Gallstone ileus 30 years after cholecystectomy and hepaticojejunostomy.

Gallstone ileus occurs when the small or large intestine is obstructed by a gallstone and is a rare occurrence in a post-cholecystectomy patient. Non-specific clinical symptoms and inconsistent imaging results often lead to a delay in diagnosis. Complex anatomy, such as a Roux-en-Y biliary-enteric anastomosis, can increase the risk of stone formation and further confound a potential case of gallstone ileus. Here, we present a rare case of gallstone ileus at the anastomosis site of a Roux-en-Y hepaticojejunostomy done 30 years prior for a common bile duct injury during a cholecystectomy. The possibility of negative CT findings, pattern of presentation on imaging as intussusception, and potential pathomechanism of gallstone formation in post-cholecystectomy patients are discussed. Through this case and review of similar cases, we emphasise the need for further study of post-cholecystectomy gallstone ileus and the importance of clinical suspicion during diagnosis.

Drug-target prediction utilizing heterogeneous bio-linked network embeddings.

To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug-target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug-target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, 'Asthma' 'Hypertension', and 'Dementia'. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.

Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.

The drug discovery process is conventionally regarded as resource intensive and complex. Therefore, research effort has been put into a process called drug repositioning with the use of computational methods. Similarity-based methods are common in predicting drug-target association or the interaction between drugs and targets based on various features the drugs and targets have. Heterogeneous network topology involving many biomedical entities interactions has yet to be used in drug-target association. Deep learning can disclose features of vertices in a large network, which can be incorporated with heterogeneous network topology in order to assist similarity-based solutions to provide more flexibility for drug-target prediction. Here we describe a similarity-based drug-target prediction method that utilizes a topology-based similarity measure and two inference methods based on the similarities. We used DeepWalk, a deep learning method, to calculate the vertex similarities based on Linked Tripartite Network (LTN), which is a heterogeneous network created from different biomedical-linked datasets. The similarities are further used to feed to the inference methods, drug-based similarity inference (DBSI) and target-based similarity inference (TBSI), to obtain the predicted drug-target associations. Our previous experiments have shown that by utilizing deep learning and heterogeneous network topology, the proposed method can provide more promising results than current topology-based similarity computation methods.

THINK OF THE CHILDREN: SEX SELECTION AND CHILD WELFARE.

This column considers the phenomenon of social sex selection and whether its legal prohibition can be justified in Australasia. It looks at whether the liberal autonomy framework is an adequate ethical basis for assessing sex selection and whether sex selection may raise ethical concerns about the nature of parenting and the welfare .of future children in the Australasian context. It argues that with sex selection comes the implicit instrumentalisation and commodification of children, which both stem from and encourage attitudes contrary to those underpinning the virtues required of parents. It concludes that in lieu of robust arguments in favour of sex selection and in light of the probable (or at least plausible) negative impact on the nature of parenting and the welfare of future children, the legal prohibition on social sex selection should be maintained in Australasia.

Advance care planning among Asian Americans and Native Hawaiians receiving haemodialysis.

AIM: To explore the attitudes about death and dying, advance care planning (ACP), and completion of ACP among Asian Americans (AAs) and Native Hawaiians (NHs) receiving haemodialysis. This study was a descriptive, cross-sectional survey design. METHOD: A convenience sample of 50 participants aged 30-82 years was recruited from four outpatient dialysis centers in Honolulu, Hawaii and interviewed face-to-face using a 43-item end-of-life community survey. A majority of participants perceived dying as an important part of life and were comfortable talking about death, but expressed concerns and fears about end-of-life issues. Aspects of ACP, such as planning a funeral service, getting finances in order, and completing the will were important. While most participants' attitudes about ACP were positive, less than half (40%) had completed ACP. Most participants preferred initiating end-of-life conversations with family. CONCLUSIONS: The main conclusions drawn from this study are that there is a need for ACP and secondly that AAs and NHs would prefer to discuss ACP with family members rather than health or legal professionals. Findings from this preliminary study build on the need to use a theoretical framework in which to develop sound instruments and effective interventions to promote ACP completion among AAs and NHs receiving haemodialysis.