With an estimated incidence of 2 to 8 per 100 000 children per year, childhood arterial ischemic stroke is a rare clinical event with potentially severe outcome and resulting disabilities with long-term social and financial effects.^1,2 In contrast to the traditional assumption that most pediatric patients with arterial ischemic stroke have a good long-term prognosis because of neuronal plasticity, pediatric patients presenting with large-vessel occlusion in the anterior or posterior circulation or high pediatric National Institutes of Health Stroke Scale (PedNIHSS) score (score range: 0 [no deficit] to 34 [maximum deficit]) on admission are affected by high morbidity and mortality.^2-9

The differentiation of type 1 and type 2 myocardial infarction (T1MI, T2MI) is important, but challenging in the emergency department. We aimed to investigate the clinical characteristics and cardiovascular outcome of T2MI patients and to develop a clinical decision tool to differentiate T1MI and T2MI patients. We prospectively enrolled 1548 patients with suspected MI. All patients were followed for up to 2 years to assess mortality. We used logistic regression with backward step-down selection to determine the most important predictors of T2MI. Based on these regression coefficients, we developed a diagnostic prediction model (score) to diagnose T2MI. T2MI was the final diagnosis of 99 patients. Patients with T2MI showed a high 1-year mortality rate (13.8%), which equals that of T1MI patients (9.4%). Female sex (Beta 1.27 [95% confidence interval; CI 0.67–1.90]), not having radiating chest pain (Beta 1.62 [CI 0.96–2.34]) and a baseline high-sensitivity troponin I concentration ≤ 40.8 ng/L (Beta 1.30 [CI 0.74–1.89]) were the strongest predictors for T2MI. Their combination resulted in an area under the curve of 0.71 to discriminate T1MI and T2MI. The binary score based on this model assigns one point to each of the predictors. Patients with the highest score value of 3 had a 72% probability of T2MI. T2MI patients are a heterogeneous population with high-cardiovascular risk. A score based on laboratory and clinical parameters might help to differentiate T1MI and T2MI patients. The additional use of this score in clinical routine needs to be investigated prospectively. www.clinicaltrials.gov (NCT02355457)

Oral cancer became a very common condition. WHO estimates that there are 4 cases of lip and oral cavity cancer for every 100,000 people worldwide. The early diagnosis of cancers is currently a top focus in the health sector. Recent systematic reviews and meta-analyses have identified promising biomarkers for early detection in several original research investigations. However, it is still unclear the quality of these evidence and which biomarker performs the best in terms of early detection. Therefore, the objective was, to map the methodological and reporting quality of available oral squamous cell carcinoma (OSCC) or head/neck squamous cell carcinoma (HNSCC) systematic reviews and meta-analysis. Secondly, to evaluate diagnostic accuracy of salivary biomarkers for common craniofacial cancers and to compare the diagnostic value of different salivary biomarkers. PubMed, Scopus, Web of Science, Embase and Cochrane Library electronic databases were used to map the methodological and reporting quality of the systematic reviews and meta-analysis conducted on the HNSCC, OSCC using the AMSTAR-2 checklist. The inclusion criteria were systematic reviews and meta-analysis published in the topic of HNSCC and OSCC biomarkers. Exclusion criteria were no animal studies; original primary studies, due to limitation of competency in other languages articles with language other than English were excluded. The sensitivity and specificity were calculated for salivary biomarkers and ranked according to network meta-analysis principles. A total of N = 5893 patients were included from four meta-analysis studies. All together, these included n = 37 primary studies. n = 94 biomarkers were pooled from these four meta-analyses and categorised into the stages at which they were detected (I-IV). In OSCC, Chemerin and MMP-9 displayed the highest sensitivity, registering 0.94 (95% CI 0.78, 1.00) and a balanced accuracy of 0.93. Phytosphingosine closely followed, with a sensitivity of 0.91 (95% CI 0.68, 0.99) and a balanced accuracy of 0.87. For HNSCC, the top three biomarkers are Actin, IL-1β Singleplex, and IL-8 ELISA. Actin leads with a sensitivity of 0.91 (95% CI 0.68-0.99), a specificity of 0.67, and an overall accuracy of 0.79. Subsequently, IL-1β Singleplex exhibits a sensitivity of 0.62 (95% CI 0.30-0.88), a specificity of 0.89, and an accuracy of 0.75, followed by IL-8 ELISA with a sensitivity of 0.81 (95% CI 0.54-0.97), a specificity of 0.59, and an accuracy of 0.70. In conclusion, there was highest sensitivity for MMP-9 and chemerin salivary biomarkers. There is need of further more studies to identify biomarkers for HNSCC and OSCC.

Since early antimicrobial therapy is mandatory in septic patients, immediate diagnosis and distinction from non-infectious SIRS is essential but hampered by the similarity of symptoms between both entities. We aimed to develop a diagnostic model for differentiation of sepsis and non-infectious SIRS in critically ill children based on routinely available parameters (baseline characteristics, clinical/laboratory parameters, technical/medical support). This is a secondary analysis of a randomized controlled trial conducted at a German tertiary-care pediatric intensive care unit (PICU). Two hundred thirty-eight cases of non-infectious SIRS and 58 cases of sepsis (as defined by IPSCC criteria) were included. We applied a Random Forest approach to identify the best set of predictors out of 44 variables measured at the day of onset of the disease. The developed diagnostic model was validated in a temporal split-sample approach. A model including four clinical (length of PICU stay until onset of non-infectious SIRS/sepsis, central line, core temperature, number of non-infectious SIRS/sepsis episodes prior to diagnosis) and four laboratory parameters (interleukin-6, platelet count, procalcitonin, CRP) was identified in the training dataset. Validation in the test dataset revealed an AUC of 0.78 (95% CI: 0.70–0.87). Our model was superior to previously proposed biomarkers such as CRP, interleukin-6, procalcitonin or a combination of CRP and procalcitonin (maximum AUC = 0.63; 95% CI: 0.52–0.74). When aiming at a complete identification of sepsis cases (100%; 95% CI: 87–100%), 28% (95% CI: 20–38%) of non-infectious SIRS cases were assorted correctly. Our approach allows early recognition of sepsis with an accuracy superior to previously described biomarkers, and could potentially reduce antibiotic use by 30% in non-infectious SIRS cases. External validation studies are necessary to confirm the generalizability of our approach across populations and treatment practices. ClinicalTrials.gov number: NCT00209768; registration date: September 21, 2005.

Catheter ablation for atrial fibrillation induces intracardiac neural damage characterized by glial S100B release, which promotes nerve sprouting.