Recent Advances in Habenula Imaging Technology: A Comprehensive Review.

The habenula (Hb) is involved in many natural human behaviors, and the relevance of its alterations in size and neural activity to several psychiatric disorders and addictive behaviors has been presumed and investigated in recent years using magnetic resonance imaging (MRI). Although the Hb is small, an increasing number of studies have overcome the difficulties in MRI. Conventional structural-based imaging also has great defects in observing the Hb contrast with adjacent structures. In addition, more and more attention should be paid to the Hb's functional, structural, and quantitative imaging studies. Several advanced MRI methods have recently been employed in clinical studies to explore the Hb and its involvement in psychiatric diseases. This review summarizes the anatomy and function of the human Hb; moreover, it focuses on exploring the human Hb with noninvasive MRI approaches, highlighting strategies to overcome the poor contrast with adjacent structures and the need for multiparametric MRI to develop imaging markers for diagnosis and treatment follow-up. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images.

OBJECTIVE: The study aims to propose an accurate labelling method of interscapular BAT (iBAT) in rats using dynamic MR fat fraction (FF) images with noradrenaline (NE) stimulation and then develop an automatic iBAT segmentation method using a U-Net model. MATERIALS AND METHODS: Thirty-four rats fed different diets or housed at different temperatures underwent successive MR scans before and after NE injection. The iBAT were labelled automatically by identifying the regions with obvious FF change in response to the NE stimulation. Further, these FF images along with the recognized iBAT mask images were used to develop a deep learning network to accomplish the robust segmentation of iBAT in various rat models, even without NE stimulation. The trained model was then validated in rats fed with high-fat diet (HFD) in comparison with normal diet (ND). RESULT: A total of 6510 FF images were collected using a clinical 3.0 T MR scanner. The dice similarity coefficient (DSC) between the automatic and manual labelled results was 0.895 ± 0.022. For the network training, the DSC, precision rate, and recall rate were found to be 0.897 ± 0.061, 0.901 ± 0.068 and 0.899 ± 0.086, respectively. The volumes and FF values of iBAT in HFD rats were higher than ND rats, while the FF decrease was larger in ND rats after NE injection. CONCLUSION: An automatic iBAT segmentation method for rats was successfully developed using the dynamic labelled FF images of activated BAT and deep learning network.

From clinical variables to multiomics analysis: a margin morphology-based gross classification system for hepatocellular carcinoma stratification.

OBJECTIVE: Selecting interventions for patients with solitary hepatocellular carcinoma (HCC) remains a challenge. Despite gross classification being proposed as a potential prognostic predictor, its widespread use has been restricted due to inadequate studies with sufficient patient numbers and the lack of established mechanisms. We sought to investigate the prognostic impacts on patients with HCC of different gross subtypes and assess their corresponding molecular landscapes. DESIGN: A prospective cohort of 400 patients who underwent hepatic resection for solitary HCC was reviewed and analysed and gross classification was assessed. Multiomics analyses were performed on tumours and non-tumour tissues from 49 patients to investigate the mechanisms underlying gross classification. Inverse probability of treatment weight (IPTW) was used to control for confounding factors. RESULTS: Overall 3-year survival rates varied significantly among the four gross subtypes (type I: 91%, type II: 80%, type III: 74.6%, type IV: 38.8%). Type IV was found to be independently associated with poor prognosis in both the entire cohort and the IPTW cohort. The four gross subtypes exhibited three distinct transcriptional modules. Particularly, type IV tumours exhibited increased angiogenesis and immune score as well as decreased metabolic pathways, together with highest frequency of TP53 mutations. Patients with type IV HCC may benefit from adjuvant intra-arterial therapy other than the other three subtypes. Accordingly, a modified trichotomous margin morphological gross classification was established. CONCLUSION: Different gross types of HCC showed significantly different prognosis and molecular characteristics. Gross classification may aid in development of precise individualised diagnosis and treatment strategies for HCC.

MRI-Based Artificial Intelligence in Rectal Cancer.

Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Radiomics nomogram for the prediction of microvascular invasion of HCC and patients' benefit from postoperative adjuvant TACE: a multi-center study.

OBJECTIVES: To evaluate the performance of a radiomics nomogram developed based on gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA) MRI for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization (PA-TACE). METHODS: A total of 260 eligible patients were retrospectively enrolled from three hospitals (140, 65, and 55 in training, standardized external, and non-standardized external validation cohort). Radiomics features and image characteristics were extracted from Gd-EOB-DTPA MRI image before hepatectomy for each lesion. In the training cohort, a radiomics nomogram which incorporated the radiomics signature and radiological predictors was developed. The performance of the radiomics nomogram was assessed with respect to discrimination calibration, and clinical usefulness with external validation. A score (m-score) was constructed to stratify the patients and explored whether it could accurately predict patient who benefit from PA-TACE. RESULTS: A radiomics nomogram integrated with the radiomics signature, max-D(iameter) > 5.1 cm, peritumoral low intensity (PTLI), incomplete capsule, and irregular morphology had favorable discrimination in the training cohort (AUC = 0.982), the standardized external validation cohort (AUC = 0.969), and the non-standardized external validation cohort (AUC = 0.981). Decision curve analysis confirmed the clinical usefulness of the novel radiomics nomogram. The log-rank test revealed that PA-TACE significantly decreased the early recurrence in the high-risk group (p = 0.006) with no significant effect in the low-risk group (p = 0.270). CONCLUSIONS: The novel radiomics nomogram combining the radiomics signature and clinical radiological features achieved preoperative non-invasive MVI risk prediction and patient benefit assessment after PA-TACE, which may help clinicians implement more appropriate interventions. CLINICAL RELEVANCE STATEMENT: Our radiomics nomogram could represent a novel biomarker to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization, which may help clinicians to implement more appropriate interventions and perform individualized precision therapies. KEY POINTS: • The novel radiomics nomogram developed based on Gd-EOB-DTPA MRI achieved preoperative non-invasive MVI risk prediction. • An m-score based on the radiomics nomogram could stratify HCC patients and further identify individuals who may benefit from the PA-TACE. • The radiomics nomogram could help clinicians to implement more appropriate interventions and perform individualized precision therapies.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Application of postmortem MRI for identification of medulla oblongata contusion as a cause of death: a case report.

Whiplash injury is common in traffic accidents, and severe whiplash is characterized by cervical spinal cord injuries with cervical dislocation or fracture, that can be diagnosed by postmortem computed tomography (PMCT), postmortem magnetic resonance (PMMR), or conventional autopsy. However, for cervical spinal cord injury without fracture and dislocation, PMMR can be more informative because it provides higher resolution of soft tissues. We report the case of a 29-year-old male who died immediately following a traffic accident, in which the vehicle hit an obstacle at a high speed, causing deformation of the bumper and severe damage of the vehicle body. PMCT indicated no significant injuries or diseases related to death, but PMMR showed patchy abnormal signals in the medulla oblongata, and the lower edge of the cerebellar tonsil was herniated out of the foramen magnum. The subsequent pathological and histological results confirmed that death was caused by medulla oblongata contusion combined with cerebellar tonsillar herniation. Our description of this case of a rare but fatal whiplash injury in which there was no fracture or dislocation provides a better understanding of the potentially fatal consequences of cervical spinal cord whiplash injury without fracture or dislocation and of the underlying lethal mechanisms. Compared with PMCT, PMMR provides important diagnostic information in forensic practice for the identification of soft tissue injuries, and is therefore an important imaging modality for diagnosis of whiplash injury when there is no fracture or dislocation.

Automatic segmentation of human supraclavicular adipose tissue using high-resolution T2-weighted magnetic resonance imaging.

OBJECTIVE: To achieve efficient segmentation of human supraclavicular adipose tissue (sclavAT) using high-resolution T2-weighted magnetic resonance images. METHODS: High-resolution 1.0 mm isotropic 3D T2-weighted images covering human supraclavicular area were acquired in transverse or coronary plane from 29 volunteers using a 3.0 T MRI scanner. There were typically 144/288 slices for the transverse/coronary scans for each subject, which amounts to a total of 6816 images in 29 volunteers. A U-NET network was trained to segment the supraclavicular adipose tissue (sclavAT). The performance of the automatic segmentation method was evaluated by comparing the output results with the manual labels using the quantitative indices of dice similarity coefficient (DSC), precision rate (PR), and recall rate (RR). The auto-segmented images were used to calculate the sclavAT volumes and registered to the MR fat fraction (FF) images to quantify the fat component of the sclavAT area. The relationship between body mass index (BMI), the volume and FF of sclavAT area was evaluated for all subjects. RESULTS: The DSC, PR and RR of the automatic sclavAT segmentation method on the testing datasets were 0.920 ± 0.048, 0.915 ± 0.070 and 0.930 ± 0.058. The volume and the mean FF of sclavAT were both found to be strongly correlated to BMI, with the correlation coefficient of 0.703 and 0.625 (p < 0.05), respectively. The averaged computation time of the automatic segmentation method was approximately 0.06 s per slice, compared to more than 5 min for manual labeling. CONCLUSION: The present study demonstrates that the proposed automatic segmentation method using U-Net network is able to identify human sclavAT efficiently and accurately.

Upconversion nanoparticle-based fluorescence resonance energy transfer sensing platform for the detection of cathepsin B activity in vitro and in vivo.

A simple fluorescence resonance energy transfer (FRET) sensing platform (termed as USP), comprised of upconversion nanoparticles (UCNPs) as the energy donor and Cy5 as the energy acceptor, has been synthesized for cathepsin B (CTSB) activity detection in vitro and in vivo. When Cy5-modified peptide substrate (peptide-Cy5) of CTSB is covalently linked on the surface of UCNPs, the FRET between the UCNPs (excitation: 980 nm; emission: 541 nm/655 nm) and Cy5 (excitation: 645 nm) leads to a reduction in the red upconversion luminescence (UCL) signal intensity of UCNPs. Cy5 can be liberated from UCNPs in the presence of CTSB through the cleavage of peptide-Cy5 by CTSB, leading to the recovery of the red UCL signal of UCNPs. Because the green UCL signal of UCNPs remains constant during the CTSB digestion, it can be considered as an internal reference. The findings demonstrate the ability of USP to detect CTSB with the linear detection ranges of 1 to 100 ng·mL-1 in buffer and 2 × 103 to 1 × 105 cells in 0.2 mL cell lysates. The limits of detection (LODs) are 0.30 ng·mL-1 in buffer and 887 cells in 0.2 mL of cell lysates (S/N = 3). The viability of USP to detect CTSB activity in tumor-bearing mice is has further been investigated using in vivo fluorescent imaging.

Diagnosis of Acute Pancreatitis Using Postmortem Computed Tomography and Postmortem Magnetic Resonance Imaging: A Case Report and a Review of Literature.

ABSTRACT: Acute pancreatitis (AP) is inflammation of the pancreas, which may be due to a wide variety of etiologies that share a final common pathway of premature activation of pancreatic enzymes and resultant autodigestion of pancreatic parenchyma. Acute pancreatitis is easy to diagnose clinically, with the presence of at least 2 of the 3 criteria (upper abdominal pain, serum amylase or lipase level greater than 3 times the upper limit of normal, or characteristic findings on imaging studies) of the revised Atlanta classification. However, postmortem imaging examinations of pancreatitis are extremely rare, and very few successful cases have been reported. Here, we present a case report of a single patient who underwent autopsy and postmortem imaging. Postmortem computed tomography (PMCT) and postmortem magnetic resonance imaging (PMMRI) showed peripancreatic inflammation and acute peripancreatic fluid collection in the left anterior pararenal space, which is consistent with the examination by autopsy. The advantages of PMMRI in AP have also been demonstrated. Our study also confirmed the advantage of PMCT angiography in the diagnosis of AP. To the best of our knowledge, this is the first report of PMCT and PMMRI combined with postmortem pathology in the diagnosis of AP.

A PET/CT nomogram incorporating SUVmax and CT radiomics for preoperative nodal staging in non-small cell lung cancer.

OBJECTIVES: To develop and validate a PET/CT nomogram for preoperative estimation of lymph node (LN) staging in patients with non-small cell lung cancer (NSCLC). METHODS: A total of 263 pathologically confirmed LNs from 124 patients with NCSLC were retrospectively analyzed. Positron-emission tomography/computed tomography (PET/CT) examination was performed before treatment according to the clinical schedule. In the training cohort (N = 185), malignancy-related features, such as SUVmax, short-axis diameter (SAD), and CT radiomics features, were extracted from the regions of LN based on the PET/CT scan. The Minimum-Redundancy Maximum-Relevance (mRMR) algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model were used for feature selection and radiomics score building. The radiomics score (Rad-Score) and SUVmax were incorporated in a PET/CT nomogram using the multivariable logistic regression analysis. The performance of the proposed model was evaluated with discrimination, calibration, and clinical application in an independent testing cohort (N = 78). RESULTS: The radiomics scores consisting of 14 selected features were significantly associated with LN status for both training cohort with AUC of 0.849 (95% confidence interval (CI), 0.796-0.903) and testing cohort with AUC of 0.828 (95% CI, 0.782-0.919). The PET/CT nomogram incorporating radiomics score and SUVmax showed moderate improvement of the efficiency with AUC of 0.881 (95% CI, 0.834-0.928) in the training cohort and AUC of 0.872 (95% CI, 0.797-0.946) in the testing cohort. The decision curve analysis indicated that the PET/CT nomogram was clinically useful. CONCLUSION: The PET/CT nomogram, which incorporates Rad-Score and SUVmax, can improve the diagnostic performance of LN metastasis. KEY POINTS: • The PET/CT nomogram (Int-Score) based on lymph node (LN) PET/CT images can reliably predict LN status in NSCLC. • Int-Score is a relatively objective diagnostic method, which can play an auxiliary role in the process of clinicians making treatment decisions.

Distortion correction of single-shot EPI enabled by deep-learning.

PURPOSE: A distortion correction method for single-shot EPI was proposed. Point-spread-function encoded EPI (PSF-EPI) images were used as the references to correct traditional EPI images based on deep neural network. THEORY AND METHODS: The PSF-EPI method can obtain distortion-free echo planar images. In this study, a 2D U-net based network was trained to achieve the distortion correction of single-shot EPI (SS-EPI) images, using PSF-EPI images as targets in the training stage. Anatomical T2W-TSE images were also fed into the network to improve the quality of the results. The applications in diffusion-weighted images were used as examples in this work. The network was trained on data acquired on healthy volunteers and tested on data of both healthy volunteers and patients. The corrected EPI images from the proposed method were also compared with those from field-mapping and top-up based distortion correction methods. RESULTS: Experimental results showed that the proposed method can correct for EPI distortions better than both the field-mapping and top-up based methods, and the results were close to the distortion-free images from PSF-EPI. Additionally, inclusion of T2W-TSE images helped improve distortion correction of the SS-EPI images without contaminating the output noticeably. The experiments with patients and different MRI platforms demonstrated the generalization feasibility of the proposed method preliminarily. CONCLUSION: Through the correction of diffusion-weighted images, the proposed deep-learning based method was demonstrated to have the feasibility to correct for the distortion of EPI images.

Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging.

BACKGROUND: The detection of Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network (ResNet) to estimate the KRAS mutation status in CRC patients based on pre-treatment contrast-enhanced CT imaging. METHODS: We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were divided into a training cohort (n = 117) and a testing cohort (n = 40). We developed an ResNet model that used portal venous phase CT images to estimate KRAS mutations in the axial, coronal, and sagittal directions of the training cohort and evaluated the model in the testing cohort. Several groups of expended region of interest (ROI) patches were generated for the ResNet model, to explore whether tissues around the tumor can contribute to cancer assessment. We also explored a radiomics model with the random forest classifier (RFC) to predict KRAS mutations and compared it with the DL model. RESULTS: The ResNet model in the axial direction achieved the higher area under the curve (AUC) value (0.90) in the testing cohort and peaked at 0.93 with an input of 'ROI and 20-pixel' surrounding area. AUC of radiomics model in testing cohorts were 0.818. In comparison, the ResNet model showed better predictive ability. CONCLUSIONS: Our experiments reveal that the computerized assessment of the pre-treatment CT images of CRC patients using a DL model has the potential to precisely predict KRAS mutations. This new model has the potential to assist in noninvasive KRAS mutation estimation.

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.

Renal Clearable Peptide Functionalized NaGdF4 Nanodots for High-Efficiency Tracking Orthotopic Colorectal Tumor in Mouse.

The effective delivery of bioimaging probes to a selected cancerous tissue has extensive significance for biological studies and clinical investigations. Herein, the peptide functionalized NaGdF4 nanodots (termed as, pPeptide-NaGdF4 nanodots) have been prepared for highly efficient magnetic resonance imaging (MRI) of tumor by formation of Gd-phosphonate coordinate bonds among hydrophobic NaGdF4 nanodots (4.2 nm in diameter) with mixed phosphorylated peptide ligands including a tumor targeting phosphopeptide and a cell penetrating phosphopeptide. The tumor targeting pPeptide-NaGdF4 nanodots have paramagnetic property with ultrasmall hydrodynamic diameter (HD, c.a., 7.3 nm) which greatly improves their MRI contrast ability of tumor and facilitates renal clearance. In detail, the capability of the pPeptide-NaGdF4 nanodots as high efficient contrast agent for in vivo MRI is evaluated successfully through tracking small drug induced orthotopic colorectal tumor (c.a., 195 mm3 in volume) in mouse.

A higher frequency of IL-10-producing B cells in Hepatitis B virus-associated membranous nephropathy.

The purpose of this study is to elucidate the potential role of interleukin (IL)-10(+) regulatory B cells and other B cell subsets in the development of hepatitis B virus-associated membranous nephropathy (HBV-MN). A total of 14 patients with new onset HBV-MN, 12 individuals with immune-tolerant HBV infection (HBV-IT), and 12 healthy controls (HC) were examined for the percentages of CD38(+) , CD86(+) , CD27(+) , CD95(+) and IL-10(+) B cells by flow cytometry. Serum IL-10 concentration was examined by enzyme-linked immunosorbent assay (ELISA). The percentages of CD38(+) CD19(+) , CD86(+) CD19(+) , CD38(+) CD86(+) CD19(+) , and CD95(+) CD19(+) B cells were significantly higher in HBV-MN patients than the HBV-IT and HC. The percentages of CD5(+) CD19(+) , IL-10(+) CD19(+) B cells and serum IL-10 level in HBV-MN patients were significantly higher than the HC, and lower than the HBV-IT. Percentages of CD38(+) CD19(+) , and CD86(+) CD19(+) B cells were reduced after treatment, while the percentages of CD5(+) CD1d(+) CD19(+) , CD5(+) CD1d(+) IL-10(+) CD19(+) , and IL-10(+) CD19(+) B cells were increased. The 24 h urinary protein concentration was positively correlated with the percentage of CD38(+) CD19(+) , and negatively correlated with the percentage of IL-10(+) CD19(+) B cells and serum IL-10 level. Similarly, the value of eGFR was negatively correlated with the percentage of CD38(+) CD19(+) , and positively correlated with the percentage of IL-10(+) CD19(+) B cells and serum IL-10 level. Serum IL-10 level and the percentage of IL-10(+) CD19(+) were negatively correlated with the percentages of CD38(+) CD19(+) , and CD86(+) CD19(+) B cells. These results suggest that CD86(+) CD19(+) , CD38(+) CD86(+) CD19(+) , CD95(+) CD19(+) , and especially CD38(+) CD19(+) and IL-10(+) CD19(+) cells may participate in the pathogenesis of HBV-MN.

The imbalance between Tregs, Th17 cells and inflammatory cytokines among renal transplant recipients.

BACKGROUND: A significant barrier to organ transplantation is the cellular rejection that occurs and mediated by antibodies, T cells, and innate immune cells. This study was aimed to determine the number of CD4(+)CD25(+)Foxp3(+) Treg, CD4(+)IFN-γ(-)IL-17(+) Th17, CD4(+)IFN-γ(+)IL-17(-) Th1 and CD4(+)IFN-γ(+)IL-17(+) Th1/17 cells in renal transplant recipients (RTR). METHODS: Renal transplantation was performed for a total of 35 patients with end-stage renal failure. The number of CD4(+)CD25(+)Foxp3(+) Treg, CD4(+)IFN-γ(-)IL-17(+) Th17, CD4(+)IFN-γ(+)IL-17(-) Th1 and CD4(+)IFN-γ(+)IL-17(+) Th1/17 cells, and the serum level of IFN-γ, TNF-α, IL-2, IL-4, IL-6, IL-10, and IL-17 were measured in pre- and post-transplant patients and 10 healthy controls (HC) using flow cytometry and Cytometric Bead Array (CBA). The association between the number of different subsets of CD4(+) T-cells and clinical parameters were analyzed among the pre- and post-transplant patients, and the healthy controls. RESULTS: The number of CD4(+)IFN-γ(-)IL-17(+) Th17, CD4(+)IFN-γ(+)IL-17(-) Th1 and CD4(+)IFN-γ(+)IL-17(+) Th1/17 cells were significantly increased in patients with End-Stage Renal Failure (ESRF) compared to the HC. Stratification analysis indicated that AMR (Acute antibody mediated acute rejection), AR (acute rejection) and CR (chronic rejection) groups displayed greater number of CD4(+)IFN-γ(-)IL-17(+) Th17, CD4(+)IFN-γ(+)IL-17(-) Th1 and CD4(+)IFN-γ(+)IL-17(+) Th1/17 cells as well as high level of serum IL-2, IFN-γ, TNF-α and IL-17. But, the AMR, AR and CR groups have shown lower level of CD4(+)CD25(+)Foxp3(+) T cells and serum IL-10 compared to transplant stable (TS) patients. Moreover, the number of Tregs were negatively correlated with the number of Th17 cells in RTR patients. The number of Tregs and Th17 cells were positively correlated with the eGFR and serum creatinine values, respectively. CONCLUSION: The imbalance between different types of CD4(+) T cells and dysregulated inflammatory cytokines may contribute towards renal transplantation rejection.

Employing Tryptone as a General Phase Transfer Agent to Produce Renal Clearable Nanodots for Bioimaging.

Hydrophobic ultrasmall nanoparticles synthesized in nonpolar solvents exhibit great potential in biomedical applications. However, a major challenge when applying these nanomaterials in biomedical research is the lack of a versatile strategy to render them water dispersible while preserving the hydrodynamic diameter (HD) to be less than 8 nm for efficient renal clearance. To address this problem, tryptone is employed as the novel ligand to fabricate a simple, versatile, and inexpensive strategy for transferring hydrophobic NaGdF(4) nanodots (3 nm in diameter) from organic phase into aqueous phase without any complicated organic synthesis. The paramagnetic properties of NaGdF(4) nanodots are well retained after the phase transfer process. In particular, the tryptone-NaGdF(4) nanodots have ultrasmall HD (ca., 7.5 nm), which greatly improves their tumor accumulation and facilitates renal clearance within 24 h postinjection. The as-prepared tryptone-NaGdF(4) nanodots can also be further functionalized with other molecules for extensively biomedical and bioanalytical applications. Furthermore, the proposed strategy can easily be extended to transfer other types of inorganic nanoparticles from hydrophobic to hydrophilic for facilitating biomedical applications.

Lectin-conjugated Fe2O3@Au core@Shell nanoparticles as dual mode contrast agents for in vivo detection of tumor.

Here, we report the covalent conjugation of lectin on Fe2O3@Au core@shell nanoparticle (lectin-Fe2O3@Au NP) for T2-weighted magnetic resonance (MR) and X-ray computed tomography (CT) dual-modality imaging. The lectin-Fe2O3@Au NPs are prepared by coupling lectins to the Fe2O3@Au NP surfaces through bifunctional PEG NHS ester disulfide (NHS-PEG-S-S-PEG-NHS) linkers. After the nonspecific adsorption sites on the nanoparticle surface are blocked by thiolated PEG (PEG-SH), the lectin-Fe2O3@Au NPs exhibit excellent stability in biological medium and inappreciable cytotoxicity. A series of in vitro and in vivo experiments were then carried out for evaluating the capabilities of three selected lectin (ConA, RCA and WGA)-Fe2O3@Au NPs. The results revealed that the lectin-Fe2O3@Au NPs had a capacity not only for dual mode MR and CT imaging in vitro but also for MR and CT imaging of colorectal cancer in vivo. The experimental results also suggest that lectin could be used as tumor targeting ligand for synthesizing nanoparticle-based contrast agents.

Preoperative detection of lymphovascular invasion in rectal cancer using intravoxel incoherent motion imaging based on radiomics.

BACKGROUND: Lymphovascular invasion (LVI) status plays an important role in treatment decision-making in rectal cancer (RC). Intravoxel incoherent motion (IVIM) imaging has been shown to detect LVI; however, making better use of IVIM data remains an important issue that needs to be discussed. PURPOSE: We proposed to explore the best way to use IVIM quantitative parameters and images to construct radiomics models for the noninvasive detection of LVI in RC. METHODS: A total of 83 patients (LVI negative (LVI-): LVI positive (LVI+) = 51:32) with postoperative pathology-confirmed LVI status in RC were divided into a training group (n = 58) and a validation group (n = 25). Images were acquired from a 3.0 Tesla machine, including oblique axial T2 weighted imaging (T2WI) and IVIM with 11 b values. The ADC, D, D* and f values were measured on IVIM maps. The ROIs of tumors were delineated on T2WI, DWI, ADCmap , and Dmap images, and three mapping methods were used: ROIs_mapping from DWI, ROIs_mapping from ADCmap , and ROIs_mapping from Dmap . Three-dimensional radiomics features were extracted from the delineated ROIs. Multivariate logistic regression was used for radiomics feature selection. Radiomics models based on different mapping methods were developed. Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were used to evaluate the performance of the models. RESULTS: Model B, which was constructed with radiomics features from ADCmap , Dmap and fmap by "ROIs_mapping from DWI" and T2WI (AUC 0.894), performed better than other models based on single sequence (AUC 0.600-0.806) and even better than Model A, which was based on "ROIs_mapping from ADC" and T2WI (AUC 0.838). Furthermore, an integrated model was constructed with Model B and the IVIM parameter (f value) with an AUC of 0.920 (95% CI: 0.820-1.000), which was higher than that of Model B, in the validation group. CONCLUSIONS: The integrated model incorporating the radiomics features and IVIM parameters accurately detected LVI of RC. The "ROIs_mapping from DWI" method provided the best results.