Bioscience trends最新文献_第3页

Multimodal optimal matching and augmentation method for small sample gesture recognition. 小样本手势识别的多模态优化匹配与增强方法。

IF 5.7 4区生物学

Bioscience trends Pub Date : 2025-03-06 Epub Date: 2025-01-25 DOI: 10.5582/bst.2024.01370

Wenli Zhang, Bo Liu, Tingsong Zhao, Shuyan Qie

{"title":"Multimodal optimal matching and augmentation method for small sample gesture recognition.","authors":"Wenli Zhang, Bo Liu, Tingsong Zhao, Shuyan Qie","doi":"10.5582/bst.2024.01370","DOIUrl":"10.5582/bst.2024.01370","url":null,"abstract":"In human-computer interaction, gesture recognition based on physiological signals offers advantages such as a more natural and fast interaction mode and less constrained by the environment than visual-based. Surface electromyography-based gesture recognition has significantly progressed. However, since individuals have physical differences, researchers must collect data multiple times from each user to train the deep learning model. This data acquisition process can be particularly burdensome for non-healthy users. Researchers are currently exploring transfer learning and data augmentation techniques to enhance the accuracy of small-sample gesture recognition models. However, challenges persist, such as negative transfer and limited diversity in training samples, leading to suboptimal recognition performance. Therefore, We introduce motion information into sEMG-based recognition and propose a multimodal optimal matching and augmentation method for small sample gesture recognition, achieving efficient gesture recognition with only one acquisition per gesture. Firstly, this method utilizes the optimal matching signal selection module to select the most similar signals from the existing data to the new user as the training set, reducing inter-domain differences. Secondly, the similarity calculation augmentation module enhances the diversity of the training set. Finally, the Modal-type embedding enhances the information interaction between each mode signal. We evaluated the effectiveness on Self-collected Stroke Patient, the Ninapro DB1 dataset and the Ninapro DB5 dataset and achieved accuracies of 93.69%, 91.65% and 98.56%, respectively. These results demonstrate that the method achieved performance comparable to traditional recognition models while significantly reducing the collected data.","PeriodicalId":8957,"journal":{"name":"Bioscience trends","volume":" ","pages":"125-139"},"PeriodicalIF":5.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Growth and differentiation factor 15: An emerging therapeutic target for brain diseases. 生长与分化因子15：脑疾病的新治疗靶点

IF 5.7 4区生物学

Bioscience trends Pub Date : 2025-03-06 Epub Date: 2025-01-25 DOI: 10.5582/bst.2024.01305

Yingying Zhou, Lei Dou, Luyao Wang, Jiajie Chen, Ruxue Mao, Lingqiang Zhu, Dan Liu, Kai Zheng

{"title":"Growth and differentiation factor 15: An emerging therapeutic target for brain diseases.","authors":"Yingying Zhou, Lei Dou, Luyao Wang, Jiajie Chen, Ruxue Mao, Lingqiang Zhu, Dan Liu, Kai Zheng","doi":"10.5582/bst.2024.01305","DOIUrl":"10.5582/bst.2024.01305","url":null,"abstract":"Growth and differentiation factor 15 (GDF15), a member of the transforming growth factor-βsuperfamily, is considered a stress response factor and has garnered increasing attention in recent years due to its roles in neurological diseases. Although many studies have suggested that GDF15 expression is elevated in patients with neurodegenerative diseases (NDDs), glioma, and ischemic stroke, the effects of increased GDF15 expression and the potential underlying mechanisms remain unclear. Notably, many experimental studies have shown the multidimensional beneficial effects of GDF15 on NDDs, and GDF15 overexpression is able to rescue NDD-associated pathological changes and phenotypes. In glioma, GDF15 exerts opposite effects, it is both protumorigenic and antitumorigenic. The causes of these conflicting findings are not comprehensively clear, but inhibiting GDF15 is helpful for suppressing tumor progression. GDF15 is also regarded as a biomarker of poor clinical outcomes in ischemic stroke patients, and targeting GDF15 may help prevent this disease. Thus, we systematically reviewed the synthesis, transcriptional regulation, and biological functions of GDF15 and its related signaling pathways within the brain. Furthermore, we explored the potential of GDF15 as a therapeutic target and assessed its clinical applicability in interventions for brain diseases. By integrating the latest research findings, this study provides new insights into the future treatment of neurological diseases.","PeriodicalId":8957,"journal":{"name":"Bioscience trends","volume":" ","pages":"72-86"},"PeriodicalIF":5.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intestinal microbiota distribution and changes in different stages of Parkinson's disease: A meta-analysis, bioinformatics analysis and in vivo simulation. 帕金森病不同阶段肠道菌群分布和变化：荟萃分析、生物信息学分析和体内模拟

IF 5.7 4区生物学

Bioscience trends Pub Date : 2025-03-06 Epub Date: 2025-01-25 DOI: 10.5582/bst.2024.01352

Tingyue Jiang, Yu Wang, Wenxin Fan, Yifan Lu, Ge Zhang, Jiayuan Li, Renzhi Ma, Mengmeng Liu, Jinli Shi

{"title":"Intestinal microbiota distribution and changes in different stages of Parkinson's disease: A meta-analysis, bioinformatics analysis and in vivo simulation.","authors":"Tingyue Jiang, Yu Wang, Wenxin Fan, Yifan Lu, Ge Zhang, Jiayuan Li, Renzhi Ma, Mengmeng Liu, Jinli Shi","doi":"10.5582/bst.2024.01352","DOIUrl":"10.5582/bst.2024.01352","url":null,"abstract":"Parkinson's disease (PD) is a progressive disease that requires effective staging management. The role of intestinal microbiota in PD has been studied, but its changes at different stages are not clear. In this study, meta- analysis, bioinformatics analysis and in vivo simulation were used to explore the intestinal microbiota distribution of PD patients and models at different stages. Two PD models at different stages were established in rotenone-treated rats and MPTP-induced mice. The differences in the intestinal microbiota among the different stages of PD patients or models were compared and analyzed. There were significant differences between PD patients and controls, including Actinobacteriota, Deltaproteobacteria, Clostridiales, Lachnospiraceae, Parabacteroides, etc. Through bioinformatics analysis, we revealed significant differences between PD patients at different stages and controls, including Actinobacteriota, Methanobacteria, Erysipelotrichales, Prevotellaceae, Parabacteroides, Parabacteroides gordonii, etc. Through meta-analysis, we found that Actinobacteriota and Erysipelotrichaceae had significantly increased in the chronic MPTP model, while Prevotellaceae had significantly decreased. PD rats and mice presented significant damage to motor function, coordination, autonomous activity ability and gastrointestinal function, and the damage in the late group was greater than that in the early group. There were significant differences in intestinal microbiota between PD patients or models at different stages and the control groups. In the early stage, the dominant microbiota are Akkermansia, Alistipes, Anaerotruncus, Bilophila, Rikenellaceae, Verrucomicrobia and Verrucomicrobiae, whereas in the late stage, the dominant microbiota are Actinobacteriota and Erysipelotrichaceae. These differences can lay a foundation for subsequent research on the treatment and mechanism of PD at different stages.","PeriodicalId":8957,"journal":{"name":"Bioscience trends","volume":" ","pages":"87-101"},"PeriodicalIF":5.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Plasma extracellular vesicle pathognomonic proteins as the biomarkers of the progression of Parkinson's disease. 血浆细胞外囊泡病理特异蛋白作为帕金森病进展的生物标志物

IF 5.7 4区生物学

Bioscience trends Pub Date : 2025-03-06 Epub Date: 2025-02-09 DOI: 10.5582/bst.2024.01369

Chien-Tai Hong, Chen-Chih Chung, Yi-Chen Hsieh, Lung Chan

{"title":"Plasma extracellular vesicle pathognomonic proteins as the biomarkers of the progression of Parkinson's disease.","authors":"Chien-Tai Hong, Chen-Chih Chung, Yi-Chen Hsieh, Lung Chan","doi":"10.5582/bst.2024.01369","DOIUrl":"10.5582/bst.2024.01369","url":null,"abstract":"Parkinson's disease (PD) is a progressive neurodegenerative disorder for which reliable blood biomarkers to predict disease progression remain elusive. Plasma extracellular vesicles (EVs) have gained attention as a promising biomarker platform due to their stability and ability to cross the blood-brain barrier. This study explored the potential of EV-cargo proteins, specifically α-synuclein, tau, and β-amyloid, as biomarkers of PD progression. A cohort of 55 people with PD (PwP) and 58 healthy controls (HCs) underwent annual assessments of plasma EV proteins, cognition, and motor symptoms. EVs were isolated and validated using standardized methods, with pathognomonic proteins quantified via immunomagnetic reduction assays. Associations between biomarker changes and clinical symptom progression were analyzed. Over an average of 3.96 visits for PwP and 2.25 visits for HCs, PwP exhibited a distinct pattern of plasma EV protein changes linked to motor symptom progression, particularly in the Unified PD Rating Scale (UPDRS) part II score. Notably, changes in plasma EV α-synuclein levels were significantly correlated with changes in motor and cognitive symptoms, suggesting its central role in disease progression. These findings highlight the potential of plasma EV biomarkers, especially α-synuclein, as indicators of ongoing pathogenesis and as candidates for evaluating α-synuclein-targeted therapies in PD.","PeriodicalId":8957,"journal":{"name":"Bioscience trends","volume":" ","pages":"116-124"},"PeriodicalIF":5.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The glamor of and insights regarding hydrotherapy, from simple immersion to advanced computer-assisted exercises: A narrative review. 关于水疗的魅力和见解，从简单的浸入到先进的计算机辅助练习：叙述回顾。

IF 5.7 4区生物学

Bioscience trends Pub Date : 2025-03-06 Epub Date: 2025-01-03 DOI: 10.5582/bst.2024.01356

Yaohan Peng, Yucong Zou, Tetsuya Asakawa

引用次数: 0

Post-stroke dysphagia: Neurological regulation and recovery strategies. 脑卒中后吞咽困难：神经调节和恢复策略。

IF 5.7 4区生物学

Bioscience trends Pub Date : 2025-03-06 Epub Date: 2025-02-22 DOI: 10.5582/bst.2025.01029

Xinyue Li, Minmin Wu, Jiongliang Zhang, Donghui Yu, Yuting Wang, Yumeng Su, Xiangyu Wei, Xun Luo, Qing Mei Wang, Luwen Zhu

{"title":"Post-stroke dysphagia: Neurological regulation and recovery strategies.","authors":"Xinyue Li, Minmin Wu, Jiongliang Zhang, Donghui Yu, Yuting Wang, Yumeng Su, Xiangyu Wei, Xun Luo, Qing Mei Wang, Luwen Zhu","doi":"10.5582/bst.2025.01029","DOIUrl":"10.5582/bst.2025.01029","url":null,"abstract":"Swallowing is a complex process requiring precise coordination of numerous muscles in the head and neck to smoothly guide ingested material from the mouth to the stomach. Animal and human studies have revealed a complex network of neurons in the brainstem, cortex, and cerebellum that coordinate normal swallowing. The interactions between these regions ensure smooth and efficient swallowing. However, the current understanding of the neurophysiological mechanisms involved in post-stroke dysphagia (PSD) is incomplete, and complete functional connectivity for swallowing recovery remains understudied and requires further exploration. In this review, we discussed the neuroanatomy of swallowing and the pathogenesis of PSD and summarized the factors affecting PSD recovery. We also described the plasticity of neural networks affecting PSD, including enhancing activation of neural pathways, cortical reorganization, regulation of extracellular matrix dynamics and its components, modulation of neurotransmitter delivery, and identification of potential therapeutic targets for functional recovery in PSD. Finally, we discussed the therapeutic strategies based on functional compensation and motor learning. This review aimed to provide a reference for clinicians and researchers to promote the optimization of PSD treatments and explore future research directions.","PeriodicalId":8957,"journal":{"name":"Bioscience trends","volume":" ","pages":"31-52"},"PeriodicalIF":5.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143490671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Serum proteomics reveals early biomarkers of Alzheimer's disease: The dual role of APOE-ε4. 血清蛋白质组学揭示阿尔茨海默病的早期生物标志物：APOE-ε4的双重作用。

IF 5.7 4区生物学

Bioscience trends Pub Date : 2025-03-06 Epub Date: 2025-01-23 DOI: 10.5582/bst.2024.01365

Ya-Nan Ma, Ying Xia, Kenji Karako, Peipei Song, Wei Tang, Xiqi Hu

{"title":"Serum proteomics reveals early biomarkers of Alzheimer's disease: The dual role of APOE-ε4.","authors":"Ya-Nan Ma, Ying Xia, Kenji Karako, Peipei Song, Wei Tang, Xiqi Hu","doi":"10.5582/bst.2024.01365","DOIUrl":"10.5582/bst.2024.01365","url":null,"abstract":"Alzheimer's disease (AD), the leading cause of dementia, significantly impacts global public health, with cases expected to exceed 150 million by 2050. Late-onset Alzheimer's disease (LOAD), predominantly influenced by the APOE-ε4 allele, exhibits complex pathogenesis involving amyloid-β (Aβ) plaques, neurofibrillary tangles (NFTs), neuroinflammation, and blood-brain barrier (BBB) disruption. Proteomics has emerged as a pivotal technology in uncovering molecular mechanisms and identifying biomarkers for early diagnosis and intervention in AD. This paper reviews the genetic and molecular roles of APOE-ε4 in the pathology of AD, including its effects on Aβ aggregation, tau phosphorylation, neuroinflammation, and BBB integrity. Additionally, it highlights recent advances in serum proteomics, revealing APOE-ε4-dependent and independent protein signatures with potential as early biomarkers for AD. Despite technological progress, challenges such as population diversity, standardization, and distinguishing AD-specific biomarkers remain. Directions for future research emphasize multicenter longitudinal studies, multi-omics integration, and the clinical translation of proteomic findings to enable early detection of AD and personalized treatment strategies. Proteomics advances in AD research hold the promise of improving patient outcomes and reducing the global disease burden.","PeriodicalId":8957,"journal":{"name":"Bioscience trends","volume":" ","pages":"1-9"},"PeriodicalIF":5.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143021678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The APP Score: A simple serum biomarker model to enhance prognostic prediction in hepatocellular carcinoma. APP评分：提高肝细胞癌预后预测的简单血清生物标志物模型。

IF 5.7 4区生物学

Bioscience trends Pub Date : 2025-01-14 Epub Date: 2024-12-05 DOI: 10.5582/bst.2024.01228

Jinyu Zhang, Qionglan Wu, Jinhua Zeng, Yongyi Zeng, Jingfeng Liu, Jianxing Zeng

{"title":"The APP Score: A simple serum biomarker model to enhance prognostic prediction in hepatocellular carcinoma.","authors":"Jinyu Zhang, Qionglan Wu, Jinhua Zeng, Yongyi Zeng, Jingfeng Liu, Jianxing Zeng","doi":"10.5582/bst.2024.01228","DOIUrl":"10.5582/bst.2024.01228","url":null,"abstract":"The prognosis for patients with hepatocellular carcinoma (HCC) depends on tumor stage and remnant liver function. However, it often includes tumor morphology, which is usually assessed with imaging studies or pathologic analysis, leading to limited predictive performance. Therefore, the aim of this study was to develop a simple and low-cost prognostic score for HCC based on serum biomarkers in routine clinical practice. A total of 3,100 patients were recruited. The least absolute shrinkage and selector operation (LASSO) algorithm was used to select the significant factors for overall survival. The prognostic score was devised based on multivariate Cox regression of the training cohort. Model performance was assessed by discrimination and calibration. Albumin (ALB), alkaline phosphatase (ALP), and alpha-fetoprotein (AFP) were selected by the LASSO algorithm. The three variables were incorporated into multivariate Cox regression to create the risk score (APP score = 0.390* ln (ALP) + 0.063* ln(AFP) - 0.033*ALB). The C-index, K-index, and time-dependent AUC of the score displayed significantly better predictive performance than 5 other models and 5 other staging systems. The model was able to stratify patients into three different risk groups. In conclusion, the APP score was developed to estimate survival probability and was used to stratify three strata with significantly different outcomes, outperforming other models in training and validation cohorts as well as different subgroups. This simple and low-cost model could help guide individualized follow-up.","PeriodicalId":8957,"journal":{"name":"Bioscience trends","volume":" ","pages":"567-583"},"PeriodicalIF":5.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and validation of a machine-learning model to predict lymph node metastasis of intrahepatic cholangiocarcinoma: A retrospective cohort study. 预测肝内胆管癌淋巴结转移的机器学习模型的开发和验证：一项回顾性队列研究。

IF 5.7 4区生物学

Bioscience trends Pub Date : 2025-01-14 Epub Date: 2024-12-05 DOI: 10.5582/bst.2024.01282

Shizheng Mi, Guoteng Qiu, Zhihong Zhang, Zhaoxing Jin, Qingyun Xie, Ziqi Hou, Jun Ji, Jiwei Huang

{"title":"Development and validation of a machine-learning model to predict lymph node metastasis of intrahepatic cholangiocarcinoma: A retrospective cohort study.","authors":"Shizheng Mi, Guoteng Qiu, Zhihong Zhang, Zhaoxing Jin, Qingyun Xie, Ziqi Hou, Jun Ji, Jiwei Huang","doi":"10.5582/bst.2024.01282","DOIUrl":"10.5582/bst.2024.01282","url":null,"abstract":"Lymph node metastasis in intrahepatic cholangiocarcinoma significantly impacts overall survival, emphasizing the need for a predictive model. This study involved patients who underwent curative liver resection between different time periods. Three machine learning models were constructed with a training cohort (2010-2016) and validated with a separate cohort (2019-2023). A total of 170 patients were included in the training set and 101 in the validation cohort. The lymph node status of patients not undergoing lymph node dissection was predicted, followed by survival analysis. Among the models, the support vector machine (SVM) had the best discrimination, with an area under the curve (AUC) of 0.705 for the training set and 0.754 for the validation set, compared to the random forest (AUC: 0.780/0.693) and the logistic regression (AUC: 0.703/0.736). Kaplan-Meier analysis indicated that patients in the positive lymph node group or predicted positive group had significantly worse overall survival (OS: p < 0.001 for both) and disease-free survival (DFS: p < 0.001 for both) compared to negative groups. An online user-friendly calculator based on the SVM model has been developed for practical application.","PeriodicalId":8957,"journal":{"name":"Bioscience trends","volume":" ","pages":"535-544"},"PeriodicalIF":5.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applications of and issues with machine learning in medicine: Bridging the gap with explainable AI. 机器学习在医学中的应用和问题：用可解释的人工智能弥合差距。

IF 5.7 4区生物学

Bioscience trends Pub Date : 2025-01-14 Epub Date: 2024-12-08 DOI: 10.5582/bst.2024.01342

Kenji Karako, Wei Tang

{"title":"Applications of and issues with machine learning in medicine: Bridging the gap with explainable AI.","authors":"Kenji Karako, Wei Tang","doi":"10.5582/bst.2024.01342","DOIUrl":"10.5582/bst.2024.01342","url":null,"abstract":"In recent years, machine learning, and particularly deep learning, has shown remarkable potential in various fields, including medicine. Advanced techniques like convolutional neural networks and transformers have enabled high-performance predictions for complex problems, making machine learning a valuable tool in medical decision-making. From predicting postoperative complications to assessing disease risk, machine learning has been actively used to analyze patient data and assist healthcare professionals. However, the \"black box\" problem, wherein the internal workings of machine learning models are opaque and difficult to interpret, poses a significant challenge in medical applications. The lack of transparency may hinder trust and acceptance by clinicians and patients, making the development of explainable AI (XAI) techniques essential. XAI aims to provide both global and local explanations for machine learning models, offering insights into how predictions are made and which factors influence these outcomes. In this article, we explore various applications of machine learning in medicine, describe commonly used algorithms, and discuss explainable AI as a promising solution to enhance the interpretability of these models. By integrating explainability into machine learning, we aim to ensure its ethical and practical application in healthcare, ultimately improving patient outcomes and supporting personalized treatment strategies.","PeriodicalId":8957,"journal":{"name":"Bioscience trends","volume":" ","pages":"497-504"},"PeriodicalIF":5.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0