Search

Abstract Objectives To analyze psychological states and needs of patients after allogeneic hematopoietic stem cell transplantation (allo‐HSCT). Methods Questionnaires were sent to 101 allo‐HSCT survivors and 96 questionnaires were returned. The questionnaire covered several categories: (1) demographics and general information, (2) physical conditions, (3) psychologic status and sleep quality, (4) survivor's comments on transplantation, (5) demands and needs, (6) preferred forms and channels of information. Results Depression and poor sleep quality were major concerns troubling allo‐HSCT survivors. A notable discrepancy exists between clinically diagnosed depression (4.2%) and self‐ reported depression based on BDI‐13 (55.2%). Young adults (18–49 years old), chronic graft‐versus‐host disease, the ECOG performance score of 2–4, surviving within 5 years since HSCT, no or low dose of ATG used and being single were significantly associated with self‐reported depression. Based on the PSQI score, 75% of survivors experienced varying degrees of sleep quality impairment. Young adults, chronic GVHD and the ECOG score of 2–4 were significantly associated with worse sleep quality. The majority of patients reported unmet needs on physical and psychosocial aspects. Nutrition information was the most important topic, followed by disease treatments and fatigue. Differences in informational needs were found in the survivors according to age, time since HSCT and gender. WeChat public account and WeChat applet, mobile interactive platform and one‐to‐one conversation were the favorite channel for information. Conclusions Clinicians should establish more appropriate survivorship care plans focusing on survivors' psychologic states, demands and needs.

Under the background of low-carbon development, the construction of green public buildings is an inescapable responsibility and mission. At present, there are relatively few studies on carbon emission of public buildings in China and there is a lack of unified and standardized carbon accounting methods. Therefore, based on the domestic research status, this paper reviews the research progress and trend of carbon emission in public buildings. Sorting out the common accounting boundaries and accounting scope in the field of public buildings; By constructing the interactive accounting method framework of public buildings, the process and method of carbon accounting are sorted out. Various points for attention in the process of accounting are clarified from data acquisition，data management, accounting analysis and other aspects. This study provides a theoretical framework for carbon accounting in the field of public buildings and lays a foundation for establishing a unified carbon emission accounting method system in the field of green public buildings.

In medical image retrieval, accurately retrieving relevant images significantly impacts clinical decision making and diagnostics. Traditional image-retrieval systems primarily rely on single-dimensional image data, while current deep-hashing methods are capable of learning complex feature representations. However, retrieval accuracy and efficiency are hindered by diverse modalities and limited sample sizes. Objective: To address this, we propose a novel deep learning-based hashing model, the Deep Attention Fusion Hashing (DAFH) model, which integrates advanced attention mechanisms with medical imaging data. Methods: The DAFH model enhances retrieval performance by integrating multi-modality medical imaging data and employing attention mechanisms to optimize the feature extraction process. Utilizing multimodal medical image data from the Cancer Imaging Archive (TCIA), this study constructed and trained a deep hashing network that achieves high-precision classification of various cancer types. Results: At hash code lengths of 16, 32, and 48 bits, the model respectively attained Mean Average Precision (MAP@10) values of 0.711, 0.754, and 0.762, highlighting the potential and advantage of the DAFH model in medical image retrieval. Conclusions: The DAFH model demonstrates significant improvements in the efficiency and accuracy of medical image retrieval, proving to be a valuable tool in clinical settings. [ABSTRACT FROM AUTHOR]