Individual risk prediction: Comparing random forests with Cox proportional-hazards model by a simulation study

With big data becoming widely available in healthcare, machine learning algorithms such as random forest (RF) that ignores time-to-event information and random survival forest (RSF) that handles right-censored data are used for individual risk prediction alternatively to the Cox proportional hazards (Cox-PH) model. We aimed to systematically compare RF and RSF with Cox-PH. RSF with three split criteria [log-rank (RSF-LR), log-rank score (RSF-LRS), maximally selected rank statistics (RSF-MSR)]; RF, Cox-PH, and Cox-PH with splines (Cox-S) were evaluated through a simulation study based on real data. One hundred eighty scenarios were investigated assuming different associations between the predictors and the outcome (linear/linear and interactions/nonlinear/nonlinear and interactions), training sample sizes (500/1000/5000), censoring rates (50%/75%/93%), hazard functions (increasing/decreasing/constant), and number of predictors (seven, 15 including noise variables). Methods' performance was evaluated with time-dependent area under curve and integrated Brier score. In all scenarios, RF had the worst performance. In scenarios with a low number of events (⩽70), Cox-PH was at least noninferior to RSF, whereas under linearity assumption it outperformed RSF. Under the presence of interactions, RSF performed better than Cox-PH as the number of events increased whereas Cox-S reached at least similar performance with RSF under nonlinear effects. RSF-LRS performed slightly worse than RSF-LR and RSF-MSR when including noise variables and interaction effects. When applied to real data, models incorporating survival time performed better. Although RSF algorithms are a promising alternative to conventional Cox-PH as data complexity increases, they require a higher number of events for training. In time-to-event analysis, algorithms that consider survival time should be used.     

Standardized Aronia melanocarpa extract regulates redox status in patients receiving hemodialysis with anemia

Molecular and Cellular Biochemistry - The aim of our study was to investigate the effects of one-month consumption of polyphenol-rich standardized Aronia melanocarpa extract (SAE) on redox status...     

Complex Patterns of Chromosome 11 Aberrations in Myeloid Malignancies Target CBL,MLL, DDB1 and LMO2

Exome sequencing of primary tumors identifies complex somatic mutation patterns. Assignment of relevance of individual somatic mutations is difficult and poses the next challenge for interpretation of next generation sequencing data. Here we present an approach how exome sequencing in combination with SNP microarray data may identify targets of chromosomal aberrations in myeloid malignancies. The rationale of this approach is that hotspots of chromosomal aberrations might also harbor point mutations in the target genes of deletions, gains or uniparental disomies (UPDs). Chromosome 11 is a frequent target of lesions in myeloid malignancies. Therefore, we studied chromosome 11 in a total of 813 samples from 773 individual patients with different myeloid malignancies by SNP microarrays and complemented the data with exome sequencing in selected cases exhibiting chromosome 11 defects. We found gains, losses and UPDs of chromosome 11 in 52 of the 813 samples (6.4%). Chromosome 11q UPDs frequently associated with mutations of CBL. In one patient the 11qUPD amplified somatic mutations in both CBL and the DNA repair gene DDB1. A duplication within MLL exon 3 was detected in another patient with 11qUPD. We identified several common deleted regions (CDR) on chromosome 11. One of the CDRs associated with de novo acute myeloid leukemia (P=0.013). One patient with a deletion at the LMO2 locus harbored an additional point mutation on the other allele indicating that LMO2 might be a tumor suppressor frequently targeted by 11p deletions. Our chromosome-centered analysis indicates that chromosome 11 contains a number of tumor suppressor genes and that the role of this chromosome in myeloid malignancies is more complex than previously recognized.     

Identification of a Functional Connectome for Long-Term Fear Memory in Mice

Long-term memories are thought to depend upon the coordinated activation of a broad network of cortical and subcortical brain regions. However, the distributed nature of this representation has made it challenging to define the neural elements of the memory trace, and lesion and electrophysiological approaches provide only a narrow window into what is appreciated a much more global network. Here we used a global mapping approach to identify networks of brain regions activated following recall of long-term fear memories in mice. Analysis of Fos expression across 84 brain regions allowed us to identify regions that were co-active following memory recall. These analyses revealed that the functional organization of long-term fear memories depends on memory age and is altered in mutant mice that exhibit premature forgetting. Most importantly, these analyses indicate that long-term memory recall engages a network that has a distinct thalamic-hippocampal-cortical signature. This network is concurrently integrated and segregated and therefore has small-world properties, and contains hub-like regions in the prefrontal cortex and thalamus that may play privileged roles in memory expression.     

Pollution in mediterranean-climate rivers

This review examines information generated over the past decade on the pollution of rivers in regions with a mediterranean-type climate (med-climate). Pollution has clearly increased in the last 100 years and is correlated with the development of industry, agriculture and human population. Important efforts have been made in some med-climate countries in order to characterise the chemical status of rivers. In addition, the number of chemical substances detectable in mediterranean-climate rivers (med-rivers), as well as the limits of detection, have improved from the development of better analytical methods. New substances detected in rivers are gaining attention. We discuss available knowledge regarding real and potential effects of pollutants on the biota and ecosystems in med-rivers, taking into account natural environmental characteristics of these rivers. The extreme seasonal conditions in med-rivers add to the potential risk because these characteristics can enhance pollutant effects. Efforts and policies to prevent or reduce pollution effects on med-rivers are linked to the knowledge about pollution pressures associated with the degree of economic development. Aquatic communities in med-rivers are more sensitive to pollutants because they are exposed to strong natural and human stressors.     

Insights from the reconstitution of the divergent outer kinetochore of Drosophila melanogaster

Accurate chromosome segregation during mitosis and meiosis is crucial for cellular and organismal viability. Kinetochores connect chromosomes with spindle microtubules and are essential for chromosome segregation. These large protein scaffolds emerge from the centromere, a specialized region of the chromosome enriched with the histone H3 variant CENP-A. In most eukaryotes, the kinetochore core consists of the centromere-proximal constitutive centromere-associated network (CCAN), which binds CENP-A and contains 16 subunits, and of the centromere-distal Knl1 complex, Mis12 complex, Ndc80 complex (KMN) network, which binds microtubules and contains 10 subunits. In the fruitfly, Drosophila melanogaster, the kinetochore underwent remarkable simplifications. All CCAN subunits, with the exception of centromeric protein C (CENP-C), and two KMN subunits, Dsn1 and Zwint, cannot be identified in this organism. In addition, two paralogues of the KMN subunit Nnf1 (Nnf1a and Nnf1b) are present. Finally, the Spc105R subunit, homologous to human Knl1/CASC5, underwent considerable sequence changes in comparison with other organisms. We combined biochemical reconstitution with biophysical and structural methods to investigate how these changes reflect on the organization of the Drosophila KMN network. We demonstrate that the Nnf1a and Nnf1b paralogues are subunits of distinct complexes, both of which interact directly with Spc105R and with CENP-C, for the latter of which we identify a binding site on the Mis12 subunit. Our studies shed light on the structural and functional organization of a highly divergent kinetochore particle.     

Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum

Background

Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics.

Methods

This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013–14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course.

Results

Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001).

Conclusion

This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics.