irPFS. The C-index was used to determine the performance of the nomogram. Bulk RNA sequencing of tumors from 42 patients in The Cancer Genome Atlas was used to investigate the RS-associated immune infiltration patterns.Results Overall, 89 of 294 patients (median age, 57 years (IQR 48–66 years); 171 males) had an objective response to immunotherapy. The RS included 13 CT features that yielded AUCs of 12-month irPFS of 0.787, 0.810 and 0.785 in the training, internal validation, and external validation 1 cohorts, respectively, and an AUC of 24-month irPFS of 0.805 in the external validation 2 cohort. Patients with low RS had longer irPFS in each cohort (p

Recovering dropout-affected gene expression values is a challenging problem in bioinformatics. Here, the authors propose a data-driven framework, that first learns the underlying data distribution and then recovers the expression values by imposing a self-consistency on the expression matrix.

A chemical study of Aesculus wilsonii Rehd. (also called Suo Luo Zi) and the in vitro anti-inflammatory effects of the obtained compounds was conducted. Retrieving results through SciFinder showed that there were four unreported compounds, aeswilosides I–IV (1–4), along with fourteen known isolates (5–18). Their structures were elucidated by extensive spectroscopic methods such as UV, IR, NMR, [α]D, and MS spectra, as well as acid hydrolysis. Among the known ones, compounds 5, 6, 8–10, and 12–16 were obtained from the Aesculus genus for the first time; compounds 7, 11, 17, and 18 were first identified from this plant. The NMR data of 5 and 18 were reported first. The effects of 1–18 on the release of nitric oxide (NO) from lipopolysaccharide (LPS)-induced RAW264.7 cells were determined. The results showed that at concentrations of 10, 25, and 50 μM, the novel compounds, aeswilosides I (1) and IV (4), along with the known ones, 1-(2-methylbutyryl)phloroglucinyl-glucopyranoside (10) and pisuminic acid (15), displayed significant inhibitory effects on NO production in a concentration-dependent manner. It is worth mentioning that compound 10 showed the best NO inhibitory effect with a relative NO production of 88.1%, which was close to that of the positive drug dexamethasone. The Elisa experiment suggested that compounds 1, 4, 10, and 15 suppressed the release of TNF-α and IL-1β as well. In conclusion, this study enriches the spectra of compounds with potential anti-inflammatory effects in A. wilsonii and provides new references for the discovery of anti-inflammatory lead compounds, but further mechanistic research is still needed.