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Rosa roxburghii Tratt, a Rosaceae plant endemic to China, produces fruit with high nutritional and medicinal value. The effects of R. roxburghii must on the growth, nutrient composition, and antioxidant activity of Pleurotus ostreatus mycelia was investigated. We measured the mycelial growth rate, proximate composition, amino acid and crude polysaccharide content, and the antioxidant activity of the crude polysaccharides of P. ostreatus mycelia cultivated under different concentrations of R. roxburghii must (2%, 4%, and 8%, v/v). Low concentrations of R. roxburghii must (2% and 4%) promoted mycelial growth, while a high concentration (8%) inhibited mycelial growth. Low concentrations of R. roxburghii must had no significant effects on the soluble substances, fat, ash, and crude fiber in P. ostreatus mycelia, but significantly increased the crude protein and total amino acid contents (p < 0.05). The addition of R. roxburghii must at low concentrations significantly increased the crude polysaccharide content in mycelia (p < 0.05) but had no impact on the scavenging of hydroxyl radicals and 2,2-diphenyl-1-picrylhydrazyl (DPPH). Therefore, R. roxburghii must at low concentration can be used as a substrate for P. ostreatus cultivation to increase the protein and polysaccharide contents in mycelia. [ABSTRACT FROM AUTHOR]

Background: Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop and test an individualized machine learning model to assess risk factors and predict in-hospital mortality in in these patients., Methods: From January 2012 to December 2021, this study collected data on elderly patients with heart failure and hypertension from the Chongqing Medical University Medical Data Platform. Least absolute shrinkage and the selection operator was used for recognizing key clinical variables. The optimal predictive model was chosen among eight machine learning algorithms on the basis of area under curve. SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations was employed to interpret the outcome of the predictive model., Results: This study ultimately comprised 4647 elderly individuals with hypertension and heart failure. The Random Forest model was chosen with the highest area under curve for 0.850 (95% CI 0.789-0.897), high accuracy for 0.738, recall 0.837, specificity 0.734 and brier score 0.178. According to SHapley Additive exPlanations results, the most related factors for in-hospital mortality in elderly patients with heart failure and hypertension were urea, length of stay, neutrophils, albumin and high-density lipoprotein cholesterol., Conclusions: This study developed eight machine learning models to predict in-hospital mortality in elderly patients with hypertension as well as heart failure. Compared to other algorithms, the Random Forest model performed significantly better. Our study successfully predicted in-hospital mortality and identified the factors most associated with in-hospital mortality., Competing Interests: Declarations Conflict of interest The authors declare no competing interests. Ethical approval and consent to participate The Ethics Committee of Chong Qing Medical University approved the study with the approval number: 2024033(dated Apr.24.2024). Written informed consent for participation was not required for this study due to its retrospective design, and the study was undertaken in accordance with national legislation and institutional requirements. Consent for publication All authors gave consent for the publication of the article., (© 2024. The Author(s).)

Excessive nutrients in aquatic ecosystems are the main driving factors for eutrophication and water quality deterioration. However, the influence of nutrients in overlying water on sediment heavy metals is not well understood. In this study, the effects of nitrate nitrogen (NO 3 -N) addition and phosphate addition in the overlying water on the environmental behaviors of chromium (Cr), copper (Cu), and cadmium (Cd) in coastal river sediments were investigated. Fresh estuary sediments and synthetic saltwater were used in microcosm studies conducted for 13 d. To determine the biological effect, unsterilized and sterilized treatments were considered. The results showed that the diffusion of Cr and Cu was inhibited in the unsterilized treatments with increased NO 3 -N. However, under the NO 3 -N sterilized treatments, Cr and Cu concentrations in the overlying water increased. This was mostly related to changes in the microbial regulation of dissolved organic carbon and pH in the unsterilized treatments. Further, in the unsterilized treatments, NO 3 -N addition considerably increased the concentrations of the acid-soluble (Cr, Cu, and Cd increased by 5%-8%, 29%-41%, and 31%-42%, respectively) and oxidizable (Cr, Cu, and Cd increased by 10%, 5%, and 14%, respectively) fractions. Additionally, compared with that in the unsterilized treatments, Cu and Cd concentrations in P-3 treatments decreased by 7% and 63%, respectively. By producing stable metal ions, microorganisms reduced the amount of unstable heavy metals in the sediment and heavy metal concentration in the overlying water, by considerably enhancing the binding ability of phosphate and heavy metal ions. This study provides a theoretical basis for investigating the coupling mechanisms between heavy metals and nutrients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

As a special geographical location between rivers and oceans, coastal estuaries always face severe heavy metal contaminations, especially in semi-closed bay. In this study, the spatial distribution, chemical fraction, ecological risks, and potential sources of heavy metals (Pb, Cr, Cu, As, Cd, Zn, and Ni) in surface sediments and sediment cores were investigated in Dingzi Bay, Shandong Peninsula. The I geo values and modified potential ecological risk index (MRI) indicated that Cd and As presented high environmental risks in the surface and sediment cores. The high concentration sites were mainly located in the middle and the mouth of the Dingzi Bay. The source identification indicated that most heavy metals in surface sediments originated from shipping and aquaculture, while As and Ni from industrial pollution. The correlation coefficients showed that high proportion of fine particle, TN, TOC, TP, and AVS in surface sediments could significantly elevate the bioavailability of most heavy metals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)