Search

Objectives: This study examined people's preference for the location to receive palliative care services and determined the associated factors. Methods: A questionnaire with reference to the Chinese version of the Hospice Attitude Scale and the Death Correspondence Scale was designed, piloted, revised, and distributed online and in person to collect data (N = 762). Binary logistic regression was used to analyze the effects of relevant factors. Results: The average age of the participants was 38.1, with a relatively even gender distribution. Over 90% of the participants were either single/never married (44.9%) or married with children (46.0%). 58.1% of the respondents (N = 428) indicated that they would like to receive palliative care at home, compared to 41.9% who preferred receiving such care in institutions or other places (N = 309). Each time people's attitudes toward death became one point more positive, they were 10.2% more likely to choose to receive palliative care services at home. People with a neutral attitude toward palliative care, single/never married or divorced with children, and having/had an occupation in health and social work had higher odds of preferring receiving palliative care at home. Those who had poor self-rated health or with an educational background of primary school or lower or some college had lower odds of preferring receiving palliative care at home. Conclusions: The research showed that attitudes toward death and other factors were associated with people's preferences for palliative care locations. More accessible and affordable community-based and home-based palliative care services should be further explored and provided. [ABSTRACT FROM AUTHOR]

Therapy-related leukemia carries a poor prognosis, and leukemia after chemotherapy is a growing risk in clinic, whose mechanism is still not well understood. Ikaros transcription factor is an important regulator in hematopoietic cells development and differentiation. In the absence of Ikaros, lymphoid cell differentiation is blocked at an extremely early stage, and myeloid cell differentiation is also significantly affected. In this work, we showed that chemotherapeutic drug etoposide reduced the protein levels of several isoforms of Ikaros including IK1, IK2 and IK4, but not IK6 or IK7, by accelerating protein degradation, in leukemic cells. To investigate the molecular mechanism of Ikaros degradation induced by etoposide, immunoprecipitation coupled with LC-MS/MS analysis was conducted to identify changes in protein interaction with Ikaros before and after etoposide treatment, which uncovered KCTD5 protein. Our further study demonstrates that KCTD5 is the key stabilizing factor of Ikaros and chemotherapeutic drug etoposide induces Ikaros protein degradation through decreasing the interaction of Ikaros with KCTD5. These results suggest that etoposide may induce leukemic transformation by downregulating Ikaros via KCTD5, and our work may provide insights to attenuate the negative impact of chemotherapy on hematopoiesis. [ABSTRACT FROM AUTHOR]

Facial expression recognition has long been an area of great interest across a wide range of fields. Deep learning is commonly employed in facial expression recognition and demonstrates excellent performance in large-sample classification tasks. However, deep learning models often encounter challenges when confronted with small-sample expression classification problems, as they struggle to extract sufficient relevant features from limited data, resulting in subpar performance. This paper presents a novel approach called the Multi-CNN Logical Reasoning System, which is based on local area recognition and logical reasoning. It initiates the process by partitioning facial expression images into two distinct components: eye action and mouth action. Subsequently, it utilizes logical reasoning based on the inherent relationship between local actions and global expressions to facilitate facial expression recognition. Throughout the reasoning process, it not only incorporates manually curated knowledge but also acquires hidden knowledge from the raw data. Experimental results conducted on two small-sample datasets derived from the KDEF and RaFD datasets demonstrate that the proposed approach exhibits faster convergence and higher prediction accuracy when compared to classical deep learning-based algorithms. [ABSTRACT FROM AUTHOR]

In order to handle semi-supervised clustering scenarios where only part of the pairwise constraint information is available in the target dataset, on the basis of nonnegative matrix factorization (NMF) architecture, this paper proposes a nonnegative matrix factorization-based clustering algorithm using joint regularization of manifold learning and pairwise constraints (NMF-JRMLPC) by learning given pairwise constraint knowledge and using manifold regularization theory. On the one hand, graph Laplacian is introduced to depict the manifold structure information contained in a large number of unlabeled samples, and on the other hand, the must-link or cannot-link pair-constraint rules among known samples are integrated into the target optimization design, which greatly improves the clustering performance of the algorithm. In addition, the [l2,1] norm based loss function design also helps to optimize the robustness of NMF-JRMLPC. Experimental results on eight real datasets confirm the validity of the proposed method.