Springer最新文献

{"title":"Farmer perceptions of the vulnerabilities of traditional livestock farming systems under global change.","authors":"Zebensui Morales-Reyes, Jomar M Barbosa, José A Sánchez-Zapata, Irene Pérez-Ibarra","doi":"10.1007/s13280-025-02150-8","DOIUrl":"10.1007/s13280-025-02150-8","url":null,"abstract":"<p><p>The continuity of traditional extensive livestock farming is being challenged by rapid socioeconomic and environmental changes, threatening livelihoods and ecosystem services critical to food security and sustainability. We conducted a large-scale assessment involving 255 livestock farmers across six extensive livestock farming systems in Spain to understand their perceptions of vulnerabilities. Using the Coupled Infrastructure Systems framework, we identified 24 different vulnerabilities, mainly caused by external socioeconomic and biophysical disturbances, such as resource costs, low profitability of livestock products, climate variability, and conflicts with wildlife. The main factors explaining these vulnerabilities were primary productivity, farm location, presence of large predators, and climatic conditions. The findings highlight the complex interplay of these factors and provide important insights for the maintenance of extensive livestock farming systems in Europe. This information is crucial for informing policy decisions aimed at supporting these farming systems and ensuring their contribution to food security, sustainability and biodiversity conservation.</p>","PeriodicalId":461,"journal":{"name":"Ambio","volume":" ","pages":"1353-1371"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12214157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The multiple roles of religious actors in advancing a sustainable future.","authors":"Jens Koehrsen, Christopher D Ives","doi":"10.1007/s13280-025-02166-0","DOIUrl":"10.1007/s13280-025-02166-0","url":null,"abstract":"<p><p>Religious actors have great potential for influencing transformation processes toward environmentally sustainable societies. Influencing peoples' worldviews, values, and group norms, they can promote (or block) pro-environmental attitudes, lifestyles, and political decision-making. Yet, current scholarship is ambivalent about religion's contribution to environmental sustainability. This perspective article outlines various roles religious actors can assume in sustainability transitions. We suggest a systematization of four roles-(1) pioneering, (2) path-following, (3) passive observing, and (4) prohibiting change-and portray five conditions that influence and catalyze these roles-(a) theological commitment, (b) internal support, (c) resources, (d) social and political influence, and (e) wider societal conditions. Generating this conceptual clarity is crucial as it allows researchers and policy actors to recognize the diversity of religious expressions with respect to sustainability action, and grasp the conditions under which religious actors are best equipped to address sustainability challenges.</p>","PeriodicalId":461,"journal":{"name":"Ambio","volume":" ","pages":"1318-1333"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12214149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143787601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors.","authors":"Oleg V Tinkov, Veniamin Y Grigorev","doi":"10.1007/s11030-024-11066-6","DOIUrl":"10.1007/s11030-024-11066-6","url":null,"abstract":"<p><p>Histone deacetylase 3 (HDAC3) inhibitors keep significant therapeutic promise for treating oncological, neurodegenerative, and inflammatory diseases. In this work, we developed robust QSAR regression models for HDAC3 inhibitory activity and acute toxicity (LD₅₀, intravenous administration in mice). A total of 1751 compounds were curated for HDAC3 activity, and 15,068 for toxicity. The models employed molecular descriptors such as Morgan fingerprints, MACCS-166 keys, and Klekota-Roth, PubChem fingerprints integrated with machine learning algorithms including random forest, gradient boosting regressor, and support vector machine. The HDAC3 QSAR models achieved Q²_test values of up to 0.76 and RMSE values as low as 0.58, while toxicity models attained Q²_test values of 0.63 and RMSE values down to 0.41, with applicability domain (AD) coverage exceeding 68%. Internal validation by fivefold cross-validation (Q²cv = 0.70 for HDAC3 and 0.60 for toxicity) and y-randomization confirmed model reliability. Shapley additive explanation (SHAP) was also used to explain the influence of modeling features on model prediction results. The most predictive QSAR models are integrated into the developed HDAC3_VS_assistant application, which is freely available at https://hdac3-vs-assistant-v2.streamlit.app/ . Virtual screening conducted using the HDAC3_VS_assistant web application allowed us to reveal a number of potential inhibitors, and the nature of their bonds with the active HDAC3 site was additionally investigated by molecular docking.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3165-3187"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating machine learning and structural dynamics to explore B-cell lymphoma-2 inhibitors for chronic lymphocytic leukemia therapy.","authors":"Rima Bharadwaj, Amer M Alanazi, Vivek Dhar Dwivedi, Sarad Kumar Mishra","doi":"10.1007/s11030-024-11079-1","DOIUrl":"10.1007/s11030-024-11079-1","url":null,"abstract":"<p><p>Chronic lymphocytic leukemia (CLL) is a malignancy caused by the overexpression of the anti-apoptotic protein B-cell lymphoma-2 (BCL-2), making it a critical therapeutic target. This study integrates computational screening, molecular docking, and molecular dynamics to identify and validate novel BCL-2 inhibitors from the ChEMBL database. Starting with 836 BCL-2 inhibitors, we performed ADME and Lipinski's Rule of Five (RO5) filtering, clustering, maximum common substructure (MCS) analysis, and machine learning models (Random Forest, SVM, and ANN), yielding a refined set of 124 compounds. Among these, 13 compounds within the most common substructure (MCS1) cluster showed promising features and were prioritized. A docking-based re-evaluation highlighted four lead compounds-ChEMBL464268, ChEMBL480009, ChEMBL464440, and ChEMBL518858-exhibiting notable binding affinities. Although a reference molecule outperformed in docking, molecular dynamics (MD), and binding energy analyses, it failed ADME and Lipinski criteria, unlike the selected leads. Further validation through MD simulations and MM/GBSA energy calculations confirmed stable binding interactions for the leads, with ChEMBL464268 showing the highest stability and binding affinity (ΔGtotal = - 80.35 ± 11.51 kcal/mol). Free energy landscape (FEL) analysis revealed stable energy minima for these complexes, underscoring conformational stability. Despite moderate activity (pIC₅₀ values from 4.3 to 5.82), the favorable pharmacokinetic profiles of these compounds position them as promising BCL-2 inhibitor leads, with ChEMBL464268 emerging as the most promising candidate for further CLL therapeutic development.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3233-3252"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.","authors":"M Janbozorgi, S Kaveh, M S Neiband, A Mani-Varnosfaderani","doi":"10.1007/s11030-024-11096-0","DOIUrl":"10.1007/s11030-024-11096-0","url":null,"abstract":"<p><p>Adenosine receptors (A₁, A_2a, A_2b, A₃) play critical roles in cellular signaling and are implicated in various physiological and pathological processes, including inflammations and cancer. The main aim of this research was to investigate structure-activity relationships (SAR) to derive models that describe the selectivity and activity of inhibitors targeting Adenosine receptors. Structural information for 16,312 inhibitors was collected from BindingDB and analyzed using machine learning methods. 450 molecular descriptors were calculated for each molecule and compounds were classified based on their activity levels and therapeutic targets. The variable importance in projection (VIP) algorithm identified key discriminating features. Classification models were built using supervised Kohonen networks (SKN) and counter-propagation artificial neural networks (CPANN) algorithms. Model validity was assessed via cross-validation, applicability domain analysis, and test sets. These models were then used to screen a random subset of 2 million molecules from the ZINC database. Three descriptors-hydrophilic factor (Hy), ratio of multiple path count over path count (PCR), and asphericity (ASP)-were identified as critical for discriminating active and inactive inhibitors. SKN models exhibited high sensitivity (0.88-0.99) and yielded an average area under the curve (AUC) of 0.922 for virtual screening. This study aimed to enhance the development of highly selective Adenosine receptor ligands for diverse therapeutic applications by identifying critical molecular features specific to each isoform.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3253-3272"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy.","authors":"Alireza Poustforoosh","doi":"10.1007/s11030-025-11114-9","DOIUrl":"10.1007/s11030-025-11114-9","url":null,"abstract":"<p><p>The drug combination is an attractive approach for cancer treatment. PARP and kinase inhibitors have recently been explored against cancer cells, but their combination has not been investigated comprehensively. In this study, we used various drug combination databases to build ML models for drug combinations against brain cancer cells. Some decision tree-based models were used for this purpose. The results were further evaluated using molecular docking and molecular dynamics (MD) simulation. The possibility of the hit drug combinations for crossing the Blood-brain barrier (BBB) was also examined. Based on the obtained results, the combination of niraparib, as the PARP inhibitor, and lapatinib, as the kinase inhibitor, exhibited more considerable outcomes with a remarkable model performance (accuracy of 0.915) and prediction confidence of 0.92. The protein tweety homolog 3 and BTB/POZ domain-containing protein 2 are the main targets of niraparib and lapatinib with - 10.2 and - 8.5 scores, respectively. Due to the outcomes, this drug combination can use the CAT1 transporter on the BBB surface and effectively cross the BBB. Based on the obtained results, niraparib-lapatinib can be a promising drug combination candidate for brain cancer treatment. This combination is worth to be examined by experimental investigation in vitro and in vivo.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3293-3303"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"iDCNNPred: an interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer.","authors":"Ravishankar Jaiswal, Girdhar Bhati, Shakil Ahmed, Mohammad Imran Siddiqi","doi":"10.1007/s11030-024-11055-9","DOIUrl":"10.1007/s11030-024-11055-9","url":null,"abstract":"<p><p>Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 expression, accounting for 15-20% of breast cancer cases. It is challenging due to low therapeutic response, heterogeneity, and aggressiveness. The PI3Ka isoform is a promising therapeutic target, often hyperactivated in TNBC, contributing to uncontrolled growth and cancer cell formation. We have proposed an interpretable deep convolutional neural network prediction (iDCNNPred) system using 2D molecular images to classify bioactivity and identify potential PI3Ka inhibitors. We built Custom-DCNN models and pre-trained models such as AlexNet, SqueezeNet, and VGG19 by using the Bayesian optimization algorithm, and found that our Custom-DCNN model performed better than a pre-trained model with lower complexity and memory usage. All top-performed models were screened with the Maybridge Chemical library to find predictive hit molecules. The screened molecules were further evaluated for protein-ligand interaction with molecular docking and finally 12 promising hits were shortlisted for biological validation using in-vitro PI3K inhibition studies. After biological evaluation, 4 potent molecules with different structural moieties were identified, and these molecules present new starting scaffolds for further improvement in terms of their potency and selectivity as PI3K inhibitors with the help of medicinal chemistry efforts. Furthermore, we also showed the significance of the interpretation and visualization of the model's predictions by the Grad-CAM technique, enhancing the robustness, transparency, and interpretability of the model's predictions. The data and script files and prediction run of models used for this study to reproduce the experiment are available in the GitHub repository at https://github.com/ravishankar1307/iDCNNPred.git .</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3077-3100"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis.","authors":"Aga Basit Iqbal, Tariq Ahmad Masoodi, Ajaz A Bhat, Muzafar A Macha, Assif Assad, Syed Zubair Ahmad Shah","doi":"10.1007/s11030-025-11133-6","DOIUrl":"10.1007/s11030-025-11133-6","url":null,"abstract":"&lt;p&gt;&lt;p&gt;The viability of cells and the integrity of the genome depend on the detection and repair of damaged DNA through intricate mechanisms. Cancer treatment employs chemotherapy or radiation therapy to eliminate neoplastic cells by causing substantial damage to their DNA. In many cases, improved DNA repair mechanisms lead to resistance to these medicines; therefore, it is essential to expand efforts to develop drugs that can sensitise cells to these treatments by inhibiting the DNA repair process. Multiple studies have demonstrated a correlation between the overexpression of Apurinic/Apyrimidinic Endonuclease (APE1), the primary mammalian enzyme responsible for excising apurinic or apyrimidinic sites in DNA, and the resistance of cells to cancer therapies; in contrast, APE1 downregulation increases cellular susceptibility to DNA-damaging agents. Thus, the effectiveness of existing therapies can be improved by promoting the targeted sensitization of cancer cells while protecting healthy cells. The current study aims to employ explainable artificial intelligence (XAI) to enhance the accuracy and reliability of machine learning models for the prediction of APE1 inhibitors. Various ML-based regression models are employed to predict the pIC50 value of different medicines. Bayesian optimization and the Permutation Feature Importance (PFI) approach are employed to determine the best hyperparameters of machine learning models and to discover the most significant features for recognizing drug candidates that target APE1 enzymes, respectively. To acquire comprehensive elucidations for the predictive models in our research, two XAI methodologies, namely SHAP and LIME, are used. The SHAP analysis reveals that the features 'C1SP2' and 'ASP-2' are essential in influencing the model's predictions. The SHAP values demonstrate variability for features such as 'maxHBint2' and 'GATS1s,' signifying that their impact is dependent on specific instances within the dataset. The LIME study corroborates these findings, demonstrating that 'C1SP2' and 'ASP-2' are the most significant positive contributors, whereas features like 'SHCHnX,' 'nHdCH2,' and 'GATS1s' result in a decrease in the predicted values. Due to the limited sample size of the APE1 dataset, direct training on this dataset posed challenges in model generalization and reliability. To overcome this limitation, the BACE-1 dataset is leveraged for model training, enabling the ML models to learn from a more extensive and diverse chemical space. Among the tested algorithms, XGBoost demonstrated superior predictive performance, achieving R&lt;sup&gt;2&lt;/sup&gt; = 0.890, MAE = 0.186, and RMSE = 0.245, significantly surpassing state-of-the-art methods, such as LightGBM and QSAR-ML, which attained R&lt;sup&gt;2&lt;/sup&gt; scores of 0.798 and 0.630, respectively. These results highlight the robustness of our approach, demonstrating its enhanced generalization capability and superior predictive accuracy compared to existing methodologies.&lt;/","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3371-3390"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of Bifurcation Morphology on Exercise-Induced PAH Deposition in the Lungs: A Computational Modeling Approach for Air Quality Research.","authors":"Justus Kavita Mutuku, Hsin-Chieh Kung, Wei-Hsin Chen, Chien-Er Huang, Kuan Shiong Khoo, Pau Loke Show","doi":"10.1007/s10237-025-01968-1","DOIUrl":"10.1007/s10237-025-01968-1","url":null,"abstract":"<p><p>This study examines the influence of lung geometry, physical activity intensity, and aerosol concentration on the deposition efficiencies (DEs) of particulate matter with surface-bound polycyclic aromatic hydrocarbons (PM-_PAHs) in human lung generations 3-6. Two-phase flows were effected in ANSYS 2020R2 platform using planar and orthogonal lung geometries, with two levels of physical activities, 4 metabolic equivalents (4 METs), and 8 METs. Aerosol concentrations of 0.95 μg‧m^-3, 1.57 μg‧m^-3, and 2.04 μg‧m^-3 represent rural, urban, and industrial areas, respectively. Relative differences in DEs for 1 μm, 3.2 μm, and 5.6 μm exhibit variations between the two geometries with ranges of 0%-84.4% for 4 METs and 1.2%-50.7% for 8 METs. The first carina region was the most significant hotspot for the 5.6 μm particles. On the other hand, the 1 μm and 3.2 μm aerosols infiltrated and deposited evenly at the lower sections of the lungs. Regarding PM-_PAHs doses, spatial variations indicate an industrial > urban > rural hierarchy. This investigation suggests that individuals in industrial and urban locations should manage the intensity of their outdoor activities to minimize exposure to PM-_PAHs. These findings are instrumental for public health interventions aimed at reducing exposure to PM-_PAHs and preventing associated health problems.</p>","PeriodicalId":489,"journal":{"name":"Biomechanics and Modeling in Mechanobiology","volume":" ","pages":"1295-1312"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Propagating instabilities in long collapsible tubes of nonlinear biological material.","authors":"Aris G Stamou, Ilias Gavriilidis, Ioanna D Karetsa, Spyros A Karamanos","doi":"10.1007/s10237-025-01973-4","DOIUrl":"10.1007/s10237-025-01973-4","url":null,"abstract":"<p><p>Proper functionality of human body relies on several continuous physical processes, many of which are carried out through biological ducts/tubes. For instance, veins, arteries and airways into the human body are natural conduit systems where blood and air are conveyed. Those elastic tubular components are prone to structural instability (buckling) and eventually collapse under critical conditions of net external pressure, resulting in malfunctioning of main physical processes. In the present work, collapsible elastic tubes are studied from a structural mechanics perspective, examining their resistance to collapse under uniform external pressure, emphasizing on the influence of nonlinear material behavior. The problem is approached numerically using nonlinear finite element models, to analyze tubes with diameter-to-thickness ratio ranging from 9 to 30, considering different nonlinear elastic material properties and focusing on the post-buckling phenomenon of \"buckling propagation\". It is demonstrated that small softening deviations from linear elastic behavior may cause a localized collapse pattern followed by its propagation along the tube with a pressure lower than the collapse pressure. Results from two-dimensional (ring) and more rigorous three-dimensional (3D) finite element models are obtained in terms of the collapse pressure value and the propagation pressure value, i.e., the minimum pressure required for a localized buckling pattern to propagate, and the two models provide very similar predictions. A simple analytical model is also employed to explain the phenomenon of collapse localization and its subsequent propagation. In addition, special emphasis is given on the correlation between the 3D results and those from ring analysis in terms of the propagation profile and the energy required for the collapse pattern to advance. Finally, comparison with numerical results from tubes made of elastic-plastic material is performed to elucidate some special features of the propagation phenomenon.</p>","PeriodicalId":489,"journal":{"name":"Biomechanics and Modeling in Mechanobiology","volume":" ","pages":"1363-1384"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12246029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}