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Incompatible living donor kidney transplant recipients (ILDKTr) have pre-existing donor-specific antibody (DSA) that, despite desensitization, may persist or reappear with resulting consequences, including delayed graft function (DGF) and acute rejection (AR). To quantify the risk of DGF and AR in ILDKT and downstream effects, we compared 1406 ILDKTr to 17 542 compatible LDKT recipients (CLDKTr) using a 25-center cohort with novel SRTR linkage. We characterized DSA strength as positive Luminex, negative flow crossmatch (PLNF); positive flow, negative cytotoxic crossmatch (PFNC); or positive cytotoxic crossmatch (PCC). DGF occurred in 3.1% of CLDKT, 3.5% of PLNF, 5.7% of PFNC, and 7.6% of PCC recipients, which translated to higher DGF for PCC recipients (aOR = 1.03 1.682.72 ). However, the impact of DGF on mortality and DCGF risk was no higher for ILDKT than CLDKT (p interaction > .1). AR developed in 8.4% of CLDKT, 18.2% of PLNF, 21.3% of PFNC, and 21.7% of PCC recipients, which translated to higher AR (aOR PLNF = 1.45 2.093.02 ; PFNC = 1.67 2.403.46 ; PCC = 1.48 2.243.37 ). Although the impact of AR on mortality was no higher for ILDKT than CLDKT (p interaction = .1), its impact on DCGF risk was less consequential for ILDKT (aHR = 1.34 1.621.95 ) than CLDKT (aHR = 1.96 2.292.67 ) (p interaction = .004). Providers should consider these risks during preoperative counseling, and strategies to mitigate them should be considered.

Islet allotransplantation in the United States (US) is facing an imminent demise. Despite nearly three decades of progress in the field, an archaic regulatory framework has stymied US clinical practice. Current regulations do not reflect the state-of-the-art in clinical or technical practices. In the US, islets are considered biologic drugs and "more than minimally manipulated" human cell and tissue products (HCT/Ps). In contrast, across the world, human islets are appropriately defined as "minimally manipulated tissue" and not regulated as a drug, which has led to islet allotransplantation (allo-ITx) becoming a standard-of-care procedure for selected patients with type 1 diabetes mellitus. This regulatory distinction impedes patient access to islets for transplantation in the US. As a result only 11 patients underwent allo-ITx in the US between 2016 and 2019, and all as investigational procedures in the settings of a clinical trials. Herein, we describe the current regulations pertaining to islet transplantation in the United States. We explore the progress which has been made in the field and demonstrate why the regulatory framework must be updated to both better reflect our current clinical practice and to deal with upcoming challenges. We propose specific updates to current regulations which are required for the renaissance of ethical, safe, effective, and affordable allo-ITx in the United States.

Multiphysics coupling of neutronics/thermal-hydraulics models is essential for accurate modeling of nuclear reactor systems with physics feedback. In this work, SCALE/TRACE coupling is used for neutronic analysis and spent fuel validation of BWR assemblies, which have strong coolant feedback. 3D axial power profiles with coolant feedback are captured in these advanced simulations. The methodology is applied to two BWR assemblies (2F2DN23/SF98 and 2F2D1/F6), discharged from the Fukushima Daini-2 unit. Coupling is performed externally, where the SCALE/T5-DEPL module transfers axial power data in all axial nodes to TRACE, which in turn calculates the coolant density and temperature for each of these nodes. Within a burnup step, the data exchange process is repeated until convergence of all coupling parameters (axial power, coolant density, and coolant temperature) is observed. Analysis of axial power, criticality, and coolant properties at the assembly level is used to verify the coupling process. The 2F2D1/F6 benchmark seems to have insignificant void feedback compared to 2F2DN23/SF98 case, which experiences large power changes during operation. Spent fuel isotopic data are used to validate the coupling methodology, which demonstrated good results for uranium isotopes and satisfactory results for other actinides. This work has a major challenge of lack of documented data to build the coupled models (boundary conditions, control rod history, spatial location in the core, etc.), which encourages more advanced methods to approximate such missing data to achieve better modeling and simulation results.

This work presents two different methods for quantifying and propagating the uncertainty associated with fuel composition at end of life for cask criticality calculations. The first approach, the computational approach uses parametric uncertainty including those associated with nuclear data, fuel geometry, material composition, and plant operation to perform forward depletion on Monte-Carlo sampled inputs. These uncertainties are based on experimental and prior experience in criticality safety. The second approach, the data-driven approach relies on using radiochemcial assay data to derive code bias information. The code bias data is used to perturb the isotopic inventory in the data-driven approach. For both approaches, the uncertainty in keff for the cask is propagated by performing forward criticality calculations on sampled inputs using the distributions obtained from each approach. It is found that the data driven approach yielded a higher uncertainty than the computational approach by about 500 pcm. An exploration is also done to see if considering correlation between isotopes at end of life affects keff uncertainty, and the results demonstrate an effect of about 100 pcm.

BACKGROUND Desensitization protocols for HLA-incompatible living donor kidney transplantation (ILDKT) vary across centers. The impact of these, as well as other practice variations, on ILDKT outcomes remains unknown. METHODS We sought to quantify center-level variation in mortality and graft loss following ILDKT using a 25-center cohort of 1358 ILDKT recipients with linkage to Scientific Registry of Transplant Recipients for accurate outcome ascertainment. We used multilevel Cox regression with shared frailty to determine the variation in post-ILDKT outcomes attributable to between-center differences and to identify any center-level characteristics associated with improved post-ILDKT outcomes. RESULTS After adjusting for patient-level characteristics, only 6 centers (24%) had lower mortality and 1 (4%) had higher mortality than average. Similarly, only 5 centers (20%) had higher graft loss and 2 had lower graft loss than average. Only 4.7% of the differences in mortality (P < 0.01) and 4.4% of the differences in graft loss (P < 0.01) were attributable to between-center variation. These translated to a median hazard ratio of 1.36 for mortality and 1.34 of graft loss for similar candidates at different centers. Post-ILDKT outcomes were not associated with the following center-level characteristics: ILDKT volume and transplanting a higher proportion of highly sensitized, prior transplant, preemptive, or minority candidates. CONCLUSIONS Unlike most aspects of transplantation in which center-level variation and volume impact outcomes, we did not find substantial evidence for this in ILDKT. Our findings support the continued practice of ILDKT across these diverse centers.

The work presents validation of the TRAC/RELAP Advanced Computational Engine (TRACE) code for natural circulation two-phase flow in a vertical annulus. Natural circulation experiments were recently...

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem. Keywords: Uncertainty quantification, GMDH, Surrogate modeling, Deep learning, Reactor simulations

Reliance on point estimates when developing hybrid energy systems can over/underestimate system performance. Analyzing the sensitivity and uncertainty of large-scale hybrid systems can be a challenging task due to the large number of design parameters to be explored. In this work, a comprehensive and efficient sensitivity/uncertainty methodology is applied on two fuel cell hybrid systems to help analysts to investigate hybrid systems more efficiently. This methodology also includes a step-by-step approach to perform design optimization under uncertainty of energy systems. The two hybrid systems are: molten carbonate fuel cell with thermoelectric generator (MCFC-TEG) and phosphoric acid fuel cell with refrigerator (PAFC-REF). Various sensitivity and uncertainty methods are utilized to analyze the design parameters and their effect on the performance of these two systems. These methods perform local and regression-based sensitivity analysis, Monte Carlo uncertainty propagation, parameter screening, and variance decomposition. Detailed approach is adopted to identify and rank the influential design parameters for each system. Results demonstrate that the optimum power output of the MCFC-TEG has 10% uncertainty, driven mainly by the operating temperature, cahtode activation energy, TEG figure of merit, and TEG thermal conductivity. However, PAFC-REF is more reliable with larger power output and 1.4% uncertainty, driven by the charge transfer coefficient, heat transfer in the refrigeration cycle, cold reservoir temperature, and operating temperature. Based on this identification, design optimization under uncertainty is performed using these sensitive parameters to improve the system performance through increasing the power output and reducing its uncertainty.

Fourth-generation nuclear reactor concepts are focused on reaching a high level of fuel burnup, high coolant temperatures, and high safety levels (including passive safety systems). A desig...

The documentation of most nuclear thermal-hydraulics codes does not provide sufficient information on uncertainty of physical models (e.g. interfacial heat transfer coefficients). These models were derived based on experimental data and implemented as empirical correlations in the computational code. The uncertainty quantification for the relevant output quantity (e.g. Peak Cladding Temperature) requires estimation of the Probability Density Functions (PDFs) of the code inputs, such as physical models. In this paper, we investigate the effect of boundary conditions (outlet pressure, inlet liquid temperature, and inlet flow rate) on the uncertainty of two physical models (the interfacial friction coefficient and the wall to liquid friction coefficient). The boundary conditions effect was accounted for by adding a bias term to the mathematical framework of two existing methods for Inverse Uncertainty Quantification (IUQ): the Maximum Likelihood Estimation (MLE) method and the Maximum A Posterior (MAP) method. The two methods were demonstrated using the BFBT benchmark, experimental data was compared to code predictions of the RSTART thermal-hydraulics code for two different cases: without and with bias term. The results show an evident improvement in code prediction when the bias term is used. Finally, a validation set of experimental data was used to investigate the possibility of data overfitting, and the proposed methodology showed absence of overfitting when bias terms are used.

In present work the coupling of neutronics code PARCS and thermo-hydraulics code TRACE was performed with adoption of GRS (Gesellschaft fur Anlagen- und Reaktorsicherheit) uncertainty method for rod ejection accident in VVER-1000 reactor core. The determination of input parameters, which represent source of possible uncertainties, for both codes was based on the Phenomenon Identification and Ranking Tables (PIRT) for rod ejection accident in pressurized water reactor (PWR) established by the U.S. Nuclear Regulatory Commission (U.S. NRC). Following input parameters in PARCS were selected: delayed neutron fraction precursor groups, macroscopic cross-section data for density of moderator, temperature of fuel and temperature of moderator. For TRACE code input uncertainty of thermal conductivity of fuel, gap and cladding were defined. The results obtained from uncertainty analyses imply that macroscopic cross-section data of moderator temperature is the most influential uncertainty among the input parameters in a neutronics code. On the other hand, uncertainty of thermal conductivity of uranium fuel significantly affects the results of coupled calculation. The influence of input uncertainties were investigated on the calculated results, mainly for core thermal power, fuel centerline temperature, total reactivity, moderator density, and fuel temperature reactivity.

Proper quantification of the uncertainties in the input parameters of a thermal-hydraulics code (e.g. physical models) is an essential part of the forward Uncertainty Quantification (UQ) problem. Such quantification can be systematically achieved by solving the Inverse Uncertainty Quantification (IUQ) problem using data from code predictions and experimental measurements. The IUQ problem is highly dependent on several factors such as geometry and discretization. In this paper, we study the effect of mesh refinement on the statistical parameters of three uncertain physical models (interfacial friction coefficient, wall to liquid friction coefficient and critical heat flux). A mathematical framework based on MLE and MAP algorithms is implemented to perform IUQ for the physical models of the thermal-hydraulics code RSTART based on experimental data from the OECD/NEA BWR Full-size Fine-mesh Bundle Test (BFBT) benchmark. Sensitivity analysis required for the IUQ problem was achieved by implementing discrete adjoint methods in the thermal-hydraulics code. The statistical parameters of the three physical models were obtained by implementing the MLE and MAP methods using code predictions that correspond to three different mesh sizes. Results from MLE and MAP showed a noticeable effect of mesh refinement on the estimates of the mean and a negligible effect on the estimates of the variance of the physical models. The compensation of error due to mesh refinement was studied by running the code with perturbation values calculated using the three different mesh sizes. It was shown that the results based on the most refined mesh led to best agreement between experimental data and code prediction.

This paper analyzes a new method for reducing boiling water reactor fuel temperature during a Loss of Coolant Accident (LOCA). The method uses a device called Reverse Flow Restriction Device (RFRD) at the inlet of fuel bundles in the core to prevent coolant loss from the bundle inlet due to the reverse flow after a large break in the recirculation loop. The device allows for flow in the forward direction which occurs during normal operation, while after the break, the RFRD device changes its status to prevent reverse flow. In this paper, a detailed simulation of LOCA has been carried out using the U.S. NRC's TRACE code to investigate the effect of RFRD on the flow rate as well as peak clad temperature of BWR fuel bundles during three different LOCA scenarios: small break LOCA (25% LOCA), large break LOCA (100% LOCA), and double-ended guillotine break (200% LOCA). The results demonstrated that the device could substantially block flow reversal in fuel bundles during LOCA, allowing for coolant to remain in the core during the coolant blowdown phase. The device can retain additional cooling water after activating the emergency systems, which maintains the peak clad temperature at lower levels. Moreover, the RFRD achieved the reflood phase (when the saturation temperature of the clad is restored) earlier than without the RFRD. Keywords: LOCA, Reverse flow, TRACE, BWR, Thermal-hydraulic

A framework for model evaluation and uncertainty quantification (UQ) is presented with applications oriented to nuclear engineering simulation codes. Our framework is inspired by the previous research on Bayesian statistics and model averaging. The methodology is demonstrated by performing UQ of three thermal-hydraulic computer codes used for two-phase flow simulation inside nuclear reactors, and conclusions regarding their performance are drawn. The complexity of the framework implementation depends upon the information to be drawn about the models as well as the nature of the models and the data. Uncertainties inherent in the input parameters and experimental data, along with predictive and model-form uncertainty can be quantified in this methodology. A composite (average) model based on the competent models can be created for improved response prediction. Two benchmarks featuring steady-state void fraction data in full-scale light water reactor (LWR) channels are used to demonstrate the methodology. The results show that the three models/codes demonstrate variable competitiveness in reproducing the data (i.e. goodness of fit with the data). There is no consistent trend at which each code excels. The predictive uncertainty (representing individual model deficiency or discrepancy) dominates the model-form uncertainty for many cases in this study due to two reasons: (1) presence of a single competent model for a specific response and (2) poor agreement with experimental data for certain responses at which nuclear codes struggle, such as low pressure and subcooled boiling conditions. In general, improvements in composite predictions (based on posterior model weights) are observed for BFBT data, while slight improvement is found for PSBT. For PSBT, the predictive uncertainty of RELAP5 and TRACE dominates the response uncertainty causing weak improvement. Additional efforts are needed to improve the closure models of these codes in future to reduce the model discrepancy contribution. This framework can be utilized for this purpose at which various closure models for the same code can be assessed in terms of their effect on the final response uncertainty. The proposed framework is flexible and extendable to other types of physics, models, and data. Developing the underlying methodology of calculating the model weights is the main focus in the subsequent studies.

In this study, an analysis on burnup credit for cask criticality safety in BWR spent fuel is conducted. Accurate burnup credit can be used to reduce overly conservative safety margins to increase shipping and storage efficiency while maintaining criticality level within regulatory limits. This analysis is based on advanced lattice depletion models that capture various complexities associated with BWR operation. This paper describes the second part of the two-part study which performs an out-of-core analysis of spent fuel in a transportation/storage cask. The first part of the study (Radaideh et al., 2019) developed the set of depletion models used here. In this paper, the spent fuel compositions resulting from these depletion models are used for cask criticality calculations. Uncertainty quantification of cask k e f f is performed by combining the uncertainty in isotope inventory, nuclear data, and the statistical sampling in KENO-V.a. The uncertainty in isotopic inventory is quantified by performing a validation analysis by comparing spent fuel compositions calculated by 2D TRITON to experimentally determined spent-fuel assay data for three reactors: Fukushima Daini-2, Cooper-1, and Gundermmingen-A. The validation results demonstrate good agreement for the uranium isotopes as compared to the plutonium isotopes. Also, it was found that the uncertainty in cask k e f f is dominated by the isotopic uncertainty and can reach about 2500 pcm, and as low as about 1700 pcm. Final results show that axial power profile, axial coolant density, control rod modeling, and the presence of gadolinium in 3D simulations have the largest effects on BWR burnup credit. This implies the need for detailed 3D modeling for accurate BWR burnup credit analysis. In addition, based on the UQ analysis considering both actinide only and actinide and fission products sets, the cask remains subcritical within 2 σ for all depletion cases analyzed (C0-C9), even though the cask is assumed to be flooded with water and the lattices are discharged at their peak reactivity.

Analyzing the variance of complex computer models is an essential practice to assess and improve that model by identifying the influential parameters that cause the output variance. Variance-based sensitivity analysis is the process of decomposing the output variance into components associated with each input parameter. In this study, we applied a new concept of variance-based sensitivity analysis inspired by the game theory proposed by Shapley. The technique is called the Shapley effect, and it investigates the contribution of each input parameter as well as its interactions with every other parameter in the system by exploring all possible permutations between them. The Shapley effect is compared to the common Sobol indices technique (first order and total effects) to investigate their performance under correlated and uncorrelated parameters. The Shapley effect demonstrated superior performance when compared to the Sobol indices for correlated input parameters. Shapley effect captured the correlation between the input parameters, expressing the variance contribution in a single index instead of two indices, and normalization of the fractional indices is preserved without over or underestimation. On the other hand, the two algorithms we selected to calculate Sobol indices under correlated inputs experienced different issues including: over/underestimating the output variance, first order effect could be larger than the total effect, possibility of negative indices, unnormalized fractional indices, and difficulty to interpret the results. However, Sobol showed satisfactory performance when the inputs are uncorrelated as the numerical values and input ranking were in good agreement with Shapley effect. The main disadvantage of Shapley effect is its large computational cost especially for high dimensional problems where the number of possible input subsets becomes very large. The results of our tests showed that the thermal fission cross-section carried most of the uncertainty at BOL, and its contribution declines after fuel burnup, which is replaced by the uncertainty contribution of the fast cross-section parameters.

In this study, the Energy Absorption Buildup Factor (EABF) of different tissue equivalent materials have been calculated using the Monte Carlo code MCNP5. The results of MCNP5 calculations are validated against the results obtained using the Geometric Progression (G-P) fitting method. The energy range for the gamma rays was 0.1–15 MeV and penetration depth was up to 20 mfps. Chemical compositions for different tissue materials are implemented into the MCNP5 to estimate the buildup factor for each tissue type. Energy and penetration depth dependence of the EABF are also analyzed. The results show that the adipose tissue has the largest buildup factor while the bone tissue has the lowest. Comparison of MCNP5 results with the G-P fitting method demonstrated a good agreement, with 90% of all MCNP5 EABF calculated values deviating less than 5% from the G-P. The average deviation for all EABF values is 2.3%. The results from this study can be used for radiotherapy treatment, radiation dosimetry and for medical diagnostics based on the practical thicknesses and energy ranges investigated.

Delayed neutrons, which are described by kinetic parameters, are significant for nuclear reactor operation as they make nuclear reactors controllable. Modern lattice physics codes such as TRITON and Polaris in SCALE code system, generate kinetic parameters for a given material composition. The calculation is performed by summation of the delayed neutron data for each precursor isotope and then weighting is performed by real and/or adjoint neutron spectrum. Quantifying uncertainties in the weighted kinetic parameters is important for assembly and core calculations. Understanding uncertainty in modeling scenarios involve kinetic parameters (e.g. transients), requires propagating uncertainty in the weighted kinetic parameters due to uncertainties in the fundamental nuclear data libraries, including delayed neutron data. In this work, uncertainty analysis of the homogenized (also called weighted or macroscopic) kinetic parameters has been performed using Sampler, a module in SCALE code system, to investigate the effect of fundamental nuclear and delayed neutron data uncertainties on the weighted kinetic parameters. Two major sources of uncertainties were considered: (1) fundamental nuclear data (i.e. cross-sections, fission yield, decay data) and (2) nuclide-dependent group-wise delayed neutron data based on reported experimental measurements. In this study, a new capability was developed through SCALE code system to allow propagation of delayed neutron data uncertainties. Preliminary analysis demonstrated 7% uncertainty in β eff at Beginning of Life (BOL) and increased to 15% after fuel burnup. Decay constant groups showed lower uncertainty than delayed neutron fraction groups, both at BOL and End of Life (EOL). Delayed neutron fraction responses showed high correlation to each other which is expected to be due to the cross-section covariances reported in SCALE data libraries as well as the normalization condition of the nuclide-dependent DNF. Different sources of U-235 delayed neutron data were compared, and the results showed that the uncertainty calculated by Tuttle data was bounded by other sources. The current study can be extended to calculate kinetic parameters and their uncertainties for more advanced LWR applications.

Verification, validation and uncertainty quantification (VVUQ) have become a common practice in thermal-hydraulics analysis. An important step in the uncertainty analysis is the sensitivity analysis of various uncertain input parameters. The common approach for computing the sensitivities, e.g. variance-based and regression-based methods, requires solving the governing equation multiple times, which is expensive in terms of computational cost. An alternative approach to compute the sensitivities is the adjoint method. The cost of solving an adjoint equation is comparable to the cost of solving the governing equation. Once the adjoint solution is available, the sensitivities to any number of parameters can be obtained with little cost. However, successful application of adjoint sensitivity analysis to two-phase flow simulations is rare. In this work, an adjoint sensitivity analysis framework is developed based on the discrete adjoint method and a new implicit forward solver. The framework is tested with the faucet flow problem and the BFBT benchmark. Adjoint sensitivities are shown to match analytical sensitivities very well in the faucet flow problem. The adjoint method is used to propagate uncertainty in input parameters to the void fraction in the BFBT benchmark test. The uncertainty propagation with the adjoint method is verified with the Monte Carlo method and is shown to be efficient.

A new method is proposed to solve the two-phase two-fluid six-equation model. A Roe-type numerical flux is formulated based on a very structured Jacobian matrix. The Jacobian matrix with arbitrary equation of state is formulated and simplified with the help of a few auxiliary variables, e.g. isentropic speed of sound. Because the Jacobian matrix is very structured, the eigenvalue and eigenvector can be obtained analytically. An explicit Roe-type numerical solver is constructed based on the analytical eigenvalue and eigenvector. A critical feature of the method is that the formulation of the solver does not depend on the form of the equation of state. The proposed method is applicable to realistic two-phase problems. It is applied to the BWR Full-size Fine-mesh Bundle Test (BFBT) benchmark. Considering simplified physical models, the solutions are in very good agreement with those from both existing codes and experiment data. The numerical solver using analytical eigenvalue and eigenvector is shown to be stable and robust.

Introduction: Living donor transplantation of kidneys accounts for one quarter of transplants performed in the United States. Careful screening of psychiatric history is a standard part of the donor evaluation. Little is known about the impact of psychiatric history on post-donation course and pain experience. Research Question: This study investigated whether psychiatric history was associated with pain and related outcomes among living kidney donors. Design: A retrospective medical record review was conducted of 75 living kidney donors who underwent laparoscopic donor nephrectomy. All donor candidates completed a psychological evaluation and were approved for donation by a multidisciplinary committee. History of psychiatric diagnosis and psychiatric medication use were obtained from donors’ psychological evaluation reports. Data on pain and related outcomes (ie, history of prescribed pain medication, post-donation pain, opioid use, length of hospital stay, post-donation emergency department visits), as well as demographic and donation-related characteristics were also abstracted from medical records. Results: Psychiatric history, including current or historical psychiatric diagnosis or psychiatric medication use, in living kidney donors who were evaluated and approved for donation by a transplant psychologist was not associated with greater perceived pain, greater use of opioid pain medication in the post-operative period, longer hospital stays, or more frequent post-donation emergency department visits. Discussion: The findings demonstrate that carefully screened donors with a psychiatric history have comparable pain-related outcomes as donors without a psychiatric history. This study highlights the importance of the pre-donation psychological evaluation in promoting positive postdonation outcomes through careful selection of donor candidates.

The Food and Drug Administration (FDA) has been regulating human islets for allotransplantation as a biologic drug in the US. Consequently, the requirement of a biological license application (BLA) approval before clinical use of islet transplantation as a standard of care procedure has stalled the development of the field for the last 20 years. Herein, we provide our commentary to the multiple FDA’s position papers and guidance for industry arguing that BLA requirement has been inappropriately applied to allogeneic islets, which was delivered to the FDA Cellular, Tissue and Gene Therapies Advisory Committee on 15 April 2021. We provided evidence that BLA requirement and drug related regulations are inadequate in reassuring islet product quality and potency as well as patient safety and clinical outcomes. As leaders in the field of transplantation and endocrinology under the “Islets for US Collaborative” designation, we examined the current regulatory status of islet transplantation in the US and identified several anticipated negative consequences of the BLA approval. In our commentary we also offer an alternative pathway for islet transplantation under the regulatory framework for organ transplantation, which would address deficiencies of in current system.