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

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

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

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

Molecular Absorption-Aware User Assignment, Spectrum, and Power Allocation in Dense THz Networks with Multi-Connectivity

This paper develops a unified framework to maximize the network sum-rate in a multi-user, multi-BS downlink terahertz (THz) network by optimizing user associations, number and bandwidth of sub-bands in a THz transmission window (TW), bandwidth of leading and trailing edge-bands in a TW, sub-band assignment, and power allocations. The proposed framework incorporates multi-connectivity and captures the impact of molecular absorption coefficient variations in a TW, beam-squint, molecular absorption noise, and link blockages. To make the problem tractable, we first propose a convex approximation of the molecular absorption coefficient using curve fitting in a TW, determine the feasible bandwidths of the leading and trailing edge-bands, and then derive closed-form optimal solution for the number of sub-bands considering beam-squint constraints. We then decompose joint user associations, sub-band assignment, and power allocation problem into two sub-problems, i.e., \textbf{(i)} joint user association and sub-band assignment, and \textbf{(ii)} power allocation. To solve the former problem, we analytically prove the unimodularity of the constraint matrix which enables us to relax the integer constraint without loss of optimality. To solve power allocation sub-problem, a fractional programming (FP)-based centralized solution as well as an alternating direction method of multipliers (ADMM)-based light-weight distributed solution is proposed. The overall problem is then solved using alternating optimization until convergence. Complexity analysis of the algorithms and numerical convergence are presented. Numerical findings validate the effectiveness of the proposed algorithms and extract useful insights about the interplay of the density of base stations (BSs), Average order of multi-connectivity (AOM), molecular absorption, {hardware impairment}, {imperfect CSI}, and link blockages.This paper has been accepted for publication in IEEE journal

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

Oncologic outcomes following radical nephroureterectomy for upper tract urothelial carcinoma: a literature review

Background and Objective: Radical nephroureterectomy (RNU) represents the gold standard treatment for non-metastatic upper tract urothelial cancer. We sought to provide a comprehensive review of reported oncologic outcomes of the RNU procedure and of factors that might impact these outcomes. Methods: A non-systematic review of the literature was conducted by performing an electronic literature search using PubMed with "radical nephroureterectomy"and "oncologic outcomes"as free text search terms. Both original articles and systematic reviews were considered. Search was limited to articles in English that were published in the last 20 years. Key Content and Findings: Open and laparoscopic RNU offer comparable oncologic outcomes. In more recent years, the discussion has de facto shifted towards the "oncological safety"of robotic RNU, which also seems to offer comparable oncologic outcomes. Several studies have looked at the impact of different treatment-, patient- and tumor-related factors. Among treatment-related factors, attention has been given to diagnostic ureteroscopy and the risk of intravesical recurrence. Surgical wait time and perioperative blood transfusion have also been studied. Perioperative chemotherapy, specifically adjuvant therapy, was shown to improve survival. Among patient-related factors, baseline chronic kidney disease, diabetes mellitus, body mass index, and systemic inflammation have gained recent attention. Some tumor related factors, such as stage, grade, location, and multifocality may negatively impact survival outcomes. Lymphovascular invasion and histologic variants are clinically significant pathological findings. Conclusions: RNU is a procedure with measured long-term oncologic outcomes. Minimally invasive techniques have gained an established role as they seem to offer comparable oncologic "safety", although special attention is needed in relation to the method of bladder cuff excision. Robotic RNU is gaining popularity, and while evidence remains limited, the current literature supports the oncologic safety of this procedure. Several factors, which can be categorized as treatment-related, patient-related, and tumor-related, might impact the oncologic outcomes of UTUC patients undergoing RNU. These factors can provide crucial information to stratify patients based on their relative risk of disease recurrence and mortality which may guide clinical decision-making