Results 171 to 180 of about 612,057 (203)

Bottom‐Up Programming of Cell States in Cancer Organoids with Defined Synthetic Adhesion Cues

Advanced Materials, EarlyView.
A bottom‐up biomaterial platform is developed to program transcriptomic states in pancreatic cancer organoids by tuning adhesion cues within synthetic matrices. By combining a Design of Experiments framework with multiobjective optimization, matrix compositions are identified that enrich specific cellular programs like EMT.
Ali Nadernezhad, Verena J. Kast, Dagmar Pette, Franziska Baenke, Daniel E. Stange, Carsten Werner, Daniela Loessner   +6 more
wiley   +1 more source

A Nano‐Interception Strategy for Chronic Heart Failure: Prussian Blue Nanoparticles Disrupt Fibroblast‐Immune Communication via CCL2 Sequestration

Advanced Materials, EarlyView.
A nano‐interception strategy disrupts pathogenic fibroblast–macrophage crosstalk in chronic heart failure. Scalable Prussian blue nanoparticles selectively sequester CCL2 via ultrahigh‐affinity binding, preventing CCR2+ macrophage recruitment and breaking a key fibro‐inflammatory circuit. This approach demonstrates robust efficacy in murine and porcine
Bo Chen, Gao Wei, Guowei Zeng, Haoyu Li, Rongrong Wu, Hongyu Yan, Zijie Zhou, Kai Luo, Xiaoyang Zhang, Cong Li, Longming Huang, Lingyan Cheng, Xinjie Zhang, Jinghao Zheng, Bozhong Shi, Xiaojun Cai, Xiaomin He   +16 more
wiley   +1 more source

Perceptions of breast cancer risk after breast density notification in a population-based screening program. [PDF]

Breast Cancer Res Treat
't Hart D, Marriott R, Stone J.
europepmc   +1 more source

Viral Infection‐Inspired Autonomous Detection of Fusion‐Competent Viruses for Screening and Environmental Surveillance

Advanced Materials, EarlyView.
Inspired by viral entry mechanisms, the FUSION assay enables autonomous detection of respiratory viruses via membrane fusion–triggered CRISPR‐Cas13a activation. VEACON selectively fuses with fusion‐competent viruses, triggering fluorescence within confined vesicles.
Jae Chul Park, Yidam Song, Hyun‐Woo Choi, Jaeuk Sung, Harin Jin, WonSeok Gwak, Kirim Yoo, Soohwang Lee, Jongeon Park, Jeongmin Kim, Hye‐Jun Jo, Jahyun Koo, Youngdo Jeong, Kwan Hyi Lee, Seung‐Jung Kee, Hojun Kim   +15 more
wiley   +1 more source

Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review. [PDF]

J Imaging
Alhusari K, Dhou S.
europepmc   +1 more source

Determining Breast Density in Patients with Metabolic Syndrome: A Cross Sectional Study.

J Pharm Bioallied Sci
Sarvetamin HT.
europepmc   +1 more source

Prediction of mammographic breast density based on clinical breast ultrasound images using deep learning: a retrospective analysis. [PDF]

Lancet Reg Health Am
Bunnell A, Valdez D, Wolfgruber TK, Quon B, Hung K, Hernandez BY, Seto TB, Killeen J, Miyoshi M, Sadowski P, Shepherd JA.   +10 more
europepmc   +1 more source

A New Breast Density Assessment Method Using Portable Document Format. [PDF]

Asian Pac J Cancer Prev
Uematsu T, Nakashima K, Itoh T, Nasu H, Igarashi T, Notsu A.   +5 more
europepmc   +1 more source

Automated Breast Density Assessment for Full-Field Digital Mammography and Digital Breast Tomosynthesis. [PDF]

Cancer Prev Res (Phila)
Jiang S, Bennett DL, Chen S, Toriola AT, Colditz GA.   +4 more
europepmc   +1 more source

Exploring Dense Breast Density in Mammography: A Comparative Analysis of Breast Cancer Risk. [PDF]

Cureus
Cardona Ortegón JD, Valencia S, Campaña Perilla L, Guerra Barón JD, Romero JA.   +4 more
europepmc   +1 more source