Object Postoperative shunt infection is the most common and feared complication of ventriculoperitoneal (VP) shunt placement for treatment of hydrocephalus. The rate of shunt infection is highest in the 1st postoperative month. The most common organisms responsible for shunt infection include coagulase-negative Staphylococcus and Staphylococcus aureus. This suggests a transfer of patient's skin flora via the surgeons' glove as a possible means of infection. The authors conducted a study to determine if the rate of postoperative shunt infections could be reduced simply by changing gloves before handling the shunt catheter. Methods A total of 111 neonates born with congenital hydrocephalus requiring a VP shunt were enrolled retrospectively and divided into 2 groups: a control group of 54 neonates treated with standard protocol VP shunt placement (Group A) and a treatment group of 57 neonates in whom, after initially double gloving, the outer pair of gloves was removed before handling the shunt catheter (Group B). Shunt infection rates were compared up to 6 months postoperatively. Results There was a statistically significant reduction of infection rate from 16.33% in Group A (control) to 3.77% in Group B (p = 0.0458). Conclusions The study shows that a changing of gloves before handling the shunt catheter may be a simple and cost-effective way to reduce the burden of postoperative shunt infections.

This study shows that the removal of an outer pair of gloves before handling instrumentation may be a simple, cost-effective, and practical way to reduce the burden of postoperative infection following lumbar spinal fusion.

Social correspondence is one of the most significant columns that the public dependent on. Notably, language is the best way to communicate and associate with one another both verbally and nonverbally. There is a persistent communication gap among deaf and non-deaf communities because non-deaf people have less understanding of sign languages. Every region/country has its sign language. In Pakistan, the sign language of Urdu is a visual gesture language that is being used for communication among deaf peoples. However, the dataset of Pakistan Sign Language (PSL) is not available publicly. The dataset of PSL has been generated by acquiring images of different hand configurations through a webcam. In this work, 40 images of each hand configuration with multiple orientations have been captured. In addition, we developed, an interactive android mobile application based on machine learning that minimized the communication barrier between the deaf and non-deaf communities by using the PSL dataset. The android application recognizes the Urdu alphabet from input hand configuration.