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Abstract Background Since chronic kidney disease is highly insidious in the early stages, most diagnosed patients have already developed irreversible renal failure. There is a lack of effective implementation and standardization of health education management for patients with chronic kidney disease, and the impact of health education management on health information literacy is not yet known. Methods This randomized controlled clinical trial was conducted from March 2022 to March 2023. A total of 130 patients with chronic kidney disease stage 3–4 were selected from a tertiary hospital in Zhuhai via a convenience sampling method and were then randomly assigned to intervention and control groups. The Health Information Literacy Questionnaire for Chronic Kidney Disease was used to assess the health literacy of the patients at month 0 and month 12. In addition, the renal function-related indices of the patients were measured before and after the intervention. The study adhered to SPIRIT guidelines. Results There was no significant difference between the two groups in health information literacy, platelet count, estimated glomerular filtration rate, or albumin level. However, the intervention group showed a significant improvement in health behavioral status such as information acquisition, checkups, and physical activity. Subgroup analysis revealed that hemoglobin was significantly higher in non-employed patients than in the intervention group. There was a significant improvement in hemoglobin level after receiving health guidance in patients with intermediate education, and acquisition capacity and estimated glomerular filtration rate in the intervention group were lower than in the control group for patients with elementary education. Conclusion Receiving health coaching helped to improve self-management behaviors and hemoglobin levels of patients with stage 3–4 chronic kidney disease. Education level is an important factor influencing the effectiveness of health coaching techniques. Trial registration This study was prospectively registered in the China Clinical Trial Registry on November 10, 2021, with registration number: ChiCTR2100053103.

Abstract Histones are important chromatin-organizing proteins in eukaryotes and archaea. They form superhelical structures around which DNA is wrapped. Recent studies have shown that some archaea and bacteria contain alternative histones that exhibit different DNA binding properties, in addition to highly divergent sequences. However, the vast majority of these histones are identified in metagenomes and thus are difficult to study in vivo. The recent revolutionary breakthroughs in computational protein structure prediction by AlphaFold2 and RoseTTAfold allow for unprecedented insights into the potential function and structure of previously uncharacterized proteins. Here, we categorize the prokaryotic histone space into 17 distinct groups based on AlphaFold2 predictions. We identify a superfamily of histones, termed α3 histones, which are common in archaea and present in several bacteria. Importantly, we establish the existence of a large family of histones throughout archaea and in some bacteriophages that, instead of wrapping DNA, bridge DNA, thereby diverging from conventional nucleosomal histones.

Ferrocenyl conjugated oxazepine/quinoline derivatives were presented through the reaction of hexadehydro-Diels–Alder (HDDA) generated arynes with ferrocenyl oxazolines under mild conditions via ring-expanding or rearrangement processes. Water molecule participated in this unexpected rearrangement process to produce quinoline skeletons, and DFT calculations supported a ring-expanding and intramolecular hydrogen migration process for the formation of oxazepine derivatives. Two variants of this chemistry, expanded the reactivity between ferrocenyl conjugated substances and arynes, further providing an innovative approach for the synthesis of ferrocene derivatives.

Abstract Purpose Previous reports argue that preoperative sleep conditions of patients can influence the dosage of general anaesthesia drugs. Therefore, we aimed to investigate the dose-effect relationship of preoperative sleep disorders on the induction of general anaesthesia with remimazolam tosilate and calculate the Median effective (ED50) and 95% effective (ED95) dosages. Methods Included in our study were 56 patients who underwent laparoscopic cholecystectomy at our hospital. A separate group of 27 patients with sleep disorders (SD group) and 29 patients without sleep disorders (NSD group) using the Pittsburgh Sleep Quality Index (PSQI) were also included. According to the Dixon ‘up-and-down’ design, patients received remimazolam at preselected concentrations starting at 0.2 mg/kg. After the administration of remimazolam, loss of consciousness was observed. By observing whether consciousness disappeared within a minute, we adjusted the dose of remimazolam by 0.1 mg/kg (up and down) in the following patient. The Median effective dose (ED50), 95% effective dose (ED95), and 95% confidence interval (CI) of remimazolam for effective sedation were calculated. Results The ED50 of remimazolam was 0.226 mg/kg (95%CI 0.221–0.232 mg/kg) in the SD group and 0.191 mg/kg (95%CI, 0.183–0.199 mg/kg) in the NSD group. The ED95 of remimazolam was 0.237 mg/kg (95%CI 0.231–0.262 mg/kg) in the SD group and 0.209 mg/kg (95%CI 0.200–0.254 mg/kg) in the NSD group. Conclusions In the SD group, the ED50 and ED95 of remimazolam during anaesthesia induction were 0.226 and 0.237 mg/kg, respectively. The induction dose of remimazolam in the SD group was significantly higher than that in the NSD group.

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.