Docosahexaenoic acid suppresses arachidonic acid-induced proliferation of LS-174T human colon carcinoma cells

AIM： To investigate the impact of arachidonic acid （AA） and docosahexaenoic acid （DHA） and their combination on colon cancer cell growth. METHODS： The LS-174T colon cancer cell line was used to study the role of the prostaglandin precursor AA and the omega-3 polyunsaturated fatty acid DHA on cell growth. Cell viability was assessed in XTT assays. For analysis of cell cycle and cell death, flow cytometry and DAPI staining were applied. Expression of cyclooxygenase-2 （COX-2）, p21 and bcl-2 in ceils incubated with AA or DHA was examined by real-time RT-PCR. Prostaglandin E2 （PGE2） generation in the presence of AA and DHA was measured using a PGE2- ELISA. RESULTS： AA increased cell growth, whereas DHA reduced viability of LS 174T cells in a time- and dosedependent manner. Furthermore, DHA down- regulated mRNA of bcl-2 and up-regulated p21. Interestingly, DHA was able to suppress AA-induced cell proliferation and significantly lowered AA-derived PGE2 formation. DHA also down-regulated COX-2 expression. In addition to the effect on PGE2 formation, DHA directly reduced PGE2-induced cell proliferation in a dosedependent manner. CONCLUSION： These results suggest that DHA can inhibit the pro-proliferative effect of abundant AA or PGE2.

A Survey of Multimodal Controllable Diffusion Models

Diffusion models have recently emerged as powerful generative models,producing high-fidelity samples across domains.Despite this,they have two key challenges,including improving the time-consuming iterative generation process and controlling and steering the generation process.Existing surveys provide broad overviews of diffusion model advancements.However,they lack comprehensive coverage specifically centered on techniques for controllable generation.This survey seeks to address this gap by providing a comprehensive and coherent review on controllable generation in diffusion models.We provide a detailed taxonomy defining controlled generation for diffusion models.Controllable generation is categorized based on the formulation,methodologies,and evaluation metrics.By enumerating the range of methods researchers have developed for enhanced control,we aim to establish controllable diffusion generation as a distinct subfield warranting dedicated focus.With this survey,we contextualize recent results,provide the dedicated treatment of controllable diffusion model generation,and outline limitations and future directions.To demonstrate applicability,we highlight controllable diffusion techniques for major computer vision tasks application.By consolidating methods and applications for controllable diffusion models,we hope to catalyze further innovations in reliable and scalable controllable generation.