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

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

Following limited clinical exposure during the coronavirus disease 2019 pandemic, a simulation-based platform aimed at providing a unique and safe learning tool was established. The aim was to improve the skills, knowledge and confidence of new ENT doctors.

The course was developed through 5 iterations over 28 months, moving from a half-day session to 2 full-day courses with more scenarios. Participant, faculty and local simulation team feedback drove course development. High-fidelity scenarios were provided, ranging from epistaxis to stridor, using technology including SimMan3 G mannequin, mask-Ed™ and nasendoscopy simulators.

Participant feedback consistently demonstrated that the knowledge and skills acquired enhanced preparedness for working in ENT, with impact being sustained in clinical practice.

Preparing healthcare professionals adequately is essential to enhancing patient safety. This simulation course has been effective in supporting new doctors in ENT and has subsequently been rolled out at a national level.

Major depressive disorder (MDD) is characterized by both clinical symptoms and cognitive deficits. Prior studies have typically examined either symptoms or cognition correlated with brain measures, thus causing a notable paucity of stable brain markers that capture the full characteristics of MDD. Brain controllability derived from newly proposed brain model integrating both metabolism (energy cost) and dynamics from a control perspective has been considered as a sensitive biomarker for characterizing brain function. Thus, identifying such a biomarker of controllability related to both symptoms and cognition may provide a promising state monitor of MDD.

To assess the associations between two multi-dimensional clinical (symptoms and cognition) and brain controllability data of MDD in an integrative model.

Sparse canonical correlation analysis (sCCA) was used to investigate the association between brain controllability at a network level and both clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. The potential mediation effect of cognition on relationship between controllability and symptoms was also tested.

Average controllability was significantly correlated with both symptoms and cognition (rmean=0.54, PBonferroni=0.03). Average controllability of dorsal attention network (DAN) (r=0.46) and visual network (r=0.29) had the highest correlation with both symptoms and cognition. Among clinical variables, depressed mood (r=-0.23) , suicide(r=-0.25), work and activities(r=-0.27), gastrointestinal symptoms (r=-0.25) were significantly negatively associated with average controllability, while cognitive flexibility (r=0.29) was most strongly positively correlated with average controllability. Additionally, cognitive flexibility fully mediated the association between average controllability of DAN and depressed mood (indirect effect=-0.11, 95% CI [-0.18, -0.04], P=0.001) in MDD.

Brain average controllability was correlated with both clinical symptoms and cognition in first-episode medication-naïve patients with MDD. The results suggest that average controllability of DAN and visual network reached high associations with clinical variates in MDD, thus these brain features may serve as stable biomarkers to control the brain functional states transitions to be relevant to cognitions deficits and clinical symptoms of MDD. Additionally, altered average controllability of DAN in patients could induce impairment of cognitive flexibility, and thus cause severe depressed mood, indicating that controllability of DAN may be a potential intervention target for alleviating depressed mood through improving cognitive flexibility in MDD.

None Declared

This study aimed to establish a model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using artificial intelligence algorithms.

Data from 295 patients with hypopharyngeal squamous cell carcinoma were analysed retrospectively. Training sets comprised 70 per cent of the data and test sets the remaining 30 per cent. A total of 22 clinical parameters were included as training features. In total, 12 different types of machine learning algorithms were used for model construction. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve and Cohen's kappa co-efficient were used to evaluate model performance.

The XGBoost algorithm achieved the best model performance. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve and kappa value of the model were 80.9 per cent, 92.6 per cent, 62.9 per cent, 77.7 per cent and 58.1 per cent, respectively.

This study successfully identified a machine learning model for predicting three-year survival status for patients with hypopharyngeal squamous cell carcinoma that can offer a new prognostic evaluation method for the clinical treatment of these patients.

This study was a systematic review to investigate the progression of untreated obstructive sleep apnoea in order to evaluate whether mild obstructive sleep apnoea should be treated from the standpoint of disease progression.

The database search study outcomes that were collected included Apnea Hypopnea Index and Respiratory Disturbance Index. A meta-analysis of obstructive sleep apnoea severity over time intervals was performed.

A total of 17 longitudinal studies and 1 randomised, controlled trial were included for review. For patients with mild obstructive sleep apnoea, mean pre-study and post-study Apnea Hypopnea Index was 5.21 and 8.03, respectively, over a median interval of 53.1 months. In patients with moderate to severe obstructive sleep apnoea, mean pre-study and post-study Apnea Hypopnea Index was 28.9 and 30.3, respectively, over a median interval of 57.8 months. Predictors for disease progression in mild obstructive sleep apnoea are patients aged less than 60 years and those with a baseline body mass index less than 25.

Mild obstructive sleep apnoea progression is observed, but it does not appear to reach any clinically significant progression to moderate or severe obstructive sleep apnoea.

Guidance for the management of thyroid nodules has evolved over time, from initial evaluation based predominantly on clinical grounds to now including the established role of ultrasound and fine needle aspiration cytology in their assessment. There is, however, significant variation in the management of thyroid nodules depending on which national guidelines are followed. In addition, there are certain clinical situations such as pregnancy and paediatric thyroid nodules that have differing evaluation priorities.

This review aimed to provide an overview of currently accepted practices for the initial investigation and subsequent management of patients with thyroid nodules for the non-specialist. The review also addresses areas of variance between the systems in common clinical use, as well as newer, evolving technologies, including molecular testing in the evaluation of malignancy in thyroid nodules.

Apart from the psychiatric symptoms, cognitive deficits are also the core symptoms of schizophrenia. Brain network control theory provided information on the role of a specific brain region in the cognitive control process, helping understand the neural mechanism of cognitive impairment in schizophrenia.

To characterize the control properties of functional brain network in first-episode untreated patients with schizophrenia and the relationships between controllability and psychiatric symptoms, as well as exploring the predictive value of controllability in differentiating patients from healthy controls (HCs).

Average and modal controllability of brain networks were calculated and compared between 133 first-episode untreated patients with schizophrenia and 135 HCs. The associations between controllability and clinical symptoms were evaluated using sparse canonical correlation analysis. Support vector machine (SVM) and SVM-recursive feature elimination combined with the controllability were performed to establish the individual prediction model.

Compared to HCs, the patients with schizophrenia showed increased average controllability and decreased modal controllability in dorsal anterior cingulate cortex (dACC). Brain controllability predominantly in somatomotor, default mode, and visual networks was associated with the positive symptomatology of schizophrenia. The established model could identify patients with an accuracy of 0.68. Furthermore, the most discriminative features were located in dACC, medial prefrontal lobe, precuneus and superior temporal gyrus.

Altered controllability in dACC may play a critical role in the neuropathological mechanisms of cognitive deficit in schizophrenia, which could drive the brain function to different states to cope with varied cognitive tasks. As symptom-related biomarkers, controllability could be also beneficial to individual prediction in schizophrenia.

No significant relationships.

There is a growing consensus on brain networks that it is not immutable but rather a dynamic complex system for adapting environment. The neuroimaging research studying how brain regions work collaboratively with dynamic methods had demonstrated its effectiveness in revealing the neural mechanisms of schizophrenia.

To investigate altered dynamic brain functional topology in first-episode untreated schizophrenia patients (SZs) and establish classification models to find objective brain imaging biomarkers.

Resting-state-functional magnetic resonance data for SZs and matched healthy controls were obtained(Table1).

Power-264-template was used to extract nodes and sliding-window approach was carried out to establish functional connectivity matrices. Functional topology was assessed by eigenvector centrality(EC) and node efficiency and its time-fluctuating was evaluated with coefficient of variation(CV). Group differences of dynamic topology and correlation analysis between Positive and Negative Syndrome Scale(PANSS) scores and topology indices showing group differences, which also were used in establishing classification models, was examed.

The CV of node efficiency in angular and paracingulate gyrus was larger in SZs. There are 13 nodes assigned into several brain networks displaying altered CV of EC between groups(Figure1.A). Fluctuation of EC of the node in DMN, which was lower in SZs, showed negative correlation with PANSS total scores(Figure1.B). Dynamic functional topology of above nodes was used to train classification models and demonstrated 80% and 71% accuracy for support vector classification(SVC) and random forest(RF), respectively(Figure2).

Dynamic functional topology illustrated a capability in identifying SZs. Aberrated dynamics of DMN relevant to severity of patient’s symptoms could reveal the reason why it contributed to classification.

No significant relationships.

Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.

To describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.

RADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.

A total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses.

RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.

No significant relationships.