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  1. Differentially expressed proteins in ER+ MCF7 and ER- MDA- MB-231 human breast cancer cells by RhoGDI-α silencing and overexpression
    Hooshmand S, Ghaderi A, Yusoff K, Thilakavathy K, Rosli R, Mojtahedi Z
    Asian Pac J Cancer Prev, 2014;15(7):3311-7.
    PMID: 24815488
    BACKGROUND: The consequence of Rho GDP dissociation inhibitor alpha (RhoGDIα) activity on migration and invasion of estrogen receptor positive (ER+) and negative (ER-) breast cancer cells has not been studied using the proteomic approach. Changes in expression of RhoGDIα and other proteins interacting directly or indirectly with RhoGDIα in MCF7 and MDA-MB-231, with different metastatic potentials is of particular interest.

    MATERIALS AND METHODS: ER+ MCF7 and ER- MDA-MB-231 cell lines were subjected to two-dimensional electrophoresis (2-DE) and spots of interest were identified by matrix-assisted laser desorption/ionization time of- flight/time- of-flight (MALDI-TOF/TOF) mass spectrometry (MS) analysis after downregulation of RhoGDIα using short interfering RNA (siRNA) and upregulated using GFP-tagged ORF clone of RhoGDIα.

    RESULTS: The results showed a total of 35 proteins that were either up- or down-regulated in these cells. Here we identifed 9 and 15 proteins differentially expressed with silencing of RhoGDIα in MCF-7 and the MDA-MB-231 cells, respectively. In addition, 10 proteins were differentially expressed in the upregulation of RhoGDIα in MCF7, while only one protein was identified in the upregulation of RhoGDIα in MDA-MB-231. Based on the biological functions of these proteins, the results revealed that proteins involved in cell migration are more strongly altered with RhoGDI-α activity. Although several of these proteins have been previously indicated in tumorigenesis and invasiveness of breast cancer cells, some ohave not been previously reported to be involved in breast cancer migration. Hence, these proteins may serve as useful candidate biomarkers for tumorigenesis and invasiveness of breast cancer cells.

    CONCLUSIONS: Future studies are needed to determine the mechanisms by which these proteins regulate cell migration. The combination of RhoGDIα with other potential biomarkers may be a more promising approach in the inhibition of breast cancer cell migration.

  2. Fulltext Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm
    Tehrany PM, Zabihi MR, Ghorbani Vajargah P, Tamimi P, Ghaderi A, Norouzkhani N, et al.
    Int Wound J, 2023 Nov;20(9):3768-3775.
    PMID: 37312659 DOI: 10.1111/iwj.14275
    Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital-acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation-maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU.
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