Effect of oral genistein administration in early and late phases of allergic encephalomyelitis

Objective(s): Experimental allergic encephalomyelitis (EAE) is an autoimmune disease validated as animal model of multiple sclerosis (MS). Administration of genistein, a phytoestrogenic component of soy, to mice at the onset of EAE is known to attenuate the clinical signs of the disease. The potential effects of genistein on established EAE is less studied. In the current study, we aimed to compare the effects of genistein administration on EAE severity in early and late phases of the disease. Materials and Methods: The C57BL/6 mice were induced with EAE, using MOG 35-55 and gavaged with genistein (300 mg/kg) either after the appearance of the first clinical sign or 30 days post disease induction for ten days. 24 hr after the last gavage, mice were sacrificed. Brains and spleens were removed for assessing lymphocyte proliferation, cell cytotoxicity, and cytokine profile. Spinal cords were dissected to assess the amount of demyelination using Luxol fast blue/cresyl violet staining. Results: Administering mice with genistein, after the establishment of EAE, did not reverse the clinical signs of disease. However, treating with genistein at the onset of disease alleviated the clinical signs by reducing neuronal demyelination. Genistein suppressed the production of IFN-γ and enhanced IL-10 secretion in splenocyte and brain. Genistein also reduced IL-12 and TNF-α secretion in splenocytes, suppressed the proliferation of T-cells, and reduced the cell cytotoxicity. Conclusion: Genistein oral therapy might only reduce EAE severity if started in early phases of the disease

The Internet Use and Community Involvement in Tehran Iran

This is exploring the impact of the internet on local community involvement in Tehran, Iran. It investigates how the internet changes community involvement and argues that the Internet has created new forms of community involvement instead of local community involvement. This study has employed quantitative research methods. The sample for this research was drawn from the population of Internet users, namely people who accessed and used the Internet in Tehran, Iran. The results of the study indicate that there was no significant correlation between the amount of Internet use and local community involvement. People who spend more time online (high Internet user) do not have a greater local community involvement than people who use Internet less of the time. By contrast in terms of type of Internet use and social capital the study found that people who used the Internet for local news and reading newspapers online were more involved in the local community. The study illustrated that the Internet encourages people to some extent to become involved in the national or global community.nbs

Genistein induces a protective immunomodulatory effect in a mouse model of cervical cancer

Background: Genistein (GEN), a naturally occurring flavonoid present in soy bean, has attracted scientific interest for its possible benefits in cancer. Objective: The potential immunomodulatory effects of genistein on the immune system and against TC-1 tumor cell line were evaluated in adult female C57BL/6 mice. Methods: Mice were treated with GEN 10 days before to 10 days after the tumor induction. Thirty days after the last GEN treatment, lymphocyte proliferation, Lactase Dehydrogenase (LDH) cytolytic activity and cytokine secretion were analyzed in GEN and control groups. Results: The results showed that ingestion of genistein significantly increased lymphocyte proliferation and LDH release. Furthermore, the treatment with genistein also caused a significant increment in interferon gamma (IFN-γ). In addition, the treatment achieved significant therapeutic effect in tumor models compared to the control group. These results indicated that the effect of GEN on tumor growth may be attributed to its effect on lymphocyte proliferation, cytolytic activity and IFN-γ production. Conclusion: These results demonstrate that GEN exerts an immunomodulatory effect in a mouse model of Human Papillomavirus (HPV) associated-cervical cancer

Protection of mice by a λ-based therapeutic vaccine against cancer associated with human papillomavirus type 16

Objective: Human papillomavirus (HPV) oncoproteins (i.e. E6 and E7) are constitutively expressed in cervical cancer cells. The proteins are ideal targets to be used for developing therapeutic vaccines against existing HPV-associated carcinomas. To date, whole bacteriophage ('phage')-λ particles, rather than purified 'naked' DNA, have been described as highly efficient delivery vehicles for a DNA vaccine. Methods: In this study, a safe and efficient λ-based therapeutic cancer vaccine, recombinant λ-ZAP E7 phage, was developed by inserting a HPV16 E7 gene into the Lambda ZAP® cytomegalovirus vector. λ-ZAP E7 phages were employed to immunize mice against the E7-expressing murine tumor cell line (TC-1), which is used as a tumor model in an H-2b murine system. Results: The tumor-bearing mice indicated a significant inhibition of tumor growth after 3 injections of 2 × 1012 particles of recombinant phages. Released lactate dehydrogenase, interferon-γ and granzyme B from spleen cells and lymphocyte proliferation of spleen cells, which all demonstrate the enhancement of cell-mediated immunity, suggested the phages could be a potent gene delivery system in animal models. Conclusion: Our results suggest the recombinant phages can be used as effective biological tools for inducing E7-specific protective immune responses. Hence, the study introduces a possible therapeutic strategy against cervical cancer and other HPV-related neoplasia. Copyright © 2010 S. Karger AG, Basel

Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions

Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality. We use a Bayesian probabilistic approach to estimate the anatomy and to quantify the uncertainty about the shape of the left atrium derived from Cardiac Magnetic Resonance images. We show that we can quantify uncertain shape, encode uncertainty about the left atrial shape due to imaging artefacts, and quantify the effect of uncertain shape on simulations of left atrial activation times

Theoretical investigation of InAs/GaSb type-II pin superlattice infrared detector in the mid wavelength infrared range

In this study, we present the theoretical investigation of type-II InAs/GaSb superlattice p-i-n detector. Kronig-Penney and envelope function approximation is used to calculate band gap energy and superlattice minibands. Variational method is also used to calculate exciton binding energies. Our results show that carriers overlap increases at GaSb/InAs interface on the higher energy side while it decreases at InAs/GaSb interface on the lower energy side with increasing reverse bias due to shifting the hole wavefunction toward to the GaSb/InAs interface decisively. Binding energies increase with increasing electric field due to overall overlap of electron and hole wave functions at the both interfaces in contrast with type I superlattices. This predicts that optical absorption is enhanced with increasing electric field. © 2013 American Institute of Physics

Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

Purpose: Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images. Methods: Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center). Results: In terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21–14.81%) and FL-PL (CI:11.82–13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32–12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34–26.10%). Furthermore, the Mann–Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R2 = 0.94), FL-SQ (R2 = 0.93), and FL-PL (R2 = 0.92), while CB model achieved a far lower coefficient of determination (R2 = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed. Conclusion: Deep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers