Cerebral ischemia detection using artificial intelligence (CIDAI)-A study protocol

Downregulation of Preso protects against ischemic/reperfusion-mediated neuronal injury through regulating PSD95-nNOS/YAP pathways

Cerebral ischemic/reperfusion (I/R) injury has become a great challenge harming patients' life. This study aims to explore the regulatory role of Preso during cerebral I/R injury and to elucidate the potential mechanism. Here, we established a middle cerebral artery occlusion/reperfusion (MCAO/IR) rat model and an oxygen-glucose deprivation/reoxygenation (OGD/R)-mediated PC12 cell model to evaluate the expression and role of Preso following cerebral I/R injury. Histopathological injury and infarct size were assessed by hematoxylin and eosin (HE) and 2,3,5-Triphenyltertrazolium chloride (TTC) staining. Double immunofluorescence staining was performed to assess neuronal apoptosis in brain tissues. Cell counting kit-8 (CCK-8) and flow cytometry were performed to evaluate cell viability and apoptosis, respectively. The reactive oxygen species (ROS) and nitric oxide (NO) levels were detected using their respective detection kits, and the expression of corresponding proteins was examined adopting Western blot. The results showed that Preso was upregulated in OGD/R-induced PC12 cells and MCAO rats. Preso knockdown significantly reduced OGD/R-caused viability loss, apoptosis and oxidative stress in PC12 cells, and reduced infarct size, attenuated histological injury, and inhibited apoptosis and oxidative stress in the brain tissues from MCAO rats, as well as inhibiting the expression of postsynaptic density protein-95 (PSD95) and nitric oxide synthase (nNOS) and repressing YAP phosphorylation in vitro. In addition, the protective role of Preso knockdown against cerebral I/R injury was partly strengthened by IC87201, the nNOS/PSD95 interaction inhibitor, or weakened by Verteporfin (Vert), an inhibitor of YAP. In conclusion, Perso knockdown might exert a protective role against cerebral I/R injury via regulating PSD95-nNOS and YAP pathways, providing a potential therapeutic target for the treatment of ischemic stroke.

Heart rate markers for prediction of fetal acidosis in an experimental study on fetal sheep

To overcome the difficulties in interpreting fetal heart rate (FHR), several tools based on the autonomic nervous system and heart rate variability (HRV) have been developed. The objective of this study was to use FHR and HRV parameters for the prediction of fetal hypoxia. It was an experimental study in the instrumented fetal sheep. Repeated umbilical cord occlusions were performed to achieve severe acidosis. Hemodynamic parameters, ECG, and blood gases were analyzed. The variables used were heart rate baseline, HRV analysis (RMSSD, SDNN, LF, HF, HFnu, Fetal Stress Index (FSI), …), and morphological analysis of decelerations. The gold standard used to classify hypoxia was the fetal arterial pH (pH < 7.10). Different multivariable statistical methods (logistic regression and decision trees) were applied for the detection of acidosis. 21 lambs were instrumented. A total of 130 pairs of FHR/fetal pH analysis were obtained of which 29 in the acidosis group and 101 in the non-acidosis group. After logistic regression model with bootstrap resampling and stepwise backward selection, only one variable was selected, FSI. The AUC of FSI alone in this model was 0.81 with a sensitivity of 0.66, specificity of 0.88, PPV of 0.61, and NPV of 0.90 considering a threshold of 68. Decision trees with CHAID and CART algorithms showed a sensitivity of 0.48 and 0.59, respectively, and a specificity of 0.94 for both. All employed methods identified HRV variables as the most predictive of acidosis. The primary variables selected automatically were those from the HRV. Supporting the use of FHRV measures for the screening of fetal acidosis during labour is interesting.

Robotics and Artificial Intelligence in Endovascular Neurosurgery

The use of artificial intelligence (AI) and robotics in endovascular neurosurgery promises to transform neurovascular care. We present a review of the recently published neurosurgical literature on artificial intelligence and robotics in endovascular neurosurgery to provide insights into the current advances and applications of this technology.

The PubMed database was searched for "neurosurgery" OR "endovascular" OR "interventional" AND "robotics" OR "artificial intelligence" between January 2016 and August 2021. A total of 1296 articles were identified, and after applying the inclusion and exclusion criteria, 38 manuscripts were selected for review and analysis. These manuscripts were divided into four categories: 1) robotics and AI for the diagnosis of cerebrovascular pathology, 2) robotics and AI for the treatment of cerebrovascular pathology, 3) robotics and AI for training in neuroendovascular procedures, and 4) robotics and AI for clinical outcome optimization.

The 38 articles presented include 23 articles on AI-based diagnosis of cerebrovascular disease, 10 articles on AI-based treatment of cerebrovascular disease, two articles on AI-based training techniques for neuroendovascular procedures, and three articles reporting AI prediction models of clinical outcomes in vascular disorders of the brain. Innovation with robotics and AI focus on diagnostic efficiency, optimizing treatment and interventional procedures, improving physician procedural performance, and predicting clinical outcomes with the use of artificial intelligence and robotics. Experimental studies with robotic systems have demonstrated safety and efficacy in treating cerebrovascular disorders, and novel microcatheterization techniques may permit access to deeper brain regions. Other studies show that pre-procedural simulations increase overall physician performance. Artificial intelligence also shows superiority over existing statistical tools in predicting clinical outcomes.

The recent advances and current usage of robotics and AI in the endovascular neurosurgery field suggest that the collaboration between physicians and machines has a bright future for the improvement of patient care. The aim of this work is to equip the medical readership, in particular the neurosurgical specialty, with tools to better understand and apply findings from research on artificial intelligence and robotics in endovascular neurosurgery.

Machine learning analysis of heart rate variability to detect delayed cerebral ischemia in subarachnoid hemorrhage

OBJECTIVES

Approximately 30% of patients with aneurysmal subarachnoid hemorrhage (aSAH) develop delayed cerebral ischemia (DCI). DCI is associated with increased mortality and persistent neurological deficits. This study aimed to analyze heart rate variability (HRV) data from patients with aSAH using machine learning to evaluate whether specific patterns could be found in patients developing DCI.

MATERIAL & METHODS

This is an extended, in-depth analysis of all HRV data from a previous study wherein HRV data were collected prospectively from a cohort of 64 patients with aSAH admitted to Sahlgrenska University Hospital, Gothenburg, Sweden, from 2015 to 2016. The method used for analyzing HRV is based on several data processing steps combined with the random forest supervised machine learning algorithm.

RESULTS

HRV data were available in 55 patients, but since data quality was significantly low in 19 patients, these were excluded. Twelve patients developed DCI. The machine learning process identified 71% of all DCI cases. However, the results also demonstrated a tendency to identify DCI in non-DCI patients, resulting in a specificity of 57%.

CONCLUSIONS

These data suggest that machine learning applied to HRV data might help identify patients with DCI in the future; however, whereas the sensitivity in the present study was acceptable, the specificity was low. Possible confounders such as severity of illness and therapy may have affected the result. Future studies should focus on developing a robust method for detecting DCI using real-time HRV data and explore the limits of this technology in terms of its reliability and accuracy.

ATP2B1-AS1 Promotes Cerebral Ischemia/Reperfusion Injury Through Regulating the miR-330-5p/TLR4-MyD88-NF-κB Signaling Pathway

We aim to explore the expression and function of long non-coding RNA (lncRNA) ATP2B1-AS1 in a cerebral ischemia/reperfusion (I/R) injury. In this study, we established a middle cerebral artery occlusion/reperfusion (MCAO/IR) rat model and an OGD/R PC12 cell model to evaluate the expression and role of ATP2B1-AS1 in the cerebral I/R injury. We found that the expression of ATP2B1-AS1 was upregulated in both in vitro and in vivo cerebral I/R injury models. Knockdown of ATP2B1-AS1 increased the cell viability, inhibited apoptosis, and decreased the expressions of inflammation cytokines. The target of ATP2B1-AS1 was predicted and validated to be miR-330-5p. MiR-330-5p abrogated the regulatory effect of ATP2B1-AS1 on cell viability, apoptosis, and cytokines of OGD/R PC12 cells. Furthermore, the results showed that miR-330-5p targeted TLR4, which was also upregulated in the infarcted area of MCAO/IR rats and OGD/R PC12 cells. Overexpression of ATP2B1-AS1 increased the expressions of TLR4, MyD88, and NF-κB p65 of OGD/R PC12 cells, while the effect of ATP2B1-AS1 was abrogated by miR-330-5p. In addition, knockdown of ATP2B1-AS1 decreased the latency time, increased the time of passing the platform position, reduced the cerebral infarct volume, decreased neurological deficit scores, and reduced the number of damaged neurons of MCAO/IR rats that were subjected to the Morris water maze test. Taken together, our study indicates that ATP2B1-AS1 may be an attractive therapeutic target for the treatment of cerebral ischemic injuries.

Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.

Artificial Intelligence Research and Its Contributions to the European Union’s Political Governance: Comparative Study between Member States

In the last six decades, many advances have been made in the field of artificial intelligence (AI). Bearing in mind that AI technologies are influencing societies and political systems differently, it can be useful to understand what are the common issues between similar states in the European Union and how these political systems can collaborate with each other, seeking synergies, finding opportunities and saving costs. Therefore, we carried out an exploratory research among similar states of the European Union, in terms of scientific research in areas of AI technologies, namely: Portugal, Greece, Austria, Belgium and Sweden. A key finding of this research is that intelligent decision support systems (IDSS) are essential for the political decision-making process, since politics normally deals with complex and multifaceted decisions, which involve trade-offs between different stakeholders. As public health is becoming increasingly relevant in the field of the European Union, the IDSSs can provide relevant contributions, as it may allow sharing critical information and assist in the political decision-making process, especially in response to crisis situations.