Nurses’ interventions to improve care quality in elderly care settings

The population is ageing in the world. The most significant change in the human history is the rapid increase in the elder people. According to China's national data, China is also aging. Family harmony but also about social stability. Increase in aging population impacts not only enjoy the old life is helping elderly to challenge we need to solve urgently at present. The aim of this study was to explore the nursing interventions that can improve the quality of elderly care nursing in nursing homes. The purpose of the study was to help Chinese nurses to provide better aged care services to the elderly and improve the quality life and well-being of elderly living in Chinese nursing homes. A literature review and inductive content analysis was the method used in this study. Data was searched and obtained from Cinahl and Medline. A total of nine articles were reviewed and analyzed. Nursing staff use the person-oriented nursing concept to take care of the elderly, and provide specialized nursing care through the good cooperation between the teams, as well as the use of professional knowledge and ability, can improve the quality of care for the elderly in nursing homes. The applicability of these results include the possibility to provide better quality nursing services, and offer the Chinese nurses tools to improve their own professional skills and improve the nursing quality care of the elderly

Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors

Over the past few decades, concentrations of carbon dioxide (CO2), a key greenhouse gas, have risen at a global rate of approximately 2 ppm/a. China is the largest CO2 emitter and is the principle contributor to the increase in global CO2 levels. Based on a satellite-retrieved atmospheric carbon dioxide column average dry air mixing ratio (XCO2) dataset, derived from the greenhouse gas observation satellite (GOSAT), this paper evaluates the spatial and temporal variations of XCO2 characteristics in China during 2009–2016. Moreover, the factors influencing changes in XCO2 were investigated. Results showed XCO2 concentrations in China increased at an average rate of 2.28 ppm/a, with significant annual seasonal variations of 6.78 ppm. The rate of change of XCO2 was greater in south China compared to other regions across China, with clear differences in seasonality. Seasonal variations in XCO2 concentrations across China were generally controlled by vegetation dynamics, characterized by the Normalized Difference Vegetation Index (NDVI). However, driving factors exhibited spatial variations. In particular, a distinct belt (northeast–southwest) with a significant negative correlation (r < −0.75) between XCO2 and NDVI was observed. Furthermore, in north China, human emissions were identified as the dominant influencing factor of total XCO2 variations (r > 0.65), with forest fires taking first place in southwest China (r > 0.47). Our results in this study can provide us with a potential way to better understand the spatiotemporal changes of CO2 concentration in China with NDVI, human activity and biomass burning, and could have an enlightening effect on slowing the growth of CO2 concentration in China

Gap-Filling of 8-Day Terra MODIS Daytime Land Surface Temperature in High-Latitude Cold Region with Generalized Additive Models (GAM)

Land surface temperature (LST) is a crucial parameter driving the dynamics of the thermal state on land surface. In high-latitude cold region, a long-term, stable LST product is of great importance in examining the distribution and degradation of permafrost under pressure of global warming. In this study, a generalized additive model (GAM) approach was developed to fill the missing pixels of the MODIS/Terra 8-day Land Surface Temperature (MODIS LST) daytime products with the ERA5 Land Skin Temperature (ERA5ST) dataset in a high-latitude watershed in Eurasia. Comparison at valid pixels revealed that the MODIS LST was 4.8–13.0 °C higher than ERA5ST, which varies with land covers and seasons. The GAM models fairly explained the LST differences between the two products from multiple covariates including satellite-extracted environmental variables (i.e., normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and normalized difference snow index (NDSI) as well as locational information. Considering the dramatic seasonal variation of vegetation and frequent snow in the cold region, the gap-filling was conducted in two seasons. The results revealed the root mean square errors (RMSE) of 2.7 °C and 3.4 °C between the valid MODIS LST and GAM-simulated LST data in the growing season and snowing season, respectively. By including the satellite-extracted land surface information in the GAM model, localized variations of land surface temperature that are often lost in the reanalysis data were effectively compensated. Specifically, land surface wetness (NDWI) was found to be the greatest contributor to explaining the differences between the two products. Vegetation (NDVI) was useful in the growing season and snow cover (NDSI) cannot be ignored in the snow season of the study region. The km-scale gap-filled MODIS LST products provide spatially and temporally continuous details that are useful for monitoring permafrost degradation in cold regions in scenarios of global climate change

High-Resolution Daily Emission Inventory of Biomass Burning in the Amur-Heilong River Basin Based on MODIS Fire Radiative Energy Data

Open biomass burning (OBB) is one of the major factors that influences the regional climate environment and surface vegetation landscape, and it significantly affects the regional carbon cycle process and atmospheric environment. The Amur-Heilong River Basin (ARB) is a fire-prone region in high-latitude boreal forests. In this study, we used fire radiative power (FRP) obtained from a Moderate-resolution Imaging Spectroradiometer (MODIS) to estimate OBB emissions from the ARB and established a long-term series (2003–2020) with a high spatiotemporal resolution and a daily 1 km emissions inventory. The results show that the annual average emissions of CO2, CO, CH4, NMHCs, NOx, NH3, SO2, BC, OC, PM2.5, and PM10 were estimated to be 153.57, 6.16, 0.21, 0.78, 0.28, 0.08, 0.06, 0.04, 0.39, 0.66, and 0.85 Tg/a, respectively. Taking CO2 as an example, grassland fire in the dry season (mainly in April and October) was the largest contributor (87.18 Tg/a), accounting for 56.77% of the total CO2 emissions from the ARB, followed by forest fire prone to occur in April–May (56.53 Tg/a, 36.81%) and crop fire during harvest season (9.86 Tg/a, 6.42%). Among the three countries in the ARB, Russia released the most total CO2 emissions (2227.04 Tg), much higher than those of China (338.41 Tg) and Mongolia (198.83 Tg). The major fire types were crop fires (40.73%) on the Chinese side and grass fires on the Russian (56.67%) and Mongolian (97.56%) sides. Over the past decade, OBB CO2 emissions have trended downward (−0.79 Tg/a) but crop burning has increased significantly (+0.81 Tg/a). Up to 83.7% of crop fires occurred in China (2010–2020), with a concentrated and southward trend. Comparisons with the Global Fire Emission Dataset (GFED4.1s), the Fire INventory from NCAR (FINNv2.2), and the Global Fire Assimilation System (GFASv1.2) showed that our newly established emission inventory was in good agreement with these three datasets in the ARB. However, this multi-year, daily 1 km high-resolution emission inventory has the advantages of detecting more small fire emissions that were overlooked by coarse-grid datasets. The methods described here can be used as an effective means of estimating greenhouse gas and aerosol emissions from biomass combustion

Predictive value of lesion morphology in rectal cancer based on MRI before surgery

Abstract Objective To explore the relationship of MRI morphology of primary rectal cancer with extramural vascular invasion (EMVI), metastasis and local recurrence. Materials and methods This retrospective study included 153 patients with rectal cancer. Imaging factors and histopathological index including nodular projection (NP), cord sign (CS) at primary tumor margin, irregular nodules (IN) of mesorectum, MRI-detected peritoneal reflection invasion (PRI), range of rectal wall invasion (RRWI), patterns and length of tumor growth, maximal extramural depth (EMD), histologically confirmed local node involvement (hLN), MRI T stage, MRI N stage, MRI-detected extramural vascular invasion (mEMVI) and histologically confirmed extramural vascular invasion (hEMVI) were evaluated. Determining the relationship between imaging factors and hEMVI, synchronous metastasis and local recurrence by univariate analysis and multivariable logistic regression, and a nomogram validated internally via Bootstrap self-sampling was constructed based on the latter. Results Thirty-eight cases of hEMVI, fourteen cases of synchronous metastasis and ten cases of local recurrence were observed among 52 NP cases. There were 50 cases of mEMVI with moderate consistency with hEMVI (Kappa = 0.614). NP, CS, EMD and mEMVI showed statistically significant differences in the negative and positive groups of hEMVI, synchronous metastasis, and local recurrence. Compared to patients with local mass growth, the rectal tumor with circular infiltration had been found to be at higher risk of synchronous metastasis and local recurrence (P < 0.05). NP and IN remained as significant predictors for hEMVI, and mEMVI was a predictor for synchronous metastasis, while PRI and mEMVI were predictors for local recurrences. The nomogram for predicting hEMVI demonstrated a C-index of 0.868, sensitivity of 86.0%, specificity of 79.6%, and accuracy of 81.7%. Conclusion NP, CS, IN, large EMD, mEMVI, and circular infiltration are significantly associated with several adverse prognostic indicators. The nomogram based on NP has good predictive performance for preoperative EMVI. mEMVI is a risk factor for synchronous metastasis. PRI and mEMVI are risk factors for local recurrence

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

Abstract Background The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM. Aim Our study presents a novel and significant contribution by developing an interpretable fusion model that effectively integrates both free‐text medical record data and structured laboratory data to predict LM in postoperative CRC patients. Methods We used a robust dataset of 1463 patients and leveraged state‐of‐the‐art natural language processing (NLP) and machine learning techniques to construct a two‐layer fusion framework that demonstrates superior predictive performance compared to single modal models. Our innovative two‐tier algorithm fuses the results from different data modalities, achieving balanced prediction results on test data and significantly enhancing the predictive ability of the model. To increase interpretability, we employed Shapley additive explanations to elucidate the contributions of free‐text clinical data and structured clinical data to the final model. Furthermore, we translated our findings into practical clinical applications by creating a novel NLP score‐based nomogram using the top 13 valid predictors identified in our study. Results The proposed fusion models demonstrated superior predictive performance with an accuracy of 80.8%, precision of 80.3%, recall of 80.5%, and an F1 score of 80.8% in predicting LMs. Conclusion This fusion model represents a notable advancement in predicting LMs for postoperative CRC patients, offering the potential to enhance patient outcomes and support clinical decision‐making

Altered Resting-State Functional Networks in Nondialysis Patients with Stage 5 Chronic Kidney Disease: A Graph–Theoretical Analysis

This study aimed to investigate the topological characteristics of the resting-state functional network and the underlying pathological mechanism in nondialysis patients with stage 5 chronic kidney disease (CKD5 ND). Eighty-five subjects (21 patients with CKD5 ND, 32 patients with CKD on maintenance hemodialysis (HD), and 32 healthy controls (HCs)) underwent laboratory examinations, neuropsychological tests, and brain magnetic resonance imaging. The topological characteristics of networks were compared with a graph–theoretical approach, and correlations between neuropsychological scores and network properties were analyzed. All participants exhibited networks with small-world attributes, and global topological attributes were impaired in both groups of patients with CKD 5 (ND and HD) compared with HCs (p p p p p p = 0.01) was positively correlated with MoCA scores. In conclusion, all CKD5 ND patients exhibited changes in functional network topological properties and were closely associated with mild cognitive impairment. More interestingly, the topological property changes in CKD5 ND patients were dominated by basal ganglia areas, which may be more helpful to understand and possibly reveal the underlying pathological mechanisms of cognitive impairment in CKD5 ND

DataSheet_1_Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer.xlsx

BackgroundMedical imaging is critical in clinical practice, and high value radiological reports can positively assist clinicians. However, there is a lack of methods for determining the value of reports.ObjectiveThe purpose of this study was to establish an ensemble learning classification model using natural language processing (NLP) applied to the Chinese free text of radiological reports to determine their value for liver lesion detection in patients with colorectal cancer (CRC).MethodsRadiological reports of upper abdominal computed tomography (CT) and magnetic resonance imaging (MRI) were divided into five categories according to the results of liver lesion detection in patients with CRC. The NLP methods including word segmentation, stop word removal, and n-gram language model establishment were applied for each dataset. Then, a word-bag model was built, high-frequency words were selected as features, and an ensemble learning classification model was constructed. Several machine learning methods were applied, including logistic regression (LR), random forest (RF), and so on. We compared the accuracy between priori choosing pertinent word strings and our machine language methodologies.ResultsThe dataset of 2790 patients included CT without contrast (10.2%), CT with/without contrast (73.3%), MRI without contrast (1.8%), and MRI with/without contrast (14.6%). The ensemble learning classification model determined the value of reports effectively, reaching 95.91% in the CT with/without contrast dataset using XGBoost. The logistic regression, random forest, and support vector machine also achieved good classification accuracy, reaching 95.89%, 95.04%, and 95.00% respectively. The results of XGBoost were visualized using a confusion matrix. The numbers of errors in categories I, II and V were very small. ELI5 was used to select important words for each category. Words such as “no abnormality”, “suggest”, “fatty liver”, and “transfer” showed a relatively large degree of positive correlation with classification accuracy. The accuracy based on string pattern search method model was lower than that of machine learning.ConclusionsThe learning classification model based on NLP was an effective tool for determining the value of radiological reports focused on liver lesions. The study made it possible to analyze the value of medical imaging examinations on a large scale.</p