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, bla_PER-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.

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. 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 . 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. 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.

In a time-to-event model, Cox proportional hazards (CoxPH) analysis is the most frequently used method for estimating overall survival. However, CoxPH analysis is limited to explaining only single or partial risk effects among clinicopathological factors. We introduced DeepCoxPH, a risk score estimation strategy based on deep learning (DL) and CoxPH, to improve risk stratification for overall survival analysis. The abstracted weight from DL and hazard ratios from CoxPH were transformed into a risk score estimation in the fully adjusted model. DeepCoxPH exhibited more comprehensive risk weight estimation for overall survival. DeepCoxPH was applied to predict 10-year overall survival in breast cancer. A Kaplan–Meier curve revealed that DeepCoxPH improved discrimination of high-and low-risk stratification in both short-and long-term breast cancer overall survival. To our knowledge, this is the first report of risk score estimation based on machine learning and parametric-statistical analysis aimed at identifying risk stratification for overall survival through consideration of comprehensive risk effects among multiple clinicopathological factors.

In the era of advanced precision medicine, next-generation genomic data are crucial to achieve breakthroughs in cancer medicine. Effective cancer mortality risk estimation for genomic data associated with cancer remains a vital challenge. The combination of machine learning algorithms and conventional survival analysis can advance the detection of high-risk missense mutation variants and candidate genes associated with cancer mortality in next-generation genomic data. In this study, a fuzzy logic system combined with machine learning algorithms and conventional survival analysis named FuzzyDeepCoxPH was proposed to identify high-risk missense mutation variants and candidate genes highly associated with cancer mortality. DL-derived abstracted weights and Cox proportional hazards (CoxPH) ratios were used to develop four model-based risk scores to consider the factor importance associated with risk stratification, time-varying effects, and individual and interaction effects among features. Fuzzy rules based on a fuzzy logic system were designed to integrate these considerations by merging four model-based risk scores to develop advanced risk estimation. The clinical features and next-generation sequencing of deoxyribonucleic acid and ribonucleic acid genomic data were used to evaluate FuzzyDeepCoxPH performance. The results indicated that FuzzyDeepCoxPH can effectively distinguish high-risk variants and candidate genes related to cancer mortality. In FuzzyDeepCoxPH, the fuzzy logic system was applied to combine DL-based and CoxPH-based models to provide a comprehensive cancer mortality risk estimation for cancer medicine.

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. 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). 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). 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.