KTP-532 Laser Assisted Excision of a Rare Laryngeal Angiolipoma: A Case Report with Review of Literature.

Angiolipoma is a benign mesenchymal tumor and its occurrence in head and neck region is very rare. Only 2 cases of Laryngeal angiolipomas have been reported in the medical literature. We present one such rare case in a 32-year-old male who presented with complaints of change in voice and foreign body sensation in the throat since past 9 months along with features suggestive of obstructive sleep apnoea and dysphagia. Contrast enhanced CT scan of the neck showed a cystic lesion arising from right ventricle extending superiorly till the vallecula, partially obstructing the airway. Suspecting a supraglottic cyst, trans-oral microlaryngoscopic KTP-532 laser assisted excision was planned, intraoperatively a solid tumor was encountered. We discuss herein the clinical presentation and management of this rare neoplasm with review of literature.

Multifocal atypical teratoid/rhabdoid tumour in an infant-a rare case report.

BACKGROUND: Atypical teratoid/rhabdoid tumours (AT/RT) are uncommon but aggressive, malignant tumours in the paediatric age group. Presentation of concomitant supratentorial and infratentorial lesions in an infant is extremely rare. We discuss an infant diagnosed with such lesions. Systematic PubMed search was conducted using keywords 'atypical teratoid /rhabdoid tumor', 'paediatric' and 'multifocal'. Reports were included for patients younger than 18 years with two or more lesions. The search yielded additional five cases and were tabulated. Age, sex, location, treatment given and survival/outcome were noted. CASE REPORT: A 10-month-old child presented with complaints of drowsiness and intractable vomiting. Imaging showed multifocal supra- and infratentorial lesions with obstructive hydrocephalus. The child underwent ventriculoperitoneal shunt followed by surgical removal of the posterior fossa lesion. Histopathological features were consistent with AT/RT. CONCLUSIONS: Multifocal AT/RT are very rare. The impact of multifocality in the outcome is not known as very few reports are available. Newer targeted therapies may offer insight in improving outcomes in the future.

A survey on cancer detection via convolutional neural networks: Current challenges and future directions.

Cancer is a condition in which abnormal cells uncontrollably split and damage the body tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical images play an indispensable role in detecting various cancers; however, manual interpretation of these images by radiologists is observer-dependent, time-consuming, and tedious. An automatic decision-making process is thus an essential need for cancer detection and diagnosis. This paper presents a comprehensive survey on automated cancer detection in various human body organs, namely, the breast, lung, liver, prostate, brain, skin, and colon, using convolutional neural networks (CNN) and medical imaging techniques. It also includes a brief discussion about deep learning based on state-of-the-art cancer detection methods, their outcomes, and the possible medical imaging data used. Eventually, the description of the dataset used for cancer detection, the limitations of the existing solutions, future trends, and challenges in this domain are discussed. The utmost goal of this paper is to provide a piece of comprehensive and insightful information to researchers who have a keen interest in developing CNN-based models for cancer detection.

Mapping the Genomic Regions Controlling Germination Rate and Early Seedling Growth Parameters in Rice.

Seed vigor is the key performance parameter of good quality seed. A panel was prepared by shortlisting genotypes from all the phenotypic groups representing seedling growth parameters from a total of 278 germplasm lines. A wide variation was observed for the traits in the population. The panel was classified into four genetic structure groups. Fixation indices indicated the existence of linkage disequilibrium in the population. A moderate to high level of diversity parameters was assessed using 143 SSR markers. Principal component, coordinate, neighbor-joining tree and cluster analyses showed subpopulations with a fair degree of correspondence with the growth parameters. Marker-trait association analysis detected eight novel QTLs, namely qAGR4.1, qAGR6.1, qAGR6.2 and qAGR8.1 for absolute growth rate (AGR); qRSG6.1, qRSG7.1 and qRSG8.1 for relative shoot growth (RSG); and qRGR11.1 for relative growth rate (RGR), as analyzed by GLM and MLM. The reported QTL for germination rate (GR), qGR4-1, was validated in this population. Additionally, QTLs present on chromosome 6 controlling RSG and AGR at 221 cM and RSG and AGR on chromosome 8 at 27 cM were detected as genetic hotspots for the parameters. The QTLs identified in the study will be useful for improvement of the seed vigor trait in rice.

Fusion of Local and Global Feature Representation With Sparse Autoencoder for Improved Melanoma Classification.

Automated skin cancer diagnosis is challenging due to inter-class uniformity, intra-class variation, and the complex structure of dermoscopy images. Convolutional neural networks (CNN) have recently made considerable progress in melanoma classification, even in the presence of limited skin images. One of the drawbacks of these methods is the loss of image details caused by downsampling high-resolution skin images to a low resolution. Further, most approaches extract features only from the whole skin image. This paper proposes an ensemble feature fusion and sparse autoencoder (SAE) based framework to overcome the above issues and improve melanoma classification performance. The proposed method extracts features from two streams, local and global, using a pre-trained CNN model. The local stream extracts features from image patches, while the global stream derives features from the whole skin image, preserving both local and global representation. The features are then fused, and an SAE framework is subsequently designed to enrich the feature representation further. The proposed method is validated on ISIC 2016 dataset and the experimental results indicate the superiority of the proposed approach.

MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis From CT Images.

Timely and accurate diagnosis of coronavirus disease 2019 (COVID-19) is crucial in curbing its spread. Slow testing results of reverse transcription-polymerase chain reaction (RT-PCR) and a shortage of test kits have led to consider chest computed tomography (CT) as an alternative screening and diagnostic tool. Many deep learning methods, especially convolutional neural networks (CNNs), have been developed to detect COVID-19 cases from chest CT scans. Most of these models demand a vast number of parameters which often suffer from overfitting in the presence of limited training data. Moreover, the linearly stacked single-branched architecture based models hamper the extraction of multi-scale features, reducing the detection performance. In this paper, to handle these issues, we propose an extremely lightweight CNN with multi-scale feature learning blocks called as MFL-Net. The MFL-Net comprises a sequence of MFL blocks that combines multiple convolutional layers with 3 ×3 filters and residual connections effectively, thereby extracting multi-scale features at different levels and preserving them throughout the block. The model has only 0.78M parameters and requires low computational cost and memory space compared to many ImageNet pretrained CNN architectures. Comprehensive experiments are carried out using two publicly available COVID-19 CT imaging datasets. The results demonstrate that the proposed model achieves higher performance than pretrained CNN models and state-of-the-art methods on both datasets with limited training data despite having an extremely lightweight architecture. The proposed method proves to be an effective aid for the healthcare system in the accurate and timely diagnosis of COVID-19.

Unraveling the genomic regions controlling the seed vigour index, root growth parameters and germination per cent in rice.

High seed vigour ensures good quality seed and higher productivity. Early seedling growth parameters indicate seed vigour in rice. Seed vigour via physiological growth parameters is a complex trait controlled by many quantitative trait loci. A panel was prepared representing a population of 274 rice landraces by including genotypes from all the phenotypic groups of sixseedling stage physiological parameters including germination % for association mapping. Wide variations for the six studiedtraits were observed in the population. The population was classified into 3 genetic groups. Fixation indices indicated the presence of linkage disequilibrium in the population. The population was classified into subpopulations and each subpopulation showed correspondence with the 6 physiological traits. A total of 5 reported QTLs viz., qGP8.1 for germination % (GP); qSVII2.1, qSVII6.1 and qSVII6.2 for seed vigour index II (SVII), and qRSR11.1 for root-shoot ratio (RSR) were validated in this mapping population. In addition, 13 QTLs regulating the physiological parameters such as qSVI 11.1 for seed vigour index I; qSVI11.1 and qSVI12.1 for seed vigour index II; qRRG10.1, qRRG8.1, qRRG8.2, qRRG6.1 and qRRG4.1 for rate of root growth (RRG); qRSR2.1, qRSR3.1 and qRSR5.1 for root-shoot ratio (RSR) while qGP6.2 and qGP6.3 for germination %were identified. Additionally, co-localization or co-inheritance of QTLs, qGP8.1 and qSVI8.1 for GP and SVI-1; qGP6.2 and qRRG6.1 for GP and RRG, and qSVI11.1 and qRSR11.1 for SVI and RSR were detected. The QTLs identified in this study will be useful for improvement of seed vigour trait in rice.

An Efficient Deep Learning Method for Detection of COVID-19 Infection Using Chest X-ray Images.

The research community has recently shown significant interest in designing automated systems to detect coronavirus disease 2019 (COVID-19) using deep learning approaches and chest radiography images. However, state-of-the-art deep learning techniques, especially convolutional neural networks (CNNs), demand more learnable parameters and memory. Therefore, they may not be suitable for real-time diagnosis. Thus, the design of a lightweight CNN model for fast and accurate COVID-19 detection is an urgent need. In this paper, a lightweight CNN model called LW-CORONet is proposed that comprises a sequence of convolution, rectified linear unit (ReLU), and pooling layers followed by two fully connected layers. The proposed model facilitates extracting meaningful features from the chest X-ray (CXR) images with only five learnable layers. The proposed model is evaluated using two larger CXR datasets (Dataset-1: 2250 images and Dataset-2: 15,999 images) and the classification accuracy obtained are 98.67% and 99.00% on Dataset-1 and 95.67% and 96.25% on Dataset-2 for multi-class and binary classification cases, respectively. The results are compared with four contemporary pre-trained CNN models as well as state-of-the-art models. The effect of several hyperparameters: different optimization techniques, batch size, and learning rate have also been investigated. The proposed model demands fewer parameters and requires less memory space. Hence, it is effective for COVID-19 detection and can be utilized as a supplementary tool to assist radiologists in their diagnosis.

Serendipitous Discovery of a Benign Obturator Nerve Schwannoma: Case report with a brief clinical review.

Schwannomas are typically benign tumours of the peripheral nerves. However, they seldom arise from the obturator nerve. We report a case of an uncommon swelling (2.5 × 3.5 cm) in a 65-year-old male cadaver, found during a routine dissection session for first Bachelor of Medicine and Surgery students in the Department of Anatomy, Kasturba Medical College, Manipal, India, in 2019. It was seen originating from the left obturator nerve in the pelvis at the level of the sacral promontory. Histopathological investigation revealed a schwannoma. The hypocellular tumour was arranged in a sweeping fascicle pattern with patches of myxoid degeneration. Obturator schwannomas, though rare, can exist in cadavers, as seen in the present case. Hence, it should be considered as a differential diagnosis for clinical cases of pelvic masses and eliminated only after thorough radiological examination. Knowledge about the existence of such schwannomas is therefore essential.

COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis.

AIM: : COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images. METHODS: : Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images; and 306 healthy control images. We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L). Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR. Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models. Micro-averaged (MA) F1 score was used as the measuring indicator. The final determined model was named CCSHNet. RESULTS: : On the test set, CCSHNet achieved sensitivities of four classes of 95.61%, 96.25%, 98.30%, and 97.86%, respectively. The precision values of four classes were 97.32%, 96.42%, 96.99%, and 97.38%, respectively. The F1 scores of four classes were 96.46%, 96.33%, 97.64%, and 97.62%, respectively. The MA F1 score was 97.04%. In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods. CONCLUSIONS: : CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs.

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study.

The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases and the novel virus is still spreading rapidly all over the world. Many hospitals across the globe are not yet equipped with an adequate amount of testing kits and the manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and troublesome. It is hence very important to design an automated and early diagnosis system which can provide fast decision and greatly reduce the diagnosis error. The chest X-ray images along with emerging Artificial Intelligence (AI) methodologies, in particular Deep Learning (DL) algorithms have recently become a worthy choice for early COVID-19 screening. This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection. We evaluate the effectiveness of eight pre-trained Convolutional Neural Network (CNN) models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classification of COVID-19 from normal cases. Also, comparative analyses have been made among these models by considering several important factors such as batch size, learning rate, number of epochs, and type of optimizers with an aim to find the best suited model. The models have been validated on publicly available chest X-ray images and the best performance is obtained by ResNet-34 with an accuracy of 98.33%. This study will be useful for researchers to think for the design of more effective CNN based models for early COVID-19 detection.

Lung Secretoglobin Scgb1a1 Influences Alveolar Macrophage-Mediated Inflammation and Immunity.

Alveolar macrophage (AM) is a mononuclear phagocyte key to the defense against respiratory infections. To understand AM's role in airway disease development, we examined the influence of Secretoglobin family 1a member 1 (SCGB1A1), a pulmonary surfactant protein, on AM development and function. In a murine model, high-throughput RNA-sequencing and gene expression analyses were performed on purified AMs isolated from mice lacking in Scgb1a1 gene and were compared with that from mice expressing the wild type Scgb1a1 at weaning (4 week), puberty (8 week), early adult (12 week), and middle age (40 week). AMs from early adult mice under Scgb1a1 sufficiency demonstrated a total of 37 up-regulated biological pathways compared to that at weaning, from which 30 were directly involved with antigen presentation, anti-viral immunity and inflammation. Importantly, these pathways under Scgb1a1 deficiency were significantly down-regulated compared to that in the age-matched Scgb1a1-sufficient counterparts. Furthermore, AMs from Scgb1a1-deficient mice showed an early activation of inflammatory pathways compared with that from Scgb1a1-sufficient mice. Our in vitro experiments with AM culture established that exogenous supplementation of SCGB1a1 protein significantly reduced AM responses to microbial stimuli where SCGB1a1 was effective in blunting the release of cytokines and chemokines (including IL-1b, IL-6, IL-8, MIP-1a, TNF-a, and MCP-1). Taken together, these findings suggest an important role for Scgb1a1 in shaping the AM-mediated inflammation and immune responses, and in mitigating cytokine surges in the lungs.

Genetic mapping of physiological traits associated with terminal stage drought tolerance in rice.

BACKGROUND: Drought during reproductive stage is among the main abiotic stresses responsible for drastic reduction of grain yield in rainfed rice. The genetic mechanism of reproductive stage drought tolerance is very complex. Many physiological and morphological traits are associated with this stress tolerance. Robust molecular markers are required for detection and incorporation of these correlated physiological traits into different superior genetic backgrounds. Identification of gene(s)/QTLs controlling reproductive stage drought tolerance and its deployment in rainfed rice improvement programs are very important. RESULTS: QTLs linked to physiological traits under reproductive stage drought tolerance were detected by using 190 F7 recombinant inbred lines (RIL) mapping population of CR 143-2-2 and Krishnahamsa. Wide variations were observed in the estimates of ten physiological traits studied under the drought stress. The RIL population was genotyped using the bulk- segregant analysis (BSA) approach. A total of 77 SSR polymorphic markers were obtained from the parental polymorphisms survey of 401 tested primers. QTL analysis using inclusive composite interval mapping detected a total of three QTLs for the physiological traits namely relative chlorophyll content (qRCC1.1), chlorophyll a (qCHLa1.1), and proline content (qPRO3.1) in the studied RIL population. The QTL, qPRO3.1 is found to be a novel one showing LOD value of 13.93 and phenotypic variance (PVE) of 78.19%. The QTL was located within the marker interval of RM22-RM517 on chromosome 3. Another novel QTL, qRCC1.1 was mapped on chromosome 1 at a distance of 142.8 cM and found to control relative chlorophyll content during terminal drought stress. A third novel QTL was detected in the population that controlled chlorophyll a content (qCHLa1.1) under the terminal stress period. The QTL was located on chromosome 1 at a distance of 81.8 cM and showed 64.5% phenotypic variation. CONCLUSIONS: The three novel QTLs, qRCC1.1, qCHLa1.1 and qPRO3.1 controlling relative chlorophyll content, chlorophyll a and proline content, respectively were identified in the mapping population derived from CR 143-2-2 and Krishnahamsa. These 3 QTLs will be useful for enhancement of terminal drought stress tolerance through marker-assisted breeding approach in rice.

A Cadaveric Report on a Giant Ureteric Stone Led Right Hydro Ureter and Severe Hydronephrosis.

BACKGROUND: The ureter shows natural constrictions in its course, and these are the potential site for the impaction of the renal calculus. Giant ureteral stones are associated with insidious growth and late presentation, often leading to renal failure. CASE PRESENTATION: In the present case, we observed a huge ureteric stone obstructing the right ureterovesical junction in a 58 year-old male cadaver. We also found hydroureter distal to the impaction of the calculus, renal damage and severe hydronephrosis on the right side. Histopathological analysis showed conditions of arterio-nephro-sclerosis and eroded ureter secondary to the calculus. Ureteric stones obstruction may result in hydroureter, hydronephrosis and progressive renal damage leading to irreversible renal function. The present case provides valuable information regarding the gross and histopathological alterations in ureteric calculi. CONCLUSION: It further enables clinicians to be armed with the knowledge of preventive approaches to educate patients with previous calculi, or those who may develop in the future.

Application of fast curvelet Tsallis entropy and kernel random vector functional link network for automated detection of multiclass brain abnormalities.

Binary classification of brain magnetic resonance (MR) images has made remarkable progress and many automated systems have been developed in the last decade. Multiclass classification of brain MR images is comparatively more challenging and has great clinical significance. Hence, it has recently become an active area of research in biomedical image processing. In this paper, an automated multiclass brain MR classification framework is proposed to categorize the MR images into five classes such as brain stroke, degenerative disease, infectious disease, brain tumor, and normal brain. A texture based feature descriptor is proposed using curvelet transform and Tsallis entropy to extract salient features from MR images. The potential of Tsallis entropy features is compared with Shannon entropy features. A kernel extension of random vector functional link network (KRVFL) is used to perform multiclass classification and improve the generalization performance at faster training speed. To validate the proposed method, two standard multiclass brain MR datasets (MD-1 and MD-2) are used. The proposed system obtained classification accuracies of 97.33% and 94.00% for MD-1 and MD-2 datasets respectively using 5-fold cross validation approach. The experimental results demonstrated the effectiveness of our system compared to the state-of-the-art schemes and hence, can be utilized as a supportive tool by physicians to verify their screening.

Genes, pathways and transcription factors involved in seedling stage chilling stress tolerance in indica rice through RNA-Seq analysis.

BACKGROUND: Rice plants show yellowing, stunting, withering, reduced tillering and utimately low productivity in susceptible varieties under low temperature stress. Comparative transcriptome analysis was performed to identify novel transcripts, gain new insights into different gene expression and pathways involved in cold tolerance in rice. RESULTS: Comparative transcriptome analyses of 5 treatments based on chilling stress exposure revealed more down regulated genes in susceptible and higher up regulated genes in tolerant genotypes. A total of 13930 and 10599 differentially expressed genes (DEGs) were detected in cold susceptible variety (CSV) and cold tolerant variety (CTV), respectively. A continuous increase in DEGs at 6, 12, 24 and 48 h exposure of cold stress was detected in both the genotypes. Gene ontology (GO) analysis revealed 18 CSV and 28 CTV term significantly involved in molecular function, cellular component and biological process. GO classification showed a significant role of transcription regulation, oxygen, lipid binding, catalytic and hydrolase activity for tolerance response. Absence of photosynthesis related genes, storage products like starch and synthesis of other classes of molecules like fatty acids and terpenes during the stress were noticed in susceptible genotype. However, biological regulations, generation of precursor metabolites, signal transduction, photosynthesis, regulation of cellular process, energy and carbohydrate metabolism were seen in tolerant genotype during the stress. KEGG pathway annotation revealed more number of genes regulating different pathways resulting in more tolerant. During early response phase, 24 and 11 DEGs were enriched in CTV and CSV, respectively in energy metabolism pathways. Among the 1583 DEG transcription factors (TF) genes, 69 WRKY, 46 bZIP, 41 NAC, 40 ERF, 31/14 MYB/MYB-related, 22 bHLH, 17 Nin-like 7 HSF and 4C3H were involved during early response phase. Late response phase showed 30 bHLH, 65 NAC, 30 ERF, 26/20 MYB/MYB-related, 11 C3H, 12 HSF, 86 Nin-like, 41 AP2/ERF, 55 bZIP and 98 WRKY members TF genes. The recovery phase included 18 bHLH, 50 NAC, 31 ERF, 24/13 MYB/MYB-related, 4 C3H, 4 HSF, 14 Nin-like, 31 bZIP and 114 WRKY TF genes. CONCLUSIONS: Transcriptome analysis of contrasting genotypes for cold tolerance detected the genes, pathways and transcription factors involved in the stress tolerance.

Enhanced immunosuppression improves early allograft function in a porcine kidney transplant model of donation after circulatory death.

It remains controversial whether renal allografts from donation after circulatory death (DCD) have a higher risk of acute rejection (AR). In the porcine large animal kidney transplant model, we investigated the AR and function of DCD renal allografts compared to the non-DCD renal allografts and the effects of increased immunosuppression. We found that the AR was significantly increased along with elevated MHC-I expression in the DCD transplants receiving low-dose immunosuppression; however, AR and renal function were significantly improved when given high-dose immunosuppressive therapy postoperatively. Also, high-dose immunosuppression remarkably decreased the mRNA levels of ifn-g, il-6, tgf-b, il-4, and tnf-a in the allograft at day 5 and decreased serum cytokines levels of IFN-g and IL-17 at day 4 and day 5 after operation. Furthermore, Western blot analysis showed that higher immunosuppression decreased phosphorylation of signal transducer and activator of transcription 3 and nuclear factor kappa-light-chain-enhancer of activated B cells-p65, increased phosphorylation of extracellular-signal-regulated kinase, and reduced the expression of Bcl-2-associated X protein and caspase-3 in the renal allografts. These results suggest that the DCD renal allograft seems to be more vulnerable to AR; enhanced immunosuppression reduces DCD-associated AR and improves early allograft function in a preclinical large animal model.

Isolation and In Vitro Culture of Murine and Human Alveolar Macrophages.

Alveolar macrophages are terminally differentiated, lung-resident macrophages of prenatal origin. Alveolar macrophages are unique in their long life and their important role in lung development and function, as well as their lung-localized responses to infection and inflammation. To date, no unified method for identification, isolation, and handling of alveolar macrophages from humans and mice exists. Such a method is needed for studies on these important innate immune cells in various experimental settings. The method described here, which can be easily adopted by any laboratory, is a simplified approach to harvesting alveolar macrophages from bronchoalveolar lavage fluid or from lung tissue and maintaining them in vitro. Because alveolar macrophages primarily occur as adherent cells in the alveoli, the focus of this method is on dislodging them prior to harvest and identification. The lung is a highly vascularized organ, and various cell types of myeloid and lymphoid origin inhabit, interact, and are influenced by the lung microenvironment. By using the set of surface markers described here, researchers can easily and unambiguously distinguish alveolar macrophages from other leukocytes, and purify them for downstream applications. The culture method developed herein supports both human and mouse alveolar macrophages for in vitro growth, and is compatible with cellular and molecular studies.

Anti-CD47 monoclonal antibody therapy reduces ischemia-reperfusion injury of renal allografts in a porcine model of donation after cardiac death.

We investigated whether blockade of the CD47 signaling pathway could reduce ischemia-reperfusion injury (IRI) of renal allografts donated after cardiac death (DCD) in a porcine animal model of transplantation. Renal allografts were subjected to 30 minutes of warm ischemia, 3.5 hours of cold ischemia, and then perfused with a humanized anti-CD47 monoclonal antibody (CD47mAb) in the treatment group or HTK solution in the control group (n = 4/group). The animals were euthanized five days after transplantation. At the time of reperfusion, indocyanine green-based in vivo imaging showed that CD47mAb-treated organs had greater and more uniform reperfusion. On post-transplant days 3-5, the treatment group had lower values compared to the control for creatinine and blood urea nitrogen. Histological examination of allograft tissues showed a significant decrease of acute tubular injury in the CD47mAb-treated group compared to control. Compared to the control group, CD47mAb treatment significantly decreased genes expression related to oxidative stress (sod-1, gpx-1, and txn), the inflammatory response (il-2, il-6, inf-g, and tgf-b), as well as reduced protein levels of BAX, Caspase-3, MMP2, and MMP9. These data demonstrate that CD47mAb blockade decreases IRI and subsequent tissue injury in DCD renal allografts in a large animal transplant model.