Health status of the population in Naqu, Tibet and its latent class analysis: a cross-sectional survey

BackgroundThrough a survey and analysis of the population’s present state of health, it is possible to give data support for improving the health status of inhabitants in Naqu, Tibet. Additionally, it is possible to provide specific recommendations for the development of medical and healthcare facilities in Tibet.MethodsThe health scores of the participants were based on their responses to the four main sections of the questionnaire: dietary habits, living habits, health knowledge, and clinical disease history, and the variability of health status among groups with different characteristics was analyzed based on the scores. The four major sections were used to create classes of participants using latent class analysis (LCA). Using logistic regression, the factors influencing the classification of latent classes of health status were investigated.ResultsA total of 995 residents from 10 counties in Naqu were selected as the study subjects. And their demographic characteristics were described. The mean health score of residents after standardization was 81.59 ± 4.68. With the exception of gender, health scores differed between groups by age, education level, different occupations, marital status, and monthly income. The health status in Naqu, Tibet, was divided into two groups (entropy = 0.29, BLRT = 0.001, LMRT = 0.001) defined as the “good health group” and the “general health group.” A monthly income of more than ¥5000 adverse to good health in Naqu, Tibet.DiscussionSingle, well-educated young adults in Naqu, Tibet, have outstanding health. The vast majority of people in Tibet’s Naqu region were in good health. Furthermore, the population’s latent health status was divided into two classes, each with good dietary and living habits choices, low health knowledge, and a history of several clinical diseases. Univariate and multivariate logistic regression analysis showed that monthly income more than ¥5000 was an independent risk factor for poor health status

Ductility Variation and Improvement of Strain-Hardening Cementitious Composites in Structural Utilization

Strain-hardening cementitious composite (SHCC) has the obvious advantages of excellent material properties such as its high tensile and compressive strengths, high tensile strain capacity, and excellent durability against multi-cracking performance with very fine crack widths. In particular, the multi-cracking performance of SHCC during structural utilization is obviously reduced compared to that of SHCC in uniaxial tension tests using dumbbell-shaped specimens of small size. The corresponding tensile strain capacity of SHCC during structural utilization is, thus, significantly decreased compared to that of SHCC in uniaxial tension tests. However, the reduction in the ductility of SHCC during structural utilization has not been sufficiently understood, and further study is required. This paper presents an experimental investigation into the ductility variation of flexural-failed and shear-failed SHCC members as well as the ductility improvement of SHCC members with steel reinforcement compared with that of SHCC in uniaxial tension tests using small-sized specimens. This study focuses on not only the decrease in the crack elongation performance of the SHCC material during structural utilization but also the increase in the crack elongation performance of SHCC members with steel reinforcement. The results demonstrate that the crack elongation performance of flexural-failed and shear-failed SHCC members is significantly reduced compared to that of SHCC in the uniaxial tension tests. Moreover, it was confirmed that steel reinforcement can effectively improve the SHCC member, increasing the strain-hardening capacity and multi-cracking performance. The load-carrying capacity of the flexural-failed SHCC member with steel reinforcement seemed to increase linearly with an increase in the reinforcement ratio, accompanied by an increase in the distribution of multiple fine cracks in the flexural-failed SHCC member with steel reinforcement

Risk factor analysis and nomogram establishment and verification of brain astrocytoma patients based on SEER database

Abstract Astrocytoma is a common brain tumor that can occur in any part of the central nervous system. This tumor is extremely harmful to patients, and there are no clear studies on the risk factors for astrocytoma of the brain. This study was conducted based on the SEER database to determine the risk factors affecting the survival of patients with astrocytoma of the brain. Patients diagnosed with brain astrocytoma in the SEER database from 2004 to 2015 were screened by inclusion exclusion criteria. Final screened brain astrocytoma patients were classified into low grade and high grade according to WHO classification. The risk factors affecting the survival of patients with low-grade and high-grade brain astrocytoma were analyzed by univariate Kaplan–Meier curves and log-rank tests, individually. Secondly, the data were randomly divided into training set and validation set according to the ratio of 7:3, and the training set data were analyzed by univariate and multivariate Cox regression, and the risk factors affecting the survival of patients were screened and nomogram was established to predict the survival rates of patients at 3 years and 5 years. The area under the ROC curve (AUC value), C-index, and Calibration curve are used to evaluate the sensitivity and calibration of the model. Univariate Kaplan–Meier survival curve and log-rank test showed that the risk factors affecting the prognosis of patients with low-grade astrocytoma included Age, Primary site, Tumor histological type, Grade, Tumor size, Extension, Surgery, Radiation, Chemotherapy and Tumor number; risk factors affecting the prognosis of patients with high-grade astrocytoma include Age, Primary site, Tumor histological type, Tumor size, Extension, Laterality, Surgery, Radiation, Chemotherapy and Tumor number. Through Cox regression, independent risk factors of patients with two grades were screened separately, and nomograms of risk factors for low-grade and high-grade astrocytoma were successfully established to predict the survival rate of patients at 3 and 5 years. The AUC values of low-grade astrocytoma training set patients were 0.829 and 0.801, and the C-index was 0.818 (95% CI 0.779, 0.857). The AUC values of patients in the validation set were 0.902, 0.829, and the C-index was 0.774 (95% CI 0.758, 0.790), respectively. The AUC values of high-grade astrocytoma training set patients were 0.814 and 0.806, the C-index was 0.774 (95% CI 0.758, 0.790), the AUC values of patients in the validation set were 0.802 and 0.823, and the C-index was 0.766 (95% CI 0.752, 0.780), respectively, and the calibration curves of the two levels of training set and validation set were well fitted. This study used data from the SEER database to identify risk factors affecting the survival prognosis of patients with brain astrocytoma, which can provide some guidance for clinicians

Phosphorus recovery by core-shell γ-Al2O3/Fe3O4 biochar composite from aqueous phosphate solutions

Biochar can act as an adsorbent for phosphate removal from water sources, which can be highly beneficial in limiting eutrophication and recycling elemental phosphorus (P). However, it is difficult to use a single biochar material to overcome problems such as low adsorption efficiency, difficulty in reuse, and secondary pollution. This study addresses these challenges using a novel core-shell structure &gamma;-Al2O3/Fe3O4 biochar adsorbent (AFBC) with significant P uptake capabilities in terms of its high adsorption capacity (205.7 mg g&minus;1), magnetic properties (saturation magnetization 24.70 emu g&minus;1), and high reuse stability (91.0% removal efficiency after five adsorption-desorption cycles). The highest partition coefficient 1.04 mg g&minus;1 &mu;M&minus;1, was obtained at a concentration of 322.89 &mu;M. Furthermore, AFBC exhibited strong regeneration ability in multiple cycle trials, making it extremely viable for sustainable resource management. P removal mechanisms, i.e., electrostatic attraction and inner-sphere complexation, were explained using Fourier transform infrared (FT-IR) spectra and X-ray photoelectron spectroscopy (XPS) measurements. A surface complexation model was established by considering the formation of monodentate mononuclear and bidentate binuclear surface complexes of P to illustrate the adsorption process. Owing to its high adsorption efficiency, easy separation from water, and environmental friendliness, AFBC is a potential adsorbent for P recovery from polluted waters.</p

Data_Sheet_2_Application of machine learning algorithms to identify people with low bone density.docx

BackgroundOsteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis.ObjectiveThis study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data.MethodsUsing NHANES 2017–2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013–2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance.ResultsA total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase.ConclusionThe machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.</p

Data_Sheet_1_Application of machine learning algorithms to identify people with low bone density.docx

BackgroundOsteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis.ObjectiveThis study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data.MethodsUsing NHANES 2017–2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013–2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance.ResultsA total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase.ConclusionThe machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.</p

Data_Sheet_1_Association of dietary pattern and Tibetan featured foods with high-altitude polycythemia in Naqu, Tibet: A 1:2 individual-matched case-control study.pdf

This study focused on the association of dietary patterns and Tibetan featured foods with high-altitude polycythemia (HAPC) in Naqu, Tibet, to explore the risk factors of HAPC in Naqu, Tibet, to raise awareness of the disease among the population and provide evidence for the development of prevention and treatment interventions. A 1:2 individual-matched case-control study design was used to select residents of three villages in the Naqu region of Tibet as the study population. During the health examination and questionnaire survey conducted from December 2020 to December 2021, a sample of 1,171 cases was collected. And after inclusion and exclusion criteria and energy intake correction, 100 patients diagnosed with HAPC using the “Qinghai criteria” were identified as the case group, while 1,059 patients without HAPC or HAPC -related diseases were identified as the control group. Individuals were matched by a 1:2 propensity score matching according to gender, age, body mass index (BMI), length of residence, working altitude, smoking status, and alcohol status. Dietary patterns were determined by a principal component analysis, and the scores of study subjects for each dietary pattern were calculated. The effect of dietary pattern scores and mean daily intake (g/day) of foods in the Tibetan specialty diet on the prevalence of HAPC was analyzed using conditional logistic regression. After propensity score matching, we found three main dietary patterns among residents in Naqu through principal component analysis, which were a “high protein pattern,” “snack food pattern,” and “vegetarian food pattern.” All three dietary patterns showed a high linear association with HAPC (p < 0.05) and were risk factors for HAPC. In the analysis of the relationship between Tibetan featured foods and the prevalence of HAPC, the results of the multifactorial analysis following adjustment for other featured foods showed that there was a positive correlation between the average daily intake of tsampa and the presence of HAPC, which was a risk factor. Additionally, there was an inverse correlation between the average daily intake of ghee tea and the presence of HAPC, which was a protective factor.</p