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3,705 results on '"Shu, Qiang"'

18. Perioperative toripalimab and chemotherapy in locally advanced gastric or gastro-esophageal junction cancer: a randomized phase 2 trial

Author

Yuan, Shu-Qiang, Nie, Run-Cong, Jin, Ying, Liang, Cheng-Cai, Li, Yuan-Fang, Jian, Rui, Sun, Xiao-Wei, Chen, Ying-Bo, Guan, Wen-Long, Wang, Zi-Xian, Qiu, Hai-Bo, Wang, Wei, Chen, Shi, Zhang, Dong-Sheng, Ling, Yi-Hong, Xi, Shao-Yan, Cai, Mu-Yan, Huang, Chun-Yu, Yang, Qiu-Xia, Liu, Zhi-Min, Guan, Yuan-Xiang, Chen, Yong-Ming, Li, Ji-Bin, Tang, Xiong-Wen, Peng, Jun-Sheng, Zhou, Zhi-Wei, Xu, Rui-Hua, and Wang, Feng

Published
2024
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25. Construction of a gene model related to the prognosis of patients with gastric cancer receiving immunotherapy and exploration of COX7A1 gene function

Author

Si-yu Wang, Yu-xin Wang, Ao Shen, Xian-qi Yang, Cheng-cai Liang, Run-jie Huang, Rui Jian, Nan An, Yu-long Xiao, Li-shuai Wang, Yin Zhao, Chuan Lin, Chang-ping Wang, Zhi-ping Yuan, and Shu-qiang Yuan

Subjects
Gastric cancer (GC), Biomarker, Prognostic model, Immunotherapy, COX7A1, Gene function, Medicine
Abstract

Abstract Background GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning. Methods Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy. Results First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS). Conclusions The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients.

Published
2024
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26. Preparation and application of profiled luminescent polyester fiber with reversible photochromism materials

Author

Tao Yaoding, Zhang Shouyun, Xu Mei, Shu Qiang, Gao Shang, Liu Yanan, Wang Peisong, and Cheng Ruijia

Subjects
reversible photochromism, profiled bright light, colorful polyester, preparation technology, applied research, Polymers and polymer manufacture, TP1080-1185
Abstract

In this study, we realized a highly luminescent polyester fiber using a special spinneret orifice comprised of eight C-shaped pores and specific process parameters. A moderate amount of reversible photochromism materials was added along with the specific color masterbatch of suitable materials. With its high light transmittance and reflectance, the fabric has high glossiness and excellent transparency and exhibits dazzling color effects with changes in ambient light intensity. The raw materials used are polyethylene terephthalate (PET) chips with a low melting point of 110–150°C, 0.3–0.5% (active ingredient 30%) masterbatch, and 40–50% (active ingredient 50%) reversible photochromism masterbatch. The following process parameters were also chosen: for the PET chips, the drying temperature was 80–90°C, the drying time was 12–14 h, the masterbatch drying temperature was 70–80°C, the drying time was 8–10 h, the spinning temperature was 220–230°C, the cooling air temperature was 15–17°C, the cooling air speed was 0.45–0.50 m·min−1, the first hot roller temperature was 75–80°C, and the secondary hot roller had the heater turned off.

Published
2024
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44. Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System

Author

Yu, Gang, Yu, Zhongzhi, Shi, Yemin, Wang, Yingshuo, Liu, Xiaoqing, Li, Zheming, Zhao, Yonggen, Sun, Fenglei, Yu, Yizhou, and Shu, Qiang

Subjects
Computer Science - Artificial Intelligence
Abstract

Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among the symptoms of these diseases precludes prompt diagnosis upon the patients' arrival. In pediatrics, the patients' limited ability in expressing their situation makes precise diagnosis even harder. This becomes worse in primary hospitals, where the lack of medical imaging devices and the doctors' limited experience further increase the difficulty of distinguishing among similar diseases. In this paper, a pediatric fine-grained diagnosis-assistant system is proposed to provide prompt and precise diagnosis using solely clinical notes upon admission, which would assist clinicians without changing the diagnostic process. The proposed system consists of two stages: a test result structuralization stage and a disease identification stage. The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage. A novel deep learning algorithm was developed for the disease identification stage, where techniques including adaptive feature infusion and multi-modal attentive fusion were introduced to fuse structured and text data together. Clinical notes from over 12000 patients with respiratory diseases were used to train a deep learning model, and clinical notes from a non-overlapping set of about 1800 patients were used to evaluate the performance of the trained model. The average precisions (AP) for pneumonia, RTI, bronchitis and asthma are 0.878, 0.857, 0.714, and 0.825, respectively, achieving a mean AP (mAP) of 0.819.

Published
2021
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45. Residual circulating tumor DNA after adjuvant chemotherapy effectively predicts recurrence of stage II‐III gastric cancer

Author

Shu‐Qiang Yuan, Run‐Cong Nie, You‐Sheng Huang, Ying‐Bo Chen, Si‐Yu Wang, Xiao‐Wei Sun, Yuan‐Fang Li, Ze‐Kun Liu, Yan‐Xing Chen, Yi‐Chen Yao, Yu Xu, Hai‐Bo Qiu, Yao Liang, Wei Wang, Ze‐Xian Liu, Qi Zhao, Rui‐Hua Xu, Zhi‐Wei Zhou, and Feng Wang

Subjects
gastric cancer, ctDNA, chemotherapy, postoperative, recurrence, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Circulating tumor DNA (ctDNA) is a promising biomarker for predicting relapse in multiple solid cancers. However, the predictive value of ctDNA for disease recurrence remains indefinite in locoregional gastric cancer (GC). Here, we aimed to evaluate the predictive value of ctDNA in this context. Methods From 2016 to 2019, 100 patients with stage II/III resectable GC were recruited in this prospective cohort study (NCT02887612). Primary tumors were collected during surgical resection, and plasma samples were collected perioperatively and within 3 months after adjuvant chemotherapy (ACT). Somatic variants were captured via a targeted sequencing panel of 425 cancer‐related genes. The plasma was defined as ctDNA‐positive only if one or more variants detected in the plasma were presented in at least 2% of the primary tumors. Results Compared with ctDNA‐negative patients, patients with positive postoperative ctDNA had moderately higher risk of recurrence [hazard ratio (HR) = 2.74, 95% confidence interval (CI) = 1.37–5.48; P = 0.003], while patients with positive post‐ACT ctDNA showed remarkably higher risk (HR = 14.99, 95% CI = 3.08‐72.96; P < 0.001). Multivariate analyses indicated that both postoperative and post‐ACT ctDNA positivity were independent predictors of recurrence‐free survival (RFS). Moreover, post‐ACT ctDNA achieved better predictive performance (sensitivity, 77.8%; specificity, 90.6%) than both postoperative ctDNA and serial cancer antigen. A comprehensive model incorporating ctDNA for recurrence risk prediction showed a higher C‐index (0.78; 95% CI = 0.71–0.84) than the model without ctDNA (0.71; 95% CI = 0.64–0.79; P = 0.009). Conclusions Residual ctDNA after ACT effectively predicts high recurrence risk in stage II/III GC, and the combination of tissue‐based and circulating tumor features could achieve better risk prediction.

Published
2023
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