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Liver-directed gene targeting as a potential therapy for Fabry Disease
Fabry disease (FD) is an X-linked inherited, lysosomal storage disorder caused by mutations in the Alpha Galactosidase-A (GLA) gene. This gene encodes for the GLA enzyme which is responsible for the catabolism of glycosphingolipids like globotriaosylceramide (Gb3). Accumulation of Gb3 in lysosomes results in systemic clinical manifestations and reduced lifespan. Enzyme replacement therapy (ERT) and chaperone therapy are the available treatments for FD however, noncurative and with limitations.
We developed a potential therapeutic approach based on the permanent genetic modification of hepatocytes to express the GLA enzyme by targeting the albumin locus in vivo. To model late-onset and early-onset FD, we treated juvenile (P30) and neonatal (P5) Fabry mice with an AAV8 donor vector containing mAlb homology arms and a codon-optimized version of the human GLA cDNA. This treatment was coupled with the AAV-mediated delivery of the CRISPR/SaCas9 platform to increase targeting efficiency. Treatment of juvenile Fabry mice (donor, 3.0E13 vg/kg; SaCas9, 6.0E12 vg/kg) resulted in elevated GLA enzyme activity which was stable till the termination of the experiment at 5 months of age, accompanied by a 70-80% reduction in Lyso-Gb3 accumulation in liver, kidneys and heart, compared to untreated mutant mice. To increase the safety of the procedure, concerns related to the use of programmable nucleases were avoided by applying a nuclease-free approach. Juvenile animals were treated only with the donor vector (3.0E13 vg/kg), coupled with the treatment with fludarabine, which enhances the gene targeting rate. This nuclease-free strategy resulted in increased plasma GLA activity compared to donor-only treated mice, accompanied by 80-95% of lyso-Gb3 clearance.
When we treated neonatal P5 Fabry mice with donor and SaCas9 AAVs, the treatment was significantly more efficient than in juvenile animals due to the increased targeting rate observed in proliferating hepatocytes present in a growing liver. In fact, we were able to completely clear lyso-Gb3 in plasma and in the different organs with the highest dose of 3.0E14 vg/kg of donor vector, while a dose of 3.0E13 vg/kg resulted in a reduction of 95-98% in plasma and target organs.
A dose escalation study with AAV-mediated episomal gene therapy was also done as a proof-of-principle in juvenile Faby KO mice using a strong liver-specific promoter and the human codon-optimized GLA transgene. Animals treated at 3.0E12 vg/kg and higher doses were able to reduce substrate accumulation by 98-100% in plasma and target tissues. Treatment with the lowest dose of the AAV vector (3.0E11vg/kg) resulted in the clearance of 85-95% lyso-Gb3 in the bloodstream and tissues proving the efficacy of the treatment for late-onset FD. ERT-treated animals were considered for comparative evaluation of the treatment.
This data is inclined towards a promising one-shot therapy using an AAV-based integrative gene-editing approach for early-onset and AAV-based episomal gene therapy for late-onset FD to revert the phenotype irrespective of the Fabry disease-causing mutation. To test the translational applicability of this integrative strategy, AAV-LK03 vectors containing human ALB homology arms have been tested in human liver cell lines and will be validated in primary cultures of human hepatocytes, and in humanized mice to generate consistent preclinical support.</br
Combined Nutrition and Exercise Interventions in Community Groups
Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews
A Multi-level Analysis on Implementation of Low-Cost IVF in Sub-Saharan Africa: A Case Study of Uganda.
Introduction: Globally, infertility is a major reproductive disease that affects an estimated 186 million people worldwide. In Sub-Saharan Africa, the burden of infertility is considerably high, affecting one in every four couples of reproductive age. Furthermore, infertility in this context has severe psychosocial, emotional, economic and health consequences. Absence of affordable fertility services in Sub-Saharan Africa has been justified by overpopulation and limited resources, resulting in inequitable access to infertility treatment compared to developed countries. Therefore, low-cost IVF (LCIVF) initiatives have been developed to simplify IVF-related treatment, reduce costs, and improve access to treatment for individuals in low-resource contexts. However, there is a gap between the development of LCIVF initiatives and their implementation in Sub-Saharan Africa. Uganda is the first country in East and Central Africa to undergo implementation of LCIVF initiatives within its public health system at Mulago Women’s Hospital.
Methods: This was an exploratory, qualitative, single, case study conducted at Mulago Women’s Hospital in Kampala, Uganda. The objective of this study was to explore how LCIVF initiatives have been implemented within the public health system of Uganda at the macro-, meso- and micro-level. Primary qualitative data was collected using semi-structured interviews, hospital observations informal conversations, and document review. Using purposive and snowball sampling, a total of twenty-three key informants were interviewed including government officials, clinicians (doctors, nurses, technicians), hospital management, implementers, patient advocacy representatives, private sector practitioners, international organizational representatives, educational institution, and professional medical associations. Sources of secondary data included government and non-government reports, hospital records, organizational briefs, and press outputs. Using a multi-level data analysis approach, this study undertook a hybrid inductive/deductive thematic analysis, with the deductive analysis guided by the Consolidated Framework for Implementation Research (CFIR).
Findings: Factors facilitating implementation included international recognition of infertility as a reproductive disease, strong political advocacy and oversight, patient needs & advocacy, government funding, inter-organizational collaboration, tension to change, competition in the private sector, intervention adaptability & trialability, relative priority, motivation &advocacy of fertility providers and specialist training. While barriers included scarcity of embryologists, intervention complexity, insufficient knowledge, evidence strength & quality of intervention, inadequate leadership engagement & hospital autonomy, poor public knowledge, limited engagement with traditional, cultural, and religious leaders, lack of salary incentives and concerns of revenue loss associated with low-cost options.
Research contributions: This study contributes to knowledge of factors salient to implementation of LCIVF initiatives in a Sub-Saharan context. Effective implementation of these initiatives requires (1) sustained political support and favourable policy & legislation, (2) public sensitization and engagement of traditional, cultural, and religious leaders (3) strengthening local innovation and capacity building of fertility health workers, in particular embryologists (4) sustained implementor leadership engagement and inter-organizational collaboration and (5) proven clinical evidence and utilization of LCIVF initiatives in innovator countries. It also adds to the literature on the applicability of the CFIR framework in explaining factors that influence successful implementation in developing countries and offer opportunities for comparisons across studies
Studying the interplay between ageing and Parkinson's disease using the zebrafish model
Parkinson’s disease (PD) is a neurodegenerative disorder characterised by the loss of dopaminergic neurons in the substantia nigra. Ageing is the major risk factor for developing PD but the interplay between ageing and PD remains elusive.
To investigate the effect of ageing on PD-relevant pathological mechanisms, zebrafish mutant lines harbouring mutations in ageing-associated genes (klotho-/-, sirt1-/-, satb1a-/-, satb1b-/- and satb1a-/-;satb1b-/-) were generated, using CRISPR/Cas9 gene editing. Likewise, a chemical model for SIRT1 deficiency was utilised.
klotho-/- zebrafish displayed an accelerated ageing phenotype at 3mpf and reduced survival to 6mpf. Dopaminergic neuron number, MPP+ susceptibility and microglial number were unaffected in klotho-/- larvae. NAD+ levels were decreased in 6mpf klotho-/- brains. However, ATP levels and DNA damage were unaffected. sirt1-/- zebrafish did not display a phenotype through adulthood. il-1β and il-6 were not upregulated in sirt1-/- larvae, and chemical inhibition of sirt1 did not increase microglial number. cdkn1a, il-1β and il-6 were not upregulated in satb1a-/- and satb1b-/- larvae. Dopaminergic neuron number and MPP+ susceptibility were unaffected in satb1a-/- larvae. However, satb1b-/- larvae demonstrated a moderate decrease in dopaminergic neuron number but equal susceptibility to MPP+ as satb1b+/+ larvae. Adult satb1a-/- but not adult satb1b-/- zebrafish were emaciated. satb1a-/-;satb1b-/- zebrafish did not display a phenotype through adulthood.
Transgenic zebrafish expressing human wildtype α-Synuclein (Tg(eno2:hsa.SNCA-ires-EGFP)) were crossed with klotho-/- and sirt1-/- zebrafish, and treated with a sirt1-specific inhibitor. Neither genetic cross affected survival. The klotho mutation did not increase microglial number in Tg(eno2:hsa.SNCA-ires-EGFP) larvae. Likewise, sirt1 inhibition did not induce motor impairment or cell death in Tg(eno2:hsa.SNCA-ires-EGFP) larvae.
In conclusion, the suitability of zebrafish for studying ageing remains elusive, as only 1 ageing-associated mutant line displayed accelerated ageing. However, zebrafish remain an effective model for studying PD-relevant pathological mechanisms due to the availability of CRISPR/Cas9 gene editing, neuropathological and neurobehavioral tools
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
Current Challenges in the Application of Algorithms in Multi-institutional Clinical Settings
The Coronavirus disease pandemic has highlighted the importance of artificial intelligence in multi-institutional clinical settings. Particularly in situations where the healthcare system is overloaded, and a lot of data is generated, artificial intelligence has great potential to provide automated solutions and to unlock the untapped potential of acquired data. This includes the areas of care, logistics, and diagnosis. For example, automated decision support applications could tremendously help physicians in their daily clinical routine. Especially in radiology and oncology, the exponential growth of imaging data, triggered by a rising number of patients, leads to a permanent overload of the healthcare system, making the use of artificial intelligence inevitable. However, the efficient and advantageous application of artificial intelligence in multi-institutional clinical settings faces several challenges, such as accountability and regulation hurdles, implementation challenges, and fairness considerations. This work focuses on the implementation challenges, which include the following questions: How to ensure well-curated and standardized data, how do algorithms from other domains perform on multi-institutional medical datasets, and how to train more robust and generalizable models? Also, questions of how to interpret results and whether there exist correlations between the performance of the models and the characteristics of the underlying data are part of the work. Therefore, besides presenting a technical solution for manual data annotation and tagging for medical images, a real-world federated learning implementation for image segmentation is introduced. Experiments on a multi-institutional prostate magnetic resonance imaging dataset showcase that models trained by federated learning can achieve similar performance to training on pooled data. Furthermore, Natural Language Processing algorithms with the tasks of semantic textual similarity, text classification, and text summarization are applied to multi-institutional, structured and free-text, oncology reports. The results show that performance gains are achieved by customizing state-of-the-art algorithms to the peculiarities of the medical datasets, such as the occurrence of medications, numbers, or dates. In addition, performance influences are observed depending on the characteristics of the data, such as lexical complexity. The generated results, human baselines, and retrospective human evaluations demonstrate that artificial intelligence algorithms have great potential for use in clinical settings. However, due to the difficulty of processing domain-specific data, there still exists a performance gap between the algorithms and the medical experts. In the future, it is therefore essential to improve the interoperability and standardization of data, as well as to continue working on algorithms to perform well on medical, possibly, domain-shifted data from multiple clinical centers
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