Use of artificial intelligence in sports medicine: a report of 5 fictional cases

Background Artificial intelligence (AI) is one of the most promising areas in medicine with many possibilities for improving health and wellness. Already today, diagnostic decision support systems may help patients to estimate the severity of their complaints. This fictional case study aimed to test the diagnostic potential of an AI algorithm for common sports injuries and pathologies. Methods Based on a literature review and clinical expert experience, five fictional “common” cases of acute, and subacute injuries or chronic sport-related pathologies were created: Concussion, ankle sprain, muscle pain, chronic knee instability (after ACL rupture) and tennis elbow. The symptoms of these cases were entered into a freely available chatbot-guided AI app and its diagnoses were compared to the pre-defined injuries and pathologies. Results A mean of 25–36 questions were asked by the app per patient, with optional explanations of certain questions or illustrative photos on demand. It was stressed, that the symptom analysis would not replace a doctor’s consultation. A 23-yr-old male patient case with a mild concussion was correctly diagnosed. An ankle sprain of a 27-yr-old female without ligament or bony lesions was also detected and an ER visit was suggested. Muscle pain in the thigh of a 19-yr-old male was correctly diagnosed. In the case of a 26-yr-old male with chronic ACL instability, the algorithm did not sufficiently cover the chronic aspect of the pathology, but the given recommendation of seeing a doctor would have helped the patient. Finally, the condition of the chronic epicondylitis in a 41-yr-old male was correctly detected. Conclusions All chosen injuries and pathologies were either correctly diagnosed or at least tagged with the right advice of when it is urgent for seeking a medical specialist. However, the quality of AI-based results could presumably depend on the data-driven experience of these programs as well as on the understanding of their users. Further studies should compare existing AI programs and their diagnostic accuracy for medical injuries and pathologies.Peer Reviewe

Compartmentalized Jet Polymerization as a High-Resolution Process to Continuously Produce Anisometric Microgel Rods with Adjustable Size and Stiffness

In the past decade, anisometric rod-shaped microgels have attracted growing interest in the materials-design and tissue-engineering communities. Rod-shaped microgels exhibit outstanding potential as versatile building blocks for 3D hydrogels, where they introduce macroscopic anisometry, porosity, or functionality for structural guidance in biomaterials. Various fabrication methods have been established to produce such shape-controlled elements. However, continuous high-throughput production of rod-shaped microgels with simultaneous control over stiffness, size, and aspect ratio still presents a major challenge. A novel microfluidic setup is presented for the continuous production of rod-shaped microgels from microfluidic plug flow and jets. This system overcomes the current limitations of established production methods for rod-shaped microgels. Here, an on-chip gelation setup enables fabrication of soft microgel rods with high aspect ratios, tunable stiffness, and diameters significantly smaller than the channel diameter. This is realized by exposing jets of a microgel precursor to a high intensity light source, operated at specific pulse sequences and frequencies to induce ultra-fast photopolymerization, while a change in flow rates or pulse duration enables variation of the aspect ratio. The microgels can assemble into 3D structures and function as support for cell culture and tissue engineering. © 2019 DWI – Leibniz Institute for Interactive Materials. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Synthetic 3D PEG-Anisogel Tailored with Fibronectin Fragments Induce Aligned Nerve Extension

An enzymatically cross-linked polyethylene glycol (PEG)-based hydrogel was engineered to promote and align nerve cells in a three-dimensional manner. To render the injectable, otherwise bioinert, PEG-based material supportive for cell growth, its mechanical and biochemical properties were optimized. A recombinant fibronectin fragment (FNIII9*-10/12-14) was coupled to the PEG backbone during gelation to provide cell adhesive and growth factor binding domains in close vicinity. Compared to full-length fibronectin, FNIII9*-10/12-14 supports nerve growth at similar concentrations. In a 3D environment, only the ultrasoft 1 w/v% PEG hydrogels with a storage modulus of ∼10 Pa promoted neuronal growth. This gel was used to establish the first fully synthetic, injectable Anisogel by the addition of magnetically aligned microelements, such as rod-shaped microgels or short fibers. The Anisogel led to linear neurite extension and represents a large step in the direction of clinical translation with the opportunity to treat acute spinal cord injuries

Rapid and Robust Coating Method to Render Polydimethylsiloxane Surfaces Cell-Adhesive

Polydimethylsiloxane (PDMS) is a synthetic material with excellent properties for biomedical applications because of its easy fabrication method, high flexibility, permeability to oxygen, transparency, and potential to produce high-resolution structures in the case of lithography. However, PDMS needs to be modified to support homogeneous cell attachments and spreading. Even though many physical and chemical methods, like plasma treatment or extracellular matrix coatings, have been developed over the last decades to increase cell surface interactions, these methods are still very time-consuming, often not efficient enough, complex, and can require several treatment steps. To overcome these issues, we present a novel, robust, and fast one-step PDMS coating method using engineered anchor peptides fused to the cell-adhesive peptide sequence (glycine-arginine-glycine-aspartate-serine, GRGDS). The anchor peptide attaches to the PDMS surface predominantly by by simply dipping PDMS in a solution containing the anchor peptide, presenting the GRGDS sequence on the surface available for cell adhesion. The binding performance and kinetics of the anchor peptide to PDMS are characterized, and the coatings are optimized for efficient cell attachment of fibroblasts and endothelial cells. Additionally, the applicability is proven using PDMS-based directional nanotopographic gradients, showing a lower threshold of 5 mu m wrinkles for fibroblast alignment

Global carbon budget 2019

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use change (ELUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), EFF was 9.5±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.9±0.02 GtC yr−1 (2.3±0.01 ppm yr−1), SOCEAN 2.5±0.6 GtC yr−1, and SLAND 3.2±0.6 GtC yr−1, with a budget imbalance BIM of 0.4 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr−1, reaching 10 GtC yr−1 for the first time in history, ELUC was 1.5±0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr−1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr−1 (2.4±0.1 ppm yr−1), SOCEAN was 2.6±0.6 GtC yr−1, and SLAND was 3.5±0.7 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein et al., 2019)

Global Carbon Budget 2022

Accurate assessment of anthropogenic carbon dioxide (CO$_2$) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO$_2$ emissions (E$_{FOS}$) are based on energy statistics and cement production data, while emissions from land-use change (E$_{LUC}$), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO$_2$ concentration is measured directly, and its growth rate (G$_{ATM}$) is computed from the annual changes in concentration. The ocean CO$_2$ sink (S$_{OCEAN}$) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO$_2$ sink (S$_{LAND}$) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (B$_{IM}$), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, E$_{FOS}$ increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr$^{−1}$ (9.9 ± 0.5 GtC yr$^{−1}$ when the cement carbonation sink is included), and E$_{LUC}$ was 1.1 ± 0.7 GtC yr$^{−1}$, for a total anthropogenic CO$_2$ emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr$^{−1}$ (40.0 ± 2.9 GtCO$_2$). Also, for 2021, G$_{ATM}$ was 5.2 ± 0.2 GtC yr$^{−1}$ (2.5 ± 0.1 ppm yr$^{−1}$), S$_{OCEAN}$ was 2.9  ± 0.4 GtC yr$^{−1}$, and S$_{LAND}$ was 3.5 ± 0.9 GtC yr$^{−1}$, with a B$_{IM}$ of −0.6 GtC yr$^{−1}$ (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO$_2$ concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in E$_{FOS}$ relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO$_2$ concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr$^{−1}$ persist for the representation of annual to semi-decadal variability in CO$_2$ fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO$_2$ flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b)

Biofunctionalized aligned microgels provide 3D cell guidance to mimic complex tissue matrices

Natural healing is based on highly orchestrated processes, in which the extracellular matrix plays a key role. To resemble the native cell environment, we introduce an artificial extracellular matrix (aECM) with the capability to template hierarchical and anisotropic structures in situ, allowing a minimally-invasive application via injection. Synthetic, magnetically responsive, rod-shaped microgels are locally aligned and fixed by a biocompatible surrounding hydrogel, creating a hybrid anisotropic hydrogel (Anisogel), of which the physical, mechanical, and chemical properties can be tailored. The microgels are rendered cell-adhesive with GRGDS and incorporated either inside a cell-adhesive fibrin or bioinert poly(ethylene glycol) hydrogel to strongly interact with fibroblasts. GRGDS-modified microgels inside a fibrin-based Anisogel enhance fibroblast alignment and lead to a reduction in fibronectin production, indicating successful replacement of structural proteins. In addition, YAP-translocation to the nucleus increases with the concentration of microgels, indicating cellular sensing of the overall anisotropic mechanical properties of the Anisogel. For bioinert surrounding PEG hydrogels, GRGDS-microgels are required to support cell proliferation and fibronectin production. In contrast to fibroblasts, primary nerve growth is not significantly affected by the biomodification of the microgels. In conclusion, this approach opens new opportunities towards advanced and complex aECMs for tissue regeneration

Biofunctionalized aligned microgels provide 3D cell guidance to mimic complex tissue matrices

Natural healing is based on highly orchestrated processes, in which the extracellular matrix plays a key role. To resemble the native cell environment, we introduce an artificial extracellular matrix (aECM) with the capability to template hierarchical and anisotropic structures in situ, allowing a minimally-invasive application via injection. Synthetic, magnetically responsive, rod-shaped microgels are locally aligned and fixed by a biocompatible surrounding hydrogel, creating a hybrid anisotropic hydrogel (Anisogel), of which the physical, mechanical, and chemical properties can be tailored. The microgels are rendered cell-adhesive with GRGDS and incorporated either inside a cell-adhesive fibrin or bioinert poly(ethylene glycol) hydrogel to strongly interact with fibroblasts. GRGDS-modified microgels inside a fibrin-based Anisogel enhance fibroblast alignment and lead to a reduction in fibronectin production, indicating successful replacement of structural proteins. In addition, YAP-translocation to the nucleus increases with the concentration of microgels, indicating cellular sensing of the overall anisotropic mechanical properties of the Anisogel. For bioinert surrounding PEG hydrogels, GRGDS-microgels are required to support cell proliferation and fibronectin production. In contrast to fibroblasts, primary nerve growth is not significantly affected by the biomodification of the microgels. In conclusion, this approach opens new opportunities towards advanced and complex aECMs for tissue regeneration

Granular Cellulose Nanofibril Hydrogel Scaffolds for 3D Cell Cultivation

The replacement of diseased and damaged organs remains an challenge in modern medicine. However, through the use of tissue engineering techniques, it may soon be possible to (re)generate tissues and organs using artificial scaffolds. For example, hydrogel networks made from hydrophilic precursor solutions can replicate many properties found in the natural extracellular matrix (ECM) but often lack the dynamic nature of the ECM, as many covalently crosslinked hydrogels possess elastic and static networks with nanoscale pores hindering cell migration without being degradable. To overcome this, macroporous colloidal hydrogels can be prepared to facilitate cell infiltration. Here, an easy method is presented to fabricate granular cellulose nanofibril hydrogel (CNF) scaffolds as porous networks for 3D cell cultivation. CNF is an abundant natural and highly biocompatible material that supports cell adhesion. Granular CNF scaffolds are generated by pre-crosslinking CNF using calcium and subsequently pressing the gel through micrometer-sized nylon meshes. The granular solution is mixed with fibroblasts and crosslinked with cell culture medium. The obtained granular CNF scaffold is significantly softer and enables well-distributed fibroblast growth. This cost-effective material combined with this efficient and facile fabrication technique allows for 3D cell cultivation in an upscalable manner. © 2020 The Authors. Published by Wiley-VCH Gmb