Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without non-alcoholic fatty liver.

BACKGROUND: The prevalence of non-alcoholic fatty liver (NAFLD) has increased recently. Subjects with NAFLD are known to have higher chance for renal function impairment. Many past studies used traditional multiple linear regression (MLR) to identify risk factors for decreased estimated glomerular filtration rate (eGFR). However, medical research is increasingly relying on emerging machine learning (Mach-L) methods. The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD (NAFLD+, NAFLD-) and to rank their importance. AIM: To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD. METHODS: A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort, accounting for 32 independent variables including demographic, biochemistry and lifestyle parameters (independent variables), while eGFR was used as the dependent variable. Aside from MLR, three Mach-L methods were applied, including stochastic gradient boosting, eXtreme gradient boosting and elastic net. Errors of estimation were used to define method accuracy, where smaller degree of error indicated better model performance. RESULTS: Income, albumin, eGFR, High density lipoprotein-Cholesterol, phosphorus, forced expiratory volume in one second (FEV1), and sleep time were all lower in the NAFLD+ group, while other factors were all significantly higher except for smoking area. Mach-L had lower estimation errors, thus outperforming MLR. In Model 1, age, uric acid (UA), FEV1, plasma calcium level (Ca), plasma albumin level (Alb) and T-bilirubin were the most important factors in the NAFLD+ group, as opposed to age, UA, FEV1, Alb, lactic dehydrogenase (LDH) and Ca for the NAFLD- group. Given the importance percentage was much higher than the 2nd important factor, we built Model 2 by removing age. CONCLUSION: The eGFR were lower in the NAFLD+ group compared to the NAFLD- group, with age being was the most important impact factor in both groups of healthy Chinese women, followed by LDH, UA, FEV1 and Alb. However, for the NAFLD- group, TSH and SBP were the 5th and 6th most important factors, as opposed to Ca and BF in the NAFLD+ group.

Uncovering a dose-response relationship between positive fecal immunochemical test (FIT) and all-cause, cardiovascular and cancer-related mortality.

BACKGROUND: Fecal immunochemical test (FIT) is for colorectal cancer (CRC) screening. Its association with non-CRC mortality has been overlooked. Given the quantitative FIT values, its dose-response relationships with different causes of deaths and years of life shortened were assessed. METHODS: This retrospective study included 546,214 adults aged ≥ 20 who attended a health surveillance program from 1994 to 2017 and were followed up until the end of 2020. FIT ≥ 20 µg Hb/g was defined as positive. The Cox model was used to assess adjusted hazard ratios (aHR). RESULTS: Positive FIT was associated with increased all-cause mortality (aHR: 1.34, 95 % CI: 1.25-1.44) and all-cancer mortality (aHR: 1.71, 95 % CI: 1.55-1.89), with a reduction of life expectancy by 4 years. The association remained even with CRC excluded. With each 10 µg Hb/g increase in FIT above 20 µg Hb/g, life expectancy was reduced by one year, and mortality increased by 4 %. About 18.6 % of deaths with positive FIT were attributed to cardiovascular disease (CVD), followed by CRC (13.5 %) and upper gastrointestinal (GI) cancers (4.5 %). The all-cause mortality rate after excluding CRC for positive FIT was 3.56/1,000 person-year, comparable to the all-cause mortality rate of 3.69/1,000 person-year for hypertension. CONCLUSION: Positive FIT was associated with increased mortality in a dose-response manner and shortened life expectancy by 4 years, an overlooked risk comparable to hypertension, even with CRC excluded. After a negative colonoscopy, subjects with positive FIT should undergo a workup on CVD risk factors and look for other upper GI cancers.

The risk factors determined by four machine learning methods for the change of difference of bone mineral density in post-menopausal women after three years follow-up.

The prevalence of osteoporosis has drastically increased recently. It is not only the most frequent but is also a major global public health problem due to its high morbidity. There are many risk factors associated with osteoporosis were identified. However, most studies have used the traditional multiple linear regression (MLR) to explore their relationships. Recently, machine learning (Mach-L) has become a new modality for data analysis because it enables machine to learn from past data or experiences without being explicitly programmed and could capture nonlinear relationships better. These methods have the potential to outperform conventional MLR in disease prediction. In the present study, we enrolled a Chinese post-menopause cohort followed up for 4 years. The difference of T-score (δ-T score) was the dependent variable. Information such as demographic, biochemistry and life styles were the independent variables. Our goals were: (1) Compare the prediction accuracy between Mach-L and traditional MLR for δ-T score. (2) Rank the importance of risk factors (independent variables) for prediction of δ T-score. Totally, there were 1698 postmenopausal women were enrolled from MJ Health Database. Four different Mach-L methods namely, Random forest (RF), eXtreme Gradient Boosting (XGBoost), Naïve Bayes (NB), and stochastic gradient boosting (SGB), to construct predictive models for predicting δ-BMD after four years follow-up. The dataset was then randomly divided into an 80% training dataset for model building and a 20% testing dataset for model testing. A 10-fold cross-validation technique for hyperparameter tuning was used. The model with the lowest root mean square error for the validation dataset was viewed as the best model for each ML method. The averaged metrics of the RF, SGB, NB, and XGBoost models were used to compare the model performance of the benchmark MLR model that used the same training and testing dataset as the Mach-L methods. We defined that the priority demonstrated in each model ranked 1 as the most critical risk factor and 22 as the last selected risk factor. For Pearson correlation, age, education, BMI, HDL-C, and TSH were positively and plasma calcium level, and baseline T-score were negatively correlated with δ-T score. All four Mach-L methods yielded lower prediction errors than the MLR method and were all convincing Mach-L models. From our results, it could be noted that education level is the most important factor for δ-T Score, followed by DBP, smoking, SBP, UA, age, and LDL-C. All four Mach-L outperformed traditional MLR. By using Mach-L, the most important six risk factors were selected which are, from the most important to the least: DBP, SBP, UA, education level, TG and sleeping hour. δ T score was positively related to SBP, education level, UA and TG and negatively related to DBP and sleeping hour in postmenopausal Chinese women.

Machine Learning Prediction of Prediabetes in a Young Male Chinese Cohort with 5.8-Year Follow-Up.

The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPGbase, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPGbase, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPGbase was the most important factor to predict future prediabetes. However, after removing FPGbase, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years.

Using Machine Learning to Identify the Relationships between Demographic, Biochemical, and Lifestyle Parameters and Plasma Vitamin D Concentration in Healthy Premenopausal Chinese Women.

INTRODUCTION: Vitamin D plays a vital role in maintaining homeostasis and enhancing the absorption of calcium, an essential component for strengthening bones and preventing osteoporosis. There are many factors known to relate to plasma vitamin D concentration (PVDC). However, most of these studies were performed with traditional statistical methods. Nowadays, machine learning methods (Mach-L) have become new tools in medical research. In the present study, we used four Mach-L methods to explore the relationships between PVDC and demographic, biochemical, and lifestyle factors in a group of healthy premenopausal Chinese women. Our goals were as follows: (1) to evaluate and compare the predictive accuracy of Mach-L and MLR, and (2) to establish a hierarchy of the significance of the aforementioned factors related to PVDC. METHODS: Five hundred ninety-three healthy Chinese women were enrolled. In total, there were 35 variables recorded, including demographic, biochemical, and lifestyle information. The dependent variable was 25-OH vitamin D (PVDC), and all other variables were the independent variables. Multiple linear regression (MLR) was regarded as the benchmark for comparison. Four Mach-L methods were applied (random forest (RF), stochastic gradient boosting (SGB), extreme gradient boosting (XGBoost), and elastic net). Each method would produce several estimation errors. The smaller these errors were, the better the model was. RESULTS: Pearson's correlation, age, glycated hemoglobin, HDL-cholesterol, LDL-cholesterol, and hemoglobin were positively correlated to PVDC, whereas eGFR was negatively correlated to PVDC. The Mach-L methods yielded smaller estimation errors for all five parameters, which indicated that they were better methods than the MLR model. After averaging the importance percentage from the four Mach-L methods, a rank of importance could be obtained. Age was the most important factor, followed by plasma insulin level, TSH, spouse status, LDH, and ALP. CONCLUSIONS: In a healthy Chinese premenopausal cohort using four different Mach-L methods, age was found to be the most important factor related to PVDC, followed by plasma insulin level, TSH, spouse status, LDH, and ALP.

Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese.

BACKGROUND: The incidence of chronic kidney disease (CKD) has dramatically increased in recent years, with significant impacts on patient mortality rates. Previous studies have identified multiple risk factors for CKD, but they mostly relied on the use of traditional statistical methods such as logistic regression and only focused on a few risk factors. AIM: To determine factors that can be used to identify subjects with a low estimated glomerular filtration rate (L-eGFR < 60 mL/min per 1.73 m2) in a cohort of 1236 Chinese people aged over 65. METHODS: Twenty risk factors were divided into three models. Model 1 consisted of demographic and biochemistry data. Model 2 added lifestyle data to Model 1, and Model 3 added inflammatory markers to Model 2. Five machine learning methods were used: Multivariate adaptive regression splines, eXtreme Gradient Boosting, stochastic gradient boosting, Light Gradient Boosting Machine, and Categorical Features + Gradient Boosting. Evaluation criteria included accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), F-1 score, and balanced accuracy. RESULTS: A trend of increasing AUC of each was observed from Model 1 to Model 3 and reached statistical significance. Model 3 selected uric acid as the most important risk factor, followed by age, hemoglobin (Hb), body mass index (BMI), sport hours, and systolic blood pressure (SBP). CONCLUSION: Among all the risk factors including demographic, biochemistry, and lifestyle risk factors, along with inflammation markers, UA is the most important risk factor to identify L-eGFR, followed by age, Hb, BMI, sport hours, and SBP in a cohort of elderly Chinese people.

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.

BACKGROUND: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density [BMD] or fracture rate) as a categorical one, which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of BMD, seeking to (1) compare the prediction accuracy between different machine learning methods and traditional multiple linear regression (MLR) and (2) rank the importance of 25 different risk factors. METHODS: The study sample includes 24 412 women older than 55 years with 25 related variables, applying traditional MLR and five different machine learning methods: classification and regression tree, Naïve Bayes, random forest, stochastic gradient boosting, and eXtreme gradient boosting. The metrics used for model performance comparisons are the symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error. RESULTS: Machine learning approaches outperformed MLR for all four prediction errors. The average importance ranking of each factor generated by the machine learning methods indicates that age is the most important factor determining T-score, followed by estimated glomerular filtration rate (eGFR), body mass index (BMI), uric acid (UA), and education level. CONCLUSION: In a group of women older than 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level.

Ethnic differences in the age-related distribution of serum prostate-specific antigen values: A study in a Taiwanese male population.

This study investigates age-specific prostate-specific antigen (PSA) distributions in Taiwanese men and recommends reference ranges for this population after comparison with other studies. From January 1999 to December 2016, a total of 213,986 Taiwanese men aged above 19 years old without history of prostate cancer, urinary tract infection, or prostate infection were recruited from the Taiwan MJ cohort, an ongoing prospective cohort of health examinations conducted by the MJ Health Screening Center in Taiwan. Participants were divided into seven age groups. Simple descriptive statistical analyses were carried out and quartiles and 95th percentiles were calculated for each group as reference ranges for serum PSA in screening for prostate cancer in Taiwanese men. Serum PSA concentration correlated with age (r = 0.274, p<0.001). The median serum PSA concentration (5th to 95th percentile) ranged from 0.7 ng/ml (0.3 to 1.8) for men 20-29 years old (n = 6,382) to 1.6 ng/ml (0.4 to 8.4) for men over 79 years old (n = 504). The age-specific PSA reference ranges are as follows: 20-29 years, 1.80 ng/ml; 30-39 years, 1.80 ng/ml; 40-49 years, 2.0 ng/ml; 50-59 years, 3.20 ng/ml; 60-69 years, 5.60 ng/ml; 70-79 years, 7.40 ng/ml; over 80 years, 8.40 ng/ml. Almost no change occurred in the median serum PSA value in men 50 years old or younger, while a gradual increase was observed in men over 50. Taiwanese men aged 60 years above showed higher 95th percentile serum PSA values compared to Caucasian men and men in other Asian countries but were closer to those of Asian American and African American men. Results indicate significantly different PSA levels correlating to different ethnicities, suggesting that Oesterling's age-specific PSA reference ranges might not be appropriate for Taiwanese men. Our results should be further studied to validate the age-specific PSA reference ranges for Taiwanese men presented in this study.

The optimal pulse pressures for healthy adults with different ages and sexes correlate with cardiovascular health metrics.

Background: Pulse pressure (PP) may play a role in the development of cardiovascular disease, and the optimal PP for different ages and sexes is unknown. In a prospective cohort, we studied subjects with favorable cardiovascular health (CVH), proposed the mean PP as the optimal PP values, and demonstrated its relationship with healthy lifestyles. Methods and results: Between 1996 and 2016, a total of 162,636 participants (aged 20 years or above; mean age 34.9 years; 26.4% male subjects; meeting criteria for favorable health) were recruited for a medical examination program. PP in male subjects was 45.6 ± 9.4 mmHg and increased after the age of 50 years. PP in female subjects was 41.8 ± 9.5 mmHg and increased after the age of 40 years, exceeding that of male subjects after the age of 50 years. Except for female subjects with a PP of 40-70 mmHg, PP increase correlates with both systolic blood pressure (BP) increase and diastolic BP decrease. Individuals with mean PP values are more likely to meet health metrics, including body mass index (BMI) <25 kg/m2 (chi-squared = 9.35, p<0.01 in male subjects; chi-squared = 208.79, p < 0.001 in female subjects) and BP <120/80 mmHg (chi-squared =1,300, p < 0.001 in male subjects; chi-squared =11,000, p < 0.001 in female subjects). We propose a health score (Hscore) based on the sum of five metrics (BP, BMI, being physically active, non-smoking, and healthy diet), which significantly correlates with the optimal PP. Conclusion: The mean PP (within ±1 standard deviation) could be proposed as the optimal PP in the adult population with favorable CVH. The relationship between health metrics and the optimal PP based on age and sex was further demonstrated to validate the Hscore.

"Sugar-Sweetened Beverages" Is an Independent Risk From Pancreatic Cancer: Based on Half a Million Asian Cohort Followed for 25 Years.

Although the link between sugar-sweetened beverages (SSB) and pancreatic cancer has been suggested for its insulin-stimulating connection, most epidemiological studies showed inconclusive relationship. Whether the result was limited by sample size is explored. This prospective study followed 491,929 adults, consisting of 235,427 men and 256,502 women (mean age: 39.9, standard deviation: 13.2), from a health surveillance program and there were 523 pancreatic cancer deaths between 1994 and 2017. The individual identification numbers of the cohort were matched with the National Death file for mortality, and Cox models were used to assess the risk. The amount of SSB intake was recorded based on the average consumption in the month before interview by a structured questionnaire. We classified the amount of SSB intake into 4 categories: 0-<0.5 serving/day, ≥0.5-<1 serving per day, ≥1-<2 servings per day, and ≥2 servings per day. One serving was defined as equivalent to 12 oz and contained 35 g added sugar. We used the age and the variables at cohort enrolment as the reported risks of pancreatic cancers. The cohort was divided into 3 age groups, 20-39, 40-59, and ≥60. We found young people (age <40) had higher prevalence and frequency of sugar-sweetened beverages than the elderly. Those consuming 2 servings/day had a 50% increase in pancreatic cancer mortality (HR = 1.55, 95% CI: 1.08-2.24) for the total cohort, but a 3-fold increase (HR: 3.09, 95% CI: 1.44-6.62) for the young. The risk started at 1 serving every other day, with a dose-response relationship. The association of SSB intake of ≥2 servings/day with pancreatic cancer mortality among the total cohort remained significant after excluding those who smoke or have diabetes (HR: 2.12, 97% CI: 1.26-3.57), are obese (HR: 1.57, 95% CI: 1.08-2.30), have hypertension (HR: 1.90, 95% CI: 1.20-3.00), or excluding who died within 3 years after enrollment (HR: 1.67, 95% CI: 1.15-2.45). Risks remained in the sensitivity analyses, implying its independent nature. We concluded that frequent drinking of SSB increased pancreatic cancer in adults, with highest risk among young people.

Current-Visit and Next-Visit Prediction for Fatty Liver Disease With a Large-Scale Dataset: Model Development and Performance Comparison.

BACKGROUND: Fatty liver disease (FLD) arises from the accumulation of fat in the liver and may cause liver inflammation, which, if not well controlled, may develop into liver fibrosis, cirrhosis, or even hepatocellular carcinoma. OBJECTIVE: We describe the construction of machine-learning models for current-visit prediction (CVP), which can help physicians obtain more information for accurate diagnosis, and next-visit prediction (NVP), which can help physicians provide potential high-risk patients with advice to effectively prevent FLD. METHODS: The large-scale and high-dimensional dataset used in this study comes from Taipei MJ Health Research Foundation in Taiwan. We used one-pass ranking and sequential forward selection (SFS) for feature selection in FLD prediction. For CVP, we explored multiple models, including k-nearest-neighbor classifier (KNNC), Adaboost, support vector machine (SVM), logistic regression (LR), random forest (RF), Gaussian naïve Bayes (GNB), decision trees C4.5 (C4.5), and classification and regression trees (CART). For NVP, we used long short-term memory (LSTM) and several of its variants as sequence classifiers that use various input sets for prediction. Model performance was evaluated based on two criteria: the accuracy of the test set and the intersection over union/coverage between the features selected by one-pass ranking/SFS and by domain experts. The accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve were calculated for both CVP and NVP for males and females, respectively. RESULTS: After data cleaning, the dataset included 34,856 and 31,394 unique visits respectively for males and females for the period 2009-2016. The test accuracy of CVP using KNNC, Adaboost, SVM, LR, RF, GNB, C4.5, and CART was respectively 84.28%, 83.84%, 82.22%, 82.21%, 76.03%, 75.78%, and 75.53%. The test accuracy of NVP using LSTM, bidirectional LSTM (biLSTM), Stack-LSTM, Stack-biLSTM, and Attention-LSTM was respectively 76.54%, 76.66%, 77.23%, 76.84%, and 77.31% for fixed-interval features, and was 79.29%, 79.12%, 79.32%, 79.29%, and 78.36%, respectively, for variable-interval features. CONCLUSIONS: This study explored a large-scale FLD dataset with high dimensionality. We developed FLD prediction models for CVP and NVP. We also implemented efficient feature selection schemes for current- and next-visit prediction to compare the automatically selected features with expert-selected features. In particular, NVP emerged as more valuable from the viewpoint of preventive medicine. For NVP, we propose use of feature set 2 (with variable intervals), which is more compact and flexible. We have also tested several variants of LSTM in combination with two feature sets to identify the best match for male and female FLD prediction. More specifically, the best model for males was Stack-LSTM using feature set 2 (with 79.32% accuracy), whereas the best model for females was LSTM using feature set 1 (with 81.90% accuracy).

Vitamin D in gynecological diseases.

BACKGROUND: Most reproductive system studies suggest the protective effects of vitamin D, but vitamin D deficiency and insufficiency are growing global health issues. The present study investigates the association between vitamin D deficiency/insufficiency and gynecologic diseases to identify illness risks at different serum vitamin D levels in Taiwan. METHODS: A total of 7699 female adults aged ≥20 years with results for both serum vitamin D and gynecologic-associated diseases were drawn from the Taiwan MJ cohort. We analyzed the correlation between serum vitamin D levels and results from reproductive system evaluations, including history of dysmenorrhea, results of Pap smear, high-risk human papillomavirus (HPV) infection of the cervix, mammography, and ultrasound of breast and pelvis. RESULTS: Over 80% of participants showed vitamin D deficiency/insufficiency. Participants with abnormal Pap smear results, high-risk HPV infection, and history of dysmenorrhea showed significantly lower levels of serum vitamin D (p < 0.001-0.05). Serum vitamin D deficiency was significantly associated with positive high-risk HPV infection of the cervix (p < 0.05) and dysmenorrhea (p < 0.001). After controlling for age as a confounding variable for each gynecologic disease, level of serum vitamin D was significantly associated with abnormal breast ultrasound (odds ratio = 0.724) and uterus ultrasound (odds ratio = 0.673 - 0.8), and dysmenorrhea (odds ratio = 0.829). CONCLUSION: Associations were found between vitamin D deficiency and endometriosis, uterine myoma, dysmenorrhea, abnormal Pap smear results, and high-risk HPV infection of the cervix. Therefore, vitamin D supplements may present a cost-effective benefit for the prevention and treatment of gynecologic diseases, and thus reduction of healthcare expenditures.

Concordance analysis of intrapartum cardiotocography between physicians and artificial intelligence-based technique using modified one-dimensional fully convolutional networks.

BACKGROUND: Cardiotocography is a common method of electronic fetal monitoring (EFM) for fetal well-being. Data-driven analyses have shown potential for automated EFM assessment. For this preliminary study, we used a novel artificial intelligence method based on fully convolutional networks (FCNs), with deep learning for EFM evaluation and correct recognition, and its possible role in evaluation of nonreassuring fetal status. METHODS: We retrospectively collected 3239 EFM labor records from 292 deliveries and neonatal Apgar scores between December 2018 and July 2019 at a single medical center. We analyzed these data using an FCN model and compared the results with clinical practice. RESULTS: The FCN model recognized EFM traces like physicians, with an average Cohen's kappa coefficient of agreement of 0.525 and average area under the receiver operating characteristic curve of 0.892 for six fetal heart rate (FHR) categories. The FCN model showed higher sensitivity for predicting fetal compromise (0.528 vs 0.132) but a higher false-positive rate (0.632 vs 0.012) compared with clinical practice. CONCLUSION: FCN is a modern technique that may be useful for EFM trace recognition based on its multiconvolutional layered analysis. Our model showed a competitive ability to identify FHR patterns and the potential for evaluation of nonreassuring fetal status.

Valproic acid enhances Oct4 promoter activity in myogenic cells.

Induced pluripotent stem (iPS) cells are reprogrammed from somatic cells through ectopic expression of stem cell-specific transcription factors, including Oct4, Nanog, Sox2, Lin28, Klf4, and c-Myc. Although iPS cells are similar to embryonic stem (ES) cells in their pluripotency, their inherited defects, such as insertion mutagenesis, employment of oncogenes, and low efficiency, associated with the reprogramming procedure have hindered their clinical application. A study has shown that valproic acid (VPA) treatment can significantly enhance the reprogramming efficiency and avoid the usage of oncogenes. To understand how VPA can enhance pluripotency, we stably transfected an Oct4 promoter driven luciferase reporter (Oct4-1.9k-Luc) into P19 embryonic carcinoma (EC) cells and C2C12 myoblasts and examined their response to VPA. We found that VPA could both activate Oct4 promoter and rescue its inhibition by retinoic acid (RA). In C2C12 myoblasts, VPA treatment also enhanced endogenous Oct4 expression but repressed that of MyoD. Furthermore, both RARalpha over-expression and mutation of a proximal hormone response element (HRE) blocked the activation effect of VPA on Oct4 promoter, implying that VPA may exert its activation effect through factors targeting this HRE. Taken together, these observations identify a molecular mechanism by which VPA directly regulate Oct4 expression to ensure the acquirement and maintenance of pluripotency.

Changes to the meiotic spindle and zona pellucida of mature mouse oocytes following different cryopreservation methods.

This study is to investigate the change of morphology of the meiotic spindle and the extent of zona hardening relating to the morphological survival and developmental competence of thawed oocytes. Four- to 8-week-old female mice (C57BL/6) primed with an intraperitoneal injection of pregnant mare's serum gonadotropin and human chorionic gonadotropin. Cryopreserved oocytes using two protocols: vitrificaton using ethylene glycol (EG) and slow freezing using propanediol (PROH). The freezing oocytes were thawed and were fertilized and subsequently cultured in vitro. Spindle/chromosome imagery, dissolution of zona pellucida, and post-thawing survival and development were comparable between two groups. The vitrification cryopreservation method proved to be better than the slow-freezing protocol when comparing the frequency of normal-shaped spindle development post-thawing. The difference in the time required for the dissolution of the zona pellucida under treatment of pronase that was determined to exist between the two cryopreservation methods was statistically significant (P<0.005). The survival rate of post-thawed mature oocytes was significantly greater for the vitrification group than it was for the slow-freezing cryopreservation group (P=0.005). The vitrification cryopreservation of mature murine oocytes would appear to be more satisfactory than the slow controlled-rate freezing method as regards the post-thawing oocyte survival and also the incidence of the normal spindle apparatus in the ooplasm.

Congenital diaphragmatic hernia with familial occurrence in a Taiwanese pedigree.

Congenital diaphragmatic hernia (CDH) is a developmental defect that accounts for 8% of all major congenital anomalies and is associated with a high mortality rate despite optimal postnatal treatment. Its etiology is uncertain. We report a case of familial CDH in a Taiwanese family. We believe autosomal recessive inheritance is the possible genetic etiology of CDH in this family.

A mobile multi-agent information system for ubiquitous fetal monitoring.

Electronic fetal monitoring (EFM) systems integrate many previously separate clinical activities related to fetal monitoring. Promoting the use of ubiquitous fetal monitoring services with real time status assessments requires a robust information platform equipped with an automatic diagnosis engine. This paper presents the design and development of a mobile multi-agent platform-based open information systems (IMAIS) with an automated diagnosis engine to support intensive and distributed ubiquitous fetal monitoring. The automatic diagnosis engine that we developed is capable of analyzing data in both traditional paper-based and digital formats. Issues related to interoperability, scalability, and openness in heterogeneous e-health environments are addressed through the adoption of a FIPA2000 standard compliant agent development platform-the Java Agent Development Environment (JADE). Integrating the IMAIS with light-weight, portable fetal monitor devices allows for continuous long-term monitoring without interfering with a patient's everyday activities and without restricting her mobility. The system architecture can be also applied to vast monitoring scenarios such as elder care and vital sign monitoring.

Hypermethylation of two consecutive tumor suppressor genes, BLU and RASSF1A, located at 3p21.3 in cervical neoplasias.

OBJECTIVES: Although initiated by human papillomavirus (HPV), cervical carcinogenesis demands other cofactors to shape its natural course. Epigenetic effects such as DNA methylation, are considered to contribute to carcinogenesis process. METHODS: The methylation status of BLU and RASSF1A, as well as the HPV infection status, were assessed in a full spectrum of cervical neoplasia, including 45 low-grade squamous intraepithelial lesions (LSIL), 63 high-grade squamous intraepithelial lesions (HSIL), 107 squamous cell carcinomas (SCC), 23 adenocarcinomas (AC), and 44 normal control tissues. RESULTS: The BLU was methylated in 76.9% of SCC, 57.4% of HSIL, 20.0% of LSIL and 12.5% of normal tissues (P<0.001). The RASSF1A was methylated in 15% of SCC, 17.5% of HSIL, but not in LSIL or normal tissues (P<0.001). In AC, 43.5% of patients showed BLU methylation and 26.1% RASSF1A methylation, significantly higher than the corresponding control frequencies of 12.5% (P=0.005) and 0% (P=0.001), respectively. There was an insignificant trend toward loss of BLU methylation with advancing clinical stages of SCC (84.8%, 67.7%, and 63.6% in stages I, II, and III/IV, respectively; P=0.08). Patients with LSIL infected with high-risk HPV showed a higher rate of BLU methylation than those without HPV (38.8% vs 9.1%, respectively; P=0.057). The methylation of RASSF1A was inversely related to HPV infection in patients with HSIL/SCC (P=0.003). CONCLUSIONS: These results suggest that the methylation of BLU and RASSF1A genes is associated with cervical carcinogenesis, which could be clinically important in the future molecular screening of cervical neoplasia.

Painless ovarian torsion mimicking a uterine myoma.

OBJECTIVE: Uterine myoma is the most common benign solid pelvic tumor seen in women and is easily demonstrated by pelvic ultrasonography. However, a chronic ovarian torsion with entire necrosis may be an exceptional mimicker. We herein present an unusual case of painless ovarian torsion similar to a subserous uterine myoma. CASE REPORT: A 35-year-old female virgin presented with a palpable midline pelvic mass that had been present for 2 months. Ultrasound revealed a well-defined, heterogeneous solid mass with echogenic rim that resembled a uterine myoma, 10.9 x 9.9 x 7.3 cm in size, just upon the uterus. Surgical exploration disclosed an enlarged stony ovary and swollen tube that were both twisted. The ovary was clogged up with red meat-like necrotic tissue. The pathologic findings were compatible with ovarian torsion, and subsequent infarction and necrosis. CONCLUSION: Ovarian torsion is a significant cause of acute lower abdominal pain in women and is a gynecologic surgical emergency. Nevertheless, surgical strategies are usually impeded because of ambiguous warning signs. Clinicians may be misled by certain conditions such as silent ovarian torsion. Although there may be no specific indication, the diagnosis of ovarian torsion should be considered on finding a pelvic mass.