Plano Cancer Institute

ABSTRACTBackgroundGlucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) have gained rapid popularity for both medical and consumer‐directed weight loss. This growth has been accompanied by increased public discussion regarding facial aesthetic changes, commonly referred to as “Ozempic face,” characterized by volume depletion and cutaneous laxity.ObjectiveTo quantify temporal patterns of public search interest in a widely known GLP‐1 RA and evaluate corresponding awareness of its cosmetic facial sequelae using infodemiologic methods.MethodsGoogle Trends data for “Ozempic” and related frequently co‐searched queries were analyzed from November 2021 to December 2024. Trends in relative search volume (RSV) were examined, with particular focus on terms associated with facial aesthetics, including “Ozempic face” and “plastic surgeons Ozempic face.”ResultsRSV for “Ozempic” showed a steady upward trajectory over the study period. Queries related to facial aesthetic consequences exhibited substantial proportional increases. Notably, “Ozempic face” demonstrated a 4600% rise in RSV, and searches for “plastic surgeons Ozempic face” similarly grew markedly.ConclusionsPublic interest in GLP‐1 RAs is strongly associated with rising awareness and concern about their facial aesthetic effects. These trends suggest that aesthetic practitioners should expect more patient inquiries regarding GLP‐1–related facial changes and should proactively integrate counseling and corrective treatment options into clinical practice.

With the exponential growth of remote sensing data, efficiently analyzing large-scale satellite imagery remains challenge due to computational complexity and resource constraints. This paper in turn suggests a superior serverless cloud-computing architecture to overcome such constraints with Hunger Game Optimization (HGO) to introduce live resource scheduling and Deep Reinforcement Learning (DRL) to introduce intelligent workflow. The suggested architecture processes cloudless Landsat images between the years 1991 and 2022 in both dry and rainy seasons. Data will be obtained on the Landsat series, namely <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5,7,8$</tex> and 9, with a 30meter spatial resolution, therefore providing a vast temporal and spatial coverage to be used in the analysis to follow. Experimentally, the framework has been shown to provide an average <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7.8 x$</tex> speedup compared to traditional server-based models, and at the same time have a 28% higher resource utilization. Notably, the classification accuracy is maintained at a high rate, with the overall F1-score of 92.3%. The findings demonstrate the effectiveness of combining HGO and DRL in a serverless cloud, which is a scalable, cost-efficient, and accurate solution to long-term remote sensing data analysis, and environmental monitoring.

Abstract Presentation slides for the ISTFA 2025 Tutorial session “Fundamental Considerations in the Justification, Design and Construction of an Analytical Laboratory for High Tech Imaging and Processing Tools.”

CVDs are the major cause of death around the world, and intelligent diagnostics systems to identify the risks early are required. The traditional diagnostic procedures do not frequently work with complex and high-dimensional medical data. In order to address this drawback, this paper suggests a hybrid deep ensemble model that involves the use of the ResNet101 as a deep feature extractor with the Random Forest-based Gradient Boosting (RFGB) classifier as a powerful predictor. Significant features are extracted by the ResNet101 and then they are processed by the RFGB ensemble that combines the strengths of both Random Forest and Gradient Boosting to achieve higher classification accuracy. The proposed ResNet101-RFGB model attained 96% accuracy as compared to the traditional classifiers like Navie-Bayes and Decision Tree (DT), which achieved an accuracy of 88% and 92% respectively. This shows that it has better performance in managing multidimensional data and in improving the accuracy of diagnosis and this means that ensemble learning to a large extent can enhance the dependability and scalability of cardiovascular disease diagnostic systems.

The increasing use of federated learning (FL) in healthcare IoT demands rigorous verification to ensure the correctness of remote model training without compromising patient data privacy. However, existing approaches either assume full trust in clients or introduce high computational and communication costs when integrating cryptographic guarantees. In this work, we propose a lightweight, privacy-preserving federated learning framework that integrates zk-SNARK-based verifiable training over a ring topology. Our system ensures that each client’s model update and aggregation step can be independently verified without revealing sensitive data or requiring a central auditor. We design an efficient proof composition strategy (CGro16) tailored for chained convolution operations and commitment schemes optimized for healthcare models. We also introduce a matrix polynomial-based masking mechanism (MatProofs) to support zero-knowledge commitments for convolutional neural networks (CNNs).Experimental results on standard benchmarks (MNIST, CIFAR-100) show up to 47% reduction in proof generation time and 39% lower memory overhead compared to baseline zk-SNARK schemes. The protocol is also benchmarked on edge devices (Jetson Nano, Raspberry Pi), confirming its suitability for remote and wearable healthcare scenarios.

Abstract The high rigidity and load‐bearing capacity of concrete‐filled stainless steel‐tube (CFSST) structures make them a common choice for high‐rise building construction. CFSST columns exhibit intricate interactions between concrete and stainless steel, leading to challenges in accurately modeling their behavior under axial loading conditions. Stainless steel tubular circular columns with axially loaded concrete and ultra‐high strength concrete are predicted to perform using a hybrid technique. The proposed hybrid strategy is the collaborative implementation of Attention Pyramid Convolutional Neural Network (APCNN) and Giant Trevally Optimizer (GTO). It is hence called the APCNN‐GTO technique. The primary goal of the proposed approach is to increase the CFSST columns' ultimate axial load‐carrying capability in comparison to conventional reinforced concrete (RC). The data is initially extracted from the dataset of steel tubular columns filled with concrete. They then feed the data into a pre‐processing approach based on federated neural collaborative filtering (FedNCF). To extract the best features, the dual‐tree biquaternion wavelet transform (DTBWT) is used for the pre‐processing result. For circular CFSST columns subjected to axial loading, the ultimate axial load is predicted using the APCNN technique. Next, APCNN's weight parameters are tuned with GTO. The proposed technique's implementation in the MATLAB platform is described as follows: the proposed solution outperforms existing approaches such as CNN, FNN, and RNN by 98% and has a 0.1% lower error rate, paving the path for advanced applications in high‐rise and heavy‐load construction.

Abstract Disclosure: M. Ayman: None. S. Manikonda: None. Background: Osteoarthritis (OA) is a leading cause of disability worldwide, particularly affecting the knee joint. Total knee replacement (TKR) remains a common intervention for patients with advanced OA. Obesity: is a well-established risk factor for the development and progression of knee OA, as excess weight places additional stress on weight-bearing joints, exacerbating symptoms. Thus, weight loss is a primary therapeutic target for managing knee OA and reducing the need for surgical intervention. While weight loss is known to reduce symptoms of OA and delay or avoid the need for TKR, pharmacological interventions like Ozempic (semaglutide), a GLP-1 receptor agonist used for weight management, may offer a promising approach. This systematic review and meta-analysis aim to evaluate whether Ozempic: leads to significant reductions in TKR procedures in patients with osteoarthritis. Methods: We systematically reviewed studies evaluating the effects of Ozempic on weight loss and knee OA symptoms, particularly focusing on any association with reduced TKR requirements. Eligible studies were identified from databases including PubMed, Cochrane, and Scopus. Both randomized controlled trials (RCTs) and observational studies were included. Data on weight loss, OA symptoms, and TKR procedures were extracted and analyzed. Results: Of the 12 studies that met inclusion criteria, 8 were RCTs, and 4 were observational studies. Ozempic significantly reduced body weight in OA patients (mean weight loss of 10-18%) over periods ranging from 12 to 52 weeks. OA symptom scores, including pain and functional outcomes, improved in 75% of studies, with a 30-45% reduction in symptoms. The meta-analysis revealed a 25% reduction in the likelihood of TKR procedures in Ozempic-treated patients, compared to controls. Clinical Implications Given the substantial healthcare burden of OA and TKR surgeries, Ozempic: represents a promising pharmacologic intervention for reducing the need for surgical procedures in obese OA patients. The observed weight loss and symptom improvements could offer an alternative to surgery, improving patient outcomes and reducing healthcare costs Conclusion: Ozempic appears to be effective in reducing body weight and improving OA symptoms, potentially delaying or reducing the need for TKR in patients with OA. Further large-scale trials are needed to confirm these findings and assess the long-term benefits of Ozempic for OA management. Presentation: Saturday, July 12, 2025

To assess social media as a potential method of bridging the gap between randomized controlled trial evidence and the implementation of pneumatic retinopexy (PnR). Cross-sectional study. Vitreoretinal surgeons from a Telegram chat group that was initiated in May 2020 for educating ophthalmologists about PnR. Between July 25, 2023, and September 25, 2023, longitudinal chat usage was recorded using an automated chatbot, and a subgroup of surgeons was surveyed about the effect of the Telegram group on their PnR practice. Telegram group membership increased from 43 members in May 2020 to 885 members in June 2024, with representation from 64 different countries. A subset of 653 members sent a mean (SD) of 2.6 (28.2) messages and were active 56.4 (195.0) times between July and September 2023. Eighty-one surgeons from 35 different countries completed the survey. Between the year before and year after joining the Telegram group, the proportions of surgeons that treated >25% of RRD cases with PnR (8.6% vs 44.4%; p < .001) and self-reported >80% primary anatomical reattachment rate (27.2% vs 48.1%; p < .001) increased. Social media may be leveraged to support the refinement of surgical techniques such as PnR for physician trainees and practicing surgeons, as well as increase surgical adoption into routine practice, a process that can otherwise take several decades. A randomized implementation trial that compares social media to other dissemination and implementation strategies while incorporating effectiveness and safety outcomes is warranted.

Incorporation of antibacterial agents into restorative materials often compromises the physical properties of glass ionomer cement(GIC). Therefore, it is of interest to evaluate the antibacterial efficacy and compressive strength of FUJI IX GIC modified with 2%chlorhexidine, 1% cetrimide, 2.5% triclosan, or 1.5% antibiotics (metronidazole, minocycline and ciprofloxacin), added to the powder ata powder-liquid ratio of 3.6/1. Compressive strength was assessed using 50 cylindrical specimens (4 mm diameter, 6 mm height, 10 pergroup) tested on a Universal Testing Machine, while antibacterial efficacy was evaluated via agar diffusion on 50 disc-shaped specimens(10 mm diameter, 2 mm thickness, 10 per group) measuring inhibition zones on BHI agar. Statistical analysis involved ANOVA followed byDunnett's t-test, with p < 0.05 considered significant; all modified GICs exhibited antibacterial activity but showed significantlyreduced compressive strength compared to the control. The antibiotic-modified GIC demonstrated superior antibacterial efficacy andcompressive strength among the experimental groups, though the control group had the highest overall compressive strength.