Restricted reaction of layered double hydroxide nanoparticles with phosphate in a confined microsphere space.

Over the past decades, considerable efforts have been made to find useful solutions for phosphate pollution control. The state transition of nanomaterials from freely dispersed to encapsulated provides a realizable route for their application in phosphate elimination. The separation convenience offered by encapsulation has been widely recognized, however, the unique binding mode of nanostructures and phosphate in the confined space remains unclear, limiting its further development. Here, carboxymethyl cellulose (CMC) microspheres were used as hosts to deploy layered double hydroxide (LDH) nanoparticles. On this basis, we described an attempt to explore the adsorption behavior of LDH and phosphate in the microsphere space. Compared to their freely dispersed analogues, LDH particles exhibited higher structural stability, wider pH adaptability, and better phosphate selectivity when spatially confined in the CMC microsphere. Nevertheless, the kinetic process was severely inhibited by three orders of magnitude. Besides, the saturated phosphate adsorption capacity was also reduced to 74.6 % of the freely dispersed system. A combinative characterization revealed that the highly electronegative CMC host not only causes electrostatic repulsion to phosphate, but also extracts the electron density of the metal center of LDH, weakening its ability to act as a Lewis acid site for phosphate binding. Meanwhile, the microsphere encapsulation also hinders the ion exchange function of interlayer anions and phosphate. This study offers an objective insight into the reaction of LDH and phosphate in the confined microsphere space, which may contribute to the advanced design of encapsulation strategies for nanoparticles.

Clustering of lifestyle behaviours and analysis of their associations with MAFLD: a cross-sectional study of 196,515 individuals in China.

BACKGROUND: The aggregation of lifestyle behaviours and their association with metabolic-associated fatty liver disease (MAFLD) remain unclear. We identified lifestyle patterns and investigated their association with the risk of developing MAFLD in a sample of Chinese adults who underwent annual physical examinations. METHODS: Annual physical examination data of Chinese adults from January 2016 to December 2020 were used in this study. We created a scoring system for lifestyle items combining a statistical method (multivariate analysis of variance) and clinical expertise (Delphi method). Subsequently, principal component analysis and two-step cluster analysis were implemented to derive the lifestyle patterns of men and women. Binary logistic regression analysis was used to explore the prevalence risk of MAFLD among lifestyle patterns stratified by sex. RESULTS: A total of 196,515 subjects were included in the analysis. Based on the defined lifestyle scoring system, nine and four lifestyle patterns were identified for men and women, respectively, which included "healthy or unhealthy" patterns and mixed patterns containing a combination of healthy and risky lifestyle behaviours. This study showed that subjects with an unhealthy or mixed pattern had a significantly higher risk of developing MAFLD than subjects with a relatively healthy pattern, especially among men. CONCLUSIONS: Clusters of unfavourable behaviours are more prominent in men than in women. Lifestyle patterns, as important factors influencing the development of MAFLD, show significant sex differences in the risk of MAFLD. There is a strong need for future research to develop targeted MAFLD interventions based on the identified behavioural clusters by sex stratification.

Impact renaming non-alcoholic fatty liver disease to metabolic associated fatty liver disease in prevalence, characteristics and risk factors.

BACKGROUND: Recently, a group of hepatologists proposed to rename non-alcoholic fatty liver disease (NAFLD) as metabolic associated fatty liver disease (MAFLD) with modified diagnostic criteria. It is important to note, however, that there are some differences between the diagnostic criteria used for NAFLD and MAFLD. Since the research on MAFLD is just beginning, however, evidence on its incidence and prevalence in the general population and in specific subpopulations remains limited. AIM: To assess epidemiology of fatty liver in new definition and compare MAFLD with NAFLD. Exploring risk factors of MAFLD individuals. METHODS: This was a retrospective, cross-sectional study. A total of 85242 adults were selected from the Chinese health management database in 2017-2022. The data of general information, laboratory indicators, lifestyle management and psychological status were obtained. MAFLD was diagnosed as ultrasound diagnosis of fatty liver and at least one between these three conditions: Overweight/obesity, type 2 diabetes mellitus (T2DM) or metabolic dysregulation. Metabolic factors were not considered in NAFLD diagnosis standard. The clinical characteristics of MAFLD and NAFLD were analysed using descriptive statistics. Continuous variables normally distributed were expressed as means ± SD. Categorical variables were expressed as frequencies and proportions. Binary logistic regression was used to determine risk factors of the MAFLD. RESULTS: The prevalence of MAFLD and NAFLD was 40.5% and 31.0%, respectively. The MAFLD or NAFLD population is more likely to be older (M: 47.19 ± 10.82 vs 43.43 ± 11.96; N: 47.72 ± 11.17 vs 43.71 ± 11.66), male (M: 77.21% vs 44.43%; N: 67.90% vs 53.12%) and high body mass index (M: 26.79 ± 2.69 vs 22.44 ± 2.48; N: 26.29 ± 2.84 vs 23.29 ± 3.12) than the non-MAFLD or non-MAFLD population. In multivariate analysis, general information (e.g., ≥ 2 metabolic abnormalities OR = 3.38, (95%CI: 2.99-3.81), P < 0.001; diastolic blood pressure OR = 1.01, (95%CI: 1.00-1.01), P = 0.002), laboratory results [e.g.,total bilirubin (TBIL) OR = 0.98, (95%CI: 0.98-0.99), P < 0.001; serum uric acid(SUA) OR = 1.01, (95%CI: 1.01-1.01), P < 0.001], and lifestyle factors [e.g., drink beverage OR = 0.32, (95%CI: 0.17-0.63), P = 0.001] were influence factors for MAFLD. Our study results offer new insight into potential risk factors associated with fatty liver disease, including SUA, TBIL and creatinine, all of which are related to chronic renal disease (CKD). CONCLUSION: MAFLD is more prevalent than NAFLD, with two-fifths of individuals meeting the diagnosis criteria. MAFLD and NAFLD populations have different clinical characteristics. CKD may be related with MAFLD.

Development and validation of a nomogram for predicting metabolic-associated fatty liver disease in the Chinese physical examination population.

AIM: We aim to develop and validate a nomogram including readily available clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in the Chinese physical examination population. METHODS: The annual physical examination data of Chinese adults from 2016 to 2020 were retrospectively analyzed. We extracted the clinical data of 138 664 subjects and randomized participants to the development and validation groups (7:3). Significant predictors associated with MAFLD were identified by using univariate and random forest analyses, and a nomogram was constructed to predict the risk of MAFLD based on a Lasso logistic model. Receiver operating characteristic curve analysis, calibration curves, and decision curve analysis were used to verify the discrimination, calibration, and clinical practicability of the nomogram, respectively. RESULTS: Ten variables were selected to establish the nomogram for predicting MAFLD risk: sex, age, waist circumference (WC), uric acid (UA), body mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting plasma glucose (FPG), triglycerides (TG), and alanine aminotransferase (ALT). The nomogram built on the nonoverfitting multivariable model showed good prediction of discrimination (AUC 0.914, 95% CI: 0.911-0.917), calibration, and clinical utility. CONCLUSIONS: This nomogram can be used as a quick screening tool to assess MAFLD risk and identify individuals at high risk of MAFLD, thus contributing to the improved management of MAFLD.

Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients.

Objective: Hospitalized cancer patients are at high risk of venous thromboembolism (VTE). However, no predictive model has been specifically developed for this population. Machine learning (ML) is advantageous for model development. This study was aimed at developing predictive models using three different ML algorithms and logistic regression for VTE risk among hospitalized cancer patients and comparing their predictive performance. Methods: A retrospective case-control study was conducted on hospitalized cancer patients at Hunan Cancer Hospital, China, between October 1, 2021, and February 30, 2022. Patients diagnosed with vein thrombosis before or after admission were excluded. Patient, tumor, treatment, and laboratory indicator information was obtained from the hospital information system. The data were randomly split into distributions of 80% for training and 20% for testing. Logistic regression and three ML algorithms-the support vector machine, random forest, and extreme gradient boosting (XGBoost)-were used to develop the models. Model performance was compared using F1, G-mean, area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall rate, and specificity. Feature rankings were achieved based on the permutation scores of the selected features in the optimal model. Results: A total of 1100 patients (mean [SD] age, 54.75 [11.08] years; 485 [44.09%] male) were included in this study. There were 340 patients (30.9%) in the VTE group. The XGBoost model achieved the best performance with the following evaluation metrics: F1 (0.750), G-mean (0.816), AUROC (0.818), accuracy (0.845), precision (0.750), recall rate (0.750), and specificity (0.888). D-dimer level, diabetes, hypertension, pleural metastasis, and hematological malignancies were identified as the five most significant features of the XGBoost model. Conclusions: Four predictive models were developed using ML algorithms. The XGBoost model was the optimal predictive model compared with the other three models. This study indicates that ML may play an important role in VTE risk estimation among hospitalized patients with cancer and provides a reference for thromboprophylaxis.

Chemotherapy-Induced Nausea and Vomiting in Breast Cancer Patients: A Multicenter Prospective Observational Study.

OBJECTIVE: This study aimed to assess the occurrence of chemotherapy-induced nausea and vomiting (CINV) in acute phase (24 h after chemotherapy) and delayed phase (2-5 days after chemotherapy) after standard antiemetic therapy and to explore the risk factors of CINV in the acute and delayed phases. METHODS: This prospective and observational study analyzed the data of 400 breast cancer patients scheduled for chemotherapy in two hospitals. The self-report survey was developed to assess the occurrence of CINV and their associated factors. On day 2 and day 6 of chemotherapy, CINV was evaluated by the Multinational Association of Supportive Care in Cancer Antiemetic Tool (MAT). The incidence of acute and delayed CINV was expressed as frequency and percentage. RESULTS: Among 400 patients, 29.8% and 23.5% experienced acute and delayed CINV, respectively. Logistic regression analysis showed that the risk factors associated with acute CINV included pain/insomnia, history of CINV, and highly emetogenic chemotherapy. The history of motion sickness (MS), history of CINV, number of chemotherapy cycles completed, and the incidence of acute CINV were significant risk factors for delayed CINV (all P < 0.05). CONCLUSIONS: The results of this study are helpful for nurses to identify high-risk patients with CINV, formulate effective treatment plans, and reduce the incidence of CINV.

Nomogram for Predicting Chemotherapy-Induced Nausea and Vomiting for Breast Cancer Patients.

Chemotherapy-induced nausea and vomiting (CINV) is a common side effect of cancer treatment. The factors influencing CINV in breast cancer patients remain unclear. In this study, we developed a nomogram for predicting the occurrence of CINV in this group using prospective clinical data. We pooled data from multiple studies which focused on the emetogenic chemotherapy. Then, we collected 334 breast cancer patients at Hunan Cancer Hospital (training set) to analyze the demographic and clinical variables. Using multivariate logistic regression, we identified the five significant factors that were associated with CINV: history of CINV, chemotherapy regimen, chemotherapy cycle, metastasis, and symptoms of distress. Then, we construct a prediction nomogram. The external validation set comprised an additional 66 patients. The reliability of the nomogram was assessed by bootstrap resampling. The C-index was 0.78 (95% confidence interval [CI], 0.73-0.85) for the training set and 0.74 (95% CI, 0.62-0.85) for the validation set. Calibration curves showed good concordance between predicted and actual occurrence of CINV. In conclusions, our nomogram model can reliably predict the occurrence of CINV in breast cancer patients based on five significant variables, which might be useful in clinical decision-making.