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While prostate multiparametric-magnetic resonance imaging (MP-MRI) has improved the diagnosis of clinically significant prostate cancer (CSPC), the complementary use of prostate-specific antigen (PSA) levels to risk-stratify for CSPC requires further study. The objective of this project was to determine if prostate MP-MRI and PSA can provide complementary insights into CSPC risk stratification.In an IRB-approved study, pathologic outcomes from patients who underwent MR/US fusion-targeted prostate biopsy were stratified by various parameters including PSA, PSA density (PSAD), age, race, and PI-RADS v2 score. CSPC was defined as a Gleason score ≥7. Logistic regression was used to determine odds ratios (OR) with 95% confidence intervals (CI). P values were reported as two-sided with p 0.05 considered statistically significant. ROC curves were generated for assessing the predictive value of tests and sensitivity + specificity optimization was performed to determine optimal testing cutoffs.A total of 327 patients with 709 lesions total were analyzed. PSAD and PI-RADS scores provided complementary predictive value for diagnosis of CSPC (AUC PSAD: 0.67, PI-RADS: 0.72, combined: 0.78, p 0.001). When controlling for PI-RADS score, age, and race, multivariate analysis showed that PSAD was independently associated with CSPC (OR 1.03 per 0.01 PSAD increase, 95% CI 1.02-105, p 0.001). The optimal cutoff of PSAD ≥ 0.1 ng/ml/cc shows that a high versus low PSAD was roughly equivalent to an increase in 1 in PI-RADS score for the presence of CSPC (4% of PI-RADS ≤3 PSAD low, 6% of PI-RADS 3 PSAD high vs. 5% of PI-RADS 4 PSAD low, 22% of PI-RADS 4 PSAD high vs. 29% of PI-RADS 5 PSAD low, 46% of PI-RADS 5 PSAD high were found to have CSPC).PSAD with a cutoff of 0.1 ng/ml/cc appears to be a useful marker that can stratify the risk of CSPC in a complementary manner to prostate MP-MRI.

INTRODUCTION AND OBJECTIVE:While PSA has traditionally been used for prostate cancer (PCa) risk stratification, prostate MRI has more recently allowed improved diagnosis of clinically significant P...

Automatic detection of helmet for motorcyclists from real-time surveillance videos is a rising application in computer science. Object detection and classification using deep learning was recently well-known over the years. Researchers used these techniques for solving several surveillance-related problems. Several deep learning models adopt for automatic detection of helmet for motorcyclists but they cannot achieve state-of-the-art results due to different difficulties such as low resolution, whether conditions, occlusion and illumination etc. In this paper, we proposed a methodology for surveillance videos that automatically detect the helmet wear by motorcyclist or not. For this purpose, we used the Faster R-CNN model. First, we apply Region Proposal Network (RPN) starting with the input image that has been delivered into the backbone. Then RPN weights are settled and proposals from RPN are utilized to train the Faster RCNN model. For training, we used the self-generated dataset of three different locations in Lahore, Pakistan. The experimental results detect 97.26% accuracy on real-time surveillance videos for the detection of helmet for motorcyclists.

There is a growing problem of waste management faced by developing countries. Karachi is the biggest city of Pakistan having a population of approximately 24 million. Statistics indicate that on a daily basis, about 12,000 tons of solid waste is generated in Karachi alone, of which forty percent can be found on the city streets. Improper management of solid waste is causing the spread of infectious diseases and environmental pollution. This study examines how the solid waste management process is implemented in Karachi and the challenges faced by the responsible authorities. Solid waste management combines all activities performed in order to keep the city clean on a regular basis and handle waste collection and disposal, sewerages, water treatment, recycling, and health and hygiene issues. This study is qualitative in nature and the mediums of observations and interviews were used to collect data. A total of twenty respondents were interviewed using both structured and unstructured questions. These included residents of selected areas of Karachi as well as municipal officials who are responsible for solid waste management in the city. Results revealed that the citizens are dissatisfied with the current strategies of solid waste management implemented by the municipalities. The public thinks that the municipalities are ill-equipped to handle the developing situation of substantial waste in the city due to which they have to confront many problems. The municipalities, on the other hand, were found to be underfunded and ineffective in the department of solid waste management and therefore, they lack the proper instruments to ensure their efficiency. Residents of Karachi were also a contributing element in the growing waste problem by means of their participation in activities such as illegal dumping and violation of laws. With the ratio of solid waste, increasing per day in the urban center, there is an immediate need to implement major steps to improve the current situation. A more consolidated strategy for solid waste management needs to be designed and implemented which streamlines the processes of waste collection and disposal. Citizens of Karachi also need to be educated on how they can play their part in reducing the quantity of solid waste through the promotion of habits such as recycling.