Robot-Assisted Partial Splenectomy for Splenic Epidermoid Cyst

The splenic cyst is a rare disease with unknown etiology. The inner wall of the cyst has lining epithelium. The cyst can be unilocular or multilocular. According to pathology, it can be divided into four types: epidermoid cyst, dermoid cyst, cystic lymphangioma, and cystic hemangioma. Ultrasound examination is often the first choice for splenic cysts because of its nonradiation, low cost, and convenient examination. The images are mostly cystic masses with clear borders and dark areas without echoes, after the detection of splenic space-occupying lesions by ultrasonography, CT, and MRI. Here, we report robot-assisted partial splenectomy for a splenic cyst. Imaging diagnosis of abdominal CT enhancement: the cystic space-occupying of the spleen is considered. We should improve the preoperative examination and exclude operative contraindications. During the operation, there was about 8 cm of the upper pole of the spleen, and the boundary was clear. There was no obvious abnormality in the exploration of the abdominal viscera. The operation was successful. The operative time was 115 minutes, and the blood loss was 20 ml. On the first day after the operation, the patient took a liquid diet. The time of first anal exhaust was on the second day after operation. The patient was discharged at the fourth day. Postoperative pathology revealed epidermoid cyst. The therapy strategy of the splenic cyst is ambiguous. Better understanding of the splenic segmental anatomy and surgical skills has made minimally invasive partial splenectomy a preferred treatment for splenic cysts. In this paper, we report a case of splenic epidermoid cyst managed successfully by robot-assisted partial splenectomy

Using non-exceedance probabilities of policy-relevant malaria prevalence thresholds to identify areas of low transmission in Somalia

Background Countries planning malaria elimination must adapt from sustaining universal control to targeted intervention and surveillance. Decisions to make this transition require interpretable information, including malaria parasite survey data. As transmission declines, observed parasite prevalence becomes highly heterogeneous with most communities reporting estimates close to zero. Absolute estimates of prevalence become hard to interpret as a measure of transmission intensity and suitable statistical methods are required to handle uncertainty of area-wide predictions that are programmatically relevant. Methods A spatio-temporal geostatistical binomial model for Plasmodium falciparum prevalence (PfPR) was developed using data from cross-sectional surveys conducted in Somalia in 2005, 2007–2011 and 2014. The fitted model was then used to generate maps of non-exceedance probabilities, i.e. the predictive probability that the region-wide population-weighted average PfPR for children between 2 and 10 years (PfPR2–10) lies below 1 and 5%. A comparison was carried out with the decision-making outcomes from those of standard approaches that ignore uncertainty in prevalence estimates. Results By 2010, most regions in Somalia were at least 70% likely to be below 5% PfPR2–10 and, by 2014, 17 regions were below 5% PfPR2–10 with a probability greater than 90%. Larger uncertainty is observed using a threshold of 1%. By 2011, only two regions were more than 90% likely of being < 1% PfPR2–10 and, by 2014, only three regions showed such low level of uncertainty. The use of non-exceedance probabilities indicated that there was weak evidence to classify 10 out of the 18 regions as < 1% in 2014, when a greater than 90% non-exceedance probability was required. Conclusion Unlike standard approaches, non-exceedance probabilities of spatially modelled PfPR2–10 allow to quantify uncertainty of prevalence estimates in relation to policy relevant intervention thresholds, providing programmatically relevant metrics to make decisions on transitioning from sustained malaria control to strategies that encompass methods of malaria elimination

MOESM1 of Using non-exceedance probabilities of policy-relevant malaria prevalence thresholds to identify areas of low transmission in Somalia

Additional file 1. Model formulation and validation

MOESM2 of Using non-exceedance probabilities of policy-relevant malaria prevalence thresholds to identify areas of low transmission in Somalia

Additional file 2. Model outputs per region