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

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

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

Application Areas of Traditional Molecular Genetic Methods and NGS in relation to Hereditary Urological Cancer Diagnosis

Next generation sequencing (NGS) is widely used for diagnosing hereditary cancer syndromes. Often, exome sequencing and extended gene panel approaches are the only means that can be used to detect a pathogenic germline mutation in the case of multiple primary tumors, early onset, a family history of cancer, or a lack of specific signs associated with a particular syndrome. Certain germline mutations of oncogenes and tumor suppressor genes that determine specific clinical phenotypes may occur in mutation hot spots. Diagnosis of such cases, which involve hereditary cancer, does not require NGS, but may be made using PCR and Sanger sequencing. Diagnostic criteria and professional community guidelines developed for hereditary cancers of particular organs should be followed when ordering molecular diagnostic tests for a patient. This review focuses on urological oncology associated with germline mutations. Clinical signs and genetic diagnostic laboratory tests for hereditary forms of renal cell cancer, prostate cancer, and bladder cancer are summarized. While exome sequencing, or, conversely, traditional molecular genetic methods are the procedure of choice in some cases, in most situations, sequencing of multigene panels that are specifically aimed at detecting germline mutations in early onset renal cancer, prostate cancer, and bladder cancer seems to be the basic solution for molecular genetic diagnosis of hereditary cancers

Sticky Architecture: Encoding Pressure Sensitive Adhesion in Polymer Networks

Pressure sensitive adhesives (PSAs) are ubiquitous materials within a spectrum that span from office supplies to biomedical devices. Currently, the ability of PSAs to meet the needs of these diverse applications relies on trial-and-error mixing of assorted chemicals and polymers, which inherently entails property imprecision and variance over time due to component migration and leaching. Herein, we develop a precise additive-free PSA design platform that predictably leverages polymer network architecture to empower comprehensive control over adhesive performance. Utilizing the chemical universality of brush-like elastomers, we encode work of adhesion ranging 5 orders of magnitude with a single polymer chemistry by coordinating brush architectural parameters–side chain length and grafting density. Lessons from this design-by-architecture approach are essential for future implementation of AI machinery in molecular engineering of both cured and thermoplastic PSAs incorporated into everyday use

Sticky Architecture: Encoding Pressure Sensitive Adhesion in Polymer Networks

Pressure sensitive adhesives (PSAs) are ubiquitous materials within a spectrum that span from office supplies to biomedical devices. Currently, the ability of PSAs to meet the needs of these diverse applications relies on trial-and-error mixing of assorted chemicals and polymers, which inherently entails property imprecision and variance over time due to component migration and leaching. Herein, we develop a precise additive-free PSA design platform that predictably leverages polymer network architecture to empower comprehensive control over adhesive performance. Utilizing the chemical universality of brush-like elastomers, we encode work of adhesion ranging 5 orders of magnitude with a single polymer chemistry by coordinating brush architectural parameters–side chain length and grafting density. Lessons from this design-by-architecture approach are essential for future implementation of AI machinery in molecular engineering of both cured and thermoplastic PSAs incorporated into everyday use

Sticky Architecture: Encoding Pressure Sensitive Adhesion in Polymer Networks

Pressure sensitive adhesives (PSAs) are ubiquitous materials within a spectrum that span from office supplies to biomedical devices. Currently, the ability of PSAs to meet the needs of these diverse applications relies on trial-and-error mixing of assorted chemicals and polymers, which inherently entails property imprecision and variance over time due to component migration and leaching. Herein, we develop a precise additive-free PSA design platform that predictably leverages polymer network architecture to empower comprehensive control over adhesive performance. Utilizing the chemical universality of brush-like elastomers, we encode work of adhesion ranging 5 orders of magnitude with a single polymer chemistry by coordinating brush architectural parameters–side chain length and grafting density. Lessons from this design-by-architecture approach are essential for future implementation of AI machinery in molecular engineering of both cured and thermoplastic PSAs incorporated into everyday use

Sticky Architecture: Encoding Pressure Sensitive Adhesion in Polymer Networks

Pressure sensitive adhesives (PSAs) are ubiquitous materials within a spectrum that span from office supplies to biomedical devices. Currently, the ability of PSAs to meet the needs of these diverse applications relies on trial-and-error mixing of assorted chemicals and polymers, which inherently entails property imprecision and variance over time due to component migration and leaching. Herein, we develop a precise additive-free PSA design platform that predictably leverages polymer network architecture to empower comprehensive control over adhesive performance. Utilizing the chemical universality of brush-like elastomers, we encode work of adhesion ranging 5 orders of magnitude with a single polymer chemistry by coordinating brush architectural parameters–side chain length and grafting density. Lessons from this design-by-architecture approach are essential for future implementation of AI machinery in molecular engineering of both cured and thermoplastic PSAs incorporated into everyday use

Sticky Architecture: Encoding Pressure Sensitive Adhesion in Polymer Networks

Pressure sensitive adhesives (PSAs) are ubiquitous materials within a spectrum that span from office supplies to biomedical devices. Currently, the ability of PSAs to meet the needs of these diverse applications relies on trial-and-error mixing of assorted chemicals and polymers, which inherently entails property imprecision and variance over time due to component migration and leaching. Herein, we develop a precise additive-free PSA design platform that predictably leverages polymer network architecture to empower comprehensive control over adhesive performance. Utilizing the chemical universality of brush-like elastomers, we encode work of adhesion ranging 5 orders of magnitude with a single polymer chemistry by coordinating brush architectural parameters–side chain length and grafting density. Lessons from this design-by-architecture approach are essential for future implementation of AI machinery in molecular engineering of both cured and thermoplastic PSAs incorporated into everyday use