BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1456-0) contains supplementary material, which is available to authorized users.

Aim-To determine the incidence, presentation, aetiology, and outcome of nontraumatic coma in children aged between 1 month and 16 years. Methods-In this prospective, population based, epidemiological study in the former Northern NHS region of the UK, cases were notified following any hospital admission or community death associated with non-traumatic coma. Coma was defined as a Glasgow Coma Score below 12 for more than six hours. Results-The incidence of non-traumatic coma was 30.8 per 100 000 children under 16 per year (6.0 per 100 000 general population per year). The age specific incidence was notably higher in the first year of life (160 per 100 000 children per year). CNS specific presentations became commoner with increasing age. In infants, nearly two thirds of presentations were with non-specific, systemic signs. Infection was the commonest overall aetiology. Aetiology remained unknown in 14% despite extensive investigation and/or autopsy. Mortality was highly dependent on aetiology, with aetiology specific mortality rates varying from 3% to 84%. With follow up to approximately 12 months, overall series mortality was 46%.

The results of this study reinforce the need for monitoring as well as educational initiatives for Muslims with diabetes who fast during Ramadan. Telemonitoring offers an attractive option requiring further research. (Clinical Trial Registry No. NCT02189135).

Increasingly, online counseling is considered to be a cost-effective and highly accessible method of providing basic counseling and mental health services. To examine the potential of online delivery as a way of increasing overall usage of services, this study looked at students’ attitudes toward and likelihood of using both online and/or face-to-face counseling. A survey was conducted with 409 students from six universities in Malaysia participating. Approximately 35% of participants reported that they would be likely to utilize online counseling services but would be unlikely to participate in face-to-face counseling. Based on these results, it is suggested that offering online counseling, in addition to face-to-face services, could be an effective way for many university counseling centers to increase the utilization of their services and thus better serve their communities.

ObjectiveWe evaluated the beliefs, experience and diabetes management strategies of type 2 diabetes mellitus (T2DM) Muslim patients that chose to fast during Ramadan.Research design and methodsA semistructured focus group interview was conducted with 53 participants with T2DM. Participants were purposefully sampled and asked to share their perspective on Ramadan fasting. All interviews were audio recorded, transcribed verbatim and analyzed thematically.ResultsParticipants reported optimism towards fasting during Ramadan, as they believed that fasting was beneficial to their overall well-being, and a time for family bonding. Most participants made limited attempts to discuss with their doctors on the decision to fast and self-adjusted their medication based on experience and symptoms during this period. They also reported difficulty in managing their diet, due to fear of hypoglycemia and the collective social aspect of fasting.ConclusionMuslims are optimistic about their well-being when fasting during Ramadan. Many choose to fulfill their religious obligation despite being discouraged by their doctors. Collaboration with religious authorities should be explored to ensure patients receive adequate education before fasting during Ramadan.Trial registration numberNCT02189135; Results.