Results 101 to 110 of about 10,169,709 (296)

Advancing Clinical Medicine with Raman Spectroscopy: Current Trends and Future Perspectives

open access: yesAdvanced Photonics Research
This review explores the potential of Raman spectroscopy and microscopy (RS) in clinical medicine, focusing on the diagnostic and therapeutic applications across multiple disciplines. For example, RS has proven effective in distinguishing between healthy
Jiří Bufka   +5 more
doaj   +1 more source

Discerning protein pools by selective staining with self‐labeling tags

open access: yesFEBS Letters, EarlyView.
Cell surface proteins have an intra‐ and extracellular pool. Combining genetic fusion to self‐labeling tags that can be addressed with small molecule fluorophores allows separating these pools. We highlight recent developments and techniques for state‐of‐the‐art interrogation of cell surface proteins in the complex tissue setting.
Kati Fischermanns, Johannes Broichhagen
wiley   +1 more source

Molecular Nanomagnets with Photomagnetic Properties: Design Strategies and Recent Advances

open access: yes
The magnetic properties of molecular nanomagnets can be finely modulated by light, which provides great potential in optical switches, smart sensors, and data storage devices.
Wei Shi   +3 more
core   +1 more source

Molecular and nanostructural mechanisms of deformation, strength and toughness of spider silk fibrils [PDF]

open access: yes, 2010
Spider silk is one of the strongest, most extensible and toughest biological materials known, exceeding the properties of many engineered materials including steel.
Keten, S.   +15 more
core   +1 more source

BERTology of Molecular Property Prediction

open access: yesCoRR
Chemical language models (CLMs) have emerged as promising competitors to popular classical machine learning models for molecular property prediction (MPP) tasks. However, an increasing number of studies have reported inconsistent and contradictory results for the performance of CLMs across various MPP benchmark tasks.
Mohammad Mostafanejad   +2 more
openaire   +3 more sources

Peripheral lysosomes recruit PLEKHG3 to focal adhesions and restrain protrusion dynamics

open access: yesFEBS Letters, EarlyView.
Proximity‐dependent labeling at the LAMTOR complex revealed the Rho GEF PLEKHG3 as a lysosome‐proximal protein directing the study toward the influence of lysosome positioning on actin dynamics and cell motility. We show that PLEKHG3 colocalizes with lysosomes at focal adhesion sites and observe that forced peripheral dispersion of lysosomes hinders ...
Rainer Ettelt   +8 more
wiley   +1 more source

High and low molecular weight crossovers in the longest relaxation time dependence of linear cis-1,4 polyisoprene by dielectric relaxations [PDF]

open access: yes, 2010
The dielectric relaxation of cis-1,4 Polyisoprene [PI] is sensitive not only to the local and segmental dynamics but also to the larger scale chain (end-to-end) fluctuations.
Alegría, Angel   +4 more
core   +1 more source

Engineering peptides into antibodies—opportunities and strategies for therapeutic innovation

open access: yesFEBS Letters, EarlyView.
Peptides and antibodies occupy complementary therapeutic niches. Peptides recognize difficult targets in a compact format, while antibodies add specificity, long half‐life, and effector functions. This review examines strategies that merge both modalities—peptide grafting into loops, terminal and Fc fusions, and bioconjugation—highlighting how ...
Jinling Wang   +2 more
wiley   +1 more source

Molecular Dynamics Simulations of Lead and Lithium in Liquid Phase [PDF]

open access: yes, 2011
Pb17Li is today a reference breeder material in diverse fusion R&D programs worldwide. Extracting dynamic and structural properties of liquid LiPb mixtures via molecular dynamics simulations, represent a crucial step for multiscale modeling efforts ...
Fraile García, Alberto   +5 more
core   +1 more source

FP2VEC: a new molecular featurizer for learning molecular properties

open access: yesBioinformatics, 2019
Abstract Motivation One of the most successful methods for predicting the properties of chemical compounds is the quantitative structure–activity relationship (QSAR) methods. The prediction accuracy of QSAR models has recently been greatly improved by employing deep learning technology.
Woosung Jeon, Dongsup Kim
openaire   +2 more sources

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