Exploiting Machine Learning Technologies to Study the Compound Effects of Serum Creatinine and Electrolytes on the Risk of Acute Kidney Injury in Intensive Care Units.

Assessing the risk of acute kidney injury (AKI) has been a challenging issue for clinicians in intensive care units (ICUs). In recent years, a number of studies have been conducted to investigate the associations between several serum electrolytes and AKI. Nevertheless, the compound effects of serum creatinine, blood urea nitrogen (BUN), and clinically relevant serum electrolytes have yet to be comprehensively investigated. Accordingly, we initiated this study aiming to develop machine learning models that illustrate how these factors interact with each other. In particular, we focused on ICU patients without a prior history of AKI or AKI-related comorbidities. With this practice, we were able to examine the associations between the levels of serum electrolytes and renal function in a more controlled manner. Our analyses revealed that the levels of serum creatinine, chloride, and magnesium were the three major factors to be monitored for this group of patients. In summary, our results can provide valuable insights for developing early intervention and effective management strategies as well as crucial clues for future investigations of the pathophysiological mechanisms that are involved. In future studies, subgroup analyses based on different causes of AKI should be conducted to further enhance our understanding of AKI.

Plasmon-Mediated Photoelectrochemical Hot-Hole Oxidation Coupling Reactions of Adenine on Nanostructured Silver Electrodes.

Surface-enhanced Raman spectroscopy (SERS) has been widely applied in the identification and characterization of DNA structures with high efficiency. Especially, the SERS signals of the adenine group have exhibited high detection sensitivity in several biomolecular systems. However, there is still no unanimous conclusion regarding the interpretation of some special kinds of SERS signals of adenine and its derivatives on silver colloids and electrodes. This Letter presents a new photochemical azo coupling reaction for adenyl residues, in which the adenine is selectively oxidized to (E)-1,2-di(7H-purin-6-yl) diazene (azopurine) in the presence of silver ions, silver colloids, and electrodes of nanostructures under visible light irradiation. The product, azopurine, is first found to be responsible for the SERS signals. This photoelectrochemical oxidative coupling reaction of adenine and its derivatives is promoted by plasmon-mediated hot holes and is regulated by positive potentials and pH of solutions, which opens up new avenues for studying azo coupling in the photoelectrochemistry of adenine-containing biomolecules on electrode surfaces of plasmonic metal nanostructures.

Age and seasonal variation and establishment of reference intervals for water-soluble vitamins determined by liquid chromatography tandem mass spectrometry.

OBJECTIVES: We aimed to establish reference intervals for water-soluble vitamins determined by liquid chromatography tandem mass spectrometry to improve the diagnosis of vitamin deficiency and outcomes of associated conditions. METHODS: In this retrospective analysis of 24 810 specimens, we aimed to examine sex-, age-, and season-related variations in vitamin levels in different groups, set reference-value intervals for vitamin levels, and evaluate these reference values against those recommended by manufacturers. RESULTS: Levels of vitamins B3, B5, B6, B7, and B12 were higher, and those of vitamins B2, B9, and C were lower, in men than in women. There were seasonal variations in levels of vitamins B1, B3, B5, B6, B9, B12, and C. Levels of vitamins B1, B2, B3, B5, B6, B7, B9, and C differed across age groups; vitamin B1 displayed significant differences between ages 0 to 14 years and adults compared with reference change values. The lower limits of vitamins B1 (ages 15-100 y), B2, B3, B7, and C were lower, and that of vitamin B5 was higher, than the recommended reference values. Finally, the upper limits of vitamins B1, B3, B5, B6, and B7 were lower than the recommended values. CONCLUSIONS: For values obtained using liquid chromatography tandem mass spectrometry, the lower limits of reference intervals for vitamins B1 (ages 15-100 y), B2, B3, B7, and C should be lowered, that of vitamin B5 should be raised, and the upper limits of reference intervals for vitamins B1, B3, B5, B6, and B7 should be lowered.

Thyroid disease as a risk factor for cerebrovascular disease.

BACKGROUND: Thyroid disease is the medical condition impairing function of the thyroid. Among this disorder category, hyperthyroidism is that the thyroid gland produces excessive amounts of thyroid hormones whereas hypothyroidism is that the thyroid gland does not produce enough thyroid hormone. Various studies have supported the comorbid association between thyroid disease and cardiovascular disorder. However, there is insufficient evidence to prove the relationship between cerebrovascular disease (CVD) and thyroid disease. METHODS: In this study, we tried to verify that thyroid disease increases the risk of CVD development employing a population-based database, National Health Insurance Research Database of Taiwan. A total of 16,808 hyperthyroidism cases and 5793 hypothyroidism patients with corresponding control subjects were studied, respectively. Hazard ratio (HR) by the Cox regression was used to quantify risk of CVD in different groups of subjects, that is, case patients versus matched controls. Further stratification studies for risk factors of CVD were performed to evaluate the comorbid association between CVD and hyperthyroidism/hypothyroidism. RESULTS: Evaluation results have shown that hyperthyroidism increased 38% of the hazard of developing follow-up CVD (adjusted HR, 1.38) whereas hypothyroidism increased even higher the risk (adjusted HR, 1.89). Further stratification studies for risk factors of CVD suggested that the comorbid association between hypothyroidism and CVD was comparable to those influences from cardiac risk factors, such as diabetes mellitus, hyperlipidemia, hypertension, or renal failure and so forth. CONCLUSIONS: Thyroid disease may predispose to onset of CVD. Advanced analysis is required to investigate the pathologic mechanism underlying the association between CVD and thyroid disease.

Identification of cucurbitacins and assembly of a draft genome for Aquilaria agallocha.

BACKGROUND: Agarwood is derived from Aquilaria trees, the trade of which has come under strict control with a listing in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora. Many secondary metabolites of agarwood are known to have medicinal value to humans, including compounds that have been shown to elicit sedative effects and exhibit anti-cancer properties. However, little is known about the genome, transcriptome, and the biosynthetic pathways responsible for producing such secondary metabolites in agarwood. RESULTS: In this study, we present a draft genome and a putative pathway for cucurbitacin E and I, compounds with known medicinal value, from in vitro Aquilaria agallocha agarwood. DNA and RNA data are utilized to annotate many genes and protein functions in the draft genome. The expression changes for cucurbitacin E and I are shown to be consistent with known responses of A. agallocha to biotic stress and a set of homologous genes in Arabidopsis thaliana related to cucurbitacin bio-synthesis is presented and validated through qRT-PCR. CONCLUSIONS: This study is the first attempt to identify cucurbitacin E and I from in vitro agarwood and the first draft genome for any species of Aquilaria. The results of this study will aid in future investigations of secondary metabolite pathways in Aquilaria and other non-model medicinal plants.

Application of density estimation algorithms in analyzing co-morbidities of migraine.

In this study, we will propose a density estimation based data analysis procedure to investigate the co-morbid associations between migraine and the suspected diseases. The primary objective of this study has aimed to develop a novel analysis procedure that can discover insightful knowledge from large medical databases. The entire analysis procedure consists of two stages. During the first stage, a kernel density estimation algorithm named relaxed variable kernel density estimation (RVKDE) is invoked to identify the samples of interest. Then, in the second stage, a density estimation algorithm based on generalized Gaussian components and named G2DE is invoked to provide a summarized description of the distribution. The results obtained by applying the proposed two-staged procedure to analyze co-morbidities of migraine revealed that the proposed procedure could effectively identify a number of clusters of samples with distinctive characteristics. The results further revealed that the distinctive characteristics of the clusters extracted by the proposed procedure were in conformity with the observations reported in recently published articles. Accordingly, it is conceivable that the proposed analysis procedure can be exploited to provide valuable clues of pathogenesis and facilitate development of proper treatment strategies.

Women with endometriosis are more likely to suffer from migraines: a population-based study.

Previous research suggests that a co-morbid relationship exists between migraine and endometriosis; however, results have been inconsistent. In addition, female hormones, which are important in the pathogenesis and management of endometriosis, have been reported to precipitate migraine attacks and may confound the results. The aim of this population-based cohort study was to explore the relationship between migraine and endometriosis in women of reproductive age (18-51 years). Data were derived from the National Health Insurance Research Database of Taiwan, which contains outpatient and inpatient records from 2000 to 2007. Our study cohort included 20,220 endometriosis patients and 263,767 controls without endometriosis. We analyzed the prevalence of migraine in these women as recorded during the eight years of the database. Our results found that patients with endometriosis were more likely to suffer migraine headaches compared to controls (odds ratio [OR], 1.70; 95% confidence interval [CI] [1.59, 1.82]; p<0.001). In addition, the co-morbid association between migraine and endometriosis remained significant after the data were controlled for age and frequently utilized hormone therapies (OR, 1.37; 95% CI, [1.27, 1.47]; p<0.001). The results of this cohort study support the existence of a co-morbid relationship between migraine and endometriosis, even after adjusting for the possible effects of female hormone therapies on migraine attacks.

A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins.

BACKGROUND: Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research. RESULTS: In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the high-sensitivity mode and the high-specificity mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the high-sensitivity mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the high-specificity mode. CONCLUSION: Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.

Prediction of protein secondary structures with a novel kernel density estimation based classifier.

BACKGROUND: Though prediction of protein secondary structures has been an active research issue in bioinformatics for quite a few years and many approaches have been proposed, a new challenge emerges as the sizes of contemporary protein structure databases continue to grow rapidly. The new challenge concerns how we can effectively exploit all the information implicitly deposited in the protein structure databases and deliver ever-improving prediction accuracy as the databases expand rapidly. FINDINGS: The new challenge is addressed in this article by proposing a predictor designed with a novel kernel density estimation algorithm. One main distinctive feature of the kernel density estimation based approach is that the average execution time taken by the training process is in the order of O(nlogn), where n is the number of instances in the training dataset. In the experiments reported in this article, the proposed predictor delivered an average Q3 (three-state prediction accuracy) score of 80.3% and an average SOV (segment overlap) score of 76.9% for a set of 27 benchmark protein chains extracted from the EVA server that are longer than 100 residues. CONCLUSION: The experimental results reported in this article reveal that we can continue to achieve higher prediction accuracy of protein secondary structures by effectively exploiting the structural information deposited in fast-growing protein structure databases. In this respect, the kernel density estimation based approach enjoys a distinctive advantage with its low time complexity for carrying out the training process.