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.

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.

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.

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.

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.

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.