RESUMO
Of the 750,000 strokes in the United States every year, 15% patients suffer from hemorrhagic stroke. Intracerebral hemorrhage (ICH) is a subtype of hemorrhagic stroke. Despite advances in acute management, patients with hemorrhagic stroke continue to suffer from high mortality and survivors suffer from multidomain impairments in the physical, cognitive, and mental health domains which could last for months to years from their index stroke. Long-term prognosis after ICH is critically dependent on the quality and efficacy of care a patient receives during the acute phase of care. With ongoing care consolidation in stroke systems of care, the number of ICH patients who need to undergo interhospital transfers (IHTs) is increasing. However, the associations between IHT and ICH outcomes have not been well described in literature. In this review, we describe the epidemiology of IHT for ICH, the relationship between IHT and ICH patient outcomes, and proposed improvements to the IHT process to ensure better long-term patient outcomes. Our review indicates that evidence regarding the safety and benefit of IHT for ICH patients is conflicting, with some studies reporting poorer outcomes for transferred patients compared to direct admissions via emergency rooms and other studies showing no effect on outcomes. The American Heart Association guidelines for ICH provide recommendations for timely blood pressure control and anticoagulation reversal to improve patient outcomes. The American Heart Association stroke systems of care guidelines provide recommendations for transfer agreements and but do not provide details on how patients should be managed while undergoing IHT. Large, prospective, and multicenter studies comparing outcomes of IHT patients to direct admissions are necessary to provide more definitive guidance to optimize IHT protocols and aid clinical decision-making.
RESUMO
Phosphorylation of proteins on tyrosine (Tyr) residues evolved in metazoan organisms as a mechanism of coordinating tissue growth1. Multicellular eukaryotes typically have more than 50 distinct protein Tyr kinases that catalyse the phosphorylation of thousands of Tyr residues throughout the proteome1-3. How a given Tyr kinase can phosphorylate a specific subset of proteins at unique Tyr sites is only partially understood4-7. Here we used combinatorial peptide arrays to profile the substrate sequence specificity of all human Tyr kinases. Globally, the Tyr kinases demonstrate considerable diversity in optimal patterns of residues surrounding the site of phosphorylation, revealing the functional organization of the human Tyr kinome by substrate motif preference. Using this information, Tyr kinases that are most compatible with phosphorylating any Tyr site can be identified. Analysis of mass spectrometry phosphoproteomic datasets using this compendium of kinase specificities accurately identifies specific Tyr kinases that are dysregulated in cells after stimulation with growth factors, treatment with anti-cancer drugs or expression of oncogenic variants. Furthermore, the topology of known Tyr signalling networks naturally emerged from a comparison of the sequence specificities of the Tyr kinases and the SH2 phosphotyrosine (pTyr)-binding domains. Finally we show that the intrinsic substrate specificity of Tyr kinases has remained fundamentally unchanged from worms to humans, suggesting that the fidelity between Tyr kinases and their protein substrate sequences has been maintained across hundreds of millions of years of evolution.
Assuntos
Fosfotirosina , Proteínas Tirosina Quinases , Especificidade por Substrato , Tirosina , Animais , Humanos , Motivos de Aminoácidos , Evolução Molecular , Espectrometria de Massas , Fosfoproteínas/química , Fosfoproteínas/metabolismo , Fosforilação , Fosfotirosina/metabolismo , Proteínas Tirosina Quinases/efeitos dos fármacos , Proteínas Tirosina Quinases/metabolismo , Proteoma/química , Proteoma/metabolismo , Proteômica , Transdução de Sinais , Domínios de Homologia de src , Tirosina/metabolismo , Tirosina/químicaRESUMO
Protein phosphorylation is one of the most widespread post-translational modifications in biology1,2. With advances in mass-spectrometry-based phosphoproteomics, 90,000 sites of serine and threonine phosphorylation have so far been identified, and several thousand have been associated with human diseases and biological processes3,4. For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible3. Here we used synthetic peptide libraries to profile the substrate sequence specificity of 303 Ser/Thr kinases, comprising more than 84% of those predicted to be active in humans. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.
Assuntos
Fosfoproteínas , Proteínas Serina-Treonina Quinases , Proteoma , Serina , Treonina , Humanos , Fosforilação , Proteínas Serina-Treonina Quinases/metabolismo , Serina/metabolismo , Especificidade por Substrato , Treonina/metabolismo , Proteoma/química , Proteoma/metabolismo , Conjuntos de Dados como Assunto , Fosfoproteínas/química , Fosfoproteínas/metabolismo , Linhagem Celular , Fosfosserina/metabolismo , Fosfotreonina/metabolismoRESUMO
Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200-256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80-324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87-0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91-0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92-0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings.