Benha University

BackgroundThe rising prevalence of obesity and type 2 diabetes mellitus (T2DM) among patients undergoing total joint arthroplasty (TJA) presents a significant clinical challenge, increasing the risk of postoperative complications. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have emerged as a potential perioperative optimization strategy, but their impact on TJA outcomes remains debated. This systematic review was conducted to synthesize the evidence on the risks and benefits of GLP-1 RA use in adult patients undergoing primary TJA—specifically, total hip arthroplasty (THA), total knee arthroplasty (TKA), and total shoulder arthroplasty (TSA).MethodsA systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The PubMed, Scopus, and Web of Science databases were searched from their inception to June 16, 2025, for studies comparing postoperative outcomes in adult patients undergoing primary total joint arthroplasty (TJA) and using GLP-1 RAs versus a control group. Data on study characteristics, patient demographics, and postoperative outcomes were extracted. Due to significant heterogeneity and overlap in data sources, a narrative synthesis rather than meta-analysis of the findings was conducted. Study quality was assessed using the Newcastle–Ottawa Scale (NOS).ResultsFifteen retrospective cohort studies involving an aggregate total of 318,143 patients (114,365 THA, 125,505 TKA, 78,273 TSA) were included, with 56,132 receiving GLP-1 RAs. In THA and TKA, GLP-1 RAs use was associated with a reduced risk of periprosthetic joint infection (PJI) (e.g., 1.6% vs. 2.9% at 2 years for THA) and lower 90-day readmission rates (e.g., 1.1% vs. 2.0% for TKA and 1.6% vs. 2.0% for THA). When analyzed by exposure timing, the reduced PJI risk was most consistent in studies that defined GLP-1 RA use in the immediate perioperative period. Reported mean length of stay (LOS) was generally similar or slightly shorter among GLP-1 RA users compared to controls. Multiple studies reported either a reduction or no significant difference in the risk of 90-day emergency department visits. The short-term revision rates and dislocations were infrequent and did not differ significantly between groups in most of the included studies. In the TSA, evidence was inconsistent, with reduced odds of 90-day surgical site infection (SSI) (OR 0.25) in one study; however, no clear trend was observed. Gastrointestinal side effects and conflicting systemic risks were noted across procedures.ConclusionCurrent observational data suggest that perioperative GLP-1 RA use in patients undergoing total hip or knee arthroplasty is not associated with a consistent increase in short-term revision rates and may be associated with a reduced risk of postoperative infection. Evidence regarding TSA remains inconclusive. However, given the retrospective nature of the evidence, substantial overlap in data sources, heterogeneity in exposure definitions, and short-term follow-up, these findings should be considered hypothesis-generating. Future prospective, randomized controlled trials with standardized exposure definitions and longer follow-up are required to confirm these associations and establish causality.Supplementary InformationThe online version contains supplementary material available at 10.1186/s42836-026-00375-w.

Abstract Background Advances in imaging have significantly enhanced the management of lymphoma, particularly through 18 F-FDG PET/CT, which combines metabolic and anatomical assessment. However, reliance on SUVmax alone provides a limited view of total disease burden. Recently, metabolic volumetric metrics such as total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) have emerged as promising tools, offering more robust prognostic information. This study aims to evaluate the predictive role of TMTV and TLG, as measured on an interim PET-CT scan (after three cycles of chemotherapy), and the change in these values on the end-of-treatment scan (after six cycles), in assessing theraputic response in patients with Hodgkin (HL) and non-Hodgkin lymphoma (NHL), and to correlate these measures with established prognostic indices. Results Ninety-one patients (HL: 38; NHL: 53) were analyzed. Comparison between interim and end-of-treatment scans showed significant reductions in TMTV and TLG in HL group, while reductions in NHL group did not reach statistical significance. For predicting complete metabolic response, ΔTLG demonstrated a diagnostic accuracy of 98.1% (at cut-off of > − 4.2) and ΔTMTV 96.2% (at cut-off of > − 1) in the NHL group, while in the HL group, ΔTLG achieved an accuracy of 92.1% (at cut-off of > − 7) and ΔMTV 89.5% (at cut-off of > − 5). In HL, TLG > 200 and MTV > 20 were associated with progressive disease, while in NHL, thresholds of TLG > 150 and MTV > 15 yielded AUCs of 0.768 and 0.761, respectively. Significant correlations were observed between end-of-treatment TMTV, TLG, and IPS in HL (r = 0.545 and 0.549; p < 0.05), but not with IPI in NHL. Conclusions TMTV and TLG are reliable PET/CT-derived biomarkers for early response prediction in lymphoma. Their dynamic changes outperform conventional SUVmax and correlate significantly with prognostic scores in HL. Incorporating these metabolic volumetric metrics may enhance individualized treatment strategies.

The Atomic Clock Ensemble in Space (ACES) mission, currently operating aboard the International Space Station (ISS), is designed to provide high-precision time and frequency measurements and to test fundamental aspects of relativistic physics. Gravitational redshift (GRS), a fundamental prediction of General Relativity (GR), implies that clocks positioned at different gravitational potentials experience relative time dilation. Previous GRS experiments have focused primarily on microwave technologies, with negligible experimental coverage in the optical domain, particularly for ground-to-space links. Motivated by the European Laser Timing (ELT) experiment and the high-precision laser-cooled cesium clock aboard ACES, we introduce and evaluate an optical time-transfer method designed to achieve high-accuracy measurements of GRS. In the absence of actual ELT/ACES optical data, a high-fidelity numerical simulation framework was developed to assess the performance of this method. The framework incorporates representative ELT/ACES mission parameters, including the space-based cesium clock and the H-MASER clock located at the reference ground station, both providing frequency stability at the level of 10−15 for 1000 s averaging time. Applying a ±1σ filtering criterion, we obtain a simulated dataset comprising 33 ELT/ACES passes, representing a total observation time of 4.38 h over a single week. Analysis of this high-fidelity dataset reveals a GRS deviation from GR of (−7.19±0.63)×10−5, achieving a 3.4 orders of magnitude improvement over the best previous laser-ranging experiment conducted at the University of Maryland (UMD), USA, 51 years ago. These simulation results demonstrate that the optical time-transfer link constitutes a powerful tool for testing fundamental physics and, when combined with next-generation optical atomic clocks, enables unprecedented capabilities in space-based timekeeping and geoscience applications.

Tropospheric zenith wet delay (ZWD) is one of the major error sources for space geodetic techniques and plays a vital role in meteorological research. Accurate prior estimates for ZWD can significantly improve the performance of geodetic applications, such as precise kinematic positioning. Current single machine learning ZWD models have limitations in modeling the high spatiotemporal variations of moisture in the lower atmosphere and in their generalization capabilities. To mitigate these limitations, this work introduces a hybrid learning framework that combines multiple machine learning models. The proposed model offers stronger generalization capabilities, improving the ZWD modeling and forecasting accuracy.When comparing the RMSE, the proposed model outperforms existing machine and deep learning-based ZWD models, the empirical GPT-3 model, and the traditional models such as the Saastamoinen and Askne & Nordius models. In the blind case, when surface meteorological data are not used, the RMSE is reduced by 25.76% compared to the GPT-3 model. When using surface meteorological parameters, the proposed model achieves improvements of 47.05% and 34.24% compared to Saastamoinen and Askne & Nordius, respectively.The generalization capabilities of the models were evaluated at non-modeled sites. The proposed model demonstrates improvements in overall external performance, with a particularly significant increase of 26.14% in the blind case compared to GPT-3. When sites access meteorological data, the model shows improvements of 45.23% and 34.31% compared to Saastamoinen and Askne & Nordius, respectively.The spatiotemporal analysis shows the improved stability and precision of the proposed model over the other models evaluated in this work, indicating promising prospects for it in real-time and rapid geodetic applications.

Industry 5.0 represents a fundamental shift in engineering and manufacturing, moving beyond automation and digitalization toward human-centric, resilient, and sustainable production systems through the intentional integration of human intelligence, creativity, emotional insight, and ethical judgment with advanced technologies such as artificial intelligence, collaborative robotics, digital twins, and the Internet of Things. This paper presents a comprehensive review of the core principles, enabling technologies, emerging standards, and industrial applications of Industry 5.0, demonstrating how advanced human–machine collaboration supported by intelligent automation and ethical AI enhances adaptability, flexibility, resilience, and sustainability, while also revealing critical barriers to large-scale adoption, including fragmented standards, socio-technical integration challenges, ethical governance complexities, and workforce transformation demands. Building on these findings, the study proposes a strategic transformation framework structured around a five-phase roadmap—vision alignment, capability development, socio-technical integration, systemic adaptation, and continuous improvement—that combines human creativity, emotional intelligence, and moral reasoning with transparent, adaptive, and collaborative technologies to enable trustworthy human–machine synergy, environmental stewardship, and organizational agility. By delivering measurable improvements in resilience, inclusivity, and sustainability performance, the proposed framework provides actionable guidance for implementing Industry 5.0 across diverse sectors, including manufacturing, healthcare, logistics, and education, and ultimately reframes industrial transformation by positioning human values, ethical governance, and planetary well-being at the core of next-generation intelligent and sustainable production ecosystems.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that presents as a symmetrical polyarthritis characterized by joint pain, swelling, and stiffness. This study aimed to evaluate the expression of microRNA-24-3p (miRNA-24-3p), in the plasma of RA patients and compare it with that of healthy controls with the goal of establishing a specific expression profile for early diagnosis and improved patient care. This case-control study was carried out on 100 RA patients attending Rehabilitation and Physical Medicine department and 50 age and sex-matched control individuals. Disease severity score (DAS-28) was used to assess the disease’s level. Real-time polymerase chain reaction (qPCR) was used for detection of plasma level of miRNA-24-3p and other lab tests including blood picture, c-reactive protein (CRP), erythrocytes sedimentation rate (ESR) and anti-cyclic citrullinated peptide (anti-CCP) were also performed. The miRNA-24-3p expression was statistically significant higher among RA cases than controls (p < 0.001) and was positively associated with higher ESR, white blood cells (WBCs) and along with the number of tendered/swallow joints (p < 0.05). Higher CRP, ESR, Anti-CCP, and miRNA-24-3p expression were found to be significant predictors of DAS28 scores in univariable analysis. Higher CRP and miRNA-24-3p expression exhibit substantial impact in multivariate analysis, indicating a strong statistical significance in their association with higher DAS28 grades. The elevated expression of miR-24-3p suggests its probable contribution to both the onset of RA and the activity measured by DAS28 score. Consequently, assessing its expression levels may serve as a valuable tool for the diagnosis and management of RA.