Objective: To explore the value of contrast-enhanced computed tomography (CT) radiomics combined with machine learning algorithms in the preoperative prediction of perineural invasion (PNI) in oral squamous cell carcinoma (OSCC), aiming to provide evidence for assisting clinical treatment decision-making. Methods​: This study was approved by the Ethics Committee of the Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. A total of 250 OSCC patients confirmed by postoperative pathology were included, comprising 128 PNI-positive and 122 PNI-negative cases. The dataset was randomly divided into training (n=175), validation (n=38), and independent testing (n=37) sets in a ratio of 7:1.5:1.5. Regions of interest were delineated on preoperative images, and radiomic features were extracted. After dimensionality reduction and feature selection using methods like Least Absolute Shrinkage and Selection Operator (LASSO) regression, multiple machine learning models, including support vector machine (SVM), random forest, Light gradient boosting machine (LightGBM), and a Stacking ensemble model, were constructed. Model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, calibration curves, and decision curve analysis (DCA). Model interpretability was analyzed using Shapley additive explanations (SHAP) and grouped permutation feature importance analysis. Results ​: ​ Among the 250 samples analyzed, the LightGBM model based on radiomics demonstrated the best performance on the independent test set, with an AUC of 0.781, outperforming models like SVM (AUC = 0.730) and Random Forest (AUC = 0.691), as well as clinical models (AUCs ranging 0.549-0.711). The LightGBM model showed good calibration (Brier score 0.198), and DCA indicated high clinical net benefit over a wide threshold probability range. Paired DeLong tests revealed no statistically significant differences in AUC between the ensemble (Stacking) model and the corresponding best-performing radiomics-based model. SHAP analysis and grouped permutation feature importance analysis further indicated that the primary discriminative information for the model came from radiomic texture features. Conclusion ​ The LightGBM model based on contrast-enhanced CT radiomics demonstrated good discriminative ability for preoperative prediction of PNI in OSCC. In the independent test set, it achieved the highest AUC. This model holds promise as a non-invasive auxiliary tool for preoperative risk assessment. Given the limited sample size of the independent test set, these results require further validation in larger cohorts and external datasets.

ObjectiveCell adhesion is a critical process that regulates cellular physiological functions. Quantitative characterization of adhesion dynamics is essential for elucidating the intrinsic mechanical mechanisms underlying cellular activities. Although atomic force microscopy-based single-cell force spectroscopy is widely used for single-cell adhesion measurements, it requires complex chemical modifications for preparation of live-cell probes, leading to limitations such as cumbersome operation, low throughput, and potential impacts on cell viability. Fluidic force microscopy, which combines atomic force microscopy with microfluidic probes, is a technique allowing the operation of force-controlled nanopipettes in aqueous environments. By applying negative or positive pressure via a pressure controller, a single living cell can be captured onto or released from the cantilever under physiological conditions. This procedure offers a simple workflow and high assay throughput for single-cell adhesion measurements without the need for chemical functionalization. In this study, fluidic force microscopy-based single-cell force spectroscopy was adopted to achieve long-term quantitative characterization of single-cell adhesion dynamics in a simpler and more efficient manner, comparing the dynamic differences in adhesion establishment between two cell lines with different differentiation levels. MethodsHEK 293T and hTERT RPE-1 cells were non-invasively captured on the cantilever of a fluidic force microscope via its integrated microfluidic system during 40 h of adhesion culture. Cell-substrate detachment assays were performed, and force-distance curves were recorded to extract key mechanical adhesion parameters, including adhesion force, adhesion energy, and maximum detachment distance. These measurements were combined with real-time monitoring of cell spreading area to systematically characterize the dynamic evolution of single-cell adhesion. ResultshTERT RPE-1 cells rapidly entered a stable adhesion phase within 1 h after seeding, with both area-normalized adhesion force and area-normalized adhesion energy reaching peak values. In contrast, HEK 293T cells required 4 h to achieve stable adhesion. Subsequently, the adhesion force, adhesion energy and maximum detachment distance of hTERT RPE-1 and HEK 293T cells stabilized at approximately 240 nN vs. 30 nN, 2.2 pJ vs. 0.12 pJ and 6 μm vs. 4 μm, respectively. hTERT RPE-1 cells reached the peak of area-normalized adhesion parameters earlier than HEK 293T cells, with their peak area-normalized adhesion force and area-normalized adhesion energy being substantially elevated relative to HEK 293T cells. HEK 293T cells presented stronger linear correlations among adhesion energy, maximum detachment distance and adhesion force compared with hTERT RPE-1 cells. For both cell lines, cell spreading area exhibited a weak correlation with adhesion force. Whereas the area-normalized adhesion parameters of HEK 293T cells remained relatively constant throughout the adhesion process, hTERT RPE-1 cells exhibited elevated values in the early phase, followed by a gradual decline. These results indicated distinct dynamic adhesion patterns between the two cell types, with hTERT RPE-1 cells exhibiting stronger adhesion strength and higher adhesion efficiency. ConclusionIn this study, fluidic force microscopy-based single-cell force spectroscopy was successfully applied to perform long-term in situ quantitative measurement of the adhesion dynamics in single adherent cells. The approach revealed divergent adhesion patterns between HEK 293T and hTERT RPE-1 cells, suggesting a close association between cell differentiation and adhesion behaviors. These findings provide quantitative mechanical evidence for further understanding the underlying mechanisms of cell adhesion.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Objective To compare the safety and efficacy of curettage and their combination with uterine artery embolization(UAE)in the treatment of cesarean scar pregnancy(CSP)patients with a low score(≤4)in the ultrasound quantification scoring system.Methods Based on our inclusion and exclusion criteria of this randomized controlled study,the women with CSP who had an ultrasonic quantitative score≤4 and were treated in our department from May 2020 to August 2023 were enrolled,and then randomly divided into a curettage group(n=48)and a UAE combination group(n=47)in a ratio of 1∶1.General information,intraoperative conditions,and use of rescue measures within 3 months after operation were collected in the 2 groups of patients.All the patients were followed up until October 2024 to observe the pregnancy outcomes and determine the impact on the menstrual volume after the resumption of normal menstruation.Results The patients from the both groups completed the follow-up.Except for the maximum gestational sac diameter,there were no significant differences in other baseline data between the 2 groups,and the curettage group had notably more patients having a gestational sac diameter≤25 mm than the combination group[37(77.1%)vs 27(57.4%),P<0.05].No statistical differences were observed between the 2 groups in the intraoperative bleeding volume and use of rescue measures within 3 months after surgery.The combination group had obviously more patients with reduced menstrual volume after the resumption of normal menstruation than the dilation and curettage group[30(63.8%)vs 13(27.1%),P<0.001].There were no statistically differences in pregnancy outcomes and the number of days to resume menstruation between the 2 groups.Conclusion For CSP patients with a score of≤4 in the ultrasound quantification scoring system,curettage show no significant difference in therapeutic effectiveness,and even have better efficacy and safety when compared with curettage combined with UAE.

Objective To modify YC-1,an inhibitor of hypoxia-inducible factor-1α(HIF-1α)into nanoparticles and explore its effect on reversing chemoresistance of colorectal cancer cells.Methods Nano-inhibitor mPEG350-YC-1(MYC-1)was prepared by the carboxyl condensation reaction of the active functional group hydroxyl(-OH)in the YC-1 molecule with methoxy polyethylene glycol carboxylic acid,and was verified by 1H nuclear magnetic resonance(1H-NMR).Its morphology was analyzed by transmission electron microscope(TEM),and its particle size distribution was analyzed by dynamic light scattering(DLS).By interacting FITC-labeled MYC-1(FYC-1)with HCT15 cells,the uptake ability of FYC-1 by the cells was observed using laser confocal microscopy.The cytotoxicity of MYC-1 was measured with CCK-8 assay.The sensitization effect of MYC-1 on the chemotherapy drug 5-Fu was detected through toxicity tests of resistant HCT15 cells(resistant HCT15,R-HCT15)and live/dead cell staining.The mechanism of MYC-1 reversing drug resistance was determined with immunofluorescence staining of HIF-1α and western blotting.Finally,a subcutaneous transplanted tumor model of R-HCT15 cells was constructed.The tumor bearing mice were randomly divided into PBS,5-Fu,MYC-1,and MYF groups,with 3 mice in each group.The tumor volume and weight were observed after treatment in each group to evaluate the ability of MYC-1 to reverse 5-Fu resistance in colorectal cancer.Results The nano-inhibitor MYC-1 was successfully prepared.TEM and DLS showed that MYC-1 could self-assemble into nanoparticles with a diameter of approximately 19.96±2.97 nm in aqueous solution.FYC-1 was also successfully prepared.When FYC-1 was interacted with HCT15 cells,FYC-1 could be better taken up by the cells,indicating that the amphiphilic MYC-1 could be better endocytosed into the cells to exert its function.When MYC-1 and 5-Fu acted in combination in colon cancer R-HCT15 cells,the resistance index(RI)was decreased from 7.99 to 1.55,and the relative reversal rate(RRR)of RI was 80.6%.Live(green)/dead(red)cell staining revealed that MYC-1 increased the toxicity of 5-Fu to R-HCT15 cells.Immunofluorescence staining(P<0.01)and Western blotting indicated that MYC-1 effectively inhibited the intracellular expression of HIF-1α.The combined action of MYC-1 and 5-Fu on mice with R-HCT15 subcutaneous transplanted tumors had the best therapeutic effect when compared with PBS(P<0.001),5-Fu(P<0.01),and MYC-1(P<0.01).Immunofluorescence staining of HIF-1α in tumor tissues displayed that the expression of HIF-1α was decreased in the MYC-1 and MYF groups.HE staining showed that there was no obvious damage to the important organs in each treatment group.Conclusion Nano-inhibitor MYC-1 can reverse the drug resistance of colorectal cancer to 5-Fu chemotherapy by reducing the expression of HIF-1α protein in colorectal cancer cells,thereby effectively improving the therapeutic effect of 5-Fu.

Patients suffering from nerve injury often experience exacerbated pain responses and complain of memory deficits. The dorsal hippocampus (dHPC), a well-defined region responsible for learning and memory, displays maladaptive plasticity upon injury, which is assumed to underlie pain hypersensitivity and cognitive deficits. However, much attention has thus far been paid to intracellular mechanisms of plasticity rather than extracellular alterations that might trigger and facilitate intracellular changes. Emerging evidence has shown that nerve injury alters the microarchitecture of the extracellular matrix (ECM) and decreases ECM rigidity in the dHPC. Despite this, it remains elusive which element of the ECM in the dHPC is affected and how it contributes to neuropathic pain and comorbid cognitive deficits. Laminin, a key element of the ECM, consists of α-, β-, and γ-chains and has been implicated in several pathophysiological processes. Here, we showed that peripheral nerve injury downregulates laminin β1 (LAMB1) in the dHPC. Silencing of hippocampal LAMB1 exacerbates pain sensitivity and induces cognitive dysfunction. Further mechanistic analysis revealed that loss of hippocampal LAMB1 causes dysregulated Src/NR2A signaling cascades via interaction with integrin β1, leading to decreased Ca²⁺ levels in pyramidal neurons, which in turn orchestrates structural and functional plasticity and eventually results in exaggerated pain responses and cognitive deficits. In this study, we shed new light on the functional capability of hippocampal ECM LAMB1 in the modulation of neuropathic pain and comorbid cognitive deficits, and reveal a mechanism that conveys extracellular alterations to intracellular plasticity. Moreover, we identified hippocampal LAMB1/integrin β1 signaling as a potential therapeutic target for the treatment of neuropathic pain and related memory loss.
Animals ; Laminin/genetics* ; Hippocampus/metabolism* ; Neuralgia/metabolism* ; Cognitive Dysfunction/etiology* ; Male ; Peripheral Nerve Injuries/metabolism* ; Extracellular Matrix/metabolism* ; Integrin beta1/metabolism* ; Pyramidal Cells/metabolism* ; Signal Transduction

Objective:To evaluate the diagnostic value of micro flow imaging (MFI) supported by Image Pro Plus (IPP) software for BI-RADS classification of breast nodules and the correlation between its parameters and Ki-67 expression level.Methods:A total of 205 cases of breast solid nodules with ultrasound BI-RADS classification of 4 categories were enrolled in this study. BI-RADS classification, CDFI score, MFI score, CDFI-IPP score and MFI-IPP score by IPP software were performed for each nodule. Ki-67 immunohistochemical staining of invasive ductal carcinoma was performed to analyze the correlation between MFI-IPP-VI and Ki-67 expression levels.Results:The combined BI-RADS classification for diagnosis of malignant nodules had the largest area under the ROC curve, which was significantly higher than that of BI-RADS, MFI-IPP-VI, MFI score, CDFI-IPP-VI, and CDFI score (all P<0.05). The combined BI-RADS classification had the best diagnostic efficacy for the diagnosis of malignant nodules with a sensitivity of 96.4%, specificity of 65.6% and accuracy of 82.4%. In addition, the agreement between experienced and inexperienced sonographers applying MFI-IPP-VI was higher than MFI (Kappa=0.780 vs. Kappa=0.743). The mean value of Ki-67 in invasive ductal carcinoma was 21.93%±16.37%, and Spearman's correlation analysis showed a positive correlation between MFI-IPP-VI and Ki-67 expression level ( r=0.815, P<0.05). Conclusions:MFI-IPP can assist sonographers to classify breast nodules by BI-RADS, providing an effective basis for puncture biopsy. MFI-IPP-VI offers the possibility of noninvasively predicting the expression level of Ki-67 in invasive ductal carcinoma.