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Quantitative assessment of partial vascular occlusions in a swine pedicle flap model using spatial frequency domain imaging.

ABSTRACT: The use of tissue transfer flaps has become a common and effective technique for reconstructing or replacing damaged tissue. While the overall failure rate associated with these procedures is relatively low (5-10%), the failure rate of tissue flaps that require additional surgery is significantly higher (40-60%). The reason for this is largely due to the absence of a technique for objectively assessing tissue health after surgery. Here we have investigated spatial frequency domain imaging (SFDI) as a potential tool to do this. By projecting wide-field patterned illumination at multiple wavelengths onto a tissue surface, SFDI is able to quantify absolute concentrations of oxygenated and deoxygenated hemoglobin over a large field of view. We have assessed the sensitivity of SFDI in a swine pedicle flap model by using a controlled vascular occlusion system that reduced blood flow by 25%, 50%, 75%, or 100% of the baseline values in either the vein or artery. SFDI was able to detect significant changes for oxygenated hemoglobin, deoxygenated hemoglobin, or tissue oxygen saturation in partial arterial occlusions of at least 50% and partial venous occlusions of at least 25%. This shows SFDI is sensitive enough to quantify changes in the tissue hemoglobin state during partial occlusions and thus has the potential to be a powerful tool for the early prediction of tissue flap failure.

SUBMITTER: Ponticorvo A

PROVIDER: S-EPMC3567716 | biostudies-other | 2013 Feb

REPOSITORIES: biostudies-other

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Project description:A semi-quantitative risk assessment was developed to classify pig farms in terms of the probability of introduction of African swine fever virus (ASFV). Following on-farm data collection via a specific checklist, we applied a modified failure mode and effect analysis (FMEA) to calculate the risk priority codes (RPC's), indicating increasing risk levels ranging from 1 to 5. The importance of biosecurity measures was attributed by experts. To consider geographic risk factors, we classified pig farms based on local density of farmed pigs, and on the estimated wild boar population density. The combination of RPC's with geographical risk factors resulted into a final ranking of pig farms in terms of the risk of ASFV introduction. Furthermore, the estimation of frequency and levels of non-compliance with biosecurity measures was used to identify weak points in risk prevention at farm level. The outcome of the risk assessment was affected by choices in assigning non-compliance scores and importance to specific components of biosecurity. The method was applied in 60 commercial farms in major pig production areas in Italy. Furthermore, we applied a reduced version of our checklist in 12 non-commercial/small commercial (≤20 pigs) farms in the northern Apennines. In commercial farms, highest RPC's were obtained for biosecurity measures associated with personnel practices and farm buildings/planimetry. Intervention should be addressed to training of personnel on biosecurity and ASF, to avoid contacts with other pig herds, and to improve practices in the entrance into the farm. Sharing trucks with other farms, and loading/unloading of pigs were other weak points. Fencing was classified as insufficient in 70% of the commercial farms. Among these farms, breeding units were characterised by the lowest risk of ASFV introduction (although differences among median ranks were not statistically significant: P-value = 0.07; Kruskal-Wallis test), and increasing herd size was not significantly correlated with a higher risk (Kendall's τ = -0.13; P-value = 0.14). Density of farmed pig was greatest in the main pig production area in northern Italy. Conversely, exposure to wild boars was greatest for non-commercial/small commercial farms on the Apennines, which were also characterised by non-compliance with critical biosecurity measures.

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Quantitative assessment of partial vascular occlusions in a swine pedicle flap model using spatial frequency domain imaging.

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