Unmasking the mental health scars of COVID-19: A longitudinal investigation of children and adolescents in post-lockdown China.

The long-term mental health consequences of COVID-19 in children and adolescents remain unclear. We investigated the impact of COVID-19 infection on mental health after China's zero-COVID policy relaxation, focusing on symptom-specific and social-family risk factors for mental health issues in children and adolescents. In a longitudinal study, 8348 youths (aged 10-18) were assessed twice (T1: September to October 2022 and T2: April to May 2023). Mental health changes (Δ=T1-T2) were compared between COVID-19-infected (COVID+, n = 4108) and non-infected (COVID-, n = 4240). After balancing social-family confounding factors at T1 with propensity score-based inverse probability weights, multivariable logistic regression was employed to assess associations between COVID-19 infection and the onset/worsening of mental health symptoms. Multivariable logistic regression was conducted to explore specific acute COVID-19 symptoms and social-family risk factors associated with the onset/worsening of mental health symptoms in COVID + group. Compared to COVID- group, COVID + group exhibited lower overall mental health improvement (Δ). COVID + group was associated with increased risks of depression worsening (OR 1.20, 95 % CI 1.04-1.39), anxiety worsening (OR 1.30, 95 % CI 1.15-1.47), stress worsening (OR 1.23, 95 % CI 1.03-1.46), insomnia worsening (OR 1.21, 95 % CI 1.05-1.39), and emotional symptoms worsening (OR 1.72, 95 % CI 1.27-2.33). Moderate-to-severe difficulty thinking, breathlessness, and gastrointestinal symptoms were specific COVID-19 symptoms associated with worsening of various mental health outcomes. Furthermore, academic difficulties, economic disadvantages, family conflicts, food addiction, and alcohol consumption were identified as social-family risk factors for worsening mental health symptoms in COVID + youths. COVID-19 infection leaves lasting mental health scars in youths, extending beyond the acute phase. Specific symptoms, particularly cognitive dysfunction and respiratory/gastrointestinal distress play a significant role in this vulnerability. Social-family factors further modulate these effects, highlighting the need for comprehensive interventions that address both biological and psychosocial aspects. This study provides valuable insights for tailoring mental health support to youths navigating the consequences of the COVID-19 pandemic.

Prediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study.

BACKGROUND: Depression and anxiety are common and disabling mental health problems in children and young adults. Group cognitive behavioral therapy (GCBT) is considered that an efficient and effective treatment for these significant public health concerns, but not all participants respond equally well. The aim of this study was to examine the predictive ability of heart rate variability (HRV), based on sensor data from consumer-grade wearable devices to detect GCBT effectiveness in early intervention. METHODS: In a study of 33 college students with depression and anxiety, participants were randomly assigned to either GCBT group or a wait-list control (WLC) group. They wore smart wearable devices to measure their physiological activities and signals in daily life. The HRV parameters were calculated and compared between the groups. The study also assessed correlations between participants' symptoms, HRV, and GCBT outcomes. RESULTS: The study showed that participants in GCBT had significant improvement in depression and anxiety symptoms after four weeks. Higher HRV was associated with greater improvement in depressive and anxious symptoms following GCBT. Additionally, HRV played a noteworthy role in determining how effective GCBT was in improve anxiety(P = 0.002) and depression(P = 0.020), and its predictive power remained significant even when considering other factors. CONCLUSION: HRV may be a useful predictor of GCBT treatment efficacy. Identifying predictors of treatment response can help personalize treatment and improve outcomes for individuals with depression and anxiety. TRIAL REGISTRATION: The trial has been retrospectively registered on [22/06/2023] with the registration number [NCT05913349] in the ClinicalTrials.gov. Variations in heart rate variability (HRV) have been associated with depression and anxiety, but the relationship of baseline HRV to treatment outcome in depression and anxiety is unclear. This study predicted GCBT effectiveness using HRV measured by wearable devices. 33 students with depression and anxiety participated in a trial comparing GCBT and wait-list control. HRV parameters from wearables correlated with symptoms (PHQ, PSS) and GCBT effectiveness. Baseline HRV levels are strongly associated with GCBT treatment outcomes. HRV may serve as a useful predictor of efficacy of GCBT treatment,facilitating personalized treatment approaches for individuals with depression and anxiety.

Miniature bioinspired artificial compound eyes: microfabrication technologies, photodetection and applications.

As an outstanding visual system for insects and crustaceans to cope with the challenges of survival, compound eye has many unique advantages, such as wide field of view, rapid response, infinite depth of field, low aberration and fast motion capture. However, the complex composition of their optical systems also presents significant challenges for manufacturing. With the continuous development of advanced materials, complex 3D manufacturing technologies and flexible electronic detectors, various ingenious and sophisticated compound eye imaging systems have been developed. This paper provides a comprehensive review on the microfabrication technologies, photoelectric detection and functional applications of miniature artificial compound eyes. Firstly, a brief introduction to the types and structural composition of compound eyes in the natural world is provided. Secondly, the 3D forming manufacturing techniques for miniature compound eyes are discussed. Subsequently, some photodetection technologies for miniature curved compound eye imaging are introduced. Lastly, with reference to the existing prototypes of functional applications for miniature compound eyes, the future development of compound eyes is prospected.

A novel approach to deriving the fine-scale daily NO2 dataset during 2005-2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment.

Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005-2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 µg/m3, while cross-validation across China during 2013-2020 showed an R2 of 0.73 and RMSE of 9.6 µg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 µg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012-2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China.

Switchover from singlet oxygen to superoxide radical through a photoinduced two-step sequential energy transfer process.

The competitive nature of type II photosensitizers in the transfer of excitation energy for the generation of singlet oxygen (1O2) presents significant challenges in the design of type I photosensitizers to produce the superoxide anion radical (O2˙-). In this study, we present an efficient method for the direct transformation of type II photosensitizers into type I photosensitizers through the implementation of an artificial light-harvesting system (ALHSs) involving a two-step sequential energy transfer process. The designed supramolecular complex (DNPY-SBE-ß-CD) not only has the ability to generate 1O2 as type II photosensitizers, but also demonstrates remarkable fluorescence properties in aqueous solution, which renders it an efficient energy donor for the development of type I photosensitizers ALHSs, thereby enabling the efficient generation of O2˙-. Meanwhile, to ascertain the capability and practicality of this method, two organic reactions were conducted, namely the photooxidation reaction of thioanisole and oxidative hydroxylation of arylboronic acids, both of which display a high level of efficiency and exhibit significant catalytic performance. This work provides an efficient method for turning type II photosensitizers into type I photosensitizers by a two-step sequential energy transfer procedure.

Effectiveness of a Biofeedback Intervention Targeting Mental and Physical Health Among College Students Through Speech and Physiology as Biomarkers Using Machine Learning: A Randomized Controlled Trial.

Biofeedback therapy is mainly based on the analysis of physiological features to improve an individual's affective state. There are insufficient objective indicators to assess symptom improvement after biofeedback. In addition to psychological and physiological features, speech features can precisely convey information about emotions. The use of speech features can improve the objectivity of psychiatric assessments. Therefore, biofeedback based on subjective symptom scales, objective speech, and physiological features to evaluate efficacy provides a new approach for early screening and treatment of emotional problems in college students. A 4-week, randomized, controlled, parallel biofeedback therapy study was conducted with college students with symptoms of anxiety or depression. Speech samples, physiological samples, and clinical symptoms were collected at baseline and at the end of treatment, and the extracted speech features and physiological features were used for between-group comparisons and correlation analyses between the biofeedback and wait-list groups. Based on the speech features with differences between the biofeedback intervention and wait-list groups, an artificial neural network was used to predict the therapeutic effect and response after biofeedback therapy. Through biofeedback therapy, improvements in depression (p = 0.001), anxiety (p = 0.001), insomnia (p = 0.013), and stress (p = 0.004) severity were observed in college-going students (n = 52). The speech and physiological features in the biofeedback group also changed significantly compared to the waitlist group (n = 52) and were related to the change in symptoms. The energy parameters and Mel-Frequency Cepstral Coefficients (MFCC) of speech features can predict whether biofeedback intervention effectively improves anxiety and insomnia symptoms and treatment response. The accuracy of the classification model built using the artificial neural network (ANN) for treatment response and non-response was approximately 60%. The results of this study provide valuable information about biofeedback in improving the mental health of college-going students. The study identified speech features, such as the energy parameters, and MFCC as more accurate and objective indicators for tracking biofeedback therapy response and predicting efficacy. Trial Registration ClinicalTrials.gov ChiCTR2100045542.

Visual cortex repetitive transcranial magnetic stimulation (rTMS) reversing neurodevelopmental impairments in adolescents with major psychiatric disorders (MPDs): A cross-species translational study.

AIMS: Neurodevelopmental impairments are closely linked to the basis of adolescent major psychiatric disorders (MPDs). The visual cortex can regulate neuroplasticity throughout the brain during critical periods of neurodevelopment, which may provide a promising target for neuromodulation therapy. This cross-species translational study examined the effects of visual cortex repetitive transcranial magnetic stimulation (rTMS) on neurodevelopmental impairments in MPDs. METHODS: Visual cortex rTMS was performed in both adolescent methylazoxymethanol acetate (MAM) rats and patients with MPDs. Functional magnetic resonance imaging (fMRI) and brain tissue proteomic data in rats and fMRI and clinical symptom data in patients were analyzed. RESULTS: The regional homogeneity (ReHo) analysis of fMRI data revealed an increase in the frontal cortex and a decrease in the posterior cortex in the MAM rats, representing the abnormal neurodevelopmental pattern in MPDs. In regard to the effects of rTMS, similar neuroimaging changes, particularly reduced frontal ReHo, were found both in MAM rats and adolescent patients, suggesting that rTMS may reverse the abnormal neurodevelopmental pattern. Proteomic analysis revealed that rTMS modulated frontal synapse-associated proteins, which may be the underpinnings of rTMS efficacy. Furthermore, a positive relationship was observed between frontal ReHo and clinical symptoms after rTMS in patients. CONCLUSION: Visual cortex rTMS was proven to be an effective treatment for adolescent MPDs, and the underlying neural and molecular mechanisms were uncovered. Our study provides translational evidence for therapeutics targeting the neurodevelopmental factor in MPDs.

The altered temporal properties of dynamic functional connectivity associated with suicide attempt in bipolar disorders.

OBJECTIVE: The suicide risk in bipolar disorder (BD) is the highest among psychiatric disorders, and the neurobiological mechanism of suicide in BD remains unclear. The study aimed to investigate the underlying relevance between the implicated abnormalities of dynamic functional connectivity (FC) and suicide attempt (SA) in BD. METHODS: We used the sliding window method to analyze the dynamic FC patterns from resting-state functional MRI data in 81 healthy controls (HC) and 114 BD patients (50 with SA and 64 with none SA). Then, the temporal properties of dynamic FC and the relationship between altered measures and clinical variables were explored. RESULTS: We found that one of the five captured brain functional states was more associated with SA. The SA patients showed significantly increased fractional window and dwell time in the suicide-related state, along with increased number of state transitions compared with none SA (NSA). In addition, the connections within subcortical network-subcortical network (SubC-SubC), default mode network-subcortical network (DMN-SubC), and attention network-subcortical network (AN-SubC) were significantly changed in SA patients relative to NSA and HC in the suicide-related state. Crucially, the above-altered measures were significantly correlated with suicide risk. CONCLUSIONS: Our findings suggested that the impaired dynamic FC within SubC-SubC, DMN-SubC, and AN-SubC were the important underlying mechanism in understanding SA for BD patients. It highlights the temporal properties of whole-brain dynamic FC could serve as the valuable biomarker for suicide risk assessment in BD.

A neuroimaging-based precision medicine framework for depression.

BACKGROUND: Symptom-based diagnostic criteria of depression leads to notorious heterogeneity and subjectivity. METHODS: The study was conducted in two stages at two sites: development of a neuroimaging-based subtyping and precise repetitive transcranial magnetic stimulation (rTMS) strategy for depression at Center 1 and its clinical application at Center 2. Center 1 identified depression subtypes and subtype-specific rTMS targets based on amplitude of low frequency fluctuation (ALFF) in a cohort of 238 major depressive disorder patients and 66 healthy controls (HC). Subtypes were identified using a Gaussian Mixture Model, and subtype-specific rTMS targets were selected based on dominant brain regions prominently differentiating depression subtypes from HC. Subsequently, one classifier was employed and 72 hospitalized, depressed youths at Center 2 received two-week precise rTMS. MRI and clinical assessments were obtained at baseline, midpoint, and treatment completion for evaluation. RESULTS: Two neuroimaging subtypes of depression, archetypal and atypical depression, were identified based on distinct frontal-posterior functional imbalance patterns as measured by ALFF. The dorsomedial prefrontal cortex was identified as the rTMS target for archetypal depression, and the occipital cortex for atypical depression. Following precise rTMS, ALFF alterations were normalized in both archetypal and atypical depressed youths, corresponding with symptom response of 90.00% in archetypal depression and 70.73% in atypical depression. CONCLUSIONS: A precision medicine framework for depression was developed based on objective neurobiomarkers and implemented with promising results, actualizing a subtyping-treatment-evaluation closed loop in depression. Future randomized controlled trials are warranted.

Superoxide radical generator based on triphenylamine-based supramolecular organic framework for green light photocatalysis.

Supramolecular organic frameworks (SOFs) mostly require high-energy purple or blue light for photocatalytic reactions, while highly abundant and low-energy light systems have rarely been explored. Therefore, it is necessary to construct 2D SOFs for low-energy light-induced photocatalysis. This study describes the design and synthesis of a water-soluble two-dimensional (2D) supramolecular organic framework (TP-SOF) using the host-guest interaction between a triphenylamine derivative (TP-3Py) and cucurbit[8]uril (CB[8]). The formation of the 2D SOF can be attributed to the synergistic impact resulting from the orientated head-to-tail superposition mode between the vinylpyridine arms of TP-3Py and CB[8], which results in a significant redshift in the UV-vis absorption spectrum, especially displaying a strong absorption band in the green light region. The monomeric TP-3Py can effectively produce singlet oxygen (1O2) and realize the photocatalytic oxidation of thioanisole in the aqueous solution. In comparison to monomeric TP-3Py, the confinement effect of CB[8] results in a notable enhancement in the production efficiency of superoxide anion radicals (O2•-), exhibiting promising prospects in the field of photocatalytic oxidation reaction, which facilitates the application of TP-SOF as a very efficient photosensitizer for the promotion of the oxidative hydroxylation of arylboronic acids under green light in the aqueous solution, giving a high yield of 91%. The present study not only presents a compelling illustration of photocatalysis utilizing a 2D SOF derived from triphenylamine, but also unveils promising avenues for the photocatalytic oxidation of SOF employing low-energy light systems.

Effects of resistance training on alleviating hypoxia-induced muscle atrophy: Focus on acetylation of FoxO1.

This study aims to explore the role of FoxO1 and its acetylation in the alleviation of hypoxia-induced muscle atrophy by resistance training. Forty male Sprague-Dawley rats were randomly divided into four groups: normoxic control group (C), normoxic resistance training group (R), hypoxic control group (H) and hypoxic resistance training group (HR). Rats in R and HR groups were trained on an incremental weight-bearing ladder every other day, while those in H and HR groups were kept in an environment containing 12.4% O2 . After 4 weeks, muscles were collected for analysis. Differentiated L6 myoblasts were analysed in vitro after hypoxia exposure and plasmids transfection (alteration in FoxO1 acetylation). The lean body mass loss, wet weight and fibre cross-sectional area of extensor digitorum longus of rats were decreased after 4 weeks hypoxia, and the adverse reactions above was reversed by resistance training. At the same time, the increase in hypoxia-induced autophagy was suppressed, which was accompanied by a decrease in the expression of nuclear FoxO1 and cytoplasmic Ac-FoxO1 by resistance training. The L6 myotube diameter increased and the expression of autophagic proteins were inhibited under hypoxia via intervening by FoxO1 deacetylation. Overall, resistance training alleviates hypoxia-induced muscle atrophy by inhibiting nuclear FoxO1 and cytoplasmic Ac-FoxO1-mediated autophagy.

Aberrant Hippocampal Development in Early-onset Mental Disorders and Promising Interventions: Evidence from a Translational Study.

Early-onset mental disorders are associated with disrupted neurodevelopmental processes during adolescence. The methylazoxymethanol acetate (MAM) animal model, in which disruption in neurodevelopmental processes is induced, mimics the abnormal neurodevelopment associated with early-onset mental disorders from an etiological perspective. We conducted longitudinal structural magnetic resonance imaging (MRI) scans during childhood, adolescence, and adulthood in MAM rats to identify specific brain regions and critical windows for intervention. Then, the effect of repetitive transcranial magnetic stimulation (rTMS) intervention on the target brain region during the critical window was investigated. In addition, the efficacy of this intervention paradigm was tested in a group of adolescent patients with early-onset mental disorders (diagnosed with major depressive disorder or bipolar disorder) to evaluate its clinical translational potential. The results demonstrated that, compared to the control group, the MAM rats exhibited significantly lower striatal volume from childhood to adulthood (all P <0.001). In contrast, the volume of the hippocampus did not show significant differences during childhood (P >0.05) but was significantly lower than the control group from adolescence to adulthood (both P <0.001). Subsequently, rTMS was applied to the occipital cortex, which is anatomically connected to the hippocampus, in the MAM models during adolescence. The MAM-rTMS group showed a significant increase in hippocampal volume compared to the MAM-sham group (P <0.01), while the volume of the striatum remained unchanged (P >0.05). In the clinical trial, adolescents with early-onset mental disorders showed a significant increase in hippocampal volume after rTMS treatment compared to baseline (P <0.01), and these volumetric changes were associated with improvement in depressive symptoms (r = - 0.524, P = 0.018). These findings highlight the potential of targeting aberrant hippocampal development during adolescence as a viable intervention for early-onset mental disorders with neurodevelopmental etiology as well as the promise of rTMS as a therapeutic approach for mitigating aberrant neurodevelopmental processes and alleviating clinical symptoms.

Functional network characteristics in adolescent psychotic mood disorder: associations with symptom severity and treatment effects.

Adolescent psychotic mood disorder (MDP) is a specific phenotype that characterized by more severe symptoms and prognosis compared to nonpsychotic mood disorder (MDNP). But the underlying neural mechanisms remain unknown, and graph theory analysis can help to understand possible mechanisms of psychotic symptoms from the perspective of functional networks. A total of 177 adolescent patients with mood disorders were recruited, including 61 MDP and 116 MDNP. Functional networks were constructed, and topological properties were compared between the two groups at baseline and after treatment, and the association between properties changes and symptom improvement was explored. Compared to the MDNP group, the MDP group exhibited higher small-world properties (FDR q = 0.003) and normalized clustering coefficients (FDR q = 0.008) but demonstrated decreased nodal properties in the superior temporal gyrus (STG), Heschl's gyrus, and medial cingulate gyrus (all FDR q < 0.05). These properties were found to be correlated with the severity of psychotic symptoms. Topological properties also changed with improvement of psychotic symptoms after treatment, and changes in degree centrality of STG in the MDP was significantly positive correlated with improvement of psychotic symptoms (r = 0.377, P = 0.031). This study indicated that functional networks are more severely impaired in patients with psychotic symptoms. Topological properties, particularly those associated with the STG, hold promise as emerging metrics for assessing symptoms and treatment efficacy in patients with psychotic symptoms.

Predicting treatment response in adolescents and young adults with major depressive episodes from fMRI using graph isomorphism network.

BACKGROUND: Major depressive episode (MDE) is the main clinical feature of mood disorders (major depressive disorder and bipolar disorder) in adolescents and young adults and accounts for most of the disease course. However, 30%-40% of MDE patients not responding to clinical first-line interventions. It is crucial to predict treatment response in the early stages and identify biomarkers associated with treatment response. Graph Isomorphism Network (GIN), a deep learning method, is promising for predicting treatment response for individual MDE patients with more powerful representation ability to capture the features of brain functional connectivity. METHODS: In this study, GIN was used to predict individual treatment response in 198 adolescents and young adults with MDE. The most discriminating regions were also identified for the treatment response prediction. RESULTS: Using GIN approach, the baseline functional connectivity could predict 79.8% responders and 67.4% non-responders to treatment (accuracy 74.24%). Furthermore, the most discriminating brain regions were mainly involved in paralimbic and subcortical areas. CONCLUSIONS: GIN has shown potential in predicting treatment response for individual patients, which may enable personalized treatment decisions. Furthermore, targeted interventions focused on modulating the activity and connectivity within paralimbic and subcortical regions could potentially improve treatment outcomes and enable personalized interventions for adolescents and young adults with MDE.

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI.

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09907-x.

The effect of rhythm training on the motor coordination abilities of 8-12-year-old freestyle swimmers.

This study aims to evaluate the effect of rhythm training in the early coordination development and to find out its role in the transfer of specific performance in young swimmers. Eighty young swimmers aged eight to twelve were divided randomly into a control group (CON, n = 40, M/F=17/23) or an experimental group (EXP, n = 40, M/F=19/21). A total of 72 swimmers completed all the training programs and tests included in this study (CON, n = 35, M/F=15/20; EXP, n = 37, M/F=16/21). The training programme lasted 12 weeks. During the study period, all groups participated in the same swimming training program with the control group participating six times a week, and the experimental group participating four times a week with an additional two rhythm trainings each week. The height, weight, walking backwards ability, jumping sideways ability, and 25 m freestyle were measured for each participant and an index of coordination was analyzed from recordings of the participant's 25 m freestyle. The results indicated that participation in rhythm training resulted in improved general motor and swimming-specific coordination among the swimmers, but the results varied by number of years of sport-specific training experience. The swimmers with more training experience improved more in swimming-specific coordination but less in general motor coordination. This study strengthens the evidence for the effectiveness of early rhythm training in swimmers, indicating that it is feasible to design programs to address general and sport-specific coordination in young athletes.

Functional connectivity between the cerebellar vermis and cerebrum distinguishes early treatment response for major depressive episodes in adolescents.

BACKGROUND: The absence of biomarkers for predicting treatment response in adolescent mood disorder calls for further research. The vermis, a component of the cerebellum, is involved in mood disorder pathophysiology and relates to clinical symptoms and treatment outcomes. We investigated vermis functional connectivity (FC) as an early marker for treatment response identification. METHOD: One hundred thirty-two adolescents with mood disorders including major depressive disorder or bipolar disorder, were recruited, who were experiencing a major depressive episode. All adolescents underwent baseline and 2-week treatment resting-state MRI scans. Hamilton Rating Scale for Depression (HAMD) assessments were completed to assess the severity of symptoms. Patients were divided into treatment-responsive (≥50 % HAMD reduction, n = 75) and treatment-unresponsive subgroups (n = 57). Vermis FCs were compared between subgroups at baseline. And we compared the pre- and post-treatment FC differences within subgroups. RESULT: Higher vermis-left temporal lobe FC in treatment-responsive group compared to treatment-unresponsive group at baseline. The FC value showed positive prognosis for the efficacy, with the area under the curve (AUC) of 0.760 (95 % confidence interval: 0.678-0.843, p < 0.001), suggesting higher vermis-temporal FC is benefit to improve treatment-response. Furthermore, post-treatment analysis showed significant increases in the vermis-right frontal lobe FC values between in all patients, suggesting that vermis-frontal FCs were independent of treatment-outcome. LIMITATION: Sample size was relatively small, which may limit the generalizability of our results. CONCLUSION: Our study revealed that the FC between the vermis and the cortex is not only associated with symptom alleviation but also predictive of treatment outcomes.

Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study.

Background: Depression is a widespread mental disorder that affects a significant portion of the population. However, the assessment of depression is often subjective, relying on standard questions or interviews. Acoustic features have been suggested as a reliable and objective alternative for depression assessment. Therefore, in this study, we aim to identify and explore voice acoustic features that can effectively and rapidly predict the severity of depression, as well as investigate the potential correlation between specific treatment options and voice acoustic features. Methods: We utilized voice acoustic features correlated with depression scores to train a prediction model based on artificial neural network. Leave-one-out cross-validation was performed to evaluate the performance of the model. We also conducted a longitudinal study to analyze the correlation between the improvement of depression and changes in voice acoustic features after an Internet-based cognitive-behavioral therapy (ICBT) program consisting of 12 sessions. Results: Our study showed that the neural network model trained based on the 30 voice acoustic features significantly correlated with HAMD scores can accurately predict the severity of depression with an absolute mean error of 3.137 and a correlation coefficient of 0.684. Furthermore, four out of the 30 features significantly decreased after ICBT, indicating their potential correlation with specific treatment options and significant improvement in depression (p < 0.05). Conclusion: Voice acoustic features can effectively and rapidly predict the severity of depression, providing a low-cost and efficient method for screening patients with depression on a large scale. Our study also identified potential acoustic features that may be significantly related to specific treatment options for depression.

Attention guided learnable time-domain filterbanks for speech depression detection.

Depression, as a global mental health problem, is lacking effective screening methods that can help with early detection and treatment. This paper aims to facilitate the large-scale screening of depression by focusing on the speech depression detection (SDD) task. Currently, direct modeling on the raw signal yields a large number of parameters, and the existing deep learning-based SDD models mainly use the fixed Mel-scale spectral features as input. However, these features are not designed for depression detection, and the manual settings limit the exploration of fine-grained feature representations. In this paper, we learn the effective representations of the raw signals from an interpretable perspective. Specifically, we present a joint learning framework with attention-guided learnable time-domain filterbanks for depression classification (DALF), which collaborates with the depression filterbanks features learning (DFBL) module and multi-scale spectral attention learning (MSSA) module. DFBL is capable of producing biologically meaningful acoustic features by employing learnable time-domain filters, and MSSA is used to guide the learnable filters to better retain the useful frequency sub-bands. We collect a new dataset, the Neutral Reading-based Audio Corpus (NRAC), to facilitate the research in depression analysis, and we evaluate the performance of DALF on the NRAC and the public DAIC-woz datasets. The experimental results demonstrate that our method outperforms the state-of-the-art SDD methods with an F1 of 78.4% on the DAIC-woz dataset. In particular, DALF achieves F1 scores of 87.3% and 81.7% on two parts of the NRAC dataset. By analyzing the filter coefficients, we find that the most important frequency range identified by our method is 600-700Hz, which corresponds to the Mandarin vowels /e/ and /eˆ/ and can be considered as an effective biomarker for the SDD task. Taken together, our DALF model provides a promising approach to depression detection.

The Value of the Alvarado Score for the Diagnosis of Acute Appendicitis in Children: A Systematic Review and Meta-Analysis.

BACKGROUND: Relevant guidelines recommend the use of the Alvarado score (AS) to assist in the diagnosis of acute appendicitis (AA) in children. To provide reference evidence for the clinical application of AS, we performed a meta-analysis of studies related to the diagnostic accuracy of AS in children with AA. METHODS: We searched the relevant literature from databases including CNKI, WanFangdata, VIP, CBM, the Cochrane Library, PubMed, Embase, and Web of Science databases from the date of database creation to April 30, 2022, and screened them according to nadir criteria, followed by data extraction and then combined effect sizes to assess the accuracy of AS for diagnosis in children. RESULTS: Twenty-six studies involving 2579 cases were finally included, including 19 studies with Alvarado score and 8 studies with modified Alvarado Score (1 study included both Alvarado Score and modified Alvarado Score). The combined sensitivity (SE) of AS for diagnosing AA in children was 76.0% (95% CI 74.0-78.0%; I2 = 95.1%); combined specificity (SP) was 71.0% (95% CI 68.0-74.0%; I2 = 86.4%); combined positive likelihood ratio (LR+) was 2.43 (95% CI 1.92- 3.07; I2 = 78.7%); combined negative likelihood ratio (LR-) was 0.28 (95% CI 0.20-0.41; I2 = 94.2%); combined AUC = 0.8092, Q∗ = 0.7439; combined diagnostic ratio (DOR) was 8.96 (95% CI 5.65 -14.21; I2 = 76.2%). The combined effect size I2 was greater than 50% for all children with a modified AS diagnosis of AA, so all analyses used a random-effects model, which showed a combined SE of 87.0% (95% CI 85.0 - 88.0%; I2 = 85.5%); the combined SP was 47.0% (95% CI 43.0 - 51.0%. I2 = 88.7%); combined LR+ was 1.68 (95% CI 1.31-2.17; I2 = 85.9%); combined LR-was 0.28 (95% CI 0.20-0.39; I2 = 74.3%); combined AUC = 0.8672 and Q∗ = 0.7978. The combined DOR was 6.43 (95% CI 3.38-12.26; I2 = 80.0%). CONCLUSION: The results of this meta-analysis suggest that the accuracy of AS in diagnosing AA in children is moderate, and AS can be an auxiliary tool for the diagnosis of AA in children, relying on AS alone for the diagnosis of AA is not recommended; AS can be further improved scientifically to increase its diagnostic value.