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Background & objectives: Breast cancer is one of the most important cancers in women worldwide. Taxol as a chemotherapeutic agent, is used for treatment of breast cancer.The aim of this study was to investigate alterations in the expression of mir-1246 and mir-224 in four breast cancer cell lines after Taxol treatment with the goal of introducing them as a biochemical marker for determining response or resistance of breast cancer to the Taxol therapy.
Methods: In this in vitro study, four breast cancer cell lines including MCF-7, MDA-MB-231, SKBR-3 and BT-474 were cultured in RPMI1640 medium supplemented with 10% FBS and antibiotics. Then, MTT assay was performed to determine IC50 concentration of Taxol. Cells were treated for 24 hours and then RNA extraction and cDNA synthesis were performed. Alterations in the expression level of mir-1246 and mir-224 were quantitated using qRT- PCR.
Results: After treatment with Taxol, the expression level of mir-1246 was significantly up-regulated in two HER2-overexpressing cell lines, BT-474 (113 fold) and SKBR-3 (1.4 fold), and down-regulated in two HER2-negative cell lines, MCF-7 (45.5 fold) and MDA-MB-231 (7.7 fold). Expression of mir-224 was detected only in two cell lines including SKBR-3 and MDA-MB-231, and was down-regulated after treatment with Taxol (2.1 and 17.2 fold, respectively).
Conclusions: According to the different pattern of alteration in the expression level of mir-1246 in HER2-overexpressing breast cancer cell lines compared to HER2-negative cell lines after treatment with Taxol, this miRNA could be a useful biomarker for responsiveness to Taxol in  different types of HER2-positive and -negative breast cancers.
 

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Introduction & objectives: Application of traditional medicine and identification of herbs to treat cancer are being on the rise. Little information is available on the anticancer effects of Smyrnium cordifolium bioss species. For this purpose, the present study investigated the cytotoxic and apoptotic effects of the alcoholic extract of S. cordifulium.
Methods: After preparing the plant and its alcoholic extract, different concentrations of the extract (0, 2, 10, 50 and 250 μg/ml) were added to the culture medium of MCF-7 cells. MTT assay was used to evaluate cytotoxicity of different extract concentrations. In addition, acridine orange-ethidium bromide staining was used to assess apoptosis rates. Data was analyzed by SPSS software at the significance level of 5%.
Results: the results of this study showed that S. cordifolium extract at 250μg/ml concentration had a more inhibitory effect on proliferation compared to other treatment groups. Moreover, this concentration (250μg/ml) had a significant effect on apoptosis in comparison with other concentrations.
Conclusion: In conclusion, it seems that alcoholic extract of S. cordifolium can partially reduce proliferation of cancer cells.
 

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Background & objectives: There is evidence that COVID-19 may have some long-term effects on the patients' blood biochemical and hematological factors. The purpose of this study was to investigate and compare the changes in biochemical and hematological factors in adults with and without past history of COVID-19.
Methods: The present study is a cross-sectional study including 72 cases with a past history of COVID-19 (PCR+ test) and 52 controls without a history of COVID-19. All participants were from two prospective cohort studies, namely Yazd Health Study (YaHS) and Shahedieh Cohort Study. Biochemical parameters such as blood glucose, lipid profiles, liver, kidney and hematological factors of cases and controls were extracted from the databases and analyzed.
Results: The mean red blood cell count in patients with COVID-19 was significantly higher than in non-COVID-19 patients (p<0.05), and mean corpuscular volume (MCV) was significantly lower in patients with COVID-19 (p>0.001). However, no significant difference was found between the two groups in terms of lipid and kidney profiles and liver enzymes.
Conclusion: No significant difference was found between the two groups of patients exposed to COVID-19 and patients not exposed to COVID-19 in terms of liver enzymes, lipid and kidney profiles, which probably indicate that COVID-19 has no long-term effect on these variables. However, a significant difference was observed between the two groups (p<0.05) regarding the number of red blood cells and MCV.
 

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A severe acute respiratory syndrome caused by coronavirus 2 infection (COVID-19)was identified in December 2019 and caused a massive pandemic that claimed millions of lives worldwide. The Chimpanzee adenovirus vector vaccine is one of the few vaccines against coronavirus disease in 2020. This case report introduces, a patient who developed thrombocytopenia, fever, and lethargy nine days after vaccination with the chimpanzee adenovirus carrier vaccine. The patient was admitted to the hospital and thrombotic thrombocytopenic purpura (TTP) symptoms appeared during hospitalization.

 

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Immunotherapy has emerged as a promising and effective approach in cancer treatment by stimulating the body’s immune system to target and eliminate malignant cells. Despite its significant therapeutic potential, several challenges remain, including accurate patient selection, identification of appropriate therapeutic targets, and the minimization of adverse effects.
Artificial intelligence (AI) plays a critical role in addressing these challenges by analyzing complex genomic, proteomic, and clinical datasets. Machine learning and deep learning algorithms can accurately identify patients likely to respond to immunotherapy, enabling the development of personalized treatment plans while avoiding unnecessary interventions in low-response individuals.
A key application of AI is predicting the efficacy of immune checkpoint inhibitors such as PD-1 and CTLA-4. By integrating medical imaging and genomic data, AI models can forecast treatment outcomes, enhance diagnostic precision, and reduce healthcare costs. Furthermore, AI is increasingly used in drug development, where it simulates novel molecular structures and predicts their therapeutic efficacy, thereby accelerating drug discovery and lowering development expenses. AI also contributes to identifying and managing side effects, improving the safety profile of immunotherapy.
Nevertheless, the implementation of AI in oncology is not without limitations. These include the need for high-quality, annotated datasets, algorithmic interpretability, and ethical concerns such as data privacy, algorithm transparency, and psychological impacts of extensive genetic testing, excessive diagnostic testing, potential treatment discrimination, and unclear legal responsibilities.
This article concludes that with robust data infrastructure and the advancement of interpretable AI models, the full potential of AI in cancer immunotherapy can be realized. This synergy promises a major leap toward precision medicine and a brighter future in cancer care.
 

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