Method of Detecting and Targeting Mutations in Cancer

While there are many differences between tumor and non-tumor cells, the basic underlying distinction is in the DNA. Tumor cells harbor mutations, at least some of which are not present in non-tumor cells. Thus, a method of directly targeting cells containing specific mutations has potential for detection or treatment of cancer without the toxicity associated with more indirect approaches. Also, as mutations are a necessary component of malignancy, such a method is potentially applicable to all tumors. 

I propose a method by which several recently developed techniques can be utilized in a novel way to accomplish the goal of directly targeting mutations for cancer detection and therapy. The model can be summarized as follows: (1) Determine potential target mutations present in tumor cells but not non-tumor cells. (2) Construct molecules that will bind to DNA at the sites of mutation, but will not bind to DNA in normal cells. And, as a consequence of the molecules binding to the mutation, the cells will be destroyed. (3) Deliver these molecules to all cells (or at least all tumor cells). I hypothesize that such molecules can now be constructed using sequence-specific DNA binding proteins (such as customized zinc-finger DNA binding proteins) fused to transcriptional activator domains (such as VP16) and reporter or toxin genes. The necessary genes can be linked to the DNA binding proteins utilizing a recently described method based on expressed protein ligation. 
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Qualified difference sets from unions of cyclotomic classes

Qualified difference sets (QDS) composed of unions of cyclotomic classes are discussed. An exhaustive computer search for such QDS and modified QDS that also possess the zero residue has been conducted for all powers n=4,6,8 and 10. Two new families were discovered in the case n=8 and some new isolated systems were discovered for n=6 and n=10

Is Your Teaching Style Racism Free?

https://digitalcommons.lmu.edu/facultypubnight/1015/thumbnail.jp

Baseline Predictors of Early Weight Loss During a Standard Behavioral Weight Loss Intervention

Lifestyle interventions that target behavior change for weight loss are effective treatments for overweight and obesity. Despite the effectiveness of these interventions, there is variability in weight loss among individuals, with some individuals being more successful with weight loss resulting from these behavioral interventions. There is a lack of consistent baseline factors to assist in identifying for whom these behavioral interventions may be most effective as a treatment for overweight and obesity. Recently, early weight loss in response to these behavioral interventions has been associated with long-term weight loss success. However, studies have not examined whether baseline factors may be predictive of early weight loss success in response to a behavioral intervention. PURPOSE: This study examined the following aims: 1) the association between early weight loss in a behavioral intervention and weight loss at 6 and 12 months; 2) the association between baseline factors (current behaviors, weight history, psychosocial factors) and early weight loss at 4, 8, and 12 weeks of an intervention. METHODS: Participants (N=383) who participated in a behavioral weight loss intervention (age 45.0±7.9 years, BMI 32.4±3.8 kg/m2) self-reported baseline behaviors, weight history, and psychosocial factors. Weight was measured at baseline, 4 weeks, 8 weeks, 12 weeks, 6 months, and 12 months of the intervention. Multi-level regression was applied to examine the association between baseline factors and weight loss trajectory from 0-4, 0-8, and 0-12 weeks. RESULTS: Early weight loss at 4, 8, and 12 weeks was associated with weight loss at 6 and 12 months. Several baseline behaviors, weight history, and psychosocial factors were associated with early weight loss trajectory; however, the effect that each individual variable might have on weight loss trajectory was modest. CONCLUSIONS: These findings demonstrate there are baselines factors reflective of current behaviors, weight history, and psychosocial domains associated with early weight loss. These findings may suggest that there are baseline factors that could potentially be intervention targets to enhance early weight loss, which may then contribute to long-term weight loss. However, further examination of these factors should be the focus of future research