Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Segmentation agreement and the reliability of radiomics features

Radiomics features extracted from medical images have been shown to correlate with tumor histological biomarkers and patient clinical information. An accurate selection of reliable features is crucial for an efficient prediction/stratification analysis in clinical applications. This work introduces a computational method for evaluating the reliability of radiomics features with respect to image segmentation. To do so, we define four reliability scores that link segmentation variability to feature quality, consistency, robustness and instability. These scores can be used to establish a ranking that helps identifying the best features to employ for the application at hand, allowing an a-priori evaluation of radiomics reliability in clinical applications. We show the performance of the method with Magnetic Resonance images of meningioma patients. In this case, we identified three main groups of features displaying similar behaviors with respect to image segmentation variability

Longitudinal Cluster Analysis of Hemodialysis Patients with COVID-19 in the Pre-Vaccination Era

Coronavirus disease 2019 (COVID-19) in hemodialysis patients (HD) is characterized by heterogeneity of clinical presentation and outcomes. To stratify patients, we collected clinical and laboratory data in two cohorts of HD patients at COVID-19 diagnosis and during the following 4 weeks. Baseline and longitudinal values were used to build a linear mixed effect model (LME) and define different clusters. The development of the LME model in the derivation cohort of 17 HD patients (66.7 ± 12.3 years, eight males) allowed the characterization of two clusters (cl1 and cl2). Patients in cl1 presented a prevalence of females, higher lymphocyte count, and lower levels of lactate dehydrogenase, C-reactive protein, and CD8 + T memory stem cells as a possible result of a milder inflammation. Then, this model was tested in an independent validation cohort of 30 HD patients (73.3 ± 16.3 years, 16 males) assigned to cl1 or cl2 (16 and 14 patients, respectively). The cluster comparison confirmed that cl1 presented a milder form of COVID-19 associated with reduced disease activity, hospitalization, mortality rate, and oxygen requirement. Clustering analysis on longitudinal data allowed patient stratification and identification of the patients at high risk of complications. This strategy could be suitable in different clinical settings

Synthesis, in vitro evaluation, and molecular modeling investigation of benzenesulfonimide peroxisome proliferator-activated receptors α antagonists

Recent evidences suggest a moderate activation of Peroxisome Proliferator-Activated Receptors (PPARs) could be favorable in metabolic diseases, reducing side effects given from full agonists. PPAR partial agonists and antagonists represent, to date, interesting tools to better elucidate biological processes modulated by these receptors. In this work are reported new benzenesulfonimide compounds able to block PPARα, synthesized and tested by transactivation assays and gene expression analysis. Some of these compounds showed a dose-dependent antagonistic behavior on PPARα, submicromolar potency, different profiles of selectivity versus PPARγ, and a repressive effect on CPT1A expression. Dockings and molecular dynamics on properly selected benzenesulfonimide derivatives furnished fresh insights into the molecular determinant most likely responsible for PPARα antagonism

Sulfonimide and Amide Derivatives as Novel PPARα Antagonists: Synthesis, Antiproliferative Activity, and Docking Studies.

An agonist-antagonist switching strategy was performed to discover novel PPARα antagonists. Phenyldiazenyl derivatives of fibrates were developed, bearing sulfonimide or amide functional groups. A second series of compounds was synthesized, replacing the phenyldiazenyl moiety with amide or urea portions. Final compounds were screened by transactivation assay, showing good PPARα antagonism and selectivity at submicromolar concentrations. When tested in cancer cell models expressing PPARα, selected derivatives induced marked effects on cell viability. Notably, 3c, 3d, and 10e displayed remarkable antiproliferative effects in two paraganglioma cell lines, with CC50 lower than commercial PPARα antagonist GW6471 and a negligible toxicity on normal fibroblast cells. Docking studies were also performed to elucidate the binding mode of these compounds and to help interpretation of SAR data

Multicenter comparative multimodality surveillance of women at genetic-familial high risk for breast cancer (HIBCRIT Study): Interim results

PURPOSE: To prospectively compare clinical breast examination (CBE), mammography, ultrasonography (US), and contrast material-enhanced magnetic resonance (MR) imaging for screening women at genetic-familial high risk for breast cancer and report interim results, with pathologic findings as standard. MATERIALS AND METHODS: Institutional review board of each center approved the research; informed written consent was obtained. CBE, mammography, US, and MR imaging were performed for yearly screening of BRCA1 or BRCA2 mutation carriers, first-degree relatives of BRCA1 or BRCA2 mutation carriers, or women enrolled because of a strong family history of breast or ovarian cancer (three or more events in first- or second-degree relatives in either maternal or paternal line; these included breast cancer in women younger than 60 years, ovarian cancer at any age, and male breast cancer at any age). RESULTS: Two hundred seventy-eight women (mean age, 46 years +/- 12 [standard deviation]) were enrolled. Breast cancer was found in 11 of 278 women at first round and seven of 99 at second round (14 invasive, four intraductal; eight were <or=10 mm in diameter). Detection rate per year was 4.8% (18 of 377) overall; 4.3% (11 of 258) in BRCA1 or BRCA2 mutation carriers and first-degree relatives of BRCA1 or BRCA2 mutation carriers versus 5.9% (seven of 119) in women enrolled because of strong family history; and 5.3% (nine of 169) in women with previous personal breast and/or ovarian cancer versus 4.3% (nine of 208) in those without. In six (33%) of 18 patients, cancer was detected only with MR imaging. Sensitivity was as follows: CBE, 50% (95% confidence interval [CI]: 29%, 71%); mammography, 59% (95% CI: 36%, 78%); US, 65% (95% CI: 41%, 83%); and MR imaging, 94% (95% CI: 82%, 99%). Positive predictive value was as follows: CBE, 82% (95% CI: 52%, 95%); mammography, 77% (95% CI: 50%, 92%); US, 65% (95% CI: 41%, 83%); and MR imaging, 63% (95% CI: 43%, 79%). CONCLUSION: Addition of MR imaging to the screening regimen for high-risk women may enable detection of otherwise unsuspected breast cancers