Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b.

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.

Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population.

Purpose: Approximately 20% of couples face infertility challenges and struggle to conceive naturally. Despite advances in artificial reproduction, its success hinges on sperm quality. Our previous study used five machine learning (ML) algorithms, random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting, to model health data from 1375 Taiwanese males and identified ten risk factors affecting sperm count. Methods: We employed the CART algorithm to generate decision trees using identified risk factors to predict healthy sperm counts. Four error metrics, SMAPE, RAE, RRSE, and RMSE, were used to evaluate the decision trees. We identified the top five decision trees based on their low errors and discussed in detail the tree with the least error. Results: The decision tree featuring the least error, comprising BMI, UA, ST, T-Cho/HDL-C ratio, and BUN, corroborated the negative impacts of metabolic syndrome, particularly high BMI, on sperm count, while emphasizing the link between good sleep and male fertility. Our study also sheds light on the potentially significant influence of high BUN on spermatogenesis. Two novel risk factors, T-Cho/HDL-C and UA, warrant further investigation. Conclusion: The ML algorithm established a predictive model for healthcare personnel to assess low sperm counts. Refinement of the model using additional data is crucial for improved precision. The risk factors identified offer avenues for future investigations.

Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators.

In many countries, especially developed nations, the fertility rate and birth rate have continually declined. Taiwan's fertility rate has paralleled this trend and reached its nadir in 2022. Therefore, the government uses many strategies to encourage more married couples to have children. However, couples marrying at an older age may have declining physical status, as well as hypertension and other metabolic syndrome symptoms, in addition to possibly being overweight, which have been the focus of the studies for their influences on male and female gamete quality. Many previous studies based on infertile people are not truly representative of the general population. This study proposed a framework using five machine learning (ML) predictive algorithms-random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting-to identify the major risk factors affecting male sperm count based on a major health screening database in Taiwan. Unlike traditional multiple linear regression, ML algorithms do not need statistical assumptions and can capture non-linear relationships or complex interactions between dependent and independent variables to generate promising performance. We analyzed annual health screening data of 1375 males from 2010 to 2017, including data on health screening indicators, sourced from the MJ Group, a major health screening center in Taiwan. The symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error were used as performance evaluation metrics. Our results show that sleep time (ST), alpha-fetoprotein (AFP), body fat (BF), systolic blood pressure (SBP), and blood urea nitrogen (BUN) are the top five risk factors associated with sperm count. ST is a known risk factor influencing reproductive hormone balance, which can affect spermatogenesis and final sperm count. BF and SBP are risk factors associated with metabolic syndrome, another known risk factor of altered male reproductive hormone systems. However, AFP has not been the focus of previous studies on male fertility or semen quality. BUN, the index for kidney function, is also identified as a risk factor by our established ML model. Our results support previous findings that metabolic syndrome has negative impacts on sperm count and semen quality. Sleep duration also has an impact on sperm generation in the testes. AFP and BUN are two novel risk factors linked to sperm counts. These findings could help healthcare personnel and law makers create strategies for creating environments to increase the country's fertility rate. This study should also be of value to follow-up research.

A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques.

With the rapid development of medicine and technology, machine learning (ML) techniques are extensively applied to medical informatics and the suboptimal health field to identify critical predictor variables and risk factors. Metabolic syndrome (MetS) and chronic kidney disease (CKD) are important risk factors for many comorbidities and complications. Existing studies that utilize different statistical or ML algorithms to perform CKD data analysis mostly analyze the early-stage subjects directly, but few studies have discussed the predictive models and important risk factors for the stage-III CKD high-risk health screening population. The middle stages 3a and 3b of CKD indicate moderate renal failure. This study aims to construct an effective hybrid important risk factor evaluation scheme for subjects with MetS and CKD stages III based on ML predictive models. The six well-known ML techniques, namely random forest (RF), logistic regression (LGR), multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost), and a light gradient boosting machine (LightGBM), were used in the proposed scheme. The data were sourced from the Taiwan health examination indicators and the questionnaire responses of 71,108 members between 2005 and 2017. In total, 375 stage 3a CKD and 50 CKD stage 3b CKD patients were enrolled, and 33 different variables were used to evaluate potential risk factors. Based on the results, the top five important variables, namely BUN, SBP, Right Intraocular Pressure (R-IOP), RBCs, and T-Cho/HDL-C (C/H), were identified as significant variables for evaluating the subjects with MetS and CKD stage 3a or 3b.

Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol.

PURPOSE: Cardiovascular disease (CVD) is a major worldwide health burden. As the risk factors of CVD, hypertension, and hyperlipidemia are most mentioned. Early stage hypertension in the population with dyslipidemia is an important public health hazard. This study was the application of data-driven machine learning (ML), demonstrating complex relationships between risk factors and outcomes and promising predictive performance with vast amounts of medical data, aimed to investigate the association between dyslipidemia and the incidence of early stage hypertension in a large cohort with normal blood pressure at baseline. METHODS: This study analyzed annual health screening data for 71,108 people from 2005 to 2017, including data for 27 risk-related indicators, sourced from the MJ Group, a major health screening center in Taiwan. We used five machine learning (ML) methods-stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), least absolute shrinkage and selection operator regression (Lasso), ridge regression (Ridge), and gradient boosting with categorical features support (CatBoost)-to develop a multi-stage ML algorithm-based prediction scheme and then evaluate important risk factors at the early stage of hypertension, especially for groups with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels within or out of the reference range. RESULTS: Age, body mass index, waist circumference, waist-to-hip ratio, fasting plasma glucose, and C-reactive protein (CRP) were associated with hypertension. The hemoglobin level was also a positive contributor to blood pressure elevation and it appeared among the top three important risk factors in all LDL-C/HDL-C groups; therefore, these variables may be important in affecting blood pressure in the early stage of hypertension. A residual contribution to blood pressure elevation was found in groups with increased LDL-C. This suggests that LDL-C levels are associated with CPR levels, and that the LDL-C level may be an important factor for predicting the development of hypertension. CONCLUSION: The five prediction models provided similar classifications of risk factors. The results of this study show that an increase in LDL-C is more important than the start of a drop in HDL-C in health screening of sub-healthy adults. The findings of this study should be of value to health awareness raising about hypertension and further discussion and follow-up research.

Evaluating the comparative efficiency of medical centers in Taiwan: a dynamic data envelopment analysis application.

BACKGROUND: People in Taiwan enjoy comprehensive National Health Insurance coverage. However, under the global budget constraint, hospitals encounter enormous challenges. This study was designed to examine Taiwan medical centers' efficiency and factors that influence it. METHODS: We obtained data from open sources of government routine publications and hospitals disclosed by law to the National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. The dynamic data envelopment analysis (DDEA) model was adopted to estimate all medical centers' efficiencies during 2015-2018. Beta regression models were used to model the efficiency level obtained from the DDEA model. We applied an input-oriented approach under both the constant returns-to-scale (CRS) and variable returns-to-scale (VRS) assumptions to estimate efficiency. RESULTS: The findings indicated that 68.4% (13 of 19) of medical centers were inefficient according to scale efficiency. The mean efficiency scores of all medical centers during 2015-2018 under the CRS, VRS, and Scale were 0.85, 0.930, and 0.95,respectively. Regression results showed that an increase in the population less than 14 years of age, assets, nurse-patient ratio and bed occupancy rate could increase medical centers' efficiency. The rate of emergency return within 3-day and patient self-pay revenues were associated significantly with reduced hospital efficiency (p < 0.05). The result also showed that the foundation owns medical center has the highest efficiency than other ownership hospitals. CONCLUSIONS: The study results provide information for hospital managers to consider ways they could adjust available resources to achieve high efficiency.

The Health Check-Up Data-Analysis for Risk Assessment of Chronic Kidney Disease (CKD) in Taiwan.

Early detection of chronic kidney disease (CKD) for high-risk population adults is very important. It has a common risk factor and causal relationship with chronic diseases such as diabetes, hypertension and cardiovascular disease etc. The results of this study provide that for early high-risk factors detection in CKD healthy population can be used by home care to recommend adjuvant treatment.

Risk assessment of metabolic syndrome prevalence involving sedentary occupations and socioeconomic status.

OBJECTIVES: To determine whether occupation type, distinguished by socioeconomic status (SES) and sedentary status, is associated with metabolic syndrome (MetS) risk. METHODS: We analysed two data sets covering 73 506 individuals. MetS was identified according to the criteria of the modified Adult Treatment Panel III. Eight occupational categories were considered: professionals, technical workers, managers, salespeople, service staff, administrative staff, manual labourers and taxi drivers; occupations were grouped into non-sedentary; sedentary, high-SES; and sedentary, non-high-SES occupations. A multiple logistic regression was used to determine significant risk factors for MetS in three age-stratified subgroups. R software for Windows (V.3.5.1) was used for all statistical analyses. RESULTS: MetS prevalence increased with age. Among participants aged ≤40 years, where MetS prevalence was low at 6.23%, having a non-sedentary occupation reduced MetS risk (OR=0.88, p<0.0295). Among participants aged >60 years, having a sedentary, high-SES occupation significantly increased (OR=1.39, p<0.0247) MetS risk. CONCLUSIONS: The influence of occupation type on MetS risk differs among age groups. Non-sedentary occupations and sedentary, high-SES occupations decrease and increase MetS risk, respectively, among younger and older adults, respectively. Authorities should focus on individuals in sedentary, high-SES occupations.

Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease.

PURPOSE: As global aging progresses, the health management of chronic diseases has become an important issue of concern to governments. Influenced by the aging of its population and improvements in the medical system and healthcare in general, Taiwan's population of patients with chronic kidney disease (CKD) has tended to grow year by year, including the incidence of high-risk cases that pose major health hazards to the elderly and middle-aged populations. METHODS: This study analyzed the annual health screening data for 65,394 people from 2010 to 2015 sourced from the MJ Group - a major health screening center in Taiwan - including data for 18 risk indicators. We used five prediction model analysis methods, namely, logistic regression (LR) analysis, C5.0 decision tree (C5.0) analysis, stochastic gradient boosting (SGB) analysis, multivariate adaptive regression splines (MARS), and eXtreme gradient boosting (XGboost), with estimated glomerular filtration rate (e-GFR) data to determine G3a, G3b & G4 stage CKD risk factors. RESULTS: The LR analysis (AUC=0.848), SGB analysis (AUC=0.855), and XGboost (AUC=0.858) generated similar classification performance levels and all outperformed the C5.0 and MARS methods. The study results showed that in terms of CKD risk factors, blood urea nitrogen (BUN) and uric acid (UA) were identified as the first and second most important indicators in the models of all five analysis methods, and they were also clinically recognized as the major risk factors. The results for systolic blood pressure (SBP), SGPT, SGOT, and LDL were similar to those of a related study. Interestingly, however, socioeconomic status-related education was found to be the third important indicator in all three of the better performing analysis methods, indicating that it is more important than the other risk indicators of this study, which had different levels of importance according to the different methods. CONCLUSION: The five prediction model methods can provide high and similar classification performance in this study. Based on the results of this study, it is recommended that education as the socioeconomic status should be an important factor for CKD, as high educational level showed a negative and highly significant correlation with CKD. The findings of this study should also be of value for further discussions and follow-up research.

Data analysis of ambient intelligence in a healthcare simulation system: a pilot study in high-end health screening process improvement.

BACKGROUND: This study aimed to reduce the total waiting time for high-end health screening processes. METHOD: The subjects of this study were recruited from a health screening center in a tertiary hospital in northern Taiwan from September 2016 to February 2017, where a total of 2342 high-end customers participated. Three policies were adopted for the simulation. RESULTS: The first policy presented a predetermined proportion of customer types, in which the total waiting time was increased from 72.29 to 83.04 mins. The second policy was based on increased bottleneck resources, which provided significant improvement, decreasing the total waiting time from 72.29 to 28.39 mins. However, this policy also dramatically increased the cost while lowering the utilization of this health screening center. The third policy was adjusting customer arrival times, which significantly reduced the waiting time-with the total waiting time reduced from 72.29 to 55.02 mins. Although the waiting time of this policy was slightly longer than that of the second policy, the additional cost was much lower. CONCLUSIONS: Scheduled arrival intervals could help reduce customer waiting time in the health screening department based on the "first in, first out" rule. The simulation model of this study could be utilized, and the parameters could be modified to comply with different health screening centers to improve processes and service quality.

Quality Improvement of Bowel Preparation for Screening Colonoscopies: A Study of Hospital Team Resource Management in Taiwan.

BACKGROUND AND OBJECTIVES: This study aimed to investigate the impact of patient education using a medical team resource management (TRM) method on the adequacy of bowel preparation. METHODS: The study setting was a single hospital in northern Taiwan, and a total of 2104 (884 female, 1220 male) healthy subjects who underwent a health checkup colonoscopy screening were enrolled before and after the application of the TRM program intervention. The efficacy of the TRM intervention and the factors affecting bowel preparation were estimated using multivariate logistic regression. RESULTS: The prevalence of adequate bowel preparation improved significantly from the preintervention period to the postintervention and validation periods, which had prevalence of 79.0%, 81.3%, and 84.0%, respectively. Using the preintervention period prevalence as a reference, the adjusted odds ratios (aORs) for adequate bowel preparation in the postintervention and validation periods were 2.199 (95% confidence interval [CI]: 1.538-3.142) and 2.035 (1.525-2.716), respectively. Men had a lower probability of adequate cleansing than women (aOR = 0.757; 95% CI = 0.598-0.957), and purgative containing polyethylene glycol had a lower probability of adequate cleansing than purgative containing sodium phosphate (aOR = 0.366; 95% CI: 0.277-0.483). CONCLUSIONS: Bowel preparation quality for colonoscopy could be improved by enhancing patient education via TRM, and we suggest that effective quality improvement schemes should be proposed for health-screening programs.

Chitosan oligosaccharide modified liposomes enhance lung cancer delivery of paclitaxel.

Lung cancer is one of the leading causes of cancer-related death worldwide. Various therapeutic failed in the effective treatment of the lung cancer due to their limited accumulation and exposure in tumors. In order to promote the chemotherapeutics delivery to lung tumor, we introduced chitosan oligosaccharide (CSO) modification on the liposomes. CSO conjugated Pluronic P123 polymers with different CSO grafting amounts, called as CP50 and CP20, were synthesized and used to prepare CSO modified liposomes (CP50-LSs and CP20-LSs). CP50-LSs and CP20-LSs displayed significantly enhanced cellular uptake in A549 cells in vitro as well as superior tumor accumulation in vivo compared with non-CSO modified liposomes (P-LSs). This phenomenon was related to the increased affinity between CSO modified liposomes and tumor cells following massive adsorption of collagen, which was highly expressed in lung tumors. In the A549 tumor-bearing mouse model, intravenous injection of paclitaxel (PTX)-loaded CP50-LSs every 3 days for 21 days resulted in optimal antitumor therapeutic performance with an inhibition rate of 86.4%. These results reveal that CSO modification provides promising applicability for nanomedicine design in the lung cancer treatment.

Evaluating the Operational Efficiency and Quality of Tertiary Hospitals in Taiwan: The Application of the EBITDA Indicator to the DEA Method and TOBIT Regression.

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015-2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals' inefficiency decision-making units through reference group analysis and slack variable analysis.

Adjusting Quality Control Chart Limits for WBC, RBC, Hb, and PLT Counts to Reduce Daily Control Risks in Hospital Laboratory.

BACKGROUND: To continuously improve medical quality and provide clinicians with more accurate blood test reports, this study collected blood quality control data in 2017 from a medical examination laboratory in a teaching level hospital located in Taoyuan City, Taiwan. MATERIAL AND METHODS: The quality control data were arranged and analyzed from daily complete blood count (CBC), including white blood cells (WBC), red blood cells (RBC), hemoglobin (Hb), and platelets (PLT) recorded by a laboratory blood analyzer. Using the empirical Bayesian method, we estimated the variation of concentrations of the last and current batches to establish a novel control chart with adjusted upper and lower limits for the current batch, and then compared results with the traditional Shewhart method. The average run length (ARL) and sensitivity of the empirical Bayesian method were explored. RESULTS: The study found that ARL showed a qualified capability for the four blood routine tests when using the empirical Bayesian method. Compared to the Levey-Jennings control chart, the novel control chart presents an alert earlier when a deviation occurs and shows a fake alert later when there is no deviation. CONCLUSION: The parallel tests showed that the longer the time is, the better the test's proficiency. We concluded that the empirical Bayesian method could be applied effectively to improve the capability of daily control in CBC laboratory tests.

A Data-Driven Assessment of the Metabolic Syndrome Criteria for Adult Health Management in Taiwan.

According to the modified Adult Treatment Panel III, five indices are used to define metabolic syndrome (MetS): waist circumference (WC), high blood pressure, fasting glucose, triglycerides (TG), and high-density lipoprotein cholesterol. Our work evaluates the importance of these indices. In addition, we attempted to identify whether trends and patterns existed among young, middle-aged, and older people. Following the analysis, a decision tree algorithm was used to analyze the importance of the five criteria for MetS because the algorithm in question selects the attribute with the highest information gain as the split node. The most important indices are located on the top of the tree, indicating that these indices can effectively distinguish data in a binary tree and the importance of this criterion. That is, the decision tree algorithm specifies the priority of the influence factors. The decision tree algorithm examined four of the five indices because one was excluded. Moreover, the tree structures differed among the three age groups. For example, the first key index for middle-aged and older people was TG whereas for younger people it was WC. Furthermore, the order of the second to fourth indices differed among the groups. Because the key index was identified for each age group, researchers and practitioners could provide different health care strategies for individuals based on age. High-risk middle-aged and healthy older people maintained low values of TG, which might be the most crucial index. When a person can avoid the first and second indices provided by the decision tree, they are at lower risk of MetS. Therefore, this paper provides a data-driven guideline for MetS prevention.

Risk assessment and quality improvement of liquid waste management in Taiwan University chemical laboratories.

The policy of establishing new universities across Taiwan has led to an increase in the number of universities, and many schools have constructed new laboratories to meet students' academic needs. In recent years, there has been an increase in the number of laboratory accidents from the liquid waste in universities. Therefore, how to build a safety system for laboratory liquid waste disposal has become an important issue in the environmental protection, safety, and hygiene of all universities. This study identifies the risk factors of liquid waste disposal and presents an agenda for practices to laboratory managers. An expert questionnaire is adopted to probe into the risk priority procedures of liquid waste disposal; then, the fuzzy theory-based FMEA method and the traditional FMEA method are employed to analyze and improve the procedures for liquid waste disposal. According to the research results, the fuzzy FMEA method is the most effective, and the top 10 potential disabling factors are prioritized for improvement according to the risk priority number (RNP), including "Unclear classification", "Gathering liquid waste without a funnel or a drain pan", "Lack of a clearance and transport contract", "Liquid waste spill during delivery", "Spill over", "Decentralized storage", "Calculating weight in the wrong way", "Compatibility between the container material and the liquid waste", "Lack of dumping and disposal tools", and "Lack of a clear labels for liquid waste containers". After tracking improvements, the overall improvement rate rose to 60.2%.

Improvement of the quality of work in a biochemistry laboratory via measurement system analysis.

An adequate and continuous monitoring of operational variations can effectively reduce the uncertainty and enhance the quality of laboratory reports. This study applied the evaluation rule of the measurement system analysis (MSA) method to estimate the quality of work conducted in a biochemistry laboratory. Using the gauge repeatability & reproducibility (GR&R) approach, variations in quality control (QC) data among medical technicians in conducting measurements of five biochemical items, namely, serum glucose (GLU), aspartate aminotransferase (AST), uric acid (UA), sodium (Na) and chloride (Cl), were evaluated. The measurements of the five biochemical items showed different levels of variance among the different technicians, with the variances in GLU measurements being higher than those for the other four items. The ratios of precision-to-tolerance (P/T) for Na, Cl and GLU were all above 0.5, implying inadequate gauge capability. The product variation contribution of Na was large (75.45% and 31.24% in normal and abnormal QC levels, respectively), which showed that the impact of insufficient usage of reagents could not be excluded. With regard to reproducibility, high contributions (of more than 30%) of variation for the selected items were found. These high operator variation levels implied that the possibility of inadequate gauge capacity could not be excluded. The analysis of variance (ANOVA) of GR&R showed that the operator variations in GLU measurements were significant (F=5.296, P=0.001 in the normal level and F=3.399, P=0.015 in the abnormal level, respectively). In addition to operator variations, product variations of Na were also significant for both QC levels. The heterogeneity of variance for the five technicians showed significant differences for the Na and Cl measurements in the normal QC level. The accuracy of QC for five technicians was identified for further operational improvement. This study revealed that MSA can be used to evaluate product and personnel errors and to improve the quality of work in a biochemical laboratory through proper corrective actions.

Resistance training exercise program for intervention to enhance gait function in elderly chronically ill patients: multivariate multiscale entropy for center of pressure signal analysis.

Falls are unpredictable accidents, and the resulting injuries can be serious in the elderly, particularly those with chronic diseases. Regular exercise is recommended to prevent and treat hypertension and other chronic diseases by reducing clinical blood pressure. The "complexity index" (CI), based on multiscale entropy (MSE) algorithm, has been applied in recent studies to show a person's adaptability to intrinsic and external perturbations and widely used measure of postural sway or stability. The multivariate multiscale entropy (MMSE) was advanced algorithm used to calculate the complexity index (CI) values of the center of pressure (COP) data. In this study, we applied the MSE & MMSE to analyze gait function of 24 elderly, chronically ill patients (44% female; 56% male; mean age, 67.56 ± 10.70 years) with either cardiovascular disease, diabetes mellitus, or osteoporosis. After a 12-week training program, postural stability measurements showed significant improvements. Our results showed beneficial effects of resistance training, which can be used to improve postural stability in the elderly and indicated that MMSE algorithms to calculate CI of the COP data were superior to the multiscale entropy (MSE) algorithm to identify the sense of balance in the elderly.

Application of indices Cp and Cpk to improve quality control capability in clinical biochemistry laboratories.

The traditional criteria for acceptability of analytic quality may not be objective in clinical laboratories. To establish quality control procedures intended to enhance Westgard multi-rules for improving the quality of clinical biochemistry tests, we applied the Cp and Cpk quality-control indices to monitor tolerance fitting and systematic variation of clinical biochemistry test results. Daily quality-control data of a large Taiwanese hospital in 2009 were analyzed. The test items were selected based on an Olympus biochemistry machine and included serum albumin, aspartate aminotransferase, cholesterol, glucose and potassium levels. Cp and Cpk values were calculated for normal and abnormal levels, respectively. The tolerance range was estimated with data from 50 laboratories using the same instruments and reagents. The results showed a monthly trend of variation for the five items under investigation. The index values of glucose were lower than those of the other items, and their values were usually <2. In contrast to the Cp value for cholesterol, Cpk of cholesterol was lower than 2, indicating a systematic error that should be further investigated. This finding suggests a degree of variation or failure to meet specifications that should be corrected. The study indicated that Cp and Cpk could be applied not only for monitoring variations in quality control, but also for revealing inter-laboratory qualitycontrol capability differences.

Facile preparation of well-dispersed CeO2-ZnO composite hollow microspheres with enhanced catalytic activity for CO oxidation.

In this article, well-dispersed CeO2-ZnO composite hollow microspheres have been fabricated through a simple chemical reaction followed by annealing treatment. Amorphous zinc-cerium citrate hollow microspheres were first synthesized by dispersing zinc citrate hollow microspheres into cerium nitrate solution and then aging at room temperature for 1 h. By calcining the as-produced zinc-cerium citrate hollow microspheres at 500 °C for 2 h, CeO2-ZnO composite hollow microspheres with homogeneous composition distribution could be harvested for the first time. The resulting CeO2-ZnO composite hollow microspheres exhibit enhanced activity for CO oxidation compared with CeO2 and ZnO, which is due to well-dispersed small CeO2 particles on the surface of ZnO hollow microspheres and strong interaction between CeO2 and ZnO. Moreover, when Au nanoparticles are deposited on the surface of the CeO2-ZnO composite hollow microspheres, the full CO conversion temperature of the as-produced 1.0 wt % Au-CeO2-ZnO composites reduces from 300 to 60 °C in comparison with CeO2-ZnO composites. The significantly improved catalytic activity may be ascribed to the strong synergistic interplay between Au nanoparticles and CeO2-ZnO composites.