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

A Platform for Enhancing the Vision of Patients Suffering from Age-Related Macular Degeneration Disease

RÉSUMÉ La dégénérescence maculaire liée à l’âge est la principale cause de cécité en Amérique du Nord sans traitement médical fiable. Cette maladie progressive se manifeste par une déficience visuelle sévère de la vision centrale à ses étapes intermédiaires ou par une perte totale de la vision centrale dans ses étapes ultérieures en raison de la dégradation de la couche photoréceptrice de la rétine. Il n’existe actuellement aucun traitement fiable pour ces maladies et très peu de mesures préventives qui ralentissent leur progression. De nombreuses approches ont été proposées pour aider à atténuer ce problème. Une canne et des chiens-guides sont parmi les méthodes les plus fiables au monde pour aider les malvoyants. Les nouvelles approches visent à tirer parti de la puissance de la technologie pour offrir une meilleure expérience au patient grâce à des guides de navigation électroniques ou des aides visuelles. Le travail présenté dans cette thèse est une plate-forme destinée à la stimulation optique pour la dégénérescence maculaire liée à l’âge, ceci en émulant le traitement rétinien se produisant dans la macula en temps réel et en envoyant un signal optogénétique à un dispositif de stimulation photo-électrique à la couche rétinienne souhaitée pour restaurer la vision dans une rétine endommagée. Ceci est réalisé avec un modèle de traitement d’image rapide, simplifié et ajustable de la rétine utilisant un filtrage spatial et temporel. Une capture vidéo en direct et des images à l’échelle sont ensuite utilisées pour adapter la prothèse visuelle à plusieurs résolutions. L’aspect temporel de la macula est émulé à l’aide d’un filtre temporel multi-images personnalisé et d’une intégration décroissante qui correspond à la vitesse de la voie temporelle dans la rétine, tandis que l’aspect spatial est modélisé comme une différence de gaussien. Ensemble, ils se combinent en un modèle simple et puissant qui représente la voie principale de la rétine humaine. Le modèle est mis en œuvre de manière à être extensible et efficace en termes de calcul pour obtenir les meilleurs résultats dans un budget de temps strict. Le modèle implémenté est suffisamment rapide pour fonctionner en temps réel sur les appareils Raspberry Pi 3 et Raspberry Pi 4. La sortie de la plate-forme génère un signal à envoyer à un stimulateur micro-LED conçu par Leila Montazeri et al. Le stimulateur générera un signal lumineux focalisé qui agit sur les cellules ganglionnaires rétiniennes traitées avec des opsines photosensibles, dans le but de restaurer une certaine acuité visuelle dans la vision centrale. En raison des limites de fabrication, seule une matrice micro-LED 8x8 peut être produite. Cela signifie que seule une image 8x8 peut être envoyée au micro-stimulateur, et il est donc important de représenter les images par un très petit nombre de pixels.----------ABSTRACT Age-related macular degeneration is the leading cause of blindness in North America with no reliable medical treatment. This progressive disease manifests itself with severe visual impairment in the central vision in its intermediate stages or with the total loss of central vision in its later stages due to the decay in the photoreceptor layer in the retina. There are currently no reliable treatments for these diseases and very few preventative measures that slow down their progression. There have been many approaches proposed to help mitigate this problem. A walking stick and guide dogs are among the most reliably used around the world to provide aid for the visually impaired. Newer approaches aim to leverage the power of technology to provide a better patient experience through electronic navigation guides or visual aids. The work presented in this thesis is a platform intended for driving age-related macular degeneration optic stimulation by emulating retinal processing occurring in the macula in real-time and sending an optogenetic signal to a photo-electric stimulation device to the desired retinal layer to recreate vision within a damaged retina. This is achieved with a fast, simplified, and tunable image processing model of the retina using spatial and temporal filtering. Live video capture and scaled images are then used to fit the visual prosthesis with multiple resolutions. The temporal aspect of the macula is emulated using a custom-written multi-frame temporal filter and a decaying integration that matches the speed of the temporal pathway in the retina, while the spatial aspect is modeled as a di˙erence of Gaussian. Together they combine into a simple and powerful model that represents the major pathway in the human retina. The model is implemented in a way to be expandable and computationally eÿcient to get the best results under a strict time budget. The implemented model is fast enough to work in real-time on Raspberry Pi 3 and Raspberry Pi 4 devices. The output of the platform generates a signal to be sent to a micro-LED stimulator designed by Leila Montazeri et al. The stimulator will generate a focused light signal that works on retinal ganglion cells treated with light-sensitive opsins, with the aim of restoring some visual acuity in the central vision. Due to manufacturing limitations, only an 8x8 micro-LED matrix can be produced, hence it is important to represent images with a very small number of pixels. We chose to focus on facial expressions since they are crucial for human communication. In addition, facial expression recognition has many uses in several fields, such as entertainment, personalized medicine, and human-machine interaction

Mobile-Optimized Facial Expression Recognition Techniques

This paper presents two novel facial expression recognition techniques: the real-time ensemble for facial expression recognition (REFER) and the facial expression recognition network (FERNet). Both approaches can detect facial expressions from various poses, distances, angles, and resolutions, and both techniques exhibit high computational efficiency and portability. REFER outperforms the existing approaches in terms of cross-dataset accuracy, making it an ideal network to use on fresh data. FERNet is a compact convolutional neural network that uses both geometric and texture features to achieve up to 98% accuracy on the MUG dataset. Both approaches can process 14 frames per second (FPS) from a live video capture on a battery-powered Raspberry Pi 4

Overcoming Therapy Resistance in Colon Cancer by Drug Repurposing

Colorectal cancer (CRC) is the third most common cancer in the world. Despite improvement in standardized screening methods and the development of promising therapies, the 5-year survival rates are as low as 10% in the metastatic setting. The increasing life expectancy of the general population, higher rates of obesity, poor diet, and comorbidities contribute to the increasing trends in incidence. Drug repurposing offers an affordable solution to achieve new indications for previously approved drugs that could play a protagonist or adjuvant role in the treatment of CRC with the advantage of treating underlying comorbidities and decreasing chemotherapy toxicity. This review elaborates on the current data that supports drug repurposing as a feasible option for patients with CRC with a focus on the evidence and mechanism of action promising repurposed candidates that are widely used, including but not limited to anti-malarial, anti-helminthic, anti-inflammatory, anti-hypertensive, anti-hyperlipidemic, and anti-diabetic agents