About

Jianan Zhang, Ph.D., is an Assistant Professor in the Department of Food Science and Technology at the University of California, Davis. Her research program investigates how diet impacts human health, focusing on both host metabolism and microbial metabolism. Her long-term goal is to understand the interactions between diet, host, and microbes and to uncover the molecular mechanisms underlying dietary influences in health and disease contexts. Dr. Zhang employs various in vitro and in vivo models to pursue two primary research directions. She examines the molecular mechanisms by which metabolites from edible fats and oils affect gut health, utilizing multi-omics techniques such as lipidomics and metagenomics to identify key metabolites and enzymatic pathways. Additionally, she investigates how dietary bioactive compounds influence metabolic disorders, with particular emphasis on the role of the gut microbiome in plant-based diets. A distinctive feature of her research is the integration of sex-specific differences in metabolism and health outcomes, aiming to advance personalized nutrition and individualized medication strategies that consider the complex interplay between diet, host biology, and the gut microbiome.

Research topics

• Computer Science
• Social psychology
• Medicine
• Psychology
• Artificial Intelligence
• Sociology
• Political Science
• Pathology
• Applied psychology
• Literature
• Human–computer interaction
• Media studies
• Physical therapy
• World Wide Web
• Law
• Nursing
• Art

Selected publications

• The Attentional Mechanism of Short Videos’ Persuasive Effects

  2026-04-25

  articleOpen access

  Short video platforms such as TikTok and Instagram Reels are increasingly becoming key information sources, but how multimodal video information influences message processing and persuasion remains unclear. Grounded in the literature of Message Sensation Value (MSV), this study examines whether MSV of short videos––based on their multimodal features––attracts or distracts viewers’ attention and subsequently facilitates or hinders persuasion. This study presents an online survey experiment and an online eye-tracking experiment (combined N = 2,617) that tested the effects of varying levels of short video MSV on attention, message processing, message recognition, and persuasive outcomes across science and health contexts. Results suggest MSV attracts viewers’ attention linearly, but its impacts on subsequent message processing and persuasive outcomes follow a curvilinear pattern. Short videos with moderate MSV not only can maximize message processing, message recognition, and persuasion but also minimize psychological reactance. Leveraging sensational short videos with moderate MSV can be an effective message design approach to communicating critical science and health issues to the general public in the contemporary competitive media environment.

  PublisherDOI

• Untangling Direct and Indirect Impacts of Integrating AI Agents in Learning Apps

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Computer-based case simulations enhances clinical reasoning skills of non-dental medical students as measured by mini-CEX

  BMC Medical Education · 2026-01-29

  articleOpen access

  PURPOSE: Traditional didactic lecture-based models in stomatology education, which rely on passive learning through lectures and observation, have limitations in fostering clinical reasoning. This study aimed to assess the effectiveness of Computer-based Case Simulations (CCS) in enhancing the clinical reasoning skills of non-dental medical undergraduates, using the Mini-Clinical Evaluation Exercise (Mini-CEX) as an outcome measure. METHODS: The study involved 328 non-dentistry medical undergraduates enrolled in four different educational programs: Bilingual, Pediatrics, Clinical Medicine I and Clinical Medicine II. Both the control and intervention group completed a Mini-CEX prior to training to establish a baseline. The control group received traditional didactic training (lectures + passive clinical observation), while the intervention group underwent CCS. Educational effectiveness was evaluated via theoretical test scores and Mini-CEX assessments. RESULTS: A pre-clerkship survey revealed that non-dentistry undergraduates prioritized learning about various dental diseases and developing clinical diagnostic and therapeutic thinking skills over the technical and procedural skills involved in the delivery of patient care. The intervention group, demonstrated significantly higher theoretical test scores compared with the control group across all classes (Bilingual Class: 98.1 ± 1.22 vs. 97.3 ± 0.97, Cohen's d = 1.129; Pediatric Class: 97.9 ± 0.85 vs. 96.5 ± 1.35, Cohen's d = 1.072; Clinical Medicine Class Ⅰ: 98.0 ± 0.91 vs. 97.0 ± 1.08, Cohen's d = 1.000; Clinical Medicine ClassⅡ: 99.2 ± 1.04 vs. 97.7 ± 1.74, Cohen's d = 1.432; all P < 0.05). There was no significant difference in the Mini-CEX score between the groups before the clerkship (P > 0.05). Although both groups showed improvements in Mini-CEX scores post-clerkship, the intervention group exhibited a significantly greater increase (Cohen's d > 0.5, P < 0.01), indicating superior clinical skill development. CONCLUSION: The results suggest that Computer-based case simulations (CCS) were associated with enhanced clinical knowledge and superior development of clinical reasoning skills in non-dentistry medical undergraduates compared to traditional methods, as measured by theoretical examination and Mini-CEX assessment. Future research should explore the long-term retention of clinical reasoning and the feasibility of scaling CCS in resource-limited settings.

  PublisherDOI

• Understanding Policy Synergy and Capacity Utilization Through a Dual-Incentive Perspective: Evidence from Cleaner Production Regulation in China

  Sustainability · 2026-03-13

  articleOpen access1st author

  This study examined how policy synergy in cleaner production regulation affects firms’ capacity utilization in China. Using firm-level panel data, this study empirically examined the impact of policy synergy in cleaner production regulation—operationalized through the coordination between the incentive and constraint instruments—on the enterprises’ capacity utilization (CU). The results showed that higher levels of policy synergy significantly enhanced the capacity utilization, with stronger effects observed in state-owned enterprises, firms in competitive industries, high-R&amp;D investment firms, and regions with lower public environmental attention. The mechanism analysis indicated that policy synergy improved capacity utilization primarily by enhancing the resource allocation efficiency. Further, the analyst attention positively moderated this relationship, amplifying the effect of coordinated policy instruments. Overall, this study clarifies the mechanisms and boundary conditions through which the policy synergy under a dual-incentive governance framework affects the firms’ capacity utilization, thereby offering theoretical insights into policy coordination and practical guidance for the design of cleaner production regulation.

  PublisherOA PDFDOI

• Neurotrophin-3 produced by motor neurons non-cell autonomously regulate the development of pre-motor interneurons in the developing spinal cord

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-30

  preprintOpen access

  The development of multicellular organisms requires proper interplays between cell-autonomous genetic programs controlled by combinations of transcription factors that regulate the differentiation of distinct cell populations and non-cell autonomous processes that coordinate the proliferation, the fate, the survival, the respective location, and the proper interactions of these populations. During the development of the nervous system, non-cell autonomous mechanisms determine neuronal fate, survival, distribution, axon guidance, and connectivity. Although similar processes are suggested to be at work in the formation of spinal motor circuits, the molecular mechanisms involved remain mostly elusive. Here, we provide evidence that the Onecut transcription factors regulate a non-cell autonomous mechanism that modulate pre-motor interneuron development. We show that conditional inactivation of the Onecut factors in spinal motor neurons affects the differentiation and the positioning of pre-motor interneuron populations. We identify that Neurotrophin-3 produced by motor neurons under the control of the Onecut factors non-cell autonomously regulate the production and the distribution of pre-motor interneuron populations. Thus, we elucidated one of the non-cell autonomous mechanisms that coordinate the formation of the spinal motor circuits.

  PublisherOA PDFDOI

• Human-Delivered Conversation Versus AI Chatbot Conversation in Increasing Heart Attack Knowledge in Women in the United States: Quasi-Experimental Studies

  Journal of Medical Internet Research · 2025-09-22

  articleOpen access

  Background: Artificial intelligence (AI) chatbots, driven by advances in natural language processing, can analyze and generate human language through computational linguistics and machine learning. Despite the rapid development of large language models, little investigation has been conducted to assess whether AI chatbot-delivered educational conversations can achieve a similar level of efficacy as human-delivered conversations. Objective: This study aims to evaluate and explore the potential efficacy of human-delivered conversations versus AI chatbot conversations in increasing women's knowledge and awareness of symptoms and response to a heart attack in the United States. Methods: This is a secondary analysis of 2 datasets collected from the AI Chatbot Development Project. Women aged 25 years or older were recruited through flyers and social media. The first dataset contained conversational data where a research interventionist engaged in educational conversations with participants (human dataset), whereas the second dataset contained conversational data where an AI chatbot named HeartBot engaged in the same educational conversations with participants (HeartBot dataset). Knowledge and awareness of symptoms and response to a heart attack were measured at the pre- and post-interaction with either the human or HeartBot. Perceived message effectiveness and conversational quality were measured at the post-survey. Ordinal logistic regression analyses were conducted to explore factors predicting participants' knowledge, adjusting for age, race or ethnicity, intervention group type, education, word count, message effectiveness, and message humanness. Results: A total of 171 participants (mean age=41.06 y, SD=12.08) in the Human dataset and 92 participants (mean age=45.85 y, SD=11.94) in the HeartBot dataset completed the study. Both human-delivered conversations and HeartBot conversations were associated with significant improvements in participants' ability to recognize heart attack symptoms (adjusted odds ratio [AOR] 15.19, 95% CI 8.46-27.25, P<.001; AOR 7.18, 95% CI 3.59-14.36, P<.001), differentiate between symptoms (AOR 9.44, 95% CI 5.60-15.91, P<.001; AOR 5.44, 95% CI 2.76-10.74, P<.001), call emergency services (AOR 6.87, 95% CI 4.09-11.55, P<.001; AOR 5.74, 95% CI 2.84-11.60, P<.001), and seek emergency care within 60 minutes of symptom onset (AOR 8.68, 95% CI 4.98-15.15, P<.001; AOR 2.86, 95% CI 1.55-5.28, P<.001), even after adjusting for covariates. Comparing the 2 datasets via interaction tests showed a statistically significant improvement in human-delivered conversations versus HeartBot conversation for all but the calling an ambulance question (P=.09). Conclusions: The study's findings provide new insights into the fully automated AI HeartBot, compared to the human-driven text message conversations, and suggest that it has potential in improving women's knowledge and awareness of heart attack symptoms and appropriate response behaviors. Nevertheless, the current evidence remains preliminary. A randomized controlled trial is warranted to validate this study's findings.

  PublisherDOI

• Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers

  ArXiv.org · 2025-07-16

  preprintOpen accessSenior author

  The prevalence of short form video platforms, combined with the ineffectiveness of age verification mechanisms, raises concerns about the potential harms facing children and teenagers in an algorithm-moderated online environment. We conducted multimodal feature analysis and thematic topic modeling of 4,492 short videos recommended to children and teenagers on Instagram Reels, TikTok, and YouTube Shorts, collected as a part of an algorithm auditing experiment. This feature-level and content-level analysis revealed that unsafe (i.e., problematic, mentally distressing) short videos (a) possess darker visual features and (b) contain explicitly harmful content and implicit harm from anxiety-inducing ordinary content. We introduce a useful framework of online harm (i.e., explicit, implicit, unintended), providing a unique lens for understanding the dynamic, multifaceted online risks facing children and teenagers. The findings highlight the importance of protecting younger audiences in critical developmental stages from both explicit and implicit risks on social media, calling for nuanced content moderation, age verification, and platform regulation.

  PublisherOA PDFDOI

• #BigTech @Minors: social media algorithms have actionable knowledge about child users and at-risk teens

  Telematics and Informatics · 2025-11-15

  article
  PublisherDOI

• Enhancing physical activity through a relational artificial intelligence chatbot: A feasibility and usability study

  Digital Health · 2025-01-01 · 5 citations

  articleOpen accessSenior author

  Objective: This study presents a pilot randomized controlled trial to assess the usability, feasibility, and initial efficacy of a mobile app-based relational artificial intelligence (AI) chatbot (Exerbot) intervention for increasing physical activity behavior. Methods: The study was conducted over a 1-week period, during which participants were randomized to either converse with a baseline chatbot without relational capacity (control group) or a relational chatbot using social relational communication strategies. Objectively measured physical activity data were collected using smartphone pedometers. Results: The study was feasible in enrolling a sample of 36 participants and with a 94% retention rate after 1 week. Daily engagement rate with the AI chatbot reached over 88% across the groups. Findings revealed that the control group experienced a significant decrease in steps on the final day, whereas the group interacting with the relational chatbot maintained their step counts throughout the study period. Importantly, individuals who engaged with the relational chatbot reported a stronger social bond with the chatbot compared to those in the control group. Conclusions: Leveraging AI chatbot and the relationship-building capabilities of AI holds promise in the development of cost-effective, accessible, and sustainable behavior change interventions. This approach may benefit individuals with limited access to conventional in-person behavior interventions. Clinical trial registrations: ClinicalTrials.gov; NCT05794308; https://clinicaltrials.gov/ct2/show/NCT05794308.

  PublisherDOI

• Advances in Research on Desalination Technology for High-Sodium Wastewater

  Sustainability · 2025-01-04 · 3 citations

  articleOpen access

  Amidst escalating global water scarcity challenges, addressing industrial and agricultural wastewater treatment has emerged as a critical concern within environmental conservation efforts. Wastewater desalination technology not only mitigates salt pollution’s impact on ecosystems but also facilitates sustainable water resource management with significant economic and ecological advantages. This study delves into fundamental principles, methodologies, and application prospects in wastewater desalination technology by conducting a comprehensive assessment encompassing physical, chemical, and biological treatment approaches while scrutinizing their practical applicability through analysis of respective merits and drawbacks. Furthermore, this study illuminates specific operational impacts associated with diverse desalinization techniques employed in industrial or agricultural contexts based on prior research findings. The findings underscore that judicious selection of suitable desalinization methods along with optimization of operational parameters are pivotal factors influencing improved rates of sustainable wastewater desalinization. Finally, this paper proposes future directions and research focuses for wastewater desalination technology to provide a reference for related fields.

  PublisherOA PDFDOI

Frequent coauthors

• Yifan Zhang

  Hefei University of Technology

  90 shared

• Fei Guo

  Huazhong Agricultural University

  48 shared

• Xiaodong Li

  RMIT University

  48 shared

• Yoo Jung Oh

  13 shared

• Ping Zhang

  12 shared

• Zhengwu Zhang

  12 shared

• Lu-Wen Wang

  Shandong University

  12 shared

• Changgee Chang

  Indiana University – Purdue University Indianapolis

  12 shared

Education

• PhD, Annenberg School for Communication

  University of Pennsylvania

  2016

• MA, Professional Communication

  Clemson University

  2011