The study aims to investigate the quality of life (QOL) and the psychological situation in Chinese patients with rosacea. A total of 196 healthy controls and 201 rosacea patients were involved in the final analysis. The general information, the Dermatology Life Quality Index (DLQI) and the Hospital Anxiety and Depression Scale (HADS) were collected. Significantly higher DLQI, anxiety and depression score were observed in the rosacea group compared to the control group (p < .01). Total DLQI score of patients was positively related with anxiety (r = .526, p < .001) and depression scores (r = .399, p < .001) in HADS. Rosacea had significant psychological impact on Chinese patients and had substantial influence on their QOL. Physicians should address the psychosocial needs of rosacea patients as much as its physical symptoms.

Sunscreen protection is not related in one uniform way to the amount of product applied to human skin. Consumers may achieve an even lower than expected sunburn protection from high SPF products than from low SPF sunscreens.

Our data indicate that there is certain relationship between the degree of stinging and the skin barrier. The stinging test method modified by lower concentration aqueous lactic acid and the assessment combined with PTEWL and PCAP is suitable for Chinese to evaluate the skin susceptibility.

From the study we concluded that the skin types of the investigated Chinese females are principally type III (more than 70%), and then type II and type IV. The different ways of answering the questionnaire did not affect the results remarkably. The measurements of photobiology parameters confirmed that there is a certain correlation between skin types and MED or MPPD determined in this group of volunteers.

Compressed sensing (CS) is a signal processing framework, which reconstructs a signal from a small set of random measurements obtained by measurement matrices. Due to the strong randomness of measurement matrices, the reconstruction performance is unstable. Additionally, current reconstruction algorithms are relatively independent of the compressed sampling process and have high time complexity. To this end, a deep learning based stacked sparse denoising autoencoder compressed sensing (SSDAE_CS) model, which mainly consists of an encoder sub-network and a decoder sub-network, is proposed and analyzed in this paper. Instead of traditional linear measurements, a multiple nonlinear measurements encoder sub-network is trained to obtain measurements. Meanwhile, a trained decoder sub-network solves the CS recovery problem by learning the structure features within the training data. Specifically, the two sub-networks are integrated into SSDAE_CS model through end-to-end training for strengthening the connection between the two processes, and their parameters are jointly trained to improve the overall performance of CS. Finally, experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods in terms of reconstruction performance, time cost, and denoising ability. Most importantly, the proposed model shows excellent reconstruction performance in the case of a few measurements.