White-Emitting Gold Nanocluster Assembly with Dynamic Color Tuning.

We report that constructed Au nanoclusters (NCs) can afford amazing white emission synergistically dictated by the Au(0)-dominated core-state fluorescence and Au(I)-governed surface-state phosphorescence, with record-high absolute quantum yields of 42.1% and 53.6% in the aqueous solution and powder state, respectively. Moreover, the dynamic color tuning is achieved in a wide warm-to-cold white-light range (with the correlated color temperature varied from 3426 to 24 973 K) by elaborately manipulating the ratio of Au(0) to Au(I) species and thus the electron transfer rate from staple motif to metal kernel. This study not only exemplifies the successful integration of multiple luminescent centers into metal NCs to accomplish efficient white-light emission but also inspires a feasible pathway toward customizing the optical properties of metal NCs by regulating electron transfer kinetics.

Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong.

INTRODUCTION/OBJECTIVES: A non-laboratory-based pre-diabetes/diabetes mellitus (pre-DM/DM) risk prediction model developed from the Hong Kong Chinese population showed good external discrimination in a primary care (PC) population, but the estimated risk level was significantly lower than the observed incidence, indicating poor calibration. This study explored whether recalibrating/updating methods could improve the model's accuracy in estimating individuals' risks in PC. METHODS: We performed a secondary analysis on the model's predictors and blood test results of 919 Chinese adults with no prior DM diagnosis recruited from PC clinics from April 2021 to January 2022 in HK. The dataset was randomly split in half into a training set and a test set. The model was recalibrated/updated based on a seven-step methodology, including model recalibrating, revising and extending methods. The primary outcome was the calibration of the recalibrated/updated models, indicated by calibration plots. The models' discrimination, indicated by the area under the receiver operating characteristic curves (AUC-ROC), was also evaluated. RESULTS: Recalibrating the model's regression constant, with no change to the predictors' coefficients, improved the model's accuracy (calibration plot intercept: -0.01, slope: 0.69). More extensive methods could not improve any further. All recalibrated/updated models had similar AUC-ROCs to the original model. CONCLUSION: The simple recalibration method can adapt the HK Chinese pre-DM/DM model to PC populations with different pre-test probabilities. The recalibrated model can be used as a first-step screening tool and as a measure to monitor changes in pre-DM/DM risks over time or after interventions.

Development and validation of HBV surveillance models using big data and machine learning.

BACKGROUND: The construction of a robust healthcare information system is fundamental to enhancing countries' capabilities in the surveillance and control of hepatitis B virus (HBV). Making use of China's rapidly expanding primary healthcare system, this innovative approach using big data and machine learning (ML) could help towards the World Health Organization's (WHO) HBV infection elimination goals of reaching 90% diagnosis and treatment rates by 2030. We aimed to develop and validate HBV detection models using routine clinical data to improve the detection of HBV and support the development of effective interventions to mitigate the impact of this disease in China. METHODS: Relevant data records extracted from the Family Medicine Clinic of the University of Hong Kong-Shenzhen Hospital's Hospital Information System were structuralized using state-of-the-art Natural Language Processing techniques. Several ML models have been used to develop HBV risk assessment models. The performance of the ML model was then interpreted using the Shapley value (SHAP) and validated using cohort data randomly divided at a ratio of 2:1 using a five-fold cross-validation framework. RESULTS: The patterns of physical complaints of patients with and without HBV infection were identified by processing 158,988 clinic attendance records. After removing cases without any clinical parameters from the derivation sample (n = 105,992), 27,392 cases were analysed using six modelling methods. A simplified model for HBV using patients' physical complaints and parameters was developed with good discrimination (AUC = 0.78) and calibration (goodness of fit test p-value >0.05). CONCLUSIONS: Suspected case detection models of HBV, showing potential for clinical deployment, have been developed to improve HBV surveillance in primary care setting in China. (Word count: 264).

This study has developed a suspected case detection model for HBV, which can facilitate early identification and treatment of HBV in the primary care setting in China, contributing towards the achievement of WHO's elimination goals of HBV infections.We utilized the state-of-art natural language processing techniques to structure the data records, leading to the development of a robust healthcare information system which enhances the surveillance and control of HBV in China.

Silica monolayer isolation of electrodeposited SERS substrate and online probing of molecule adsorption onto mineral microparticles in relation to flotation.

In the minerals processing industry, the surface chemistry of mineral particles and its real-time detection can significantly enhance process performance, and ultimately leading to automotive and intelligent control. The adsorption of collector molecule onto bulk mineral specimens could be investigated with the help of shell-isolated nanoparticle enhanced Raman spectroscopy (SHINERS). However, this method is unsuitable for the online detection of particles fluid consisted of micro-sized chalcocite that encountered in industrial production processes. In this work, a novel strategy of shell-isolated nanoparticles synthesis by electrodeposition of gold nanoparticles film and isolation of this film with crosslinked silica monolayer was proposed. The adsorption of 2-mercaptobenzothiazole (MBT), a typical flotation collector, onto a copper sulfide mineral, chalcocite was measured in-situ with the help of such a SERS substrate. Enhancement factors of 106-107 was calculated based on an idealized model. Furthermore, we discussed the stability of the silica isolation monolayer under high-power laser irradiation.

Multi-Species Surface Reconstruction for High-Efficiency Perovskite Nanocrystal Light-Emitting Diodes.

Although colloidal perovskite nanocrystal (PNC) solution has exhibited near-unity photoluminescence quantum yield (PLQY), the luminance would be severely quenched when the PNC solution is assembled into thin films due to the agglomeration and fusion of NCs caused by the exfoliation of surface ligands and non-radiative Förster resonance energy transfer (FRET) from small to large particle sizes, which seriously affected the performances of light-emitting diodes (LEDs). Here, we used Guanidine thiocyanate (GASCN) and Sodium thiocyanate (NaSCN) to achieve effective CsPbI3 PNC surface reconstruction. Due to the strong coordination ability of these small molecules with the anions and cations on the surface of the PNCs, they can provide strong surface protection against PNC fusion during centrifugal purification process and repair the surface defects of PNCs, so that the original uniform size distribution of PNCs can be maintained and FRET between close-packed PNC films is effectively suppressed, which allows the emission characteristics of the films to be preserved. As a result, highly oriented, smooth and nearly defect-free high-quality PNC thin films are obtained, with PLQY as high as 95.1 %, far exceeding that of the original film, and corresponding LEDs exhibit a maximum external quantum efficiency of 24.5 %.

High-Performance Blue Perovskite Light-Emitting Diodes Enabled by Synergistic Effect of Additives.

While quasi-two-dimensional (quasi-2D) perovskites have good properties of cascade energy transfer, high exciton binding energy, and high quantum efficiency, which will benefit high-efficiency blue PeLEDs, inefficient domain distribution management and unbalanced carrier transport impede device performance improvement. Herein, (2-(9H-carbazol-9-yl)ethyl)phosphonic acid (2PACz) and methyl 2-aminopyridine-4-carboxylate (MAC) were simultaneously introduced to a blue quasi-2D perovskite film. Relying on the synergistic effect of 2PACz and MAC, it not only modulates the phase distribution inhibiting the n = 2 phase but also greatly improves the electrical property of the quasi-2D perovskite film. As a result, the as-modified blue quasi-2D PeLED demonstrated an external quantum efficiency (EQE) of 17.08% and a luminance of 10142 cd m-2. This study exemplifies the synergistic effect among dual additives and offers a new effective additive strategy modulating phase distribution and building balanced carrier transport, which paves the way for the fabrication of highly efficient blue PeLEDs.

Multiscale Bioresponses of Metal Nanoclusters.

Metal nanoclusters (NCs) are well-recognized novel nano-agents that hold great promise for applications in nanomedicine because of their ultrafine size, low toxicity, and high renal clearance. As foreign substances, however, an in-depth understanding of the bioresponses to metal NCs is necessary but is still far from being realized. Herein, this review is deployed to summarize the biofates of metal NCs at various biological levels, emphasizing their multiscale bioresponses at the molecular, cellular, and organismal levels. In the parts-to-whole schema, the interactions between biomolecules and metal NCs are discussed, presenting typical protein-dictated nano-bio interfaces, hierarchical structures, and in vivo trajectories. Then, the accumulation, internalization, and metabolic evolution of metal NCs in the cellular environment and as-imparted theranostic functionalization are demonstrated. The organismal metabolism and transportation processes of the metal NCs are subsequently distilled. Finally, this review ends with the conclusions and perspectives on the outstanding issues of metal NC-mediated bioresponses in the near future. This review is expected to provide inspiration for tailoring the customization of metal NC-based nano-agents to meet practical requirements in different sectors of nanomedicine.

Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review.

Early detection of pre-diabetes (pre-DM) can prevent DM and related complications. This review examined studies on non-laboratory-based pre-DM risk prediction tools to identify important predictors and evaluate their performance. PubMed, Embase, MEDLINE, CINAHL were searched in February 2023. Studies that developed tools with: (1) pre-DM as a prediction outcome, (2) fasting/post-prandial blood glucose/HbA1c as outcome measures, and (3) non-laboratory predictors only were included. The studies' quality was assessed using the CASP Clinical Prediction Rule Checklist. Data on pre-DM definitions, predictors, validation methods, performances of the tools were extracted for narrative synthesis. A total of 6398 titles were identified and screened. Twenty-four studies were included with satisfactory quality. Eight studies (33.3%) developed pre-DM risk tools and sixteen studies (66.7%) focused on pre-DM and DM risks. Age, family history of DM, diagnosed hypertension and obesity measured by BMI and/or WC were the most common non-laboratory predictors. Existing tools showed satisfactory internal discrimination (AUROC: 0.68-0.82), sensitivity (0.60-0.89), and specificity (0.50-0.74). Only twelve studies (50.0%) had validated their tools externally, with a variance in the external discrimination (AUROC: 0.31-0.79) and sensitivity (0.31-0.92). Most non-laboratory-based risk tools for pre-DM detection showed satisfactory performance in their study populations. The generalisability of these tools was unclear since most lacked external validation.

Who reports insufficient and disturbed sleep? Results from a representative population-based health survey in Hong Kong.

OBJECTIVES: To highlight the prevalence of sleep problems and identify associated risk factors among a representative sample recruited from the general population of Hong Kong. DESIGN, SETTING AND PARTICIPANTS: Participants included 12 022 individuals (aged 15 or above) who took part in the Population Health Survey 2014/15, a territory-wide survey conducted by the Department of Health of the Government of the Hong Kong Special Administrative Region. PRIMARY AND SECONDARY OUTCOME MEASURES: Outcomes were the prevalence of (1) insufficient sleep (<6 hours sleep per day) and (2) any sleep disturbance (difficulty initiating sleep, intermittent awakenings, early awakening) ≥3 times per week in the past 30 days. Multivariable logistic regression identified associations between sleep problems and sociodemographic, clinical and lifestyle factors. RESULTS: 9.7% of respondents reported insufficient sleep and 10.5% reported sleep disturbances ≥3 times a week. Female gender, monthly household income <$12 250 (Hong Kong dollar), lower education level, mental health condition and physical health condition were significantly associated with both insufficient and disturbed sleep (all p<0.05). Unemployment, homemaker, insufficient physical activity, current/former smoking status and harmful alcohol consumption were associated with sleep disturbances only (all p<0.01). CONCLUSIONS: Sleep problems are highly prevalent in Hong Kong. As such problems are associated with a range of health conditions, it is important to facilitate improvements in sleep. Our results show that harmful alcohol consumption, insufficient physical activity and current smoking are modifiable risk factors for sleep disturbances. Public health campaigns should focus on these risk factors in order to promote a healthy lifestyle and ultimately reduce sleep disturbances. Targeted interventions for high-risk groups may also be warranted, particularly for those with doctor-diagnosed physical and mental health conditions.

Development and validation of a diabetes mellitus and prediabetes risk prediction function for case finding in primary care in Hong Kong: a cross-sectional study and a prospective study protocol paper.

INTRODUCTION: Diabetes mellitus (DM) is a major non-communicable disease with an increasing prevalence. Undiagnosed DM is not uncommon and can lead to severe complications and mortality. Identifying high-risk individuals at an earlier disease stage, that is, pre-diabetes (pre-DM), is crucial in delaying progression. Existing risk models mainly rely on non-modifiable factors to predict only the DM risk, and few apply to Chinese people. This study aims to develop and validate a risk prediction function that incorporates modifiable lifestyle factors to detect DM and pre-DM in Chinese adults in primary care. METHODS AND ANALYSIS: A cross-sectional study to develop DM/Pre-DM risk prediction functions using data from the Hong Kong's Population Health Survey (PHS) 2014/2015 and a 12-month prospective study to validate the functions in case finding of individuals with DM/pre-DM. Data of 1857 Chinese adults without self-reported DM/Pre-DM will be extracted from the PHS 2014/2015 to develop DM/Pre-DM risk models using logistic regression and machine learning methods. 1014 Chinese adults without a known history of DM/Pre-DM will be recruited from public and private primary care clinics in Hong Kong. They will complete a questionnaire on relevant risk factors and blood tests on Oral Glucose Tolerance Test (OGTT) and haemoglobin A1C (HbA1c) on recruitment and, if the first blood test is negative, at 12 months. A positive case is DM/pre-DM defined by OGTT or HbA1c in any blood test. Area under receiver operating characteristic curve, sensitivity, specificity, positive predictive value and negative predictive value of the models in detecting DM/pre-DM will be calculated. ETHICS AND DISSEMINATION: Ethics approval has been received from The University of Hong Kong/Hong Kong Hospital Authority Hong Kong West Cluster (UW19-831) and Hong Kong Hospital Authority Kowloon Central/Kowloon East Cluster (REC(KC/KE)-21-0042/ER-3). The study results will be submitted for publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: US ClinicalTrial.gov: NCT04881383; HKU clinical trials registry: HKUCTR-2808; Pre-results.

Non-laboratory-based risk assessment model for case detection of diabetes mellitus and pre-diabetes in primary care.

INTRODUCTION: More than half of diabetes mellitus (DM) and pre-diabetes (pre-DM) cases remain undiagnosed, while existing risk assessment models are limited by focusing on diabetes mellitus only (omitting pre-DM) and often lack lifestyle factors such as sleep. This study aimed to develop a non-laboratory risk assessment model to detect undiagnosed diabetes mellitus and pre-diabetes mellitus in Chinese adults. METHODS: Based on a population-representative dataset, 1,857 participants aged 18-84 years without self-reported diabetes mellitus, pre-diabetes mellitus, and other major chronic diseases were included. The outcome was defined as a newly detected diabetes mellitus or pre-diabetes by a blood test. The risk models were developed using logistic regression (LR) and interpretable machine learning (ML) methods. Models were validated using area under the receiver-operating characteristic curve (AUC-ROC), precision-recall curve (AUC-PR), and calibration plots. Two existing diabetes mellitus risk models were included for comparison. RESULTS: The prevalence of newly diagnosed diabetes mellitus and pre-diabetes mellitus was 15.08%. In addition to known risk factors (age, BMI, WHR, SBP, waist circumference, and smoking status), we found that sleep duration, and vigorous recreational activity time were also significant risk factors of diabetes mellitus and pre-diabetes mellitus. Both LR (AUC-ROC = 0.812, AUC-PR = 0.448) and ML models (AUC-ROC = 0.822, AUC-PR = 0.496) performed well in the validation sample with the ML model showing better discrimination and calibration. The performance of the models was better than the two existing models. CONCLUSIONS: Sleep duration and vigorous recreational activity time are modifiable risk factors of diabetes mellitus and pre-diabetes in Chinese adults. Non-laboratory-based risk assessment models that incorporate these lifestyle factors can enhance case detection of diabetes mellitus and pre-diabetes.

Unintentional Injury Burden in Hong Kong: Results from a Representative Population-Based Survey.

Unintentional injuries are major causes of mortality and morbidity. Although generally perceived as accidents, it is possible to identify those at higher risk and implement appropriate prevention measures. This study aims to investigate the common causes of unintentional injuries and their associated risk factors among a large representative sample. Data of 12,022 individuals who completed the Hong Kong Population Health Survey 2014/15 were extracted. The primary outcome was the prevalence of having unintentional injury(-ies) in the previous 12 months that was severe enough to limit daily activities. Multivariable logistic regression analyses were conducted to identify associations between injuries and sociodemographic, clinical and lifestyle factors. 14.5% of respondents reported episode(s) of unintentional injury in the past 12 months in the population level. The main causes of top three most severe unintentional injuries were sprains (24.0%), falls (19.9%) and being hit/struck (19.6%). 13.2% injury episodes were work-related among the most severe episode. Factors independently associated with significantly higher risks of injury included currently employed, homemaker or student, born in Hong Kong (as compared with immigrants), doctor-diagnosed chronic conditions, harmful alcohol consumption, insufficient sleep, and disturbed sleep. To summarize, unintentional injuries are highly prevalent and associated with harmful drinking, insufficient sleep, and disturbed sleep, which are potential modifiable risk factors for prevention.

Generative adversarial networks for imputing missing data for big data clinical research.

BACKGROUND: Missing data is a pervasive problem in clinical research. Generative adversarial imputation nets (GAIN), a novel machine learning data imputation approach, has the potential to substitute missing data accurately and efficiently but has not yet been evaluated in empirical big clinical datasets. OBJECTIVES: This study aimed to evaluate the accuracy of GAIN in imputing missing values in large real-world clinical datasets with mixed-type variables. The computation efficiency of GAIN was also evaluated. The performance of GAIN was compared with other commonly used methods, MICE and missForest. METHODS: Two real world clinical datasets were used. The first was that of a cohort study on the long-term outcomes of patients with diabetes (50,000 complete cases), and the second was of a cohort study on the effectiveness of a risk assessment and management programme for patients with hypertension (10,000 complete cases). Missing data (missing at random) to independent variables were simulated at different missingness rates (20, 50%). The normalized root mean square error (NRMSE) between imputed values and real values for continuous variables and the proportion of falsely classified (PFC) for categorical variables were used to measure imputation accuracy. Computation time per imputation for each method was recorded. The differences in accuracy of different imputation methods were compared using ANOVA or non-parametric test. RESULTS: Both missForest and GAIN were more accurate than MICE. GAIN showed similar accuracy as missForest when the simulated missingness rate was 20%, but was more accurate when the simulated missingness rate was 50%. GAIN was the most accurate for the imputation of skewed continuous and imbalanced categorical variables at both missingness rates. GAIN had a much higher computation speed (32 min on PC) comparing to that of missForest (1300 min) when the sample size is 50,000. CONCLUSION: GAIN showed better accuracy as an imputation method for missing data in large real-world clinical datasets compared to MICE and missForest, and was more resistant to high missingness rate (50%). The high computation speed is an added advantage of GAIN in big clinical data research. It holds potential as an accurate and efficient method for missing data imputation in future big data clinical research. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT03299010 ; Unique Protocol ID: HKUCTR-2232.

Prediction models and nomograms for 10-year risk of end-stage renal disease in Chinese type 2 diabetes mellitus patients in primary care.

AIMS: To develop and validate 10-year risk prediction models, nomograms and charts for end-stage renal disease (ESRD) in Chinese patients with type 2 diabetes mellitus (T2DM) in primary care, in order to guide individualized treatment. MATERIALS AND METHODS: This was a 10-year population-based observational cohort study. A total of 141 516 Chinese T2DM patients without history of cardiovascular disease or ESRD who were managed in public primary care clinics in 2008 were included and followed up until December 2017. Two-thirds of these patients were randomly selected to develop sex-specific ESRD risk prediction models using Cox regressions. The validity and accuracy of the models were tested on the remaining third of patients using Harrell's C-index. We selected variables based on their clinical and statistical importance to construct the nomograms and charts. RESULTS: The median follow-up period was 9.75 years. The cumulative incidence of ESRD was 6.0% (men: 6.1%, women: 5.9%). Age, diabetes duration, systolic blood pressure (SBP), SBP variability, diastolic blood pressure, triglycerides, glycated haemoglobin (HbA1c), HbA1c variability, urine albumin to creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) were significant predictors for both sexes. Smoking and total cholesterol to HDL cholesterol ratio were additional significant predictors for men and women, respectively. The models showed Harrell's C-statistics of 0.889/0.889 (women/men). Age, eGFR, UACR, SBP and HbA1c were selected for both sexes to develop nomograms and charts. CONCLUSIONS: Using routinely available variables, the 10-year ESRD risk of Chinese T2DM patients in primary care can be predicted with approximately 90% accuracy. We have developed different tools to facilitate routine ESRD risk prediction in primary care, so that individualized care can be provided to prevent or delay ESRD in T2DM patients.

Trend in health-related quality of life and health utility and their decrements due to non-communicable diseases and risk factors: analysis of four population-based surveys between 1998 and 2015.

PURPOSE: To revisit the population norms of health-related quality of life (HRQoL) and health utility for the Hong Kong general population, compare these scores over past health surveys, and assess the association of scores with non-communicable diseases (NCDs) and their risk factors. METHODS: HRQoL data measured by the standard Short Form 12 Health Survey-version 2 (SF-12v2) were extracted from the surveys in 1998, 2003/2004, 2008/2009 and 2014/2015. SF-12v2 data were mapped to the Short-form 6-dimension (SF-6D) preference-based measure to generate the health utility scores. Population weighting based on the sex and age in the second quarter of 2015 was applied when generating population normative values. Linear regression models were fitted to assess the effect of the number of NCDs and modifiable lifestyle factors on HRQoL and health utility. RESULTS: The general population mean scores of SF-12v2 domains and SF-6D in 2014/15 were higher compared to past surveys. Linear increases in General Health, Vitality and Mental Health domains were observed from 1998 to 2014/15. More doctor-diagnosed NCDs, insufficient physical activity and fruit/vegetable consumption, poor sleep quality and insufficient or excessive amount of sleep (< 6/≥ 10 h) were all associated with worse physical- and mental-related HRQoL and health utility. CONCLUSION: This study compared HRQoL and health utility in the Hong Kong general population derived from multiple surveys and found an improving trend over twenty years. More NCDs were associated with worse HRQoL. It is suggested that promoting adequate physical activity, consumption of fruit/vegetable and 6-9 h of sleep could improve health.

Screening for chronic conditions with reproductive factors using a machine learning based approach.

A large proportion of cases with chronic conditions including diabetes or pre-diabetes, hypertension and dyslipidemia remain undiagnosed. To include reproductive factors (RF) might be able to improve current screening guidelines by providing extra effectiveness. The objective is to study the relationships between RFs and chronic conditions' biomarkers. A cross-sectional study was conducted. Demographics, RFs and metabolic biomarkers were collected. The relationship of the metabolic biomarkers were shown by correlation analysis. Principal component analysis (PCA) and autoencoder were compared by cross-validation. The better one was adopted to extract a single marker, the general chronic condition (GCC), to represent the body's chronic conditions. Multivariate linear regression was performed to explore the relationship between GCC and RFs. In total, 1,656 postmenopausal females were included. A multi-layer autoencoder outperformed PCA in the dimensionality reduction performance. The extracted variable by autoencoder, GCC, was verified to be representative of three chronic conditions (AUC for patoglycemia, hypertension and dyslipidemia were 0.844, 0.824 and 0.805 respectively). Linear regression showed that earlier age at menarche (OR = 0.9976) and shorter reproductive life span (OR = 0.9895) were associated with higher GCC. Autoencoder performed well in the dimensionality reduction of clinical metabolic biomarkers. Due to high accessibility and effectiveness, RFs have potential to be included in screening tools for general chronic conditions and could enhance current screening guidelines.

Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network.

Background and Purpose: The risk of recurrent stroke following a transient ischemic attack (TIA) or minor stroke is high, despite of a significant reduction in the past decade. In this study, we investigated the feasibility of using artificial neural network (ANN) for risk stratification of TIA or minor stroke patients. Methods: Consecutive patients with acute TIA or minor ischemic stroke presenting at a tertiary hospital during a 2-year period were recruited. We collected demographics, clinical and imaging data at baseline. The primary outcome was recurrent ischemic stroke within 1 year. We developed ANN models to predict the primary outcome. We randomly down-sampled patients without a primary outcome to 1:1 match with those with a primary outcome to mitigate data imbalance. We used a 5-fold cross-validation approach to train and test the ANN models to avoid overfitting. We employed 19 independent variables at baseline as the input neurons in the ANN models, using a learning algorithm based on backpropagation to minimize the loss function. We obtained the sensitivity, specificity, accuracy and the c statistic of each ANN model from the 5 rounds of cross-validation and compared that of support vector machine (SVM) and Naïve Bayes classifier in risk stratification of the patients. Results: A total of 451 acute TIA or minor stroke patients were enrolled. Forty (8.9%) patients had a recurrent ischemic stroke within 1 year. Another 40 patients were randomly selected from those with no recurrent stroke, so that data from 80 patients in total were used for 5 rounds of training and testing of ANN models. The median sensitivity, specificity, accuracy and c statistic of the ANN models to predict recurrent stroke at 1 year was 75%, 75%, 75%, and 0.77, respectively. ANN model outperformed SVM and Naïve Bayes classifier in our dataset for predicting relapse after TIA or minor stroke. Conclusion: This pilot study indicated that ANN may yield a novel and effective method in risk stratification of TIA and minor stroke. Further studies are warranted for verification and improvement of the current ANN model.

Residents' satisfaction with primary medical and health services in Western China.

BACKGROUND: Currently, China is in the process of medical and health care reform, and the establishment of primary medical and health services covering urban and rural residents is an important aspect of this process. Studying the satisfaction of residents of underdeveloped areas with their primary medical and health services and identifying the factors that can increase the satisfaction of different groups may improve patient compliance and ultimately improve health. Moreover, such research may provide a reference for the development of medical and health undertakings in similarly underdeveloped areas. METHODS: A face-to-face survey was conducted on a stratified random sample of 2200 residents in Gansu by using structured questionnaires. Demographic characteristics were collated, and questionnaires were factor-analysed and weighted using SPSS software to obtain scores for each factor, as well as total satisfaction scores. The characteristics of poorly satisfied populations were determined by a multiple linear regression analysis using SAS software. A cluster analysis was performed using SAS software for classification and a separate discussion of populations. RESULTS: The hypertension self-awareness rate (11.29%) of the sampled population was lower than the average hypertension prevalence (23.85%), as recorded in the 2014 Health Statistical Yearbook of the region. The disease knowledge awareness factor was the lowest factor (2.857), whereas the policy awareness factor was the highest factor (4.772). The overall satisfaction was moderate (3.898). The multivariate linear regression model was significant (p <0.05). The regression coefficients were -0.041 for minors; 0.065 for unemployed people; and 0.094 for people with an elementary school educational level, a value lower than that of other population groups. A cluster analysis was used to divide the respondents into five groups. The overall satisfaction was lowest in the second population group (rural, middle-aged)(Fz = 3.64) and was highest in the fourth population group(minors) (Fz = 4.13). Different population groups showed different satisfaction rates in F1 to F6. CONCLUSION: Hypertensive patients had low self-awareness, and residents had a poor grasp of disease and limited health knowledge. Their overall satisfaction was moderate. Residents expressed comparatively high satisfaction with the current policy. Minors, adults with low level of education, unemployed people and other vulnerable groups expressed low overall satisfaction. The degree of satisfaction varied greatly among the different groups. Targeted medical and health practices should be implemented for different groups; additionally, the public health practice should be strengthened.