publications | Zhengdong Wu

For a full list, see Google Scholar.

2026

Under Review

A randomized experiment of artificial intelligence fact-checking extension to combat HPV vaccine misinformation in China

Zhengdong Wu, Zhiyuan Hou, Yao Rao, Hui Zheng, Liubing Gong, Hui Peng, Kathy Leung, Joseph T. Wu, and Leesa Lin

2026

Under review at npj Digital Medicine

Bib

@unpublished{wu2026factcheck,
  title = {A randomized experiment of artificial intelligence fact-checking extension to combat HPV vaccine misinformation in China},
  author = {Wu, Zhengdong and Hou, Zhiyuan and Rao, Yao and Zheng, Hui and Gong, Liubing and Peng, Hui and Leung, Kathy and Wu, Joseph T. and Lin, Leesa},
  note = {Under review at npj Digital Medicine},
  year = {2026}
}

medRxiv
Artificial Intelligence Agents in Mental Health: A Systematic Review and Meta Analysis

Lexuan Zhu, Wenkong Wang, Zhiying Liang, Wenjia Tan, Bingyi Chen, Xinxin Lin, Zhengdong Wu, Huizi Yu, Xiang Li, Jiyuan Jiao, Sijia He, and 14 more authors

medRxiv preprint, Apr 2026

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The rapid rise of large language models (LLMs) and foundation models has accelerated efforts to build artificial intelligence (AI) agents for mental health assessment, triage, psychotherapy support and clinical decision assistance. Yet a gap persists between healthcare and AI-focused work: while both communities use the language of “agents,” clinical research largely describes monolithic chatbots, whereas AI studies emphasize agentic properties such as autonomous planning, multiagent coordination, tool and database use and integration with multimodal mental health data streams. In this Review, we conduct a systematic analysis of mental health AI agent systems from 2023 to 2025 using a six-dimensional audit framework: (i) system type (base model lineage, interface modality and workflow composition, from rule-based tools to role-aware multi-agent foundation-model systems), (ii) data scope (modalities and provenance, from elicited self-report and chatbot dialogues to electronic health records, biosensing and synthetic corpora), (iii) mental health focus (mapped to ICD-11 diagnostic groupings), (iv) demographics (age strata, geography and sex representation), (v) downstream tasks (screening/triage, clinical decision support, therapeutic interventions, documentation, ethical–legal support and education/simulation) and (vi) evaluation types (automated metrics, language quality benchmarks, safety stress tests, expert review and clinician or patient involvement). Across this corpus, we find that most systems (1) concentrate on depression, anxiety and suicidality, with sparse coverage of severe mental illness, neurocognitive disorders, substance use and complex comorbidity; (2) rely heavily on text-based self-report rather than clinically verified longitudinal data or genuinely multimodal inputs; (3) are implemented as single-agent chatbots powered by general-purpose LLMs rather than role-structured, workflow-integrated pipelines; and (4) are evaluated primarily via offline metrics or vignette-based scenarios, with few prospective, clinician- or patient-in-the-loop studies. At the same time, an emerging class of agentic systems assigns foundation models explicit roles as planners, retrieval agents, safety auditors or supervisors coordinating other models and tools. These multiagent, tool-augmented workflows promise personalization, safety monitoring and greater transparency, but they also introduce new risks around reliability, bias amplification, privacy, regulatory accountability and the blurring of clinical versus non-clinical roles. We conclude by outlining priorities for the next generation of mental health AI agents: clinically grounded, role-aware multi-agent architectures; transparent and privacy-preserving use of clinical and elicited data; demographic and cultural broadening beyond predominantly Western adult samples; and evaluation pipelines that progress from offline benchmarks to longitudinal, real-world studies with routine safety auditing and clear governance of responsibilities between agents and human clinicians.
@article{zhu2026agentsreview, title = {Artificial Intelligence Agents in Mental Health: A Systematic Review and Meta Analysis}, author = {Zhu, Lexuan and Wang, Wenkong and Liang, Zhiying and Tan, Wenjia and Chen, Bingyi and Lin, Xinxin and Wu, Zhengdong and Yu, Huizi and Li, Xiang and Jiao, Jiyuan and He, Sijia and Dai, Guangxin and Niu, Jiahui and Zhong, Yi and Zheng, Yongbo and Sun, Jie and Han, Andi and Li, Lingyao and Zhou, Jiayan and Hua, Wenyue and Chan, Ngan Yin and Lu, Lin and Wing, Yun Kwok and Ma, Xin and Fan, Lizhou}, journal = {medRxiv preprint}, year = {2026}, month = apr, doi = {10.64898/2026.04.21.26351365}, }
arXiv
An evidence-guided reinforcement learning method to improve psychiatric reasoning in small language models

Xinxin Lin, Guangxin Dai, Yi Zhong, Xiang Li, Xue Xiao, Jian Liu, Yixin Zhang, Lingming Hu, Zhengdong Wu, Yongbo Zheng, Runchuan Zhu, and 17 more authors

arXiv preprint arXiv:2602.06449, Feb 2026

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Privacy and computational constraints limit the use of large language models in psychiatry, while adapting small language models (SLMs) often requires substantial data and expert annotation. We developed ClinMPO, an evidence-guided reinforcement-learning framework guided by the psychiatrist-defined Clinical Psychiatry Thinking Strategy (CPTS). ClinMPO uses ClinRM, a reward model trained on 18,569 question–answer pairs from 4,474 psychiatry articles. We evaluated four Qwen3 sizes on 1,737 model-screened questions. ClinMPO outperformed Base, supervised fine-tuning and standard group relative policy optimization across scales. From responses by 300 senior pre-licensure medical students, we established the human baseline, a medical-student reference. The 4B model approached this baseline, whereas the 8B model surpassed it and ranked first among 31 models and post-training variants. ClinMPO improved performance across two complementary schemes covering ICD-11 diagnostic categories and psychiatric practice competencies. Blinded assessment by three clinicians showed improved rationale quality across CPTS criteria. These findings highlight how existing clinical evidence and specialist knowledge can be incorporated into the development of medical AI systems through evidence-guided learning.
@article{lin2026psychrl, title = {An evidence-guided reinforcement learning method to improve psychiatric reasoning in small language models}, author = {Lin, Xinxin and Dai, Guangxin and Zhong, Yi and Li, Xiang and Xiao, Xue and Liu, Jian and Zhang, Yixin and Hu, Lingming and Wu, Zhengdong and Zheng, Yongbo and Zhu, Runchuan and Zhao, Ming and Yu, Huizi and Zhang, Yi and Lu, Fangting and Wu, Shuo and Zhao, Jun and Yin, Ping and Chan, Joey W. Y. and Chan, Ngan Yin and Wang, Yumei and Yang, Lejin and Xing, Yanqiu and Chen, Sijing and Wing, Yun Kwok and Lu, Lin and Ma, Xin and Fan, Lizhou}, journal = {arXiv preprint arXiv:2602.06449}, year = {2026}, month = feb, doi = {10.48550/arXiv.2602.06449} }
JAMA Netw. Open
AI Chatbot Use for Human Papillomavirus Vaccine Literacy in Japan: A Randomized Clinical Trial

Kana Kobayashi, Zhengdong Wu, Hana Tomoi, Ken Masuda, Heidi J. Larson, Kathy Leung, Joseph T. Wu, and Leesa Lin

JAMA Network Open, Sep 2026

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Importance. Persistent misinformation and low confidence impede human papillomavirus (HPV) vaccine uptake in Japan. Artificial intelligence (AI)-driven conversational agents may provide scalable vaccine education, but randomized evidence for HPV vaccine communication remains limited.
Objective. To determine whether an AI-driven HPV vaccine chatbot improves caregiver HPV vaccine literacy compared with a government leaflet.
Design, setting, and participants. Parallel-group randomized clinical trial (RCT) conducted through a national research panel of female caregivers in Japan with unvaccinated daughters aged 12 to 18 years from November 30 to December 27, 2024, with 2-week follow-up.
Interventions. Participants were randomized 1:1 to an AI-driven HPV vaccine chatbot or standard government HPV vaccine leaflet.
Main outcomes and measures. The primary outcome was an HPV vaccine literacy, evaluated immediately after intervention to estimate immediate intervention efficacy and evaluated at 2-week follow-up to estimate durability. HPV vaccine literacy was measured using 7 factual questions about HPV and HPV vaccination; scores are shown on a 0 to 7 scale, with higher scores indicating more correct responses.The secondary outcome was decision to vaccinate at 2-week follow-up.
Results. Among 848 randomized participants, 704 of 841 (83.7%) were included in the immediate modified intention-to-treat (mITT) analysis (353 chatbot, 351 leaflet) and 477 of 841 (56.7%) in the mITT analysis at 2 weeks (237 chatbot, 240 leaflet). A total of 447 of 703 participants (63.6%) were aged 40 to 49 years and had college or university education (472 of 703 participants [67.1%]). Immediately after the intervention, analysis of covariance-adjusted literacy was higher with the chatbot (adjusted mean difference, 0.30 points on the 0 to 7 scale; 95% CI, 0.18-0.43 points; P < .001). At the 2-week durability assessment, mean (SD) literacy increased by 0.40 (1.21) with the chatbot vs 0.28 (1.13) points with the leaflet; adjusted literacy again favored the chatbot (adjusted mean difference, 0.30 points; 95% CI, 0.13-0.47; P < .001). At 2 weeks, caregivers’ vaccination decisions did not differ between groups (102 of 253 participants [40.3%] vs 103 of 260 participants [39.6%]; adjusted odds ratio, 0.74; 95% CI, 0.41 to 1.33; P = .31).
Conclusions and relevance. In this RCT of female caregivers in Japan, an AI-driven HPV vaccine chatbot produced higher adjusted HPV vaccine literacy than a government leaflet immediately after intervention and at 2-week follow-up, without a measurable short-term effect on vaccination decision. Conversational AI may complement official materials and health care professional recommendations.
Trial registration. ClinicalTrials.gov Identifier: NCT06702423.
@article{kobayashi2026japan, title = {AI Chatbot Use for Human Papillomavirus Vaccine Literacy in Japan: A Randomized Clinical Trial}, author = {Kobayashi, Kana and Wu, Zhengdong and Tomoi, Hana and Masuda, Ken and Larson, Heidi J. and Leung, Kathy and Wu, Joseph T. and Lin, Leesa}, journal = {JAMA Network Open}, volume = {9}, number = {9}, pages = {e2634696}, year = {2026}, month = sep, doi = {10.1001/jamanetworkopen.2026.34696}, }
JMIR
Global Sentiment Toward Health AI at the Dawn of the ChatGPT Era: Empirical Analysis of Twitter (X) Discourse

Lily Minh Wass, Zhengdong Wu, José Vizoso, Joseph T. Wu, and Leesa Lin

Journal of Medical Internet Research, May 2026

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Background. Artificial intelligence (AI) is increasingly proposed for use in health and health care systems. Beyond technical performance, public perceptions and affective responses influence whether AI technologies are accepted and adopted in real-world contexts. Social media platforms such as X (formerly Twitter) provide large-scale, real-time insight into public discourse surrounding emerging technologies, yet remain underused for examining how health AI is discussed, evaluated, and emotionally framed.
Objective. This study aimed to develop and apply large language model (LLM)-based methods for exploratory social listening on health AI. This is the first study to map large-scale sentiment, emotional expressions, and confidence-related signals in online discussions of applications of AI to health.
Methods. We collected 786,750 English-language posts from X (Twitter) published between January 1 and December 5, 2023, using health- and AI-related keywords. We benchmarked an LLM-based annotation framework by using OpenAI’s GPT-3.5-Turbo and GPT-4, comparing model classifications with trained human researchers. Annotations included overall sentiment and 6 evaluative domains frequently referenced in the literature surrounding attitudes toward health AI-usefulness, safety, privacy, ethics, quality, and trust. After cleaning, GPT-3.5-Turbo used the best-performing prompts to label 388,009 posts. A subset (n=268,347) was further analyzed using Emollama-7b, an open-source model fine-tuned from Meta’s LLaMA2-7B, for emotion detection, and latent Dirichlet allocation for thematic analysis. Comparisons were made across World Health Organization regions.
Results. Compared against human annotations, optimized prompts achieved weighted F1-scores above 0.60 across evaluative domains and sentiment classification. Global discourse about health AI was 65.26% (95% CI 65.11%-65.4%) positive and 83.62% (95% CI 83.48%-83.76%) emotionally optimistic, although substantial regional variation was observed in sentiment (P<.001). The Eastern Mediterranean and South-East Asia regions expressed significantly higher levels of positive sentiment and evaluative agreement in the studied features of health AI, alongside frequent discussion of the tech industry and commercial development. In comparison, the Western Pacific region expressed lower confidence and significantly more mentions of research topics (19.27%, 95% CI 18.5%-20.07%). Privacy was the most prominent global concern, with 33.31% (95% CI 32.98%-33.66%) of privacy-related posts expressing perceived risks. In the Region of the Americas, 18.19% (95% CI 17.92%-18.44%) of posts discussed algorithms and data governance, significantly higher than overall.
Conclusions. This study offers the first systematic characterization of online health AI discourse at scale, mapping stances toward key features of AI, emotional tone, and discussion topics across regions. LLM-powered social listening is demonstrated as a feasible approach for identifying dominant narratives and regionally distinct concerns, capable of surfacing opinions absent from traditional media. This can extend to studying discourse on other evolving health technologies where public surveying is limited. While methodological refinement and multilingual expansion are needed, this framework can inform timely policy development, risk communication, and responsible health AI governance.
@article{wass2026sentiment, title = {Global Sentiment Toward Health AI at the Dawn of the ChatGPT Era: Empirical Analysis of Twitter (X) Discourse}, author = {Wass, Lily Minh and Wu, Zhengdong and Vizoso, José and Wu, Joseph T. and Lin, Leesa}, journal = {Journal of Medical Internet Research}, volume = {28}, pages = {e80346}, year = {2026}, month = may, doi = {10.2196/80346}, }
npj Vaccines
Evaluating large language models on multilingual vaccine knowledge: a benchmark study

Siyuan Chen, Lily Wass, Zhengdong Wu, Lucas Garay, José Vizoso, Kathy Leung, Joseph Wu, and Leesa Lin

npj Vaccines, Jun 2026

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Large language models (LLMs) are increasingly used by clinicians and the public for vaccine information, yet their factual accuracy across languages and vaccine domains remains insufficiently characterized. We evaluated 13 LLMs using VaxEval, a multilingual vaccine-knowledge benchmark of 1886 vaccine-related multiple-choice questions spanning 14 vaccines in English (71%), Spanish (13%), and Chinese (16%). All items underwent quality control, with reference answers verified against authoritative guidance and peer-reviewed sources. Model performance was evaluated under zero-shot, few-shot, and chain-of-thought (CoT) prompting, with exact-match accuracy defined as selecting the pre-specified reference option. We used mixed-effect logistic regression to estimate associations between model group (newer flagship models vs earlier models), prompting strategy, language, and vaccine type, and answer correctness. Mean accuracy across models was 86.0% in English, 83.7% in Spanish, and 80.0% in Chinese. Flagship models had higher odds of correctness than earlier versions (OR 1.57; 95% CI 1.50-1.65; P < .001). Few-shot prompting was associated with higher correctness (OR 1.17; P < .001), whereas CoT prompting was associated with lower correctness (OR 0.79; P < .001). Performance varied by vaccine type and question category, underscoring the need for rigorous evaluation, structured guardrails, and targeted refinement before using LLMs for vaccine communication.
@article{chen2026llmvaccine, title = {Evaluating large language models on multilingual vaccine knowledge: a benchmark study}, author = {Chen, Siyuan and Wass, Lily and Wu, Zhengdong and Garay, Lucas and Vizoso, José and Leung, Kathy and Wu, Joseph and Lin, Leesa}, journal = {npj Vaccines}, volume = {11}, pages = {187}, year = {2026}, month = jun, doi = {10.1038/s41541-026-01500-1}, }

2025

Nat. Med.
A vaccine chatbot intervention for parents to improve HPV vaccination uptake among middle school girls: a cluster randomized trial

Zhiyuan Hou^*, Zhengdong Wu^*, Zhiqiang Qu^*, Liubing Gong, Hui Peng, Mark Jit, Heidi J. Larson, Joseph T. Wu, and Leesa Lin

Nature Medicine, Jun 2025

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Conversational artificial intelligence, in the form of chatbots powered by large language models, offers a new approach to facilitating human-like interactions, yet its efficacy in enhancing vaccination uptake remains under-investigated. This study assesses the effectiveness of a vaccine chatbot in improving human papillomavirus (HPV) vaccination among female middle school students aged 12-15 years across diverse socioeconomic settings in China, where HPV vaccination is primarily paid out-of-pocket. A school-based cluster randomized trial was conducted from 18 January to 31 May 2024. The study included 2,671 parents from 180 middle school classes stratified by socioeconomic setting, school and grade level in Shanghai megacity, and urban and rural regions of Anhui Province. Participants were randomly assigned to either the intervention group (90 classes, 1,294 parents), which engaged with the chatbot for two weeks, or the control group (90 classes, 1,377 parents), which received usual care. The primary outcome was the receipt or scheduled appointment of the HPV vaccine for participants’ daughters. In intention-to-treat analyses, 7.1% of the intervention group met this outcome versus 1.8% of the control group (P < 0.001) over a two-week intervention period. In addition, there was a statistically significant increase in HPV vaccination-specific consultations with health professionals (49.1% versus 17.6%, P < 0.001), along with enhanced vaccine literacy (P < 0.001) and rumor discernment (P < 0.001) among participants using the chatbot. These findings indicate that the chatbot effectively increased vaccination and improved parental vaccine literacy, although further research is necessary to scale and sustain these gains. Clinical trial registration: NCT06227689 .
@article{hou2025chatbot, title = {A vaccine chatbot intervention for parents to improve HPV vaccination uptake among middle school girls: a cluster randomized trial}, author = {Hou, Zhiyuan and Wu, Zhengdong and Qu, Zhiqiang and Gong, Liubing and Peng, Hui and Jit, Mark and Larson, Heidi J. and Wu, Joseph T. and Lin, Leesa}, journal = {Nature Medicine}, volume = {31}, number = {6}, pages = {1855--1862}, year = {2025}, month = jun, doi = {10.1038/s41591-025-03618-6}, }

2024

npj Vaccines
Mapping global public perspectives on mRNA vaccines and therapeutics

Jiaxiang Xu, Zhengdong Wu, Lily Wass, Heidi J. Larson, and Leesa Lin

npj Vaccines, 2024

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The development and rollout of mRNA vaccines during COVID-19 marked a significant advancement in vaccinology, yet public hesitation to vaccination was prevalent, indicating the potential risk that future mRNA-based medical innovations will fail to be adopted. Utilizing a combined approach of large language models with manual validation and unsupervised machine learning, we conducted a social listening analysis to assess attitudes towards mRNA vaccines and therapeutics on Twitter from June 2022 to May 2023, contrasting online perspectives with data from the Vaccine Adverse Event Reporting System. Our findings reveal widespread negative sentiment and a global lack of confidence in the safety, effectiveness, and trustworthiness of mRNA vaccines and therapeutics, with frequent discussions of severe vaccine side effects, rumors, and misinformation. This underscores the need for targeted communication strategies to foster acceptance of medical treatments and strengthen public trust in order to enhance societal resilience to future health challenges.
@article{xu2024mrna, title = {Mapping global public perspectives on mRNA vaccines and therapeutics}, author = {Xu, Jiaxiang and Wu, Zhengdong and Wass, Lily and Larson, Heidi J. and Lin, Leesa}, journal = {npj Vaccines}, volume = {9}, number = {1}, pages = {218}, year = {2024}, doi = {10.1038/s41541-024-01019-3}, }

2023

npj Digit. Med.
Effectiveness of chatbots on COVID vaccine confidence and acceptance in Thailand, Hong Kong, and Singapore

Kristi Yoonsup Lee, Saudamini Vishwanath Dabak, Vivian Hanxiao Kong, Minah Park, Shirley L. L. Kwok, Madison Silzle, Chayapat Rachatan, Alex Cook, Aly Passanante, Ed Pertwee, Zhengdong Wu, and 6 more authors

npj Digital Medicine, 2023

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Chatbots have become an increasingly popular tool in the field of health services and communications. Despite chatbots’ significance amid the COVID-19 pandemic, few studies have performed a rigorous evaluation of the effectiveness of chatbots in improving vaccine confidence and acceptance. In Thailand, Hong Kong, and Singapore, from February 11th to June 30th, 2022, we conducted multisite randomised controlled trials (RCT) on 2,045 adult guardians of children and seniors who were unvaccinated or had delayed vaccinations. After a week of using COVID-19 vaccine chatbots, the differences in vaccine confidence and acceptance were compared between the intervention and control groups. Compared to non-users, fewer chatbot users reported decreased confidence in vaccine effectiveness in the Thailand child group [Intervention: 4.3 % vs. Control: 17%, P = 0.023]. However, more chatbot users reported decreased vaccine acceptance [26% vs. 12%, P = 0.028] in Hong Kong child group and decreased vaccine confidence in safety [29% vs. 10%, P = 0.041] in Singapore child group. There was no statistically significant change in vaccine confidence or acceptance in the Hong Kong senior group. Employing the RE-AIM framework, process evaluation indicated strong acceptance and implementation support for vaccine chatbots from stakeholders, with high levels of sustainability and scalability. This multisite, parallel RCT study on vaccine chatbots found mixed success in improving vaccine confidence and acceptance among unvaccinated Asian subpopulations. Further studies that link chatbot usage and real-world vaccine uptake are needed to augment evidence for employing vaccine chatbots to advance vaccine confidence and acceptance.
@article{lee2023covidchatbot, title = {Effectiveness of chatbots on COVID vaccine confidence and acceptance in Thailand, Hong Kong, and Singapore}, author = {Lee, Kristi Yoonsup and Dabak, Saudamini Vishwanath and Kong, Vivian Hanxiao and Park, Minah and Kwok, Shirley L. L. and Silzle, Madison and Rachatan, Chayapat and Cook, Alex and Passanante, Aly and Pertwee, Ed and Wu, Zhengdong and Elkin, Javier A. and Larson, Heidi J. and Lau, Eric H. Y. and Leung, Kathy and Wu, Joseph T. and Lin, Leesa}, journal = {npj Digital Medicine}, volume = {6}, number = {1}, pages = {96}, year = {2023}, doi = {10.1038/s41746-023-00843-6}, }

2021

J. Transl. Med.
An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation

Yihan Zhang, Dong Yang, Zifeng Liu, Chaojin Chen, Mian Ge, Xiang Li, Tongsen Luo, Zhengdong Wu, Chenguang Shi, Bohan Wang, Xiaoshuai Huang, and 3 more authors

Journal of Translational Medicine, 2021

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Background. Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making.
Methods. Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms.
Results. 430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model.
Conclusions. Our GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT.
@article{zhang2021aki, title = {An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation}, author = {Zhang, Yihan and Yang, Dong and Liu, Zifeng and Chen, Chaojin and Ge, Mian and Li, Xiang and Luo, Tongsen and Wu, Zhengdong and Shi, Chenguang and Wang, Bohan and Huang, Xiaoshuai and Zhang, Xiaodong and Zhou, Shaoli and Hei, Ziqing}, journal = {Journal of Translational Medicine}, volume = {19}, number = {1}, pages = {321}, year = {2021}, doi = {10.1186/s12967-021-02990-4}, }