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Abstract Background: The study was planned to explain the interactions of isoflavones with estrogen receptors alpha (ERα), beta (ERβ) and vascular endothelial growth factor (VEGF) expressions in ovarian and uterine tissues during different stages of estrous cycle of regular cyclic female Wistar rats. Methods: Thirty-two regular cyclic females were divided equally into control group: fed casein-based diet and isoflavones group: fed casein-based diet and gavaged 50 mg/kg/day soy isoflavones extract 40%. Regularity of estrus cycles was monitored. Final body weight (FBW), weight gain (BWG), ovarian and uterine weights were estimated. Histopathology and immunohistochemistry for ERα, ERβ and VEGF in ovarian and uterine tissues were performed. Results: All females (100 %, n=16) in control group showed regularity in estrous cycle compared to 62.5% (n=10) in isoflavones group. Estrus and diestrus phases revealed prolongation and shortening in isoflavones rats than control, respectively. Non-significant variation was noted in the duration of the whole cycle of both groups. FBW and BWG significantly decreased however, ovarian and uterine weights increased significantly in all estrous phases of isoflavones group than control. Histopathology demonstrated increase in number of follicles/ ovaries besides, hyperplasia and proliferation of luminal epithelium with hydropic degeneration in isoflavones group. Also, uterine connective tissue stroma showed edema in isoflavones group during all estrous phases. Immunostaining percentages of ERα, ERβ and VEGF protein expression significantly elevated in isoflavones group during all estrous phases. Conclusion: Isoflavones induced irregularity of estrous cycle that encountered by increased and altered ERα, ERβ and VEGF expressions in ovarian and uterine tissues. [Open Vet J 2022; 12(5.000): 639-648]

Atopic dermatitis (AD) is a common itching disorder that begins in infancy and may occur in persons with a family history of atopic disease. The disease is characterized by several changes in the epidermal layer with elevated serum immunoglobulin E (IgE) antibodies and histamine. Until now, there is no evident treatment for AD disease; however, topical therapy including topical corticosteroids has been used in both children and adults. Although the preferable route is the topical route, the low penetration across the stratum corneum (SC) layer is the great challenge for researchers and scientists. Nowadays the available drugs have severe side effects and low skin availability. Nanocarriers including liposomes, nanoparticles, nano-mixtures, nanogels, nano-emulsions and others, offer a good solution to these problems. Nanocarriers enable treatment of different forms of dermatitis, enhance drug bioavailability at the site of inflammation, reduce the side effects and increase the safety profiles. Nanoparticulate systems can possibly enhance topical medication delivery because of their ability to upgrade the drug loading, dissolution, and protect the unstable drug from degradation. This review offers an overview of AD types, different management modalities, systemic versus topical treatment, in addition to different types of lipid based nanocarriers that have been investigated for the management of AD.

The present case study describes our experience in treating a young woman diagnosed with a relapsing case of diffuse large cell lymphoma, who was heavily pre-treated with chemotherapy and radiotherapy. Our only chance to improve her survival was by using high-dose chemotherapy, followed by peripheral stem cell rescue. Unfortunately, in this patient, collecting sufficient stem cells for bone marrow transplantation proved to be very difficult since she had already been heavily treated with chemotherapy and radiotherapy. Currently, granulocyte colony-stimulating factor (G-CSF) alone or G-CSF plus chemotherapy are the most commonly used treatments for stem cell mobilization. However, 5–30% of patients do not respond to these agents. Plerixafor is a new hematopoietic stem cell-mobilizing drug that antagonizes the binding of chemokine stromal cell-derived factor-1α to CXC chemokine receptor 4. It is indicated in combination with G-CSF to mobilize hematopoietic stem cells to the peripheral blood for collection and subsequent autologous transplantation in patients with non-Hodgkin’s lymphoma and multiple myeloma [Kessans et al.: Pharmacotherapy 2010;30:485–492; Jantunen: Expert Opin Biol Ther 2011;11:1241–1248]. Based on our findings, we consider plerixafor to be a very efficient and practical solution to mobilize and collect stem cells among all patients in such a situation, enabling us to proceed to autologous bone marrow transplantation and peripheral stem cell rescue in order to improve the patients’ overall survival.

The increasing rates of neurodevelopmental disorders (NDs) are threatening pregnant women, parents, and clinicians caring for healthy infants and children. NDs can initially start through embryonic development due to several reasons. Up to three in 1000 pregnant women have embryos with brain defects; hence, the primitive detection of embryonic neurodevelopmental disorders (ENDs) is necessary. Related work done for embryonic ND classification is very limited and is based on conventional machine learning (ML) methods for feature extraction and classification processes. Feature extraction of these methods is handcrafted and has several drawbacks. Deep learning methods have the ability to deduce an optimum demonstration from the raw images without image enhancement, segmentation, and feature extraction processes, leading to an effective classification process. This article proposes a new framework based on deep learning methods for the detection of END. To the best of our knowledge, this is the first study that uses deep learning techniques for detecting END. The framework consists of four stages which are transfer learning, deep feature extraction, feature reduction, and classification. The framework depends on feature fusion. The results showed that the proposed framework was capable of identifying END from embryonic MRI images of various gestational ages. To verify the efficiency of the proposed framework, the results were compared with related work that used embryonic images. The performance of the proposed framework was competitive. This means that the proposed framework can be successively used for detecting END.

Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and premature infants, with fewer studies focusing on images for fetuses. These studies associated fetal scans to scans after birth for the detection and classification of brain defects early in the neonatal age. This type of brain abnormality is named small for gestational age (SGA). This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born). As far as we could know, this is the first study to classify brain abnormalities of fetuses of widespread gestational ages (GAs). The study incorporates several machine learning classifiers, such as diagonal quadratic discriminates analysis (DQDA), K-nearest neighbour (K-NN), random forest, naïve Bayes, and radial basis function (RBF) neural network classifiers. Moreover, several bagging and Adaboosting ensembles models have been constructed using random forest, naïve Bayes, and RBF network classifiers. The performances of these ensembles have been compared with their individual models. Our results show that our novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs. Using the KNN classifier, we were able to achieve the highest classification accuracy and area under receiving operating characteristics of 95.6% and 99% respectively. In addition, ensemble classifiers improved the results of their respective individual models.