Browsing by Author "Lee, J. Jack"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Accuracy of In Vivo Multimodal Optical Imaging for Detection of Oral Neoplasia
    (AACR, 2012) Pierce, Mark C.; Schwarz, Richard A.; Bhattar, Vijayashree S.; Mondrik, Sharon; Williams, Michelle D.; Lee, J. Jack; Richards-Kortum, Rebecca; Gillenwater, Ann M.
    If detected early, oral cancer is eminently curable. However, survival rates for oral cancer patients remain low, largely due to late-stage diagnosis and subsequent difficulty of treatment. To improve cliniciansﾒ ability to detect early disease and to treat advanced cancers, we developed a multimodal optical imaging system (MMIS) to evaluate tissue in situ, at macroscopic and microscopic scales. The MMIS was used to measure 100 anatomic sites in 30 patients, correctly classifying 98% of pathologically confirmed normal tissue sites, and 95% of sites graded as moderate dysplasia, severe dysplasia, or cancer. When used alone, MMIS classification accuracy was 35% for sites determined by pathology as mild dysplasia. However, MMIS measurements correlated with expression of candidate molecular markers in 87% of sites with mild dysplasia. These findings support the ability of noninvasive multimodal optical imaging to accurately identify neoplastic tissue and premalignant lesions. This in turn may have considerable impact on detection and treatment of patients with oral cancer and other epithelial malignancies.
  • Thumbnail Image
    Item
    Bayesian decision-theoretic method and semi-parametric approach with applications in clinical trial designs and longitudinal studies
    (2013-11-25) Jiang, Fei; Lee, J. Jack; Cox, Dennis D.; Scott, David W.; Ma, Yanyuan; Tapia, Richard A.
    The gold of biostatistical researches is to develop statistical tools that improves human health or increases understanding of human biology. One area of the studies focuses on designing clinical trials to find out if new drugs or treatments are efficacious. The other area focuses on studying diseases related variables, which gives better understanding of the diseases. The thesis explores these areas from both theoretical and practical points of views. In addition, the thesis develop statistical devices which improve the existing methods in these areas. Firstly, the thesis proposes a Bayesian decision-theoretic group sequential – adaptive randomization phase II clinical trial design. The design improves the trial efficiency by increasing statistical power and reducing required sample sizes. The design also increases patients’ individual benefit, because it enhances patients’ opportunities of receiving better treatments. Secondly, the thesis develops a semiparametric restricted moment model and a score imputation estimation for survival analysis. The method is more robust than the parametric alternatives. In addition to data analysis, the method is used to design a seamless phase II/III clinical trial. The seamless phase II/III clinical trial design shortens the durations between phase II and III studies, and improves the efficiency of the traditional designs by utilizing additional short term information for making decisions. Finally, the thesis develops a partial linear time varying semi-parametric single-index risk score model and a fused B-spline/kernel estimation for longitudinal data analysis. The method models confounder effects linearly. In addition, it uses a nonparametric nonlinear function, namely the single-index risk score, to model the effects of interests. The fused B-spline/kernel technique estimates both the parametric and nonparametric components consistently. The methodology is applied to study the onsite of Huntington’s disease in determining certain time varying covariate effects on the disease risk.
  • Thumbnail Image
    Item
    Heterogeneous antibodies against SARS-CoV-2 spike receptor binding domain and nucleocapsid with implications for COVID-19 immunity
    (American Society for Clinical Investigation, 2020) McAndrews, Kathleen M.; Dowlatshahi, Dara P.; Dai, Jianli; Becker, Lisa M.; Hensel, Janine; Snowden, Laura M.; Leveille, Jennifer M.; Brunner, Michael R.; Holden, Kylie W.; Hopkins, Nikolas S.; Harris, Alexandria M.; Kumpati, Jerusha; Whitt, Michael A.; Lee, J. Jack; Ostrosky-Zeichner, Luis L.; Papanna, Ramesha; LeBleu, Valerie S.; Allison, James P.; Kalluri, Raghu
    Evaluation of potential immunity against the novel severe acute respiratory syndrome (SARS) coronavirus that emerged in 2019 (SARS-CoV-2) is essential for health, as well as social and economic recovery. Generation of antibody response to SARS-CoV-2 (seroconversion) may inform on acquired immunity from prior exposure, and antibodies against the SARS-CoV-2 spike protein receptor binding domain (S-RBD) are speculated to neutralize virus infection. Some serology assays rely solely on SARS-CoV-2 nucleocapsid protein (N-protein) as the antibody detection antigen; however, whether such immune responses correlate with S-RBD response and COVID-19 immunity remains unknown. Here, we generated a quantitative serological ELISA using recombinant S-RBD and N-protein for the detection of circulating antibodies in 138 serial serum samples from 30 reverse transcription PCR–confirmed, SARS-CoV-2–hospitalized patients, as well as 464 healthy and non–COVID-19 serum samples that were collected between June 2017 and June 2020. Quantitative detection of IgG antibodies against the 2 different viral proteins showed a moderate correlation. Antibodies against N-protein were detected at a rate of 3.6% in healthy and non–COVID-19 sera collected during the pandemic in 2020, whereas 1.9% of these sera were positive for S-RBD. Approximately 86% of individuals positive for S-RBD–binding antibodies exhibited neutralizing capacity, but only 74% of N-protein–positive individuals exhibited neutralizing capacity. Collectively, our studies show that detection of N-protein–binding antibodies does not always correlate with presence of S-RBD–neutralizing antibodies and caution against the extensive use of N-protein–based serology testing for determination of potential COVID-19 immunity.