METHODS: The two-dimensional images of normal heart from gated computed tomography scan datasets were used to create a 3D model of the heart. The slices were then processed using the software BioModroid and printed with the 3D printer. The evaluation of the model was performed by a questionnaire answered by four cardiothoracic surgeons, 12 cardiologists, five radiologists, and nine surgical registrars.
RESULTS: Eighty-six percent of the anatomy structures showed in this model scored 100% accuracy. Structures such as circumflex branch of left coronary artery, great cardiac vein, papillary muscle, and coronary sinus were each rated 77%, 70%, 70%, and 57% accurate. Among 30 clinicians, a total of 93% rated the model accuracy as good and above; 64% of the clinicians evaluated this model as an excellent teaching tool for anatomy class. As a visual aid for surgery or interventional procedures, the model was rated excellent (40%), good (50%), average (23%), and poor (3%); 70% of the clinicians scored the model as above average for training purpose. Overall, this 3D rapid prototyping cardiac model was rated as excellent (33%), good (50%), and average (17%).
CONCLUSION: This 3D rapid prototyping heart model will be a valuable source of anatomical education and cardiac interventional management.
METHOD: The Delphi method was used to develop consensus statements through identification of clinical questions on diagnostic endoscopy. Three consensus meetings were conducted to consolidate the statements and voting. We conducted a systematic literature search on evidence for each statement. The statements were presented in the second consensus meeting and revised according to comments. The final voting was conducted at the third consensus meeting on the level of evidence and agreement.
RESULTS: Risk stratification should be conducted before endoscopy and high risk endoscopic findings should raise an index of suspicion. The presence of premalignant mucosal changes should be documented and use of sedation is recommended to enhance detection of superficial upper GI neoplasms. The use of antispasmodics and mucolytics enhanced visualisation of the upper GI tract, and systematic endoscopic mapping should be conducted to improve detection. Sufficient examination time and structured training on diagnosis improves detection. Image enhanced endoscopy in addition to white light imaging improves detection of superficial upper GI cancer. Magnifying endoscopy with narrow-band imaging is recommended for characterisation of upper GI superficial neoplasms. Endoscopic characterisation can avoid unnecessary biopsy.
CONCLUSION: This consensus provides guidance for the performance of endoscopic diagnosis and characterisation for early gastric and oesophageal neoplasia based on the evidence. This will enhance the quality of endoscopic diagnosis and improve detection of early upper GI cancers.
RESULTS: This study sought to identify the QTLs associated with fatty acid composition and vegetative traits for compactness in the crop. It integrated two interspecific backcross two (BC2) mapping populations to improve the genetic resolution and evaluate the consistency of the QTLs identified. A total 1963 markers (1814 SNPs and 149 SSRs) spanning a total map length of 1793 cM were integrated into a consensus map. For the first time, some QTLs associated with vegetative parameters and carotene content were identified in interspecific hybrids, apart from those associated with fatty acid composition. The analysis identified 8, 3 and 8 genomic loci significantly associated with fatty acids, carotene content and compactness, respectively.
CONCLUSIONS: Major genomic region influencing the traits for compactness and fatty acid composition was identified in the same chromosomal region in the two populations using two methods for QTL detection. Several significant loci influencing compactness, carotene content and FAC were common to both populations, while others were specific to particular genetic backgrounds. It is hoped that the QTLs identified will be useful tools for marker-assisted selection and accelerate the identification of desirable genotypes for breeding.