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تُو کیوں اُس کو سوچ رہا ہے

تُو کیوں اس کو سوچ رہا ہے
وہ تو تجھ کو بھول چکا ہے

دل میں کیسا خوف بھرا ہے
پھول کھلے تو ڈر لگتا ہے

گئی رتوں میں تلاش کرے گا
آج وہ جس کو چھوڑ رہا ہے

میں کہتا ہوں اُسے بھلا دے
یہ کیا روگ لگا بیٹھا ہے

یادیں تو بس بوجھ ہیں دل کا
اور یادوں میں کیا رکھّا ہے

کوئی جو پوچھے حال مرا تو
کہہ دیتا ہوں سب اچھا ہے

پتا پتا ڈالی ڈالی
کس کے غم میں زرد ہوا ہے

ہر سُو پھیلا خوف کا عالَم
خوف یہ کیسے پھیل گیا ہے

صادقؔ تیرا مسئلہ کیا ہے
تو کیوں ماضی میں رہتا ہے

Efficacy of Atorvastatin Plus Pegylated Interferon and Ribavirin Versus Pegylated Interferon and Ribavirin Alone in Chronic Hepatitis C Patients with Genotype-3a Atorvastatin for the treatment of chronic HCV with Genotype-3a

Background: Chronic hepatitis C infection has created a huge burden of disease causing serious health effects. The combination therapy used to treat hepatitis C virus (HCV) infection includes Pegylated interferon and Ribavirin. As cholesterol biosynthesis plays a pivotal role in HCV replication, the use of various statins has been associated with higher sustained viral response Objective: To compare the efficacy of atorvastatin plus pegylated interferon and ribavirin versus pegylated interferon and ribavirin alone in patients of chronic hepatitis C with genotype-3a Methods: This Randomized controlled trial was conducted at outpatient department, Mayo Hospital Lahore for six months i.e. May to November 2017. After ethical approval, 60patients of ages 25 to 55 years of either gender with chronic hepatitis C with genotype 3a were included in the study. Informed consent was taken from all patients. Then patients were randomly allocated into two groups “A” and “B” using random number table. Patients in Group A received standard of care treatment for chronic hepatitis C i.e. Pegylated interferon and ribavirin while the patients in Group B also received tab atorvastatin along with the standard treatment. Patients were follow up for 4 week. Blood samples were collected and HCV RNA detection. All this information were entered in proformaResults: In standard therapy group, the mean age of patients was 39.50±8.39years. In atorvastatin plus standard therapy group, the mean age of patients was 34.30±6.78years. In standard therapy group, there were 25 (83.3%) males and 5 (16.7%) females. In atorvastatin plus standard therapy group, there were 16 (53.3%) males and 14 (46.7%) females. After 4 weeks, Rapid VirologicalResponse (RVR) was achieved in 4 (13.3%) patients in standard therapy group while in 14 (46.7%) in atorvastatin plus standard therapy group. The difference was significant (p<0.05) Conclusions: Atorvastatin in combination with Pegylated interferon and ribavirin have better efficacy as compared toPegylated interferon & ribavirin alone in chronic hepatitis C-3a.

Facial Expression Recognition in Unconstrained Environment

Facial expressions are the most effective form of non-verbal communication which is used to express one’s emotions. It not only expresses our expressions, but also communicates a wealth of information during social interaction. Facial expressions play an important and effective role in Human-Computer Interaction. Unlike humans, facial expression recognition is a challenging task for computers and research work in this domain can still be considered in seminal form. The prevalent facial expression techniques tend to provide accurate and impressive results for the images captured under controlled environment with a cooperative subject. However, majority of these techniques fail to provide adequate results for the images captured in uncontrolled environment. Achieving a higher classification accuracy rate for the images captured in uncontrolled environments is a major challenge being faced by researchers in this domain. There are many factors such as illumination, image orientations, noise and low-resolution, which hinder the development of facial expression recognition system in uncontrolled environment. Even the datasets prepared under the constrained environments sometimes need preprocessing before the classification stage. The core objective of this dissertation is to analyze existing techniques and develop an effective framework that is capable of classification of human facial expressions both under constrained and unconstrained environments. In this dissertation, we have explored, investigated and proposed frameworks to deal with two of the most common, influential and challenging issues that affect the accuracy of a facial expression recognition for images captured in an uncontrolled environment, namely illumination and low-resolution. First, a novel framework for feature extraction named Weber Local Binary Image Cosine Transform has been developed which not only extracts significant features by integrating features extracted using local binary pattern and weber local descriptor but also utilizes most discriminant features by applying the frequencybased components. Low-resolution and multi-orientation facial images have been used for the classification and a significant improvement in the classification accuracy rate has been achieved with the proposed framework. The developed framework has proved to be not only reliable but also computationally efficient across multiple datasets in the presence of noise and orientations. The Proposed framework has been tested on four datasets including JAFEE, MMI, CK+ and SFEW datasets. The second major contribution is the development of an illumination invariant technique. The proposed framework has been named Weber Local Binary Image Cosine Transform (WLBI-CT) and it advocates for the need of simultaneous contrast enhancement and brightness preservation for datasets containing real world images in unconstrained environment. The empirical results for the SFEW dataset are promising. In order to evaluate the performance of the proposed framework, rigorous set of experiments are presented in this thesis. The empirical results meet the standard quantitative measure criteria. The comparison of our work with various other stateof-the-art techniques is also presented using various benchmarks for these factors. The results are impressive even in the case of inclusion of noise and occlusion effects.
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