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Journals Computational and Structural B...

Computational and Structural Biotechnology Journal

https://read.qxmd.com/read/38525105/smad4-regulates-tgf-%C3%AE-1-mediated-hedgehog-activation-to-promote-epithelial-to-mesenchymal-transition-in-pancreatic-cancer-cells-by-suppressing-gli1-activity
#1
JOURNAL ARTICLE
Hangcheng Guo, Zujian Hu, Xuejia Yang, Ziwei Yuan, Mengsi Wang, Chaoyue Chen, Lili Xie, Yuanyuan Gao, Wangjian Li, Yongheng Bai, Chunjing Lin
Pancreatic cancer (PC) is an aggressive and metastatic gastrointestinal tumor with a poor prognosis. Persistent activation of the TGF-β/Smad signaling induces PC cell (PCC) invasion and infiltration via epithelial-to-mesenchymal transition (EMT). Hedgehog signaling is a crucial pathway for the development of PC via the transcription factors Gli1/2/3. This study aimed to investigate the underlying molecular mechanisms of action of hedgehog activation in TGF-β1-triggered EMT in PCCs (PANC-1 and BxPc-3)...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38510977/differential-gene-expression-analysis-pipelines-and-bioinformatic-tools-for-the-identification-of-specific-biomarkers-a-review
#2
REVIEW
Diletta Rosati, Maria Palmieri, Giulia Brunelli, Andrea Morrione, Francesco Iannelli, Elisa Frullanti, Antonio Giordano
In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38510976/decoding-phenotypic-screening-a-comparative-analysis-of-image-representations
#3
JOURNAL ARTICLE
Adriana Borowa, Dawid Rymarczyk, Marek Żyła, Maciej Kańdula, Ana Sánchez-Fernández, Krzysztof Rataj, Łukasz Struski, Jacek Tabor, Bartosz Zieliński
Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop a universal representation model for HCS data, mainly data generated using U2OS cells and CellPainting protocol, using supervised and self-supervised learning approaches...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38510975/comprehensive-analysis-of-m-6-a-methylome-alterations-after-azacytidine-plus-venetoclax-treatment-for-acute-myeloid-leukemia-by-nanopore-sequencing
#4
JOURNAL ARTICLE
Zaifeng Zhang, Lili Zhang, Jiangtao Li, Ru Feng, Chang Li, Ye Liu, Gaoyuan Sun, Fei Xiao, Chunli Zhang
N6 adenosine methylation (m6 A), one of the most prevalent internal modifications on mammalian RNAs, regulates RNA transcription, stabilization, and splicing. Growing evidence has focused on the functional role of m6 A regulators on acute myeloid leukemia (AML). However, the global m6 A levels after azacytidine (AZA) plus venetoclax (VEN) treatment in AML patients remain unclear. In our present study, bone marrow (BM) sample pairs (including pre-treatment [AML] and post-treatment [complete remission (CR)] samples) were harvested from three AML patients who had achieved CR after AZA plus VEN treatment for Nanopore direct RNA sequencing...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38510974/cyclodextrins-establishing-building-blocks-for-ai-driven-drug-design-by-determining-affinity-constants-in-silico
#5
JOURNAL ARTICLE
Amelia Anderson, Ángel Piñeiro, Rebeca García-Fandiño, Matthew S O'Connor
Cyclodextrins (CDs) are cyclic carbohydrate polymers that hold significant promise for drug delivery and industrial applications. Their effectiveness depends on their ability to encapsulate target molecules with strong affinity and specificity, but quantifying affinities in these systems accurately is challenging for a variety of reasons. Computational methods represent an exceptional complement to in vitro assays because they can be employed for existing and hypothetical molecules, providing high resolution structures in addition to a mechanistic, dynamic, kinetic, and thermodynamic characterization...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38510973/drexml-a-command-line-tool-and-python-package-for-drug-repurposing
#6
JOURNAL ARTICLE
Marina Esteban-Medina, Víctor Manuel de la Oliva Roque, Sara Herráiz-Gil, María Peña-Chilet, Joaquín Dopazo, Carlos Loucera
We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38510972/phosphopeptide-binding-to-the-n-sh2-domain-of-tyrosine-phosphatase-shp2-correlates-with-the-unzipping-of-its-central-%C3%AE-sheet
#7
JOURNAL ARTICLE
Michelangelo Marasco, John Kirkpatrick, Teresa Carlomagno, Jochen S Hub, Massimiliano Anselmi
SHP2 is a tyrosine phosphatase that plays a regulatory role in multiple intracellular signaling cascades and is known to be oncogenic in certain contexts. In the absence of effectors, SHP2 adopts an autoinhibited conformation with its N-SH2 domain blocking the active site. Given the key role of N-SH2 in regulating SHP2, this domain has been extensively studied, often by X-ray crystallography. Using a combination of structural analyses and molecular dynamics (MD) simulations we show that the crystallographic environment can significantly influence the structure of the isolated N-SH2 domain, resulting in misleading interpretations...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38510535/role-of-artificial-intelligence-in-digital-pathology-for-gynecological-cancers
#8
REVIEW
Ya-Li Wang, Song Gao, Qian Xiao, Chen Li, Marcin Grzegorzek, Ying-Ying Zhang, Xiao-Han Li, Ye Kang, Fang-Hua Liu, Dong-Hui Huang, Ting-Ting Gong, Qi-Jun Wu
The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38495555/computational-methods-for-alignment-and-integration-of-spatially-resolved-transcriptomics-data
#9
REVIEW
Yuyao Liu, Can Yang
Most of the complex biological regulatory activities occur in three dimensions (3D). To better analyze biological processes, it is essential not only to decipher the molecular information of numerous cells but also to understand how their spatial contexts influence their behavior. With the development of spatially resolved transcriptomics (SRT) technologies, SRT datasets are being generated to simultaneously characterize gene expression and spatial arrangement information within tissues, organs or organisms...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38495554/multi-omics-analysis-reveals-promiscuous-o-glycosyltransferases-involved-in-the-diversity-of-flavonoid-glycosides-in-periploca-forrestii-apocynaceae
#10
JOURNAL ARTICLE
Xiaotong Wang, Lan Wu, Wanran Zhang, Shi Qiu, Zhichao Xu, Huihua Wan, Jiang He, Wenting Wang, Mengyue Wang, Qinggang Yin, Yuhua Shi, Ranran Gao, Li Xiang, Weijun Yang
Flavonoid glycosides are widespread in plants, and are of great interest owing to their diverse biological activities and effectiveness in preventing chronic diseases. Periploca forrestii , a renowned medicinal plant of the Apocynaceae family, contains diverse flavonoid glycosides and is clinically used to treat rheumatoid arthritis and traumatic injuries. However, the mechanisms underlying the biosynthesis of these flavonoid glycosides have not yet been elucidated. In this study, we used widely targeted metabolomics and full-length transcriptome sequencing to identify flavonoid diversity and biosynthetic genes in P...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38495121/mir-21-responsive-nanocarrier-targeting-ovarian-cancer-cells
#11
JOURNAL ARTICLE
Liting Han, Tao Song, Xinyu Wang, Yan Luo, Chuanqi Gu, Xin Li, Jinda Wen, Zhibin Wen, Xiaolong Shi
In recent years, DNA origami-based nanocarriers have been extensively utilized for efficient cancer therapy. However, developing a nanocarrier capable of effectively protecting cargos such as RNA remains a challenge. In this study, we designed a compact and controllable DNA tubular origami (DTO) measuring 120 nm in length and 18 nm in width. The DTO exhibited appropriate structural characteristics for encapsulating and safeguarding cargo. Inside the DTO, we incorporated 20 connecting points to facilitate the delivery of cargoes to various ovarian and normal epithelial cell lines...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38487369/stabilization-of-the-retromer-complex-analysis-of-novel-binding-sites-of-bis-1-3-phenyl-guanylhydrazone-2a-to-the-vps29-vps35-interface
#12
JOURNAL ARTICLE
Elisa Fagnani, Francesco Bonì, Pierfausto Seneci, Davide Gornati, Luca Muzio, Eloise Mastrangelo, Mario Milani
The stabilization of the retromer protein complex can be effective in the treatment of different neurological disorders. Following the identification of bis-1,3-phenyl guanylhydrazone 2a as an effective new compound for the treatment of amyotrophic lateral sclerosis, in this work we analyze the possible binding sites of this molecule to the VPS35/VPS29 dimer of the retromer complex. Our results show that the affinity for different sites of the protein assembly depends on compound charge and therefore slight changes in the cell microenvironment could promote different binding states...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38464935/network-based-analysis-of-heterogeneous-patient-matched-brain-and-extracranial-melanoma-metastasis-pairs-reveals-three-homogeneous-subgroups
#13
JOURNAL ARTICLE
Konrad Grützmann, Theresa Kraft, Matthias Meinhardt, Friedegund Meier, Dana Westphal, Michael Seifert
Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome and methylome data followed by network-based impact propagation of patient-specific alterations. This innovative data analysis strategy allowed to predict potential impacts of patient-specific driver candidate genes on other genes and pathways...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38455069/identification-and-validation-of-protein-biomarkers-for-predicting-gastrointestinal-stromal-tumor-recurrence
#14
JOURNAL ARTICLE
Juan Sun, Jie Li, Yixuan He, Weiming Kang, Xin Ye
We conducted a proteomic analysis using mass spectrometry to identify and validate protein biomarkers for accurately predicting recurrence risk in gastrointestinal stromal tumors (GIST) patients, focusing on differentially expressed proteins in metastatic versus primary GIST tissues. We selected five biomarkers-GPX4, RBM4, TPM3, PFKFB2, and PGAM5-and validated their expressions in primary tumors of recurrent and non-recurrent GIST patients via immunohistochemistry. Our analysis of the association between these biomarkers with recurrence-free survival (RFS) and overall survival (OS), along with their interrelationships, revealed that immunohistochemistry confirmed significantly higher expressions of these biomarkers in primary GIST tissues of recurrent patients...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38455068/uncovering-the-potential-of-apod-as-a-biomarker-in-gastric-cancer-a-retrospective-and-multi-center-study
#15
JOURNAL ARTICLE
Zisong Wang, Hongshan Chen, Le Sun, Xuanyu Wang, Yihang Xu, Sufang Tian, Xiaoping Liu
Gastric cancer (GC) poses a significant health challenge worldwide, necessitating the identification of predictive biomarkers to improve prognosis. Dysregulated lipid metabolism is a well-recognized hallmark of tumorigenesis, prompting investigation into apolipoproteins (APOs). In this study, we focused on apolipoprotein D (APOD) following comprehensive analyses of APOs in pan-cancer. Utilizing data from the TCGA-STAD and GSE62254 cohorts, we elucidated associations between APOD expression and multiple facets of GC, including prognosis, tumor microenvironment (TME), cancer biomarkers, mutations, and immunotherapy response, and identified potential anti-GC drugs...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38444982/design-rules-applied-to-silver-nanoparticles-synthesis-a-practical-example-of-machine-learning-application
#16
JOURNAL ARTICLE
Irini Furxhi, Lara Faccani, Ilaria Zanoni, Andrea Brigliadori, Maurizio Vespignani, Anna Luisa Costa
The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In this study, we manually extracted synthesis-related data from the literature and leveraged various machine learning algorithms...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38435301/analysis-of-haemonchus-embryos-at-single-cell-resolution-identifies-two-eukaryotic-elongation-factors-as-intervention-target-candidates
#17
JOURNAL ARTICLE
Pasi K Korhonen, Tao Wang, Neil D Young, Joseph J Byrne, Tulio L Campos, Bill C H Chang, Aya C Taki, Robin B Gasser
Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38434250/privacy-preserving-federated-machine-learning-on-fair-health-data-a-real-world-application
#18
JOURNAL ARTICLE
A Anil Sinaci, Mert Gencturk, Celia Alvarez-Romero, Gokce Banu Laleci Erturkmen, Alicia Martinez-Garcia, María José Escalona-Cuaresma, Carlos Luis Parra-Calderon
OBJECTIVE: This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets. MATERIALS AND METHODS: Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38434249/how-do-medical-professionals-make-sense-or-not-of-ai-a-social-media-based-computational-grounded-theory-study-and-an-online-survey
#19
JOURNAL ARTICLE
Sebastian Weber, Marc Wyszynski, Marie Godefroid, Ralf Plattfaut, Bjoern Niehaves
To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations...
December 2024: Computational and Structural Biotechnology Journal
https://read.qxmd.com/read/38425487/curvature-enhanced-graph-convolutional-network-for-biomolecular-interaction-prediction
#20
JOURNAL ARTICLE
Cong Shen, Pingjian Ding, Junjie Wee, Jialin Bi, Jiawei Luo, Kelin Xia
Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further incorporated into the weight function for the feature aggregation in message-passing procedure...
December 2024: Computational and Structural Biotechnology Journal
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