Advanced Diagnosis of Acute Kidney Injury
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 24
Special Issue Editors
2. School of Medicine, IIBISMED, Universidad Mayor de San Simon, Cochabamba, Bolivia
Interests: AKI biomarkers; point-of-care testing; multi-omics integration; machine learning risk prediction; urinary proteomics; biomarker-guided decision support; functional renal imaging
Special Issue Information
Dear Colleagues,
Acute kidney injury (AKI) remains a common, heterogeneous syndrome with high morbidity and mortality; delayed or imprecise diagnosis contributes substantially to poor outcomes.
This Special Issue will focus on advances that improve early detection, mechanistic phenotyping, and actionable clinical decision making. Topics of interest include novel and composite biomarkers (protein, metabolite, and nucleic acid-based), urine and plasma proteomics/metabolomics, extracellular vesicle and microRNA signatures, and functional renal imaging techniques. We welcome studies that translate bench discoveries into point-of-care platforms, microfluidic and lateral-flow diagnostics, and wearable/bedside technologies. Interdisciplinary work applying multi-omics integration, machine learning risk models, and EHR-based surveillance to enhance sensitivity, specificity, and clinical utility is encouraged, as are validation studies performed in diverse settings including low- and middle-income countries. Implementation, regulatory, and biomarker-guided clinical decision-support solutions that demonstrate impact on patient management or trials will also be prioritized.
Our aim is to present translational research and high-quality clinical validation studies that move AKI diagnosis toward earlier, actionable, and equitable care.
Dr. Rolando Claure-Del Granado
Dr. Nuttha Lumlertgul
Guest Editors
Manuscript Submission Information
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Keywords
- AKI biomarkers
- point-of-care testing
- multi-omics integration
- machine learning risk prediction
- urinary proteomics
- biomarker-guided decision support
- functional renal imaging
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