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As the cavitation is a long - term disturbing problem in hydropower industry, carrying out anti - cavitation research for hydropower machinery is of great significance for promoting hydropower development and scientific carbon reduction. In this work, the cavitation mechanism and prevention strategy for hydropower mechinery were briefly explained. Based on this, the current research status and application of anti - cavitation coating materials were emphatically discussed. Finally, the current problems and the future research direction of new anti - cavitation materials were prospected.

Dendritic cells (DCs) are crucial in initiating and shaping both innate and adaptive immune responses. Clinical studies and experimental models have highlighted their significant involvement in various autoimmune diseases, positioning them as promising therapeutic targets. Nicotinamide (NAM), a form of vitamin B3, with its anti-inflammatory properties, has been suggested, while the involvement of NAM in DCs regulation remains elusive. Here, through analyzing publicly available databases, we observe substantial alterations in NAM levels and NAM metabolic pathways during DCs activation. Furthermore, we discover that NAM, but not Nicotinamide Mononucleotide (NMN), significantly inhibits DCs over-activation in vitro and in vivo. The suppression of DCs hyperactivation effectively alleviates symptoms of psoriasis. Mechanistically, NAM impairs DCs activation through a Poly (ADP-ribose) polymerases (PARPs)-NF-κB dependent manner. Notably, phosphoribosyl transferase (NAMPT) and PARPs are significantly upregulated in lipopolysaccharide (LPS)-stimulated DCs and psoriasis patients; elevated NAMPT and PARPs expression in psoriasis patients correlates with higher psoriasis area and severity index (PASI) scores. In summary, our findings underscore the pivotal role of NAM in modulating DCs functions and autoimmune disorders. Targeting the NAMPT-PARP axis emerges as a promising therapeutic approach for DC-related diseases. [ABSTRACT FROM AUTHOR]

To explore the effects of “hospital–community integrated transitional care” model on quality of life in patients with chronic obstructive pulmonary disease (COPD).

The common method for the determination of Au in geological samples is by Atomic Absorption Spectrometry (AAS) and Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) after enrichment by polyurethane foam (PUF). Compared with activated carbon, PUF has a good selectivity but has a low absorption capacity. PUF loaded by different extraction agents or modified with different functional groups can improve the absorption capacity. PUF was treated with hydrochloric acid to prepare PUF-NH2. Infrared Spectrum and Scanned Electron Microscope results show the peak of PUF-NH2 shows obvious red shift, and the amount of amino group increases obviously. Macromolecule of PUF-NH2 shows obvious fracture. The amino group after hydrolysis is reduced, which is easy to achieve in an oxide-reduction reaction with Au ions during the absorption of Au to form Au nanoparticles. Modified PUF-NH2 achieved absorption capacity of 96 mg/g, which is an 8-fold improvement compared to PUF. Using PUF-NH2 to enrich Au in geological samples, the samples were first ashed and then digested by 50% aqua regia. Gold was determined by ICP-OES. The recovery by standard-addition ranged from 95.0% to 105.0% and the detection limit was 0.15 μg/g. The experimental results show that the enrichment degree and analytical sensitivity are improved by using PUF-NH2, which is suitable for the determination Au in low grade ores.

Aims: To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients. Method: This was a retrospective study of perioperative medical data from patients undergoing non‐cardiac and non‐neurology surgery over 65 years old from January 2014 to August 2019. Forty‐six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC‐ROC), sensitivity, specificity, and precision. Results: In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765–0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%. Conclusions: The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model. [ABSTRACT FROM AUTHOR]

Abstract: We report in this study the synthesis, crystal culture, and single-crystal X-ray crystallography of two new kinds of double stilbene, which were readily prepared from trinitro-p-xylene and p-tolualdehyde in [...]