Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia.

The augmentation of clozapine with electroconvulsive therapy (ECT) has been an optimal treatment option for patients with treatment- or clozapine-resistant schizophrenia. Using data from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics survey, which was the largest international psychiatry research collaboration in Asia, our study aimed to develop a machine learning algorithm-based substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in terms of precision medicine. A random forest model and least absolute shrinkage and selection operator (LASSO) model were used to develop a substantial prediction model for the augmented use of clozapine with ECT. Among the 3744 Asian patients with schizophrenia, those treated with a combination of clozapine and ECT were characterized by significantly greater proportions of females and inpatients, a longer duration of illness, and a greater prevalence of negative symptoms and social or occupational dysfunction than those not treated. In the random forest model, the area under the curve (AUC), which was the most preferred indicator of the prediction model, was 0.774. The overall accuracy was 0.817 (95% confidence interval, 0.793−0.839). Inpatient status was the most important variable in the substantial prediction model, followed by BMI, age, social or occupational dysfunction, persistent symptoms, illness duration > 20 years, and others. Furthermore, the AUC and overall accuracy of the LASSO model were 0.831 and 0.644 (95% CI, 0.615−0.672), respectively. Despite the subtle differences in both AUC and overall accuracy of the random forest model and LASSO model, the important variables were commonly shared by the two models. Using the machine learning algorithm, our findings allow the development of a substantial prediction model for the augmented use of clozapine with ECT in Asian patients with schizophrenia. This substantial prediction model can support further studies to develop a substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in a strict epidemiological context.

Elucidation of the Inhibitory Mechanisms of Nipponoparmelia laevior Lichen Extract against Influenza A (H1N1) Virus through Proteomic Analyses.

Lichens contain diverse bioactive secondary metabolites with various chemical and biological properties, which have been widely studied. However, details of the inhibitory mechanisms of their secondary metabolites against influenza A virus (IAV) have not been documented. Here, we investigated the antiviral effect of lichen extracts, obtained from South Korea, against IAV in MDCK cells. Of the lichens tested, Nipponoparmelia laevior (LC24) exhibited the most potent inhibitory effect against IAV infection. LC24 extract significantly increased cell viability, and reduced apoptosis in IAV-infected cells. The LC24 extract also markedly reduced (~ 3.2 logfold) IAV mRNA expression after 48 h of infection. To understand the antiviral mechanism of LC24 against IAV, proteomic (UPLC-HDMSE) analysis was performed to compare proteome modulation in IAV-infected (V) vs. mock (M) and LC24+IAV (LCV) vs. V cells. Based on Ingenuity Pathway Analysis (IPA), LC24 inhibited IAV infection by modulating several antiviral-related genes and proteins (HSPA4, HSPA5, HSPA8, ANXA1, ANXA2, HIF-1α, AKT1, MX1, HNRNPH1, HNRNPDL, PDIA3, and VCP) via different signaling pathways, including HIF-1α signaling, unfolded protein response, and interferon signaling. These molecules were identified as the specific biomarkers for controlling IAV in vitro and further confirmation of their potential against IAV in vivo is required. Our findings provide a platform for further studies on the application of lichen extracts against IAV.

Psychotropic drug-prescribing correlates of disorganized speech in Asians with schizophrenia: The REAP-AP study.

BACKGROUND: Although disorganized speech is seen as one of the nuclear features of schizophrenia, there have been few reports of disorganized speech-associated psychotropic drug-prescribing patterns in large samples of schizophrenia patients. OBJECTIVE: We aimed to examine the prevalence of disorganized speech and its correlates in terms of psychotropic drug prescribing, using the data from the Research on Asian Psychotropic Patterns for Antipsychotics (REAP-AP) study. METHOD: A total of 3744 patients with the ICD-10 diagnosis of schizophrenia were enrolled from 71 survey centers in 15 Asian countries/areas. An essential criterion of disorganized speech was that it was "severe enough to impair substantially effective communication" as defined in the DSM-5. A binary logistic model was fitted to identify the psychotropic drug-prescribing correlates of disorganized speech. RESULTS: After adjusting for the potential effects of confounding variables, the binary logistic regression model showed that the presence of disorganized speech was directly associated with adjunctive use of mood stabilizers (P < 0.001) and cumulative diazepam equivalent dose (P < 0.0001), and inversely associated with adjunctive use of anti-Parkinson drugs (P < 0.0001). CONCLUSION: The association between disorganized speech and adjunctive use of mood stabilizers could perhaps be understood in the context of a relationship with impulsiveness/aggressiveness, or in terms of deconstructing the Kraepelinian dualism.

Two new guaiane sesquiterpene lactones from the aerial parts of Artemisia vulgaris.

Two new guaiane sesquiterpene lactones, vulgarolides A and B (1 and 2), were isolated from Artemisia vulgaris aerial parts using various chromatographic separations. The structure elucidation was performed by combination of spectroscopic experiments including 1D and 2D NMR, HR ESI MS, and CD. Their in vitro cytotoxic activities against five human cancer cell lines were also evaluated using SRB method.

Effects of Annatto (Bixa orellana L.) Seeds Powder on Physicochemical Properties, Antioxidant and Antimicrobial Activities of Pork Patties during Refrigerated Storage.

This study investigated the effect of the powder produced by ball-milling the outer layer of annatto (Bixa orellana L.) seeds on the physicochemical properties as well as the antioxidant and antimicrobial activities of pork patties over 14 d of refrigerated storage (4±1℃). Five pork patty treatments were produced containing three different concentrations of annatto seeds, 0.1, 0.25 and 0.5% (ANT0.1, ANT0.25, ANT0.5), 0.1% ascorbic acid (AA0.1), and a control (CTL). Based on the results, annatto seed powder appeared to show antioxidant activity. The Hunter color values of pork patties were affected by the addition of annatto seed powder, which increased the redness and yellowness values, but decreased the lightness of the patties (p<0.05). To evaluate the antioxidative effects of annatto on pork patties, thiobarbituric acid reactive substances (TBARS) and peroxide values (POV) were analyzed over 14 d of refrigerated storage. Treatments containing annatto seed showed lower TBARS and POV than control (CTL) samples (p<0.05). The volatile basic nitrogen (VBN) of the pork patties containing annatto seeds were lower than that of CTL at the end of storage (p<0.05). Although no differences in total bacterial counts were observed between control and treated patties, those containing annatto seeds had lower microbial counts for Enterobacteriacease than CTL or AA 0.1%. Therefore, annatto seed powder might be a good source of natural antioxidants for the production of meat products.