Validation of Diagnostic Codes to Identify Glaucoma in Taiwan's Claims Data: A Multi-Institutional Study.

Background: Healthcare databases play a crucial role in improving our understanding of glaucoma epidemiology, which is the leading cause of irreversible blindness globally. However, the accuracy of diagnostic codes used in these databases to detect glaucoma is still uncertain. Aim: To assess the accuracy of ICD-9-CM and ICD-10-CM codes in identifying patients with glaucoma, including two distinct subtypes, primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG). Methods: We analyzed electronic medical records data from a 2% random sample of patients who newly underwent visual field examination in Taiwan's largest multi-institutional healthcare system from 2011 to 2020. The diagnosis of glaucoma was confirmed by two ophthalmologists, based on the glaucoma diagnostic criteria. The positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity for ICD-9-CM codes 365.1X and 365.2X, and ICD-10-CM codes H4010X, H4011X, H4012X, H4020X, H4021X, H4022X, H4023X and H4024X for glaucoma were calculated. Results: We randomly selected 821 patients (mean age: 56.9 years old; female: 50.5%) from the original cohort of 41,050 newly receiving visual field examination in the study. Among 464 cases with an ICD-9-CM glaucoma code, the sensitivity, specificity, PPV and NPV for glaucoma were 86.5, 96.5, 91.9, and 90.9%, respectively. Among 357 cases with an ICD-10-CM glaucoma code, the sensitivity, specificity, PPV and NPV for glaucoma were 87.0, 92.8, 92.2 and 87.9%, respectively. The accuracy of diagnostic codes to identify POAG and PACG remained consistent. Conclusion: The diagnostic codes were highly reliable for identifying cases of glaucoma in Taiwan's routine healthcare practice. These results provide confidence when using ICD-9-CM and ICD-10-CM codes to define glaucoma cases in healthcare database research in Taiwan.

Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study.

BACKGROUND: Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia. OBJECTIVE: This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods. METHODS: Using Taiwan's National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization. RESULTS: Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions. CONCLUSIONS: Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world.

Real-world patient characteristics and treatment patterns of polycythemia vera in Taiwan between 2016 and 2017: a nationwide cross-sectional study.

Background: Polycythemia vera (PV) patients often experience constitutional symptoms and are at risk of thromboembolism as well as disease progression to myelofibrosis or acute myeloid leukemia. Not only is PV often overlooked but treatment options are also limited, however. Objectives: To explore the patient characteristics and treatment pattern of PV patients in Taiwan, and compare with other countries reported in the literature. Design: This is a nationwide cross-sectional study. Methods: The National Health Insurance Research Database in Taiwan, which covers 99% of the population, was utilized. Patients were identified during the cross-sectional period between 2016 and 2017, and their retrospective data were retrieved from 2001 to 2017. Results: A total of 2647 PV patients were identified between 1 January 2016 and 31 December 2017. This study described the demographic information of these patients, including number of patients by risk stratification and by sex, age at diagnosis, age at cross-sectional period, rate of bone marrow aspiration/biopsy at diagnosis, comorbidities, number of postdiagnosis thrombosis, number of disease progression, and death. The mortality rate of PV patients (4.1%) over 60 of age was higher than the general population of the same age group (2.8%). This study also compared the different treatment patterns between sexes and risk groups. Hydroxyurea was deferred to an older age, but conversely was prescribed at higher dose to younger patients. Alarmingly, a high proportion of patients did not receive phlebotomy or hydroxyurea for at least 2 years. Furthermore, discrepancies in prevalence, age at diagnosis, sex ratio, incidence of thrombosis and mortality were also found when compared with data reported in other countries. Conclusion: The clinical landscape of PV in Taiwan between 2016 and 2017 was examined. Distinctive patterns of phlebotomy and hydroxyurea were identified. Overall, these findings highlight the importance of understanding the patient characteristics and treatment patterns of PV in different regions to better inform clinical practice and improve patient outcomes.

Risk factors for acute kidney injury after high-dose methotrexate therapy: a single-center study and narrative review.

PURPOSE: To assess the risk factors associated with high-dose methotrexate (HDMTX) (≥ 1 g/m2) treatment-induced acute kidney injury (AKI). METHODS: Patients who received HDMTX from July 2014 to August 2019 in one medical center were enrolled. The patients' demographic, laboratory, and medication data were collected and compared between groups with or without AKI. Risk factors of HDMTX-induced AKI were explored using univariate and multivariate logistic regression analyses. Additionally, we searched and summarized previous studies to identify key correlates of AKI in a narrative review. RESULTS: We enrolled 59 patients who had received 200 HDMTX courses. The incidence of HDMTX-induced nephrotoxicity was 9.5%. Multivariate logistic regression revealed that male sex (odds ratio [OR], 4.20; P = .037), and angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) (OR, 5.18; P = .016) were significantly associated with AKI. Diuretics with urinary acidification, such as loop diuretics, were also a key factor in AKI (OR, 4.91; P = .018). Furthermore, a forest plot identified 21 predictors from nine additional cohort studies showing correlations with the development of AKI. CONCLUSION: Male sex, ACEIs/ARBs, and diuretics with urinary acidification are associated with AKI. Furthermore, laboratory data should be monitored to assess AKI risk before HDMTX therapy, especially in elderly patients with obesity, diabetes, or acute lymphoblastic leukemia.

Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease.

Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.

Impact of recording length and other arrhythmias on atrial fibrillation detection from wrist photoplethysmogram using smartwatches.

This study aimed to evaluate whether quantitative analysis of wrist photoplethysmography (PPG) could detect atrial fibrillation (AF). Continuous electrocardiograms recorded using an electrophysiology recording system and PPG obtained using a wrist-worn smartwatch were simultaneously collected from patients undergoing catheter ablation or electrical cardioversion. PPG features were extracted from 10, 25, 40, and 80 heartbeats of the split segments. Machine learning with a support vector machine and random forest approach were used to detect AF. A total of 116 patients were evaluated. We annotated > 117 h of PPG. A total of 6475 and 3957 segments of 25-beat pulse-to-pulse intervals (PPIs) were annotated as AF and sinus rhythm, respectively. The accuracy of the 25 PPIs yielded a test area under the receiver operating characteristic curve (AUC) of 0.9676, which was significantly better than the AUC for the 10 PPIs (0.9453; P < .001). PPGs obtained from another 38 patients with frequent premature ventricular/atrial complexes (PVCs/PACs) were used to evaluate the impact of other arrhythmias on diagnostic accuracy. The new AF detection algorithm achieved an AUC of 0.9680. The appropriate data length of PPG for optimizing the PPG analytics program was 25 heartbeats. Algorithm modification using a machine learning approach shows robustness to PVCs/PACs.

Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system.

BACKGROUND: Behavioral and psychological symptoms of dementia (BPSD) affect 90% of persons with dementia (PwD), resulting in various adverse outcomes and aggravating care burdens among their caretakers. This study aimed to explore the potential of artificial intelligence-based facial expression recognition systems (FERS) in predicting BPSDs among PwD. METHODS: A hybrid of human labeling and a preconstructed deep learning model was used to differentiate basic facial expressions of individuals to predict the results of Neuropsychiatric Inventory (NPI) assessments by stepwise linear regression (LR), random forest (RF) with importance ranking, and ensemble method (EM) of equal importance, while the accuracy was determined by mean absolute error (MAE) and root-mean-square error (RMSE) methods. RESULTS: Twenty-three PwD from an adult day care center were enrolled with ≥ 11,500 FERS data series and 38 comparative NPI scores. The overall accuracy was 86% on facial expression recognition. Negative facial expressions and variance in emotional switches were important features of BPSDs. A strong positive correlation was identified in each model (EM: r = 0.834, LR: r = 0.821, RF: r = 0.798 by the patientwise method; EM: r = 0.891, LR: r = 0.870, RF: r = 0.886 by the MinimPy method), and EM exhibited the lowest MAE and RMSE. CONCLUSIONS: FERS successfully predicted the BPSD of PwD by negative emotions and the variance in emotional switches. This finding enables early detection and management of BPSDs, thus improving the quality of dementia care.

Artificial-Intelligence-Assisted Discovery of Genetic Factors for Precision Medicine of Antiplatelet Therapy in Diabetic Peripheral Artery Disease.

An increased risk of cardiovascular events was identified in patients with peripheral artery disease (PAD). Clopidogrel is one of the most widely used antiplatelet medications. However, there are heterogeneous outcomes when clopidogrel is used to prevent cardiovascular events in PAD patients. Here, we use an artificial intelligence (AI)-assisted methodology to identify genetic factors potentially involved in the clopidogrel-resistant mechanism, which is currently unclear. Several discoveries can be pinpointed. Firstly, a high proportion (>50%) of clopidogrel resistance was found among diabetic PAD patients in Taiwan. Interestingly, our result suggests that platelet function test-guided antiplatelet therapy appears to reduce the post-interventional occurrence of major adverse cerebrovascular and cardiac events in diabetic PAD patients. Secondly, AI-assisted genome-wide association study of a single-nucleotide polymorphism (SNP) database identified a SNP signature composed of 20 SNPs, which are mapped into 9 protein-coding genes (SLC37A2, IQSEC1, WASHC3, PSD3, BTBD7, GLIS3, PRDM11, LRBA1, and CNR1). Finally, analysis of the protein connectivity map revealed that LRBA, GLIS3, BTBD7, IQSEC1, and PSD3 appear to form a protein interaction network. Intriguingly, the genetic factors seem to pinpoint a pathway related to endocytosis and recycling of P2Y12 receptor, which is the drug target of clopidogrel. Our findings reveal that a combination of AI-assisted discovery of SNP signatures and clinical parameters has the potential to develop an ethnic-specific precision medicine for antiplatelet therapy in diabetic PAD patients.

Neuroprotective Effects of Novel Treatments on Acute Optic Neuritis-A Meta-Analysis.

Optic neuritis, inflammation of the optic nerve, can cause visual impairment through retinal nerve fiber layer (RNFL) degeneration. Optical coherence tomography could serve as a sensitive noninvasive tool for measuring RNFL thickness and evaluating the neuroprotective effects of treatment. We conducted a meta-analysis to compare RNFL loss between novel add-on treatments and corticosteroid therapy at least 3 months after acute optic neuritis. The outcome measures were mean differences (MDs) in (1) RNFL thickness compared with the baseline in the affected and unaffected eye and (2) LogMAR visual acuity (VA). Seven studies involving five novel agents (memantine, erythropoietin, interferon-beta, phenytoin, and clemastine) were analyzed. When compared with the baseline RNFL thickness of the affected eye, the neuroprotective effects of novel add-on treatments could not be demonstrated. The difference in visual outcomes was also not significant between the two treatment groups. One study revealed that phenytoin has the potential to alleviate RNFL loss when the baseline thickness of the unaffected eye is considered. Larger randomized controlled trials with suitable outcome measures are warranted to evaluate the neuroprotective effects of novel treatments. Further studies should also tailor therapies to specific patient populations and investigate a more targeted treatment for acute optic neuritis.

Machine learning of treadmill exercise test to improve selection for testing for coronary artery disease.

BACKGROUND AND AIMS: The high false-positive rate of the treadmill exercise test (TET) may lead to unnecessary invasive coronary angiography. We aimed to develop a machine learning-based algorithm to improve the diagnostic performance of TET. METHODS: Study included 2325 patients who underwent TET and subsequent coronary angiography within one-year interval. The mean age was 58.7 (48.1-69.3) years, 1731 (74.5%) were male, 1858 (79.9%) had positive TET result, and 812 (34.9%) had obstructive coronary artery disease (≥70% stenosis in at least one vessel). The study population were randomly divided into training (70%) and testing (30%) groups for algorithm development. A total of 93 features, including exercise performance, hemodynamics and ST-segment changes were extracted from the TET results. Clinical features included comorbidity, smoking, height, weight, and Framingham risk score. Support vector machine, logistic regression, random forest, k-nearest neighbor and extreme gradient boosting machine learning algorithms were used to build the predictive models. The performance of each model was compared with that of conventional TET. RESULTS: Four of the five models exhibited comparable diagnostic performance and were better than conventional TET. The random forest algorithm had an area under the curve (AUC) of 0.73. When used with clinical features, the AUC improved to 0.74. The major advantage of the algorithm is the reduction of the false-positive rate compared with conventional TET (55% vs. 76.3%, respectively), while maintaining comparable sensitivity (85%). CONCLUSIONS: Using the information obtained from conventional TET, a more accurate diagnosis can be made by incorporating an artificial intelligence-based model.

Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer's Disease.

Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer's disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma metabolites of nineteen MCI patients proceeding to AD (P-MCI) and twenty-nine stable MCI (S-MCI) patients by untargeted metabolomics profiling. Alterations in the plasma metabolites between the P-MCI and S-MCI groups, as well as between the P-MCI and AD groups, were compared over the observation period. With the help of machine learning-based stratification, a 20-metabolite signature panel was identified that was associated with the presence and progression of AD. Furthermore, when the metabolic signature panel was used for classification of the three patient groups, this gave an accuracy of 73.5% using the panel. Moreover, when specifically classifying the P-MCI and S-MCI subjects, a fivefold cross-validation accuracy of 80.3% was obtained using the random forest model. Importantly, indole-3-propionic acid, a bacteria-generated metabolite from tryptophan, was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology. Our study establishes a metabolite panel to assist in the stratification of MCI patients and to predict conversion to AD.

Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure.

Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual's quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individuals, we identified a SNP signature composed of 13 SNPs. These were annotated and mapped into six protein-coding genes (GAD2, APP, RASGEF1C, MACROD2, DMD, and DOCK1), a pseudogene (PGAM1P5), and various non-coding RNA genes (LINC01968, LINC00687, LOC105372209, LOC101928047, LOC105372208, and LOC105371356). The SNP signature was found to have a good performance when predicting HF progression, namely with an accuracy rate of 0.857 and an area under the curve of 0.912. Intriguingly, analysis of the protein connectivity map revealed that DMD, RASGEF1C, MACROD2, DOCK1, and PGAM1P5 appear to form a protein interaction network in the heart. This suggests that, together, they may contribute to the pathogenesis of HF. Our findings demonstrate that a combination of AI-assisted identifications of SNP signatures and clinical parameters are able to effectively identify asymptomatic high-risk subjects that are predisposed to HF.

Suppressive Effect of Two Cucurbitane-Type Triterpenoids from Momordica charantia on Cutibacterium acnes-Induced Inflammatory Responses in Human THP-1 Monocytic Cell and Mouse Models.

Cutibacterium acnes (formerly Propionibacterium acnes) is one of the major bacterial species responsible for acne vulgaris. Numerous bioactive compounds from Momordica charantia Linn. var. abbreviata Ser. have been isolated and examined for many years. In this study, we evaluated the suppressive effect of two cucurbitane-type triterpenoids, 5ß,19-epoxycucurbita-6,23-dien-3ß,19,25-triol (Kuguacin R; KR) and 3ß,7ß,25-trihydroxycucurbita-5,23-dien-19-al (TCD) on live C. acnes-stimulated in vitro and in vivo inflammatory responses. Using human THP-1 monocytes, KR or TCD suppressed C. acnes-induced production of interleukin (IL)-1ß, IL-6 and IL-8 at least above 56% or 45%, as well as gene expression of these three pro-inflammatory cytokines. However, a significantly strong inhibitory effect on production and expression of tumor necrosis factor (TNF)-α was not observed. Both cucurbitanes inhibited C. acnes-induced activation of the myeloid differentiation primary response 88 (MyD88) (up to 62%) and mitogen-activated protein kinases (MAPK) (at least 36%). Furthermore, TCD suppressed the expression of pro-caspase-1 and cleaved caspase-1 (p10). In a separate study, KR or TCD decreased C. acnes-stimulated mouse ear edema by ear thickness (20% or 14%), and reduced IL-1ß-expressing leukocytes and neutrophils in mouse ears. We demonstrated that KR and TCD are potential anti-inflammatory agents for modulating C. acnes-induced inflammation in vitro and in vivo.

In Vitro and In Vivo Screening of Wild Bitter Melon Leaf for Anti-Inflammatory Activity against Cutibacterium acnes.

Cutibacterium acnes (formerly Propionibacterium acnes) is a key pathogen involved in the development and progression of acne inflammation. The numerous bioactive properties of wild bitter melon (WBM) leaf extract and their medicinal applications have been recognized for many years. In this study, we examined the suppressive effect of a methanolic extract (ME) of WBM leaf and fractionated components thereof on live C. acnes-induced in vitro and in vivo inflammation. Following methanol extraction of WBM leaves, we confirmed anti-inflammatory properties of ME in C. acnes-treated human THP-1 monocyte and mouse ear edema models. Using a bioassay-monitored isolation approach and a combination of liquid-liquid extraction and column chromatography, the ME was then separated into n-hexane, ethyl acetate, n-butanol and water-soluble fractions. The hexane fraction exerted the most potent anti-inflammatory effect, suppressing C. acnes-induced interleukin-8 (IL-8) production by 36%. The ethanol-soluble fraction (ESF), which was separated from the n-hexane fraction, significantly inhibited C. acnes-induced activation of mitogen-activated protein kinase (MAPK)-mediated cellular IL-8 production. Similarly, the ESF protected against C. acnes-stimulated mouse ear swelling, as measured by ear thickness (20%) and biopsy weight (23%). Twenty-four compounds in the ESF were identified using gas chromatograph-mass spectrum (GC/MS) analysis. Using co-cultures of C. acnes and THP-1 cells, ß-ionone, a compound of the ESF, reduced the production of IL-1ß and IL-8 up to 40% and 18%, respectively. ß-ionone also reduced epidermal microabscess, neutrophilic infiltration and IL-1ß expression in mouse ear. We also found evidence of the presence of anti-inflammatory substances in an unfractionated phenolic extract of WBM leaf, and demonstrated that the ESF is a potential anti-inflammatory agent for modulating in vitro and in vivo C. acnes-induced inflammatory responses.

Fluoroquinolones as an alternative treatment for Klebsiella pneumoniae liver abscess and impact on hospital length of stay.

Klebsiella pneumoniae liver abscess (KPLA) is an endemic disease in East Asia. Patients with KPLA usually require prolonged intravenous (i.v.) ß-lactam therapy and hospitalisation. Fluoroquinolones have high oral bioavailability and the potential to shorten the duration of i.v. therapy. The aim of this study was to investigate the feasibility of fluoroquinolones as an alternative treatment for KPLA in Taiwan. Consecutive patients with KPLA in a medical centre in Taiwan between July 2012 and August 2019 were retrospectively enrolled. Clinical characteristics and outcomes were compared between cases treated with ß-lactams and fluoroquinolones. A multivariate logistic regression model and propensity-score adjusted analysis were performed to identify independent risk factors for prolonged hospitalisation. A total of 234 patients with KPLA were identified during the study period. Most patients received ß-lactams (n = 199; 85.0%), whilst only 35 (15.0%) received fluoroquinolones as the major therapy. Fluoroquinolones had similar clinical efficacy to ß-lactams even in critically ill patients. Patients treated with fluoroquinolones had a shorter i.v. antibiotics duration (18.9 ± 7.6 days vs. 28.5 ± 14.7 days; P < 0.001) and hospital length of stay (LOS) (20.9 ± 8.3 days vs. 29.5 ± 16.2 days; P < 0.001) than patients treated with ß-lactams. Major therapy with fluoroquinolones was an independent protective factor for hospital LOS > 14 days in all patients and for hospital LOS > 21 days in critically ill patients. In conclusion, fluoroquinolones were an effective alternative treatment for KPLA that resulted in a shorter duration of i.v. therapy and hospital LOS.

Ethanolic Extract of Origanum vulgare Suppresses Propionibacterium acnes-Induced Inflammatory Responses in Human Monocyte and Mouse Ear Edema Models.

Acne vulgaris (acne) is a common inflammatory skin disorder, and Propionibacterium acnes plays a major role in the development and progression of acne inflammation. Herbs possessing antimicrobial and anti-inflammatory activity have been applied as a medical option for centuries. In this study, we examined the suppressive effect of ethanolic oregano (Origanum vulgare) extract on live P. acnes-induced in vivo and in vitro inflammation. Following ethanol extraction of oregano leaves, four compounds with strong antioxidant activity, including rosmarinic acid, quercetin, apigenin, and carvacrol, were identified by high-performance liquid chromatography. Using the mouse ear edema model, we demonstrated that ethanol oregano extracts (EOE) significantly suppressed P. acnes-induced skin inflammation, as measured by ear thickness (32%) and biopsy weight (37%). In a separate study, using the co-culture of P. acnes and human THP-1 monocytes, EOE reduced the production of interleukin (IL)-8, IL-1ß and tumor necrosis factor (TNF)-α up to 40%, 37%, and 18%, respectively, as well as the expression of these three pro-inflammatory mediators at the transcriptional level. Furthermore, EOE inhibited the translocation of nuclear factor-kappa B (NF-κB) into the nucleus possibly by inactivating toll-like receptor-2 (TLR2). The suppressive effect of EOE on live P. acnes-induced inflammatory responses could be due, in part, to the anti-inflammatory and antioxidant properties, but not the anti-microbial effect of EOE.

Confidence intervals for the ratio of two median residual lifetimes with left-truncated and right-censored data.

The confidence intervals for the ratio of two median residual lifetimes are developed for left-truncated and right-censored data. The approach of Su and Wei (1993) is first extended by replacing the Kaplan-Meier survival estimator with the estimator of the conditional survival function (Lynden-Bell, 1971). This procedure does not involve a nonparametric estimation of the probability density function of the failure time. However, the Su and Wei type confidence intervals are very conservative even for larger sample size. Therefore, this article proposes an alternative confidence interval for the ratio of two median residual lifetimes, which is not only without nonparametric estimation of the density function of failure times but is also computationally simpler than the Su and Wei type confidence interval. A simulation study is conducted to examine the accuracy of these confidence intervals and the implementation of these confidence intervals to two real data sets is illustrated.

Dynamic association rules for gene expression data analysis.

BACKGROUND: The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. RESULTS: We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. CONCLUSIONS: In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance.