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BackgroundThe use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the prediction of patient outcomes. Machine learning may also be able to enhance and augment anesthesia clinical procedures such as airway management. In this study, we sought to develop a machine learning algorithm that could classify vocal cords and tracheal airway anatomy real-time during video laryngoscopy or bronchoscopy as well as compare the performance of three novel convolutional networks for detecting vocal cords and tracheal rings.MethodsFollowing institutional approval, a clinical dataset of 775 video laryngoscopy and bronchoscopy videos was used. The dataset was divided into two categories for use for training and testing. We used three convolutional neural networks (CNNs): ResNet, Inception and MobileNet. Backpropagation and a mean squared error loss function were used to assess accuracy as well as minimize bias and variance. Following training, we assessed transferability using the generalization error of the CNN, sensitivity and specificity, average confidence error, outliers, overall confidence percentage, and frames per second for live video feeds. After the training was complete, 22 models using 0 to 25,000 steps were generated and compared.ResultsThe overall confidence of classification for the vocal cords and tracheal rings for ResNet, Inception and MobileNet CNNs were as follows: 0.84, 0.78, and 0.64 for vocal cords, respectively, and 0.69, 0.72, 0.54 for tracheal rings, respectively. Transfer learning following additional training resulted in improved accuracy of ResNet and Inception for identifying the vocal cords (with a confidence of 0.96 and 0.93 respectively). The two best performing CNNs, ResNet and Inception, achieved a specificity of 0.985 and 0.971, respectively, and a sensitivity of 0.865 and 0.892, respectively. Inception was able to process the live video feeds at 10 FPS while ResNet processed at 5 FPS. Both were able to pass a feasibility test of identifying vocal cords and tracheal rings in a video feed.ConclusionsWe report the development and evaluation of a CNN that can identify and classify airway anatomy in real time. This neural network demonstrates high performance. The availability of artificial intelligence may improve airway management and bronchoscopy by helping to identify key anatomy real time. Thus, potentially improving performance and outcomes during these procedures. Further, this technology may theoretically be extended to the settings of airway pathology or airway management in the hands of experienced providers. The researchers in this study are exploring the performance of this neural network in clinical trials.

Shape-memory polymers (SMPs) are smart materials that have gained significant attention in recent years owing to their widespread application in smart structures and devices. Digital light processing (DLP), a vat-photopolymerization-based technique, is a significantly faster technology for printing a complete layer in a single step. The current study reports a facile and fast method for the 3D printing of SMP-based smart structures using a DLP 3D printer and a customized resin. A liquid crystal (LC, RM257) was combined with the resin to introduce shape-memory properties. The combination of LCs in photocurable resin provides the opportunity to directly 3D-print thermoresponsive structures, avoiding the complexity of SMP resin preparation. The structures were printed with different geometries, and the shape-memory response was measured. Lattice structures were fabricated and programmed to obtain tunable mechanical properties. Furthermore, the strain-sensing response was measured to demonstrate the utility of these lattice structures as smart patches for joint-movement sensing. The SMPs can be prepared conveniently and can potentially be used for various applications, such as smart tools, toys, and meta-material sensors. Shape-Memory Polymers: A Game-Changer in 3D Printing and Smart Devices Shape-memory polymers, also known as shape-shifting materials, can morph due to changes in their environment. However, the optimal use of these materials for complex 3D designs is still uncertain. Scientists from King Abdullah University of Science and Technology utilized a commercially available 3D printer to investigate this issue. They merged a shape-memory polymer with a liquid crystal (a substance that can modify its characteristics with temperature), to produce a resin for use in the 3D printer. They showed the possibility of printing diverse complex objects, from lattice structures to toys, which could transform their shape when heated and regain their original shape upon reheating. This study underscores the potential of 3D printing for developing smart materials with shape-memory features. This technology could be advantageous in various domains, including medicine and robotics. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author. In this work, 3D printing shape of memory polymer (SMP) based smart structures is conducted using a Digital light processing 3D printer and a customized resin in combination with liquid crystals. Lattice structures are fabricated and programmed to achieve tunable mechanical properties. The strain-sensing response is measured to demonstrate the utility of these lattice structures as smart patches for joint movement sensing. Changes in the electrical resistance are measured during the stretching and compression of the structure. The SMPs can be prepared conveniently and can potentially be used for various applications, such as smart tools, toys, and meta-material sensors.

Herein, we report the physicochemical, thermal, mechanical and biological characteristics, including bioactivity, biodegradation and cytocompatibility of additive manufacturing-enabled novel nanocomposite scaffolds. The scaffolds comprise a blend of polylactic acid (PLA) and poly-ε-caprolactone (PCL) reinforced with halloysite nanotubes (HNTs). The nanoengineered filaments were developed by melt blending, and the nanocomposite scaffolds were manufactured by fused filament fabrication. Uniform dispersion of HNTs in the PLA/PCL blend is revealed via scanning electron microscopy. Mechanical property loss due to the addition of PCL to realize a suitable biodegradation rate of PLA was fully recovered by the addition of HNTs. Bioactivity, as revealed by the fraction of apatite growth quantified from XRD analysis, was 5.4, 6.3, 6.8 and 7.1% for PLA, 3, 5 and 7 wt% HNT in PLA/PCL blend, respectively, evidencing enhancement in the bioactivity. The degradation rate, in terms of weight loss, was reduced from 4.6% (PLA) to 1.3% (PLA/PCL) upon addition of PCL, which gradually increased to 4.4% by the addition of HNTs (at 7 wt% HNT). The results suggest that the biodegradation rate, mechanical properties and biological characteristics, including cytocompatibility and cell adhesion, of the 3D printed, microarchitected PLA/PCL/HNT composite scaffolds can be tuned by an appropriate combination of HNT and PCL content in the PLA matrix, demonstrating their promise for bone replacement and regeneration applications. Graphical abstract

Impairment of spatial memory, including an inability to recall previous locations and navigate the world, is often one of the first signs of functional disability on the road to cognitive impairment. While there are many screening and diagnostic tools which attempt to measure spatial memory ability, they are often not representative of real-life situations and can therefore lack applicability. One potential solution to this problem involves the use of virtual reality (VR), which immerses individuals in a virtually-simulated environment, allowing for scenarios more representative of real-life without any of the associated risks. Here, we review the evidence surrounding the use of VR for the screening and diagnosis of spatial memory impairments, including potential limitations and how it compares to standard neuropsychological tests. We will also discuss the evidence regarding the potential use of VR in the rehabilitation of spatial memory deficits, which has not been well studied, but which could be game-changing if proven successful.

Fiber‐optic technology emerged originally for applications in data transmission and telecommunications. However, sensors based on fiber‐optics have been developed rapidly because of their excellent sensing performances and capability to function in remote and harsh environments. The usage of fiber‐optic sensors has flourished in many fields over the past 30 years due to the fiber‐optic's inherent advantages: cost‐effectiveness, miniaturized size, light weight, and immunity to electromagnetic interference. This work reviews the fiber‐optic sensors based on Bragg gratings, long period gratings, interferometers, surface plasmon resonance, fluorescence, and light diffusion. Brief theory of sensing principle, fabrication method, applications, advantages and disadvantages of the different fiber‐optic sensors, are addressed. Recent progress in numerous sensing fields, including environmental, industrial, and biomedical are discussed for each class of fiber‐optic sensors. The review highlights the methods and techniques used to overcome the sensing challenges. Finally, prospect of future developments of fiber‐optic sensors is summarized. Fiber‐optic sensors based on Bragg gratings, long‐period gratings, interferometry, surface plasmon resonance (SPR), fluorescence, and light diffusion are analyzed. The foundations of chemical and biosensing are addressed and the recent progress in numerous sensing fields, including environmental, industrial, and biomedical applications, are discussed. The review highlights the strategies and techniques used to overcome the sensing challenges.

Sharda et al discuss antiracism as a professional competence. The CanMEDS roles of professional and health advocate as defined seem to be at odds. Medicine's current concept of professionalism is not designed to encourage advocacy in progressive movements or the critical interrogation of racism within the medical profession itself. It is time to challenge traditional concepts of what constitutes professional behavior in medicine so that physicians can be equipped to advocate for a society that supports health for all. Although tools for embedding antiracism training into medical programs exist, much work remains for Canadian institutions to firmly embed antiracism into medical education.

Color blindness or color vision deficiency (CVD) is a congenital ocular deficiency that hampers patients’ daily life activities. CVD patients rely mostly on using wearable visual aids that enhance color distinction by blocking problematic wavelengths of light. Contributing to that, this study examines the fabrication of 3D‐printed colored contact lens for color blindness management. A vat photopolymerization based 3D printing technology is utilized to fabricate the contact lenses for CVD patients. An in‐house prepared resin is mixed with a low‐cost colored ink to attain the desired blocking range (520–580 nm). The fabricated lens blocks more than 50% of light at the problematic wavelengths, along with exhibiting minimal leakage when stored and examined in water and contact lens storage solution. Average contact angle and water content values are 48° and 56%, respectively. Mechanical properties demonstrate the physical adequacy of the contact lens. The CVD filtering efficacy of the tinted contact lens and its potential as a CVD wearable is evaluated by comparing its optical performance with that of commercial products. Finally, cytotoxicity analysis of the lens to dermal fibroblast cells reveals the biocompatibility of the lens as the cell viability remains greater than 75% after 24 h. Color blind contact lenses are currently expensive due to their costly manufacturing and materials involved in dyeing them. Herein, an additively manufactured tinted contact lens is reported, which is economical and has an optical efficacy similar to the commercial products. Moreover, successful deployment of such contact lenses ensures patients have access to low‐cost, customizable, and effective color‐blind wearable solutions.

Ocular diseases can cause vision problems or even blindness if they are not detected early. Some ocular diseases generate irregular physical changes in the eye; therefore, reliable diagnostic technology for continuous monitoring of the eye is an unmet clinical need. In this study, a pulsed laser (Nd:YAG) was used to create optical nanostructures on a hydrogel-based commercial contact lens. Simulations were used to determine the spacing of the nanostructures, which were then produced and tested on the lens in ambient humidity and fully hydrated environments. The nanostructures produced a 4° diffraction angle difference in response to the environmental changes. Vision obstruction was considered while designing the nanostructure features on the lens. The curved nanostructures exhibited a series of visible rainbow colors with an average range of 8° under normal room light. A spherical surface was also used to simulate the human eye, and application of a force (curvature change) caused the nanostructure spacing to change, influencing the visible color of the contact lenses. A smartphone camera application was used to measure the progress of ocular diseases by analyzing the RGB color values of the visible color. The nanostructures were also responsive to K+ ion variations in artificial tear fluids, with a 12 mmol L−1 sensitivity, which may allow the detection of ocular ionic strength changes.A pulsed laser created optical nanostructures (holograms) on hydrogel-based soft contact lenses. The nanostructures produced varying diffraction patterns in response to the environmental changes. Vision obstruction was considered while designing the nanostructure features on the lens surface. A change in curvature of the contact lens caused the nanostructure spacing to change, influencing the visible color of the hologram. A smartphone camera application was used to monitor the diffraction colors by analyzing the RGB color values.

This article explores the novel gold nanoparticle-enhanced photodynamic therapy of methylene blue against recalcitrant pathogenic Candida albicans biofilm. Physiochemical (X-ray diffraction, ultraviolet-visible absorption, photon cross-correlation, FTIR, and fluorescence spectroscopy) and electron microscopy techniques were used to characterize gold nanoparticles as well as gold nanoparticle-methylene blue conjugate. A 38.2-J/cm(2) energy density of 660-nm diode laser was applied for activation of gold nanoparticle-methylene blue conjugate and methylene blue against C. albicans biofilm and cells. Antibiofilm assays, confocal laser scanning, and electron microscopy were used to investigate the effects of the conjugate. Physical characteristics of the gold nanoparticles (21 ± 2.5 nm and 0.2 mg/mL) and methylene blue (20 μg/mL) conjugation were confirmed by physicochemical and electron microscopy techniques. Antibiofilm assays and microscopic studies showed significant reduction of biofilm and adverse effect against Candida cells in the presence of conjugate. Fluorescence spectroscopic study confirmed type I photo toxicity against biofilm. Gold nanoparticle conjugate-mediated photodynamic therapy may be used against nosocomially acquired refractory Candida albicans biofilm.