Background

Neurocognitive dysfunction is reported in women with breast cancer even prior to receipt of adjuvant therapy; however, there is little understanding of underlying mechanisms. We tested the hypothesis that pretreatment neurocognitive dysfunction in newly diagnosed patients is related to immunological activation, as indexed by pro-inflammatory cytokines.

Methods

One hundred seventy-four postmenopausal patients with newly diagnosed breast cancer underwent a comprehensive neuropsychological evaluation (assessment of cognitive function, mood, and fatigue) and measurement of key cytokine levels prior to surgery. Age-matched control participants without cancer were evaluated concurrently. Multivariable regression analyses examined the contribution of circulating Interleukin-6 (IL-6), interleukin-1 receptor antagonist (IL-1ra), and soluble TNF receptor type two (sTNF-RII) in predicting neurocognitive performance in patients after controlling for key factors thought to impact functioning. All tests of statistical significance were two-sided.

Results

Memory performance was statistically significantly reduced, in patients compared with controls (P = .02). Of the three cytokines measured, only IL-1ra was statistically significantly elevated in cancer patients when compared with control participants (mean ± SD, 375 ± 239 pg/mL vs 291 ± 169 pg/mL, P = .007). After controlling for age, education, race, mood, fatigue, body mass index, and comorbidity, cytokines independently explained 6.0% of the total variance in memory performance (P = .01) in cancer patients but not control participants, with higher sTNF-RII associated with worse functioning. Exploratory analyses found that comorbidity statistically significantly explained variance in processing speed and executive functioning (P = .03 and P = .03, respectively).

Conclusion

An association of TNF with memory, previously reported in patients after exposure to chemotherapy, was found prior to initiation of any treatment, including surgery. This association requires further investigation as sTNF-RII was not higher in cancer patients relative to control participants.

Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.

A variety of genetic techniques have been devised to determine cell lineage relationships during tissue development. Some of these systems monitor cell lineages spatially and/or temporally without regard to gene expression by the cells, whereas others correlate gene expression with the lineage under study. The GAL4 Technique for Real-time and Clonal Expression (G-TRACE) system allows for rapid, fluorescent protein-based visualization of both current and past GAL4 expression patterns and is therefore amenable to genome-wide expression-based lineage screens. Here we describe the results from such a screen, performed by undergraduate students of the University of California, Los Angeles (UCLA) Undergraduate Research Consortium for Functional Genomics (URCFG) and high school summer scholars as part of a discovery-based education program. The results of the screen, which reveal novel expression-based lineage patterns within the brain, the imaginal disc epithelia, and the hematopoietic lymph gland, have been compiled into the G-TRACE Expression Database (GED), an online resource for use by the Drosophila research community. The impact of this discovery-based research experience on student learning gains was assessed independently and shown to be greater than that of similar programs conducted elsewhere. Furthermore, students participating in the URCFG showed considerably higher STEM retention rates than UCLA STEM students that did not participate in the URCFG, as well as STEM students nationwide.