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Background and ObjectivesThe association between patterns of physical/mental activity and dementia and how it is affected by disease susceptibility remains unknown. We aimed to examine the association between patterns of physical and mental activity and dementia and whether it can be modified by disease susceptibility to dementia.MethodsIn a prospective cohort study based on UK Biobank, 501,376 dementia-free participants were recruited in 2006–2010 and followed from 1 year after the recruitment date until the end of 2019 for ascertainment of dementia. Data on physical (i.e., physical activity at leisure time, housework-related activity, and transportation) and mental (i.e., intelligence, social contact, and use of electronic device) activity were collected using questionnaires at recruitment. Cox models were used to estimate the associations of physical and mental activity-related items, as well as major activity patterns identified by principal component analysis, with the risk of dementia, adjusted for multiple confounders. The modification role of disease susceptibility on such associations was assessed through stratified analyses by the polygenic risk score (PRS) of dementia generated based on summary statistics of independent genome-wide association studies, by the APOE genotype, and by the self-reported family history of dementia.ResultsThe mean age at recruitment was 56.53, and 45.60% of the participants were male. During a mean follow-up of 10.66 years, 5,185 dementia cases were identified. When analyzed separately, multiple studied items related to physical and mental activity showed significant associations with the risk of dementia. The pattern analyses revealed that a higher level of adherence to activity patterns related to frequent vigorous and other exercises (hazard ratio 0.65, 95% CI 0.59–0.71), housework-related activity (0.79, 0.72–0.85), and friend/family visit (0.85, 0.75–0.96) was associated with a lower risk of dementia. We obtained comparable results for vascular dementia and Alzheimer disease as well as in the stratified analyses by the PRS for dementia, APOE genotype, or family history of dementia.DiscussionActivity patterns more adherent to frequent vigorous and other exercises, housework-related activity, and friend/family visit were associated with a reduced risk of multiple types of dementia. Such associations are independent of disease susceptibility, highlighting the potential of these physical and mental activity patterns, as effective interventions, in the primary prevention of dementia.

Cervical cancer is the leading cause of morbidity and mortality in women throughout the world, However, the underlying mechanism of cervical cancer progression is still unclear. In the current study, we found that lymphoid-specific helicase (HELLS) was significantly increased in cervical cancer. The function of HELLS was assessed by gain- and loss-function experiments. Functionally, the cell viability was assessed by CCK8 and cell proliferation was analyzed by colony formation assay. We found that HELLS overexpression significantly promoted cell viability and colony formation of cervical cancer cell, whereas HELLS inhibition suppressed these effect. Moreover, MDA and ROS induced by ferroptosis inducers erastin were restored by HELLS-OE. Mechanically, we confirm that HELLS promote cervical cancer proliferation by regulating Nrf2-mediated ferroptosis. In conclusion, our data suggest HELLS promote cervical cancer proliferation by inhibiting Nrf2 expression, and HELLS knockdown might be an effective treatment for cervical cancer.

Cervical cancer is the leading cause of morbidity and mortality in women throughout the world, human papillomavirus 16 (HPV16) is the main type of HPV causing invasive cervical cancer. However, the underlying mechanism of the high carcinogenicity of HPV16 remains unclear. In the current study, we documented that metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a long noncoding RNA, is upregulated in HPV16-positive cervical cancer tissue and cell lines. The results of immunohistochemistry and immunofluorescence showed that MALAT1 was mainly localized in the cytoplasm. To clarify the biological functions of MALAT1 in cervical cancer cells, we performed gain- and loss-of-function experiments to explore the underlying molecular mechanism. Functionally, the proliferation of cervical cancer was detected by Cell Counting Kit-8 (CCK-8) and colony formation assay in MALAT1 overexpression or knockdown cells, our data showed that MALAT1 promotes the proliferation of cervical cancer cells. Mechanistically, our results suggested that MALAT1 upregulates Methionine adenosyltransferase 2A (MAT2A) by sponging miR-485-5p. Moreover, the gain-of-function assay validated the function of MAT2A in HPV16-positive cervical cancer proliferation. Taken together, our results demonstrated that MALAT1 acts as a competitive endogenous RNA (ceRNA) to regulate MAT2A by sponging miR-485-5p in HPV16-positive cervical cancer, suggesting that MALAT1 may act as a potential therapeutic target for HPV16-positive cervical cancer.

Aim Depression and anxiety are both common psychological outcomes triggered via trauma-related experience. The current study aimed to identify the prevalence of depression and anxiety among youth survivors, explore the heterogeneity patterns of depression and anxiety symptoms in the chronic phase, and integrate early available information to predict patterns. Methods The study is a longitudinal study conducted at 2 weeks and 12 months after the Lushan earthquake. Finally, 1725 children and adolescents' survivors who accomplished both two-time stages assessments were included. The heterogeneous patterns of depression and anxiety symptoms assessed by latent profile analysis (LPA) were used to predict heterogeneity patterns of depression and anxiety symptoms by early attained variables. Results A three-class solution characterized mild depression/mild anxiety (64.3%), moderate depression/moderate anxiety (25.9%), and high depression/high anxiety (9.8%) parallel patterns was the most suitable model in our research. Demographic characteristics, earthquake-related exposures, sleep, and somatic symptoms were variables that can be used to predict the parallel profiles. Conclusions Detection of heterogeneity patterns, especially for youth survivors at high risk, based on the self-reported measurement attained at the early stage, is feasible. Our study may promote operational strategies by enabling targeted intervention.

Background The outbreak of the 2019 novel coronavirus disease (COVID-19) not only caused physical abnormalities, but also caused psychological distress, especially for undergraduate students who are facing the pressure of academic study and work. We aimed to explore the prevalence rate of probable anxiety and probable insomnia and to find the risk factors among a longitudinal study of undergraduate students using the approach of machine learning. Methods The baseline data (T1) were collected from freshmen who underwent psychological evaluation at two months after entering the university. At T2 stage (February 10th to 13th, 2020), we used a convenience cluster sampling to assess psychological state (probable anxiety was assessed by general anxiety disorder-7 and probable insomnia was assessed by insomnia severity index-7) based on a web survey. We integrated information attained at T1 stage to predict probable anxiety and probable insomnia at T2 stage using a machine learning algorithm (XGBoost). Results Finally, we included 2009 students (response rate: 80.36%). The prevalence rate of probable anxiety and probable insomnia was 12.49% and 16.87%, respectively. The XGBoost algorithm predicted 1954 out of 2009 students (translated into 97.3% accuracy) and 1932 out of 2009 students (translated into 96.2% accuracy) who suffered anxiety and insomnia symptoms, respectively. The most relevant variables in predicting probable anxiety included romantic relationship, suicidal ideation, sleep symptoms, and a history of anxiety symptoms. The most relevant variables in predicting probable insomnia included aggression, psychotic experiences, suicidal ideation, and romantic relationship. Conclusion Risks for probable anxiety and probable insomnia among undergraduate students can be identified at an individual level by baseline data. Thus, timely psychological intervention for anxiety and insomnia symptoms among undergraduate students is needed considering the above factors.

Background A growing body of research suggests that major depressive disorder (MDD) is one of the most common psychiatric conditions associated with suicide ideation (SI). However, how a combination of easily accessible variables built a utility clinically model to estimate the probability of an individual patient with SI via machine learning is limited. Methods We used the electronic medical record database from a hospital located in western China. A total of 1916 Chinese patients with MDD were included. Easily accessible data (demographic, clinical, and biological variables) were collected at admission (on the first day of admission) and were used to distinguish SI with MDD from non-SI using a machine learning algorithm (neural network). Results The neural network algorithm distinguished 1356 out of 1916 patients translating into 70.08% accuracy (70.68% sensitivity and 67.09% specificity) and an area under the curve (AUC) of 0.76. The most relevant predictor variables in identifying SI from non-SI included free thyroxine (FT4), the total scores of Hamilton Depression Scale (HAMD), vocational status, and free triiodothyronine (FT3). Conclusion Risk for SI among patients with MDD can be identified at an individual subject level by integrating demographic, clinical, and biological variables as possible as early during hospitalization (at admission).

Objective The functions of both the central and peripheral autonomic nervous system, indexed by heart rate variability (HRV), are affected by psychology and physiology. This review summarizes the results of studies comparing the evaluation of HRV parameters between individuals with posttraumatic stress disorder (PTSD) and healthy controls. Methods Eligible studies (n=499) were identified through literature searches of the EMBASE, Medline, PubMed and Web of Science databases. Nineteen studies met our inclusion criteria. A random effects model was used, and standardized mean differences for high-frequency HRV(HF-HRV), low-frequency HRV(LF-HRV) and the root mean square of successive R-R interval differences (RMSSD) were calculated. Results Significant effects were found for HF-HRV [p

Background The homeobox A cluster (HOXA) gene family is participated in multiple biological functions in human cancers. To date, little is known about the expression profile and clinical significance of HOXA genes in cervical cancer. Methods We downloaded RNASeq data of cervical cancer from The Cancer Genome Atlas (TCGA) database. The difference in HOXA family expression was analyzed using independent samples t test. Cox proportional hazard regression analysis was used to assess the effect of HOXA family expression on survival, and a nomogram predicting survival was generated. We assessed the infiltration difference in immune cells and expression difference of immunity biomarkers between two groups with different expression level of HOXA genes through Immune Cell Abundance Identifier (ImmuCellAI) and independent samples t test, respectively. Results Our results showed that the HOXA1 gene was upregulated, while the HOXA10 and HOXA11 were downregulated in cervical cancer. Downregulation of HOXA1 was related to a poor outcome for cervical cancer patient. We also identified a significantly increased abundance of T helper 2 cells (Th2) and higher expression of PD‐L1 in cervical cancer patients with lower expression of HOXA10 and HOXA11. The gene set enrichment analysis (GSEA) results indicated that HOXA1 and HOXA11 were involved in immune responses pathways and participated in the activation of a variety of classic signaling pathways related to the progression of human cancer. Conclusion This study comprehensively analyzed different HOXA genes applying public database to determine their expression patterns, potential diagnostic, prognostic, and treatment values in cervical cancer., Our study comprehensively analyzed different HOXA genes applying public database to determine their expression patterns, potential diagnostic, prognostic, and treatment values in cervical cancer. Firstly, we compared the expression of HOXA members in cervical cancer than normal controls, and results showed only three HOXA members with statistical significance (HOXA1, HOXA10, and HOXA11). Then, the prognostic potential was assessed by the following steps: (1) Kaplan‐Meier plot showed low expression of seven HOXA members was correlated with favor overall survival of cervical cancer (including HOXA1, HOXA2, HOAX3, HOXA4, HOXA5, HOXA6, and HOXA9). (2) Univariate Cox proportional hazards regression analysis confirmed the prognostic value of HOXA1, HOXA2, HOXA3, and HOXA4. (3) Multivariate Cox proportional hazards regression analysis determined the prognostic value of HOXA1 and HOXA3. Eventually, the HOXA1 could be served as an independent prognostic biomarker for poor outcome of cervical cancer. Subsequently, a significantly increased abundance of T helper 2 cells (Th2) and higher expression of PD‐L1 were observed in cervical cancer patients with lower expression of HOXA10 and HOXA11, indicative of the fact that HOXA10 and HOXA11 could be served as biomarkers of response to immunotherapy.

Objectives Early recognition and intervention of patients with the anti‐N‐methyl‐d‐aspartate receptor (NMDAR) encephalitis are important to achieve a better prognosis. The study aims to summarize the real‐world perspectives of anti‐NMDAR encephalitis patients in China via electronic medical records (EMRs). Methods Using EMRs of patients from 2013 to 2019 from West China Hospital in China, a retrospective research was conducted to demonstrate the temporary rank of clinical characteristics and disease prognosis of anti‐NMDAR encephalitis. The modified Rankin Scale (mRS) scores were used to divide the anti‐NMDAR‐encephalitis into two groups (poor prognosis vs. good prognosis). Chi‐square test and logistic regression were used to analyze factors associated with prognosis. Results Here, 78 patients were included. The most common clinical characteristics are cognitive dysfunction (86.0%) and thought disorder (86.0%). Cognitive dysfunction, thought disorder, and seizures tended to appear soon after prodrome symptoms. Logistics analysis results showed that cognitive dysfunction (OR = 4.48, 95% CI = 1.09–18.47), the score of (GCS ≤ 8) (OR = 4.52, 95% CI = 1.18–17.32), positive antibodies in serum (OR = 4.89, 95% CI = 1.19–20.13) and delay immunotherapy (OR = 4.76, 95% CI = 1.79–12.60) were risk factors of poor clinical outcomes. Conclusions There are two peaks in the development of autoimmune encephalitis (AE). The first peak is cognitive dysfunction, and the second peak is autonomic dysfunction. Cognitive dysfunction and GCS score ≤8 at admission, antibodies positive in serum, and delay immunotherapy were risk factors for a poor prognosis at discharge., Using electronic medical records (EMRs) of patients between 2013 to 2019 from West China Hospital in China, a retrospective research was conducted to demonstrate the temporary rank of clinical characteristics and disease prognosis of anti‐N‐methyl‐D‐aspartate receptor (NMDAR) encephalitis. We found that the most common clinical characteristics are cognitive dysfunction (86.0%) and thought disorder (86.0%). Logistics analysis results showed that cognitive dysfunction (OR=4.48, 95%, CI=1.09‐18.47), the score of (GCS≤8)(OR=4.52, 95%, CI=1.18‐17.32), positive antibodies in serum(OR=4.89, 95%CI=1.19‐20.13) and delay immunotherapy (OR=4.76, 95%, CI=1.79‐12.60) were risk factors of poor clinical outcomes.

Background: Patients with major depressive disorder (MDD) often present with co-occurring anxiety symptoms. The network method provides a novel view on understanding the co-occurrence of depressive and anxiety symptoms. Thus, the purpose of our study was to explore it by applying network analysis methods.Methods: We used electronic medical records from West China Hospital in China. In total, 3,424 patients who met the criteria for MDD were included. R-studio 3.6 was used to estimate the network structure. First, we estimated the network structure of depression and anxiety symptoms using the graphic LASSO algorithm. Then, we estimated the centrality indices of nodes to determine which symptoms are more central in the network. We then estimated the bridge centrality indices using the bridge function via the R package networktools.Results: Some strong connections were found like “easy to wake up,” “wake up early,” and “difficulty falling asleep,” “suicidal thoughts,” and “hopelessness.” “Depressed mood,” “somatic anxiety,” “hopelessness,” “anxiety mood,” and “tension” have the higher centrality indices. Results revealed eight bridge symptoms (e.g., concentration/memory difficulty, gastrointestinal symptoms) in the co-occurrence network structure.Conclusions: This research suggests that the described approach in mapping the presence of anxiety symptoms in individuals with major depression might potentially increase diagnostic precision and help choose more targeted interventions and potentially reduce the occurrence of treatment resistance.

Rationale and objectives The hippocampus, especially the CA1, CA3, and dentate gyrus (DG) subfields, is reported to be associated with post-traumatic stress disorder (PTSD) after trauma. However, neuroimaging studies of the associations between PTSD and hippocampal subfield volumes have failed to yield consistent findings. The aim of this study is to examine whether the dopamine D2 receptor (DRD2) Taq1A polymorphism, which is associated with both hippocampal function and PTSD, moderated the association between PTSD severity and hippocampal CA1, CA3 and DG volumes. Methods T1-weighted images were acquired from 142 trauma survivors from the 2008 Wenchuan earthquake using a 3.0-T magnetic resonance imaging system. Hippocampal subfield segmentations were performed with FreeSurfer v6.0. We used the simple moderation model from the PROCESS v3.4 tool for SPSS 23.0 to examine the association between the rs1800497 polymorphism, PTSD severity, and hippocampal CA3 and DG volumes. Results A significant genotype × PTSD symptom severity interaction was found for the left CA3 volume (ΔF = 5.01, p = 0.008, ΔR2 = 0.05). Post hoc, exploratory analyses deconstructing the interaction revealed that severe PTSD symptomatology were associated with reduced left CA3 volume among TC heterozygotes (t = − 2.86, p = 0.005). Conclusions This study suggests that DRD2 Taq1A polymorphism moderates the association between PTSD symptomatology and left CA3 volume, which promotes an etiological understanding of the hippocampal atrophy at the subfield level. This highlights the complex effect of environmental stress, and provides possible mechanism for the relationship between the dopaminergic system and hippocampal function in PTSD.

Background The fear of insecurity and uncertainty caused by the 2019 coronavirus disease (COVID-19), the separation and loss of certain important relationships, and great changes in lifestyle have awakened strong emotional responses, which may cause psychological problems in the general population. However, there is little research on how people who pay attention to anxiety and depression cope with the negative psychological impact during an epidemic or major disaster. This study aimed to identify what behaviors can effectively reduce negative emotions during an epidemic. Methods From 1 February to 8 March 2020, we conducted a web-based survey and collected information on general demographic data. Probable depression, anxiety symptoms, and coping behaviors were assessed with the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and self-made coping behaviors questionnaires. Result Among 17 249 responders, 7923 and 9326 completed assessments of depression and anxiety respectively, and all responders completed the coping behaviors questionnaires. Our survey population showed a high prevalence rate of possible depression disorders (2746 of 7923, 34.66%) and anxiety disorders (5309 of 9326, 56.93%). Compared with other groups, the elderly, women, people of lower education, and people with lower income were more likely to suffer depression and/or anxiety. In terms of marital status, the cohabiting group showed the highest rate of depression and/or anxiety. Among the careers, students and housewives were high-risk groups suffering from depression and/or anxiety. After adjusting for social-demographic factors (e.g. age, sex), depression and anxiety were positively associated with self-injury, doing housework, and having sex or masturbating, and negatively associated with singing, drawing, or writing, dating friends online, singing, attending lectures, and doing yoga. Conclusion Our findings identified some spontaneous coping behaviors that can probably relieve the psychological impact of vulnerable groups during the COVID-19 epidemic.

Background A growing body of research suggests that major depressive disorder (MDD) is one of the most common psychiatric conditions associated with suicide ideation (SI). However, how a combination of easily accessible variables built a utility clinically model to estimate the probability of an individual patient with SI via machine learning is limited. Methods We used the electronic medical record database from a hospital located in western China. A total of 1916 Chinese patients with MDD were included. Easily accessible data (demographic, clinical, and biological variables) were collected at admission (on the first day of admission) and were used to distinguish SI with MDD from non-SI using a machine learning algorithm (neural network). Results The neural network algorithm distinguished 1356 out of 1916 patients translating into 70.08% accuracy (70.68% sensitivity and 67.09% specificity) and an area under the curve (AUC) of 0.76. The most relevant predictor variables in identifying SI from non-SI included free thyroxine (FT4), the total scores of Hamilton Depression Scale (HAMD), vocational status, and free triiodothyronine (FT3). Conclusion Risk for SI among patients with MDD can be identified at an individual subject level by integrating demographic, clinical, and biological variables as possible as early during hospitalization (at admission).

Background Despite considerable studies focused on the symptoms of posttraumatic stress disorder (PTSD), little is understood about how symptoms of PTSD change over time. The study aimed to use a network analysis approach to understand the nature of the association between PTSD symptoms at different time points among children and adolescents who experience an earthquake. Methods A longitudinal study enrolled 1623 youth survivors who completed 3 assessments with the Children's Revised Impact of Event Scale at 2 weeks, 3 months and 6 months after the Lushan earthquake. The epicentre was Baoxing Country that located in the Ya'an city (Sichuan Province in China). A network analysis approach was used to investigate how symptom networks change at different time points. Results A total of 1623 youth survivors (768 male and 855 female) completed three assessments. Different centrality symptoms existed at different time points. Flashback and upset by reminders were centrality symptoms at 2 weeks, 3 months and 6 months. The overall network connectivity was significantly stronger at 3 months than at 2 weeks (5.663 vs. 5.140, s = 0.523, p = 0.000), and significantly stronger at 6 months than at 2 weeks (6.094 vs. 5.663, s = 0.432, p = 0.020). Limitations Participants from a specific region might limit the generalizability of our results. A self-report questionnaire was used to assess PTSD symptoms. Conclusions Re-experience cluster (flashback and upset by reminders) and their interactions might play a key role in PTSD symptom evolution. Over time, the global connectivity becomes stronger, suggesting that youth survivors are more vulnerable in the chronic phases.

Evidence has identified risk factors associated with individuals with trauma exposure who develop posttraumatic stress disorder (PTSD). How to combine risk factors to predict probable PTSD in young survivors using machine learning is limited. The study aimed to integrated multiple measures at 2 weeks after the earthquake using machine learning for the prediction of probable PTSD at 3 months after earthquake.A total of 2099 young survivors with earthquake exposure were included. We integrated multiple domains of variables to 'train' a machine learning algorithm (XGBoost). Thirty-one combination types were implemented and evaluated. The resulting XGBoost was utilized in identifying individual participants as either probable PTSD or no PTSD.Any combination type predicted young survivor probable PTSD, with prediction accuracies ranging between 66%-80% (p 0.05). In particular, the combination of earthquake experience, everyday functioning, somatic symptoms and sleeping correctly predicted 683 out of 802 cases of probable PTSD, translating to a classical accuracy of 74.476% (85.156% sensitivity and 60.366% specificity) and an area under the curve of 0.80. The most relevant variables (e.g. age, sex, property loss and a sedentary lifestyle) revealed in the present study.Participants from a specific district might limit the generalizability of our results. Self-report questionnaires and non-standardized measures were used to assess symptoms.Detection of probable PTSD according to self-reported measurement data is feasible, may improve operational efficiencies via enabling targeted intervention, before manifestation of symptoms.