Background Sacrococcygeal pilonidal sinus disease (PSD) is an infection of the skin and subcutaneous tissue at the upper part of the natal cleft of the buttocks. Excision and healing by granulation “lay-open” method is still more preferable than other methods of midline closure or using flaps but the healing time is lengthy. The present study was performed to assess the healing promotion effect of platelet-rich plasma (PRP) on the pilonidal sinus wounds treated by the lay-open method. Methods One hundred patients suffering from PSD were randomly divided into two groups, they were treated by the lay-open method, at General surgery department, Kafr El-Sheik University hospital, Egypt, during the period from December 2018 to December 2019. Group (A) was adopted the regular dressing postoperatively, while group (B) was treated with PRP injection into the wound at 4 and 12 postoperative days. Results Accelerated rate of wound healing was detected in group (B) in day 10, with a significant difference detected in days 15, 20, 25 and 30 postoperative, with a mean time of complete healing 45 ± 2.6 days in group B, while it was 57 ± 2.4 days in group A with a p-value of 0.001 which indicates considerable effect in the treated group. Conclusions PRP injection is an effective new technique in accelerating the healing of pilonidal wound after surgery, with a significant decrease in post-operative pain, complications and an early return to work. Trial registration retrospectively registered. Trial registration number: 12/35/1016 issued on December 2018 from the Institution Review Board at Kafr El Sheikh University. ClinicalTrials.gov identifier: NCT04430413

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The continuing increase in functional requirements of modern hardware designs means the traditional functional verification process becomes inefficient in meeting the time-to-market goal with sufficient level of confidence in the design. Therefore, the need for enhancing the process is evident. Machine learning (ML) models proved to be valuable for automating major parts of the process, which have typically occupied the bandwidth of engineers; diverting them from adding new coverage metrics to make the designs more robust. Current research of deploying different (ML) models prove to be promising in areas such as stimulus constraining, test generation, coverage collection and bug detection and localization. An example of deploying artificial neural network (ANN) in test generation shows 24.5× speed up in functionally verifying a dual-core RISC processor specification. Another study demonstrates how k-means clustering can reduce redundancy of simulation trace dump of an AHB-to-WHISHBONE bridge by 21%, thus reducing the debugging effort by not having to inspect unnecessary waveforms. The surveyed work demonstrates a comprehensive overview of current (ML) models enhancing the functional verification process from which an insight of promising future research areas is inferred.

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