Is infarct core growth linear? Infarct volume estimation by computed tomography perfusion imaging

Objectives Current guidelines for recanalization treatment are based on the time elapsed between symptom onset and treatment and visualization of existing penumbra in computed tomography perfusion (CTP) imaging. The time window for treatment options relies on linear growth of infarction although individual infarct growth rate may vary. We aimed to test how accurately the estimated follow-up infarct volume (eFIV) can be approximated by using a linear growth model based on CTP baseline imaging. If eFIV did not fall within the margins of +/- 19% of the follow-up infarct volume (FIV) measured at 24 h from non-enhanced computed tomography images, the results would imply that the infarct growth is not linear. Materials and Methods All consecutive endovascularly treated (EVT) patients from 11/2015 to 9/2019 at the Helsinki University Hospital with large vessel occlusion (LVO), CTP imaging, and known time of symptom onset were included. Infarct growth rate was assumed to be linear and calculated by dividing the ischemic core volume (CTPcore) by the time from symptom onset to baseline imaging. eFIV was calculated by multiplying the infarct growth rate with the time from baseline imaging to recanalization or in case of futile recanalization to follow-up imaging at 24 h, limited to the penumbra. Collateral flow was estimated by calculating hypoperfusion intensity ratio (HIR). Results Of 5234 patients, 48 had LVO, EVT, CTP imaging, and known time of symptom onset. In 40/48 patients (87%), infarct growth was not linear. HIR did not differ between patients with linear and nonlinear growth (p > .05). As expected, in over half of the patients with successful recanalization eFIV exceeded FIV. Conclusions Infarct growth was not linear in most patients and thus time elapsed from symptom onset and CTPcore appear to be insufficient parameters for clinical decision-making in EVT candidates.Peer reviewe

Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA

Genome-wide association studies have identified numerous loci linked with complex diseases, for which the molecular mechanisms remain largely unclear. Comprehensive molecular profiling of circulating metabolites captures highly heritable traits, which can help to uncover metabolic pathophysiology underlying established disease variants. We conduct an extended genome-wide association study of genetic influences on 123 circulating metabolic traits quantified by nuclear magnetic resonance metabolomics from up to 24,925 individuals and identify eight novel loci for amino acids, pyruvate and fatty acids. The LPA locus link with cardiovascular risk exemplifies how detailed metabolic profiling may inform underlying aetiology via extensive associations with very-low-density lipoprotein and triglyceride metabolism. Genetic fine mapping and Mendelian randomization uncover wide-spread causal effects of lipoprotein(a) on overall lipoprotein metabolism and we assess potential pleiotropic consequences of genetically elevated lipoprotein(a) on diverse morbidities via electronic health-care records. Our findings strengthen the argument for safe LPA-targeted intervention to reduce cardiovascular risk.Peer reviewe

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Alkali activation-granulation of hazardous fluidized bed combustion fly ashes

Abstract The increasing amount of fluidized bed combustion (FBC) ash is putting pressure on researchers to invent novel methods for utilizing the ash. The low reactivity and heavy metal content constrict the use of FBC ash in the same way as coal ash from pulverized combustion. Four FBC fly ashes from different power plants were granulated with sodium silicate solution in order to produce artificial aggregates. All aggregates matched the definition for lightweight aggregate according to the EN 13055–1 standard. The strongest aggregates were produced from fly ashes that had the highest X-ray amorphous material content and the highest amount of selectively soluble SiO₂ and Al₂O₃. However, the same leaching problem (leaching of the anionic species) as with coal fly ashes was observed with the FBC fly ashes. The simultaneous high shear granulation and alkali activation of FBC ashes showed that artificial aggregates with satisfactory physical properties, such as density and strength, can be obtained even from low-reactive fly ashes that contain heavy metals

Whitening CNN-based rotor system fault diagnosis model features

Abstract Intelligent fault diagnosis (IFD) models have the potential to increase the level of automation and the diagnosis accuracy of machine condition monitoring systems. Many of the latest IFD models rely on convolutional layers for feature extraction from vibration data. The majority of these models employ batch normalisation (BN) for centring and scaling the input for each neuron. This study includes a novel examination of a competitive approach for layer input normalisation in the scope of fault diagnosis. Network deconvolution (ND) is a technique that further decorrelates the layer inputs reducing redundancy among the learned features. Both normalisation techniques are implemented on three common 1D-CNN-based fault diagnosis models. The models with ND mostly outperform the baseline models with BN in three experiments concerning fault datasets from two different rotor systems. Furthermore, the models with ND significantly outperform the baseline models with BN in the common CWRU bearing fault tests with load domain shifts, if the data from drive-end and fan-end sensors are employed. The results show that whitened features can improve the performance of CNN-based fault diagnosis models

Previous radiotherapy improves treatment responses and causes a trend toward longer time to progression among patients with immune checkpoint inhibitor-related adverse events

Abstract Background: Immune-related adverse events (irAEs) are frequently encountered by patients during immune checkpoint inhibitor (ICI) treatment and are associated with better treatment outcomes. The sequencing of radiotherapy (RT) and ICIs is widely used in current clinical practice, but its effect on survival has remained unclear. Methods: In a real-world multicenter study including 521 patients who received ICI treatment for metastatic or locally advanced cancer, RT schedules and timing, irAEs, time to progression, overall survival, and treatment responses were retrospectively reviewed. Results: Patients who received previous RT and developed irAE (RT +/AE +) had the best overall response rate (ORR 44.0%). The ORR was 40.1% in the RT −/AE + group, 26.7% in the RT −/AE − group and 18.3% in the RT + /AE − group (p &lt; 0.001). There was a significantly longer time to progression (TTP) in the RT + /AE + group compared to the RT −/AE − and RT + /AE − groups (log rank p = 0.001 and p &lt; 0.001, respectively), but the trend toward longer TTP in the RT + /AE + group did not reach statistical significance in pairwise comparison to that in the RT −/AE + group. Preceding RT timing and intent had no statistically significant effect on TTP. In a multivariate model, ECOG = 0 and occurrence of irAEs remained independent positive prognostic factors for TTP (HR 0.737; 95% CI 0.582–0.935; p = 0.012, and HR 0.620; 95% CI 0.499–0.769; p &lt; 0.001, respectively). Conclusions: Better ORR and a trend toward longer TTP were demonstrated for patients with RT preceding ICI treatment and development of irAEs, which suggests that RT may boost the therapeutic effect of immunotherapy in patients with metastatic cancers