RESULTS: We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).
CONCLUSIONS: Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.
OBJECTIVES: To determine the cost-effectiveness of universal HLA-B*15:02 screening in preventing carbamazepine-induced Stevens-Johnson syndrome/toxic epidermal necrolysis in an ethnically diverse Malaysian population.
METHODS: A hybrid model of a decision tree and Markov model was developed to evaluate three strategies for treating newly diagnosed epilepsy among adults: (i) carbamazepine initiation without HLA-B*15:02 screening (current practice); (ii) universal HLA-B*15:02 screening prior to carbamazepine initiation; and (iii) alternative treatment [sodium valproate (VPA)] prescribing without HLA-B*15:02 screening. Base-case analysis and sensitivity analyses were performed over a lifetime time horizon. Incremental cost-effectiveness ratios were calculated.
RESULTS: Both universal HLA-B*15:02 screening and VPA prescribing were dominated by current practice. Compared with current practice, universal HLA-B*15:02 screening resulted in a loss of 0·0255 quality-adjusted life years (QALYs) at an additional cost of 707 U.S. dollars (USD); VPA prescribing resulted in a loss of 0·2622 QALYs at an additional cost of USD 4127, owing to estimated differences in antiepileptic treatment efficacy.
CONCLUSIONS: Universal HLA-B*15:02 screening is unlikely to be a cost-effective intervention in Malaysia. However, with the emergence of an ethnically diverse population in many other countries, this may render HLA-B*15:02 screening a viable intervention when an increasing proportion of the population is at risk and an equally effective yet safer antiepileptic drug is available.
Methods: A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry for the years 2006-2013 were enrolled. In the development of univariate and multivariate logistic regression model for the STEMI patients, Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied. The performance of the model was assessed through convergence diagnostics, overall model fit, model calibration and discrimination.
Results: A set of six risk factors for cardiovascular death among STEMI male patients were identified from the Bayesian multivariate logistic model namely age, diabetes mellitus, family history of CVD, Killip class, chronic lung disease and renal disease respectively. Overall model fit, model calibration and discrimination were considered good for the proposed model.
Conclusion: Bayesian risk prediction model for CVD male patients identified six risk factors associated with mortality. Among the highest risks were Killip class (OR=18.0), renal disease (2.46) and age group (OR=2.43) respectively.
OBJECTIVE: This study aimed to examine the cost-effectiveness of second-line endocrine therapies for the treatment of postmenopausal women with HR + and HER2 - mBC.
METHODS: A Markov model was developed to analyze the cost-effectiveness of the therapies over a 15-year time horizon from a public healthcare payer's perspective. The efficacy and utility parameters were determined via a systematic search of the literature. Direct medical care costs were used. A discount rate of 2% was applied for costs and outcomes. Subgroup analysis was performed for non-visceral metastasis. A series of sensitivity analyses, including probabilistic sensitivity analysis (PSA) and threshold analysis were performed.
RESULTS: Base-case analyses estimated incremental cost-effectiveness ratios (ICERs) of 3 million and 6 million Japanese yen (JPY)/quality-adjusted life year (QALY) gained for TOR and FUL 500 mg relative to EXE, respectively. FUL 250 mg and EXE + EVE were dominated. The overall survival (OS) highly influenced the ICER. With a willingness-to-pay (WTP) threshold of 5 million JPY/QALY, the probability of TOR being cost-effective was the highest. Subgroup analysis in non-visceral metastasis revealed 0.4 and 10% reduction in ICER from the base-case results of FUL5 500 mg versus EXE and TOR versus EXE, respectively, while threshold analysis indicated EVE and FUL prices should be reduced 73 and 30%, respectively.
CONCLUSION: As a second-line therapy for postmenopausal women with HR +/HER2 - mBC, TOR may be cost-effective relative to other alternatives and seems to be the most favorable choice, based on a WTP threshold of 5 million JPY/QALY. FUL 250 mg is expected to be as costly and effective as EXE. The cost-effectiveness of EXE + EVE and FUL 500 mg could be improved by a large price reduction. However, the results are highly sensitive to the hazard ratio of OS. Policy makers should carefully interpret and utilize these findings.