OBJECTIVES: To investigate the impact of prenatal fear stress on placental amino acid transport and emotion and cognition development in offspring rats. METHODS: Thirty pregnant Wistar rats were randomized equally into control and fear stress (induced using an observational foot shock model) groups. In each group, placental and serum samples were collected from 6 dams on gestational day 20, and the remaining rats delivered naturally and the offspring rats were raised under the same conditions until 8 weeks of age. Emotional and cognitive outcomes of the offspring rats were assessed with behavioral tests, and placental structure was examined using HE staining. Bioinformatics analysis was used to identify differentially expressed placental transporter genes under fear stress. The expressions of system A and system L amino acid transporters, along with other specialized transporters, were detected using qRT-PCR and Western blotting. Fetal serum amino acid concentrations were determined by HPLC. The correlations between fetal amino acid levels and behavioral outcomes of the offspring rats were analyzed. RESULTS: The dams with fear stress showed reduced open-field activity and increased freezing behavior with significantly decreased placental weight, fetal weight, and fetal-to-placental ratio. Bioinformatics analysis revealed 28 differentially expressed transporter genes involved mainly in amino acid transport. In the fear stress group, fetal serum amino acid levels were significantly lowered and Slc38a1, Slc43a1, Slc43a2, Slc7a8, Slc6a6, Slc1a1 and Slc6a9 mRNA and protein expressions were all downregulated. The offspring rats in fear stress group exhibited decreased novel object preference and spontaneous alternation with reduced open arm exploration and increased immobility in emotional tests. Lower early-life amino acid levels was found to correlate with impaired adult cognition. CONCLUSIONS Prenatal fear stress in rats impairs placental amino acid transporter expression and reduces fetal serum amino acid levels, potentially contributing to long-term cognitive deficits in the offspring rats.
Animals ; Female ; Pregnancy ; Placenta/metabolism* ; Fear ; Rats ; Rats, Wistar ; Cognition ; Prenatal Exposure Delayed Effects ; Stress, Psychological ; Amino Acids/blood* ; Amino Acid Transport Systems/metabolism*

Objective:To develop and validate a deep learning-based multimodal model integrating clinical and radiomic features for predicting acute kidney injury（AKI）risk after partial nephrectomy.Methods:A retrospective analysis was conducted on 416 patients who underwent partial nephrectomy at Zhongshan Hospital，Fudan University from January 2023 to January 2025. The cohort included 100 AKI patients［defined by a ≥ 25% reduction in postoperative evaluated glomerular filtration rate（eGFR）within 48 hours sustained for >24 hours］and 316 non-AKI patients（1∶3 ratio，randomly matched with 16 additional cases for redundancy）. Clinical and radiomic features were extracted from preoperative contrast-enhanced CT scans using PyRadiomics. Demographics included 259 males and 158 females，with a median age of 57（49，65）years，body mass index of（24.1 ± 3.3）kg/m2，preoperative eGFR of（88.5 ± 18.3）ml/（min·1.73 m2），postoperative eGFR（48-hour）of（76.0 ± 21.9）ml/（min·1.73 m2），Zhongshan Score（ZSscore）of 7.34 ± 2.01，and R.E.N.A.L. score of 7.50 ± 1.71. All tumors were T 1a stage. Patients were divided into training（n = 312）and test（n = 104）sets（3∶1 ratio）. A clinical model was constructed via multivariate logistic regression，while radiomic and combined（clinical + radiomic）models utilized an artificial neural network（ANN）with 1 input layer，5 hidden layers，1 output layer，and 10 5 training epochs. Model performance was evaluated by using receiver operating characteristic（ROC）curves and area under the curve（AUC），and was compared to the Martini model. Feature contributions were interpreted via SHapley Additive exPlanations（SHAP）. Results:In the test set，the results of multivariate logistic regression showed that patient’s weight，preoperative eGFR，R.E.N.A.L. score，surgical approach，and operation time were risk factors for AKI（ P < 0.05）. The AUC of the clinical feature prediction model constructed based on the above factors was 0.852（95% CI 0.775?0.929）. In the test set，the AUC of the Martini model was 0.725（95% CI 0.565?0.791）. The radiomic model，trained on 1 315 imaging features，achieved an AUC of 0.898（95% CI 0.804?0.993）with 94.2%（98/104）accuracy. The combined clinical and radiomic model，integrating 1 315 radiomic features and clinical features，demonstrated superior performance with an AUC of 0.946（95% CI 0.887?1.000）and 96.2%（100/104）accuracy，outperforming both the clinical model（ P = 0.03）and the Martini model（ P < 0.01）. SHAP analysis identified the top five predictors in the combined model：ZSscore（SHAP value：0.78），long-run low gray-level emphasis（SHAP value：0.61），run-length non-uniformity（SHAP value：0.58），size-zone non-uniformity（SHAP value：0.46），and gray-level co-occurrence matrix joint energy（SHAP value：0.36）. Conclusions:The deep learning-based multimodal model integrating clinical and radiomic features accurately predicts AKI risk after partial nephrectomy，offering a novel strategy for preoperative risk stratification and personalized intervention.

Objective This study examines serum lipid metabolism characteristics in colorectal cancer patients and its diagnostic potential.Methods Serum samples from 57 colorectal cancer patients and 54 healthy controls underwent lipidomic analysis using ultra-high performance liquid chromatography-time-of-flight mass spec-trometry,combined with principal component analysis(PCA)and orthogonal partial least squares discriminant analysis(OPLS-DA).Differential lipids were identified based on criteria of P<0.05,VIP>1,and fold change<0.67 or>1.5.These lipids were further evaluated using receiver operating characteristic(ROC)analysis to identify biomarkers with strong diagnostic value.Results Five classes and 66 differential lipids were identified,with phos-phatidylcholine(PC)and triglyceride(TG)comprising 59.09%.KEGG pathway enrichment indicated involvement in glycerophospholipid and glycerol ester metabolism pathways.ROC analysis identified Sphinganine,MG(19∶0),LysoPC(18∶2),PA(42∶6),PC(36∶5),PC(36∶4),PC(38∶6),and PC(40∶8)as having areas under the curve greater than 0.85.Conclusion The lipid metabolic profile of colorectal cancer(CRC)patients can be systematically analyzed through the efficient enrichment of lipid metabolites in serum using the UPLC-Q/TOF-MS technique,in conjunction with a modified Bligh-Dyer method.The identification of eight specific lipids including Sphinganine,MG(19∶0),LysoPC(18∶2),PA(42∶6),PC(36∶5),PC(36∶4),PC(38∶6),and PC(40∶8)offer novel insights and parameters for differentiating between healthy individuals and those diagnosed with colorec-tal cancer.

Objective To investigate the effects of five-element music on depressive behaviors and intestinal flora in offspring of fear-stress rats during pregnancy.Methods Thirty-six 0.5-day pregnant Wistar rats were divided randomly into a control group,fear-stress group,and five-element music group,and the subsequent litters continued the maternal grouping.Pregnant rats in the fear-stress and five-element music groups were modeled on days 1～19 of gestation using the bystander electric method,while the five-element music group was also exposed to Feather Tune five-element music intervention.The fear behavior and serum glucocorticoid(GC)levels in pregnant rats were assessed on day 20 of pregnancy by open field test and enzyme-linked immunosorbent assay,respectively,to evaluate the effectiveness of the model.Depression in 3-week-old offspring was evaluated by open field,tail suspension,and sucrose preference tests.Norepinephrine(NE),dopamine(DA),and 5-hydroxytryptamine(5-HT)levels in the hippocampus of the offspring were measured by high-performance liquid chromatography.Changes in the intestinal flora of the offspring were analyzed by 16S rRNA sequencing.Expression levels of the proximal colonic proteins claudin1,occludin,and ZO1 in the offspring were measured by Western blot assay.Results Pregnant rats in the fear-stress group stayed longer in the area around the open field(P＜0.05),had fewer entries into the central area(P＜0.001),and had higher serum GC levels compared with those in the blank group(P＜0.001).In contrast,five-element music exposure reversed these behavioral changes(P＜0.05)and serum GC levels(P＜0.001)in the fear-stress group.Offspring in the fear-stress group exhibited decreased open-field crossing frequency(P＜0.01),reduced sucrose preference index,and longer immobility time in the tail suspension test(P＜0.05),compared with those in the blank group,together with significantly decreased NE,DA,and 5-HT levels in the hippocampus(P＜0.05).Rats in the fear-stress group showed decreased diversity of the intestinal flora(P＜0.01)and significant alterations in flora structure,including higher abundance of Proteobacteria,Enterobacteriaceae,Enterococcus,and Escherichia(P＜0.05),and lower abundance of Spirochaetes,Spirochaetaceae,Lachnospiraceae,Ruminococcaceae,Treponema,Prevotella,Coprococcus,Allobaculum,Ruminococcus,and Dorea(P＜0.05).The proximal colonic proteins Claudin1,Occludin,and ZO1 were significantly downregulated(P＜0.05).The open-field crossing frequency,sucrose preference index,and duration of tail suspension immobilization were improved in the five-element music group compared with those in the fear-stress group(P＜0.05),while DA and 5-HT levels were significantly restored in the hippocampus(P＜0.05),species diversity of the intestinal flora increased(P＜0.01)and changes in the abundance of the flora were reversed,and the proximal colonic proteins Occludin and ZO1 expression were significantly upregulated(P＜0.05).Conclusions Five-element music intervention during pregnancy can ameliorate fear-stress-induced depression behaviors and intestinal flora disorders in the offspring.

Objective To investigate the effects of five-element music on depressive behaviors and intestinal flora in offspring of fear-stress rats during pregnancy.Methods Thirty-six 0.5-day pregnant Wistar rats were divided randomly into a control group,fear-stress group,and five-element music group,and the subsequent litters continued the maternal grouping.Pregnant rats in the fear-stress and five-element music groups were modeled on days 1～19 of gestation using the bystander electric method,while the five-element music group was also exposed to Feather Tune five-element music intervention.The fear behavior and serum glucocorticoid(GC)levels in pregnant rats were assessed on day 20 of pregnancy by open field test and enzyme-linked immunosorbent assay,respectively,to evaluate the effectiveness of the model.Depression in 3-week-old offspring was evaluated by open field,tail suspension,and sucrose preference tests.Norepinephrine(NE),dopamine(DA),and 5-hydroxytryptamine(5-HT)levels in the hippocampus of the offspring were measured by high-performance liquid chromatography.Changes in the intestinal flora of the offspring were analyzed by 16S rRNA sequencing.Expression levels of the proximal colonic proteins claudin1,occludin,and ZO1 in the offspring were measured by Western blot assay.Results Pregnant rats in the fear-stress group stayed longer in the area around the open field(P＜0.05),had fewer entries into the central area(P＜0.001),and had higher serum GC levels compared with those in the blank group(P＜0.001).In contrast,five-element music exposure reversed these behavioral changes(P＜0.05)and serum GC levels(P＜0.001)in the fear-stress group.Offspring in the fear-stress group exhibited decreased open-field crossing frequency(P＜0.01),reduced sucrose preference index,and longer immobility time in the tail suspension test(P＜0.05),compared with those in the blank group,together with significantly decreased NE,DA,and 5-HT levels in the hippocampus(P＜0.05).Rats in the fear-stress group showed decreased diversity of the intestinal flora(P＜0.01)and significant alterations in flora structure,including higher abundance of Proteobacteria,Enterobacteriaceae,Enterococcus,and Escherichia(P＜0.05),and lower abundance of Spirochaetes,Spirochaetaceae,Lachnospiraceae,Ruminococcaceae,Treponema,Prevotella,Coprococcus,Allobaculum,Ruminococcus,and Dorea(P＜0.05).The proximal colonic proteins Claudin1,Occludin,and ZO1 were significantly downregulated(P＜0.05).The open-field crossing frequency,sucrose preference index,and duration of tail suspension immobilization were improved in the five-element music group compared with those in the fear-stress group(P＜0.05),while DA and 5-HT levels were significantly restored in the hippocampus(P＜0.05),species diversity of the intestinal flora increased(P＜0.01)and changes in the abundance of the flora were reversed,and the proximal colonic proteins Occludin and ZO1 expression were significantly upregulated(P＜0.05).Conclusions Five-element music intervention during pregnancy can ameliorate fear-stress-induced depression behaviors and intestinal flora disorders in the offspring.

Objective This study examines serum lipid metabolism characteristics in colorectal cancer patients and its diagnostic potential.Methods Serum samples from 57 colorectal cancer patients and 54 healthy controls underwent lipidomic analysis using ultra-high performance liquid chromatography-time-of-flight mass spec-trometry,combined with principal component analysis(PCA)and orthogonal partial least squares discriminant analysis(OPLS-DA).Differential lipids were identified based on criteria of P<0.05,VIP>1,and fold change<0.67 or>1.5.These lipids were further evaluated using receiver operating characteristic(ROC)analysis to identify biomarkers with strong diagnostic value.Results Five classes and 66 differential lipids were identified,with phos-phatidylcholine(PC)and triglyceride(TG)comprising 59.09%.KEGG pathway enrichment indicated involvement in glycerophospholipid and glycerol ester metabolism pathways.ROC analysis identified Sphinganine,MG(19∶0),LysoPC(18∶2),PA(42∶6),PC(36∶5),PC(36∶4),PC(38∶6),and PC(40∶8)as having areas under the curve greater than 0.85.Conclusion The lipid metabolic profile of colorectal cancer(CRC)patients can be systematically analyzed through the efficient enrichment of lipid metabolites in serum using the UPLC-Q/TOF-MS technique,in conjunction with a modified Bligh-Dyer method.The identification of eight specific lipids including Sphinganine,MG(19∶0),LysoPC(18∶2),PA(42∶6),PC(36∶5),PC(36∶4),PC(38∶6),and PC(40∶8)offer novel insights and parameters for differentiating between healthy individuals and those diagnosed with colorec-tal cancer.

Objective:To develop and validate a deep learning-based multimodal model integrating clinical and radiomic features for predicting acute kidney injury（AKI）risk after partial nephrectomy.Methods:A retrospective analysis was conducted on 416 patients who underwent partial nephrectomy at Zhongshan Hospital，Fudan University from January 2023 to January 2025. The cohort included 100 AKI patients［defined by a ≥ 25% reduction in postoperative evaluated glomerular filtration rate（eGFR）within 48 hours sustained for >24 hours］and 316 non-AKI patients（1∶3 ratio，randomly matched with 16 additional cases for redundancy）. Clinical and radiomic features were extracted from preoperative contrast-enhanced CT scans using PyRadiomics. Demographics included 259 males and 158 females，with a median age of 57（49，65）years，body mass index of（24.1 ± 3.3）kg/m2，preoperative eGFR of（88.5 ± 18.3）ml/（min·1.73 m2），postoperative eGFR（48-hour）of（76.0 ± 21.9）ml/（min·1.73 m2），Zhongshan Score（ZSscore）of 7.34 ± 2.01，and R.E.N.A.L. score of 7.50 ± 1.71. All tumors were T 1a stage. Patients were divided into training（n = 312）and test（n = 104）sets（3∶1 ratio）. A clinical model was constructed via multivariate logistic regression，while radiomic and combined（clinical + radiomic）models utilized an artificial neural network（ANN）with 1 input layer，5 hidden layers，1 output layer，and 10 5 training epochs. Model performance was evaluated by using receiver operating characteristic（ROC）curves and area under the curve（AUC），and was compared to the Martini model. Feature contributions were interpreted via SHapley Additive exPlanations（SHAP）. Results:In the test set，the results of multivariate logistic regression showed that patient’s weight，preoperative eGFR，R.E.N.A.L. score，surgical approach，and operation time were risk factors for AKI（ P < 0.05）. The AUC of the clinical feature prediction model constructed based on the above factors was 0.852（95% CI 0.775?0.929）. In the test set，the AUC of the Martini model was 0.725（95% CI 0.565?0.791）. The radiomic model，trained on 1 315 imaging features，achieved an AUC of 0.898（95% CI 0.804?0.993）with 94.2%（98/104）accuracy. The combined clinical and radiomic model，integrating 1 315 radiomic features and clinical features，demonstrated superior performance with an AUC of 0.946（95% CI 0.887?1.000）and 96.2%（100/104）accuracy，outperforming both the clinical model（ P = 0.03）and the Martini model（ P < 0.01）. SHAP analysis identified the top five predictors in the combined model：ZSscore（SHAP value：0.78），long-run low gray-level emphasis（SHAP value：0.61），run-length non-uniformity（SHAP value：0.58），size-zone non-uniformity（SHAP value：0.46），and gray-level co-occurrence matrix joint energy（SHAP value：0.36）. Conclusions:The deep learning-based multimodal model integrating clinical and radiomic features accurately predicts AKI risk after partial nephrectomy，offering a novel strategy for preoperative risk stratification and personalized intervention.

Objective:To explore the application of anterior esophageal wall full layer fixation and gastric tube guidance in total laparoscopic overlap method for intracorporeal esophagojejunostomy.Methods:Overlap esophagojejunostomy with anterior esophageal wall full layer fixation and gastric tube guidance is suitable for patients with advanced gastric cancer (clinical stage: cT1b~4aN0~3M0) and esophageal invasion <3 cm, who underwent radical total gastrectomy+ overlap esophagojejunostomy. The main operation procedure was performed as follows: A titanium clip was used for fixation of the full anterior wall of esophagus before overlap esophagojejunostomy, and the side‐to‐side esophagojejunostomy was performed with the linear stapler under the guidance of gastric tube. Then the titanium clip was removed after confirming that the correct cavity was entered. Finally, the common outlet was closed by two barbed sutures. A descriptive case series study was conducted. The clinical data of patients who underwent laparoscopic radical gastrectomy and overlap esophagojejunostomy with anterior esophageal wall full layer fixation and gastric tube guidance in Guangdong Provincial Hospital of Chinese medicine and the First Affiliated Hospital of Guangzhou University of Chinese medicine from May 2021 to June 2023 were retrospectively analyzed.Results:A total of 42 patients were collected, and all of them were successfully completed laparoscopic total radical gastrectomy without conversion to laparotomy or perioperative death. The esophagojejunostomy time, operative time, intraoperative blood loss was 17(5‐25) minutes, (258.8±38.0) minutes and 50(20‐200) ml, respectively. The incidence of esophageal false lumen was 0%, and there were no intraoperative complications. The time of gastric tube removal, initial fluid diet intake and the duration of postoperative hospital were 2(1‐5) , 4(1‐8) and 8(4‐21) days, respectively. There were no postoperative anastomotic hemorrhage, anastomotic stenosis and other related complications. One patient (2.38%) developed a Clavien‐Dindo IIIb complication, which was abdominal hemorrhage after operation. The second surgical exploration confirmed that the patient was bleeding due to gastroduodenal artery rupture. After intraoperative suture hemostasis, fluid expansion, blood transfusion and other treatments, the patient was discharged on the 15th day after the operation. Three patients (7.14%) developed Clavien‐Dindo grade II complications, including anastomotic leakage, chylous leakage and pulmonary infection, and were discharged after conservative treatment such as anti‐infection and prolonged retention of drainage tube.Conclusions:Laparoscopic overlap method for intracorporeal esophagojejunostomy with anterior esophageal wall fixation and gastric tube guidance can shorten the time of esophagojejunostomy and prevent the occurrence of false lumen, and do not increase anastomose‐related complications.

Background and Purpose:The early prediction of local tumor progression-free survival(LTPFS)after radiofrequency ablation(RFA)for colorectal cancer(CRC)lung metastases has significant clinical importance.The application of radiomics in the prediction of tumor prognosis has been explored.This study aimed to construct a radiomics-based nomogram for predicting LTPFS after RFA in CRC patients with lung metastases.Methods:This study retrospectively analyzed 172 CRC patients with 401 lung metastases admitted to Department of Interventional Radiology,Fudan University Shanghai Cancer Center from August 2016 to January 2019.This study was reviewed by the medical ethics committee of Fudan University Shanghai Cancer Center(ethics number:2402291-24).After augmentation of pre-ablation and immediate post-ablation computed tomography(CT)images,the target metastases and ablation regions were segmented manually to extract the radiomic features.Maximum relevance and minimum redundancy algorithm(MRMRA)and least absolute shrinkage and selection operator(LASSO)regression models were applied for feature selection.The clinical model,the radiomics model,and the fusion model were constructed based on the selected radiomic features and clinical variables screened by the multivariate analysis.The Harrell concordance index(C-index)and area under receiver operating characteristic(ROC)curves(AUC)were calculated to evaluate the prediction performance.Finally,the corresponding nomogram of the best model was drawn.Results:Among all the lung metastases,102(25.4%)had final recurrence,and 299(74.6%)had complete response(CR).The median follow-up time was 21 months(95%CI:19.466-22.534),and the LTPFS rates at 1,2,and 3 years after RFA were 76.5%(95%CI:72.0-80.4),72.1%(95%CI:66.6-76.9)and 69.9%(95%CI:64.0-75.1).In both the training and test dataset,the fusion model based on the final 12 radiomic features through the LASSO regression and 4 clinical variables screened by multivariate analysis achieved the highest AUC values for LTPFS,with C-index values of 0.890(95%CI:0.854-0.927)and 0.843(95%CI:0.768-0.916),respectively.Conclusion:The fusion model based on radiomic features and clinical variables is feasible for predicting LTPFS after RFA of CRC patients with lung metastases,whose performance is superior to the single radiomic and clinical model.At the same time,the nomogram of the fusion model can intuitively predict the prognosis of CRC patients with lung metastases after RFA,thus assisting clinicians in developing individualized follow-up review plans for patients and adjusting treatment strategies flexibly.

Objective:To explore the value of the classifier model based on CT radiomic characteristics in predicting the activity of pulmonary cystic echinococcosis (CE).Methods:The study was designed as cross-sectional. A retrospective analysis was performed on 81 patients diagnosed with pulmonary CE in the First Affiliated Hospital of Xinjiang Medical University from January 2010 to October 2020. The Python function divided 17 cases into an internal verification group and 64 cases into a training group with a ratio of 2∶8. In addition, 16 patients diagnosed with pulmonary CE from the Fourth Affiliated Hospital of Xinjiang Medical University from October 2020 to 2024 were included in the external validation group. All patients underwent CT examination, and radiomics features were extracted using Radcloud platform of Huimedi Huiying. The intraclass correlation coefficient was performed on the features, then feature screening was performed using the SelectKBest method, variance thresholding method, and least absolute shrinkage and selection operator. Finally, three classifiers (including support vector machine (SVM), K-neighborhood (KNN), and logistic regression (LR)) were used to build the models. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s efficiency.Results:Among 81 patients with lung CE, 58 were male, and 23 were female. twenty-eight lesions were active and 53 were inactive. A total of 11 optimal features were selected. Based on the selected features, the SVM classifier model, KNN classifier model, and LR classifier model were established. The KNN classifier model in the training group had the highest AUC value (0.93) and the highest specificity (0.98) in predicting lung CE activity. In the internal validation group, the SVM classifier model had the highest AUC value (0.92) and the highest specificity (0.91) in predicting lung CE activity. The LR classifier model performed best with the highest AUC of 0.85 for predicting lung CE activity in the external validation group, and the specificity of the three models was 0.92.Conclusion:The classifier model established based on CT radiomic features has a certain value in predicting lung CE activity, and may be helpful in clinical decision-making.