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1.
Pediatr Nephrol ; 38(11): 3513-3518, 2023 11.
Article in English | MEDLINE | ID: mdl-36952039

ABSTRACT

Nephrotic syndrome (NS) consists of the clinical triad of hypoalbuminaemia, high levels of proteinuria and oedema, and describes a heterogeneous group of disease processes with different underlying drivers. The existence of circulating factor disease (CFD) as a driver of NS has been epitomised by a subset of patients who exhibit disease recurrence after transplantation, alongside laboratory work. Several circulating factors have been proposed and studied, broadly grouped into protease components such as soluble urokinase-type plasminogen activator (suPAR), hemopexin (Hx) and calcium/calmodulin-serine protease kinase (CASK), and other circulating proteases, and immune components such as TNF-α, CD40 and cardiotrophin-like cytokine-1 (CLC-1). While currently there is no definitive way of assessing risk of CFD pre-transplantation, promising work is emerging through the study of 'multi-omic' bioinformatic data from large national cohorts and biobanks.


Subject(s)
Nephrotic Syndrome , Humans , Proteinuria , Receptors, Urokinase Plasminogen Activator
2.
Biomedicines ; 11(2)2023 Feb 10.
Article in English | MEDLINE | ID: mdl-36831050

ABSTRACT

A small subset of people with nephrotic syndrome (NS) have genetically driven disease. However, the disease mechanisms for the remaining majority are unknown. Epigenetic marks are reversible but stable regulators of gene expression with utility as biomarkers and therapeutic targets. We aimed to identify and assess all published human studies of epigenetic mechanisms in NS. PubMed (MEDLINE) and Embase were searched for original research articles examining any epigenetic mechanism in samples collected from people with steroid resistant NS, steroid sensitive NS, focal segmental glomerulosclerosis or minimal change disease. Study quality was assessed by using the Joanna Briggs Institute critical appraisal tools. Forty-nine studies met our inclusion criteria. The majority of these examined micro-RNAs (n = 35, 71%). Study quality was low, with only 23 deemed higher quality, and most of these included fewer than 100 patients and failed to validate findings in a second cohort. However, there were some promising concordant results between the studies; higher levels of serum miR-191 and miR-30c, and urinary miR-23b-3p and miR-30a-5p were observed in NS compared to controls. We have identified that the epigenome, particularly DNA methylation and histone modifications, has been understudied in NS. Large clinical studies, which utilise the latest high-throughput technologies and analytical pipelines, should focus on addressing this critical gap in the literature.

3.
Arch Dis Child ; 107(3): 223-228, 2022 03.
Article in English | MEDLINE | ID: mdl-34301619

ABSTRACT

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse and interpret extremely large amounts of data, which can then be applied to create predictive models. Such applications of this technology are now ubiquitous in our day-to-day lives: predictive text, spam filtering, and recommendation systems in social media, streaming video and e-commerce to name a few examples. It is only more recently that ML has started to be implemented against the vast amount of data generated in healthcare. The emerging role of AI in refining healthcare delivery was recently highlighted in the 'National Health Service Long Term Plan 2019'. In paediatrics, workforce challenges, rising healthcare attendance and increased patient complexity and comorbidity mean that demands on paediatric services are also growing. As healthcare moves into this digital age, this review considers the potential impact ML can have across all aspects of paediatric care from improving workforce efficiency and aiding clinical decision-making to precision medicine and drug development.


Subject(s)
Delivery of Health Care/methods , Machine Learning/trends , Pediatrics/methods , Adolescent , Artificial Intelligence , Child , Clinical Decision-Making , Computers , Delivery of Health Care/trends , Drug Development/methods , Female , Forecasting , Humans , Infant , Male , Pediatrics/trends , Precision Medicine , Risk Assessment/methods , State Medicine , Workforce
4.
Med Teach ; 38(12): 1289, 2016 Dec.
Article in English | MEDLINE | ID: mdl-27766912

Subject(s)
Learning , Peer Group , Humans , Teaching
5.
Adv Hematol ; 2016: 3905907, 2016.
Article in English | MEDLINE | ID: mdl-27313619

ABSTRACT

Haematopoietic stem cell transplantation is a well-established treatment option for both hematological malignancies and nonmalignant conditions such as aplastic anemia and haemoglobinopathies. For those patients lacking a suitable matched sibling or matched unrelated donor, haploidentical donors are an alternative expedient donor pool. Historically, haploidentical transplantation led to high rates of graft rejection and GVHD. Strategies to circumvent these issues include T cell depletion and management of complications thereof or T replete transplants with GVHD prophylaxis. This review is an overview of these strategies and contemporaneous outcomes for hematological malignancies in adult haploidentical stem cell transplant recipients.

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