Military personnel have greater psychological stress and are at higher suicide attempt risk compared with the general population. High mental stress may cause suicidal ideations which are crucially driving suicide attempts. However, traditional statistical methods could only find a moderate degree of correlation between psychological stress and suicidal ideation in non-psychiatric individuals. This paper utilizes machine learning techniques including logistic regression, decision tree, random forest, gradient boosting regression tree, support vector machine, and multilayer perceptron to predict the presence of suicidal ideation by six important psychological stress domains of the military males and females. The accuracies of all the six machine learning methods are over 98%. Among them, the multilayer perceptron and support vector machine provide the best predictions of suicide ideation approximately to 100%. As compared with the BSRS-5 score ≥ 7, a conventional criterion, for the presence of suicidal ideation ≥ 1, the proposed algorithms can improve the performances of accuracy, sensitivity, specificity, precision, the AUC of ROC curve and the AUC of PR curve up to 5.7%, 35.9%, 4.6%, 65.2%, 4.3% and 53.2%, respectively; and for the presence of more severely intense suicidal ideation ≥ 2, the improvements are 6.1%, 26.2%, 5.8%, 83.5%, 2.8% and 64.7%, respectively.

Background: ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have examined the quality, similarity (abstracts being similar to the original one), and accuracy of the abstracts generated by ChatGPT when researchers provide full-text basic research papers. Objective: We aimed to assess the applicability of an artificial intelligence (AI) model in generating abstracts for basic preclinical research. Methods: We selected 30 basic research papers from Nature, Genome Biology, and Biological Psychiatry. Excluding abstracts, we inputted the full text into ChatPDF, an application of a language model based on ChatGPT, and we prompted it to generate abstracts with the same style as used in the original papers. A total of 8 experts were invited to evaluate the quality of these abstracts (based on a Likert scale of 0-10) and identify which abstracts were generated by ChatPDF, using a blind approach. These abstracts were also evaluated for their similarity to the original abstracts and the accuracy of the AI content. Results: The quality of ChatGPT-generated abstracts was lower than that of the actual abstracts (10-point Likert scale: mean 4.72, SD 2.09 vs mean 8.09, SD 1.03; P<.001). The difference in quality was significant in the unstructured format (mean difference –4.33; 95% CI –4.79 to –3.86; P<.001) but minimal in the 4-subheading structured format (mean difference –2.33; 95% CI –2.79 to –1.86). Among the 30 ChatGPT-generated abstracts, 3 showed wrong conclusions, and 10 were identified as AI content. The mean percentage of similarity between the original and the generated abstracts was not high (2.10%-4.40%). The blinded reviewers achieved a 93% (224/240) accuracy rate in guessing which abstracts were written using ChatGPT. Conclusions: Using ChatGPT to generate a scientific abstract may not lead to issues of similarity when using real full texts written by humans. However, the quality of the ChatGPT-generated abstracts was suboptimal, and their accuracy was not 100%.

We examined the cardiovascular safety, acceptability, and trajectory of the antidepressant effects of psilocybin after single- or two-dose administration. Four major electronic databases were systematically searched. Data were pooled using a multivariate random-effects meta-analysis. Primary outcomes were changes in depressive symptoms. Secondary outcomes were cardiovascular safety and acceptability. Ten studies were included. The estimated effect sizes (standardized mean difference (SMD) with 95% confidence intervals) for psilocybin were −0.75 (−1.15 to −0.35) on day 1, −1.74 (−2.15 to −1.32) at 1 week, −1.35 (−1.77 to −0.93) at 1 month, −0.91 (−1.31 to −0.51) at 3 months, and −1.12 (−1.56 to −0.68) at 6 months. Higher doses and two sessions of psilocybin treatment were significantly associated with superior antidepressant effects. The all-cause discontinuation and heart rate after psilocybin administration were comparable to placebo; meanwhile, psilocybin increased systolic and diastolic blood pressure by 19.00 mmHg and 8.66 mmHg, respectively. There were no significant differences between SMD derived from placebo-controlled trials compared to those from pre–post changes and SMD in randomized controlled trials (RCTs) compared to those in non-RCTs. The present study demonstrates that single- or two-dose psilocybin administration has rapid and sustained antidepressant effects for up to 6 months, with favorable cardiovascular safety and acceptability.

Objective: A certain portion of patients with depression is under-diagnosed and has attracted the attention in the field of natural language processing (NLP). In this study, we intended to explore the feasibility of transferring unstructured textual records into a screening tool to early detect depression. Methods: We recruited 22,355 medical records in Mandarin traditional Chinese from the psychiatry emergency department of a military psychiatry center from 2004 to 2019. We preprocessed all the context of present illness histories as corpus and the presence of clinical diagnoses of depression as an outcome. A state-of-the-art NLP model was developed based on a pretrained bidirectional encoder representation from transformers (BERT) model along with several convolutional neural network (CNN) and trained by the training set (80% of original data) of total samples (BERT_general) and of civilian samples (BERT_civilian) and of military samples (BERT_military) independently. The receiver operating characteristic (ROC) and area under curve (AUC) of three trained models were compared for predicting depression for the test dataset (20% of original data) of general and specific samples. Results: The experimental results demonstrated excellent performance of BERT_general for general samples (AUC = 0.93, sensitivity = 0.817, specificity = 0.920 for optimal cut-off point) and civilian sample (AUC = 0.91, sensitivity = 0.851, specificity = 0.851 for optimal cut-off point). BERT_general showed a significant underperformance of for military samples (AUC = 0.79, sensitivity = 0.712, specificity = 0.732, p < 0.05 for optimal cut-off point). That of BERT_military was slight higher (AUC = 0.82, sensitivity = 0.708, specificity = 0.786 for optimal cut-off point) for military samples. Conclusion: This study showed the feasibility of applying deep learning technique as a depression-detection assistant tool in Mandarin Chinese medical records. However, the subjects' specific situation, e.g., military status, is warranted for further investigation.

Background/Objectives: Tea, the world’s second most consumed beverage after water, contains diverse phytochemicals that have garnered growing interest for their potential ability to modulate inflammasome activation. This study examined the antioxidant and anti-inflammatory properties of oolong tea (OLT) extracts, with a specific focus on their regulatory effects on NLRP3 inflammasome assembly—a critical mediator in chronic inflammatory diseases. Methods: OLT extracts were prepared from the Jin-Xuan cultivar with quantification for bioactive components (total phenolics, flavonoids, condensed tannins, and proanthocyanidins). J774A.1 murine macrophages were primed with LPS and stimulated with ATP to induce inflammasome activation. Therapeutic potentials of OLT extracts were assessed by measuring cytokine secretion, expression of NLRP3 inflammasome-related proteins (NLRP3, ASC, Caspase-1, and IL-1β), inflammasome complex formation, and ROS generation via biochemical assays, immunoblotting, and fluorescence microscopy. Results: OLT extracts, particularly at 100 µg/mL, markedly suppressed both the priming and activation phases of NLRP3 inflammasome formation. OLT treatment reduced IL-1β secretion by more than 50%, attenuated ASC oligomerization and speck formation, inhibited caspase-1 cleavage, and lowered intracellular ROS levels by approximately 50%. Conclusions: These findings suggest that OLT extracts exert potent anti-NLRP3 inflammasome activity and offer immunomodulation potential in preventing inflammation-related diseases such as infections, cancer, and neurodegenerative disorders. Further in vivo investigations, followed by clinical applications and epidemiological studies, are warranted to validate these preventive effects in human populations.

Preliminary meta-analyses suggested that fluvoxamine was effective in treating COVID-19 infection. However, the reliability of this evidence has not yet been examined. MEDLINE, CENTRAL, EMBASE, PsycINFO, and ClinicalTrials.gov were searched to identify any randomized controlled trials (RCTs) from the inception of the databases to 5 February 2023. We used trial sequential analysis (TSA) to examine the reliability of the current existing evidence on the benefits of fluvoxamine on COVID-19 infection. The primary outcome was clinical deterioration, as defined in the original study (reported as odds ratio (OR), with 95% confidence intervals), and the secondary outcome was hospitalization. In the TSA, we used the relative risk reduction thresholds of 10, 20, and 30%. The updated meta-analysis of the five RCTs showed that fluvoxamine was not associated with lower odds of clinical deterioration when compared with a placebo (OR: 0.81; 0.59–1.11). The effect of fluvoxamine lay within the futility boundary (i.e., lack of effect) when using a 30% relative risk reduction threshold. The effect estimates lay between the superiority and futility boundary using the 10% and 20% threshold, and the required size of information was not reached for these two thresholds. The effect of fluvoxamine on the odds of hospitalization was not statistically significant (0.76; 0.56–1.03). In conclusion, there is no reliable evidence that fluvoxamine, when compared to a placebo, reduces the relative risk of clinical deterioration among adult patients with COVID-19 infection by 30%, and a relative risk reduction of 20% or 10% is still uncertain. The role of fluvoxamine as a COVID-19 treatment cannot be justified.