BACKGROUND: Kinovea software that tracking semi-automatically the motion in video screen has been used to study motion-related tasks in several studies. However, the validation of this software in open field test to assess locomotor activity have not been studied yet. Therefore, this study aimed to examine the reliability and validity of this software in analyzing locomotor activities.METHODS: Thirty male Institute Cancer Research mice were subjected in this study. The results examined by this software and the classical method were compared. Test-retest reliability and inter-rater reliability were analyzed with Pearson's correlation coefficient and intraclass correlation coefficient (ICC). The validity of this software was analyzed with Pearson's correlation coefficient.RESULTS: This software showed good test-retest reliability (ICC=0.997, 95% confidence interval [CI]=0.975–0.994, p < 0.001). This software also showed good inter-rater reliability (ICC=0.987, 95% CI=0.973–0.994, p < 0.001). Furthermore, in three analyses for the validity of this software, there were significant correlations between two methods (Pearson's correlation coefficient=0.928–0.972, p < 0.001). In addition, this software showed good reliability and validity in the analysis locomotor activity according to time interval.CONCLUSION: This study showed that this software in analyzing drug-induced locomotor activity has good reliability and validity. This software can be effectively used in animal study using the analysis of locomotor activity.
Animals ; Humans ; Male ; Methods ; Mice ; Motor Activity ; Reproducibility of Results

BACKGROUND: Kinovea software that tracking semi-automatically the motion in video screen has been used to study motion-related tasks in several studies. However, the validation of this software in open field test to assess locomotor activity have not been studied yet. Therefore, this study aimed to examine the reliability and validity of this software in analyzing locomotor activities. METHODS: Thirty male Institute Cancer Research mice were subjected in this study. The results examined by this software and the classical method were compared. Test-retest reliability and inter-rater reliability were analyzed with Pearson's correlation coefficient and intraclass correlation coefficient (ICC). The validity of this software was analyzed with Pearson's correlation coefficient. RESULTS: This software showed good test-retest reliability (ICC=0.997, 95% confidence interval [CI]=0.975–0.994, p < 0.001). This software also showed good inter-rater reliability (ICC=0.987, 95% CI=0.973–0.994, p < 0.001). Furthermore, in three analyses for the validity of this software, there were significant correlations between two methods (Pearson's correlation coefficient=0.928–0.972, p < 0.001). In addition, this software showed good reliability and validity in the analysis locomotor activity according to time interval. CONCLUSION This study showed that this software in analyzing drug-induced locomotor activity has good reliability and validity. This software can be effectively used in animal study using the analysis of locomotor activity.

Background: Locomotor activity in mice may have an ultradian rest-activity rhythm. However, to date, no study has shown how locomotor activity can be explained statistically using fitted cosine curves. Therefore, this study explored whether the ultradian rhythm of locomotor activity in mice could be analyzed using cosine fitting analysis. Methods: The locomotor activity of 20 male mice under a 12/12-hour dark/light cycle for 2 days was fitted to a cosine function to obtain the best fit. The mean absolute error (MAE) values were used to determine the explanatory power of the calculated cosine model for locomotor activity. The cosine fitting analysis was performed using R statistical software (version 4.1.1). Results: The mean MAE was 0.2944, whereas the mean MAE for integrating the individual analyses in the two experimental groups was 0.3284. The periods of the estimated ultradian rest-activity rhythm ranged from 1.602 to 4.168 hours. Conclusions These results suggest that locomotor activity data reflect an ultradian rhythm better than a circadian rhythm. Locomotor activity can be statistically fitted to a cosine curve under well-controlled conditions. In the future, it will be necessary to explore whether this cosine-fitting analysis can be used to analyze ultradian rhythms under different experimental conditions.

Objective: Data processing in analysis of circadian rhythm was performed in various ways. However, there was a lack of evidence for the optimal analysis of circadian rest-activity rhythm. Therefore, we aimed to perform mathematical simulations of data processing to investigate proper evidence for the optimal analysis of circadian rest-activity rhythm. Methods: Locomotor activities of 20 ICR male mice were measured by infrared motion detectors. The data of locomotor activities was processed using data summation, data average, and data moving average methods for cosinor analysis. Circadian indices were estimated according to time block, respectively. Also, statistical F and p-values were calculated by zero-amplitude test. Results: The data moving average result showed well-fitted cosine curves independent of data processing time. Meanwhile, the amplitude, MESOR, and acrophase were properly estimated within 800 seconds in data summation and data average methods. Conclusion These findings suggest that data moving average would be an optimal method for data processing in a cosinor analysis and data average within 800-second data processing time might be adaptable. The results of this study can be helpful to analyze circadian restactivity rhythms and integrate the results of the studies using different data processing methods.

Objective: Even though studies using machine learning on sleep-wake states have been performed, studies in various conditions are still necessary. This study aimed to examine the performance of the prediction model of locomotor activities on sleep-wake states using machine learning algorithms. Methods: The processed data using moving average of locomotor activities were used as predicting features. The sleep-wake states were used as true labels. The prediction models were established by machine learning classifiers such as support vector machine with radial basis function (SVM-RBF), linear discriminant analysis (LDA), naïve Bayes, and random forest (RF). The prediction model was evaluated by a six-fold cross validation. Results: The SVM-RBF and RF showed acceptable performance within a window of moving average from 480 to 1,200 seconds. The highest accuracy (0.869) was shown by the RF at the interval of 480 seconds. Meanwhile, the highest area under the curve (0.939) was shown by LDA at the interval of 870 seconds. Conclusion This study suggested that the prediction model on sleep-wake state using machine learning could show an improvement of the model performance when using moving average with raw data. The prediction model using locomotor activity can be useful in research on sleep-wake state.

Objective: As dopamine is closely linked to locomotor activities, animal studies on locomotor activities using dopaminergic agents were widely done. However, most of animal studies were performed for a short period that there is a lack of longitudinal study on the effects of dopaminergic agents on locomotor activities. This study aimed to examine the longterm effect of a dopamine D2, D3 agonist quinpirole on locomotor activities in mice using a home-cage monitoring system. Methods: The locomotor activities of Institute Cancer Research mice were measured by infrared motion detectors in home-cages under the 12-hour dark and 12-hour light condition for three days after the quinpirole injection. Quinpirole was injected at a concentration of 0.5 mg/kg intraperitoneally in the beginning of the dark phase. The locomotor activities before and after the quinpirole administration were compared by the Wilcoxon signed-rank test and one-way repeated measures ANOVA. Results: After the quinpirole administration, the 24-hour total locomotor activity did not change (p = 0.169), but activities were significantly increased in the 12-hour dark phase sum (p = 0.013) and decreased in the 12-hour light phase sum (p = 0.009). Significant increases in the activities were observed in the dark-light difference (p = 0.005) and dark-light ratio (p = 0.005) as well. Conclusion This study suggests that quinpirole injection entrains the circadian rest-activity rhythm of locomotor activities. Therefore, quinpirole can be a drug that mediates locomotor activity as a dopamine agonist as well as a modulator of the circadian rhythms.

Background: Dopamine D2 and D3 receptor agonist quinpirole have been tried as one of drug-induced bipolar animal models. An open-field test is used to assess locomotor activity related to anxiety. Not many studies have analyzed the effects of quinpirole dosages on locomotor activity. The purpose of this study was to look at the locomotor activity of quinpirole-injected mice in an open-field test. Methods: The open-field test was used to observe the locomotor activities of 28 mice. Quinpirole was administrated at 0.05-5 mg/kg and normal saline were used as a control. The Mann-Whitney U-test was employed to compare the locomotor activities in the quinpirole and control groups. Results: Quinpirole-induced locomotor activities reduced as time elapsed during the first 30 minutes following the injection in most mice, then increased or fluctuated in the later 30 minutes. As the dosage was increased, there was a stronger initial inhibition, followed by a rapid and further increase in the last 30 minutes. Conclusion This study showed the differential effects of quinpirole-induced locomotor activities depending on dosage, and initial suppression of locomotor activities by quinpirole was observed. Additionally, longitudinal observation of more than 1 hour would be required to look into the biphasic pattern of quinpirole in an animal model.

Objective: The purpose of this study is to investigate the association of the apolipoprotein E (APOE) e4 genotype with cognition, brain volume, glucose metabolism, and amyloid deposition in patients with Alzheimer disease (AD). Methods: This is cross-sectional study of 69 subjects with AD. All subjects were divided into carriers and non-carriers of the e4 allele. Forty APOE e4 carriers and 29 APOE e4 non-carriers underwent neuropsychological, structural magnetic resonance imaging, [18F]fluorodeoxyglucose positron emission tomography scans (PET) and [18F]florbetaben amyloid PET. Analysis of co-variance was conducted to compare the differences on cognition, brain volume, glucose metabolism and amyloid deposition between APOE e4 carriers and non-carriers after controlling demographics. Results: APOE e4 carriers had 50% lower scores of Seoul Verbal Learning Test (delayed recall) compared to non-carriers (0.88±1.65 vs. 1.76±1.75, p<0.05). However, APOE e4 carriers performed better on other cognitive tests than non-carriers (Korean version of Boston Naming Test [11.04±2.55 vs. 9.66±2.82, p<0.05], Rey Complex Figure Test [25.73±8.56 vs. 20.15±10.82, p<0.05], and Stroop test [color response] [48.28±26.33 vs. 31.56±27.03, p<0.05]). APOE e4 carriers had slightly smaller hippocampal volume than non-carriers (3.09±0.38 vs. 3.32±0.38, p<0.05), but greater total brain cortical thickness (1.45±1.55 vs. 1.37±1.24, p<0.05). Amyloid deposition did not differ significantly between APOE e4 carriers and non-carriers, and no signifi-cant difference in glucose metabolism was found between groups. Conclusion We found that APOE e4 genotype is associated with cognition, brain volume in AD, suggesting that APOE e4 genotype could play an important role in the underlying pathogenesis of AD.

Objectives: ：It is found that imbalance in activity-dependent neuroprotective protein(ADNP) and the homologous protein ADNP2 in schizophrenia may impact the disease progression. Yet, further research is required to clarify their connection to schizophrenia. This is a pilot study for a family-based association analysis of ADNP2 gene in a Korean population with schizophrenia. Methods: ：Twenty-seven probands with schizophrenia were recruited with their parents and siblings. We have used lifetime dimensions of psychosis scale for measuring psychotic features. Promising endophenotypic markers such as age at interview, apparent onset, apparent onset of psychosis, and first treatment age were also included. We analyzed 2 single nucleotide polymorphisms (SNPs) of ADNP gene. Then, we performed family based association test(FBAT) and linkage disequilibrium analyses for each individual SNPs. Results: ：A significant SNP of ADNP2 gene in chromosome 18 (p-value<0.05) for the qualitative phenotype of schizophrenia was found (rs575682; ADNP2). The result was replicated for the quantitative phenotype of apparent onset, apparent onset of psychosis, and the first treatment age. We also found one significant SNP of ADNP2 gene in chromosome 18 (p-value<0.05) for the quantitative phenotype of any delusions. (rs575682; ADNP2) No SNPs were found for the quantitative phenotype of any hallucinations. Conclusion ：Our results showed that quantitative traits such as age of onset, any delusions, and any hallucinations could be continuous with qualitative trait in schizophrenia. However, a caution must be taken in interpreting these results because there were clear limitations in FBAT analyses which included nominal number of SNPs in the small incomplete pedigrees.

BACKGROUND/OBJECTIVES: Male hypogonadism is a condition where the body does not produce enough testosterone and significantly impacts health. Age, obesity, genetics, and oxidative stress are some physiological factors that may contribute to testosterone deficiency.Previous studies have shown many pharmacological benefits of Schisandra chinensis (S. chinensis) Baillon as an anti-inflammatory and antioxidant. However, the molecular mechanism of attenuating hypogonadism is yet to be well established. This research was undertaken to study the effects of S. chinensis extract (SCE) on testosterone deficiency.MATERIALS/METHODS: S. chinensis fruit was pulverized and extracted using 60% aqueous ethanol. HPLC analysis was performed to analyze and quantify the lignans of the SCE. RESULTS: The 2,2-diphenyl-2-picrylhydrazyl and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) scavenging assays confirmed that the SCE and its major lignans (schisandrol A and gomisin N) inhibit oxidative stress. Effects of SCE analysis on the testosterone level under oxidative stress conditions revealed that both schisandrol A and gomisin N were able to recover the lowered testosterone levels. Through mRNA expression of TM3 Leydig cell, we observed that the SCE lignans were able to induce the enzymes involved in testosterone biosynthesis-related genes such as 3β-HSD4 (P < 0.01 for SCE, and P < 0.001 for schisandrol A and gomisin N), 17β-HSD3 (P < 0.001 for SCE, schisandrol A and gomisin N), and 17, 20-desmolase (P < 0.01 for schisandrol A, and P < 0.001 for SCE and gomisin N). CONCLUSIONS These results support that SCE and its active components could be potential therapeutic agents for regulating and increasing testosterone production.