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Background: The long-term effects of statin use on rehospitalization due to ischemic stroke (reHospIS) in hyperlipidemic patients are still unknown. Therefore, we aimed to assess the long-term risks of reHospIS for hyperlipidemic patients who were taking statins and nonstatin lipid-lowering medicines on a regular basis.Methods and Materials: The National Health Insurance Research Database in Taiwan was used to conduct a 6-year cohort study of patients >45 years old (n = 9,098) who were newly diagnosed with hyperlipidemia and hospitalized for the first or second time due to ischemic stroke (IS). The risk of reHospIS was assessed using Cox proportional hazards regression model.Results: Nonstatin lipid-lowering medicines regular users were associated with a higher risk of reHospIS compared to stains users (hazard ratio, HR = 1.29–1.39, p < 0.05). Rosuvastatin was the most preferred lipid-lowering medicine with lower HRs of reHospIS in hyperlipidemic patients whether they developed diabetes or not. Bezafibrate regular users of hyperlipidemic patients developing diabetes (HR = 2.15, p < 0.01) had nearly 50% lower reHospIS risks than those without diabetes (HR = 4.27, p < 0.05). Age, gender, drug dosage, comorbidities of diabetes and heart failure (HF), and characteristics of the first hospitalization due to IS were all adjusted in models. Moreover, increasing trends of HRs of reHospIS were observed from Rosuvastatin, nonstatin lipid-lowering medicines, Lovastatin, and Gemfibrozil to Bezafibrate users.Conclusion: Statins were associated with long-term secondary prevention of reHospIS for hyperlipidemic patients. Rosuvastatin seemed to have the best protective effects. On the other hand, Bezafibrate appears to be beneficial for hyperlipidemic patients developing diabetes. Further research into the combination treatment of statin and nonstatin lipid-lowering medicines in hyperlipidemic patients developing diabetes is warranted.

Study Objectives: Smoking and sleep are modifiable factors associated with the chronic kidney diseases. However, the interaction of smoking and sleep on the renal function are still unclear. Therefore, we aimed to evaluate the interactive impacts of smoking and sleep on the renal function.Methods: Data were obtained from the National Health and Nutrition Examination Survey. The study population were categorized into nine subgroups by smoking (smoking every day, sometimes, and non-smokers recently) and sleep duration (short duration ≤ 6 h, normal duration 6–9 h, and longer duration ≥ 9 h on the weekdays).Results: The study group with a short sleep duration had significantly higher serum cotinine and hydrocotinine levels compared with the other two sleep groups. After adjusting the demographic characteristics (age, race, body mass index, and marital status), sleep quality (snoring or breathing cessation), and comorbidities (diabetes mellitus, hypertension, high cholesterol, anemia, congestive heart failure, coronary heart disease, and stroke), non-smokers with short or long sleep duration had significant lower estimated glomerular filtration rate (eGFR) levels than the study group who smoked every day and slept ≤ 6 h. The effects of sleep duration on eGFR levels varied with smoking status. For the study group smoking every day, eGFR levels increased as sleep duration decreased, whereas for the study group smoking sometimes, eGFR levels increased as sleep duration increased. The U-shaped effects of eGFR levels were observed among non-smokers whose normal sleep duration was associated with better eGFR levels. Normal sleep duration was an important protective factor of the renal function for non-smokers than smokers.Conclusions: The effects of sleep duration on eGFR levels varied with smoking status. Normal sleep duration was a protective factor and more crucial for non-smokers than for smokers.

Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0–81.4% and 74.6–78% respectively (rfm, ACC 63.2–65.5%, AUC 61.9–74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p –8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

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Backgrounds: Marathons and road running have become popular in recent years, sudden cardiac death may occur even in young populations. Early detection the cardiac function changes during running is imperative. Methods: This study used the American Zephyr™ product BioHarness 3.0. It can measure basic physiological parameters, including heart-rate, respiratory rate, temperature, maximum oxygen consumption and activity. Interpretation: We investigated the correlations among the 2 new physiological parameters, including cardiac force index (CFI) = weight * activity / heart rate, cardiac force ratio (CFR) = CFI of running / CFI of walking, and finish times for 3000 meters of running. After analysis of multi-variates logistic regression model, accumulation of heart activities during running was statistically significant with the qualification of the 3000-meter running test (p = 0.04). Finally, we compared the correlations of CFR(s) between runners with stronger and weaker cardiac force during 3000-meter running. In conclusion, this study results suggested that real-time CFR changes could be a predication model for the 3000-meter running performance. Funding Statement: This study was supported from the grants of AFTYGH-10752 and AFTYGH-10812, Taoyuan Armed Forces General Hospital, Taiwan, ROC. Declaration of Interests: The authors state: "None declared." Ethics Approval Statement: This was a cross-sectional study which was approved by the human trial committee of the Tri-Service General Hospital, Taiwan. Approval for this study was provided by the Institutional Review Board (IRB) of the Tri-Service General Hospital, Taiwan (Approval No: TSGH- IRB-1-104-05-147).

Background Long-distance running can be a form of stress to the heart. Technological improvements combined with the public’s gradual turn toward mobile health (mHealth), self-health, and exercise effectiveness have resulted in the widespread use of wearable exercise products. The monitoring of dynamic cardiac function changes during running and running performance should be further studied. Objective We investigated the relationship between dynamic cardiac function changes and finish time for 3000-meter runs. Using a wearable device based on a novel cardiac force index (CFI), we explored potential correlations among 3000-meter runners with stronger and weaker cardiac functions during running. Methods This study used the American product BioHarness 3.0 (Zephyr Technology Corporation), which can measure basic physiological parameters including heart rate, respiratory rate, temperature, maximum oxygen consumption, and activity. We investigated the correlations among new physiological parameters, including CFI = weight * activity / heart rate, cardiac force ratio (CFR) = CFI of running / CFI of walking, and finish times for 3000-meter runs. Results The results showed that waist circumference, smoking, and CFI were the significant factors for qualifying in the 3000-meter run. The prediction model was as follows: ln (3000 meters running performance pass probability / fail results probability) = –2.702 – 0.096 × [waist circumference] – 1.827 × [smoke] + 0.020 × [ACi7]. If smoking and the ACi7 were controlled, contestants with a larger waist circumference tended to fail the qualification based on the formula above. If waist circumference and ACi7 were controlled, smokers tended to fail more often than nonsmokers. Finally, we investigated a new calculation method for monitoring cardiac status during exercise that uses the CFI of walking for the runner as a reference to obtain the ratio between the cardiac force of exercise and that of walking (CFR) to provide a standard for determining if the heart is capable of exercise. A relationship is documented between the CFR and the performance of 3000-meter runs in a healthy 22-year-old person. During the running period, data are obtained while participant slowly runs 3000 meters, and the relationship between the CFR and time is plotted. The runner’s CFR varies with changes in activity. Since the runner’s acceleration increases, the CFR quickly increases to an explosive peak, indicating the runner’s explosive power. At this period, the CFI revealed a 3-fold increase (CFR=3) in a strong heart. After a time lapse, the CFR is approximately 2.5 during an endurance period until finishing the 3000-meter run. Similar correlation is found in a runner with a weak heart, with the CFR at the beginning period being 4 and approximately 2.5 thereafter. Conclusions In conclusion, the study results suggested that measuring the real-time CFR changes could be used in a prediction model for 3000-meter running performance.

Background and objective: To combine the diagnosis of OSA with titration of positive airway pressure (PAP), current guidelines recommend that split-night polysomnography (PSG) be performed if an AHI of ≥40/h is recorded over 2 h. However, the diagnostic validity of partial-night PSG is uncertain. This study aimed to test the validity of partial-night PSG and to determine the optimum AHI cut-off points. Methods: Patients who visited the sleep centre at a tertiary medical centre between January and December 2008, for symptoms related to sleep disorders (sleepiness, snoring, sleep disturbance), and who completed full-night PSG, were evaluated for this study. Full-night PSG data were processed to obtain partial-night PSG data, from which AHI were computed as a reference for diagnosing severe OSA. Full-night and partial-night PSG data obtained over different recording times (expressed as x-h PSG, where x = 1–6) were compared using receiver operating characteristic (ROC) curve analysis. The diagnostic validity of 2-h PSG with different AHI cut-off points (25/h to 45/h) was also calculated. Results: Data from 198 PSG recordings was processed. For 2-h PSG, an AHI cut-off point of 30/h gave the highest accuracy of 90.9%. Comparing areas under the ROC curves (AUC), 2-h PSG (AUC = 0.97) was as good as 2.5-h PSG (AUC = 0.977, P = 0.057) and 3-h PSG (AUC = 0.978, P = 0.125), but was better than 1.5-h PSG (AUC = 0.955, P = 0.016). Conclusions: Partial-night PSG is effective for diagnosing severe OSA. If there is an unabridged PSG recording indicating an AHI of ≥30/h for 2 h, severe OSA can be diagnosed and PAP titration initiated.

Optimal molecular markers for detecting colorectal cancer (CRC) in a blood-based assay were evaluated.A matched (by variables of age and sex) case-control design (111 CRC and 227 non-cancer samples) was applied. Total RNAs isolated from the 338 blood samples were reverse-transcribed, and the relative transcript levels of candidate genes were analyzed. The training set was made of 162 random samples of the total 338 samples. A logistic regression analysis was performed, and odds ratios for each gene were determined between CRC and non-cancer. The samples (n = 176) in the testing set were used to validate the logistic model, and an inferred performance (generality) was verified. By pooling 12 public microarray datasets(GSE 4107, 4183, 8671, 9348, 10961, 13067, 13294, 13471, 14333, 15960, 17538, and 18105), which included 519 cases of adenocarcinoma and 88 controls of normal mucosa, we were able to verify the selected genes from logistic models and estimate their external generality.The logistic regression analysis resulted in the selection of five significant genes (P0.05; MDM2, DUSP6, CPEB4, MMD, and EIF2S3), with odds ratios of 2.978, 6.029, 3.776, 0.538 and 0.138, respectively. The five-gene model performed stably for the discrimination of CRC cases from controls in the training set, with accuracies ranging from 73.9% to 87.0%, a sensitivity of 95% and a specificity of 95%. In addition, a good performance in the test set was obtained using the discrimination model, providing 83.5% accuracy, 66.0% sensitivity, 92.0% specificity, a positive predictive value of 89.2% and a negative predictive value of 73.0%. Multivariate logistic regressions analyzed 12 pooled public microarray data sets as an external validation. Models that provided similar expected and observed event rates in subgroups were termed well calibrated. A model in which MDM2, DUSP6, CPEB4, MMD, and EIF2S3 were selected showed the result in logistic regression analysis (H-L P = 0.460, R2= 0.853, AUC = 0.978, accuracy = 0.949, specificity = 0.818 and sensitivity = 0.971).A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.

To verify gene expression profiles for colorectal cancer using 12 internet public microarray datasets.Logistic regression analysis was performed, and odds ratios for each gene were determined between colorectal cancer (CRC) and controls. Twelve public microarray datasets of GSE 4107, 4183, 8671, 9348, 10961, 13067, 13294, 13471, 14333, 15960, 17538, and 18105, which included 519 cases of adenocarcinoma and 88 normal mucosa controls, were pooled and used to verify 17 selective genes from 3 published studies and estimate the external generality.We validated the 17 CRC-associated genes from studies by Chang et al (Model 1: 5 genes), Marshall et al (Model 2: 7 genes) and Han et al (Model 3: 5 genes) and performed the multivariate logistic regression analysis using the pooled 12 public microarray datasets as well as the external validation. The goodness-of-fit test of Hosmer-Lemeshow (H-L) showed statistical significance (P = 0.044) for Model 2 of Marshall et al in which observed event rates did not match expected event rates in subgroups of the model population. Expected and observed event rates in subgroups were similar, which are called well calibrated, in Models 1, 3 and 4 with non-significant P values of 0.460, 0.194 and 1.000 for H-L tests, respectively. A 7-gene model of CPEB4, EIF2S3, MGC20553, MS4A1, ANXA3, TNFAIP6 and IL2RB was pairwise selected, which showed the best results in logistic regression analysis (H-L P = 1.000, R (2) = 0.951, areas under the curve = 0.999, accuracy = 0.968, specificity = 0.966 and sensitivity = 0.994).A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.

Background: Injuries are the leading causes of death and contribute greatly to morbidity in children. Our study examined injuries' age and gender-specific variations over time among children 0-19, from 1989 to 2007. Materials and Methods: Numbers of deaths caused by injury are drawn from Taiwan's official Vital Statistics System. Mortality was age-adjusted to the US 2000 standard population. Temporal trends were analyzed by linear regression. Results: Both genders' annual mortality rates and proportional mortality ratios of unintentional injuries declined significantly during 1989-2007. Conversely, an increasing trend of intentional deaths occurred. In general, during 1992-2007, increasing the rates of suicide deaths in ages 10-19 and of homicide deaths in ages 0-9 occurred. Boys had more suicide deaths than did girls. Conclusions: Unlike unintentional injuries, intentional injuries increased over the 1989-2007 period. Deaths in the subgroups of ages 0-19 and categorized by genders were caused by varying injuries.