The study of the association between immune monitoring and pneumonia in kidney transplant recipients through machine learning models

Prediction of peripheral blood lymphocyte subpopulations after renal transplantation

Immune monitoring is essential for maintaining immune homeostasis after renal transplantation (RT). Peripheral blood lymphocyte subpopulations (PBLSs) are widely used biomarkers for immune monitoring, yet there is no established standard reference for PBLSs during immune reconstitution post-RT. PBLS data from stable recipients at various time points post-RT were collected. Binary and multiple linear regressions, along with a mixed-effect linear model, were used to analyze the correlations between PBLSs and clinical parameters. Predictive models for PBLS reference values were developed using Gradient Boosting Regressor, and the models' performance was also evaluated in infected recipients. A total of 1,736 tests from 494 stable recipients and 98 tests from 82 infected recipients were included. Age, transplant time, induction therapy, dialysis duration, serum creatinine, albumin, hemoglobin, and immunosuppressant drug concentration were identified as major factors influencing PBLSs. CD4+ and CD8+ T cells and NK cells increased rapidly, stabilizing within three months post-RT. In contrast, B cells peaked at around two weeks and gradually plateaued after four months. Both static and dynamic predictive models provided accurate reference values for PBLSs at any time post-RT, with the static model showing superior performance in distinguishing stable, infected and sepsis patients. Key factors influencing PBLS reconstitution after RT were identified. The predictive models accurately reflected PBLS reconstitution patterns and provided practical, personalized reference values for PBLSs, contributing to precision-guided care. The study was registered on Chinese Clinical Trial Registry (ChiCTR2300068666).

Correlation between peripheral lymphocyte subsets monitoring and COVID-19 pneumonia in kidney transplant recipients

OBJECTIVES

In kidney transplant recipients (KTRs), immune monitoring of peripheral lymphocyte subsets (PLS) reflects the real immune status and aids in the diagnosis of the occurrence and development of infectious diseases, including COVID-19. Exploring the PLS of COVID-19 pneumonia in KTRs is important.

METHODS

In this study, a total of 103 KTRs were divided into mild pneumonia (MP) and severe pneumonia (SP) groups, as well as a stable group. The clinical information and PLS data were assessed via t or Mann-Whitney test and receiver operating curve analysis. Logistic regression was employed to identify the risk factors, and Spearman's correlation analysis was used to identify correlations.

RESULTS

Lymphopenia is a common manifestation of COVID-19 in KTRs, and it is positively related to the severity of COVID-19 pneumonia. The CD3 + T-cell count had the highest AUC between the MP and the SP. Multiple PLS measures were found to be independent risk factors for COVID-19 pneumonia progression in KTRs.

CONCLUSIONS

This study revealed a robust correlation between PLS and severe COVID-19 pneumonia progression in KTRs. PLS monitoring could facilitate real-time diagnosis and therapy for infection, as well as timely and precisive adjustment of immunosuppression instructions, for KTRs with COVID-19.

Present and Future Applications of Artificial Intelligence in Kidney Transplantation

Artificial intelligence (AI) has a wide and increasing range of applications across various sectors. In medicine, AI has already made an impact in numerous fields, rapidly transforming healthcare delivery through its growing applications in diagnosis, treatment and overall patient care. Equally, AI is swiftly and essentially transforming the landscape of kidney transplantation (KT), offering innovative solutions for longstanding problems that have eluded resolution through traditional approaches outside its spectrum. The purpose of this review is to explore the present and future applications of artificial intelligence in KT, with a focus on pre-transplant evaluation, surgical assistance, outcomes and post-transplant care. We discuss its great potential and the inevitable limitations that accompany these technologies. We conclude that by fostering collaboration between AI technologies and medical practitioners, we can pave the way for a future where advanced, personalised care becomes the standard in KT and beyond.

Proliferation of MDSCs may indicate a lower CD4+ T cell immune response in schistosomiasis japonica

BACKGROUND

Schistosoma japonicum (S. japonicum) is the main species of Schistosoma prevalent in China. Myeloid-derived suppressor cells (MDSCs) are important immunoregulatory cells and generally expand in parasite infection, but there is little research relating to MDSCs in Schistosoma infection.

METHODS

Fifty-six S. japonicum-infected patients were included in this study. MDSCs and percentages and absolute cell numbers of lymphocyte subsets, including CD3+ T cells, CD4+ T cells, CD8+ T cells, B cells and natural killer (NK) cells were detected using flow cytometry. The degree of liver fibrosis was determined using color Doppler ultrasound.

RESULTS

Patients infected with S. japonicum had a much higher percentage of MDSCs among peripheral blood mononuclear cells (PBMCs) than the healthy control. Regarding subpopulations of MDSCs, the percentage of granulocytic myeloid-derived suppressor cells (G-MDSCs) was clearly increased. Correlation analysis showed that the absolute cell counts of T-cell subsets correlated negatively with the percentages of MDSCs and G-MDSCs among PBMCs. The percentage of G-MDSCs in PBMCs was also significantly higher in patients with liver fibrosis diagnosed by color doppler ultrasound (grade > 0), and the percentage of G-MDSCs in PBMCs and liver fibrosis grading based on ultrasound showed a positive correlation.

CONCLUSION

S. japonicum infection contributes to an increase in MDSCs, especially G-MDSCs, whose proliferation may inhibit the number of CD4+ T cells in peripheral blood. Meanwhile, there is a close relationship between proliferation of G-MDSCs and liver fibrosis in S. japonicum-infected patients.

The Inflammatory State of Follicular Fluid Combined with Negative Emotion Indicators can Predict Pregnancy Outcomes in Patients with PCOS

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder syndrome with an incidence of 6% to 10% in women of reproductive age. Women with PCOS not only exhibit abnormal follicular development and fertility disorders, but also have a greater tendency to develop anxiety and depression. Our aim was to evaluate the ability of inflammatory factors in follicular fluid to predict embryonic developmental potential and pregnancy outcome and to construct a machine learning model that can predict IVF pregnancy outcomes based on indicators such as basic sex hormones, embryonic morphology, the follicular microenvironment, and negative emotion. In this study, inflammatory factors (CRP, IL-6, and TNF-α) in follicular fluid samples obtained from 225 PCOS and 225 non-PCOS women were detected via ELISA. For patients with PCOS, the levels of CRP and IL-6 in the follicular fluid in the pregnant group were significantly lower than those in the nonpregnant group. For non-patients with PCOS, only the level of IL-6 in the follicular fluid was significantly lower in the pregnant group than in the nonpregnant group. In addition, for both PCOS and non-patients with PCOS, compared with those in the pregnant group, patients in the nonpregnant group showed more pronounced signs of anxiety and depression. Finally, the factors that were significantly different between the two subgroups (pregnancy and nonpregnancy) of patients with or without PCOS were identified by an independent sample t test first and further analysed by multilayer perceptron (MLP) and random forest (RF) models to distinguish the two clinical pregnancy outcomes according to the classification function. The accuracy of the RF model in predicting pregnancy outcomes in patients with or without PCOS was 95.6% and 91.1%, respectively. The RF model is more suitable than the MLP model for predicting pregnancy outcomes in IVF patients. This study not only identified inflammatory factors that can affect embryonic development and assessed the anxiety and depression tendencies of PCOS patients, but also constructed an AI model that predict pregnancy outcomes through machine learning methods, which is a beneficial clinical tool.

A novel method to predict white blood cells after kidney transplantation based on machine learning

Background

Abnormal white blood cell count after kidney transplantation is an important adverse clinical outcome. The abnormal white blood cell count in patients after surgery may be caused by the use of immunosuppressive agents and other factors. A lower white blood cell count than normal will greatly increase the probability of adverse outcomes such as infection and reduce the success rate of surgery.

Objective

To establish a machine learning prediction model of leukocyte drop to abnormal level after kidney transplantation, and provide reference for clinical treatment.

Methods

A total of 546 kidney transplant patients were selected as the study subjects. The time correlation feature of the ratio of the duration time of each variable to the total time in different intervals was innovatively introduced. Least absolute shrinkage and selection operator algorithm was used for correlation analysis of 85 candidate variables, and the top 20 variables were retained in the end. Eight machine learning algorithms, including Logistic-L1, Logistic-L2, support vector machine, decision tree, random forest, multilayer perceptron, extreme gradient boosting and light gradient boosting machine, were used for the five-fold cross-validation on all data sets, and the algorithm with the best performance was selected as the final prediction algorithm based on the average area under the curve.

Results

As the final prediction model, the accuracy, sensitivity, specificity and area under the curve values of the multilayer perceptron model in test set were 71.34%, 61.18%, 82.28% and 77.30%, respectively. The most important factors affecting leukopenia after surgery were the proportion of time of lymphocyte less than normal, blood group AB, gender, and platelet CV.

Conclusions

The multilayer perceptron model explored in this study shows significant potential in predicting abnormal white blood cell counts after kidney transplantation. This model can help stratify risk following transplantation, subject to external and/or prospective validation.

Supervised Machine Learning Methods for Seasonal Influenza Diagnosis

Influenza has been a stationary disease in Mexico since 2009, and this causes a high cost for the national public health system, including its detection using RT-qPCR tests, treatments, and absenteeism in the workplace. Despite influenza's relevance, the main clinical features to detect the disease defined by international institutions like the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) do not follow the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480 records, including clinical and demographic data of patients with a positive/negative RT-qPCR influenza tests, from 2010 to 2020 in the public healthcare institutions of Mexico City. The performance of the methods for classifying influenza cases were evaluated with indices like accuracy, specificity, sensitivity, precision, the f1-measure and the area under the curve (AUC). Results indicate that random forest and bagging classifiers were the best supervised methods; they showed promise in supporting clinical diagnosis, especially in places where performing molecular tests might be challenging or not feasible.

Machine learning-based investigation of the relationship between immune status and left ventricular hypertrophy in patients with end-stage kidney disease

Background

Left ventricular hypertrophy (LVH) is the most frequent cardiac complication among end-stage kidney disease (ESKD) patients, which has been identified as predictive of adverse outcomes. Emerging evidence has suggested that immune system is implicated in the development of cardiac hypertrophy in multiple diseases. We applied machine learning models to exploring the relation between immune status and LVH in ESKD patients.

Methods

A cohort of 506 eligible patients undergoing immune status assessment and standard echocardiography simultaneously in our center were retrospectively analyzed. The association between immune parameters and the occurrence of LVH were evaluated through univariate and multivariate logistic analysis. To develop a predictive model, we utilized four distinct modeling approaches: support vector machine (SVM), logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF).

Results

In comparison to the non-LVH group, ESKD patients with LVH exhibited significantly impaired immune function, as indicated by lower cell counts of CD3⁺ T cells, CD4⁺ T cells, CD8⁺ T cells, and B cells. Additionally, multivariable Cox regression analysis revealed that a decrease in CD3⁺ T cell count was an independent risk factor for LVH, while a decrease in NK cell count was associated with the severity of LVH. The RF model demonstrated superior performance, with an average area under the curve (AUC) of 0.942.

Conclusion

Our findings indicate a strong association between immune parameters and LVH in ESKD patients. Moreover, the RF model exhibits excellent predictive ability in identifying ESKD patients at risk of developing LVH. Based on these results, immunomodulation may represent a promising approach for preventing and treating this disease.

Standardization of neutrophil CD64 and monocyte HLA-DR measurement and its application in immune monitoring in kidney transplantation

Background

Infections cause high mortality in kidney transplant recipients (KTRs). The expressions of neutrophil CD64 (nCD64) and monocyte HLA-DR (mHLA-DR) provide direct evidence of immune status and can be used to evaluate the severity of infection. However, the intensities of nCD64 and mHLA-DR detected by flow cytometry (FCM) are commonly measured by mean fluorescence intensities (MFIs), which are relative values, thus limiting their application. We aimed to standardize nCD64 and mHLA-DR expression using molecules of equivalent soluble fluorochrome (MESF) and to explore their role in immune monitoring for KTRs with infection.

Methods

The study included 50 KTRs diagnosed with infection, 65 immunologically stable KTRs and 26 healthy controls. The blood samples were collected and measured simultaneously by four FCM protocols at different flow cytometers. The MFIs of nCD64 and mHLA-DR were converted into MESF by Phycoerythrin (PE) Fluorescence Quantitation Kit. The intraclass correlation coefficients (ICCs) and the Bland-Altman plots were used to evaluate the reliability between the four FCM protocols. MESFs of nCD64 and mHLA-DR, nCD64 index and sepsis index (SI) with the TBNK panel were used to evaluate the immune status. Comparisons among multiple groups were performed with ANOVA one-way analysis. Receiver operating characteristics (ROC) curve analysis was performed to diagnose infection or sepsis. Univariate and multivariate logistic analysis examined associations of the immune status with infection.

Results

MESFs of nCD64 and mHLA-DR measured by four protocols had excellent reliability (ICCs 0.993 and 0.957, respectively). The nCD64, CD64 index and SI in infection group were significantly higher than those of stable KTRs group. Patients with sepsis had lower mHLA-DR but higher SI than non-sepsis patients. ROC analysis indicated that nCD64 had the highest area under the curve (AUC) for infection, and that mHLA-DR had the highest AUC for sepsis. Logistic analysis indicated that nCD64 > 3089 and B cells counts were independent risk factors for infection.

Conclusion

The standardization of nCD64 and mHLA-DR made it available for widespread application. MESFs of nCD64 and mHLA-DR had good diagnostic performance on infection and sepsis, respectively, which could be promising indicators for immune status of KTRs and contributed to individualized treatment.

The Association between Regulatory T Cell Subpopulations and Severe Pneumonia Post Renal Transplantation

Severe pneumonia accounts for the majority of morbidity and mortality in renal allograft recipients due to immunosuppressant maintenance. Regulatory T cells (Tregs), which are involved in tackling infections under immunosuppressive conditions, are rarely uncovered. We aimed to investigate the relationship between various Treg subpopulations and severe pneumonia after kidney transplantation (KTx). KTx recipients with pneumonia were divided into severe pneumonia and mild pneumonia groups. The frequencies and absolute numbers (Ab No.) of total Tregs (CD4+CD25+FoxP3+), six subsets of Tregs (Helios+/-, CD39+/-, and CD45RA+/-), and T cells, B cells, and NK cells were assessed from peripheral blood via flow cytometry using the $t$ or Mann-Whitney test and receiver operating curve analysis. We also determined the median fluorescence intensity (MFI) of human leukocyte antigen- (HLA-) DR on monocytes and CD64 on neutrophils. Logistic regression was used to identify the risk factors of disease progression, and Pearson’s correlation analysis was performed to identify relationships between the measured immune indices and patients’ clinical information. Our research indicated that Treg subpopulations were strongly associated with severe pneumonia progression post KTx. Based on the monitoring of Treg subpopulations, better-individualized prevention and therapy might be achieved for patients with severe pneumonia post KTx.

The Expansion of Myeloid-Derived Suppressor Cells Correlates With the Severity of Pneumonia in Kidney Transplant Patients

Background

Pneumonia is one of the most frequent but serious infectious complications post kidney transplantation. Severe pneumonia induces sustained immunosuppression, but few parameters concerning immune status are used to assess the severity of pneumonia. Myeloid-derived suppressor cells (MDSCs) are induced under infection and have the strong immunosuppressive capacity, but the correlation between MDSCs and pneumonia in kidney transplant recipients (KTRs) is unknown.

Methods

Peripheral blood MDSCs were longitudinally detected in 58 KTRs diagnosed with pneumonia using flow cytometry and in 29 stable KTRs as a control. The effectors of MDSCs were detected in the plasma. Spearman's rank correlation analysis was performed to determine the correlation between MDSCs and the severity of pneumonia as well as lymphopenia.

Results

The frequency of MDSCs and effectors, including arginase-1, S100A8/A9, and S100A12, were significantly increased in the pneumonia group compared with the stable group. CD11b⁺CD14⁺HLA-DR^low/−CD15⁻ monocytic-MDSCs (M-MDSCs) were higher in the pneumonia group but showed no significant difference between the severe and non-severe pneumonia subgroups. CD11b⁺CD14⁻CD15⁺ low-density granulocytic-MDSCs (G-MDSCs) were specifically increased in the severe pneumonia subgroup and correlated with the severity of pneumonia as well as lymphopenia. During the study period of 2 weeks, the frequencies of MDSCs and G-MDSCs were persistently increased in the severe pneumonia subgroup.

Conclusions

MDSCs and G-MDSCs were persistently increased in KTRs with pneumonia. G-MDSCs were correlated with the severity of pneumonia and could thus be an indicator concerning immune status for assessing pneumonia severity.

The Role of Lymphocyte Subset in Predicting Allograft Rejections in Kidney Transplant Recipients

BACKGROUND

Allograft rejection remains a significant challenge in managing post-transplant recipients despite the improvement in immunologic risk assessment and immunosuppressive therapy. Published literature including animal studies has demonstrated that the cells responsible for rejection are beyond the innate T and B cells, and other studies revealed evidence supporting natural killer (NK) cells' role in kidney allograft injury. This study aims to find the association between the peripheral blood lymphocyte subset counts, primarily NK cells, and the kidney allograft biopsy findings.

METHODS

This is a prospective cross-sectional study among a total of 100 kidney allograft biopsies in 61 kidney transplant recipients. The peripheral blood for the lymphocyte subset was sent just before the allograft biopsy. The patients' immunosuppression and other laboratory investigations were managed as per clinical practices by the attending nephrologist.

RESULTS

Overall, the mean age of our patients was 43.72 ± 10.68 years old, and 55.7% of recipients were male. Higher counts of T cells (CD4+; 658.8 ± 441.4 cells/µL; P = .043) and NK cells (CD3-CD16+CD56+; 188 [interquartile range = 133.0-363.0 cells/µL]; P = .002) were associated with higher risk of allograft rejection in the initial analysis. Patients with an allograft age <12 months had significantly higher total T cells, CD4+ T cells, and NK cells in the rejection groups. However, after assessing factors associated with rejection in the multivariate analysis, we only found that being ABO-incompatible and having >497 CD4+ cells/µL had a higher odds of allograft rejection.

CONCLUSIONS

Higher CD4± counts were associated with a higher risk of allograft rejection. However, there was no significant increase in CD8±, CD19±, and NK cells count in our cohort with allograft rejection.

Models to predict the short-term survival of acute-on-chronic liver failure patients following liver transplantation

Background

Acute-on-chronic liver failure (ACLF) is featured with rapid deterioration of chronic liver disease and poor short-term prognosis. Liver transplantation (LT) is recognized as the curative option for ACLF. However, there is no standard in the prediction of the short-term survival among ACLF patients following LT.

Method

Preoperative data of 132 ACLF patients receiving LT at our center were investigated retrospectively. Cox regression was performed to determine the risk factors for short-term survival among ACLF patients following LT. Five conventional score systems (the MELD score, ABIC, CLIF-C OFs, CLIF-SOFAs and CLIF-C ACLFs) in forecasting short-term survival were estimated through the receiver operating characteristic (ROC). Four machine-learning (ML) models, including support vector machine (SVM), logistic regression (LR), multi-layer perceptron (MLP) and random forest (RF), were also established for short-term survival prediction.

Results

Cox regression analysis demonstrated that creatinine (Cr) and international normalized ratio (INR) were the two independent predictors for short-term survival among ACLF patients following LT. The ROC curves showed that the area under the curve (AUC) ML models was much larger than that of conventional models in predicting short-term survival. Among conventional models the model for end stage liver disease (MELD) score had the highest AUC (0.704), while among ML models the RF model yielded the largest AUC (0.940).

Conclusion

Compared with the traditional methods, the ML models showed good performance in the prediction of short-term prognosis among ACLF patients following LT and the RF model perform the best. It is promising to optimize organ allocation and promote transplant survival based on the prediction of ML models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02164-6.

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

Multi-omics network characterization reveals novel microRNA biomarkers and mechanisms for diagnosis and subtyping of kidney transplant rejection

BACKGROUND

Kidney transplantation is an optimal method for treatment of end-stage kidney failure. However, kidney transplant rejection (KTR) is commonly observed to have negative effects on allograft function. MicroRNAs (miRNAs) are small non-coding RNAs with regulatory role in KTR genesis, the identification of miRNA biomarkers for accurate diagnosis and subtyping of KTR is therefore of clinical significance for active intervention and personalized therapy.

METHODS

In this study, an integrative bioinformatics model was developed based on multi-omics network characterization for miRNA biomarker discovery in KTR. Compared with existed methods, the topological importance of miRNA targets was prioritized based on cross-level miRNA-mRNA and protein-protein interaction network analyses. The biomarker potential of identified miRNAs was computationally validated and explored by receiver-operating characteristic (ROC) evaluation and integrated "miRNA-gene-pathway" pathogenic survey.

RESULTS

Three miRNAs, i.e., miR-145-5p, miR-155-5p, and miR-23b-3p, were screened as putative biomarkers for KTR monitoring. Among them, miR-155-5p was a previously reported signature in KTR, whereas the remaining two were novel candidates both for KTR diagnosis and subtyping. The ROC analysis convinced the power of identified miRNAs as single and combined biomarkers for KTR prediction in kidney tissue and blood samples. Functional analyses, including the latent crosstalk among HLA-related genes, immune signaling pathways and identified miRNAs, provided new insights of these miRNAs in KTR pathogenesis.

CONCLUSIONS

A network-based bioinformatics approach was proposed and applied to identify candidate miRNA biomarkers for KTR study. Biological and clinical validations are further needed for translational applications of the findings.

Artificial intelligence and kidney transplantation

Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.

Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence

After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD). Despite this success, the high risk of premature death and frequent occurrence of graft failure remain important clinical and research challenges. The current burst of studies and other innovative initiatives using artificial intelligence (AI) for a wide range of analytical and practical applications in biomedical areas seems to correlate with the same trend observed in publications in the kidney transplantation field, and points toward the potential of such novel approaches to address the aforementioned aim of improving long-term outcomes of kidney transplant recipients (KTR). However, at the same time, this trend underscores now more than ever the old methodological challenges and potential threats that the research and clinical community needs to be aware of and actively look after with regard to AI-driven evidence. The purpose of this narrative mini-review is to explore challenges for obtaining applicable and adequate kidney transplant data for analyses using AI techniques to develop prediction models, and to propose next steps in the field. We make a call to act toward establishing the strong collaborations needed to bring innovative synergies further augmented by AI, which have the potential to impact the long-term care of KTR. We encourage researchers and clinicians to submit their invaluable research, including original clinical and imaging studies, database studies from registries, meta-analyses, and AI research in the kidney transplantation field.