In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and s...
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.
Antibody-based therapeutics constitute a rapidly growing class of pharmaceutical compounds. However, monoclonal antibodies, which specifically engage only one target, often lack the mechanistic intricacy to treat comp...
Antibody-based therapeutics constitute a rapidly growing class of pharmaceutical compounds. However, monoclonal antibodies, which specifically engage only one target, often lack the mechanistic intricacy to treat complex diseases. To expand the utility of antibody therapies, significant efforts have been invested in designing multispecific antibodies, which engage multiple targets using a single molecule. These efforts have culminated in remarkable translational progress, including nine US Food and Drug Administration–approved multispecific antibodies, with countless others in various stages of preclinical or clinical development. In this review, we discuss several categories of multispecific antibodies that have achieved clinical approval or shown promise in earlier stages of development. We focus on the molecular mechanisms used by multispecific antibodies and how these mechanisms inform their customized design and formulation. In particular, we discuss multispecific antibodies that target multiple disease markers, multiparatopic antibodies, and immune-interfacing antibodies. Overall, these innovative multispecific antibody designs are fueling exciting advances across the immunotherapeutic landscape.
A cheap, reliable discriminator and integrator device for the specific analysis of multi-unit neural activity has been described. Only solid-state components are used. Several options are available for displaying the ...
A cheap, reliable discriminator and integrator device for the specific analysis of multi-unit neural activity has been described. Only solid-state components are used. Several options are available for displaying the instrument output. Un système bon marché et fidèle de discrimination et d'intégration destiné à l'analyse spécifique de l'activité nerveuse multi-unitaire est décrit. Seules des composantes état-solide sont utilisées. Plusieurs modèles de présentation des sorties de l'instrument sont disponibles.
The neural properties of the pigeon lingual chemoreceptor mechanism are investigated from electrophysiological data extracted from the peripheral laryngo-lingual nerve; a branch of the IX nerve trunk. Of 32 chemicals ...
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The neural properties of the pigeon lingual chemoreceptor mechanism are investigated from electrophysiological data extracted from the peripheral laryngo-lingual nerve; a branch of the IX nerve trunk. Of 32 chemicals tested individual pigeon variations are noted to many solutions. Salt and acid solutions are very effective stimuli; solutions commonly associated with the sweet or bitter taste sensation in man are notably ineffective as stimuli in the pigeon. In most instances there is good correlation between electrophysiological and behavioral response. A new measure, the ionic stimulating efficiency factor derived from both the relative effectiveness measure and response rise time data shows a ranking of NH 4 + > K + > Na + ≥ Ca ++ and Cl − > Br − = I − for several salt solutions. Both anions and cations are involved in chemoreception. Beidler's fundamental taste equation [2] can be fitted to NaCl response data but only above a certain threshold value of 0.2 M concentration. Acetic acid data does not appear to fit Beidler's equation. Burst-like clusters and pattern temporal variation are prominent features of single-fiber activity. Intensity coding is mediated by both fiber recruitment and impulse frequency variation.
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