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Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but one in three patients continue to have seizures one year after surgery. In order to improve the chances of good outcomes, computational models of seizure dynamics are being integrated into surgical planning to simulate the effects of the planned surgeries. These modelling frameworks require several conceptual and methodological choices, as well as large amounts of patient-specific data, which hinders their clinical applicability. To address this problem, we considered the patient-specific brain network, derived from magnetoencephalography (MEG) recordings, and a simple epidemic spreading model as the dynamical basis for seizure propagation. This simple model was enough to reproduce the seizure propagation patterns derived from stereo-tactical electroencephalography recordings (SEEG) of all considered patients (N= 15), when the patients’ resected areas (RA) were used as the origin of epidemic spreading. The model yielded a more accurate fit for the seizure-free (SF,N= 11) than the non-SF (NSF) group and, even though the difference between the groups was not significant, the goodness-of-fit distinguished NSF from SF patients with an area under the curve AUC = 84.1%. We also explored the definition of a population model that combined data from different patients to fit the model parameters but was still individualized by considering the patient-specific MEG network. Even though the goodness-of-fit decreased compared to the individualized models, the difference between the SF and NSF groups held, and in fact became stronger and significant (p= 0.023), and the group classification also improved slightly (AUC= 88.6%). Therefore, combining data from different patients may pave the way not only to generalize this framework to patients without SEEG recordings, but also to reduce the risk of over-fitting and improve the stability of the models. Finally, we considered the individualized models to derive alternative hypothesis of the seizure onset zones and to test the surgical strategyin silicofor each patient. We found that RA regions were on average more likely to originate the seizures, but that alternative explanations were possible. Virtual resections of the RA when considering these alternative seeds significantly reduced seizure propagation, and to a greater extend for SF than NSF patients (although the difference was not significant). Overall, our findings indicate that spreading models based on the patient-specific MEG network can be used to predict surgical outcomes, with better fit results and greater reduction on seizure spreading linked to higher likelihood of seizure freedom after surgery.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome in a patient-by-patient basis, we developed a framework of individualized computational models that combine epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). The ESSES parameters were fitted in a retrospective study (N= 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES could not only reproduce the iEEG-recorded seizures, but significantly better so for patients with good (seizure-free, SF) than bad (non-seizure-free, NSF) outcome (area under the curveAUC= 0.73). Once the model parameters were set in the retrospective study, ESSES can be applied also to patients without iEEG data. We illustrate here the clinical applicability of ESSES with apseudo-prospective study(N= 34) with a blind setting (to the resection strategy and surgical outcome) that emulated the presurgical conditions. ESSES could predict the chances of good outcome afteranyresection by finding patient-specific optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan also overlapped more with the optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting optimal resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated on a fully independent cohort without the need for iEEG recordings.

Fact-checking databases, as important results of fact checkers’ epistemic work, are increasingly tied together in new overarchinginfrastructures, but these are understudied and lack transparencydespite being an important societal baseline for whether claimsare false. This article conceptualizes fact-checking as infrastructureand constructs a mixed-methods approach to examine overlapsand differences and thereby detect biases to increase transparency in COVID-19 misinformation infrastructure at scale. AnalyzingPoynter and Google as such overarching infrastructures, we foundonly a small overlap. Fewer fact-checkers contribute to Google,with fewer stories than to Poynter. 75% of claims in Google arefact-checked by Asian and North American fact-checkers (44% forPoynter) but none by South Americans (20% for Poynter). Morestories in Poynter originate from Facebook than outside socialmedia (43% vs. 17%), while Google shows the opposite (16% vs.38%). In Google, claims originate to a larger extent from publicpersons. We find similar large topics on “statistics” and “cures,”but also differences regarding smaller topics (e.g., “vaccines”) andtypes of misinformation (e.g., “virus characteristics”). Thus, the article shows that the infrastructures have inherent biases and arguethat making visible such biases will increase transparency forstakeholders using it.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on MEG brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual brain networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation. The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome. MEG-based networks can provide a good approximation of structural connectivity for computational models of seizure propagation, and facilitate their clinical use.

Introduction: Children treated for cancer with a central venous catheter are often bothered by scarring. We studied whether glucocorticosteroid during patch occlusion has a beneficial effect on scar outcome in children and adolescents treated for neoplastic disease. Methods: A double-blinded placebo-controlled randomized clinical trial was performed. The main outcome was the Vancouver Scar Scale (VSS). Secondary outcomes were scar width and scar quality measured using the Patient and observer scar assessment (POSAS). The patients were divided into an intervention groups and a control group. The intervention group was randomized into two groups; Active and placebo group. The active treatment consisted of cream with glucocorticosteroid and fusidic acid. The placebo treatment consisted of cream with fusidic acid. Both groups were also treated with a silicone gel patch for three months after central venous catheter removal. The control group did not receive any specific skin care. Results: Assessment at 6 months were completed for 44 in the intervention group (21 in the active and 23 in the placebo group) and 47 in the comtrol group. The intervention group had a significantly lower VSS and a smaller scar compared to the control group (p = 0.00, p = 0.02). The POSAS by the patients and the study nurse showed no significant difference between the intervention and control groups (p = 0.84, p= 0.36). Conclusions: Silicone gel sheet alone or in combination with application of glucocorticosteroid during sheet occlusion improved scar outcome after removal of central venous catheter in children treated for neoplastic diseases.

Purpose To assess (1) the repeatability and (2) the impact of reconstruction methods and delineation on the repeatability of 105 radiomic features in non-small-cell lung cancer (NSCLC) 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomorgraphy/computed tomography (PET/CT) studies. Procedures Eleven NSCLC patients received two baseline whole-body PET/CT scans. Each scan was reconstructed twice, once using the point spread function (PSF) and once complying with the European Association for Nuclear Medicine (EANM) guidelines for tumor PET imaging. Volumes of interest (n = 19) were delineated twice, once on PET and once on CT images. Results Sixty-three features showed an intraclass correlation coefficient ≥ 0.90 independent of delineation or reconstruction. More features were sensitive to a change in delineation than to a change in reconstruction (25 and 3 features, respectively). Conclusions The majority of features in NSCLC [18F]FDG-PET/CT studies show a high level of repeatability that is similar or better compared to simple standardized uptake value measures. Electronic supplementary material The online version of this article (doi:10.1007/s11307-016-0940-2) contains supplementary material, which is available to authorized users.

ObjectiveIdentifying epilepsy patients for whom clinical MEG is likely to be beneficial avoids or optimizes burdensome ancillary investigations. We determined whether it could be predicted upfront if MEG would be able to generate a hypothesis about the location of the epileptogenic zone (EZ), and in which patients MEG fails to do so.MethodsMEG recordings of 382 epilepsy patients with inconclusive findings regarding EZ localization prior to MEG were acquired for preoperative evaluation. MEG reports were categorized for several demographic, clinical and MEG variables. First, demographic and clinical variables were associated with MEG localization ability for upfront prediction. Second, all variables were compared between patients with and without MEG location in order to characterize patients without MEG location.ResultsOur patient group had often complex etiology and did not contain the (by other means) straightforward and well-localized cases, such as those with concordant tumor and EEG location. For our highly-selected patient group, MEG localization ability cannot be predicted upfront, although the odds of a recording with MEG location were significantly higher in the absence of a tumor and in the presence of widespread MRI abnormalities. Compared to the patients with MEG location, patients without MEG location more often had a tumor, widespread EEG abnormalities, non-lateralizing MEG abnormalities, non-concordant MEG/EEG abnormalities and less often widespread MRI abnormalities or epileptiform MEG activity. In a subgroup of 48 patients with known surgery outcome, more patients with concordant MEG and resection area were seizure-free than patients with discordant results.ConclusionsMEG potentially adds information about the location of the EZ even in patients with a complex etiology, and the clinical advice is to not withhold MEG in epilepsy surgery candidates. Providing a hypothesis about the location of the EZ using MEG is difficult in patients with inconclusive EEG and MRI findings, and in the absence of specific epileptiform activity. More refined methods are needed for patients where MEG currently does not contribute to the hypothesis about the location of the EZ.