Patients discharged from emergency departments (ED) with antibiotics for common infections often receive unnecessarily prolonged durations, representing a target for transition of care (TOC) antimicrobial stewardship intervention.

This study aimed to evaluate the effectiveness of TOC pharmacists’ review on decreasing the duration of discharge oral antibiotics in patients discharged from the ED at an academic medical center. Pharmacist interventions were guided by an antibiotic duration of therapy guidance focused on respiratory, urinary, and skin infections developed and implemented by the antimicrobial stewardship program. Pharmacist interventions from January 27, 2023, to December 29, 2023, were analyzed to quantify the total number of antibiotic days saved and the percentage of provider acceptance.

The ED TOC pharmacists reviewed a total of 157 oral antibiotic prescriptions. 86.6% percent of the reviews required pharmacist interventions. The most common indications for the discharge antibiotics were urinary tract infections (50.0%) and skin infections (23.4%). The total number of antibiotic days saved was 155 days with the provider acceptance rate of 76.5%. In 21% of cases, providers did not count the antibiotic doses administered in the ED, contributing to unnecessarily prolonged duration. 10.2% of patients re-presented to the ED while 6.4% of patients were hospitalized within 30 days of index ED discharge.

The transitions of care pharmacist-led intervention was successful in optimizing the duration of discharge oral antibiotics in the ED utilizing prospective audit and feedback based on institutional guidance. The ED represents a high-yield setting for TOC-directed antimicrobial stewardship.

While the cross-sectional relationship between internet gaming disorder (IGD) and depression is well-established, whether IGD predicts future depression remains debated, and the underlying mechanisms are not fully understood. This large-scale, three-wave longitudinal study aimed to clarify the predictive role of IGD in depression and explore the mediating effects of resilience and sleep distress.

A cohort of 41,215 middle school students from Zigong City was assessed at three time points: November 2021 (T1), November 2022 (T2) and November 2023 (T3). IGD, depression, sleep distress and resilience were measured using standardized questionnaires. Multiple logistic regression was used to examine the associations between baseline IGD and both concurrent and subsequent depression. Mediation analyses were conducted with T1 IGD as the predictor, T2 sleep distress and resilience as serial mediators and T3 depression as the outcome. To test the robustness of the findings, a series of sensitivity analyses were performed. Additionally, sex differences in the mediation pathways were explored.

(1) IGD was independently associated with depression at baseline (T1: adjusted odds ratio [AOR] = 4.76, 95% confidence interval [CI]: 3.79–5.98, p < 0.001), 1 year later (T2: AOR = 1.42, 95% CI: 1.16–1.74, p < 0.001) and 2 years later (T3: AOR = 1.24, 95% CI: 1.01–1.53, p = 0.042); (2) A serial multiple mediation effect of sleep distress and resilience was identified in the relationship between IGD and depression. The mediation ratio was 60.7% in the unadjusted model and 33.3% in the fully adjusted model, accounting for baseline depression, sleep distress, resilience and other covariates. The robustness of our findings was supported by various sensitivity analyses; and (3) Sex differences were observed in the mediating roles of sleep distress and resilience, with the mediation ratio being higher in boys compared to girls.

IGD is a significant predictor of depression in adolescents, with resilience and sleep distress serving as key mediators. Early identification and targeted interventions for IGD may help prevent depression. Intervention strategies should prioritize enhancing resilience and improving sleep quality, particularly among boys at risk.

Major depressive disorder (MDD) is a prevalent and disabling condition. Approximately 30-50% of patients do not respond to first-line medication or psychotherapy. Therefore, several studies have investigated the predictive potential of pretreatment severity rating or neuroimaging features to guide clinical approaches that can speed optimal treatment selection.

To evaluate the performance of 1) severity ratings (scores of Hamilton Depression/Anxiety Scale, illness duration, and sleep quality, etc.) and demographic characteristic and 2) brain magnetic resonance imaging (MRI) features in predicting treatment outcomes for MDD. Second, to assess performance variations among varied modalities and interventions in MRI studies.

We searched studies in PubMed, Embase, Web of Science, and Science Direct databases before March 22, 2023. We extracted a confusion matrix for prediction in each study. Separate meta-analyses were performed for clinical and MRI studies. The logarithm of diagnostic odds ratio [log(DOR)], sensitivity, and specificity were conducted using Reitsma’s random effect model. The area under curve (AUC) of summary receiver operating characteristic (SROC) curve was calculated.

Subgroup analyses were conducted in MRI studies based on modalities: resting-state functional MRI (rsfMRI), task-based fMRI (tbfMRI), and structural MRI (sMRI), and interventions: antidepressant (including selective serotonin reuptake inhibitors [SSRI]) and electroconvulsive therapy (ECT). Meta-regression was conducted 1) between clinical and MRI studies and 2) among modality or intervention subgroups in MRI studies.

We included ten studies used clinical features covering 6494 patients, yielded a log(DOR) of 1.42, AUC of 0.71, sensitivity of 0.61, and specificity of 0.74. In terms of MRI, 44 studies with 2623 patients were included, revealing an overall log(DOR) of 2.53. The AUC, sensitivity, and specificity were 0.89, 0.78, and 0.75.

Studies using MRI features had a higher sensitivity (0.89 vs. 0.61) in predicting treatment outcomes than clinical features (P < 0.001). RsfMRI had higher specificity (0.79 vs. 0.69) than tbfMRI subgroup (P = 0.01). No significant differences were found between sMRI and other modalities, nor between antidepressants (SSRIs and others) and ECT. Antidepressant studies primarily identified predictive imaging features in limbic and default mode networks, while ECT mainly focused on limbic network.

Our findings suggest a robust promise for pretreatment brain MRI features in predicting treatment outcomes in MDD, offering higher accuracy than clinical studies. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. For MRI studies, overlapping but distinct network level measures predicted outcomes for antidepressants and ECT.

None Declared

Obsessive-compulsive disorder (OCD) is a common psychiatric disorder. It is considered that dysregulation of cytokine levels is related to the pathophysiological mechanism of OCD. However, the results of previous studies on cytokine levels in OCD are inconsistent.

To perform a meta-analysis assessing cytokine levels in peripheral blood of OCD patients.

We searched in PubMed, Web of Science, and Embase from inception to March 31, 2023 for eligible studies. We conducted multivariate meta-analysis in combined proinflammatory cytokines (interleukin-6 [IL-6], IL-1β, IL-2, tumor necrosis factor-α [TNF-α], and interferon-γ [IFN-γ]) and combined anti-inflammatory cytokines (IL-10 and IL-4) respectively, and calculated the same meta-analysis in each cytokine. We also performed sensitivity analysis and publication bias tests, as well as subgroup analysis (i.e. different age groups, varied cytokine measurement methods, medication treated or naïve, and presence of psychiatric comorbidities) and meta-regression analysis (variables including patients’ sex ratio, age, age at symptom onset, illness duration, scores of Y-BOCS, family history of psychiatric disorders, and BMI).

17 original studies (13, 13, 10, 5, 4, 3, 2 studies for IL-6, TNF-α, IL-1β, IL-10, IL-2, IL-4, and IFN-γ, respectively), 573 patients (mean age, 25.2; 50.3% female) and 498 healthy controls (HC; mean age, 25.3; 51.4% female) were included. The results showed that the levels of combined pro- or anti-inflammatory cytokines and each signle cytokine were not significantly different between OCD patients and HC (all P>0.05), with significant heterogeneities in all analyses (I2 from 79.1% to 91.7%). We did not find between-group differences in cytokine levels in all subgroup analyses. Meta-regression analysis suggested that age at onset (P=0.0003) and family history (P=0.0062) might be the source of heterogeneity in TNF-α level. Sensitivity analysis confirmed that all results were stable, except for IL-4 where different cytokine measurement methods may be the contributing factor. Egger test did not find publication bias.

Our study showed no difference in cytokine levels between OCD patients and HC, but age at onset and family history may affect TNF-α level. Confounding factors such as age at onset, family history, and cytokine measurement methods should be controlled in future studies to further explore the immune mechanism of OCD.

None Declared

Adolescence is a period marked by highest vulnerability to the onset of depression, with profound implications for adult health. Neuroimaging studies have revealed considerable atrophy in brain structure in these patients with depression. Of particular importance are regions responsible for cognitive control, reward, and self-referential processing. However, the causal structural networks underpinning brain region atrophies in adolescents with depression remain unclear.

This study aimed to investigate the temporal course and causal relationships of gray matter atrophy within the brains of adolescents with depression.

We analyzed T1-weighted structural images using voxel-based morphometry in first-episode adolescent patients with depression (n=80, 22 males; age = 15.57±1.78) and age, gender matched healthy controls (n=82, 25 males; age = 16.11±2.76) to identify the disease stage-specific gray matter abnormalities. Then, with granger causality analysis, we arranged the patients’ illness duration chronologically to construct the causal structural covariance networks that investigated the causal relationships of those atypical structures.

Compared to controls, smaller volumes in ventral medial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC), middle cingulate cortex (MCC) and insula areas were identified in patients with less than 1 year illness duration, and further progressed to the subgenual ACC, regions of default, frontoparietal networks in longer duration. Causal network results revealed that dACC, vmPFC, MCC and insula were prominent nodes projecting exerted positive causal effects to regions of the default mode and frontoparietal networks. The dACC, vmPFC and insula also had positive projections to the reward network, which included mainly the thalamus, caudate and putamen, while MCC also exerted a positive causal effect on the insula and thalamus.

These findings revealed the progression of structural atrophy in adolescent patients with depression and demonstrated the causal relationships between regions involving cognitive control, reward and self-referential processes.

None Declared

The concept of Mental Health Literacy (MHL) is inherently multidimensional. However, the interrelationships among its various dimensions remain insufficiently elucidated. In recent years, the textual analysis of social media posts has emerged as a promising methodological approach for longitudinal research in this domain.

This study aimed to investigate whether temporal causal associations exist between recognition of mental illness (R), mental illness stigma (S), help-seeking efficacy (HE), maintenance of positive mental health (M), and help-seeking attitude (HA).

Tweets were collocted at three distinct time points: T1, T2, and T3, spanning the period from November 1, 2021, to December 31, 2022. We employed a machine-learning approach to categorize the posts into five MHL facets. Using these facets, we trained a machine learning model, specifically Bidirectional Encoder Representations from Transformers (BERT), to determine the MHL scores. To be eligible, an account must have an R facet score at T1, and M, S, HE facet scores at T2, as well as an HA facet score at T3. In total, we retrieved 4,471,951 MHL-related tweets from 941 users. We further employed structural equation modeling to validate the causal relationships within the MHL framework.

In the evaluation, BERT achieved average accuracy scores exceeding 89% across the five MHL facets in the validation set, along with F1-scores ranging between 0.75 and 0.89. Among the five MHL facets—maintenance of positive mental health, recognition of mental illness, help-seeking efficacy, and help-seeking attitudes—each demonstrated a statistically significant positive correlation with the others. Conversely, mental illness stigma exhibited a statistically significant negative correlation with the remaining four facets. In the analysis using single-mediation models, each of the individual mediator variables—namely, mental illness stigma, help-seeking efficacy, and maintenance of positive mental health—exhibited significant indirect effects. In the multiple-mediation model, two mediator variables—help-seeking efficacy and maintenance of positive mental health—demonstrated significant indirect effects. These findings suggested that the recognition of mental illness exerted an influence on help-seeking attitudes through one or more of these mediators.

By leveraging machine learning techniques for the textual analysis of social media and employing a longitudinal research design with panel data, this study elucidates the potential mechanisms through which the MHL framework influences attitudes toward seeking mental health services. These insights hold significant implications for the design of future interventions and the development of targeted policies aimed at promoting help-seeking behaviors.

None Declared

There’s large heterogeneity present in major depressive disorder (MDD) and controversial evidence on alterations of brain functional connectivity (FC), making it hard to elucidate the neurobiological basis of MDD. Subtyping is one promising solution to characterize this heterogeneity.

To identify neurophysiological subtypes of MDD based on FC derived from resting-state functional magnetic resonance imaging using large multisite data and investigate the differences in genetic mechanisms and neurotransmitter basis of FC alterations, and the differences of FC-related cognition between each subtype.

Consensus clustering of FC patterns was applied to a population of 829 MDD patients from REST-Meta-MDD database after data cleaning and image quality control. Gene transcriptomic data derived from Allen Human Brain Atlas and neurotransmitter receptor/transporter density data acquired by using neuromap toolbox were used to characterize the molecular mechanism underlying each FC-based subtype by identifying the gene set and neurotransmitters/transporters showing high spatial similarity with the profiles of FC alterations between each subtype and 770 healthy controls. The FC-related cognition in each subtype was also selected by lasso regression.

Two stable neurophysiological MDD subtypes were found and labeled as hypoconnectivity (n=527) and hyperconnectivity (n=299) characterized by the FC differences in each subtype relative to controls, respectively. The two subtypes did not differ in age, sex, and scores of Hamilton Depression/Anxiety Scale.

The genes related to FC alterations were enriched in ion transmembrane transport, synaptic transmission/organization, axon development, and regulation of neurotransmitter level for both subtypes, but specifically enriched in glial cell differentiation for hypoconnectivity subtype, while enriched in regulation of presynaptic membrane and regulation of neuron differentiation for hyperconnectivity subtype.

FC alterations were associated with the density of 5-HT2a receptor in both subtypes. For hyperconnectivity subtype, FC alterations were also correlated with the density of norepinephrine transporter, glutamate receptor, GABA receptor, 5-HT1b receptor, and cannabinoid receptor.

Both subtypes showed correlations between FC and categorization, motor inhibition, and localization. The FC in hypoconnectivity subtype correlated with response inhibition, selective attention, face recognition, sleep, empathy, expertise, uncertainty, and anticipation, while that was related to inference, speech perception, and reward anticipation in hyperconnectivity subtype.

Our findings suggested the presence of two neuroimaging subtypes of MDD characterized by hypo or hyper-connectivity. The two subtypes had both shared and distinct genetic mechanisms, neurotransmitter receptor/transporter profiles, and cognition types.

None Declared