Background: The metacestode larval stage of the fox-tapeworm Echinococcus multilocularis causes alveolar echinococcosis by tumour-like growth within the liver of the intermediate host. Metacestode growth and development is stimulated by host-derived cytokines such as insulin, fibroblast growth factor, and epidermal growth factor via activation of cognate receptor tyrosine kinases expressed by the parasite. Little is known, however, concerning signal transmission to the parasite nucleus and cross-reaction with other parasite signalling systems. Methodology/Principal findings: Using bioinformatic approaches, cloning, and yeast two-hybrid analyses we identified a novel mitogen-activated kinase (MAPK) cascade module that consists of E. multilocularis orthologs of the tyrosine kinase receptor interactor Growth factor receptor-bound 2, EmGrb2, the MAPK kinase kinase EmMEKK1, a novel MAPK kinase, EmMKK3, and a close homolog to c-Jun N-terminal kinase (JNK), EmMPK3. Whole mount in situ hybridization analyses indicated that EmMEKK1 and EmMPK3 are both expressed in E. multilocularis germinative (stem) cells but also in differentiated or differentiating cells. Treatment with the known JNK inhibitor SP600125 led to a significantly reduced formation of metacestode vesicles from stem cells and to a specific reduction of proliferating stem cells in mature metacestode vesicles. Conclusions/Significance: We provide evidence for the expression of a MEKK1-JNK MAPK cascade module which, in mammals, is crucially involved in stress responses, cytoskeletal rearrangements, and apoptosis, in E. multilocularis stem cells. Inhibitor studies indicate an important role of JNK signalling in E. multilocularis stem cell survival and/or maintenance. Our data are relevant for molecular and cellular studies into crosstalk signalling mechanisms that govern Echinococcus stem cell function and introduce the JNK signalling cascade as a possible target of chemotherapeutics against echinococcosis.

The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographical disparities in stroke care access persist, there is a growing need for predictive models that quantify individual benefits. However, current imaging methods for estimating outcomes may not fully capture the dynamic nature of cerebral ischaemia and lack a patient-specific assessment of thrombectomy benefits. Our study introduces a deep learning approach to predict individual responses to thrombectomy in acute ischaemic stroke patients. The proposed models provide predictions for both tissue and clinical outcomes under two scenarios: one assuming successful reperfusion and another assuming unsuccessful reperfusion. The resulting simulations of penumbral salvage and difference in National Institutes of Health Stroke Scale (NIHSS) at discharge quantify the potential individual benefits of the intervention. Our models were developed on an extensive dataset from routine stroke care, which included 405 ischaemic stroke patients who underwent thrombectomy. We used acute data for training (n = 304), including multimodal CT imaging and clinical characteristics, along with post hoc markers such as thrombectomy success, final infarct localization and NIHSS at discharge. We benchmarked our tissue outcome predictions under the observed reperfusion scenario against a thresholding-based clinical method and a generalized linear model. Our deep learning model showed significant superiority, with a mean Dice score of 0.48 on internal test data (n = 50) and 0.52 on external test data (n = 51), versus 0.26/0.36 and 0.34/0.35 for the baselines, respectively. The NIHSS sum score prediction achieved median absolute errors of 1.5 NIHSS points on the internal test dataset and 3.0 NIHSS points on the external test dataset, outperforming other machine learning models. By predicting the patient-specific response to thrombectomy for both tissue and clinical outcomes, our approach offers an innovative biomarker that captures the dynamics of cerebral ischaemia. We believe this method holds significant potential to enhance personalized therapeutic strategies and to facilitate efficient resource allocation in acute stroke care.

Background/Objectives: Delayed cerebral ischemia (DCI) after an aneurysmal subarachnoid hemorrhage (aSAH) often presents with bilateral vasospasm and cortical spreading depolarizations. Computer tomography perfusion (CTP) is the prevailing screening method for detecting early changes in the cerebral blood flow. Commonly used CTP thresholds include an rCBF < 30% for the core volume and a Tmax > 6 s for hypoperfused tissue detection in acute ischemic stroke. These stroke algorithm computing thresholds compared to the contralateral hemisphere may or may not apply to detect tissue at risk of DCI. We aimed to quantify the volumetric agreement of three different stroke algorithms compared to the final infarct volumes as the standard. Methods: Furthermore, 123 CTP datasets of 75 patients with aSAH suspicious of DCI were processed using Intellispace Portal (ISP), Cercare Threshold, and Cercare Artificial Intelligence (AI) to calculate the tissue-at-risk (hypoperfused) and non-viable tissue (core) volumes. CT infarct volumes in plain CTs were segmented in the follow-up study by using a 3D slicer. Results: The calculated core volumes corresponded best to the final infarct volumes if DCI-related treatment was performed subsequently. Additional postprocessing improved the calculation of core volumes but overestimated the tissue at risk of hypoperfusion in DCI. Whereas the accuracy of tissue-at-risk prediction accelerated without treatment, underlining the importance of intra-arterial spasmolysis and induced hypertension in the prevention of DCI. Conclusions: Cercare AI and ISP revealed a sensitivity of 100% each, with a serious low specificity of <5% that was independent of treatment. Overall, the Cercare Threshold, applying the commonly used stroke thresholds, performed the best in predicting tissue at risk of hypoperfusion in DCI.