Assessment of cytotoxicity, acute, subacute toxicities and antioxidant activities (in vitro) of Sanghuangporus vaninii crude polysaccharide.

ETHNOPHARMACOLOGY RELEVANCE: Sanghuangporus vaninii (S. vaninii), as a traditional large medicinal fungus, has a history of more than 2000 years in Chinese history and has been widely used to treat female diseases such as vaginal discharge, amenorrhea, and uterine bleeding, and recent pharmacological studies have also found that it has antioxidant, anti-inflammatory, and anti-tumor physiological activity, which has received more and more attention. AIM OF THE STUDY: The objective was to evaluate cytotoxicity and the acute, subacute toxicity, and in vitro antioxidant activity of S. vaninii crude polysaccharide (SVP). MATERIALS AND METHODS: The monosaccharide composition of SVP was determined by HPLC (high-performance liquid chromatography). The cytotoxicity of different concentrations of SVP on three types of cells (HT-22, Kupffer macrophages, HEK293) was assessed using CCk-8. The acute toxicity in vivo was evaluated for 14 days after the administration of SVP (2500,5000, or 10,000 mg/mL). For the evaluation of subacute toxicity, mice were daily treated for 28 days with SVP (2500,5000, or 10,000 mg/mL). In addition, DPPH, hydroxyl radical, and superoxide anion radical were used to evaluate the in vitro antioxidant activity of SVP. RESULTS: SVP was not toxic in all three cell lines tested. In vitro antioxidant tests on the extracts showed that SVP possessed a strong antioxidant capacity in vitro. In the acute study, the no-observed-adverse-effect level (NOAEL) in male and female rats was 10，000 mg/kg body weight. There were also no deaths or severe toxicity associated with SVP in subacute studies. However, SVP treatment had a decreasing effect on body weight in mice of both sexes (2500, 5000, and 10000 mg/kg). At doses (5000 and 10,000 mg/kg), SVP had a reduced effect on food intake in both male and female mice. In addition, there were significant effects on organ coefficients of the liver, lung, and kidney. Hematological analysis showed significantly lower LYM (%) values in mice of both sexes, with significantly lower MCH (pg) values obtained in males (5000 mg/kg and 10000 mg/kg) and higher GRAN (%) values in females. In addition, the RDW-SD (fL) values were significantly lower in the male mice given the highest dose. Biochemical tests showed that there were no significant changes in ALT, AST, TP, and Cr levels after SVP treatment. In histopathological analysis, mild liver toxicity was observed in both female mice treated with 10,000 mg/kg SVP. CONCLUSION: The extract of SVP showed a predominance of polysaccharide compounds, with non-toxic action in vivo. Our approach revealed SVP on the chemical composition and suggests a high margin of safety in the popular use of medicinal fungi. In conclusion, our results suggest that SVP is safe, and can be used as health care products and food.

Artificial Intelligence-Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study.

BACKGROUND: Surgical site infection (SSI) is one of the most common types of health care-associated infections. It increases mortality, prolongs hospital length of stay, and raises health care costs. Many institutions developed risk assessment models for SSI to help surgeons preoperatively identify high-risk patients and guide clinical intervention. However, most of these models had low accuracies. OBJECTIVE: We aimed to provide a solution in the form of an Artificial intelligence-based Multimodal Risk Assessment Model for Surgical site infection (AMRAMS) for inpatients undergoing operations, using routinely collected clinical data. We internally and externally validated the discriminations of the models, which combined various machine learning and natural language processing techniques, and compared them with the National Nosocomial Infections Surveillance (NNIS) risk index. METHODS: We retrieved inpatient records between January 1, 2014, and June 30, 2019, from the electronic medical record (EMR) system of Rui Jin Hospital, Luwan Branch, Shanghai, China. We used data from before July 1, 2018, as the development set for internal validation and the remaining data as the test set for external validation. We included patient demographics, preoperative lab results, and free-text preoperative notes as our features. We used word-embedding techniques to encode text information, and we trained the LASSO (least absolute shrinkage and selection operator) model, random forest model, gradient boosting decision tree (GBDT) model, convolutional neural network (CNN) model, and self-attention network model using the combined data. Surgeons manually scored the NNIS risk index values. RESULTS: For internal bootstrapping validation, CNN yielded the highest mean area under the receiver operating characteristic curve (AUROC) of 0.889 (95% CI 0.886-0.892), and the paired-sample t test revealed statistically significant advantages as compared with other models (P<.001). The self-attention network yielded the second-highest mean AUROC of 0.882 (95% CI 0.878-0.886), but the AUROC was only numerically higher than the AUROC of the third-best model, GBDT with text embeddings (mean AUROC 0.881, 95% CI 0.878-0.884, P=.47). The AUROCs of LASSO, random forest, and GBDT models using text embeddings were statistically higher than the AUROCs of models not using text embeddings (P<.001). For external validation, the self-attention network yielded the highest AUROC of 0.879. CNN was the second-best model (AUROC 0.878), and GBDT with text embeddings was the third-best model (AUROC 0.872). The NNIS risk index scored by surgeons had an AUROC of 0.651. CONCLUSIONS: Our AMRAMS based on EMR data and deep learning methods-CNN and self-attention network-had significant advantages in terms of accuracy compared with other conventional machine learning methods and the NNIS risk index. Moreover, the semantic embeddings of preoperative notes improved the model performance further. Our models could replace the NNIS risk index to provide personalized guidance for the preoperative intervention of SSIs. Through this case, we offered an easy-to-implement solution for building multimodal RAMs for other similar scenarios.