Prevalence of Metabolic Syndrome in Chinese Patients With Erythrodermic Psoriasis: A Case-Control Study.

Erythroderma psoriasis (EP) is a rare and severe form of psoriasis, which is a chronic inflammatory skin disease that usually occurs simultaneously with cardiovascular disease (CVD). Metabolic syndrome (MetS) is a significant precursor of CVD. This study was to investigate the association between EP and MetS in the Chinese population. We conducted a retrospective study on 86 consecutive patients with EP and 100 healthy controls from Huashan Hospital between 2013 and 2018. Demographic, biochemical parameters for MetS, and other relevant data including the severity of EP, family history of EP, age of onset, and treatment history involved in those individuals were recorded. The prevalence of MetS in erythrodermic psoriatic patients was 88.37%, which was significantly higher than that of controls (P < 0.0001). Erythrodermic psoriatic patients also had a higher prevalence of MetS components, including abdominal obesity, dyslipidemia and hypertension, whereas hyperglycemia was similar. Adjusted for confounding factors, MetS, abdominal obesity, hypertension, smoking and alcohol use were positive independent predictors of EP (odds ratio > 1, P < 0.05). The area under the receiver operating characteristic curve calculated from determined risk factors for predicting the EP's incidence was 0.934 (95% CI 0.902-0.966). There was no correlation between the severity of EP and the prevalence of MetS. Compared with patients with mild EP, patients with moderate-to-severe EP had higher body mass index, waist circumstance and blood pressure (P < 0.05). We concluded that the prevalence of MetS and its components was higher in patients with EP. MetS an independent predictor of EP, which can favor CVD and should be encouraged to correct these cardiovascular risk factors aggressively for managing EP.

Automatic Radiographic Position Recognition from Image Frequency and Intensity.

Purpose: With the development of digital X-ray imaging and processing methods, the categorization and analysis of massive digital radiographic images need to be automatically finished. What is crucial in this processing is the automatic retrieval and recognition of radiographic position. To address these concerns, we developed an automatic method to identify a patient's position and body region using only frequency curve classification and gray matching. Methods: Our new method is combined with frequency analysis and gray image matching. The radiographic position was determined from frequency similarity and amplitude classification. The body region recognition was performed by image matching in the whole-body phantom image with prior knowledge of templates. The whole-body phantom image was stitched by radiological images of different parts. Results: The proposed method can automatically retrieve and recognize the radiographic position and body region using frequency and intensity information. It replaces 2D image retrieval with 1D frequency curve classification, with higher speed and accuracy up to 93.78%. Conclusion: The proposed method is able to outperform the digital X-ray image's position recognition with a limited time cost and a simple algorithm. The frequency information of radiography can make image classification quicker and more accurate.