Search

Objective: To determine the prevalance of Candida albicans amongst patients attending the Gynae& Obstetrics Department at Sheikh Zayed Hospital, Rahim Yar Khan (SZH-RYK). Study Design: Descriptive Cross-sectional hospital-based study. Setting: Department of Pathology, SZH-RYK. Period: November 2018 to February 2019. Material & Method: The data on vaginal swabs were collected from different women with symptoms of vaginal infection, cultured on Sabouraud Dextrose agar. For identification of the isolated organisms,colony morphology, wet preparation, Gram staining procedure, a Germ tube test was carried out. A proforma was designed with the help of the supervisor includes various variables. Results: Out of 215 HVS samples, about 125 of Candida spp. were isolated, C. Albicans isolated was 64% while yeast other than C. Albicans isolated was 36%. 26-30 years age group had a higher percentage of 65.6% of C. Albicans. Prevalence of C. Albicans was highest in pregnant women 43.2% especially in third trimester 55.6%. The pruritus was found to be 77.9%, the cheesy discharge was found to be 73.9%, prevalence among diabetic women was highest 52.0% and prevalence among antibiotic users was also highest 45.0%. Conclusion: This study concluded that pruritus as a key symptom of VVC and prevalence of C. Albicans among pregnant women is more than in non-pregnant women.

Remote sensing in image processing is popular in urban monitoring, forest detection, and disaster Monitoring. The high-resolution satellite images are classified into their respective classes through their distinctive features. Innovation in image acquisition has played a critical role in the process of recognition. However, the geometric and photometric variations require the extraction of invariant features. This paper presents a robust strategy that can classify such high-resolution images, also in case of changes in geometry and photometry. The employed dataset consists of images located in the Headwater Region of China. The images of the database include variations in illumination, viewpoint, and scale. Robust and distinctive features collected from the fully connected layer of the DNN model are classified through a multi-class support vector machine. The Gaussian kernel type parameter of SVM is used for the classification in our experiments. The results show our proposed approach provides 93.8% classification accuracy, which is better than many recently reported works.