BackgroundThere has been an increasing interest in understanding the usefulness of wrist-based accelerometer data for physical activity (PA) assessment due to the ease of use and higher user compliance than other body placements. PA assessment studies have relied on machine learning methods which take accelerometer data in forms of variables, or feature vectors.MethodsIn this work, we introduce automated shape feature derivation methods to transform epochs of accelerometer data into feature vectors. As the first step, recurring patterns in the collected data are identified and placed in a codebook. Similarities between epochs of accelerometer data and codebook’s patterns are the basis of feature calculations. In this paper, we demonstrate supervised and unsupervised approaches to learn codebooks. We evaluated these methods and compared them with the standard statistical measures for PA assessment. The experiments were performed on 146 participants who wore an ActiGraph GT3X+ accelerometer on the right wrist and performed 33 activities of daily living.ResultsOur evaluations show that the shape feature derivation methods were able to perform comparably with the standard wrist model (F1-score: 0.89) for identifying sedentary PAs (F1-scores of 0.86 and 0.85 for supervised and unsupervised methods, respectively). This was also observed for identifying locomotion activities (F1-scores: 0.87, 0.83, and 0.81 for the standard wrist, supervised, unsupervised models, respectively). All the wrist models were able to estimate energy expenditure required for PAs with low error (rMSE: 0.90, 0.93, and 0.90 for the standard wrist, supervised, and unsupervised models, respectively).ConclusionThe automated shape feature derivation methods offer insights into the performed activities by providing a summary of repeating patterns in the accelerometer data. Furthermore, they could be used as efficient alternatives (or additions) for manually engineered features, especially important for cases where the latter fail to provide sufficient information to machine learning methods for PA assessment.

Glioblastoma represent a great challenge and current therapies are negligibly effective, with disease recurrence being inevitable. T cell therapy has emerged as a viable treatment for brain malignancies. While promising, the efficacy of this approach is often limited by a complex immunosuppressive tumor microenvironment. These complexities mean that more sophisticated T cell products are required. The brain tumor microenvironment provides local restraints via metabolic competition suppressing antitumor immunity, specifically inhibiting infiltration and effector functions of host and adoptively transferred tumor-reactive T cells. The objective of this project is to test new treatments to reverse immune dysfunction in brain cancer through the regulation of T cell metabolic signaling. We propose that modulating glucose signaling can potentiate T cell anti-tumor activity. The glucose signaling pathway of T cells was modulated through overexpression of glucose transporters and the function of metabolically modified T cells was investigated using murine and human models. We revealed a competition for glucose between T cells and tumor cells, with tumor cells imposing glucose restriction mediating T cell hyporesponsiveness. Overexpression of glucose transporters such as Glut1 and Glut3 enhanced T cell glucose utilization and provided a survival/growth advantage and greater activation, specifically in glucose-restricted conditions. We established that glucose transporter overexpression improves intratumoral infiltration and expansion of adoptively transferred CAR T cells, resulting in improved survival. Our study integrates fundamental concepts of tumor and immune metabolism in the design of immunotherapy and confirms that immunometabolism represents a viable target for new cancer therapy to treat brain tumors. Citation Format: Tanya Ghosh, Avirup Chakraborty, Linchun Jin, Diana Feier, Aryeh Silver, Maryam Rahman, Catherine Flores, Matthew Sarkisian, Jianping Huang, Duane A. Mitchell, Loic P. Deleyrolle. Optimizing CAR T therapy via metabolic engineering for the treatment of glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 904.

Diabetes mellitus (DM) is a widespread metabolic disorder with a yearly 6.7 million deaths worldwide. Several treatment options are available but with common side effects like weight gain, cardiovascular diseases, neurotoxicity, hepatotoxicity, and nephrotoxicity. Therefore, ethnomedicine is gaining the interest of researchers in the treatment of DM. Ethnomedicine works by preventing intestinal absorption and hepatic production of glucose as well as enhancing glucose uptake in muscles and fatty tissues and increasing insulin secretion. A variety of plants have entered clinical trials but very few have gained approval for use. This current study provides an evaluation of such clinical trials. For this purpose, an extensive literature review was performed from a database using keywords like “ethnomedicine diabetes clinical trial”, “clinical trials”, “clinical trial in diabetes”, “diabetes”, “natural products in diabetes”, “ethno-pharmacological relevance of natural products in diabetes”, etc. Clinical trials of 20 plants and natural products were evaluated based on eligibility criteria. Major limitations associated with these clinical trials were a lack of patient compliance, dose-response relationship, and an evaluation of biomarkers with a small sample size and treatment duration. Measures in terms of strict regulations can be considered to achieve quality clinical trials. A specific goal of this systematic review is to discuss DM treatment through ethnomedicine based on recent clinical trials of the past 7 years.