The role of metalloproteinase and hypoxia conditions in endometrial cells and embryo implantation

In the process of implantation, metalloproteinase enzymes play a key role in basement membrane degradation and endometrial extracellular matrix. The activity of these enzymes is impeded by binding Tissue Inhibitors of Metalloproteinase (TIMP). The oxygen concentration in the mammalian uterus at the time of implantation is about 2-5%. It is seen that the imposition of hypoxia on cancer cells increases the expression of metalloproteinase enzymes and reduces the expression of metalloproteinase inhibitors, resulting in increased cell invasion. To know the effect of Hypoxia-Inducible Factor (HIF) and other related factors, we decided to evaluate hypoxic conditions on endometrial epithelial cells of the uterus and roll of matrix metalloproteinases (MMPs) on angiogenesis and invasion of the embryo during implantation. In this study, human and mouse endometrial epithelial cells were incubated for 24-48 hours in hypoxic conditions. Subsequently, the expression level of TIMP-1 was measured in mouse and human epithelial cells by Real-Time PCR technique. The cell viability in hypoxic conditions was evaluated by MTT assay. Our results demonstrated that hypoxia reduced the quantitative gene expression of TIMP-1 in the human and mouse endometrial epithelial cells compared to the control group. It can be concluded that applying hypoxic conditions by reducing the TIMP-1 expression and consequently increasing MMP expression, may improve the embryo implantation rate

The Effects of an Herbal Mixture on the Clinical Symptoms of Women with Polycystic Ovary Syndrome

Background & Objective: Polycystic ovary syndrome is the most common endocrine disorder in women. Multiple-drug treatments and prolonged treatments often cause patients to discontinue the treatment. Considering the side-effects of chemical drugs, the present study aimed to investigate the effects of the combination of cumin and fennel extract on the clinical symptoms of women with polycystic ovary syndrome. Materials and Methods: This clinical trial was conducted on 70 patients with polycystic ovary syndrome, who were selected randomly from the patients referring to the teaching hospitals in Shahrekord, Iran. The patients were randomly divided into two groups of intervention and control. The intervention group received capsules containing fennel and black cumin, and the control group received placebo twice per day for four months. Before and after the intervention, the clinical symptoms of the subjects were evaluated in both groups. Data analysis was performed in SPSS. Results: In the intervention group, a significant reduction was observed in hirsutism, while the menstrual duration increased compared to the control group (P0.05). Conclusion: According to the results, the combination of fennel and cumin extract could effectively improve the clinical symptoms of the patients with polycystic ovary syndrome. Therefore, the herbal mixture could be used as a non-toxic medication for the treatment of these patients. Keywords: Cumin, Fennel, Polycystic Ovary Syndrome, Clinical Symptom

Effects of combination therapy with Bunium persicum and Foeniculum vulgare extracts on patients with polycystic ovary syndrome

Background: Considering the side effects of common drugs used to treat polycystic ovary syndrome (PCOS), researchers have turned their attention to natural compounds, including medicinal plants. Foeniculum vulgare has estrogenic properties and has been traditionally used to treat gynecological disorders. Bunium persicum has medical aspects that have not yet been evaluated, so the aim of this study was to evaluate the effects of combination therapy with these extracts on clinical symptoms of women with PCOS. Materials and Methods: In this double-blind clinical trial study, 70 women with PCOS referred to infertility clinics, were selected and randomly divided into two groups. The intervention group received B. persicum capsule 60 mg plus F. vulgare capsule 25 mg) twice daily for 4 months and the control groups received routine intervention. Before and after the intervention, levels of luteinizing hormone (LH), follicle-stimulating hormone, progesterone, prolactin, testosterone and dehydroepiandrosterone sulfate (DHEAS) levels, hirsutism score, and menstrual pattern were recorded and endometrial thickness and follicle count were determined by ultrasound. Data were analyzed by the SPSS21 software. Results: Treatment with B. persicum and F. vulgare extracts significantly decreased LH and DHEAS levels, hirsutism score, and significantly increased menstrual duration compared to the control group. Before the intervention, 5.7% of the intervention and control groups had the normal menstrual pattern, while after the intervention 31.4% of the intervention group and 25.7% of the control group had the normal pattern. Conclusion: Regarding the effect of these extracts combination and because they have no side-effects, which is a great advantage over chemical drugs, using of these plants recommend

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

<p>Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.</p&gt