Fully Automated Analysis of the Anatomic and Mechanical Axes From Pediatric Standing Lower Limb Radiographs Using Convolutional Neural Networks.

BACKGROUND: Lower limb alignment is the quantification of a set of parameters that are commonly measured radiographically to test for and track a wide range of skeletal pathologies. Determining limb alignment is a commonly performed yet laborious task in the pediatric orthopaedic setting and is therefore an interesting goal for automation. METHODS: We employ a machine learning approach using convolutional neural networks (CNNs) to segment pediatric weight-bearing lower limb radiographs. The results are then used with custom Matlab code to extract anatomic landmarks and to determine lower limb alignment parameters. RESULTS: Measurements obtained from the automated workflow proposed here were compared with manual measurements performed by orthopaedic surgery fellows. Mechanical axis deviation was determined within a mean of 2.02 mm. Lateral distal femoral angle and medial proximal tibial angle were determined with a mean deviation of 1.73 and 2.90 degrees, respectively. The calculation speed for the full set of mechanical and anatomic axis parameters was found to be ~2 seconds per radiograph. CONCLUSIONS: The CNN-based approach proposed in this work was shown to produce results comparable to orthopaedic surgery fellows at fast calculation speed. Although further work is needed to validate these results against radiographs and measurements from other centers, we see this as a promising start and a functional path that can be employed in further research. CLINICAL RELEVANCE: CNNs are a promising approach to automating commonly performed, repetitive tasks, especially those pertaining to image processing. The time savings are particularly important in clinical research applications where large sets of radiographs are routinely available and require analysis. With further development of these algorithms, we anticipate significantly improved agreement with expert-measured results and the calculation speed.

The effect of type-2 diabetes conditions on neutrophil rolling adhesion.

OBJECTIVE: Type 2 diabetes mellitus (T2D) is the result of a dysregulation of insulin production and signalling, leading to an increase in both glucose concentration and pro-inflammatory cytokines such as interleukin (IL)-6 and tumour necrosis factor (TNF)-α. Previous work showed that T2D patients exhibited immune dysfunction associated with increased adhesion molecule expression on endothelial cell surfaces, accompanied by decreased neutrophil rolling velocity on the endothelial cell surface. Changes in cell rolling adhesion have direct vascular and immune complications such as atherosclerosis and reduced healing time in T2D patients. While previous studies focused primarily on how endothelial cells affect neutrophil rolling under T2D conditions, little is known about changes to neutrophils that affect their rolling. In this study, we aim to show how the rolling behaviour of neutrophils is affected by T2D conditions on a controlled substrate. RESULTS: We found that neutrophils cultured in T2D-serum mimicking media increased cell rolling velocity compared to neutrophils under normal conditions. Specifically, glucose alone is responsible for higher rolling velocity. While cytokines further increase the rolling velocity, they also reduce the cell size. Both glucose and cytokines likely reduce the function of P-selectin Glycoprotein Ligand-1 (PSGL-1) on neutrophils.

Clinical Presentations and Outcomes of Children in Canada With Recurrent Invasive Pneumococcal Disease From the IMPACT Surveillance Network.

BACKGROUND: Invasive pneumococcal disease due to Streptococcus pneumoniae can cause mortality and severe morbidity due to sepsis, meningitis and pneumonia, particularly in young children and the elderly. Recurrent invasive pneumococcal disease is rare yet serious sequelae of invasive pneumococcal disease that is associated with the immunocompromised and leads to a high mortality rate. METHOD: This retrospective study reviewed recurrent invasive pneumococcal disease cases from the Canadian Immunization Monitoring Program, ACTive (IMPACT) between 1991 and 2019, an active network for surveillance of vaccine-preventable diseases and adverse events following immunization for children ages 0-16 years. Data were collected from 12 pediatric tertiary care hospitals across all 3 eras of public pneumococcal conjugate vaccine implementation in Canada. RESULTS: The survival rate within our cohort of 180 recurrent invasive pneumococcal disease cases was 98.3%. A decrease of 26.4% in recurrent invasive pneumococcal disease due to vaccine serotypes was observed with pneumococcal vaccine introduction. There was also a 69.0% increase in the rate of vaccination in children with preexisting medical conditions compared with their healthy peers. CONCLUSION: The decrease in recurrent invasive pneumococcal disease due to vaccine-covered serotypes has been offset by an increase of non-vaccine serotypes in this sample of Canadian children.

Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay.

Rolling adhesion, facilitated by selectin-mediated interactions, is a highly dynamic, passive motility in recruiting leukocytes to the site of inflammation. This phenomenon occurs in postcapillary venules, where blood flow pushes leukocytes in a rolling motion on the endothelial cells. Stable rolling requires a delicate balance between adhesion bond formation and their mechanically-driven dissociation, allowing the cell to remain attached to the surface while rolling in the direction of flow. Unlike other adhesion processes occurring in relatively static environments, rolling adhesion is highly dynamic as the rolling cells travel over thousands of microns at tens of microns per second. Consequently, conventional mechanobiology methods such as traction force microscopy are unsuitable for measuring the individual adhesion events and the associated molecular forces due to the short timescale and high sensitivity required. Here, we describe our latest implementation of the adhesion footprint assay to image the P-selectin: PSGL-1 interactions in rolling adhesion at the molecular level. This method utilizes irreversible DNA-based tension gauge tethers to produce a permanent history of molecular adhesion events in the form of fluorescence tracks. These tracks can be imaged in two ways: (1) stitching together thousands of diffraction-limited images to produce a large field of view, enabling the extraction of adhesion footprint of each rolling cell over thousands of microns in length, (2) performing DNA-PAINT to reconstruct super-resolution images of the fluorescence tracks within a small field of view. In this study, the adhesion footprint assay was used to study HL-60 cells rolling at different shear stresses. In doing so, we were able to image the spatial distribution of the P-selectin: PSGL-1 interaction and gain insight into their molecular forces through fluorescence intensity. Thus, this method provides the groundwork for the quantitative investigation of the various cell-surface interactions involved in rolling adhesion at the molecular level.

Quantitative interpretation of cell rolling velocity distribution.

Leukocyte rolling adhesion, facilitated by selectin-mediated interactions, is a highly dynamic process in which cells roll along the endothelial surface of blood vessel walls to reach the site of infection. The most common approach to investigate cell-substrate adhesion is to analyze the cell rolling velocity in response to shear stress changes. It is assumed that changes in rolling velocity indicate changes in adhesion strength. In general, cell rolling velocity is studied at the population level as an average velocity corresponding to given shear stress. However, no statistical investigation has been performed on the instantaneous velocity distribution. In this study, we first developed a method to remove systematic noise and revealed the true velocity distribution to exhibit a log-normal profile. We then demonstrated that the log-normal distribution describes the instantaneous velocity at both the population and single-cell levels across the physiological flow rates. The log-normal parameters capture the cell motion more accurately than the mean and median velocities, which are prone to systematic error. Lastly, we connected the velocity distribution to the molecular adhesion force distribution and showed that the slip-bond regime of the catch-slip behavior of the P-selectin/PSGL-1 interaction is responsible for the variation of cell velocity.

Quantifying molecular tension-classifications, interpretations and limitations of force sensors.

Molecular force sensors (MFSs) have grown to become an important tool to study the mechanobiology of cells and tissues. They provide a minimally invasive means to optically report mechanical interactions at the molecular level. One of the challenges in molecular force sensor studies is the interpretation of the fluorescence readout. In this review, we divide existing MFSs into three classes based on the force-sensing mechanism (reversibility) and the signal output (analog/digital). From single-molecule force spectroscopy (SMFS) perspectives, we provided a critical discussion on how the sensors respond to force and how the different sensor designs affect the interpretation of their fluorescence readout. Lastly, the review focuses on the limitations and attention one must pay in designing MFSs and biological experiments using them; in terms of their tunability, signal-to-noise ratio (SNR), and perturbation of the biological system under investigation.

Quantifying Molecular Forces with Serially Connected Force Sensors.

To understand the mechanical forces involved in cell adhesion, molecular force sensors have been developed to study tension through adhesion proteins. Recently, a class of molecular force sensors called tension gauge tethers (TGTs) have been developed that rely on irreversible force-dependent dissociation of a DNA duplex to study cell adhesion forces. Although the TGT offers a high signal-to-noise ratio and is ideal for studying fast/single-molecular adhesion processes, quantitative interpretation of experimental results has been challenging. Here, we use a computational approach to investigate how TGT fluorescence readout can be quantitatively interpreted. In particular, we studied force sensors made of a single TGT, multiplexed single TGTs, and two TGTs connected in series. Our results showed that fluorescence readout using a single TGT can result from drastically different combinations of force history and adhesion event density that span orders of magnitude. In addition, the apparent behavior of the TGT is influenced by the tethered receptor-ligand, making it necessary to calibrate the TGT with every new receptor-ligand. To solve this problem, we proposed a system of two serially connected TGTs. Our result shows that not only is the ratiometric readout of serial TGT independent of the choice of receptor-ligand, it is able to reconstruct force history with sub-pN force resolution. This is also not possible by simply multiplexing different types of TGTs together. Last, we systematically investigated how the sequence composition of the two serially connected TGTs can be tuned to achieve different dynamic range. This computational study demonstrated how serially connected irreversible molecular dissociation processes can accurately quantify molecular force and laid the foundation for subsequent experimental studies.