Mycobacterium tuberculosis and M. bovis infection in Feedlot Deer (Cervus unicolor swinhoei and C. nippon taiouanus) in Taiwan

Background/purposeMycobacterium bovis frequently infects wild and farm deer species with tuberculosis. This study investigated mycobacterial infection in two native deer species Cervus unicolor swinhoei (Formosan Sambar, Sambar) and C. nippon taiouanus (Formasan Sika, Sika).MethodsBased on different sampling sources of 19 intradermal tuberculin test (ITT) Sambar, mycobacterial infection and/or species were detected by acid-fast stain, duplex polymerase chain reaction (PCR) and multiplex nested PCR (mnPCR) methods, traditional mycobacterial culture and gross lesion. Blood samples of 167 Sambar deer and 147 Sika deer were then tested by duplex PCR and mnPCR methods to investigate the prevalence of mycobacterial infection. Sequence variations of these mycobacterial species were analyzed as well.ResultsDuplex PCR and mnPCR assays could differentiate between MTBC (M. bovis and M. tuberculosis) and M. avium, as well as between M. bovis and M. tuberculosis, respectively. These PCR methods showed a higher detection rate than traditional culture and matched the gross lesions examined in 19 ITT-examined Sambar. Therefore, the mycobacterial infection in blood samples of 314 deer samples was detected using these PCR methods. Duplex PCR and mnPCR showed an identical prevalence of 16.1% in Sambar and 8.2% in Sika and a significant difference in prevalence between these two deer species. M. bovis and M. tuberculosis were the species detected in feedlot Sambar and Sika. M. tuberculosis was found only and first in Sambar fed in central Taiwan. Sequence analysis revealed diverse genetic variations in M. bovis and M. tuberculosis associated with deer subspecies.ConclusionMultiplex PCR methods were established, and M. bovis and M. tuberculosis were identified in feedlot deer in Taiwan. Sequence variations indicated diverse sources of both mycobacterial species

A moment kernel machine for clinical data mining to inform medical decision making

Machine learning-aided medical decision making presents three major challenges: achieving model parsimony, ensuring credible predictions, and providing real-time recommendations with high computational efficiency. In this paper, we formulate medical decision making as a classification problem and develop a moment kernel machine (MKM) to tackle these challenges. The main idea of our approach is to treat the clinical data of each patient as a probability distribution and leverage moment representations of these distributions to build the MKM, which transforms the high-dimensional clinical data to low-dimensional representations while retaining essential information. We then apply this machine to various pre-surgical clinical datasets to predict surgical outcomes and inform medical decision making, which requires significantly less computational power and time for classification while yielding favorable performance compared to existing methods. Moreover, we utilize synthetic datasets to demonstrate that the developed moment-based data mining framework is robust to noise and missing data, and achieves model parsimony giving an efficient way to generate satisfactory predictions to aid personalized medical decision making

The genetic correlation and causal association between key factors that influence vascular calcification and cardiovascular disease incidence

Background: Serum calcium (Ca), vitamin D (VD), and vitamin K (VK) levels are key determinants of vascular calcification, which itself impacts cardiovascular disease (CVD) risk. The specific relationships between the levels of these different compounds and particular forms of CVD, however, remain to be fully defined. Objective: This study was designed to explore the associations between these serum levels and CVDs with the goal of identifying natural interventions capable of controlling vascular calcification and thereby protecting against CVD pathogenesis, extending the healthy lifespan of at-risk individuals.Methods: Linkage disequilibrium score (LDSC) regression and a two-sample Mendelian randomization (MR) framework were leveraged to systematically examine the causal interplay between these serum levels and nine forms of CVD, as well as longevity through the use of large publically accessible Genome-Wide Association Studies (GWAS) datasets. The optimal concentrations of serum Ca and VD to lower CVD risk were examined through a restrictive cubic spline (RCS) approach.Results: After Bonferroni correction, the positive genetic correlations were observed between serum Ca levels and myocardial infarction (MI) (p = 1.356E–04), as well as coronary artery disease (CAD) (p = 3.601E–04). Negative genetic correlations were detected between levels of VD and CAD (p = 0.035), while elevated VK1 concentrations were causally associated with heart failure (HF) [odds ratios (OR) per 1-standard deviation (SD) increase: 1.044], large artery stroke (LAS) (OR per 1-SD increase: 1.172), and all stroke (AS) (OR per 1-SD increase: 1.041). Higher serum Ca concentrations (OR per 1-SD increase: 0.865) and VD levels (OR per 1-SD increase: 0.777) were causally associated with reduced odds of longevity. These findings remained consistent in sensitivity analyses, and serum Ca and VD concentrations of 2.376 mmol/L and 46.8 nmol/L, respectively, were associated with a lower CVD risk (p &lt; 0.001). Conclusion: Our findings support a genetic correlation between serum Ca and VD and CVD risk, and a causal relationship between VK1 levels and CVD risk. The optimal serum Ca (2.376 mmol/L) and VD levels (46.8 nmol/L) can reduce cardiovascular risk.</p

Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BACKGROUND: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. RESULTS: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… CONCLUSIONS: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

Nardosinane-Type Sesquiterpenoids from the Formosan Soft Coral Paralemnalia thyrsoides

Five new nardosinane-type sesquiterpenoids, paralemnolins Q–U (1–5), along with three known compounds (6–8), were isolated from the Formosan soft coral Paralemnalia thyrsoides. The structures of new metabolites were elucidated on the basis of extensive spectroscopic methods, and the absolute configuration of 1 was determined by the application of Mosher’s method on 1. Among these metabolites, 1 and 3 are rarely found nardosinane-type sesquiterpenoids, possessing novel polycyclic structures. Compounds 1, 3, 6 and 7 were found to possess neuroprotective activity

A delta-doped quantum well system with additional modulation doping

A delta-doped quantum well with additional modulation doping may have potential applications. Utilizing such a hybrid system, it is possible to experimentally realize an extremely high two-dimensional electron gas (2DEG) density without suffering inter-electronic-subband scattering. In this article, the authors report on transport measurements on a delta-doped quantum well system with extra modulation doping. We have observed a 0-10 direct insulator-quantum Hall (I-QH) transition where the numbers 0 and 10 correspond to the insulator and Landau level filling factor ν = 10 QH state, respectively. In situ titled-magnetic field measurements reveal that the observed direct I-QH transition depends on the magnetic component perpendicular to the quantum well, and the electron system within this structure is 2D in nature. Furthermore, transport measurements on the 2DEG of this study show that carrier density, resistance and mobility are approximately temperature (T)-independent over a wide range of T. Such results could be an advantage for applications in T-insensitive devices