Postoperative Healing after Surgical Removal of Mandibular Third Molar: A Comparative Study between Two Proteolytic Enzymes.

OBJECTIVE: The postoperative symptoms after third molar removal surgery were commonly uneventful. The aim of this study was to compare two proteolytic enzymes from different origin and to prove which enzyme provides a faster healing. One of the proteolytic enzymes involves a combination with a flavonoid. This involves trypsin, bromelain, along with a flavonoid rutin. Another proteolytic enzyme is serratiopeptidase that helps in degradation of insoluble proteins causing reduced swelling in the operated site. MATERIALS AND METHODS: In this study, voluntary subjects of clinically indicated bilateral lower third impaction were selected. The subjects were recommended to undergo extraction of impacted teeth in two sittings, one extraction per visit. In the first sitting, subjects were prescribed trypsin, bromelain, and rutin combination after removal of 48 teeth and in the second sitting subjects were prescribed serratiopeptidase after removal of 38 teeth. There was a time interval of 2-3 weeks in between the sittings. The post findings such as mouth opening, swelling, and pain scale were noted. RESULTS: The results showed that the proteolytic enzyme combination of trypsin, bromelain, and rutin is better than serratiopeptidase. CONCLUSION: It is recommended that trypsin, bromelain, and rutin combination can be used effectively for postoperative purpose to facilitate wound healing.

Neural networks prediction of preterm delivery with first trimester bleeding.

OBJECTIVE: This paper illustrates a retrospective study of the outcome of those pregnancies that continued from an initial episode of bleeding in first trimester. METHODS: Neural networks is used for the prediction of preterm delivery, using various inputs such as the age of women, gestational age when the bleeding occurred, duration of the bleeding days, amount of bleeding, number of episodes, presence or absence of hematoma and placentation position. RESULTS: The success rate of prediction obtained using the feed forward backpropogation network is 70%. Hence, this model can be used to identify women at the risk of premature delivery for planning antenatal care and clinical interventions in pregnancy.