Dimensionality reduction approach for many-objective epistasis analysis.

In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.

Multiobjective optimization-driven primer design mechanism: towards user-specified parameters of PCR primer.

Primers are critical for polymerase chain reaction (PCR) and influence PCR experimental outcomes. Designing numerous combinations of forward and reverse primers involves various primer constraints, posing a computational challenge. Most PCR primer design methods limit parameters because the available algorithms use general fitness functions. This study designed new fitness functions based on user-specified parameters and used the functions in a primer design approach based on the multiobjective particle swarm optimization (MOPSO) algorithm to address the challenge of primer design with user-specified parameters. Multicriteria evaluation was conducted simultaneously based on primer constraints. The fitness functions were evaluated using 7425 DNA sequences and compared with a predominant primer design approach based on optimization algorithms. Each DNA sequence was run 100 times to calculate the difference between the user-specified parameters and primer constraint values. The algorithms based on fitness functions with user-specified parameters outperformed the algorithms based on general fitness functions for 11 primer constraints. Moreover, MOPSO exhibited superior implementation in all experiments. Practical gel electrophoresis was conducted to verify the PCR experiments and established that MOPSO effectively designs primers based on user-specified parameters.

Exploring the association of metal mixture in blood to the kidney function and tumor necrosis factor alpha using machine learning methods.

This research aimed to approach relationships between metal mixture in blood and kidney function, tumor necrosis factor alpha (TNF-α) by machine learning. Metals levels were measured by Inductively Couple Plasma Mass Spectrometry in blood from 421 participants. We applied K Nearest Neighbor (KNN), Naive Bayes classifier (NB), Support Vector Machines (SVM), random forest (RF), Gradient Boosting Decision Tree (GBDT), Categorical boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Whale Optimization-based XGBoost (WXGBoost) to identify the effect of plasma metals, TNF-α, and estimated glomerular filtration rate (eGFR by CKD-EPI equation). We conducted not only toxic metals, lead (Pb), arsenic (As), cadmium (Cd) but also included trace essential metals, selenium (Se), copper (Cu), zinc (Zn), cobalt (Co), to predict the interaction of TNF-α, TNF-α/white blood count, and eGFR. The high average TNF-α level group was observed among subjects with higher Pb, As, Cd, Cu, and Zn levels in blood. No associations were shown between the low and high TNF-α level group in blood Se and Co levels. Those with lower eGFR group had high Pb, As, Cd, Co, Cu, and Zn levels. The crucial predictor of TNF-α level in metals was blood Pb, and then Cd, As, Cu, Se, Zn and Co. The machine learning revealed that As was the major role among predictors of eGFR after feature selection. The levels of kidney function and TNF-α were modified by co-exposure metals. We were able to acquire highest accuracy of over 85% in the multi-metals exposure model. The higher Pb and Zn levels had strongest interaction with declined eGFR. In addition, As and Cd had synergistic with prediction model of TNF-α. We explored the potential of machine learning approaches for predicting health outcomes with multi-metal exposure. XGBoost model added SHAP could give an explicit explanation of individualized and precision risk prediction and insight of the interaction of key features in the multi-metal exposure.

Effect of clinical factors on trajectory of functional performance in patients undergoing hemodialysis.

PURPOSE: This study aimed to investigate the association between clinical factors and temporary changes in functional performance in patients undergoing hemodialysis. METHODS: This was a retrospective, longitudinal observational study conducted from 2015 to 2017. Eight-two patients undergoing hemodialysis in the outpatient clinic were enrolled. Functional performance was measured using the Karnofsky Performance Status (KPS) scale. Collected data for analysis included demographics, laboratory parameters, and KPS scale scores. All participants were grouped into a high KPS cluster and a low KPS cluster based on dynamic changes in KPS scales from 2015 to 2017. RESULTS: Participants in the high KPS cluster demonstrated an approximate trend, and those in the low KPS cluster demonstrated a low pattern. By stepwise selection model analysis, age (OR 1.12, 95% CI 1.03-1.23, p = 0.011), serum BUN (OR 1.08, 95% CI 1.02-1.16, p = 0.015), calcium levels (OR 3.24, 95% CI 1.2-8.73, p = 0.02), and beta-2-microglobulin (OR > 1.0, CI >1.00-<1.01, p = 0.031) showed risk for the low KPS cluster. Male sex (OR 0.20, 95% CI 0.04-0.96, p = 0.045) and albumin level (OR 0.02, 95% CI 0-0.4, p = 0.009) showed a low risk for the low KPS cluster. CONCLUSIONS: A different trajectory pattern was observed between the high and low KPS clusters in a 3-year period. Risk factors for the low KPS cluster were age, serum BUN, calcium, and beta-2-microglobulin levels. Male sex and serum albumin levels reduced the risk for the low KPS cluster.

Importance of non-medical reasons for dropout in patients on peritoneal dialysis.

BACKGROUND: To describe the associated factors for non-medical reasons for dropout in peritoneal dialysis (PD) patients. METHODS: A retrospective cohort study was performed using registry data of adult patients commencing PD as their initial renal replacement therapy in one hospital-facilitated PD center in Taiwan between 2014 and 2018. The collected data included socio-demographics and relevant medical and PD-related parameters. Kaplan-Meier analysis was used to determine the impact of non-medical reasons and medical reasons on PD dropout. RESULTS: The analysis included 224 PD patients, of whom 37 dropped out for non-medical reasons and 187 for medical reasons during the study period. There was significant difference between the two cohorts in age (62.3 years vs. 56.1 years, P = 0.010) and PD vintage (median 3.4 years vs. 4.8 years, P = 0.001). Diabetes was more predominant in the cohort for non-medical reasons than in the one for medical reasons (54.1% vs. 27.3% respectively, P = 0.001). In non-medical reason cohort, two leading reasons given for dropping out were lacking of caregivers (n = 12) and losing confidence (n = 10), whereas PD-related peritonitis (n = 101) was the main medical reason for PD dropout. Using Kaplan-Meier curve analysis, patients in the non-medical reason cohort demonstrated higher cumulative dropout rate compared to patients in the medical reason cohort during a 10-year period (P < 0.001). CONCLUSIONS: The main characteristics of PD dropout patients for non-medical reasons are age, diabetes, patients' perception and caregiver support.

Multiobjective multifactor dimensionality reduction to detect SNP-SNP interactions.

Motivation: Single-nucleotide polymorphism (SNP)-SNP interactions (SSIs) are popular markers for understanding disease susceptibility. Multifactor dimensionality reduction (MDR) can successfully detect considerable SSIs. Currently, MDR-based methods mainly adopt a single-objective function (a single measure based on contingency tables) to detect SSIs. However, generally, a single-measure function might not yield favorable results due to potential model preferences and disease complexities. Approach: This study proposes a multiobjective MDR (MOMDR) method that is based on a contingency table of MDR as an objective function. MOMDR considers the incorporated measures, including correct classification and likelihood rates, to detect SSIs and adopts set theory to predict the most favorable SSIs with cross-validation consistency. MOMDR enables simultaneously using multiple measures to determine potential SSIs. Results: Three simulation studies were conducted to compare the detection success rates of MOMDR and single-objective MDR (SOMDR), revealing that MOMDR had higher detection success rates than SOMDR. Furthermore, the Wellcome Trust Case Control Consortium dataset was analyzed by MOMDR to detect SSIs associated with coronary artery disease. Availability and implementation: MOMDR is freely available at https://goo.gl/M8dpDg. Supplementary information: Supplementary data are available at Bioinformatics online.

PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions.

Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of susceptibility genes for complex diseases remain a great challenge for geneticists. In this study, a particle swarm optimization (PSO)-based multifactor dimensionality reduction (MDR) approach was proposed, denoted by PBMDR. MDR was used to detect multilocus interactions based on the PSO algorithm. A test data set was simulated from the genotype frequencies of 26 SNPs from eight breast-cancer-related gene. In simulated disease models, we demonstrated that PBMDR outperforms existing global optimization algorithms in terms of its ability to explore and power to detect specific SNP-genotype combinations. In addition, the PBMDR algorithm was compared with other algorithms, including PSO and chaotic PSOs, and the results revealed that the PBMDR algorithm yielded higher accuracy and chi-square values than other algorithms did.

Comparison of Solute Clearance, Hospitalization Rate, and Aortic Arch Calcification between Online Hemodiafiltration and High-Flux Hemodialysis: A 6-Year Observational Study.

BACKGROUND/AIMS: Studies on the long-term clinical benefits of hemodiafiltration (HDF) and high-flux hemodialysis (HFHD) are very limited. This study aimed to investigate the hospitalization rate and aortic arch calcification (AAC) of these two dialysis modalities over 6 years. METHODS: Participants who received regular HDF and HFHD in one hospital-facilitated hemodialysis center were prospectively enrolled after matching for age, sex, and diabetes between January 2009 and December 2014. Medical records were reviewed retrospectively on demographics, laboratory variables, calcified scores in aortic arch measured by chest radiography, and rates of hospital admission. Cox proportional hazard regression and linear regression were used to obtain the outcome results. RESULTS: The HDF and HFHD groups consisted of 108 and 102 participants, respectively. Levels of laboratory variables including small soluble solutes and Kt/V were not statistically different over the 6-year period between the HDF and HFHD groups. Calcified scores of the aortic arch increased over 6 years in both groups. The changes in the mean calcified scores were significant when compared between the two groups (0.44-1.82 in HFHD, 0.79-1.8 in HDF, respectively, p = 0.008). Hospitalization rates were 735 per 1,000 patients in the HDF group and 852 per 1,000 patients in the HFHD group, respectively. No significant difference was observed in frequency and days of hospitalization between HDF and HFHD. CONCLUSION: Hospitalization rates and AAC were observed to be equal for HDF and HFHD.

Time-varying serum albumin levels and all-cause mortality in prevalent peritoneal dialysis patients: a 5-year observational study.

BACKGROUND: In this study, we investigated the association of time-varying serum albumin levels with mortality over a 5-year period in one cohort of patients undergoing long-term peritoneal dialysis (PD) therapy. METHODS: The participants in this study enrolled 302 patients who underwent long-term PD at a single PD center in Taiwan. We reviewed medical records from 2011 to 2015 retrospectively. Time-averaged albumin level and serum albumin reach rate (defined as the percentage of serum albumin measurements that reached ≥3.5 g/dL) were applied as the predictor variables in the first 2 years (2011-2012). All-cause mortality was used as the outcome variable in the subsequent 3 years (2013-2015). Hazard function of all-cause mortality in the study participants was examined by using Cox proportional hazard regression models . RESULTS: Patients with different albumin reach rates (75-< 100%, 50-< 75%, 1-< 50%) did not exhibit a significantly increased risk for all-cause mortality. Patients with a 0% albumin reach rate exhibited a significantly increased risk for all-cause mortality (hazard ratio [HR] 7.59, 95% confidence interval [CI], 2.38-24.21) by fully adjusted analysis. Patients with time-averaged albumin levels of < 3.5 g/dL (HR 15.49, 95% CI 1.74-137.72) exhibited a higher risk for all-cause mortality than those with serum albumin levels ≥4.0 g/dL. CONCLUSIONS: This study demonstrated that higher serum albumin reach rates and higher time-averaged serum albumin levels are associated with a lower mortality rate over a 5-year period among patients undergoing long-term PD.

Comparison of tunneled central venous catheters and native arteriovenous fistulae by evaluating the mortality and morbidity of patients with prevalent hemodialysis.

PURPOSE: We examined the association between catheter use for maintenance hemodialysis (HD) and mortality/hospitalization in a cohort of patients with prevalent HD. METHODS: In this study, 70 HD patients with tunneled cuffed central venous catheters (TCVCs) from a Taiwanese HD center during 2014-2016 were enrolled and compared with 70 matched HD patients with native arteriovenous fistulae (AVF). The compared variables included demographic parameters and laboratory and dialysis-related indices. Cox regression analysis was used to assess the risk of mortality/hospitalization within a year. RESULTS: Low baseline serum albumin levels were found in patients with TCVCs (3.64 g/dL vs 3.79 g/dL, p = 0.030). The mortality rates of patients with AVF and TCVCs were 14 per 1000 patients and 171 per 1000 patients, respectively. Infection was the leading cause of mortality/hospitalization in patients with TCVCs. Using multivariate analyses, the risk of death was found to be significantly higher in patients with TCVCs than in those with AVF (Hazard ratio [HR] 12.15, 95% CI 1.16-127.17; p = 0.037). Patients with TCVC also had a higher hospitalization rate (HR 1.33, 95% CI 0.71-2.49; p = 0.369) (not statistically significant). CONCLUSION: Catheter use for maintenance HD was associated with increased all-cause mortality.

Biofilm Formation in Acinetobacter Baumannii: Genotype-Phenotype Correlation.

Strains of Acinetobacter baumannii are commensal and opportunistic pathogens that have emerged as problematic hospital pathogens due to its biofilm formation ability and multiple antibiotic resistances. The biofilm-associated pathogens usually exhibit dramatically decreased susceptibility to antibiotics. This study was aimed to investigate the correlation of biofilm-forming ability, antibiotic resistance and biofilm-related genes of 154 A. baumannii isolates which were collected from a teaching hospital in Taiwan. Biofilm-forming ability of the isolates was evaluated by crystal violet staining and observed by scanning electron microscopy. Antibiotic susceptibility was determined by disc diffusion method and minimum inhibitory concentration; the biofilm-related genes were screened by polymerase chain reaction. Results showed that among the 154 tested isolates, 15.6% of the clinical isolates were weak biofilm producers, while 32.5% and 45.4% of them possessed moderate and strong biofilm formation ability, respectively. The experimental results revealed that the multiple drug resistant isolates usually provided a higher biofilm formation. The prevalence of biofilm related genes including bap, blaPER-1, csuE and ompA among the isolated strains was 79.2%, 38.3%, 91.6%, and 68.8%, respectively. The results indicated that the antibiotic resistance, the formation of biofilm and the related genes were significantly correlated. The results of this study can effectively help to understand the antibiotic resistant mechanism and provides the valuable information to the screening, identification, diagnosis, treatment and control of clinical antibiotic-resistant pathogens.

CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies.

MOTIVATION: Detecting epistatic interactions in genome-wide association studies (GWAS) is a computational challenge. Such huge numbers of single-nucleotide polymorphism (SNP) combinations limit the some of the powerful algorithms to be applied to detect the potential epistasis in large-scale SNP datasets. APPROACH: We propose a new algorithm which combines the differential evolution (DE) algorithm with a classification based multifactor-dimensionality reduction (CMDR), termed DECMDR. DECMDR uses the CMDR as a fitness measure to evaluate values of solutions in DE process for scanning the potential statistical epistasis in GWAS. RESULTS: The results indicated that DECMDR outperforms the existing algorithms in terms of detection success rate by the large simulation and real data obtained from the Wellcome Trust Case Control Consortium. For running time comparison, DECMDR can efficient to apply the CMDR to detect the significant association between cases and controls amongst all possible SNP combinations in GWAS. AVAILABILITY AND IMPLEMENTATION: DECMDR is freely available at https://goo.gl/p9sLuJ . CONTACT: chuang@isu.edu.tw or e0955767257@yahoo.com.tw. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cumulative receiver operating characteristics for analyzing interaction between tissue visfatin and clinicopathologic factors in breast cancer progression.

BACKGROUND: Visfatin has been reported to be associated with breast cancer progression, but the interaction between the visfatin and clinicopathologic factors in breast cancer progression status requires further investigation. To address this problem, it is better to simultaneously consider multiple factors in sensitivity and specificity assays. METHODS: In this study, a dataset for 105 breast cancer patients (84 disease-free and 21 progressing) were chosen. Individual and cumulative receiver operating characteristics (ROC) were used to analyze the impact of each factor along with interaction effects. RESULTS: In individual ROC analysis, only 3 of 13 factors showed better performance for area under curve (AUC), i.e., AUC > 7 for hormone therapy (HT), tissue visfatin, and lymph node (LN) metastasis. Under our proposed scoring system, the cumulative ROC analysis provides higher AUC performance (0.746-0.886) than individual ROC analysis in predicting breast cancer progression. Considering the interaction between these factors, a minimum of six factors, including HT, tissue visfatin, LN metastasis, tumor stage, age, and tumor size, were identified as being highly interactive and associated with breast cancer progression, providing potential and optimal discriminators for predicting breast cancer progression. CONCLUSION: Taken together, the cumulative ROC analysis provides better prediction for breast cancer progression than individual ROC analysis.

Factors Associated with Functional Performance among Patients on Hemodialysis in Taiwan.

BACKGROUND/AIMS: We investigated factors associated with functional performance in hemodialysis (HD) patients as well as their relationships with mortality. METHODS: We enrolled 790 HD patients who were followed up from 2009 to 2013. Functional performance was evaluated by Karnofsky Performance Status Scale (KPSS) scores. We examined the associations of clinical variables and all-cause mortality with KPSS scores. RESULTS: Of the participants, 460 had high KPSS scores (range 90-100) and 330 had low KPSS scores (below 80). On multivariate analysis, age and weekly HD sessions were associated with significantly increased odds of a lower KPSS score (age: OR 1.05, 95% CI 1.04-1.07, p < 0.001; weekly HD: OR 2.10, 95% CI 1.37-3.21, p = 0.001). A low KPSS score was a significant predictor of increased all-cause mortality (hazard ratio 1.49; 95% CI 1.02-2.16, p = 0.037), as determined using Cox regression analysis. CONCLUSION: Functional performance was associated with clinical variables and all-cause mortality in HD patients.

Longitudinal analysis of cardiac structure and function in incident-automated peritoneal dialysis: comparison between icodextrin solution and glucose-based solution.

BACKGROUND: This study aimed to evaluate the longitudinal changes in cardiac structure and function in incident-automated peritoneal dialysis (APD) patients. METHODS: We conducted a 2-year prospective, randomized, open-label, parallel-group study to compare the efficacy of icodextrin solution versus glucose-based solution. Echocardiography was performed at baseline, 1 and 2 years. Echocardiographic parameters over 2 years were evaluated for each group, using the Friedman test. Generalized linear regression analysis was used to test the associations between baseline clinical variables and echocardiographic changes, and a multivariate model was used to analyze cardiac function between the two groups. RESULTS: A total of 43 APD patients were enrolled in the beginning of this study. Twenty patients in the icodextrin group (ICO) and 18 patients in the glucose group (GLU) completed the study. In left ventricular (LV) systolic function measurements, ejection fraction (EF) increased significantly in the GLU group. Measurements of LV diastolic function and septal early mitral annulus velocity (EMV) increased significantly from baseline to 24-months in the ICO group (5.43-5.51 ms). The GLU group showed a significant decrease in peak early diastolic velocity (EDV) (70.67-68.25 cm/s), but a significant increase in septal EMV (5.94-7.57 ms) from baseline to 24-months. No significant association was found between the baseline clinical variables and echocardiographic changes within 24 months in the generalized linear regression analysis. Multivariate models were used to investigate changes in the four primary endpoints, namely, myocardial performance index (MPI), left ventricular ejection fraction (LVEF), deceleration time (DT), and E/e' ratio. These primary endpoints show no significant association with the baseline values in both the ICO and GLU groups. CONCLUSION: The present study demonstrates that long-dwell icodextrin solution can maintain reasonable cardiac structure and function in incident-APD patients. TRIAL REGISTRATION: ISRCTN14931270 (retrospectively registered on 23/03/2018).

Use of Genetic Algorithm Combinational Single-nucleotide Polymorphisms Could Modify the Association of Blood Lead Levels and Bone Matrix Density.

BACKGROUND: Previous studies have not explored the relationship between a single gene and a single disease. Our study aims to investigate the association of multiple genotypes with blood lead levels and bone matrix density (BMD) by using genetic algorithms. METHODS: Our research focused on 506 employees (245 males and 261 females) of a lead battery factory in Taiwan. We collected data on their BMD, blood lead level, and 6 SNPs (ACE, alpha-adducin, Bsm, Tag, Apa, and ALAD); these factors were analyzed using discrete binary particle swarm optimization (DBPSO) and logistic regression analysis. RESULTS: We found no association between blood lead level and bone density, which may be due to the relatively young age of this population (mean age = 45.1 years). However, the genotype that contained both Bsm type bb and ALAD type 1-1 may play an important role in protecting against lower bone density among these employees. CONCLUSIONS: In conclusion, this study found that the Bsm and ALAD genes influence bone density among lead workers. However, the mechanism and exact relationship between these two genes and bone density require further investigation.

tRNAfeature: An algorithm for tRNA features to identify tRNA genes in DNA sequences.

The identification of transfer RNAs (tRNAs) is critical for a detailed understanding of the evolution of biological organisms and viruses. However, some tRNAs are difficult to recognize due to their unusual sub-structures and may result in the detection of the wrong anticodon. Therefore, the detection of unusual sub-structures of tRNA genes remains an important challenge. In this study, we propose a method to identify tRNA genes based on tRNA features. tRNAfeature attempts to refold the sequence with single-stranded regions longer than those found in the canonical and conventional structural models for tRNA. We predicted a set of 53926 archaeal, eubacterial and eukaryotic tRNA genes annotated in tRNADB-CE and scanned the tRNA genes in whole genome sequencing. The results indicate that tRNAfeature is more powerful than other existing methods for identifying tRNAs.

Analysis of high-order SNP barcodes in mitochondrial D-loop for chronic dialysis susceptibility.

OBJECTIVES: Positively identifying disease-associated single nucleotide polymorphism (SNP) markers in genome-wide studies entails the complex association analysis of a huge number of SNPs. Such large numbers of SNP barcode (SNP/genotype combinations) continue to pose serious computational challenges, especially for high-dimensional data. METHODS: We propose a novel exploiting SNP barcode method based on differential evolution, termed IDE (improved differential evolution). IDE uses a "top combination strategy" to improve the ability of differential evolution to explore high-order SNP barcodes in high-dimensional data. RESULTS: We simulate disease data and use real chronic dialysis data to test four global optimization algorithms. In 48 simulated disease models, we show that IDE outperforms existing global optimization algorithms in terms of exploring ability and power to detect the specific SNP/genotype combinations with a maximum difference between cases and controls. In real data, we show that IDE can be used to evaluate the relative effects of each individual SNP on disease susceptibility. CONCLUSION: IDE generated significant SNP barcode with less computational complexity than the other algorithms, making IDE ideally suited for analysis of high-order SNP barcodes.

An association between time-varying serum albumin level and the mortality rate in maintenance haemodialysis patients: a five-year clinical cohort study.

BACKGROUND: Until now, no long-term studies relating serum albumin level to mortality rate in prevalent haemodialysis (HD) patients have been conducted. We aimed to examine the association between serum albumin level and mortality over a 5-year period. METHODS: This study included 781 patients who received maintenance HD in a large, hospital-facilitated HD centre. Five-year medical records (2009-2013) were retrospectively reviewed, and the cut-off level for serum albumin level was set at 3.5 g/dL. The analysed albumin levels were expressed as time-averaged levels (first 24-month data) and albumin target reach rate over the first 2-year interval. Univariate and multivariate Cox proportional hazard regression models were used to examine the hazard function of the all-cause and cardiovascular mortality of the study participants in the subsequent 3-year period (2011-2013). RESULTS: Compared to those with a 100 % albumin reach rate (3.5 g/dL), the participants with 75- < 100, 50- < 75, and 1- < 50 % albumin reach rates exhibited significantly increased risk for all-cause mortality (HR 1.72, 95 % CI 1.19-2.47; HR 3.14, 95 % CI 1.91-5.16; HR 3.66, 95 % CI 2.18-6.16, respectively). A similar trend for all-cause mortality was demonstrated in participants with time-averaged albumin levels <4 g/dL (HR 1.57, 95 % CI 1.00-2.46 for 3.5-4.0 g/dL; HR 3.66, 95 % CI 2.11-6.32 for <3.5 g/dL). Compared to a 100 % albumin reach rate, the 50- < 75 and 1- < 50 % groups (HR 4.28, 95 % CI 1.82-10.01; HR 3.23, 95 % CI 1.22-8.54 respectively) showed significantly higher cardiovascular mortality rates. Similarly, participants with a time-averaged serum albumin level <3.5 g/dL exhibited a higher risk for cardiovascular mortality (HR 3.24, 95 % CI: 1.23-8.56). CONCLUSIONS: This long-term study demonstrated that higher reach rates of serum albumin levels and higher time-averaged serum albumin levels are associated with a lower mortality rate in patients undergoing maintenance HD.

Chemical Composition, Antioxidant, and Antibacterial Activity of Wood Vinegar from Litchi chinensis.

The antioxidant and antibacterial activities of wood vinegar from Litchi chinensis, and its components have been studied. The chemical compositions of wood vinegar were analyzed by gas chromatography-mass spectrometry (GC-MS). A total of 17 chemical compounds were identified, representing 83.96% of the compositions in the wood vinegar. Three major components, included 2,6-dimethoxyphenol (syringol, 29.54%), 2-methoxyphenol (guaiacol, 12.36%), and 3,5-dimethoxy-4-hydroxytoluene (11.07%), were found in the wood vinegar. Antioxidant activities of the acids were investigated from the aspects of 1,1-Diphyl-2-picrylhydrazyl (DPPH) free radicals scavenging capacity, superoxide anion radical scavenging capacity, and reducing power. The pyroligneous acid exhibited high antioxidant activity which was comparable to the reference standards (vitamin C and butylated hydroxyl toluene) at the same dose with IC50 values of 36.5 ppm calculated by the DPPH radical scavenging assay, 38.38 g Trolox equivalent/100 g DW by the trolox equivalent antioxidant capacity (TEAC) assay, and 67.9 by the reducing power analysis. Antibacterial activity was evaluated using the disc diffusion and microdilution methods against a group of clinically antibiotic resistant isolates. The major components exhibited broad spectrum inhibition against all the bacterial strains with a range of disc inhibition zoon between 15-19 mm. The minimum inhibition concentration and minimum bactericide concentration against the test strains was ranging in 0.95-3.80 µL/100 µL and 1.90-3.80 µL/100 µL, respectively. Most of the antibiotic resistant strains were more susceptible to the wood vinegar than the non-antibiotic resistant strain except the strain of ornithine resistant Staphylococcus aureus. Based on the chemical profile, it was considered that the strongest antioxidant and antibacterial activity of Litchi chinensis wood vinegar was due to its highly phenolic compositions. This study revealed that the Litchi chinensis wood vinegar is valuable to develop as alternative food antioxidant and antibiotics.