Background: Behavioral changes in animals reflect functional changes in their central nervous system. Neuroactive drugs that act on different neural pathways can induce specific behavioral responses; therefore, it is possible to infer the activities of neuroactive drugs by studying the behavioral changes induced by drugs of interest in animals. Methods: In this study, AB strain zebrafish larvae at 7 days post fertilization (dpf) were treated with different concentrations of drugs that act on different neural pathways. Changes in the swimming distances of zebrafish larvae under different illumination conditions and the differences in locomotor activities between light and dark conditions (lighting motor index) were analyzed. Results: Among the drugs studied, different concentrations of sulpiride had no effect on larval locomotor activity either under light or dark conditions. Progressively decreased spontaneous movements were observed in zebrafish larvae treated with increasing doses of MK-801 and valproic acid. With increasing concentrations of pentylenetetrazole and yohimbine, the spontaneous movement of larval zebrafish presented a bell-shaped response. When the illumination changed from light to dark, zebrafish larvae not treated with drugs demonstrated increased locomotor activities. However, high levels of yohimbine, pentylenetetrazole decreased the degree of change in the lighting motor index. Conclusions: In conclusion, drugs that affect different neural pathways exert different influences on the locomotor activities of zebrafish larvae. This study presents an initial effort to establish a framework that correlates the drug activities and the behavioral responses of zebrafish larvae under drug treatments, which may provide a potential identification of the pathways of novel drugs with neurological activities through their behavioral influences.

Tuberous sclerosis complex (TSC) is a genetic condition characterized by the occurrence of hamartomatous wounds stemming from the dysfunction of the mammalian target of rapamycin (mTOR) pathway. We investigated the clinical phenotypes and genetic variants in 243 unrelated probands and their families in China. Exome sequencing, targeted sequencing or multiplex ligation-dependent probe amplification (MLPA) was performed in 174 children with TSC, among whom 31 (17.82%) patients/families were identified as having pathogenic or likely pathogenic variants in the TSC1 gene, 120 (68.97%) as having pathogenic or likely pathogenic variants in the TSC2 gene and 23 (13.21%) as having no pathogenic or likely pathogenic variants identified (NMI). In the 31 patients with pathogenic or likely pathogenic TSC1 variants, 10 novel variants were detected among 26 different variants. In all 120 patients with TSC2 variants, 39 novel variants were found among a total of 107 different variants. We compared the phenotypes of the individuals with TSC1 pathogenic variants, TSC2 pathogenic variants and NMI. Patients with TSC2 variants were first diagnosed at a younger age (p = 0.003) and had more retinal hamartomas (p = 0.003) and facial angiofibromas (p = 0.027) (age ≥ 3 years) than individuals with TSC1 variants. Compared with individuals with TSC1/TSC2 pathogenic variants, NMI individuals had fewer cortical tubers (p = 0.003). Compared with individuals with TSC1 pathogenic variants, NMI patients had more retinal hamartomas (p = 0.035), and compared with individuals with TSC2 pathogenic variants, they had less epilepsy (p = 0.003) and fewer subependymal nodules (SENs) (p = 0.004).

Aims: To evaluate the quality‐of‐life (QOL) impairment and identify the possible risk factors in patients with tuberous sclerosis complex (TSC) in China.Methods: The parent proxy‐report PedsQL 4.0 Generic Core Scales were administered to 124 caregivers of children with TSC (aged 2‐18 years). For comparison, the survey was also conducted in a demographically group‐matched sample of healthy controls (HCs) (aged 2‐18 years).Results: A total of 124 children with TSC and 206 HCs were recruited. The mean parent proxy‐report total scale score, physical health summary score, and psychosocial health summary score for children with TSC were 65.0 (SD 19.7), 77.6 (SD 22.9), and 58.0 (SD 21.3), respectively, compared with the HC values of 83.6 (SD 14.3), 87.2 (SD 16.9), and 82.8 (SD 15.9). There were statistically significant differences between the two groups (P < .0001). TSC2 mutation (P = .033), epilepsy (P = .011), seizure before 2 years old (P = .001), course of epilepsy (more than 2 years) (P = .001), high reported seizure frequency (more than once a month) (HRSF) (P = .007), multiple antiepileptic drugs (≥2) (P = .002), intellectual disability (ID) (mild and moderate ID, P < .0001, and severe and profound ID, P < .0001), and TANDs (P < .0001) (ADHD, P = .004; agoraphobia, P = .007; and social anxiety disorder, P < .0001) were closely related to lower QOL scores.Conclusion: This study is the first large cohort study on QOL in children with TSC in China. The results of the PedsQL 4.0 indicated that the QOL of children with TSC is significantly lower than that of HCs. TSC2 mutation, epilepsy, early onset, long disease course and HRSF, ID, and TANDs are risk factors for poor QOL.

Aims: Status epilepticus (SE) is the most common neurological emergency in pediatric patients. This study aimed to screen for prognostic biomarkers of SE in the cerebrospinal fluid (CSF) using metabolomics.Methods: Ultra‐performance liquid chromatography quadrupole time‐of‐flight tandem mass spectrometry (UPLC‐QTOF‐MS) was conducted to identify prognostic biomarkers in CSF metabolomics by comparing the poor outcome group (N = 13) with the good outcome group (N = 15) of children with SE. Differentially expressed metabolites were identified using Mann–Whitney U test corrected by Benjamini‐Hochberg and partial least squares discriminant analysis (PLS‐DA).Results: The PLS‐DA model identified and validated significant metabolic differences between the poor and good outcome groups of children with SE (PLS‐DA with R²Y = 0.992 and Q² = 0.798). A total of 49 prognosis‐related metabolites were identified. Of these metabolites, 20 including glutamyl‐glutamine, 3‐iodothyronamine, and L‐fucose had an area under the curve (AUC) ≥ 80% in prognostic prediction of SE. The logistic regression model combining glutamyl‐glutamine and 3‐iodothyronamine produced an AUC value of 0.976, with a sensitivity of 0.863 and specificity of 0.956. Pathway analysis revealed that dysregulation of the citrate cycle (TCA) and arginine biosynthesis may contribute to poor SE prognosis.Conclusions: This study highlighted the prognosis‐related metabolomic disturbances in the CSF of children with SE and identified potential prognostic biomarkers. A prognostic prediction model combining glutamyl‐glutamine and 3‐iodothyronamine with high predictive value was established.