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ObjectiveKale is an important bulk vegetable crop worldwide, its main growth characteristics are outer leaves and leaf bulbs. The traits of leaf bulb kale are crucial for adjusting water and fertilizer parameters in the field to achieve maximum yield. However, various factors such as soil quality, light exposure, leaf overlap, and shading can affect the growth of in practical field conditions. The similarity in color and texture between leaf bulbs and outer leaves complicates the segmentation process for existing recognition models. In this paper, the segmentation of kale outer leaves and leaf bulbs in complex field background was proposed, using pixel values to determine leaf bulb size for intelligent field management. A semantic segmentation algorithm, UperNet-ESA was proposed to efficiently and accurately segment nodular kale outer leaf and leaf bulb in field scenes using the morphological features of the leaf bulbs and outer leaves of nodular kale to realize the intelligent management of nodular kale in the field.MethodsThe UperNet-ESA semantic segmentation algorithm, which uses the unified perceptual parsing network (UperNet) as an efficient semantic segmentation framework, is more suitable for extracting crop features in complex environments by integrating semantic information across different scales. The backbone network was improved using ConvNeXt, which is responsible for feature extraction in the model. The similarity between kale leaf bulbs and outer leaves, along with issues of leaf overlap affecting accurate target contour localization, posed challenges for the baseline network, leading to low accuracy. ConvNeXt effectively combines the strengths of convolutional neural networks (CNN) and Transformers, using design principles from Swin Transformer and building upon ResNet50 to create a highly effective network structure. The simplicity of the ConvNeXt design not only enhances segmentation accuracy with minimal model complexity, but also positions it as a top performer among CNN architectures. In this study, the ConvNeXt-B version was chosen based on considerations of computational complexity and the background characteristics of the knotweed kale image dataset. To enhance the model's perceptual acuity, block ratios for each stage were set at 3:3:27:3, with corresponding channel numbers of 128, 256, 512 and 1 024, respectively. Given the visual similarity between kale leaf bulbs and outer leaves, a high-efficiency channel attention mechanism was integrated into the backbone network to improve feature extraction in the leaf bulb region. By incorporating attention weights into feature mapping through residual inversion, attention parameters were cyclically trained within each block, resulting in feature maps with attentional weights. This iterative process facilitated the repeated training of attentional parameters and enhanced the capture of global feature information. To address challenges arising from direct pixel addition between up-sampling and local features, potentially leading to misaligned context in feature maps and erroneous classifications at kale leaf boundaries, a feature alignment module and feature selection module were introduced into the feature pyramid network to refine target boundary information extraction and enhance model segmentation accuracy.Results and DiscussionsThe UperNet-ESA semantic segmentation model outperforms the current mainstream UNet model, PSPNet model, DeepLabV3+ model in terms of segmentation accuracy, where mIoU and mPA reached 92.45% and 94.32%, respectively, and the inference speed of up to 16.6 frames per second (fps). The mPA values were better than that of the UNet model, PSPNet model, ResNet-50 based, MobilenetV2, and DeepLabV3+ model with Xception as the backbone, showing improvements of 11.52%, 13.56%, 8.68%, 4.31%, and 6.21%, respectively. Similarly, the mIoU exhibited improvements of 12.21%, 13.04%, 10.65%, 3.26% and 7.11% compared to the mIoU of the UNet-based model, PSPNet model, and DeepLabV3+ model based on the ResNet-50, MobilenetV2, and Xception backbones, respectively. This performance enhancement can be attributed to the introduction of the ECA module and the improvement made to the feature pyramid network in this model, which strengthen the judgement of the target features at each stage to obtain effective global contextual information. In addition, although the PSPNet model had the fastest inference speed, the overall accuracy was too low to for developing kale semantic segmentation models. On the contrary, the proposed model exhibited superior inference speed compared to all other network models.ConclusionsThe experimental results showed that the UperNet-ESA semantic segmentation model proposed in this study outperforms the original network in terms of performance. The improved model achieves the best accuracy-speed balance compared to the current mainstream semantic segmentation networks. In the upcoming research, the current model will be further optimized and enhanced, while the kale dataset will be expanded to include a wider range of samples of nodulated kale leaf bulbs. This expansion is intended to provide a more robust and comprehensive theoretical foundation for intelligent kale field management.

ObjectiveCurrently, the lack of computerized systems to monitor the quality of cabbage transplants is a notable shortcoming in the agricultural industry, where transplanting operations play a crucial role in determining the overall yield and quality of the crop. To address this problem, a lightweight and efficient algorithm was developed to monitor the status of cabbage transplants in a natural environment.MethodsFirst, the cabbage image dataset was established, the cabbage images in the natural environment were collected, the collected image data were filtered and the transplanting status of the cabbage was set as normal seedling (upright and intact seedling), buried seedling (whose stems and leaves were buried by the soil) and exposed seedling (whose roots were exposed), and the dataset was manually categorized and labelled using a graphical image annotation tool (LabelImg) so that corresponding XML files could be generated. And the dataset was pre-processed with data enhancement methods such as flipping, cropping, blurring and random brightness mode to eliminate the scale and position differences between the cabbages in the test and training sets and to improve the imbalance of the data. Then, a cabbage transplantation state detection model based on YOLOv8s (You Only Look Once Version 8s) was designed. To address the problem that light and soil have a large influence on the identification of the transplantation state of cabbage in the natural environment, a multi-scale attention mechanism was embedded to increase the number of features in the model, and a multi-scale attention mechanism was embedded to increase the number of features in the model. Embedding the multi-scale attention mechanism to increase the algorithm's attention to the target region and improve the network's attention to target features at different scales, so as to improve the model's detection efficiency and target recognition accuracy, and reduce the leakage rate; by combining with deformable convolution, more useful target information was captured to improve the model's target recognition and convergence effect, and the model complexity increased by C3-layer convolution was reduced, which further reduced the model complexity. Due to the unsatisfactory localization effect of the algorithm, the focal extended intersection over union loss (Focal-EIoU Loss) was introduced to solve the problem of violent oscillation of the loss value caused by low-quality samples, and the influence weight of high-quality samples on the loss value was increased while the influence of low-quality samples was suppressed, so as to improve the convergence speed and localization accuracy of the algorithm.Results and DiscussionsEventually, the algorithm was put through a stringent testing phase, yielding a remarkable recognition accuracy of 96.2% for the task of cabbage transplantation state. This was an improvement of 2.8% over the widely used YOLOv8s. Moreover, when benchmarked against other prominent target detection models, the algorithm emerged as a clear winner. It showcased a notable enhancement of 3% and 8.9% in detection performance compared to YOLOv3-tiny. Simultaneously, it also managed to achieve a 3.7% increase in the recall rate, a metric that measured the efficiency of the algorithm in identifying actual targets among false positives. On a comparative note, the algorithm outperformed YOLOv5 in terms of recall rate by 1.1%, 2% and 1.5%, respectively. When pitted against the robust faster region-based convolutional neural network (Faster R-CNN), the algorithm demonstrated a significant boost in recall rate by 20.8% and 11.4%, resulting in an overall improvement of 13%. A similar trend was observed when the algorithm was compared to the single shot multibox detector (SSD) model, with a notable 9.4% and 6.1% improvement in recall rate. The final experimental results show that when the enhanced model was compared with YOLOv7-tiny, the recognition accuracy was increased by 3%, and the recall rate was increased by 3.5%. These impressive results validated the superiority of the algorithm in terms of accuracy and localization ability within the target area. The algorithm effectively eliminates interferenced factors such as soil and background impurities, thereby enhancing its performance and making it an ideal choice for tasks such as cabbage transplantation state recognition.ConclusionsThe experimental results show that the proposed cabbage transplantation state detection method can meet the accuracy and real-time requirements for the identification of cabbage transplantation state, and the detection accuracy and localization accuracy of the improved model perform better when the target is smaller and there are weeds and other interferences in the background. Therefore, the method proposed in this study can improve the efficiency of cabbage transplantation quality measurement, reduce the time and labor, and improve the automation of field transplantation quality survey.

Chronic obstructive pulmonary disease (COPD) is one of the major chronic airway diseases, with the highest prevalence among individuals over 60 years old, significantly impacting the health of elderly patients. Both domestic and international COPD prevention and treatment guidelines recommend pulmonary rehabilitation as a complementary and effective adjunct to pharmacological treatment. Pulmonary rehabilitation can significantly improve patients' respiratory function and physical fitness, alleviate symptoms, and reduce hospitalization and mortality rates. However, the specific contents of pulmonary rehabilitation for elderly COPD patients differ depending on the stage of the disease. This expert consensus elaborates on the assessment and treatment contents of pulmonary rehabilitation for elderly COPD patients in the acute exacerbation period, acute exacerbation remission period, and outpatient stable period, aiming to provide guidance for pulmonary rehabilitation treatment at different stages of the disease. This consensus identifies common functional impairments in elderly COPD patients, including respiratory dysfunction, exercise tolerance impairment, psychological issues, swallowing dysfunction and cognitive impairment. It recommends a comprehensive assessment of elderly COPD patients' multifaceted impairments based on the International Classification of Functioning, Disability, and Health (ICF) framework, covering four aspects：body structure, body function (respiratory function, cardiac function, exercise function, psychological and cognitive function, swallowing function, and nutritional factors), activity and participation, and environmental factors. Pulmonary rehabilitation is suitable for all patients exhibiting relevant symptoms and/or those at high risk of acute exacerbations (high-level evidence). As the disease progresses, the rehabilitation team, treatment plan, and treatment location should be adjusted in a timely manner, forming a continuous rehabilitation cycle. Smoking cessation is a crucial intervention to slow down the decline in lung function and the progression of COPD (high-level evidence). Through adequate nutritional support, elderly COPD patients can improve their nutritional status, enhance physical strength and quality of life, better manage the disease, and promote rehabilitation (low to moderate-level evidence). Oxygen therapy is recommended for patients with advanced COPD who remain hypoxemic at rest (low to moderate-level evidence). Some emerging rehabilitation treatments (such as neuromuscular electrical stimulation, acupuncture, and certain traditional Chinese medicines) may have some value for elderly COPD patients (low to moderate-level evidence. This expert consensus divides rehabilitation treatment into three stages：Phase I Pulmonary Rehabilitation, Phase II Pulmonary Rehabilitation, and Phase III Pulmonary Rehabilitation, based on the different stages of elderly COPD patients. It further details the target population, implementation location, rehabilitation content, assessment content, participating disciplines, and rehabilitation goals for each stage, emphasizing the need for clinical respiratory specialists, primary care physicians, rehabilitation physicians/therapists, nurses, nutritionists, psychologists, and social workers to actively coordinate and collaborate throughout the full-cycle rehabilitation process for elderly COPD patients.

Elderly lung cancer is the leading cause of cancer-related deaths worldwide, posing significant challenges to public health. This consensus systematically reviews relevant clinical guidelines and literature both domestically and internationally, integrating recent clinical experiences and research findings. It summarizes new clinical rehabilitation concepts from experts in rehabilitation medicine, respiratory medicine, and nursing regarding elderly lung cancer. The consensus provides detailed discussions on the definition, risk factors, classification, staging, and clinical characteristics of elderly lung cancer, with a focus on functional impairments and their rehabilitation assessment and treatment strategies. Functional impairments encompass multiple aspects, including pulmonary function, cardiac function, exercise capacity, pain, psychological issues, cognition, speech, swallowing, sensory functions, and bowel and bladder dysfunction. The consensus offers comprehensive academic guidance and clinical practice recommendations for rehabilitation professionals across various healthcare settings. It emphasizes that the full-cycle rehabilitation of elderly lung cancer requires multidisciplinary teamwork to ensure seamless integration of clinical care, rehabilitation, and nursing, maximizing patient functional recovery and quality of life. By conducting a comprehensive assessment of patients' overall conditions and formulating personalized rehabilitation treatment plans, effective improvements in functional impairments, symptom relief, and even prolongation of survival can be achieved. Additionally, the consensus outlines the service process for full-cycle rehabilitation of lung cancer, highlighting the interconnectedness of tertiary hospitals, secondary hospitals, community hospitals, and families, as well as the importance of adjusting rehabilitation models based on local circumstances. The promotion and implementation of this consensus aim to provide standardized guidance for the assessment and treatment of full-cycle rehabilitation in elderly lung cancer patients, with the expectation of improving rehabilitation outcomes and quality of life, thereby contributing to the development of elderly lung cancer rehabilitation in China.

Alternaria toxin， a secondary metabolite produced by Alternaria， is widely found in food products all over the world and poses a crucial threat to human health. Therefore， the analysis of toxicity and the search for prevention and control measures of the toxin， have become the focus of current research. This study mainly summarizes the toxicity， exposure risk， pollution control， and toxicity intervention methods of four common Alternaria toxins. It provides chain spore mold toxin risk assessment， safety evaluation standards， and a reference for the establishment of prevention and control measures to ensure food safety and preserve human health.

Krüppel-like zinc-finger transcription factor 5 (KLF5) is a vital regulator of breast cancer (BC) onset and progression. The mechanism by which KLF5 regulates BC is still not clearly known. In this study, bioinformatics analysis shows that BC-affected individuals with elevated KLF5 expression levels have poor clinical outcomes. We further verify that miR-145-5p regulated KLF5 expression to promote cell apoptosis and inhibit cell proliferation in BC via dual-luciferase reporter assay, western blot analysis, qRT-PCR, CCK-8 assay and cell apoptosis assay. In addition, based on bioinformatics analysis, the binding of ENST00000422059 with miR-145-5p is confirmed by dual-luciferase reporter assay. Subsequently, FISH, western blot analysis, qRT-PCR, CCK-8 and cell apoptosis assays verified that ENST00000422059 increases KLF5 protein expression by sponging miRNA to promote cell proliferation and inhibit cell apoptosis. Finally, ENST00000422059 is found to accelerate tumor progression by regulating the miR-145-5p/KLF5 axis in vivo. In conclusion, this study suggests that ENST00000422059 upregulates KLF5 by sponging miR-145-5p to promote BC progression.

Bladder cancer (BCa) is a common malignant tumor of the urinary tract. Significant progress has been made in both basic and clinical research on BCa, which has further elucidated the drivers of BCa development at the gene and protein levels, and explored the structure and interactions of the tumor microenvironment. The early diagnostic index nuclear matrix protein 22 (NMP22) of BCa has great clinical limitations, and new biomarkers are constantly being developed to make early diagnosis more accurate. A breakthrough has been made in the comprehensive treatment of BCa, including the combination of immune checkpoint inhibitor therapy on cisplatin-based first-line chemotherapy, the application of antibody-drug conjugate (ADC), and the combination of these drugs in different stages of BCa. In order to better describe the recently achieved research results, a review of the research progress of BCa in 2022 was presented.

[Purpose/Significance] Emotional experience is a purely subjective feeling of the interaction between users and products. Exploring user participation behavior and influencing factors in online health communities under the influence of user emotional experience has obvious significance for retaining users. [Method/Process] A total of 25 users of the online health platform were selected as the research objects. The semi-structured interview method was used, and the basic information of the interviewees was combined with the questionnaire. With the help of grounded theory, the original interview data were coded, and the factors influencing user participation in online health communities were analyzed. Then, the emotional experience and feedback mechanism model of user participation in online health communities was constructed by combining the "emotion-cognitive recursive model". Finally, effective cases were screened from the questionnaire, and the configuration of the influencing factors of user participation behavior and affective feedback was explored based on the principle of qualitative comparative analysis and calculation of fuzzy sets. [Results/Conclusions] Through the grounded theory coding, six factors affecting user participation behavior are obtained: risk perception, platform cognition, demand motivation, platform experience, user emotion and environment. Then combined with the cognitive-affective model, the seven main categories derived from the rooted coding were divided into three dimensions: cognitive assessment, affective experience and participatory behavior. The feedback mechanism of emotional experience after participating behavior is further studied. It is found that the cognitive evaluation including risk perception, platform cognition and demand motivation affects the emotional experience of users, thus affecting their actual participation behavior. In the process of user participation in online health community, the factors of cognitive assessment, emotional experience and participation behavior interact with each other, and have a two-way effect on user participation behavior and user cognitive assessment. Further verification by fuzzy set qualitative comparative analysis (fsQCA) shows that among the six factors affecting user participation behavior, platform experience and demand motivation are the core conditions affecting user participation in online health community, and the three single factor variables of user emotion, environment and platform cognition do not necessarily affect the generation of user participation behavior. The extraction of influencing factors and the definition of user behavior are not comprehensive, and some real variables and key engagement behaviors may be overlooked. In addition, there is an imbalance in the number of samples on each platform. Follow-up related research can improve the pertinence of the platform and expand the sample capacity.

We studied 1110 patients with β-thalassemia major aged ≤25 years who received transplants with grafts from HLA-matched related (n = 677; 61%), HLA-mismatched related (n = 78; 7%), HLA-matched unrelated (n = 252; 23%), and HLA-mismatched unrelated (n = 103; 9%) donors between 2000 and 2016. Ninety percent of transplants were performed in the last decade. Eight-five percent of patients received ≥20 transfusions and 88% were inadequately chelated. All patients received myeloablative-conditioning regimen. Overall and event-free survival were highest for patients aged ≤6 years and after HLA-matched related and HLA-matched unrelated donor transplantation. The 5-year probabilities of overall survival for patients aged ≤6 years, 7 to 15 years, and 16 to 25 years, adjusted for donor type and conditioning regimen were 90%, 84%, and 63%, respectively (P < .001). The corresponding probabilities for event-free survival were 86%, 80%, and 63% (P < .001). Overall and event-free survival did not differ between HLA-matched related and HLA-matched unrelated donor transplantation (89% vs 87% and 86% vs 82%, respectively). Corresponding probabilities after mismatched related and mismatched unrelated donor transplantation were 73% vs 83% and 70% vs 78%. In conclusion, if transplantation is considered as a treatment option it should be offered early (age ≤6 years). An HLA-matched unrelated donor is a suitable alternative if an HLA-matched relative is not available.

A metal-free B(C6F5)3 and HFIP synergistically catalyzed bisarylation reaction between difluoroacetaldehyde ethyl hemiacetal and anilines was developed to efficiently synthesize symmetrical and unsymmetrical difluoromethylated diarylmethanes without using stoichiometric activating agents or in concentrated strong acid media. This efficient, scalable and operationally simple protocol displays high atom economy and broad substrate tolerance, allowing quick access to diverse symmetrical and unsymmetrical difluoromethylated diarylmethane products under mild conditions. Furthermore, the recyclability and reusability of the HFIP solvent make the reaction environmentally friendly. [ABSTRACT FROM AUTHOR]

Background: Equine joint disease including septic arthritis (SA) and osteoarthritis (OA) is a critical cause of equine lameness. Platelet‐rich plasma (PRP) is one of the most popular regenerative therapies to treat equine OA, even SA, but the evidence in support of the treatment is conflicting. Objectives: The aim of the study was to systematically review the current evidence on PRP products used for SA and OA, as well as the efficacy of PRP products as treatment for OA on the basis of a meta‐analysis of the available literature. Study design: Systematic review and meta‐analysis. Methods: A systematic search of relevant databases (PubMed, Web of Science, Scopus) was performed to identify studies published from 2013 to 2023, in accordance with the PRISMA guidelines. Randomised controlled trials, non‐randomised trials and controlled laboratory studies that used at least one type of PRP products were included. Dichotomous outcomes were presented using odds ratios (ORs) and 95% confidence intervals (95% CIs). Results: A total of 21 publications were identified in the systematic review and 5 of them in the meta‐analysis. These publications involved various types of PRP products and reported different outcomes. Although most of the studies were associated with a high risk of bias, the overall estimated effect was consistent with a significant improvement in the PRP products treatment group compared with the control group (OR: 15.32; 95% CI: 3.00–78.15; p < 0.05). There was a significant improvement in clinical performance outcomes between the groups (OR: 36.64; 95% CI: 3.69–364.30; p < 0.05). Conclusion: PRP products as intra‐articular treatment are likely efficacious for treatment of equine OA and have potential for treating SA. These conclusions might be affected by the limited number of randomised controlled studies and high variability of different types of PRP products. To better evaluate the efficacy of PRP, a widely recognised classification system and the utilisation of randomised, blinded, equivalency or non‐inferiority trials are required. [ABSTRACT FROM AUTHOR]