Effect of filgrastim (Recombinant human granulocyte colony stimulating factor) on spatial memory in aged rats

Background: Apart from functioning as a multimodal hematopoietic growth factor, granulocyte colony-stimulating factor (G-CSF) causes intense consequences on the brain. It has been viewed that G-CSF boosts the improvement from the neurologic deficits in rodent models of central nervous system diseases. Aims and Objective: To evaluate the efficacy of G-CSF as an intervention for improving cognitive deficits commonly associated with aging. Materials and Methods: In this study, male Wistar rats aged 21 months were treated for 2 weeks with G-CSF intraperitoneally at doses of 10, 50, and 70 mg/kg/day. The learning process was assessed by the reference memory task in the Morris water maze, by comparing the G-CSF–treated group with the control animals. All the rats received Morris water maze training (four trials/day for 5 days) in order to assess the hippocampal-dependent spatial learning and received a 60-s probe trial test of spatial memory retention 24 h after the twentieth trial. Result: Over 5 days of training, G-CSF (10, 50, and70 mg/kg/day) significantly reduced the latency and path length to finding the escape platform (P < 0.01). In probe trials (without platform), on the last day of training, the G-CSF–treated group spent significantly longer time in the platform quadrant when compared with the control animals (P < 0.01). Among the treated groups, the 50-mg/kg dosage of G-CSF induced the best rehearsals memory. Conclusion: The findings observed in this study support G-CSF as a promising therapeutic aid for cognitive enhancement in the aging phenomenon. © 2015 Hamid Sepehri

Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework

Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users' quality-of-service (QoS) constraints, guaranteeing constraint satisfaction becomes a fundamental challenge. In this paper, we propose a novel unsupervised learning framework to solve the classical power control problem in a multi-user interference channel, where the objective is to maximize the network sumrate under users' minimum data rate or QoS requirements and power budget constraints. Utilizing a differentiable projection function, two novel deep learning (DL) solutions are pursued. The first is called Deep Implicit Projection Network (DIPNet), and the second is called Deep Explicit Projection Network (DEPNet). DIPNet utilizes a differentiable convex optimization layer to implicitly define a projection function. On the other hand, DEPNet uses an explicitly-defined projection function, which has an iterative nature and relies on a differentiable correction process. DIPNet requires convex constraints; whereas, the DEPNet does not require convexity and has a reduced computational complexity. To enhance the sum-rate performance of the proposed models even further, Frank-Wolfe algorithm (FW) has been applied to the output of the proposed models. Extensive simulations depict that the proposed DNN solutions not only improve the achievable data rate but also achieve zero constraint violation probability, compared to the existing DNNs. The proposed solutions outperform the classic optimization methods in terms of computation time complexity.Comment: accepted in IEEE Transactions on Communication

Production of polycolonal antibody against domain 2-4 of protective antigen of Bacillus anthracis in laboratory animals

زمینه و هدف: آنتراکس که مسبب آن باکتری باسیلوس آنتراسیس می‌باشد یک بیماری عفونی حاد است و اغلب در گیاه‌خواران و انسان اتفاق می‌افتد. فرم رویشی باسیلوس آنتراسیس یک اگزوتوکسین سه جزیی شامل آنتی ژن حفاظت کننده (Protective Antigen=PA)، فاکتور کشنده (Lethal Factor=LF) و فاکتور ادم (Edema Factor=EF) می‌باشد. آنتی ژن حفاظت کننده به عنوان یک ایمونوژن اولیه برای توسعه ایمنی حمایتی بر علیه آنتراکس بررسی شده است. این مطالعه با هدف تولید آنتی بادی علیه ناحیه 4-2 آنتی ژن حفاظت کننده این باکتری در حیوانات آزمایشگاهی طراحی و اجرا شده است. روش بررسی: در این مطالعه تجربی ناحیه 4-2 ژن PA از پلاسمید pXOI با جایگاه های آنزیمی BamHI و HindIII به روش PCR تکثیر و در وکتورها کلون و ساب کلون شد. وکتور (pET28a(+ در باکتری اشرشیاکلی سویه BL21(DE3) ترانسفورم گردید. بعد از القاء با IPTG، بیان پروتئین ژن PA مشاهده شد. پروتئین تخلیص شده در 4 نوبت به موش و خرگوش تزریق شد؛ سپس آنتی بادی تولید شده از سرم موش و خرگوش جداسازی و توسط آزمون الایزا تایید گردید. یافته ها: ناحیه 4-2 ژن PAکلون شده در وکتور بیانی pET28a(+) به وسیله ی توالی یابی، PCR، آنالیز آنزیمی، الکتروفورز در ژل پلی اکریل آمید و لکه گذاری وسترن مورد تأیید قرار گرفت. افزایش تیتر آنتی بادی در خون موش و خرگوش توسط آزمون الایزا تایید گردید. نتیجه گیری: با توجه به ایمونوژن بودن پروتئین PA، می‌توان از آن در طراحی واکسن و همچنین به عنوان ادجوانت قوی سیستم مخاطی استفاده نمود

Deep Unsupervised Learning for Network Resource Allocation Problems with Convex and Non-Convex Constraints

Deep neural networks (DNNs) are currently emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users' quality-of-service (QoS) constraints or base station quota, guaranteeing constraint satisfaction becomes a fundamental challenge. In this thesis, I propose a novel unsupervised learning framework to solve the classical power control and user assignment problem in a multi-user interference channel, where the objective is to maximize the network sum-rate with QoS, power budget, and base station quota constraints. The proposed method utilizes a differentiable projection function, defined both implicitly and explicitly, to project the output of the DNN to the feasible set of the problem. Extensive simulations depict that the proposed DNN solutions not only improve the achievable data rate, but also achieve zero constraint violation probability, compared to the existing DNNs, and also outperform the optimization-based benchmarks in computation time

Preoperative assessment of meningioma aggressiveness by Thallium-201 brain SPECT

Introduction: Meningioma is usually a benign brain tumor, but sometimes with aggressive course. The aim of this study was to assess the ability of 201Tl Brain SPECT to differentiate the pathologic grade of meningioma preoperatively. Methods: Thirty lesions in 28 patients were evaluated in this study. Early (20 minutes) and late (3 hours) brain SPECT images were performed and early uptake ratio (EUR), late uptake ratio (LUR) and retention index (RI) were calculated. All patients were operated and pathologic grade of tumors were defined according to World Health Organization grading system. Results: SPECT results were compared in different pathologic groups. Data analysis clarified no significant difference of EUR in benign and aggressive meningioma (P=0.2). However LUR and RI were significantly higher in aggressive tumors (P=0.001 and P=0.02, respectively). Conclusion: According to our data Tl-201 Brain SPECT with early and late imaging has 80 sensitivity and specificity to differentiate malignant from benign meningioma

Errors Related to Medication Reconciliation: A Prospective Study in Patients Admitted to the Post CCU

Abstract Medication errors are one of the important factors that increase fatal injuries to the patients and burden significant economic costs to the health care. An appropriate medical history could reduce errors related to omission of the previous drugs at the time of hospitalization. The aim of this study, as first one in Iran, was evaluating the discrepancies between medication histories obtained by pharmacists and physicians/nurses and first order of physician. From September 2012 until March 2013, patients admitted to the post CCU of a 550 bed university hospital, were recruited in the study. As a part of medication reconciliation on admission, the physicians/nurses obtained medication history from all admitted patients. For patients included in the study, medication history was obtained by both physician/nurse and a pharmacy student (after training by a faculty clinical pharmacist) during the first 24 h of admission. 250 patients met inclusion criteria. The mean age of patients was 61.19 ± 14.41 years. Comparing pharmacy student drug history with medication lists obtained by nurses/physicians revealed 3036 discrepancies. On average, 12.14 discrepancies, ranged from 0 to 68, were identified per patient. Only in 20 patients (8%) there was 100 % agreement among medication lists obtained by pharmacist and physician/nurse. Comparing the medications by list of drugs ordered by physician at first visit showed 12.1 discrepancies on average ranging 0 to 72. According to the results, omission errors in our setting are higher than other countries. Pharmacybased medication reconciliation could be recommended to decrease this type of error