Advances in oligonucleotide drug delivery Roberts, Thomas C; Langer, Robert; Wood, Matthew J A
Nature reviews. Drug discovery,
10/2020, Volume:
19, Issue:
10
Journal Article
Peer reviewed
Open access
Oligonucleotides can be used to modulate gene expression via a range of processes including RNAi, target degradation by RNase H-mediated cleavage, splicing modulation, non-coding RNA inhibition, gene ...activation and programmed gene editing. As such, these molecules have potential therapeutic applications for myriad indications, with several oligonucleotide drugs recently gaining approval. However, despite recent technological advances, achieving efficient oligonucleotide delivery, particularly to extrahepatic tissues, remains a major translational limitation. Here, we provide an overview of oligonucleotide-based drug platforms, focusing on key approaches - including chemical modification, bioconjugation and the use of nanocarriers - which aim to address the delivery challenge.
We are witnessing the advent of a new era of robots - drones - that can autonomously fly in natural and man-made environments. These robots, often associated with defence applications, could have a ...major impact on civilian tasks, including transportation, communication, agriculture, disaster mitigation and environment preservation. Autonomous flight in confined spaces presents great scientific and technical challenges owing to the energetic cost of staying airborne and to the perceptual intelligence required to negotiate complex environments. We identify scientific and technological advances that are expected to translate, within appropriate regulatory frameworks, into pervasive use of autonomous drones for civilian applications.
Antisense oligonucleotides (ASOs) were first discovered to influence RNA processing and modulate protein expression over two decades ago; however, progress translating these agents into the clinic ...has been hampered by inadequate target engagement, insufficient biological activity, and off-target toxic effects. Over the years, novel chemical modifications of ASOs have been employed to address these issues. These modifications, in combination with elucidation of the mechanism of action of ASOs and improved clinical trial design, have provided momentum for the translation of ASO-based strategies into therapies. Many neurological conditions lack an effective treatment; however, as research progressively disentangles the pathogenic mechanisms of these diseases, they provide an ideal platform to test ASO-based strategies. This steady progress reached a pinnacle in the past few years with approvals of ASOs for the treatment of spinal muscular atrophy and Duchenne muscular dystrophy, which represent landmarks in a field in which disease-modifying therapies were virtually non-existent. With the rapid development of improved next-generation ASOs toward clinical application, this technology now holds the potential to have a dramatic effect on the treatment of many neurological conditions in the near future.
The spread of misinformation is a global phenomenon, with implications for elections, state-sanctioned violence, and health outcomes. Yet, even though scholars have investigated the capacity of ...fact-checking to reduce belief in misinformation, little evidence exists on the global effectiveness of this approach. We describe fact-checking experiments conducted simultaneously in Argentina, Nigeria, South Africa, and the United Kingdom, in which we studied whether fact-checking can durably reduce belief in misinformation. In total, we evaluated 22 fact-checks, including two that were tested in all four countries. Fact-checking reduced belief in misinformation, with most effects still apparent more than 2 wk later. A meta-analytic procedure indicates that fact-checks reduced belief in misinformation by at least 0.59 points on a 5-point scale. Exposure to misinformation, however, only increased false beliefs by less than 0.07 points on the same scale. Across continents, fact-checks reduce belief in misinformation, often durably so.
In this study, an IgG-degrading enzyme derived from
Streptococcus pyogenes
(IdeS) that cleaves human IgG into F(ab′)
2
and Fc fragments reduced or eliminated donor-specific antibodies and permitted ...HLA-incompatible renal transplantation.
Good Gig, Bad Gig Wood, Alex J; Graham, Mark; Lehdonvirta, Vili ...
Work, employment and society,
02/2019, Volume:
33, Issue:
1
Journal Article
Peer reviewed
Open access
This article evaluates the job quality of work in the remote gig economy. Such work consists of the remote provision of a wide variety of digital services mediated by online labour platforms. ...Focusing on workers in Southeast Asia and Sub-Saharan Africa, the article draws on semi-structured interviews in six countries (N = 107) and a cross-regional survey (N = 679) to detail the manner in which remote gig work is shaped by platform-based algorithmic control. Despite varying country contexts and types of work, we show that algorithmic control is central to the operation of online labour platforms. Algorithmic management techniques tend to offer workers high levels of flexibility, autonomy, task variety and complexity. However, these mechanisms of control can also result in low pay, social isolation, working unsocial and irregular hours, overwork, sleep deprivation and exhaustion.
Fluid-driven origami-inspired artificial muscles Li, Shuguang; Vogt, Daniel M.; Rus, Daniela ...
Proceedings of the National Academy of Sciences - PNAS,
12/2017, Volume:
114, Issue:
50
Journal Article
Peer reviewed
Open access
Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by ...their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ∼600 kPa, and produce peak power densities over 2 kW/kg—all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration.
Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, application of these models in clinically realistic environments can result in poor ...generalization and decreased accuracy, mainly due to the domain shift across different hospitals, scanner vendors, imaging protocols, and patient populations etc. Common transfer learning and domain adaptation techniques are proposed to address this bottleneck. However, these solutions require data (and annotations) from the target domain to retrain the model, and is therefore restrictive in practice for widespread model deployment. Ideally, we wish to have a trained (locked) model that can work uniformly well across unseen domains without further training. In this paper, we propose a deep stacked transformation approach for domain generalization. Specifically, a series of {n} stacked transformations are applied to each image during network training. The underlying assumption is that the "expected" domain shift for a specific medical imaging modality could be simulated by applying extensive data augmentation on a single source domain, and consequently, a deep model trained on the augmented "big" data (BigAug) could generalize well on unseen domains. We exploit four surprisingly effective, but previously understudied, image-based characteristics for data augmentation to overcome the domain generalization problem. We train and evaluate the BigAug model (with {n}={9} transformations) on three different 3D segmentation tasks (prostate gland, left atrial, left ventricle) covering two medical imaging modalities (MRI and ultrasound) involving eight publicly available challenge datasets. The results show that when training on relatively small dataset (n = 10~32 volumes, depending on the size of the available datasets) from a single source domain: (i) BigAug models degrade an average of 11%(Dice score change) from source to unseen domain, substantially better than conventional augmentation (degrading 39%) and CycleGAN-based domain adaptation method (degrading 25%), (ii) BigAug is better than "shallower" stacked transforms (i.e. those with fewer transforms) on unseen domains and demonstrates modest improvement to conventional augmentation on the source domain, (iii) after training with BigAug on one source domain, performance on an unseen domain is similar to training a model from scratch on that domain when using the same number of training samples. When training on large datasets (n = 465 volumes) with BigAug, (iv) application to unseen domains reaches the performance of state-of-the-art fully supervised models that are trained and tested on their source domains. These findings establish a strong benchmark for the study of domain generalization in medical imaging, and can be generalized to the design of highly robust deep segmentation models for clinical deployment.
A new family of high-surface-area polyethylene fiber adsorbents named the AF series was recently developed at the Oak Ridge National Laboratory (ORNL). The AF series adsorbents were synthesized by ...radiation-induced graft polymerization of acrylonitrile and itaconic acid (at different monomer/comonomer mol ratios) onto high surface area polyethylene fibers. The degree of grafting (%DOG) of AF series adsorbents was found to be 154–354%. The grafted nitrile groups were converted to amidoxime groups by treating with hydroxylamine. The amidoximated adsorbents were then conditioned with 0.44 M KOH at 80 °C followed by screening at ORNL with sodium-based synthetic aqueous solution, spiked with 8 ppm uranium. The uranium adsorption capacity in simulated seawater screening ranged from 170 to 200 g-U/kg-ads irrespective of %DOG. A monomer/comonomer molar ratio in the range of 7.57–10.14 seemed to be optimum for highest uranium loading capacity. Subsequently, the adsorbents were also tested with natural seawater at Pacific Northwest National Laboratory (PNNL) using flow-through column experiments to determine uranium loading capacity with varying KOH conditioning times at 80 °C. The highest adsorption capacity of AF1 measured after 56 days of marine testing was demonstrated as 3.9 g-U/kg-adsorbent and 3.2 g-U/kg-adsorbent for 1 and 3 h of KOH conditioning at 80 °C, respectively. Based on capacity values of several AF1 samples, it was observed that changing KOH conditioning from 1 to 3 h at 80 °C resulted in a 22–27% decrease in uranium adsorption capacity in seawater.
Exosomes are emerging targets for biomedical research. However, suitable methods for the isolation of blood plasma-derived exosomes without impurities have not yet been described.
Therefore, we ...investigated the efficiency and purity of exosomes isolated with potentially suitable methods; differential ultracentrifugation (UC) and size exclusion chromatography (SEC).
Exosomes were isolated from rat and human blood plasma by various UC and SEC conditions. Efficiency was investigated at serial UC of the supernatant, while in case of SEC by comparing the content of exosomal markers of various fractions. Purity was assessed based on the presence of albumin. We found that the diameter of the majority of isolated particles fell into the size range of exosomes, however, albumin was also present in the preparations, when 1h UC at 4°C was applied. Furthermore, with this method only a minor fraction of total exosomes could be isolated from blood as deduced from the constant amount of exosomal markers CD63 and TSG101 detected after serial UC of rat blood plasma samples. By using UC for longer time or with shorter sedimentation distance at 4°C, or UC performed at 37°C, exosomal yield increased, but albumin impurity was still observed in the isolates, as assessed by transmission electron microscopy, dynamic light scattering and immunoblotting against CD63, TSG101 and albumin. Efficiency and purity were not different in case of using further diluted samples. By using SEC with different columns, we have found that although a minor fraction of exosomes can be isolated without significant albumin content on Sepharose CL-4B or Sephacryl S-400 columns, but not on Sepharose 2B columns, the majority of exosomes co-eluted with albumin.
Here we show that it is feasible to isolate exosomes from blood plasma by SEC without significant albumin contamination albeit with low vesicle yield.
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