Life-span characterization of epilepsy and comorbidities in Dravet syndrome mice carrying a targeted deletion of exon 1 of the Scn1a gene.

OBJECTIVE: Dravet Syndrome (DS) is a catastrophic form of paediatric epilepsy associated with multiple comorbidities mainly caused by mutations in the SCN1A gene. DS progresses in three different phases termed febrile, worsening and stabilization stage. Mice that are haploinsufficient for Scn1a faithfully model each stage of DS, although various aspects have not been fully described, including the temporal appearance and sex differences of the epilepsy and comorbidities. The aim of the present study was to investigate the epilepsy landscape according to the progression of DS and the long-term co-morbidities in the Scn1a(+/-)tm1Kea DS mouse line that are not fully understood yet. METHODS: Male and female F1.Scn1a(+/+) and F1.Scn1a(+/-)tm1Kea mice were assessed in the hyperthermia model or monitored by video electroencephalogram (vEEG) and wireless video-EEG according to the respective stage of DS. Long-term comorbidities were investigated through a battery of behaviour assessments in ~6 month-old mice. RESULTS: At P18, F1.Scn1a(+/-)tm1Kea mice showed the expected sensitivity to hyperthermia-induced seizures. Between P21 and P28, EEG recordings in F1.Scn1a(+/-)tm1Kea mice combined with video monitoring revealed a high frequency of SRS and SUDEP (sudden unexpected death in epilepsy). Power spectral analyses of background EEG activity also revealed that low EEG power in multiple frequency bands was associated with SUDEP risk in F1.Scn1a(+/-)tm1Kea mice during the worsening stage of DS. Later, SRS and SUDEP rates stabilized and then declined in F1.Scn1a(+/-)tm1kea mice. Incidence of SRS ending with death in F1.Scn1a(+/-)tm1kea mice displayed variations with the time of day and sex, with female mice displaying higher numbers of severe seizures resulting in greater SUDEP risk. F1.Scn1a(+/-)tm1kea mice ~6 month-old displayed fewer behavioural impairments than expected including hyperactivity, impaired exploratory behaviour and poor nest building performance. SIGNIFICANCE: These results reveal new features of this model that will optimize use and selection of phenotype assays for future studies on the mechanisms, diagnosis, and treatment of DS.

Translational Application of 3D Bioprinting for Cartilage Tissue Engineering.

Cartilage is an avascular tissue with extremely limited self-regeneration capabilities. At present, there are no existing treatments that effectively stop the deterioration of cartilage or reverse its effects; current treatments merely relieve its symptoms and surgical intervention is required when the condition aggravates. Thus, cartilage damage remains an ongoing challenge in orthopaedics with an urgent need for improved treatment options. In recent years, major advances have been made in the development of three-dimensional (3D) bioprinted constructs for cartilage repair applications. 3D bioprinting is an evolutionary additive manufacturing technique that enables the precisely controlled deposition of a combination of biomaterials, cells, and bioactive molecules, collectively known as bioink, layer-by-layer to produce constructs that simulate the structure and function of native cartilage tissue. This review provides an insight into the current developments in 3D bioprinting for cartilage tissue engineering. The bioink and construct properties required for successful application in cartilage repair applications are highlighted. Furthermore, the potential for translation of 3D bioprinted constructs to the clinic is discussed. Overall, 3D bioprinting demonstrates great potential as a novel technique for the fabrication of tissue engineered constructs for cartilage regeneration, with distinct advantages over conventional techniques.

Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy.

Objective.Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals.Approach.In this study, a machine learning-based seizure detection approach, 'Epi-AI', which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach.Main results.Epi-AI demonstrated a superior sensitivity, 91.4%-98.8%, and specificity, 93.1%-98.8%, in Models I-III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I-III ranged from 66.9%-91.3% sensitivity and 60.8%-97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I-III at 73.2%-80.1% and 75.8%-98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity.Significance.Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.