Abatacept for acute graft-versus-host disease prophylaxis after unrelated donor hematopoietic cell transplantation.

Abatacept plus calcineurin inhibitors/methotrexate (CNI/MTX) is the first FDA-approved regimen for acute graft-versus-host disease (aGVHD) prophylaxis during unrelated-donor hematopoietic cell transplantation (URD-HCT). We investigated its impact in URD-HCT patients using Center for International Blood and Marrow Transplant Research data for 7/8-human leukocyte antigen (HLA)-mismatched (MMUD) or 8/8-HLA-matched (MUD) URD-HCT recipients between 2011-2018. Primary outcomes included day-180, 1-year, and 2-year overall survival (OS) and relapse-free survival (RFS) for abatacept+CNI/MTX vs CNI/MTX, CNI/MTX+antithymocyte globulin (ATG), and post-transplant cyclophosphamide-based prophylaxis (PT-Cy); other outcomes included aGVHD, chronic GVHD, non-relapse mortality, and relapse. For 7/8-MMUDs, day-180 OS (primary endpoint supporting FDA approval) was significantly higher for abatacept+CNI/MTX vs CNI/MTX (98%vs75%; p=0.0028). Two-year OS was significantly higher for abatacept+CNI/MTX vs CNI/MTX (83%vs55%; p=0.0036), CNI/MTX+ATG (83%vs46%; p=0.0005) and similar to PT-Cy (80%vs68%; p=0.2325). Two-year RFS was significantly higher for abatacept+CNI/MTX vs CNI/MTX (74%vs49%; p=0.0098) and CNI/MTX+ATG (77%vs35%; p=0.0002), and similar vs PT-Cy (72%vs56%; p=0.1058). For 8/8-MUDs, 2-year OS was similar with abatacept+CNI/MTX vs CNI/MTX (70%vs62%; p=0.2569), CNI/MTX+ATG (75%vs64%; p=0.1048), and PT-Cy (74%vs69%; p=0.5543). Two-year RFS for abatacept+CNI/MTX was numerically higher vs CNI/MTX (63%vs52%; p=0.1497) with an improved hazard ratio (HR: 0.46 [0.25-0.86]), and vs CNI/MTX+ATG (66%vs55%; p=0.1193; HR: 0.39 [0.21-0.73]). Two-year RFS was similar vs PT-Cy (68%vs57%; p=0.2356; HR: 0.54 [0.26-1.11]). For both 7/8-MMUD and 8/8-MUD recipients, abatacept+CNI/MTX prophylaxis improved survival outcomes vs CNI/MTX and CNI/MTX+ATG; outcomes were similar to PT-Cy-based regimens. Abatacept+CNI/MTX has potential to facilitate unrelated donor pool expansion for HCT.

Evolving brain network dynamics in early childhood: Insights from modular graph metrics.

Modular dynamic graph theory metrics effectively capture the patterns of dynamic information interaction during human brain development. While existing research has employed modular algorithms to examine the overall impact of dynamic changes in community structure throughout development, there is a notable gap in understanding the cross-community dynamic changes within different functional networks during early childhood and their potential contributions to the efficiency of brain information transmission. This study seeks to address this gap by tracing the trajectories of cross-community structural changes within early childhood functional networks and modeling their contributions to information transmission efficiency. We analyzed 194 functional imaging scans from 83 children aged 2 to 8 years, who participated in passive viewing functional magnetic resonance imaging sessions. Utilizing sliding windows and modular algorithms, we evaluated three spatiotemporal metrics-temporal flexibility, spatiotemporal diversity, and within-community spatiotemporal diversity-and four centrality metrics: within-community degree centrality, eigenvector centrality, between-community degree centrality, and between-community eigenvector centrality. Mixed-effects linear models revealed significant age-related increases in the temporal flexibility of the default mode network (DMN), executive control network (ECN), and salience network (SN), indicating frequent adjustments in community structure within these networks during early childhood. Additionally, the spatiotemporal diversity of the SN also displayed significant age-related increases, highlighting its broad pattern of cross-community dynamic interactions. Conversely, within-community spatiotemporal diversity in the language network exhibited significant age-related decreases, reflecting the network's gradual functional specialization. Furthermore, our findings indicated significant age-related increases in between-community degree centrality across the DMN, ECN, SN, language network, and dorsal attention network, while between-community eigenvector centrality also increased significantly for the DMN, ECN, and SN. However, within-community eigenvector centrality remained stable across all functional networks during early childhood. These results suggest that while centrality of cross-community interactions in early childhood functional networks increases, centrality within communities remains stable. Finally, mediation analysis was conducted to explore the relationships between age, brain dynamic graph metrics, and both global and local efficiency based on community structure. The results indicated that the dynamic graph metrics of the SN primarily mediated the relationship between age and the decrease in global efficiency, while those of the DMN, language network, ECN, dorsal attention network, and SN primarily mediated the relationship between age and the increase in local efficiency. This pattern suggests a developmental trajectory in early childhood from global information integration to local information segregation, with the SN playing a pivotal role in this transformation. This study provides novel insights into the mechanisms by which early childhood brain functional development impacts information transmission efficiency through cross-community adjustments in functional networks.

MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence.

PURPOSE: This study aims to develop and evaluate a novel cardiovascular MR sequence, MyoFold, designed for the simultaneous quantifications of myocardial tissue composition and wall motion. METHODS: MyoFold is designed as a 2D single breathing-holding sequence, integrating joint T1/T2 mapping and cine imaging. The sequence uses a 2-fold accelerated balanced SSFP (bSSFP) for data readout and incorporates electrocardiogram synchronization to align with the cardiac cycle. MyoFold initially acquires six single-shot inversion-recovery images, completed during the diastole of six successive heartbeats. T2 preparation (T2-prep) is applied to introduce T2 weightings for the last three images. Subsequently, over the following six heartbeats, segmented bSSFP is performed for the movie of the entire cardiac cycle, synchronized with an electrocardiogram. A neural network trained using numerical simulations of MyoFold is used for T1 and T2 calculations. MyoFold was validated through phantom and in vivo experiments, with comparisons made against MOLLI, SASHA, T2-prep bSSFP, and the conventional cine. RESULTS: In phantom studies, MyoFold exhibited a 10% overestimation in T1 measurements, whereas T2 measurements demonstrated high accuracy. In vivo experiments revealed that MyoFold T1 had comparable accuracy to SASHA and precision similar to MOLLI. MyoFold demonstrated good agreement with T2-prep bSSFP in myocardial T2 measurements. No significant differences were observed in the quantification of left-ventricle wall thickness and function between MyoFold and the conventional cine. CONCLUSION: MyoFold presents as a rapid, simple, and multitasking approach for quantitative cardiovascular MR examinations, offering simultaneous assessment of tissue composition and wall motion. The sequence's multitasking capabilities make it a promising tool for comprehensive cardiac evaluations in clinical settings.

DeepFittingNet: A deep neural network-based approach for simplifying cardiac T1 and T2 estimation with improved robustness.

PURPOSE: To develop and evaluate a deep neural network (DeepFittingNet) for T1 /T2 estimation of the most commonly used cardiovascular MR mapping sequences to simplify data processing and improve robustness. THEORY AND METHODS: DeepFittingNet is a 1D neural network composed of a recurrent neural network (RNN) and a fully connected (FCNN) neural network, in which RNN adapts to the different number of input signals from various sequences and FCNN subsequently predicts A, B, and Tx of a three-parameter model. DeepFittingNet was trained using Bloch-equation simulations of MOLLI and saturation-recovery single-shot acquisition (SASHA) T1 mapping sequences, and T2 -prepared balanced SSFP (T2 -prep bSSFP) T2 mapping sequence, with reference values from the curve-fitting method. Several imaging confounders were simulated to improve robustness. The trained DeepFittingNet was tested using phantom and in-vivo signals, and compared to the curve-fitting algorithm. RESULTS: In testing, DeepFittingNet performed T1 /T2 estimation of four sequences with improved robustness in inversion-recovery T1 estimation. The mean bias in phantom T1 and T2 between the curve-fitting and DeepFittingNet was smaller than 30 and 1 ms, respectively. Excellent agreements between both methods was found in the left ventricle and septum T1 /T2 with a mean bias <6 ms. There was no significant difference in the SD of both the left ventricle and septum T1 /T2 between the two methods. CONCLUSION: DeepFittingNet trained with simulations of MOLLI, SASHA, and T2 -prep bSSFP performed T1 /T2 estimation tasks for all these most used sequences. Compared with the curve-fitting algorithm, DeepFittingNet improved the robustness for inversion-recovery T1 estimation and had comparable performance in terms of accuracy and precision.

Evaluation of parametrial infiltration in patients with IB-IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in 18 F-fluorodeoxy glucose positron emission tomography/MR images.

Parametrial infiltration (PMI) is an essential factor in staging and planning treatment of cervical cancer. The purpose of this study was to develop a radiomics model for accessing PMI in patients with IB-IIB cervical cancer using features from 18 F-fluorodeoxy glucose (18 F-FDG) positron emission tomography (PET)/MR images. In this retrospective study, 66 patients with International Federation of Gynecology and Obstetrics stage IB-IIB cervical cancer (22 with PMI and 44 without PMI) who underwent 18 F-FDG PET/MRI were divided into a training dataset (n = 46) and a testing dataset (n = 20). Features were extracted from both the tumoral and peritumoral regions in 18 F-FDG PET/MR images. Single-modality and multimodality radiomics models were developed with random forest to predict PMI. The performance of the models was evaluated with F1 score, accuracy, and area under the curve (AUC). The Kappa test was used to observe the differences between PMI evaluated by radiomics-based models and pathological results. The intraclass correlation coefficient for features extracted from each region of interest (ROI) was measured. Three-fold crossvalidation was conducted to confirm the diagnostic ability of the features. The radiomics models developed by features from the tumoral region in T2 -weighted images (F1 score = 0.400, accuracy = 0.700, AUC = 0.708, Kappa = 0.211, p = 0.329) and the peritumoral region in PET images (F1 score = 0.533, accuracy = 0.650, AUC = 0.714, Kappa = 0.271, p = 0.202) achieved the best performances in the testing dataset among the four single-ROI radiomics models. The combined model using features from the tumoral region in T2 -weighted images and the peritumoral region in PET images achieved the best performance (F1 score = 0.727, accuracy = 0.850, AUC = 0.774, Kappa = 0.625, p < 0.05). The results suggest that 18 F-FDG PET/MRI can provide complementary information regarding cervical cancer. The radiomics-based method integrating features from the tumoral and peritumoral regions in 18 F-FDG PET/MR images gave a superior performance for evaluating PMI.

Cognitive Challenges Are Better in Distinguishing Binge From Nonbinge Drinkers: An Exploratory Deep-Learning Study of fMRI Data of Multiple Behavioral Tasks and Resting State.

BACKGROUND: Studies have identified imaging markers of binge drinking. Functional connectivity during both task challenges and resting state was shown to distinguish binge and nonbinge drinkers. However, no studies have compared the efficacy of task and resting data in the classification. HYPOTHESIS: Task outperforms resting-state functional magnetic resonance imaging (fMRI) data in the differentiation of binge and nonbinge drinkers. We tested the hypothesis via multiple deep learning algorithms. STUDY TYPE: Cross-sectional; retrospective. POPULATION: A total of 149 binge (107 men) and 151 demographically matched, nonbinge (92 men) drinkers curated from the Human Connectome Project, with 80% randomly selected for model development and 20% for validation/test. FIELD STRENGTH/SEQUENCE: A 3 T; fMRI with a blood oxygen level-dependent (BOLD) gradient-echo echo-planar sequence. ASSESSMENT: FMRI data of resting state and seven behavioral tasks were acquired. Graph convolutional network (GCN), long short-term memory, convolutional, and recurrent neural network models were built to distinguish bingers and nonbingers using connectivity matrices of 8, 116, and 268 regions of interest (ROI). Nodal metrics including betweenness centrality, degree centrality, clustering coefficient, efficiency, local efficiency, and shortest path length were calculated from the GCN model. STATISTICAL TESTS: Model performance was quantified by the area under the curve (AUC) in receiver operating characteristic analysis. A P value < 0.05 was considered statistically significant. RESULTS: Task outperformed resting data in classification by approximately 8% by AUC in the test set. Across models and ROI sets, the gambling, motor, language and working memory tasks, each with AUC of 0.614, 0.612, 0.605, and 0.603, performed better than resting data (AUC = 0.548). Models with 116 ROIs (AUC = 0.602) consistently outperformed those with 8 ROIs (AUC = 0.569). Task data performed best with GCN (AUC = 0.619). Nodal metrics of left supplementary motor area and right cuneus showed significant group main effect across tasks. CONCLUSION: Neural responses to cognitive challenges relative to resting state better characterize binge drinking. The performance of different network models may depend on behavioral tasks and the number of ROIs. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline.

OBJECTIVES: This study aims at a fully automatic pipeline for measuring the magnetic resonance parkinsonism index (MRPI) using deep learning methods. METHODS: MRPI is defined as the product of the pons area to the midbrain area ratio and the middle cerebellar peduncle (MCP) width to the superior cerebellar peduncle (SCP) width ratio. In our proposed pipeline, we first used nnUNet to segment the brainstem and then employed HRNet to identify two key boundary points so as to sub-divide the whole brainstem into midbrain and pons. HRNet was also employed to predict the MCP endpoints for measuring the MCP width. Finally, we segmented the SCP on an oblique coronal plane and calculated its width. A total of 400 T1-weighted magnetic resonance images (MRIs) were used to train the nnUNet and HRNet models. Five-fold cross-validation was conducted to evaluate our proposed pipeline's performance on the training dataset. We also evaluated the performance of our proposed pipeline on three external datasets. Two of them had two raters manually measuring the MRPI values, providing insights into automatic accuracy versus inter-rater variability. RESULTS: We obtained average absolute percentage errors (APEs) of 17.21%, 18.17%, 20.83%, and 22.83% on the training dataset and the three external validation datasets, while the inter-rater average APE measured on the first two external validation datasets was 11.31%. Our proposed pipeline significantly improved the MRPI quantification accuracy over a representative state-of-the-art traditional approach (p < 0.001). CONCLUSION: The proposed automatic pipeline can accurately predict MRPI that is comparable with manual measurement. CLINICAL RELEVANCE STATEMENT: This study presents an automated magnetic resonance parkinsonism index measurement tool that can analyze large amounts of magnetic resonance images, enhance the efficiency of Parkinsonism-Plus syndrome diagnosis, reduce the workload of clinicians, and minimize the impact of human factors on diagnosis. KEY POINTS: • We propose an automatic pipeline for measuring the magnetic resonance parkinsonism index from magnetic resonance images. • The effectiveness of the proposed pipeline is successfully established on multiple datasets and comparisons with inter-rater measurements. • The proposed pipeline significantly outperforms a state-of-the-art quantification approach, being much closer to ground truth.

Near-Infrared-Absorbing B-N Lewis Pair-Functionalized Anthracenes: Electronic Structure Tuning, Conformational Isomerism, and Applications in Photothermal Cancer Therapy.

B-N-fused dianthracenylpyrazine derivatives are synthesized to generate new low gap chromophores. Photophysical and electrochemical, crystal packing, and theoretical studies have been performed. Two energetically similar conformers are identified by density functional theory calculations, showing that the core unit adopts a curved saddle-like shape (x-isomer) or a zig-zag conformation (z-isomer). In the solid state, the z-isomer is prevalent according to an X-ray crystal structure of a C6F5-substituted derivative (4-Pf), but variable-temperature nuclear magnetic resonance studies suggest a dynamic behavior in solution. B-N fusion results in a large decrease of the HOMO-LUMO gap and dramatically lowers the LUMO energy compared to the all-carbon analogues. 4-Pf in particular shows significant absorbance at greater than 700 nm while being almost transparent throughout the visible region. After encapsulation in the biodegradable polymer DSPE-mPEG2000, 4-Pf nanoparticles (4-Pf-NPs) exhibit good water solubility, high photostability, and an excellent photothermal conversion efficiency of ∼41.8%. 4-Pf-NPs are evaluated both in vitro and in vivo as photothermal therapeutic agents. These results uncover B-N Lewis pair functionalization of PAHs as a promising strategy toward new NIR-absorbing materials for photothermal applications.

MOF derived core-shell CuO/C with temperature-controlled oxygen-vacancy for real time analysis of glucose.

Introducing oxygen-vacancy into the surface of the non-enzymatic sensor is supposed to be an effective way to improve inherently low catalytic activity and specificity of non-enzymatic sensors. In this work, CuO/C was synthesized at different temperatures using metal-organic frameworks as sacrificial templates to receive additional content of oxygen-vacancy. The product with the highest oxygen vacancy was found at 400 °C (named CuO/C-400 °C), which increased catalytically active sites and enhanced the charge-transfer efficiency. The sensing performance was afterward explored by amperometry under an optimal applied potential at 0.5 V (vs. SCE), presenting a broad detection range from 5.0 µM to 25.325 mM (R2 = 0.9998) with a sensitivity of 244.71 µA mM- 1 cm- 2, and a detection limit of 1 µM. Furthermore, the reliability and selectivity of CuO/C-400 °C sensors were extensively explored in the presence of artificial serum/saliva samples with gradient glucose concentrations. The human blood samples were also detected with high recoveries compared with the clinical Hexokinase method. Hence, the prepared CuO/C-400 °C sensor with a broad detection range and high selectivity can be applied for the diabetes diagnosis ex vivo without further dilution for real-time analysis in practical applications.

Classification of gastric cancerous tissues by a residual network based on optical coherence tomography images.

Optical coherence tomography (OCT) is a noninvasive, radiation-free, and high-resolution imaging technology. The intraoperative classification of normal and cancerous tissue is critical for surgeons to guide surgical operations. Accurate classification of gastric cancerous OCT images is beneficial to improve the effect of surgical treatment based on the deep learning method. The OCT system was used to collect images of cancerous tissues removed from patients. An intelligent classification method of gastric cancerous tissues based on the residual network is proposed in this study and optimized with the ResNet18 model. Four residual blocks are used to reset the model structure of ResNet18 and reduce the number of network layers to identify cancerous tissues. The model performance of different residual networks is evaluated by accuracy, precision, recall, specificity, F1 value, ROC curve, and model parameters. The classification accuracies of the proposed method and ResNet18 both reach 99.90%. Also, the model parameters of the proposed method are 44% of ResNet18, which occupies fewer system resources and is more efficient. In this study, the proposed deep learning method was used to automatically recognize OCT images of gastric cancerous tissue. This artificial intelligence method could help promote the clinical application of gastric cancerous tissue classification in the future.

Inversion of Nitrogen Concentration in Apple Canopy Based on UAV Hyperspectral Images.

As the major nutrient affecting crop growth, accurate assessing of nitrogen (N) is crucial to precise agricultural management. Although improvements based on ground and satellite data nitrogen in monitoring crops have been made, the application of these technologies is limited by expensive costs, covering small spatial scales and low spatiotemporal resolution. This study strived to explore an effective approach for inversing and mapping the distributions of the canopy nitrogen concentration (CNC) based on Unmanned Aerial Vehicle (UAV) hyperspectral image data in a typical apple orchard area of China. A Cubert UHD185 imaging spectrometer mounted on a UAV was used to obtain the hyperspectral images of the apple canopy. The range of the apple canopy was determined by the threshold method to eliminate the effect of the background spectrum from bare soil and shadow. We analyzed and screened out the spectral parameters sensitive to CNC, including vegetation indices (VIs), random two-band spectral indices, and red-edge parameters. The partial least squares regression (PLSR) and backpropagation neural network (BPNN) were constructed to inverse CNC based on a single spectral parameter or a combination of multiple spectral parameters. The results show that when the thresholds of normalized difference vegetation index (NDVI) and normalized difference canopy shadow index (NDCSI) were set to 0.65 and 0.45, respectively, the canopy's CNC range could be effectively identified and extracted, which was more refined than random forest classifier (RFC); the correlation between random two-band spectral indices and nitrogen concentration was stronger than that of other spectral parameters; and the BPNN model based on the combination of random two-band spectral indices and red-edge parameters was the optimal model for accurately retrieving CNC. Its modeling determination coefficient (R2) and root mean square error (RMSE) were 0.77 and 0.16, respectively; and the validation R2 and residual predictive deviation (RPD) were 0.75 and 1.92. The findings of this study can provide a theoretical basis and technical support for the large-scale, rapid, and non-destructive monitoring of apple nutritional status.

Molecular Contrast Optical Coherence Tomography and Its Applications in Medicine.

The growing need to understand the molecular mechanisms of diseases has prompted the revolution in molecular imaging techniques along with nanomedicine development. Conventional optical coherence tomography (OCT) is a low-cost in vivo imaging modality that provides unique high spatial and temporal resolution anatomic images but little molecular information. However, given the widespread adoption of OCT in research and clinical practice, its robust molecular imaging extensions are strongly desired to combine with anatomical images. A range of relevant approaches has been reported already. In this article, we review the recent advances of molecular contrast OCT imaging techniques, the corresponding contrast agents, especially the nanoparticle-based ones, and their applications. We also summarize the properties, design criteria, merit, and demerit of those contrast agents. In the end, the prospects and challenges for further research and development in this field are outlined.

Automatic Segmentation of Laser-Induced Injury OCT Images Based on a Deep Neural Network Model.

Optical coherence tomography (OCT) has considerable application potential in noninvasive diagnosis and disease monitoring. Skin diseases, such as basal cell carcinoma (BCC), are destructive; hence, quantitative segmentation of the skin is very important for early diagnosis and treatment. Deep neural networks have been widely used in the boundary recognition and segmentation of diseased areas in medical images. Research on OCT skin segmentation and laser-induced skin damage segmentation based on deep neural networks is still in its infancy. Here, a segmentation and quantitative analysis pipeline of laser skin injury and skin stratification based on a deep neural network model is proposed. Based on the stratification of mouse skins, a laser injury model of mouse skins induced by lasers was constructed, and the multilayer structure and injury areas were accurately segmented by using a deep neural network method. First, the intact area of mouse skin and the damaged areas of different laser radiation doses are collected by the OCT system, and then the labels are manually labeled by experienced histologists. A variety of deep neural network models are used to realize the segmentation of skin layers and damaged areas on the skin dataset. In particular, the U-Net model based on a dual attention mechanism is used to realize the segmentation of the laser-damage structure, and the results are compared and analyzed. The segmentation results showed that the Dice coefficient of the mouse dermis layer and injury area reached more than 0.90, and the Dice coefficient of the fat layer and muscle layer reached more than 0.80. In the evaluation results, the average surface distance (ASSD) and Hausdorff distance (HD) indicated that the segmentation results are excellent, with a high overlap rate with the manually labeled area and a short edge distance. The results of this study have important application value for the quantitative analysis of laser-induced skin injury and the exploration of laser biological effects and have potential application value for the early noninvasive detection of diseases and the monitoring of postoperative recovery in the future.

Recent Advance in Biological Responsive Nanomaterials for Biosensing and Molecular Imaging Application.

In recent decades, as a subclass of biomaterials, biologically sensitive nanoparticles have attracted increased scientific interest. Many of the demands for physiologically responsive nanomaterials in applications involving the human body cannot be met by conventional technologies. Due to the field's importance, considerable effort has been expended, and biologically responsive nanomaterials have achieved remarkable success thus far. This review summarizes the recent advancements in biologically responsive nanomaterials and their applications in biosensing and molecular imaging. The nanomaterials change their structure or increase the chemical reaction ratio in response to specific bio-relevant stimuli (such as pH, redox potentials, enzyme kinds, and concentrations) in order to improve the signal for biologically responsive diagnosis. We use various case studies to illustrate the existing issues and provide a clear sense of direction in this area. Furthermore, the limitations and prospects of these nanomaterials for diagnosis are also discussed.

Reporter Genes for Brain Imaging Using MRI, SPECT and PET.

The use of molecular imaging technologies for brain imaging can not only play an important supporting role in disease diagnosis and treatment but can also be used to deeply study brain functions. Recently, with the support of reporter gene technology, optical imaging has achieved a breakthrough in brain function studies at the molecular level. Reporter gene technology based on traditional clinical imaging modalities is also expanding. By benefiting from the deeper imaging depths and wider imaging ranges now possible, these methods have led to breakthroughs in preclinical and clinical research. This article focuses on the applications of magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) reporter gene technologies for use in brain imaging. The tracking of cell therapies and gene therapies is the most successful and widely used application of these techniques. Meanwhile, breakthroughs have been achieved in the research and development of reporter genes and their imaging probe pairs with respect to brain function research. This paper introduces the imaging principles and classifications of the reporter gene technologies of these imaging modalities, lists the relevant brain imaging applications, reviews their characteristics, and discusses the opportunities and challenges faced by clinical imaging modalities based on reporter gene technology. The conclusion is provided in the last section.

The Design of Abnormal Microenvironment Responsive MRI Nanoprobe and Its Application.

Magnetic resonance imaging (MRI) is often used to diagnose diseases due to its high spatial, temporal and soft tissue resolution. Frequently, probes or contrast agents are used to enhance the contrast in MRI to improve diagnostic accuracy. With the development of molecular imaging techniques, molecular MRI can be used to obtain 3D anatomical structure, physiology, pathology, and other relevant information regarding the lesion, which can provide an important reference for the accurate diagnosis and treatment of the disease in the early stages. Among existing contrast agents, smart or activatable nanoprobes can respond to selective stimuli, such as proving the presence of acidic pH, active enzymes, or reducing environments. The recently developed environment-responsive or smart MRI nanoprobes can specifically target cells based on differences in the cellular environment and improve the contrast between diseased tissues and normal tissues. Here, we review the design and application of these environment-responsive MRI nanoprobes.

A Brief History and Future Prospects of CEST MRI in Clinical Non-Brain Tumor Imaging.

Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging tool which allows the specific detection of metabolites that contain exchangeable amide, amine, and hydroxyl protons. Decades of development have progressed CEST imaging from an initial concept to a clinical imaging tool that is used to assess tumor metabolism. The first translation efforts involved brain imaging, but this has now progressed to imaging other body tissues. In this review, we summarize studies using CEST MRI to image a range of tumor types, including breast cancer, pelvic tumors, digestive tumors, and lung cancer. Approximately two thirds of the published studies involved breast or pelvic tumors which are sites that are less affected by body motion. Most studies conclude that CEST shows good potential for the differentiation of malignant from benign lesions with a number of reports now extending to compare different histological classifications along with the effects of anti-cancer treatments. Despite CEST being a unique 'label-free' approach with a higher sensitivity than MR spectroscopy, there are still some obstacles for implementing its clinical use. Future research is now focused on overcoming these challenges. Vigorous ongoing development and further clinical trials are expected to see CEST technology become more widely implemented as a mainstream imaging technology.

Neural responses to negative facial emotions: Sex differences in the correlates of individual anger and fear traits.

Studies have examined sex differences in emotion processing in health and illness. However, it remains unclear how these neural processes may relate to individual differences in affective traits. We addressed this issue with a dataset of 970 subjects (508 women) curated from the Human Connectome Project. Participants were assessed with the NIH Toolbox Emotion Measures and fMRI while identifying negative facial emotion and neutral shape targets in alternating blocks. Imaging data were analyzed with published routines and the results were reported at a corrected threshold. Men scored similarly in Anger- but lower in Fear-Affect, as compared to women. Men as compared with women engaged the occipital-temporal visual cortex, retrosplenial cortex (RSC), and both anterior and posterior cingulate cortex to a greater extent during face versus shape identification. Women relative to men engaged higher activation of bilateral middle frontal cortex. In regional brain responses to face versus shape identification, men relative to women showed more significant modulations by both Anger- and Fear- Affect traits. The left RSC and right RSC/precuneus each demonstrated activities during face vs. shape identification in negative correlation with Anger- and Fear- Affect scores in men only. Anger affect was positively correlated with prolonged RT in identifying face vs. shape target in men but not women. In contrast, women relative to men showed higher Fear-Affect score and higher activation in the right middle frontal cortex, which was more strongly correlated with prolonged RT during face vs. shape identification. Together, men and women with higher Fear-Affect demonstrated lower accuracy in identifying negative facial emotion versus neutral shape target, a relationship mediated by activity of the RSC. These findings add to the literature of sex and trait individual differences in emotion processing and may help research of sex-shared and sex-specific behavioral and neural markers of emotional disorders.

Graft Cryopreservation Does Not Impact Overall Survival after Allogeneic Hematopoietic Cell Transplantation Using Post-Transplantation Cyclophosphamide for Graft-versus-Host Disease Prophylaxis.

The COVID-19 pandemic has created significant barriers to timely donor evaluation, cell collection, and graft transport for allogeneic hematopoietic stem cell transplantation (allo-HCT). To ensure availability of donor cells on the scheduled date of infusion, many sites now collect cryopreserved grafts before the start of pretransplantation conditioning. Post-transplantation cyclophosphamide (ptCY) is an increasingly used approach for graft-versus-host disease (GVHD) prophylaxis, but the impact of graft cryopreservation on the outcomes of allo-HCT using ptCY is not known. Using the Center for International Blood and Marrow Transplant Research (CIBMTR) database, we compared the outcomes of HCT using cryopreserved versus fresh grafts in patients undergoing HCT for hematologic malignancy with ptCY. We analyzed 274 patients with hematologic malignancy undergoing allo-HCT between 2013 and 2018 with cryopreserved grafts and ptCY. Eighteen patients received bone marrow grafts and 256 received peripheral blood stem cell grafts. These patients were matched for age, graft type, disease risk index (DRI), and propensity score with 1080 patients who underwent allo-HCT with fresh grafts. The propensity score, which is an assessment of the likelihood of receiving a fresh graft versus a cryopreserved graft, was calculated using logistic regression to account for the following: disease histology, Karnofsky Performance Score (KPS), HCT Comorbidity Index, conditioning regimen intensity, donor type, and recipient race. The primary endpoint was overall survival (OS). Secondary endpoints included acute and chronic graft-versus-host disease (GVHD), non-relapse mortality (NRM), relapse/progression and disease-free survival (DFS). Because of multiple comparisons, only P values <.01 were considered statistically significant. The 2 cohorts (cryopreserved and fresh) were similar in terms of patient age, KPS, diagnosis, DRI, HCT-CI, donor/graft source, and conditioning intensity. One-year probabilities of OS were 71.1% (95% confidence interval [CI], 68.3% to 73.8%) with fresh grafts and 70.3% (95% CI, 64.6% to 75.7%) with cryopreserved grafts (P = .81). Corresponding probabilities of OS at 2 years were 60.6% (95% CI, 57.3% to 63.8%) and 58.7% (95% CI, 51.9% to 65.4%) (P = .62). In matched-pair regression analysis, graft cryopreservation was not associated with a significantly higher risk of mortality (hazard ratio [HR] for cryopreserved versus fresh, 1.05; 95% CI, .86 to 1.29; P = .60). Similarly, rates of neutrophil recovery (HR, .91; 95% CI, .80 to 1.02; P = .12), platelet recovery (HR, .88; 95% CI, .78 to 1.00; P = .05), grade III-IV acute GVHD (HR, .78; 95% CI, .50 to 1.22; P = .27), NRM (HR, 1.16; 95% CI, .86 to 1.55; P = .32) and relapse/progression (HR, 1.21; 95% CI, .97 to 1.50; P = .09) were similar with cryopreserved grafts versus fresh grafts. There were somewhat lower rates of chronic GVHD (HR, 78; 95% CI, .61 to .99; P = .04) and DFS (HR for treatment failure, 1.19; 95% CI, 1.01 to 1.29; P = .04) with graft cryopreservation that were of marginal statistical significance after adjusting for multiple comparisons. Overall, our data indicate that graft cryopreservation does not significantly delay hematopoietic recovery, increase the risk of acute GVHD or NRM, or decrease OS after allo-HCT using ptCY.

Hematopoietic Cell Transplantation with Cryopreserved Grafts for Severe Aplastic Anemia.

With the COVID-19 pandemic and the ensuing barriers to the collection and transport of donor cells, it is often necessary to collect and cryopreserve grafts before initiation of transplantation conditioning. The effect on transplantation outcomes in nonmalignant disease is unknown. This analysis examined the effect of cryopreservation of related and unrelated donor grafts for transplantation for severe aplastic anemia in the United States during 2013 to 2019. Included are 52 recipients of cryopreserved grafts who were matched for age, donor type, and graft type to 194 recipients who received noncryopreserved grafts. Marginal Cox regression models were built to study the effect of cryopreservation and other risk factors associated with outcomes. We recorded higher 1-year rates of graft failure (hazard ratio [HR], 2.26; 95% confidence interval, 1.17 to 4.35; P = .01) and of 1-year overall mortality (HR, 3.13; 95% CI, 1.60 to 6.11; P = .0008) after transplantation of cryopreserved compared with noncryopreserved grafts, with adjustment for sex, performance score, comorbidity, cytomegalovirus serostatus, and ABO blood group match. The incidence of acute and chronic graft-versus-host disease did not differ between the 2 groups. Adjusted probabilities of 1-year survival were 73% (95% CI, 60% to 84%) in the cryopreserved graft group and 91% (95% CI, 86% to 94%) in the noncryopreserved graft group. These data support the use of noncryopreserved grafts whenever possible in patients with severe aplastic anemia.