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Introduction: Donation after circulatory death (DCD) liver transplantation (LT) makes up well less than 1% of all LTs with a Model for End-Stage Liver Disease (MELD)≥35 in the United States. We hypothesized DCD-LT yields acceptable ischemia-reperfusion and reasonable outcomes for recipients with MELD≥35., Methods: We analyzed recipients with lab-MELD≥35 at transplant within the UCSF (n=41) and the UNOS (n=375) cohorts using multivariate Cox regression and propensity score matching., Results: In the UCSF cohort, five-year patient survival was 85% for DCD-LTs and 86% for matched-Donation after Brain Death donors-(DBD) LTs (p=0.843). Multivariate analyses showed that younger donor/recipient age and more recent transplants (2011-2021 versus 1999-2010) were associated with better survival. DCD vs. DBD graft use did not significantly impact survival (HR: 1.2, 95%CI 0.6-2.7). The transaminase peak was approximately doubled, indicating suggesting an increased ischemia-reperfusion hit. DCD-LTs had a median post-LT length of stay of 11 days, and 34% (14/41) were on dialysis at discharge versus 12 days and 22% (9/41) for DBD-LTs. 27% (11/41) DCD-LTs versus 12% (5/41) DBD-LTs developed a biliary complication (p=0.095). UNOS cohort analysis confirmed patient survival predictors, but DCD graft emerged as a risk factor (HR: 1.5, 95%CI 1.3-1.9) with five-year patient survival of 65% versus 75% for DBD-LTs (p=0.016). This difference became non-significant in a sub-analysis focusing on MELD 35-36 recipients. Analysis of MELD≥35 DCD recipients showed that donor age of <30yo independently reduced the risk of graft loss by 30% (HR, 95%CI: 0.7 (0.9-0.5), p=0.019). Retransplant status was associated with a doubled risk of adverse event (HR, 95%CI: 2.1 (1.4-3.3), p=0.001). The rejection rates at 1y were similar between DCD- and DBD-LTs, (9.3% (35/375) versus 1,541 (8.7% (1,541/17,677), respectively)., Discussion: In highly selected recipient/donor pair, DCD transplantation is feasible and can achieve comparable survival to DBD transplantation. Biliary complications occurred at the expected rates. In the absence of selection, DCD-LTs outcomes remain worse than those of DBD-LTs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Meier, Nunez, Syed, Feng, Tavakol, Freise, Roberts, Ascher, Hirose and Roll.)

Background: Small bowel obstruction (SBO) is a common hospital admission diagnosis. Identification of patients who will require a surgical resection because of a nonviable small bowel remains a challenge. Through a prospective cohort study, the authors aimed to validate risk factors and scores for intestinal resection, and to develop a practical clinical score designed to guide surgical versus conservative management., Patients and Methods: All patients admitted for an acute SBO between 2004 and 2016 in the center were included. Patients were divided in three categories depending on the management: conservative, surgical with bowel resection, and surgical without bowel resection. The outcome variable was small bowel necrosis. Logistic regression models were used to identify the best predictors., Results: Seven hundred and thirteen patients were included in this study, 492 in the development cohort and 221 in the validation cohort. Sixty-seven percent had surgery, of which 21% had small bowel resection. Thirty-three percent were treated conservatively. Eight variables were identified with a strong association with small bowel resection: age 70 years of age and above, first episode of SBO, no bowel movement for greater than or equal to 3 days, abdominal guarding, C-reactive protein greater than or equal to 50, and three abdominal computer tomography scanner signs: small bowel transition point, lack of small bowel contrast enhancement, and the presence of greater than 500 ml of intra-abdominal fluid. Sensitivity and specificity of this score were 65 and 88%, respectively, and the area under the curve was 0.84 (95% CI: 0.80-0.89)., Conclusion: The authors developed and validated a practical clinical severity score designed to tailor management of patients presenting with an SBO., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Introduction: In pancreatic islet transplantation, the exact contribution of human leukocyte antigen (HLA) matching to graft survival remains unclear. Islets may be exposed to allogenic rejection but also the recurrence of type 1 diabetes (T1D). We evaluated the HLA-DR matching, including the impact of diabetogenic HLA-DR3 or HLA-DR4 matches., Methods: We retrospectively examined the HLA profile in 965 transplant recipients and 2327 islet donors. The study population was obtained from patients enrolled in the Collaborative Islet Transplant Registry. We then identified 87 recipients who received a single-islet infusion. Islet-kidney recipients, 2nd islet infusion, and patients with missing data were excluded from the analysis (n=878)., Results: HLA-DR3 and HLA-DR4 were present in 29.7% and 32.6% of T1D recipients and 11.6% and 15.8% of the donors, respectively. We identified 52 T1D islet recipients mismatched for HLA-DR (group A), 11 with 1 or 2 HLA-DR-matches but excluding HLA-DR3 and HLA- DR4 (group B), and 24 matched for HLA-DR3 or HLA-DR4 (group C). Insulin-independence was maintained in a significantly higher percentage of group B recipients from year one through five post-transplantation (p<0.01). At five-year post-transplantation, 78% of group B was insulin-independent compared to 24% (group A) and 35% (group C). Insulin-independence correlated with significantly better glycemic control (HbA1c <7%), fasting blood glucose, and reduced severe hypoglycemic events. Matching HLA-A-B-DR (≥3) independently of HLA- DR3 or HLA-DR4 matching did not improve graft survival., Conclusion: This study suggests that matching HLA-DR but excluding the diabetogenic HLA-DR3 and/or 4 is a significant predictor for long-term islet survival., Competing Interests: Authors CB, FB and EP were employed by The EMMES Company, LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ballou, Barton, Payne, Berney, Villard, Meier, Baidal, Alejandro, Robien, Eggerman, Kamoun and Muller.)

Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by arterial obstruction. The quality of collateral circulation has been established as a key factor in determining the likelihood of a favorable clinical outcome and goes a long way to determining the choice of a stroke care model. Though many imaging and grading methods exist for quantifying collateral blood flow, the actual grading is mostly done through manual inspection. This approach is associated with a number of challenges. First, it is time-consuming. Second, there is a high tendency for bias and inconsistency in the final grade assigned to a patient depending on the experience level of the clinician. We present a multi-stage deep learning approach to predict collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data. First, we formulate a region of interest detection task as a reinforcement learning problem and train a deep learning network to automatically detect the occluded region within the 3D MR perfusion volumes. Second, we extract radiomic features from the obtained region of interest through local image descriptors and denoising auto-encoders. Finally, we apply a convolutional neural network and other machine learning classifiers to the extracted radiomic features to automatically predict the collateral flow grading of the given patient volume as one of three severity classes - no flow (0), moderate flow (1), and good flow (2). Results from our experiments show an overall accuracy of 72% in the three-class prediction task. With an inter-observer agreement of 16% and a maximum intra-observer agreement of 74% in a similar experiment, our automated deep learning approach demonstrates a performance comparable to expert grading, is faster than visual inspection, and eliminates the problem of grading bias., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Tetteh, Navarro, Meier, Kaesmacher, Paetzold, Kirschke, Zimmer, Wiest and Menze.)

The ideal preservation temperature for donation after circulatory death kidney grafts is unknown. We investigated whether subnormothermic (22 °C) ex vivo kidney machine perfusion could improve kidney metabolism and reduce ischemia-reperfusion injury., Methods: To mimic donation after circulatory death procurement, kidneys from 45-kg pigs underwent 60 min of warm ischemia. Kidneys were then perfused ex vivo for 4 h with Belzer machine perfusion solution UW at 22 °C or at 4 °C before transplantation. Magnetic resonance spectroscopic imaging coupled with LCModel fitting was used to assess energy metabolites. Kidney perfusion was evaluated with dynamic-contrast enhanced MRI. Renal biopsies were collected at various time points for histopathologic analysis., Results: Total adenosine triphosphate content was 4 times higher during ex vivo perfusion at 22 °C than at 4 °C perfusion. At 22 °C, adenosine triphosphate levels increased during the first hours of perfusion but declined afterward. Similarly, phosphomonoesters, containing adenosine monophosphate, were increased at 22 °C and then slowly consumed over time. Compared with 4 °C, ex vivo perfusion at 22 °C improved cortical and medullary perfusion. Finally, kidney perfusion at 22 °C reduced histological lesions after transplantation (injury score: 22 °C: 10.5 ± 3.5; 4 °C: 18 ± 2.25 over 30)., Conclusions: Ex vivo kidney perfusion at 22°C improved graft metabolism and protected from ischemia-reperfusion injuries upon transplantation. Future clinical studies will need to define the benefits of subnormothermic perfusion in improving kidney graft function and patient's survival., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 The Author(s). Transplantation Direct. Published by Wolters Kluwer Health, Inc.)

Background and Objectives: Very poor outcome despite IV thrombolysis (IVT) and mechanical thrombectomy (MT) occurs in approximately 1 of 4 patients with ischemic stroke and is associated with a high logistic and economic burden. We aimed to develop and validate a multivariable prognostic model to identify futile recanalization therapies (FRTs) in patients undergoing those therapies., Methods: Patients from a prospectively collected observational registry of a single academic stroke center treated with MT and/or IVT were included. The data set was split into a training (N = 1,808, 80%) and internal validation (N = 453, 20%) cohort. We used gradient boosted decision tree machine learning models after k-nearest neighbor imputation of 32 variables available at admission to predict FRT defined as modified Rankin scale 5-6 at 3 months. We report feature importance, ability for discrimination, calibration, and decision curve analysis., Results: A total of 2,261 patients with a median (interquartile range) age of 75 years (64-83 years), 46% female, median NIH Stroke Scale 9 (4-17), 34% IVT alone, 41% MT alone, and 25% bridging were included. Overall, 539 (24%) had FRT, more often in MT alone (34%) as compared with IVT alone (11%). Feature importance identified clinical variables (stroke severity, age, active cancer, prestroke disability), laboratory values (glucose, C-reactive protein, creatinine), imaging biomarkers (white matter hyperintensities), and onset-to-admission time as the most important predictors. The final model was discriminatory for predicting 3-month FRT (area under the curve 0.87, 95% CI 0.87-0.88) and had good calibration (Brier 0.12, 0.11-0.12). Overall performance was moderate (F1-score 0.63 ± 0.004), and decision curve analyses suggested higher mean net benefit at lower thresholds of treatment (up to 0.8)., Conclusions: This FRT prediction model can help inform shared decision making and identify the most relevant features in the emergency setting. Although it might be particularly useful in low resource healthcare settings, incorporation of further multifaceted variables is necessary to further increase the predictive performance., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS., Competing Interests: Conflicts of Interest The conflicts of interest have not been entered yet.

The success of pancreas islet isolation largely depends on donor characteristics, including extracellular matrix composition of which collagen is the main element. We hypothesized that isolation yields are proportional to collagen digestion percentage, and aimed to determine a threshold that predicts isolation success. The amount of pancreas collagen (I-V) was determined using colorimetry prior to and after the digestion process in 52 human islet isolations. Collagen I-V and VI were also assessed histologically. We identified a collagen digestion threshold of ≥ 60% as an independent factor beyond which an islet preparation has a ninefold increased odds of yielding ≥ 250 000 islet equivalents (IEQ) (P = 0.009) and a sixfold increased odds of being transplanted (P = 0.015). Preparations with ≥ 60% collagen digestion (n = 35) yielded 283 017 ± 164 214 IEQ versus 180 142 ± 85 397 in the < 60% collagen digestion group (n = 17) (P = 0.016); respectively 62.9% versus 29.4% of those were transplanted (P = 0.024). Common donor characteristics, initial collagen content, enzyme blend, and digestion times were not associated with collagen digestion percentage variations. Donor age positively correlated with the amount of collagen VI (P = 0.013). There was no difference in islet graft survival between high and low digestion groups. We determined that a 60% pancreas collagen digestion is the threshold beyond which an islet isolation is likely to be successful and transplanted., (© 2020 Steunstichting ESOT. Published by John Wiley & Sons Ltd.)

Background: Sarcopenia has been identified as a predictive variable for surgical outcomes. We hypothesized that sarcopenia could be a key measure to identify frail patients and potentially predict poorer outcomes among recipients of simultaneous pancreas and kidney (SPK) transplants., Methods: We estimated sarcopenia by measuring psoas muscle mass index (PMI). PMI was assessed on perioperative computed tomography (CT) scans of SPK recipients., Results: Of the 141 patients identified between 2010 and 2018, 107 had a CT scan available and were included in the study. The median follow-up was 4 years (range, 0.5-9.1 y). Twenty-three patients had a low PMI, and 84 patients had a normal PMI. Patient characteristics were similar between the 2 groups except for body mass index, which was significantly lower in low PMI group ( P  < 0.001). Patient and kidney graft survival were not statistically different between groups ( P  = 0.851 and P  = 0.357, respectively). A multivariate Cox regression analysis showed that patients with a low PMI were 6 times more likely to lose their pancreas allograft (hazard ratios, 5.4; 95% confidence intervals, 1.4-20.8; P  = 0.015). Three out of 6 patients lost their pancreas graft due to rejection in the low PMI group, compared with 1 out of 9 patients in the normal PMI group. Among low PMI patients who had a follow-up CT scan, 62.5% (5/8) of those with a functional pancreas graft either improved or resolved sarcopenia, whereas 75.0% (3/4) of those who lost their pancreas graft continued to lose muscle mass., Conclusion: Sarcopenia could represent one of the predictors of pancreas graft failure and should be evaluated and potentially optimized in SPK recipients., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2020 The Author(s). Transplantation Direct. Published by Wolters Kluwer Health, Inc.)

Background: The lack of organs for kidney transplantation is a growing concern. Expansion in organ supply has been proposed through the use of organs after circulatory death (donation after circulatory death [DCD]). However, many DCD grafts are discarded because of long warm ischemia times, and the absence of reliable measure of kidney viability. P magnetic resonance imaging (pMRI) spectroscopy is a noninvasive method to detect high-energy phosphate metabolites, such as ATP. Thus, pMRI could predict kidney energy state, and its viability before transplantation., Methods: To mimic DCD, pig kidneys underwent 0, 30, or 60 min of warm ischemia, before hypothermic machine perfusion. During the ex vivo perfusion, we assessed energy metabolites using pMRI. In addition, we performed Gadolinium perfusion sequences. Each sample underwent histopathological analyzing and scoring. Energy status and kidney perfusion were correlated with kidney injury., Results: Using pMRI, we found that in pig kidney, ATP was rapidly generated in presence of oxygen (100 kPa), which remained stable up to 22 h. Warm ischemia (30 and 60 min) induced significant histological damages, delayed cortical and medullary Gadolinium elimination (perfusion), and reduced ATP levels, but not its precursors (AMP). Finally, ATP levels and kidney perfusion both inversely correlated with the severity of kidney histological injury., Conclusions: ATP levels, and kidney perfusion measurements using pMRI, are biomarkers of kidney injury after warm ischemia. Future work will define the role of pMRI in predicting kidney graft and patient's survival.

Chronic pain disorders appear to be associated with altered body perception. The clinical tools of two-point discrimination (TPD), left/right judgment task (LRJ) and body image drawing (BID) can all be used to assess altered body perception in people with chronic low back pain (CLBP). The aim of this observational study was to examine whether values from TPD, LRJ and BID can determine altered body perception between unilateral CLBP patients' painful and pain-free trunk sides, through the evaluation of some of the underlying mechanisms of body perception. Twenty-seven eligible participants completed all tasks. Inclusion criteria were: unilateral CLBP with duration of over 12 weeks; pain level higher than two out of ten on the numeric rating scale; a minimum score of four points on the Roland Morris Disability Questionnaire (RMDQ). Findings from TPD and BID tests showed an alteration in body awareness. However, no significant interaction effects were found between the affected sides and their measurements (TPD p = 0.310, LRJ response time p = 0.571, LRJ accuracy p = 0.190, BID p = 0.751). The profiling of people with high levels of distorted body perception for other factors known to contribute to CLBP may be a useful direction for further investigation., (Copyright © 2018. Published by Elsevier Ltd.)

Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Encapsulated hepatocyte transplantation and encapsulated mesenchymal stem cell transplantation are newly developed potential treatments for acute and chronic liver diseases, respectively. Cells are microencapsulated in biocompatible semipermeable alginate-based hydrogels. Microspheres protect cells against antibodies and immune cells, while allowing nutrients, small/medium size proteins and drugs to diffuse inside and outside the polymer matrix. Microencapsulated cells are assessed in vitro and designed for experimental transplantation and for future clinical applications.Here, we describe the protocol for microencapsulation of hepatocytes and mesenchymal stem cells within hybrid poly(ethylene glycol)-alginate hydrogels.

The detection rate of cystic lesions of the pancreas has increased following the widespread use of high-resolution imaging technologies. CT-scan, MRI and echo-endoscopy are diagnostic modalities. Pseudocyst is the most common lesion. It is benign and can be managed with endoscopic treatment. Mucinous cystic neoplasia and Intraductal Papillary Mucinous Neoplasia (IPMN) carry a risk for malignant transformation. The surgical treatment of these lesions has to be discussed by a multidisciplinary board. Serous cystic neoplasia and pseudopapillar and solid neoplasia are two rare types of lesion. The aim of this article is to present the diagnostic pathway and the management of these lesions from the general practitioner point of view.

In 2016, peritoneal carcinomatosis can be considered as a chronic disease that can be treated and sometimes cured. Hyperthermic Intra PEritoneal Chemotherapy (HIPEC) is a procedure developed in the eighties. Combined with CytoReductive (CR) surgery, this protocol underwent a considerable expansion in Washington Cancer Institute. CR combined with HIPEC was demonstrated to be the only curative treatment for PseudoMyxoma Peritonei syndrome (PMP). It is actually approved in the management of peritoneal carcinomatosis of ovarian, colorectal, or peritoneal primitive (mesothelioma) origin but is still studied for gastric cancer. CR/HIPEC is associated with an important mortality and morbidity. This article takes stock of indications to CR/HIPEC.