Background: This study examined the relationships among negative symptoms, self-stigma, and quality of life in individuals with schizophrenia, and investigated whether these relationships differ between patients with high versus low levels of negative symptoms.Methods: This cross-sectional study included 403 inpatients with schizophrenia. Participants were assessed using the Positive and Negative Syndrome Scale (PANSS), the Internalized Stigma of Mental Illness Scale (ISMI), the Schizophrenia Quality of Life Scale (SQLS), and other relevant measures. Descriptive statistics, correlation analyses, and multiple regression models were conducted. Of the 500 inpatients with schizophrenia who were screened, 403 met the inclusion criteria and were included in the final analysis.Results: Negative symptoms were significantly associated with overall self-stigma and its subdimensions. Among participants with high levels of negative symptoms, self-stigma was significantly associated with general psychopathology, objective support, and avoidance coping. In contrast, only avoidance coping showed a significant association in the low-symptom group. Regarding quality of life, stereotype endorsement and stigma resistance were significantly associated with higher SQLS scores (indicating worse quality of life) in the low-symptom group, whereas no significant associations were observed in the high-symptom group.Conclusions: Negative symptoms are closely associated with internalized stigma in individuals with schizophrenia. The psychosocial correlates and consequences of self-stigma appear to vary according to the severity of negative symptoms. These findings highlight the importance of tailored interventions, such as enhancing social support for individuals with prominent negative symptoms and promoting adaptive coping strategies for those with milder symptoms. Antecedentes: Este estudio examin & oacute; las relaciones entre los s & iacute;ntomas negativos, el autoestigma y la calidad de vida en individuos con esquizofrenia, e investig & oacute; si estas relaciones difieren entre pacientes con niveles altos frente a bajos de s & iacute;ntomas negativos.M & eacute;todos: Este estudio transversal incluy & oacute; 403 pacientes hospitalizados con esquizofrenia. Los participantes fueron evaluados mediante la Escala de S & iacute;ndromes Positivos y Negativos (PANSS), la Escala de Estigma Internalizado de la Enfermedad Mental (ISMI), la Escala de Calidad de Vida en Esquizofrenia (SQLS) y otras medidas relevantes. Se realizaron an & aacute;lisis descriptivos, an & aacute;lisis de correlaci & oacute;n y modelos de regresi & oacute;n m & uacute;ltiple. De los 500 pacientes hospitalizados con esquizofrenia que fueron cribados, 403 cumplieron los criterios de inclusi & oacute;n y fueron incluidos en el an & aacute;lisis final.Resultados: Los s & iacute;ntomas negativos se asociaron significativamente con el autoestigma global y sus subdimensiones. Entre los participantes con altos niveles de s & iacute;ntomas negativos, el autoestigma se asoci & oacute; significativamente con la psicopatolog & iacute;a general, el apoyo objetivo y el afrontamiento evitativo. En contraste, solo el afrontamiento evitativo mostr & oacute; una asociaci & oacute;n significativa en el grupo con niveles bajos de s & iacute;ntomas. Respecto a la calidad de vida, la adhesi & oacute;n a estereotipos y la resistencia al estigma se asociaron significativamente con puntuaciones m & aacute;s altas en la SQLS (indicando peor calidad de vida) en el grupo con niveles bajos de s & iacute;ntomas, mientras que no se observaron asociaciones significativas en el grupo con niveles altos.Conclusi & oacute;ns: Los s & iacute;ntomas negativos est & aacute;n estrechamente asociados con el estigma internalizado en individuos con esquizofrenia. Los correlatos psicosociales y las consecuencias del autoestigma parecen variar seg & uacute;n la gravedad de los s & iacute;ntomas negativos. Estos hallazgos destacan la importancia de intervenciones individualizadas, tales como el fortalecimiento del apoyo social para individuos con s & iacute;ntomas negativos prominentes, y la promoci & oacute;n de estrategias de afrontamiento adaptativas para aquellos con s & iacute;ntomas m & aacute;s leves.

Speech-based depression detection has become a hot research topic. Personalized information such as personality and speaking style may cause overlap of speech features among individuals with varying depression levels, raising the risk of model misclassification. A potential strategy is to specifically account for the effect of personalized information during modeling to leverage its intrinsic depression cues. Accordingly, we proposed the Adaptive Embedding Personalized Information Model (AEPIM), which comprises three modules: the Personalized Information Extraction Module (PIEM), the Depression Information Extraction Module (DIEM), and the Self-Adaptive Fusion Module (SAFM). PIEM employs contrastive learning to extract personalized information from longitudinal data. DIEM and SAFM are then trained jointly to learn more discriminative depression representations. To validate AEPIM’s effectiveness, we constructed a longitudinal dataset containing two rounds of data, which is rare in this field. Such data are crucial for analyzing personalized information, supporting the establishment of accurate relationships between speech features and depression levels, thereby assisting in individualized depression diagnosis. Experimental results demonstrate that AEPIM outperforms existing methods, reducing RMSE and MAE by at least 13.8% and 13.0% in Round 1, and by 7.5% and 5.2% in Round 2, respectively. Out-of-domain generalization was assessed on two cross-sectional datasets, indicating its effectiveness on external data. These improvements suggest that AEPIM holds significant potential for practical applications, such as long-term monitoring of depression. The code is available at https://github.com/yuanjq2023-stack/AEPIM.

Background:The interrelationships among the negative symptoms of schizophrenia and factors affecting quality of life remain unclear. To address this gap, the present study explores the complex interrelationships among anhedonia, self-stigma, self-esteem, psychological resilience, and social support in individuals with schizophrenia. Methods:A comprehensive battery of measures was used to assess 447 patients with schizophrenia. Undirected network analysis was employed to examine the centrality of various factors, and Bayesian network analysis was used to explore the indirect influence of upstream variables on anhedonia through psychological factors. Results:Self-stigma was identified as the most central factor. Among the subdimensions of anhedonia, abstract anticipatory pleasure and contextual consummatory pleasure demonstrated the highest centrality. Negative symptoms of schizophrenia and social support were found to indirectly influence different aspects of anhedonia through psychological factors such as self-esteem and resilience. In one of the identified pathways, social support influenced psychological resilience, which in turn affected abstract anticipatory pleasure, contextual consummatory pleasure, and contextual anticipatory pleasure. Conclusions:Our findings suggest that social support is associated with pleasure experiences partly through psychological resilience and underscore the potential mediating role of psychological factors in linking clinical symptoms, social resources, and hedonic capacity in schizophrenia.

Automatic depression recognition based on facial movements in videos has become a research hotspot. However, existing methods tend to confuse individual inherent facial behavioral habits with characteristics specific to depression, which leads to misjudgments. To address this, we propose a Multi-scale Personalized Depression Recognition Model (MPDRM) that mitigates the negative impact of individual differences, enabling the model to focus on general and robust facial depression cues. The proposed model consists of three main components: the Multi-scale Depression Feature Network (MDFN), the Multi-scale Personality Feature Network (MPFN), and the Relational Attention Recognition Module (RARM). The MDFN extracts depression-related information, while the contrastive learning-based MPFN extracts stable personalized information. In both MDFN and MPFN, we insert the Multi-scale Motion Pattern Extraction Module (MMP) to capture rich multi- scale spatiotemporal facial features. Finally, the RARM is designed to enhance the representation of depression and output the results. Cross-validation on a specifically constructed longitudinal dataset demonstrates that our model outperforms other models. Experimental results indicate that suppressing personalized information of facial movements can effectively improve the accuracy of depression recognition.

BACKGROUND:Major depressive disorder (MDD) is a prevalent and severe psychiatric condition for which objective diagnostic tools are lacking. Heart rate variability (HRV), an index of autonomic nervous system (ANS) function, has shown potential for distinguishing patients with MDD. This study aimed to improve classification performance by leveraging circadian rhythm features derived from multiple HRV indices using a support vector machine (SVM) approach. METHODS:Twenty-four-hour electrocardiographic recordings were collected from 116 patients with MDD and 63 healthy controls (HCs). Thirteen HRV indices, spanning the time domain, frequency domain, and nonlinear measures, were extracted, along with five corresponding circadian rhythm features (CRFs) for each index. To reduce feature dimensionality, a recursive feature elimination strategy based on the SVM was applied. The selected CRFs were then used to train the SVM classifier. Separate SVM models were constructed using CRFs derived from linear indices, nonlinear indices, and their combinations. Furthermore, their performance was compared across multiple metrics. RESULTS:The SVM model constructed using CRFs from all HRV indices achieved an accuracy of 97.80 %, sensitivity of 98.01 %, and specificity of 97.60 %. The models based on the CRFs from linear HRV indices outperformed those based on nonlinear indices, whereas the combination of all HRV indices yielded the best overall performance. CONCLUSIONS:The results highlight the potential utility of HRV CRFs for objective MDD classification. Moreover, with an equal number of features, the CRFs derived from linear HRV indices proved to be more effective than those derived from nonlinear indices in discriminating patients with MDD.

BACKGROUND:Simpler and more feasible light therapy protocols, and objective indicators for assessing its effectiveness is lacking. We aimed to evaluate the efficacy of light therapy on subthreshold depression (SD) among college students and explore facial expressions as an objective biomarker across different treatment groups. METHODS:From September 13, 2021, to January 4, 2022, college students with SD were recruited from a university in Hubei Province, randomly assigned to Bright Light Therapy (BLT) group (10,000 lx), Dim Light Therapy (DLT) group (200 lx), or Waiting List Control (WLC) group (no intervention). Self-reported questionnaire and facial expressions were assessed for all participants before and after intervention. Repeated measures ANOVA and logistic regression were conducted to compare baseline and post-intervention differences among three groups. RESULTS:135 participants were enrolled and 121 participants completed the study. Depression symptom and sleep quality scores significantly decreased in both BLT and DLT groups (P < 0.001), while no significant changes were observed in WLC group. BLT (OR, 4.50; 95 % CI, 1.11-18.27; P = 0.035) and DLT group (OR, 4.17; 95 % CI, 1.04-16.79; P = 0.045) had higher efficacy rates than WLC group. For facial expressions, DLT group showed significant increases in two happy-related facial action units (AU) including AU14 values (positive, negative and neutral stimuli) and AU26 values (neutral and negative stimuli). BLT group showed a significant decrease in fear-related AU20 values under negative stimuli (P < 0.001). CONCLUSION:Light therapy improves depressive symptoms and sleep quality in individuals with SD, and facial expressions can serve as an objective biomarker to support its effectiveness.

ObjectiveTo investigate self-stigma’s influence on schizophrenia patients’ quality of life and its mediated impact by various factors.MethodsThis study adopted a cross-sectional design and randomly selected 170 hospitalized patients with schizophrenia for evaluation. The assessment tools included the Positive and Negative Syndrome Scale (PANSS), Internalized Stigma of Mental Illness Scale (ISMI), Schizophrenia Quality of Life Scale (SQLS), and Coping Questionnaire for Schizophrenia Patients (CQSP), among others. Correlation analysis, regression analysis, and mediation analysis were used to test the correlation and mediation effects.ResultsSelf-stigma had a significant impact on quality of life (T = 8.13, p = 0.00). When self-stigma is used as a mediator, the problem-solving factor in coping strategies has an indirect effect on quality of life, which is significant (AB = -0.16, P = 0.02), while the avoidance factor in coping strategies has a direct effect on quality of life, which is significant (C’ = 0.54, p < 0.001), and an indirect effect, which is also significant (AB = 0.25, p < 0.001).ConclusionThe study highlights the significant impact of self-stigma on the quality of life of schizophrenia patients, emphasizing the crucial roles of self-esteem and coping strategies. These findings suggest clinical interventions to improve quality of life should focus on reducing self-stigma, especially enhancing self-esteem and promoting adaptive coping strategies. By addressing these factors, we can better support the mental health and well-being of those with schizophrenia, offering an effective approach to rehabilitation.

Little is known about the association of ambient ozone with ovarian reserve. Based on a retrospective cohort study of 6008 women who attended a fertility center in Hubei, China, during 2018-2021, we estimated ozone exposure levels by calculating averages during the development of follicles (2-month [W1], 4-month [W2], 6-month [W3]) and 1-year before measurement (W4) according to Tracking Air Pollution in China database. We used multivariate logistic regression and linear regression models to investigate association of ozone exposure with anti-mullerian hormone (AMH), the preferred indicator of ovarian reserve. Each 10 mu g/m(3) increases in ozone were associated with 2.34% (0.68%, 3.97%), 2.08% (0.10%, 4.01%), 4.20% (1.67%, 6.67%), and 8.91% (5.79%, 11.93%) decreased AMH levels during W1-W4; AMH levels decreased by 15.85%, 11.90%, 16.92% in the fourth quartile during W1, W3, and W4 when comparing the extreme quartile, with significant exposure response relationships during W4 (P < 0.05). Ozone exposure during W1 was positively associated with low AMH. Additionally, we detected significant effect modification by age, body mass index, and temperature in ozone-associated decreased AMH levels. Our findings highlight the potential adverse impact of ozone pollution on female ovarian reserve, especially during the secondary to small antral follicle stage and 1-year before measurement.

Depression detection based on text analysis has emerged as a research hotspot. Existing research indicates that patients’ personalized characteristics are the primary factor contributing to differences in reported experiences, which poses challenges for automated depression detection methods. To address this, we pioneered defining the fundamental components of personalized information within the text-based depression detection field and proposed the Personalized Information Embedding (PIE) model. The model narrows the gap between generic clinical symptoms and personalized patient experiences in detection, introducing a novel method for computing personalized information representations. Then, we constructed a unique depression intervention dataset containing 108 cases of subjects, the first longitudinally gathering experimental dataset in text-based depression detection. Extensive experimental evidence demonstrates that compared to advanced models, PIE demonstrates statistically significant improvements in performance (with the maximum reductions in RMSE of 0.309 and MAE of 0.232) and generalizability (with standard deviation reductions in RMSE by 75.43% and MAE by 69.77%), and the out-of-domain generalizability of personalized information representations has been validated on two larger external datasets. Additionally, we conducted case studies to analyze how personalized information leads to improved model capabilities. This research serves as a pilot and reference for developing personalized models in text-based depression detection.

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

Alzheimer’s disease (AD), one of the leading diseases of the nervous system, is accompanied by symptoms such as loss of memory, thinking and language skills. Both mild cognitive impairment (MCI) and very mild cognitive impairment (VMCI) are the transitional pathological stages between normal aging and AD. While the changes in whole-brain structural and functional information have been extensively investigated in AD, The impaired structure–function coupling remains unknown. The current study employed the OASIS-3 dataset, which includes 53 MCI, 90 VMCI, and 100 Age-, gender-, and education-matched normal controls (NC). Several structural and functional parameters, such as the amplitude of low-frequency fluctuations (ALFF), voxel-based morphometry (VBM), and The ALFF/VBM ratio, were used To estimate The whole-brain neuroimaging changes In MCI, VMCI, and NC. As disease symptoms became more severe, these regions, distributed in the frontal-inf-orb, putamen, and paracentral lobule in the white matter (WM), exhibited progressively increasing ALFF (ALFFNC < ALFFVMCI < ALFFMCI), which was similar to the tendency for The cerebellum and putamen in the gray matter (GM). Additionally, as symptoms worsened in AD, the cuneus/frontal lobe in the WM and the parahippocampal gyrus/hippocampus in the GM showed progressively decreasing structure–function coupling. As the typical focal areas in AD, The parahippocampal gyrus and hippocampus showed significant positive correlations with the severity of cognitive impairment, suggesting the important applications of the ALFF/VBM ratio in brain disorders. On the other hand, these findings from WM functional signals provided a novel perspective for understanding the pathophysiological mechanisms involved In cognitive decline in AD.

ObjectiveSchizophrenia is a debilitating mental disorder with a high disability rate that is characterized by negative symptoms such as apathy, hyperactivity, and anhedonia that can make daily life challenging and impair social functioning. In this study, we aim to investigate the effectiveness of homestyle rehabilitation in mitigating these negative symptoms and associated factors.MethodsA randomized controlled trial was conducted to compare the efficacy of hospital rehabilitation and homestyle rehabilitation for negative symptoms in 100 individuals diagnosed with schizophrenia. The participants were divided randomly into two groups, each persisting for 3 months. The primary outcome measures were the Scale for Assessment of Negative Symptoms (SANS) and Global Assessment of Functioning (GAF). The secondary outcome measures included the Positive Symptom Assessment Scale (SAPS), Calgary Schizophrenia Depression Scale (CDSS), Simpson-Angus Scale (SAS), and Abnormal Involuntary Movement Scale (AIMS). The trial aimed to compare the effectiveness of the two rehabilitation methods.ResultsHomestyle rehabilitation for negative symptoms was found to be more effective than hospital rehabilitation, according to the changes in SANS (T = 2.07, p = 0.04). Further analysis using multiple regression indicated that improvements in depressive symptoms (T = 6.88, p < 0.001) and involuntary motor symptoms (T = 2.75, p = 0.007) were associated with a reduction in negative symptoms.ConclusionHomestyle rehabilitation may have greater potential than hospital rehabilitation in improving negative symptoms, making it an effective rehabilitation model. Further research is necessary to investigate factors such as depressive symptoms and involuntary motor symptoms, which may be associated with the improvement of negative symptoms. Additionally, more attention should be given to addressing secondary negative symptoms in rehabilitation interventions.

BACKGROUND:The heterogeneity of the clinical symptoms and presumptive neural pathologies has stunted progress toward identifying reproducible biomarkers and limited therapeutic interventions' effectiveness for the first episode drug-naïve major depressive disorders (FEDN-MDD). This study combined the dynamic features of fMRI data and normative modeling to quantitative and individualized metrics for delineating the biological heterogeneity of FEDN-MDD. METHOD:Two hundred seventy-four adults with FEDN-MDD and 832 healthy controls from International Big-Data Center for Depression Research were included. Subject-specific dynamic brain networks and network fluctuation characteristics were computed for each subject using the group information-guided independent component analysis. Then, we mapped the heterogeneity of the dynamic features (network fluctuation characteristics and dynamic functional connectivity within brain networks) in the patients group via normative modeling. RESULTS:The FEDN-MDD whose network fluctuation characteristics deviate from the normative model also showed significant differences within the default mode network, executive control network, and limbic network compared with healthy controls. Furthermore, the network fluctuation characteristics are significantly increased in patients with FEDN-MDD. About 4.74 % of the patients showed a deviation of dynamic functional connectivity, and only 3.35 % of the controls deviated from the normative model in above 100 connectivities. More patients than healthy controls showed extreme dynamic variabilities in above 100 connectivities. CONCLUSIONS:This work evaluates the efficacy of an individualized approach based on normative modeling for understanding the heterogeneity of abnormal dynamic functional connectivity patterns in FEDN-MDD, and could be used as complementary to classical case-control comparisons.

The long-term physical and mental sequelae of COVID-19 are a growing public health concern, yet there is considerable uncertainty about their prevalence, persistence and predictors. We conducted a comprehensive, up-to-date meta-analysis of survivors' health consequences and sequelae for COVID-19. PubMed, Embase and the Cochrane Library were searched through Sep 30th, 2021. Observational studies that reported the prevalence of sequelae of COVID-19 were included. Two reviewers independently undertook the data extraction and quality assessment. Of the 36,625 records identified, a total of 151 studies were included involving 1,285,407 participants from thirty-two countries. At least one sequelae symptom occurred in 50.1% (95% CI 45.4-54.8) of COVID-19 survivors for up to 12 months after infection. The most common investigation findings included abnormalities on lung CT (56.9%, 95% CI 46.2-67.3) and abnormal pulmonary function tests (45.6%, 95% CI 36.3-55.0), followed by generalized symptoms, such as fatigue (28.7%, 95% CI 21.0-37.0), psychiatric symptoms (19.7%, 95% CI 16.1-23.6) mainly depression (18.3%, 95% CI 13.3-23.8) and PTSD (17.9%, 95% CI 11.6-25.3), and neurological symptoms (18.7%, 95% CI 16.2-21.4), such as cognitive deficits (19.7%, 95% CI 8.8-33.4) and memory impairment (17.5%, 95% CI 8.1-29.6). Subgroup analysis showed that participants with a higher risk of long-term sequelae were older, mostly male, living in a high-income country, with more severe status at acute infection. Individuals with severe infection suffered more from PTSD, sleep disturbance, cognitive deficits, concentration impairment, and gustatory dysfunction. Survivors with mild infection had high burden of anxiety and memory impairment after recovery. Our findings suggest that after recovery from acute COVID-19, half of survivors still have a high burden of either physical or mental sequelae up to at least 12 months. It is important to provide urgent and appropriate prevention and intervention management to preclude persistent or emerging long-term sequelae and to promote the physical and psychiatric wellbeing of COVID-19 survivors.

Late-life depression (LLD) is an important public health problem among the aging population. Recent studies found that mindfulness-based cognitive therapy (MBCT) can effectively alleviate depressive symptoms in major depressive disorder. The present study explored the clinical effect and potential neuroimaging mechanism of MBCT in the treatment of LLD. We enrolled 60 participants with LLD in an 8-week, randomized, controlled trial (ChiCTR1800017725). Patients were randomized to the treatment-as-usual (TAU) group or a MBCT+TAU group. The Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA) were used to evaluate symptoms. Magnetic resonance imaging (MRI) was used to measure changes in resting-state functional connectivity and structural connectivity. We also measured the relationship between changes in brain connectivity and improvements in clinical symptoms. HAMD total scores in the MBCT+TAU group were significantly lower than in the TAU group after 8 weeks of treatment (p < 0.001) and at the end of the 3-month follow-up (p < 0.001). The increase in functional connections between the amygdala and middle frontal gyrus (MFG) correlated with decreases in HAMA and HAMD scores in the MBCT+TAU group. Diffusion tensor imaging analyses showed that fractional anisotropy of the MFG-amygdala significantly increased in the MBCT+TAU group after 8-week treatment compared with the TAU group. Our study suggested that MBCT improves depression and anxiety symptoms that are associated with LLD. MBCT strengthened functional and structural connections between the amygdala and MFG, and this increase in communication correlated with improvements in clinical symptoms. Randomized Controlled Trial; Follow-Up Study; fMRI; Brain Connectivity.

IntroductionAutism spectrum disorder (ASD) is a lifelong condition. Autistic symptoms can persist into adulthood. Studies have reported that autistic symptoms generally improved in adulthood, especially restricted and repetitive behaviors and interests (RRBIs). We explored brain networks that are related to differences in RRBIs in individuals with ASDs among different ages.MethodsWe enrolled 147 ASD patients from the Autism Brain Imaging Data Exchange II (ABIDEII) database. The participants were divided into four age groups: children (6–9 years old), younger adolescents (10–14 years old), older adolescents (15–19 years old), and adults (≥20 years old). RRBIs were evaluated using the Repetitive Behaviors Scale-Revised 6. We first explored differences in RRBIs between age groups using the Kruskal–Wallis test. Associations between improvements in RRBIs and age were analyzed using a general linear model. We then analyzed RRBIs associated functional connectivity (FC) links using the network-based statistic method by adjusting covariates. The association of the identified FC with age group, and mediation function of the FC on the association of age-group and RRBI were further analyzed.ResultsMost subtypes of RRBIs improved with age, especially stereotyped behaviors, ritualistic behaviors, and restricted behaviors (p = 0.012, 0.014, and 0.012, respectively). Results showed that 12 FC links were closely related to overall RRBIs, 17 FC links were related to stereotyped behaviors. Among the identified 29 FC links, 15 were negatively related to age-groups. The mostly reported core brain regions included superior occipital gyrus, insula, rolandic operculum, angular, caudate, and cingulum. The decrease in FC between the left superior occipital lobe and right angular (effect = −0.125 and −0.693, respectively) and between the left insula and left caudate (effect = −0.116 and −0.664, respectively) might contribute to improvements in multiple RRBIs with age.ConclusionWe identified improvements in RRBIs with age in ASD patients, especially stereotyped behaviors, ritualistic behaviors, and restricted behaviors. The decrease in FC between left superior occipital lobe and right angular and between left insula and left caudate might contribute to these improvements. Our findings improve our understanding of the pathogenesis of RRBIs and suggest potential intervention targets to improve prognosis in adulthood.

Objective: A comparative analysis was performed to investigate the potential risk factors of Adverse Events Following Immunization (AEFI) after receiving different booster vaccines. Methods: From 18 January 2021 to 21 January 2022, the Health Care Workers (HCWs) of Guizhou Provincial Staff Hospital (Guizhou Province, China) who received a third Booster vaccine, that was either homologous (i.e., (i) a total of three doses of Vero cell vaccine or (ii) three doses of CHO cell vaccine) or (iii) heterologous with two first doses of Vero cell vaccine, being either CHO cell vaccine or adenovirus type-5 (Ad5) vectored COVID-19 vaccine, were asked to complete a self-report questionnaire form to provide information on any AEFI that may have occurred in the first 3 days after vaccination with the booster. The frequency of AEFI corresponding to the three different booster vaccines was compared, and the risk factors for predicting AEFI were determined by multivariate logistic regression analysis. Results: Of the 904 HCWs who completed the survey, 792 met the inclusion criteria. The rates of AEFI were 9.8% (62/635) in the homologous Vero cell booster group, 17.3% (13/75) in the homologous CHO cell booster group, and 20.7% (17/82) in the heterologous mixed vaccines booster group, and the rates were significantly different (χ2 = 11.5, p = 0.004) between the three groups of vaccines. Multivariate logistic regression analysis showed that: (1) compared to the homologous Vero cell booster group, the risk of AEFI was about 2.1 times higher (OR = 2.095, 95% CI: 1.056–4.157, p = 0.034) in the CHO cell booster group and 2.5 times higher (OR = 2.476, 95% CI: 1.352–4.533, p = 0.003) in the mixed vaccines group; (2) the odds for women experiencing AEFI were about 2.8 times higher (OR = 2.792, 95% CI: 1.407–5.543, p = 0.003) than men; and (3) compared to the non-frontline HCWs, the risk of AEFI was about 2.6 times higher (OR = 2.648, 95% CI: 1.473–4.760, p = 0.001) in the doctors. Conclusion: The AEFI in all three booster groups are acceptable, and serious adverse events are rare. The risk of AEFI was higher in doctors, which may be related to the high stress during the COVID-19 epidemic. Support from government and non-governmental agencies is important for ensuring the physical and mental health of HCWs.

Perceived stress impairs cognitive function across the adult lifespan, but the extent to which cognition decline is variable across individuals. Individual differences in the stress response are described as personality traits. Substantial individual differences in the magnitude of cognitive impairment that is induced by short-term perceived stress are poorly understood. The present study tested the hypothesis that the relationship between short-term perceived stress and different aspects of cognition is mediated by personality traits. The study included 1066 participants with behavior and neuroimaging data from the Human Connectome Project after excluding individuals with missing variables. In the result, the parallel multiple mediation model demonstrated that the influence of perceived stress on the total and crystalized cognition is mainly mediated by neuroticism (indirect effect = −0.04, p < 0.05) and conscientiousness (indirect effect = 0.05, p < 0.05) in adults. Cortical thickness value ( n = 1066) of the right superior frontal gyrus (SFG) showed not only positive correlations with short-term perceived stress and neuroticism, but negative associations with cognition. The chain mediation model found that the right SFG and neuroticism play a small but significant chain mediating effect between stress and total cognition. The strength of the resting-state functional connectivity ( n = 968) between the left orbitofrontal cortex versus the left superior medial frontal cortex was positively correlated with crystallized cognition and negatively associated with conscientiousness. These results extend previous findings by the impacts of short-term perceived stress on cognitive function is mediated by neuroticism and the right SFG was the underlying neural mechanism.

BACKGROUND:Vaccination is an important preventive measure against the coronavirus disease 19 (COVID-19) pandemic. We aimed to examine the willingness to vaccination and influencing factors among college students in China. METHODS:From March 18 to April 26, 2021, we conducted a cross-sectional online survey among college students from 30 universities in Wuhan, Hubei Province, China. The survey was composed of the sociodemographic information, psychological status, experience during pandemic, the willingness of vaccination and related information. Students' attitudes towards vaccination were classified as 'vaccine acceptance', 'vaccine hesitancy', and 'vaccine resistance'. Multinomial logistic regression analyses were performed to identify the influencing factors associated with vaccine hesitancy and resistance. RESULTS:Among 23,143 students who completed the survey, a total of 22,660 participants were included in the final analysis with an effective rate of 97.9% after excluding invalid questionnaires. A total of 60.6% of participants would be willing to receive COVID-19 vaccine, 33.4% were hesitant to vaccination, and 6.0% were resistant to vaccination. Social media platforms and government agencies were the main sources of information vaccination. Worry about the efficacy and adverse effects of vaccine were the top two common reason of vaccine hesitancy and resistance. Multiple multinomial logistic regression analysis identified that participants who worried about the adverse effects of vaccination were more likely to be vaccine hesitancy (aOR = 2.44, 95% CI = 2.30, 2.58) and resistance (aOR = 2.71, 95% CI = 2.40, 3.05). CONCLUSION:More than half of college students are willing to receive the COVID-19 vaccine, whereas nearly one-third college students are still hesitant or resistant. It is crucial to provide sufficient and scientific information on the efficacy and safety of vaccine through social media and government agencies platforms to promote vaccine progress against COVID-19 and control the pandemic in China.

Objective As COVID-19 persists around the world, it is necessary to explore the long-term mental health effects in COVID-19 survivors. In this study, we investigated the mental health outcomes of survivors of COVID-19 at 6 and 12 months postdiagnosis. Methods Posttraumatic stress disorder (PTSD checklist for the DSM-5, PCL-5), depression (PHQ-9), anxiety (Generalized Anxiety Disorder Scale, GAD-7), resilience (Connor-Davidson Resilience Scale, CD-RISC-10), perceived social support (PSSS), personality traits (Chinese Big Five Personality Inventory-15, CBF-PI-15), and sociodemographic information were examined among 511 survivors of COVID-19 (48.1%, females; Mage = 56.23 years at first assessment) at 6 and 12 months postdiagnosis. The data were analyzed with Wilcoxon signed rank tests and multivariable logistic regression models. Results The prevalence of anxiety, depression, and posttraumatic stress disorder (PTSD) at 6 and 12 months after diagnosis was 13.31% and 6.26%; 20.35% and 11.94%; and 13.11% and 6.07%, respectively. The risk factors for all symptoms were as follows: higher neuroticism; lower openness, extraversion, agreeableness, and resilience; greater life disruptions due to COVID-19; poorer living standards; and increased symptoms of PTSD or depression at 6 months postdiagnosis. Conclusion The mental health of COVID-19 survivors improved between 6 and 12 months postdiagnosis. Mental health workers should pay long-term attention to this group, especially to survivors with risk factors.