Building and Validation of an Acute Event Prediction Model for Severe Mental Disorders.

Background: The global incidence of acute events in psychiatric patients is intensifying, and models to successfully predict acute events have attracted much attention. Objective: To explore the influence factors of acute incident severe mental disorders (SMDs) and the application of Rstudio statistical software, and build and verify a nomogram prediction model. Methods: SMDs were taken as research objects. The questionnaire survey method was adopted to collect data. Patients with acute event independent factors were screened. R software multivariable Logistic regression model was constructed and a nomogram was drawn. Results: A total of 342 patients with SMDs were hospitalized, and the number of patients who encountered acute events was 64, which accounted for 18.70% of all patients. Statistical significances were found in many aspects (all P ˂ 0.05). Such aspects included Medication adherence, disease diagnosis, marital status, caregivers, social support and the hospitalization environment (odds ratio (OR) = 4.08, 11.62, 12.06, 10.52, 0.04 and 0.61, respectively) were independent risk factors for the acute events of patients with SMDs. The prediction model was modeled, and the AUC was 0.77 and 0.80. The calibration curve shows that the model has good calibration. The clinical decision curve shows that the model has a good clinical effect. Conclusion: The constructed risk prediction model shows good prediction effectiveness in the acute events of patients with SMDs, which is helpful for the early detection of clinical mental health staff at high risk of acute events.

Quantitative magnetic resonance spectroscopy of depression: The value of short-term metabolite changes in predicting treatment response.

Background: Although various prediction models of the antidepressant response have been established, the results have not been effectively applied to heterogeneous depression populations, which has seriously limited their clinical value. This study tried to build a more specific and stable model to predict treatment response in depression based on short-term changes in hippocampal metabolites. Materials and methods: Seventy-four major depressive disorder (MDD) patients and 20 healthy controls in the test set were prospectively collected and retrospectively analyzed. Subjects underwent magnetic resonance spectroscopy (MRS) once a week during 6 weeks of treatment. Hippocampal regions of interest (ROIs) were extracted by using a voxel iteration scheme combined with standard brain templates. The short-term differences in hippocampal metabolites between and within groups were screened. Then, the association between hippocampal metabolite changes and clinical response was analyzed, and a prediction model based on logistic regression was constructed. In addition, a validation set (n = 60) was collected from another medical center to validate the predictive abilities. Results: After 2-3 weeks of antidepressant treatment, the differences in indicators (tCho wee0-2, tCho wee0-3 and NAA week0-3) were successfully screened. Then, the predictive abilities of these three indicators were revealed in the logistic regression model, and the optimal prediction effect was found in d(tCho) week0-3-d(NAA) week0-3 (AUC = 0.841, 95%CI = 0.736-0.946). In addition, their predictive abilities were further confirmed with the validation set. Limitations: The small sample size and the need for multiple follow-ups limited the statistical ability to detect other findings. Conclusion: The predictive model in this study presented accurate prediction and strong verification effects, which may provide early guidance for adjusting the treatment regimens of depression and serve as a checkpoint at which the eventual treatment outcome can be predicted.

Montelukast ameliorated pemetrexed-induced cytotoxicity in hepatocytes by mitigating endoplasmic reticulum (ER) stress and nucleotide oligomerization domain-like receptor protein 3 (NLRP3) activation.

Pemetrexed (PEM) is an effective chemotherapeutic drug used for the treatment of clinical non-small-cell lung cancer (NSCLC) and is reported to induce severe hepatotoxicity. Exploring potential drugs which could counteract the side effects of PEM is of great clinical interest. Here, we aim to examine the beneficial effects of Montelukast, a novel anti-asthma drug, against PEM-induced cytotoxicity in hepatocytes, and to explore the underlying mechanism. We found that Montelukast reduces cytotoxicity of PEM in hepatocytes, confirmed by its increasing cell viability and reducing lactate dehydrogenase (LDH) release. In addition, Montelukast attenuated PEM-induced oxidative stress by reducing mitochondrial reactive oxygen species (ROS), increasing reduced glutathione (GSH), and downregulating NADPH oxidase 4 (NOX-4) expression. Importantly, Montelukast suppressed PEM-induced activation of the nucleotide oligomerization domain-like receptor protein 3 (NLRP3) inflammasome and mitigated endoplasmic reticulum (ER) stress by reducing NLRP3, growth arrest, and DNA damage-inducible protein 34 (GADD34), CEBP-homologous protein (CHOP), and also blocking the eukaryotic initiation factor 2 (eIF-2α)/activating transcription factor 4 (ATF4) signaling pathway. Lastly, we found that Montelukast inhibited the transcriptional activity of nuclear factor kappa-B (NF-κB). Montelukast exerted a protective action against PEM-induced cytotoxicity in hepatocytes by mitigating ER stress and NLRP3 activation.

Pulmonary embolism caused by postpartum ovarian vein thrombophlebitis after vaginal delivery: Case report and brief review of the literature.

Puerperal ovarian vein thrombophlebitis is a rare pathology. We present the case of a 23-year-old female who presented with fever and cough that occurred after vaginal delivery and persisted after several courses of antibiotics between the 2nd and 20th day postpartum. CT pulmonary angiography revealed right subsegmental pulmonary embolism. An abdominal ultrasonographic examination led to the diagnosis of ovarian vein thrombosis. She was treated with warfarin for 2 weeks with a good response. Our case highlights the importance of prompt ultrasonographic diagnosis and clinical treatment of ovarian vein thrombosis to prevent morbidity and mortality.