Unexplained acute kidney injury (AKI) in children owing to diethylene glycol (DEG) contamination during drug production has gained attention in recent years. This qualitative study investigated the effects of DEG exposure on the incidence of unknown AKI in children. A systematic review following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines was proposed to search for studies using predefined search terms in the PubMed, EBSCO, and Web of Science databases without publication date restrictions. The inclusion criteria are observational study, case study, case report, and case series design; and having provided accurate data for DEG poisoning and AKI diagnosis in children. All authors performed the study screening, data extraction, and data synthesis processes. Consensus was reached by mutual agreement. The data synthesis was conducted according to the DEG and unexplained AKI in children by examining the statistical data using Microsoft Excel 2017 and storing the data using the cloud service of Universitas Islam Indonesia. Of the 115 included studies, 21 met the inclusion criteria, including 2 case-control studies, 1 cross-sectional study, 4 case studies, and 14 case reports. DEG-contaminated paracetamol caused unexplained AKI in children. Other drugs including cough expectorants, antihistamines, and sedatives were administered. Chemicals other than DEG, such as propylene glycol and ethylene glycol, also induce AKI owing to overprescription and unintentional exposure. A recent epidemic of unexplained AKI showed contaminated paracetamol as the poisoning agent regardless of formula.

Background: Accurate diagnosis of Alzheimer's disease (AD) in Indonesia remains challenging due to limited access to cerebrospinal fluid (CSF) analysis and PET imaging. Plasma amyloid-β42 (Aβ ₄₂ ) may offer a less-invasive and affordable alternative, particularly when combined with cognitive assessment. However, its diagnostic performance in Indonesian populations is not well established. Objective: To exploratorily assess the diagnostic accuracy of plasma Aβ ₄₂ , alone and as a composite index combined with Montreal Cognitive Assessment-Indonesian version (MoCA-INA) scores, as potential alternatives to CSF biomarkers for AD diagnosis in a resource-limited setting. Methods: A cross-sectional study was conducted involving 25 AD patients and 20 cognitively normal controls. Plasma and CSF biomarker levels were measured using ultrasensitive immunoassay. Cognitive function was evaluated using MoCA-INA. Receiver operating characteristic curve analysis was used to assess diagnostic accuracy. Results: Plasma Aβ ₄₂ with the level of 9.6 pg/ml showed moderate diagnostic accuracy (AUC = 0.78, sensitivity = 69.2%, specificity = 70.6%), while its combination with MoCA-INA Score improved accuracy (adjusted AUC = 0.93, sensitivity = 80.0%, specificity = 84.0%). No significant correlation was found between plasma and CSF biomarkers. Plasma p-tau181 was excluded from the final composite index because it showed an unexpected direction of change. Conclusions: Plasma Aβ ₄₂ combined with MoCA-INA as a composite index, demonstrated potential diagnostic performance for AD in resource-limited settings. However, these exploratory findings should be interpreted cautiously and require further validation in larger and independent cohorts before clinical application.

Background/Objectives: Alzheimer’s disease (AD) is a leading cause of dementia globally, yet standard diagnostic markers like cerebrospinal fluid (CSF) analysis and molecular imaging are invasive and resource-intensive. While artificial intelligence (AI)-based volumetric magnetic resonance imaging (MRI) offers a scalable and non-invasive alternative, data correlating these structural metrics with fluid biomarkers and cognitive status in Southeast Asian populations are scarce. This study addresses this critical gap by examining the within-cohort relationship between CSF biomarkers and regional brain volumes derived from AI-assisted MRI in Indonesian patients with clinically diagnosed AD, providing novel data for an underrepresented population. Methods: Twenty-one AD patients from three national referral hospitals in Indonesia underwent lumbar puncture for CSF biomarker analysis and 3 Tesla structural brain MRI. Brain volumes were analyzed using United Imaging Intelligence software, focusing on AD-relevant regions (hippocampus, entorhinal cortex, parahippocampus, precuneus, and posterior cingulate cortex [PCC]). Results: Spearman’s correlation revealed significant positive associations between CSF Aβ42 levels and several brain regions. Strong correlations were found with the right entorhinal volume indexed to intracranial volume (VICV) (r = 0.601, p = 0.004), right PCC VICV (r = 0.603, p = 0.004), right entorhinal volume (r = 0.533, p = 0.013), and right hippocampus VICV (r = 0.503, p = 0.020). Furthermore, MoCA-InA scores demonstrated highly significant positive correlations with CSF Aβ42 concentrations (r = 0.720, p < 0.001), right Hippocampus VICV (r = 0.703, p < 0.001), and right PCC VICV (r = 0.695, p < 0.001). No significant correlations were found between CSF pTau or the pTau/Aβ42 ratio and regional volumes. Conclusions: These results highlight the entorhinal cortex and PCC as early affected regions where CSF Aβ42 correlates with preserved volume, supporting their role as structural markers in early AD. The absence of pTau associations may reflect early-stage pathology or limitations of cross-sectional volumetry. In resource-limited settings, AI-assisted volumetric MRI demonstrates potential utility as a non-invasive tool for stratifying amyloid-associated brain atrophy and staging disease severity.