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.

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.

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.

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.

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.

Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning.

Bipolar disorder (BD) is associated with marked suicidal susceptibility, particularly during a major depressive episode. However, the evaluation of suicidal risk remains challenging since it relies mainly on self-reported information from patients. Hence, it is necessary to complement neuroimaging features with advanced machine learning techniques in order to predict suicidal behavior in BD patients. In this study, a total of 288 participants, including 75 BD suicide attempters, 101 BD nonattempters and 112 healthy controls, underwent a resting-state functional magnetic resonance imaging (rs-fMRI). Intrinsic brain activity was measured by amplitude of low-frequency fluctuation (ALFF). We trained and tested a two-level k-nearest neighbors (k-NN) model based on resting-state variability of ALFF with fivefold cross-validation. BD suicide attempters had increased dynamic ALFF values in the right anterior cingulate cortex, left thalamus and right precuneus. Compared to other machine learning methods, our proposed framework had a promising performance with 83.52% accuracy, 78.75% sensitivity and 87.50% specificity. The trained models could also replicate and validate the results in an independent cohort with 72.72% accuracy. These findings based on a relatively large data set, provide a promising way of combining fMRI data with machine learning technique to reliably predict suicide attempt at an individual level in bipolar depression. Overall, this work might enhance our understanding of the neurobiology of suicidal behavior by detecting clinically defined disruptions in the dynamics of instinct brain activity.

Shared and unique imaging-derived endo-phenotypes of two typical antidepressant-applicative depressive patients.

OBJECTIVES: Determining the clinical homogeneous and heterogeneous sets among depressive patients is the key to facilitate individual-level treatment decision. METHODS: The diffusion tensor imaging (DTI) data of 62 patients with major depressive disorder (MDD) and 39 healthy controls were used to construct a Latent Dirichlet Allocation (LDA) Bayesian model. Another 48 MDD patients were used to verify the robustness. The LDA model was employed to identify both shared and unique imaging-derived factors of two typically antidepressant-targeted depressive patients, selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). Furthermore, we applied canonical correlation analysis (CCA) between each factor loading and Hamilton depression rating scale (HAMD) sub-score, to explore the potential neurophysiological significance of each factor. RESULTS: The results revealed the imaging-derived connectional fingerprint of all patients could be situated along three latent factor dimensions; such results were also verified by the out-of-sample dataset. Factor 1, uniquely expressed by SNRI-targeted patients, was associated with retardation (r = 0.4, p = 0.037) and characterized by coupling patterns between default mode network and cognitive control network. Factor 3, uniquely expressed by SSRI-targeted patients, was associated with cognitive impairment (r = 0.36, p = 0.047) and characterized by coupling patterns within cognitive control and attention network, and the connectivity between threat and reward network. Shared factor 2, characterized by coupling patterns within default mode network, was associated with anxiety (r = 0.54, p = 0.005) and sleep disturbance (r = 0.37, p = 0.032). CONCLUSIONS: Our findings suggested that quantification of both homogeneity and heterogeneity within MDD may have the potential to inform rational design of pharmacological therapies. KEY POINTS: • The shared and unique manifestations guiding pharmacotherapy of depressive patients are caused by the homogeneity and heterogeneity of underlying structural connections of the brain. • Both shared and unique factor loadings were found in different antidepressant-targeted patients. • Significant correlations between factor loading and HAMD sub-scores were found.

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.

Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic.

OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on depression and anxiety and explore the influencing factors during the COVID-19 epidemic in China. METHODS: A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Depression and anxiety symptoms were assessed using Patient Health Questionnaire 9 (PHQ9) and Generalized Anxiety Disorder 7 (GAD7) respectively. Latent class analysis was performed based on depression and anxiety symptoms in medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. RESULTS: In this study, three distinct subgroups were identified, namely, the poor mental health group, the mild mental health group and the low symptoms group. The number of medical students in each class is 4325, 9321 and 16,017 respectively. The multinomial logistic regression results showed that compared with the low symptoms group, the factors influencing depression and anxiety in the poor mental health group and mild mental health group were sex, educational level, drinking, individual psychiatric disorders, family psychiatric disorders, knowledge of COVID-19, fear of being infected, and participate in mental health education on COVID-19. CONCLUSIONS: Our findings suggested that latent class analysis can be used to categorize different medical students according to their depression and anxiety symptoms during the outbreak of COVID-19. The main factors influencing the poor mental health group and the mild mental health group are basic demographic characteristics, disease history, COVID-19 related factors and behavioural lifestyle. School administrative departments can carry out targeted psychological counseling according to different subgroups to promote the physical and mental health of medical students.

Early identification of bipolar from unipolar depression before manic episode: Evidence from dynamic rfMRI.

OBJECTIVE: Misdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD. METHOD: The module allegiance from resting-fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD. RESULTS: Based on our classification model, difference mainly reflected in default-mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well-overlapped, except for cognitive control network (CCN). CONCLUSION: The functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.

Causal associations between pediatric asthma and united airways disease: a two-sample Mendelian randomization analysis.

Background: Prior observational research has indicated a potential link between pediatric asthma and united airways disease (UAD). However, these findings could be subject to confounding factors and reverse causation. Therefore, our study utilizes Mendelian randomization (MR) method to further investigate the causal relationship between pediatric asthma and UAD. Methods: We conducted a comprehensive two-sample Mendelian randomization (MR) analysis to investigate the association between pediatric asthma and seven groups of UAD, including chronic sinusitis, chronic rhinitis, nasopharyngitis and pharyngitis, chronic diseases of tonsils and adenoids, chronic laryngitis and laryngotracheitis, chronic bronchitis, bronchiectasis, chronic obstructive pulmonary disease (COPD). The present study employed a range of methods for two-sample MR analysis, including inverse variance weighted (IVW), MR-Egger regression, Simple mode, weighted median, and weighted models. The conclusion of the MR analysis primarily relies on the IVW results, while other analytical methods are utilized as supplementary evidence to ensure result robustness in this MR analysis. And sensitivity analyses were conducted, including heterogeneity test, horizontal pleiotropy test, MR-PRESSO test, and leave-one-out analysis to validate the results. Results: The results of the MR analysis indicate significant causal effects of pediatric asthma on chronic rhinitis, nasopharyngitis and pharyngitis (IVW: OR = 1.15, 95%CI: 1.05-1.26, p-value = 0.003), chronic diseases of tonsils and adenoids (IVW: OR = 1.07, 95%CI: 1.00-1.15, p-value = 0.038), chronic bronchitis (IVW: OR = 1.51, 95%CI: 1.42-1.62, p-value <0.001), bronchiectasis (IVW: OR = 1.51, 95%CI: (1.30-1.75), p-value <0.001), and COPD (IVW: OR = 1.43, 95%CI: 1.34-1.51, p-value <0.001). However, no significant causal association was observed between pediatric asthma and chronic sinusitis (IVW: OR = 1.00, 95%CI: 1.00-1.00, p-value = 0.085), chronic laryngitis and laryngotracheitis (IVW: OR = 1.05, 95%CI: 0.90-1.21, p-value = 0.558). Conclusion: Our findings support a potential causal relationship between pediatric asthma and UAD, suggesting that pediatric asthma may be a potential risk factor for various UAD.

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.

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.

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.

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.

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.

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.

Repetitive Transcranial Magnetic Stimulation Reversing Abnormal Brain Function in Mood Disorders with Early Life Stress: from preclinical models to clinical applications.

BACKGROUND: Early life stress (ELS) significantly increases the risk of mood disorders and affects the neurodevelopment of the primary cortex. HYPOTHESIS: Modulating the primary cortex through neural intervention can ameliorate the impact of ELS on brain development and consequently alleviate its effects on mood disorders. METHOD: We induced the chronic unpredictable mild stress (CUMS) model in adolescent rats, followed by applying repetitive transcranial magnetic stimulation (rTMS) to their primary cortex in early adulthood. To assess the applicability of primary cortex rTMS in humans, we recruited individuals aged 17-25 with mood disorders who had experienced ELS and performed primary cortex rTMS on them. Functional magnetic resonance imaging (fMRI) and depression-related behavioral and clinical symptoms were conducted in both rats and human subjects before and after the rTMS. RESULTS: In animals, fMRI analysis revealed increased activation in the primary cortex of CUMS rats and decrease subcortical activation. Following the intervention of primary cortex rTMS, the abnormal functional activity was reversed. Similarly, in mood disorders patients with ELS, increased activation in the primary cortex and decreased activation in the frontal cortex were observed. During rTMS intervention, similar neuroimaging improvements were noted, particularly decreased activation in the primary cortex. This suggests that targeted rTMS in the primary cortex can reverse the abnormal neuroimaging. CONCLUSION: This cross-species translational study has identified the primary cortex as a key region in mood disorders patients with ELS. Targeting the primary cortex with rTMS can correct abnormal functional activity while improving symptoms. Our study provides translational evidence for therapeutics targeting the ELS factor of mood disorders patients.

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.

Construction of an anthropometric discriminant model for identification of elite swimmers: an adaptive lasso approach.

Background: Anthropometric characteristics are important factors that affect swimming performance. The aim of this study is to build a discriminant model using anthropometric factors to identify elite short-to-medium-distance freestyle swimmers through an adaptive Lasso approach. Methods: The study recruited 254 swimmers (145 males and 109 females) who were divided them into elite (aged 17.9 ± 2.2 years, FINA points 793.8 ± 73.8) and non-elite (aged 17.1 ± 1.3 years, FINA points 560.6 ± 78.7) groups. Data for 73 variables were obtained, including basic information, anthropometric and derivative indicators. After filtering out highly correlated variables, 24 candidate variables were retained to be used in adaptive Lasso to select variables for prediction of elite swimmers. Deviance and area under the curve (AUC) were applied to assess the goodness of fit and prediction accuracy of the model, respectively. Results: The adaptive Lasso selected 12 variables using the whole sample, with an AUC being 0.926 (95% CI [0.895-0.956]; P = 2.42 × 10-29). In stratified analysis by gender, nine variables were selected for male swimmers with an AUC of 0.921 (95% CI [0.880-0.963]; P = 8.82 × 10-17), and eight variables were for female swimmers with an AUC of 0.941 (95% CI [0.898-0.984]; P = 7.67 × 10-15). Conclusion: The adaptive Lasso showed satisfactory performance in selecting anthropometric characteristics to identify elite swimmers. Additional studies with longitudinal data or data from other ethnicities are needed to validate our findings.