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Objective·To systematically integrate relevant influencing factors of advanced cancer patients' engagement behavior in advance care planning (ACP).Methods·The systematic search of Chinese and English literature on factors influencing ACP engagement behavior in advanced cancer patients from inception to December 2022 in China National Knowledge Infrastructure (CNKI), Wanfang, China Biomedical Literature Database (Sinomed), PubMed, Cochrane Library, Embase, CINAHL, and PsycINFO was conducted. After the literature quality evaluation, content extraction and summary were conducted by two researchers, and the data of quantitative research and qualitative research were extracted and integrated respectively. The final influencing factors of ACP engagement behavior of advanced cancer patients were obtained. With the help of the theoretical domain, they were mapped to the capability, opportunity, motivation-behavior (COM-B) model step by step.Results·A total of 21 studies were included and 27 factors were summarized, including 9 theoretical domains. Mapping to the COM-B model included 9 capability factors (knowledge of ACP, education level, accurate knowledge of prognosis, knowledge of the time of disease diagnosis, prior experience, subjective life expectancy, age, cancer site, and disease symptom burden), 13 opportunity factors (gender, marital status, race/ethnicity, religious belief, dependent children, family economic condition, place of living, housing type, family support, social support, doctor-patient relationship, acculturation, and whether or not to establish a hospice service center) and 5 motivational factors (ACP attitude, ACP belief, ACP motivation, anxiety and depression, and death attitude). Among them, doctor-patient relationship, religious belief, ACP attitude, educational level, marital status, family support, knowledge of ACP, accurate knowledge of prognosis, age, place of living, attitude toward death, prior experience, and race/ethnicity were more influential factors on ACP engagement behavior.Conclusion·Based on the COM-B model, the influencing factors of ACP engagement behavior in advanced cancer patients can be comprehensively summarized. Future studies can use the above factors as an entry point to design continuous, multifaceted, and comprehensive interventions based on the COM-B model to promote the practice of ACP engagement behavior in advanced cancer patients.

The following statement has been added to the subsection 2.1 of Materials and methods of the paper by Zeng et al.1: ‘The study is approved by the Chinese Ethics Committee of Registering Clinical Trials (ID: ChiECRCT-20160046) and conformed to the standards set by the Declaration of Helsinki’.

In order to further improve the performance of the SSD (single shot multibox detector) algorithm, and solve the problems of unbalanced feature map information and difficulty in small target recognition during multi-scale prediction of the SSD algorithm, in this paper, plug-and-play modules are designed to fully integrate the information contained in feature maps of different scales and model the relationships within feature maps to enhance the representation ability of feature maps. Firstly, a novel feature fusion method is designed to solve the problem of information disparity in cross-scale feature fusion. Secondly, according to the idea of pooling pyramid, a depth feature extraction module is designed to extract the information of different receptive fields, so as to improve the detection ability of the model to object of different sizes. Finally, in order to further optimize the feature map, highlight the effective information of the feature map for the current task, and establish the global long-distance relationship between pixels and the importance relationship between each channel, a lightweight attention module is proposed. Through the above mechanism, the structure of SSD model is modified in this paper, which effectively improves the detection accuracy and robustness of SSD algorithm. Extensive experiments have been conducted on PASCAL VOC datasets to verify the efficiency of the proposed method. On PASCAL VOC2007 test datasets, the proposed method improves 2.9 percentage points mean average precision (mAP) over SSD algorithm, while maintaining the ability of real-time detection.

In the target tracking algorithm, the feature model’s ability to quickly learn image features and the ability to adapt to changes in target features during tracking has always been one of the main research directions of target tracking algorithms. Especially for discriminative target trackers based on image block learning, these two points have become decisive factors affecting the efficiency and robustness of the tracker. However, the performance of most existing similar algorithms on these two abilities cannot achieve satisfactory results. To solve this problem, an efficient and robust feature model is proposed. The feature model first uses extreme learning machine autoencoder (ELM-AE) to quickly perform random feature mapping on complex image features of the target and background image blocks, and then uses the transfer learning ability of transfer representation learning (TRL) to improve the adaptability of random feature space. The feature model is named transfer representation learning with ELM-AE (TRL-ELM-AE). Compared with original complex image features, this model can provide the classifier with more compact and expressive shared features, so that the classifier can learn and classify more quickly and efficiently. In addition, in the target tracking process, the target and background usually change continuously over time. Although the feature migration capability of TRL can already adapt to this, in order to further improve the robustness of the tracker, a strategy of dynamically updating training samples is adopted. Through a large number of experimental and analysis results on the 11 target tracking challenge scenarios proposed by OTB, it is proven that the proposed target tracker has significant advantages over the existing target tracker.

The inhibition of Nav1.7 is a promising strategy for the development of analgesic treatments. Spider venom‐derived peptide toxins are recognized as significant sources of Nav1.7 inhibitors. However, their development has been impeded by limited selectivity. In this study, eight peptide toxins from three distinct spider venom Nav channel families demonstrated robust inhibition of hNav1.7, rKv4.2, and rKv4.3 (rKv4.2/4.3) currents, exhibiting a similar mode of action. The analysis of structure and function relationship revealed a significant overlap in the pharmacophore responsible for inhibiting hNav1.7 and rKv4.2 by HNTX‐III, although Lys25 seems to play a more pivotal role in the inhibition of rKv4.2/4.3. Pharmacophore‐guided rational design is employed for the development of an mGpTx1 analogue, mGpTx1‐SA, which retains its inhibition of hNav1.7 while significantly reducing its inhibition of rKv4.2/4.3 and eliminating cardiotoxicity. Moreover, mGpTx1‐SA demonstrates potent analgesic effects in both inflammatory and neuropathic pain models, accompanied by an improved in vivo safety profile. The results suggest that off‐target inhibition of rKv4.2/4.3 by specific spider peptide toxins targeting hNav1.7 may arise from a conserved binding motif. This insight promises to facilitate the design of hNav1.7‐specific analgesics, aimed at minimizing rKv4.2/4.3 inhibition and associated toxicity, thereby enhancing their suitability for therapeutic applications. [ABSTRACT FROM AUTHOR]

Cytogenetic alterations form the basis for risk stratification for multiple myeloma (MM) and guide the selection of therapy; however, current pathology assays performed on bone marrow samples can produce false-negatives due to the unpredictable distribution and rarity of MM cells. Here, we report on a microfluidic device used to facilitate CD45 depletion to enhance the detection of cytogenetic alterations in plasma cells (PCs). Bone marrow samples from 48 patients with MM were each divided into two aliquots. One aliquot was subjected to classic flow cytometry and fluorescent in situ hybridization (FISH). The other first went through CD45+ cell depletion, further enriched by microfluidic size selection. The enriched samples were then analyzed using flow cytometry and FISH and compared to those analyzed using the classic method only. Unlike the traditional method, the microfluidic device removed the CD45+ leukocytes and specifically selected PCs from the remaining white blood cells. Therefore, the microfluidic method (MF-CD45-TACs) significantly increased the percentage of CD38+ /CD138+ cells to 37.7 ± 20.4% (P