An automated interface for sedimentation velocity analysis in SEDFIT.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) is an indispensable tool for the study of particle size distributions in biopharmaceutical industry, for example, to characterize protein therapeutics and vaccine products. In particular, the diffusion-deconvoluted sedimentation coefficient distribution analysis, in the software SEDFIT, has found widespread applications due to its relatively high resolution and sensitivity. However, a lack of suitable software compatible with Good Manufacturing Practices (GMP) has hampered the use of SV-AUC in this regulatory environment. To address this, we have created an interface for SEDFIT so that it can serve as an automatically spawned module with controlled data input through command line parameters and output of key results in files. The interface can be integrated in custom GMP compatible software, and in scripts that provide documentation and meta-analyses for replicate or related samples, for example, to streamline analysis of large families of experimental data, such as binding isotherm analyses in the study of protein interactions. To test and demonstrate this approach we provide a MATLAB script mlSEDFIT.

An automated interface for sedimentation velocity analysis in SEDFIT.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) is an indispensable tool for the study of particle size distributions in biopharmaceutical industry, for example, to characterize protein therapeutics and vaccine products. In particular, the diffusion-deconvoluted sedimentation coefficient distribution analysis, in the software SEDFIT, has found widespread applications due to its relatively high resolution and sensitivity. However, a lack of available software compatible with Good Manufacturing Practices (GMP) has hampered the use of SV-AUC in this regulatory environment. To address this, we have created an interface for SEDFIT so that it can serve as an automatically spawned module with controlled data input through command line parameters and output of key results in files. The interface can be integrated in custom GMP compatible software, and in scripts that provide documentation and meta-analyses for replicate or related samples, for example, to streamline analysis of large families of experimental data, such as binding isotherm analyses in the study of protein interactions. To test and demonstrate this approach we provide a MATLAB script mlSEDFIT.

Patient and surgical predictors of 3D correction in posterior spinal fusion: a systematic review.

BACKGROUND: Restoration of three-dimensional (3D) alignment is critical in correcting patients with adolescent idiopathic scoliosis using posterior spinal fusion (PSF). However, current studies mostly rely on 2D radiographs, resulting in inaccurate assessment of surgical correction and underlying predictive factors. While 3D reconstruction of biplanar radiographs is a reliable and accurate tool for quantifying spinal deformity, no study has reviewed the current literature on its use in evaluating surgical prognosis. PURPOSE: To summarize the current evidence on patient and surgical factors affecting sagittal alignment and curve correction after PSF based on 3D parameters derived from reconstruction of biplanar radiographs. METHODS: A comprehensive search was conducted by three independent investigators on Medline, PubMed, Web of Science, and Cochrane Library to obtain all published information on predictors of postoperative alignment and correction after PSF. Search items included "adolescent idiopathic scoliosis," "stereoradiography," "three-dimensional," "surgical," and "correction." The inclusion and exclusion criteria were carefully defined to include clinical studies. Risk of bias was assessed with the Quality in Prognostic Studies tool, and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development, and Evaluations approach. 989 publications were identified, with 444 unique articles subjected to full-text screening. Ultimately, 41 articles were included. RESULTS: Strong predictors of better curve correction included preoperative normokyphosis (TK > 15°), a corresponding rod contour, intraoperative vertebral rotation and translation, and upper and lower instrumented vertebrae selected based on sagittal and axial inflection points. For example, for Lenke 1 patients with junctional vertebrae above L1, fusion to NV-1 (1 level above the neutral vertebra) achieved optimal curve correction while preserving motion segments. Pre-op coronal Cobb angle and axial rotation, distal junctional kyphosis, pelvic incidence, sacral slope, and type of instrument were identified as predictors with moderate evidence. For Lenke 1C patients, > 50% LIV rotation was found to increase spontaneous lumbar curve correction. Pre-op thoracolumbar apical translation and lumbar lordosis, Ponte osteotomies, and rod material were found to be predictors with low evidence. CONCLUSIONS: Rod contouring and UIV/LIV selection should be based on preoperative 3D TK in order to achieve normal postoperative alignment. Specifically, Lenke 1 patients with high-lying rotations should be fused distally at NV-1, while hypokyphotic patients with large lumbar curves and truncal shift should be fused at NV to improve lumbar alignment. Lenke 1C curves should be corrected using > 50% LIV rotation counterclockwise to the lumbar rotation. Further investigation should compare surgical correction between pedicle-screw and hybrid constructs using matched cohorts. DJK and overbending rods are potential predictors of postoperative alignment.

An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation.

BACKGROUND: Assessment of spine alignment is crucial in the management of scoliosis, but current auto-analysis of spine alignment suffers from low accuracy. We aim to develop and validate a hybrid model named SpineHRNet+, which integrates artificial intelligence (AI) and rule-based methods to improve auto-alignment reliability and interpretability. METHODS: From December 2019 to November 2020, 1,542 consecutive patients with scoliosis attending two local scoliosis clinics (The Duchess of Kent Children's Hospital at Sandy Bay in Hong Kong; Queen Mary Hospital in Pok Fu Lam on Hong Kong Island) were recruited. The biplanar radiographs of each patient were collected with our medical machine EOS™. The collected radiographs were recaptured using smartphones or screenshots, with deidentified images securely stored. Manually labelled landmarks and alignment parameters by a spine surgeon were considered as ground truth (GT). The data were split 8:2 to train and internally test SpineHRNet+, respectively. This was followed by a prospective validation on another 337 patients. Quantitative analyses of landmark predictions were conducted, and reliabilities of auto-alignment were assessed using linear regression and Bland-Altman plots. Deformity severity and sagittal abnormality classifications were evaluated by confusion matrices. FINDINGS: SpineHRNet+ achieved accurate landmark detection with mean Euclidean distance errors of 2·78 and 5·52 pixels on posteroanterior and lateral radiographs, respectively. The mean angle errors between predictions and GT were 3·18° and 6·32° coronally and sagittally. All predicted alignments were strongly correlated with GT (p < 0·001, R2 > 0·97), with minimal overall difference visualised via Bland-Altman plots. For curve detections, 95·7% sensitivity and 88·1% specificity was achieved, and for severity classification, 88·6-90·8% sensitivity was obtained. For sagittal abnormalities, greater than 85·2-88·9% specificity and sensitivity were achieved. INTERPRETATION: The auto-analysis provided by SpineHRNet+ was reliable and continuous and it might offer the potential to assist clinical work and facilitate large-scale clinical studies. FUNDING: RGC Research Impact Fund (R5017-18F), Innovation and Technology Fund (ITS/404/18), and the AOSpine East Asia Fund (AOSEA(R)2019-06).

Calibrating analytical ultracentrifuges.

Analytical ultracentrifugation (AUC) is based on the concept of recording and analyzing macroscopic macromolecular redistribution that results from a centrifugal force acting on the mass of suspended macromolecules in solution. Since AUC rests on first principles, it can provide an absolute measurement of macromolecular mass, sedimentation and diffusion coefficients, and many other quantities, provided that the solvent density and viscosity are known, and provided that the instrument is properly calibrated. Unfortunately, a large benchmark study revealed that many instruments exhibit very significant systematic errors. This includes the magnification of the optical detection system used to determine migration distance, the measurement of sedimentation time, and the measurement of the solution temperature governing viscosity. We have previously developed reference materials, tools, and protocols to detect and correct for systematic measurement errors in the AUC by comparison with independently calibrated standards. This 'external calibration' resulted in greatly improved precision and consistency of parameters across laboratories. Here we detail the steps required for calibration of the different data dimensions in the AUC. We demonstrate the calibration of three different instruments with absorbance and interference optical detection, and use measurements of the sedimentation coefficient of NISTmAb monomer as a test of consistency. Whereas the measured uncorrected sedimentation coefficients span a wide range from 6.22 to 6.61 S, proper calibration resulted in a tenfold reduced standard deviation of sedimentation coefficients. The calibrated relative standard deviation and mean error of 0.2% and 0.07%, respectively, is comparable with statistical errors and side-by-side repeatability in a single instrument.

Nucleic acid-induced dimerization of HIV-1 Gag protein.

HIV-1 Gag is a highly flexible multidomain protein that forms the protein lattice of the immature HIV-1 virion. In vitro, it reversibly dimerizes, but in the presence of nucleic acids (NAs), it spontaneously assembles into virus-like particles (VLPs). High-resolution structures have revealed intricate details of the interactions of the capsid (CA) domain of Gag and the flanking spacer peptide SP1 that stabilize VLPs, but much less is known about the assembly pathway and the interactions of the highly flexible NA-binding nucleocapsid (NC) domain. Here, using a novel hybrid fluorescence proximity/sedimentation velocity method in combination with calorimetric analyses, we studied initial binding events by monitoring the sizes and conformations of complexes of Gag with very short oligonucleotides. We observed that high-affinity binding of oligonucleotides induces conformational changes in Gag accompanied by the formation of complexes with a 2:1 Gag/NA stoichiometry. This NA-liganded dimerization mode is distinct from the widely studied dimer interface in the CA domain and from protein interactions arising in the SP1 region and may be mediated by protein-protein interactions localized in the NC domain. The formation of the liganded dimer is strongly enthalpically driven, resulting in higher dimerization affinity than the CA-domain dimer. Both detailed energetic and conformational analyses of different Gag constructs revealed modulatory contributions to NA-induced dimerization from both matrix and CA domains. We hypothesize that allosterically controlled self-association represents the first step of VLP assembly and, in concert with scaffolding along the NA, can seed the formation of two-dimensional arrays near the NA.

Enhanced Sample Handling for Analytical Ultracentrifugation with 3D-Printed Centerpieces.

The centerpiece of the sample cell assembly in analytical ultracentrifugation holds the sample solution between windows, sealed against high vacuum, and is shaped such that macromolecular migration in centrifugal fields exceeding 200 000g can proceed undisturbed by walls or convection while concentration profiles are imaged with optical detection systems aligned perpendicular to the plane of rotation. We have recently shown that 3D printing using various materials allows inexpensive and rapid manufacturing of centerpieces. In the present work, we expand this endeavor to examine the accuracy of the measured sedimentation process, as well as short-term durability of the centerpieces. We find that 3D-printed centerpieces can be used many times and can provide data equivalent in quality to commonly used commercial epoxy resin centerpieces. Furthermore, 3D printing enables novel designs adapted to particular experimental objectives because they offer unique opportunities, for example, to create well-defined curved surfaces, narrow channels, and embossed features. We present examples of centerpiece designs exploiting these capabilities for improved AUC experiments. This includes narrow sector centerpieces that substantially reduce the required sample volume while maintaining the standard optical path length; thin centerpieces with integrated window holders to provide very short optical pathlengths that reduce optical aberrations at high macromolecular concentrations; long-column centerpieces that increase the observable distance of macromolecular migration for higher-precision sedimentation coefficients; and three-sector centerpieces that allow doubling the number of samples in a single run while reducing the sample volumes. We find each of these designs allows unimpeded macromolecular sedimentation and can provide high-quality sedimentation data.