Parastomal hernia: an overview.

Parastomal hernia (PH) is one of the most frequent ostomy complications, and the reported incidence in the literature is highly variable. As highlighted by the Association of Stoma Care Nurses UK, this complication develops mainly in children and older men over 70, but many predisposing factors are related to the individual patient and surgery. There is no standardised system for assessing PH. The main assessment techniques include objective examination, ultrasound scan and computed tomography. Prevention is based on various interventions by surgeons and stoma care nurses (SCNs). The SCN's primary interventions include accurate patient evaluation, pre-operative ostomy siting, education about body weight management and advice on appropriate exercises. The treatment of PH can be conservative or surgical, and the choice is based on the patient's clinical condition. Ostomy can significantly impact on a patient's quality of life (QoL), and the presence of PH can further aggravate the situation. This overview of PH considers the incidence, aetiology, prevention, treatment and impact on QoL.

Peristomal Skin Complications: Detailed Analysis of a Web-Based Survey and Predictive Risk Factors.

Patients with a stoma are at risk of developing peristomal skin complications (PSCs) that can negatively impact their quality of life. This study aims to identify potential risk factors for dermatitis, pruritis/xerosis, infections, and ulcerations among patients with a stoma and evaluate preventive measures. This cross-sectional study involved data regarding 232 Italian patients with a stoma. A questionnaire was used to collect patient characteristics, comorbidities, and stoma management data. The most frequent PSCs observed were dermatitis and pruritis/xerosis in approximately 60% of patients. Psoriasis was strongly correlated with dermatitis, while being overweight or obese increased the risk of pruritis/xerosis. Class 2 obesity and atopic dermatitis were associated with an increased risk of infections. Being underweight, completely nonautonomous, and having inflammatory bowel disease were associated with a higher risk of ulcerations, while radiotherapy was a strong risk factor for ulceration. Preventive measures such as using hydrocolloid barriers, TNT gauze cleansing, and low pH detergent were effective in preventing dermatitis. Appropriate stoma care and maintenance, including the use of protective film and careful monitoring of weight and comorbidities, are crucial in minimizing the risk of complications associated with a stoma.

A global perspective on the intrinsic dimensionality of COVID-19 data.

We develop a novel global perspective of the complexity of the relationships between three COVID-19 datasets, the standardised per-capita growth rate of COVID-19 cases and deaths, and the Oxford Coronavirus Government Response Tracker COVID-19 Stringency Index (CSI) which is a measure describing a country's stringency of lockdown policies. We use a state-of-the-art heterogeneous intrinsic dimension estimator implemented as a Bayesian mixture model, called Hidalgo. Our findings suggest that these highly popular COVID-19 statistics may project onto two low-dimensional manifolds without significant information loss, suggesting that COVID-19 data dynamics are generated from a latent mechanism characterised by a few important variables. The low dimensionality imply a strong dependency among the standardised growth rates of cases and deaths per capita and the CSI for countries over 2020-2021. Importantly, we identify spatial autocorrelation in the intrinsic dimension distribution worldwide. The results show how high-income countries are more prone to lie on low-dimensional manifolds, likely arising from aging populations, comorbidities, and increased per capita mortality burden from COVID-19. Finally, the temporal stratification of the dataset allows the examination of the intrinsic dimension at a more granular level throughout the pandemic.

A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data.

The use of large datasets for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In particular, models for partially exchangeable data are needed for inference on nested datasets, where the observations are assumed to be organized in different units and some sharing of information is required to learn distinctive features of the units. In this manuscript, we propose a nested common atoms model (CAM) that is particularly suited for the analysis of nested datasets where the distributions of the units are expected to differ only over a small fraction of the observations sampled from each unit. The proposed CAM allows a two-layered clustering at the distributional and observational level and is amenable to scalable posterior inference through the use of a computationally efficient nested slice sampler algorithm. We further discuss how to extend the proposed modeling framework to handle discrete measurements, and we conduct posterior inference on a real microbiome dataset from a diet swap study to investigate how the alterations in intestinal microbiota composition are associated with different eating habits. We further investigate the performance of our model in capturing true distributional structures in the population by means of a simulation study.

Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies.

The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two competing densities, the null and the alternative distributions. We investigate the use of weighted densities and, in particular, non-local densities as working alternative distributions, to enforce separation from the null and thus refine the screening procedure. We show how these weighted alternatives improve various operating characteristics, such as the Bayesian false discovery rate, of the resulting tests for a fixed mixture proportion with respect to a local, unweighted likelihood approach. Parametric and nonparametric model specifications are proposed, along with efficient samplers for posterior inference. By means of a simulation study, we exhibit how our model compares with both well-established and state-of-the-art alternatives in terms of various operating characteristics. Finally, to illustrate the versatility of our method, we conduct three differential expression analyses with publicly-available datasets from genomic studies of heterogeneous nature.

The generalized ratios intrinsic dimension estimator.

Modern datasets are characterized by numerous features related by complex dependency structures. To deal with these data, dimensionality reduction techniques are essential. Many of these techniques rely on the concept of intrinsic dimension (id), a measure of the complexity of the dataset. However, the estimation of this quantity is not trivial: often, the id depends rather dramatically on the scale of the distances among data points. At short distances, the id can be grossly overestimated due to the presence of noise, becoming smaller and approximately scale-independent only at large distances. An immediate approach to examining the scale dependence consists in decimating the dataset, which unavoidably induces non-negligible statistical errors at large scale. This article introduces a novel statistical method, Gride, that allows estimating the id as an explicit function of the scale without performing any decimation. Our approach is based on rigorous distributional results that enable the quantification of uncertainty of the estimates. Moreover, our method is simple and computationally efficient since it relies only on the distances among data points. Through simulation studies, we show that Gride is asymptotically unbiased, provides comparable estimates to other state-of-the-art methods, and is more robust to short-scale noise than other likelihood-based approaches.

Is purchasing of vegetable dishes affected by organic or local labels? Empirical evidence from a university canteen.

Previous studies have shown that the use of labels such as "organic" or "local" may improve the perceived healthiness, attractiveness and taste evaluation of healthy food products and increase their likelihood of being purchased. The aim of this work was to examine whether labeling vegetable items could promote consumption of at least one vegetable dish among students in a university canteen. We analyzed the purchasing of vegetable dishes among 458 students during an eight-week intervention in a university canteen, where vegetable items alternatively received neutral, organic and local labels. We implemented a multilevel Bayesian analysis to incorporate prior knowledge extracted from data preceding the experiment and to account for potential confounders related to the design of the experiment. Our results suggest that the labels "organic," "local" "organic & local" were not strongly associated with ordering at least one vegetable plate. Additional studies are warranted to further investigate the potential impact of vegetable plate labeling on customers' purchasing choices.

The frequency of early stomal, peristomal and skin complications.

BACKGROUND: The incidence of early complications after stoma formation (within 30 days of surgery) is difficult to determine and has been reported to be in a range of 3%-82%. AIM: The aim of this study was to analyse the onset of stomal, peristomal and skin complications one month (30 days) after ostomy creation. METHOD: This review analysed enteral stoma therapy nurse reports on patients who had an ostomy created between January 2016 and December 2020. FINDINGS: Complications were analysed according to ostomy type: colostomy, ileostomy and urostomy. There were 1292 incidences of complications: skin complications were the most common (26%), and abscess the least common (0%). CONCLUSION: A majority (63%) of patients experienced at least one or more complications within 30 days of surgery. Haemorrhage was reported as a complication (2%) but the authors found no data on its incidence in the literature. In addition to early complications, late complications were detected.

SACS Evolution: a peristomal health tool for the prevention of peristomal skin disorders.

BACKGROUND: The ostomy patient's quality of life also depends on their peristomal health and thus first and foremost on keeping healthy peristomal skin. This is by no means an easy task, given that the incidence of peristomal skin disorders can be a factor in up to 80% of cases. Over the past 15 years, several tools have been developed to classify peristomal skin lesions to facilitate the management of the problem. These tools, however, meet the needs of healthcare professionals and those of patients. Hence the decision to work on an advanced version of the SACS©, currently the most widely used tool, with the setting up of the SACS Evolution Consensus Group. METHODS: By applying the simplified Delphi method, the SACS Evolution Consensus Group critically re-read what had been garnered from the literature in the light of their personal clinical experience, identified the key issues to be addressed, and processed and refined the relevant statements. This made it possible to achieve the challenging objective of creating a peristomal health tool to help prevent peristomal skin lesions. RESULTS: SACS Evolution is, indeed, a promising tool for preventing peristomal lesions. It is structured on two different levels, one for the patient and one for the healthcare professional, each characterized by specific language and based on an innovative concept of healthy peristomal skin, which is essential for those who work on peristomal health. CONCLUSIONS: The SACS Evolution Consensus Group thus aims to validate this peristomal health tool and has already started a validation process so that the tool can be used in clinical practice.

Two-group Poisson-Dirichlet mixtures for multiple testing.

The simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed by Efron, identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literature, many approaches have suggested using mixtures of Dirichlet Processes in the two-group model framework. Here, we investigate employing mixtures of two-parameter Poisson-Dirichlet Processes instead, and show how they provide a more flexible and effective tool for large-scale hypothesis testing. Our model further employs nonlocal prior densities to allow separation between the two mixture components. We obtain a closed-form expression for the exchangeable partition probability function of the two-group model, which leads to a straightforward Markov Chain Monte Carlo implementation. We compare the performance of our method for large-scale inference in a simulation study and illustrate its use on both a prostate cancer data set and a case-control microbiome study of the gastrointestinal tracts in children from underdeveloped countries who have been recently diagnosed with moderate-to-severe diarrhea.

Data segmentation based on the local intrinsic dimension.

One of the founding paradigms of machine learning is that a small number of variables is often sufficient to describe high-dimensional data. The minimum number of variables required is called the intrinsic dimension (ID) of the data. Contrary to common intuition, there are cases where the ID varies within the same data set. This fact has been highlighted in technical discussions, but seldom exploited to analyze large data sets and obtain insight into their structure. Here we develop a robust approach to discriminate regions with different local IDs and segment the points accordingly. Our approach is computationally efficient and can be proficiently used even on large data sets. We find that many real-world data sets contain regions with widely heterogeneous dimensions. These regions host points differing in core properties: folded versus unfolded configurations in a protein molecular dynamics trajectory, active versus non-active regions in brain imaging data, and firms with different financial risk in company balance sheets. A simple topological feature, the local ID, is thus sufficient to achieve an unsupervised segmentation of high-dimensional data, complementary to the one given by clustering algorithms.

Computed Tomographic Trochlear Depth Measurement in Normal Dogs.

OBJECTIVES: The main purpose of this study was to describe the relationship between patellar maximal craniocaudal thickness and femoral trochlear groove depth in normal dogs and to valuate the intra-observer or inter-observer variability of maximal trochlear depth and maximal patellar craniocaudal thickness using computed tomography. METHODS: Trochlear groove depth and patellar maximal craniocaudal thickness of 40 limbs (20 dogs) were measured by three independent veterinarians using three-dimensional multiplanar reconstruction computed tomography images. The patellar maximal craniocaudal thickness/trochlear depth ratio was determined. RESULTS: The mean ratio of these stifles was 0.46 (range 0.24-0.70), meaning that the mean maximal depth of the trochlea was 46% of the mean maximal-patellar thickness. CLINICAL SIGNIFICANCE: A wide range of maximal-patellar-craniocaudal-thickness/maximal trochlear-depth ratio was found suggesting that breed studies should be performed to determine a breed-specific patellar-thickness/trochlear-depth ratio. To make decisions on when and where to perform a sulcoplasty during patellar luxation surgery, patella/trochlea thickness relationship should be measured for each breed with patellar tracking from stifle hyperflexion to stifle hyperextension.