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.

Booklice Liposcelis bostrychophila naturally infected by Rickettsia felis cause fever and experimental pneumonia in mammals

BACKGROUND

Rickettsia felis is emergent in tropical areas. Despite its high morbidity, its natural history has not yet been fully determined. We investigated the role of the common household booklouse, Liposcelis bostrychophila, recently found to harbour R. felis.

METHODS

Blood samples from 372 febrile patients from Senegalese villages, as well as nasal and skin samples from 264 asymptomatic individuals, were tested for cat flea-associated and booklice-associated strains of R. felis. Dust samples from beds were collected to isolate booklice and R. felis. Mice were infected with aerosol of R. felis strain from naturally infected booklice.

RESULTS

Forty febrile patients (11%) were infected by R. felis, including 26 (7%) by the booklice-associated strain. Nine nasal samples (3.4%) and 28 skin samples (10.6%) contained R. felis, including seven and 24, respectively, with the booklice-associated strain. The presence of live L. bostrychophila was observed in 32 dust samples (16.8%); R. felis was identified in 62 dust samples (32.5%). Several mice samples were positive for R. felis; interstitial lymphohistiocytic infiltrates were identified in lungs.

CONCLUSIONS

L. bostrychophila may be a reservoir of R. felis. The booklice-associated strain is pathogenic in mammals causing pneumonia. Human infection may be acquired via inhalation of infected booklice particles.

Inflammatory Stimuli and Fecal Microbiota Transplantation Accelerate Pancreatic Carcinogenesis in Transgenic Mice, Accompanied by Changes in the Microbiota Composition

An association between gut microbiota and the development of pancreatic ductal adenocarcinoma (PDAC) has been previously described. To better understand the bacterial microbiota changes accompanying PDAC promotion and progression stimulated by inflammation and fecal microbiota transplantation (FMT), we investigated stool and pancreatic microbiota by 16s RNA-based metagenomic analysis in mice with inducible acinar transgenic expressions of KrasG12D, and age- and sex-matched control mice that were exposed to inflammatory stimuli and fecal microbiota obtained from mice with PDAC. Time- and inflammatory-dependent stool and pancreatic bacterial composition alterations and stool alpha microbiota diversity reduction were observed only in mice with a Kras mutation that developed advanced pancreatic changes. Stool Actinobacteriota abundance and pancreatic Actinobacteriota and Bifidobacterium abundances increased. In contrast, stool abundance of Firmicutes, Verrucomicrobiota, Spirochaetota, Desulfobacterota, Butyricicoccus, Roseburia, Lachnospiraceae A2, Lachnospiraceae unclassified, and Oscillospiraceae unclassified decreased, and pancreatic detection of Alloprevotella and Oscillospiraceae uncultured was not observed. Furthermore, FMT accelerated tumorigenesis, gradually decreased the stool alpha diversity, and changed the pancreatic and stool microbial composition in mice with a Kras mutation. Specifically, the abundance of Actinobacteriota, Bifidobacterium and Faecalibaculum increased, while the abundance of genera such as Lachnospiraceace A2 and ASF356, Desulfovibrionaceace uncultured, and Roseburia has decreased. In conclusion, pancreatic carcinogenesis in the presence of an oncogenic Kras mutation stimulated by chronic inflammation and FMT dynamically changes the stool and pancreas microbiota. In particular, a decrease in stool microbiota diversity and abundance of bacteria known to be involved in short-fatty acids production were observed. PDAC mouse model can be used for further research on microbiota-PDAC interactions and towards more personalized and effective cancer therapies.