Toxicity assessment of particulate matter emitted from different types of vehicles on marine microalgae.

Air pollution caused by vehicle emissions remains a serious environmental threat in urban areas. Sedimentation of atmospheric aerosols, surface wash, drainage water, and urbane wastewater can bring vehicle particle emissions into the aquatic environment. However, the level of toxicity and mode of toxic action for this kind of particles are not fully understood. Here we explored the aquatic toxic effects of particulate matter emitted from different types of vehicles on marine microalgae Porphyridium purpureum and Heterosigma akashiwo. We used flow cytometry to evaluate growth rate inhibition, changes in the level of esterase activity, changes in membrane potential and size changes of microalgae cells under the influence of particulate matter emitted by motorcycles, cars and specialized vehicles with different types of engines and powered by different types of fuel. Both microalgae species were highly influenced by the particles emitted by diesel-powered vehicles. These particle samples had the highest impact on survival, esterase activity, and membrane potential of microalgae and caused the most significant increase in microalgae cell size compared to the particles produced by gasoline-powered vehicles. The results of the algae-bioassay strongly correlate with the data of laser granulometry analyses, which indicate that the most toxic samples had a significantly higher percentage of particles in the size range less than 1 µm. Visual observation with an optical microscope showed intensive agglomeration of the particles emitted by diesel-powered vehicles with microalgae cells. Moreover, within the scope of this research, we did not observe the direct influence of metal content in the particles to the level of their aquatic toxicity, and we can conclude that physical damage is the most probable mechanism of toxicity for vehicle emitted particles.

Successful re-construction of a large tissue defect using a free pre-fabricated flap with purely implanted arterialised venous loop.

Flap pre-fabrication represents an interesting technique in tissue-defect reconstruction. In this article we report the successful clinical application of a free pre-fabricated flap using an isolated arterialised venous loop as an implanted vascular pedicle for reconstruction of an extended soft-tissue defect on the leg, as well as clinical neo-vascularisation development following arterialised venous loop implantation. The procedure appears potentially useful in selected patients due to the easy harvest of an isolated vein loop as well as a favourable functional and aesthetic result at the donor site.

Anatomical sketch understanding: recognizing explicit and implicit structure.

OBJECTIVE: Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applications ranging from automated patient records to medical education software could benefit greatly from the richer and more natural interfaces that would be enabled by the ability to understand sketches. In this paper we take the first steps toward developing a system that can understand anatomical sketches. METHODS: Understanding an anatomical sketch requires the ability to recognize what anatomical structure has been sketched and from what view (e.g. parietal view of the brain), as well as to identify the anatomical parts and their locations in the sketch (e.g. parts of the brain), even if they have not been explicitly drawn. We present novel algorithms for sketch recognition and for part identification. We evaluate the accuracy of the recognition algorithm on sketches obtained from medical students. We evaluate the part identification algorithm by comparing its results to the judgment of an experienced physician. RESULTS: The sketch recognition algorithm achieves a recognition accuracy of 75.5%, far above the baseline random classification accuracy of 6.7%. Comparison of the results of the part identification algorithm with the judgment of an experienced physician shows close agreement in terms of location, orientation, size, and shape of the identified parts. CONCLUSIONS: The performance of our prototype in terms of accuracy and running time provides strong evidence that development of robust sketch understanding systems for medical domains is an attainable goal. Further work needs to be done to extend the approach to sketches containing multiple and partial anatomical structures, as well as to be able to interpret sketch annotations.