Knowing When to Ignore the Numbers: Single-Center Experience Transplanting Deceased Donor Kidneys with Poor Perfusion Parameters.

BACKGROUND: Hypothermic machine perfusion is frequently used in evaluating marginal kidneys with poor perfusion parameters (PPP) contributing to delays in kidney placement or discard. We examined outcomes in deceased donor kidney transplants with PPP compared with those with optimal perfusion parameters (OPP). STUDY DESIGN: We conducted a retrospective single-center cohort study from 2001 to 2021 comparing PPP (n = 91) with OPP (n = 598) deceased donor kidney transplants. PPP was defined as terminal flow ≤80 mL/min and terminal resistance ≥0.40 mmHg/mL/min. OPP was defined as terminal flow ≥120 mL/min and terminal resistance ≤0.20 mmHg/mL/min. RESULTS: Mean terminal flow was PPP 66 ± 16 vs OPP 149 ± 21 mL/min and resistance was PPP 0.47 ± 0.10 vs OPP 0.15 ± 0.04 mmHg/mL/min (both p < 0.001). Donor age, donation after cardiac death, and terminal serum creatinine levels were similar between groups. Mean Kidney Donor Profile Index was higher among PPP donors (PPP 65 ± 23% vs OPP 52 ± 27%, p < 0.001). The PPP transplant group had more females and lower weight and BMI. Delayed graft function was comparable (PPP 32% vs OPP 27%, p = 0.33) even though cold ischemia times trended toward longer in PPP kidneys (PPP 28 ± 10 vs OPP 26 ± 9 hours, p = 0.09). One-year patient survival (PPP 98% vs OPP 97%, p = 0.84) and graft survival (PPP 91% vs OPP 92%, p = 0.23) were equivalent. PPP did predict inferior overall and death-censored graft survival long-term (overall hazard ratio 1.63, 95% CI 1.19 to 2.23 and death-censored hazard ratio 1.77, 95% CI 1.15 to 2.74). At 1 year, the estimated glomerular filtration rate was higher with OPP kidneys (PPP 40 ± 17 vs OPP 52 ± 19 mL/min/1.73 m 2 , p < 0.001). CONCLUSIONS: Short-term outcomes in PPP kidneys were comparable to OPP kidneys despite higher Kidney Donor Profile Index and longer cold ischemia times, suggesting a role for increased utilization of these organs with careful recipient selection.

Modeling COVID-19 pandemic with financial markets models: The case of Jaén (Spain).

The main objective of this work is to test whether some stochastic models typically used in financial markets could be applied to the COVID-19 pandemic. To this end, we have implemented the ARIMAX and Cox-Ingersoll-Ross (CIR) models originally designed for interest rate pricing but transformed by us into a forecasting tool. For the latter, which we denoted CIR*, both the Euler-Maruyama method and the Milstein method were used. Forecasts obtained with the maximum likelihood method have been validated with 95% confidence intervals and with statistical measures of goodness of fit, such as the root mean square error (RMSE). We demonstrate that the accuracy of the obtained results is consistent with the observations and sufficiently accurate to the point that the proposed CIR* framework could be considered a valid alternative to the classical ARIMAX for modelling pandemics.

Regenerative medicine applications: An overview of clinical trials

Insights into the use of cellular therapeutics, extracellular vesicles (EVs), and tissue engineering strategies for regenerative medicine applications are continually emerging with a focus on personalized, patient-specific treatments. Multiple pre-clinical and clinical trials have demonstrated the strong potential of cellular therapies, such as stem cells, immune cells, and EVs, to modulate inflammatory immune responses and promote neoangiogenic regeneration in diseased organs, damaged grafts, and inflammatory diseases, including COVID-19. Over 5,000 registered clinical trials on ClinicalTrials.gov involve stem cell therapies across various organs such as lung, kidney, heart, and liver, among other applications. A vast majority of stem cell clinical trials have been focused on these therapies' safety and effectiveness. Advances in our understanding of stem cell heterogeneity, dosage specificity, and ex vivo manipulation of stem cell activity have shed light on the potential benefits of cellular therapies and supported expansion into clinical indications such as optimizing organ preservation before transplantation. Standardization of manufacturing protocols of tissue-engineered grafts is a critical first step towards the ultimate goal of whole organ engineering. Although various challenges and uncertainties are present in applying cellular and tissue engineering therapies, these fields' prospect remains promising for customized patient-specific treatments. Here we will review novel regenerative medicine applications involving cellular therapies, EVs, and tissue-engineered constructs currently investigated in the clinic to mitigate diseases and possible use of cellular therapeutics for solid organ transplantation. We will discuss how these strategies may help advance the therapeutic potential of regenerative and transplant medicine.

Alterations in the molecular composition of COVID-19 patient urine, detected using Raman spectroscopic/computational analysis.

We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine 'molecular fingerprint', representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016-2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9-1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) <90 mL/min/1.73m2 (range 59-84 mL/min/1.73m2). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis-Rametrix®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of 'Long COVID-19' symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography-Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.

Modelling bursts and chaos regularization in credit risk with a deterministic nonlinear model

In this paper, we suggest a deterministic approach for modelling credit risk time series even in distressed periods (including COVID-19). We examine the Moody’s Seasoned Aaa Corporate Bond Yield Relative to Yield on 10-Year Treasury Constant Maturity as well as the ICE BofA US High Yield Index Option-Adjusted Spread and we find that the proposed model could fit well the alternation between periods of low and high volatility. This result is compared to the ARIMA-EGARCH model to determine how a chaotic deterministic model stands with respect to a stochastic model expressly designed for handling moving average, autoregression, cointegration and heteroscedastic volatility. According to recent literature, we find that both models give comparable results.