Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism

Background: Long-COVID is characterized by prolonged, diffuse symptoms months after acute COVID-19. Accurate diagnosis and targeted therapies for Long-COVID are lacking. We investigated vascular transformation biomarkers in Long-COVID patients. Methods: A case–control study utilizing Long-COVID patients, one to six months (median 98.5 days) post-infection, with multiplex immunoassay measurement of sixteen blood biomarkers of vascular transformation, including ANG-1, P-SEL, MMP-1, VE-Cad, Syn-1, Endoglin, PECAM-1, VEGF-A, ICAM-1, VLA-4, E-SEL, thrombomodulin, VEGF-R2, VEGF-R3, VCAM-1 and VEGF-D. Results: Fourteen vasculature transformation blood biomarkers were significantly elevated in Long-COVID outpatients, versus acutely ill COVID-19 inpatients and healthy controls subjects (P \u3c 0.05). A unique two biomarker profile consisting of ANG-1/P-SEL was developed with machine learning, providing a classification accuracy for Long-COVID status of 96%. Individually, ANG-1 and P-SEL had excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, P \u3c 0.0001; validated in a secondary cohort). Specific to Long-COVID, ANG-1 levels were associated with female sex and a lack of disease interventions at follow-up (P \u3c 0.05). Conclusions: Long-COVID patients suffer prolonged, diffuse symptoms and poorer health. Vascular transformation blood biomarkers were significantly elevated in Long-COVID, with angiogenesis markers (ANG-1/P-SEL) providing classification accuracy of 96%. Vascular transformation blood biomarkers hold potential for diagnostics, and modulators of angiogenesis may have therapeutic efficacy

Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism

Background: Long-COVID is characterized by prolonged, diffuse symptoms months after acute COVID-19. Accurate diagnosis and targeted therapies for Long-COVID are lacking. We investigated vascular transformation biomarkers in Long-COVID patients. Methods: A case–control study utilizing Long-COVID patients, one to six months (median 98.5 days) post-infection, with multiplex immunoassay measurement of sixteen blood biomarkers of vascular transformation, including ANG-1, P-SEL, MMP-1, VE-Cad, Syn-1, Endoglin, PECAM-1, VEGF-A, ICAM-1, VLA-4, E-SEL, thrombomodulin, VEGF-R2, VEGF-R3, VCAM-1 and VEGF-D. Results: Fourteen vasculature transformation blood biomarkers were significantly elevated in Long-COVID outpatients, versus acutely ill COVID-19 inpatients and healthy controls subjects (P \u3c 0.05). A unique two biomarker profile consisting of ANG-1/P-SEL was developed with machine learning, providing a classification accuracy for Long-COVID status of 96%. Individually, ANG-1 and P-SEL had excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, P \u3c 0.0001; validated in a secondary cohort). Specific to Long-COVID, ANG-1 levels were associated with female sex and a lack of disease interventions at follow-up (P \u3c 0.05). Conclusions: Long-COVID patients suffer prolonged, diffuse symptoms and poorer health. Vascular transformation blood biomarkers were significantly elevated in Long-COVID, with angiogenesis markers (ANG-1/P-SEL) providing classification accuracy of 96%. Vascular transformation blood biomarkers hold potential for diagnostics, and modulators of angiogenesis may have therapeutic efficacy

A Distinct Metabolite Signature in Military Personnel Exposed to Repetitive Low-Level Blasts

Military Breachers and Range Staff (MBRS) are subjected to repeated sub-concussive blasts, and they often report symptoms that are consistent with a mild traumatic brain injury (mTBI). Biomarkers of blast injury would potentially aid blast injury diagnosis, surveillance and avoidance. Our objective was to identify plasma metabolite biomarkers in military personnel that were exposed to repeated low-level or sub-concussive blast overpressure. A total of 37 military members were enrolled (18 MBRS and 19 controls), with MBRS having participated in 8–20 breaching courses per year, with a maximum exposure of 6 blasts per day. The two cohorts were similar except that the number of blast exposures were significantly higher in the MBRS, and the MBRS cohort suffered significantly more post-concussive symptoms and poorer health on assessment. Metabolomics profiling demonstrated significant differences between groups with 74% MBRS classification accuracy (CA). Feature reduction identified 6 metabolites that resulted in a MBRS CA of 98%, and included acetic acid (23.7%), formate (22.6%), creatine (14.8%), acetone (14.2%), methanol (12,7%), and glutamic acid (12.0%). All 6 metabolites were examined with individual receiver operating characteristic (ROC) curve analyses and demonstrated areas-under-the-curve (AUCs) of 0.82–0.91 (P ≤ 0.001) for MBRS status. Several parsimonious combinations of three metabolites increased accuracy of ROC curve analyses to AUCs of 1.00 (P \u3c 0.001), while a combination of volatile organic compounds (VOCs; acetic acid, acetone and methanol) yielded an AUC of 0.98 (P \u3c 0.001). Candidate biomarkers for chronic blast exposure were identified, and if validated in a larger cohort, may aid surveillance and care of military personnel. Future point-of-care screening could be developed that measures VOCs from breath, with definitive diagnoses confirmed with plasma metabolomics profiling

Critically Ill COVID-19 Patients Exhibit Anti-SARS-CoV-2 Serological Responses

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is a global health care emergency. Anti-SARS-CoV-2 serological profiling of critically ill COVID-19 patients was performed to determine their humoral response. Blood was collected from critically ill ICU patients, either COVID-19 positive (+) or COVID-19 negative (−), to measure anti-SARS-CoV-2 immunoglobulins: IgM; IgA; IgG; and Total Ig (combined IgM/IgA/IgG). Cohorts were similar, with the exception that COVID-19+ patients had a greater body mass indexes, developed bilateral pneumonias more frequently and suffered increased hypoxia when compared to COVID-19-patients (p \u3c 0.05). The mortality rate for COVID-19+ patients was 50%. COVID-19 status could be determined by anti-SARS-CoV-2 serological responses with excellent classification accuracies on ICU day 1 (89%); ICU day 3 (96%); and ICU days 7 and 10 (100%). The importance of each Ig isotype for determining COVID-19 status on combined ICU days 1 and 3 was: Total Ig, 43%; IgM, 27%; IgA, 24% and IgG, 6%. Peak serological responses for each Ig isotype occurred on different ICU days (IgM day 13 \u3e IgA day 17 \u3e IgG persistently increased), with the Total Ig peaking at approximately ICU day 18. Those COVID-19+ patients who died had earlier or similar peaks in IgA and Total Ig in their ICU stay when compared to patients who survived (p \u3c 0.005). Critically ill COVID-19 patients exhibit anti-SARS-CoV-2 serological responses, including those COVID-19 patients who ultimately died, suggesting that blunted serological responses did not contribute to mortality. Serological profiling of critically ill COVID-19 patients may aid disease surveillance, patient cohorting and help guide antibody therapies such as convalescent plasma

Developing Interventional Radiology Anticoagulation Guidelines: Process and Benefits †

We created, posted, and updated radiology department anticoagulation guidelines and identified various steps in the process, including triggering events, consensus building, legal analysis, education, and distribution of the guidelines to nurses and clinicians. Supporting data collected retrospectively, before and after implementation, included nursing satisfaction survey results and the number of procedure cancellations. After the guidelines were developed and posted, significantly fewer procedures were cancelled, nursing satisfaction was higher, and radiologists performed procedures with less variability. Anecdotally, radiologists had fewer queries about anticoagulation. The development and dissemination of radiologic procedure anticoagulation guidelines should be considered as a departmental quality improvement project

Additional file 1 of The plasma proteome differentiates the multisystem inflammatory syndrome in children (MIS-C) from children with SARS-CoV-2 negative sepsis

Supplementary Material

Pediatric sepsis inflammatory blood biomarkers that correlate with clinical variables and severity of illness scores

Abstract Background Sepsis is a dysregulated systemic inflammatory response triggered by infection, resulting in organ dysfunction. A major challenge in clinical pediatrics is to identify sepsis early and then quickly intervene to reduce morbidity and mortality. As blood biomarkers hold promise as early sepsis diagnostic tools, we aimed to measure a large number of blood inflammatory biomarkers from pediatric sepsis patients to determine their predictive ability, as well as their correlations with clinical variables and illness severity scores. Methods Pediatric patients that met sepsis criteria were enrolled, and clinical data and blood samples were collected. Fifty-eight inflammatory plasma biomarker concentrations were determined using immunoassays. The data were analyzed with both conventional statistics and machine learning. Results Twenty sepsis patients were enrolled (median age 13 years), with infectious pathogens identified in 75%. Vasopressors were administered to 85% of patients, while 55% received invasive ventilation and 20% were ventilated non-invasively. A total of 24 inflammatory biomarkers were significantly different between sepsis patients and age/sex-matched healthy controls. Nine biomarkers (IL-6, IL-8, MCP-1, M-CSF, IL-1RA, hyaluronan, HSP70, MMP3, and MMP10) yielded AUC parameters > 0.9 (95% CIs: 0.837-1.000; p < 0.001). Boruta feature reduction yielded 6 critical biomarkers with their relative importance: IL-8 (12.2%), MCP-1 (11.6%), HSP70 (11.6%), hyaluronan (11.5%), M-CSF (11.5%), and IL-6 (11.5%); combinations of 2 biomarkers yielded AUC values of 1.00 (95% CI: 1.00–1.00; p < 0.001). Specific biomarkers strongly correlated with illness severity scoring, as well as other clinical variables. IL-3 specifically distinguished bacterial versus viral infection (p < 0.005). Conclusions Specific inflammatory biomarkers were identified as markers of pediatric sepsis and strongly correlated to both clinical variables and sepsis severity

Pediatric severe traumatic brain injury mortality prediction determined with machine learning-based modeling

Introduction: Severe traumatic brain injury (sTBI) is a leading cause of mortality in children. As clinical prognostication is important in guiding optimal care and decision making, our goal was to create a highly discriminative sTBI outcome prediction model for mortality. Methods: Machine learning and advanced analytics were applied to the patient admission variables obtained from a comprehensive pediatric sTBI database. Demographic and clinical data, head CT imaging abnormalities and blood biochemical data from 196 children and adolescents admitted to a tertiary pediatric intensive care unit (PICU) with sTBI were integrated using feature ranking by way of a forest of randomized decision trees, and a model was generated from a reduced number of admission variables with maximal ability to discriminate outcome. Results: In total, 36 admission variables were analyzed using feature ranking with variable weighting to determine their predictive importance for mortality following sTBI. Reduction analysis utilizing Borata feature selection resulted in a parsimonious six-variable model with a mortality classification accuracy of 82%. The final admission variables that predicted mortality were: partial thromboplastin time (22%); motor Glasgow Coma Scale (21%); serum glucose (16%); fixed pupil(s) (16%); platelet count (13%) and creatinine (12%). Using only these six admission variables, a t-distributed stochastic nearest neighbor embedding algorithm plot demonstrated visual separation of sTBI patients that lived or died, with high mortality predictive ability of this model on the validation dataset (AUC = 0.90) which was confirmed with a conventional area-under-the-curve statistical approach on the total dataset (AUC = 0.91; P \u3c 0.001). Conclusions: Machine learning-based modeling identified the most clinically important prognostic factors resulting in a pragmatic, high performing prognostic tool for pediatric sTBI with excellent discriminative ability to predict mortality risk with 82% classification accuracy (AUC = 0.90). After external multicenter validation, our prognostic model might help to guide treatment decisions, aggressiveness of therapy and prepare family members and caregivers for timely end-of-life discussions and decision making. Level of evidence: III; Prognostic

Putative Concussion Biomarkers Identified in Adolescent Male Athletes Using Targeted Plasma Proteomics

Sport concussions can be difficult to diagnose and if missed, they can expose athletes to greater injury risk and long-lasting neurological disabilities. Discovery of objective biomarkers to aid concussion diagnosis is critical to protecting athlete brain health. To this end, we performed targeted proteomics on plasma obtained from adolescent athletes suffering a sports concussion. A total of 11 concussed male athletes were enrolled at our academic Sport Medicine Concussion Clinic, as well as 24 sex-, age- and activity-matched healthy control subjects. Clinical evaluation was performed and blood was drawn within 72 h of injury. Proximity extension assays were performed for 1,472 plasma proteins; a total of six proteins were considered significantly different between cohorts (P \u3c 0.01; five proteins decreased and one protein increased). Receiver operating characteristic curves on the six individual protein biomarkers identified had areas-under-the-curves (AUCs) for concussion diagnosis ≥0.78; antioxidant 1 copper chaperone (ATOX1; AUC 0.81, P = 0.003), secreted protein acidic and rich in cysteine (SPARC; AUC 0.81, P = 0.004), cluster of differentiation 34 (CD34; AUC 0.79, P = 0.006), polyglutamine binding protein 1 (PQBP1; AUC 0.78, P = 0.008), insulin-like growth factor-binding protein-like 1 (IGFBPL1; AUC 0.78, P = 0.008) and cytosolic 5\u27-nucleotidase 3A (NT5C3A; AUC 0.78, P = 0.009). Combining three of the protein biomarkers (ATOX1, SPARC and NT5C3A), produced an AUC of 0.98 for concussion diagnoses (P \u3c 0.001; 95% CI: 0.95, 1.00). Despite a paucity of studies on these three identified proteins, the available evidence points to their roles in modulating tissue inflammation and regulating integrity of the cerebral microvasculature. Taken together, our exploratory data suggest that three or less novel proteins, which are amenable to a point-of-care immunoassay, may be future candidate biomarkers for screening adolescent sport concussion. Validation with protein assays is required in larger cohorts