The role of time-resolved imaging of contrast kinetics (TRICKS) magnetic resonance angiography (MRA) in the evaluation of head-neck vascular anomalies: A preliminary experience

Objectives: In this preliminary report, we describe our experience with time-resolved imaging of contrast kinetics-MR angiography (TRICKS-MRA) in the assessment of head-neck vascular anomalies (HNVAs). Methods: We prospectively studied six consecutive patients with clinically suspected or diagnosed HNVAs. All of them underwent TRICKS-MRA of the head and neck as part of the routine for treatment planning. A digital subtraction angiography (DSA) was also performed. Results: TRICKS-MRA could be achieved in all cases. Three subjects were treated based on TRICKS-MRA imaging findings and subsequent DSA examination. In all of them, DSA confirmed the vascular architecture of HNVAs shown by TRICKS-MRA. In the other three patients, a close follow up to assess the evolution of the suspected haemangioma was preferred. Conclusions: TRICKS sequences add important diagnostic information in cases of HNVAs, helpful for therapeutic decisions and post-treatment follow up. We recommend TRICKSMRA use (if technically possible) as part of routine MRI protocol for HNVAs, representing a possible alternative imaging tool to conventional DSA

The ketogenic diet increases in vivo glutathione levels in patients with epilepsy

The Ketogenic Diet (KD) is a high-fat, low-carbohydrate diet that has been utilized as the first line treatment for contrasting intractable epilepsy. It is responsible for the presence of ketone bodies in blood, whose neuroprotective effect has been widely shown in recent years but remains unclear. Since glutathione (GSH) is implicated in oxidation-reduction reactions, our aim was to monitor the effects of KD on GSH brain levels by means of magnetic resonance spectroscopy (MRS). MRS was acquired from 16 KD patients and seven age-matched Healthy Controls (HC). We estimated metabolite concentrations with linear combination model (LCModel), assessing differences between KD and HC with t-test. Pearson was used to investigate GHS correlations with blood serum 3-B-Hydroxybutyrate (3HB) concentrations and with number of weekly epileptic seizures. The results have shown higher levels of brain GSH for KD patients (2.5 ± 0.5 mM) compared to HC (2.0 ± 0.5 mM). Both blood serum 3HB and number of seizures did not correlate with GSH concentration. The present study showed a significant increase in GSH in the brain of epileptic children treated with KD, reproducing for the first time in humans what was previously observed in animal studies. Our results may suggest a pivotal role of GSH in the antioxidant neuroprotective effect of KD in the human brain

The Prevention and Control of HIV/AIDS, TB and Vector-borne Diseases in Informal Settlements: Challenges, Opportunities and Insights

Today’s urban settings are redefining the field of public health. The complex dynamics of cities, with their concentration of the poorest and most vulnerable (even within the developed world) pose an urgent challenge to the health community. While retaining fidelity to the core principles of disease prevention and control, major adjustments are needed in the systems and approaches to effectively reach those with the greatest health risks (and the least resilience) within today’s urban environment. This is particularly relevant to infectious disease prevention and control. Controlling and preventing HIV/AIDS, tuberculosis and vector-borne diseases like malaria are among the key global health priorities, particularly in poor urban settings. The challenge in slums and informal settlements is not in identifying which interventions work, but rather in ensuring that informal settlers: (1) are captured in health statistics that define disease epidemiology and (2) are provided opportunities equal to the rest of the population to access proven interventions. Growing international attention to the plight of slum dwellers and informal settlers, embodied by Goal 7 Target 11 of the Millennium Development Goals, and the considerable resources being mobilized by the Global Fund to fight AIDS, TB and malaria, among others, provide an unprecedented potential opportunity for countries to seriously address the structural and intermediate determinants of poor health in these settings. Viewed within the framework of the “social determinants of disease” model, preventing and controlling HIV/AIDS, TB and vector-borne diseases requires broad and integrated interventions that address the underlying causes of inequity that result in poorer health and worse health outcomes for the urban poor. We examine insights into effective approaches to disease control and prevention within poor urban settings under a comprehensive social development agenda

Grey matter volume alterations in CADASIL: a voxel-based morphometry study

CADASIL is a hereditary disease characterized by cerebral subcortical microangiopathy leading to early onset cerebral strokes and progressive severe cognitive impairment. Until now, only few studies have investigated the extent and localization of grey matter (GM) involvement. The purpose of our study was to evaluate GM volume alterations in CADASIL patients compared to healthy subjects. We also looked for correlations between global and regional white matter (WM) lesion load and GM volume alterations. 14 genetically proved CADASIL patients and 12 healthy subjects were enrolled in our study. Brain MRI (1.5 T) was acquired in all subjects. Optimized-voxel based morphometry method was applied for the comparison of brain volumes between CADASIL patients and controls. Global and lobar WM lesion loads were calculated for each patient and used as covariate-of-interest for regression analyses with SPM-8. Compared to controls, patients showed GM volume reductions in bilateral temporal lobes (p < 0.05; FDR-corrected). Regression analysis in the patient group revealed a correlation between total WM lesion load and temporal GM atrophy (p < 0.05; uncorrected), not between temporal lesion load and GM atrophy. Temporal GM volume reduction was demonstrated in CADASIL patients compared to controls; it was related to WM lesion load involving the whole brain but not to lobar and, specifically, temporal WM lesion load. Complex interactions between sub-cortical and cortical damage should be hypothesized

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Interpretable surface-based detection of focal cortical dysplasias:a Multi-centre Epilepsy Lesion Detection study

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy

Technico-economic assessment of thermal energy storage integration with low-temperature district heating.

International audienc

Techno-Economic Assessment of Active Latent Heat Thermal Energy Storage Systems with Low-Temperature District Heating

International audienc