Protein S purpose rules and also specialized medical viewpoints

The NIRS system can assess cerebral ischemia and oxygenation non-invasively and changes of HbO2 and HbT may be used as reference parameters to assess the level of CPP in an animal model of traumatic brain injury.
To predict response to neoadjuvant chemotherapy (NAC) of gastric cancer (GC), prior to surgery, would be pivotal to customize patient treatment. The aim of this study is to investigate the reliability of computed tomography (CT) texture analysis (TA) in predicting the histo-pathological response to NAC in patients with resectable locally advanced gastric cancer (AGC).
Seventy (40 male, mean age 63.3 years) patients with resectable locally AGC, treated with NAC and radical surgery, were included in this retrospective study from 5 centers of the Italian Research Group for Gastric Cancer (GIRCG). Population was divided into two groups 29 patients from one center (internal cohort for model development and internal validation) and 41 from other four centers (external cohort for independent external validation). Gross tumor volume (GTV) was segmented on each pre- and post-NAC multidetector CT (MDCT) image by using a dedicated software (RayStation), and 14 TA parameters were then extrapolated. Correlation between TA parameters and complete pathological response (tumor regression grade, TRG1), was initially investigated for the internal cohort. The univariate significant variables were tested on the external cohort and multivariate logistic analysis was performed.
In multivariate logistic regression the only significant TA variable was delta gray-level co-occurrence matrix (GLCM) contrast (P=0.001, Nagelkerke R
0.546 for the internal cohort and P=0.014, Nagelkerke R
0.435 for the external cohort). Receiver operating characteristic (ROC) curves, generated from the logistic regression of all the patients, showed an area under the curve (AUC) of 0.763.
Post-NAC GLCM contrast and dissimilarity and delta GLCM contrast TA parameters seem to be reliable for identifying patients with locally AGC responder to NAC.
Post-NAC GLCM contrast and dissimilarity and delta GLCM contrast TA parameters seem to be reliable for identifying patients with locally AGC responder to NAC.
Predicting the mutation statuses of 2 essential pathogenic genes [epidermal growth factor receptor (
) and Kirsten rat sarcoma (
)] in non-small cell lung cancer (NSCLC) based on CT is valuable for targeted therapy because it is a non-invasive and less costly method. Although deep learning technology has realized substantial computer vision achievements, CT imaging being used to predict gene mutations remains challenging due to small dataset limitations.
We propose a multi-channel and multi-task deep learning (MMDL) model for the simultaneous prediction of
and
mutation statuses based on CT images. First, we decomposed each 3D lung nodule into 9 views. Then, we used the pre-trained inception-attention-resnet model for each view to learn the features of the nodules. By combining 9 inception-attention-resnet models to predict the types of gene mutations in lung nodules, the models were adaptively weighted, and the proposed MMDL model could be trained end-to-end. The MMDL model utilized multiple channels to characterize the nodule more comprehensively and integrate patient personal information into our learning process.
We trained the proposed MMDL model using a dataset of 363 patients collected by our partner hospital and conducted a multi-center validation on 162 patients in The Cancer Imaging Archive (TCIA) public dataset. The accuracies for the prediction of
and
mutations were, respectively, 79.43% and 72.25% in the training dataset and 75.06% and 69.64% in the validation dataset.
The experimental results demonstrated that the proposed MMDL model outperformed the latest methods in predicting
and
mutations in NSCLC.
The experimental results demonstrated that the proposed MMDL model outperformed the latest methods in predicting EGFR and KRAS mutations in NSCLC.
The weightings of iterative reconstruction algorithm can affect CT radiomic quantification. But, the effect of ASiR-V levels on the reproducibility of CT radiomic features between ultra-low-dose computed tomography (ULDCT) and low-dose computed tomography (LDCT) is still unknown. The purpose of study is to investigate whether adaptive statistical iterative reconstruction-V (ASiR-V) levels affect radiomic feature quantification using ULDCT and to assess the reproducibility of radiomic features between ULDCT and LDCT.
Sixty-three patients with pulmonary nodules underwent LDCT (0.70±0.16 mSv) and ULDCT (0.15±0.02 mSv). LDCT was reconstructed with ASiR-V 50%, and ULDCT with ASiR-V 50%, 70%, and 90%. Radiomics analysis was applied, and 107 features were extracted. The concordance correlation coefficient (CCC) was calculated to describe agreement among ULDCTs and between ULDCT and LDCT for each feature. The proportion of features with CCC >0.9 among ULDCTs and between ULDCT and LDCT, and the mean CCC for allcts in pGGNs than in SNs. The reproducibility of radiomic features was highest between ULDCT90% and LDCT50%.
To prospectively demonstrate the feasibility of performing dual-phase SPECT/CT for the assessment of the small joints of the hands of rheumatoid arthritis (RA) patients, and to evaluate the reliability of the quantitative and qualitative measures derived from the resulting images.
A SPECT/CT imaging protocol was developed in this pilot study to scan both hands simultaneously in participants with RA, in two phases of
Tc-MDP radiotracer uptake, namely the soft-tissue blood pool phase (within 15 minutes after radiotracer injection) and osseous phase (after 3 hours). Joints were evaluated qualitatively (normal
abnormal uptake) and quantitatively [by measuring a newly developed metric, maximum corrected count ratio (MCCR)]. Qualitative and quantitative evaluations were repeated to assess reliability.
Four participants completed seven studies (all four were imaged at baseline, and three of them at follow-up after 1-month of arthritis therapy). A total of 280 joints (20 per hand) were evaluated. selleck chemical The MCCR from soft-tissue phase scans was significantly higher for clinically abnormal joints compared to clinically normal ones; P<0.