ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

Purpose To summarize the clinical pathological and immunohistochemical characteristics of esophage-al carcinoma with ductal differentiation of esophageal gland,and analyze the somatic mutation characteristics,key driv-ing mutation genes,and significantly mutated genes based on whole exome sequencing.Methods The clinicopatho-logical features of 9 cases of esophageal carcinoma with esophageal duct differentiation were retrospectively analyzed,and the immunohistochemistry EnVision two-step method was used to stain them,and 3 of the samples were subjected to whole exome sequencing and data analysis.Results Among the 9 patients,6 were males and 3 were females.The average age was 68.3 years old(61-80 years old).All 9 cases were located in the middle-lower segment of the e-sophagus.The diameter of the lesion was from 1.5 cm to 3.5 cm.Most areas of the tumor had a double-layer epithelial structure,including the inner layer of luminal epithelium and the outer layer of basal epithelium.Focal areas could be seen with keratinization and mucinous cells.Immunohistochemistry showed that CK7 was positive in the inner epitheli-um,while p63 was positive in the outer basal epithelium.S-100,SOX10 and c-myb were all negative,and p53 was mutated(diffuse strongly positive).The results of whole exome sequencing analysis showed somatic mutation character-istics(796 SNV,37 InDel,482 CNV),key driving mutation genes(12),and significantly mutated genes(TP53).No intraepithelial neoplasia was observed on the surface squamous epithelium of all cases,and no Barrett's esophagus or ectopic gastric mucosa was observed.The average follow-up time was 21.9 months(8 days-51 months),with 8 ca-ses surviving and 1 case dying of severe pulmonary infection 8 days after surgery.Conclusion Esophageal carcinoma with ductal differentiation of esophageal gland is a rare epithelial derived malignant tumor of the esophagus,character-ized by unique morphological,immunohistochemical,and molecular changes.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

Glioma represents the most prevalent malignant tumor of the central nervous system, with chemotherapy serving as an essential adjunctive treatment. However, most chemotherapeutic agents exhibit limited ability to penetrate the blood-brain barrier (BBB). This study introduced a novel dual-targeting strategy for glioma therapy by modulating the formation of nanobody-driven protein coronas to enhance the brain and tumor-targeting efficiency of hydrophobic cisplatin prodrug-loaded lipid nanoparticles (C8Pt-Ls). Specifically, nanobodies (Nbs) with fibrinogen-binding capabilities were conjugated to the surface of C8Pt-Ls, resulting in the generation of Nb-C8Pt-Ls. Within the bloodstream, Nb-C8Pt-Ls could bound more fibrinogen, forming the protein corona that specifically interacted with LRP-1, a receptor highly expressed on the BBB. This interaction enabled a "Hitchhiking Effect" mechanism, facilitating efficient trans-BBB transport and promoting effective brain targeting. Additionally, the protein corona interacted with LRP-1, which is also overexpressed in glioma cells, achieving precise tumor targeting. Computational simulations and SPR detection clarified the molecular interaction mechanism of the Nb-fibrinogen-(LRP-1) complex, confirming its binding specificity and stability. Our results demonstrated that this strategy significantly enhanced C8Pt accumulation in brain tissues and tumors, induced apoptosis in glioma cells, and improved therapeutic efficacy. This study provides a novel framework for glioma therapy and underscores the potential of protein corona modulation-based dual-targeting strategies in advancing treatments for brain tumors.

Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

AIM:To establish a cell model of visceral hypersensitivity and nerve hyperplasia in irritable bowel syndrome(IBS)by conducting an in vitro co-culture of mouse P815 mast cells and N2a nerve cells and explore its possible mechanism.METHODS:Enzyme-linked immunosorbent assay(ELISA)with three replicates was used to confirm the C48/80-induced P815 degranulation.The length of neurites was observed under bright field microscopy to determine the number of differentiated neurons,thereby selecting the concentration of retinoic acid(RA)for stimulating the differentia-tion of N2a cells,with four replicates.A co-culture system of P815 and N2a cells was established using Transwell cham-bers with four replicates.The following groups were established:N2a cells cultured alone,N2a cells co-cultured with P815 cells,N2a cells co-cultured with P815 cells plus C48/80,and N2a cells plus RA group.After co-culturing,the num-ber of differentiated N2a cells was observed under bright field.The expression of nerve growth factor(NGF),tyrosine ki-nase receptor A(TRKA),growth-associated protein-43(GAP43),neuron-specific enolase(NSE),synapsin(SYN),and postsynaptic density protein-95(PSD-95)at both protein and gene levels in N2a cells was detected using Western blot and polymerase chain reaction(PCR),with four replicates.RESULTS:The best condition for N2a differentiation was stimulation with 10 μmol/L RA for 24 hours,whereas the best condition for degranulation was stimulation of P815 cells with 20 mg/L C48/80 for 24 hours.Compared with N2a cells cultured alone,the differentiation ratio of the N2a+P815+C48/80 and N2a+RA groups was significantly increased(P<0.01),and the protein and mRNA expressions of NGF,TRKA,GAP43,NSE,SYN,and PSD-95 were significantly increased(P<0.05).CONCLUSION:Our results revealed that mast cell degranulation enhances the level of nerve hyperplasia in enteric nerve cells and promotes changes in nerve structure and function.Synaptic remodeling regulated by abnormal expression of key proteins such as NGF,TRKA,and GAP43 is involved in the nerve hyperplasia induced by mast cell degranulation.

Purpose To summarize the clinical pathological and immunohistochemical characteristics of esophage-al carcinoma with ductal differentiation of esophageal gland,and analyze the somatic mutation characteristics,key driv-ing mutation genes,and significantly mutated genes based on whole exome sequencing.Methods The clinicopatho-logical features of 9 cases of esophageal carcinoma with esophageal duct differentiation were retrospectively analyzed,and the immunohistochemistry EnVision two-step method was used to stain them,and 3 of the samples were subjected to whole exome sequencing and data analysis.Results Among the 9 patients,6 were males and 3 were females.The average age was 68.3 years old(61-80 years old).All 9 cases were located in the middle-lower segment of the e-sophagus.The diameter of the lesion was from 1.5 cm to 3.5 cm.Most areas of the tumor had a double-layer epithelial structure,including the inner layer of luminal epithelium and the outer layer of basal epithelium.Focal areas could be seen with keratinization and mucinous cells.Immunohistochemistry showed that CK7 was positive in the inner epitheli-um,while p63 was positive in the outer basal epithelium.S-100,SOX10 and c-myb were all negative,and p53 was mutated(diffuse strongly positive).The results of whole exome sequencing analysis showed somatic mutation character-istics(796 SNV,37 InDel,482 CNV),key driving mutation genes(12),and significantly mutated genes(TP53).No intraepithelial neoplasia was observed on the surface squamous epithelium of all cases,and no Barrett's esophagus or ectopic gastric mucosa was observed.The average follow-up time was 21.9 months(8 days-51 months),with 8 ca-ses surviving and 1 case dying of severe pulmonary infection 8 days after surgery.Conclusion Esophageal carcinoma with ductal differentiation of esophageal gland is a rare epithelial derived malignant tumor of the esophagus,character-ized by unique morphological,immunohistochemical,and molecular changes.

AIM:To establish a cell model of visceral hypersensitivity and nerve hyperplasia in irritable bowel syndrome(IBS)by conducting an in vitro co-culture of mouse P815 mast cells and N2a nerve cells and explore its possible mechanism.METHODS:Enzyme-linked immunosorbent assay(ELISA)with three replicates was used to confirm the C48/80-induced P815 degranulation.The length of neurites was observed under bright field microscopy to determine the number of differentiated neurons,thereby selecting the concentration of retinoic acid(RA)for stimulating the differentia-tion of N2a cells,with four replicates.A co-culture system of P815 and N2a cells was established using Transwell cham-bers with four replicates.The following groups were established:N2a cells cultured alone,N2a cells co-cultured with P815 cells,N2a cells co-cultured with P815 cells plus C48/80,and N2a cells plus RA group.After co-culturing,the num-ber of differentiated N2a cells was observed under bright field.The expression of nerve growth factor(NGF),tyrosine ki-nase receptor A(TRKA),growth-associated protein-43(GAP43),neuron-specific enolase(NSE),synapsin(SYN),and postsynaptic density protein-95(PSD-95)at both protein and gene levels in N2a cells was detected using Western blot and polymerase chain reaction(PCR),with four replicates.RESULTS:The best condition for N2a differentiation was stimulation with 10 μmol/L RA for 24 hours,whereas the best condition for degranulation was stimulation of P815 cells with 20 mg/L C48/80 for 24 hours.Compared with N2a cells cultured alone,the differentiation ratio of the N2a+P815+C48/80 and N2a+RA groups was significantly increased(P<0.01),and the protein and mRNA expressions of NGF,TRKA,GAP43,NSE,SYN,and PSD-95 were significantly increased(P<0.05).CONCLUSION:Our results revealed that mast cell degranulation enhances the level of nerve hyperplasia in enteric nerve cells and promotes changes in nerve structure and function.Synaptic remodeling regulated by abnormal expression of key proteins such as NGF,TRKA,and GAP43 is involved in the nerve hyperplasia induced by mast cell degranulation.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.