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Abstract Aim To investigate the status and influencing factors of health information literacy in patients with intermediate‐stage chronic kidney disease (CKD). Design A prospective clinical study. Methods We used a CKD health information literacy questionnaire to survey 130 patients with intermediate‐stage CKD and valuate their health needs and health knowledge. We performed the study in strict accordance with the Guidelines for Clinical Trial Protocols. We registered the study in the Chinese Clinical Trial Registration Center (registration No. ChiCTR2100053103; approval No. K56‐1). Results The overall health information literacy of CKD was relatively low. Low education level, advanced age and unemployment were influencing factors. The scores of assessment ability, literacy awareness, application ability, integration ability and CKD health knowledge reserve were relatively low. The generalized linear model showed that the older the men, the lower their health information literacy.

In this study, we explored the effects and principles of light-emitting diodes (LEDs) in preventing acute radiodermatitis (RD) in breast cancer patients. Twenty patients with breast cancer, who met the inclusion and exclusion criteria and had undergone modified radical mastectomy, were selected and administered standard preventive radiotherapy. Fifteen days after the first radiotherapy dose, the patients were randomly divided into a control group and an experimental group and subjected to additional LED exposure. Twenty-one days after LED illumination, the preventive effect of LED on RD was evaluated by statistically analyzing the RD scores of the two groups. The preventive effects were inferred by histological and gene expression analyses. The results showed that compared with the control group, the RD score of the experimental group did not change significantly, so that the prevention effect of LED was significant. Our results showed that LED exposure increased skin appendage (p=0.041), accelerated microvascular growth (p=0.007), and increased IL-10 and MMP-9 expression (p

Bladder cancer (BC) is one of the ten most common cancers worldwide with late detection and early age of diagnosis. There is abundant evidence that early detection and timely intervention can lead to a better prognosis of BC. Substantial evidence has indicated that microRNAs (miRNAs) are specific to different tumour types and are remarkably stable, indicating that serum miRNAs may serve as potential cancer diagnostic markers. This study aimed to identify suitable serum miRNAs to create a panel that can be used to diagnose primary BC. In this study, 18 miRNAs that were differentially expressed in BC were obtained from the PubMed or Gene Expression Omnibus database. Then, 18 BC-related-miRNAs were verified in screening and validation sets created using 56 (28 primary BC vs. 28 NCs) and 168 (84 primary BC vs. 84 NCs) serum samples, respectively. Quantitative reverse transcription-PCR (qRT-PCR) was performed to verify the identity of the differential miRNAs. A multi-miRNA panel with superior diagnostic performance was constructed. TCGA and KEGG databases were used to conduct the survival analysis and bioinformatics analysis, respectively. Six serum miRNAs (miR-221-5p, miR-181a-5p, miR-98-5p, miR-15a-5p, miR-222-3p, and miR-197-3p) were significantly aberrantly expressed in the BC patients, while four miRNAs from among them (miR-221-5p, miR-181a-5p, miR-15a-5p, miR-222-3p) were assembled into a panel that showed high diagnostic value (AUC = 0.875, 95% CI: 0.815 − 0.921; sensitivity: 82.14%; and specificity: 85.71%) based on the logistic regression analysis. The survival analysis showed that miR-181a-5p was closely associated with BC prognosis (Log-rank p-value < 0.05). The combination of the four miRNAs (miR-221-5p, miR-181a-5p, miR-15a-5p and miR-222-3p) may be a novel non-invasive serological biomarker for BC screening. Early detection and timely intervention can lead to a better prognosis of bladder cancer. This study aimed to identify suitable serum miRNAs to create a panel that can be used to diagnose primary bladder cancer. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Water and fertilizer integration technology can effectively improve nutrient utilization efficiency. However, the existing water and fertilizer machines have some shortcomings, such as huge cost, single fertilizer injection, need for cleaning water and so on, which hinder the development of water and fertilizer integration technology. Aiming at the problems of precise and low-cost compounding of compound fertilizer at the local farm, the water and fertilizer integrated intelligent irrigation and fertilization system were taken as the research object. In this research, new concept of an intelligent sensing system was proposed， and accurate proportioning system of water and fertilizer concentration was constructed and implemented. Firstly, a fast on-line method of intelligent sensing model of water and fertilizer was established based on a series of concentration gradient compound fertilizer solutions. The conductivity values of these formulated solutions were tested by contactless conductivity detection electrodes. Subsequently, the data analysis algorithms were discussed and compared to fit regression model. Based on the intelligent sensing model of water and fertilizer , the framework structure of in-situ intelligent sensing and accurate proportioning system of water and fertilizer concentration was designed, and the working principle of the system was also explained. The system proposed includs a first-level water and fertilizer concentration intelligent perception model building subsystem and a second-level water and fertilizer accurate proportioning subsystem. The first-level subsystem was designed as a portable device, which mainly included a precise pump for quantitative dosing, a large-range online conductivity sensor, a plastic bucket and supporting control and model building software. The second-level subsystem was designed as a dynamic and precise fertilizer distribution device. The effectiveness of the system was verified by three types of water intelligent fertilizer application so as to guide the in-situ water and fertilizer concentration ratio. The testing results showed that the second-order polynomial fitting curve under regularization conditions was the best model to express the relationship between the conductivity and the concentration of water and fertilizer, and the correlation coefficients R2 was higher than 0.999. Combined with the proportion of each index of compound fertilizer, the concentration of each index of compound fertilizer that the user cares about can be obtained according to this model. The results of three types of water intelligent fertilizer application showed that the conductivity of natural water had an effect on the water and fertilizer system, and the relative deviation was more than 0.1. The online water and fertilizer perception and ratio system proposed in this research realized the elimination of the interference of the local water conductivity on the accuracy of the ratio of water and fertilizer, and the accurate calculation of compound fertilizer was achieved through model calculation. This system has a simple structure and accurate ratio, low cost， and can be easily combined with the existing water and fertilizer integrated machine or artificial fertilizer system. The system could be widely used in facility agriculture, orchard cultivation and field cash crop cultivation, et al.

(1) Background: Vegetation is an important component of ecosystems. Investigating the spatio-temporal dynamic changes in vegetation in various Shaanxi Province regions is crucial for the preservation of the local ecological environment and sustainable development. (2) Methods: In this study, the KNDVI vegetation index over the 20-year period from 2003 to 2022 was calculated using MODIS satellite image data that was received from Google Earth Engine (GEE). Sen and MK trend analysis as well as partial correlation analysis were then utilized to examine the patterns in vegetation change in various Shaanxi Province regions. This paper selected meteorological factors, such as potential evapotranspiration (PET), precipitation (PRE), and temperature (TMP); human activity factors, such as land-use type and population density; and terrain factors, such as surface elevation, slope direction, and slope gradient, as the influencing factors for vegetation changes in the research area in order to analyze the driving forces of vegetation spatio-temporal changes. These factors were analyzed using a geo-detector. (3) Results: The vegetation in the research area presented a growth trend from 2003 to 2022, and the area of vegetation improvement was 189,756 km2, accounting for 92.15% of the total area. Among them, the area of significantly improved regions was 174,262 km2, accounting for 84.63% of the total area, and the area of slightly improved regions was 15,495 square kilometers, accounting for 7.52% of the total area. (4) Conclusions: The strengthening of bivariate factors and nonlinear enhancement were the main interaction types affecting vegetation changes. The combination of interaction factors affecting vegetation change in Shaanxi Province includes PRE ∩ PET as well as TMP ∩ PET. Therefore, climate conditions were the main driving force of KNDVI vegetation changes in Shaanxi Province. The data supported by this research are crucial for maintaining the region's natural ecosystem. [ABSTRACT FROM AUTHOR]

Achieving global food security requires an understanding of the current status and the future trends in the yield gap for cropping systems worldwide. The aim of this study was to scientifically understand the existing yield gap research, recognize the knowledge base and influential articles, and uncover key research themes and how these have evolved over the past three decades. Bibliometric methods were used to analyze articles related to the yield gap available in the largest scientific database, the Web of Science. A total of 6049 relevant articles published from 1993 to 2023 were numerically analyzed for patterns, trends, and clusters. The findings identified a few primary authors of widely cited publications. Geographically, the United States and China were the two major contributors to the publication pool, with articles from China mostly affiliated with the Chinese Academy of Sciences and China Agricultural University, while the United States had a more scattered distribution of affiliated institutions. The research on yield gaps primarily focused on biological factors, such as the effects of crop varieties, agronomic management, climate change, and soil conditions, with a limited exploration of social and economic factors. Within the auspices of the current food issues worldwide, this study provides a thorough view of the progress and key topics in crop yield gap research, contributing to the existing body of knowledge and providing guidance for researchers, policymakers, and stakeholders involved in agricultural productivity enhancement and sustainable food production. Amid the increasing trend in hunger worldwide over the past decade, we thus concluded that, by establishing appropriate benchmarks, re-prioritizing research needs, and focusing on transforming natural resources rather than inputs, the crop yield gap approach can be useful in terms of the clear inclusion of local contexts and socioeconomic constraints. [ABSTRACT FROM AUTHOR]