Search

Flexible piezoresistive sensors are highly desirable for tactile sensing and wearable electronics. However, the reported flexible piezoresistive sensors have the inherent trade-off effect between high sensitivity and wide pressure ranges. Herein, we report a flexible piezoresistive sensor with a three-dimensional (3D) porous microstructured sensing layer composed of silver nanowires (AgNWs) and a poly(vinylidene fluoride) (PVDF) matrix, exhibiting high sensitivity and wide pressure ranges. Benefiting from the conductive networks of AgNWs and the 3D porous structure of PVDF, the porous AgNWs/PVDF composite (PAPC)-based flexible piezoresistive sensor exhibits high sensitivities of 0.014 and 0.009 kPa

The presence of p53 mutationscombined with high Ki-67 expression can predict poor prognosis and overall survival in IDH-wildtypeglioblastoma patients. This study aims to use magnetic resonance imaging (MRI) features and apparent diffusion coefficient(ADC) values to predict the expression of p53 and Ki-67 in IDH-wildtype glioblastomas to informclinical treatment. We retrospectively collected clinical and imaging data for 103 IDH-wildtype glioblastoma patients in our hospital between June 2021 and February 2023. The datasets were divided into four groups: p53(+)/Ki-67(+), p53(+)/Ki67(-), p53(-)/Ki67(+), and p53(-)/Ki67(-) according to mutant status of p53 and expression level of Ki-67. Six MRI morphological features and four ADC values for the tumor parenchyma were analyzed. In the 103 patients, 21 had p53(-)/Ki-67(-), 12 had p53(+)/Ki-67(-), 28 had p53(-)/Ki-67(+), and 42 had p53(+)/Ki-67(+). Tumor location showed statistically significant differences among the 4 groups (P < 0.05). ADC parameters were negatively correlated with p53 and Ki-67 expression status and could distinguish the co-expression of p53 and Ki-67 (P < 0.05). In distinguishing between the p53(+)/Ki-67(+) and p53(-)/Ki-67(-) groups, when ADCmin was 0.85×10-3 mm2/s, the sensitivity, specificity and accuracy reached their maximum values of 61.90%, 92.90% and 82.54%, respectively. In conclusion, the expression of p53 and Ki-67 in IDH-wildtypeglioblastoma can be evaluated by ADC values. ADC values can be used as a non-invasive quantitative parameter to assist in the clinical diagnosis of IDH-wildtype glioblastoma molecular states.

Background Endometrial cancer (EC) has been increasing in incidence and mortality rates over the years. To investigate the feasibility of ADC in preoperative non-invasive prediction of myometrial infiltration and Ki-67 and p53 expression levels in patients with EC. Methods we performed the retrospective analysis of 105 patients with EC who underwent preoperative magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) and were confirmed by pathology after operation from January 2017 to December 2021 in our hospital. Two independent radiologists measured the ADC values (ADCmax, ADCmean, and ADCmin) of EC on the ADC image by comparing the MRI enhancement and DWI images, respectively. Statistical methods were used to calculate the correlation between clinical information, ADC values and myometrial infiltration and Ki-67 and p53 expression in EC patients. A nomogram prediction model was constructed and evaluated via receiver operating characteristic (ROC) curve and calibration curve analysis. Results The ADC values were significantly correlated with the myometrial infiltration and Ki-67 and p53 expression levels in EC patients (all P P max, ADCmean, and ADCmin were combined with the FIGO stage to construct the nomogram model. The nomogram model, ADCmax, ADCmean, ADCmin, and FIGO stage predicted AUC values of 0.809, 0.707, 0.693, 0.694, and 0.599 for myometrial infiltration, respectively; the AUC values for predicting Ki-67 expression levels were 0.897, 0.879, 0.849, 0.808 and 0.550, respectively. The nomogram model was constructed by combining the ADCmax, ADCmean, ADCmin. The AUC values predicted by the nomogram, ADCmax, ADCmean, ADCmin were 0.665, 0.615, 0.641 and 0.654, respectively. Conclusions The nomogram model based on ADC values combined with the FIGO stage could be a useful method for the preoperative non-invasive assessment of myometrial infiltration and Ki-67 and p53 expression in EC patients.

Purpose: To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating atypical meningioma (AtM) from transitional meningioma (TrM) and its correlation with the Ki-67 proliferation index. Methods: Clinical, imaging, and pathological data of 78 patients with AtM and 80 patients with TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile (ADCp1), 10th percentile (ADCp10), 50th percentile (ADCp50), 90th percentile (ADCp90), and 99th percentile (ADCp99)) were generated. The Mann–Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 proliferation index was analyzed. Results: All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different (P < 0.05). The optimal cutoff value of 147.00 × 10 – 3 mm2/s, ADCp99 achieved the best diagnostic performance for distinguishing AtM from TrM: area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.764, 58.75%, 82.05%, 70.25%, 77.05%, and 65.98%, respectively. All histogram parameters were negatively correlated with Ki-67 expression (r = -0.012 to -0.293). Conclusion: ADC histogram analysis is a potential tool for noninvasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 proliferation index.

Purpose: To assess differences in pericoronary adipose tissue (PCAT) in patients with different plaque types by using several quantitative parameters of PCAT and investigate the relationship between PCAT and different plaque types.Methods: We retrospectively recruited 488 patients diagnosed with stable coronary artery disease (CAD) via coronary computed tomographic angiography, including 279 with calcified plaques (CP), 153 with non-calcified plaques (NCP), and 56 with mixed plaques (MP). Volume, fat attenuation index (FAI), and 10th percentile, 90th percentile, median, and minimum Hounsfield unit (HU) values of PCAT surrounding plaques were quantified using semi-automated software. Clinical features and quantitative PCAT parameters were compared between different plaque types.Results: No between-group differences were observed for age, sex, body mass index, risk factors, and plaque location. Length and PCAT volume in the NCP group were lower than those of the CP and MP groups (P < 0.001), whereas there were no significant differences between the CP and MP groups (P > 0.05). Patients with NCP and MP had a higher FAI and 10th percentile, 90th percentile, median, and minimum HU values of PCAT than CP (P < 0.001); however these values were not significantly different between the NCP and MP groups (P > 0.05). Conclusion: The quantitative parameters of PCAT, as a biosensor for CAD, vary among the different plaque types.

Purpose: To investigate the value of a cli-radiomics model based on multi-parameter magnetic resonance imaging (MRI) in differentiating fibroblastic meningiomas from non-fibroblastic meningiomas.Methods: Clinical, imaging, and postoperative pathological data of 423 patients (128 fibroblastic meningiomas and 295 non-fibroblastic meningiomas) were randomly categorized into training (n=296) and validation (n=127) groups at a 7:3 ratio. The Selectpercentile and LASSO were used to selected the highly correlated features from 3376 radiomics features. Different classifiers were used to train and verify the model. The receiver operating characteristic (ROC) curves, ACC, SEN, and SPE were drawn to evaluate the performance. The optimal radiomics model was selected, calibration curves and decision curve analysis were used to verify the clinical utility and consistency of the nomogram constructed from the radiomics features and clinical factors.Results: There were thirteen radiomic features selected from T1C and T2WI after dimensionality reduction. The prediction performance of RF radiomics model is slightly lower than that of the cli-radiomics model. The area under the curve (AUC), SEN, SPE, and ACC of the cli-radiomics model training set are 0.836 (95% confidence interval [CI], 0.795-0.878), 0.922, 0.583, and 0.686; the AUC, SEN, SPE, and ACC of the validation set were 0.756 (95% CI, 0.660-0.846), 0.816, 0.596, and 0.661, respectively.Conclusion: The diagnostic efficacy of the cli-radiomics model of fibroblastic meningioma and non-fibroblastic meningioma was better than that of the radiomics prediction model alone, and can be used as a potential tool for clinical surgical planning and evaluation of patient prognosis.

To explore the value of MRI conventional features and apparent diffusion coefficient (ADC) on the differential diagnosis of atypical meningioma (AtM) and anaplastic meningioma (AnM).This retrospective study analyzed the preoperative clinical data, MRI conventional features, and DWI data of 55 AtM and 25 AnM confirmed by pathology in our hospital. The clinical features, MRI conventional features, ADCmean, ADCmin, and relative ADC (rADC) values were compared between the two tumors by Chi-square test or an independent sample t-test. Receiver operating characteristic curve (ROC) and binary logistic regression analysis were used to evaluate the diagnostic efficacy of each parameter to differentiate between these tumors.The MRI conventional features had a certain ability to distinguish AnM and AtM, with an area under the curve value (AUC) of 0.824 (95% CI, 0.723-0.900). The ADCmean, ADCmin, and rADC values were significantly higher in AtM compared to AnM (all P 0.05). ADCmean had the best identification effect with an AUC of 0.867 (95% CI, 0.772-0.933) among them, at an cut-off of 0.817 × 10MRI conventional features combined with ADCmean, as a non-invasive method, has potential clinical value in the preoperative diagnosis of AtM and AnM.

Background: To investigate the spectral computed tomography (CT) findings of peripheral adenocarcinoma (P-AC) and peripheral squamous cell carcinoma (P-SCC) in lung. Methods: In this retrospective study, A total of 273 patients (150 patients with P-AC and 123 patients with P-SCC) confirmed by surgery and pathology who underwent chest contrast enhanced CT scan with GSI mode, including arterial phase (AP) and venous phase (VP). During two phases, The CT40keV, CT70keV, CT100keV values, iodine concentration (IC), water concentration (WC), effective atomic number (Zeff) were measured and the slope of the spectral curve (K) was calculated. Differences between two groups were compared using two-sample t-test, Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC) was also calculated to calculate diagnostic efficacies.Results: There was significant difference in gender between the two groups (P < 0.05), No significant difference between other clinical features and symptoms (P > 0.05). For AP and VP, the CT40keV, CT70keV, K70keV, IC and Zeff of P-AC were significantly higher than those of P-SCC (PConclusions: Spectral CT can provide reference for the differentiation of P-AC and P-SCC.