Probing artificial intelligence in neurosurgical training: ChatGPT takes a neurosurgical residents written exam

Introduction

Artificial Intelligence tools are being introduced in almost every field of human life, including medical sciences and medical education, among scepticism and enthusiasm.

Research question

to assess how a generative language tool (Generative Pretrained Transformer 3.5, ChatGPT) performs at both generating questions and answering a neurosurgical residents' written exam. Namely, to assess how ChatGPT generates questions, how it answers human-generated questions, how residents answer AI-generated questions and how AI answers its self-generated question.

Materials and methods

50 questions were included in the written exam, 46 questions were generated by humans (senior staff members) and 4 were generated by ChatGPT. 11 participants took the exam (ChatGPT and 10 residents). Questions were both open-ended and multiple-choice.8 questions were not submitted to ChatGPT since they contained images or schematic drawings to interpret.

Results

formulating requests to ChatGPT required an iterative process to precise both questions and answers. Chat GPT scored among the lowest ranks (9/11) among all the participants). There was no difference in response rate for residents' between human-generated vs AI-generated questions that could have been attributed to less clarity of the question. ChatGPT answered correctly to all its self-generated questions.

Discussion and conclusions

AI is a promising and powerful tool for medical education and for specific medical purposes, which need to be further determined. To request AI to generate logical and sound questions, that request must be formulated as precise as possible, framing the content, the type of question and its correct answers.

Structure-activity relationship of GPR15L peptide analogues and investigation of their interaction with the GPR15 receptor

The 57-mer full-length GPR15L(25-81) peptide has been identified as the principal endogenous agonist of the G protein-coupled receptor GPR15. Its main activity resides in the C-terminal 11-mer GPR15L(71-81), which has full efficacy but ~40-fold lower potency than the full-length peptide. Here, we systematically investigated the structure-activity-relationship of GPR15L(71-81) by truncations/extensions, alanine-scanning, and N- and C-terminal capping. The synthesized peptide analogues were tested at GPR15 stably expressed in HEK293A cells using a homogenous time-resolved Förster resonance energy transfer based G_i cAMP functional assay. We show that the C-terminal α carboxyl group and the residues Leu⁷⁸ , Pro⁷⁵ , Val⁷⁴ , and Trp⁷² are critical for receptor interaction and contribute significantly to the peptide potency. Furthermore, we tested the ability of GPR15L(71-81), C-terminally amidated GPR15L(71-81), and GPR15L(25-81) to activate the three GPR15 receptor mutants in a bioluminescence resonance energy transfer-based G protein activation assay. The results demonstrate that the Lys192 and Glu272 residues in GPR15 are important for the potency of the GPR15L peptide. Overall, our study identifies critical residues in the peptide and receptor sequences for future drug design.

Reaction Pathways for the Enzymatic Degradation of Poly(Ethylene Terephthalate): What Characterizes an Efficient PET-Hydrolase?

Bioprocessing of polyester waste has emerged as a promising tool in the quest for a cyclic plastic economy. One key step is the enzymatic breakdown of the polymer, and this entails a complicated pathway with substrates, intermediates, and products of variable size and solubility. We have elucidated this pathway for poly(ethylene terephthalate) (PET) and four enzymes. Specifically, we combined different kinetic measurements and a novel stochastic model and found that the ability to hydrolyze internal bonds in the polymer (endo-lytic activity) was a key parameter for overall enzyme performance. Endo-lytic activity promoted the release of soluble PET fragments with two or three aromatic rings, which, in turn, were broken down with remarkable efficiency (k_cat /K_M values of about 10⁵  M^-1 s^-1 ) in the aqueous bulk. This meant that approximatly 70 % of the final, monoaromatic products were formed via soluble di- or tri-aromatic intermediates.

The Sabatier principle as a tool for discovery and engineering of industrial enzymes

The recent breakthrough in all-atom, protein structure prediction opens new avenues for a range of computational approaches in enzyme design. These new approaches could become instrumental for the development of technical biocatalysts, and hence our transition toward more sustainable industries. Here, we discuss one approach, which is well-known within inorganic catalysis, but essentially unexploited in biotechnology. Specifically, we review examples of linear free-energy relationships (LFERs) for enzyme reactions and discuss how LFERs and the associated Sabatier Principle may be implemented in algorithms that estimate kinetic parameters and enzyme performance based on model structures.

Replica-Exchange Enveloping Distribution Sampling Using Generalized AMBER Force-Field Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules

Free-energy differences between pairs of end-states can be estimated based on molecular dynamics (MD) simulations using standard pathway-dependent methods such as thermodynamic integration (TI), free-energy perturbation, or Bennett’s acceptance ratio. Replica-exchange enveloping distribution sampling (RE-EDS), on the other hand, allows for the sampling of multiple end-states in a single simulation without the specification of any pathways. In this work, we use the RE-EDS method as implemented in GROMOS together with generalized AMBER force-field (GAFF) topologies, converted to a GROMOS-compatible format with a newly developed GROMOS++ program amber2gromos, to compute relative hydration free energies for a series of benzene derivatives. The results obtained with RE-EDS are compared to the experimental data as well as calculated values from the literature. In addition, the estimated free-energy differences in water and in vacuum are compared to values from TI calculations carried out with GROMACS. The hydration free energies obtained using RE-EDS for multiple molecules are found to be in good agreement with both the experimental data and the results calculated using other free-energy methods. While all considered free-energy methods delivered accurate results, the RE-EDS calculations required the least amount of total simulation time. This work serves as a validation for the use of GAFF topologies with the GROMOS simulation package and the RE-EDS approach. Furthermore, the performance of RE-EDS for a large set of 28 end-states is assessed with promising results.

Modulation of epileptic networks by transient interictal epileptic activity: A dynamic approach to simultaneous EEG-fMRI

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EEG-fMRI has been instrumental in characterizing brain networks in epilepsy.

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Its value is documented in the pre-surgical assessment of drug-resistant epilepsy.

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The delineation of brain areas to resect is fundamental for the post-surgical outcome.

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Standard EEG-fMRI in epilepsy assesses static functional connectivity of the network.

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EEG-fMRI dynamic connectivity identifies transitory features of specific connections.

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We integrate dynamic fMRI connectivity and dynamic patterns of simultaneous scalp EEG.

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This allows to better characterize the spatiotemporal aspects of epileptic networks.

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This may help in more efficiently target the surgical intervention.

Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.

A snapshot of European neurosurgery December 2019 vs. March 2020: just before and during the Covid-19 pandemic

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or Covid-19), which began as an epidemic in China and spread globally as a pandemic, has necessitated resource management to meet emergency needs of Covid-19 patients and other emergent cases. We have conducted a survey to analyze caseload and measures to adapt indications for a perception of crisis.

Methods

We constructed a questionnaire to survey a snapshot of neurosurgical activity, resources, and indications during 1 week with usual activity in December 2019 and 1 week during SARS-CoV-2 pandemic in March 2020. The questionnaire was sent to 34 neurosurgical departments in Europe; 25 departments returned responses within 5 days.

Results

We found unexpectedly large differences in resources and indications already before the pandemic. Differences were also large in how much practice and resources changed during the pandemic. Neurosurgical beds and neuro-intensive care beds were significantly decreased from December 2019 to March 2020. The utilization of resources decreased via less demand for care of brain injuries and subarachnoid hemorrhage, postponing surgery and changed surgical indications as a method of rationing resources. Twenty departments (80%) reduced activity extensively, and the same proportion stated that they were no longer able to provide care according to legitimate medical needs.

Conclusion

Neurosurgical centers responded swiftly and effectively to a sudden decrease of neurosurgical capacity due to relocation of resources to pandemic care. The pandemic led to rationing of neurosurgical care in 80% of responding centers. We saw a relation between resources before the pandemic and ability to uphold neurosurgical services. The observation of extensive differences of available beds provided an opportunity to show how resources that had been restricted already under normal conditions translated to rationing of care that may not be acceptable to the public of seemingly affluent European countries.

Electronic supplementary material

The online version of this article (10.1007/s00701-020-04482-8) contains supplementary material, which is available to authorized users.

Substrate binding in the processive cellulase Cel7A: Transition state of complexation and roles of conserved tryptophan residues

Cellobiohydrolases effectively degrade cellulose and are of biotechnological interest because they can convert lignocellulosic biomass to fermentable sugars. Here, we implemented a fluorescence-based method for real-time measurements of complexation and decomplexation of the processive cellulase Cel7A and its insoluble substrate, cellulose. The method enabled detailed kinetic and thermodynamic analyses of ligand binding in a heterogeneous system. We studied WT Cel7A and several variants in which one or two of four highly conserved Trp residues in the binding tunnel had been replaced with Ala. WT Cel7A had on/off-rate constants of 1 × 105 m-1 s-1 and 5 × 10-3 s-1, respectively, reflecting the slow dynamics of a solid, polymeric ligand. Especially the off-rate constant was many orders of magnitude lower than typical values for small, soluble ligands. Binding rate and strength both were typically lower for the Trp variants, but effects of the substitutions were moderate and sometimes negligible. Hence, we propose that lowering the activation barrier for complexation is not a major driving force for the high conservation of the Trp residues. Using so-called Φ-factor analysis, we analyzed the kinetic and thermodynamic results for the variants. The results of this analysis suggested a transition state for complexation and decomplexation in which the reducing end of the ligand is close to the tunnel entrance (near Trp-40), whereas the rest of the binding tunnel is empty. We propose that this structure defines the highest free-energy barrier of the overall catalytic cycle and hence governs the turnover rate of this industrially important enzyme.

The network integration of epileptic activity in relation to surgical outcome

OBJECTIVE

Epilepsy is a network disease with epileptic activity and cognitive impairment involving large-scale brain networks. A complex network is involved in the seizure and in the interictal epileptiform discharges (IEDs). Directed connectivity analysis, describing the information transfer between brain regions, and graph analysis are applied to high-density EEG to characterise networks.

METHODS

We analysed 19 patients with focal epilepsy who had high-density EEG containing IED and underwent surgery. We estimated cortical activity during IED using electric source analysis in 72 atlas-based cortical regions of the individual brain MRI. We applied directed connectivity analysis (information Partial Directed Coherence) and graph analysis on these sources and compared patients with good vs poor post-operative outcome at global, hemispheric and lobar level.

RESULTS

We found lower network integration reflected by global, hemispheric, lobar efficiency during the IED (p < 0.05) in patients with good post-surgical outcome, compared to patients with poor outcome. Prediction was better than using the IED field or the localisation obtained by electric source imaging.

CONCLUSIONS

Abnormal network patterns in epilepsy are related to seizure outcome after surgery.

SIGNIFICANCE

Our finding may help understand networks related to a more "isolated" epileptic activity, limiting the extent of the epileptic network in patients with subsequent good post-operative outcome.

Spontaneous appearance of de novo intracranial arteriovenous malformation in hepatic cirrhosis

BACKGROUND

Intracranial arteriovenous malformations (AVMs) are rare lesions that can be congenital or acquired in early childhood, with fatal outcome in approximately 30% of cases. De novo formation during adulthood without established predisposing vascular pathology or previous brain insult is even less frequent.

CASE DESCRIPTION

We present a case of de novo brain AVM in an alcoholic Child-B cirrhosis setting. Thirty previously reported cases presented de novo AVM in patients of all ages that had another previous brain pathology or insult, such as AVM resection. Seventeen of those cases occurred in adult patients, with only 2 showing no significant predisposing factor. The present pathophysiological review covers and completes Mullan's hemodynamic "two-hit" model, associating probable thrombotic predisposition to AVM with brain insult triggering a later stage based on angiogenic stimuli.

CONCLUSIONS

This case report and literature review renews previously discussed hemodynamic theories and contributes to a fuller understanding of the pathogenesis and progression of AVM. We postulate a causal link between hepatopathy and de novo AVM, which should be strengthened and interpreted based on recent genetic data and future prospective studies.

Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning

The bile acid activated transcription factor farnesoid X receptor (FXR) has revealed therapeutic potential as a molecular drug target for the treatment of hepatic and metabolic disorders. Despite strong efforts in FXR ligand development, the structural diversity among the known FXR modulators is limited. Only four molecular frameworks account for more than 50 % of the FXR modulators annotated in ChEMBL. Here, we leverage machine learning methods to expand the chemical space of FXR-targeting small molecules by employing an ensemble of three complementary machine learning approaches. A counter-propagation artificial neural network, a k-nearest neighbor learner, and a three-dimensional pharmacophore descriptor were combined to retrieve novel FXR ligands from a collection of more than 3 million compounds. The ensemble machine learning model identified six new FXR modulators among ten top-ranked candidates. These active hits comprise both FXR activators and antagonists with micromolar potencies. With four novel FXR ligand scaffolds, these computationally identified bioactive compounds appreciably expand the chemical space of known FXR modulators and may serve as starting points for hit-to-lead expansion.

Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning

Invited for this month's cover picture is the group of Prof. Dr. Gisbert Schneider from the Swiss Federal Institute of Technology (ETH) Zurich (Switzerland). The cover picture illustrates the application of machine-learning methods to expand the chemical space of farnesoid X receptor (FXR)-targeting small molecules, by employing an ensemble of three complementary machine-learning approaches (counter-propagation artificial neural network, k-nearest neighbor learner, and three-dimensional pharmacophore model). Read the full text of their Full Paper at 10.1002/open.201800156.

Front Cover: Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning (ChemistryOpen 1/2019)

The Front Cover shows the application of machine‐learning methods to expand the chemical space of farnesoid X receptor (FXR)‐targeting small molecules, by employing an ensemble of three complementary machine‐learning approaches (counter‐propagation artificial neural network, k‐nearest neighbor learner, and three‐dimensional pharmacophore model). The ensemble machine‐learning model identified six new FXR modulators from a library of 3 million compounds. These computationally identified bioactive compounds possess four novel scaffolds and appreciably expand the chemical space of known FXR modulators. More information can be found in the Full Paper by D. Merk et al. on page 7 in Issue 1, 2019 (DOI: 10.1002/open.201800156).

[E-Cigarettes: Assessment of Health Effects and Potential Benefits for Smokers]

E-cigarettes are electronic devices used to vaporize a flavored liquid resulting in aerosols to be inhaled by the user. The high variety of e-cigarettes and liquids makes it difficult to assess the health effects of e-cigarette use. The ingredients of the liquids - apart from nicotine - are generally recognized as safe for oral and dermal use, however, toxicological data on health effects upon chronic inhalation are missing. Studies have detected several harmful substances in e-cigarette aerosol, including carcinogens, but in lower concentrations than tobacco smoke. Evidence suggests that e-cigarette use entails acute health effects such as inflammatory responses in the lung. In animal and in vitro studies e-cigarette aerosol showed adverse effects like cytotoxicity, increased infiltration of inflammatory cells and pro-inflammatory cytokines, increased oxidative stress and moderate inflammation. Compared to tobacco smoking e-cigarettes are very likely to be less harmful, however, due to scarce data the extent of harm reduction cannot be quantified. Although smokers often use e-cigarettes to quit smoking, currently they are not approved as smoking cessation aids. As e-cigarette aerosol is a new source of air pollution, it is reasonable to assume that it poses an increased health risk to bystanders, in particular to vulnerable persons such as children and people with health conditions. E-cigarettes can have a positive effect on public health, if many smokers completely switch from smoking tobacco to e-cigarettes and, finally, also stop e-cigarette use, and if at the same time adverse consequences (such as increased initiation among youths) remain limited.

Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations

Parametrization of small organic molecules for classical molecular dynamics simulations is not trivial. The vastness of the chemical space makes approaches using building blocks challenging. The most common approach is therefore an individual parametrization of each compound by deriving partial charges from semiempirical or ab initio calculations and inheriting the bonded and van der Waals (Lennard-Jones) parameters from a (bio)molecular force field. The quality of the partial charges generated in this fashion depends on the level of the quantum-chemical calculation as well as on the extraction procedure used. Here, we present a machine learning (ML) based approach for predicting partial charges extracted from density functional theory (DFT) electron densities. The training set was chosen with the goal to provide a broad coverage of the known chemical space of druglike molecules. In addition to the speed of the approach, the partial charges predicted by ML are not dependent on the three-dimensional conformation in contrast to the ones obtained by fitting to the electrostatic potential (ESP). To assess the quality and compatibility with standard force fields, we performed benchmark calculations for the free energy of hydration and liquid properties such as density and heat of vaporization.

Correlation of pain, functional impairment, and health-related quality of life with radiological grading scales of lumbar degenerative disc disease

BACKGROUND

It is generally believed that radiological signs of lumbar degenerative disc disease (DDD) are associated with increased pain and functional impairment as well as lower health-related quality of life (HRQoL). Our aim was to assess the association of the Modic and Pfirrmann grading scales with established outcome questionnaires and the timed-up-and-go (TUG) test.

METHODS

In a prospective two-center study with patients scheduled for lumbar spine surgery, visual analogue scale (VAS) for back and leg pain, Roland-Morris Disability Index, Oswestry Disability Index and HRQoL, as determined by the Short-Form (SF)-12 and the Euro-Qol, were recorded. Functional mobility was measured with the TUG test. Modic type (MOD) and Pfirrmann grade (PFI) of the affected lumbar segment were assessed with preoperative imaging. Uni- and multivariate logistic regression analysis was performed to estimate the effect size of the relationship between clinical and radiological findings.

RESULTS

Two hundred eighty-four patients (mean age 58.5, 119 (42 %) females) were enrolled. None of the radiological grading scales were significantly associated with any of the subjective or objective clinical tests. There was a tendency for higher VAS back pain (3.48 vs. 4.14, p = 0.096) and lower SF-12 physical component scale (31.2 vs. 29.4, p = 0.065) in patients with high PFI (4-5) as compared to patients with low PFI (0-3). In the multivariate analysis, patients with MOD changes of the vertebral endplates were 100 % as likely as patients without changes to show an impaired TUG test performance (odds ratio (OR) 1.00, 95 % confidence interval (CI) 0.56-1.80, p = 0.982). Patients with high PFI were 145 % as likely as those with low PFI to show an impaired TUG test performance (OR 1.45, 95 % CI 0.79-2.66, p = 0.230).

CONCLUSIONS

There was no association between established outcome questionnaires of symptom severity and two widely used radiological classifications in patients undergoing surgery for lumbar DDD.

[Minimally invasive spine surgery: past and present]

In the early twentieth century, the understanding of spine biomechanics and the advent of surgical techniques of the lumbar spine, led to the currently emerging concept of minimal invasive spine surgery, By reducing surgical access, blood loss, infection rate and general morbidity, functional prognosis of patients is improved. This is a real challenge for the spine surgeon, who has to maintain a good operative result by significantly reducing surgical collateral damages due to the relatively traumatic conventional access.

[Cervical disc herniation--diagnosis and treatment]

A cervical disc herniation (CDH) is a frequently encountered pathology in primary care medicine. It may give rise to a compression of a nerve root (a radiculopathy, with or without sensory-motor deficit) or of the spinal cord (myelopathy). The majority of CDHs can be supported by means of a conservative treatment. When a radiculopathy is found and a clinico-radiological correlation is present, a moderate neurological deficit appears suddenly, or if it is progressive under conservative treatment or if pain is poorly controlled by well-conducted conservative treatment performed during 6 to 8 months, surgery is then recommended. A symptomatic cervical myelopathy is, by itself, an indication for a surgical treatment.

Improving workflows of neuro-interventional procedures with autostereoscopic 3D visualization of multi-modality imaging in hybrid interventional suites

PURPOSE

Recent developments in interventional neuroradiology techniques, medical imaging modalities, endovascular stenting and embolization materials lead to an increasing number of patients with cerebral aneurysms and arteriovenous malformations that are eligible for endovascular treatment and have opened new perspectives for novel ways for patient treatment in general. In this paper, we describe a software tool for 3D image fusion of multi-modal acquisitions to assist endovascular treatment of cerebral malformations. The software and an autostereoscopic 3D display were implemented and tested in clinical applications in a hybrid interventional suite that is used for radio-interventional as well as neurosurgical procedures. Our hypothesis is that fusion of image data acquired prior to intervention procedures with images acquired during those procedures should allow better visualizing and navigating through complex cerebral vasculature. This should also improve workflows of neuro-interventional procedures.

METHODS

Preoperative and intra-operative acquisitions of vascular images of the brain were performed and transferred to a dedicated imaging workstation to be processed with our image fusion and visualization software tool. The tool was developed as a plugin extension to the open-source DICOM viewer OsiriX and is based on a modular and scalable architecture. Several processing modules were implemented to allow spatial co-registration and fusion of preoperative and intra-operative modalities. A special extension was also implemented for interactive autostereosopic, glass-free 3D visualization of fused results.

RESULTS

The software platform was validated and evaluated in nine in vivo procedures by expert users. All patient cases were related to interventional treatment of neuro-vascular diseases. The emphasis was laid on the added value of spatial co-registration and fusion of preoperative and intra-operative modalities, as well as the overall impact on workflow during the intervention. The co-registered and fused images were visualized on an autostereoscopic 3D monitor installed in hybrid interventional suite. All experiments were evaluated and scored by interventional physicians and technicians.

CONCLUSIONS

Displaying 3D-4D representations of brain vascular anomalies based on multi-modal acquisitions on a 3D autostereoscopic display is beneficial for the workflow and efficiency of interventional radiologists. The implemented software tool fulfills the premise of applicability of an open-source platform for more advanced, multi-modal visualization and processing of brain vascular structures for image-guided therapeutic interventions.

[Lumbar disc herniation--diagnosis and treatment]

A lumbar disc herniation (LDH) is a condition frequently encountered in primary care medicine. It may give rise to a compression of one or more nerve roots, which can lead to a nerve root irritation, a so-called radiculopathy, with or without a sensorimotor deficit. The majority of LDHs can be supported by means of a conservative treatment consisting of physical therapy, ergotherapy, analgetics, anti-inflammatory therapy or corticosteroids, which may be eventually administered by infiltrations. If a clinico-radiological correlation is present and moderate neurological deficit appears suddenly, if it is progressive under conservative treatment or if pain is poorly controlled by well-conducted conservative treatment performed during four to six months, surgery is then recommended.

Intra-aneurysmal flow patterns: illustrative comparison among digital subtraction angiography, optical flow, and computational fluid dynamics

BACKGROUND AND PURPOSE

Digital subtraction angiography is the gold standard vascular imaging and it is used for all endovascular treatment of intracranial anerysms. Optical flow imaging has been described as a potential method to evaluate cerebral hemodynamics through DSA. In this study, we aimed to compare the flow patterns measured during angiography, by using an optical flow method, with those measured by using computational fluid dynamics in intracranial aneurysms.

MATERIALS AND METHODS

A consecutive series of 21 patients harboring unruptured saccular intracranial aneurysms who underwent diagnostic angiography before treatment was considered. High-frame-rate digital subtraction angiography was performed to obtain an intra-aneurysmal velocity field by following the cardiac-modulated contrast wave through the vascular structures by using optical flow principles. Additionally, computational fluid dynamics modeling was performed for every case by using patient-specific inlet-boundary conditions measured with the optical flow method from both DSA and 3D rotational angiography datasets. Three independent observers compared qualitatively both the inflow direction and the apparent recirculation in regular DSA, optical flow images, and computational fluid dynamics flow patterns for each patient; κ statistics were estimated.

RESULTS

We included 21 patients. In 14 of these 21, the flow patterns were conclusive and matching between the optical flow images and computational fluid dynamics within the same projection view (κ = .91). However, in only 8 of these 14 patients the optical flow images were conclusive and matching regular DSA images (observer κ = 0.87). In 7 of the 21 patients, the flow patterns in the optical flow images were inconclusive, possibly due to improper projection angles.

CONCLUSIONS

The DSA-based optical flow technique was considered qualitatively consistent with computational fluid dynamics outcomes in evaluating intra-aneurysmal inflow direction and apparent recirculation. Moreover, the optical flow technique may provide the premises for new solutions for improving the visibility of flow patterns when contrast motion in DSA is not apparent. This technique is a diagnostic method to evaluate intra-aneurysmal flow patterns and could be used in the future for validation and patient evaluation.

[Cell transplantation: current treatments and future prospects]

Regenerative medicine aims to replace a body function or specific cell loss. It includes therapies at the forefront of modem medicine, issuing from translational biomedical research. Transplantation of organs and cells has revolutionized the management of patients for whom medical treatment is a failure. Unfortunately, organ shortage is limiting treatment possibility. As an example, among the 15,000 patients with type I diabetes in Switzerland, only approximately 30 can receive a pancreas or an islet transplant per year. Second example, 500 patients die each year in Switzerland from alcoholic cirrhosis because no treatment is available. Transplantation of islet cells, hepatocytes, mesenchymal stem cells or dopaminergic neurons represents hope fora therapy available for large populations of patients.