We investigated the effect of lysophosphatidic acid (LPA) in experimental acetaminophen (APAP)-induced acute liver injury. LPA administration significantly reduced APAP-challenged acute liver injury, showing attenuated liver damage, liver cell death and aspartate aminotransferase and alanine aminotransferase levels. APAP overdose-induced mortality was also significantly decreased by LPA administration. Regarding the mechanism involved in LPA-induced protection against acute liver injury, LPA administration significantly increased the glutathione level, which was markedly decreased in APAP challenge-induced acute liver injury. LPA administration also strongly blocked the APAP challenge-elicited phosphorylation of JNK, ERK and GSK3β, which are involved in the pathogenesis of acute liver injury. Furthermore, LPA administration decreased the production of TNF-α and IL-1β in an experimental drug-induced liver injury animal model. Mouse primary hepatocytes express LPA₁(,)₃–₆, and injection of the LPA receptor antagonist KI16425 (an LPA₁(,)₃-selective inhibitor) or H2L 5765834 (an LPA₁(,)₃(,)₅-selective inhibitor) did not reverse the LPA-induced protective effects against acute liver injury. The therapeutic administration of LPA also blocked APAP-induced liver damage, leading to an increased survival rate. Collectively, these results indicate that the well-known bioactive lipid LPA can block the pathogenesis of APAP-induced acute liver injury by increasing the glutathione level but decreasing inflammatory cytokines in an LPA₁(,)₃(,)₅-independent manner. Our results suggest that LPA might be an important therapeutic agent for drug-induced liver injury.

Objectives: ZZThe aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-basedmedical records. Methods: ZZElectronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes withthree diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independentvalidation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF)and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vectorclassification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find aneffective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results: ZZFive-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis(accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final workingDL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showedslightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions ZZThe current results suggest that the vectorization may have more impact on the performance of classification thanthe machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category,and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machinelearning models.

Objectives: ZZThe aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-basedmedical records. Methods: ZZElectronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes withthree diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independentvalidation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF)and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vectorclassification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find aneffective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results: ZZFive-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis(accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final workingDL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showedslightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions ZZThe current results suggest that the vectorization may have more impact on the performance of classification thanthe machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category,and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machinelearning models.

PURPOSE: Osteitis refers to the development of new bone formation and remodeling of bone in chronic rhinosinusitis (CRS) patients; it is typically associated with eosinophilia, nasal polyps (NPs), and recalcitrant CRS. However, the roles of ossification in CRS with or without NPs remain unclear due to the lack of appropriate animal models. Thus, it is necessary to have a suitable animal model for greater advances in the understanding of CRS pathogenesis.METHODS: BALB/c mice were administered ovalbumin (OVA) and staphylococcal enterotoxin B (SEB). The numbers of osteoclasts and osteoblasts and bony changes were assessed. Micro computed tomography (micro-CT) scans were conducted to measure bone thickness. Immunofluorescence, immunohistochemistry, and quantitative polymerase chain reaction were performed to evaluate runt-related transcription factor 2 (RUNX2), osteonectin, interleukin (IL)-13, and RUNX2 downstream gene expression. Gene set enrichment analysis was performed in mucosal tissues from control and CRS patients. The effect of resveratrol was evaluated in terms of osteogenesis in a murine eosinophilic CRS NP model.RESULTS: The histopathologic changes showed markedly thickened bones with significant increase in osteoblast numbers in OVA/SEB-treated mice compared to the phosphate-buffered saline-treated mice. The structural changes in bone on micro-CT were consistent with the histopathological features. The expression of RUNX2 and IL-13 was increased by the administration of OVA/SEB and showed a positive correlation. RUNX2 expression mainly co-localized with osteoblasts. Bioinformatic analysis using human CRS transcriptome revealed that IL-13-induced bony changes via RUNX2. Treatment with resveratrol, a candidate drug against osteitis, diminished the expression of IL-13 and RUNX2, and the number of osteoblasts in OVA/SEB-treated mice.CONCLUSIONS: In the present study, we found the histopathological and radiographic evidence of osteogenesis using a previously established murine eosinophilic CRS NP model. This animal model could provide new insights into the pathophysiology of neo-osteogenesis and provide a basis for developing new therapeutics.
Animals ; Computational Biology ; Core Binding Factor Alpha 1 Subunit ; Enterotoxins ; Eosinophilia ; Eosinophils ; Fluorescent Antibody Technique ; Gene Expression ; Humans ; Immunohistochemistry ; Interleukin-13 ; Interleukins ; Mice ; Models, Animal ; Mucous Membrane ; Nasal Polyps ; Nose ; Osteitis ; Osteoblasts ; Osteoclasts ; Osteogenesis ; Osteonectin ; Ovalbumin ; Polymerase Chain Reaction ; Sinusitis ; Transcription Factors ; Transcriptome

Objective: The incidence of bystander cardiopulmonary resuscitation in out-of-hospital cardiac arrest has increased rapidly over the past 10 years. On the other hand, automated external defibrillators (AEDs) are still only used in a minority of cases. This study investigated the feasibility of mobile video call guidance to facilitate AED use for laypeople. Methods: Ninety laypersons were randomized into three groups: mobile video call guided, voice call guided, and nonguided. The participants were exposed to a simulated cardiac arrest requiring AED use and guided by video call, voice call, or not. The simulation experiments were saved as a video clip, and other researchers blinded to simulation assessed the performance according to a prespecified checklist after the simulations. The performance score and analyzed time intervals from AED arrival to defibrillation in the three groups were compared. Results: The basic characteristics were similar in the three groups. Performance scores in the checklist for using AEDs were higher in the mobile video call guided group, particularly in a category of ‘power on AED’ and ‘correctly attaches pads’ than non-guided groups. The performance scores in the category of ‘safely delivers a shock and resume compression’ were also higher in the mobile video call group. On the other hand, the time interval to defibrillation was significantly longer in the mobile video call group. Conclusion This study showed that mobile video call guidance might be an alternative method for laypeople to facilitate AED use, but further well-designed research will be needed.

Objective: To explore the quality of life and mental health status of adolescents in Zhengzhou, and to compare with HongKong and Taiwan. Methods: A total of 6 401 students from 12 primary and secondary schools in Zhengzhou City. A total of 3 642 students from HongKong and 1 547 students from Taiwan were selected by cluster sampling. And Padiatric Quality of Life Inventory Version 4.0, Self-Esteem Scale, General Self-efficacy Scale, Depression Anxiety Stress Scale and self-made general situation questionnaire were used to conduct questionnaire survey. Results: The total score of quality of life and the scores of each dimension in Zhengzhou were significantly higher than those in HongKong, while self-esteem and anxiety were lower than those of Taiwan adolescents（P<0.05). In addition to self-esteem, anxiety and stress, the scores of quality of life and mental health of adolescents of different grades and genders in Zhengzhou were statistically different(t=13.53，20.71，10.92，20.26，14.68，-16.03，21.26；6.16，3.81，-2.22，-0.33，8.76，4.16，2.71，P<0.01). The quality of life of adolescents in HongKong and Taiwan in different grades and genders were basically the same as those in Zhengzhou, and the differences of depression and stress scores in grades were the same as those in Zhengzhou. Conclusion The overall quality of life and mental health of adolescents in Zhengzhou is better than that in Hong Kong and Taiwan. It is necessary to explore the relationship between the quality of life and mental health of adolescents in order to improve their quality of life.

Objective: The loudness dependence of the auditory evoked potential (LDAEP) is a reliable indicator that is inversely related to central serotonergic activity, and recent studies have suggested an association between LDAEP and suicidal ideation. This study investigated differences in LDAEP between patients with major depressive disorder and high suicidality and those with major depressive disorder and low suicidality compared to healthy controls. Methods: This study included 67 participants: 23 patients with major depressive disorder with high suicidality (9 males, mean age 29.3 ± 15.7 years, total score of SSI-BECK ≥ 15), 22 patients with major depressive disorder with low suicidality (9 males, mean age 42.2 ± 14.4 years, total score of SSI-BECK ≤ 14), and 22 healthy controls (11 males, mean age 31.6 ± 8.7 years). Participants completed the following assessments: Patient Health Questionnaire-9, Beck Depression Inventory-II, Beck Scale for Suicidal ideation, State Anxiety Scale of the State-Trait Anxiety Inventory, Beck Anxiety Inventory, and LDAEP (measured at electrode Cz). Results: There were no sex-related differences among groups (p = 0.821). The high-suicidality group exhibited significantly higher LDAEP compared to the low-suicidality group (0.82 ± 0.79 vs. 0.26 ± 0.36, p = 0.014). No significant differences were found between the control and high-suicidality (p = 0.281) or the control and low-suicidality groups (p = 0.236). Conclusion LDAEP was applied to demonstrate the association between serotonergic activity and suicidal ideation and suicide risk in major depression and may be a candidate of biological marker for preventing suicide in this study.

Objective: The loudness dependence of the auditory evoked potential (LDAEP) is a reliable indicator that is inversely related to central serotonergic activity, and recent studies have suggested an association between LDAEP and suicidal ideation. This study investigated differences in LDAEP between patients with major depressive disorder and high suicidality and those with major depressive disorder and low suicidality compared to healthy controls. Methods: This study included 67 participants: 23 patients with major depressive disorder with high suicidality (9 males, mean age 29.3 ± 15.7 years, total score of SSI-BECK ≥ 15), 22 patients with major depressive disorder with low suicidality (9 males, mean age 42.2 ± 14.4 years, total score of SSI-BECK ≤ 14), and 22 healthy controls (11 males, mean age 31.6 ± 8.7 years). Participants completed the following assessments: Patient Health Questionnaire-9, Beck Depression Inventory-II, Beck Scale for Suicidal ideation, State Anxiety Scale of the State-Trait Anxiety Inventory, Beck Anxiety Inventory, and LDAEP (measured at electrode Cz). Results: There were no sex-related differences among groups (p = 0.821). The high-suicidality group exhibited significantly higher LDAEP compared to the low-suicidality group (0.82 ± 0.79 vs. 0.26 ± 0.36, p = 0.014). No significant differences were found between the control and high-suicidality (p = 0.281) or the control and low-suicidality groups (p = 0.236). Conclusion LDAEP was applied to demonstrate the association between serotonergic activity and suicidal ideation and suicide risk in major depression and may be a candidate of biological marker for preventing suicide in this study.