Research and application advances in rehabilitation assessment of stroke.

Stroke has a high incidence and disability rate, and rehabilitation is an effective means to reduce the disability rate of patients. To systematize rehabilitation assessment, which is the foundation for rehabilitation therapy, we summarize the assessment methods commonly used in research and clinical applications, including the various types of stroke rehabilitation scales and their applicability, and related biomedical detection technologies, including surface electromyography (sEMG), motion analysis systems, transcranial magnetic stimulation (TMS), magnetic resonance imaging (MRI), and combinations of different techniques. We also introduce some assessment techniques that are still in the experimental phase, such as the prospective application of artificial intelligence (AI) with optical correlation tomography (OCT) in stroke rehabilitation. This review provides a useful bibliography for the assessment of not only the severity of stroke injury, but also the therapeutic effects of stroke rehabilitation, and establishes a solid base for the future development of stroke rehabilitation skills.

Determination of significant parameters in remote ischemic postconditioning for ischemic stroke in experimental models: A systematic review and meta-analysis study.

OBJECTIVES: To systematically review studies using remote ischemia postconditioning (RIPostC) for ischemic stroke in experimental models and obtain factors that significantly influence treatment outcomes. MATERIALS AND METHODS: Peer-reviewed studies were identified and selected based on the eligibility criteria, followed by extraction of data on potentially influential factors related to model preparation, postconditioning, and measure time based on outcome measures including infarct size, neurological scales, and cell tests with autophagy, apoptosis, normal-neuron, and damaged-neuron counting. Then, all data were preprocessed, grouped, and meta-analyzed with the indicator of the standardized mean difference. RESULTS: Fifty-seven studies with 224 experiments (91 for infarct size, 92 for neurological scales, and 41 for cell-level tests) were included. There was little statistical difference between different model preparations, treated body parts, number of treatments, and sides. And treatment effect was generally a positive correlation with the duration of conditioning time to stroke onset with exceptions at some time points. Based on infarct size, the number of cycles per treatment, duration of occlusion, and release per cycle showed significant differences. Combined with the effect sizes by other measures, the occlusion/release duration of 8-10 min per cycle is better than 5 min, and three cycles per treatment were most frequently used with good effects. Effect also varied when measuring at different times, showing statistical differences in infarct size and most neurological scales. RIPostC is confirmed as an effective therapeutic intervention for ischemic stroke, while the RIPostC-mediated autophagy level being activated or inhibited remained conflicting. CONCLUSIONS: Conditioning time, number of cycles per treatment, duration of occlusion, and release per cycle were found to influence the treatment effects of RIPostC significantly. More studies on the relevant influential factors and autophagy mechanisms are warranted.

Preliminary exploration of response the course of radiotherapy for stage III non-small cell lung cancer based on longitudinal CT radiomics features.

PURPOSE: Explore the longitudinal CT-based radiomics to demonstrate the changing trend of radiotherapy response and to determine at which point after the onset of treatment radiomics exhibit the greatest change for stage III NSCLC patients. METHODS AND MATERIALS: Ten stage III NSCLC patients in line with inclusion criteria were enrolled retrospectively, each of whom received radiotherapy or concurrent chemo-radiotherapy and performed eight series of follow-up CT imaging. Longitudinal radiomics were extracted on region of interest from the eight registered images, then two steps were conducted to select significant features as indicators of tumor change: 1) stable features were selected by Kendall rank correlation; 2) texture feature types with a steadily changing trend were retained and intensity features with stable change trends were selected to represent the large number of them. Next, the trend and rate of tumor change were analyzed using the Delta method and Curve-fitting method. Finally, the statistics in the distribution of stable features in patients were calculated. RESULTS: 675 stable features were selected from a total number of 1371 radiomics features, then 12 texture features types were retained and three intensity features were chosen to represent their own category. Among the final selected feature types, it was found that the two time points were weeks 1 and 3 with the higher rate of change. One patient had very few stable tumor features out of a total of 101 features, and the rate of change of features of another patient was conspicuously higher than the average level with number of 301 features. CONCLUSION: The longitudinal CT radiomics could demonstrate the change trend of tumor and at which point exhibit the greatest change during radiotherapy, and potentially be used for treatment decisions concerning adaptive radiotherapy.

Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions.

PURPOSE: The study is to explore potential features and develop classification models for distinguishing benign and malignant lung lesions based on CT-radiomics features and PET metabolic parameters extracted from PET/CT images. MATERIALS AND METHODS: A retrospective study was conducted in baseline 18 F-flurodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 135 patients. The dataset was utilized for feature extraction of CT-radiomics features and PET metabolic parameters based on volume of interest, then went through feature selection and model development with strategy of five-fold cross-validation. Specifically, model development used support vector machine, PET metabolic parameters selection used Akaike's information criterion, and CT-radiomics were reduced by the least absolute shrinkage and selection operator method then forward selection approach. The diagnostic performances of CT-radiomics, PET metabolic parameters and combination of both were illustrated by receiver operating characteristic (ROC) curves, and compared by Delong test. Five groups of selected PET metabolic parameters and CT-radiomics were counted, and potential features were found and analyzed with Mann-Whitney U test. RESULTS: The CT-radiomics, PET metabolic parameters, and combination of both among five subsets showed mean area under the curve (AUC) of 0.820 ±â€¯0.053, 0.874 ±â€¯0.081, and 0.887 ±â€¯0.046, respectively. No significant differences in ROC among models were observed through pairwise comparison in each fold (P-value from 0.09 to 0.81, Delong test). The potential features were found to be SurfaceVolumeRatio and SUVpeak (P < 0.001 of both, U test). CONCLUSION: The classification models developed by CT-radiomics features and PET metabolic parameters based on PET/CT images have substantial diagnostic capacity on lung lesions.

Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.

PURPOSE: To evaluate the performance of machine learning (ML)-based computed tomography (CT) radiomics analysis for discriminating between low grade (WHO/ISUP I-II) and high grade (WHO/ISUP III-IV) clear cell renal cell carcinomas (ccRCCs). METHODS: A total of 164 low grade and 107 high grade ccRCCs were retrospectively analyzed in this study. Radiomic features were extracted from corticomedullary phase (CMP) and nephrographic phase (NP) CT images. Intraclass correlation coefficient (ICC) was calculated to quantify the feature's reproducibility. The training and validation cohort consisted of 163 and 108 cases. Least absolute shrinkage and selection operator (LASSO) regression method was used for feature selection. The machine learning (ML) classifiers were k-NearestNeighbor (KNN), Logistic Regression (LR), multilayer perceptron (MLP), Random Forest (RF), and support vector machine (SVM). The performance of classifiers was mainly evaluated and compared by certain metrics. RESULTS: Seven CMP features (ICC range, 0.990-0.999) and seven NP features (ICC range, 0.931-0.999) were selected. The accuracy of CMP, NP and the combination of CMP and NP ranged from 82.2%-85.9 %, 82.8%-94.5 % and 86.5%-90.8 % in the training cohort, and 90.7%-95.4%, 77.8%-79.6 % and 91.7%-93.5 % in the validation cohort. The AUC of CMP, NP and the combination of CMP and NP ranged from 0.901 to 0.938, 0.912 to 0.976, 0.948 to 0.968 in the training cohort, and 0.957 to 0.974, 0.856 to 0.875, 0.960 to 0.978 in the validation cohort. CONCLUSIONS: ML-based CT radiomics analysis can be used to predict the WHO/ISUP grade of ccRCCs preoperatively.

Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis.

RATIONALE AND OBJECTIVES: To use machine learning-based magnetic resonance imaging radiomics to predict metachronous liver metastases (MLM) in patients with rectal cancer. MATERIALS AND METHODS: This study retrospectively analyzed 108 patients with rectal cancer (54 in MLM group and 54 in nonmetastases group). Feature selection were performed in the radiomic feature sets extracted from images of T2-weighted image (T2WI) and venous phase (VP) sequence respectively, and the combining feature set with 2058 radiomic features incorporating two sequences with the least absolute shrinkage and selection operator method. Five-fold cross-validation and two machine learning algorithms (support vector machine [SVM]; logistic regression [LR]) were utilized for predictive model constructing. The diagnostic performance of the models was evaluated by receiver operating characteristic curves with indicators of accuracy, sensitivity, specificity and area under the curve, and compared by DeLong test. RESULTS: Five, 8, and 22 optimal features were selected from 1029 T2WI, 1029 VP, and 2058 combining features, respectively. Four-group models were constructed using the five T2WI features (ModelT2), the 8 VP features (ModelVP), the combined 13 optimal features (Modelcombined), and the 22 optimal features selected from 2058 features (Modeloptimal). In ModelVP, the LR was superior to the SVM algorithm (P = 0.0303). The Modeloptimal using LR algorithm showed the best prediction performance (P = 0.0019-0.0081) with accuracy, sensitivity, specificity, and area under the curve of 0.80, 0.83, 0.76, and 0.87, respectively. CONCLUSION: Radiomics models based on baseline rectal magnetic resonance imaging has high potential for MLM prediction, especially the Modeloptimal using LR algorithm. Moreover, except for ModelVP, the LR was not superior to the SVM algorithm for model construction.

Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade.

OBJECTIVES: To discriminate low grade (Fuhrman I/II) and high grade (Fuhrman III/IV) clear cell renal cell carcinoma (CCRCC) by using CT-based radiomic features. METHODS: 161 and 99 patients diagnosed with low and high grade CCRCCs from January 2011 to May 2018 were enrolled in this study. 1029 radiomic features were extracted from corticomedullary (CMP), and nephrographic phase (NP) CT images of all patients. We used interclass correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO) regression method to select features, then the selected features were constructed three classification models (CMP, NP and with their combination) to discriminate high and low grades CCRCC. These three models were built by logistic regression method using 5-fold cross validation strategy, evaluated with receiver operating characteristics curve (ROC) and compared using DeLong test. RESULTS: We found 11 and 24 CMP and NP features were independently significantly associated with the Fuhrman grades. The model of CMP, NP and Combined model using radiomic feature set showed diagnostic accuracy of 0.719 (AUC [area under the curve], 0.766; 95% CI [confidence interval]: 0.709-0.816; sensitivity, 0.602; specificity, 0.838), 0.738 (AUC, 0.818; 95% CI:0.765-0.838; sensitivity, 0.693; specificity, 0.838), 0.777(AUC, 0.822; 95% CI: 0.769-0.866; sensitivity, 0.677; specificity, 0.839). There were significant differences in AUC between CMP model and Combined model (P = 0.0208), meanwhile, the differences between CMP model and NP model, NP model and Combined model reached no significant (P = 0.0844, 0.7915). CONCLUSIONS: Radiomic features could be used as biomarker for the preoperative evaluation of the CCRCC Fuhrman grades.

Theoretical investigations of the H···π and X (X = F, Cl, Br, I)···π complexes between hypohalous acids and benzene.

The H···π and X (X = F, Cl, Br, I)···π interactions between hypohalous acids and benzene are investigated at the MP2/6-311++G(2d,2p) level. Four hydrogen-bonded and three halogen-bonded complexes were obtained. Ab initio calculations indicate that the X···π interaction between HOX and C(6)H(6) is mainly electrostatically driven, and there is nearly an equal contribution from both electrostatic and dispersive energies in the case of XOH-C(6)H(6) complexes. Natural bond orbital (NBO) analysis reveals that there exists charge transfer from benzene to hypohalous acids. Atom in molecules (AIM) analysis locates bond critical points (BCP) linking the hydrogen or halogen atom and carbon atom in benzene.

What differs on the enzymatic acetylation mechanisms for arylamines and arylhydrazines substrates? A theoretical study.

The acetylation mechanisms of several selected typical substrates from experiments, including arylamines and arylhydrazines, are investigated with the density functional theory in this paper. The results indicate that all the transition states are characterized by a four-membered ring structure, and hydralazine (HDZ) is the most potent substrate. The bioactivity for all the compounds is increased in a sequence of PABA ≈ 4-AS < 4-MA < 5-AS ≈ INH < HDZ. The conjunction effect and the delocalization of the lone pairs of N atom play a key role in the reaction. All the results are consistent with the experimental data.

Prediction of the binding modes between BB-83698 and peptide deformylase from Bacillus stearothermophilus by docking and molecular dynamics simulation.

BB-83698 is a first potent inhibitor of peptide deformylase in this novel class to enter clinical trials. In this study, automated docking, molecular dynamics simulation and binding free energy calculations with the linear interaction energy (LIE) method are first applied to investigate the binding of BB-83698 to the peptide deformylase from Bacillus stearothermophilus. The lowest docking energy structure from each cluster is selected as different representative binding modes. Compared with the experimental data, the results show that the binding of BB-83698 in Mode 1 is the most stable, with a binding free energy of -41.35 kJ/mol. The average structure of the Mode 1 complex suggests that inhibitor interacts with Ile59 and Gly109 by hydrogen bond interaction and with Pro47, Pro57, Ile59 and Leu146 by hydrophobic interaction are essential for the activity of BB-83698. Mode 2 represents a new binding mode. Additionally, if the hydrophilic group is introduced to the benzo-[1,3]-dioxole ring, the binding affinity of BB-83698 to the peptide deformylase from B. stearothermophilus will be greatly improved.

A global 12-dimensional ab initio potential energy surface and dynamical studies for the SiH4+H-->SiH3+H2 reaction.

A global 12-dimensional ab initio interpolated potential energy surface (PES) for the SiH(4)+H-->SiH(3)+H(2) reaction is presented. The ab initio calculations are based on the unrestricted quadratic configuration interaction treatment with all single and double excitations together with the cc-pVTZ basis set, and the modified Shepard interpolation method of Collins and co-workers [K. C. Thompson et al., J. Chem. Phys. 108, 8302 (1998); M. A. Collins, Theor. Chem. Acc. 108, 313 (2002); R. P. A. Bettens and M. A. Collins, J. Chem. Phys. 111, 816 (1999)] is applied. Using this PES, classical trajectory and variational transition state theory calculations have been carried out, and the computed rate constants are in good agreement with the available experimental data.

Docking studies of nickel-peptide deformylase (PDF) inhibitors: exploring the new binding pockets.

The binding modes of a series of known activity inhibitors docking to Peptide deformylase (PDF) have been studied using molecular docking software AutoDock3.0.5. In this study, good correlation (R(2)=0.894) between calculated binding energies and experimental inhibitory activities is obtained. We find that some shallow pockets near the known active pocket are very important which can accommodate the side-chains of the inhibitor. Moreover, a new binding pocket is also explored. All these may provide something useful for designing the potent inhibitors.