Minimum clinical utility standards for wearable seizure detectors: A simulation study.

OBJECTIVE: Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. METHODS: Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario. RESULTS: The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. SIGNIFICANCE: The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.

Feasibility and acceptability of an ultra-long-term at-home EEG monitoring system (EEG@HOME) for people with epilepsy.

BACKGROUND: Recent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. Previous studies provided feedback from patients, but few explored the experience of them using independently multiple devices independently at home. PURPOSE: The study, assessed through a mixed methods design, the direct experiences of people with epilepsy independently using a non-invasive monitoring system (EEG@HOME) for an extended duration of 6 months, at home. We aimed to investigate factors affecting engagement, gather qualitative insights, and provide recommendations for future home epilepsy monitoring systems. MATERIALS AND METHODS: Adults with epilepsy independently were trained to use a wearable dry EEG system, a wrist-worn device, and a smartphone app for seizure tracking and behaviour monitoring for 6 months at home. Monthly acceptability questionnaires (PSSUQ, SUS) and semi-structured interviews were conducted to explore participant experience. Adherence with the procedure, acceptability scores and systematic thematic analysis of the interviews, focusing on the experience with the procedure, motivation and benefits and opinion about the procedure were assessed. RESULTS: Twelve people with epilepsy took part into the study for an average of 193.8 days (range 61 to 312) with a likelihood of using the system at six months of 83 %. The e-diary and the smartwatch were highly acceptable and preferred to a wearable EEG system (PSSUQ score of 1.9, 1.9, 2.4). Participants showed an acceptable level of adherence with all solutions (Average usage of 63 %, 66 %, 92 %) reporting more difficulties using the EEG twice a day and remembering to complete the daily behavioural questionnaires. Clear information and training, continuous remote support, perceived direct and indirect benefits and the possibility to have a flexible, tailored to daily routine monitoring were defined as key factors to ensure compliance with long-term monitoring systems. CONCLUSIONS: EEG@HOME study demonstrated people with epilepsy' interest and ability in active health monitoring using new technologies. Remote training and support enable independent home use of new non-invasive technologies, but to ensure long term acceptability and usability systems will require to be integrated into patients' routines, include healthcare providers, and offer continuous support and personalized feedback.

Modulated Electro-Hyperthermia Accelerates Tumor Delivery and Improves Anticancer Activity of Doxorubicin Encapsulated in Lyso-Thermosensitive Liposomes in 4T1-Tumor-Bearing Mice.

Modulated electro-hyperthermia (mEHT) is an adjuvant cancer therapy that enables tumor-selective heating (+2.5 °C). In this study, we investigated whether mEHT accelerates the tumor-specific delivery of doxorubicin (DOX) from lyso-thermosensitive liposomal doxorubicin (LTLD) and improves its anticancer efficacy in mice bearing a triple-negative breast cancer cell line (4T1). The 4T1 cells were orthotopically injected into Balb/C mice, and mEHT was performed on days 9, 12, and 15 after the implantation. DOX, LTLD, or PEGylated liposomal DOX (PLD) were administered for comparison. The tumor size and DOX accumulation in the tumor were measured. The cleaved caspase-3 (cC3) and cell proliferation were evaluated by cC3 or Ki67 immunohistochemistry and Western blot. The LTLD+mEHT combination was more effective at inhibiting tumor growth than the free DOX and PLD, demonstrated by reductions in both the tumor volume and tumor weight. LTLD+mEHT resulted in the highest DOX accumulation in the tumor one hour after treatment. Tumor cell damage was associated with cC3 in the damaged area, and with a reduction in Ki67 in the living area. These changes were significantly the strongest in the LTLD+mEHT-treated tumors. The body weight loss was similar in all mice treated with any DOX formulation, suggesting no difference in toxicity. In conclusion, LTLD combined with mEHT represents a novel approach for DOX delivery into cancer tissue.

Novel Fe3O4 Nanoparticles with Bioactive Glass-Naproxen Coating: Synthesis, Characterization, and In Vitro Evaluation of Bioactivity.

Immune response to biomaterials, which is intimately related to their surface properties, can produce chronic inflammation and fibrosis, leading to implant failure. This study investigated the development of magnetic nanoparticles coated with silica and incorporating the anti-inflammatory drug naproxen, aimed at multifunctional biomedical applications. The synthesized nanoparticles were characterized using various techniques that confirmed the presence of magnetite and the formation of a silica-rich bioactive glass (BG) layer. In vitro studies demonstrated that the nanoparticles exhibited bioactive properties, forming an apatite surface layer when immersed in simulated body fluid, and biocompatibility with bone cells, with good viability and alkaline phosphatase activity. Naproxen, either free or encapsulated, reduced nitric oxide production, an inflammatory marker, while the BG coating alone did not show anti-inflammatory effects in this study. Overall, the magnetic nanoparticles coated with BG and naproxen showed promise for biomedical applications, especially anti-inflammatory activity in macrophages and in the bone field, due to their biocompatibility, bioactivity, and osteogenic potential.

Perceived seizure risk in epilepsy: Chronic electronic surveys with and without concurrent electroencephalography.

OBJECTIVE: Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self-reported and electroencephalographically (EEG)-confirmed seizures in ambulatory patients with epilepsy in their natural home environments. METHODS: Long-term e-surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). RESULTS: Fifty-four subjects returned 10 269 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12-3.61, AUC = .61, p = .02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (OR = 5.37, 95% CI = 3.53-8.16, AUC = .76, p < .001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87-5.95, AUC = .69, p < .001) remained significant when prior self-reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures. SIGNIFICANCE: Our results suggest that patients may tend to self-forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.

Seizure occurrence is linked to multiday cycles in diverse physiological signals.

OBJECTIVE: The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. METHODS: In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter-Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. RESULTS: Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. SIGNIFICANCE: Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time-varying approaches to epilepsy care.

Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review.

In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long-term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state-of-the-art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty-three full-text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic-detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure-detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.

Diagnostic yield and limitations of in-hospital documentation in patients with epilepsy.

OBJECTIVE: To determine the diagnostic yield of in-hospital video-electroencephalography (EEG) monitoring to document seizures in patients with epilepsy. METHODS: Retrospective analysis of electronic seizure documentation at the University Hospital Freiburg (UKF) and at King's College London (KCL). Statistical assessment of the role of the duration of monitoring, and subanalyses on presurgical patient groups and patients undergoing reduction of antiseizure medication. RESULTS: Of more than 4800 patients with epilepsy undergoing in-hospital recordings at the two institutions since 2005, seizures with documented for 43% (KCL) and 73% (UKF).. Duration of monitoring was highly significantly associated with seizure recordings (p < .0001), and presurgical patients as well as patients with drug reduction had a significantly higher diagnostic yield (p < .0001). Recordings with a duration of >5 days lead to additional new seizure documentation in only less than 10% of patients. SIGNIFICANCE: There is a need for the development of new ambulatory monitoring strategies to document seizures for diagnostic and monitoring purposes for a relevant subgroup of patients with epilepsy in whom in-hospital monitoring fails to document seizures.

Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models.

This study describes a generalized cross-patient seizure-forecasting approach using recurrent neural networks with ultra-long-term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures were identified by a board-certified epileptologist. One-minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise-added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo-prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave-one-patient-out cross-validation method, and the area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave-one-signal-out method with repeated training and testing for each classifier. Cross-patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution (p < .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross-patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two-channel ultra-long-term sqEEG.

Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models.

OBJECTIVE: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. METHODS: We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. RESULTS: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. SIGNIFICANCE: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.

Signal quality and power spectrum analysis of remote ultra long-term subcutaneous EEG.

OBJECTIVE: Ultra long-term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long-term quality and consistency of the sqEEG signal, which is the objective of this study. METHODS: The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG™ SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long-term temporal trends in signal impedance and power spectral features were investigated with subject-specific linear regression models and group-level linear mixed-effects models. RESULTS: sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8-13Hz) and nocturnal peaks in the sigma range (12-16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods. SIGNIFICANCE: The spectral characteristics of minimally invasive, ultra long-term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain-computer interfaces.

Wearable devices for seizure detection: Practical experiences and recommendations from the Wearables for Epilepsy And Research (WEAR) International Study Group.

The Wearables for Epilepsy And Research (WEAR) International Study Group identified a set of methodology standards to guide research on wearable devices for seizure detection. We formed an international consortium of experts from clinical research, engineering, computer science, and data analytics at the beginning of 2020. The study protocols and practical experience acquired during the development of wearable research studies were discussed and analyzed during bi-weekly virtual meetings to highlight commonalities, strengths, and weaknesses, and to formulate recommendations. Seven major essential components of the experimental design were identified, and recommendations were formulated about: (1) description of study aims, (2) policies and agreements, (3) study population, (4) data collection and technical infrastructure, (5) devices, (6) reporting results, and (7) data sharing. Introducing a framework of methodology standards promotes optimal, accurate, and consistent data collection. It also guarantees that studies are generalizable and comparable, and that results can be replicated, validated, and shared.

Seizure detection at home: Do devices on the market match the needs of people living with epilepsy and their caregivers?

In patients with epilepsy, the potential to prevent seizure-related injuries and to improve the unreliability of seizure self-report have fostered the development and marketing of numerous seizure detection devices for home use. Understanding the requirements of users (patients and caregivers) is essential to improve adherence and mitigate barriers to the long-term use of such devices. Here we reviewed the evidence on the needs and preferences of users and provided an overview of currently marketed devices for seizure detection (medically approved or with published evidence for their performance). We then compared devices with known needs. Seizure-detection devices are expected to improve safety and clinical and self-management, and to provide reassurance to users. Key factors affecting a device's usability relate to its design (attractive appearance, low visibility, low intrusiveness), comfort of use, confidentiality of recorded data, and timely support from both technical and clinical ends. High detection sensitivity and low false alarm rates are paramount. Currently marketed devices are focused primarily on the recording of non-electroencephalography (EEG) signals associated with tonic-clonic seizures, whereas the detection of focal seizures without major motor features remains a clear evidence gap. Moreover, there is paucity of evidence coming from real-life settings. A joint effort of clinical and nonclinical experts, patients, and caregivers is required to ensure an optimal level of acceptability and usability, which are key aspects for a successful continuous monitoring aimed at seizure detection at home.

Signal quality and patient experience with wearable devices for epilepsy management.

Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor-quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in-hospital or in-home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high-quality, marginal-quality, or poor-quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good-quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good-quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good-, marginal-, and poor-quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist-worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high-quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.

Amazon plant diversity revealed by a taxonomically verified species list.

Recent debates on the number of plant species in the vast lowland rain forests of the Amazon have been based largely on model estimates, neglecting published checklists based on verified voucher data. Here we collate taxonomically verified checklists to present a list of seed plant species from lowland Amazon rain forests. Our list comprises 14,003 species, of which 6,727 are trees. These figures are similar to estimates derived from nonparametric ecological models, but they contrast strongly with predictions of much higher tree diversity derived from parametric models. Based on the known proportion of tree species in neotropical lowland rain forest communities as measured in complete plot censuses, and on overall estimates of seed plant diversity in Brazil and in the neotropics in general, it is more likely that tree diversity in the Amazon is closer to the lower estimates derived from nonparametric models. Much remains unknown about Amazonian plant diversity, but this taxonomically verified dataset provides a valid starting point for macroecological and evolutionary studies aimed at understanding the origin, evolution, and ecology of the exceptional biodiversity of Amazonian forests.

Short-lasting retroorbital pain attacks as a form of ictal epileptic headache: Case report.

BACKGROUND AND OBJECTIVES: Ictal epileptic headache is a rare form of painful seizure, habitually consisting of migrainous or tension-type headache. We describe a case of a patient with short-lasting, severe retroorbital pain attacks caused by frontal lobe epilepsy. CASE REPORT: A 25-year-old male patient presented with recurrent attacks of paroxysmal, short-lasting, excruciating left periorbital and facial pain mainly occurring from sleep. After intracranial EEG exploration and resection of a right prefrontal focal cortical dysplasia, long-term seizure and headache remission was obtained. DISCUSSION: Our case extends the clinical and neuroanatomical spectrum of ictal epileptic headache and suggests that long-term remission can be obtained by resective epilepsy surgery. It also reinforces the role of the prefrontal cortex in the pain matrix and pain generation. CONCLUSION: Despite its rarity, ictal epileptic headache should be suspected in selected patients, particularly those with other ictal symptoms and signs, history of epileptic seizures, or neuroimaging abnormalities.

"Cough Hemicrania" - An Overlapping Form of Headache: Case Reports.

OBJECTIVE: To describe two patients with recurring unilateral brief headaches that fulfilled criteria for both primary cough headache (CH) and chronic paroxysmal hemicrania (CPH). BACKGROUND: CH is typically a bilateral headache, specifically triggered by cough, straining, or other Valsalva maneuvers. The report of cases sharing features with other primary headache disorders, such as CPH, suggest common pathogenic mechanisms. METHODS: Case reports. RESULTS: Two patients (one man), aged 55 and 64, had a two-year history of daily, unilateral, side-locked headache attacks, lasting about 15 minutes, and associated with ipsilateral tearing. Headaches were triggered by cough, sneezing, laughing, or bending forward. Both patients experienced a marked and sustained improvement with indomethacin, and both relapsed when it was interrupted. CONCLUSIONS: These cases suggest the existence of transitional phenotypes, or shared pathogenic mechanisms, between CH and CPH, two indomethacin-responsive headaches. A more comprehensive analysis of different CH subtypes is necessary to understand their relation with other primary headaches.

Familial SUNCT in mother and son.

BACKGROUND: Trigeminal autonomic cephalalgias comprise a heterogeneous group of lateralized headaches associated with ipsilateral autonomic symptoms. They are usually localized within the territory of one or more rami of the trigeminal nerve, but may be localized outside its cutaneous territory. Although these headaches are considered primary disorders, the evidence supporting their genetic nature is lacking, particularly concerning their neuralgic forms, with the exception of a familial case described partly based on a historical account. CASE REPORTS: We report on a mother and son with episodic, short-lasting, intense, paroxysmal headaches, with the same localization in the left retroauricular region, associated with prominent conjunctival injection and tearing, which are consistent with the diagnosis of SUNCT (short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing). DISCUSSION: These cases corroborate the existence of hereditary forms of this disorder, thus supporting its primary nature.

Early assembly of the most massive galaxies.

The current consensus is that galaxies begin as small density fluctuations in the early Universe and grow by in situ star formation and hierarchical merging. Stars begin to form relatively quickly in sub-galactic-sized building blocks called haloes which are subsequently assembled into galaxies. However, exactly when this assembly takes place is a matter of some debate. Here we report that the stellar masses of brightest cluster galaxies, which are the most luminous objects emitting stellar light, some 9 billion years ago are not significantly different from their stellar masses today. Brightest cluster galaxies are almost fully assembled 4-5 billion years after the Big Bang, having grown to more than 90 per cent of their final stellar mass by this time. Our data conflict with the most recent galaxy formation models based on the largest simulations of dark-matter halo development. These models predict protracted formation of brightest cluster galaxies over a Hubble time, with only 22 per cent of the stellar mass assembled at the epoch probed by our sample. Our findings suggest a new picture in which brightest cluster galaxies experience an early period of rapid growth rather than prolonged hierarchical assembly.

Differentiation of human adipose-derived stem cells seeded on mineralized electrospun co-axial poly(ε-caprolactone) (PCL)/gelatin nanofibers.

Mineralized poly(ε-caprolactone)/gelatin core-shell nanofibers were prepared via co-axial electrospinning and subsequent incubation in biomimetic simulated body fluid containing ten times the calcium and phosphate ion concentrations found in human blood plasma. The deposition of calcium phosphate on the nanofiber surfaces was investigated through scanning electronic microscopy and X-ray diffraction. Energy dispersive spectroscopy results indicated that calcium-deficient hydroxyapatite had grown on the fibers. Fourier transform infrared spectroscopy analysis suggested the presence of hydroxyl-carbonate-apatite. The results of a viability assay (MTT) and alkaline phosphatase activity analysis suggested that these mineralized matrices promote osteogenic differentiation of human adipose-derived stem cells (hASCs) when cultured in an osteogenic medium and have the potential to be used as a scaffold in bone tissue engineering. hASCs cultured in the presence of nanofibers in endothelial differentiation medium showed lower rates of proliferation than cells cultured without the nanofibers. However, endothelial cell markers were detected in cells cultured in the presence of nanofibers in endothelial differentiation medium.