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Background: Breast cancer is one of the most prevalent types of cancer in Iranian women and the second cause of death in women worldwide. Gene mutations are the key determinants of the disease; therefore, the genetic study of this disease is of paramount importance. One of the genetic evaluation methods of this disease is microarray technology, which allows the examination of the simultaneous expression of thousands of genes. Clustering is the method for analyzing high-dimension data, which we used in the present research for collecting similar genes in separated clusters. Method: A descriptive and inferential statistical analysis was carried out to evaluate unsupervised learning models of gene expression analysis and five bi-clustering methods (including PLAID (PL), Fabia, Bimax, Cheng & Church (CC), and Xmotif) were compared. For this purpose, we obtained the microarray gene expression data for lapatinib-resistant breast cancer cell lines from previously published research. The enrichment efficacy of the clusters was evaluated with gene ontology, and the results of these five models were compared with the Jaccard index, variance stability, least-square error, and goodness of fit indices. Furthermore, the results of the best model were assessed for building a genes sets network with Bayesian networks. Results: After preprocessing, clustering was performed on the data with the dimension (4710 x 18) of the genes. Four models, except for CC, successfully found bi-clusters in the data set. The data evaluation revealed that the results of the models were almost the same, but the PL model performed better than the others, finding 11 bi-clusters; this model was used to build the network of gene sets. Conclusion: According to the results, the PL method was suitable for clustering the data. Accordingly, it could be recommended for data analysis. In addition, the gene sets network formed on gene expression data was incompetent. [ABSTRACT FROM AUTHOR]

In situations where the molecular mechanism of many ocular disorders is unknown, owing to the difficulties associated with sampling from ocular tissues, human tear film can be a promising medium in ophthalmic research. The present study demonstrates an in-depth gel-based proteome optimization survey to approach more appropriate and efficient systems in various situations such as normal and dry-eye syndromes. Therefore, systematic and statistical evaluations were performed on different preparation methods, including acetone, acetone-methanol, chloroform-methanol-water, trichloroacetic acid (TCA)-acetone, tri- n-butylphosphate-acetone-methanol precipitations and ammonium sulfate fractionation at three different percentages of saturations (50, 70 and 90%). Methods were compared quantitatively on both one- and two-dimensional patterns. Some important parameters such as total protein recovery yield, densitometric analysis of some protein contaminants, banding patterns and total spot numbers along with statistical models for proper clustering were considered. Findings revealed noticeable impacts of preparation methods on all aspects of gel-based separations as well as recovery yield (ranging from 5.29 ± 0.96 to 22.56 ± 1.77 µg/mm) and banding and pattern resolution. In addition to all these, the most important point is that the total protein spot number on the final two-dimensional patterns (varied from 528.00 ± 19.00 to 657.00 ± 21.52 for different methods) were also noticeably increased in comparison with previously published reports (maximum of 250 spots), which is essential for a more comprehensive analysis. Increasing the proteome coverage in the present study is supposed to originate from improved solubility and effective rehydration during the sample application and isoelectric focusing (IEF) procedure along with proper sample preparation. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: The clinical pathogenesis of COVID-19 necessitates a comprehensive and homogeneous study to understand the disease mechanisms. Identifying clinical symptoms and laboratory parameters as key predictors can guide prognosis and inform effective treatment strategies. This study analyzed comorbidities and laboratory metrics to predict COVID-19 mortality using a homogeneous model., Method: A retrospective cohort study was conducted on 7500 COVID-19 patients admitted to Rasoul Akram Hospital between 2022 and 2022. Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores., Results: The frequency and mortality rates of COVID-19 among males (19.3 %) were higher than those among females (17 %) (p = 0.01, OR = 0.85, 95 % CI = 0.76-0.96). Cancer (p < 0.05, OR = 1.9, 95 % CI = 1.48-2.4) and Alzheimer's (p < 0.05, OR = 2.36, 95 % CI = 1.89-2.9) were the two most common comorbidities associated with long-term hospitalization (LTH). Kidney disease (KD) was identified as the most lethal comorbidity (45 % of KD patients) (OR = 5.6, 95 % CI = 5.05-6.04, p < 0.001). Age > 55 was the most predictive parameter for mortality (p < 0.001, OR = 6.5, 95 % CI = 1.03-1.04), and the CT scan score showed no predictive value for death (p > 0.05). WBC, Cr, CRP, ALP, and VBG-HCO3 were the most significant critical data associated with death prediction across all comorbidities (p < 0.05)., Conclusion: COVID-19 is particularly lethal for elderly adults; thus, age plays a crucial role in disease prognosis. Regarding death prediction, various comorbidities rank differently, with KD having a significant impact on mortality outcomes., Competing Interests: Declaration of Competing Interest I, Nazanin Mojtabavi, on behalf of all co-authors, hereby declare that there is no conflict of interest regarding the research presented in this manuscript entitled "[Retrospective Analysis of COVID-19 Clinical and Laboratory Data: Constructing a Multivariable Model Across Different Comorbidities]". I affirm that neither myself nor any of my co-authors have any financial, personal, or professional interests that could influence the interpretation or presentation of the research findings contained within this manuscript., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: The unique expression pattern of prostate stem cell antigen (PSCA) in a number of prevalent neoplasms has made the antigen a great target for cancer researches, and many clinical methods have been developed based on the application of this tumor marker. Hence, optimal PSCA laboratory production can be considered a hallmark for many researchers., Objective: An analytical study was designed to improve the quality and quantity of PSCA production., Materials and Methods: The effects of different compositions of lysis buffers and some ultrasound durations were assessed by calculation of the protein recovery followed by PSCA specific blotting experiments. Then, based on the results of the web-based characterization, interference removal, followed by re-solubilization of the protein in various buffers, was designed, applied, and assessed., Results: Since the selection of an appropriate methodology depends merely on the research purposes, we tried to discuss the pros and cons of the investigated methods according to the hydrophobic nature of PSCA as well as its dramatic tendency to aggregate in the form of inclusion bodies in the expression hosts., Conclusions: We introduced a newly designed method to fit the delicate immunological surveys and overcome some limiting factors in PSCA production., (Copyright: © 2021 The Author(s); Published by Iranian Journal of Biotechnology.)