This study aimed to develop and validate an integrated prediction model based on clinicoradiological data and computed tomography (CT)-radiomics for differentiating between benign and malignant parotid gland (PG) tumors via multicentre cohorts.A cohort of 87 PG tumor patients from hospital #1 who were diagnosed between January 2017 and January 2020 were used for prediction model training. A total of 378 radiomic features were extracted from a single tumor region of interest (ROI) of each patient on each phase of CT images. Imaging features were extracted from plain CT and contrast-enhanced CT (CECT) images. After dimensionality reduction, a radiomics signature was constructed. A combination model was constructed by incorporating the rad-score and CT radiological features. An independent group of 38 patients from hospital #2 was used to validate the prediction models. The model performances were evaluated by receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the models. The radiomics signature model was constructed and the rad-score was calculated based on selected imaging features from plain CT and CECT images.Analysis of variance and multivariable logistic regression analysis showed that location, lymph node metastases, and rad-score were independent predictors of tumor malignant status. The ROC curves showed that the accuracy of the support vector machine (SVM)-based prediction model, radiomics signature, location and lymph node status in the training set was 0.854, 0.772, 0.679, and 0.632, respectively; specificity was 0.869, 0.878, 0.734, and 0.773; and sensitivity was 0.731, 0.808, 0.723, and 0.742. In the test set, the accuracy was 0.835, 0.771, 0.653, and 0.608, respectively; the specificity was 0.741, 0.889, 0.852, and 0.812; and the sensitivity was 0.818, 0.790, 0.731, and 0.716.The combination model based on the radiomics signature and CT radiological features is capable of evaluating the malignancy of PG tumors and can help clinicians guide clinical tumor management.
To observe the longitudinal changes of nerve repair in rats after tissue-engineered construct implantation at magnetic resonance (MR) imaging and to determine whether the enhanced nerve regeneration with use of tissue-engineered constructs could be monitored with gadofluorine M-enhanced MR imaging or nerve T2 relaxation time measurement.All experimental protocols were approved by the institutional Animal Use and Care Committee. Tissue-engineered constructs were prepared by seeding mesenchymal stem cells (MSCs) into chitosan nerve tubes. Thirty-six rats with sciatic nerve transection injury underwent nerve tube implantation with (n = 18) or without (n = 18) MSC seeding. Sequential T2 measurement, gadofluorine M-enhanced MR imaging, and sciatic function index measurement were performed over an 8-week follow-up period, with histologic assessments performed at regular intervals. T2 relaxation times and signal intensity at gadofluorine M-enhanced T1-weighted imaging were measured and were compared by using repeated-measures analysis of variance followed by the Student-Neuman-Keuls post-hoc test for multiple pairwise comparisons.Nerve T2 relaxation times and gadofluorine M enhancement, as well as functional changes, showed a similar time course. Nerves implanted with MSC-seeded tubes achieved slightly better functional recovery and enhanced nerve regeneration while showing a slower return to baseline T2 relaxation time and a more rapid decline in gadofluorine M enhancement compared with nerves implanted with chitosan tubes alone. T2 values of the distal portion of transected nerves showed a more rapid return to baseline level than did gadofluorine M enhancement.Peripheral nerve repair with use of tissue-engineered constructs can be monitored by using gadofluorine M-enhanced MR imaging and T2 relaxation time measurements. T2 relaxation time seems more sensitive than gadofluorine M-enhanced MR imaging for detecting nerve regeneration.
To discuss the clinical efficacy of phentolamine in the treatment of feeding intolerance in premature infants with low birth weight.Seventy-one low-birth-weight infants with feeding intolerance were randomly divided into the phentolamine group and the erythromycin group (38 patients and 33 patients, respectively). The infants were given basic treatment, such as gastric lavage, temporary fasting, nutritional support and abdominal massage. The phentolamine group was intravenously pumped with phentolamine as the basis of basic treatment, while the erythromycin group was given erythromycin as the basis of basic treatment. The time for gastrointestinal symptoms to disappear, the time the basic standard was reached, the time parenteral nutrition was used, the total time enteral feeding was implemented, the length of stay, and the increase in physical indexes according to the corrected gestational age of 40 weeks of the two groups were compared.There was no significant difference between the phentolamine group and the erythromycin group in vomiting disappearance time or the increase in physical indicators at the corrected gestational age of 40 weeks (P>0.05), while the abdominal distension disappearance time, the time of restoration to birth weight, the time to reach the basic standard, the total time of parenteral nutrition, the total time of enteral feeding, and the length of stay in the phentolamine group were shorter than those in the erythromycin group, with significant differences (P<0.05).Phentolamine has a significant effect on alleviating symptoms and shortening the treatment time while treating feeding intolerance in premature infants with low birth weight, without adverse events, so it is worthy of clinical promotion.
The vascular endothelial glycocalyx is a dense, bush-like structure that is synthesized and secreted by endothelial cells and evenly distributed on the surface of vascular endothelial cells. The blood-brain barrier (BBB) is mainly composed of pericytes endothelial cells, glycocalyx, basement membranes, and astrocytes. The glycocalyx in the BBB plays an indispensable role in many important physiological functions, including vascular permeability, inflammation, blood coagulation, and the synthesis of nitric oxide. Damage to the fragile glycocalyx can lead to increased permeability of the BBB, tissue edema, glial cell activation, up-regulation of inflammatory chemokines expression, and ultimately brain tissue damage, leading to increased mortality. This article reviews the important role that glycocalyx plays in the physiological function of the BBB. The review may provide some basis for the research direction of neurological diseases and a theoretical basis for the diagnosis and treatment of neurological diseases.
Abstract Background Radiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO). Methods A total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study. Clinicoradiological profiles, including head CT without contrast enhancement and computed tomographic angiography (CTA), were collected. The time from stroke onset (TFS) was classified into two subcategories: ≤ 4.5 h, and > 4.5 h. The middle cerebral artery (MCA) territory on CT images was segmented to extract and score the radiomics features associated with the TFS. In addition, the clinicoradiological factors related to the TFS were identified. Subsequently, a combined model of the radiomics signature and clinicoradiological factors was constructed to distinguish the TFS ≤ 4.5 h. Finally, we evaluated the overall performance of our constructed model in an external validation sample of ischemic stroke patients with acute MCAO in the M1 segment. Results The area under the curve (AUC) of the radiomics signature for discriminating the TFS in the development and validation cohorts was 0.770 (95% confidence interval (CI): 0.665–0.875) and 0.792 (95% CI: 0.633–0.950), respectively. The AUC of the combined model comprised of the radiomics signature, age and ASPECTS on CT in the development and validation cohorts was 0.808 (95% CI: 0.701–0.916) and 0.833 (95% CI: 0.702–0.965), respectively. In the external validation cohort, the AUC of the radiomics signature was 0.755 (95% CI: 0.614–0.897), and the AUC of the combined model was 0.820 (95% CI: 0.712–0.928). Conclusions The CT-based radiomics signature is a valuable tool for discriminating the TFS in patients with acute MCAO in the M1 segment, which may guide the use of thrombolysis therapy in patients with indeterminate stroke onset time.
Ischemic stroke remains the leading cause of death and disability, while the main mechanisms of dominant neurological damage in stroke contain excitotoxicity, oxidative stress, and inflammation. The clinical application of many neuroprotective agents is limited mainly due to their inability to cross the blood-brain barrier (BBB), short half-life and low bioavailability. These disadvantages can be better eliminated/reduced by nanoparticle as the carrier of these drugs. This review expounded the currently hot researched nanomedicines from the perspective of the mechanism of ischemic stroke. In addition, this review describes the bionic nanomedicine delivery strategies containing cells, cell membrane vesicles and exosomes that can effectively avoid the risk of clearance by the reticuloendothelial system. The potential challenges and application prospect for clinical translation of these delivery platforms were also discussed.
Postinterventional cerebral hyperdensity (PCHD) is commonly seen in acute ischemic patients after mechanical thrombectomy. We propose a new classification of PCHD to investigate its correlation with hemorrhagic transformation (HT). The clinical prognosis of PCHD was further studied.Data from 189 acute stroke patients were analyzed retrospectively. According to the European Cooperative Acute Stroke Study criteria (ECASS), HT was classified as hemorrhagic infarction (HI-1 and HI-2) and parenchymal hematoma (pH-1 and pH-2). Referring to the classification of HT, PCHD was classified as PCHD-1, PCHD-2, PCHD-3, and PCHD-4. The prognosis included early neurological deterioration (END) and the modified Rankin Scale (mRS) score at 3 months.The incidence of HT was 14.8% (12/81) in the no-PCHD group and 77.8% (84/108) in the PCHD group. PCHD was highly correlated with HT (r = 0.751, p < 0.01). After stepwise regression analysis, PCHD and the National Institutes of Health Stroke Scale (NIHSS) score at admission were found to be independent factors for END (p < 0.001, p = 0.015, respectively). The area of curves (AUC) of PCHD, the NIHSS at admission, and the combined model were 0.810, 0.667, and 0.832, respectively. The optimal diagnostic cutoff of PCHD for END was PCHD > 2. PCHD, the NIHSS score at admission, and good vascular recanalization (VR) were independently associated with 3-month mRS (all p < 0.05). The AUC of PCHD, the NIHSS at admission, good VR, and the combined model were 0.779, 0.733, 0.565, and 0.867, respectively. And the best cutoff of PCHD for the mRS was PCHD > 1.The relationship of PCHD and HT suggested PCHD was an early risk indicator for HT. The occurrence of PCHD-3 and PCHD-4 was a strong predictor for END. PCHD-1 is considered to be relatively benign in relation to the 3-month mRS.
Malignant middle cerebral artery infarction (mMCAi) is a serious complication of cerebral infarction usually associated with poor patient prognosis. In this retrospective study, we analyzed clinical information as well as non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) data from patients with cerebral infarction in the middle cerebral artery (MCA) territory acquired within 24 h from symptoms onset. Then, we aimed to develop a model based on the radiomics signature to predict the development of mMCAi in cerebral infarction patients. Patients were divided randomly into training (n = 87) and validation (n = 39) sets. A total of 396 texture features were extracted from each NCCT image from the 126 patients. The least absolute shrinkage and selection operator regression analysis was used to reduce the feature dimension and construct an accurate radiomics signature based on the remaining texture features. Subsequently, we developed a model based on the radiomics signature and Alberta Stroke Program Early CT Score (ASPECTS) based on NCCT to predict mMCAi. Our prediction model showed a good predictive performance with an AUC of 0.917 [95% confidence interval (CI), 0.863-0.972] and 0.913 [95% CI, 0.795-1] in the training and validation sets, respectively. Additionally, the decision curve analysis (DCA) validated the clinical efficacy of the combined risk factors of radiomics signature and ASPECTS based on NCCT in the prediction of mMCAi development in patients with acute stroke across a wide range of threshold probabilities. Our research indicates that radiomics signature can be an instrumental tool to predict the risk of mMCAi.
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