Humans and animals have a fundamental ability to use experiences and environmental information to organize behavior. It often happens that humans and animals make decisions and prepare actions under uncertain situations. Uncertainty would significantly affect the state of animals' minds, but may not be reflected in behavior. How to "read animals' mind state" under different situations is a challenge. Here, we report that neuronal activity in the medial prefrontal cortex (mPFC) of rats can reflect the environmental uncertainty when the task situation changes from certain to uncertain. Rats were trained to perform behavioral tasks under certain and uncertain situations. Under certain situations, rats were required to simply repeat two nose-poking actions that each triggered short auditory tone feedback (single-task situation). Whereas under the uncertain situation, the feedback could randomly be either the previous tone or a short musical rhythm. No additional action was required upon the music feedback, and the same secondary nose-poking action was required upon the tone feedback (dual-task situation); therefore, the coming task was uncertain before action initiation. We recorded single-unit activity from the mPFC when the rats were performing the tasks. We found that in the dual task, when uncertainty was introduced, many mPFC neurons were actively engaged in dealing with the uncertainty before the task initiation, suggesting that the rats could be aware of the task situation change and encode the information in the mPFC before the action of task initiation.
Animals ; Prefrontal Cortex/cytology* ; Uncertainty ; Neurons/physiology* ; Male ; Rats ; Rats, Long-Evans ; Action Potentials/physiology* ; Acoustic Stimulation

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective:A nomogram model for predicting double positivity of microvascular invasion (MVI) and vascular endothelial-to-mesenchymal transition (VETC) in patients with hepatocellular carcinoma (HCC) was constructed and its predictive performance was evaluated.Methods:A retrospective analysis was conducted on 326 HCC patients who were treated at the Third People's Hospital of Nantong and the First Affiliated Hospital of Soochow University from January 2013 to June 2023, including 240 males and 86 females, with an average age of (58.7±9.0) years. The 326 patients were randomly divided into a training set ( n=228) and a test set ( n=98) at a ratio of 7: 3 using the random number table method. The training set was divided into a double-positive group ( n=54) and a control group ( n=174) based on whether the HCC patients were double positive for MVI and VETC. Univariate and multivariate logistic regression analyses were performed to identify the influencing factors of double positivity of microvascular invasion in HCC patients, and a nomogram for predicting double positivity of microvascular invasion patterns was constructed based on the multivariate. The predictive performance and clinical net benefit of the nomogram were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Results:There were statistically significant differences in alpha-fetoprotein, gamma-glutamyl transferase, and phosphatidylinositol proteoglycan between the two groups (all P<0.05). Multivariate logistic regression analysis showed that LI-RADS category ( OR=8.58, 95% CI: 1.87-39.38), intratumoral hemorrhage ( OR=2.16, 95% CI: 1.14-4.07), and intratumoral arteries ( OR=2.59, 95% CI: 1.19-5.64) were all influencing factors of double positivity of microvascular invasion patterns in HCC patients (all P<0.05). Based on the multivariate results, a nomogram was constructed. In the training set, the area under the ROC curve for predicting double positivity of microvascular invasion patterns in HCC patients was 0.769 (95% CI: 0.720-0.814). In the test set, the area under the ROC curve for predicting double positivity of microvascular invasion patterns in HCC patients was 0.756 (95% CI: 0.622-0.850). The calibration curve showed a good fit between the predicted model and the ideal curve. Decision curve analysis showed that the clinical applicability was good when the threshold was 0.01-0.80 in the training set and 0.01-0.65 in the test set. Conclusion:The nomogram model based on LI-RADS category, intratumoral hemorrhage, and intratumoral arteries can effectively predict double positivity of microvascular invasion patterns in HCC patients and has good clinical applicability.

OBJECTIVE To analyze the relationship between interleukin-35(IL-35)and serine/arginine-rich splicing factor 6(SRSF6)levels and pleural thickness in patients with tuberculous pleurisy,as well as their values of joint assessment for the risk of pleural tuberculoma.METHODS A total of 100 patients with tuberculous pleurisy admit-ted to the Second Affiliated Hospital of Zhejiang University School of Medicine,Linping Campus,from Jan.2022 to Jul.2024 were selected.The IL-35 and SRSF6 levels in pleural effusion and pleural thickness were measured.The relationship between IL-35,SRSF6 levels and pleural thickness were analyzed.After a one-year follow-up,patients with tuberculosis were divided into the occurrence group and non-occurrence group according to whether pleurotuberculoma developed.The risk factors for developing pleurotuberculoma and the value of IL-35 and SRSF6 in accessing the risk of developing pleurotuberculoma were analyzed.RESULTS In patients with tuberculous pleuri-sy,IL-35 and SRSF6 in pleural effusion was(529.78±146.85)ng/L and(1.44±0.43)ng/ml,respectively,and the pleural thickness was(4.07±0.53)mm.IL-35 and SRSF6 levels in pleural effusion were positively correlated with pleural thickness(P<0.05).Logistic analysis showed that the risk of pleural tuberculoma significantly in-creased in patients with tuberculous pleurisy when there was pleural fluid separation,abnormal pleural thickness and elevated levels of IL-35 and SRSF6 in pleural effusion(P<0.05).Receiver operating characteristic(ROC)curve analysis showed that the area under the curve(AUC),specificity and sensitivity of combined IL-35 and SRSF6 in predicting the occurrence of pleural tuberculoma in patients with tuberculous pleurisy were significantly higher than those of either marker alone(P<0.05).CONCLUSION IL-35 and SRSF6 levels are positively correla-ted with the pleural thickness of tuberculous pleurisy,and the combined detection of IL-35 and SRSF6 can effec-tively assess the risk of pleural tuberculoma in tuberculous pleurisy patients.

Objective To explore the mechanism underlying the role of disulfidptosis-related genes(DRGs)in the disease progression of metabolically associated fatty liver disease(MAFLD)based on bioinformatics.Methods In this study,the GEO database was utilized to screen for eligible MAFLD expression data,conduct differential gene analysis,and identify DRGs through consistent clustering to subtype MAFLD patients.The immune infiltration status among subtypes was further evaluated,and the infiltration of immune cells was analyzed using the CIBERSORT algorithm.The gene modules related to the disease were selected through weighted gene co-expression network analysis(WGCNA).Subsequently,a diagnostic model was constructed based on DRGs using machine learning models,and the performance of the model was verified.Finally,the stability of DRGs among different subtypes was evaluated using an external dataset,and the significance of the results was analyzed using statistical tests.Results Through the analysis of the dataset GSE31803,six disulfide death genes,namely,SLC3A2,NCKAP1,CYFIP1,FLNA,MYL6 and MYH10,which were closely related to the clinical characteristics of MAFLD,were screened out.MAFLD patients were classified into two subtypes,with subtype 1 having a higher level of immune cell infiltration.Key gene modules were identified through WGCNA.Through machine learning screening,the support vector machine(SVM)model was determined as the optimal classification model.External validation confirmed the stability and effectiveness of the key genes in different subtypes of MAFLD.Conclusion Based on DRGs,two highly heterogeneous subtypes of MAFLD were identified,which exhibited significant differences in clinical characteristics,biological processes and immune status,indicating that DRGs play a crucial role in the occurrence and development of MAFLD.

Polymyxin serves as the"last line of defense"for treating infection with multidrug-resistant Gram-ne-gative bacteria.However,the emergence and spread of polymyxin-resistant genes such as mcr-1 severely weakens its clinical efficacy.This paper systematically summarizes the antimicrobial and resistance mechanisms of polymy-xin,comprehensively summarizes the current research progresses in polymyxin sensitizers particular focusing on three aspects:natural compounds,synthetic small molecules,and drug repurposing.Furthermore,this paper explores the innovative strategies of gene intervention,new targets,and nanotechnology-based formulations in the develop-ment of sensitizer,aiming to provide systematic theoretical support and research ideas against polymyxin resistance.

Objective To explore the mechanism underlying the role of disulfidptosis-related genes(DRGs)in the disease progression of metabolically associated fatty liver disease(MAFLD)based on bioinformatics.Methods In this study,the GEO database was utilized to screen for eligible MAFLD expression data,conduct differential gene analysis,and identify DRGs through consistent clustering to subtype MAFLD patients.The immune infiltration status among subtypes was further evaluated,and the infiltration of immune cells was analyzed using the CIBERSORT algorithm.The gene modules related to the disease were selected through weighted gene co-expression network analysis(WGCNA).Subsequently,a diagnostic model was constructed based on DRGs using machine learning models,and the performance of the model was verified.Finally,the stability of DRGs among different subtypes was evaluated using an external dataset,and the significance of the results was analyzed using statistical tests.Results Through the analysis of the dataset GSE31803,six disulfide death genes,namely,SLC3A2,NCKAP1,CYFIP1,FLNA,MYL6 and MYH10,which were closely related to the clinical characteristics of MAFLD,were screened out.MAFLD patients were classified into two subtypes,with subtype 1 having a higher level of immune cell infiltration.Key gene modules were identified through WGCNA.Through machine learning screening,the support vector machine(SVM)model was determined as the optimal classification model.External validation confirmed the stability and effectiveness of the key genes in different subtypes of MAFLD.Conclusion Based on DRGs,two highly heterogeneous subtypes of MAFLD were identified,which exhibited significant differences in clinical characteristics,biological processes and immune status,indicating that DRGs play a crucial role in the occurrence and development of MAFLD.

Polymyxin serves as the"last line of defense"for treating infection with multidrug-resistant Gram-ne-gative bacteria.However,the emergence and spread of polymyxin-resistant genes such as mcr-1 severely weakens its clinical efficacy.This paper systematically summarizes the antimicrobial and resistance mechanisms of polymy-xin,comprehensively summarizes the current research progresses in polymyxin sensitizers particular focusing on three aspects:natural compounds,synthetic small molecules,and drug repurposing.Furthermore,this paper explores the innovative strategies of gene intervention,new targets,and nanotechnology-based formulations in the develop-ment of sensitizer,aiming to provide systematic theoretical support and research ideas against polymyxin resistance.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.