Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

Astrocytes are a crucial type of glial cells in the central nervous system, not only maintaining brain homeostasis, but also actively participating in the transmission of information within the brain. Astrocytes have a complex structure that includes the soma, various levels of processes, and end-feet. With the advancement of genetically encoded calcium indicators and imaging technologies, researchers have discovered numerous localized and small calcium activities in the fine processes and end-feet. These calcium activities were termed as microdomain calcium activities, which significantly differ from the calcium activities in the soma and can influence the activity of local neurons, synapses, and blood vessels. This article elaborates the detection and analysis, characteristics, sources, and functions of microdomain calcium activities, and discusses the impact of aging and neurodegenerative diseases on these activities, aiming to enhance the understanding of the role of astrocytes in the brain and to provide new insights for the treatment of brain disorders.
Astrocytes/cytology* ; Humans ; Animals ; Calcium/metabolism* ; Calcium Signaling/physiology* ; Brain/physiology* ; Aging/physiology* ; Membrane Microdomains/physiology* ; Neurodegenerative Diseases/physiopathology*

OBJECTIVES: We propose a multimodal model integrating social media text and image data for automated assessment of psychological stress in college students to support the development of intelligent mental health services in higher education institutions. METHODS: Based on deep learning technology, we designed an evaluation framework comprising a text sentiment modeling module, an image sentiment modeling module, and a multimodal fusion prediction module. Text sentiment features were extracted using Bi-LSTM, and image semantic cues were extracted via U-Net. A feature concatenation strategy was used to enable cross-modal semantic collaboration to achieve automatic identification of 3 psychological stress levels: mild, moderate, and severe. We constructed a multimodal annotated dataset using social platform data from 1577 students across multiple universities in Guangdong Province. After data cleaning, 252 samples were randomly selected for model training and testing. RESULTS: In the 3-classification task, the model demonstrated outstanding performance on the test set, and achieved an accuracy of 92.86% and an F1 score of 0.9276, exhibiting excellent stability and consistency. Confusion matrix analysis further revealed the model's ability to effectively distinguish between different pressure levels. CONCLUSIONS The multimodal psychological stress assessment model developed in this study effectively integrates unstructured social behavior data to enhance the scientific rigor and practical applicability of psychological state recognition, and thus provides support for developing intelligent psychological service systems.
Humans ; Stress, Psychological/diagnosis* ; Students/psychology* ; Universities ; Social Media ; Deep Learning

Background Due to the prominent particularity of medical work,nurses are prone to developing depression,which not only poses a considerable threat to the physical and mental health of nurses,but also affects the quality of nursing to some extent.Occupational stress is a known contributor to depression in nurses,whereas relatively little research has been done to investigate the underlying mediation path.Objective To explore the mediation effect of perceived organizational support and psychological capital on the relationship between occupational stress and depression among nurses,and to provide theoretical references for the prevention and treatment of occupational stress-induced depression in nurses.Methods From March to August 2019,a stratified random sampling method was utilized to select a sample size of 720 nurses in each department of a grade A tertiary hospital in Shandong Province.All subjects were assessed using Effort-Reward Imbalance Questionnaire(ERI),Perceived Organization Support Questionnaire(POS),Psychological Capital Questionnaire(PCQ)and Center for Epidemiologic Studies Depression Scale(CES-D).Pearson correlation was adopted to examine the correlation among above scales,and multilevel mediation analyses were conducted with model 6 in Process macro(version 3.3)for SPSS 26.0 using Bootstrap re-sampling method.Results A total of 658 nurses(91.39%)completed the effective questionnaire survey.ERI score was positively correlated with CES-D score(r=0.499,P<0.01),and negatively correlated with POS and PCQ scores(r=-0.318,-0.275,P<0.01).POS score was positively correlated with PCQ score(r=0.455,P<0.01),and negatively correlated with CES-D score(r=-0.401,P<0.01).PCQ score was negatively correlated with CES-D score(r=-0.567,P<0.01).The total effect value of occupational stress on depression was 0.466(95%CI:0.399～0.534).Perceived organizational support(indirect effect value of 0.027,95%CI:0.006～0.057)and psychological capital(indirect effect value of 0.059,95%CI:0.013～0.120),separately from each other,mediated the relationship between occupational stress and depression,and a chained mediation effect of perceived organizational support and psychological capital was also demonstrated(indirect effect value of 0.051,95%CI:0.031～0.082),accounting for 10.94%of the total effect.Conclusion Occupational stress is a influencing factor of nurses' depression,which can affect the occurrence of depression both directly and indirectly through either separate or chained mediation of perceived organizational support and psychological capital.

Background::With an increasing proportion of multiparas, proper interpregnancy intervals (IPIs) are urgently needed. However, the association between IPIs and adverse perinatal outcomes has always been debated. This study aimed to explore the association between IPIs and adverse outcomes in different fertility policy periods and for different previous gestational ages.Methods::We used individual data from China’s National Maternal Near Miss Surveillance System between 2014 and 2019. Multivariable Poisson models with restricted cubic splines were used. Each adverse outcome was analyzed separately in the overall model and stratified models. The stratified models included different categories of fertility policy periods (2014–2015, 2016–2017, and 2018–2019) and infant gestational age in previous pregnancy (<28 weeks, 28–36 weeks, and ≥37 weeks).Results::There were 781,731 pregnancies enrolled in this study. A short IPI (≤6 months) was associated with an increased risk of preterm birth (OR [95% CI]: 1.63 [1.55, 1.71] for vaginal delivery [VD] and 1.10 [1.03, 1.19] for cesarean section [CS]), low Apgar scores and small for gestational age (SGA), and a decreased risk of diabetes mellitus in pregnancy, preeclampsia or eclampsia, and gestational hypertension. A long IPI (≥60 months) was associated with an increased risk of preterm birth (OR [95% CI]: 1.18 [1.11, 1.26] for VD and 1.39 [1.32, 1.47] for CS), placenta previa, postpartum hemorrhage, diabetes mellitus in pregnancy, preeclampsia or eclampsia, and gestational hypertension. Fertility policy changes had little effect on the association of IPIs and adverse maternal and neonatal outcomes. The estimated risk of preterm birth, low Apgar scores, SGA, diabetes mellitus in pregnancy, and gestational hypertension was more profound among women with previous term births than among those with preterm births or pregnancy loss.Conclusion::For pregnant women with shorter or longer IPIs, more targeted health care measures during pregnancy should be formulated according to infant gestational age in previous pregnancy.

Objective:To understand the epidemiological characteristics of human respiratory syncytial virus (HRSV) among acute respiratory infection (ARI) cases in 16 provinces of China from 2009 to 2023.Methods:The data of this study were collected from the ARI surveillance data from 16 provinces in China from 2009 to 2023, with a total of 28 278 ARI cases included in the study. The clinical specimens from ARI cases were screened for HRSV nucleic acid from 2009 to 2023, and differences in virus detection rates among cases of different age groups, regions, and months were analyzed.Results:A total of 28 278 ARI cases were enrolled from January 2009 to September 2023. The age of the cases ranged from<1 month to 112 years, and the age M ( Q1, Q3) was 3 years (1 year, 9 years). Among them, 3 062 cases were positive for HRSV nucleic acid, with a total detection rate of 10.83%. From 2009 to 2019, the detection rate of HRSV was 9.33%, and the virus was mainly prevalent in winter and spring. During the Corona Virus Disease 2019 (COVID-19) pandemic, the detection rate of HRSV fluctuated between 6.32% and 18.67%. There was no traditional winter epidemic peak of HRSV from the end of 2022 to the beginning of 2023, and an anti-seasonal epidemic of HRSV occurred from April to May 2023. About 87.95% (2 693/3 062) of positive cases were children under 5 years old, and the difference in the detection rate of HRSV among different age groups was statistically significant ( P<0.001), showing a decreasing trend of HRSV detection rate with the increase of age ( P<0.001). Among them, the HRSV detection rate (25.69%) was highest in children under 6 months. Compared with 2009-2019, the ranking of HRSV detection rates in different age groups changed from high to low between 2020 and 2023, with the age M (Q1, Q3) of HRSV positive cases increasing from 1 year (6 months, 3 years) to 2 years (11 months, 3 years). Conclusion:Through 15 years of continuous HRSV surveillance analysis, children under 5 years old, especially infants under 6 months old, are the main high-risk population for HRSV infection. During the COVID-19 pandemic, the prevalence and patterns of HRSV in China have changed.

Objective:To investigate the changes in cardiopulmonary exercise testing (CPET) characteristics before and after the outbreak of COVID-19 in patients with acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI).Methods:This is a cross-sectional study that included ACS patients who underwent PCI at the General Hospital of the Northern Theater Command from July 2018 to February 2023. Based on the timeline of the COVID-19 pandemic, patients were divided into two groups: the pre-pandemic group and the during-pandemic group, with January 2020 as the dividing line. Clinical data were collected from both groups, and a comparative analysis was performed on their postoperative CPET outcomes, including peak oxygen uptake (peak VO 2), peak metabolic equivalents (peak MET), and other indicators. Weber′s classification was used to assess cardiac function. In addition, the 7-tiem generalized anxiety disorder scale (GAD-7) and the patient health questionnaire-9 (PHQ-9) were used to assess the patients′ psychological anxiety and depression states, respectively. Multivariate logistic regression was used to analyze the influencing factors of CPET after PCI. Results:A total of 4 310 post-PCI ACS patients were included, with an average age of (58.7±9.1) years, and 3 464 (80.37%) were male. There were 1 698 patients in the pre-pandemic group and 2 612 patients in the during-pandemic group. The main indicator of the CPET, peak VO 2 (15.04±3.93) ml·min -1·kg -1 in the during-pandemic group, was lower than that in the pre-pandemic group (15.52±3.68) ml·min -1·kg -1, and the difference was statistically significant ( P<0.001). Multivariate logistic regression analysis showed that advanced age, female gender, high body mass index, elevated high-sensitivity C-reactive protein, reduced high-density lipoprotein cholesterol, smoking history, history of myocardial infarction, more severe ACS classification, and mild to moderate degree of depression were related to poor cardiopulmonary outcomes ( P<0.05). Conclusion:The COVID-19 pandemic had a negative impact on the cardiopulmonary outcomes of ACS patients after PCI. Reduced physical activity, and increased psychological stress should be given consideration and attention regarding their impact on patients′ cardiopulmonary function.

Alzheimer's disease(AD)is a common neurological degenerative disease.Collapsin response mediator protein2(CRMP2)plays an important role in the progression of AD.Hyperphosphorylation of CRMP2 results in decreased stability of axonal terminal microtubules and abnormal axoplasmic transport of neurons,which leads to abnormal mitochondrial dynamics of neurons,inhibits the ability of lysosomal autophagy,and leads to excessive activation of NMDA receptors.The phosphorylation of CRMP2 provides a new idea for AD drug develop-ment.In this review,the molecular mechanisms of CRMP2 involved in AD are reviewed,which can provide refer-ences for the development of targeted drugs.

Objective:To understand the epidemiological characteristics of human respiratory syncytial virus (HRSV) among acute respiratory infection (ARI) cases in 16 provinces of China from 2009 to 2023.Methods:The data of this study were collected from the ARI surveillance data from 16 provinces in China from 2009 to 2023, with a total of 28 278 ARI cases included in the study. The clinical specimens from ARI cases were screened for HRSV nucleic acid from 2009 to 2023, and differences in virus detection rates among cases of different age groups, regions, and months were analyzed.Results:A total of 28 278 ARI cases were enrolled from January 2009 to September 2023. The age of the cases ranged from<1 month to 112 years, and the age M ( Q1, Q3) was 3 years (1 year, 9 years). Among them, 3 062 cases were positive for HRSV nucleic acid, with a total detection rate of 10.83%. From 2009 to 2019, the detection rate of HRSV was 9.33%, and the virus was mainly prevalent in winter and spring. During the Corona Virus Disease 2019 (COVID-19) pandemic, the detection rate of HRSV fluctuated between 6.32% and 18.67%. There was no traditional winter epidemic peak of HRSV from the end of 2022 to the beginning of 2023, and an anti-seasonal epidemic of HRSV occurred from April to May 2023. About 87.95% (2 693/3 062) of positive cases were children under 5 years old, and the difference in the detection rate of HRSV among different age groups was statistically significant ( P<0.001), showing a decreasing trend of HRSV detection rate with the increase of age ( P<0.001). Among them, the HRSV detection rate (25.69%) was highest in children under 6 months. Compared with 2009-2019, the ranking of HRSV detection rates in different age groups changed from high to low between 2020 and 2023, with the age M (Q1, Q3) of HRSV positive cases increasing from 1 year (6 months, 3 years) to 2 years (11 months, 3 years). Conclusion:Through 15 years of continuous HRSV surveillance analysis, children under 5 years old, especially infants under 6 months old, are the main high-risk population for HRSV infection. During the COVID-19 pandemic, the prevalence and patterns of HRSV in China have changed.