Nanoparticle–Cartilage Interaction: Pathology-Based Intra-articular Drug Delivery for Osteoarthritis Therapy

Osteoarthritis is the most prevalent chronic and debilitating joint disease, resulting in huge medical and socioeconomic burdens. Intra-articular administration of agents is clinically used for pain management. However, the effectiveness is inapparent caused by the rapid clearance of agents. To overcome this issue, nanoparticles as delivery systems hold considerable promise for local control of the pharmacokinetics of therapeutic agents. Given the therapeutic programs are inseparable from pathological progress of osteoarthritis, an ideal delivery system should allow the release of therapeutic agents upon specific features of disorders. In this review, we firstly introduce the pathological features of osteoarthritis and the design concept for accurate localization within cartilage for sustained drug release. Then, we review the interactions of nanoparticles with cartilage microenvironment and the rational design. Furthermore, we highlight advances in the therapeutic schemes according to the pathology signals. Finally, armed with an updated understanding of the pathological mechanisms, we place an emphasis on the development of “smart” bioresponsive and multiple modality nanoparticles on the near horizon to interact with the pathological signals. We anticipate that the exploration of nanoparticles by balancing the efficacy, safety, and complexity will lay down a solid foundation tangible for clinical translation.

Multisensory action effects facilitate the performance of motor sequences

Research has shown that contingent, distinct action effects have a beneficial influence on motor sequence performance. Previous studies showed the beneficial influence of task-irrelevant action effects from one modality (auditory) on motor sequence performance, compared with no task-irrelevant action effects. The present study investigated the influence of task-irrelevant action effects on motor sequence performance from a multiple-modality perspective. We compared motor sequence performances of participants who received different task-irrelevant action effects in an auditory, visual, or audiovisual condition. In the auditory condition, key presses produced tones of a C-major scale that mapped to keys from left to right in ascending order. In the visual condition, key presses produced rectangles in different locations on the screen that mapped to keys from left to right in ascending order. In the audiovisual condition, both tone and rectangle effects were produced simultaneously by key presses. There were advantages for the audiovisual group in motor sequence initiation and execution. The results implied that, compared with unimodal action effects, action effects from multiple sensory modalities can prime an action faster and strengthen associations between successive actions, leading to faster motor sequence performance.

A pan-Atlantic ‘multiple modal belt’?

Multiple modality is spread across the wider Atlantic region, both within individual varieties and across variety types. Based on corpus-based evidence, it is argued that first and second tiers of multiple modals carry high diagnostic value and that regionally separated Anglophone areas differ in their preference for first- and second-tier components in modal constructions. Semantics is a diagnostic typologically as there exists a continuum, the “Multiple Modal Belt,” which consists of three main clusters that are primarily differentiated by their respective compositional preferences: North American varieties favor epistemic ‘weak probability’ elements (~might) as first-tier modals, Caribbean varieties ‘high probability’ or ‘certainty’ (~must). Multiple causation and contact-induced change are offered as explanations for supra- and sub-regional variation in the Atlantic region, and there is strong evidence that the preference for second-tier components originally represented Scottish origin and subsequent diffusion with locally differing contact scenarios. Locally distinct preferences for semantic compositionality – particularly based on preference for first-tier ‘high-probability’ modals – are used to model a geo-typological clustering of varieties throughout the wider Atlantic region.

SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract)

In recent years, there has been a substantial rise in the consumption of news via online platforms. The ease of publication and lack of editorial rigour in some of these platforms have further led to the proliferation of fake news. In this paper, we study the problem of detecting fake news on the FakeNewsNet repository, a collection of full length articles along with associated images. We present SpotFake+, a multimodal approach that leverages transfer learning to capture semantic and contextual information from the news articles and its associated images and achieves the better accuracy for fake news detection. To the best of our knowledge, this is the first work that performs a multimodal approach for fake news detection on a dataset that consists of full length articles. It outperforms the performance shown by both single modality and multiple-modality models. We also release the pretrained model for the benefit of the community.