Article
Chemistry, Multidisciplinary
Khan Haroon, Huitong Ruan, Haoran Zheng, Shengju Wu, Ze Liu, Xiaojing Shi, Yaohui Tang, Guo-Yuan Yang, Zhijun Zhang
Summary: Delivering large therapeutic molecules to the brain for the treatment of ischemic stroke is challenging. In this study, the researchers developed a targeted delivery system using small extracellular vesicles (sEVs) loaded with a neuroprotective peptide called NR2B9c. The sEVs were modified with a rabies virus glycoprotein to improve brain targeting. In vitro and in vivo experiments showed that the sEVs-COCKTAIL exhibited potential against reactive oxygen species and cellular apoptosis, resulting in increased behavioral recovery and reduced neuronal apoptosis after stroke. The study provides a promising modality for stroke therapy.
JOURNAL OF CONTROLLED RELEASE
(2023)
Article
Pharmacology & Pharmacy
Mengyu Li, Rachel Yoon Kyung Chang, Yu Lin, Sandra Morales, Elizabeth Kutter, Hak-Kim Chan
Summary: The spray-dried phage cocktail powder demonstrated stability, inhalability, and efficacy against various MDR P. aeruginosa strains causing pulmonary infections, broadening the bactericidal spectrum and reducing resistance emergence compared to single-phage formulations.
INTERNATIONAL JOURNAL OF PHARMACEUTICS
(2021)
Article
Chemistry, Multidisciplinary
Lei Zhou, Chang Du, Rong Zhang, Changming Dong
Summary: The study developed a novel multifunctional nanocarrier PDOXCBs for combined photothermal-chemotherapy treatment, demonstrating a significant synergistic effect in vitro.
CHINESE CHEMICAL LETTERS
(2021)
Article
Toxicology
Jing Gao, Yuanjin Zhang, Xueqin Lei, Yuan Xu, Zhenliang Sun, Xin Wang
Summary: Hydroxygenkwanin (HGK), a natural flavonoid extracted from Daphne genkwa Sieb.et Zucc., exhibits a wide range of pharmacological activities and it competitively inhibits CYP1A2 and 2C enzymes. The inhibitory effect of HGK on CYP enzymes is weaker than diosmetin, possibly due to the substitution of hydroxyl and methoxy in the A and B rings of the flavone skeleton, which may lead to potential drug-drug interactions and toxicity.
TOXICOLOGY IN VITRO
(2022)
Article
Biochemistry & Molecular Biology
Pan Sun, Yuying Cao, Jicheng Qiu, Jingyuan Kong, Suxia Zhang, Xingyuan Cao
Summary: This study aimed to evaluate the inhibitory effects of LKMS on canine CYP450 enzymes. The results showed that LKMS inhibited the activity of CYP2A6, CYP2B6, and CYP2D6 through mixed inhibition. However, further in vivo studies are needed due to the in vitro nature of this study.
Article
Oncology
Ken Ogasawara, Rebecca N. Wood-Horrall, Mark Thomas, Michael Thomas, Liangang Liu, Mary Liu, Yongjun Xue, Sekhar Surapaneni, Leonidas N. Carayannopoulos, Simon Zhou, Maria Palmisano, Gopal Krishna
Summary: The study evaluated the impact of fedratinib on the pharmacokinetics of digoxin, rosuvastatin, and metformin. The results indicated that fedratinib has minimal influence on the exposure of these drugs, but it does decrease the renal clearance of metformin.
CANCER CHEMOTHERAPY AND PHARMACOLOGY
(2021)
Article
Pharmacology & Pharmacy
Jinhui Wang, Feifei Chen, Hui Jiang, Jia Xu, Deru Meng, Peiwu Geng, Dapeng Dai, Jingbo Hu, Yunfang Zhou, Quan Zhou, Shuanghu Wang
Summary: Poziotinib is an irreversible pan-HER tyrosine kinase inhibitor used for the treatment of various cancers. It interacts with CYP enzymes, particularly inhibiting CYP2B1 and CYP2C11 activity in rats. This suggests the potential for drug interactions with these enzymes and the need for caution in clinical settings.
FRONTIERS IN PHARMACOLOGY
(2021)
Article
Pharmacology & Pharmacy
Chih-hsuan Hsin, Annett Kuehne, Yi Gu, Gabriele Jedlitschky, Yohannes Hagos, Dirk Gruendemann, Uwe Fuhr
Summary: This study investigated the inhibitory effects of individual probe substrates on major transporters in vitro, using a clinically tested cocktail of adefovir, digoxin, metformin, sitagliptin, and pitavastatin. Only sitagliptin showed significant inhibition on several transporters, suggesting a need for dose reduction in the cocktail.
EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES
(2023)
Article
Pharmacology & Pharmacy
Tingting Li, Jiangling Huang, Min Wang, Hangxiang Wang
Summary: Therapeutic nanoparticles assembled from small molecular entities offer new possibilities for drug delivery systems. The use of microfluidics to fabricate hybrid nanoparticles for synergistic delivery of multiple chemotherapeutic drugs shows promise in reducing drug dosages while enhancing therapeutic effects. This approach, with better size control and reproducibility, could have significant potential for combination therapy in various cancer types.
INTERNATIONAL JOURNAL OF PHARMACEUTICS
(2021)
Article
Pharmacology & Pharmacy
Liuyun Gong, Yinliang Lu, Jing Wang, Xinyue Li, Jing Zhao, Yuetong Chen, Rongze Ma, Jinlu Ma, Tianya Liu, Suxia Han
Summary: Nanomedicine provides an opportunity to encapsulate multiple drugs in a nano-carrier, helping to address the issue of inconsistent pharmacokinetics of different therapeutic agents. In this study, a two-step super-assembled strategy was used to unify the pharmacokinetics of a peptide and a small molecular compound, resulting in a nano-pill with enhanced therapeutic effect for hepatoma treatment.
JOURNAL OF PHARMACEUTICAL ANALYSIS
(2023)
Article
Pharmacology & Pharmacy
Meng Fu, Lin Luo, Sheng Feng, Hongda Lin, Zekun Lu, Fei Gu, Yang Fan, Bing Wu, Jianying Huang, Kai Shen
Summary: This study evaluated the effects of SHR0302 on the pharmacokinetics of cytochrome P450 (CYP) probe substrates. The results demonstrated that co-administration of SHR0302 did not have a clinically meaningful effect on the exposure of drugs metabolized by CYP3A4, CYP2C8, CYP2C9 and CYP2C19 in healthy subjects.
BRITISH JOURNAL OF CLINICAL PHARMACOLOGY
(2023)
Article
Pharmacology & Pharmacy
Camille Lenoir, Youssef Daali, Victoria Rollason, Francois Curtin, Yvonne Gloor, Marija Bosilkovska, Bernhard Walder, Cem Gabay, Michael John Nissen, Jules Alexandre Desmeules, Didier Hannouche, Caroline Flora Samer
Summary: The study aimed to evaluate the impact of orthopedic surgery on the activity of human CYP enzymes, finding that acute inflammation significantly decreased the activity of CYP1A2, CYP2C19, and CYP3A, while increasing the activity of CYP2B6 and CYP2C9.
CLINICAL PHARMACOLOGY & THERAPEUTICS
(2021)
Article
Pharmacology & Pharmacy
Camille Lenoir, Jean Terrier, Yvonne Gloor, Francois Curtin, Victoria Rollason, Jules Alexandre Desmeules, Youssef Daali, Jean-Luc Reny, Caroline Flora Samer
Summary: The study found that SARS-CoV-2 infection affects the activity of specific CYP isoforms in COVID-19 patients, with changes observed in CYP1A2, CYP2B6, CYP2C9, CYP2C19, and CYP3A. This has implications for the metabolism of drugs and potential impacts on drug treatment efficacy in patients with COVID-19.
CLINICAL PHARMACOLOGY & THERAPEUTICS
(2021)
Editorial Material
Microbiology
Agustina Taglialegna
Summary: Gencay et al. developed a cocktail of engineered phages to specifically target clinically relevant strains of Escherichia coli.
NATURE REVIEWS MICROBIOLOGY
(2023)
Article
Microbiology
Yue Li, Peilin Lv, Deshi Shi, Hongze Zhao, Xu Yuan, Xiue Jin, Xiliang Wang
Summary: Salmonella enterica is a common and significant foodborne pathogen with increasing multi-drug resistance. Researchers isolated several Salmonella phages from environmental samples and found that some of them had broad host ranges and lytic capacities against Salmonella. These phages also showed inhibitory effects against Salmonella in a chicken model. The findings suggest that a phage cocktail could serve as an effective antimicrobial treatment to mitigate infections caused by multi-drug resistant Salmonella.
FRONTIERS IN MICROBIOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Yuanyuan Bu, Cangzhi Jia, Xudong Guo, Fuyi Li, Jiangning Song
Summary: This study introduces a deep learning-based stacking ensemble method called COPPER for predicting plant exclusive virus-derived small interfering RNAs (vsiRNAs). COPPER utilizes word2vec and fastText to generate sequence features and employs a hybrid deep learning framework to enable precise predictions. Benchmarking experiments and comparisons with other methods demonstrate that COPPER significantly improves the predictive performance for plant vsiRNAs.
BRIEFINGS IN FUNCTIONAL GENOMICS
(2023)
Article
Environmental Sciences
Ke Ju, Liyong Lu, Wen Wang, Ting Chen, Chenyu Yang, En Zhang, Zongyou Xu, Shanshan Li, Jiangning Song, Jay Pan, Yuming Guo
Summary: Long-term exposure to air pollutants is likely to be associated with mental disorders. However, there is limited and inconsistent evidence to assess causality, especially in developing countries. This study found that air pollution significantly affects mental health, but habitual physical activity can mitigate this negative effect.
ENVIRONMENTAL RESEARCH
(2023)
Article
Multidisciplinary Sciences
Zhongxiao Li, Yuwei Cong, Xin Chen, Yupeng Chen, Jiping Qi, Jingxian Sun, Tao Yan, He Yang, Junsi Liu, Xin Gao, Enzhou Lu, Lixiang Wang, Jiafeng Li, Hong Hu, Cheng Zhang, Quan Yang, Jiawei Yao, Penglei Yao, Qiuyi Jiang, Wenwu Liu, Jiangning Song, Lawrence Carin, Shiguang Zhao
Summary: This study developed an end-to-end deep learning architecture called ViT-WSI for brain tumor analysis, which can classify the major types and subtypes of primary brain tumors. By using gradient-based attribution analysis, ViT-WSI is able to discover histopathological features for diagnosis. Additionally, ViT-WSI has shown high predictive power for inferring the status of three glioma molecular markers directly from H&E-stained histopathological images.
Article
Health Care Sciences & Services
Jing Xu, Jiarui Ou, Chen Li, Zheng Zhu, Jian Li, Hailun Zhang, Junchen Chen, Bin Yi, Wu Zhu, Weiru Zhang, Guanxiong Zhang, Qian Gao, Yehong Kuang, Jiangning Song, Xiang Chen, Hong Liu
Summary: A machine learning model was developed to diagnose Psoriatic arthritis (PsA) and analyze its progression risk using 3961 patients' clinical records. General additive models (GAMs) and the Kaplan-Meier method were applied to analyze the efficacy of various drugs on psoriasis treatment and inhibiting PsA progression. The results showed accurate prediction of PsA and its progression and analysis of drug efficacy.
NPJ DIGITAL MEDICINE
(2023)
Article
Biochemical Research Methods
Zixuan Wang, Yi Zhou, Tatsuya Takagi, Jiangning Song, Yu-Shi Tian, Tetsuo Shibuya
Summary: This study proposes a novel feature selection method, called Iso-GA, for cancer classification. Iso-GA combines the manifold learning algorithm Isomap with the genetic algorithm (GA) to consider the latent nonlinear structure of gene expression in microarray data. The performance of Iso-GA was evaluated on eight benchmark microarray datasets and outperformed other benchmarking gene selection methods.
BMC BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Ruyi Chen, Fuyi Li, Xudong Guo, Yue Bi, Chen Li, Shirui Pan, Lachlan J. M. Coin, Jiangning Song
Summary: A-to-I editing is a common RNA editing event where adenosine (A) bases are changed to inosine (I) bases. Accurate identification of A-to-I editing sites is crucial for understanding RNA-level modifications and their roles in molecular functions. In this study, a novel stacked-ensemble learning model called ATTIC was developed to accurately predict A-to-I editing sites in three species. ATTIC outperforms state-of-the-art tools for predicting A-to-I editing sites.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Xiaoti Jia, Pei Zhao, Fuyi Li, Zhaohui Qin, Haoran Ren, Junzhou Li, Chunbo Miao, Quanzhi Zhao, Tatsuya Akutsu, Gensheng Dou, Zhen Chen, Jiangning Song
Summary: Lysine 2-hydroxyisobutylation (K-hib) plays important roles in various biological processes. A deep learning-based approach called ResNetKhib has been developed to accurately predict cell type-specific K-hib sites, outperforming other prediction tools.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Jing Xu, Fuyi Li, Chen Li, Xudong Guo, Cornelia Landersdorfer, Hsin-Hui Shen, Anton Y. Peleg, Jian Li, Seiya Imoto, Jianhua Yao, Tatsuya Akutsu, Jiangning Song
Summary: Antimicrobial peptides (AMPs) are short peptides with various functional activities against target organisms and have the potential to be alternatives to antibiotics in the face of increasing antibiotic resistance. Existing computational approaches for identifying AMPs lack the ability to predict functional activities comprehensively. In this study, we developed a deep learning-based framework, iAMPCN, which significantly improved the prediction performance of AMPs and their functional activities. The model outperformed state-of-the-art approaches and can be used as a valuable tool for identifying potential AMPs with specific functional activities.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biology
Fuyi Li, Xudong Guo, Yue Bi, Runchang Jia, Miranda E. Pitt, Shirui Pan, Shuqin Li, Robin B. Gasser, Lachlan JM. Coin, Jiangning Song
Summary: A deep learning-based approach called Digerati was developed for the rapid and accurate identification of PE and PPE family proteins. Digerati achieved a significantly better performance (-18-20%) than alignment-based methods. It is expected to facilitate high-throughput identification and analysis of PE/PPE family members.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Meng Wang, Guitian Liu, Meng Liu, Cui Tai, Zixin Deng, Jiangning Song, Hong-Yu Ou
Summary: ICEberg 3.0 is an upgraded database that provides comprehensive insights into bacterial integrative and conjugative elements (ICEs). It contains detailed information about ICEs, categorizes cargo gene functions, and aids in the analysis and exploration of ICEs from the human microbiome. This enhances the understanding of ICE biology and its implications for microbial communities.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Biochemistry & Molecular Biology
Jiahao Guan, Yongkui Chen, Ying-Xian Goh, Meng Wang, Cui Tai, Zixin Deng, Jiangning Song, Hong-Yu Ou
Summary: TADB 3.0 is an updated database that provides comprehensive information on bacterial toxin-antitoxin gene loci. It includes experimental and predicted loci, as well as visualized networks showing the relationships between loci and mobile genetic elements.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Multidisciplinary Sciences
Yu Zhao, Bing He, Fan Xu, Chen Li, Zhimeng Xu, Xiaona Su, Haohuai He, Yueshan Huang, Jamie Rossjohn, Jiangning Song, Jianhua Yao
Summary: In this study, a deep learning framework called DeepAIR is presented for accurately predicting the binding between AIRs and antigens by integrating both sequence and structure features. DeepAIR achieves high prediction performance in both TCR binding affinity and BCR binding reactivity, and successfully identifies patients with nasopharyngeal carcinoma and inflammatory bowel disease in test data.
Article
Biochemical Research Methods
Jiani Ma, Chen Li, Yiwen Zhang, Zhikang Wang, Shanshan Li, Yuming Guo, Lin Zhang, Hui Liu, Xin Gao, Jiangning Song
Summary: This study proposes a novel multi-view graph autoencoder framework, MULGA, for predicting drug-protein interactions and drug repositioning. The results show that MULGA outperforms existing methods in DPI prediction, and the effectiveness of each proposed component is verified through ablation studies. Importantly, MULGA identifies important drugs targeting the spike glycoprotein of SAR-CoV-2, providing additional insights and potentially useful treatment options for COVID-19.
Article
Biochemical Research Methods
Fang Ge, Chen Li, Shahid Iqbal, Arif Muhammad, Fuyi Li, Maha A. Thafar, Zihao Yan, Apilak Worachartcheewan, Xiaofeng Xu, Jiangning Song, Dong-Jun Yu
Summary: Determining the pathogenicity and functional impact of variants is crucial for understanding genetic mechanisms of human diseases. This study developed a deep-learning-based computational solution called VPatho, which accurately identifies the pathogenicity and functional impact of variants. Experimental results demonstrate the superior performance of VPatho and its application in blind testing analysis using the XGBOD model.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Matee Ullah, Fazal Hadi, Jiangning Song, Dong-Jun Yu
Summary: The study introduces a new two-level stacked autoencoder network (2L-SAE-SM) that improves the performance of protein subcellular localization prediction by integrating heterogeneous feature sets. Extensive benchmarking experiments demonstrate the effectiveness of the proposed framework for feature set integration and show that the proposed method outperforms existing state-of-the-art methods in protein subcellular localization prediction.