Semi-Automated Biomarker Discovery from Pharmacodynamic Effects on EEG in ADHD Rodent Models

In Vitro and In Vivo Models for Evaluating the Oral Toxicity of Nanomedicines

Toxicity studies for conventional oral drug formulations are standardized and well documented, as required by the guidelines of administrative agencies such as the US Food & Drug Administration (FDA), the European Medicines Agency (EMA) or European Medicines Evaluation Agency (EMEA), and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA). Researchers tend to extrapolate these standardized protocols to evaluate nanoformulations (NFs) because standard nanotoxicity protocols are still lacking in nonclinical studies for testing orally delivered NFs. However, such strategies have generated many inconsistent results because they do not account for the specific physicochemical properties of nanomedicines. Due to their tiny size, accumulated surface charge and tension, sizeable surface-area-to-volume ratio, and high chemical/structural complexity, orally delivered NFs may generate severe topical toxicities to the gastrointestinal tract and metabolic organs, including the liver and kidney. Such toxicities involve immune responses that reflect different mechanisms than those triggered by conventional formulations. Herein, we briefly analyze the potential oral toxicity mechanisms of NFs and describe recently reported in vitro and in vivo models that attempt to address the specific oral toxicity of nanomedicines. We also discuss approaches that may be used to develop nontoxic NFs for oral drug delivery.

Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering

BACKGROUND

The classification of sleep signals is a subjective and time consuming task. A large number of automatic classifiers have been published in the past decade but a sleep community has no strong confidence to use them in clinical practice and still remains using a standard manual scoring according standardized rules.

NEW METHOD

We developed a semi-supervised data-driven approach for objective and efficient evaluation of polysomnographic (PSG) data. The proposed algorithm finds a representative set of signal segments that are subsequently scored by a sleep neurologist. The remaining part of the recording is then automatically classified using these templates.

RESULTS

The method was evaluated on 36 PSG recordings (18 chronic insomniacs, 18 healthy controls). We show a faster and objective evaluation of PSG data compared to the manual scoring that is over-performing automated classifiers (accuracy increases ∼14%). The classification results are comparable on both datasets.

COMPARISON WITH EXISTING METHOD(S)

The methodology that we propose has not yet been published in the area of sleep PSG data processing. The performance of our method is comparable to various published automated approaches (a typical published classification accuracy is ∼75-95%). The method allows the evaluation of PSG recordings in more general terms and across different recording devices and standards.

CONCLUSIONS

The proposed solution is not based on a single-purpose rules or heuristics and training model is not trained on other patient's sleep recordings. The method is applicable to wide range of similar tasks and various types of physiological signals.

Surgery Under General Anesthesia Alleviated the Hyperactivity but Had No Effect on the Susceptibility to PND in ADHD Rats

Background: Attention-deficit hyperactivity disorder (ADHD) is a typical neuropsychiatric disorder characterized by inattention, impulsivity, and hyperactivity, particularly in children. Recent studies demonstrated a close relationship between the development of ADHD and surgery under general anesthesia. However, few studies illustrated if ADHD symptoms changed after surgery. Meanwhile, whether these individuals with natural neural impairment were sensitive to postoperative neurocognitive disorder (PND) still remain unclear. Methods: Spontaneously hypertensive rats (SHR) were utilized as spontaneous ADHD animal model and Wistar-Kyoto (WKY) rats as non-ADHD animal model. We evaluated the variation of neurocognitive function and locomotor activity of the rats undergoing experimental laparotomy with general anesthesia by isoflurane. Neurocognitive function was assessed by fear conditioning test for contextual memory and Morris water maze (MWM) for spatial memory. Depressive-like behavior after surgery was detected by forced swim test, and open-field test and elevated plus maze test were utilized to evaluate locomotor activities and anxiety. Furthermore, we compared electroencephalogram (EEG) signal in ADHD and WKY rats under free-moving conditions. Afterward, c-Fos staining was also utilized to detect the excitatory activity of neurons in these rats to explore the neural mechanism. Results: Locomotor activity of SHR assessed by average speed and number of line crossings in the open-field test decreased 1 week after surgery under general anesthesia, but there was no difference concerning anxiety levels between SHR and WKY rats after surgery. This phenomenon was also paralleled with the change in EEG signal (delta band 0∼3 Hz). Surgery under general anesthesia had no effect on spatial and contextual memory, while it improved spontaneous depression in SHR. The expression of c-Fos was downregulated for at least 1 week in the nucleus accumbens (NAc) area of ADHD rats' brain after surgery. Conclusion: ADHD rats were not sensitive to PND. Surgery with general anesthesia could partly improve the hyperactivity symptom of ADHD rats. This mechanism was related to the suppression of neural activity in the cerebral NAc of ADHD rats induced by general anesthetics.

Combining behavior and EEG analysis for exploration of dynamic effects of ADHD treatment in animal models

BACKGROUND

We analyze the dynamics of rodent EEG amplitude in an experiment accompanied by video recordings. Brain activity of animals is commonly acquired together with a video of behavior, but recordings are rarely combined in analysis. The data acquired is most commonly analyzed separately. To our knowledge, no study has used behavior to improve the analysis of EEG waveforms, specifically for artifact removal - other than through manual editing.

COMPARISON WITH EXISTING METHOD(S)

We explore two approaches: a traditional approach that relies on data preprocessing and artifact rejection by an expert; and an alternative approach that combines analysis of EEG with behavior extracted from video recordings.

NEW METHOD

We use the level of activity extracted from the behavioral video as a measure of confidence in the acquired EEG waveform, and as a weighting factor in averaging and statistical comparisons.

RESULTS

We find in analysis of the EEG that the two approaches lead to similar conclusions, but the analysis leveraging behavioral data achieves this while avoiding many subjective choices often required for artifact rejection and data preprocessing.

CONCLUSIONS

The methods we describe allow for the inclusion of all recorded data in the analysis, thereby making statistical tests more friendly to interpretation, and making the data processing transparent and reproducible.