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Digital and Interactive EA:Ensuring data quality and and Integrity
No full textThe rise of digital technologies is transforming Environmental Assessment (EA), enabling continuous monitoring and interactive communication of environmental impacts. However, the reliability of these advancements critically depends on the quality and integrity of underlying data. This systematic literature review examines how Artificial Intelligence (AI) and other digital tools influence data quality, integrity, and trustworthiness in the EA processes e.g. monitoring. Using the PRISMA methodology and NVivo-assisted thematic analysis of 63 peer-reviewed studies, four key application domains emerged: (1) predictive modelling and simulation, (2) automated data collection and real-time processing, (3) decision-support systems for impact evaluation, and (4) interactive digital platforms for communicating progress. To analyse these applications further, the review adopts four conceptual dimensions: socio-technical systems theory, to explore the interaction between digital tools and institutional practices; responsible innovation, to assess ethical and inclusive use of AI for environmental monitoring; environmental governance, to evaluate how digitalization affects transparency and accountability; and digital transformation, to understand the implications for data-driven decision-making. The findings reveal both opportunities and risks: to explore how data integrity i.e. through robust verification mechanisms, transparent algorithms, and secure data infrastructures is essential for credible and actionable impact assessments
A modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis
No full textBackground Lewy body dementias (LBD), comprising dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD), are defined by misfolded α-synuclein aggregation. Seed amplification assays (SAAs), such as RT-QuIC, enable sensitive detection of α-synuclein aggregates but typically provide binary readouts and require fluorescence labeling. Raman spectroscopy offers a label-free approach to detect subtle biochemical changes, and its diagnostic potential can be enhanced with machine learning. Objectives This proof-of-concept study aimed to evaluate whether Raman spectroscopy combined with machine learning can improve SAA-based discrimination of LBD from controls in cerebrospinal fluid (CSF). Methods We analyzed a small number of post-mortem CSF samples from pathologically confirmed DLB (n = 2), PDD (n = 2), and controls (n = 2) using a 7-day SAA. Raman spectra were collected on Days 1, 4, and 7 and analyzed using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). Results Following SAA, both PCA and UMAP distinguished combined LBD samples from controls within 24 h (Day 1), reflecting biochemical changes consistent with α-synuclein fibrillation. Spectral shifts indicated decreased α-helical content with increased β-sheet structures. No consistent separation between DLB and PDD was observed. Conclusion This preliminary study demonstrates that combining Raman spectroscopy with machine learning can enable rapid, label-free detection of disease-specific changes. Despite the very limited sample size, these findings highlight the potential of this novel workflow and strongly warrant its validation in larger cohorts.</p
Impact of antimicrobial stewardship on healthcare-associated infections and antibiotic prescriptions in African countries:Systematic review and meta-analysis
No full textBackground: An antimicrobial stewardship programme (AMS) aims to combat antimicrobial resistance and healthcare-associated infections (HCAIs). While studies in developed countries show AMS’s effectiveness in reducing AMR and HCAIs, its impact in African countries, given differing socioeconomics, remains unclear. Objectives: To review the impact of AMS on HCAIs, antibiotic prescriptions, cost of antimicrobial procurement, and compliance with diagnostic measures for detecting resistant HCAIs in African countries. Methods: Two reviewers (S.S. and U.C.) searched databases like CINAHL, Medline, and PubMed for studies on AMS interventions in African countries, focussing on their impact on healthcare-associated infections (HCAIs) and antibiotic prescriptions. We excluded studies on outpatients, children, or those not in English and conducted a meta-analysis using data collected from changes in HCAIs before and after intervention using a random-effects model. Results: The search identified 1153 studies, of which 14 were included in the review, while four eligible studies were included in the meta-analysis. Thirteen of the 14 studies were designed using pre- and post-study methods, and one study employed a case-control method. AMS interventions effectively reduce antibiotic consumption, the cost of antibiotic procurement, and improve diagnostic measures for the detection of resistant microorganisms. The forest plot suggested a 34% reduction in HCAIs. Conclusions: In Africa, AMS interventions, whether combined or single, reduce HCAIs and antibiotic prescriptions in healthcare settings. Surgical antibiotic prophylaxis lowers HCAIs in hospitals by one-third. However, findings are cautiously generalised due to the varied quality of studies and the limited number of African countries involved.</p
Mutations within the predicted 1 fragment-binding region of FAM83G/SACK1G abolish its interaction with the Ser/Thr kinase CK1α
Get PDFSACK1G (aka FAM83G, PAWS1) plays a central role in activating canonical WNT signalling via interaction with the Ser/Thr kinase CK1α. This loss of CK1α binding and WNT signalling underlies the pathogenesis of Palmoplantar Keratoderma (PPK) caused by several reported mutations in the SACK1G gene. We modelled the scaffold anchor of CK1 (SACK1) domain of SACK1G and used fragment-bound structures of the SACK1B (FAM83B) dimer to guide our analysis. This allowed us to computafonally predict several key residues near the fragment binding site in SACK1G that may be important for its funcfon. We mutated these residues, introduced them into SACK1G-/- DLD-1 colorectal cancer cells and investigated their ability to bind endogenous CK1α. We uncovered two SACK1G mutations, namely Y204A and I206A, that abolish interaction with CK1α similarly to the PPK pathogenic mutant A34E. Consistent with this loss of SACK1G-CK1α interaction, the molecular glue degrader of CK1α, DEG-77, fails to co-degrade the Y204A and I206A mutants while it still co-degrades native SACK1G. Our findings demonstrate the utility of our computational methods to uncover functional residues on proteins based on fragment-binding sites
Cellular and Extracellular MicroRNA Dysregulation in LRRK2-Linked Parkinson’s Disease
Get PDFCell-free microRNAs in body fluids have emerged as promising biomarker candidates in neurodegenerative diseases. While several studies have identified dysregulated miRNAs in sporadic Parkinson’s disease, it remains unclear whether distinguishable alterations of cell-free miRNAs occur in genetic forms of the disease, such as those associated with the LRRK2 G2019S mutation. In this proof-of-concept study, we used a human induced pluripotent stem cell-derived dopaminergic neuron model to investigate whether the LRRK2 G2019S mutation induces detectable changes in the intra- and extracellular miRNAome, and whether miRNA signatures identified in vitro can be validated in patient-derived cerebrospinal fluid. We differentiated dopaminergic neurons from induced pluripotent stem cells carrying the LRRK2 G2019S mutation and an isogenic gene-corrected control. Extracellular vesicles were isolated from the culture medium and used as a source of cell-free miRNA. Next, small RNA libraries were generated and analyzed. Differentially expressed microRNAs were validated in an independent batch using RT-qPCR. We further quantified candidate microRNAs in cerebrospinal fluid samples from five LRRK2 G2019S patients and matching healthy controls. The patient cohort included the fibroblast donor from whom the stem cells were originally derived. We successfully isolated extracellular vesicles from induced pluripotent stem cell-derived human dopaminergic neurons. We identified a distinct set of differentially expressed miRNAs in cellular and cell-free RNA, among which let-7g-5p and miR-21-5p were consistently upregulated and validated across independent replicates. These alterations were reflected in the cerebrospinal fluid of the original donor and partially reproduced in additional LRRK2 patients, supporting the concept of patient-specific signatures. A strong correlation between intra- and extracellular miRNA expression was observed. Our findings demonstrate that induced pluripotent stem cell-derived dopaminergic neurons can serve as a model to identify individualized, cell-free microRNA signatures associated with the LRRK2 G2019S mutation. The dysregulated miRNAs detected in vitro were mirrored in patient cerebrospinal fluid, supporting their potential as accessible molecular readouts. These results lay the groundwork for personalized biomarker strategies in genetic forms of Parkinson’s disease and warrant further validation in larger patient cohorts.</p
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