Identification of doping suspicions through artificial intelligence-powered analysis on athlete’s performance passport in female weightlifting
Ontology highlight
ABSTRACT:
SUBMITTER: Ryoo H
PROVIDER: S-EPMC11208455 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy.
2024-02-15 | GSE228560 | GEO
Project description:We examined the potential roles of the athlete's performance passport (APP) for doping detection by analyzing the relationship between weightlifting performance and sanction status. For the present study, performance data of 'not-sanctioned' (26740 datasets) and 'sanctioned' (289 datasets) male athletes were acquired from the website of the International Weightlifting Federation (www.iwf.net). One-way ANOVA, correlation analysis, and t-tests were used to analyze the relationship between athletes' use of doping and their performances across age and body weight. Athletic performance was significantly greater for athletes in the sanctioned group than those of the same age group who were not sanctioned, and this performance difference between the two groups was the greatest in their late thirties at 20.6% (not-sanctioned 292.0kg vs. sanctioned 352.3kg) (p < 0.05). From the age group analysis, out of 289 sanctioned cases, 84 cases, which was the largest proportion, were found within the top 10-25% of their performances. When stratified by body weight, athletic performance was significantly greater for the sanctioned group than the not-sanctioned group, and this performance gap was the greatest in the bodyweight category of 96 at 18.6% (not-sanctioned 310.1kg vs. sanctioned 367.8kg) (p < 0.05). From the body weight category analysis, out of 289 sanctioned cases, 75 cases, which was the largest proportion, were found within the top 10-25% of their performances. Additionally, the mean difference in performance between not-sanctioned and sanctioned groups was the largest in the body weight category of 67kg in the ages of 15-19 at 20% (not-sanctioned 234.6kg vs. sanctioned 281.5kg). These results are interpreted to mean that in male weightlifters 1) sanctioned athletes were detected in all ranges of performances regardless of age and body weight, 2) there were even higher rates of sanctioned athletes who performed within the top 10-25% of each age group and body weight category, 3) there were significant differences in performance between not-sanctioned and sanctioned group for all body weight categories, excluding +109, in the ages of 15-19 and 20-24, 4) therefore, performance data can be effectively used to better target suspected athletes for doping testing.
| S-EPMC8815941 | biostudies-literature
Project description: Not available
| S-EPMC10915451 | biostudies-literature
Project description:BackgroundThe management of acne requires the consideration of its severity; however, a universally adopted evaluation system for clinical practice is lacking. Artificial intelligence (AI) evaluation systems hold the promise of enhancing the efficiency and reproducibility of assessments. Artificial intelligence (AI) evaluation systems offer the potential to enhance the efficiency and reproducibility of assessments in this domain. While the identification of skin lesions represents a crucial component of acne evaluation, existing AI systems often overlook lesion identification or fail to integrate it with severity assessment. This study aimed to develop an AI-powered acne grading system and compare its performance with physician image-based scoring.MethodsA total of 1,501 acne patients were included in the study, and standardized pictures were obtained using the VISIA system. The initial evaluation involved 40 stratified sampled frontal photos assessed by seven dermatologists. Subsequently, the three doctors with the highest inter-rater agreement annotated the remaining 1,461 images, which served as the dataset for the development of the AI system. The dataset was randomly divided into two groups: 276 images were allocated for training the acne lesion identification platform, and 1,185 images were used to assess the severity of acne.ResultsThe average precision of our model for skin lesion identification was 0.507 and the average recall was 0.775. The AI severity grading system achieved good agreement with the true label (linear weighted kappa = 0.652). After integrating the lesion identification results into the severity assessment with fixed weights and learnable weights, the kappa rose to 0.737 and 0.696, respectively, and the entire evaluation on a Linux workstation with a Tesla K40m GPU took less than 0.1s per picture.ConclusionThis study developed a system that detects various types of acne lesions and correlates them well with acne severity grading, and the good accuracy and efficiency make this approach potentially an effective clinical decision support tool.
| S-EPMC10587552 | biostudies-literature
Project description:Artificial Intelligence-powered Drug Discovery against CTLA-4 in Cancer
| PRJNA950363 | ENA
Project description:Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitousness and accessibility are fundamental features to promote ease of use and to improve user experience. With this goal in mind, we have developed the LightDock Server, a web server for the integrative modeling of macromolecular interactions, along with several dedicated usage modes. The server builds upon the LightDock macromolecular docking framework, which has proved useful for modeling medium-to-high flexible complexes, antibody-antigen interactions, or membrane-associated protein assemblies. We believe that this free-to-use resource will be a valuable addition to the structural biology community and can be accessed online at: https://server.lightdock.org/.
| S-EPMC10320125 | biostudies-literature
Project description:Monitoring tests are commonly used to assess weightlifter’s preparedness for competition. Although various monitoring tests have been used, it is not clear which test is the strongest indicator of weightlifting performance. Therefore, the purpose of this study was to (1) determine the relationships between vertical jump, isometric mid-thigh pull (IMTP) and weightlifting performance; and (2) compare vertical jumps to IMTP as monitoring tests of weightlifting performance in a large cohort of male and female weightlifters.Fifty-two competitive weightlifters (31 males, 21 females) participated in squat and countermovement jump testing (SJ, CMJ), and IMTP testing performed on force plates. All laboratory testing data was correlated to a recent competition where the athletes had attempted to peak.Squat jump height (SJH) was the strongest correlate for men and women with the Sinclair Total (r = 0.686, p ≤ 0.01; r = 0.487, p ≤ 0.05, respectively) compared to countermovement jump height (r = 0.642, p ≤ 0.01; r = 0.413, p = 0.063), IMTP peak force allometrically scaled to body mass (r = 0.542, p ≤ 0.01; r = −0.044, p = 0.851) and rate of force development at 200 ms (r = 0.066, p = 0.723; r = 0.086, p = 0.711), respectively. Further, SJH was a stronger correlate of relative weightlifting performance compared to IMTP peak force in females (p = 0.042), but not male weightlifters (p = 0.191).Although CMJ and IMTP are still considered strong indicators of weightlifting performance, SJH appears to be the most indicative measure of weightlifting performance across a wide-range of performance levels. Thus, SJH can be used as a reliable measure to monitor weightlifting performance in male and female weightlifters.
| S-EPMC6026842 | biostudies-literature
Project description:A recent case-control study identified 28 DNA polymorphisms associated with strength athlete status. However, studies of genotype-phenotype design are required to support those findings. The aim of the present study was to investigate both individually and in combination the association of 28 genetic markers with weightlifting performance in Russian athletes and to replicate the most significant findings in an independent cohort of Japanese athletes. Genomic DNA was collected from 53 elite Russian (31 men and 22 women, 23.3 ± 4.1 years) and 100 sub-elite Japanese (53 men and 47 women, 21.4 ± 4.2 years) weightlifters, and then genotyped using PCR or micro-array analysis. Out of 28 DNA polymorphisms, LRPPRC rs10186876 A, MMS22L rs9320823 T, MTHFR rs1801131 C, and PHACTR1 rs6905419 C alleles positively correlated (p < 0.05) with weightlifting performance (i.e., total lifts in snatch and clean and jerk in official competitions adjusted for sex and body mass) in Russian athletes. Next, using a polygenic approach, we found that carriers of a high (6-8) number of strength-related alleles had better competition results than carriers of a low (0-5) number of strength-related alleles (264.2 (14.7) vs. 239.1 (21.9) points; p = 0.009). These findings were replicated in the study of Japanese athletes. More specifically, Japanese carriers of a high number of strength-related alleles were stronger than carriers of a low number of strength-related alleles (212.9 (22.6) vs. 199.1 (17.2) points; p = 0.0016). In conclusion, we identified four common gene polymorphisms individually or in combination associated with weightlifting performance in athletes from East European and East Asian geographic ancestries.
| S-EPMC8775245 | biostudies-literature
Project description:We introduce HARMONI, a three-dimensional (3D) computer vision and audio processing method for analyzing caregiver-child behavior and interaction from observational videos. HARMONI operates at subsecond resolution, estimating 3D mesh representations and spatial interactions of humans, and adapts to challenging natural environments using an environment-targeted synthetic data generation module. Deployed on 500 hours from the SEEDLingS dataset, HARMONI generates detailed quantitative measurements of 3D human behavior previously unattainable through manual efforts or 2D methods. HARMONI identifies longitudinal trends in child-caregiver interaction, including child movement, body pose, dyadic touch, visibility, and conversational turns. The integrated visual and audio analysis further reveals multimodal trends, including associations between child conversational turns and movement. Open-sourced for large-scale analysis, HARMONI facilitates advancements in human development research. HARMONI achieves 63 to 80% consistency on key attributes with human annotators on SEEDLingS and 84 to 93% consistency on videos taken from a laboratory setting while achieving >100 times savings in time.
| S-EPMC11837989 | biostudies-literature
Project description:Background/objectivesCheckpoint inhibitors, which generate durable responses in many cancer patients, have revolutionized cancer immunotherapy. However, their therapeutic efficacy is limited, and immune-related adverse events are severe, especially for monoclonal antibody treatment directed against cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), which plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with the B7 proteins CD80 and CD86. Small molecules impairing the CTLA-4/CD80 interaction have been developed; however, they directly target CD80, not CTLA-4.Subjects/methodsIn this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to identify those targeting CTLA-4. We validated the hits molecules with biochemical, biophysical, immunological, and experimental animal assays.ResultsThe primary hits obtained from the virtual screening were successfully validated in vitro and in vivo. We then optimized lead compounds and obtained inhibitors (inhibitory concentration, 1 micromole) that disrupted the CTLA-4/CD80 interaction without degrading CTLA-4.ConclusionsSeveral compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. Our findings support using AI-based frameworks to design small molecules targeting immune checkpoints for cancer therapy.
| S-EPMC10838660 | biostudies-literature