[A preliminary prediction model of depression based on whole blood cell count by machine learning method].

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.

Shifted resonances in coated metamaterial cylinders: enhanced backscattering and near-field effects.

Electromagnetic wave properties in the presence of a thin or thick coating on a cylinder are investigated. The theoretical treatment starts from the formulation of electromagnetic fields in all regions where the coating and the core are allowed to rotate. The angular velocities of the core and coating can be different and even antidirectional. The present general approach can be applied to a wide range of specific cases such as rotating and stationary and left-handed and right-handed core-coating combinations. In particular, the optical resonances due to the surface plolaritons and morphology-dependent resonances are examined. Because of the rotation, the resonances are found to shift, and the effects of velocity on such phenomena are investigated. The backscattering can thus be controlled to achieve suppression or enhancement by using proper metamaterial coatings. Physical insights as well as the numerical results are presented.