IntroductionEarly prognostication after successful cardiopulmonary resuscitation is difficult, and there is a need for novel methods to estimate the extent of brain injury and predict outcome. In this study, we evaluated the impact of the cardiac arrest syndrome on the plasma levels of selected tissue-specific microRNAs (miRNAs) and assessed their ability to prognosticate death and neurological disability.MethodsWe included 65 patients treated with hypothermia after cardiac arrest in the study. Blood samples were obtained at 24 hours and at 48 hours. For miRNA-screening purposes, custom quantitative polymerase chain reaction (qPCR) panels were first used. Thereafter individual miRNAs were assessed at 48 hours with qPCR. miRNAs that successfully predicted prognosis at 48 hours were further analysed at 24 hours. Outcomes were measured according to the Cerebral Performance Category (CPC) score at 6 months after cardiac arrest and stratified into good (CPC score 1 or 2) or poor (CPC scores 3 to 5).ResultsAt 48 hours, miR-146a, miR-122, miR-208b, miR-21, miR-9 and miR-128 did not differ between the good and poor neurological outcome groups. In contrast, miR-124 was significantly elevated in patients with poor outcomes compared with those with favourable outcomes (P < 0.0001) at 24 hours and 48 hours after cardiac arrest. Analysis of receiver operating characteristic curves at 24 and 48 hours after cardiac arrest showed areas under the curve of 0.87 (95% confidence interval (CI) = 0.79 to 0.96) and 0.89 (95% CI = 0.80 to 0.97), respectively.ConclusionsThe brain-enriched miRNA miR-124 is a promising novel biomarker for prediction of neurological prognosis following cardiac arrest.

a Extracellular vesicles (EVs) are a heterogeneous group of actively released vesicles originating from a wide range of cell types. Characterization of these EVs and their proteomes in the human plasma provides a novel approach in clinical diagnostics, as they reflect physiological and pathological states. However, EV isolation is technically challenging with the current methods having several disadvantages, requiring large sample volumes, and resulting in loss of sample and EV integrity. Here, we use an alternative, non-contact method based on a microscale acoustic standing wave technology. Improved coupling of the acoustic resonator increased the EV recovery from 30% in earlier reports to 80%, also displaying long term stability between experiment days. We report a pilot study, with 20 subjects who underwent physical exercise. Plasma samples were obtained before and 1 h after the workout. Acoustic trapping was compared to a standard high-speed centrifugation protocol, and the method was validated by flow cytometry (FCM). To monitor the device stability, the pooled frozen plasma from volunteers was used as an internal control. A key finding from the FCM analysis was a decrease in CD62E+ (E-selectin) EVs 1 h after exercise that was consistent for both methods. Furthermore, we report the first data that analyse differential EV protein expression before and after physical exercise. Olink-based proteomic analysis showed 54 significantly changed proteins in the EV fraction in response to physical exercise, whereas the EV-free plasma proteome only displayed four differentially regulated proteins, thus underlining an important role of these vesicles in cellular communication, and their potential as plasma derived biomarkers. We conclude that acoustic trapping offers a fast and efficient method comparable with high-speed centrifugation protocols. Further, it has the advantage of using smaller sample volumes (12.5 μL) and rapid contact-free separation with higher yield, and can thus pave the way for future clinical EV-based diagnostics.

AimsPulmonary congestion remains a diagnostic challenge in patients with heart failure (HF). The recommended method, chest X‐ray (CXR), lacks in accuracy, whereas quantitative tomographic lung scintigraphy [ventilation/perfusion single‐photon emission computed tomography (V/P SPECT)] has shown promising results but needs independent validation. The aim of this study is to evaluate V/P SPECT as a non‐invasive method to assess and quantify pulmonary congestion in HF patients, using right heart catheterization as reference method. The secondary objective was to investigate the performance of V/P SPECT in the clinical setting compared with CXR.Methods and resultsForty‐six consecutive patients with HF that were under consideration for heart transplantation were studied prospectively. All participants were examined with V/P SPECT, CXR, and right heart catheterization. Pulmonary artery wedge pressure served as reference method. Quantitative perfusion gradients were derived from V/P SPECT images. Ventilation/perfusion single‐photon emission computed tomography images were also assessed both by expert readers and clinical nuclear medicine physicians. Expert readers correctly identified 87% of all patients with an elevated pulmonary artery wedge pressure > 15 mmHg. The average sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for V/P SPECT assessed by the expert readers were 87%, 72%, 85%, and 75%, respectively. In the clinical nuclear medicine setting, V/P SPECT had 87% sensitivity, 63% specificity, 81% PPV, and 71% NPV. Clinically, V/P SPECT outperformed CXR, which had 27% sensitivity, 75% specificity, 67% PPV, and 35% NPV.ConclusionsVentilation/perfusion single‐photon emission computed tomography can be used as a non‐invasive method to diagnose and quantify pulmonary congestion in patients with HF and is more accurate than CXR in diagnosing pulmonary congestion in the clinical setting.

The study examines the sustainability of public and external debt burden of Pakistan and India for the period 1971–2017. The debt dynamics equation for public debt uses two components for the analysis of public debt sustainability, namely, interest rate–growth rate differential and differential of primary budget balance-to-GDP and change of reserve money-to-GDP ratio. The equation for external debt dynamics also uses two components for the assessment of external debt sustainability, namely, current account balance-to-exports ratio and differential of exports growth and interest rate. The significance of the approach used in the current study lies in the fact that in case of evaluation of countries’ debt sustainability, it is quite necessary to monitor debt trends along with emerging domestic and external vulnerabilities and systemic risks that threaten debt sustainability. This phenomenon has been captured through debt dynamics approach, which is used in the current study. The results are based on the estimation of two equations, namely, debt dynamics equation for overall public debt sustainability and debt dynamics equation for external debt sustainability. The results of the study indicate that primary budget deficit and current account deficit have played a significant role in the accumulation of public debt and external debt, respectively in Pakistan and India. The study concludes that public debt and external debt of Pakistan and India are sustainable but in a weak form.