OBJECTIVE: The elevated physical symptom burden in advanced lung cancer can disrupt patients\' emotional well-being, and current literature suggests that physicians\' good communication skills might be a buffer. However, little is known about for which group of patients this buffering effect is most effective. Based on a cross-sectional study in patients with advanced lung cancer, the present study examined whether the moderating effect of physicians\' communication skills on the association between physical symptoms and emotional distress would further depend on patients\' perceived disease understanding.
METHODS: Patients with advanced lung cancer (n = 199) completed a questionnaire including measures of physical symptoms related to lung cancer, anxiety, and depressive symptoms, perceptions of physicians\' communication skills, and self-reported understanding of their disease.
RESULTS: Hierarchical regression analyses indicated a significant three-way interaction among physical symptoms, perceptions of physicians\' communication skills, and perceived disease understanding on both anxiety and depression. Specifically, physicians\' good communication skills exerted a buffering effect only for patients with lower levels of disease understanding.
CONCLUSIONS: Our findings indicate that improving physicians\' communication skills may be especially beneficial for reducing the maladaptive emotional reactions to symptom burden for patients with limited disease understanding. When time and resources for communication are restricted, enhanced awareness and focused training may be directed at communicating with patients who possess limited knowledge about their disease.

Drug repurposing has become a widely used strategy to accelerate the process of finding treatments. While classical de novo drug development involves high costs, risks, and time-consuming paths, drug repurposing allows to reuse already-existing and approved drugs for new indications. Numerous research has been carried out in this field, both in vitro and in silico. Computational drug repurposing methods make use of modern heterogeneous biomedical data to identify and prioritize new indications for old drugs. In the current paper, we present a new complete methodology to evaluate new potentially repurposable drugs based on disease-gene and disease-phenotype associations, identifying significant differences between repurposing and non-repurposing data. We have collected a set of known successful drug repurposing case studies from the literature and we have analysed their dissimilarities with other biomedical data not necessarily participating in repurposing processes. The information used has been obtained from the DISNET platform. We have performed three analyses (at the genetical, phenotypical, and categorization levels), to conclude that there is a statistically significant difference between actual repurposing-related information and non-repurposing data. The insights obtained could be relevant when suggesting new potential drug repurposing hypotheses.

BACKGROUND: Within the global endeavour of improving population health, one major challenge is the identification and integration of medical knowledge spread through several information sources. The creation of a comprehensive dataset of diseases and their clinical manifestations based on information from public sources is an interesting approach that allows one not only to complement and merge medical knowledge but also to increase it and thereby to interconnect existing data and analyse and relate diseases to each other. In this paper, we present DISNET (http://disnet.ctb.upm.es/), a web-based system designed to periodically extract the knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks.
METHODS: We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia and PubMed websites; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques.
RESULTS: We further present the validation of our system on Wikipedia and PubMed texts, obtaining the relevant accuracy. The final output includes the creation of a comprehensive symptoms-disease dataset, shared (free access) through the system\'s API. We finally describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses.
CONCLUSIONS: DISNET allows retrieving knowledge about the signs, symptoms and diagnostic tests associated with a disease. It is not limited to a specific category (all the categories that the selected sources of information offer us) and clinical diagnosis terms. It further allows to track the evolution of those terms through time, being thus an opportunity to analyse and observe the progress of human knowledge on diseases. We further discussed the validation of the system, suggesting that it is good enough to be used to extract diseases and diagnostically-relevant terms. At the same time, the evaluation also revealed that improvements could be introduced to enhance the system\'s reliability.

Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes. One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks.

BACKGROUND: This study aimed to determine self-monitoring practices, awareness to dietary modifications and barriers to medication adherence among physically disabled type 2 diabetes mellitus patients.
METHODS: Interview sessions were conducted at diabetes clinic-Penang general hospital. The invited participants represented three major ethnic groups of Malaysia (Malay, Chinese and Indians). An open-ended approach was used to elicit answers from participants. Interview questions were related to participant\'s perception towards self-monitoring blood glucose practices, Awareness towards diet management, behaviour to diabetes medication and cues of action.
RESULTS: A total of twenty-one diabetes patients between the ages 35-67 years with physical disability (P1-P21) were interviewed. The cohort of participants was dominated by males (n = 12) and also distribution pattern showed majority of participants were Malay (n = 10), followed by Chinese (n = 7) and rest Indians (n = 4). When the participants were asked in their opinion what was the preferred method of recording blood glucose tests, several participants from low socioeconomic status and either divorced or widowed denied to adapt telemonitoring instead preferred to record manually. There were mixed responses about the barriers to control diet/calories. Even patients with high economic status, middle age 35-50 and diabetes history of 5-10 years were influenced towards alternative treatments.
CONCLUSIONS: Study concluded that patients with physical disability required extensive care and effective strategies to control glucose metabolism.
CONCLUSIONS: This study explores the patients\' perspectives regarding treatment management with physical disability.

BACKGROUND: Disability is a key indicator implicating both overall morbidity and success of public health efforts to compress the period of morbidity among geriatrics for the overall population. Disabilities are more prevalent among diabetics than among those without diabetes.
OBJECTIVE: This study aimed to determine self-monitoring practices, awareness to dietary modifications and barriers to medication adherence among physically disabled type 2 diabetes mellitus patients.
METHODS: Interview sessions were conducted at diabetes clinic - Penang general hospital. The invited participants represented three major ethnic groups of Malaysia (Malay, Chinese & Indians). An openended approach was used to elicit answers from participants. Interview questions were related to participant\'s perception towards self-monitoring blood glucose practices, Awareness towards diet management, behaviour to diabetes medication and cues of action.
RESULTS: A total of twenty-one diabetes patients between the ages 35 - 67 years with physical disability (P1-P21) were interviewed. The cohort of participants was dominated by Males (n=12) and also distribution pattern showed that majority of participants were Malay (n=10), followed by Chinese (n=7) and rest Indians (n=4). When the participants were asked in their opinion what was the preferred method of recording blood glucose tests, several participants from low socioeconomic status and either divorced or widowed denied to adapt telemontoring instead preferred to record manually. There were mixed responses about the barriers to control diet/calories. Even patients with high economic status, middle age 35-50 and diabetes history of 5-10 years were influenced towards alternative treatments.
CONCLUSIONS: Study concluded that patients with physical disability required extensive care and effective strategies to control glucose metabolism.