Results 211 to 220 of about 1,336,811 (364)
Retrospective Review on Reticular Materials: Facts and Figures Over the Last 30 Years
To shape the future course of research in reticular materials, this work reflects on the progress over the past 30 years, complemented by input from the community of 228 active researchers through a global, crowdsourced survey: ranging from demographics, how it works, publish and interact, to highlights on both academic and industrial milestones, as ...
Aamod V. Desai +8 more
wiley +1 more source
Ticagrelor use in ruptured aneurysms: A retrospective cohort study. [PDF]
Bankole NDA +8 more
europepmc +1 more source
Perinatal mortality in rural China: retrospective cohort study [PDF]
Zhongwei Wu
openalex +1 more source
Elevated spliced form of X‐box–binding protein 1 (XBP1) correlates with unfavorable responses to endocrine therapy plus CDK4/6 inhibitors in HR+/HER2− advanced breast cancer. XBP1s facilitates cell proliferation and G1/S transition by transcriptionally activating SND1, thereby activating the E2F1 pathway.
Yuting Sang +11 more
wiley +1 more source
Colchicine and cardiovascular outcomes in MINOCA: A retrospective cohort study. [PDF]
Oro P +5 more
europepmc +1 more source
Retrospective cohort study examining incidence of HIV and hepatitis C infection among injecting drug users in Dublin [PDF]
Bobby P. Smyth
openalex +1 more source
Life Factors and Melanoma: From the Macroscopic State to the Molecular Mechanism
Melanoma, an aggressive skin cancer, arises from dynamic interactions between genetic, environmental, and lifestyle factors. This review explores how age, gender, obesity, diet, exercise, smoking, alcohol, UV exposure, circadian rhythms, and medications influence melanoma risk and progression.
Hanbin Wang +4 more
wiley +1 more source
The role of glucocorticoids in recurrent idiopathic intussusception: a retrospective cohort study. [PDF]
Zhang J +7 more
europepmc +1 more source
Predicting Immunotherapy Outcomes in NSCLC Using RNA and Pathology from Multicenter Clinical Trials
LIRA, a machine learning‐based model, is developed using transcriptomic data from 891 NSCLC patients in the OAK and POPLAR cohorts. Its predictive performance is validated in multiple external cohorts. Patients stratified by LIRA‐score exhibit distinct clinical characteristics and tumor microenvironment profiles.
Zhaojun Wang +32 more
wiley +1 more source

