About this Research Topic
Kidney transplantation is a life-saving intervention for individuals suffering from end-stage renal disease. Despite the current immunosuppressive therapies, allograft rejection remains a significant problem, leading to dysfunction in solid organ transplants, particularly renal transplantation.
Traditional methods used to evaluate kidney function, such as monitoring serum creatinine and urine protein levels, have variable sensitivity and specificity. These methods, however, may not provide clinically crucial insights, as several factors can lead to fluctuations in serum creatinine in transplanted patients, such as acute rejection, renal artery stenosis, de novo kidney disease, or recurrence of the original illness.
Although histologic analysis of transplanted kidneys has long been the gold standard for diagnosing acute and chronic kidney rejection, its utility is hindered by a range of limitations, such as invasiveness, limited details, and difficulty being repeated.
Additional novel biomarkers are necessary to detect renal allograft injuries and predict allograft outcomes effectively. The development of reliable and non-invasive biomarkers for detecting and categorizing renal allograft injuries is crucial for accurately diagnosing kidney allograft rejection and ultimately improving graft survival rates and the overall quality of life for kidney transplant recipients.
An ideal biomarker should be beneficial for early diagnosis, estimation of prognosis, monitoring for recurrence, and assessment of clinical responses to therapies.
In this research collection, we are looking for novel biomarkers in kidney transplantation and donor-specific antibody detection that include but are not limited to extracellular vesicles, genomics and transcriptomics, proteomics and metabolomics, microRNA, imaging techniques, machine learning and artificial intelligence, etc.
This research topic aims to explore novel biomarkers for the detection of rejection after kidney transplantation. We encourage the submission of research studies, brief reports, and review articles.
Traditional methods used to evaluate kidney function, such as monitoring serum creatinine and urine protein levels, have variable sensitivity and specificity. These methods, however, may not provide clinically crucial insights, as several factors can lead to fluctuations in serum creatinine in transplanted patients, such as acute rejection, renal artery stenosis, de novo kidney disease, or recurrence of the original illness.
Although histologic analysis of transplanted kidneys has long been the gold standard for diagnosing acute and chronic kidney rejection, its utility is hindered by a range of limitations, such as invasiveness, limited details, and difficulty being repeated.
Additional novel biomarkers are necessary to detect renal allograft injuries and predict allograft outcomes effectively. The development of reliable and non-invasive biomarkers for detecting and categorizing renal allograft injuries is crucial for accurately diagnosing kidney allograft rejection and ultimately improving graft survival rates and the overall quality of life for kidney transplant recipients.
An ideal biomarker should be beneficial for early diagnosis, estimation of prognosis, monitoring for recurrence, and assessment of clinical responses to therapies.
In this research collection, we are looking for novel biomarkers in kidney transplantation and donor-specific antibody detection that include but are not limited to extracellular vesicles, genomics and transcriptomics, proteomics and metabolomics, microRNA, imaging techniques, machine learning and artificial intelligence, etc.
This research topic aims to explore novel biomarkers for the detection of rejection after kidney transplantation. We encourage the submission of research studies, brief reports, and review articles.
Keywords: kidney transplantation, novel biomarkers, renal biopsy, T-cellular-mediated rejection, antibody-mediated rejection, genomics, metabolomics, proteomics, transcriptomics, cell-free DNA, extracellular vesicles, machine learning, artificial intelligence, dono
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