Purpose:To study the feasibility of GM CSF and the tumor antigen activated dendritic cells in treating bladder tumor in rats.Methods:The DCs isolated from the spleen of T739 mice which were stimulated in vitro by syngenic granulocyte macrophage colony stimulating factor (GM CSF) and irradiated inactivated BTT739 Bladder tumor cells(Group D),were transfused intravenously once a week when BTT739 tumor cells were inoculated in mouse. Group A was established as control,group B and C were injected with GM CSF activated DCs and inactivated tumor vaccine.Results:In group D, the CTL activity against the BTT739 tumor and cytokine IL 4 and IFN ? was significantly higher than those in other groups ( P

In the development of radio frequency (RF) coils for better quality of the mini-type permanent magnetic resonance imager for using in the small animal imaging, the solenoid RF coil has a special advantage for permanent magnetic system based on analyses of various types.of RF coils. However, it is not satisfied for imaging if the RF coils are directly used. By theoretical analyses of the magnetic field properties produced from the solenoid coil, the research direction was determined by careful studies to raise further the uniformity of the magnetic field coil, receiving coil sensitivity for signals and signal-to-noise ratio (SNR). The method had certain advantages and avoided some shortcomings of the other different coil types, such as, birdcage coil, saddle shaped coil and phased array coil by using the alloy materials (from our own patent). The RF coils were designed, developed and made for keeled applicable to permanent magnet-type magnetic resonance imager, multi-coil combination-type, single-channel overall RF receiving coil, and applied for a patent. Mounted on three instruments (25 mm aperture, with main magnetic field strength of 0.5 T or 1.5 T, and 50 mm aperture, with main magnetic field strength of 0.48 T), we performed experiments with mice, rats, and nude mice bearing tumors. The experimental results indicated that the RF receiving coil was fully applicable to the permanent magnet-type imaging system.
Animals ; Magnetic Fields ; Magnetic Resonance Imaging ; instrumentation ; Magnets ; Mice ; Mice, Nude ; Phantoms, Imaging ; Radio Waves ; Rats

We developed a three-dimensional mini-type permanent magnetic resonance imaging (MRI) device in our lab. The purposes of this study were (1) for further development of MRI technologies, (2) for support of broadening practices of animal test modeling in medical research, and (3) for training more specialists from colleges or universities in the field of MRI. This paper describes the research and development at our lab(s), especially stressing on the design of the main magnet, the gradient coil and the radio frequency coil. In addition, the specific methodologies used in our lab(s) and the related data are emphasized. The 3D MRI technologies have met the needs of using small animals, super thin sections of live animal body and high imaging resolutions. MRI images of mice head and abdominal have been obtained successfully by using the imager that we developed. The imaging results and analyses have also been discussed.
Animals ; Equipment Design ; Imaging, Three-Dimensional ; instrumentation ; Magnetic Resonance Imaging ; instrumentation ; methods ; Mice

Objective:To compare magnetic resonance imaging (MRI)-based and computed tomography (CT)-based target volume delineation and dose coverage in partial breast irradiation (PBI) for patients with breast cancer, aiming to explore the application value of MRI localization in PBI after breast-conserving surgery.Methods:Twenty-nine patients with early breast cancer underwent simulating CT and MRI scans in a supine position. The cavity visualization score (CVS) of tumor bed (TB) was evaluated. The TB, clinical target volume (CTV), planning target volume (PTV) were delineated on CT and MRI images, and then statistically compared. Conformity indices (CI) between CT- and MRI-defined target volumes were calculated. PBI treatment plan of 40 Gy in 10 fractions was designed based on PTV-CT, and the dose coverage for PTV-MRI was evaluated.Results:The CVS on CT and MRI images was 2.97±1.40 vs. 3.10±1.40( P=0.408). The volumes of TB, CTV, PTV on MRI were significantly larger than those on CT, (24.48±16.60) cm 3vs. (38.00±19.77) cm 3, (126.76±56.81) cm 3vs. (168.42±70.54) cm 3, (216.63±81.99) cm 3vs. (279.24±101.55) cm 3, respectively, whereas the increasing percentage of CTV and PTV were significantly smaller than those of TB. The CI between CT-based and MRI-based TB, CTV, PTV were 0.43±0.13, 0.66±0.11, 0.70±0.09( P<0.001), respectively. The median percentage of PTV-MRI receiving 40 Gy dose was 81.9%(62.3% to 92.4%), significantly lower than 95.6%(95.0%~97.5%) of PTV-CT. Conclusions:The CVS between CT and MRI is not significantly different, but the MRI-based TB, CTV, PTV are significantly larger than CT-based values. The PTV-MRI is of underdose if PBI treatment plan is designed for PTV-CT. As a supplement of CT scan, MRI can enhance the accuracy of TB delineation after breast-onserving surgery.

Objective To construct a deep learning-based artificial intelligence model to automat-ically quantify left ventricular ejection fraction(LVEF)using static views of echocardiography.Meth-ods The study included data of 1,902 adults with left ventricular multi-slice echocardiographic views at end-systole and end-diastole.The collected dataset was divided into development set(1,610 cases,with 1,252 cases for model training and 358 cases for parameter adjustment),inter-nal test set(177 cases for internal validation),and external test set(115 cases for external validation and generalization testing).The model achieved left ventricular segmentation and automatic quantification of LVEF through precise identification of the left ventricular endocardial boundary and inspection of key points.The Dice coefficient was employed to evaluate the performance of the left ventricular segmentation model,while the Pearson correlation coefficient and the intraclass correlation coefficient were used to assess the correlation and consistency between the automatically measured LVEF and the reference standard.Results The left ventricular segmentation model performed well,with Dice coefficients ≥0.90 for both the internal and external independent test sets;the agreement between the automatically measured LVEF and the cardiologists'manual measurements was moderate,with Pearson correlation coefficients ranging from 0.46 to 0.71 and intragroup correlation analysis agreements from 0.39 to 0.57 for the inter-nal test set;and Pearson correlation coefficients for the independent external test set were 0.26 to 0.54 and intra-group correlation analysis agreement of 0.23 to 0.50.Conclusion In this study,a left ven-tricular segmentation model with better performance is constructed,and initial application of the model for automatic quantification of LVEF for two-dimensional echocardiography has general performance,which requires further optimisation of the algorithm to improve the model generalisation.

Objective To construct a deep learning-based artificial intelligence model to automat-ically quantify left ventricular ejection fraction(LVEF)using static views of echocardiography.Meth-ods The study included data of 1,902 adults with left ventricular multi-slice echocardiographic views at end-systole and end-diastole.The collected dataset was divided into development set(1,610 cases,with 1,252 cases for model training and 358 cases for parameter adjustment),inter-nal test set(177 cases for internal validation),and external test set(115 cases for external validation and generalization testing).The model achieved left ventricular segmentation and automatic quantification of LVEF through precise identification of the left ventricular endocardial boundary and inspection of key points.The Dice coefficient was employed to evaluate the performance of the left ventricular segmentation model,while the Pearson correlation coefficient and the intraclass correlation coefficient were used to assess the correlation and consistency between the automatically measured LVEF and the reference standard.Results The left ventricular segmentation model performed well,with Dice coefficients ≥0.90 for both the internal and external independent test sets;the agreement between the automatically measured LVEF and the cardiologists'manual measurements was moderate,with Pearson correlation coefficients ranging from 0.46 to 0.71 and intragroup correlation analysis agreements from 0.39 to 0.57 for the inter-nal test set;and Pearson correlation coefficients for the independent external test set were 0.26 to 0.54 and intra-group correlation analysis agreement of 0.23 to 0.50.Conclusion In this study,a left ven-tricular segmentation model with better performance is constructed,and initial application of the model for automatic quantification of LVEF for two-dimensional echocardiography has general performance,which requires further optimisation of the algorithm to improve the model generalisation.