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320 results on '"Huang, Jiaming"'

25. Real-world Practice Patterns and Safety of Concurrent Radiotherapy and Cabozantinib in Metastatic Renal Cell Carcinoma: Results from the International Metastatic Renal Cell Carcinoma Database Consortium

Author

Loo Gan, Chun, Huang, Jiaming, Pan, Elizabeth, Xie, Wanling, Schmidt, Andrew L., Labaki, Chris, Meza, Luis, Bouchard, Gabrielle, Li, Haoran, Jackson-Spence, Francesca, Sánchez-Ruiz, Carla, Powles, Thomas, Kumar, Shruti A., Weise, Nicole, Hall, William A., Rose, Brent S., Beuselinck, Benoit, Suarez, Cristina, Pal, Sumanta K., Choueiri, Toni K., Heng, Daniel Y.C., and McKay, Rana R.

Published
2023
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28. Self-assembly enables simple structure organic photovoltaics via green-solvent and open-air-printing: Closing the lab-to-fab gap

Author

Tang, Hua, Lv, Jie, Liu, Kuan, Ren, Zhiwei, Chandran, Hrisheekesh Thachoth, Huang, Jiaming, Zhang, Ying, Xia, Hao, Khan, Jafar I., Hu, Dingqin, Yan, Cenqi, Oh, Jiyeon, Chen, Shanshan, Chu, Shenglong, Fong, Patrick W.K., Chen, Haiyan, Xiao, Zhengguo, Yang, Changduk, Kan, Zhipeng, Laquai, Frédéric, Lu, Shirong, and Li, Gang

Published
2022
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41. Comparison of outcomes for Hispanic and non‐Hispanic patients with advanced renal cell carcinoma in the International Metastatic Renal Cell Carcinoma Database.

Author

Guram, Kripa, Huang, Jiaming, Mouchati, Christian, Abdallah, Nour, Jani, Chinmay, Navani, Vishal, Xie, Wanling, El Zarif, Talal, Adib, Elio, Gebrael, Georges, Agarwal, Neeraj, Li, Haoran, Labaki, Chris, Labban, Muhieddine, Ruiz Morales, Jose Manuel, Choueiri, Toni K., Chin Heng, Daniel Yick, Mittal, Abhenil, Hansen, Aaron R., and Rose, Brent S.

Subjects
*RENAL cell carcinoma, *DATABASES, *PROTEIN-tyrosine kinase inhibitors, *BONE metastasis, *OVERALL survival
Abstract

Background: Existing data on the impact of Hispanic ethnicity on outcomes for patients with renal cell carcinoma (RCC) is mixed. The authors investigated outcomes of Hispanic and non‐Hispanic White (NHW) patients with advanced RCC receiving systemic therapy at large academic cancer centers using the International Metastatic Renal Cell Carcinoma Database (IMDC). Methods: Eligible patients included non‐Black Hispanic and NHW patients with locally advanced or metastatic RCC initiating systemic therapy. Overall survival (OS) and time to first‐line treatment failure (TTF) were calculated using the Kaplan–Meier method. The effect of ethnicity on OS and TTF were estimated by Cox regression hazard ratios (HRs). Results: A total of 1563 patients (181 Hispanic and 1382 NHW) (mostly males [73.8%] with clear cell RCC [81.5%] treated with tyrosine kinase inhibitor [TKI] monotherapy [69.9%]) were included. IMDC risk groups were similar between groups. Hispanic patients were younger at initial diagnosis (median 57 vs. 59 years, p =.015) and less likely to have greater than one metastatic site (60.8% vs. 76.8%, p <.001) or bone metastases (23.8% vs. 33.4%, p =.009). Median OS and TTF was 38.0 months (95% confidence interval [CI], 28.1–59.2) versus 35.7 months (95% CI, 31.9–39.2) and 7.8 months (95% CI, 6.2–9.0) versus 7.5 months (95% CI, 6.9–8.1), respectively, in Hispanic versus NHW patients. In multivariable Cox regression analysis, no statistically significant differences were observed in OS (adjusted hazard ratio [HR], 1.07; 95% CI, 0.86–1.31, p =.56) or TTF (adjusted HR, 1.06; 95% CI, 0.89–1.26, p =.50). Conclusions: The authors did not observe statistically significant differences in OS or TTF between Hispanic and NHW patients with advanced RCC. Receiving treatment at tertiary cancer centers may mitigate observed disparities in cancer outcomes. The authors demonstrate that there were no statistically significant differences in overall survival or time to first‐line treatment failure between non‐Black Hispanic and non‐Hispanic White patients with advanced renal cell carcinoma (RCC) treated at these large academic cancer centers. Our findings suggest that receiving treatment at a tertiary cancer center may mitigate previously observed disparities in cancer outcomes for non‐Black Hispanic patients with advanced RCC. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Rapid and sensitive diagnosis of live Mycobacterium tuberculosis using clustered regularly interspaced short palindromic repeat‐Cas13a point‐of‐care RNA testing.

Author

Wang, Yu, Lin, Huihuang, Yang, Anqi, Huang, Jiaming, Ren, Weicong, Dong, Jiajun, Wang, Shaojie, Xu, Wenxue, Pang, Yu, Qu, Jieming, and Liu, Jia

Subjects
MYCOBACTERIUM tuberculosis, CRISPRS, POINT-of-care testing
Abstract

Mycobacterium tuberculosis (MTB) is the causal pathogen of tuberculosis (TB). Rapid and accurate detection of live MTB is important for transmission control and patient treatment. Here, we described a clustered regularly interspaced short palindromic repeat (CRISPR)‐Cas13a‐based molecular diagnosis approach for rapid and specific detection of live MTB. This detection method, which we termed CRISPR‐Live‐MTB, contained two consecutive reactions including nuclear acid sequence‐based amplification (NASBA) and CRISPR‐Cas13a collateral cleavage reaction. CRISPR‐Live‐MTB could efficiently detect MTB single‐stranded RNA (ssRNA) in 2 hours with high specificity over double‐stranded DNA (dsDNA). Importantly, CRISPR‐Live‐MTB exhibited a limit of detection of 2.4 copies for MTB ssRNA, which was 1000 times lower than that of the clinically used NASBA method. Moreover, lateral flow was integrated into the CRISPR‐Live‐MTB method to enable point‐of‐care testing application with a sensitivity of 95% and a specificity of 100%. Overall, our study demonstrated the feasibility of CRISPR‐Live‐MTB as a rapid, sensitive, and specific approach for live MTB detection. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Personalized Federated Learning with Layer-Wise Feature Transformation via Meta-Learning.

Author

Tu, Jingke, Huang, Jiaming, Yang, Lei, and Lin, Wanyu

Subjects
FEDERATED learning, MACHINE learning, INDIVIDUALIZED instruction, DATA distribution, STATISTICAL learning
Abstract

Federated learning enables multiple clients to collaboratively learn machine learning models in a privacy-preserving manner. However, in real-world scenarios, a key challenge encountered in federated learning is the statistical heterogeneity among clients. Existing work mainly focused on a single global model shared across the clients, making it hard to generalize well to all clients due to the large discrepancy in the data distributions. To address this challenge, we propose pFedLT, a novel approach that can adapt the single global model to different data distributions. Specifically, we propose to perform a pluggable layer-wise transformation during the local update phase based on scaling and shifting operations. In particular, these operations are learned with a meta-learning strategy. By doing so, pFedLT can capture the diversity of data distribution among clients, therefore, can generalize well even when the data distributions among clients exhibit high statistical heterogeneity. We conduct extensive experiments on synthetic and real-world datasets (MNIST, Fashion_MNIST, CIFAR-10, and Office+Caltech10) under different Non-IID settings. Experimental results demonstrate that pFedLT significantly improves the model accuracy by up to 11.67% and reduces the communication costs compared with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Room-temperature multiple ligands-tailored SnO2 quantum dots endow in situ dual-interface binding for upscaling efficient perovskite photovoltaics with high VOC

Author

Ren, Zhiwei, Liu, Kuan, Hu, Hanlin, Guo, Xuyun, Gao, Yajun, Fong, Patrick W. K., Liang, Qiong, Tang, Hua, Huang, Jiaming, Zhang, Hengkai, Qin, Minchao, Cui, Li, Chandran, Hrisheekesh Thachoth, Shen, Dong, Lo, Ming-Fai, Ng, Annie, Surya, Charles, Shao, Minhua, Lee, Chun-Sing, Lu, Xinhui, Laquai, Frédéric, Zhu, Ye, and Li, Gang

Published
2021
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