There are heterogeneity and complexity of symptoms across disease lineages in psychiatric disorders, and there lack objective biological markers. With the development of neuroimaging research, breakthroughs in neuroimaging techniques such as high-resolution magnetic resonance imaging, resting state functional connectivity analysis, and molecular imaging have provided new methods for exploring the neuropathological mechanisms of psychiatric disorders. Although rapid progress has been made in the study of brain imaging in psychiatric disorders, challenges still exist such as sample heterogeneity, limitations in diagnostic criteria, and research reproducibility. In the future, efforts can be made to build a "bridge" between brain imaging research and diagnosis through large-scale collaboration, establishing standardized processes, interpretable AI models, and optimizing deep learning models.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

There are heterogeneity and complexity of symptoms across disease lineages in psychiatric disorders, and there lack objective biological markers. With the development of neuroimaging research, breakthroughs in neuroimaging techniques such as high-resolution magnetic resonance imaging, resting state functional connectivity analysis, and molecular imaging have provided new methods for exploring the neuropathological mechanisms of psychiatric disorders. Although rapid progress has been made in the study of brain imaging in psychiatric disorders, challenges still exist such as sample heterogeneity, limitations in diagnostic criteria, and research reproducibility. In the future, efforts can be made to build a "bridge" between brain imaging research and diagnosis through large-scale collaboration, establishing standardized processes, interpretable AI models, and optimizing deep learning models.

Endodontic diseases are a kind of chronic infectious oral disease.Common endodontic treatment concepts are based on the removal of inflamed or necrotic pulp tissue and the replacement by gutta-percha.However,it is very essential for endodontic treatment to debride the root canal system and prevent the root canal system from bacterial reinfection after root canal therapy(RCT).Recent research,encompassing bacterial etiology and advanced imaging techniques,contributes to our understanding of the root canal system's anatomy intricacies and the technique sensitivity of RCT.Success in RCT hinges on factors like patients,infection severity,root canal anatomy,and treatment techniques.Therefore,improving disease management is a key issue to combat endodontic diseases and cure periapical lesions.The clinical difficulty assessment system of RCT is established based on patient conditions,tooth conditions,root canal configuration,and root canal needing retreatment,and emphasizes pre-treatment risk assessment for optimal outcomes.The findings suggest that the presence of risk factors may correlate with the challenge of achieving the high standard required for RCT.These insights contribute not only to improve education but also aid practitioners in treatment planning and referral decision-making within the field of endodontics.

Objective:The present study aims to investigate whether there is a shared alteration of the resting-state functional connectivity and amplitude of low frequency fluctuation (ALFF) within the limbic network across all three clinical phases of bipolar disorder.Methods:From July 2019 to December 2021, 107 patients diagnosed with bipolar disorder (bipolar disorder group) were recruited from the Institute of Mental Health at Xiangya Second Hospital, Central South University. Additionally, 46 healthy controls matched for age, gender, and education level were enrolled as the control group. According to the total scores of the Young Mania Rating Scale (YMRS) and the Hamilton Depression Rating Scale (HAMD 17), individuals with bipolar disorder can be categorized into three groups: bipolar manic group, bipolar depressive group, and bipolar euthymic group. Resting state functional magnetic resonance imaging data were collected from all the participants, and seed-based functional connectivity and ALFF in the limbic network were conducted to compare differences among three mood states and controls using One-way ANOVA and post hoc analysis. Finally, correlation analyses using Pearson or Spearman coefficients were performed between statistically significant intergroup differences in functional connectivity/ALFF values and total scores as well as factor scores on clinical rating scales. Results:In comparison to the healthy controls, there was a significant decrease in functional connectivity between the anterior cingulate gyrus and hippocampus across all three clinical phases of bipolar disorder after Bonferroni correction ( t=-2.60--2.07, P<0.05). Furthermore, the ALFF in the anterior cingulate gyrus during three phases showed a significantl reduction following false discovery rate correction ( t=-4.41--3.51, P<0.05). Moreover, a decreased functional connectivity between the left anterior cingulate gyrus and left hippocampus during the depressive phase demonstrated a significant negative correlation with work interest subscores of the HAMD 17 ( r=-0.406, P=0.021). Conclusions:Dysconnectivity and reduced activity within the anterior cingulate of the limbic network may represent common alterations of mood regulation disorder throughout all three clinical phases of bipolar disorder.

Objective:The present study aims to investigate whether there is a shared alteration of the resting-state functional connectivity and amplitude of low frequency fluctuation (ALFF) within the limbic network across all three clinical phases of bipolar disorder.Methods:From July 2019 to December 2021, 107 patients diagnosed with bipolar disorder (bipolar disorder group) were recruited from the Institute of Mental Health at Xiangya Second Hospital, Central South University. Additionally, 46 healthy controls matched for age, gender, and education level were enrolled as the control group. According to the total scores of the Young Mania Rating Scale (YMRS) and the Hamilton Depression Rating Scale (HAMD 17), individuals with bipolar disorder can be categorized into three groups: bipolar manic group, bipolar depressive group, and bipolar euthymic group. Resting state functional magnetic resonance imaging data were collected from all the participants, and seed-based functional connectivity and ALFF in the limbic network were conducted to compare differences among three mood states and controls using One-way ANOVA and post hoc analysis. Finally, correlation analyses using Pearson or Spearman coefficients were performed between statistically significant intergroup differences in functional connectivity/ALFF values and total scores as well as factor scores on clinical rating scales. Results:In comparison to the healthy controls, there was a significant decrease in functional connectivity between the anterior cingulate gyrus and hippocampus across all three clinical phases of bipolar disorder after Bonferroni correction ( t=-2.60--2.07, P<0.05). Furthermore, the ALFF in the anterior cingulate gyrus during three phases showed a significantl reduction following false discovery rate correction ( t=-4.41--3.51, P<0.05). Moreover, a decreased functional connectivity between the left anterior cingulate gyrus and left hippocampus during the depressive phase demonstrated a significant negative correlation with work interest subscores of the HAMD 17 ( r=-0.406, P=0.021). Conclusions:Dysconnectivity and reduced activity within the anterior cingulate of the limbic network may represent common alterations of mood regulation disorder throughout all three clinical phases of bipolar disorder.

Objective:To investigate the feasibility of classifying imaging patterns of dermatomyositis/polymyositis-related interstitial lung disease (DM/PM-ILD) into subtypes based on chest CT radiomics features and a model was constructed by machine learning algorithms.Methods:From November 2011 to November 2020, 107 patients diagnosed with PM/DM-ILD at the First Affiliated Hospital of Xi′an Jiaotong University were retrospectively analyzed. A total of 315 cases with chest CT were collected. Doctors pre-classified image patterns, including 105 cases with non-specific interstitial pneumonia (NSIP), 90 cases with organizing pneumonia (OP), and 66 cases with non-specific interstitial pneumonia combined with organizing pneumonia (NSIP+OP), 35 cases with common interstitial pneumonia (UIP), and 19 cases with diffuse alveolar damage (DAD), ANOVA was used to test the difference of baseline clinical information among the imaging classification groups. All images were divided into the training set and the est set by stratified random sampling at a ratio of 4∶1. In each CT scan, 3D slicer was used to segment each lung lobe, and then reconstructed into 3 mm 3 of voxels, and Pyradiomics library was used to extract the radiomic features of the whole lung and each lobe. The multi-classification goal was achieved by constructing random forest base classifiers for each of the five groups and then voting as the final model. In the process of constructing the base classifier, firstly, the balance between sample groups was achieved by SMOTETomek comprehensive sampling, and the optimal feature set was selected by independent sample t test and L1 regularized least absolute shrinkage and selection operator (LASSO) regression. In this study, the Radiomics model was constructed based on chest CT radiomics features, and the Radiomics + model was constructed by introducing gender and age information. The base classifier and the integration model use the mean accuracy and the area under the receiver operator characteristics analysis curve (AUC) to evaluate the performance, respectively. Results:There was a statistically significant difference ( P<0.05) between the ages of the NSIP, OP, NSIP+OP, UIP, and DAD groups [(57±13),(53±8),(54±10),(44±11), and (46±8)years old, respectively], F=11.82, P<0.001. In the Radiomics model, for each group of NSIP, OP, NSIP+OP, UIP, and DAD, the AUCs of the training set were 0.87, 0.91, 0.91, 0.96, and 0.99, respectively, and the AUC of the test set were 0.81, 0.82, 0.79, 0.93, 0.89. In the final Radiomics + model, for each group of NSIP, OP, NSIP+OP, UIP, and DAD, the AUCs of the training set were 0.89, 0.91, 0.92, 0.97, and 0.99, respectively, and the AUCs of the test set were 0.84, 0.82, 0.78, 0.94, 0.90. Conclusion:Based on chest CT radiomics features and key clinical features (sex, age), the Radiomics + model constructed by machine learning has good classification performance for the imaging patterns of PM/DM-LD.

Objective:To explore the status and influencing factors of incontinence-associated dermatitis among elderly inpatients in 52 hospitals nationwide, and to analyze the nursing of elderly inpatients with incontinence, so as to provide a reference for clinical intervention.Methods:On March 31, 2021, convenience sampling was used to select 14 675 elderly inpatients from 52 hospitals across the country as the research object. The self-designed Incontinence-associated Dermatitis Questionnaire for Elderly Inpatients was used to collect general demographic data, health status, incontinence, and skin nursing. Binomial Logistic regression was used to investigate the influencing factors of incontinence-associated dermatitis in elderly inpatients.Results:Among 14 675 elderly inpatients, the prevalence rates of xerosis cutis, incontinence and incontinence-associated dermatitis were 38.78% (5 691/14 675) , 11.06% (1 623/14 675) and 1.91% (280/14 675) , respectively. The prevalence of mild, moderate and severe incontinence-associated dermatitis were 1.27% (186/14 675) , 0.55% (81/14 675) , and 0.09% (13/14 675) , respectively. Among the nursing of 1 623 elderly inpatients with incontinence, the items with low implementation rate were the use neutral lotion to clean skin (14.17%, 230/1 623) , use of skin protectant after moisturizing (17.68%, 287/1 623) , moisturizing after cleansing the skin (28.90%, 469/1 623) . The results of binomial Logistic regression analysis showed that xeroderma, fecal incontinence, urinary and fecal incontinence, ≥2 kinds of combined medication, and hospital stay >30 days were risk factors for incontinence-associated dermatitis in elderly inpatients.Conclusions:The risk factors of incontinence-associated dermatitis in elderly inpatients mainly include xerosis cutis, type of incontinence, ≥2 kinds of combined medication, and hospital stay >30 days.

OBJECTIVES: There are almost one million families who lost their only child in China, and 65.6% of them had severe and long lasting depression and needed timely psycho-intervention. This study aims to explore the relationship among resilience and its influential factors, and to compare their effect on depression. METHODS: A total of 212 only-child loss person in 9 administrative regions in Changsha were assessed by using Connor-Davidson Resilience Scale, Zung Self-rating Depression Scale, Simplified Coping Style Questionnaire, Simplified Eysenck Personality Questionnaire, Social Support Rating Scale, and General Self-efficacy Scale. A hypothetical model was tested based on Kumpfer resilience framework and stress-coping theory. RESULTS: The influential factors of resilience were: positive coping (the total effect value was 0.480), support utilization (the total effect value was 0.359), neuroticism (the total effect value was -0.326), negative coping (the total effect value was 0.279), extraversion (the total effect value was 0.219), and objective support (the total effect value was 0.077). The process of individual-environment interaction showed a greater impact on resilience, which had a direct effect on depression (the total effect value was -0.344, 67.1%), and also indirect effect through self-efficacy (the total effect value was -0.169). The total effect of resilience accounted for 20.1% of the total effect of all variables. CONCLUSIONS Resilience mainly impacts depression directly, and can negatively predict depression in only-child loss parents. Resilience, located before self-efficacy, is a significant stress mediating variables. Personality traits and support utilization indirectly impact resilience via negative and positive coping. The key to promote the reorganization of resilience is the process of individual-environmental interaction, involving support utilization, positive coping, and some sorts of negative coping strategies, which plays an important role in developing a resilience intervention program and can improve the depression of the only-child loss person.
Adaptation, Psychological ; Child ; China/epidemiology* ; Extraversion, Psychological ; Humans ; Only Child ; Surveys and Questionnaires