Optimization of parameters for femoral component implantation during TKA using finite element analysis and orthogonal array testing

Abstract Background Individualized and accurate implantation of a femoral component during total knee arthroplasty (TKA) is essential in achieving equal distribution of intra-articular stress and long-term survival of the prosthesis. However, individualized component implantation remains challenging. This study aimed to optimize and individualize the positioning parameters of a femoral component in order to facilitate its accurate implantation. Methods Using computer-simulated TKA, the positioning parameters of a femoral component were optimized individually by finite element analysis in combination with orthogonal array testing. Flexion angle, valgus angle, and external rotation angle were optimized in order to reduce the peak value of the pressure on the polyethylene liner of the prosthesis. Results The optimal implantation parameters of the femoral component were as follows: 1° flexion, 5° valgus angle, and 4° external rotation. Under these conditions, the peak value of the pressure on the polyethylene liner surface was minimized to 16.46 MPa. Among the three parameters, the external rotation angle had the greatest effect on the pressure, followed by the valgus angle and the flexion angle. Conclusion Finite element analysis in combination with orthogonal array testing can optimize the implantation parameters of a femoral component for TKA. This approach would possibly reduce the wear of the polyethylene liner and prolong the survival of the TKA prosthesis, due to its capacity to minimize stress. This technique represents a new method for preoperative optimization of the implantation parameters that can achieve the best possible TKA outcome

Univariable and multivariable mendelian randomization study revealed the modifiable risk factors of urolithiasis.

BackgroundUrolithiasis is a common urological disease with increasing incidence worldwide, and preventing its risk poses significant challenges. Here, we used Mendelian randomization (MR) framework to genetically assess the causal nature of multifaceted risk factors on urolithiasis.Methods17 potential risk factors associated with urolithiasis were collected from recently published observational studies, which can be categorized basically into lifestyle factors and circulating biomarkers. The instrumental variables of risk factors were selected from large-scale genome-wide association studies (N ≤ 607,291). Summary-level data on urolithiasis were obtained from UK Biobank (UKB) (3,625 cases and 459,308 noncases) and the FinnGen consortium (5,347 cases and 213,445 noncases). The univariable and multivariable MR analyses were applied to evaluate the causal, independent effect of these potential risk factors upon urolithiasis. Effects from the two consortia were combined by the meta-analysis methods.ResultsHigher genetically predicted sex hormone-binding globulin (SHBG, OR, 0.708; 95% CI, 0.555 to 0.903), estradiol (OR, 0.179; 95% CI, 0.042 to 0.751), tea intake (OR, 0.550; 95% CI, 0.345 to 0.878), alcoholic drinks per week (OR, 0.992; 95% CI, 0.987 to 0.997), and some physical activity (e.g., swimming, cycling, keeping fit, and bowling, OR, 0.054; 95% CI, 0.008 to 0.363) were significantly associated with a lower risk of urolithiasis. In the Multivariate Mendelian Randomization (MVMR) analyses, the significant causal associations between estradiol, SHBG, tea intake, and alcoholic drinks per week with urolithiasis were robust even after adjusting for potential confounding variables. However, the previously observed causal association between other exercises and urolithiasis was no longer significant after adjusting for these factors.ConclusionsThe univariable and multivariable MR findings highlight the independent and significant roles of estradiol, SHBG, tea intake, and alcoholic drinks per week in the development of urolithiasis, which might provide a deeper insight into urolithiasis risk factors and supply potential preventative strategies

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

International audienceAbstract The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature—at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset—consisting of over 30 000 articles with manually reviewed topics—was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative