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Journal Article
Yanyan Zhu and others
Database, Volume 2025, 2025, baaf070, https://doi.org/10.1093/database/baaf070
Published: 31 October 2025
Journal Article
Marziyeh Mousazadeh and others
Database, Volume 2025, 2025, baaf042, https://doi.org/10.1093/database/baaf042
Published: 31 October 2025
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Published: 31 October 2025
Figure 1. Schematic diagram of Cys-PTMs involved in ROS-related activities: ROS scavenging (—|) and ROS producing (→).
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Published: 31 October 2025
Figure 6. Heatmap of four different ROS-related categories in taxonomic groups based on domain [ 84 , 85 ].
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Published: 31 October 2025
Figure 8. Distribution of disease categories (based on disease anatomy) in this database. Numbers of entries in each category are shown.
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Published: 31 October 2025
Figure 5. Tools of InTxDB. (A) Diamond tool: Provides sequence alignment capabilities with customizable parameters, allowing users to analyse and download alignment results. (B) HPInet Prediction tool: Predicts bacterial effector–host interactions based on sequence data or predefined PPI lists, generating net
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Published: 31 October 2025
Figure 3. LCDD interface—advanced search.
Journal Article
Sharayu Ghodeswar and Debashree Bandyopadhyay
Database, Volume 2025, 2025, baaf069, https://doi.org/10.1093/database/baaf069
Published: 31 October 2025
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Published: 31 October 2025
Figure 2. Flowchart of ROSBase curation.
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Published: 31 October 2025
Figure 3. Workflow (integrating backend and frontend) of ROSBase1.0 webserver.
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Published: 31 October 2025
Figure 5. The taxonomic classification of the database. Numbers of entries in each category are shown.
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Published: 31 October 2025
Figure 9. (a) Disease categories per protein entry (UniProt ID). (b) Pie chart representation of each disease category and corresponding UniProt IDs.
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Published: 31 October 2025
Figure 10. Interactome around Tp53, (a) derived from the STRING network and (b) regulatory pathways of Tp53; inhibition (—|) and interaction (→).
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Published: 31 October 2025
Figure 1. The workflow of InTxDB. The construction of InTxDB involves three main stages: data collection, data processing, and web construction. Data collection integrates three components: TxSE-human PPIs from databases and literature, structural data from PDB and AlphaFold DB, and interaction networks deriv
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Published: 31 October 2025
Figure 3. Two modules of InTxDB. (A) Browse module: Users can filter interaction data using options such as bacterial species, secretion system type, and protein length to quickly locate relevant information. (B) Detailed information page: Displays comprehensive details on selected interactions, including pro
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Published: 31 October 2025
Figure 4. Species of InTxDB. InTxDB provides species-specific interaction networks, allowing users to explore bacterial effector–host interactions across different bacterial species. Users can filter results by species, interaction detection method, and effector type, with options to toggle between ‘Interacti
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Published: 31 October 2025
Figure 1. The data from liposome research articles were manually extracted and incorporated into a comprehensive database (LCDD).
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Published: 31 October 2025
Figure 2. LCDD interface—basic search.
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Published: 31 October 2025
Figure 5. (A) The frequency of liposomal carriers used in various cancers, (B) the statistics of the experiment type in cancer-related liposomal formulations, and (C) the frequency of various animal models used in the in vivo experiment on liposomes in cancer-related research (NR denotes ‘not reported’).
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Published: 31 October 2025
Figure 6. (A) The statistics of the cargos loaded in liposomal carriers, (B) the size distribution of the liposomal carriers in cancer research, and (C) statistics of therapeutic, diagnostic, and theranostic liposomal formulations (NR denotes ‘not reported’).