The aim of this study was of two folds 1) Assessment of quality of life among systemic lupus women patients and 2) Identification of associates of quality of life among systemic lupus women under the study. A convenient sample of 60 adult women were recruited for this study; 30 with a definite diagnosis of systemic lupus erythematosus and the other 30 were healthy secretary women working at Al-manial Cairo University Hospital. The study was conducted at Rheumatology and Rehabilitation Department and at three medical units (9,19.31). Data pertinent to the study were collected through semi-structured interview using the following tools; 1) Socio-demographic Data Sheets (SDS) which were developed by the investigator, 2) The Translated Modified Arabic Version of Quality of Life Index Scale Cancer version by (Ferrell, Grant & Schmidt, 1992) used for SLE women patients & 3) The Combined Translated Modified Arabic Version of Quality of Life Index Scales by (Ferrans 1984; Ferrell, Grant & Schmidt, 1992 ) used for healthy women. The study findings answered the two stated research questions by documenting that no one of SLE women had reported high Quality of life as they had reported moderate Quality of life scores (164-346) to low Quality of life scores (347-520). Systemic lupus women had reported low QOL in the following dimensions respectively; psychological (83.3%), social (43.3%) spiritual (36.6%) and physical (33.3%). Socio-demographic characteristics of systemic lupus women revealed no statistical significant relation with their total quality of life scores except for significant others as the married systemic lupus women who recognized their spouse as significant others had reported the best quality of life total index scores. It is worth noting that even though socio-demographic characteristics of systemic lupus women revealed no statistical significant relation with their quality of life total index scores, the findings of the mean QOL total index scores showed that being single or housewife, having secondary school education, residing in rural areas and having first degree relatives as main significant others were associates of best QOL total index scores among the studied SLE women subjects. The current study concluded to the evidence of altered QOL among SLE women. Replication of this study is highly warranted to achieve more generaliazble results.

Background: Vitiligo is the commonest hypopigmented skin disorder. Taurine upregulated gene1(TUG1), one of Long noncoding RNAs (LncRNA) that plays a role in melanogenesis. microRNA-377 (miRNA-377) is a conserved non-coding RNA that regulates angiogenesis and promotes oxidative stress. Peroxisome proliferator-activated receptors (PPARs) are members of the nuclear hormone receptor superfamily. PPARγ activators stimulate melanogenesis. Interleukin-17(IL17) has been implicated in the pathogenesis of several immunological diseases.
Aim: This work aimed to detect the expression levels of TUG1, miRNA-377, PPARγ and IL-17 among vitiligo subjects and to investigate their possible role in the pathogenesis of vitiligo.
Methods: This study was conducted on 30 healthy control and 30 vitiligo patients. TUG1 and miRNA-377 were detected in serum by real- time-PCR, Also PPARγ and IL-17 were assessed in tissue by Real-time PCR.
Results: TUG1and PPARγ level was significantly down regulated in vitiligo group compared to control group, on the other hand miRNA-377 and IL-17 was significantly up regulated in vitiligo group compared to control group.
Conclusion: This study demonstrated the dysregulated expressions of TUG1 and miR-377 in patients with vitiligo and suggested that both contributed to its pathogenesis and that might be through PPARγ and IL-17.
Key words: Vitiligo /TUG1/ miRNA377/ PPAR γ / IL17.

This thesis presents an automatic soccer video summarization system using machine learning (ML) techniques. The proposed system is composed of ve phases. Namely; in the pre-processing phase, the system segments the whole video stream into small video shots. Then, in the shot processing
phase, it applies two types of classication (shot type classication and play / break classification) to the video shots resulted from the pre-processing phase. Afterwards, in the replay detection phase, the proposed system applies two machine learning algorithms, namely; support vector machine (SVM) and articial neural network (ANN), for emphasizing important segments with championship logo appearance. Also, in the excitement event detection phase, the proposed system uses both machine learning algorithms for detecting the scoreboard which contain an information about the score of the game. The proposed system also uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor lter for detecting goal net. Finally, in the event detection and summarization phase, the proposed system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. The event detection and summarization has attained recall 94% and precision 97.3% for soccer match videos from ve international soccer championships.