RESUMO
The spatialization of socioeconomic data can be used and integrated with other sources of information to reveal valuable insights. Such data can be utilized to infer different variations, such as the dynamics of city dwellers and their spatial and temporal variability. This work focuses on such applications to explore the underlying association between socioeconomic characteristics of different geographical regions in Dublin, Ireland, and the number of confirmed COVID cases in each area. Our aim is to implement a machine learning approach to identify demographic characteristics and spatial patterns. Spatial analysis was used to describe the pattern of interest in electoral divisions (ED), which are the legally defined administrative areas in the Republic of Ireland for which population statistics are published from the census data. We used the most informative variables of the census data to model the number of infected people in different regions at ED level. Seven clusters detected by implementing an unsupervised neural network method. The distribution of people who have contracted the virus was studied.
RESUMO
BACKGROUND: Patellofemoral pain syndrome is a descriptive term applied to patients with nonspecific anterior knee pain, and is the most common knee problem. The pain in most patellofemoral disorders is generalized to the anterior part of the knee. One important concept in patellofemoral joint function is the quadriceps angle (Q-angle). Theoretically, a higher Q-angle increases the lateral pull of the quadriceps femoris muscle on the patella and potentiates patellofemoral disorders. This study was undertaken to evaluate the relationship between the anterior knee pain and Q-angle. METHODS: This prospective study was performed on two groups; the case group consisted of 100 outpatients (44 men, and 56 women) aged between 15 and 35 years, with anterior knee pain. The control group consisted of 100 outpatients (50 men, and 50 women) with the same age distribution, who presented with different problems in the upper extremities and no knee problems. The Q-angle of each knee was measured in all participants, using a universal goniometer. RESULTS: The mean Q-angle for men, women, and all participants in the case group was 15.2, 20.1, and 18.0 degrees, respectively. In the normal control group the angles were 12.1, 16.7, and 14.9 degrees, respectively. All these differences were statistically significant (P < 0.001). CONCLUSION: These results substantiate the fact that patients with anterior knee pain have larger Q-angles than healthy individuals.
