Consequences of reaming with flat and convex reamers for bone volume and surface area of the glenoid : a basic science study

Background: The effect of reaming on bone volume and surface area of the glenoid is not precisely known. We hypothesize that (1) convex reamers create a larger surface area than flat reamers, (2) flat reamers cause less bone loss than convex reamers, and (3) the amount of bone loss increases with the amount of version correction. Methods: Reaming procedures with different types of reamers are performed on similar-sized uniconcave and biconcave glenoids created from Sawbones foam blocks. The loss of bone volume, the size of the remaining surface area, and the reaming depth are measured and evaluated. Results: Reaming with convex reamers results in a significantly larger surface area than with flat reamers for both uniconcave and biconcave glenoids (p = 0.013 and p = 0.001). Convex reamers cause more bone loss than flat reamers, but the difference is only significant for uniconcave glenoids (p = 0.007). Conclusions: In biconcave glenoids, convex reamers remove a similar amount of bone as flat reamers, but offer a larger surface area while maximizing the correction of the retroversion. In pathological uniconcave glenoids, convex reamers are preferred because of the conforming shape

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques

State-of-the-Art of Non-Radiative, Non-Visual Spine Sensing with a Focus on Sensing Forces, Vibrations and Bioelectrical Properties: A Systematic Review

In the research field of robotic spine surgery, there is a big upcoming momentum for surgeon-like autonomous behaviour and surgical accuracy in robotics which goes beyond the standard engineering notions such as geometric precision. The objective of this review is to present an overview of the state of the art in non-visual, non-radiative spine sensing for the enhancement of surgical techniques in robotic automation. It provides a vantage point that facilitates experimentation and guides new research projects to what has not been investigated or integrated in surgical robotics. Studies were identified, selected and processed according to the PRISMA guidelines. Relevant study characteristics that were searched for include the sensor type and measured feature, the surgical action, the tested sample, the method for data analysis and the system's accuracy of state identification. The 6DOF f/t sensor, the microphone and the electromyography probe were the most commonly used sensors in each category, respectively. The performance of the electromyography probe is unsatisfactory in terms of preventing nerve damage as it can only signal after the nerve is disturbed. Feature thresholding and artificial neural networks were the most common decision algorithms for state identification. The fusion of different sensor data in the decision algorithm improved the accuracy of state identification

Comparison of the transcriptomes of American chestnut (Castanea dentata) and Chinese chestnut (Castanea mollissima) in response to the chestnut blight infection

<p>Abstract</p> <p>Background1471-2229-9-51</p> <p>American chestnut (<it>Castanea dentata</it>) was devastated by an exotic pathogen in the beginning of the twentieth century. This chestnut blight is caused by <it>Cryphonectria parasitica</it>, a fungus that infects stem tissues and kills the trees by girdling them. Because of the great economic and ecological value of this species, significant efforts have been made over the century to combat this disease, but it wasn't until recently that a focused genomics approach was initiated. Prior to the Genomic Tool Development for the Fagaceae project, genomic resources available in public databases for this species were limited to a few hundred ESTs. To identify genes involved in resistance to <it>C. parasitica</it>, we have sequenced the transcriptome from fungal infected and healthy stem tissues collected from blight-sensitive American chestnut and blight-resistant Chinese chestnut (<it>Castanea mollissima</it>) trees using ultra high throughput pyrosequencing.</p> <p>Results</p> <p>We produced over a million 454 reads, totaling over 250 million bp, from which we generated 40,039 and 28,890 unigenes in total from <it>C. mollissima </it>and <it>C. dentata </it>respectively.</p> <p>The functions of the unigenes, from GO annotation, cover a diverse set of molecular functions and biological processes, among which we identified a large number of genes associated with resistance to stresses and response to biotic stimuli. <it>In silico </it>expression analyses showed that many of the stress response unigenes were expressed more in canker tissues versus healthy stem tissues in both American and Chinese chestnut. Comparative analysis also identified genes belonging to different pathways of plant defense against biotic stresses that are differentially expressed in either American or Chinese chestnut canker tissues.</p> <p>Conclusion</p> <p>Our study resulted in the identification of a large set of cDNA unigenes from American chestnut and Chinese chestnut. The ESTs and unigenes from this study constitute an important resource to the scientific community interested in the discovery of genes involved in various biological processes in Chestnut and other species. The identification of many defense-related genes differentially expressed in canker vs. healthy stem in chestnuts provides many new candidate genes for developing resistance to the chestnut blight and for studying pathways involved in responses of trees to necrotrophic pathogens. We also identified several candidate genes that may underline the difference in resistance to <it>Cryphonectria parasitica </it>between American chestnut and Chinese chestnut.</p

Strain-level metagenomic data analysis of enriched in vitro and in silico spiked food samples : paving the way towards a culture-free foodborne outbreak investigation using STEC as a case study

Culture-independent diagnostics, such as metagenomic shotgun sequencing of food samples, could not only reduce the turnaround time of samples in an outbreak investigation, but also allow the detection of multi-species and multi-strain outbreaks. For successful foodborne outbreak investigation using a metagenomic approach, it is, however, necessary to bioinformatically separate the genomes of individual strains, including strains belonging to the same species, present in a microbial community, which has up until now not been demonstrated for this application. The current work shows the feasibility of strain-level metagenomics of enriched food matrix samples making use of data analysis tools that classify reads against a sequence database. It includes a brief comparison of two database-based read classification tools, Sigma and Sparse, using a mock community obtained by in vitro spiking minced meat with a Shiga toxin-producing Escherichia coli (STEC) isolate originating from a described outbreak. The more optimal tool Sigma was further evaluated using in silico simulated metagenomic data to explore the possibilities and limitations of this data analysis approach. The performed analysis allowed us to link the pathogenic strains from food samples to human isolates previously collected during the same outbreak, demonstrating that the metagenomic approach could be applied for the rapid source tracking of foodborne outbreaks. To our knowledge, this is the first study demonstrating a data analysis approach for detailed characterization and phylogenetic placement of multiple bacterial strains of one species from shotgun metagenomic WGS data of an enriched food sample

A practical method to implement strain-level metagenomics-based foodborne outbreak investigation and source tracking in routine

The management of a foodborne outbreak depends on the rapid and accurate identification of the responsible food source. Conventional methods based on isolation of the pathogen from the food matrix and target-specific real-time polymerase chain reactions (qPCRs) are used in routine. In recent years, the use of whole genome sequencing (WGS) of bacterial isolates has proven its value to collect relevant information for strain characterization as well as tracing the origin of the contamination by linking the food isolate with the patient’s isolate with high resolution. However, the isolation of a bacterial pathogen from food matrices is often time-consuming and not always successful. Therefore, we aimed to improve outbreak investigation by developing a method that can be implemented in reference laboratories to characterize the pathogen in the food vehicle without its prior isolation and link it back to human cases. We tested and validated a shotgun metagenomics approach by spiking food pathogens in specific food matrices using the Shiga toxin-producing Escherichia coli (STEC) as a case study. Different DNA extraction kits and enrichment procedures were investigated to obtain the most practical workflow. We demonstrated the feasibility of shotgun metagenomics to obtain the same information as in ISO/TS 13136:2012 and WGS of the isolate in parallel by inferring the genome of the contaminant and characterizing it in a shorter timeframe. This was achieved in food samples containing different E. coli strains, including a combination of different STEC strains. For the first time, we also managed to link individual strains from a food product to isolates from human cases, demonstrating the power of shotgun metagenomics for rapid outbreak investigation and source tracking

Automatic breach detection during spine pedicle drilling based on vibroacoustic sensing

Pedicle drilling is a complex and critical spinal surgery task. Detecting breach or penetration of the surgical tool to the cortical wall during pilot-hole drilling is essential to avoid damage to vital anatomical structures adjacent to the pedicle, such as the spinal cord, blood vessels, and nerves. Currently, the guidance of pedicle drilling is done using image-guided methods that are radiation intensive and limited to the preoperative information. This work proposes a new radiation-free breach detection algorithm leveraging a non-visual sensor setup in combination with deep learning approach. Multiple vibroacoustic sensors, such as a contact microphone, a free-field microphone, a tri-axial accelerometer, a uni-axial accelerometer, and an optical tracking system were integrated into the setup. Data were collected on four cadaveric human spines, ranging from L5 to T10. An experienced spine surgeon drilled the pedicles relying on optical navigation. A new automatic labeling method based on the tracking data was introduced. Labeled data was subsequently fed to the network in mel-spectrograms, classifying the data into breach and non-breach. Different sensor types, sensor positioning, and their combinations were evaluated. The best results in breach recall for individual sensors could be achieved using contact microphones attached to the dorsal skin (85.8%) and uni-axial accelerometers clamped to the spinous process of the drilled vertebra (81.0%). The best-performing data fusion model combined the latter two sensors with a breach recall of 98%. The proposed method shows the great potential of non-visual sensor fusion for avoiding screw misplacement and accidental bone breaches during pedicle drilling and could be extended to further surgical applications

Automatic breach detection during spine pedicle drilling based on vibroacoustic sensing

Pedicle drilling is a complex and critical spinal surgery task. Detecting breach or penetration of the surgical tool to the cortical wall during pilot-hole drilling is essential to avoid damage to vital anatomical structures adjacent to the pedicle, such as the spinal cord, blood vessels, and nerves. Currently, the guidance of pedicle drilling is done using image-guided methods that are radiation intensive and limited to the preoperative information. This work proposes a new radiation-free breach detection algorithm leveraging a non-visual sensor setup in combination with deep learning approach. Multiple vibroacoustic sensors, such as a contact microphone, a free-field microphone, a tri-axial accelerometer, a uni-axial accelerometer, and an optical tracking system were integrated into the setup. Data were collected on four cadaveric human spines, ranging from L5 to T10. An experienced spine surgeon drilled the pedicles relying on optical navigation. A new automatic labeling method based on the tracking data was introduced. Labeled data was subsequently fed to the network in mel-spectrograms, classifying the data into breach and non-breach. Different sensor types, sensor positioning, and their combinations were evaluated. The best results in breach recall for individual sensors could be achieved using contact microphones attached to the dorsal skin (85.8\%) and uni-axial accelerometers clamped to the spinous process of the drilled vertebra (81.0\%). The best-performing data fusion model combined the latter two sensors with a breach recall of 98\%. The proposed method shows the great potential of non-visual sensor fusion for avoiding screw misplacement and accidental bone breaches during pedicle drilling and could be extended to further surgical applications