Technical Considerations in Revision Anterior Cruciate Ligament Reconstruction for Operative Techniques in Orthopaedics

As the incidence of anterior cruciate ligament (ACL) reconstruction continues to increase, the rate of revision surgery continues to climb. Revision surgery has inherent challenges that must be addressed to achieve successful results. The cause of the primary ACL reconstruction failure should be determined and careful preoperative planning should be performed to address the cause(s) of failure. Each patient undergoing revision surgery should undergo a thorough history and physical examination, receive full-length alignment radiographs, lateral radiographs, 45° flexion weight-bearing posteroanterior radiographs, and patellofemoral radiographs. The 3-dimensional computed tomography scan should be performed to assess tunnel position and widening. Magnetic resonance imaging should be used to assess for intra-articular soft tissue pathology. Meniscal tears, meniscal deficiency, anterolateral capsule injuries, bony morphology, age, activity level, connective tissue diseases, infection, graft choice, and tunnel position can all affect the success of ACL reconstruction surgery. Meniscal lesions should be repaired, and in cases of persistent rotatory instability, extra-articular procedures may be indicated. Furthermore, osteotomies may be needed to correct malalignment or excess posterior tibial slope. Depending on the placement and condition of the original femoral and tibial tunnels, revision surgery may be performed in a single procedure or in a staged manner. In most cases, the surgery can be performed in one procedure. Regardless, the surgeon must communicate with the patient openly regarding the implications of revision ACL surgery, and the treatment plan should be developed in a shared fashion between the surgeon and the patient

Calcium Alginate Gels as Stem Cell Matrix - Making Paracrine Stem Cell Activity Available for Enhanced Healing after Surgery

Regeneration after surgery can be improved by the administration of anabolic growth factors. However, to locally maintain these factors at the site of regeneration is problematic. The aim of this study was to develop a matrix system containing human mesenchymal stem cells (MSCs) which can be applied to the surgical site and allows the secretion of endogenous healing factors from the cells. Calcium alginate gels were prepared by a combination of internal and external gelation. The gelling behaviour, mechanical stability, surface adhesive properties and injectability of the gels were investigated. The permeability of the gels for growth factors was analysed using bovine serum albumin and lysozyme as model proteins. Human MSCs were isolated, cultivated and seeded into the alginate gels. Cell viability was determined by AlamarBlue assay and fluorescence microscopy. The release of human VEGF and bFGF from the cells was determined using an enzyme-linked immunoassay. Gels with sufficient mechanical properties were prepared which remained injectable through a syringe and solidified in a sufficient time frame after application. Surface adhesion was improved by the addition of polyethylene glycol 300, 000 and hyaluronic acid. Humans MSCs remained viable for the duration of 6 weeks within the gels. Human VEGF and bFGF was found in quantifiable concentrations in cell culture supernatants of gels loaded with MSCs and incubated for a period of 6 weeks. This work shows that calcium alginate gels can function as immobilization matrices for human MSCs

Calcium Alginate Gels as Stem Cell Matrix - Making Paracrine Stem Cell Activity Available for Enhanced Healing after Surgery

Regeneration after surgery can be improved by the administration of anabolic growth factors. However, to locally maintain these factors at the site of regeneration is problematic. The aim of this study was to develop a matrix system containing human mesenchymal stem cells (MSCs) which can be applied to the surgical site and allows the secretion of endogenous healing factors from the cells. Calcium alginate gels were prepared by a combination of internal and external gelation. The gelling behaviour, mechanical stability, surface adhesive properties and injectability of the gels were investigated. The permeability of the gels for growth factors was analysed using bovine serum albumin and lysozyme as model proteins. Human MSCs were isolated, cultivated and seeded into the alginate gels. Cell viability was determined by AlamarBlue assay and fluorescence microscopy. The release of human VEGF and bFGF from the cells was determined using an enzyme-linked immunoassay. Gels with sufficient mechanical properties were prepared which remained injectable through a syringe and solidified in a sufficient time frame after application. Surface adhesion was improved by the addition of polyethylene glycol 300, 000 and hyaluronic acid. Humans MSCs remained viable for the duration of 6 weeks within the gels. Human VEGF and bFGF was found in quantifiable concentrations in cell culture supernatants of gels loaded with MSCs and incubated for a period of 6 weeks. This work shows that calcium alginate gels can function as immobilization matrices for human MSCs

Accelerated evidence synthesis in orthopaedics—the roles of natural language processing, expert annotation and large language models

peer reviewedIn an era of electronical medical records, rapidly expanding publication rates of medical knowledge, and large-scale registries, orthopaedics is in a dire need of innovative approaches to facilitate the adoption of the latest knowledge in clinical practice. While machine learning (ML) has been heralded as one solution to many research tasks hampered by previous technological limitations [12], there is an increasing need to direct our attention towards subdomains of ML that are convenient for the extraction of meaningful clinical information stored in medical records. We believe natural language processing (NLP) to be one such domain of ML, with an immense future potential to catalyse rate-limiting steps in orthopaedic research

Research productivity during orthopedic surgery residency correlates with pre‑planned and protected research time: a survey of German‑speaking countries

Purpose The purpose of this study was to identify modifiable factors associated with research activity among residents working in orthopedic surgery and traumatology. Methods Residents at 796 university-affiliated hospitals in Austria, Germany, and Switzerland were invited to participate. The online survey consisted of questions that ascertained 13 modifiable and 17 non-modifiable factors associated with the residents’ current research activities. Responses of 129 residents were analyzed. Univariate linear regression was used to determine the association of individual factors with the current research activity (hours per week). The impact of significant non-modifiable factors (with unadjusted p values < 0.05) was controlled for using multivariate linear regression. Results The univariate analysis demonstrated six non-modifiable factors that were significantly associated with the current research activity: a University hospital setting (p < 0.001), an A-level hospital setting (p = 0.024), Swiss residents (p = 0.0012), the completion of a dedicated research year (p = 0.007), female gender (p = 0.016), and the department’s size (p = 0.048). Multivariate regression demonstrated that the number of protected research days per year (p < 0.029) and the percentage of protected days, that were known 1 week before (p < 0.001) or the day before (p < 0.001), were significantly associated with a higher research activity. Conclusions As hypothesized, more frequent and predictable protected research days were associated with higher research activity among residents in orthopedic surgery and traumatology. Level of evidence III

A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges.

peer reviewedArtificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world\u27s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications