Who Are the Anatomic Outliers Undergoing Total Knee Arthroplasty? A Computed Tomography-Based Analysis of the Hip-Knee-Ankle Axis Across 1,352 Preoperative Computed Tomographies Using a Deep Learning and Computer Vision-Based Pipeline.

BACKGROUND: Dissatisfaction after total knee arthroplasty (TKA) ranges from 15 to 30%. While patient selection may be partially responsible, morphological and reconstructive challenges may be determinants. Preoperative computed tomography (CT) scans for TKA planning allow us to evaluate the hip-knee-ankle axis and establish a baseline phenotypic distribution across anatomic parameters. The purpose of this cross-sectional analysis was to establish the distributions of 27 parameters in a pre-TKA cohort and perform threshold analysis to identify anatomic outliers. METHODS: There were 1,352 pre-TKA CTs that were processed. A 2-step deep learning pipeline of classification and segmentation models identified landmark images and then generated contour representations. We used an open-source computer vision library to compute measurements for 27 anatomic metrics along the hip-knee axis. Normative distribution plots were established, and thresholds for the 15th percentile at both extremes were calculated. Metrics falling outside the central 70th percentile were considered outlier indices. A threshold analysis of outlier indices against the proportion of the cohort was performed. RESULTS: Significant variation exists in pre-TKA anatomy across 27 normally distributed metrics. Threshold analysis revealed a sigmoid function with a critical point at 9 outlier indices, representing 31.2% of subjects as anatomic outliers. Metrics with the greatest variation related to deformity (tibiofemoral angle, medial proximal tibial angle, lateral distal femoral angle), bony size (tibial width, anteroposterior femoral size, femoral head size, medial femoral condyle size), intraoperative landmarks (posterior tibial slope, transepicondylar and posterior condylar axes), and neglected rotational considerations (acetabular and femoral version, femoral torsion). CONCLUSIONS: In the largest non-industry database of pre-TKA CTs using a fully automated 3-stage deep learning and computer vision-based pipeline, marked anatomic variation exists. In the pursuit of understanding the dissatisfaction rate after TKA, acknowledging that 31% of patients represent anatomic outliers may help us better achieve anatomically personalized TKA, with or without adjunctive technology.

CRISPR-Based Assays for Point-of-Need Detection and Subtyping of Influenza.

The high disease burden of influenza virus poses a significant threat to human health. Optimized diagnostic technologies that combine speed, sensitivity, and specificity with minimal equipment requirements are urgently needed to detect the many circulating species, subtypes, and variants of influenza at the point of need. Here, we introduce such a method using Streamlined Highlighting of Infections to Navigate Epidemics (SHINE), a clustered regularly interspaced short palindromic repeats (CRISPR)-based RNA detection platform. Four SHINE assays were designed and validated for the detection and differentiation of clinically relevant influenza species (A and B) and subtypes (H1N1 and H3N2). When tested on clinical samples, these optimized assays achieved 100% concordance with quantitative RT-PCR. Duplex Cas12a/Cas13a SHINE assays were also developed to detect two targets simultaneously. This study demonstrates the utility of this duplex assay in discriminating two alleles of an oseltamivir resistance (H275Y) mutation as well as in simultaneously detecting influenza A and human RNAse P in patient samples. These assays have the potential to expand influenza detection outside of clinical laboratories for enhanced influenza diagnosis and surveillance.

Designing sensitive viral diagnostics with machine learning.

Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome's conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic-target pairs, concentrated on CRISPR-based diagnostics, and train a deep neural network to accurately predict diagnostic readout. We join this model with combinatorial optimization to maximize sensitivity over the full spectrum of a virus's genomic variation. We introduce Activity-informed Design with All-inclusive Patrolling of Targets (ADAPT), a system for automated design, and use it to design diagnostics for 1,933 vertebrate-infecting viral species within 2 hours for most species and within 24 hours for all but three. We experimentally show that ADAPT's designs are sensitive and specific to the lineage level and permit lower limits of detection, across a virus's variation, than the outputs of standard design techniques. Our strategy could facilitate a proactive resource of assays for detecting pathogens.

Simplified Cas13-based assays for the fast identification of SARS-CoV-2 and its variants.

The widespread transmission and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) call for rapid nucleic acid diagnostics that are easy to use outside of centralized clinical laboratories. Here we report the development and performance benchmarking of Cas13-based nucleic acid assays leveraging lyophilised reagents and fast sample inactivation at ambient temperature. The assays, which we named SHINEv.2 (for 'streamlined highlighting of infections to navigate epidemics, version 2'), simplify the previously reported RNA-extraction-free SHINEv.1 technology by eliminating heating steps and the need for cold storage of the reagents. SHINEv.2 detected SARS-CoV-2 in nasopharyngeal samples with 90.5% sensitivity and 100% specificity (benchmarked against the reverse transcription quantitative polymerase chain reaction) in less than 90 min, using lateral-flow technology and incubation in a heat block at 37 °C. SHINEv.2 also allows for the visual discrimination of the Alpha, Beta, Gamma, Delta and Omicron SARS-CoV-2 variants, and can be run without performance losses by using body heat. Accurate, easy-to-use and equipment-free nucleic acid assays could facilitate wider testing for SARS-CoV-2 and other pathogens in point-of-care and at-home settings.

Blood-spinal cord barrier leakage is independent of motor neuron pathology in ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease involving progressive degeneration of upper and lower motor neurons. The pattern of lower motor neuron loss along the spinal cord follows the pattern of deposition of phosphorylated TDP-43 aggregates. The blood-spinal cord barrier (BSCB) restricts entry into the spinal cord parenchyma of blood components that can promote motor neuron degeneration, but in ALS there is evidence for barrier breakdown. Here we sought to quantify BSCB breakdown along the spinal cord axis, to determine whether BSCB breakdown displays the same patterning as motor neuron loss and TDP-43 proteinopathy. Cerebrospinal fluid hemoglobin was measured in living ALS patients (n = 87 control, n = 236 ALS) as a potential biomarker of BSCB and blood-brain barrier leakage. Cervical, thoracic, and lumbar post-mortem spinal cord tissue (n = 5 control, n = 13 ALS) were then immunolabelled and semi-automated imaging and analysis performed to quantify hemoglobin leakage, lower motor neuron loss, and phosphorylated TDP-43 inclusion load. Hemoglobin leakage was observed along the whole ALS spinal cord axis and was most severe in the dorsal gray and white matter in the thoracic spinal cord. In contrast, motor neuron loss and TDP-43 proteinopathy were seen at all three levels of the ALS spinal cord, with most abundant TDP-43 deposition in the anterior gray matter of the cervical and lumbar cord. Our data show that leakage of the BSCB occurs during life, but at end-stage disease the regions with most severe BSCB damage are not those where TDP-43 accumulation is most abundant. This suggests BSCB leakage and TDP-43 pathology are independent pathologies in ALS.

Equipment-free detection of SARS-CoV-2 and Variants of Concern using Cas13.

The COVID-19 pandemic, and the recent rise and widespread transmission of SARS-CoV-2 Variants of Concern (VOCs), have demonstrated the need for ubiquitous nucleic acid testing outside of centralized clinical laboratories. Here, we develop SHINEv2, a Cas13-based nucleic acid diagnostic that combines quick and ambient temperature sample processing and lyophilized reagents to greatly simplify the test procedure and assay distribution. We benchmarked a SHINEv2 assay for SARS-CoV-2 detection against state-of-the-art antigen-capture tests using 96 patient samples, demonstrating 50-fold greater sensitivity and 100% specificity. We designed SHINEv2 assays for discriminating the Alpha, Beta, Gamma and Delta VOCs, which can be read out visually using lateral flow technology. We further demonstrate that our assays can be performed without any equipment in less than 90 minutes. SHINEv2 represents an important advance towards rapid nucleic acid tests that can be performed in any location.