Results 51 to 60 of about 1,574,453 (263)
This study explores salivary RNA for breast cancer (BC) diagnosis, prognosis, and follow‐up. High‐throughput RNA sequencing identified distinct salivary RNA signatures, including novel transcripts, that differentiate BC from healthy controls, characterize histological and molecular subtypes, and indicate lymph node involvement.
Nicholas Rajan +9 more
wiley +1 more source
A‐to‐I editing of miRNAs, particularly miR‐200b‐3p, contributes to HGSOC progression by enhancing cancer cell proliferation, migration and 3D growth. The edited form is linked to poorer patient survival and the identification of novel molecular targets.
Magdalena Niemira +14 more
wiley +1 more source
Model-based Prediction of Length of Stay for Rehabilitating Stroke Patients
Accurate length-of-stay (LOS) estimates have an impact on medical costs for stroke patients. Most studies have reported only descriptive sample means or have provided linear-model-based estimates for LOS.
Chien-Lin Lin +6 more
doaj +1 more source
Mean-parametrized Conway-Maxwell-Poisson regression models for dispersed counts
Conway-Maxwell-Poisson (CMP) distributions are flexible generalizations of the Poisson distribution for modelling overdispersed or underdispersed counts.
Huang, Alan
core +1 more source
Emerging role of ARHGAP29 in melanoma cell phenotype switching
This study gives first insights into the role of ARHGAP29 in malignant melanoma. ARHGAP29 was revealed to be connected to tumor cell plasticity, promoting a mesenchymal‐like, invasive phenotype and driving tumor progression. Further, it modulates cell spreading by influencing RhoA/ROCK signaling and affects SMAD2 activity. Rho GTPase‐activating protein
Beatrice Charlotte Tröster +3 more
wiley +1 more source
Multiple Correspondence Analysis (MCA) and log-linear modeling are two techniques for multi-way contingency table analysis having different approaches and fields of applications.
S. Ben Ammou , G. Saporta
doaj +1 more source
Linear and Parallel Learning of Markov Random Fields [PDF]
We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields with untied parameters which is efficient for a large class of practical models. Our algorithm parallelizes naturally over cliques and, for graphs of bounded
de Freitas, Nando +2 more
core +1 more source
In this exploratory study, we investigated the relationship between the gut microbiota and outcome in patients with metastatic hormone receptor‐positive breast cancer, treated in a randomized clinical trial with chemotherapy alone or chemotherapy in combination with immune checkpoint blockade.
Andreas Ullern +7 more
wiley +1 more source
Modeling hepatic fibrosis in TP53 knockout iPSC‐derived human liver organoids
This study developed iPSC‐derived human liver organoids with TP53 gene knockout to model human liver fibrosis. These organoids showed elevated myofibroblast activation, early disease markers, and advanced fibrotic hallmarks. The use of profibrotic differentiation medium further amplified the fibrotic signature seen in the organoids.
Mustafa Karabicici +8 more
wiley +1 more source
When non-linear models are fitted to experimental data, parameter estimates can be poorly constrained albeit being identifiable in principle. This means that along certain paths in parameter space, the log-likelihood does not exceed a given statistical ...
Daniel Lill, Jens Timmer, Daniel Kaschek
doaj +1 more source

