Primary large B-cell lymphoma of immune-privileged sites (IP-LBCL) encompasses a spectrum of relatively rare aggressive B-cell lymphomas, such as primary central nervous system lymphoma (PCNSL), primary testicular large B-cell lymphoma (PTL), and primary vitreoretinal large B-cell lymphoma (PVRL). Macroscopically, the development of IPI-LBCL may be associated with the dysfunction of meningeal lymphatic vessels (mLVs) and the perivascular channel system formed by astrocytes. Microscopically, mutation in MYD88 and CD79B genes plays a pivotal role in the pathogenesis of IP-LBCL. Pathological examination remains the cornerstone for establishing a diagnosis of IP-LBCL. Moreover, traditional imaging is now supplemented by a suite of advanced diagnostic methods, including cytological, genetic, immunological, multiple omics, and molecular biological, which collectively enhance the diagnostic accuracy of IP-LBCL. Despite these advancements, the high recurrence rates and attendant high mortality rates pose significant challenges to achieving long-term survival in IP-LBCL patients. However, the emergence of novel therapeutic agents, such as Bruton's tyrosine kinase inhibitors (BTKi), immune checkpoint inhibitors, immunomodulators, and anti-CD19 chimeric antigen receptor T (CAR-T) cell therapy, has offered promising new avenues for the treatment of IP-LBCL, demonstrating remarkable anti-tumor efficacy in recent years. This review delves into the epidemiology, pathogenesis mechanisms, diagnosis approaches, therapeutic strategies, and prognosis factors associated with IP-LBCL. It meticulously examines the parallels and divergences between the National Comprehensive Cancer Network (NCCN) and European Society for Medical Oncology (ESMO) guidelines, enhancing the professional comprehension of the complexities inherent to IP-LBCL.

As reported, 40% of patients with diffuse large B-cell lymphoma (DLBCL) still experience refractory disease or relapse after the standard R-CHOP (cyclophosphamide, doxorubicin, vincristine, and pre-dnisone+rituximab) therapy.1,2 Various salvage therapeutic strategies, including autologous hemato-poietic stem cell transplantation, have been attempted to overcome the resistance to treatment and improve overall survival (OS). Nevertheless, the disease still relapses in 30% of patients.3,4 Regarding immunochemotherapy, chimeric antigen receptor (CAR) T-cell therapy has been shown to be promising for treating patients with refractory or relapsed DLBCL (rrDLBCL). However, CD19-CAR T-cell therapy has shown only a 40% to 54% complete remission (CR) rate in patients with rrDLBCL,5-8 which may be related to the genetic heterogeneity of the rrDLBCLs. The advent of high-throughput next-generation sequencing technology has rapidly increased our knowledge of genomic alterations of DLBCL.9-11 Several reports have proposed the concept of molecular subtypes.12-16 Distinct classification describes partially overlapping features, suggesting the existence of molecular subtypes and guiding novel-targeted therapy. It is unclear whether the diverse molecular subgroups experience different efficacy of CAR T-cell therapy. To investigate, we performed targeted deep sequencing of 92 hematologic-related genes in a cohort of 105 patients with rrDLBCL, in which most of the patients underwent CAR T-cell immunotherapy after having a poor response to multiple lines of treatment. This trial is registered on the Chinese Clinical Trials Registry as #ChiCTR1900020980. One hundred five patients with rrDLBCL diagnosed from 2019 through 2020, including 6 patients with transformed follicular lymphoma, 2 with transformed mucosa-associated lymphoid tissue lymphoma, and 2 with transformed chronic lymphocytic leukemia lymphoma, were recruited for the study. Eighty-four patients (86%, 84/105) had been treated with CAR T-cell therapy before enrollment in the study. The patients were followed up until 15 April 2021. The study was approved by the Institutional Review Board of Boren Hospital. Informed consent was obtained from all patients in accordance with the Declaration of Helsinki. The baseline clinical characteristics of the 105 patients included in the study are summarized in Table 1 and supplemental Table 1. The median age of the entire cohort at initial diag-nosis was 49 (range, 13-79) years. Twenty-nine patients (27.6%) had a relatively high-risk International Prognostic Index score of 4 to 5. The median time of relapse was 12.7 months. The median number of chemotherapy cycles was 11. Among our cohort of patients, 11 with germinal center B cells (11 of 31, 35.5%) were classified as "double hit" or "triple hit" (DH/TH), harboring translocations of MYC and BCL2/BCL6, whereas only 2 patients without germinal center B-cells (GCB; 2 of 74; 2.7%) were classified as DH/TH. Most of the patients presented with advanced disease. Written informed consent was obtained from each patient, and the study was approved by the Ethics Committee at the Beijing Boren Hospital, according to guidelines of the 1975 Declaration of Helsinki.

Background: This study aims to evaluate the prognostic value of Serum Amyloid A (SAA) and misfolded Transthyretin (TTR) in Relapsed/Refractory Diffuse Large B-Cell Lymphoma (R/RDLBCL). Methods: A total of 50 DLBCL patients were included in the present study. Among these patients, 30 patients had R/R-DLBCL, 20 patients had remission/stabilization DLBCL, and 10 patients had chronic lymphadenitis. The SELDI technique, Tris-Tricine Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (Tris-Tricine-SDS-PAGE), and shotgun-LTQ-MS method were used to determine and identify the protein of SAA and TTR in R/R-DLBCL. The bioinformatics technique was used to determine the structure and function of the protein. The clinical feature data were statistically analyzed using SPSS 21.0 software, and chi-square test, Kaplan-Meier curve and logrank test ware used. Results: A molecular weight of approximately 12,000 Da and 14,000 Da were found in serum of R/R-DLBCL in protein finger graphics and Tris-Tricine-SDS-PAGE. Then, these were identified as SAA and TTR. The high expression of SAA and TTR protein (SAA+TTR+) was significantly associated with the extranodal lesion, and high level LDH and NCCN-IPI scores (P=0.017, P=0.017 and P=0.008, respectively), and correlates with the non-GCB type (the Φ correlation coefficient was 0.538) in R/R-DLBCL patients. Furthermore, the high expression of TTR protein (TTR+) was significantly associated with high levels of LDH, extranodal lesion C-MYC expression and non-GCB in R/R-DLBCL patients (The P-value was 0.028, P=0.003, the Φ correlation coefficients were 0.305 and 0.385, respectively) and the high expression of SAA protein (SAA+) was significantly associated with B-symptoms, high level of LDH and non-GCB in R/R-DLBCL patients (The P-values were 0.02, 0.011, Φ correlation coefficient was 0.390, respectively). The survival time of the SAA+ group, TTR+ group and SAA+TTR+ group were shorter than that of the negative group (P=0.001, P=0.034 and P=0.003, respectively). Multivariate analysis showed that LDH levels were an independent risk factor of poor prognosis (P<0.05). Conclusion: Both the SAA and misfolded TTR were poor prognosis factors for R/R-DLBCL.

Analytical solutions of the two dimensional triangular and square lattice Boltzmann BGK models have been obtained for the plain Poiseuille flow and the plain Couette flow. The analytical solutions are written in terms of the characteristic velocity of the flow, the single relaxation time τ and the lattice spacing. The analytic solutions are the exact representation of these two flows without any approximation.

We are concerned here with the analysis and partition of uncertainty into component pieces, for a model prediction problem for flow in porous media.

A new algorithm is introduced for upscaling relative permeabilities, and tested in simulations of two-dimensional reservoir displacement processes. The algorithm is similar to existing algorithms for computing upscaled relative permeabilities from subgrid simulations, but uses new boundary conditions for the pressure field. The new 'effective flux boundary conditions' were introduced in a previous paper and provide a more accurate estimate of flux through high permeability channels. The algorithm was tested in conjunction with uniform grid coarsening and upscaled absolute permeabilities for a broad range of coarsenings. The permeability fields were highly heteroge-neous and layered, and were obtained from synthetic data and from conditioned realizations of actual oil reservoirs. The algorithm was tested for a wide variety of grid aspect ratios, and for both viscous-and gravity-dominated flow. Typical fine grids were of the order of 100×100 cells; the coarsest scaled-up grids were on the order of 5×5 cells. The quality of scale up was evaluated by comparing oil cut curves for the fine and coarse grid simulations. We consistently obtained excellent agreement, even at the coarsest levels of scale up.

We consider numerical solutions of the Darcy and Buckley–Leverett equations for flow in porous media. These solutions depend on a realization of a random field that describes the reservoir permeability. The main content of this paper is to formulate and analyze a probability model for the numerical coarse grid solution error. We explore the extent to which the coarse grid oil production rate is sufficient to predict future oil production rates. We find that very early oil production data is sufficient to reduce the prediction error in oil production by about 30%, relative to the prior probability prediction.

We present a prediction methodology for reservoir oil production rates which assesses uncertainty and yields confidence intervals associated with its prediction. The methodology combines new developments in the traditional areas of upscaling and history matching with a new theory for numerical solution errors and with Bayesian inference. We present recent results of coworkers and ourselves. Introduction A remarkable development in upscaling 2 allows reduction in computational work by factors of more than 10,000 compared to simulations using detailed geological models, while preserving good fidelity to the oil cut curves generated from solutions of the highly detailed geologies. In common engineering practice, the detailed geology models are too expensive for routine simulation. This is especially the case if an ensemble of realizations of the reservoir is to be explored. The ensemble allows consideration of distinct geological scenarios, an issue of greater importance in many cases than errors associated with upscaling of detailed geology to obtain a coarse grid solution. Upscaling allows rapid solutions and is a key to good history matching. We formulate history matching probabilistically to allow quantitative estimates of prediction uncertainty . A probability model is constructed for numerical solution errors. It links the history match to prediction with confidence intervals. The error analysis establishes the accuracy of fit to be demanded by the history match. It defines a Bayesian posterior probability for the unknown geology. Thus history matching defines a revised ensemble of geologies, with revised probabilities or weights. Prediction is based on the forward solution, averaged with these weights. Confidence intervals are also defined by the probability weights for the ensemble together with error probabilities for the forward solution. Results of the prediction methodology will be described, based on simulated geologies and simulated reservoir flow production rates. Efficient scaleup allows a sizable number of geologies to be considered. The Bayesian framework incorporates prior knowledge (for example from geostatistics or seismic data) into the prediction. We show that a history match to past production rates improves prediction significantly. The plan of this paper is to pick one fine grid reservoir from an ensemble and regard its solution as a stand in for production data. Other reservoirs in the ensemble are evaluated on the basis of the quality of their match to this data. They are upscaled, simulated on a coarse grid, and the upscaled solution is compared to production history from the data. Probability of mismatch between the coarse grid solution and the data weights each realization in a balanced manner according to (a) its prior probability and (b) the quality of its match to data. We thus define a posterior probability on the ensemble, which is used for prediction. Uncertainty in the prediction has two sources: uncertainty in the geology, or history match, as discussed above, and uncertainty in the forward simulation, also conducted on coarse grids. The total uncertainty receives contributions from these two sources, and its analysis leads to confidence intervals for prediction. The intended application of this prediction methodology is to guide reservoir development choices. For this purpose, simulation of an ensemble of reservoir scenarios is important to explore unknown geological possiblities. Statistical methods are important to assess the ensemble of outcomes. The methods are intended for use by reservoir managers and engineers. For this purpose, the methods will need to be augmented by inclusion of factors omitted from the present study. The significance of our methods is their ability to predict the risk, or uncertainty associated with production rate forecasts, and not just the production rates themselves. The latter feature of this method, which is not standard, is very useful for evaluation of decision alternatives. Stochastic History Matching Problem Formulation. Stochastic history matching is based on an ensemble of geological realizations. To simplify this study, we fix the geologic model aside from the SPE 66350 Prediction of Oil Production With Confidence Intervals James Glimm, SPE, SUNY at Stony Brook and Brookhaven National Laboratory; Shuling Hou, SPE, Los Alamos National Laboratory; Yoon-ha Lee, SUNY at Stony Brook; David Sharp, SPE, Los Alamos National Laboratory; and Kenny Ye, SUNY at Stony Brook. JAMES GLIMM SPE 66350 2 permeability field, which is taken to be a random variable simple form of the Darcy and Buckley-Leverett equations 0 = ∇ − = p K v λ ; 0 = ∇v , ...................(1) ( ) 0 = ⋅ ∇ + ∂ ∂ s f v t s , ...............................(2) where λ is a relative mobility, K the absolute permeability, v velocity, p pressure, s the water saturation and f the fractional flow flux. We consider these equations in a two dimensional (reservoir cross section) geometry, 1 0 ≤ ≤ x , 1 0 ≤ ≤ z in dimensionless units. Assume no flow across the boundaries 1 , 0 = z and a constant pressure drop across the boundaries 1 , 0 = x . The absolute permeability K is spatially variable, with an assumed log normal distribution. We characterize the covariance ( ) K ln by correlation lengths 50 / 1 = z l and = lx ( ) 0 1 8 0 6 0 4 0 2 0 . , . , . , . , . . Thus, ( ) K ln is actually a Gaussian mixture, and is not Gaussian. This distribution for K is called the prior distribution. Each realization is a specific choice of K . We consider an ensemble defined by 500 realizations of K , 100 for each of the five correlation lengths, selected according to the above Gaussian distribution. Each K is specified on a 100 x 100 grid (the fine grid). K and the fractional flow functions f are then upscaled to grids at the levels 5 x 5, 10 x 10, and 20 x 20. Each of the coarse grid upscaled reservoirs is also solved, in all cases for up to 1.4 pore volumes of injected fluid (1.4 PVI). We select one of the geologies, 0 i K , as representing the exact but unknown reservoir. We observe the oil cut 0 i f generated by the fine grid solution for times 0 0 t t ≤ ≤ (PVI). This data represents past, historical data, and using it, we seek to predict production for 1 0 t t t ≤ ≤ = 1.4 PVI, i.e. into the future. The solution is (a) history matching, to select a revised ensemble of geologies, which reflect agreement with history data, and (b) forward simulation, averaged over the revised (posterior) ensemble, to predict the future production. The Bayesian Framework. In the Bayesian framework, the prediction problem is solved by assigning a probability, or likelihood to any degree of mismatch between the coarse grid oil cut ( ) t c j and the observed history ( ) { } 0 0 , 0 t t t f O i ≤ ≤ = , where ( ) t fi0 is the oil cut for the reservoir 0 i K computed on the fine grid (and the fine grid is conceptually considered to be exact). The probability or likelihood of the observation given the geology K is denoted ( ) K O p | . A mismatch could arise due to measurement errors, or as we consider here, due to use of a coarse grid in a simulation analysis. According to Bayes’ theorem, the posterior probability for the geology defined by the permeability realization K is ∫ = dK K p K O p K p K O p O K p ) ( ) ( ) ( ) ( ) ( , .........................(3) where ( ) K p is the prior probability for the realization K. The prior probability is defined, for example, by methods of geostatistics 6, 7, 8, 9, , and in the present context it is defined by the above mixture of Gaussians with specified correlation lengths. In the absence of errors, there would be no mismatch, and we could accept geology j K as a history match only if ( ) ( ) t f t c i j 0 ≡ . This is of course unrealistic, as errors do occur. Since ( ) j K O p | assumes 0 i j K K = is exact, the mismatch is assumed to be due to an error in determining j c . We write j j j c f e − = as the error. Measurement errors also contribute to the mismatch likelihood, but for simplicity we concentrate on scale up and numerical solution errors only. Thus, ( ) j K O p | is the probability of

Abstract This paper describes further development and testing of the Renormalization and Nonuniform Coarsening (RNC) scale up algorithm, which was recently introduced in Ref. 13. RNC scale up combines nonuniform coarsening with upscaling of the absolute and relative permeabilities to provide coarsened descriptions of two-phase immiscible flow. We diagnose a known bias in the two-phase upscaling algorithm used in RNC. An unbiased alternative is introduced and incorporated into RNC scale up. The modified RNC scale up algorithm is tested on displacements through two dimensional cross sections with permeability and porosity data from conditioned reservoir simulations, and three dimensional linear and quarter-five-spot pattern displacements through computer generated permeability fields. Each reservoir description, measured or synthetic, contains highly heterogeneous and highly layered permeability fields. RNC scale up is used to coarsen these reservoir descriptions by a factor of 300 to 1000. The coarse grid oil cut curves show consistently close agreement with the fine grid curves in overall shape and in breakthrough time. The results are comparable to earlier results in Ref. 13 on displacements through synthetic two dimensional data.

This paper describes a new upscaling algorithm, which combines the key aspects of nonuniform coarsening methods and renormalization based multiphase methods. The algorithm is applied to a range of heterogeneous, two dimensional geological descriptions. Extensive simulation results indicate that the new algorithm is able to provide results on highly coarsened grids (5 x 5) that are in close agreement with fine grid (100 x 100) simulations, at least for the set of problems considered. This very large degree of coarsening is more than an order of magnitude greater than could be achieved with either of the original methods individually, as currently implemented. For the set of problems considered, the algorithm is unbiased and consistent, even at very large levels of scale up.

Two lattice Boltzmann models for multiphase flows, the immiscible fluid model proposed by Rothman and Keller (R–K) and the multicomponent nonideal gas lattice Boltzmann model by Shan and Chen (S–C), are studied numerically to compare their abilities to simulate the physics of multiphase flows. The test problem is the simulation of a static bubble. Isotropy, strength of surface tension, thickness of the interface, spurious currents, Laplace's law, and steadiness of the bubble are examined. The results show that the S–C model is a major improvement over the R–K model.

This paper further develops the parallel algorithm, lattice Boltzmann (LB) model for simulation of transport phenomena. The lattice Boltzmann model is based on concepts that lay between the molecular and continuum extremes and is capable of drawing information and producing phenomena from both scales. Transport processes are simulated on a lattice array of nodes or points of local interaction, making natural parallel computations. Three-dimensional laminar and turbulent flow in a cavity driven by a moving wall are simulated. The model and computer simulation correctly predicts the unsteady Taylor-Görtler-Like (TGL) vortices and corner vortices observed in experiments for mediate Reynolds numbers. Quantitative comparisons are made to those from a 2-D simulation and a 3-D experiment. The excellent agreement between the LB method and experimental work shows that the LB method has great potential for solving complex, unsteady 3-D flow. The subgrid turbulent model is introduced on the lattice Boltzmann framework and applied to 3-D cavity flow. Detailed information-rich results are obtained over a range of the Reynolds number.

It is well known that the lattice Boltzmann equation method (LBE) can model the incompressible Navier-Stokes (NS) equations in the limit where density goes to a constant. In a LBE simulation, however, the density cannot be constant because pressure is equal to density times the square of sound speed, hence a compressibility error seems inevitable for the LBE to model incompressible flows. This work uses a modified equilibrium distribution and a modified velocity to construct an LBE which models time-independent (steady) incompressible flows with significantly reduced compressibility error. Computational results in 2D cavity flow and in a 2D flow with an exact solution are reported.

A detailed analysis is presented to demonstrate the capabilities of the lattice Boltzmann method. Thorough comparisons with other numerical solutions for the two-dimensional, driven cavity flow show that the lattice Boltzmann method gives accurate results over a wide range of Reynolds numbers. Studies of errors and convergence rates are carried out. Compressibility effects are quantified for different maximum velocities and parameter ranges are found for stable simulations. The paper\u0027s objective is to stimulate further work using this relatively new approach for applied engineering problems in transport phenomena utilizing parallel computers.

A subgrid turbulence model for the lattice Boltzmann method is proposed for high Reynolds number fluid flow applications. The method, based on the standard Smagorinsky subgrid model and a single-time relaxation lattice Boltzmann method, incorporates the advantages of the lattice Boltzmann method for handling arbitrary boundaries and is easily implemented on parallel machines. The method is applied to a two-dimensional driven cavity flow for studying dynamics and the Reynolds number dependence of the flow structures. The substitution of other subgrid models, such as the dynamic subgrid model, in the framework of the LB method is discussed.

A detailed analysis is presented to demonstrate the capabilities of the lattice Boltzmann method. Thorough comparisons with other numerical solutions for the two-dimensional, driven cavity flow show that the lattice Boltzmann method gives accurate results over a wide range of Reynolds numbers. Studies of errors and convergence rates are carried out. Compressibility effects are quantified for different maximum velocities and parameter ranges are found for stable simulations. The paper's objective is to stimulate further work using this relatively new approach for applied engineering problems in transport phenomena utilizing parallel computers.

Analytical solutions of the two-dimensional triangular and square lattice Boltzmann BGK models have been obtained for the plane Poiseuille flow and the plane Couette flow. The analytical solutions are written in terms of the characteristic velocity of the flow, the single relaxation time τ, and the lattice spacing. The analytic solutions are the exact representation of these two flows without any approximation. Using the analytical solution, it is shown that in Poiseuille flow the bounce-back boundary condition introduces an error of first order in the lattice spacing. The boundary condition used by Kadanoffet al. in lattice gas automata to simulate Poiseuille flow is also considered for the triangular lattice Boltzmann BGK model. An analytical solution is obtained and used to show that the boundary condition introduces an error of second order in the lattice spacing.