RESUMEN
BACKGROUND: The molecular mechanisms underlying development of posthemorrhagic hydrocephalus (PHH) following subarachnoid hemorrhage (SAH) remain incompletely understood. Consequently, treatment strategies tailored towards the individual patient remain limited. This study aimed to identify proteomic cerebrospinal fluid (CSF) biomarkers capable of predicting shunt dependency and functional outcome in patients with SAH in order to improve informed clinical decision making. METHODS: Ventricular CSF samples were collected twice from 23 patients with SAH who required external ventricular drain (EVD) insertion (12 patients with successful EVD weaning, 11 patients in need of permanent CSF shunting due to development of PHH). The paired CSF samples were collected acutely after ictus and later upon EVD removal. Cisternal CSF samples were collected from 10 healthy control subjects undergoing vascular clipping of an unruptured aneurysm. All CSF samples were subjected to mass spectrometry-based proteomics analysis. Proteomic biomarkers were quantified using area under the curve (AUC) estimates from a receiver operating curve (ROC). RESULTS: CSF from patients with SAH displayed a distinct proteomic profile in comparison to that of healthy control subjects. The CSF collected acutely after ictus from patients with SAH was moreover distinct from that collected weeks later but appeared similar in the weaned and shunted patient groups. Sixteen unique proteins were identified as potential predictors of shunt dependency, while three proteins were identified as potential predictors of functional outcome assessed six months after ictus with the modified Rankin Scale. CONCLUSIONS: We here identified several potential proteomic biomarkers in CSF from patients with SAH capable of predicting (i) shunt dependency and thus development of PHH and (ii) the functional outcome assessed six months after ictus. These proteomic biomarkers may have the potential to aid clinical decision making by predicting shunt dependency and functional outcome following SAH.
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Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.
Asunto(s)
COVID-19 , Proteómica , Síndrome de Respuesta Inflamatoria Sistémica , Humanos , Síndrome de Respuesta Inflamatoria Sistémica/diagnóstico , Síndrome de Respuesta Inflamatoria Sistémica/sangre , COVID-19/diagnóstico , COVID-19/metabolismo , COVID-19/complicaciones , Niño , Proteómica/métodos , Femenino , Masculino , Preescolar , SARS-CoV-2 , Adolescente , Biomarcadores/sangre , Inteligencia Artificial , LactanteRESUMEN
AIMS: The underlying biological mechanisms of ventricular fibrillation (VF) during acute myocardial infarction are largely unknown. To our knowledge, this is the first proteomic study for this trait, with the aim to identify and characterize proteins that are associated with VF during first ST-elevation myocardial infarction (STEMI). METHODS AND RESULTS: We included 230 participants from a Danish ongoing case-control study on patients with first STEMI with VF (case, n = 110) and without VF (control, n = 120) before guided catheter insertion for primary percutaneous coronary intervention. The plasma proteome was investigated using mass spectrometry-based proteomics on plasma samples collected within 24â h of symptom onset, and one patient was excluded in quality control. In 229 STEMI patients {72% men, median age 62 years [interquartile range (IQR): 54-70]}, a median of 257 proteins (IQR: 244-281) were quantified per patient. A total of 26 proteins were associated with VF; these proteins were involved in several biological processes including blood coagulation, haemostasis, and immunity. After correcting for multiple testing, two up-regulated proteins remained significantly associated with VF, actin beta-like 2 [ACTBL2, fold change (FC) 2.25, P < 0.001, q = 0.023], and coagulation factor XIII-A (F13A1, FC 1.48, P < 0.001, q = 0.023). None of the proteins were correlated with anterior infarct location. CONCLUSION: Ventricular fibrillation due to first STEMI was significantly associated with two up-regulated proteins (ACTBL2 and F13A1), suggesting that they may represent novel underlying molecular VF mechanisms. Further research is needed to determine whether these proteins are predictive biomarkers or acute phase response proteins to VF during acute ischaemia.