Anti-Tumor Necrosis Factor Alpha for Retinal Diseases: Current Knowledge and Future Concepts

Tumor necrosis factor alpha (TNF-α) is a pro-inflammatory cytokine produced by macrophages and T-cells. It plays an important role both in inflammation and apoptosis. In the eye, TNF-α appears to have a role in the pathogenesis of inflammatory, edematous, neovascular and neurodegenerative disorders. Several TNF-blocking drugs have been developed and approved, and are in clinical use for inflammatory diseases such as rheumatoid arthritis, psoriasis and ankylosing spondylitis. TNF-α blockers are widely used in ophthalmology as an off-label alternative to "traditional" immunosuppressive and immune-modulatory treatments in noninfectious uveitis. Preliminary studies suggest a positive effect of intravenously administered TNF-α blockers, mainly infliximab, for treating refractory diabetic macular edema and neovascular age-related macular degeneration. Unfortunately, much of the current data raises considerable safety concerns for intravitreal use of TNF-α inhibitors, in particular, intraocular inflammatory responses have been reported after intravitreal injection of infliximab. Results of dose-finding studies and humanized antibody or antibody fragments (e.g. adalimumab) are anticipated in the coming years; these will shed light on potential benefits and risks of local and systemic TNF-α blockers used for treatment of diseases of the retina and choroid

High-accuracy detection of early Parkinson's Disease using multiple characteristics of finger movement while typing.

Parkinson's Disease (PD) is a progressive neurodegenerative movement disease affecting over 6 million people worldwide. Loss of dopamine-producing neurons results in a range of both motor and non-motor symptoms, however there is currently no definitive test for PD by non-specialist clinicians, especially in the early disease stages where the symptoms may be subtle and poorly characterised. This results in a high misdiagnosis rate (up to 25% by non-specialists) and people can have the disease for many years before diagnosis. There is a need for a more accurate, objective means of early detection, ideally one which can be used by individuals in their home setting. In this investigation, keystroke timing information from 103 subjects (comprising 32 with mild PD severity and the remainder non-PD controls) was captured as they typed on a computer keyboard over an extended period and showed that PD affects various characteristics of hand and finger movement and that these can be detected. A novel methodology was used to classify the subjects' disease status, by utilising a combination of many keystroke features which were analysed by an ensemble of machine learning classification models. When applied to two separate participant groups, this approach was able to successfully discriminate between early-PD subjects and controls with 96% sensitivity, 97% specificity and an AUC of 0.98. The technique does not require any specialised equipment or medical supervision, and does not rely on the experience and skill of the practitioner. Regarding more general application, it currently does not incorporate a second cardinal disease symptom, so may not differentiate PD from similar movement-related disorders