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1.  Clinical observation of different minimally invasive surgeries for the treatment of impacted upper ureteral calculi 
Pakistan Journal of Medical Sciences  2013;29(6):1358-1362.
Objective: To compare the clinical effects of three minimally invasive surgeries on the treatment of impacted upper ureteral calculi.
Methods: 135 patients with impacted upper ureteral calculi were selected and randomly divided into three groups (Group A-C) (n=45), which were treated with transurethral ureteroscopic lithotripsy, minimally invasive percutaneous nephrolithotomy, and retroperitoneal laparoscopic ureterolithotomy respectively. Relevant results of the three groups were compared.
Results: The surgery time of Group C was significantly longer than those of Group A and Group B (P < 0.05). The postoperative hospitalization time of Group B was significantly longer than those of Group A and Group C (P < 0.05). 37.78% (17/45) of Group A patients required extracorporeal shock wave lithotripsy, being significantly more than those in Group B (6.67%, 3/45) and Group C (0, 0/45) (P < 0.05). The postoperative calculus clearance rate of Group A (51.11%, 82.22%) was significantly lower than those of Group B (91.11%, 97.78%) and Group C (93.33%, 100%) (P < 0.05). The incidence rates of postoperative complications in Group A-C were 11.11% (5/45), 8.89% (4/45) and 6.67% (3/45) respectively without significant differences (P > 0.05).
Conclusion: The three surgical methods for impacted upper ureteral calculi should be selected according to practical conditions to improve therapeutic effects and to ensure safe surgery.
PMCID: PMC3905360  PMID: 24550953
Transurethral ureteroscopic lithotripsy; Minimally invasive percutaneous nephrolithotomy; Retroperitoneal laparoscopic ureterolithotomy; Impacted upper ureteral calculus
2.  Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties 
BMC Systems Biology  2013;7:14.
Background
High-throughput (omic) data have become more widespread in both quantity and frequency of use, thanks to technological advances, lower costs and higher precision. Consequently, computational scientists are confronted by two parallel challenges: on one side, the design of efficient methods to interpret each of these data in their own right (gene expression signatures, protein markers, etc.) and, on the other side, realization of a novel, pressing request from the biological field to design methodologies that allow for these data to be interpreted as a whole, i.e. not only as the union of relevant molecules in each of these layers, but as a complex molecular signature containing proteins, mRNAs and miRNAs, all of which must be directly associated in the results of analyses that are able to capture inter-layers connections and complexity.
Results
We address the latter of these two challenges by testing an integrated approach on a known cancer benchmark: the NCI-60 cell panel. Here, high-throughput screens for mRNA, miRNA and proteins are jointly analyzed using factor analysis, combined with linear discriminant analysis, to identify the molecular characteristics of cancer. Comparisons with separate (non-joint) analyses show that the proposed integrated approach can uncover deeper and more precise biological information. In particular, the integrated approach gives a more complete picture of the set of miRNAs identified and the Wnt pathway, which represents an important surrogate marker of melanoma progression. We further test the approach on a more challenging patient-dataset, for which we are able to identify clinically relevant markers.
Conclusions
The integration of multiple layers of omics can bring more information than analysis of single layers alone. Using and expanding the proposed integrated framework to integrate omic data from other molecular levels will allow researchers to uncover further systemic information. The application of this approach to a clinically challenging dataset shows its promising potential.
doi:10.1186/1752-0509-7-14
PMCID: PMC3610285  PMID: 23418673
Multi-omic; Emergent property; Factor analysis; Linear discriminant analysis; NCI-60 cell panel
3.  Adapting functional genomic tools to metagenomic analyses: investigating the role of gut bacteria in relation to obesity 
Briefings in Functional Genomics  2010;9(5-6):355-361.
With the expanding availability of sequencing technologies, research previously centered on the human genome can now afford to include the study of humans’ internal ecosystem (human microbiome). Given the scale of the data involved in this metagenomic research (two orders of magnitude larger than the human genome) and their importance in relation to human health, it is crucial to guarantee (along with the appropriate data collection and taxonomy) proper tools for data analysis. We propose to adapt the approaches defined for the analysis of gene-expression microarray in order to infer information in metagenomics. In particular, we applied SAM, a broadly used tool for the identification of differentially expressed genes among different samples classes, to a reported dataset on a research model with mice of two genotypes (a high density lipoprotein knockout mouse and its wild-type counterpart). The data contain two different diets (high-fat or normal-chow) to ensure the onset of obesity, prodrome of metabolic syndromes (MS). By using 16S rRNA gene as a genomic diversity marker, we illustrate how this approach can identify bacterial populations differentially enriched among different genetic and dietary conditions of the host. This approach faithfully reproduces highly-relevant results from phylogenetic and standard statistical analyses, used to explain the role of the gut microbiome in relation to obesity. This represents a promising proof-of-principle for using functional genomic approaches in the fast growing area of metagenomics, and warrants the availability of a large body of thoroughly tested and theoretically sound methodologies to this exciting new field.
doi:10.1093/bfgp/elq011
PMCID: PMC3080776  PMID: 21266343
human microbiome; functional genomic; metagenomics

Results 1-3 (3)