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*

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

Objective To investigate the effect and mechanism of Yiguan Decoction(YGJD)on the efficacy of M1 bone marrow-derived macrophages(M1-BMDMs)in the treatment of rats with liver cirrhosis induced by 2-AAF/CCl4.Methods BMDMs were isolated and induced into M1-BMDMs by lipopolysaccharide.A total of 50 male Wistar rats were randomly divided into normal group with 5 rats and model group with 45 rats.The rats for modeling were given subcutaneous injection of 50%CCl4 twice a week.Since week 7,the rats for modeling were randomly divided into model group(M group),YGJD group,M1-BMDM group,M1-BMDM+YGJD group,and sorafenib(SORA)group,and they were given subcutaneous injection of 30%CCl4 to maintain the progression of liver cirrhosis and intragastric administration of 2-AAF.CCR2 inhibitors were added to the drinking water,and each group was given the corresponding intervention.Related samples were collected at week 9.The rats were observed in terms of serum liver function parameters,liver pathology,hydroxyproline(Hyp)content in liver tissue,hepatic stellate cell activation,hepatic fibrosis and inflammation factors,and the expression levels of molecules associated with the Wnt signaling pathway.A one-way analysis of variance was used for comparison of continuous data between multiple groups,and the least significant difference t-test was used for further comparison between two groups.Results Compared with the M group,the M1-BMDM+YGJD group had significant reductions in the serum levels of alanine aminotransferase,aspartate aminotransferase,and total bilirubin(TBil)(all P<0.05)and a significant increase in the content of albumin(Alb)(P<0.05),and compared with the M1-BMDM group,the M1-BMDM+YGJD group had a significant reduction in the serum level of TBil(P<0.05)and a significant increase in the serum level of Alb(P<0.05).Compared with the M1-BMDM group,the M1-BMDM+YGJD group had significant reductions in the expression levels of CD68 and TNF-α(P<0.05).Compared with the M1-BMDM group,the M1-BMDM+YGJD group had significant reductions in Hyp content and Sirius red positive area(P<0.05).As for the non-canonical Wnt signaling pathway molecules,compared with the M1-BMDM group,the M1-BMDM+YGJD group had significantly lower mRNA and protein expression levels of Wnt5a(P<0.05)and mRNA expression level of Fzd2(P<0.05),as well as significant reductions in the mRNA expression levels of Wnt4,Wnt5b,and Fzd3(P<0.05),while there were no significant changes in the mRNA expression levels of the canonical Wnt signaling pathway molecules β-catenin,LRP5,LRP6,Fzd5,and TCF.Conclusion YGJD can enhance the therapeutic effect of M1-BMDMs on rats with liver cirrhosis induced by 2-AAF/CCl4,possibly by inhibiting the non-canonical Wnt5a/Fzd2 signaling pathway,which provides new ideas for the synergistic effect of traditional Chinese medicine on M1-BMDMs in the treatment of liver cirrhosis.

Objective:To assess the value of serum tumor specific protein 70 (SP70) for prognostic stratification in acute myeloid leukemia (AML).Methods:A cohort study design was adopted. 129 newly diagnosed AML patients from September 2022 to January 2024 at the Hematology Department of the First Affiliated Hospital of Nanjing Medical University were included, as well as a control group consisted of 120 healthy individuals and 7 cases with benign hematologic diseases during the same period (total 127 cases). Clinical data were collected from Electronic Medical Records. According to the 2023 edition of the Chinese Leukemia Diagnosis and Treatment Guidelines, AML patients with good or moderate prognosis were categorized as low-to-intermediate risk, while those with poor prognosis were high-risk group. Univariate and multivariate logistic regression analyses were used to identify variables significantly associated with AML prognostic risk. ROC analysis was used to evaluate diagnostic performance. A nomogram for predicting patient prognostic risk was constructed by R 4.0.2 software, and the internal validation was performed using bootstrapping.Results:Among 129 AML patients, there were 71 males (55.0%) and 58 females (45.0%), with 42 (32.6%) classified as high-risk and 87 (67.4%) as low-intermediate risk. The high-risk group had a significantly higher median age [62 (48, 67) years] compared to the low-intermediate risk group [50 (35, 63) years, Z=-2.381, P=0.017], and a significantly higher proportion of males (30 patients, 71.4%) compared to the low-intermediate risk group (41 patients, 47.1%, χ 2=6.760, P=0.009). Multivariate logistic regression analysis indicated that serum SP70 ( OR=2.54, 95% CI: 1.68-3.84, P<0.001), hemoglobin (HB) ( OR=0.96, 95% CI: 0.93-0.99, P<0.05), and bone marrow blast (BM blast) ( OR=1.07, 95% CI: 1.02-1.13, P<0.05) were independent risk factors for high-risk prognosis in AML patients. ROC analysis showed that the area under the curve (AUC) for SP70 predicting high-risk patients was 0.908 (cut-off value of 5.74 ng/ml, 95% CI: 0.845-0.952, sensitivity 90.5%, specificity 82.8%). The combined model of serum SP70, HB, and BM blasts had an AUC of 0.931 (95% CI: 0.890-0.973); C-index=0.925 (95% CI: 0.876-0.963),with no statistically significant difference compared to serum SP70 alone ( Z=1.693, P>0.05). Conclusion:Serum SP70 may be a promising non-invasive molecular biomarker for prognostic stratification in AML.

Objective:To assess the value of serum tumor specific protein 70 (SP70) for prognostic stratification in acute myeloid leukemia (AML).Methods:A cohort study design was adopted. 129 newly diagnosed AML patients from September 2022 to January 2024 at the Hematology Department of the First Affiliated Hospital of Nanjing Medical University were included, as well as a control group consisted of 120 healthy individuals and 7 cases with benign hematologic diseases during the same period (total 127 cases). Clinical data were collected from Electronic Medical Records. According to the 2023 edition of the Chinese Leukemia Diagnosis and Treatment Guidelines, AML patients with good or moderate prognosis were categorized as low-to-intermediate risk, while those with poor prognosis were high-risk group. Univariate and multivariate logistic regression analyses were used to identify variables significantly associated with AML prognostic risk. ROC analysis was used to evaluate diagnostic performance. A nomogram for predicting patient prognostic risk was constructed by R 4.0.2 software, and the internal validation was performed using bootstrapping.Results:Among 129 AML patients, there were 71 males (55.0%) and 58 females (45.0%), with 42 (32.6%) classified as high-risk and 87 (67.4%) as low-intermediate risk. The high-risk group had a significantly higher median age [62 (48, 67) years] compared to the low-intermediate risk group [50 (35, 63) years, Z=-2.381, P=0.017], and a significantly higher proportion of males (30 patients, 71.4%) compared to the low-intermediate risk group (41 patients, 47.1%, χ 2=6.760, P=0.009). Multivariate logistic regression analysis indicated that serum SP70 ( OR=2.54, 95% CI: 1.68-3.84, P<0.001), hemoglobin (HB) ( OR=0.96, 95% CI: 0.93-0.99, P<0.05), and bone marrow blast (BM blast) ( OR=1.07, 95% CI: 1.02-1.13, P<0.05) were independent risk factors for high-risk prognosis in AML patients. ROC analysis showed that the area under the curve (AUC) for SP70 predicting high-risk patients was 0.908 (cut-off value of 5.74 ng/ml, 95% CI: 0.845-0.952, sensitivity 90.5%, specificity 82.8%). The combined model of serum SP70, HB, and BM blasts had an AUC of 0.931 (95% CI: 0.890-0.973); C-index=0.925 (95% CI: 0.876-0.963),with no statistically significant difference compared to serum SP70 alone ( Z=1.693, P>0.05). Conclusion:Serum SP70 may be a promising non-invasive molecular biomarker for prognostic stratification in AML.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.