The first successful experiment of laser vascular welding was reported in 1979. Laser assisted vascular anastomosis
(LAVA) is looked as a particularly promising non-suture method in future. We performed a Medline literature
search on laser vessel welding combined with cross-referencing. According to the former experimental animal
studies, CO<sub>2</sub>-, argon-, diode-, KTP-, Holmium:YAG-, and Nd:YAG-lasers have been used for LAVA. Almost all
lasers have been used in combination with stay suture and/or solders in order to improve the strength on anastomosis
site. Advantages of LAVA are minimal vessel damage, faster operation and the potential for minimally invasive
application. However, the clinical application of LAVA is still seldom employed because of aneurysm formation. In
conclusion of the literature study, the diode laser is the most popular, but long-term evaluation is required.
Laser assisted vascular anastomosis (LAVA) is looked as a particularly promising non-suture method in future.
However, aneurysm formation is one of the main reasons delay the clinical application of LAVA. Some scientists
investigated the incidence of aneurysms in animal model. To systematically analyze the literature on reported
incidence of aneurysm formation in LAVA therapy, we performed a meta-analysis comparing LAVA with
conventional suture anastomosis (CSA) in animal model. Data were systematically retrieved and selected from
PUBMED. In total, 23 studies were retrieved. 18 studies were excluded, and 5 studies involving 647 animals were
included. Analysis suggested no statistically significant difference between LAVA and CSA (OR 1.24, 95%CI
0.66-2.32, P=0.51). Result of meta analysis shows that the technology of LAVA is very close to clinical application.
Abstract The Wavelet transform has been established with the Fourier transform as a data-processing method in
analytical fields. The main fields of application are related to de-noising, compression, variable reduction, and signal
suppression. Raman spectroscopy (RS) is characterized by the frequency excursion that can show the information of
molecule. Every substance has its own feature Raman spectroscopy, which can analyze the structure, components,
concentrations and some other properties of samples easily. RS is a powerful analytical tool for detection and
identification. There are many databases of RS. But the data of Raman spectrum needs large space to storing and long
time to searching. In this paper, Wavelet packet is chosen to compress Raman spectra data of some benzene series. The
obtained results show that the energy retained is as high as 99.9% after compression, while the percentage for number of
zeros is 87.50%. It was concluded that the Wavelet packet has significance in compressing the RS data.
Abstract As one kind of molecule scattering spectroscopy, Raman spectroscopy (RS) is characterized by the frequency
excursion that can show the information of molecule. RS has a broad application in biological, chemical, environmental
and industrial fields. But signals in Raman spectral analysis often have noise, which greatly influences the achievement
of accurate analytical results. The de-noising of RS signals is an important part of spectral analysis. Wavelet transform
has been established with the Fourier transform as a data-processing method in analytical fields. The main fields of
application are related to de-noising, compression, variable reduction, and signal suppression. In de-noising of Raman
Spectroscopy, wavelet is chosen to construct de-noising function because of its excellent properties. In this paper, bior
wavelet is adopted to remove the noise in the Raman spectra. It eliminates noise obviously and the result is satisfying.
This method can provide some bases for practical de-noising in Raman spectra.