Background: There is little information about cancer-related cognitive impairment (CRCI) in adolescent and young adults (AYA, 15-39 years old) due to its rare incidence. Here, we present the pre-treatment (before chemotherapy or radiotherapy) evaluation of cognitive function and ability of AYA with cancer (AYAC) in a multicentered cohort study. Methods: Newly diagnosed AYAC and age-matched healthy controls (HC) were recruited between 2018 and 2021. The primary outcome was the comparison of pre-treatment cognitive impairment defined as 2 standard deviations (SDs) below the HC on ≥1 cognitive test, or >1.5 SDs below on ≥2 tests using CANTAB® between AYAC and HC. Secondary outcomes included self-perceived cognitive ability assessed by FACT-Cog v3 and biomarkers (inflammatory cytokines and brain-derived neurotrophic factor [BDNF]). Results:We recruited 74 AYAC (median age = 34) and 118 HC (median age = 32). On objective cognitive testing, we observed three times more AYAC patients performed poorly on at least 2 cognitive tests compared to HC (40.5% vs. 13.6%, p < 0.001). AYAC self-perceived less degree of cognitive impairment than HC (p < 0.001). However, AYAC perceived a greater impact of cognitive changes on their quality of life compared to HC (p = 0.039). Elevated baseline inflammatory markers (IL-2, IL-4, IL-6, IL-8, IL-10 and IFN-γ) were observed among AYAC compared to HC, and baseline BDNF was lower in AYAC compared to HC. Interaction effects between cancer diagnosis and biomarkers were observed in predicting cognitive function. Conclusion:With the pre-existence of CRCI and risk factors of neuroinflammation even prior to systemic therapy, AYAC should receive early rehabilitation to prevent further deterioration of cognitive function after initiation of systemic therapies. (Clini calTr ials.gov Identifier: NCT03476070).

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Incorporating zeaxanthin-rich wolfberry (Lycium barbarum) into a healthy dietary pattern may augment its antioxidant potential. The present 16-week, parallel design randomized controlled trial aimed to investigate the impact of adhering to a healthy dietary pattern, either with or without whole dried wolfberry (15 g/d) on oxidative stress status (plasma malondialdehyde and 8-iso-prostaglandin F2α) in middle-aged and older adults. Changes to carotenoids status (plasma and skin carotenoids) and body composition were further evaluated to explore potential mechanisms which underlie the antioxidant properties of wolfberry. Plasma 8-iso-prostaglandin F2α, plasma zeaxanthin and skin carotenoids status were significantly raised in the wolfberry consuming group (n = 22; p < 0.05) compared to the control group which showed no changes (n = 18). Likewise in the wolfberry group only, inverse association was observed between the change values of plasma zeaxanthin and plasma 8-iso-prostaglandin F2α (−0.21 (−0.43, 0.00) ng/µmol, regression coefficient (95% CI); p = 0.05). Wolfberry consumption with a healthy dietary pattern may serve as a dietary strategy to attenuate lipid peroxidation among middle-aged and older adults who are at a heightened risk of oxidative stress induced age-related disorders. The antioxidant properties of wolfberry may be attributed to its rich zeaxanthin content.

Purpose To elucidate existing decision aids (DAs) in supporting cancer survivors’ decisions to engage in cancer survivorship care services after primary treatment. Secondary objectives are to assess the DA acceptability, impact of DAs, and implementation barriers. Methods Databases (PubMed, Embase, PsycINFO, CINAHL) were searched to collect publications from inception through September 2021. Studies describing the development or evaluation of DAs used for survivorship care services after primary cancer treatment were included. Article selection and critical appraisal were conducted independently by two authors. Results We included 16 studies that described 13 DAs and addressed multiple survivorship care domains: prevention of recurrence/new cancers in Hodgkin lymphoma survivors and breast cancer gene mutation carriers, family building options, health insurance plans, health promotion (substance use behavior, cardiovascular disease risk reduction), advanced care planning, and post-treatment follow-up intensity. The electronic format was used to design most DAs for self-administration. The content presentation covered decisional context, options, and value clarification exercises. DAs were acceptable and associated with higher knowledge but presented inconclusive decisional outcomes. Implementation barriers included lack of design features for connectivity to care, low self-efficacy, and low perceived DA usefulness among healthcare professionals. Other survivor characteristics included age, literacy, preferred timing, and setting. Conclusions A diverse range of DAs exists in survivorship care services engagement with favorable knowledge outcomes. Future work should clarify the impact of DAs on decisional outcomes. Implications for Cancer Survivors DA characterization and suggestions for prospective developers could enhance support for cancer survivors encountering complex decisions throughout the survivorship continuum.

In document-level event argument extraction, an argument is likely to appear multiple times in different expressions in the document. The redundancy of arguments underlying multiple sentences is beneficial but is often overlooked. In addition, in event argument extraction, most entities are regarded as class "others", i.e. Universum class, which is defined as a collection of samples that do not belong to any class of interest. Universum class is composed of heterogeneous entities without typical common features. Classifiers trained by cross entropy loss could easily misclassify the Universum class because of their open decision boundary. In this paper, to make use of redundant event information underlying a document, we build an entity coreference graph with the graph2token module to produce a comprehensive and coreference-aware representation for every entity and then build an entity summary graph to merge the multiple extraction results. To better classify Universum class, we propose a new loss function to build classifiers with closed boundaries. Experimental results show that our model outperforms the previous state-of-the-art models by 3.35% in F1-score.