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PurposeTo compare early postoperative outcomes after transversus abdominis release (TAR) for ventral hernia repair with open (oTAR) and robotic (rTAR) approach.MethodsA systematic search of PubMed/MEDLINE, EMBASE, SCOPUS and Web of Science databases was conducted to identify comparative studies until October 2020. A meta-analysis of postoperative short-term outcomes was performed including complications rate, operative time, length of stay, surgical site infection (SSI), surgical site occurrence (SSO), SSO requiring intervention (SSOPI), systemic complications, readmission, and reoperation rates as measure outcomes.ResultsSix retrospective studies were included in the analysis with a total of 831 patients who underwent rTAR (n = 237) and oTAR (n = 594). Robotic TAR was associated with lower risk of complications rate (9.3 vs 20.7%, OR 0.358, 95% CI 0.218–0.589, p < 0.001), lower risk of developing SSO (5.3 vs 11.5%, OR 0.669, 95% CI 0.307–1.458, p = 0.02), lower risk of developing systemic complications (6.3 vs 26.5%, OR 0.208, 95% CI 0.100–0.433, p < 0.001), shorter hospital stay (SMD − 4.409, 95% CI − 6.000 to − 2.818, p < 0.001) but longer operative time (SMD 53.115, 95% CI 30.236–75.993, p < 0.01) compared with oTAR. There was no statistically significant difference in terms of SSI, SSOPI, readmission, and reoperation rates.ConclusionRobotic TAR improves recovery by adding the benefits of minimally invasive procedures when compared to open surgery. Although postoperative complications appear to decrease with a robotic approach, further studies are needed to support the real long-term and cost-effective advantages.

The photogrammetric 3D stereo reconstruction from pairs of strereo images is rising interest in the past few years in space field downstream. Nowadays, it is conceivable that a large production of DSMs from satellite images can become the primary source of 3D information on a global scale. However, in urban areas, DSMs produced with current technology suffer from poor quality. Indeed, even using very high resolution (VHR) images, there is too little information to generate disparity maps that reproduce very well defined shaped objects such as buildings.To address this issue, one solution may be to artificially increase image resolution beyond the sensor limits. Super resolution (SR) algorithms are designed to recover high frequencies, introducing significant information in a scene characterized by strong and frequent discontinuities such as a city. State-of-the-art methods relying on Deep Learning have shown remarkable results in this sense. The aim of this work is therefore to assess the contribution of single image SR Deep Learning techniques to the stereo matching and DSMs generation in an urban context, highlighting potential advantages and limitations that can show up when introducing such a technology in a multi-view stereo pipeline. The proposed contributions are: a methodology for super resolution of VHR data that takes into account realistic simulation of a satellite product; a testbed for the evaluation of the impact of super resolution on 3D photogrammetric reconstruction; a local analysis of the consequences of deep learning SR of VHR images on stereo matching.

Increased resting energy expenditure (REE) has been hypothesized to be a potential cause of weight loss in individuals with Crohn's Disease (CD), mainly due to inflammatory response. This study aimed to develop and validate new predictive equations for REE in adults with CD. Adult patients aged between 18-65 years and with CD were recruited. Anthropometry, indirect calorimetry and bio-impedance analysis (BIA) were performed in all patients. New predictive equations were generated using two models: Model 1 with age, weight, height and CDAI as predictors, and Model 2 in which both bioimpedance-index (BI-index) and phase angle were added. Finally, accuracy prediction within ±10% was assessed and then compared with several published equations. A total of 270 CD patients (159 males, 111 females) were included and assigned to the calibration (n=180; age: 37.9±13years; BMI: 22.2±3.4kg/m2) and the validation groups (n=90; age: 38.5±13years; BMI: 22.7±3.7kg/m2). REE was directly correlated with weight (r=0.733, p=0.000) and BI-index (r=0.762, p=0.000). Models were both suitable for estimating REE at population level (bias: -0.2, bias: -0.3; respectively). Individual accuracy was high in both models (∼80%; 83%;respectively), unrelated to gender. Similarly, the Harris and Benedict, FAO and Schofield equations showed a good accuracy in males and females. Therefore, new equations specifically derived from CD patients provided a very good prediction of REE at population level. Interestingly, the formula based on raw-BIA variables was as accurate as those based on anthropometry, unrelated to gender. However, further studies are required to verify the application of those formulas and the role of raw-BIA variables in REE prediction.

Sclerosing mesenteritis (SM) is an idiopathic disorder affecting mesentery, characterized by fat necrosis, chronic inflammation and fibrosis. The clinical presentation varies from asymptomatic cases to acute abdomen. The diagnosis is suggested by imaging but can be definitely established only by biopsies. In this paper, we discuss ultrasonography-based management of SM.

Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008–2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients’ residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region’s census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated with lower odds of T2D (0.92 [0.87, 0.97]). With the modified typology, the strongest association (1.37 [1.15, 1.63]) was observed in cities in the suburban affluent-like category (vs. rural affordable-like–township), followed by cities in the rural affordable-like category (1.20 [1.05, 1.36]). We conclude that in evaluating geographic disparities in T2D onset, it is beneficial to conduct simultaneous evaluation of SDOH in multiple dimensions. Associations with the modified typology showed the importance of incorporating governmentally, behaviorally, and experientially relevant community definitions when evaluating geographic health disparities.

The aim of this paper is to investigate how optical and Synthetic Aperture Radar (SAR) data can be combined in an integrated multi-source framework to identify burned areas at the regional scale. The proposed approach is based on the use of fuzzy sets theory and a region-growing algorithm. Landsat TM and (C-band) ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired for the year 2003 have been processed to extract burned area maps over Portugal. Pre-post fire SAR backscatter temporal difference has been integrated with optical spectral indices to the aim of reducing confusion between burned areas and low-albedo surfaces. The output fuzzy score maps have been compared with reference fire perimeters provided by the Fire Atlas of Portugal. Results show that commission and omission errors in the output burned area maps are a function of the threshold applied to the fuzzy score maps; between the two extremes of the greatest producer’s accuracy (omission error < 10%) and user’s accuracy (commission error < 5%), an intermediate threshold value provides errors of about 20% over the study area. The integration of SAR backscatter allowed reducing local commission errors from 65.4% (using optical data, only) to 11.4%, showing to significantly mitigate local errors due to the presence of cloud shadows and wetland areas. Overall, the proposed method is flexible and open to further developments; also in the perspective of the European Space Agency (ESA) Sentinel missions operationally providing SAR and optical datasets.

ObjectivesTo evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions.DesignNested case–control study within the open dynamic cohort of health system patients.SettingLarge, integrated health system in 37 counties in central and northeastern Pennsylvania, USA.Participants and analysisWe used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types.ResultsBorough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds.ConclusionsUrban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes.