Biomedical subjects

L Canard

Publications and source records attributed to L Canard.

4 recordsLinked to original sources

PubMed2003

[Small bowel obstruction from adhesions: which CT severity criteria to research?].

PURPOSE: To determine the value of known computed tomographic (CT) criteria to differentiate non-complicated from complicated (strangulation, necrosis) small bowel obstruction. MATERIALS AND METHODS: 43 patients with a definitive diagnosis of small bowel obstruction based on clinical, sonographic, CT, surgical and pathological findings were included. All patients had small bowel obstruction caused by adhesions confirmed at surgery. The obstruction was non-complicated in 28 patients and complicated in 15 patients. The CT examinations from all patients were retrospectively reviewed by three experienced radiologists using a set of pre-defined criteria. Attention was focused on the following signs: reduced enhancement of the small bowel wall, mural thickening, congestion of small mesenteric veins, and ascites. Results were correlated with surgical and/or pathological data. RESULTS: For the diagnosis of complicated obstruction, reduced bowel wall enhancement had a sensitivity of 57% and a specificity of 100%, a bowel wall thickness greater than 3 mm had a sensitivity of 35% and a specificity of 100% and a bowel wall thickness less than 1 mm had a sensitivity of 35% and a specificity of 93%. Ascites and congestion of small mesenteric veins were not significant. The multivariate analysis showed that the association of bowel-wall thickening and reduced enhancement of the small bowel wall was significant (sensitivity of 71%, specificity 100%, and accuracy 90%). CONCLUSION: Among the CT criteria used to diagnose complications from small-bowel obstruction that were evaluated in this study, only three were significant with a high specificity but low sensitivity.

Adolescent↗

PubMed1997

Deciphering protein sequence information through hydrophobic cluster analysis (HCA): current status and perspectives.

Ten years after the idea of hydrophobic cluster analysis (HCA) was conceived and first published, theoretical and practical experience has shown this unconventional method of protein sequence analysis to be particularly efficient and sensitive, especially with families of sequences sharing low levels of sequence identity. This extreme sensitivity has made it possible to predict the functions of genes whose sequence similarities are hardly if at all detectable by current one-dimensional (1D) methods alone, and offers a new way to explore the enormous amount of data generated by genome sequencing. HCA also provides original tools to understand fundamental features of protein stability and folding. Since the last review of HCA published in 1990 [1], significant improvements have been made and several new facets have been addressed. Here we wish to update and summarize this information.

Amino Acid Sequence↗

PubMed1997

Visual BLAST and visual FASTA: graphic workbenches for interactive analysis of full BLAST and FASTA outputs under MICROSOFT WINDOWS 95/NT.

MOTIVATION: When routinely analysing protein sequences, detailed analysis of database search results made with BLAST and FASTA becomes exceedingly time consuming and tedious work, as the resultant file may contain a list of hundreds of potential homologies. The interpretation of these results is usually carried out with a text editor which is not a convenient tool for this analysis. In addition, the format of data within BLAST and FASTA output files makes them difficult to read. RESULTS: To facilitate and accelerate this analysis, we present for the first time, two easy-to-use programs designed for interactive analysis of full BLAST and FASTA output files containing protein sequence alignments. The programs, Visual BLAST and Visual FASTA, run under Microsoft Windows 95 or NT systems. They are based on the same intuitive graphical user interface (GUI) with extensive viewing, searching, editing, printing and multithreading capabilities. These programs improve the browsing of BLAST/FASTA results by offering a more convenient presentation of these results. They also implement on a computer several analytical tools which automate a manual methodology used for detailed analysis of BLAST and FASTA outputs. These tools include a pairwise sequence alignment viewer, a Hydrophobic Cluster Analysis plot alignment viewer and a tool displaying a graphical map of all database sequences aligned with the query sequence. In addition. Visual Blast includes tools for multiple sequence alignment analysis (with an amino acid patterns search engine), and Visual FASTA provides a GUI to the FASTA program.

Amino Acid Sequence↗

PubMed1996

Analysis, clustering and prediction of the conformation of short and medium size loops connecting regular secondary structures.

Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using knowledge-based procedures. While the core of a protein can be straight forwardly modelled from the structurally conserved regions of homologues of known structure, loops must be modelled from a selected homologue or from a loop chosen from outside the family. Here we present a loop prediction procedure that attempts to identify the conformational class of the loop rather than to select a specific loop from a database of fragments. The structures of some 2083 loops of one to eight residues in length were extracted from a database of 225 protein and protein domain structures. For each loop, the relative disposition of its bounding secondary structures is described by the separation between the tips of their axes, the angle and dihedral angle between their axes. From the clustering of the loops according to the root mean square deviation of their spatial fit, a total of 162 loop conformational classes, including 79% of loops, were identified. One-hundred and eight of these, involving 66% of the loops, were populated by at least four non-homologous loops or four loops sharing a low sequence identity. Another 54 classes, including 13% of the loops, were populated by at least three loops of low sequence similarity from three or fewer non-homologous groups. Most of the previously described loop conformations were found among the populated classes. For each class a template was constructed containing both sequence preferences and the relative disposition of bounding secondary structures among member loops. During comparative modelling, the conformation of a loop can be predicted by identifying a loop class with which its sequence and disposition of bounding secondary structures are compatible.

Amino Acid Sequence↗