White Matter High Signals Interfere with Noncontrast Computed Tomography in the Early Identification of Cerebral Infarction.

BACKGROUND: We developed an image patch classification-based method to detect early ischemic stroke. The accuracy of this method was >75%. We aimed to analyze patients' image data to identify interference factors that would affect its accuracy. METHODS: We conducted a retrospective analysis of 162 patients who were hospitalized with acute ischemic stroke. Factors related to the noncontrast computed tomography (ncCT) determination results were analyzed according to the patient's sex, age, clinical symptoms, cerebral infarction volume, cerebral infarction location, and whether or not the white matter high (WMH) signal was combined. RESULTS: The volume of cerebral infarction was positively correlated with the predicted results. The correct percentages of patients with volumes >1 and <1 mL were 59.18 and 83.19%, respectively, and the difference was statistically significant (p = 0.001). The correct percentage of the internal capsule region (47.1%) was significantly lower than that of the other groups (p = 0.011). The correct percentage of lateral ventricular paraventricular infarction was significantly lower than that of non-lateral ventricle patients (70.8 vs. 85.7%). In patients with lateral ventricular paraventricular infarction, if the WMH was combined, the correct percentage will decreased further as the Fazekas level increased. The correct percentage of lateral ventricle infarction combined with Fazekas 3 was 40.0%, which was statistically significant compared with the patient having Fazekas 0 with lateral ventricular infarction (p = 0.01). CONCLUSIONS: WMH had a similar computed tomography appearance to cerebral infarction and could interfere with the prediction of the cerebral infarction region by ncCT. This result provides a reference for clinicians to choose imaging methods for identifying acute cerebral infarction areas.

Role Exchange-Based Self-Training Semi-Supervision Framework for Complex Medical Image Segmentation.

Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images based on deep learning has made great progress, fully supervised models require a great amount of annotations, making such complex medical image segmentation a difficult problem. In this article, we propose a semi-supervised model for complex medical image segmentation, which innovatively proposes a bidirectional self-training paradigm, through dynamically exchanging the roles of teacher and student by estimating the reliability at the model level. The direction of information and knowledge transfer between the two networks can be controlled, and the probability distribution of the roles of teacher and student in the next stage will be jointly determined by the model's uncertainty and instability in the training process. We also resolve the problem that loosely coupled networks are prone to collapse when training on small-scale annotated data by proposing asymmetric supervision (AS) strategy and hierarchical dual student (HDS) structure. In particular, a bidirectional distillation loss combined with the role exchange (RE) strategy and a global-local-aware consistency loss are introduced to obtain stable mutual promotion and achieve matching of global and local features, respectively. We conduct detailed experiments on two public datasets and one private dataset and lead existing semi-supervised methods by a large margin, while achieving fully supervised performance at a labeling cost of 5%.

SWI and CTP fusion model based on sparse representation method to predict cerebral infarction trend.

Objective: SWI image signal is related to venous reflux disorder and perfusion defect. Computed tomography perfusion (CTP) contains perfusion information in space and time. There is a complementary basis between them to affect the prognosis of cerebral infarction. Methods: Sixty-six patients included in the retrospective study were designated as the training set. Effective perfusion indicator features and imaging radiomic features of the peri-infarction area on Susceptibility weighted imaging (SWI) and CTP modality images were extracted from each case. Thirty-three patients from the prospectively included group were designated as the test set of the machine learning model based on a sparse representation method. The predicted results were compared with the DWI results of the patients' 7-10 days review to assess the validity and accuracy of the prediction. Results: The AUC of the SWI + CTP integrated model was 0.952, the ACC was 0.909, the SEN was 0.889, and the SPE was 0.933. The prediction performance is the highest. Compared with the value of AUC: the SWI model is 0.874, inferior to the performance of the SWI + CTP model, and the CTP model is 0.715. Conclusion: The prediction efficiency of the changing trend of infarction volume is further improved by the correlation between the combination of the two image features.

Analysis of the evolution and causes of groundwater chemistry after ecological water replenishment of the Jialu River, China.

Ecological water replenishment is an important measure for conserving water sources and improving the water environment. To explore the evolution and causes of groundwater chemistry after ecological water replenishment in the Jialu River, this study utilized groundwater monitoring data from 2015 to 2019 following ecological water replenishment. Various methods, including Piper's trilinear diagram, Gibbs diagram, principal component analysis, and ion ratio analysis, were employed for research purposes. The results indicate that (1) since the implementation of ecological water replenishment in the Jialu River, there has been a general downwards trend in total dissolved solids (TDS) in groundwater. The dominant cation in groundwater is Ca2+, whereas HCO3- is the dominant anion. The concentration of cations in groundwater has generally decreased, with noticeable reductions in SO42- and Cl- concentrations in the upper reaches of the recharge river contributing to improved groundwater quality. (2) A comparison with 2015 reveals a gradual transition at sampling points from chemical types such as HCO3-Ca·Mg and HCO3·Cl-Ca·Mg to an ecological water replenishment chemical type (HCO3-Ca).

Posterior Circulation Ischemic Stroke From Atlantoaxial Instability and Lateral Dislocation due to Os Odontoideum: Case Report and Review of Literature.

BACKGROUND: Strokes in young individuals often stem from unusual causes. Posterior circulation ischemic stroke caused by vertebral artery insufficiency due to atlantoaxial instability or dislocation is rare. We present a case of posterior circulation ischemic stroke due to an unstable os odontoideum and review the current literature. The clinical features and imaging manifestations are described to promote awareness of etiology, early diagnosis, and assessment. CASE PRESENTATION: A 24-year-old male presented with recurrent right-sided limb numbness and weakness and cerebellar ataxia due to posterior circulation ischemic stroke. The work-up revealed thrombosis reformation in the tortuous left vertebral artery. It is noteworthy that the patient developed compression and chronic damage of the vertebral artery secondary to atlantoaxial instability and lateral dislocation due to an os odontoideum. He underwent antiplatelet and anticoagulant therapy, cervical traction, and posterior atlantoaxial screw fixation and fusion with iliac crest autograft. The postoperative course was uneventful. At 6-month follow-up, the patient had a solid fusion mass and rigid stability of the atlantoaxial joint without neurologic deficits or ischemic sequelae. CONCLUSIONS: For unexplained posterior circulation ischemic stroke, it is important to consider unstable os odontoideum as a potential etiology, especially in pediatric and young adult male patients. Atlantoaxial instability and dislocation with os odontoideum, especially when occurring laterally, may cause insufficiency of the vertebral artery and subsequent posterior circulation ischemic strokes. The significance of lateral atlantoaxial dislocation in the genesis of vertebral artery injury and the necessity for specific positional imaging are emphasized.

Prediction of poststroke cognitive impairment based on the systemic inflammatory response index.

BACKGROUND: Poststroke cognitive impairment (PSCI) is a prevalent complication among stroke survivors. Although the systemic inflammatory response index (SIRI) has been shown to be a reliable predictor of a variety of inflammatory diseases, the association between the SIRI and PSCI is still unclear. Therefore, the purpose of this study was to investigate the relationship between SIRI and PSCI, and to design a nomogram to predict the risk of PSCI in acute ischemic stroke (AIS) patients. METHODS: A total of 1342 patients with AIS were included in the study. Using the Mini-Mental State Examination scale, patients were separated into PSCI and non-PSCI groups within 2 weeks of stroke. Clinical data and SIRI values were compared between the groups. We developed the optimal nomogram for predicting PSCI using multivariate logistic regression. Finally, the nomogram was validated using the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS: In total, 690 (51.4%) patients were diagnosed with PSCI. After adjusting for potential confounders, the SIRI (OR = 1.226, OR: 1.095-1.373, p < .001) was shown to be an independent risk factor for PSCI in the logistic regression analysis. The nomogram based on patient gender, age, admission National Institutes of Health Stroke Scale scores, education, diabetes mellitus, and SIRI had good discriminative ability with an area under the curve (AUC) of 0.716. The calibration curve and Hosmer-Lemeshow test revealed excellent predictive accuracy for the nomogram. Finally, the DCA showed the good clinical utility of the model. CONCLUSION: Increased SIRI on admission is correlated with PSCI, and the nomogram built with SIRI as one of the predictors can help identify PSCI early.

Association of Bun/Cr ratio-based dehydration status with infarct volumes and stroke severity in acute ischemic stroke.

BACKGROUND: Only a few clinical research had previously investigated the dehydration status to predict the evolution of the ischemic core. The aim of this study is to clarify the association between blood urea nitrogen (BUN)/creatinine (Cr)ratio-based dehydration and infarct volume measured using DWI (Diffusion-weighted imaging) at admission in patients with AIS (Acute Ischemic Stroke). METHODS: We retrospectively recruited a total of 203 consecutive patients who were hospitalized through emergency or outpatient services within 72 h of acute ischemic stroke onset between October 2015 and September 2019. Stroke severity was measured by assessing the National Institutes of Health Stroke Scale (NIHSS) on admission. Infarct volume was measured using DWI with MATLAB software. RESULTS: In this study, 203 patients who met the study criteria were enrolled. Patients in the dehydration group (Bun/Cr ratio>15) had a higher median NIHSS score (6(IQR:4-10) VS. 5(3-7); P = 0.0015)and larger DWI infarct volume (1.55 ml (IQR:0.51-6.79) VS. (0.37 ml (0.05-1.22); P < 0.001) on admission compared with patients in normal group. Further, a statistically significant correlation was found between DWI infarct volumes and NIHSS score with nonparametric Spearman rank correlation (r = 0.77; P < 0.001). The median NIHSS scores for the DWI infarct volumes quartiles were 3 ml (IQR, 2-4), 5 ml (4-7), 6 ml (5-8), and12 ml (8-17) from lowest to highest. However, the second quartile group did not show any significant correlation with the third quartile group (P = 0.4268). Multivariable linear and logistic regression analyses were used to test dehydration (Bun/Cr ratio>15), representing a predictor of infarct volume and stroke severity. CONCLUSION: Bun/Cr ratio-based dehydration is associated with larger volumes of ischemic tissue measured using DWI and worse neurological deficit assessed by the NIHSS score in acute ischemic stroke.

The value of circulating lymphocytic subpopulations in the diagnosis and repair of ischemic stroke patients with dizziness.

Background: To determine whether dizziness can contribute to stroke as a main cause still remains challenging. This study aims to explore clinical biomarkers in the identification of ischemic stroke patients from people with dizziness and the prediction of their long-term recovery. Methods: From January 2018 to June 2019, 21 ischemic stroke patients with a main complaint of dizziness, 84 non-stroke dizziness patients and 87 healthy volunteers were recruited in this study. Then, their peripheral blood samples were collected, and the percentages of circulating lymphocytes T cells, CD4+ T cells, CD8+ T cells, T-/- cells (DNTs), CD4+ regulatory T cells (Tregs), CD8+ Tregs, B cells and regulatory B cells (Bregs) were examined to identify biomarkers with clinical value. Results: According to our data, a significant difference in the DNTs proportion was detected between non-stroke dizziness and ischemic stroke patients with dizziness (p = 0.0009). The Bregs proportion in ischemic stroke patients with dizziness was lower than that in non-stroke dizziness patients (p = 0.035). In addition, the percentage of Bregs and DNTs within lymphocytes in patients' peripheral blood exhibited a significant negative correlation with stroke occurrence (Bregs, p = 0.039; DNTs, p = 0.046). Moreover, the Bregs and DNTs within lymphocytes were negatively related to participants' age, while presented a weak relationship with clinical risks like smoking, hypertension, and diabetes. Then, area under the receiver operating characteristic curve (AUC) of Bregs and DNTs together was 0.768, the risk factors and Bregs or DNTs ranged from 0.795 and 0.792, respectively, and the AUC value of risk factors, Bregs and DNTs combination was further increased to 0.815. Furthermore, the Bregs percentage within lymphocytes at admission was also a potential predictor of repair at discharge and the following 3 months. Conclusion: Bregs and DNTs could be the clinical biomarkers together in the identification of ischemic stroke patients from people with dizziness.

Safety and efficacy of a new modified intravenous recombinant tissue plasminogen activator (rt-PA) regimen in Chinese patients with acute ischemic stroke: A descriptive retrospective cohort study with subgroup-analysis of different rt-PA dose.

Evidence for effects of the dose of recombinant tissue plasminogen activator(rt-PA) in Asian populations is inconclusive. The standard dose may cause drug waste and increase economic burden in developing country. Therefore, we preliminarily describe the safety and efficacy of a new modified dose of rt-PA regimen(0.6 or 0.9 mg/kg, with a maximum dose of 50 mg) in real world settings. 265 consecutive patients with ischemic stroke were treated with intravenous(IV) rt-PA alone from all the 323 consecutive patients treated with reperfusion therapy between January 1, 2017 and March 31, 2020. Safety and Efficacy was assessed by early neurological improvement(ENI), early neurological deterioration(END), symptomatic intracranial hemorrhage(sICH) and 90-day outcome defined by modified Rankin scale(mRS). Subgroup analysis was conducted to draw comparisons between different dose groups ([0.5,0.6)mg/kg, [0.6,0.7)mg/kg, [0.7,0.8)mg/kg, [0.8,0.9]mg/kg). Among the 265 patients, 150(56.60%) had a favorable outcome at 3 months(3 M); Mortality occurred in 17(6.40%) in 3 M; sICH in 12(4.50%); ENI in 70(26.40%); END2 in 29(10.90%) and END4 in 18(6.80%). In subgroup analysis, there was a significant difference in sICH that more patients developed sICH in [0.8-0.9]mg/kg group(P = 0.044) in univariate analysis of different dose. After adjusting, there was no significant difference between 4 dosage groups. Significant differences were seen in gender, atrial fibrillation and baseline NIHSS in the multivariable model of favorable outcome at 3 M. Our study preliminarily shows a good safety and efficacy of our modified rt-PA regimen, indicating that this regimen should be worthy of further study especially in developing country to reduce the financial burden of patients and avoid drug waste.

Identification of invisible ischemic stroke in noncontrast CT based on novel two-stage convolutional neural network model.

PURPOSE: Early identification of ischemic stroke lesion regions plays a vital role in its treatments like thrombolytic therapy and patients' recovery. Noncontrast computed tomography (ncCT) is the most widespread imaging modality in emergency departments. Unfortunately, it is extremely hard to distinguish the lesion from healthy tissue during the hyper-acute phase of stroke. In this paper, a two-stage convolutional neural network-based method was proposed to identify the invisible ischemic stroke from ncCT. METHODS: In order to combine the global and local information of images effectively, a cascaded structure with two coordinated networks was used to detect the suspicious stroke regions on the whole and optimize the detailed localization. In the first stage, an end-to-end U-net with adaptive threshold was proposed to integrate global position, symmetry and gray texture information to detect the suspicious regions. After reducing the interference from most normal regions, a ResNet-based patch classification network was used to eliminate some false positive samples on suspicious regions by mining deeper image features, contributing to a more precise localization of stroke. Finally, a MAP model was used to optimize the result by combining the classification results of each patch with their spatial constraint information. RESULTS: Three independent experiments, that is, training and testing on dataset from one hospital, on the combination of two, and on the two respectively, were performed on a total of 277 cases from two hospitals to validate the proposed model, The proposed method achieved identification accuracy of 91.89%, 87.21%, and 85.71% in the three experiments, and the final localization accuracy in terms of precise localization of stroke were 82.35%, 83.02%, and 81.40%, respectively, which indicated the robustness and clinical values of the method. CONCLUSIONS: There are some deep image feature differences between stroke region and normal region on ncCT images. The proposed two-stage convolutional neural network model can well seize these features and use them to effectively identify and locate stroke.

A Clinical-Radiomics Nomogram for Functional Outcome Predictions in Ischemic Stroke.

INTRODUCTION: Stroke remains a leading cause of death and disability worldwide. Effective and prompt prognostic evaluation is vital for determining the appropriate management strategy. Radiomics is an emerging noninvasive method used to identify the quantitative imaging indicators for predicting important clinical outcomes. This study was conducted to investigate and validate a radiomics nomogram for predicting ischemic stroke prognosis using the modified Rankin scale (mRS). METHODS: A total of 598 consecutive patients with subacute infarction confirmed by diffusion-weighted imaging (DWI), from January 2018 to December 2019, were retrospectively assessed. They were assigned to the good (mRS ≤ 2) and poor (mRS > 2) functional outcome groups, respectively. Then, 399 patients examined by MR scanner 1 and 199 patients scanned by MR scanner 2 were assigned to the training and validation cohorts, respectively. Infarction lesions underwent manual segmentation on DWI, extracting 402 radiomic features. A radiomics nomogram encompassing patient characteristics and the radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the training and validation cohorts. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram. The performance of infarction lesion volume was also evaluated using univariate analysis. RESULTS: Stroke lesion volume showed moderate performance, with an area under the curve (AUC) of 0.678. The radiomics signature, including 11 radiomics features, exhibited good prediction performance. The radiomics nomogram, encompassing clinical characteristics (age, hemorrhage, and 24 h National Institutes of Health Stroke Scale score) and the radiomics signature, presented good discriminatory potential in the training cohort [AUC = 0.80; 95% confidence interval (CI) 0.75-0.86], which was validated in the validation cohort (AUC = 0.73; 95% CI 0.63-0.82). In addition, it demonstrated good calibration in the training (p = 0.55) and validation (p = 0.21) cohorts. Decision curve analysis confirmed the clinical value of this nomogram. CONCLUSION: This novel noninvasive clinical-radiomics nomogram shows good performance in predicting ischemic stroke prognosis.

Texture analysis based on ADC maps and T2-FLAIR images for the assessment of the severity and prognosis of ischaemic stroke.

OBJECTIVES: To explore the feasibility of texture analysis based on T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images and apparent diffusion coefficient (ADC) maps in the assessment of the severity and prognosis of ischaemic stroke using the National Institutes of Health Stroke Scale (NIHSS) and modified Rankin scale (mRS) scores, respectively. METHODS: Overall, 116 patients diagnosed with subacute ischaemic stroke were included in this retrospective study. Based on T2-FLAIR images and ADC maps, 15 texture features were extracted from the ROIs of each patient using grey-level co-occurrence matrix (GLCM) and local binary pattern histogram Fourier (LBP-HF) methods. The correlations of NIHSS score on admission (NIHSSbaseline), NIHSS score 24 h after stroke onset (NIHSS24h) and mRS score with the texture features were evaluated using Spearman's partial correlations. The receiver operating characteristic (ROC) curve was used to compare the performance of the selected texture features in the evaluation of stroke severity and prognosis. RESULTS: Texture features derived from the T2-FLAIR images and ADC maps were correlated with NIHSS score and mRS score. EntropyADC and 0.75QuantileT2-FLAIR showed the best diagnostic performance for assessing stroke severity. The combination of EntropyADC and 0.75QuantileT2-FLAIR achieved a better performance in the evaluation of stroke severity (AUC = 0.7, p = 0.01) than either feature alone. Only 0.05QuantileT2-FLAIR was found to be correlated with mRS score, and none of the texture features were predictive of mRS score. CONCLUSION: Texture features derived from T2-FLAIR images and ADC maps might serve as biomarkers to evaluate stroke severity, but were insufficient to predict stroke prognosis.

Rapid Computer-Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi-Modal Recognition.

Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability-adjusted life-years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer-aided diagnosis of stroke is performed using SMF based multi-modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano-assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi-modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single-modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening.

Prediction of neurologic deterioration based on support vector machine algorithms and serum osmolarity equations.

OBJECTIVE: Dehydration on admission is correlated with neurological deterioration (ND). The primary objective of our study was to use support vector machine (SVM) algorithms to identify an ND prognostic model, based on dehydration equations. METHODS: This study included a total of 382 patients hospitalized with acute ischemic stroke. The following parameters were recorded: age, sex, laboratory values (serum sodium, potassium, chlorinum, glucose, and urea), and vascular risk factor data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of the BUN/Cr ratio as well as each of 38 equations for predicting ND. We used the Boruta algorithm for feature selection. After optimizing the SVM kernel parameters, we built an SVM model to predict ND and used the test set to obtain predictive values for assessing model accuracy. RESULTS: In total, 102 of 382 patients (26.7%) with acute ischemic stroke developed ND. In all patients, the BUN/Cr ratio and each of 38 equations were significant predictors of ND. Equation 20 [1.86 × Na+ + glucose + urea + 9] yielded the maximum area under the ROC curve, and faired best in terms of prognostic performance (a cutoff value of 284.49 mM yielded a sensitivity of 94.12% and specificity of 61.43%). Equation 32 predicted ND poststroke across population groups, and worked well in older as well as young adults; (a cutoff value of 297.08 mM yielded a sensitivity of 93.14% and specificity of 60.00%). Feature selection by the Boruta algorithm was used to decrease the number of variables from 18 to 5 in the condition. The specificity of test samples for the SVM prediction model increased from 44.1% to 89.4%, and the AUC increased from 0.700 to 0.927. CONCLUSIONS: SVM algorithms can be used to establish a prediction model for dehydration-associated ND, with good classification results.

AMPK activation by Tanshinone IIA protects neuronal cells from oxygen-glucose deprivation.

The current study tested the potential neuroprotective function of Tanshinone IIA (ThIIA) in neuronal cells with oxygen-glucose deprivation (ODG) and re-oxygenation (OGDR). In SH-SY5Y neuronal cells and primary murine cortical neurons, ThIIA pre-treatment attenuated OGDR-induced viability reduction and apoptosis. Further, OGDR-induced mitochondrial depolarization, reactive oxygen species production, lipid peroxidation and DNA damages in neuronal cells were significantly attenuated by ThIIA. ThIIA activated AMP-activated protein kinase (AMPK) signaling, which was essential for neuroprotection against OGDR. AMPKα1 knockdown or complete knockout in SH-SY5Y cells abolished ThIIA-induced AMPK activation and neuroprotection against OGDR. Further studies found that ThIIA up-regulated microRNA-135b to downregulate the AMPK phosphatase Ppm1e. Notably, knockdown of Ppm1e by targeted shRNA or forced microRNA-135b expression also activated AMPK and protected SH-SY5Y cells from OGDR. Together, AMPK activation by ThIIA protects neuronal cells from OGDR. microRNA-135b-mediated silence of Ppm1e could be the key mechanism of AMPK activation by ThIIA.

Mesenchymal stem cells enhance the differentiation of c-kit+ cardiac stem cells.

C-kit+ cardiac stem cells (CSCs) were isolated from neonatal rat and tested for the expression of Nkx2.5 and GATA-4 genes which are important in cardiac development. C-kit+ CSCs were plated into the well below the insert of transwell inserts and bone marrow mesenchymal stem cells (BMMSCs) were plated into the inserts. The expression of cardiac Troponin T (cTnT), p27, CDK2, transforming growth factor-beta receptor II (TGF-beta R II), and Smad2 protein in CSCs were tested by western blot. Expression of p27, TGF-beta R II and Smad2 was found to be upregulated in the co-culture group. In contrast, the expression of CDK2 was downregulated. Our results suggest that BMMSCs could promote the differentiation of c-kit+ CSCs, probably through paracrine activity via the TGF-beta signaling pathway.