Clinical Analysis of stereotactic body radiation therapy using extracranial gamma knife for patients with mainly bulky inoperable early stage non-small cell lung carcinoma

<p>Abstract</p> <p>Purpose</p> <p>To evaluate the clinical efficacy and toxicity of stereotactic body radiation therapy (SBRT) using extracranial gamma knife in patients with mainly bulky inoperable early stage non-small cell lung carcinoma (NSCLC).</p> <p>Materials and methods</p> <p>A total of 43 medically inoperable patients with mainly bulky Stage I/II NSCLC received SBRT using gamma knife were reviewed. The fraction dose and the total dose were determined by the radiation oncologist according to patients' general status, tumor location, tumor size and the relationship between tumor and nearby organ at risk (OAR). The total dose of 34~47.5 Gy was prescribed in 4~12 fractions, 3.5~10 Gy per fraction, one fraction per day or every other day. The therapeutic efficacy and toxicity were evaluated.</p> <p>Results</p> <p>The median follow-up was 22 months (range, 3-102 months). The local tumor response rate was 95.35%, with CR 18.60% (8/43) and PR 76.74% (33/43), respectively. The local control rates at 1, 2, 3, 5 years were 77.54%, 53.02%, 39.77%, and 15.46%, respectively, while the 1- and 2-year local control rates were 75% and 60% for tumor ≤3 cm; 84% and 71% for tumor sized 3~5 cm; 55% and 14.6% for tumor sized 5~7 cm; and 45%, 21% in those with tumor size of >7 cm. The overall survival rate at 1, 2, 3, 5 years were 92.04%, 78.04%, 62.76%, 42.61%, respectively. The toxicity of stereotactic radiation therapy was grade 1-2. Clinical stages were significantly important factor in local control of lung tumors (P = 0.000). Both clinical stages (P = 0.015) and chemotherapy (P = 0.042) were significantly important factors in overall survival of lung tumors.</p> <p>Conclusion</p> <p>SBRT is an effective and safe therapy for medically inoperable patients with early stage NSCLC. Clinical stage was the significant prognostic factors for both local tumor control and overall survival. The toxicity is mild. The overall local control for bulky tumors is poor. Tumor size is a poor prognostic factor, and the patients for adjuvant chemotherapy need to be carefully selected.</p

Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification

Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous works have made remarkable progress in leveraging attention mechanism to extract opinion words for different aspects. However, a persistent challenge is the effective management of semantic mismatches, which stem from attention mechanisms that fall short in adequately aligning opinions words with their corresponding aspect in multi-aspect sentences. To address this issue, we propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect. Specifically, we first introduce a neighboring span enhanced module which highlights various compositions of neighboring words and given aspects. In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect. Extensive experiments on three benchmark datasets demonstrate that our model achieves state-of-the-art results. The source code is available at https://github.com/AONE-NLP/ABSA-AOAN.Comment: 8 pages, 5 figure, ECAI 202

H-VFI: Hierarchical Frame Interpolation for Videos with Large Motions

Capitalizing on the rapid development of neural networks, recent video frame interpolation (VFI) methods have achieved notable improvements. However, they still fall short for real-world videos containing large motions. Complex deformation and/or occlusion caused by large motions make it an extremely difficult problem in video frame interpolation. In this paper, we propose a simple yet effective solution, H-VFI, to deal with large motions in video frame interpolation. H-VFI contributes a hierarchical video interpolation transformer (HVIT) to learn a deformable kernel in a coarse-to-fine strategy in multiple scales. The learnt deformable kernel is then utilized in convolving the input frames for predicting the interpolated frame. Starting from the smallest scale, H-VFI updates the deformable kernel by a residual in succession based on former predicted kernels, intermediate interpolated results and hierarchical features from transformer. Bias and masks to refine the final outputs are then predicted by a transformer block based on interpolated results. The advantage of such a progressive approximation is that the large motion frame interpolation problem can be decomposed into several relatively simpler sub-tasks, which enables a very accurate prediction in the final results. Another noteworthy contribution of our paper consists of a large-scale high-quality dataset, YouTube200K, which contains videos depicting a great variety of scenarios captured at high resolution and high frame rate. Extensive experiments on multiple frame interpolation benchmarks validate that H-VFI outperforms existing state-of-the-art methods especially for videos with large motions

KMT2A promotes melanoma cell growth by targeting hTERT signaling pathway.

Melanoma is an aggressive cutaneous malignancy, illuminating the exact mechanisms and finding novel therapeutic targets are urgently needed. In this study, we identified KMT2A as a potential target, which promoted the growth of human melanoma cells. KMT2A knockdown significantly inhibited cell viability and cell migration and induced apoptosis, whereas KMT2A overexpression effectively promoted cell proliferation in various melanoma cell lines. Further study showed that KMT2A regulated melanoma cell growth by targeting the hTERT-dependent signal pathway. Knockdown of KMT2A markedly inhibited the promoter activity and expression of hTERT, and hTERT overexpression rescued the viability inhibition caused by KMT2A knockdown. Moreover, KMT2A knockdown suppressed tumorsphere formation and the expression of cancer stem cell markers, which was also reversed by hTERT overexpression. In addition, the results from a xenograft mouse model confirmed that KMT2A promoted melanoma growth via hTERT signaling. Finally, analyses of clinical samples demonstrated that the expression of KMT2A and hTERT were positively correlated in melanoma tumor tissues, and KMT2A high expression predicted poor prognosis in melanoma patients. Collectively, our results indicate that KMT2A promotes melanoma growth by activating the hTERT signaling, suggesting that the KMT2A/hTERT signaling pathway may be a potential therapeutic target for melanoma

S-antigen specific T helper type 1 response is present in Behcet’s disease

PURPOSE: To investigate the frequency and phenotypic and functional characteristics of S-antigen (S-Ag) specific T cells in patients with Behcet's disease (BD). METHODS: Blood was taken from 23 active BD patients, 12 inactive BD patients, and 14 healthy controls. The clinical features of the patients were summarized. T cell response against 40 mixed S-Ag peptides was identified by interferon gamma (IFN-gamma) enzyme-linked immunospot assay (ELISPOT). CD69 and CD45RO were used to characterize the phenotype of S-Ag specific T cells. The functional property of S-Ag specific T cells was investigated by measuring the production of cytokines. RESULTS: Response to the mixed S-Ag peptides was found in 56.5% and 25% of active and inactive BD patients, respectively. The responsiveness to S-Ag peptides was unrelated to the clinical features of the patients. About 65.8% of IFN-gamma(+) CD4(+) T cells in active BD patients expressed CD69 and CD45RO concomitantly. S-Ag peptides significantly induced a production of IFN-gamma and tumor necrosis factor (TNF)-alpha but not interleukin (IL)-2, IL-4, and IL-17 by peripheral blood mononuclear cells (PBMCs) in active BD patients with a response to S-Ag. CONCLUSIONS: S-Ag specific T cells are present in certain active BD patients, and most of them are activated memory CD4(+) T cells. These T cells may be involved in the pathogenesis of BD via producing Th1-dominant cytokine