Distinct effector functions mediated by Fc regions of bovine IgG subclasses and their interaction with Fc gamma receptors.

Cattle possess three IgG subclasses. However, the key immune functions, including complement and NK cell activation, and enhancement of phagocytosis, are not fully described for bovine IgG1, 2 and 3. We produced chimeric monoclonal antibodies (mAbs) consisting of a defined variable region linked to the constant regions of bovine IgG1, 2 and 3, and expressed His-tagged soluble recombinant bovine Fc gamma receptors (FcγRs) IA (CD64), IIA (CD32A), III (CD16) and Fcγ2R. Functional assays using bovinized mAbs were developed. IgG1 and IgG3, but not IgG2, activated complement-dependent cytotoxicity. Only IgG1 could activate cattle NK cells to mobilize CD107a after antigen crosslinking, a surrogate assay for antibody-dependent cell cytotoxicity. Both IgG1 and IgG2 could trigger monocyte-derived macrophages to phagocytose fluorescently labelled antigen-expressing target cells. IgG3 induced only weak antibody-dependent cellular phagocytosis (ADCP). By contrast, monocytes only exhibited strong ADCP when triggered by IgG2. IgG1 bound most strongly to recombinant FcγRs IA, IIA and III, with weaker binding by IgG3 and none by IgG2, which bound exclusively to Fcγ2R. Immune complexes containing IgG1, 2 and 3 bound differentially to leukocyte subsets, with IgG2 binding strongly to neutrophils and monocytes and all subclasses binding platelets. Differential expression of the FcγRs on leukocyte subsets was demonstrated by surface staining and/or RT-qPCR of sorted cells, e.g., Fcγ2R mRNA was expressed in monocytes/macrophages, neutrophils, and platelets, potentially explaining their strong interactions with IgG2, and FcγRIII was expressed on NK cells, presumably mediating IgG1-dependent NK cell activation. These data reveal differences in bovine IgG subclass functionality, which do not correspond to those described in humans, mice or pigs, which is relevant to the study of these IgG subclasses in vaccine and therapeutic antibody development.

Recycling silage leachate and biochar for improving nitrate removal by woodchip bioreactor.

Woodchip bioreactor (WBR) is commonly used to remove nitrate from drainage and runoff. However, the efficiency of nitrate removal in WBR is highly variable due to the properties of filling materials. In this study, we investigated the potential of recycling two waste materials, biochar (B) and silage leachate (SL), to enhance nitrate removal by providing a better living habitat and extra available carbon for denitrification. We constructed twelve lab-scale bioreactors with different filling materials (WBR, WBR + B, WBR + SL, WBR + B + SL), hydraulic retention times (HRT: 0.5-24 h), and nitrate concentrations (5.4-33 mg L-1) to test nitrate removal efficiency (NRE) and nitrate removal rate (NRR). Our results showed that the combination of biochar and silage leachate led to the highest NRE and NRR, with improvements of 23% and 48%, respectively, compared to WBR alone. However, the benefits of adding biochar and silage leachate were less apparent at longer HRTs. According to the results of our structural equation modeling (SEM), we have attributed the improved denitrification to several factors. These factors include the decrease in dissolved oxygen, saturated hydraulic conductivity, and pH value, as well as an increase in dissolved organic carbon after the addition of silage leachate. Therefore, our study provides evidence that recycling biochar and silage leachate as an additive to WBR could be a beneficial strategy for enhancing nitrate removal. Overall, this study highlights the potential of a win-win solution to improve the efficiency of nitrate removal in water treatment processes.

Environmental regulations in the United States lead to improvements in untreated stormwater quality over four decades.

Identifying sources of pollutants in watersheds is critical to accurately predicting stormwater quality. Many existing software used to model stormwater quality rely on decades-old data sets which may not represent current runoff quality in the United States. Because of environmental regulations promulgated at the federal level over previous decades, there is a need to understand long-term trends (and potential shifts) in runoff quality to better parameterize models. Pollutant event mean concentrations (EMCs) from the National Stormwater Quality Database (NSQD) were combined with those from recent sources to understand if untreated stormwater quality has changed over the past 40 years. A significant decreasing monotonic trend (i.e., continually decreasing in a nonuniform fashion) was observed for total suspended solids (TSS), total phosphorus (TP), total Kjeldahl nitrogen (TKN), total copper (Cu), total lead (Pb), and total zinc (Zn) in the resultant database, suggesting that runoff quality has become less polluted with time. Median EMCs decreased from 99.2 to 42 mg/L, 0.34 to 0.26 mg/L, 1.27 to 1.03 mg/L, 40 to 6.8 µg/L, 110 to 3.7 µg/L, and 375 to 53.3 µg/L for TSS, TP, TN, Cu, Pb, and Zn, respectively, from the 1980s to the 2010s. These significant reductions often aligned temporally with advancements in clean manufacturing, amendments of the Clean Air Act, and other source control efforts which impact pollutant bioavailability and atmospheric deposition. Results suggest environmental regulations not specifically targeting stormwater management have had a positive impact on stormwater quality and that temporal fluctuations should be considered in modeling.

A Customizable Suite of Methods to Sequence and Annotate Cattle Antibodies.

Studying the antibody response to infection or vaccination is essential for developing more effective vaccines and therapeutics. Advances in high-throughput antibody sequencing technologies and immunoinformatic tools now allow the fast and comprehensive analysis of antibody repertoires at high resolution in any species. Here, we detail a flexible and customizable suite of methods from flow cytometry, single cell sorting, heavy and light chain amplification to antibody sequencing in cattle. These methods were used successfully, including adaptation to the 10x Genomics platform, to isolate native heavy-light chain pairs. When combined with the Ig-Sequence Multi-Species Annotation Tool, this suite represents a powerful toolkit for studying the cattle antibody response with high resolution and precision. Using three workflows, we processed 84, 96, and 8313 cattle B cells from which we sequenced 24, 31, and 4756 antibody heavy-light chain pairs, respectively. Each method has strengths and limitations in terms of the throughput, timeline, specialist equipment, and cost that are each discussed. Moreover, the principles outlined here can be applied to study antibody responses in other mammalian species.

The global significance of abiotic factors affecting nitrate removal in woodchip bioreactors.

Woodchip bioreactor (WBR) is one of the green infrastructures in the agriculture system to reduce nitrate from agricultural drainage and stormwater. A lot of abiotic factors have been reported that affect nitrate removal lacking a comprehensive understanding. In this study, we studied eight important abiotic factors, including media age, hydraulic retention time (HRT), influent nitrate concentration (Cin), temperature, dissolved organic carbon (DOC), dissolved oxygen (DO), pH, and effective porosity (ρe) of WBR-filling materials. Based on a database that included 10,179 sets of data from 63 peer-reviewed articles, the nitrate removal rate (NRR) and nitrate removal efficiency (NRE) corresponding to the eight abiotic factors by different categories were comprehensively reported. According to this database, this study found the optimal range of abiotic factors for NRR and NRE in WBR were different. Regarding NRR, the optimal range of media age, HRT, Cin, temperature, effluent DOC, DO, pH, and ρe were in the first year, 0-5 h, 10-20 mg L-1, 20-25 °C, 0-5 mg L-1, 0-0.5 mg L-1, 7-8, and 0.6-0.7, respectively. For NRE, the optimal range of media age, HRT, Cin, temperature, effluent DOC, DO, pH, and ρe were in the first year, 500-3000 h, 0-10 mg L-1, 10-15 °C, >50 mg L-1, 0-0.5 mg L-1, 4-5, and 0.4-0.5, respectively. Moreover, the principal component analysis (PCA) indicated the field studies' principal components were different from laboratory studies. Furthermore, the structural equation modeling (SEM) also revealed the causal relationship of the eight abiotic factors on NRR and NRE is totally different. Lessons from this study can be incorporated into DNBR designs, especially improving nitrate removal rates by optimizing different abiotic factors. It also provides insights regarding the contributions of different abiotic factors for NRR and NRE independently and comprehensively.

Fc-Mediated Functions of Porcine IgG Subclasses.

The pig is an important agricultural species and powerful biomedical model. We have established the pig, a large natural host animal for influenza with many physiological similarities to humans, as a robust model for testing the therapeutic potential of monoclonal antibodies. Antibodies provide protection through neutralization and recruitment of innate effector functions through the Fc domain. However very little is known about the Fc-mediated functions of porcine IgG subclasses. We have generated 8 subclasses of two porcine monoclonal anti influenza hemagglutinin antibodies. We characterized their ability to activate complement, trigger cytotoxicity and phagocytosis by immune cells and assayed their binding to monocytes, macrophages, and natural killer cells. We show that IgG1, IgG2a, IgG2b, IgG2c and IgG4 bind well to targeted cell types and mediate complement mediated cellular cytotoxicity (CDCC), antibody dependent cellular cytotoxicity (ADCC) and antibody mediated cell phagocytosis (ADCP). IgG5b and IgG5c exhibited weak binding and variable and poor functional activity. Immune complexes of porcine IgG3 did not show any Fc-mediated functions except for binding to monocytes and macrophages and weak binding to NK cells. Interestingly, functionally similar porcine IgG subclasses clustered together in the genome. These novel findings will enhance the utility of the pig model for investigation of therapeutic antibodies.

Understanding Artificial Intelligence and Predictive Analytics: A Clinically Focused Review of Machine Learning Techniques.

¼: Machine learning and artificial intelligence have seen tremendous growth in recent years and have been applied in numerous studies in the field of orthopaedics. ¼: Machine learning will soon become critical in the day-to-day operations of orthopaedic practice; therefore, it is imperative that providers become accustomed to and familiar with not only the terminology but also the fundamental techniques behind the technology. ¼: A foundation of knowledge regarding machine learning is critical for physicians so they can begin to understand the details in the algorithms that are being developed, which provide improved accuracy compared with clinicians, decreased time required, and a heightened ability to triage patients.

Identification of Anterior Cervical Spinal Instrumentation Using a Smartphone Application Powered by Machine Learning.

STUDY DESIGN: Cross-sectional study. OBJECTIVE: The purpose of this study is to develop and validate a machine learning algorithm for the automated identification of anterior cervical discectomy and fusion (ACDF) plates from smartphone images of anterior-posterior (AP) cervical spine radiographs. SUMMARY OF BACKGROUND DATA: Identification of existing instrumentation is a critical step in planning revision surgery for ACDF. Machine learning algorithms that are known to be adept at image classification may be applied to the problem of ACDF plate identification. METHODS: A total of 402 smartphone images containing 15 different types of ACDF plates were gathered. Two hundred seventy-five images (∼70%) were used to train and validate a convolution neural network (CNN) for classification of images from radiographs. One hundred twenty-seven (∼30%) images were held out to test algorithm performance. RESULTS: The algorithm performed with an overall accuracy of 94.4% and 85.8% for top-3 and top-1 accuracy, respectively. Overall positive predictive value, sensitivity, and f1-scores were 0.873, 0.858, and 0.855, respectively. CONCLUSION: This algorithm demonstrates strong performance in the classification of ACDF plates from smartphone images and will be deployed as an accessible smartphone application for further evaluation, improvement, and eventual widespread use.Level of Evidence: 3.

Highly-contiguous bovine genomes underpin accurate functional analyses and updated nomenclature of MHC class I.

The major histocompatibility complex (MHC) class I region of cattle is both highly polymorphic and, unlike many species, highly variable in gene content between haplotypes. Cattle MHC class I alleles were historically grouped by sequence similarity in the more conserved 3' end of the coding sequence to form phylogenetic allele groups. This has formed the basis of current cattle MHC class I nomenclature. We presently describe and compare five fully assembled MHC class I haplotypes using the latest cattle and yak genome assemblies. Of the five previously described "pseudogenes" in the cattle MHC class I region, Pseudogene 3 is putatively functional in all haplotypes and Pseudogene 6 and Pseudogene 7 are putatively functional in some haplotypes. This was reinforced by evidence of transcription. Based on full gene sequences as well as 3' coding sequence, we identified distinct subgroups of BoLA-3 and BoLA-6 that represent distinct genetic loci. We further examined allele-specific expression using transcriptomic data revealing that certain alleles are consistently weakly expressed compared to others. These observations will help to inform further studies into how MHC class I region variability influences T cell and natural killer cell functions in cattle.

Emerging Technologies in the Treatment of Adult Spinal Deformity.

Outcomes for adult spinal deformity continue to improve as new technologies become integrated into clinical practice. Machine learning, robot-guided spinal surgery, and patientspecific rods are tools that are being used to improve preoperative planning and patient satisfaction. Machine learning can be used to predict complications, readmissions, and generate postoperative radiographs which can be shown to patients to guide discussions about surgery. Robot-guided spinal surgery is a rapidly growing field showing signs of greater accuracy in screw placement during surgery. Patient-specific rods offer improved outcomes through higher correction rates and decreased rates of rod breakage while decreasing operative time. The objective of this review is to evaluate trends in the literature about machine learning, robot-guided spinal surgery, and patient-specific rods in the treatment of adult spinal deformity.

Correction: Protective porcine influenza virus-specific monoclonal antibodies recognize similar haemagglutinin epitopes as humans.

[This corrects the article DOI: 10.1371/journal.ppat.1009330.].

Subpopulations of swine γδ T cells defined by TCRγ and WC1 gene expression.

γδ T cells constitute a major portion of lymphocytes in the blood of both ruminants and swine. Subpopulations of swine γδ T cells have been distinguished by CD2 and CD8α expression. However, it was not clear if they have distinct expression profiles of their T-cell receptor (TCR) or WC1 genes. Identifying receptor expression will contribute to understanding the functional differences between these subpopulations and their contributions to immune protection. Here, we annotated three genomic assemblies of the swine TCRγ gene locus finding four gene cassettes containing C, J and V genes, although some haplotypes carried a null TRGC gene (TRGC4). Genes in the TRGC1 cassette were homologs of bovine TRGC5 cassette while the others were not homologous to bovine genes. Here we evaluated three principal populations of γδ T cells (CD2+/SWC5-, CD2-/SWC5+, and CD2-/SWC5-). Both CD2- subpopulations transcribed WC1 co-receptor genes, albeit with different patterns of gene expression but CD2+ cells did not. All subpopulations transcribed TCR genes from all four cassettes, although there were differences in expression levels. Finally, the CD2+ and CD2- γδ T-cell populations differed in their representation in various organs and tissues, presumably at least partially reflective of different ligand specificities for their receptors.

Half the V by 120: A practical approach to the prevention of the dialysis disequilibrium syndrome.

The dialysis disequilibrium syndrome (DDS) results from osmotic shifts between the blood and the brain compartments. Patients at risk for DDS include those with very elevated blood urea nitrogen, concomitant hypernatremia, metabolic acidosis, and low total body water volumes. By understanding the underlying pathophysiology and applying urea kinetic modeling, it is possible to avoid the occurrence of this disorder. A urea reduction ratio (URR) of no more than 40%-45% over 2 h is recommended for the initial hemodialysis treatment. The relationship between the URR and Kt/V is useful when trying to model the dialysis treatment to a specific URR target. A simplified relationship between Kt/V and URR is provided by the equation: Kt/V = -ln (1 - URR). A URR of 40% is roughly equivalent to a Kt/V of 0.5. The required dialyzer urea clearance to achieve this goal URR in a 120-min treatment can simply be calculated by dividing half the patient's volume of distribution of urea by 120. The blood flow rate and dialyzer mass transfer coefficient (K0 A) required to achieve this clearance can then be plotted on a nomogram. Other methods to reduce the risk of DDS are reviewed, including the use of continuous renal replacement therapy.

Genomic markers associated with successful treatment of hypertension with lisinopril: A pilot study.

OBJECTIVE: To identify single nucleotide variants (SNVs) associated with lisinopril effectiveness. MATERIALS AND METHODS: This was an observational study using a candidate gene approach to examine SNVs associated with lisinopril effectiveness. Drug effectiveness was defined as 10% decrease in systolic blood pressure at 1 week follow-up. We used the Illumina GWAS MEGA chip to examine variants in the renin/angiotensin pathway that may be associated with drug effectiveness. RESULTS: 61 subjects were enrolled, and 33 (54.1%) were responsive to lisinopril therapy. SNVs in AGT (p = 0.0141), REN (p = 0.0192), and ACE2 (p = 0.0002) were found to be associated with successful treatment on lisinopril. Conclusion and relevance: SNVs in the renin and angiotensin pathway are associated with lisinopril effectiveness in a pilot cohort of patients with uncontrolled hypertension.

Protective porcine influenza virus-specific monoclonal antibodies recognize similar haemagglutinin epitopes as humans.

Pigs are natural hosts for the same subtypes of influenza A viruses as humans and integrally involved in virus evolution with frequent interspecies transmissions in both directions. The emergence of the 2009 pandemic H1N1 virus illustrates the importance of pigs in evolution of zoonotic strains. Here we generated pig influenza-specific monoclonal antibodies (mAbs) from H1N1pdm09 infected pigs. The mAbs recognized the same two major immunodominant haemagglutinin (HA) epitopes targeted by humans, one of which is not recognized by post-infection ferret antisera that are commonly used to monitor virus evolution. Neutralizing activity of the pig mAbs was comparable to that of potent human anti-HA mAbs. Further, prophylactic administration of a selected porcine mAb to pigs abolished lung viral load and greatly reduced lung pathology but did not eliminate nasal shedding of virus after H1N1pdm09 challenge. Hence mAbs from pigs, which target HA can significantly reduce disease severity. These results, together with the comparable sizes of pigs and humans, indicate that the pig is a valuable model for understanding how best to apply mAbs as therapy in humans and for monitoring antigenic drift of influenza viruses in humans, thereby providing information highly relevant to making influenza vaccine recommendations.

Oral Administration of a Chemically Modified Curcumin, TRB-N0224, Reduced Inflammatory Cytokines and Cartilage Erosion in a Rabbit ACL Transection Injury Model.

OBJECTIVE: To evaluate the effects of TRB-N0224, a chemically modified curcumin (CMC) with zinc binding properties and improved pharmacokinetics, in a rabbit anterior cruciate ligament (ACL) transection injury-induced model of osteoarthritis (OA). DESIGN: Thirty-eight skeletally mature New Zealand white rabbits were studied in 4 groups: a sham with arthrotomy (n = 6), control with ACL transection (n = 6), and 2 treatment groups with ACL transection and administration of TRB-N0224 at low (25 mg/kg/day) (n = 13) and high (50 mg/kg/day) (n = 13) doses. After euthanization at 12 weeks, outcomes were measured by post-necropsy gross morphology, biomechanics, and cartilage and synovium histology. Rabbit blood ELISA quantified cytokine and matrix metalloproteinase (MMP) concentrations at 0, 4, 8, and 12 weeks. RESULTS: Both treatment doses had fewer distal femoral condyle erosive defects than the control; the low dose demonstrated a mean 78% decrease (P < 0.01). Histologically, the low- and high-dose treatment groups had fewer cartilage pathologic changes and less severe synovitis than the control. CMC alone did not have a major effect on the biomechanics of healthy cartilage or cartilage in the ACL transection model, as demonstrated in 5 of the 6 measured properties/regions (P < 0.05). ELISA results suggested that the key mediators of OA, (interleukin) IL-1ß, IL-6, TNFα (tumor necrosis factor-α), MMP-9, and MMP-13, had decreased concentrations with TRB-N0224 treatment at different time points between weeks 4 to 12 (P < 0.05). CONCLUSIONS: In the pathogenesis of OA, an imbalance exists between catabolic and anabolic mediators. These results suggest the potential of TRB-N0224 to modulate MMP and cytokine levels, slowing the macroscopic and histopathological progression of OA.

Deep Learning Automates Measurement of Spinopelvic Parameters on Lateral Lumbar Radiographs.

STUDY DESIGN: Cross-sectional database study. OBJECTIVE: The objective of this study was to develop an algorithm for the automated measurement of spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. SUMMARY OF BACKGROUND DATA: Sagittal alignment measurements are important for the evaluation of spinal disorders. Manual measurement methods are time-consuming and subject to rater-dependent error. Thus, a need exists to develop automated methods for obtaining sagittal measurements. Previous studies of automated measurement have been limited in accuracy, inapplicable to common plain films, or unable to measure pelvic parameters. METHODS: Images from 816 patients receiving lateral lumbar radiographs were collected sequentially and used to develop a convolutional neural network (CNN) segmentation algorithm. A total of 653 (80%) of these radiographs were used to train and validate the CNN. This CNN was combined with a computer vision algorithm to create a pipeline for the fully automated measurement of spinopelvic parameters from lateral lumbar radiographs. The remaining 163 (20%) of radiographs were used to test this pipeline. Forty radiographs were selected from the test set and manually measured by three surgeons for comparison. RESULTS: The CNN achieved an area under the receiver-operating curve of 0.956. Algorithm measurements of L1-S1 cobb angle, pelvic incidence, pelvic tilt, and sacral slope were not significantly different from surgeon measurement. In comparison to criterion standard measurement, the algorithm performed with a similar mean absolute difference to spine surgeons for L1-S1 Cobb angle (4.30°â€Š±â€Š4.14° vs. 4.99°â€Š±â€Š5.34°), pelvic tilt (2.14°â€Š±â€Š6.29° vs. 1.58°â€Š±â€Š5.97°), pelvic incidence (4.56°â€Š±â€Š5.40° vs. 3.74°â€Š±â€Š2.89°), and sacral slope (4.76°â€Š±â€Š6.93° vs. 4.75°â€Š±â€Š5.71°). CONCLUSION: This algorithm measures spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. The algorithm could be used to streamline clinical workflow or perform large scale studies of spinopelvic parameters.Level of Evidence: 3.

Risk Factors for 30- and 90-Day Readmissions Due To Surgical Site Infection Following Posterior Lumbar Fusion.

STUDY DESIGN: This was a retrospective cohort study. OBJECTIVE: Identify the independent risk factors for 30- and 90-day readmission because of surgical site infection (SSI) in patients undergoing elective posterior lumbar fusion (PLF). SUMMARY OF BACKGROUND DATA: SSI is a significant cause of morbidity in the 30- and 90-day windows after hospital discharge. There remains a gap in the literature on independent risk factors for readmission because of SSI after PLF procedures. In addition, readmission for SSI after spine surgery beyond the 30-day postoperative period has not been well studied. METHODS: A retrospective analysis was performed on data from the 2012 to 2014 Healthcare Cost and Utilization Project Nationwide Readmissions Database. The authors identified 65,121 patients who underwent PLF. There were 191 patients (0.30%) readmitted with a diagnosis of SSI in the 30-day readmission window, and 283 (0.43%) patients readmitted with a diagnosis of SSI in the 90-day window. Baseline patient demographics and medical comorbidities were assessed. Bivariate and multivariate analyses were performed to examine the independent risk factors for readmission because of SSI. RESULTS: In the 30-day window after discharge, this study identified patients with liver disease, uncomplicated diabetes, deficiency anemia, depression, psychosis, renal failure, obesity, and Medicaid or Medicare insurance as higher risk patients for unplanned readmission with a diagnosis of SSI. The study identified the same risk factors in the 90-day window with the addition of diabetes with chronic complications, chronic pulmonary disease, and pulmonary circulation disease. CONCLUSIONS: Independent risk factors for readmission because of SSI included liver disease, uncomplicated diabetes, obesity, and Medicaid insurance status. These findings suggest that additional intervention in the perioperative workup for patients with these risk factors may be necessary to lower unplanned readmission because of SSI after PLF surgery.

Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision.

STUDY DESIGN: Cross sectional database study. OBJECTIVE: To develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis. METHODS: Lateral lumbar radiographs were used to develop a segmentation neural network (n = 629). After synthetic augmentation, 70% of these radiographs were used for network training, while the remaining 30% were used for hyperparameter optimization. A computer vision algorithm was deployed on the segmented radiographs to calculate lumbar lordosis angles. A test set of radiographs was used to evaluate the validity of the entire pipeline (n = 151). RESULTS: The U-Net segmentation achieved a test dataset dice score of 0.821, an area under the receiver operating curve of 0.914, and an accuracy of 0.862. The computer vision algorithm identified the L1 and S1 vertebrae on 84.1% of the test set with an average speed of 0.14 seconds/radiograph. From the 151 test set radiographs, 50 were randomly chosen for surgeon measurement. When compared with those measurements, our algorithm achieved a mean absolute error of 8.055° and a median absolute error of 6.965° (not statistically significant, P > .05). CONCLUSION: This study is the first to use artificial intelligence and computer vision in a combined pipeline to rapidly measure a sagittal spinopelvic parameter without prior manual surgeon input. The pipeline measures angles with no statistically significant differences from manual measurements by surgeons. This pipeline offers clinical utility in an assistive capacity, and future work should focus on improving segmentation network performance.

The most influential papers in direct anterior approach to total hip arthroplasty.

BACKGROUND: Citation analysis is a commonly used method for appraising the impact of academic publications within a particular field of study. A gap exists in the citation analysis literature with regard to the topic of direct anterior approach (DAA) hip arthroplasty. The purpose of this study is to identify the 50 most frequently cited publications related to this topic. METHODS: The Clarivate Analytics Web of Knowledge database was utilized to search for publications relating to DAA hip arthroplasty. The top 50 most cited articles that met inclusion criteria were recorded and reviewed for various metrics. RESULTS: The top 50 publications were cited a total of 3521 times, with an average of 86.3 total citations per year between 1980 and 2019. 47 of the 50 articles identified had been published since the year 2000. Cohort designs were the most common study type. CONCLUSIONS: This analysis provides insight into factors that characterize highly cited articles on the specific topic of DAA hip arthroplasty. These factors include higher levels of evidence, recent publication, and origin in the United States. Citations of DAA hip arthroplasty papers appear to be on the rise. The curation and analysis of this set of 50 articles will provide orthopaedic surgery clinicians, researchers, and residency program directors a guide for quickly isolating influential articles on the topic of DAA hip arthroplasty. This may serve as a quick reference for clinical decision-making, foundation for further research, and curriculum on DAA hip arthroplasty.