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Highlights from the Science family of journals.
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Negatively charged lipid bilayers enhance the interaction between a chemokine and an atypical chemokine receptor.
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Membrana Dobles de Lípidos , Humanos , Membrana Dobles de Lípidos/metabolismo , Membrana Dobles de Lípidos/química , Quimiocinas/metabolismo , Receptores de Quimiocina/metabolismoRESUMEN
Highlights from the Science family of journals.
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A negative allosteric modulator of the µ-opioid receptor enhances the efficacy of naloxone.
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Naloxona , Antagonistas de Narcóticos , Receptores Opioides mu , Receptores Opioides mu/metabolismo , Humanos , Regulación Alostérica , AnimalesRESUMEN
Highlights from the Science family of journals.
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Palmitoylation of intact or cleaved gasdermin D causes plasma membrane pore formation.
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Lipoilación , Humanos , Membrana Celular/metabolismo , Péptidos y Proteínas de Señalización Intracelular/metabolismo , Péptidos y Proteínas de Señalización Intracelular/genética , Animales , Gasderminas , Proteínas de Unión a FosfatoRESUMEN
Highlights from the Science family of journals.
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Antibody fragments can act as pharmacological tools to modulate the functions of G protein-coupled receptors.
Asunto(s)
Receptores Acoplados a Proteínas G , Anticuerpos de Dominio Único , Anticuerpos de Dominio Único/inmunología , Humanos , Receptores Acoplados a Proteínas G/metabolismo , Receptores Acoplados a Proteínas G/inmunología , AnimalesRESUMEN
An unexpected integrin pairing enhances T cell receptor signaling and cytotoxicity in antitumor T cells.
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Integrinas , Neoplasias , Humanos , Transducción de Señal , Linfocitos TRESUMEN
Highlights from the Science family of journals.
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The efficacy of therapeutic T cells is enhanced by incorporating mutations associated with autoimmunity or lymphoma.
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Linfoma , Linfocitos T , Humanos , Autoinmunidad , MutaciónRESUMEN
Few studies examined blood biomarkers informative of patient-reported outcome (PRO) of disability in people with multiple sclerosis (MS). We examined the associations between serum multi-protein biomarker profiles and patient-reported MS disability. In this cross-sectional study (2017-2020), adults with diagnosis of MS (or precursors) from two independent clinic-based cohorts were divided into a training and test set. For predictors, we examined seven clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying therapy [DMT], and time interval between sample collection and closest PRO assessment) and 19 serum protein biomarkers potentially associated with MS disease activity endpoints identified from prior studies. We trained machine learning (ML) models (Least Absolute Shrinkage and Selection Operator regression [LASSO], Random Forest, Extreme Gradient Boosting, Support Vector Machines, stacking ensemble learning, and stacking classification) for predicting Patient Determined Disease Steps (PDDS) score as the primary endpoint and reported model performance using the held-out test set. The study included 431 participants (mean age 49 years, 81% women, 94% non-Hispanic White). For binary PDDS score, combined feature input of routine clinical factors and the 19 proteins consistently outperformed base models (comprising clinical features alone or clinical features plus one single protein at a time) in predicting severe (PDDS ≥ 4) versus mild/moderate (PDDS < 4) disability across multiple machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS = 0.91) and other metrics. For ordinal PDDS score, LASSO model comprising combined clinical factors and 19 proteins as feature input (R2PDDS = 0.31) again outperformed base models. The two best-performing LASSO models (i.e., binary and ordinal PDDS score) shared six clinical features (age, sex, race/ethnicity, disease subtype, disease duration, DMT efficacy) and nine proteins (cluster of differentiation 6, CUB-domain-containing protein 1, contactin-2, interleukin-12 subunit-beta, neurofilament light chain [NfL], protogenin, serpin family A member 9, tumor necrosis factor superfamily member 13B, versican). By comparison, LASSO models with clinical features plus one single protein at a time as feature input did not select either NfL or glial fibrillary acidic protein (GFAP) as a final feature. Forcing either NfL or GFAP as a single protein feature into models did not improve performance beyond clinical features alone. Stacking classification model using five functional pathways to represent multiple proteins as meta-features implicated those involved in neuroaxonal integrity as significant contributors to predictive performance. Thus, serum multi-protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus single protein biomarker, reaching clinically actionable performance.
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The actions of glucagon-like peptide 1 receptor agonists in the CNS reduce systemic inflammation.
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Diabetes Mellitus Tipo 2 , Péptido 1 Similar al Glucagón , Humanos , Encéfalo/metabolismo , Inflamación , Receptor del Péptido 1 Similar al Glucagón/agonistas , Receptor del Péptido 1 Similar al Glucagón/metabolismoRESUMEN
Highlights from the Science family of journals.
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Highlights from the Science family of journals.
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Highlights from the Science family of journals.
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Ruptures in lysosomal membranes stimulate the formation of stress granules that plug the holes to enable repair.
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Lisosomas , Gránulos de EstrésRESUMEN
Highlights from the Science family of journals.
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Highlights from the Science family of journals.
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Highlights from the Science family of journals.