RESUMEN
Background: Psoriasis is a common, chronic T cell-mediated disease characterised by erythematous, scaly plaques. Psoriasis is associated with depression, anxiety, poor quality of life, harmful use of alcohol and suicidal ideation. We performed this study to find out prevalence of psychological morbidity in our patients with severe psoriasis and to know clinical variables associated with higher risk of psychological morbidity. Method: This study is a cross-sectional, observational study conducted in a dermatology outpatient department (OPD) of a tertiary care centre. Study population included patients with severe psoriasis area severity index (PASI>10). Clinical and epidemiological data, Patient health questionnaire-9, Generalised anxiety disorder-7, Dermatology life quality index (DLQI) and Alcohol Use Disorders Identification Test questionnaires were recorded. Data were analysed using SPSS, version 24. Chi-square test and Spearman's rank correlation test (ρ) were performed, and p value of <0.05 was considered statistically significant. Result: A total of 140 patients were included in the study and consisted of 67 men and 73 women. The median age was 44 years, median duration of disease was 10 years and median PASI was 13.6. Scoring revealed moderate-to-severe depression in 69 (49.3%) patients, moderate-to-severe anxiety in 40 (28.6%), severe impairment in quality of life in 98 (70%), harmful use of alcohol in 23 (16.4%) and suicidal ideation in 11 (7.8%) patients. Young age, recent onset, higher PASI and facial involvement show significant association with depression, anxiety and poor quality of life. Conclusion: Screening for psychological morbidity should be performed in patients with severe psoriasis especially younger patients with recent onset disease. Higher DLQI points to concomitant psychological morbidity and it should be performed in all the patients.
RESUMEN
Wireless sensor networks (WSNs) are being used to facilitate monitoring of patients in hospital and home environments. These systems consist of a variety of different components/sensors and many processes like clustering, routing, security, and self-organization. Routing is necessary for medical-based WSNs because it allows remote data delivery and it facilitates network scalability in large hospitals. However, routing entails several problems, mainly due to the open nature of wireless networks, and these need to be addressed. This paper looks at two of the problems that arise due to wireless routing between the nodes and access points of a medical WSN (for IoT use): black hole and selective forwarding (SF) attacks. A solution to the former can readily be provided through the use of cryptographic hashes, while the latter makes use of a neighbourhood watch and threshold-based analysis to detect and correct SF attacks. The scheme proposed here is capable of detecting a selective forwarding attack with over 96% accuracy and successfully identifying the malicious node with 83% accuracy.
