Aqilah Hanifah Putri,1 Galuh Anindya Rahmah,1 Renata Oktavia Safitri,1 Ahmedz Widiasta,2,3 Yunisa Pamela3,41Undergraduate Program, Faculty of Medicine, Universitas Padjadjaran, Sumedang, West Java, Indonesia; 2Department of Child Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, West Java, Indonesia; 3Research Center of Medical Genetics, Faculty of Medicine, Universitas Padjadjaran, Bandung, West Java, Indonesia; 4Department of Biomedical Science, Faculty of Medicine, Universitas Padjadjaran, Sumedang, West Java, IndonesiaCorrespondence: Yunisa Pamela, Department of Biomedical Science, Faculty of Medicine, Universitas Padjadjaran, Jalan Ir. Soekarno No.KM. 21, Hegarmanah, Jatinangor, Sumedang, West Java, 45363, Indonesia, Email yunisa.pamela@unpad.ac.idAbstract: Nephrotic syndrome is the leading cause of pediatric chronic kidney disease, particularly the steroid-resistant subtype (SRNS). The current diagnostic paradigm relies on prolonged steroid trials or invasive biopsies, often delaying optimal treatment. This study systematically synthesizes evidence from cohort studies on non-invasive molecular biomarkers predicting SRNS at onset. We searched PubMed, Scopus, ScienceDirect, and MEDLINE Ultimate via EBSCO for cohort studies published between January 2015 and September 2025. The study protocol was registered with PROSPERO (CRD420251147997), and quality was assessed using the Newcastle-Ottawa Scale. Eleven studies comprising 718 study-level participant observations were included; as two studies originated from an overlapping cohort within a single research group, the number of unique children is likely lower. Candidate molecular signatures were identified across plasma proteins, cytokines, metabolites, urinary markers, transcriptomic markers, and a genetic/epigenetic model. Multi-marker panels showed high predictive utility, including a plasma protein panel (VDB+ADIPOQ+MMP2; AUC 0.780; P = 0.003) and a cytokine panel (IL-7+IL-9+MCP-1; AUC 0.846; sensitivity 64.3%; specificity 84.6%). Among individual markers, TGF-β 2 mRNA showed the highest predictive accuracy (AUC 0.972; P < 0.001) in a small single cohort, followed by plasma glutamine in an age-combined model (AUC 0.810; OR 1.01), while reduced plasma malonate showed predictive value in children older than 3 years (AUC 0.84; OR 0.94). Conversely, urinary VDBP failed to distinguish SRNS from SSNS (P = 0.417), suggesting possible confounding by proteinuria severity. These exploratory findings suggest that several molecular biomarkers, particularly composite panels, may hold promise for future early risk stratification; however, given the small number and size of cohorts, participant overlap, and methodological heterogeneity, these results should be interpreted as hypothesis-generating rather than evidence of superiority of any single biomarker class, requiring validation in larger, independent, prospective cohorts before clinical application.Keywords: biomarkers, nephrotic syndrome, pediatric, steroid-resistant
Roland Fejes,1 László Kovács,2 Dóra Bajcsi,3 Zsófia Balajthy,4 Denisia Suzana Todor,1 Béla Iványi,4 Szilárd Burcsár21Department of Internal Medicine, Hódmezővásárhely-Makó Healthcare Center, Makó, Hungary; 2Department of Rheumatology and Immunology, Albert Szent-Györgyi Health Centre, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary; 3Department of Internal Medicine, Albert Szent-Györgyi Medical Centre, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary; 4Department of Pathology, Albert Szent-Györgyi Medical Centre, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, HungaryCorrespondence: Szilárd Burcsár, Department of Rheumatology and Immunology, Albert Szent-Györgyi Health Centre, Albert Szent-Györgyi Medical School, University of Szeged, H-6725 Kálvária Boulevard 57, Szeged, Hungary, Tel +36 62 341-520, Email burcsar.szilard@med.u-szeged.huAbstract: Systemic vasculitis is traditionally classified according to the predominant size of affected vessels; however, phenotypes affecting multiple vascular calibers may not be adequately captured by rigid definitions and require integrated clinicopathological interpretation. Here we report the case of a 70-year-old Caucasian man who presented with persistent systemic inflammation initially suggestive of infection. Although inflammatory markers decreased during antibiotic therapy, residual inflammation persisted, and the subsequent clinical course was characterized by myalgia, pulmonary involvement, and lymphadenopathy. Muscle biopsy revealed noninflammatory myopathic changes, consistent with normal creatine kinase levels. Following the initiation of corticosteroid therapy, the patient rapidly developed purpura and acute nephritic syndrome. Kidney biopsy demonstrated IgA glomerulonephritis (Oxford scores: M0, E0, S0, T0, and C1), interpreted as a manifestation of IgA vasculitis (IgAV), along with sclerosing phase of necrotizing arteritis involving arcuate arteries. These findings demonstrated the coexistence of IgA-mediated glomerular/small-vessel manifestations and chronic medium-vessel arterial pathology but did not establish the exact pathological process leading to the clinical manifestation. Treatment with corticosteroids and cyclophosphamide resulted in rapid clinical remission. This case illustrates a diagnostically challenging vasculitis phenotype involving different vascular compartments that cannot be assigned to a single conventional category. The case highlights the importance of integrated clinicopathological correlation and detailed histopathological assessment in resolving diagnostic uncertainty and guiding immunosuppressive therapy. Recognition of vasculitis as a dynamic disease spectrum rather than a rigidly compartmentalized entity is essential for timely diagnosis and optimal management.Keywords: medium-vessel vasculitis, polyarteritis nodosa, IgA glomerulonephritis, vasculitis overlap, IgA vasculitis, case report
Xiayan Xu,1,* Haoting Huang,2,* Yan Xu,3 Ying Liu,3 Jiamei Yi,1 Qingrong Zou,1 Jianru Wu,2 Xiaoyu Liu21Department of Pharmacy, Shenzhen Luohu People’s Hospital (The Third Affiliated Hospital of Shenzhen University), Shenzhen, Guangdong, People’s Republic of China; 2Drug Monitoring Section, Shenzhen Institute of Pharmacovigilance and Risk Management, Shenzhen, Guangdong, People’s Republic of China; 3Drug Monitoring Section, Center for ADR Monitoring of Guangdong, Guangzhou, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaoyu Liu, Drug Monitoring Section, Shenzhen Institute of Pharmacovigilance and Risk Management, 16th Floor, Building E, Nanshan Zhigu Industrial Park, Nanshan District, Shenzhen, Guangdong, People’s Republic of China, Email liuxy4@mail.amr.sz.gov.cnBackground: Elderly individuals are particularly vulnerable to drug-induced acute kidney injury (DI-AKI) due to their distinct physiological and pathophysiological traits. DI-AKI’s non-specific symptoms complicate the identification of causative medications, highlighting the urgent need for accurate predictive tools to detect AKI risk early in this group.Aim: The objective is to construct a machine learning-based predictive model for DI-AKI in elderly patients utilizing real-world data, with the aim of offering a decision-support tool for the early clinical identification of DI-AKI risk.Methods: The electronic health records of 2,389 patients aged ≥ 60 years at Shenzhen Luohu People’s Hospital from January 2023 to December 2024 were retrospectively analyzed. Drug-induced AKI was defined by KDIGO creatinine criteria (≥ 0.3 mg/dL within 48h or ≥ 1.5×baseline within 7d) plus Naranjo score for drug attribution. Forty optimal features (30 original and 10 interaction terms) were selected, and seven machine learning algorithms were assessed using nested cross-validation with different feature selection and interactive feature construction methods. SHAP values were employed for model interpretability.Results: In a study of 2,389 patients, 39.9% (953 individuals) experienced drug-induced AKI. The Random Forest model performed best on the test set, with an Area Under the Curve (AUC) of 0.9209 [95% CI: 0.896– 0.946], showing 84.9% sensitivity, 89.1% specificity, and an 85.5% positive predictive value. Key predictors included a history of renal failure (importance score: 0.1703) and drug-disease interactions, such as those involving antihypertensives and renal failure (importance score: 0.1000).Conclusion: This machine learning model is capable of aiding in the identification of high-risk elderly patients within electronic medical record systems, with a particular emphasis on drug interactions and renal function as pivotal risk factors. By employing SHAP values for analysis, this study elucidates the contributions of these risk factors and offers support for making personalized medication decisions; However, external validation remains necessary prior to clinical implementation.Keywords: drug-induced AKI, machine learning, predictive modeling, geriatric patients
Background:The modified furosemide response index (mFRI), derived from a standardized furosemide stress test, is a biomarker of diuretic responsiveness and AKI progression. Its prognostic value in chronic kidney disease (CKD) remains unclear. This study examined the association between mFRI and clinical outcomes in critically ill patients with CKD. Methods:We conducted a retrospective cohort study using the MIMIC‑IV database. Critically ill CKD patients receiving furosemide were included. Restricted cubic splines were used to examine the non‑linear relationship between mFRI and mortality. Patients were grouped by mFRI quartiles. Multivariable Cox and logistic regression assessed outcome associations, with subgroup analyses for robustness. Results:Among 3,311 patients, a significant L‑shaped non‑linear association was observed between mFRI and 28‑day mortality (P < 0.001). Mortality risk declined sharply as mFRI increased to 0.143, then plateaued. Kaplan‑Meier analysis showed progressively lower mortality from the lowest (Q1) to the highest (Q4) mFRI quartile. Higher mFRI quartiles were associated with lower all‑cause mortality and reduced use of renal replacement therapy. Conclusion:mFRI has a non‑linear, L‑shaped association with prognosis in critically ill patients with CKD. Because it is simple, low‑cost, and repeatable, mFRI could be a practical bedside tool for risk stratification in this population.
Objective:To systematically compare the clinicopathological features and prognostic outcomes of renal-limited thrombotic microangiopathy (RL-TMA) and systemic thrombotic microangiopathy (Sys-TMA) using a semiquantitative pathological scoring system. Methods:We retrospectively analyzed 47 patients with biopsy-proven TMA between 2018 and 2025, categorized as RL-TMA (n = 42) or Sys-TMA (n = 5) based on systemic manifestations. Clinical, pathological, and follow-up data were collected. A composite renal endpoint served as the primary outcome. Kaplan-Meier and Cox regression analyses were used for survival analysis and prognostic factor identification. Results:RL-TMA patients were older, with higher hemoglobin and platelet levels, lower lactate dehydrogenase, less complement C3 consumption, and significantly fewer multiorgan dysfunctions than Sys-TMA patients. Pathologically, Sys-TMA was dominated by acute microvascular lesions, whereas RL-TMA showed more chronic changes (glomerulosclerosis, arteriolar hyalinosis) and more frequent immune complex deposition. Total immunofluorescence intensity and IgG, IgG2, IgG4, and C1q deposition were significantly higher in RL-TMA (all P < 0.05). Over a median follow-up of 16-20 months, end-stage kidney disease incidence did not differ significantly between groups (9.5% vs 0%, P > 0.05). In RL-TMA, univariate analysis showed that lower hemoglobin was associated with worse renal outcomes (HR = 0.946, P = 0.004). Serum albumin showed no clear association (HR = 0.992, P = 0.862), while pathological scores lacked independent predictive value. Kaplan-Meier analysis showed no statistically significant difference in renal progression-free survival between the two groups (P = 0.97). Conclusion:This study delineates significant differences between RL-TMA and Sys-TMA across clinical, pathological, and prognostic dimensions, suggesting that RL-TMA may represent a distinct clinicopathological entity, warranting further validation in larger prospective studies. Given the small sample size of the Sys-TMA group, these results should be interpreted as exploratory and hypothesis-generating. Hemoglobin levels showed an association with renal outcomes in univariate analysis, but this finding requires further investigation in larger cohorts before any clinical utility can be established.
