A novel approach to decision making in rice quality management using interval-valued Pythagorean fuzzy Schweizer and Sklar power aggregation operators.

The Pythagorean fuzzy set and interval-valued intuitionistic fuzzy set are the basis of the interval-valued Pythagorean fuzzy set (IVPFS) which offers an effective approach to addressing the complex uncertainty in decision-analysis processes, making it applicable across a broad spectrum of applications. This paper introduces several aggregation operators within the IVPF framework, such as the interval-valued Pythagorean fuzzy SS power weighted average operator, and the interval-valued Pythagorean fuzzy SS power geometric operator using the notion of power aggregation operators through Schweizer and Sklar (SS) operations. The existence of SS t-norms and t-conorms in the IVPF framework for addressing multi-attribute decision-making problems gives the generated operator's ability to make the information aggregation approach more adaptable compared to other current ones. The application of the proposed approach holds the potential to enhance crop yield, optimize resource utilization, and contribute to the overall sustainability of agriculture. Additionally, sensitivity and comparative analyses are provided to clarify the stability and dependability of the results acquired through this approach.

Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review.

The development of the Gleason grading system has proven to be an irreplaceable tool in prostate cancer diagnostics within urology. Despite the advancements and developments in diagnostics, there remains a discrepancy in the grading process among even the most experienced pathologists. AI algorithms have demonstrated potential in detecting cancer and assigning Gleason grades, offering a solution to the issue of significant variability among pathologists' evaluations. Our paper explores the evolving role of AI in prostate cancer histopathology, with a key focus on outcomes and the reliability of various AI algorithms for Gleason pattern assessment. We conducted a non-systematic review of the published literature to examine the role of artificial intelligence in Gleason pattern diagnostics. The PubMed and Google Scholar databases were searched to gather pertinent information about recent advancements in artificial intelligence and their impact on Gleason patterns. We found that AI algorithms are increasingly being used to identify Gleason patterns in prostate cancer, with recent studies showing promising advancements that surpass traditional diagnostic methods. These findings highlight AI's potential to be integrated into clinical practice, enhancing pathologists' workflows and improving patient outcomes. The inter-observer variability in Gleason grading has seen an improvement in efficiency with the implementation of AI. Pathologists using AI have reported successful outcomes, demonstrating its effectiveness as a supplementary tool. While some refinements are still needed before AI can be fully implemented in clinical practice, its positive impact is anticipated soon.

The Expansion of Artificial Intelligence in Modifying and Enhancing the Current Management of Abdominal Aortic Aneurysms: A Literature Review.

An abdominal aortic aneurysm (AAA) is a pathological dilation that is 3 cm or greater resulting in a bulging or balloon appearance. To meet a personalized therapeutic approach for patients, artificial intelligence (AI) can exhibit an array of applications ranging from decoding patterns from large data sets to predicting new data. The review aims to discuss how AI can assist and improve the standard of care and management plans for these patients. A comprehensive non-systematic literature review was carried out for published material on the use of AI relating to AAAs. The PubMed and Google Scholar databases were used to scout for articles relating to the title of this review. The review included 54 literature papers in this study. AI is involved on a genomic level, which assists in screening, diagnosing, and identifying individual risk factors of a patient. Personalized management plans can be created with AI predictions using patient data to reduce the risk of in-hospital mortality following a repair or due to complications. AI represents a promising group of programs aimed at improving patient management and assisting surgeons in making beneficial decisions to improve the patient's prognosis.

Current advances in the use of artificial intelligence in predicting and managing urological complications.

BACKGROUND: Artificial intelligence (AI) has emerged as a promising avenue for improving patient care and surgical outcomes in urological surgery. However, the extent of AI's impact in predicting and managing complications is not fully elucidated. OBJECTIVES: We review the application of AI to foresee and manage complications in urological surgery, assess its efficacy, and discuss challenges to its use. METHODS AND MATERIALS: A targeted non-systematic literature search was conducted using the PubMed and Google Scholar databases to identify studies on AI in urological surgery and its complications. Evidence from the studies was synthesised. RESULTS: Incorporating AI into various facets of urological surgery has shown promising advancements. From preoperative planning to intraoperative guidance, AI is revolutionising the field, demonstrating remarkable proficiency in tasks such as image analysis, decision-making support, and complication prediction. Studies show that AI programmes are highly accurate, increase surgical precision and efficiency, and reduce complications. However, implementation challenges exist in AI errors, human errors, and ethical issues. CONCLUSION: AI has great potential in predicting and managing surgical complications of urological surgery. Advancements have been made, but challenges and ethical considerations must be addressed before widespread AI implementation.

The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis.

BACKGROUND AND OBJECTIVES: This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. METHOD: A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. RESULTS: Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. CONCLUSIONS: When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

Protective effect of ischaemic preconditioning on acute and chronic renal damage following ischaemia reperfusion injury: characterisation of fibrosis development after inflammation resolution.

OBJECTIVES: Acute Kidney Injury (AKI) and Chronic Kidney Disease (CKD) are increasingly recognised as one disease continuum, rather than distinct entities, and are associated with a huge burden to healthcare services. The leading cause of AKI worldwide is Ischaemia Reperfusion Injury (IRI), most commonly seen in clinical settings of sepsis-driven hypotension. Ischaemic Preconditioning (IPC) is a strategy aimed at reducing the deleterious effects of IRI. The objectives of this study were to demonstrate an efficacious in vivo model of Kidney IRI, and the protective influence of IPC in attenuating AKI and development of renal fibrosis. METHODS: A rat model of bilateral kidney IRI was used: Male Lewis rats (n=84) were assigned to IRI, sham or IPC. In IRI, renal pedicles were clamped for 45 minutes. IPC groups underwent pulsatile IPC prior to IRI. Kidneys were retrieved at 24 hours, 48 hours, 7 days, 14 days and 28 days, and assessed histologically. RESULTS: IRI led to marked AKI (24-48 h) and renal fibrosis development by 28 days. IPC attenuated this damage, with 66% less fibrosis. Interestingly, at 14-days, the histological appearance of both IRI and IPC kidneys was rather similar, potentially representing an important transitional point at which kidneys commit to either fibrosis or recovery. This may provide a suitable inflexion point for introduction of novel anti-fibrotic therapies. CONCLUSIONS: In conclusion, we have characterised a model of kidney injury from acute to chronic phases, allowing detailed mechanistic understanding and which can be manipulated by effective treatment strategies such as IPC.

Late pregnancy in women with renal transplants: A multidisciplinary guide.

Kidney transplant recipients are at risk of complications in late pregnancy, with increased rates of pre-eclampsia, intrauterine growth restriction and preterm birth. It is recommended that these women receive more intensive monitoring after 20 weeks' gestation, ideally provided by a multidisciplinary team in a tertiary centre. This review focuses on the management of late pregnancy in kidney transplant recipients, from the perspective of different members of the multidisciplinary team. This includes evidence and guidance to inform the nephrologist, obstetrician, obstetric anaesthetist, transplant surgeon, midwife, and a summary of the woman's perspective. The review outlines a late pregnancy and early postnatal care pathway as a common algorithm to be used by the whole multidisciplinary team.

Robust Rat and Mouse Models of Bilateral Renal Ischemia Reperfusion Injury.

BACKGROUND/AIM: Acute and chronic kidney diseases are a major contributor to morbidity and mortality worldwide, with no specific treatments currently available for these. To enable understanding the pathophysiology of and testing novel treatments for acute and chronic kidney disease, a suitable in vivo model of kidney disease is essential. In this article, we describe two reliable rodent models (rats and mice) of efficacious kidney injury displaying acute to chronic kidney injury progression, which is also reversible through novel therapeutic strategies such as ischemic preconditioning (IPC). MATERIALS AND METHODS: We utilized adult male Lewis rats and adult male wildtype (C57BL/6) mice, performed a midline laparotomy, and induced warm ischemia to both kidneys by bilateral clamping of both renal vascular pedicles for a set time, to mimic the hypoxic etiology of disease commonly found in kidney injury. RESULTS: Bilateral ischemia reperfusion injury caused marked structural and functional kidney injury as exemplified by histology damage scores, serum creatinine levels, and kidney injury biomarker levels in both rodents. Furthermore, this effect displayed a dose-dependent response in the mouse model. CONCLUSION: These rodent models of bilateral kidney IRI are reliable, reproducible, and enable detailed mechanistic study of the underlying pathophysiology of both acute and chronic kidney disease. They have been carefully optimised for single operator use with a strong track record of training both surgically trained and surgically naïve operators.

Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review.

BACKGROUND: The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. METHODS: A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords. RESULTS: The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies. CONCLUSIONS: Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC's morbidity and mortality.

The influence of socioeconomic deprivation on outcomes in transplant patients infected with SARS-CoV-2 in Wales.

INTRODUCTION: SARS-CoV-2 infection has had a significant impact on vulnerable individuals including transplant patients. Socioeconomic deprivation negatively affects outcomes of many health conditions. The aim of this study was to evaluate the effect of socioeconomic deprivation on the incidence and severity of SARS-CoV-2 infection among Welsh transplant patients. METHODS: This study is a retrospective, cross-sectional study on the transplant population of Wales. The Welsh Index of Multiple Deprivation (WIMD) was used to assess the influence of socioeconomic deprivation on outcomes of Welsh transplant patients who developed SARS-CoV-2 infection. Outcome measures were the incidence of SARS-CoV-2 infection, rates of hospital and ICU admission, development of acute kidney injury (AKI) and mortality. A logistic binomial regression analysis was used to correlate the various risk factors with the incidence of SARS-CoV-2 infection. RESULTS: Two hundred and sixty-six (25%) of regular follow up patients had SARS-CoV-2 infection; of these 55 (20.7%) were admitted, 15 (5.6%) to ICU, 37 (13.9%) developed AKI, and 23 (8.6%) died. In a regression analysis, patients of younger age were associated with more (p = .001) and those with SPK (simultaneous pancreas kidney) transplant less chance of infection (p = .038), whereas social deprivation was not associated with the chance of infection (p = .14). In regression analysis increased social deprivation was associated with higher chance of AKI post SARS-CoV-2 (p = .049). CONCLUSIONS: Socioeconomic deprivation did not affect the rates or severity of SARS-CoV-2 infection apart from the degree of AKI in Welsh Transplant patients. Adherence to the preventive measures for this high-risk population must continue to remain a priority.

Experimental and numerical techniques to evaluate coal/biomass fly ash blend characteristics and potentials.

Fossil and renewable fuels are used by industrial units that produce energy-intensive products. Competitive fuel pricing encourages these fuel sources' usage globally, particularly in developing nations, which leads to large volumes of byproducts like fly ash among thermal power plant operators. The elements and compounds found in coal fly ash (CFA) and biomass fly ash (BFA) can be utilized through several engineering applications. This study aims to assess typical CFA and BFA samples quantitatively and qualitatively via techniques such as ultimate analysis (CH-S), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), X-ray diffraction (XRD), X-ray fluorescence (XRF) elemental analysis, and ash fusion temperature (AFT), to anticipate the ideal ratios of coal to biomass blends for combustion applications while adhering to environmental regulations. The optimal blend, consisting of 75 % CFA and 25 % BFA, exhibited improved carbon (C%) and hydrogen (H%) percentages, increasing from 2.5 % to 4.67 % and from 0 % to 0.12 %, respectively. These improvements were further confirmed by the observed functional groups in FTIR, indicating a rising trend in both carbon and hydroxyl groups from BFA to CFA. XRF and XRD also confirmed it and TGA also showed optimum mass loss (ML%) behavior of 14.55 % for 75CFA + 25BFA. According to slagging and fouling indices, the values of RB/A, Rs, and Fu indicate a reduction in slagging and fouling issues through the blending of CFA with BFA. Simultaneously, the fusion temperature increased from 1181 °C to 1207 °C. CFA was found to increase the AFT of the BFA from 1197 °C to 1247 °C, mitigating their propensity. This suggests that a blend of 75CFA + 25BFA results in lower to medium range of slagging and fouling. However, AFI and BAI indicate a slightly higher range. AFT analysis further validates the conclusions drawn from the indices. The ternary phase diagram shows that the ash's melting point increases in the optimum blend. This is attributed to a reduced content of K2O (<15 %) and increased proportions of >50 % CaO and SiO2, effectively inhibiting slagging, agglomeration, and deposition. Meanwhile, the blend maintains a medium level of acidity and susceptively to corrosion, as observed in the case of 75CFA + 25BFA. The identification of optimal blend ratios can be anticipated to offer essential solutions for future research, aiming to ensure smooth industrial operations and regulatory compliance in power plants.

COVID-19 economic stimulus packages, tourism industry and external debt: The influence of extreme poverty.

The COVID-19 outbreak has had a catastrophic effect on the tourism sector and poverty alleviation efforts. This is especially the case, given the crucial role the tourism sector plays in poverty alleviation and the generation of foreign exchange earnings. This study investigates the moderating influence of extreme poverty on the underlying link between the size of the tourism industry and COVID-19 Economic Stimulus Packages (ESPs) while accounting for the influence of external debt. The results show that tourism-dependent economies with a larger share of individuals living in extreme poverty introduced larger ESPs to cushion the impacts of the COVID-19 outbreak. In addition, economies with larger external debt have less fiscal and monetary leeway to alleviate the negative effects of the COVID-19 outbreak.

Quality improvement through audit: Zuclopenthixol acetate prescribing, monitoring and patient outcomes in a regional health service.

OBJECTIVE: Prescribers' expectations of Zuclopethixol Acetate's (ZA) efficacy and tolerability are shaped by clinical experience and organisational culture; however, these expectations may not be consistent with current evidence and best practice. METHODS: Quality improvement project (QIP) through a process audit of ZA prescribing, monitoring and patient outcomes (adverse events) in order to identify issues requiring intervention to align with service standards and practices. RESULTS: QIP interventions resulted in a statistically significant shift in psychiatrist oversight, identifying high dose ZA with adverse outcomes and cessation of prescribing/administration within the Emergency Department. Clinically significant changes in patterns of prescribing were observed between pre-post intervention audits. CONCLUSION: Entrenching an evidence-based QIP approach to clinical practice can effect clinically significant patterns of practice change to improve safe prescribing and drug monitoring.

Removal of ceftriaxone sodium antibiotic from pharmaceutical wastewater using an activated carbon based TiO2 composite: Adsorption and photocatalytic degradation evaluation.

The water pollution becomes a serious concern for the sustainability of ecosystems due to the existence of pharmaceutical products (ceftriaxone (CEF) antibiotic). Even in low concentration of CEF has lethal effects on ecosystem and human health. To remove CEF, TiO2 is considered as an effective and efficient nanoparticles, however its performance is reduced due to wider energy gap and rapid recombination of charge carriers. In this study, activated carbon based TiO2 (ACT-X) heterogeneous nanocomposites were synthesized to improve the intrinsic properties of TiO2 and their adsorption-photocatalytic performance for the removal of CEF. The characterization results revealed that ACT-X composites have slower recombination of charge carriers, lower energy band gap (3.05 eV), and better light absorption under visible region of light. From ACT-X composites, the ACT-4 photocatalyst has achieved highest photocatalytic degradation (99.6%) and COD removal up (99.2%). The results of radical scavengers showed that photocatalytic degradation of CEF is mainly occurred due to superoxide and hydroxyl radicals. Meanwhile, the reusability of ACT-4 up to five cycles shows more than 80% photocatalytic degradation, which make the process more economical. The highest experimental adsorption capacity is achieved up to 844.8 mg g-1 using ACT-4. The favorable and multilayer heterogeneous adsorption is carried out according to the well-fitted data with pseudo-second-order and Freundlich models, respectively. These results indicate that the carbon-based TiO2 composites can be used as a green, stable, efficient, effective, reusable, renewable, and sustainable photocatalyst to eliminate the pharmaceutical pollutants (antibiotics) via adsorption and photocatalytic degradation processes.

Semaglutide improves cardiometabolic risk factors in adults with overweight or obesity: STEP 1 and 4 exploratory analyses.

AIMS: Evaluate the effects of once-weekly subcutaneous semaglutide 2.4 mg on cardiometabolic risk factors in people with overweight/obesity without diabetes in the STEP 1 and 4 trials. MATERIALS AND METHODS: STEP 1 and 4 were phase III, 68-week, placebo-controlled trials of once-weekly semaglutide 2.4 mg combined with lifestyle intervention; STEP 4 had a 20-week semaglutide run-in and 48-week randomized withdrawal period. Participants had a body mass index ≥30 kg/m2 or ≥27 kg/m2 with one or more weight-related comorbidity, without diabetes. Pre-specified endpoints were changes in waist circumference, systolic/diastolic blood pressure (SBP/DBP), lipids, fasting plasma glucose (FPG), fasting serum insulin and antihypertensive/lipid-lowering medication use. Post-hoc assessments included non-high-density lipoprotein (HDL) cholesterol, homeostatic model assessment of insulin resistance (HOMA-IR; STEP 1 only), atherosclerotic cardiovascular disease (ASCVD) risk (American College of Cardiology/American Heart Association algorithm; STEP 1 only) and cardiometabolic risk factors by weight loss achieved (<5%, 5% to <10%, 10% to <15%, or ≥15%) (STEP 1 only). RESULTS: Of the 1961 participants in STEP 1 and 803 in STEP 4, most had one or more complication/comorbidity at baseline, with dyslipidaemia and hypertension most prevalent. In STEP 1, reductions in waist circumference, SBP, DBP, FPG, fasting serum insulin, lipids and HOMA-IR were greater with semaglutide versus placebo (p ≤ .001). Reductions in SBP, non-HDL cholesterol, low-density lipoprotein cholesterol and FPG were generally greater with semaglutide than placebo within weight-loss categories. Non-significant ASCVD risk reductions were observed with semaglutide versus placebo (p > .05). In STEP 4, improvements in waist circumference, SBP, FPG, fasting serum insulin and lipids during the semaglutide run-in (week 0-20) were maintained over week 20-68 with continued semaglutide, but deteriorated following the switch to placebo (p < .001 [week 20-68]). Net reductions in antihypertensive/lipid-lowering medication use occurred with semaglutide versus placebo (both trials). CONCLUSIONS: Semaglutide may improve cardiometabolic risk factors and reduce antihypertensive/lipid-lowering medication use versus placebo in adults with overweight/obesity without diabetes. These potential benefits were not maintained after treatment discontinuation. GOV NUMBERS: STEP 1 NCT03548935, STEP 4 NCT03548987.

Surgical Innovations in Tracheal Reconstruction: A Review on Synthetic Material Fabrication.

Background and Objectives: The aim of this review is to explore the recent surgical innovations in tracheal reconstruction by evaluating the uses of synthetic material fabrication when dealing with tracheomalacia or stenotic pathologies, then discussing the challenges holding back these innovations. Materials and Methods: A targeted non-systematic review of published literature relating to tracheal reconstruction was performed within the PubMed database to help identify how synthetic materials are utilised to innovate tracheal reconstruction. Results: The advancements in 3D printing to aid synthetic material fabrication have unveiled promising alternatives to conventional approaches. Achieving successful tracheal reconstruction through this technology demands that the 3D models exhibit biocompatibility with neighbouring tracheal elements by encompassing vasculature, chondral foundation, and immunocompatibility. Tracheal reconstruction has employed grafts and scaffolds, showing a promising beginning in vivo. Concurrently, the integration of resorbable models and stem cell therapy serves to underscore their viability and application in the context of tracheal pathologies. Despite this, certain barriers hinder its advancement in surgery. The intricate tracheal structure has posed a challenge for researchers seeking novel approaches to support its growth and regeneration. Conclusions: The potential of synthetic material fabrication has shown promising outcomes in initial studies involving smaller animals. Yet, to fully realise the applicability of these innovative developments, research must progress toward clinical trials. These trials would ascertain the anatomical and physiological effects on the human body, enabling a thorough evaluation of post-operative outcomes and any potential complications linked to the materials or cells implanted in the trachea.

Thymoglobulin Versus Alemtuzumab Versus Basiliximab Kidney Transplantation From Donors After Circulatory Death.

Introduction: The Campath, Calcineurin inhibitor (CNI) reduction, and Chronic allograft nephropathy (3C), a study comparing alemtuzumab versus basiliximab induction immunosuppression in kidney transplants, has found lower acute rejection rate with alemtuzumab but same graft survival. The aim of the current study is to evaluate the effect of induction immunosuppression (thymoglobulin, alemtuzumab, basiliximab) on the outcome of kidneys of donors after circulatory death (DCD). Methods: Data of the 274 DCD patients of the 3C obtained from the sponsor were compounded with the 140 DCD patients who received thymoglobulin in a single center with the same entry criteria as the 3C, giving 414 patients on 3 induction regimes. Results: There were more male donors (P < 0.05) and human leukocyte antigen and DR mismatched patients in the thymoglobulin group (P < 0.001). Death-censored graft survival at 6 months was 98.6% in the thymoglobulin, 95.5% in the alemtuzumab (P = 0.08), and 95.7% in the basiliximab group (P = 0.09) and at 2 years 97.9% versus 94.8% (P = 0.13, hazard ratio [HR] 2.8, 95% CI 0.7-10.9) versus 94.3% (P = 0.06, HR 3.5, 95% CI 0.9-13.6), respectively.The 2-year overall graft survival was 95% in the thymoglobulin versus 88% in the alemtuzumab (unadjusted P = 0.038, adjusted HR 2.4, 95% CI 0.99-5.9) and 91.4% in the basiliximab group (P = 0.21). The 2-year patient survival was numerically less in the alemtuzumab compared with the thymoglobulin group (91.8% vs. 97.1%, P = 0.052, HR 2.90, 95% CI 0.93-9.2). Acute rejection was 17% in the basiliximab, 4.3% in the thymoglobulin, and 6% in the alemtuzumab group (P < 0.001). Conclusion: In DCD transplants, thymoglobulin induction may provide advantage over alemtuzumab in patient survival and the same advantage as alemtuzumab over basiliximab in terms of acute rejection. Differing maintenance immunosuppression may contribute to the difference found.

SARS-CoV-2 Infection Can Lead to an Increase in Tacrolimus Levels in Renal Transplant Patients: A Cohort Study.

The aim of this study is to evaluate the effect of SARS-CoV-2 infection on serum tacrolimus levels. Tacrolimus levels of 34 transplant patients diagnosed with SARS-CoV-2 in 2020 were compared with their pre-infection values and those of a control group with alternative infections. 20 out of 34 (59%) had high levels. At diagnosis, median tacrolimus level in the SARS-CoV-2 cohort was 9.6 µg/L (2.7-23) compared to 7.9 µg/L in the control group (p = 0.07, 95% CI for difference -0.3-5.8). The ratio of post-infection to pre-infection tacrolimus values was higher in the SARS-CoV-2 group (1.7) compared to the control group (1.25, p = 0.018, 95% CI for difference 0.08-0.89). The acute kidney injury rate was 65% (13 of 20) in SARS-CoV-2 patients with a level >8 µg/dl, compared to 29% (4 of 14) in those with lower levels (p = 0.037). Median length of stay was 10 days among SARS-CoV-2 infected patients with high tacrolimus levels compared to 0 days in the rest (p = 0.04). Four patients with high levels died compared to 2 in the control group. Clinicians should be aware of this potential effect on tacrolimus levels and take appropriate measures.

An Analysis of Serological Response and Infection Outcomes Following Oxford-AstraZeneca (AZD1222) and Pfizer-BioNTech (mRNA BNT162b2) SARS-CoV-2 Vaccines in Kidney and Kidney-pancreas Transplants.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 is associated with high mortality among transplant recipients. Comparative data that define humoral responses to the Oxford-AstraZeneca (AZ) and BNT162b2 (Pfizer-BioNTech) vaccines are limited. METHODS: We recruited 920 kidney transplant patients receiving at least 1 dose of severe acute respiratory syndrome coronavirus 2 vaccine, excluding patients with virus pre-exposure. Serological status was determined with the COVID-SeroKlir ELISA (Kantaro-EKF Diagnostics). Patients with a corrected antibody level of <0.7 AU/mL were considered seronegative. RESULTS: Four hundred ninety-five AZ and 141 Pfizer patients had a sample analyzed after first dose and 593 after second dose (346 AZ versus 247 Pfizer). After first dose, 25.7% of patients seroconverted (26.6% AZ, 22.8% Pfizer). After second dose, 148 (42.8%) of AZ seroconverted compared with 130 (52.6%) of Pfizer (P = 0.02; hazard ratio, 1.48; 95% confidence interval, 1.07-2.06). When negative responders were excluded, Pfizer patients were shown to have significantly higher response than AZ patients (median 2.6 versus 1.78 AU/mL, P = 0.005).Patients on mycophenolate had a reduced seroconversion rate (42.2% versus 61.4%; P < 0.001; hazard ratio, 2.17) and reduced antibody levels (0.47 versus 1.22 AU/mL, P = 0.001), and this effect was dose dependent (P = 0.05). Prednisolone reduced the seroconversion from 58.2% to 43.6% (P = 0.03) among Pfizer but not AZ recipients. Regression analysis showed that antibody levels were reduced by older age (P = 0.002), mycophenolate (P < 0.001), AZ vaccine (versus Pfizer, P = 0.001), and male gender (P = 0.02). Sixteen of 17 serious postvaccine infections occurred to patients who did not seroconvert. CONCLUSIONS: Both seroconversion and antibody levels are lower in AZ compared with Pfizer vaccinated recipients following 2 vaccine doses. Mycophenolate was associated with lower antibody responses in a dose-dependent manner. Serious postvaccine infections occurred among seronegative recipients.