KE Sharquie, HM Al-Hamamy, AA Noaimi, IA Al-Shawi, Journal of the Saudi Society of Dermatology & Dermatologic Surgery, 2011 - Cited by 9

The present study was aimed to find out the role of humoral immunity in the pathogenesis of psoriasis. Complements C3, C4 and immunoglobulin IgE .The study included 55 Iraqi patients with psoriasis 30 (15 females ,15 males) were untreated with any drugs. The other patient group consisted of 25 (9 female and 16 male) treated with a biological treatment (infliximab) ,and 30 (13 males ,12 females) healthy control group. Blood sample were withdrawn (5) ml of venous blood for both patients and members of the control ,to conduct the Immunological tests to determine the quantitative for each of total IgE by using (ELISA) and C3,C4 by Single Radial Immunodiffuse (SIRD). The results showed significant increase in the level of probability (P <0.0

Osteoblast and osteoclast activity is disrupted in post-menopausal osteoporosis. Thus, to fully address this imbalance, therapies should reduce bone resorption and promote bone formation. Dietary factors such as phyto-oestrogens and Zn have beneficial effects on osteoblast and osteoclast activity. However, the effect of combinations of these factors has not been widely studied. We therefore examined the effect of coumestrol, daidzein and genistein in the presence or absence of zinc sulphate (Zn) on osteoclast and osteoblast activity. Osteoclast differentiation and bone resorption were significantly reduced by coumestrol (10- 7 m), daidzein (10- 5 m) and genistein (10- 7 m); and this direct anti-osteoclastic action was unaffected by Zn (10-

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi