ABSTRACT
Pulsed radiofrequency, short bursts of radiofrequency energy, has been used by pain practitioners as a non- or minimally neurodestructive technique, an alternative to radiofrequency heat lesions. The clinical advantages and mechanisms of this treatment remain unclear. To review the current clinical implication of the pulsed radiofrequency technique for male patients with chronic scrotal pain. We systematically searched the English literature available at the EMBASE, MEDLINE/PubMed, Google Scholar, and Cochrane Library from inception to 22 November 2022. Only reports on a pulsed radiofrequency application on male patients with chronic scrotal pain were included. The final analysis yielded six reports on the clinical use of pulsed radiofrequency applications in male patients with chronic scrotal pain: six full publications, three case reports, one case series, one prospective uncontrolled pilot study, and one prospective randomized, controlled clinical trial. The accumulation of these data shows that using pulsed radiofrequency generates an increasing interest in pain physicians, radiologists, and urologists for managing chronic scrotal pain. No side effects related to the pulsed radiofrequency technique were reported to date. Further research on the clinical and biological effects is justified. Large sample sizes and randomized clinical trials are warranted.
ABSTRACT
Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the pattern hidden in various software development processes and artifacts. This module is part of the smart requirements management system that is intended to be built. This system will have multiple modules to make the software requirements management phase more secure from vulnerabilities. Some of the critical challenges bothering the software development community are discussed. The background of Machine Learning approaches and their application in software development practices are explored. Some of the work done around modeling the source code and approaches used for vulnerabilities understanding in software systems are reviewed. Program representation is explored to understand some of the principles that would help in understanding the subject well. Further deeper dive into source code modeling possibilities are explored. Machine learning best practices are explored inline with the software source code modeling.
