Results 211 to 220 of about 2,763,762 (257)

Fluorination: A strategy to tune the interactions of molecules and nanoscale systems with biological matter

FEBS Open Bio, EarlyView.
This minireview explores fluorination as a key strategy to optimize therapeutic molecules and nanocarriers. By incorporating different fluorinated motifs—from single atoms to linear and branched chains—drug delivery, cellular uptake, and colloidal stability can be improved and fine‐tuned.
Martina Beccalli, Beatrice Lucia Bona, Francesca Baldelli Bombelli   +2 more
wiley   +1 more source

Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning. [PDF]

Sensors (Basel)
Kherad M, Moayyedi MK, Fotouhi-Ghazvini F, Vahabi M, Fotouhi H.   +4 more
europepmc   +1 more source

A nationwide student survey on biochemistry learning resources in Italy

FEBS Open Bio, EarlyView.
We conducted a survey among Italian university students to investigate their preferred study materials for biochemistry. We then analyzed the collected data to identify these materials. This information may be valuable to the Biochemistry educational community. The graphical abstract was created with Claude (Opus 5.5; Anthropic, 2026).
Anna Marabotti, Ivano Eberini, Maria Luisa Genova, Francesca Mensitieri, Francesco Malatesta   +4 more
wiley   +1 more source

CFD-Based Hemolysis Study of Fontan Cavopulmonary Assist Device Using Kriging Surrogate Modeling. [PDF]

Artif Organs
Sarfare S, Palazzolo A, Afaq M, Ghali G, Giridharan G, Rodefeld M.   +5 more
europepmc   +1 more source

Dactolisib outperforms Rapamycin in potentiating apoptosis induced by XPO1, Bcl‐2 and Mcl‐1 blockade in preclinical glioblastoma models

FEBS Open Bio, EarlyView.
The PAM pathway and its impact on cellular metabolism. Color‐coded key symbols represent Rapamycin (R, orange, targeting mTOR) and Dactolisib (D, green, targeting PI3K/mTOR), indicating their sites of action. Created with BioRenders.com. Glioblastoma resists therapy through interconnected survival programs.
Kai Zhao, Leonie Meyer, Madita Braun, Katharina Otte, Celine Bitter, Maite Pawlik, Hartmann Raifer, Frederik Helmprobst, Axel Pagenstecher, Hans Urban, Michael C. Burger, Michael W. Ronellenfitsch, Joachim P. Steinbach, Jarmila Jedelská, Shamim Bagheri, Behrooz H. Yousefi, Pietro Di Fazio, Markus Luster, Andreas H. Mahnken, Andreas Neubauer, Andreas Burchert, Christopher Nimsky, Jörg W. Bartsch, Tillmann Rusch   +23 more
wiley   +1 more source

Quantification of Impact of Uncertainty on Emissions in a Cement Manufacturing Plant: Surrogate Modeling-Based Approach. [PDF]

ACS Omega
Usman M, Ahmad I, Kano M, Ahmad F, Ahsan M.   +4 more
europepmc   +1 more source

Design and evaluation of FEBS Education and Training Academy module – Integrating generative artificial intelligence into life‐science education

FEBS Open Bio, EarlyView.
Life‐science educators from 14 countries redesigned their own course materials with generative AI in a three‐session, pedagogy‐first FEBS Education and Training Academy workshop built on PICRAT. Among 12 matched respondents, four more reported high confidence in each of five practical domains, and 10 of 13 reported implementing at least one idea ...
Zvonimir Mlinarić, Ali Burak Özkaya
wiley   +1 more source

Assessing the Influence of Screw Orientation on Fracture Fixation of the Proximal Humerus Using Finite Element Informed Surrogate Modeling. [PDF]

Int J Numer Method Biomed Eng
Mini D, Reynolds KJ, Taylor M.
europepmc   +1 more source

Hydrogel platforms for 3D cardiac tissue engineering and fibrotic disease modelling

FEBS Open Bio, EarlyView.
Hydrogel‐based cardiac tissue models are advancing our understanding of heart health and disease. By incorporating electrical conductivity, aligned structures, and tunable mechanical properties, these platforms better replicate the complex environment of native heart tissue.
Harrison Porritt, Annika Winbo, Jenny Malmström   +2 more
wiley   +1 more source

Surrogate modeling of electrospun PVA/PLA nanofibers using artificial neural network for biomedical applications. [PDF]

Sci Rep
Fatahian R, Erfani R.
europepmc   +1 more source