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Diagnostics
Special Issues
Personalized Diagnosis, Prognosis and Treatment of Kidney Diseases
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Personalized Diagnosis, Prognosis and Treatment of Kidney Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1982

Special Issue Editor

Dr. Chien-Hung Lin

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Guest Editor
Division of Pediatric Immunology and Nephrology, Department of Pediatrics, Taipei Veterans General Hospital, Taipei 11221, Taiwan
Interests: acute and chronic kidney disease; early prediction; molecular diagnosis; precision therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Personalized diagnosis, prognosis and treatment of kidney diseases involve medical approaches to individual patients based on their unique characteristics, including genetic profiles, biomarker levels and specific disease manifestations. The personalized approach aims to optimize patient outcomes by providing targeted and effective interventions. Innovations in precision medicine, including molecular diagnosis and targeted therapies, are being explored for specific kidney diseases. Personalized approaches offer the potential for improved disease control, reduced treatment-related complications and predicted accurate prognosis for patients with acute and chronic kidney diseases.

The purpose of this Special Issue is to explore the new frontiers in the personalized diagnosis, prognosis and treatment of kidney diseases, as well as the integration of these new perspectives for the benefit of patients.

Dr. Chien-Hung Lin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • personalized medicine
  • kidney diseases
  • genetic testing
  • biomarker analysis
  • precision medicine
  • targeted therapies
  • renal prognosis

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Published Papers (2 papers)

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Review

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20 pages, 785 KiB  
Review
The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications
by Tanja Belčič Mikič and Miha Arnol
Diagnostics 2024, 14(22), 2482; https://doi.org/10.3390/diagnostics14222482 - 7 Nov 2024
Cited by 1 | Viewed by 1138
Abstract
Kidney allograft rejection is one of the main limitations to long-term kidney transplant survival. The diagnostic gold standard for detecting rejection is a kidney biopsy, an invasive procedure that can often give imprecise results due to complex diagnostic criteria and high interobserver variability. [...] Read more.
Kidney allograft rejection is one of the main limitations to long-term kidney transplant survival. The diagnostic gold standard for detecting rejection is a kidney biopsy, an invasive procedure that can often give imprecise results due to complex diagnostic criteria and high interobserver variability. In recent years, several additional diagnostic approaches to rejection have been investigated, some of them with the aid of machine learning (ML). In this review, we addressed studies that investigated the detection of kidney allograft rejection over the last decade using various ML algorithms. Various ML techniques were used in three main categories: (a) histopathologic assessment of kidney tissue with the aim to improve the diagnostic accuracy of a kidney biopsy, (b) assessment of gene expression in rejected kidney tissue or peripheral blood and the development of diagnostic classifiers based on these data, (c) radiologic assessment of kidney tissue using diffusion-weighted magnetic resonance imaging and the construction of a computer-aided diagnostic system. In histopathology, ML algorithms could serve as a support to the pathologist to avoid misclassifications and overcome interobserver variability. Diagnostic platforms based on biopsy-based transcripts serve as a supplement to a kidney biopsy, especially in cases where histopathologic diagnosis is inconclusive. ML models based on radiologic evaluation or gene signature in peripheral blood may be useful in cases where kidney biopsy is contraindicated in addition to other non-invasive biomarkers. The implementation of ML-based diagnostic methods is usually slow and undertaken with caution considering ethical and legal issues. In summary, the approach to the diagnosis of rejection should be individualized and based on all available diagnostic tools (including ML-based), leaving the responsibility for over- and under-treatment in the hands of the clinician. Full article
(This article belongs to the Special Issue Personalized Diagnosis, Prognosis and Treatment of Kidney Diseases)
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9 pages, 5040 KiB  
Case Report
Severe Acute Kidney Injury with Necrotizing Glomerulonephritis After Piperacillin/Tazobactam Therapy in a Patient with Peritonitis: A Case Report and Literature Review
by Youn-Sik Oh, Man-Hoon Han, Yong-Jin Kim, You Hyun Jeon, Hee-Yeon Jung, Ji-Young Choi, Jang-Hee Cho, Sun-Hee Park, Chan-Duck Kim, Yong-Lim Kim and Jeong-Hoon Lim
Diagnostics 2025, 15(5), 574; https://doi.org/10.3390/diagnostics15050574 - 27 Feb 2025
Cited by 1 | Viewed by 471
Abstract
Piperacillin/tazobactam (PT), a widely utilized broad-spectrum antibiotic, has been associated with acute kidney injury (AKI). Although the precise mechanism remains uncertain, and most cases of PT-associated AKI are mild, this report describes a rare and severe complication of PT, which manifested as severe [...] Read more.
Piperacillin/tazobactam (PT), a widely utilized broad-spectrum antibiotic, has been associated with acute kidney injury (AKI). Although the precise mechanism remains uncertain, and most cases of PT-associated AKI are mild, this report describes a rare and severe complication of PT, which manifested as severe AKI with necrotizing glomerulonephritis requiring hemodialysis. A 42-year-old man was transferred to the nephrology clinic due to progressive deterioration of kidney function. Prior to the transfer, the patient had been diagnosed with appendicitis complicated by peritonitis and received intravenous PT for 8 days. Baseline kidney function was normal, but serum creatinine subsequently increased to 7.2 mg/dL. Hemodialysis was initiated to address metabolic acidosis and edema. Kidney biopsy revealed severe acute tubular injury and necrotizing glomerulonephritis. Steroid therapy was initiated based on the biopsy findings, and serum creatinine returned to normal levels after 4 weeks of treatment. This case demonstrates that severe AKI with necrotizing glomerulonephritis can occur after PT use. Prompt kidney biopsy and the timely initiation of immunosuppressive therapy are essential for a favorable outcome. Full article
(This article belongs to the Special Issue Personalized Diagnosis, Prognosis and Treatment of Kidney Diseases)
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