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.

This study aimed to determine the probability of hearing recovery in patients with idiopathic sudden sensorineural hearing loss following salvage intratympanic steroids

A retrospective review of all patients receiving salvage intratympanic steroid injections for idiopathic sudden sensorineural hearing loss was performed (January 2014 to December 2019). Twenty-two patients were identified, of whom 15 met inclusion criteria. Pre- and post-treatment audiograms were compared with the unaffected ear. Hearing recovery was categorised based on American Academy of Otolaryngology Head and Neck Surgery criteria.

Only 1 patient out of 15 (6.7 per cent) made a partial recovery, and the remainder were non-responders. The median duration of time between symptom onset and first salvage intratympanic steroid treatment was 52 days (range, 14–81 days). No adverse reactions were observed.

‘Real world’ patients with idiopathic sudden sensorineural hearing loss present differently to those in the literature. Sudden sensorineural hearing loss should be diagnosed with care and intratympanic steroid injections initiated early if considered appropriate. Patients should make an informed decision on treatment based on prognostic factors and local success rates.

To explore the diagnostic value of anteroposterior-to-transverse ratio for predicting thyroid cancer.

A total of 2306 nodules were divided into 5 groups according to their size. The ability of the anteroposterior-to-transverse ratio to predict thyroid cancer was analysed in each group on the basis of the sensitivity, specificity, accuracy and Youden index.

The median anteroposterior-to-transverse ratio was 0.83, with an interquartile range of 0.28. The area under the receiver operating characteristic curve was 0.709 (p < 0.001). When the diameter of a thyroid nodule was less than 1.5 cm, an anteroposterior-to-transverse ratio of more than 0.9 was associated with higher sensitivity, accuracy and Youden index, compared with an anteroposterior-to-transverse ratio of greater than 1.0, but the specificity was lower. When the diameter of a thyroid nodule was 1.5 cm or more, an anteroposterior-to-transverse ratio of greater than 0.9 was associated with higher sensitivity and Youden index, compared with an anteroposterior-to-transverse ratio of greater than 1.0, but specificity and accuracy were lower.

The anteroposterior-to-transverse ratio was a meaningful indicator of thyroid cancer, and its predictive effectiveness could be influenced by nodule size.