Molecular Diversity最新文献

{"title":"Canthaxanthin downregulates EGFR in NSCLC: network pharmacology, molecular docking, dynamics simulations, ADMET, and in-vitro analysis.","authors":"Janmejay Pant, Payal Mittal, Lovedeep Singh, Harneet Marwah","doi":"10.1007/s11030-025-11246-y","DOIUrl":"https://doi.org/10.1007/s11030-025-11246-y","url":null,"abstract":"<p><p>Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with current therapies often limited by toxicity and resistance. Natural compounds like canthaxanthin, a carotenoid with demonstrated anticancer properties, offer a promising alternative. This study investigates canthaxanthin's therapeutic potential in NSCLC through an integrated computational and experimental approach. Network pharmacology identified 34 shared targets between canthaxanthin and NSCLC, with EGFR, SRC, and CASP3 emerging as key hubs. Molecular docking revealed strong binding affinities (- 9.0, - 7.6, and - 8.0 kcal/mol, respectively), supported by 200-ns molecular dynamics simulations demonstrating complex stability. ADMET analysis predicted favourable pharmacokinetics and low toxicity (Class 6). In-vitro validation via MTT assay showed selective cytotoxicity against A549 cells (IC₅₀ = 23.66 µg/mL) compared to normal lung cells (HEL 299; IC₅₀ = 57.77 µg/mL), outperforming 5-fluorouracil in selectivity (SI = 2.64 vs. 2.23). Pathway enrichment implicated cancer-related signaling (PI3K-AKT, MAPK) and apoptosis. Canthaxanthin's multi-target action-inhibiting EGFR proliferation, SRC migration, and activating CASP3-mediated apoptosis-suggests a polypharmacological advantage. Computational predictions aligned with experimental results, confirming dose-dependent cytotoxicity and minimal mutagenic risk. Canthaxanthin exhibits potent, selective anti-NSCLC activity through multi-target modulation, supported by robust binding stability and low toxicity. These findings highlight its potential as an adjunct or alternative therapy, particularly for resistant NSCLC. Future studies should explore in-vivo efficacy, combination regimens, and clinical translation.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144293083","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":"Predicting peroxisome proliferator-activated receptor gamma potency of small molecules: a synergistic consensus model and deep learning binding affinity approach powered by Enalos Cloud Platform.","authors":"Maria Antoniou, Konstantinos D Papavasileiou, Antreas Tsoumanis, Georgia Melagraki, Antreas Afantitis","doi":"10.1007/s11030-025-11230-6","DOIUrl":"https://doi.org/10.1007/s11030-025-11230-6","url":null,"abstract":"<p><p>Peroxisome proliferator-activated receptor gamma (PPARγ) antagonists play a critical role in regulating glucose and lipid metabolism, making them promising candidates for antidiabetic therapies. To support the ongoing search of such compounds, this study introduces two advanced in silico models for predicting the binding affinity and biological activity of small molecules targeting PPARγ. A neural network was developed to classify compounds as strong or weak binders based on molecular docking scores. Additionally, a consensus model combining Random Forest, Support Vector Machine, and k-Nearest Neighbours algorithms was implemented to predict the antagonistic activity of small molecules. Both models were rigorously validated according to the Organisation for Economic Co-operation and Development (OECD) guidelines, to ensure generalisability and sufficient efficiency in detecting the minority class (active antagonists). Mechanistic insights into how key molecular descriptors influence PPARγ activity were discussed in a posteriori interpretation. A case study involving 34 prioritised per- and polyfluoroalkyl substances (PFAS) were screened with the developed workflows to demonstrate their practical application. The models, integrated into user-friendly web applications via the Enalos Cloud Platform, enable accessible and efficient virtual screening, supporting the discovery of PPARγ modulators.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144293085","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":"Phosphodiesterase 4B (PDE4B) inhibitors and their applications in recent years (2014 to early 2025).","authors":"Jing Zhang, Jiadai Zhai, Hui Liu, Hongying Liu, Bingxia Sun, Jie Gao, Feng Sang","doi":"10.1007/s11030-025-11242-2","DOIUrl":"https://doi.org/10.1007/s11030-025-11242-2","url":null,"abstract":"<p><p>The phosphodiesterase 4B (PDE4B) subtype, a member of the phosphodiesterase (PDE) family, plays a key role in promoting anti-inflammatory and antifibrotic effects by controlling the rate of cyclic adenosine phosphate degradation. To date, inhibitors targeting PDE4B have been widely used in the development of therapeutic agents for pulmonary fibrosis, inflammation, cancer, Alzheimer's disease, adipose tissue dysfunction and chronic liver injury. With the development of techniques such as molecular docking studies, more and more PDE4B inhibitors with different core scaffolds have been discovered, and at least six of these molecular structures have been approved for marketing or entered clinical studies. In this work, we reviewed the PDE4B inhibitors reported in the literature since 2014 and classified the most representative examples with different biological activities according to their structural characteristics. We also made a preliminary analysis of their structure-activity relationship based on the classification results and the conclusions reported in the relevant literature. In addition, we describe the inhibition selectivity of some compounds to PDE4B and PDE4D enzymes, as inhibition of PDE4D is often associated with side effects such as nausea and emesis. We hope that this work will help researchers in the design and optimization of novel PDE4B selective inhibitors and provide a reference for readers who are new to this field.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144293084","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":"Quinazolinone based broad-spectrum antiviral molecules: design, synthesis, in silico studies and biological evaluation.","authors":"Abdallah E Abdallah, Omkulthom Al Kamaly, Esmail M El-Fakharany, Yousra A El-Maradny, Abdelaaty A Shahat, Mohamed S Alesawy, Ali Hammad, Mohamed Ayman El-Zahabi, Samiha A El-Sebaey, Mona S El-Zoghbi","doi":"10.1007/s11030-025-11237-z","DOIUrl":"https://doi.org/10.1007/s11030-025-11237-z","url":null,"abstract":"<p><p>In an attempt to develop broad-spectrum antiviral agents, we designed non-nucleoside small molecules as deubiquitinating enzyme inhibitors. The newly developed candidates are based on the quinazolinone nucleus and have been biologically evaluated as antiviral agents against four viruses: adenovirus, HSV-1, coxsackievirus, and SARS-CoV-2. Additionally, activity against papain-like protease (PL^pro), a DUB enzyme of SARS-CoV-2, was evaluated. Structure-activity association was established dependent on the obtained data. Regarding adenovirus, HSV-1, and coxsackievirus, most of the new candidates showed promising antiviral activity. Among them, compounds 8d and 8c have the highest potential, with IC₅₀ values reaching from 12.77 to 15.96 μg/mL and 16.71 to 19.58 μg/mL, respectively, compared to acyclovir's IC₅₀ of 3.45-15.97 μg/mL. However, 8c outperformed acyclovir in terms of selectivity index, with selectivity indices ranging from 19.04 to 22.31, whereas acyclovir's selectivity indices ranged from 4.77 to 22.10. While 8d had selectivity indices comparable to those of acyclovir. Interestingly, compound 8d revealed very potent activity against SARS-CoV-2, showing an IC₅₀ value of 0.948 μg/mL in comparison to IC₅₀ of 1.141 μg/mL for remdesivir. Additionally, 8d displayed a far better selectivity index than remdesivir. Furthermore, 8d showed promising inhibition of papain-like protease with an IC₅₀ of 5.056 μg/mL. In addition, the proposed binding modes and affinities of the new derivatives to papain-like protease were significant. Overall, the majority of such synthesized compounds, especially compound 8d, have shown strong antiviral activity and good safety profiles, making them promising candidates for future development in antiviral therapies.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281938","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":"From genome to drug targets: computational subtractive genomics reveals novel anti-filarial targets in Wuchereria bancrofti and identifies plant-based inhibitors of β-1,4-mannosyltransferase, a high-priority target.","authors":"Muthusamy Sureshan, Kadhirvel Saraboji, Arunachalam Jothi","doi":"10.1007/s11030-025-11229-z","DOIUrl":"https://doi.org/10.1007/s11030-025-11229-z","url":null,"abstract":"<p><p>Lymphatic filariasis (LF) is a mosquito-transmitted parasitic disease, which is a main concern in tropical and subtropical countries. LF is the second major cause of chronic and irreversible disabilities worldwide, which include lymphoedema, hydrocele, and elephantiasis. According to the World Health Organization (WHO), an estimated 882 million individuals across 44 countries were reported to be at risk of acquiring LF. The nematode Wuchereria bancrofti is the predominant pathogen which causes LF, accounting for approximately 90% of filarial infections. The drugs albendazole (ALB), ivermectin (IVM), and diethylcarbamazine (DEC) are currently used to treat LF, but they are not effective against microfilariae and are known to have an inability to reverse chronic conditions, produce adverse reactions, and have developed drug resistance due to prolonged use. Further scientific studies are necessary to discover and characterize potential drug targets in the genome of W. bancrofti, which would facilitate the development of novel therapeutic approaches. This study employs a subtractive genomics approach to identify potential anti-filarial drug targets from the genome of W. bancrofti. Our analysis revealed 12 targets of W. bancrofti, which were found to be involved in important metabolic pathways such as combating oxidative stress, amino acid and nucleotide metabolism, folate biosynthesis, and DNA repair. This article highlights the proposed drug targets and their potential role in the development of effective drugs against W. bancrofti. We also propose beta-1,4-mannosyltransferase (WbEGH), one among the 12 identified targets, as a priority target based on its sequence similarity with human proteins. Further, structure-based virtual screening identified five potent phytochemicals (IMPPAT ID: 9,896,047, 49,777,225, 13,888,122, 89,483-03-4, and 14,605,093) having a better affinity with WbEGH. Furthermore, experimental validation of these identified phytochemicals would lead towards an effective method for controlling Lymphatic Filariasis.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273913","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":"Advancing the development of deep learning and machine learning models for oral drugs through diverse descriptor classes: a focus on pharmacokinetic parameters (Vdss and PPB).","authors":"Rakesh Bantu, Samiron Phukan, Simon Haydar","doi":"10.1007/s11030-025-11235-1","DOIUrl":"https://doi.org/10.1007/s11030-025-11235-1","url":null,"abstract":"<p><p>In the present study, we report a predictive deep learning (DL) and machine learning (ML) model for pharmacokinetics (PK) parameters such as volume of distribution (Vdss) and plasma protein Binding (PPB). Using DL & ML algorithms our study provides a deeper and novel insights into the role of molecular descriptors in determining the PK parameters such as Vdss and PPB. FDA approved drugs with oral route of administration and having reported PK parameters were taken as the dataset. This was used for establishment of the foundational datasets followed by computation of different molecular descriptor classes. Feature engineering by Boruta algorithm exhibited significant increase in accuracy of the models. Features identified by Boruta algorithm, were trained for different models separately for both Vdss and PPB. The highest predictive scores amongst the models were achieved in gradient boosting (GB) and Stacking Classifier with 80% and 78% for Vdss. In the case of PPB, random forest and GB algorithm predicted the highest scores of 73% and 71%, respectively, in comparison to all other algorithms. In summary we report here appropriate ML algorithms like Stacking Classifier-by utilizing an unreported feature engineering algorithm -to predict Vdss and PPB individually considering over 67 descriptors each with ≥ 80% accuracy and 73% accuracy, respectively. Additionally, we developed models based on the shared descriptors between Vdss and PPB. Quantum chemical descriptors like MLFERs (MLFER_BH, MLFER_BO & MLFER_E) and topological descriptors like piPC5, piPC6, piPC9 & TpiPC identified as the common drivers of the functional activity of Vdss and PPB together.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265013","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":"PARP1 inhibitors discovery: innovative screening strategies incorporating machine learning and fragment replacement techniques.","authors":"Jiahui Tu, Jiaqi Chen, Nan Zhou, Lianxiang Luo","doi":"10.1007/s11030-025-11238-y","DOIUrl":"https://doi.org/10.1007/s11030-025-11238-y","url":null,"abstract":"<p><p>PARP1, the most prominent member of the PARP family, mediates DNA repair and cellular stress responses. PARP inhibitors (PARPi) show clinical promise in treating BRCA1/2-mutated or homologous recombination-deficient tumors, particularly in breast and ovarian cancers. However, acquired resistance remains a significant therapeutic challenge. This study developed a PARP1 inhibitor discovery pipeline integrating machine learning with conventional virtual screening methods. We introduced a novel strategy called fragment replacement to generate new compounds with optimized properties. Using the Maybridge compound library, we developed machine learning models to predict inhibitor activity. The Random Forest classifier demonstrated superior performance (AUC = 0.971, accuracy = 0.915) in tenfold cross-validation. This machine learning-driven approach outperformed conventional virtual screening in terms of efficiency. Subsequently, we conducted virtual screening using 2D fingerprints, shapes, and docking to retain the top-ranked ligands based on a standardized score (Z2-score). XP docking and ADMET prediction were used to select two molecules with strong drug-like properties. Fragment replacement was employed to reconstruct 101 new compounds with improved drug-like characteristics and increased activity. After validation, we identified three hits with docking scores between - 11.802kcal/mol and - 10.808kcal/mol, which were superior to the positive control Talazoparib (docking score: - 9.103kcal/mol). MD simulations assessed the binding stability of the compounds to proteins, with all three selected compounds exhibiting good binding stability, thus identifying them as potential candidates for development as PARP1 inhibitors.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265015","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":"Machine learning and molecular modeling reveal potential inhibitors of the human metapneumovirus fusion protein.","authors":"Shatha Ghazi Felemban, Hayat Ali Alzahrani, Abdullah R Alzahrani, Zia Ur Rehman, Abdullah Yahya Abdullah Alzahrani, Abida Khan, Mohd Imran","doi":"10.1007/s11030-025-11232-4","DOIUrl":"https://doi.org/10.1007/s11030-025-11232-4","url":null,"abstract":"<p><p>Respiratory infections by human metapneumovirus (HMPV) are common in children, those with weakened immune systems, and older people. With its important role in viral entry, viral fusion (F) glycoprotein is a prime target for designing drugs. To discover new inhibitors of the HMPV fusion protein as a class of drugs that can target this protein and stop it from causing disease, this study employs a computational drug design approach that includes density functional theory (DFT), molecular dynamics (MD), and machine learning (ML). With the help of molecular dynamics simulations, this study verifies the binding activity of lead compounds, optimizes them using calculations based on density functional theory to evaluate electronic properties, and then uses a machine learning-based virtual screening strategy to identify possible inhibitors. PSICHIC, ML model found five lead compounds with ligand 57,414,794 with the highest predicted binding affinity (7.413) and maximum antagonist probability (0.99998). Strong binding of 57,414,794 to the HMPV fusion protein was validated by molecular docking and MM/GBSA binding free energy calculation. The drug outperformed the reference compound Remdesivir with a binding free energy of - 27.46 kcal/mol by a big margin. MD simulations validated its stability with fewer structural fluctuations and good free energy landscape (FEL) characteristics. ADMET profiling also displayed excellent gastrointestinal absorption with no Lipinski violations, supporting the drug-likeness of identified compounds. These results contribute to the search for target-based drugs against HMPV and illustrate the role of machine learning-assisted computational drug design in infectious disease research.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265014","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":"Unraveling the role of deuterium in cancer: mechanisms, detection techniques, and therapeutic potential.","authors":"Zhe Lei, Ning Su, Menglong Li, Yanan Sun, Zhenrui Pan, Kangdong Liu, Yueteng Zhang","doi":"10.1007/s11030-025-11221-7","DOIUrl":"https://doi.org/10.1007/s11030-025-11221-7","url":null,"abstract":"<p><p>Deuterium, an element recognized for its close association with cancer, is involved in every stage from tumorigenesis and basic research methodologies to clinical diagnosis, therapeutic interventions, and drug development. Several significant findings have emerged in this field. This review explores the potential mechanisms by which deuterons influence cancer initiation and progression in the form of deuterium oxide including deuterium-enriched water (DEW) and deuterium-depleted water (DDW). Besides, deuterium, a stable hydrogen isotope with unique physicochemical properties, also plays a pivotal role in detection and drug discovery. We delve into the importance of deuterium-labeled compound detection techniques-such as hydrogen-deuterium exchange mass spectrometry, deuterium metabolism imaging, Raman deuterium isotope probe techniques, and the applications of AI in Deuterium-related detection techniques-in identifying tumor biomarkers, elucidating metabolic pathways, and validating drug targets. The advantages and limitations of these techniques, particularly in the realm of imaging, are discussed. Deuterium-substituted drugs, such as donafenib, offer notable pharmacokinetic superiorities, including a significantly longer half-life and reduced toxicity compared to conventional chemotherapeutic agents. These characteristics make them promising candidates for cancer chemotherapy. In summary, this review examines the role of deuterium-related molecules in cancer development, detection, and treatment, including DEW, DDW, deuterium-substituted drugs, and deuterium-labeled compounds. Additionally, it highlights the latest advancements in deuterium-labeled compound detection technologies.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144245650","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":"Fine-tuning LLM hyperparameters to align semantic and physiological contexts of aging-related pathways.","authors":"Antos Shakhbazau","doi":"10.1007/s11030-025-11226-2","DOIUrl":"https://doi.org/10.1007/s11030-025-11226-2","url":null,"abstract":"<p><p>Biological aging is defined by many physiological pathways, calling for the identification and validation of synergistic combinations of interventions that would address multiple hallmarks of aging. Multitude of biological agents resides semantically linked in the LLM's vector space, offering avenues to leverage Artificial Intelligence's (AI) generative capabilities to explore remote and indirect connections and relationships. This study examines various models, hyperparameter configurations, response formats, scopes, and other AI generation scenarios to align semantic proximity of aging-related factors with their biological context, with LLM precision being evaluated against KEGG benchmark database. Tuning GPT-4 hyperparameters such as temperature and frequency penalty offers maximum diversity of context collected for known senotherapeutics and pathway regulators. Response confidence score, based on LLM's internal logprob metric, was found to be predictive of KEGG validation success and offers evidence of higher semantic proximity translating into lower biological distance between input molecules and their respective associations.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144245649","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}