Rapidly Progressing Neurocognitive Disorder in a Male with FXTAS and Alzheimer's Disease.

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a neurodegenerative disorder that usually begins in the early 60s and affects carriers of premutation expansion (55-200 CGG repeats) of the fragile X mental retardation 1 (FMR1) gene. Additional disorders can co-occur with FXTAS including Alzheimer's disease (AD). Here we discuss a case report of a male with 67 CGG repeats in FMR1 who had mild late-onset FXTAS symptoms followed by neurocognitive disorder symptoms consistent with AD. The patient has developed tremor and ataxia that are the two characteristic symptoms of FXTAS. In addition, he shows rapid cognitive decline, brain atrophy most substantial in the medial temporal lobe, and decreased metabolism in the brain regions that are the characteristic findings of AD. The purpose of this study is to describe a patient profile with both diseases and review the details of an overlap between these two diseases

Pragmatic language disorder in Parkinson's disease and the potential effect of cognitive reserve

It is known that patients with Parkinson\u2019s Disease (PD) may show deficits in several areas of cognition, including speech and language abilities. One domain of particular interest is pragmatics, which refers to the capacity of using language in context for a successful communication. Several studies showed that some specific aspects of pragmatics \u2013 both in production and in comprehension \u2013 might be impaired in patients with PD. However, a clear picture of pragmatic abilities in PD is still missing, as most of the existing studies focused on specific aspects of the pragmatic competence rather than on sketching a complete pragmatic profile. Moreover, little is known on the potential role of protective factors in compensating the decline of communicative skills as the disease progresses. The present study has two aims: (1) to provide a complete picture of pragmatic abilities in patients with PD, by using a comprehensive battery (Assessment of Pragmatic Abilities and Cognitive Substrates, APACS) and by investigating the relationship with other aspects of cognitive functioning (e.g., working memory and Theory of Mind) and (2) to investigate whether Cognitive Reserve, i.e., the resilience to cognitive impairment provided by life experiences and activities, may compensate for the progressive pragmatic deficits in PD. We found that patients with PD, compared to healthy matched controls, had worse performance in discourse production and in the description of scenes, and that these impairments were tightly correlated with the severity of motor impairment, suggesting reduced intentionality of engaging in a communicative exchange. Patients with PD showed also an impairment in comprehending texts and humor, suggesting a problem in inferring from stories, which was related to general cognitive impairment. Notably, we did not find any significant difference between patients and controls in figurative language comprehension, a domain that is commonly impaired in other neurodegenerative diseases. This might be indicative of a specific profile of pragmatic impairment in patients with PD, worth of further investigation. Finally, Cognitive Reserve measures showed a high degree of association with pragmatic comprehension abilities, suggesting that the modification of life-styles could be a good candidate for compensating the possible problems in understanding the pragmatic aspects of language experienced by patients with PD

The impact of artificial intelligence on the current and future practice of clinical cancer genomics.

Artificial intelligence (AI) is one of the most significant fields of development in the current digital age. Rapid advancements have raised speculation as to its potential benefits in a wide range of fields, with healthcare often at the forefront. However, amidst this optimism, apprehension and opposition continue to strongly persist. Oft-cited concerns include the threat of unemployment, harm to the doctor-patient relationship and questions of safety and accuracy. In this article, we review both the current and future medical applications of AI within the sub-speciality of cancer genomics

Adverse Drug Reaction Classification With Deep Neural Networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs