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Abstract Objective This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). Methods This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). Results A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. Conclusion The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

Objective: This study aims to develop and validate an innovative radiopathomics model that combines radiomics and pathomics features to effectively differentiate between stages I-II and stage III gastric cancer (pathological staging). Methods: Our study included 200 patients with well-defined stages of gastric cancer divided into a training cohort (n = 140) and a test cohort (n = 60). Radiomics features were extracted from contrast-enhanced CT images using PyRadiomics, while pathomics features were obtained from whole slide images of pathological specimens through a fine-tuned deep learning model (ResNet-18). After rigorous feature dimensionality reduction and selection, we constructed radiomics models (SVM_rad, LR_rad, and MLP_rad) and pathomics models (SVM_path, LR_path, and MLP_path) utilizing support vector machine (SVM), logistic regression (LR), and multilayer perceptron (MLP) algorithms. The optimal radiomics and pathomics models were chosen based on comprehensive evaluation criteria such as ROC curves, Hosmer‒Lemeshow tests, and calibration curve tests. Feature patterns extracted from the best-performing radiomics model (MLP_rad) and pathomics model (SVM_rad) were integrated to create a powerful radiopathomics nomogram. Results: From a pool of 1834 radiomics features extracted from CT images, 14 were selected to construct radiomics models. Among these, the MLP_rad model exhibited the most robust predictive performance (AUC, training cohort: 0.843; test cohort: 0.797). Likewise, 10 pathomics features were chosen from 512 extracted from whole slide images to build pathomics models, with the SVM_path model demonstrating the highest predictive efficiency (AUC, training cohort: 0.937; test cohort: 0.792). The combined radiopathomics nomogram model exhibited optimal discriminative ability (AUC, training cohort: 0.951; test cohort: 0.837), as confirmed by decision curve analysis (DCA), which indicated superior clinical effectiveness. Conclusion: This study presents a cutting-edge radiopathomics nomogram model designed to predict pathological staging in gastric cancer, distinguishing between stages I-II and stage III. Our research leverages preoperative CT images and histopathological slides to forecast gastric cancer staging accurately, potentially facilitating the estimation of staging before radical gastric cancer surgery in the future.

AbstractAim:We have investigated the effects of lysophosphatidylcholine (LPC), a product of lipid peroxidation, on Aβ1–42-induced SH-SY5Y cell apoptosis.Methods:The viability of cultured SH-SY5Y cells was measured using a CCK-8 kit. Apoptosis was determined by Chip-based flow cytometric assay. The mRNA transcription of Bcl-2, Bax, and caspase-3 were detected by using reverse transcription and real-time quantitative PCR and the protein levels of Bax and caspase-3 were analyzed by Western blotting. The cytosolic calcium concentration of SH-SY5Y cells was tested by calcium influx assay. G2A expression in SH-SY5Y cells was silenced by small interfering RNA.Results:Long-term exposure of SH-SY5Y cells to LPC augmented the neurotoxicity of Aβ1–42. Furthermore, after LPC treatment, the Bax/Bcl-xL ratio and the expression levels, as well as the activity of caspase-3 were, elevated, whereas the expression level of TRAF1 was reduced. Because LPC was reported to be a specific ligand for the orphan G-protein coupled receptor, G2A, we investigated LPC-mediated changes in calcium levels in SH-SY5Y cells. Our results demonstrated that LPC can enhance the Aβ1–42-induced elevation of intracellular calcium. Interestingly, Aβ1–42 significantly increased the expression of G2A in SH-SY5Y cells, whereas knockdown of G2A using siRNA reduced the effects of LPC on Aβ1–42-induced neurotoxicity.Conclusion:The effects of LPC on Aβ1–42-induced apoptosis may occur through the signal pathways of the orphan G-protein coupled receptor.Acta Pharmacologica Sinica (2009) 30: 388–395; doi: 10.1038/aps.2009.25 [ABSTRACT FROM AUTHOR]

Interleukin (IL)-2 is a pharmacologically important cytokine secreted byT-lymphocytes. Recombinant IL-2 (rIL-2) has been modified and producedin many systems. Mass production of rIL-2 is the prerequisite for its wideapplication. Using a site-directed mutagenesis strategy, we first generated agene coding for a new type of mutant of human IL-2 (MhIL-2), in which wereplaced the cysteine-125 in human IL-2 with alanine, the leucine-18 withmethionine, and the leucine-19 with serine. Then we investigated the possibilityof its production of MhIL-2 in a Pichia pastoris system. High-levelsecreted expression of MhIL-2 was achieved by methanol induction. Whenpurified with ultrafiltration, cation-exchange chromatography, and SephadexG100 gel filtration, about 100 mg of MhIL-2 with high purity was obtainedfrom 1 L of ferment supernatant. Biologic activity assay revealed that thepurified recombinant protein displayed increased activity on proliferation ofIL-2-dependent CTLL-2 cells. These results suggest that MhIL-2 is an improvedIL-2 mutant that might hold great promise for clinical use, and that P. pastorisis an excellent system for the mass production of biologically active hIL-2. [ABSTRACT FROM AUTHOR]

Aim: To establish a cell-based assay to screen human dopamine D1 and D5 recep-tor agonists against compounds from a natural product compound library. Methods: Synthetic responsive elements 6×cAMP response elements (CRE) and a mini promoter containing a TATA box were inserted into the pGL3 basic vector to generate the reporter gene construct pCRE/TA/Luci. CHO cells were co-trans-fected with the reporter gene construct and human D1 or D5 receptor cDNA in mammalian expression vectors. Stable cell lines were established for agonist screening. A natural product compound library from over 300 herbs has been established. The extracts from these herbs were used for human D1 and D5 receptor agonist screenings. Results: A number of extracts were identified that activated both D1 and D5 receptors. One of the herb extracts, SBG492, demon-strated distinct pharmacological characteristics with human D1 and D5 receptors. The EC50 values of SBG492 were 342.7 μg/mL for the D1 receptor and 31.7 μg/mL for the D5 receptor. Conclusion: We have established a cell-based assay for high-throughput drug screening to identify D1-like receptor agonists from natural products. Several extracts that can active D1-like receptors were discovered. These compounds could be useful tools for studies on the functions of these receptors in the brain and could potentially be developed into therapeutic drugs for the treatment of central nervous system diseases. [ABSTRACT FROM AUTHOR]