Psychotic Relapse in Schizophrenia: Routine Clustering of Mobile Sensor Data Facilitates Relapse Prediction (Preprint)
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null (Ed.)In recent years alcohol abuse and dependence have become topics of increasing concern in Uganda, but the chronic relapsing brain disease model of addiction remains only one of many ways of understanding and addressing alcohol related problems there. For many Ugandan Pentecostals and spirit mediums to be addicted is to be under the control of a being that comes from outside the self. Where these two groups differ, and here they differ strongly, is in regard to the moral valence of these external spirits and what ought to be done about them. This article draws on four years of collaborative ethnographic fieldwork to explore the affordances of these ways of viewing and experiencing addiction and recovery for Ugandans attempting to leave alcohol behind. While the idioms of bondage, dedication, and possession are at times severe, this article argues that they contain within them concepts and practices that point away from models of addiction as a chronic relapsing brain disease and towards the possibility of release.more » « less
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Background Adaptive CD19-targeted chimeric antigen receptor (CAR) T-cell transfer has become a promising treatment for leukemia. Although patient responses vary across different clinical trials, reliable methods to dissect and predict patient responses to novel therapies are currently lacking. Recently, the depiction of patient responses has been achieved using in silico computational models, with prediction application being limited. Methods We established a computational model of CAR T-cell therapy to recapitulate key cellular mechanisms and dynamics during treatment with responses of continuous remission (CR), non-response (NR), and CD19-positive (CD19 + ) and CD19-negative (CD19 − ) relapse. Real-time CAR T-cell and tumor burden data of 209 patients were collected from clinical studies and standardized with unified units in bone marrow. Parameter estimation was conducted using the stochastic approximation expectation maximization algorithm for nonlinear mixed-effect modeling. Results We revealed critical determinants related to patient responses at remission, resistance, and relapse. For CR, NR, and CD19 + relapse, the overall functionality of CAR T-cell led to various outcomes, whereas loss of the CD19 + antigen and the bystander killing effect of CAR T-cells may partly explain the progression of CD19 − relapse. Furthermore, we predicted patient responses by combining the peak and accumulated values of CAR T-cells or by inputting early-stage CAR T-cell dynamics. A clinical trial simulation using virtual patient cohorts generated based on real clinical patient datasets was conducted to further validate the prediction. Conclusions Our model dissected the mechanism behind distinct responses of leukemia to CAR T-cell therapy. This patient-based computational immuno-oncology model can predict late responses and may be informative in clinical treatment and management.more » « less
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Spheroids recapitulate the organization, heterogeneity and microenvironment of solid tumors. Herein, we targeted spatiotemporally the accelerated metabolism of proliferative cells located on the spheroid surface that ensure structure maintenance and/or growth. We demonstrate that phosphorylated carbohydrate amphiphile acts as a potent antimetabolite due to glycolysis inhibition and to in situ formation of supramolecular net around spheroid surface where alkaline phosphatase is overexpressed. The efficiency of the treatment is higher in spheroids as compared to the conventional 2D cultures because of the 2-fold higher expression of glucose transporter 1 (GLUT1). Moreover, treated spheroids do not undergo following relapse.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2196/31006
