Polyunsaturated fatty acids-induced ferroptosis suppresses pancreatic cancer growth.

Despite recent advances in science and medical technology, pancreatic cancer remains associated with high mortality rates due to aggressive growth and no early clinical sign as well as the unique resistance to anti-cancer chemotherapy. Current numerous investigations have suggested that ferroptosis, which is a programed cell death driven by lipid oxidation, is an attractive therapeutic in different tumor types including pancreatic cancer. Here, we first demonstrated that linoleic acid (LA) and α-linolenic acid (αLA) induced cell death with necroptotic morphological change in MIA-Paca2 and Suit 2 cell lines. LA and αLA increased lipid peroxidation and phosphorylation of RIP3 and MLKL in pancreatic cancers, which were negated by ferroptosis inhibitor, ferrostatin-1, restoring back to BSA control levels. Similarly, intraperitoneal administration of LA and αLA suppresses the growth of subcutaneously transplanted Suit-2 cells and ameliorated the decreased survival rate of tumor bearing mice, while co-administration of ferrostatin-1 with LA and αLA negated the anti-cancer effect. We also demonstrated that LA and αLA partially showed ferroptotic effects on the gemcitabine-resistant-PK cells, although its effect was exerted late compared to treatment on normal-PK cells. In addition, the trial to validate the importance of double bonds in PUFAs in ferroptosis revealed that AA and EPA had a marked effect of ferroptosis on pancreatic cancer cells, but DHA showed mild suppression of cancer proliferation. Furthermore, treatment in other tumor cell lines revealed different sensitivity of PUFA-induced ferroptosis; e.g., EPA induced a ferroptotic effect on colorectal adenocarcinoma, but LA or αLA did not. Collectively, these data suggest that PUFAs can have a potential to exert an anti-cancer effect via ferroptosis in both normal and gemcitabine-resistant pancreatic cancer.

Exploring the association between cancer and cognitive impairment in the Australian Imaging Biomarkers and Lifestyle (AIBL) study.

An inverse association between cancer and Alzheimer's disease (AD) has been demonstrated; however, the association between cancer and mild cognitive impairment (MCI), and the association between cancer and cognitive decline are yet to be clarified. The AIBL dataset was used to address these knowledge gaps. The crude and adjusted odds ratios for MCI/AD and cognitive decline were compared between participants with/without cancer (referred to as C+ and C- participants). A 37% reduction in odds for AD was observed in C+ participants compared to C- participants after adjusting for all confounders. The overall risk for MCI and AD in C+ participants was reduced by 27% and 31%, respectively. The odds of cognitive decline from MCI to AD was reduced by 59% in C+ participants after adjusting for all confounders. The risk of cognitive decline from MCI to AD was halved in C+ participants. The estimated mean change in Clinical Dementia Rating-Sum of boxes (CDR-SOB) score per year was 0.23 units/year higher in C- participants than in C+ participants. Overall, an inverse association between cancer and MCI/AD was observed in AIBL, which is in line with previous reports. Importantly, an inverse association between cancer and cognitive decline has also been identified.

How Can We Use Mathematical Modeling of Amyloid-ß in Alzheimer's Disease Research and Clinical Practices?

The accumulation of amyloid-ß (Aß) plaques in the brain is considered a hallmark of Alzheimer's disease (AD). Mathematical modeling, capable of predicting the motion and accumulation of Aß, has obtained increasing interest as a potential alternative to aid the diagnosis of AD and predict disease prognosis. These mathematical models have provided insights into the pathogenesis and progression of AD that are difficult to obtain through experimental studies alone. Mathematical modeling can also simulate the effects of therapeutics on brain Aß levels, thereby holding potential for drug efficacy simulation and the optimization of personalized treatment approaches. In this review, we provide an overview of the mathematical models that have been used to simulate brain levels of Aß (oligomers, protofibrils, and/or plaques). We classify the models into five categories: the general ordinary differential equation models, the general partial differential equation models, the network models, the linear optimal ordinary differential equation models, and the modified partial differential equation models (i.e., Smoluchowski equation models). The assumptions, advantages and limitations of these models are discussed. Given the popularity of using the Smoluchowski equation models to simulate brain levels of Aß, our review summarizes the history and major advancements in these models (e.g., their application to predict the onset of AD and their combined use with network models). This review is intended to bring mathematical modeling to the attention of more scientists and clinical researchers working on AD to promote cross-disciplinary research.