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Abstract Background Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction. Methods The deep learning model was trained using a large dataset of 47,245 electrocardiograms from 33,246 patients at an academic medical center. Patients were included if electrocardiograms and measurements of serum thyroid-stimulating hormone were available that had been obtained within a three day period. Serum thyroid-stimulating hormone and free thyroxine were used to define overt and subclinical hyperthyroidism. We tested the model internally using 14,420 patients and externally using two additional test sets comprising 11,498 and 596 patients, respectively. Results The performance of the deep learning model achieves areas under the receiver operating characteristic curves (AUCs) of 0.725–0.761 for hyperthyroidism detection, AUCs of 0.867–0.876 for overt hyperthyroidism, and AUC of 0.631–0.701 for subclinical hyperthyroidism, superior to a traditional features-based machine learning model. Patients identified as hyperthyroidism-positive by the deep learning model have a significantly higher risk (1.97–2.94 fold) of all-cause mortality and new-onset heart failure compared to hyperthyroidism-negative patients. This cardiovascular disease stratification is particularly pronounced in subclinical hyperthyroidism, surpassing that observed in overt hyperthyroidism. Conclusions An innovative algorithm effectively identifies overt and subclinical hyperthyroidism and contributes to cardiovascular risk assessment.

Background The electrocardiogram (ECG) may be the most popular test in the management of cardiovascular disease (CVD). Although wide applications of artificial intelligence (AI)-enabled ECG have been developed, an integrating indicator for CVD risk stratification was not investigated. Since mortality may be the most important global outcome, this study aimed to develop a survival deep learning model (DLM) to establish a critical ECG value and explore the associations with various CVD events. Methods We trained a DLM with 451,950 12-lead resting ECGs obtained from 210,552 patients, for whom 23,592 events occurred. The internal validation set included 27,808 patients with one ECG for each patient. The external validations were performed in a community hospital with 33,047 patients and two transnational data sets with 233,647 and 1631 ECGs. We distinguished the cause of mortality and additionally investigated CVD-related outcomes, including new-onset acute myocardial infarction (AMI), stroke (STK), and heart failure (HF). Results The DLM achieved C-indices of 0.858/0.836 in internal/external validation sets by using ECG over a 10-year period. The high-mortality-risk group identified by the proposed DLM presented a hazard ratio (HR) of 14.16 (95% confidence interval (CI): 11.33–17.70) compared to the low-risk group in the internal validation and presented a higher risk of cardiovascular (CV) mortality (HR: 18.50, 95% CI: 9.82–34.84), non-CV mortality (HR: 13.68, 95% CI: 10.76–17.38), AMI (HR: 4.01, 95% CI: 2.24–7.17), STK (HR: 2.15, 95% CI: 1.70–2.72), and HF (HR: 6.66, 95% CI: 4.54–9.77), which was consistent in an independent community hospital. The transnational validation also revealed HRs of 4.91 (95% CI: 2.63–9.16) and 2.29 (95% CI: 2.15–2.44) for all-cause mortality in the SaMi-Trop and Clinical Outcomes in Digital Electrocardiography 15% (CODE15) cohorts. Conclusions The mortality risk by AI-enabled ECG may be applied in passive electronic-health-record-based CVD risk screening, which may identify more asymptomatic and unaware high-risk patients.

Objectives Chest X-rays (CXRs) convey much illegible physiological information that deep learning model (DLM) has been reported interpreting successfully. Since the electrocardiogram age established by DLM was revealed as a heart biological marker, we hypothesize that CXR age has similar potential to describe the heart and lung states. Therefore, we developed a DLM to predict sex and age through CXR and analyzed its relation with future cardiovascular diseases (CVD). Methods A total of 90,396 CXRs aged 20 to 90 were collected and separated into a development set with 53,102 CXRs and demographic information pairs, a tuning set with 7073 pairs, an internal validation set with 17,364 pairs, and an external validation set with 12,857 pairs. The study trained DLM with development set for estimating age and sex and compared them to actual information. Results The mean absolute errors of predicted age were 4.803 and 4.313 years in the internal and external validation sets, respectively. The area under the curve of sex analysis was 0.9993 and 0.9988 in the internal and external validation sets, respectively. Patients whose CXR age was 5 years older than chronologic age lead to higher risk of all-cause mortality (hazard ratio (HR): 2.42, 95% confidence interval (CI): 2.00–2.92), cardiovascular (CV)-cause mortality (HR: 7.57, 95% CI: 4.55–12.60), new-onset heart failure (HR: 2.07, 95% CI: 1.56–2.76), new-onset chronic kidney disease (HR: 1.73, 95% CI: 1.46–2.05), new-onset acute myocardial infarction (HR: 1.80, 95% CI: 1.12–2.92), new-onset stroke (HR: 1.45, 95% CI: 1.10–1.90), new-onset coronary artery disease (HR: 1.26, 95% CI: 1.04–1.52), and new-onset atrial fibrillation (HR: 1.43, 95% CI: 1.01–2.02). Conclusions Using DLM to predict CXR age provided additional information for future CVDs. Older CXR age is an accessible risk classification tool for clinician use.

This bibliometric analysis explores the synergy of artificial intelligence (AI), particularly machine learning, and biomedical signal processing in predicting patient mortality risk within the intensive care unit (ICU). Utilizing a comprehensive literature review, the study assesses the research landscape by applying these techniques to ICU data. Examining diverse data sources like vital signs and electronic health records, the analysis identifies trends and gaps in existing work, emphasizing AI’s potential for resource allocation and preventative care to enhance ICU outcomes. Structured within a bibliometric framework, the review encompasses methodological approaches, results, and discussions, while addressing clinical and ethical perspectives on mortality prediction. Challenges related to data, model performance, and fairness are evaluated through a bibliometric lens. The research questions underscore the importance of understanding past literature trends in predictive analysis for ICU patients. The review methodologically explores recent studies employing word representation models, impact assessments, and risk prediction of vital signs. Global research trends in AI for critical care are identified based on bibliographic data between 2013 and 2022. Noteworthy contributions, such as a sepsis dataset, are highlighted within the bibliometric analysis. In conclusion, this bibliometric analysis positions itself at the intersection of AI and critical care, emphasizing the importance of bibliographic data in understanding past trends, methodologies, and impactful contributions. It sets the stage for future directions in the evolving landscape of ICU predictive analytics within a bibliometric framework.

Background: Over the last decade, many studies have assessed the efficacy of treatments for refractory/relapsed multiple myeloma (R/R MM). While combination therapies show greater efficacy than traditional methods, limited research has targeted elderly patients who might be less resilient to treatments. Our study aimed to evaluate treatment efficacy for these elderly patients. Methods: We carried out a comprehensive review of the literature using a systematic approach. Initially, 4966 citations were retrieved and subsequently narrowed down to 13 eligible randomized controlled trials (RCTs) through our systematic review process from databases like Embase, PubMed, and Cochrane Library from 1 January 2000 to 31 December 2022. Evidence was collated through a frequentist network meta-analysis, using the hazard ratio (HR) for evaluation. Results: Combined therapy of daratumumab, lenalidomide, and dexamethasone (DaraLenDex) was the preferred treatment for R/R MM elderly patients. Its strengths included an HR for progression-free survival (0.15; 95% CI: 0.09–0.25) and a 96% P-score. Conclusions: Our analysis suggests that, pending more comprehensive RCTs, DaraLenDex is the treatment with the highest efficacy for R/R MM in elderly patients.

Abstract Dyskalemias are common electrolyte disorders associated with high cardiovascular risk. Artificial intelligence (AI)-assisted electrocardiography (ECG) has been evaluated as an early-detection approach for dyskalemia. The aims of this study were to determine the clinical accuracy of AI-assisted ECG for dyskalemia and prognostic ability on clinical outcomes such as all-cause mortality, hospitalizations, and ED revisits. This retrospective cohort study was done at two hospitals within a health system from May 2019 to December 2020. In total, 26,499 patients with 34,803 emergency department (ED) visits to an academic medical center and 6492 ED visits from 4747 patients to a community hospital who had a 12-lead ECG to estimate ECG-K+ and serum laboratory potassium measurement (Lab-K+) within 1 h were included. ECG-K+ had mean absolute errors (MAEs) of ≤0.365 mmol/L. Area under receiver operating characteristic curves for ECG-K+ to predict moderate-to-severe hypokalemia (Lab-K+ ≤3 mmol/L) and moderate-to-severe hyperkalemia (Lab-K+ ≥ 6 mmol/L) were >0.85 and >0.95, respectively. The U-shaped relationships between K+ concentration and adverse outcomes were more prominent for ECG-K+ than for Lab-K+. ECG-K+ and Lab-K+ hyperkalemia were associated with high HRs for 30-day all-cause mortality. Compared to hypokalemic Lab-K+, patients with hypokalemic ECG-K+ had significantly higher risk for adverse outcomes after full confounder adjustment. In addition, patients with normal Lab-K+ but dyskalemic ECG-K+ (pseudo-positive) also exhibited more co-morbidities and had worse outcomes. Point-of-care bloodless AI ECG-K+ not only rapidly identified potentially severe hypo- and hyperkalemia, but also may serve as a biomarker for medical complexity and an independent predictor for adverse outcomes.

Background Artificial intelligence-enabled electrocardiogram has become a substitute tool for echocardiography in left ventricular ejection fraction estimation. However, the direct use of artificial intelligence-enabled electrocardiogram may be not trustable due to the uncertainty of the prediction. Objective The study aimed to establish an artificial intelligence-enabled electrocardiogram with a degree of confidence to identify left ventricular dysfunction. Methods The study collected 76,081 and 11,771 electrocardiograms from an academic medical center and a community hospital to establish and validate the deep learning model, respectively. The proposed deep learning model provided the point estimation of the actual ejection fraction and its standard deviation derived from the maximum probability density function of a normal distribution. The primary analysis focused on the accuracy of identifying patients with left ventricular dysfunction (ejection fraction ≤ 40%). Since the standard deviation was an uncertainty indicator in a normal distribution, we used it as a degree of confidence in the artificial intelligence-enabled electrocardiogram. We further explored the clinical application of estimated standard deviation and followed up on the new-onset left ventricular dysfunction in patients with initially normal ejection fraction. Results The area under receiver operating characteristic curves (AUC) of detecting left ventricular dysfunction were 0.9549 and 0.9365 in internal and external validation sets. After excluding the cases with a lower degree of confidence, the artificial intelligence-enabled electrocardiogram performed better in the remaining cases in internal (AUC = 0.9759) and external (AUC = 0.9653) validation sets. For the application of future left ventricular dysfunction risk stratification in patients with initially normal ejection fraction, a 4.57-fold risk of future left ventricular dysfunction when the artificial intelligence-enabled electrocardiogram is positive in the internal validation set. The hazard ratio was increased to 8.67 after excluding the cases with a lower degree of confidence. This trend was also validated in the external validation set. Conclusion The deep learning model with a degree of confidence can provide advanced improvements in identifying left ventricular dysfunction and serve as a decision support and management-guided screening tool for prognosis.

BACKGROUND: The B-type natriuretic peptide (BNP) and N-terminal pro-brain natriuretic peptide (pBNP) are predictors of cardiovascular morbidity and mortality. Since the artificial intelligence (AI)-enabled electrocardiogram (ECG) system is widely used in the management of many cardiovascular diseases (CVDs), patients requiring intensive monitoring may benefit from an AI-ECG with BNP/pBNP predictions. This study aimed to develop an AI-ECG to predict BNP/pBNP and compare their values for future mortality. METHODS: The development, tuning, internal validation, and external validation sets included 47,709, 16,249, 4001, and 6042 ECGs, respectively. Deep learning models (DLMs) were trained using a development set for estimating ECG-based BNP/pBNP (ECG-BNP/ECG-pBNP), and the tuning set was used to guide the training process. The ECGs in internal and external validation sets belonging to nonrepeating patients were used to validate the DLMs. We also followed-up all-cause mortality to explore the prognostic value. RESULTS: The DLMs accurately distinguished mild (≥500 pg/mL) and severe (≥1000 pg/mL) an abnormal BNP/pBNP with AUCs of ≥0.85 in the internal and external validation sets, which provided sensitivities of 68.0–85.0% and specificities of 77.9–86.2%. In continuous predictions, the Pearson correlation coefficient between ECG-BNP and ECG-pBNP was 0.93, and they were both associated with similar ECG features, such as the T wave axis and correct QT interval. ECG-pBNP provided a higher all-cause mortality predictive value than ECG-BNP. CONCLUSIONS: The AI-ECG can accurately estimate BNP/pBNP and may be useful for monitoring the risk of CVDs. Moreover, ECG-pBNP may be a better indicator to manage the risk of future mortality.

BackgroundProximal protection devices, such as the Mo.Ma system provides better neurological outcomes than the distal filter system in the carotid artery stenting (CAS) procedure. This study first evaluated the safety and efficacy of the Mo.Ma system during CAS in a single tertiary referral hospital from Taiwan. The outcomes of distal vs. proximal embolic protection devices were also studied.MethodsA total of 294 patients with carotid artery stenosis who underwent the CAS procedure were retrospectively included and divided into two groups: 152 patients in the distal filter system group and 142 patients in the Mo.Ma system. The outcomes of interest were compared between the two groups. The factors contributing to occlusion intolerance (OI) in the Mo.Ma system were evaluated.ResultsThe procedure success rates were more than 98% in both groups. No major stroke occurred in this study. The minor stroke rates were 2.8% (4/142) and 4.6% (7/152) in the Mo.Ma system and filter system, respectively (p = 0.419). Patients with hypoalbuminemia significantly predicted the risk of stroke with an odds ratio of 0.08 [95% confidence interval (CI), 0.01–0.68, p = 0.020] per 1 g/day of serum albumin in the filter group. A total of 12 patients developed OI in the Mo.Ma system (12/142, 8%). Low occlusion pressure predicted the occurrence of OI in the Mo.Ma group with the hazard ratios of 0.88 (95% CI: 0.82–0.96) and 0.90 (95% CI: 0.84–0.98) per 1 mmHg of occlusion systolic pressure (OSP) and diastolic pressure (ODP), respectively. We further indicated that patients with an OSP of ≥60 mmHg or an ODP of ≥44 mmHg could tolerate the procedure of occlusion time up to 400 s, while patients with an OSP of

BackgroundAlbumin, an important component of fluid balance, is associated with kidney, liver, nutritional, and cardiovascular diseases (CVD) and is measured by blood tests. Since fluid balance is associated with electrocardiography (ECG) changes, we established a deep learning model (DLM) to estimate albumin via ECG.ObjectiveThis study aimed to develop a DLM to estimate albumin via ECG and explored its contribution to future complications.Materials and MethodsA DLM was trained for estimating ECG-based albumin (ECG-Alb) using 155,078 ECGs corresponding to albumin from 79,111 patients, and another independent 13,335 patients from an academic medical center and 11,370 patients from a community hospital were used for internal and external validation. The primary analysis focused on distinguishing patients with mild to severe hypoalbuminemia, and the secondary analysis aimed to provide additional prognostic value from ECG-Alb for future complications, which included mortality, new-onset hypoalbuminemia, chronic kidney disease (CKD), new onset hepatitis, CVD mortality, new-onset acute myocardial infarction (AMI), new-onset stroke (STK), new-onset coronary artery disease (CAD), new-onset heart failure (HF), and new-onset atrial fibrillation (Afib).ResultsThe AUC to identify hypoalbuminemia was 0.8771 with a sensitivity of 56.0% and a specificity of 90.7% in the internal validation set, and the Pearson correlation coefficient was 0.69 in the continuous analysis. The most important ECG features contributing to ECG-Alb were ordered in terms of heart rate, corrected QT interval, T wave axis, sinus rhythm, P wave axis, etc. The group with severely low ECG-Alb had a higher risk of all-cause mortality [hazard ratio (HR): 2.45, 95% CI: 1.81–3.33] and the other hepatorenal and cardiovascular events in the internal validation set. The external validation set yielded similar results.ConclusionHypoalbuminemia and its complications can be predicted using ECG-Alb as a novel biomarker, which may be a non-invasive tool to warn asymptomatic patients.

BackgroundCertain variables reportedly are associated with a change in left ventricular ejection fraction (LVEF) in heart failure (HF) with reduced ejection fraction (HFrEF). However, literature describing the association between the recovery potential of LVEF and parameters of ventricular remodeling in echocardiography remains sparse.MethodsWe recruited 2,148 HF patients with LVEF < 35%. All patients underwent at least two echocardiographic images. The study aimed to compare LVEF alterations and their association with patient characteristics and echocardiographic findings.ResultsPatients with “recovery” of LVEF (follow-up LVEF ≥ 50%) were less likely to have prior myocardial infarction (MI), had a higher prevalence of atrial fibrillation (Af), were less likely to have diabetes and hypertension, and had a smaller left atrium (LA) diameter, left ventricular end-diastolic diameter (LVEDD) and left ventricular end-systolic diameter (LVESD), both in crude and in adjusted models (adjustment for age and sex). LVEDD cutoff values of 59.5 mm in men and 52.5 mm in women and LVESD cutoff values of 48.5 mm in men and 46.5 mm in women showed a year-to-year increase in the rate of recovery (follow-up LVEF ≥ 50%)/improvement (follow-up LVEF ≥ 35%), p-value < 0.05 in Kaplan–Meier estimates of the cumulative hazard curves.ConclusionsOur study shows that LVEDD and LVESD increments in echocardiography can be predictors of changes in LVEF in in HF patients with LVEF < 35%. They may be used to identify patients who require more aggressive therapeutic interventions.

BackgroundHeart failure (HF) is a global disease with increasing prevalence in an aging society. However, the survival rate is poor despite the patient receiving standard treatment. Early identification of patients with a high risk of HF is important but challenging. Left ventricular end-diastolic diameter (LV-D) increase was an independent risk factor of HF and adverse cardiovascular (CV) outcomes. In this study, we aimed to develop an artificial intelligence (AI) enabled electrocardiogram (ECG) system to detect LV-D increase early.ObjectiveWe developed a deep learning model (DLM) to predict left ventricular end-diastolic and end-systolic diameter (LV-D and LV-S) with internal and external validations and investigated the relationship between ECG-LV-D and echocardiographic LV-D and explored the contributions of ECG-LV-D on future CV outcomes.MethodsElectrocardiograms and corresponding echocardiography data within 7 days were collected and paired for DLM training with 99,692 ECGs in the development set and 20,197 ECGs in the tuning set. The other 7,551 and 11,644 ECGs were collected from two different hospitals to validate the DLM performance in internal and external validation sets. We analyzed the association and prediction ability of ECG-LVD for CV outcomes, including left ventricular (LV) dysfunction, CV mortality, acute myocardial infarction (AMI), and coronary artery disease (CAD).ResultsThe mean absolute errors (MAE) of ECG-LV-D were 5.25/5.29, and the area under the receiver operating characteristic (ROC) curves (AUCs) were 0.8297/0.8072 and 0.9295/0.9148 for the detection of mild (56 ≦ LV-D < 65 mm) and severe (LV-D ≧ 65 mm) LV-D dilation in internal/external validation sets, respectively. Patients with normal ejection fraction (EF) who were identified as high ECHO-LV-D had the higher hazard ratios (HRs) of developing new onset LV dysfunction [HR: 2.34, 95% conference interval (CI): 1.78–3.08], CV mortality (HR 2.30, 95% CI 1.05–5.05), new-onset AMI (HR 2.12, 95% CI 1.36–3.29), and CAD (HR 1.59, 95% CI 1.26–2.00) in the internal validation set. In addition, the ECG-LV-D presents a 1.88-fold risk (95% CI 1.47–2.39) on new-onset LV dysfunction in the external validation set.ConclusionThe ECG-LV-D not only identifies high-risk patients with normal EF but also serves as an independent risk factor of long-term CV outcomes.

ObjectiveThe biological age progression of the heart varies from person to person. We developed a deep learning model (DLM) to predict the biological age via ECG to explore its contribution to future cardiovascular diseases (CVDs).MethodsThere were 71,741 cases ranging from 20 to 80 years old recruited from the health examination center. The development set used 32,707 cases to train the DLM for estimating the ECG-age, and 8,295 cases were used as the tuning set. The validation set included 30,469 ECGs to follow the outcomes, including all-cause mortality, cardiovascular-cause mortality, heart failure (HF), diabetes mellitus (DM), chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke (STK), coronary artery disease (CAD), atrial fibrillation (AF), and hypertension (HTN). Two independent external validation sets (SaMi-Trop and CODE15) were also used to validate our DLM.ResultsThe mean absolute errors of chronologic age and ECG-age was 6.899 years (r = 0.822). The higher difference between ECG-age and chronological age was related to more comorbidities and abnormal ECG rhythm. The cases with the difference of more than 7 years had higher risk on the all-cause mortality [hazard ratio (HR): 1.61, 95% CI: 1.23–2.12], CV-cause mortality (HR: 3.49, 95% CI: 1.74–7.01), HF (HR: 2.79, 95% CI: 2.25–3.45), DM (HR: 1.70, 95% CI: 1.53–1.89), CKD (HR: 1.67, 95% CI: 1.41–1.97), AMI (HR: 1.76, 95% CI: 1.20–2.57), STK (HR: 1.65, 95% CI: 1.42–1.92), CAD (HR: 1.24, 95% CI: 1.12–1.37), AF (HR: 2.38, 95% CI: 1.86–3.04), and HTN (HR: 1.67, 95% CI: 1.51–1.85). The external validation sets also validated that an ECG-age >7 years compare to chronologic age had 3.16-fold risk (95% CI: 1.72–5.78) and 1.59-fold risk (95% CI: 1.45–1.74) on all-cause mortality in SaMi-Trop and CODE15 cohorts. The ECG-age significantly contributed additional information on heart failure, stroke, coronary artery disease, and atrial fibrillation predictions after considering all the known risk factors.ConclusionsThe ECG-age estimated via DLM provides additional information for CVD incidence. Older ECG-age is correlated with not only on mortality but also on other CVDs compared with chronological age.

Background/Objective: The aim of study is to assess the efficacy of each ventilator weaning method for ventilated patients in intensive care units (ICUs).Methods: A systematic search was conducted using PubMed, Embase, and China National Knowledge Infrastructure to identify randomized control studies on ventilated patients regarding extubation associated outcomes (weaning success or failure, proportion requiring re-intubation, or mortality) from inception until April 01, 2020. Commonly used ventilation modes involved pressure support ventilation, synchronized intermittent mandatory ventilation, automatic tube compensation, continuous positive airway pressure, adaptive support ventilation, neurally adjusted ventilatory assist, proportional assisted ventilation, and SmartCare. Pooled estimates regarding extubation associated outcomes were calculated using network meta-analysis.Results: Thirty-nine randomized controlled trials including 5,953 patients met inclusion criteria. SmartCare and proportional assist ventilation were found to be effective methods in increasing weaning success (odds ratio, 2.72, 95% confidence interval (CI), 1.33–5.58, P-score: 0.84; odds ratio, 2.56, 95% CI, 1.60–4.11, P-score: 0.83; respectively). Besides, proportional assist ventilation had superior in reducing proportion requiring re-intubation rate (odds ratio, 0.48, 95% CI, 0.25–0.92, P-score: 0.89) and mortality (odds ratio, 0.48, 95% CI, 0.26–0.92, P-score: 0.91) than others.Conclusion: In general consideration, our study provided evidence that weaning with proportional assist ventilation has a high probability of being the most effective ventilation mode for patients with mechanical ventilation regarding a higher rate of weaning success, a lower proportion requiring reintubation, and a lower mortality rate than other ventilation modes.

Background: Moderator effect assessment is important in personalized medicine. We mathematically prove that the average summary value is actually nonlinearly to logRR, and we assess the bias from linear meta-regression on logRR via simulation. Methods: In the meta-analysis of randomized controlled trials, the moderator effect is generally evaluated by the linear meta-regression of the logarithmic risk ratio (RR) versus the average summary value of the entire study population. Conclusions: We recommend using linear meta-regression on logarithmic odds ratio (logOR) since it has been shown that the average summary value is actually linear to logOR.

Background: In the past decade, Tri-Service General Hospital has trained many actors-background standardized patients (SPs). However, we also have recruited some nonactor SPs since 2010. Reviewing the literature, the reliability of SPs with or without actor background would be the impact factor on high-stakes OSCE has not been well studied. Aim: The purpose of this retrospective observation is to clarify whether a SP with or without a professional actor background will affect the performances of the examination when participating in the Taiwan high-stakes objective structured clinical examination (OSCE). The result will be the policy for selecting which kind of SPs to participate in the high-stack OSCE in our hospital. Methods: In this retrospective observation, we analyzed 74 actor background SPs (A-SPs), and 70 no actor background SPs (NA-SPs) who have participated in each spring test of 2015–2017 Taiwan high-stakes OSCE. The data of SPs performance have come from two parts: one is from the examinee with the global rating of the SPs performance and the other is from the examiner with eight-item checklist for the SPs performance. The scoring of both examinee and examiner is a five-point Likert scale. Results: The results show that there is no significant difference in SPs performance from the examinee scoring in the subsequent 3 years, stable and satisfactory, and let examinees feel like real patients. The scores from examiners also showed that two groups of SPs performed equally well without significant differences. Their performance is reliable and consistent, simulating to be a real patient in both groups. However, only one interest finding, when compared to the different age subdivision of SPs, showed the aged subgroup of A-SPs and NA-SPs with the significant difference in the item of reliability and consistent (4.67 ± 0.53 vs. 4.41 ± 0.50, P < 0.05), the possible reason is related to the less-experienced associate with mild memory decline in aged NA-SPs.Conclusions: Our observation gives the essential information, that the well-trained and experienced SP are necessary for participating in the high-stakes OSCE examination, even without the background of the actor. What we need is a professional SP who not only has excellent professionalism but also has a good personality literacy to engage in his/her work.

Large structural variants (SV) are understudied in human genetics research because of the difficulty to detect them in the routinely generated short-read sequencing data. Here, the authors generate optical genome maps of 154 individuals from 26 populations that allow comprehensive examination of large SVs.

Abstract Background Both SOX2 promoter methylation and air pollution have been associated with lung cancer risk. However, little has been done to assess SOX2 promoter methylation in individuals living in air pollution areas. The aim of this study was to investigate SOX2 promoter methylation in non-smoking Taiwanese adults living in areas with different levels of air pollution especially particulate matter with diameter

Abstract Nutritional factors contributing to disability and mortality are modifiable in later life. Indices would add utility. We developed a gender-specific Healthy Ageing Nutrition Index (HANI) for all-cause mortality in free-living elderly. We stratified 1898 participants aged ≥65 y from the 1999–2000 Nutrition and Health Survey in Taiwan by region and randomly allocated them into development and validation sets. Linkage to the National Death Registry database until December 31, 2008 enabled mortality prediction using Cox proportional-hazards models. Four factors (appetite, eating with others, dietary diversity score, and BMI) with best total of 25 HANI points for men; and 3 factors (cooking frequency, dietary diversity score, and BMI) with best total of 27 HANI points for women, were developed. In the validation set, the highest HANI group exhibited a greater intake of plant-derived food and associated nutrients, a favourable quality of life, and more muscle mass, compared with the lowest group. The highest HANI group predicts mortality risk lower by 44 percent in men and 61 percent in women. Adjusted mortality HRs were comparable between sets. HANI is a simple, non-invasive, inexpensive, and potentially modifiable tool for nutrition monitoring and survival prediction for older adults, superior to its individual components.

Abstract Background Differences between staged bilateral total knee replacement (TKR) and simultaneous bilateral TKR have been investigated, but few studies have investigated differences in the functional improvements resulting from these methods. Therefore, this study investigates the different functional improvements between staged bilateral total knee TKR and simultaneous bilateral TKR. Methods Among 144 potential bilateral TKR patients who were included in this study, 93 (64.6%) patients selected unilateral TKR and 51 (35.4%) selected bilateral TKR. Functional improvements were assessed using the Western Ontario and McMaster University osteoarthritis index (WOMAC) and the Medical Outcomes Trust Short Form-36 (SF-36), and patients were interviewed pre-operatively and after 6 months. A generalized equation was used to test for differences in functional improvements. Results After TKR, pain, stiffness, function and total WOMAC scores were significantly reduced in both groups, with mean changes from − 26.6 to − 41.4 and from − 27.5 to − 42.2.The mean health change of SF-36 scores, physical component and mental component scores changed to 45.2 ± 18.2, 74.0 ± 15.4 and 77.0 ± 9.6, respectively, in Group 1 and 47.1 ± 17.1, 74.0 ± 15.2 and 75.5 ± 12.1, respectively, in Group 2. Unilateral and simultaneous bilateral TKR produce similar functional improvements, although current work status may be a novel impact factor. Conclusion No differences in functional improvements were identified between patients who selected unilateral versus bilateral TKR, indicating no recommendation for one procedure over the other.

Abstract We present a new method, OMSV, for accurately and comprehensively identifying structural variations (SVs) from optical maps. OMSV detects both homozygous and heterozygous SVs, SVs of various types and sizes, and SVs with or without creating or destroying restriction sites. We show that OMSV has high sensitivity and specificity, with clear performance gains over the latest method. Applying OMSV to a human cell line, we identified hundreds of SVs >2 kbp, with 68 % of them missed by sequencing-based callers. Independent experimental validation confirmed the high accuracy of these SVs. The OMSV software is available at http://yiplab.cse.cuhk.edu.hk/omsv/ .

BACKGROUND:Chronic lymphocytic leukemia (CLL) is incurable through conventional chemoimmunotherapy regimens. Despite durable responses to front-line therapy and sustained remission rates in patients with CLL, a majority of patients eventually relapse in 5 years of initial treatment. The depth of the response may affect the length of response. Maintenance therapies were aimed to deep remissions and extend the period of disease quiescence. Lenalidomide, rituximab and ofatumumab had demonstrated some efficacy as a maintenance therapy compared to no intervention for CLL patients. The relative effect on disease control and safety between different maintenance therapies were unclear. METHODS:We performed a systematic literature review and network meta-analysis to evaluate relative effect on disease control and safety of current available maintenance therapies. We searched PubMed, Embase and Cochrane database up to March 6, 2019. Relevant reference of review article and conference abstract including European Hematology Association Annual Meeting (EHA 2018), American Society of Hematology Annual Meeting (ASH 2018) and American Society of Clinical Oncology Annual Meeting (ASCO 2018) were searched. Randomized controlled trials (RCT) involving current available maintenance therapy including "Lenalidomide", "Rituximab", "Ofatumumab", "Ibrutinib", "Idelalisib", "Venetoclax"and "Obinutuzumab"were eligible. Outcomes of interest included progression-free survival (PFS), overall survival (OS) and serious adverse events (SAE) in CLL patients received subsequent maintenance therapy. Two authors CHL and CL) independently assessed eligibility for all identified citations and extracted data from the original trial reports. The selected studies' risk of bias was assessed following the guidelines of Cochrane Collaboration Handbook. RESULTS:In total, six phase III RCTs with total 1,615 CLL patients were identified. Maintenance therapy using lenalidomide, rituximab, and ofatumumab demonstrated a statistically significant effect in prolongation of progression-free survival (HR:0.37, 95% CI: 0.27-0.50 of lenalidomide; HR:0.50, 95% CI: 0.38-0.66 of rituximab; HR:0.52, 95% CI:0.41-0.66 of ofatumumab, separately) compared with no intervention; however, for overall survival, the effect of maintenance therapy showed no significant difference versus no intervention (HR: 0.89, 95% CI: 0.70-1.14). Lenalidomide showed the best efficacy for PFS (HR: 0.37, 95% CI: 0.27-0.50, Probability of being best treatment: 96%). CONCLUSIONS:Our network meta-analysis provided an integrated overview of relative efficacy and safety of different maintenance therapies in CLL. All maintenance therapies were effective in reducing the risk of disease progression versus no intervention. Based on current best evidence, maintenance therapy with lenalidomide is the most efficacious option.

Statins constitute the mainstay treatment for atherosclerotic cardiovascular disease, which is associated with the risk of new-onset diabetes mellitus (NODM). However, the effects of individual statins on the risk of NODM remain unclear. We recruited 48,941 patients taking one of the three interested statins in a tertiary hospital between 2006 and 2018. Among them, 8337 non-diabetic patients taking moderate-intensity statins (2 mg/day pitavastatin, 10 mg/day atorvastatin, and 10 mg/day rosuvastatin) were included. The pitavastatin group had a higher probability of being NODM-free than the atorvastatin and rosuvastatin groups during the 4-year follow-up (log-rank test: p = 0.038). A subgroup analysis revealed that rosuvastatin had a significantly higher risk of NODM than pitavastatin among patients with coronary artery disease (CAD) (adjusted HR [aHR], 1.47, 95% confidence interval [CI], 1.05–2.05, p = 0.025), hypertension (aHR, 1.26, 95% CI, 1.00–1.59, p = 0.047), or chronic obstructive pulmonary disease (COPD) (aHR, 1.74, 95% CI, 1.02–2.94, p = 0.04). We concluded that compared with rosuvastatin, reduced diabetogenic effects of pitavastatin were observed among patients treated with moderate-intensity statin who had hypertension, COPD, or CAD. Additional studies are required to prove the effects of different statins on the risk of NODM.

Although proton magnetic resonance spectroscopy (1H-MRS) is a common method for the evaluation of intracranial meningiomas, controversy exists regarding which parameter of 1H-MRS best predicts the histopathological grade of an intracranial meningioma. In this study, we evaluated the results of pre-operative 1H-MRS to identify predictive factors for high-grade intracranial meningioma. Thirteen patients with World Health Organization (WHO) grade II-III meningioma (confirmed by pathology) were defined as high-grade; twenty-two patients with WHO grade I meningioma were defined as low-grade. All patients were evaluated by 1H-MRS before surgery. The relationships between the ratios of metabolites (N-acetylaspartate [NAA], creatine [Cr], and choline [Cho]) and the diagnosis of high-grade meningioma were analyzed. According to Mann-Whitney U test analysis, the Cho/NAA ratio in cases of high-grade meningioma was significantly higher than in cases of low-grade meningioma (6.34 ± 7.90 vs. 1.58 ± 0.77, p

ABSTRACT Penicillin binding proteins (PBPs) are involved in peptidoglycan synthesis, and their inactivation is linked to β-lactamase expression in ampR–β-lactamase module–harboring Gram-negative bacteria. There are seven annotated PBP genes, namely, mrcA, mrcB, pbpC, mrdA, ftsI, dacB, and dacC, in the Stenotrophomonas maltophilia genome, and these genes encode PBP1a, PBP1b, PBP1c, PBP2, PBP3, PBP4, and PBP6, respectively. In addition, S. maltophilia harbors two β-lactamase genes, L1 and L2, whose expression is induced via β-lactam challenge. The impact of PBP inactivation on L1/L2 expression was assessed in this study. Inactivation of mrdA resulted in increased L1/L2 expression in the absence of β-lactam challenge, and the underlying mechanism was further elucidated. The roles of ampNG, ampDI (the homologue of Escherichia coli ampD), nagZ, ampR, and creBC in L1/L2 expression mediated by a ΔmrdA mutant strain were assessed via mutant construction and β-lactamase activity determinations. Furthermore, the strain ΔmrdA-mediated change in the muropeptide profile was assessed using liquid chromatography mass spectrometry (LC-MS). The mutant ΔmrdA-mediated L1/L2 expression relied on functional AmpNG, AmpR, and NagZ, was restricted by AmpDI, and was less related to the CreBC two-component system. Inactivation of mrdA significantly increased the levels of total and periplasmic N-acetylglucosaminyl-1,6-anhydro-N-acetylmuramyl-l-alanyl-d-glutamyl-meso-diamnopimelic acid-d-alanine (GlcNAc-anhMurNAc tetrapeptide, or M4N), supporting that the critical activator ligands for mutant strain ΔmrdA-mediated L1/L2 expression are anhMurNAc tetrapeptides. IMPORTANCE Inducible expression of chromosomally encoded β-lactamase(s) is a key mechanism for β-lactam resistance in Enterobacter cloacae, Citrobacter freundii, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia. The muropeptides produced during the peptidoglycan recycling pathway act as activator ligands for β-lactamase(s) induction. The muropeptides 1,6-anhydromuramyl pentapeptide and 1,6-anhydromuramyl tripeptide are the known activator ligands for ampC β-lactamase expression in E. cloacae. Here, we dissected the type of muropepetides for L1/L2 β-lactamase expression in an mrdA deletion mutant of S. maltophilia. Distinct from the findings with the ampC system, 1,6-anhydromuramyl tetrapeptide is the candidate for ΔmrdA-mediated β-lactamase expression in S. maltophilia. Our work extends the understanding of β-lactamase induction and provides valuable information for combating the occurrence of β-lactam resistance.

Studies on vitrectomy with and without internal limiting membrane (ILM) peeling for idiopathic epiretinal membrane (ERM) have yielded uncertain results regarding clinical outcomes and recurrence rates.To compare the clinical outcomes of vitrectomy with and without ILM peeling for idiopathic ERM.Databases, including PubMed, Embase, Cochrane, Web of Science, Google Scholar, CNKI databases, FDA.gov, and ClinicalTrials.gov, published until July 2016, were searched to identify studies comparing the clinical outcomes following vitrectomy with ERM and ILM peeling and with only ERM peeling, for treating idiopathic ERM. Studies with sufficient data were selected. Pooled results were expressed as mean differences (MDs) and risk ratios (RRs) with corresponding 95% confidence intervals (CI) for vitrectomy with and without ILM peeling with regard to postoperative best corrected visual acuity (BCVA), central retinal thickness (CRT), and ERM recurrence rate.Eleven retrospective studies and one randomized controlled trial involving 756 eyes were identified. This demonstrated that the postoperative BCVA within 12 months was significantly better in the non-ILM peeling group (MD = 0.04, 95% CI: 0.00 to 0.08; P = 0.0460), but that the patients in the ILM peeling group had significantly better postoperative BCVA after 18 months (MD = -0.13, 95% CI: -0.23 to -0.04; P = 0.0049) than did those in the non-ILM peeling group. The non-ILM peeling group exhibited a higher reduction in postoperative CRT (MD = 51.55, 95% CI:-84.23 to -18.88; P = 0.0020) and a higher recurrence rate of ERM (RR = 0.34, 95% CI:0.16 to 0.72; P = 0.0048) than did the ILM peeling group. However, the improvement rates of BCVA (RR = 1.03, 95% CI:0.72 to 1.47; P = 0.8802) and postoperative CRTs (MD = 18.15, 95% CI:-2.29 to 38.60; P = 0.0818) were similar between the two groups.Vitrectomy with ILM peeling results in better visual improvement in long-term follow-ups and lower ERM recurrence rates, and vitrectomy with only ERM peeling is more efficacious in reduction of CRT than is vitrectomy with ILM peeling.

Current studies that compare the efficacy and safety of micafungin (MCFG) with that of triazoles for the prophylaxis and treatment of invasive fungal infections (IFIs) demonstrate a lack of sufficient evidence and yield conflicting results. To compare the efficacy and safety of MCFG and triazoles in the prevention and treatment of IFIs, we conducted a meta-analysis and trial sequential analysis (TSA).For the meta-analysis, we systematically searched the databases of PubMed, Embase and Cochrane Central Register of Controlled Trials and relevant database articles for randomized controlled studies published through November 2016. Comparative studies of the efficacy and safety of MCFG versus triazoles in the prevention and treatment of IFIs were selected. Meta-analysis was performed by R software with the "metafor" package. Pooled results were expressed as risk ratios (RRs) with corresponding 95% confidence intervals (CI). TSA was adopted to assess the studies' power with TSA version 0.9 beta.Nine current studies were included in the meta-analysis (1049 cases and 959 controls). Pooled trial comparisons indicated that MCFG does have significantly higher treatment success rates (RR = 1.13; 95% CI, 1.02-1.25; p = 0.0205) and reduces the number of overall IFIs (RR = 0.75; 95% CI, 0.61-0.92; p = 0.0056). However, MCFG demonstrates no difference in all-cause mortality (RR = 0.76; 95% CI, 0.52-1.12, p = 0.1624). For the safety evaluation, MCFG had a significantly lower incidence of severe adverse events (AEs) (RR = 0.45; 95% CI, 0.25-0.83; p = 0.0105), hepatic impairment (RR = 0.70; 95% CI, 0.50-0.97; p = 0.0363) and premature discontinuation (RR = 0.51; 95% CI, 0.34-0.76, p = 0.0010). Meta-regression analysis disclosed the correction of mean age and treatment success rates (P < 0.0001). Meanwhile, TSA demonstrated sufficient power to show efficacy.The treatment success rate of MCFG is superior to that of triazoles for the prophylaxis and treatment of IFIs, and correction of the mean patient age demonstrates that efficacy increases as patient age decreases. MCFG appears to be well-tolerated with manageable side effects and lower withdrawal rates. However, additional clinical trials should be conducted on specific drug-related mortality and AEs to gather sufficient evidence on these matters.

The direct peptide reactivity assay (DPRA) is a validated method for in vitro assessment of the skin sensitization potential of chemicals. In the present work, we describe a peptide reactivity assay using 96-well plate format and systematically identified the optimal assay conditions for accurate and reproducible classification of chemicals with known sensitizing capacity. The aim of the research is to ensure that the analytical component of the peptide reactivity assay is robust, accurate and reproducible in accordance with criteria that are used for the validation of bioanalytical methods. Analytical performance was evaluated using quality control samples (QCs; heptapeptides at low, medium and high concentrations) and incubation of control chemicals (chemicals with known sensitization capacity, weak, moderate, strong, extreme and non-sensitizers) with each of three synthetic heptapeptides, viz Cor1-C420 (Ac-NKKCDLF), cysteine- (Ac-RFAACAA) and lysine- (Ac-RFAAKAA) containing heptapeptides. The optimal incubation temperature for all three heptapeptides was 25°C. Apparent heptapeptide depletion was affected by vial material composition. Incubation of test chemicals with Cor1-C420, showed that peptide depletion was unchanged in polypropylene vials over 3-days storage in an autosampler but this was not the case for borosilicate glass vials. For cysteine-containing heptapeptide, the concentration was not stable by day 3 post-incubation in borosilicate glass vials. Although the lysine-containing heptapeptide concentration was unchanged in both polypropylene and borosilicate glass vials, the apparent extent of lysine-containing heptapeptide depletion by ethyl acrylate, differed between polypropylene (24.7%) and glass (47.3%) vials. Additionally, the peptide-chemical complexes for Cor1-C420-cinnamaldehyde and cysteine-containing heptapeptide-2,4-dinitrochlorobenzene were partially reversible during 3-days of autosampler storage. These observations further highlight the difficulty in adapting in vitro methods to high-throughput format for screening the skin sensitization potential of large numbers of chemicals whilst ensuring that the data produced are both accurate and reproducible.

Condensed Abstract: The prevalence and prognostic importance of CM occurring as a consequence of AF is poorly defined. This study investigated the incidence of CM in patients with AF, its clinical features and long-term outcomes. We demonstrated that CM is common in patients presenting acutely with newly diagnosed rapid AF, and carries a worse long-term prognosis. Systolic dysfunction was reversible in an important proportion of patients, suggesting a greater prevalence of rate-related CM in AF than has previously been postulated. This underscores the importance of appropriate rhythm management strategies and repeat imaging studies. Summary Background: Atrial fibrillation (AF) may precipitate LV dysfunction, potentially leading to cardiomyopathy (CM). The prevalence and prognostic importance of CM occurring as a consequence of AF is poorly defined. We investigated the incidence of CM in patients with AF, its clinical features and long-term outcomes. Methods: We reviewed 292 consecutive patients (average age 72 ± 13yrs) presenting acutely with AF and tachycardia over a 3 year period from June 2004. Clinical details were obtained from medical records. CM was defined as ejection fraction (EF) ≤ 50% on index admission. Results: Echo was performed 93% of patients at index admission, and 69 (24%) had CM (average EF% = 37 ± 11), 60 of which were newly diagnosed. Patients with CM had significantly higher presenting heart rate (141 ± 19 vs. 132 ± 23 bpm), larger end-diastolic (5.7 vs. 5.2 cm) and end-systolic (4.5 vs. 3.2 cm) dimensions, and larger left atrial size (4.6 vs. 4.3 cm) (P < 0.05 for all). They were also statistically more likely (P < 0.05) to be male, present with breathlessness, have a history of coronary disease, and be treated with digoxin and warfarin. Follow-up echo between 6 and 12 months was performed in 46% of patients with new CM, and average EF rose to 53 ± 12%. At an average follow-up of 2.5 years, there was a significant increase in mortality in CM patients (16% vs. 9.5%, P < 0.05). Conclusion: CM is common in patients presenting acutely with newly diagnosed rapid AF, and carries a worse long-term prognosis. Systolic dysfunction was reversible in an important proportion of patients, suggesting a greater prevalence of rate-related CM in AF than has previously been postulated. This underscores the importance of appropriate rhythm management strategies and repeat imaging studies.

We conducted a retrospective study to investigate the anesthesia-controlled time and factors that contribute to prolonged extubation in open colorectal surgery. Using our hospital database, demographic data, various time intervals (waiting for anesthesia time, anesthesia time, surgical time, emergence time, exit from operating room after extubation, total operating room time, and post-anesthesia care unit stay time), and incidence of prolonged extubation (≥ 15 mins), were compared between patients who received desflurane/fentanyl-based anesthesia and total intravenous anesthesia via target-controlled infusion with fentanyl/propofol. Logistic regression analyses were performed to assess the association between variables that contributed to prolonged extubation. In conclusion, the anesthesia-controlled time was similar in desflurane anesthesia and propofol-based total intravenous anesthesia for open colorectal surgery in our hospital. Surgical time greater than 210 minutes, as well as age, contributed to prolonged extubation.

BACKGROUND:Studies of angiotensin-converting enzyme insertion/deletion (ACE I/D) polymorphisms and the risks of knee osteoarthritis (OA) have yielded conflicting results. OBJECTIVE:To determine the association between ACE I/D and knee OA, we conducted a combined case-control study and meta-analysis. METHODS:For the case-control study, 447 knee OA cases and 423 healthy controls were recruited between March 2010 and July 2011. Knee OA cases were defined using the Kellgren-Lawrence grading system, and the ACE I/D genotype was determined using a standard polymerase chain reaction. The association between ACE I/D and knee OA was detected using allele, genotype, dominant, and recessive models. For the meta-analysis, PubMed and Embase databases were systematically searched for prospective observational studies published up until August 2015. Studies of ACE I/D and knee OA with sufficient data were selected. Pooled results were expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CI) for the D versus I allele with regard to knee OA risk. RESULTS:We found no significant association between the D allele and knee OA [OR: 1.09 (95% CI: 0.76-1.89)] in the present case-control study, and the results of other genetic models were also nonsignificant. Five current studies were included, and there were a total of six study populations after including our case-control study (1165 cases and 1029 controls). In the meta-analysis, the allele model also yielded nonsignificant results [OR: 1.37 (95% CI: 0.95-1.99)] and a high heterogeneity (I2: 87.2%). CONCLUSIONS:The association between ACE I/D and knee OA tended to yield negative results. High heterogeneity suggests a complex, multifactorial mechanism, and an epistasis analysis of ACE I/D and knee OA should therefore be conducted.

Conventional genome-wide association studies (GWAS) have been proven to be a successful strategy for identifying genetic variants associated with complex human traits. However, there is still a large heritability gap between GWAS and transitional family studies. The "missing heritability" has been suggested to be due to lack of studies focused on epistasis, also called gene-gene interactions, because individual trials have often had insufficient sample size. Meta-analysis is a common method for increasing statistical power. However, sufficient detailed information is difficult to obtain. A previous study employed a meta-regression-based method to detect epistasis, but it faced the challenge of inconsistent estimates. Here, we describe a Markov chain Monte Carlo-based method, called "Epistasis Test in Meta-Analysis" (ETMA), which uses genotype summary data to obtain consistent estimates of epistasis effects in meta-analysis. We defined a series of conditions to generate simulation data and tested the power and type I error rates in ETMA, individual data analysis and conventional meta-regression-based method. ETMA not only successfully facilitated consistency of evidence but also yielded acceptable type I error and higher power than conventional meta-regression. We applied ETMA to three real meta-analysis data sets. We found significant gene-gene interactions in the renin-angiotensin system and the polycyclic aromatic hydrocarbon metabolism pathway, with strong supporting evidence. In addition, glutathione S-transferase (GST) mu 1 and theta 1 were confirmed to exert independent effects on cancer. We concluded that the application of ETMA to real meta-analysis data was successful. Finally, we developed an R package, etma, for the detection of epistasis in meta-analysis [etma is available via the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/etma/index.html].

Allergic contact dermatitis (ACD) is a delayed-type hypersensitivity immune reaction mediated by T-lymphocytes as a result of repeated exposure of an allergen primarily on skin. ACD accounts for up to 95% of occupational skin diseases (OSDs), with epoxy resins implicated as one of the most common causes of ACD. Efficient high-throughput in vitro screening for accurate identification of compounds and materials that may pose hazardous risks in the workplace is crucial. At present, the murine local lymph node assay (LLNA) is the ‘method of choice’ for predicting the sensitizing potency of contact allergens. As the 3Rs principles of reduction, refinement and replacement in animal testing has gained political and economic momentum, several in vitro screening methods have been developed for identifying potential contact allergens. To date, these latter methods have been utilized primarily to assess the skin sensitizing potential of the chemical components of cosmetic products with scant research attention as to the applicability of these methods to industrial chemicals, particularly epoxy resins. Herein we review the currently utilized in vitro methods and identify the knowledge gaps with regard to assessing the generalizability of in vitro screening methods for assessing the skin sensitizing potential of industrial chemicals.

Extensive genetic studies have identified a large number of causal genetic variations in many human phenotypes; however, these could not completely explain heritability in complex diseases. Some researchers have proposed that the "missing heritability" may be attributable to gene-gene and gene-environment interactions. Because there are billions of potential interaction combinations, the statistical power of a single study is often ineffective in detecting these interactions. Meta-analysis is a common method of increasing detection power; however, accessing individual data could be difficult. This study presents a simple method that employs aggregated summary values from a "case" group to detect these specific interactions that based on rare disease and independence assumptions. However, these assumptions, particularly the rare disease assumption, may be violated in real situations; therefore, this study further investigated the robustness of our proposed method when it violates the assumptions. In conclusion, we observed that the rare disease assumption is relatively nonessential, whereas the independence assumption is an essential component. Because single nucleotide polymorphisms (SNPs) are often unrelated to environmental factors and SNPs on other chromosomes, researchers should use this method to investigate gene-gene and gene-environment interactions when they are unable to obtain detailed individual patient data.

Renal failure is a common morbidity in multiple myeloma (MM). Although proteinuria has been increasingly reported in malignancies, it is not routinely used to refine risk estimates of survival outcomes in patients with MM. Here we aimed to investigate initial serum albumin and 24-hour daily protein excretion (24-h DPE) before treatment as prognostic factors in patients with MM. We conducted a retrospective analysis of 102 patients with myeloma who were ineligible for haematopoietic stem cell transplantation between October 2000 and December 2012. Initial proteinuria was assessed before treatment by quantitative analysis of 24-hour urine samples. The demographic and laboratory characteristics, survival outcome, and significance of pre-treatment 24-h DPE and albumin in the new staging system of MM were analyzed. Pre-treatment proteinuria (>300 mg/day) was present in 66 patients (64.7%). The optimal cut-off value of 24-h DPE before treatment was 500 mg/day. Analysis of the time-dependent area under the curve showed that the serum albumin and 24-h DPE before treatment were better than 24-h creatinine clearance rate and β2-microglobulin. A subgroup analysis showed that an initial excess proteinuria (24-h DPE ≥ 500 mg) was associated with poor survival status (17.51 vs. 34.24 months, p = 0.002). Furthermore, initial serum albumin was an independent risk factor on multivariate analysis (

BACKGROUND: Associations between angiotensin-converting enzyme (ACE) gene insertion/deletion (I/D) polymorphisms and chronic kidney disease (CKD) have been extensively studied, with most studies reporting that individuals with the D allele have a higher risk. Although some factors, such as ethnicity, may moderate the association between ACE I/D polymorphisms and CKD risk, gender-dependent effects on the CKD risk remain controversial. OBJECTIVES: This study investigated the gender-dependent effects of ACE I/D polymorphisms on CKD risk. DATA SOURCES: PubMed, the Cochrane library, and EMBASE were searched for studies published before January 2013. STUDY ELIGIBILITY CRITERIA PARTICIPANTS AND INTERVENTIONS: Cross-sectional surveys and case-control studies analyzing ACE I/D polymorphisms and CKD were included. They were required to match the following criteria: age >18 years, absence of rare diseases, and Asian or Caucasian ethnicity. STUDY APPRAISAL AND SYNTHESIS METHODS: The effect of carrying the D allele on CKD risk was assessed by meta-analysis and meta-regression using random-effects models. RESULTS: ETHNICITY [ODDS RATIO (OR): 1.24; 95% confidence interval (CI): 1.08-1.42] and hypertension (OR: 1.55; 95% CI: 1.04-2.32) had significant moderate effects on the association between ACE I/D polymorphisms and CKD risk, but they were not significant in the diabetic nephropathy subgroup. Males had higher OR for the association between ACE I/D polymorphisms and CKD risk than females in Asians but not Caucasians, regardless of adjustment for hypertension (p

Objective. In this study, we investigated whether RAAS gene single nucleotide polymorphisms (SNPs) and their interactions were associated with end-stage renal stage (ESRD). Methodology and Results. This was a case-control study for 647 ESRD cases and 644 controls. AGT (M235T (rs699) and T174M (rs4762)), AGTR1 (A1166C (rs5186) and C573T (rs5182)), ACE (I/D (rs1799752) and G2350A (rs4343)), and CYP11B2 C-344T (rs1799998) were genotyped and compared between cases and controls to identify SNPs associated with ESRD susceptibility. Multifactor dimensionality reduction (MDR) was used to identify gene-gene interactions. Several RAAS genes were associated with ESRD: AGT M235T, ACE I/D, ACE G2350A, and CYP11B2 C-344T. By MDR analysis, a three-locus model (ACE ID/ACE G2350A/CYP11B2 C-344T) of gene-gene interaction was the best for predicting ESRD risk, and its maximum testing accuracy was 56.08% and maximum cross-validation consistency was 9/10. ESRD risk was higher with the simultaneous occurrence of ACE I/D DD-ACE G2350A AA. AGT, ACE, and CYP11B2 gene polymorphisms are associated with ESRD. Conclusions. The gene-gene interaction effects of ACE I/D, ACE G2350A, and CYP11B2 C-344T polymorphisms are more important than individual factors for ESRD development among Han Chinese.

The prevalence and prognostic importance of CM occurring as a consequence of AF is poorly defined. This study investigated the incidence of CM in patients with AF, its clinical features and long-term outcomes. We demonstrated that CM is common in patients presenting acutely with newly diagnosed rapid AF, and carries a worse long-term prognosis. Systolic dysfunction was reversible in an important proportion of patients, suggesting a greater prevalence of rate-related CM in AF than has previously been postulated. This underscores the importance of appropriate rhythm management strategies and repeat imaging studies. Background Atrial fibrillation (AF) may precipitate LV dysfunction, potentially leading to cardiomyopathy (CM). The prevalence and prognostic importance of CM occurring as a consequence of AF is poorly defined. We investigated the incidence of CM in patients with AF, its clinical features and long-term outcomes. Methods We reviewed 292 consecutive patients (average age 72 ± 13yrs) presenting acutely with AF and tachycardia over a 3 year period from June 2004. Clinical details were obtained from medical records. CM was defined as ejection fraction (EF) ≤ 50% on index admission. Results Echo was performed 93% of patients at index admission, and 69 (24%) had CM (average EF% = 37 ± 11), 60 of which were newly diagnosed. Patients with CM had significantly higher presenting heart rate (141 ± 19 vs. 132 ± 23 bpm), larger end-diastolic (5.7 vs. 5.2 cm) and end-systolic (4.5 vs. 3.2 cm) dimensions, and larger left atrial size (4.6 vs. 4.3 cm) ( P < 0.05 for all). They were also statistically more likely ( P < 0.05) to be male, present with breathlessness, have a history of coronary disease, and be treated with digoxin and warfarin. Follow-up echo between 6 and 12 months was performed in 46% of patients with new CM, and average EF rose to 53 ± 12%. At an average follow-up of 2.5 years, there was a significant increase in mortality in CM patients (16% vs. 9.5%, P < 0.05). Conclusion CM is common in patients presenting acutely with newly diagnosed rapid AF, and carries a worse long-term prognosis. Systolic dysfunction was reversible in an important proportion of patients, suggesting a greater prevalence of rate-related CM in AF than has previously been postulated. This underscores the importance of appropriate rhythm management strategies and repeat imaging studies.

Badminton is a very popular subject in Physical Education (PE). Many students enroll badminton courses in every semester which pose a tremendous teaching load to the instructors. The one-on-one guiding/feedback time provided by the instructor to each student is also greatly reduced. To overcome this challenge, some studies have tried to adopt pose recognition technique in teaching badminton. However, the lack of mobility and recognition accuracy problems hinder its applicability. To address this issue, a new pose recognition technique, OpenPose, was employed to develop a real-time pose recognition badminton teaching APP in this study. The APP was then installed on a mobile device to enhance badminton smash skill learning performance. The scores distribution of the experimental group shows majority of students achieved satisfactory performance, this implies the badminton teaching APP is helpful to all students no matter what their initial skill levels are.