This study indicates an association of smoking with worse macular function and structural integrity in retinitis pigmentosa patients, and hence a potential detrimental effect of smoking on the disease course.Cerebral small vessel disease is a common disease in the older population and is recognized as a major risk factor for cognitive decline and stroke. Small vessel disease is considered a global brain disease impacting the integrity of neuronal networks resulting in disturbances of structural and functional connectivity. A core feature of cerebral small vessel disease commonly present on neuroimaging is white matter hyperintensities. We studied high-resolution resting-state EEG, leveraging source reconstruction methods, in 35 participants with varying degree of white matter hyperintensities without clinically evident cognitive impairment in an observational study. In patients with increasing white matter lesion load, global theta power was increased independently of age. Whole-brain functional connectivity revealed a disrupted network confined to the alpha band in participants with higher white matter hyperintensities lesion load. The decrease of functional connectivity was evident in long-range connections, mostly originating or terminating in the frontal lobe. Cognitive testing revealed no global cognitive impairment; however, some participants revealed deficits of executive functions that were related to larger white matter hyperintensities lesion load. In summary, participants without clinical signs of mild cognitive impairment or dementia showed oscillatory changes that were significantly related to white matter lesion load. Hence, oscillatory neuronal network changes due to white matter lesions might act as biomarker prior to clinically relevant behavioural impairment.Epidemiological evidence suggests non-steroidal anti-inflammatory drugs reduce the risk of Alzheimer's disease. However, clinical trials have found no evidence of non-steroidal anti-inflammatory drug efficacy. This incongruence may be due to the wrong non-steroidal anti-inflammatory drugs being tested in robust clinical trials or the epidemiological findings being caused by confounding factors. Therefore, this study used logistic regression and the innovative approach of negative binomial generalized linear mixed modelling to investigate both prevalence and cognitive decline, respectively, in the Alzheimer's Disease Neuroimaging dataset for each commonly used non-steroidal anti-inflammatory drug and paracetamol. Use of most non-steroidal anti-inflammatories was associated with reduced Alzheimer's disease prevalence yet no effect on cognitive decline was observed. Paracetamol had a similar effect on prevalence to these non-steroidal anti-inflammatory drugs suggesting this association is independent of the anti-inflammatory effects and that previous results may be due to spurious associations. Interestingly, diclofenac use was significantly associated with both reduce incidence and slower cognitive decline warranting further research into the potential therapeutic effects of diclofenac in Alzheimer's disease.Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical data will lead to improved prognostic and diagnostic models in neurological disease, facilitating expert-level clinical decision tools across healthcare settings. Despite the clinical promise of artificial intelligence, machine and deep-learning algorithms are not a one-size-fits-all solution for all types of clinical data and questions. In this article, we provide an overview of the core concepts of artificial intelligence, particularly contemporary deep-learning methods, to give clinician and neuroscience researchers an appreciation of how artificial intelligence can be harnessed to support clinical decisions. We clarify and emphasize the data quality and the human expertise needed to build robust clinical artificial intelligence models in neurology. As artificial intelligence is a rapidly evolving field, we take the opportunity to iterate important ethical principles to guide the field of medicine is it moves into an artificial intelligence enhanced future.Diagnosing patients with disorders of consciousness is immensely difficult and often results in misdiagnoses, which can have fatal consequences. Despite the severity of this well-known issue, a reliable assessment tool has not yet been developed and implemented in the clinic. The main aim of this focused review is to evaluate the various event-related potential paradigms, recorded using EEG, which may be used to improve the assessment of patients with disorders of consciousness; we also provide a brief comparison of these paradigms with other measures. Notably, most event-related potential studies on the topic have focused on testing a small set of components, or even just a single component. However, to be of practical use, we argue that an assessment should probe a range of cognitive and linguistic functions at once. We suggest a novel approach that combines a set of well-tested auditory event-related potential components N100, mismatch negativity, P3a, N400, early left anterior negativity and lexical response enhancement. Combining these components in a single, task-free design will provide a multidimensional assessment of cognitive and linguistic processes, which may help physicians make a more precise diagnosis.Human mitochondrial genome (mtDNA) variations, such as mtDNA heteroplasmies (the co-existence of mutated and wild-type mtDNA), have received increasing attention in recent years for their clinical relevance to numerous diseases. But large-scale population studies of mtDNA heteroplasmies have been lagging due to the lack of a labor- and cost-effective method.  https://www.selleckchem.com/products/ipi-549.html  Here, we present a novel human mtDNA sequencing method called STAMP (sequencing by targeted amplification of multiplex probes) for measuring mtDNA heteroplasmies and content in a streamlined workflow. We show that STAMP has high-mapping rates to mtDNA, deep coverage of unique reads and high tolerance to sequencing and polymerase chain reaction errors when applied to human samples. STAMP also has high sensitivity and low false positive rates in identifying artificial mtDNA variants at fractions as low as 0.5% in genomic DNA samples. We further extend STAMP, by including nuclear DNA-targeting probes, to enable assessment of relative mtDNA content in the same assay.