High-dose-rate intraluminal brachytherapy during preoperative chemoradiation for locally advanced rectal cancers

AIM:To determine the feasibility and safety of high dose rate intraluminal brachytherapy(HDR-ILBT) boost during preoperative chemoradiation for rectal cancer.METHODS:Between 2008 and 2009,thirty-six patients with locally advanced rectal cancer(≥ T3 or N+),were treated initially with concurrent capecitabine(825 mg/m2 oral twice daily) and pelvic external beam radiotherapy(EBRT)(45 Gy in 25 fractions),then were randomized to group A;HDR-ILBT group(n = 17) to receive 5.5-7 Gy×2 to gross tumor volume(GTV) and group B;EBRT group(n = 19) to receive 5.4 Gy×3 fractions to GTV with EBRT.All patients underwent total mesorectal excision.RESULTS:Grade 3 acute toxicities were registered in 12 patients(70.6%) in group A and in 8(42.1%) in group B.Complete pathologic response of T stage(ypT0) in group A was registered in 10 patients(58.8%) and in group B,3 patients(15.8%) had ypT0(P < 0.0001).Sphincter preservation was reported in 6/9 patients(66.7%) in group A and in 5/10 patients(50%) in group B(P < 0.01).Overall radiological response was 68.15% and 66.04% in Group A and B,respectively.During a median follow up of 18 mo,late grade 1 and 2 sequelae were registered in 3 patients(17.6%) and 4 patients(21.1%) in the groups A and B,respectively.CONCLUSION:HDR-ILBT was found to be effective dose escalation technique in preoperative chemoradiation for rectal cancers,with higher response rates,downstaging and with manageable acute toxicities.

Microwave-assisted pre-ionization experiments on GLAST-Ⅲ

GLAss Spherical Tokamak(GLAST-Ⅲ)is a spherical tokamak with an insulating vacuum vessel that has a unique single-passage capability for incident microwaves.In this work,electron cyclotron resonance heating(ECRH)-assisted plasma pre-ionization in GLAST-Ⅲis explored for three radio-frequency(RF)polarizations(the O-,X-,and M-modes)at different toroidal-field(TF)strengths and filled gas pressures.The optimum hydrogen pressure is identified for efficient plasma pre-ionization.A comparison of the plasma pre-ionizations initiated by the O-,X-,and M-modes shows prominent differences in the breakdown time,location,and wave absorption.In the case of O-mode polarization,microwave absorption occurs for a relatively shorter duration,resulting in a bell-shaped electron-temperature(Te)temporal profile.Microwave absorption is dominant in the case of the X-mode,leading to a broader Te temporal profile.The M-mode discharge contains features of both the X-and O-modes.Efficient plasma pre-ionization is achieved in the X-mode polarization for the intermediate TF strengths(with a central toroidal magnetic field B0=0.075 T).Traces of the electron-number density show a similar tendency,as revealed by Te.These results suggest that the X-mode is the best candidate for efficient plasma pre-ionization at low filled gas pressures(10-2 Pa)in small tokamaks.

Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method

Brain tumors are life-threatening for adults and children.However,accurate and timely detection can save lives.This study focuses on three different types of brain tumors:Glioma,meningioma,and pituitary tumors.Many studies describe the analysis and classification of brain tumors,but few have looked at the problem of feature engineering.Methods are needed to overcome the drawbacks of manual diagnosis and conventional featureengineering techniques.An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately.While progress continues to be made,the automatic diagnoses of brain tumors still face challenges of low accuracy and high false-positive results.The model presented in this study,which offers improvements for feature extraction and classification,uses deep learning and machine learning for the assessment of brain tumors.Deep learning is used for feature extraction and encompasses the application of different models such as fine-tuned Inception-v3 and fine-tuned Xception.The classification of brain tumors is explored through deep-and machine-learning algorithms including softmax,Random Forest,Support Vector Machine,K-Nearest Neighbors,and the ensemble technique.The results of these approaches are compared with existing methods.The Inception-v3 model has a test accuracy of 94.34%and achieves the highest performance compared with other recently reported methods.This improvement may be sufficient to support a significant role in clinical applications for brain tumor analysis.Furthermore,this type of approach can be used as an effective decisionsupport tool for radiologists in medical diagnostics as a second opinion based on the magnetic resonance imaging(MRI)analysis.It may also save valuable time for radiologists who have to manually review numerous MRI images of patients.

高电荷态离子^(40)Ar^(q+)与Si表面作用中的电子发射产额

报道了在兰州重离子加速器国家实验室电子回旋共振离子源原子物理实验平台上,用高电荷态40Arq+(1≤q≤12)离子作用于半导体Si固体表面时的电子发射产额实验测量.实验中,通过改变炮弹离子的电荷态和引出电压选取其不同的势能和动能,系统地研究了入射离子势能沉积和与其在固体中的电子能损对表面电子发射产额的贡献.结果表明,作为引起表面电子发射的两个主要因素,单离子的电子发射产额与炮弹离子在固体表面的势能沉积和电子能损都有近似的正比关系.