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IMPORTANCE: Identifying the mechanisms of structural racism, such as racial and ethnic segregation, is a crucial first step in addressing the persistent disparities in access to live donor kidney transplantation (LDKT). OBJECTIVE: To assess whether segregation at the candidate’s residential neighborhood and transplant center neighborhood is associated with access to LDKT. DESIGN, SETTING, AND PARTICIPANTS: In this cohort study spanning January 1995 to December 2021, participants included non-Hispanic Black or White adult candidates for first-time LDKT reported in the US national transplant registry. The median (IQR) follow-up time for each participant was 1.9 (0.6-3.0) years. MAIN OUTCOME AND MEASURES: Segregation, measured using the Theil H method to calculate segregation tertiles in zip code tabulation areas based on the American Community Survey 5-year estimates, reflects the heterogeneity in neighborhood racial and ethnic composition. To quantify the likelihood of LDKT by neighborhood segregation, cause-specific hazard models were adjusted for individual-level and neighborhood-level factors and included an interaction between segregation tertiles and race. RESULTS: Among 162 587 candidates for kidney transplant, the mean (SD) age was 51.6 (13.2) years, 65 141 (40.1%) were female, 80 023 (49.2%) were Black, and 82 564 (50.8%) were White. Among Black candidates, living in a high-segregation neighborhood was associated with 10% (adjusted hazard ratio [AHR], 0.90 [95% CI, 0.84-0.97]) lower access to LDKT relative to residence in low-segregation neighborhoods; no such association was observed among White candidates (P for interaction = .01). Both Black candidates (AHR, 0.94 [95% CI, 0.89-1.00]) and White candidates (AHR, 0.92 [95% CI, 0.88-0.97]) listed at transplant centers in high-segregation neighborhoods had lower access to LDKT relative to their counterparts listed at centers in low-segregation neighborhoods (P for interaction = .64). Within high-segregation transplant center neighborhoods, candidates listed at predominantly minority neighborhoods had 17% lower access to LDKT relative to candidates listed at predominantly White neighborhoods (AHR, 0.83 [95% CI, 0.75-0.92]). Black candidates residing in or listed at transplant centers in predominantly minority neighborhoods had significantly lower likelihood of LDKT relative to White candidates residing in or listed at transplant centers located in predominantly White neighborhoods (65% and 64%, respectively). CONCLUSIONS: Segregated residential and transplant center neighborhoods likely serve as a mechanism of structural racism, contributing to persistent racial disparities in access to LDKT. To promote equitable access, studies should assess targeted interventions (eg, community outreach clinics) to improve support for potential candidates and donors and ultimately mitigate the effects of segregation.

Control of real world dynamical systems is challenging due to their nonlinear behaviour. Research in the area of controller design for underactuated systems have been majorly concentrated on fixed value controllers like Proportional Integral Derivative (PID), Linear Quadratic Regulator (LQR) etc. Incorporating the advantages of fixed value controllers with predictive control is efficient and is less explored. This article proposes a real time Model Predictive Control (MPC) technique that incorporates previously computed data to improve the closed loop performance of an unstable underactuated system, using an intelligent warm start method. Infusing memory at the start of the MPC algorithm improves the optimisation data, thereby guaranteeing stability. The proposed methodology is demonstrated on a two Degrees of Freedom (DoF) underactuated system whose dynamics are derived using Euler Lagrange technique. The results presented highlight the significance of integral action for set point tracking. Also, comparative study between conventional Discrete LQR (DLQR) strategy and proposed warm start Discrete MPC (DMPC) is presented. Results from the experiments validate the performance of the proposed predictive control

Introduction

Introduction:

During the early phase of the pandemic, studies revealed that COVID-19 patients with multiple myeloma (MM) had significantly higher hospitalization rates (56%), with 20% requiring critical care and 18% facing an increased risk of mortality. MM patients undergoing anticancer therapy showed a 55% higher risk of mortality when infected with COVID-19. Even in the post-pandemic world, MM patients remain at heightened risk for breakthrough infections and severe SARS-CoV-2 due to their immunocompromised status, necessitating hospitalization. While both active and remission MM patients exhibited worse outcomes than COVID-19 patients without cancer, limited research exists to elucidate the underlying causes. In this study, we aimed to (1) develop a model elucidating the pathogenesis and interplay between MM and severe SARS-CoV-2, and (2) explain the efficacy and therapeutic potential of Exosomal Vesicles (EVs) as an intervention against severe SARS-CoV-2, supported by clinical evidence.