Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Author "Zhang, Yuan"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Impacts of natural microbial exposure on gut lipid metabolism in mice
    (2024-04-18) Wang, Zihao; Zhang, Yuan; Wang, Haiguang; Revelo, Xavier; Ruan, Hai-bin
    Microbial exposure to the host influences various factors. Current murine research predominantly occurs in specific pathogen-free (SPF) environments. However, humans are exposed to pathogenic and commensal microbes on a daily basis from birth. How natural microbial exposure (NME) influences metabolic and immune responses in hosts remains unclear. Our hypothesis is that laboratory mice models housed at SPF conditions are missing essential components of the natural microbiome that impact the host response under normal diet and protect mice from diet-induced obesity. In this study, C57BL/6 mice under normal chow were divided into two groups: SPF group and NME group. Both cohorts were initiated at the age of 6 weeks, NME group involved mice cohabitation with pet store mice for another 4 weeks. Mice were sacrificed in week 10, small intestine and gonadal white adipose tissue (gWAT) were collected. To establish an obesity model, 8-week old C57BL/6 male mice were housed in SPF and NME facilities under western diet for 16 weeks. Mice were sacrificed in week 24 and inguinal white adipose tissue (iWAT) was collected. qRT-PCR was performed to measure the key genes in lipid uptake in small intestine and fluorescence bead-based antibody systems (Luminex platform) was applied to determine cytokine levels in the small intestine. Mice in the NME group exhibited a significant reduction in body weight compared to the SPF group under normal chow condition. Furthermore, a notable decrease in iWAT mass was observed in NME mice under western diet. qRT-PCR analysis revealed a down-regulation of lipid uptake genes, including CD36 and FATP4, in the NME group. NME led to more robust changes in chemokine level rather than pro-inflammatory cytokine level in the small intestine. Our results provided compelling evidence that NME exerted a protective role from diet-induced obesity and caused perturbation of cytokine levels in small intestine, thereby underscoring the pivotal role of microbiota exposure in modulating host metabolism. This revelation highlighted the critical role gut microbes play in orchestrating metabolic and immunological interactions in mammalian systems.
  • Thumbnail Image
    Item
    Modifications of Q-learning to Optimize Dynamic Treatment Regimes
    (2021-08) Zhang, Yuan
    With an emerging interest in personalized medicine and quality healthcare, the design of clinical trials that incorporates multiple stages of randomization and intervention, for example, a sequential multiple assignment randomized trial (SMART), has become a popular choice for investigators as it facilitates the construction and analysis of dynamic treatment regimes (DTRs). There exists a comprehensive body of literature on various statistical methods to analyze data collected from such trials and estimate the optimal DTR for an individual subject, among which Q-learning with linear regression is widely used due to its simplicity and ease of interpretation. This thesis discusses three important challenges that cause problems in the implementation of Q-learning and proposes multiple modifications of Q-learning to address them.The first challenge arises from the repeatedly monitored outcome of interest at intermediate stages of randomization and at longer follow-up intervals after the final stage of randomization. Clinical investigators are usually interested in identifying the optimal DTR and estimating the outcome trajectory under the optimal DTR. However, in the presence of stagewise repeated-measures outcomes, standard Q-learning fails to provide point estimates of the optimal trajectory with time-specific heterogeneous causal effects. To address this problem, we propose a modified algorithm of Q-learning with a generalized estimating equation to estimate each Q-function. The second challenge is model misspecification. Model misspecification is a common problem in Q-learning, but little attention has been given to its impact when treatment effects are heterogeneous across subjects. We describe the integrative impact of two possible types of model misspecification related to treatment effect heterogeneity: unexplained early-stage treatment effects in late-stage main effect model, and misspecified linearity between pseudo-outcomes and predictors as a result of the optimization operation. The proposed method, aiming to deal with both types of misspecification concomitantly, builds interactive models into residual-modified parametric Q-learning. The third challenge is generalizing modified Q-learning to dichotomous outcomes. It is difficult to include informative residuals from estimation of late-stage models into early-stage pseudo-outcomes due to the non-identity link function. We propose a modification based on monotonicity of preferences to address model misspecification in Q-learning with probit regression. The improvement in robustness of the proposed modification is subject to the extent of model misspecification and can be limited. Thus, we take a latent variable approach and propose a novel algorithm using sampled surrogates of the underlying continuous outcome conditional on the binary observations. The methods proposed in this thesis are assessed via simulations and illustrated using the M-bridge study, a SMART with embedded tailoring which develops and evaluates adaptive interventions for preventing binge drinking among college students.