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ObjectiveTo describe percentage length changes in the femur after total hip replacement (THR) performed before skeletal maturity.Study designRetrospective study.AnimalsTwenty-four dogs younger than 8.5 months which underwent unilateral THR and had radiographic follow up.MethodsPreoperative and follow-up radiographs were reviewed. Radiographic measurements included the length of the greater trochanter, femoral diaphysis and distal epiphysis, width of the femur 10 mm distal to the distal aspect of the greater trochanter, width of the femur at 50%, and femoral condylar offset. Percentage changes in length over time were compared among operated and contralateral femurs used as controls. Measurements were collected in triplicate in 10 dogs to evaluate consistency.ResultsAll repeated measurements had excellent consistency. The percentage increase in length of the greater trochanter was smaller in operated femurs than controls (mean difference: -11.5%, p = .017), but no differences were observed for the femoral diaphysis and distal epiphysis (-1.0%, p = .595), or the femur overall (-2.3%, p = .232). The percentage increase in femoral cortical width was greater in operated femurs than controls, both 10 mm distal to the greater trochanter (4.6% difference, p = .037) and at 50% length (8.5% difference, p = .030).ConclusionIn growing dogs, cementless THR decreased trochanteric growth by approximately 10% but did not change diaphyseal growth and femoral growth.Clinical significanceCementless THR performed in skeletally immature dogs with severe hip problems did not impact femoral length in a clinically relevant fashion.

OBJECTIVE: The updated 2019 National Kidney Foundation Kidney Disease Outcomes Quality Initiative vascular access guidelines recommend patient-centered, multi-disciplinary construction and regular update of an individualized end-stage kidney disease (ESKD) Life-Plan (LP) for each patient, a dramatic shift from previous recommendations and policy. The objective of this study was to examine barriers and facilitators to implementing the LP among key stakeholders. METHODS: Semi-structured individual interviews were analyzed using inductive and deductive coding. Codes were mapped to relevant domains in the Consolidated Framework for Implementation Research (CFIR). RESULTS: We interviewed 34 participants: 11 patients with end-stage kidney disease, 2 care partners, and 21 clinicians who care for patients with end-stage kidney disease. In both the clinician and the patient/care partner categories, saturation (where no new themes were identified) was reached at 8 participants. We identified significant barriers and facilitators to implementation of the ESKD LP across three CFIR domains: Innovation, Outer setting, and Inner setting. Regarding the Innovation domain, patients and care partners valued the concept of shared decision-making with their care team (CFIR construct: innovation design). However, both clinicians and patients had significant concerns about the complexity of decision-making around kidney substitutes and the ability of patients to digest the overwhelming amount of information needed to effectively participate in creating the LP (innovation complexity). Clinicians expressed concerns regarding the lack of existing evidence base which limits their ability to effectively counsel patients (innovation evidence base) and the implementation costs (innovation cost). Within the Outer Setting, both clinicians and patients were concerned about performance measurement pressure under the existing Fistula First policies and had concerns about reimbursement (financing). In the Inner Setting, clinicians and patients stressed the lack of available resources and access to knowledge and information. CONCLUSION: Given the complexity of decision-making around kidney substitutes and vascular access, our findings point to the need for implementation strategies, infrastructure development, and policy change to facilitate ESKD LP development.

This report describes the case of a frail 36-year-old patient who underwent an endovascular treatment of a right subclavian artery pseudoaneurysm (SAP) associated with an arteriovenous fistula secondary to a traumatic central venous catheter insertion. The deployment of a covered stent from the innominate to the right common carotid artery combined with coiling of the SAP and the internal mammary artery was performed. Two additional covered stents were deployed from the vertebral artery to the distal subclavian artery to preserve right upper extremity circulation. This case highlights the feasibility of an endovascular treatment of a complex SAP in a candidate unsuitable for open surgery.

Goal: The purpose of this study is to evaluate the importance of cardiorespiratory variables within a Reinforcement Learning (RL) recommendation system aimed at establishing optimal strategies for drug treatment of septic patients in the intensive care unit (ICU). Methods: We developed a RL model in order to establish drug administration strategies for septic patients using only a set of cardiorespiratory variables. We then compared this model with other RL models trained with a different set of features. We selected patients meeting the Sepsis-3 criteria from the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC III) database, resulting in a total of 20,496 ICU admissions. A Markov Decision Process (MDP) was built on the extracted discrete time-series. A policy iteration algorithm was used to obtain the optimal AI policy for the MDP. The policy performance was then evaluated using the WIS estimator. The process was repeated for each set of variables and compared to a set of baseline benchmark policies. Results: The model trained with cardiorespiratory variables outperformed all other models considered, resulting in a 95% confidence lower bound score of 97.48. This finding highlights the importance of cardiovascular variables in the clinical RL recommendation system. Conclusions: We established an efficient RL model for sepsis treatment in the ICU and demonstrated that cardiorespiratory variables provides critical information in devising optimal policies. Given the potentially continuous availability of cardiorespiratory features extracted from bedside physiological waveform monitoring, the proposed framework paves the way for a real time recommendation system for sepsis treatment.

BackgroundPrevious studies evaluating the accuracy of computed tomography (CT) in detecting caudal vena cava (CVC) invasion by adrenal tumors (AT) used a binary system and did not evaluate for other vessels.ObjectiveTest a 7-point scale CT grading system for accuracy in predicting vascular invasion and for repeatability among radiologists. Build a decision tree based on CT criteria to predict tumor type.MethodsRetrospective observational cross-sectional case study. Abdominal CT studies were analyzed by 3 radiologists using a 7-point CT grading scale for vascular invasion and by 1 radiologist for CT features of AT.AnimalsDogs with AT that underwent adrenalectomy and had pre- and postcontrast CT.ResultsNinety-one dogs; 45 adrenocortical carcinomas (50%), 36 pheochromocytomas (40%), 9 adrenocortical adenomas (10%) and 1 unknown tumor. Carcinoma and pheochromocytoma differed in pre- and postcontrast attenuation, contralateral adrenal size, tumor thrombus short- and long-axis, and tumor and thrombus mineralization. A decision tree was built based on these differences. Adenoma and malignant tumors differed in contour irregularity. Probability of vascular invasion was dependent on CT grading scale, and a large equivocal zone existed between 3 and 6 scores, lowering CT accuracy to detect vascular invasion. Radiologists' agreement for detecting abnormalities (evaluated by chance-corrected weighted kappa statistics) was excellent for CVC and good to moderate for other vessels. The quality of postcontrast CT study had a negative impact on radiologists' performance and agreement.Conclusions and clinical importanceFeatures of CT may help radiologists predict AT type and provide probabilistic information on vascular invasion.

Three measurement campaigns were conducted on the island of Culuccia (Sardinia, Italy) to evaluate particulate matter (PM) concentrations and the contribution of sea spray aerosol (SSA) across different seasons in a largely uncontaminated coastal environment. The goal is not only to analyze PM concentration in relation to meteorological parameters such as temperature, relative humidity (rH), and wind speed but also to provide a chemical analysis of SSA. The chemical composition of PM was determined using Raman spectroscopy and SEM-EDX, allowing for precise identification of individual particles. Results showed seasonal variations in PM composition, with sodium nitrate and sodium chloride prevalent in March and June and sulfates dominating in October. A correlation between the PM composition and meteorological parameters was observed according to the value of the deliquescence relative humidity (DRH), highlighting the reciprocal influence of rH and coarse and fine PM trends. This multi-technique approach offers valuable insights into the relative abundance of different PM compound classes based on the varying conditions for SSA formation. This enhances our understanding of the behavior of sea spray aerosol and other PM in natural coastal environments.

The Mediterranean Forecasting System produces operational analyses and reanalyses and 10 d forecasts for many essential ocean variables (EOVs), from currents, temperature, salinity, and sea level to wind waves and pelagic biogeochemistry. The products are available at a horizontal resolution of 1/24∘ (approximately 4 km) and with 141 unevenly spaced vertical levels. The core of the Mediterranean Forecasting System is constituted by the physical (PHY), the biogeochemical (BIO), and the wave (WAV) components, consisting of both numerical models and data assimilation modules. The three components together constitute the so-called Mediterranean Monitoring and Forecasting Center (Med-MFC) of the Copernicus Marine Service. Daily 10 d forecasts and analyses are produced by the PHY, BIO, and WAV operational systems, while reanalyses are produced every ∼ 3 years for the past 30 years and are extended (yearly). The modelling systems, their coupling strategy, and their evolutions are illustrated in detail. For the first time, the quality of the products is documented in terms of skill metrics evaluated over a common 3-year period (2018–2020), giving the first complete assessment of uncertainties for all the Mediterranean environmental variable analyses.

The coronavirus disease 2019 (COVID-19) pandemic has placed significant strain on the health and welfare of all health care professionals, including vascular surgeons. This review summarizes the implications of the pandemic on the health and wellness of surgeons and trainees, with a particular focus on those in vascular surgery (VS). A literature review was completed using common resource databases. We provide a brief history of burnout in VS and explore burnout and wellness in VS during this unprecedented pandemic. We then offer recommendations to address mental health needs by the VS workforce and highlight opportunities to address the gaps in the literature. The impact of COVID-19 on the professional and personal lives of surgeons and trainees in VS is notable. More than half of vascular surgeons reported some degree of anxiety. Factors associated with anxiety and burnout include COVID-19 exposure, moral injury, practice changes, and financial impacts. Trainees appeared to have more active coping strategies with dampened rates of anxiety compared to those in practice. Women appear to be disproportionately affected by the pandemic, with higher rates of anxiety and burnout. Groups underrepresented in medicine seemed to have more resilience when it came to burnout, but struggled with other inequities in the health care environment, such as structural racism and isolation. Strategies for addressing burnout include mindfulness practices, exercise, and peer and institutional support. The COVID-19 pandemic has had a substantial mental health impact on the VS workforce globally, as shifts were made in patient care, surgical practice, and work-home life concerns.

Jejunal artery pseudoaneurysms are extremely rare, accounting for

Introduction: In several fields, the process of fusing multiple two-dimensional (2D) closed lines is an important step. For instance, this is fundamental in histology and oncology in general. The treatment of a tumor consists of numerous steps and activities. Among them, segmenting the cancer area, that is, the correct identification of its spatial location by the segmentation technique, is one of the most important and at the same time complex and delicate steps. The difficulty in deriving reliable segmentations stems from the lack of a standard for identifying the edges and surrounding tissues of the tumor area. For this reason, the entire process is affected by considerable subjectivity. Given a tumor image, different practitioners can associate different segmentations with it, and the diagnoses produced may differ. Moreover, experimental data show that the analysis of the same area by the same physician at two separate timepoints may result in different lines being produced. Accordingly, it is challenging to establish which contour line is the ground truth.Methods: Starting from multiple segmentations related to the same tumor, statistical metrics and computational procedures could be exploited to combine them for determining the most reliable contour line. In particular, numerous algorithms have been developed over time for this procedure, but none of them is validated yet. Accordingly, in this field, there is no ground truth, and research is still active.Results: In this work, we developed the Two-Dimensional Segmentation Fusion Tool (TDSFT), a user-friendly tool distributed as a free-to-use standalone application for MAC, Linux, and Windows, which offers a simple and extensible interface where numerous algorithms are proposed to “compute the mean” (i.e., the process to fuse, combine, and “average”) multiple 2D lines.Conclusions: The TDSFT can support medical specialists, but it can also be used in other fields where it is required to combine 2D close lines. In addition, the TDSFT is designed to be easily extended with new algorithms thanks to a dedicated graphical interface for configuring new parameters. The TDSFT can be downloaded from the following link: https://sourceforge.net/p/tdsft.

IntroductionHypothyroidism can be easily misdiagnosed in dogs, and prediction models can support clinical decision-making, avoiding unnecessary testing and treatment. The aim of this study is to develop and internally validate diagnostic prediction models for hypothyroidism in dogs by applying machine-learning algorithms.MethodsA single-institutional cross-sectional study was designed searching the electronic database of a Veterinary Teaching Hospital for dogs tested for hypothyroidism. Hypothyroidism was diagnosed based on suggestive clinical signs and thyroid function tests. Dogs were excluded if medical records were incomplete or a definitive diagnosis was lacking. Predictors identified after data processing were dermatological signs, alopecia, lethargy, hematocrit, serum concentrations of cholesterol, creatinine, total thyroxine (tT4), and thyrotropin (cTSH). Four models were created by combining clinical signs and clinicopathological variables expressed as quantitative (models 1 and 2) and qualitative variables (models 3 and 4). Models 2 and 4 included tT4 and cTSH, models 1 and 3 did not. Six different algorithms were applied to each model. Internal validation was performed using a 10-fold cross-validation. Apparent performance was evaluated by calculating the area under the receiver operating characteristic curve (AUROC).ResultsEighty-two hypothyroid and 233 euthyroid client-owned dogs were included. The best performing algorithms were naive Bayes in model 1 (AUROC = 0.85; 95% confidence interval [CI] = 0.83–0.86) and in model 2 (AUROC = 0.98; 95% CI = 0.97–0.99), logistic regression in model 3 (AUROC = 0.88; 95% CI = 0.86–0.89), and random forest in model 4 (AUROC = 0.99; 95% CI = 0.98–0.99). Positive predictive value was 0.76, 0.84, 0.93, and 0.97 in model 1, 2, 3, and 4, respectively. Negative predictive value was 0.89, 0.89, 0.99, and 0.99 in model 1, 2, 3, and 4, respectively.DiscussionMachine learning-based prediction models were accurate in predicting and quantifying the likelihood of hypothyroidism in dogs based on internal validation performed in a single-institution, but external validation is required to support the clinical applicability of these models.

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)