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Background evidence is largely available indicating benefits to adding a pharmacist on acute care wards. The benefits of maintaining pharmacotherapeutic consultant services on a geriatric ward remain unexplored. Objectives to determine the impact of the removal of a clinical pharmacist from an acute geriatric ward on patients' Medication Appropriateness Index (MAI) scores, admission-related outcomes and drug burdens. Methods researchers consulted the archives for records of patients admitted to the geriatric care unit before and after the pharmacist's withdrawal. The primary outcome of differential MAI scores and secondary outcomes of rehospitalisations, emergency department visits, durations of hospitalisation and differential drug count were compared pre- and post-intervention. An interrupted time series analysis regression model was used for the primary outcome. Results a total of 305 patients admitted before (n  = 208) and after (n  = 97) the pharmacist's withdrawal were included in the study. The intervention had a significant impact on the primary outcome, increasing the relative differential MAI score (adjusted mean) by 9.3 points (95% confidence interval 3.9–14.6). As for the secondary outcomes, differences in admission-related outcomes were non-significant but the mean differential drug count significantly increased post-intervention from 0.02 to 1.36 (P  < 0.001). Conclusion the removal of the pharmacist led to an increase in inappropriate drug prescription. Careful consideration should be given to decisions regarding the removal of the pharmacist from acute geriatric care teams. [ABSTRACT FROM AUTHOR]

Protein fold recognition is one of the most critical tasks to explore the structures and functions of the proteins based on their primary sequence information. The existing protein fold recognition approaches rely on features reflecting the characteristics of protein folds. However, the feature extraction methods are still the bottleneck of the performance improvement of these methods. In this paper, we proposed two new feature extraction methods called MotifCNN and MotifDCNN to extract more discriminative fold-specific features based on structural motif kernels to construct the motif-based convolutional neural networks (CNNs). The pairwise sequence similarity scores calculated based on fold-specific features are then fed into support vector machines to construct the predictor for fold recognition, and a predictor called MotifCNN-fold has been proposed. Experimental results on the benchmark dataset showed that MotifCNN-fold obviously outperformed all the other competing methods. In particular, the fold-specific features extracted by MotifCNN and MotifDCNN are more discriminative than the fold-specific features extracted by other deep learning techniques, indicating that incorporating the structural motifs into the CNN is able to capture the characteristics of protein folds. [ABSTRACT FROM AUTHOR]

Protein fold recognition is critical for studying the structures and functions of proteins. The existing protein fold recognition approaches failed to efficiently calculate the pairwise sequence similarity scores of the proteins in the same fold sharing low sequence similarities. Furthermore, the existing feature vectorization strategies are not able to measure the global relationships among proteins from different protein folds. In this article, we proposed a new computational predictor called DeepSVM-fold for protein fold recognition by introducing a new feature vector based on the pairwise sequence similarity scores calculated from the fold-specific features extracted by deep learning networks. The feature vectors are then fed into a support vector machine to construct the predictor. Experimental results on the benchmark dataset (LE) show that DeepSVM-fold obviously outperforms all the other competing methods. [ABSTRACT FROM AUTHOR]

Asthma is a common chronic disease in children. Its prevalence in Taiwan has been increasing every year. Because repeated asthma attacks are life threatening, primary caregivers who take care of children with asthma may experience extremely high levels of stress. This study used qualitative research methods to identify the sources of caregiver stress. Eighteen primary caregivers were interviewed individually, and 12 were interviewed through focus-group discussions. Using data obtained from a literature review and the respondents' caregiver experiences, authors summarized and categorized stressors as physiologic, psychological, intellectual, and social. Primary caregivers taking care of children with asthma paid more attention to household cleaning; this was the biggest physiologic stressor. The biggest psychological stressor was coping with the life-threatening nature of asthma attacks, and the biggest intellectual stressor was drug safety. The close family ties of Chinese culture impose heavy social stress on primary caregivers, as does the schools' refusal to provide adequate asthma care. Further studies are warranted to investigate coping strategies for caregiver stress and to provide useful and relevant recommendations. [ABSTRACT FROM AUTHOR]

5-Aza-2ʹ-deoxycytidine (5-azaCdR) inhibits growth of breast cancer cells but increases their metastatic potential. Combining methylated DNA binding protein 2 depletion and 5-azaCdR has a synergistic antitumor effect enhancing tumor growth, while inhibiting invasiveness, pointing to a potential new epigenetic therapeutic for breast cancer.5-Aza-2ʹ-deoxycytidine (5-azaCdR) not only inhibits growth of non-invasive breast cancer cells but also increases their invasiveness through induction of pro-metastatic genes. Methylated DNA binding protein 2 (MBD2) is involved in silencing methylated tumor suppressor genes as well as activation of pro-metastatic genes. In this study, we show that a combination of MBD2 depletion and DNA methyltransferases (DNMT) inhibition in breast cancer cells results in a combined effect in vitro and in vivo, enhancing tumor growth arrest on one hand, while inhibiting invasiveness triggered by 5-azaCdR on the other hand. The combined treatment of MBD2 depletion and 5-azaCdR suppresses and augments distinct gene networks that are induced by DNMT inhibition alone. These data point to a potential new approach in targeting the DNA methylation machinery by combination of MBD2 and DNMT inhibitors. [ABSTRACT FROM AUTHOR]