Computational study of the photophysical properties and electronic structure of iridium(III) photosensitizer complexes with electron-withdrawing groups.

A series of novel [Ir(tpy)(btp)Cl]+ complexes (Ir1-Ir4) have been reported to show excellent performance as photosensitizers. The introduction of electron-withdrawing groups increases visible light absorption and the lifetime of triplet states. To improve the photophysical properties, we theoretically design Ir5-Ir9 with electron-withdrawing groups (Cl, F, COOH, CN and NO2). Surprisingly, our findings indicate that the photosensitizer performance does not strictly increase with the electron-withdrawing ability of the substituents. In this work, the geometric and electronic structures, transition features, and photophysical properties of Ir1-Ir9 are investigated. The natural transition orbital (NTO) analysis indicates that the T1 and T2 states play a role in the photochemical pathways. Ultraviolet-visible (UV-vis) absorption spectra and charge-transfer spectra (CTS) have been investigated to show that the introduction of electron-withdrawing groups not only improves the visible light absorbing ability, but also changes the nature of electron excitation, providing a future molecular design strategy for similar series of photosensitizers. The rates of (reverse) intersystem crossing and the Huang-Rhys factors are evaluated to interpret the experimental results within the framework of Marcus theory. For complexes Ir1-Ir7, the introduction of electron-withdrawing groups leads to a lower efficiency of reverse intersystem crossing and a strong non-radiative process T2 → T1, resulting in a long triplet lifetime and excellent performance as a photosensitizer. Furthermore, some newly designed complexes (Ir7-Ir9) show great potential as thermally activated delayed fluorescence emitters, contrary to our initial expectations.

LRFNet: A deep learning model for the assessment of liver reserve function based on Child-Pugh score and CT image.

BACKGROUND AND OBJECTIVE: Liver reserve function should be accurately evaluated in patients with hepatic cellular cancer before surgery to evaluate the degree of liver tolerance to surgical methods. Meanwhile, liver reserve function is also an important indicator for disease analysis and prognosis of patients. Child-Pugh score is the most widely used liver reserve function evaluation and scoring system. However, this method also has many shortcomings such as poor accuracy and subjective factors. To achieve comprehensive evaluation of liver reserve function, we developed a deep learning model to fuse bimodal features of Child-Pugh score and computed tomography (CT) image. METHODS: 1022 enhanced abdomen CT images of 121 patients with hepatocellular carcinoma and impaired liver reserve function were retrospectively collected. Firstly, CT images were pre-processed by de-noising, data amplification and normalization. Then, new branches were added between the dense blocks of the DenseNet structure, and the center clipping operation was introduced to obtain a lightweight deep learning model liver reserve function network (LRFNet) with rich liver scale features. LRFNet extracted depth features related to liver reserve function from CT images. Finally, the extracted features are input into a deep learning classifier composed of fully connected layers to classify CT images into Child-Pugh A, B and C. Precision, Specificity, Sensitivity, and Area Under Curve are used to evaluate the performance of the model. RESULTS: The AUC by our LRFNet model based on CT image for Child-Pugh A, B and C classification of liver reserve function was 0.834, 0.649 and 0.876, respectively, and with an average AUC of 0.774, which was better than the traditional clinical subjective Child-Pugh classification method. CONCLUSION: Deep learning model based on CT images can accurately classify Child-Pugh grade of liver reserve function in hepatocellular carcinoma patients, provide a comprehensive method for clinicians to assess liver reserve function before surgery.

Dual-layer spectral detector CT for contrast agent concentration, dose and injection rate reduction: Utility in imaging of the superior mesenteric artery.

PURPOSE: To investigate the feasibility of contrast agent and injection rate reduction for dual-layer spectral detector computed tomography (SDCT) imaging of the superior mesenteric artery (SMA) using virtual monochromatic image (VMI). METHODS: A total of 102 patients who underwent abdominal arterial phase-enhanced SDCT examination due to suspected abdominal diseases were prospectively selected and divided into control group, low concentration/dose groups (groups 370-0.7, 300-1.0, and 300-0.9) and low injection rate groups (groups 2-370 and 2-350). Compared with the control group, low concentration/dose groups and low injection rate groups lowered the concentration/dose or injection rate of the contrast agent to varying degrees. The raw data obtained in each group were reconstructed using hybrid-iterative reconstruction and projection spatial-spectral reconstruction algorithm. The image quality of the SMA in conventional images (CI) and in VMIs40-140 kiloelectron volt (keV) (interval: 10 keV) during the arterial phase was analyzed. Multiplanar reformation images and volume rendering images of the SMA were reconstructed. Image quality objective evaluation indexes included the CT values, contrast-to-noise ratio, signal-to-noise ratio, and diameter of the SMA. The diameter of the SMA was determined by the CT values profile curve and its full width at half maximum. Two doctors independently evaluated the subjective image quality of multiplanar reformation coronal images and volume rendering images according to a 5-point scale. Repeated analysis of variance and Friedman test were used to compare the differences in the objective evaluation indexes and subjective scores between VMIs and CI in the same group. The Dunnett's t-test or Dunnett's T3 test and Kruskal-Wallis H-test were used to compare the differences in the objective evaluation indexes and subjective scores between the experimental and control groups. RESULTS: VMIs of the SMA in each group had the best image quality at 60 keV, and VMI60 keV in each group were better than their respective CI to varying degrees. Although the objective (CT values, contrast-to-noise ratio, and signal-to-noise ratio) and subjective (subjective scores) indexes of CI in the low concentration/dose groups and low injection rate groups were lower than those of CI in the control group to varying degrees, these indexes of VMI60 keV in the low concentration/dose groups and group 2-370 were equal to or even better than the CI in the control group. CONCLUSIONS: VMI60 keV using SDCT could effectively reduce the contrast agent load while providing equivalent or better SMA image quality compared with CI obtained using a conventional contrast agent protocol. When the injection rate was lowered to 2.0 ml/s for a high-concentration contrast agent (370 mgI/ml), the SMA image quality at VMI60 keV was comparable with that of the CI in the control group.

Liver Fibrosis Staging with Gadolinium Ethoxybenzyl Diethylenetriamine Penta-Acetic Acid-enhanced: A Systematic Review and Meta-analysis.

OBJECTIVE: While liver biopsy is the golden standard for liver-fibrosis diagnosis, it is also invasive and has many limitations. Non-invasive techniques such as Magnetic Resonance Imaging (MRI) need to be further developed for liver fibrosis staging. This study aimed to evaluate the diagnostic accuracy of Gadolinium Ethoxybenzyl Diethylenetriamine Penta-acetic Acid (Gd-EOBDTPA)- enhanced MRI for liver fibrosis through systematic review and meta-analysis. METHODS: This study comprehensively searched relevant article in PubMed, Embase, and the Cochrane Library published from 2004 to 2018 to find studies analyzing the diagnostic accuracy of Gd-EOB-DTPA-enhanced MRI for liver fibrosis. Two reviewers independently screened the retrieved articles, extracted the required data from the included studies, and evaluated the methodological quality of the studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and Summary Receiver Operating Characteristics (SROC) curve were assessed. RESULTS: This study finally included 16 studies (n = 1,599) and selected a random-effects model based on the results of the I2 statistic to combine them. The areas under the SROC curve for the detection of F1 or greater, F2 or greater, F3 or greater, or F4 liver fibrosis were 0.8669, 0.8399, 0.8481, and 0.8858, respectively. CONCLUSION: Gd-EOB-DTPA-enhanced MRI showed a good diagnostic performance for staging liver fibrosis, especially for F4 liver fibrosis.

Diffusion tensor imaging quantifying the severity of chronic hepatitis in rats.

BACKGROUND: Diffusion tensor imaging (DTI) is mainly used for detecting white matter fiber in the brain. DTI was applied to assess fiber in liver disorders in previous studies. However, the data obtained have been insufficient in determining if DTI can be used to exactly stage chronic hepatitis. This study assessed the value of DTI for staging of liver fibrosis (F), necroinflammatory activity (A) and steatosis (S) with chronic hepatitis in rats. METHODS: Seventy male Sprague-Dawley rats were divided into a control group(n = 10) and an experimental group(n = 60). The rat models of chronic hepatitis were established by abdominal subcutaneous injections of 40% CCl4. All of the rats underwent 3.0 T MRI. Regions of interest (ROIs) were subjected to DTI to estimate the MR parameters (rADC value and FA value). Histopathology was used as the reference standard. Multiple linear regression was used to analyze the associations between the MR parameters and pathology. The differences in the MR parameters among the pathological stages were evaluated by MANOVA or ANOVA. The LSD test was used to test for differences between each pair of groups. ROC analysis was also performed. RESULTS: The count of each pathology was as follows: F0(n = 15), F1(n = 11), F2(n = 6), F3(n = 9), F4(n = 6); A0(n = 8), A1(n = 16), A2(n = 16), A3(n = 7); S0(n = 10), S1(n = 7), S2(n = 3), S3(n = 11), S4(n = 16). The rADC value had a negative correlation with liver fibrosis (r = - 0.392, P = 0.008) and inflammation (r = - 0.359, P = 0.015). The FA value had a positive correlation with fibrosis (r = 0.409, P = 0.005). Significant differences were found in the FA values between F4 and F0 ~ F3 (P = 0.03), while no significant differences among F0 ~ F3 were found (P > 0.05). The AUC of the FA value differentiating F4 from F0 ~ F3 was 0.909 (p < 0.001) with an 83.3% sensitivity and an 85.4% specificity when the FA value was at the cut-off of 588.089 (× 10- 6 mm2/s). CONCLUSION: The FA value for DTI can distinguish early cirrhosis from normal, mild and moderate liver fibrosis, but the rADC value lacked the ability to differentiate among the fibrotic grades. Both the FA and rADC values were unable to discriminate the stages of necroinflammatory activity and steatosis.

Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma.

The aim of this study was to evaluate diagnostic performance of radiomics models of MRI in the detection of differentiation degree (DD) and lymph node metastases (LNM) of extrahepatic cholangiocarcinoma (ECC). We retrospectively enrolled 100 patients with ECC confirmed by pathology from January 2011 to December 2018. Three hundred radiomics features were extracted from each region of interest using MaZda software. Next, the radiomics model was developed by incorporating the optimal radiomics signatures and ADC values of tumors to predict DD (model A) and LNM (model B) of ECC, respectively, through the random forest algorithm. After which, the performance of the radiomics models were further evaluated. The model A showed better performance in both training and testing cohorts to discriminate high and medium-low differentiation groups of ECC, with an average AUC of 0.78 and 0.80, respectively. The model B also yielded the good average AUC of 0.80 and 0.90 to predict the LNM of ECC in training and testing cohorts. The radiomics models based on MRI performed well in predicting DD and LNM of ECC and have significant potential in clinical noninvasive diagnosis and in the prediction of ECC.

Quantitative analysis of susceptibility-weighted magnetic resonance imaging in chronic hepatitis in rats.

PURPOSE: To assess the value of susceptibility-weighted imaging (SWI) for staging of liver fibrosis (F), necroinflammatory activity (A), and steatosis (S) of chronic hepatitis. METHODS: 100 Sprague-Dawley (SD) male rats were randomly divided into a chronic hepatitis model group (n = 88) and a control group (n = 12). The chronic hepatitis model rats were induced by intraperitoneal injection of 40% (v/v) of carbon tetrachloride (CCl4) diluted in olive oil. All rats were examined by magnetic resonance imaging (MRI) and then killed immediately to detect pathologic staging as liver fibrosis (F), necroinflammatory activity (A), and steatosis (S). Liver-to-muscle signal intensity ratios (SIRs) of SWI were analyzed and associated with histopathologic findings. RESULTS: There were ultimately 11 normal control rats and 60 chronic hepatitis model rats. Statistical data were as follows: F0 (n = 11), F1 (n = 18), F2 (n = 16), F3 (n = 13), F4 (n = 13); A0 (n = 11), A1 (n = 29), A2 (n = 21), A3 (n = 10); S0 (n = 11), S1 (n = 12), S2 (n = 12), S3 (n = 18), and S4 (n = 18). The liver-to-muscle SIR of the SWI was related to hepatic fibrosis (P < 0.05) and liver steatosis (P < 0.05) but not to necroinflammatory activity (P > 0.05). By partial correlation analysis, a significant negative correlation was shown between the liver-to-muscle SIR and staging of liver fibrosis (r = -0.68, P < 0.05) as well as a low correlation with liver steatosis (r = 0.30, P < 0.05). Except for F0-F1 and F1-F2, there were statistical differences between each of the stages of hepatic fibrosis (P < 0.05), with an area under the ROC of 0.87 for F3 or above and of 0.96 for F4. CONCLUSION: SWI can be a reliable method for staging hepatic fibrosis.