ABSTRACT
Objective:To analyze the clinical characteristics of patients with different types of coronavirus disease 2019 (COVID-19).Methods:A total of 272 eligible COVID-19 patients who were admitted to Guangzhou Eighth People′s Hospital, Guangzhou Medical University from January 22 to February 15, 2020 were retrospectively enrolled. General characteristics, the first laboratory examination and imaging data of these patients were collected. According to the clinical classification, there were 236 cases in non-severe group (mild+ common type) and 36 cases in severe group (severe+ critical type). Comparisons between groups were performed by t test, chi-square test or rank-sum test when appropriate. Results:There were 23 males and 13 females in the severe group, 103 males and 133 females in the non-severe group, and the difference was statistically significant ( χ2=5.149, P=0.023). The age of severe group was (60.5±11.2) years, which was higher than that of non-severe group (46.8±15.7) years. The difference was statistically significant ( t=6.43, P<0.01). The lymphocyte (LYM) count, platelet (PLT) count and arterial partial pressure of oxygen (PaO 2) in the severe group were 0.90(0.55, 1.10)×10 9/L, 170.00(143.50, 198.00)×10 9/L and 73.50(69.70, 83.00) mmHg(1 mmHg=0.133 kPa), respectively, which were all lower than those in the non-severe group (1.42(1.09, 1.95)×10 9/L, 187.00(148.00, 230.00)×10 9/L and 96.00(83.20, 108.00) mmHg, respectively). The differences were all statistically significant ( Z=5.59, 2.00 and 5.00, respectively, all P<0.05). The levels of creatine kinase (CK), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), C reaction protein (CRP) and procalcitonin (PCT) in the severe group were 123.00(79.00, 212.00) U/L, 32.10(27.00, 47.40) U/L, 305.50(216.00, 396.00) U/L, 37.02(23.92, 63.66) mg/L and 0.09(0.05, 0.19) μg/L, respectively, which were all higher than those in the non-severe group (68.00(48.00, 103.00) U/L, 20.10(16.70, 26.20) U/L, 179.00(150.00, 222.00) U/L, 26.55(18.11, 36.96) mg/L and 0.04(0.03, 0.06) μg/L respectively), and the differences were all statistically significant ( Z=3.89, 5.60, 5.12, 2.85 and 5.43, respectively, all P<0.01). No significant differences were observed in white blood cell count, creatine kinase isoenzyme and blood lactate between the two groups ( Z=1.53, 0.41 and 1.00, respectively, all P>0.05). Conclusion:Gender, age, LYM count, PLT count, PaO 2, CK, AST, LDH, CRP and PCT could be used to provide reference for clinical classification of COVID-19 patients.
ABSTRACT
Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.