Intestinal surgery in small animals: historical foundations,current thinking,and future horizons

Intestinal wounds require precise closure after intestinal biopsy, enterotomy, or enterectomy in small animals. Preexisting factors such as intra‐abdominal sepsis and hypoalbuminemia as well as poor surgical technique increase the risk of intestinal dehiscence, with considerable negative impact on patient morbidity and mortality. Live dog studies have demonstrated the dangers of mucosal eversion especially in the septic abdomen. Approximating patterns preserve luminal diameter, heal optimally, and have equal bursting strength compared with inverting patterns after 24 hours. Simple interrupted and simple continuous suture patterns and disposable skin staples are established alternatives for manual wound closure. Knotless quilled suture currently used in laparoscopic gastropexy techniques shows bursting strength equal to monofilament sutures in dog cadaveric intestine. Dehiscence rates with hand sewn vs titanium automated stapling anastomosis are similar in uncomplicated cases; however, auto stapling devices may be the preferred method of anastomosis when preexisting abdominal sepsis is present and when patient size allows it. Regardless of the technique, current standard of care involves leak testing and omental wrapping, followed by early postoperative feeding. The past decade has ushered in an exciting new era of laparoscopic assisted techniques that have the potential to reduce postoperative pain and patient morbidity. An understanding of these applications will establish the future of minimally invasive small animal intestinal surgery for veterinary specialists. In summary, surgeons have a variety of methods at their disposal for optimal clinical outcome in small animal intestinal surgery.     

A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data

The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniques, random forest (RF), support vector machine (SVM) and multivariate adaptive regression splines (MARSs), in time series modelling and predicting of monthly brucellosis data from 2005 (March/April) to 2017 (February/March) extracted from a national public health surveillance system in Hamadan located in west of Iran. The performances were compared based on the root mean square errors, mean absolute errors, determination coefficient (R²) and intraclass correlation coefficient criteria. Results indicated that the RF model outperformed the SVM and MARS models in modeling used data and it can be utilized successfully utilized to diagnose the behaviour of brucellosis over time. Further research with application of such novel time series models in practice provides the most appropriate method in the control and prevention of future outbreaks for the epidemiologist.