Objective:To explore the care experience of caregivers of adolescents with first-episode depres-sion.Methods:Nine family caregivers of adolescent with first-episode depression were enrolled.A qualitative re-search was carried out,and semi-structured interviews were performed to investigate the caregivers'opinion of de-pressive disorder,emotional state,caring experiences and dilemma,coping strategy,and support requirements.The data were analyzed,summarized and distilled by using the Colaizzi phenomenological 7-step analysis.Results:Four kinds of first order themes of caring experiences(complex caring experience,heavy burden of care,yearn for sup-port,achieved post-traumatic growth)were extracted,including 10 kinds of second order themes,namely shock and disbelief,pessimism and helplessness,guilt and stigma,inefficient coping strategy,impaired physical and mental health,heavy economic burden,family relationship tension,changed personal role,lack of medical support,dying to be admitted by society.Conclusion:Family caregivers of adolescent with first-episode depression may have obvious negative emotions,which faced with caring dilemma such as impaired health status or heavy economic burden,and urgently need professional resources and social support.

BACKGROUNDDendritic cells (DCs) can recognize the pathogen-associated molecular patterns (PAMP) of Aspergillus fumigatus (A. fumigatus), activating the immune response. During A. fumigatus infection, a Th and Treg response induced in the fungi-pulsed DCs is not yet well understood.

METHODSIn this study, bone marrow-derived dendritic cells (BMDCs) were separated and proliferated from C57BL/6 mice. A. fumigatus pulsed DCs were generated and cultured with CD4(+) T cells derived from the spleen of C57BL/6 mice in vitro. CD4(+) T cells differentiation after co-culture were analyzed by flow cytometry, ELISA, and real-time PCR analysis.

RESULTSThe A. fumigatus pulsed DCs exhibited increased Th1 and Treg frequency, Th1-related cytokines (IFN-γ and IL-12), Treg-related cytokines (TGF-β) and T-bet, and Foxp3 mRNA levels compared with the control group. There was no significant difference between A. fumigatus pulsed DCs group and the control group about Th17 and Th2 frequency.

CONCLUSIONSThe inactivated conidia of A. fumigatus were able to activate BMDCs and made them capable of triggering T cell responses in vitro. A. fumigatus loaded DCs was a weak inducer of Th17 and Th2, but induced a strong Th1 and Treg response.

Animals ; Aspergillus fumigatus ; pathogenicity ; Cytokines ; metabolism ; Dendritic Cells ; immunology ; microbiology ; Forkhead Transcription Factors ; metabolism ; Interleukin-12 ; metabolism ; Male ; Mice ; Mice, Inbred C57BL ; T-Lymphocytes, Helper-Inducer ; immunology ; T-Lymphocytes, Regulatory ; immunology ; Th1 Cells ; immunology ; Transforming Growth Factor beta ; metabolism

Objective To develop an intelligent recognition model based on deep learning algorithms of unmanned aerial vehicle (UAV) images, and to preliminarily explore the value of this model for remote identification, monitoring and management of cattle, a source of Schistosoma japonicum infection. Methods Oncomelania hupensis snail-infested marshlands around the Poyang Lake area were selected as the study area. Image datasets of the study area were captured by aerial photography with UAV and subjected to augmentation. Cattle in the sample database were annotated with the annotation software VGG Image Annotator to create the morphological recognition labels for cattle. A model was created for intelligent recognition of livestock based on deep learning-based Mask R-convolutional neural network (CNN) algorithms. The performance of the model for cattle recognition was evaluated with accuracy, precision, recall, F1 score and mean precision. Results A total of 200 original UAV images were obtained, and 410 images were yielded following data augmentation. A total of 2 860 training samples of cattle recognition were labeled. The created deep learning-based Mask R-CNN model converged following 200 iterations, with an accuracy of 88.01%, precision of 92.33%, recall of 94.06%, F1 score of 93.19%, and mean precision of 92.27%, and the model was effective to detect and segment the morphological features of cattle. Conclusion The deep learning-based Mask R-CNN model is highly accurate for recognition of cattle based on UAV images, which is feasible for remote intelligent recognition, monitoring, and management of the source of S. japonicum infection.