An Investigation of the Pace of the Story Time in the Narrative of “Cloudy Years” Novel by Ali-Ashraf Darvishian

“Cloudy Years” novel by Ali-Ashraf Darvishian written in 4 volumes and 1622 pages and its events contain about 33 years of hero-narrator’s life. In the article, the speed of passing the story events through the author’s narrative is measured on the basis of Gerard Genette’s opinions. Genette utilizes order, duration, and frequency categories to measure the tempo of a novel. The study and survey of the existent specimens in all the novel demonstrate that the order of the events of the novel is more lineal, even though analepses and prolepses would be seen in the narrative, and evaluating the duration of the story and narrative makes clear that the average of 7/42 days of story exhibited in every page of the novel. The comparison between the average duration of every volume of the novel with the average duration of the novel proves negative, positive, positive and negative fluctuations in every volume of the novel, in turn. The acquired statistics of how being repeated the events of story approve also the more number of singulative frequencies which has a significant relationship with more lineal passing the narrative. The findings and conclusion of this research present new points in its theoretical framework

Applying novel machine learning technology to optimize computer-aided detection and diagnosis of medical images

The purpose of developing Computer-Aided Detection (CAD) schemes is to assist physicians (i.e., radiologists) in interpreting medical imaging findings and reducing inter-reader variability more accurately. In developing CAD schemes, Machine Learning (ML) plays an essential role because it is widely used to identify effective image features from complex datasets and optimally integrate them with the classifiers, which aims to assist the clinicians to more accurately detect early disease, classify disease types and predict disease treatment outcome. In my dissertation, in different studies, I assess the feasibility of developing several novel CAD systems in the area of medical imaging for different purposes. The first study aims to develop and evaluate a new computer-aided diagnosis (CADx) scheme based on analysis of global mammographic image features to predict the likelihood of cases being malignant. CADx scheme is applied to pre-process mammograms, generate two image maps in the frequency domain using discrete cosine transform and fast Fourier transform, compute bilateral image feature differences from left and right breasts, and apply a support vector machine (SVM) method to predict the likelihood of the case being malignant. This study demonstrates the feasibility of developing a new global image feature analysis based CADx scheme of mammograms with high performance. This new CADx approach is more efficient in development and potentially more robust in future applications by avoiding difficulty and possible errors in breast lesion segmentation. In the second study, to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, I investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. To this purpose, a computer-aided image processing scheme is applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, an embedded LLP algorithm optimizes the feature space and regenerates a new operational vector with 4 features using a maximal variance approach. This study demonstrates that applying the LPP algorithm effectively reduces feature dimensionality, and yields higher and potentially more robust performance in predicting short-term breast cancer risk. In the third study, to more precisely classify malignant lesions, I investigate the feasibility of applying a random projection algorithm to build an optimal feature vector from the initially CAD-generated large feature pool and improve the performance of the machine learning model. In this process, a CAD scheme is first applied to segment mass regions and initially compute 181 features. An SVM model embedded with the feature dimensionality reduction method is then built to predict the likelihood of lesions being malignant. This study demonstrates that the random project algorithm is a promising method to generate optimal feature vectors to improve the performance of machine learning models of medical images. The last study aims to develop and test a new CAD scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia. To this purpose, the CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and two filtered images are used to form a pseudo color image. This image is fed into three input channels of a transfer learning-based convolutional neural network (CNN) model to classify chest X-ray images into 3 classes of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. This study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an essential role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia. In summary, I developed and presented several image pre-processing algorithms, feature extraction methods, and data optimization techniques to present innovative approaches for quantitative imaging markers based on machine learning systems in all these studies. The studies' simulation and results show the discriminative performance of the proposed CAD schemes on different application fields helpful to assist radiologists on their assessments in diagnosing disease and improve their overall performance

Spirituality in medical education: a choice or a necessity?

Increased attention to spirituality in health issues in recent years has led to the recognition of the need to incorporate spirituality in medical education. Accordingly, healthcare providers need educational interventions and related curricula designed and implemented to develop their spiritual competencies. As a result of the perceived need to integrate spirituality into medical education and as a response to this need, spirituality has been addressed by medical schools throughout the world. Nevertheless, few medical schools have integrated spirituality into their curriculum as a core and mandatory course. Students in different disciplines of healthcare are required to pass spirituality courses to understand the role of spirituality in the health of their patients (knowledge), appreciate and favor the inclusion of spirituality in their healthcare and be adequately motivated (attitude), and gain the required skills and abilities to address the spiritual needs of their clients (practice) in a reasonable and satisfactory level. Several studies suggest the integration of spirituality in both undergraduate and postgraduate programs as mandatory courses. So, it seems necessary to integrate spirituality into medical education as required rather than in optional courses

Comparing the effect of Metoclopramide and Ketamine as a preemptive analgesia on postoperative pain

زمینه و هدف: جهت کنترل و یا کمک به کاهش درد پس از عمل جراحی از داروها و روشهای مختلف استفاده می شود. آنالژزی پیشگیرانه (Preemptive Analgesia) یکی از روشهایی است که در آن قبل از شروع جراحی از تزریق داروهای ضد درد نظیر مخدرها، کتامین و... استفاده می شود. در برخی از تحقیقات از متوکلوپرامید وریدی جهت کاهش درد پس از عمل جراحی استفاده شده است. این مطالعه با هدف بررسی تاثیر مقایسه ای تزریق داخل وریدی دو داروی کتامین و متوکلوپرامید نیم ساعت قبل از القای بیهوشی بر روی درد و میزان مصرف مخدر پس از جراحی انجام شد. روش بررسی: در این مطالعه کارآزمایی بالینی دو سویه کور تعداد 86 بیمار کلاس 1 و 2 بیهوشی کاندیدای عمل جراحی شکم تحت بیهوشی عمومی، به صورت در دسترس انتخاب و بطور تصادفی ساده به دو گروه تقسیم شدند. در گروه اول متوکلوپرامید (mg 10) و در گروه دوم کتامین (mg/kg 3/0) نیم ساعت قبل از القای بیهوشی به صورت داخل وریدی تزریق شد. نمره درد، میزان مصرف مخدر در ریکاوری و 24 ساعت اول پس از عمل جراحی، طول مدت اقامت در ریکاوری، زمان خروج لوله تراشه و عوارض مختلف از قبیل تهوع، استفراغ، بیقراری، عوارض روانی و...ارزیابی و ثبت گردید. اطلاعات با استفاده از آزمون های آماری کای اسکوار، t و آنالیز واریانس مشاهدات تکرار شده تجزیه و تحلیل گردید. یافته ها: میانگین نمره درد (VAS) در 24 ساعت اول پس از عمل جراحی در گروه متوکلوپرامید 04/3±98/3 و در گروه کتامین 32/3±93/5 بود (05/0

The study of relationship between post dural puncture headache and hemodynamic fluctuation in patients undergoing spinal anesthesia

زمینه و هدف: به اعتقاد بسیاری از محققین علت بروز سردرد بعد از سوراخ شدن سخت شامه، نشت مایع مغزی نخاعی (CSF= Cerebrospinal Fluid) و کاهش فشار داخل مغز (ICP= Intracranial Pressure) است. تغییرات همودینامیک می تواند بر دینامیک CSF و وضعیت ICP تأثیر گذار باشد. مطالعه حاضر با هدف تعیین ارتباط نوسانات فشار خون، نبض، میزان مایع دریافتی و افدرین مصرفی با سردرد بعد از سوراخ شدن سخت شامه طراحی و اجراء شده است. روش بررسی: این مطالعه به صورت توصیفی - تحلیلی بر روی 95 بیمار با شکستگی ساق پا، نامزد بیهوشی نخاعی، با استفاده از سوزن نوعQuincke شماره 23 انجام شد. فشار خون و نبض در دقایق 1، 2، 4، 8 و 16 پس از انجام بیهوشی نخاعی اندازه گیری و ثبت شد. همچنین جمع مایع وریدی و افدرین دریافتی نیز محاسبه و ثبت گردید. سپس بروز، شدت (بر اساس پرسشنامه VAS=Visual Analog Scale) و مدت سردرد (روز) تا 5 روز بعد از انجام بیهوشی مورد بررسی قرار گرفت. در پایان ارتباط میزان بروز و شدت سردرد با نوسانات فشار خون، نبض، میزان مایع وریدی دریافتی و افدرین مصرفی با استفاده از آزمون های t مستقل و پیرسون مورد تجزیه و تحلیل قرار گرفت. یافته ها: از بیماران مورد مطالعه 3/33 دچار سردرد بعد از سوراخ شدن دورا شدند. میانگین شدت و طول مدت سردرد به ترتیب 11/2±83/5 (سانتیمتر) و 40/1±66/3 (روز) بود. بین درصد نوسانات فشارخون سیستول، دیاستول، متوسط شریانی، تعداد نبض، میزان مایع دریافتی و افدرین مصرفی با میزان بروز و شدت سردرد متعاقب بیهوشی نخاعی، رابطه معنی داری بدست نیامد. نتیجه گیری: عدم ارتباط معنی دار بین نوسانات همودینامیکی با میزان بروز و شدت سردرد می تواند نشان دهنده این موضوع باشد که علیرغم تأثیر عوامل همودینامیک بر دینامیک و وضعیت ICP، این عوامل احتمالاَ از قدرت کافی جهت تغییر در میزان نشت CSF از سوراخ دورا برخوردار نبوده و عامل تعیین کننده اصلی همان اندازه و شکل سوراخ ایجاد شده توسط سوزن های نخاعی است

General Principles of the Medical Management of Epilepsy in Children: A Literature Review

The primary aim of epilepsy treatment is seizure control, and the treatment is principally prophylactic. Although complete seizure control is the most important predictor of improved quality of life, antiepileptic drugs (AEDs) could cause severe side effects in the patients. Therefore, the risk-benefit ratio must be considered before the initiation of AED treatment. Accurate recognition and differentiation of epileptic and non-epileptic paroxysmal events and the diagnosis of the seizure type and epilepsy syndrome are essential procedures before AED treatment. It is often recommended that AED treatment start after two seizures, and being seizure-free for a minimum of two years is a prerequisite for treatment withdrawal. The AED treatment process must be initiated with a single drug at a low maintenance dose, along with further upward titration. Overall, the first attempt in AED treatment has been reported to effectively control seizures in 50-70% of the cases. Moreover, there is a consensus that being seizure-free for two years is the most valid approach to discontinue AED treatment. Approximately 50% of the children with epilepsy outgrow their disease. The present study aimed to provide a systematic method for the treatment and management of epilepsy in children