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Effect of dexmedetomidine combined with the modified Trendelenburg position on intraocular pressure in patients undergoing robot-assisted laparoscopic surgery: A randomized controlled trial

Objective This study aimed to evaluate the effect of combining dexmedetomidine infusion with a modified Trendelenburg position on intraocular pressure (IOP) in patients undergoing robot-assisted laparoscopic surgery (RALS). Methods In a single-center, prospective, 2 × 2 factorial randomized controlled trial, 160 patients scheduled for RALS were allocated to one of four groups: control (normal saline + traditional position), modified position alone, dexmedetomidine alone, or the combination of both. IOP was measured at baseline, during key surgical phases, and after extubation. The primary outcome was IOP during surgery. Secondary outcomes included the incidence of cardiovascular adverse events and the requirement for vasoactive drugs. Results A total of 153 patients completed the study protocol. Both dexmedetomidine and the modified position significantly reduced IOP during surgery (main effect of drug: F = 35.2, P  < 0.001; main effect of position: F = 28.7, P  < 0.001). Critically, a statistically significant interaction was observed between the two interventions (F = 4.8, P  = 0.030), indicating a synergistic effect. The combination group demonstrated the greatest reduction in IOP from time points T2 to T4, ranging from 3.6 to 4.8 mmHg lower than the control group. There were no significant differences in the overall incidence of adverse events among the groups. Conclusions In conclusion, for patients undergoing prolonged robotic laparoscopic surgery (with a mean operative time of 217 minutes), the combined use of dexmedetomidine infusion and a modified Trendelenburg position provides an effective and synergistic strategy for intraocular pressure control. This approach offers a practical means to enhance intraoperative ocular safety during such lengthy procedures. Trial registration Chinese Clinical Trial Registry ChiCTR2300072961

Remanufacturing workshop management in blockchain and digital twin-based cloud remanufacturing service platform

The cloud remanufacturing service platform is an online platform that provides various services and resources for the remanufacturing industry. It promotes the digital transformation of the industry, improves resource utilization efficiency, and reduces environmental impact. However, there are prominent issues in sharing remanufacturing data securely, monitoring the remanufacturing process opaquely, and dealing with high uncertainties in remanufacturing operations within the remanufacturing intelligent workshop, which is an important component of the platform. This paper combines blockchain technology and digital twin technology to study the management mode of the remanufacturing workshop under the cloud remanufacturing service platform. Firstly, the overall framework of the cloud remanufacturing service platform is constructed, and a dual-chain structure with expandable subchains for storing transaction data and workshop data is designed. Secondly, by connecting the remanufacturing workshop with smart contracts and digital twin, real-time monitoring, remote collaboration, and data analysis optimization of the remanufacturing workshop are achieved. Digital twin is applied to the remanufacturing process, and a digital twin-based remanufacturing workshop architecture is established. The key technologies involved are analyzed. Finally, the management mode of the automotive remanufacturing workshop under the cloud-based remanufacturing service platform based on blockchain and digital twin is explored.

Dimensionally constrained adversarial attack and defense in wind power forecasting

Deep neural networks (DNNs) have achieved remarkable success in wind power forecasting, but DNNs are vulnerable to adversarial attacks that can severely degrade forecast accuracy. Existing studies primarily emphasize attack effectiveness and pay limited attention to attack stealthiness. In this paper, a dimension-constrained momentum iterative fast gradient sign method (DC-MI-FGSM) is proposed for wind power forecasting, which generates highly stealthy perturbations by applying the momentum update mechanism during attack optimization and limiting the perturbation dimensions of input samples. To defend against this attack, a denoising autoencoder (DAE)-based preprocessing defense strategy is developed for wind power forecasting, which resists adversarial attacks by mapping adversarial samples back to their corresponding clean forms. The effectiveness of the proposed attack and defense methods is validated on the public SDWPF dataset under both white-box and black-box scenarios. Compared with existing attacks, DC-MI-FGSM achieves a lower average perturbation percentage (APP), indicating superior attack stealthiness. Meanwhile, it causes more severe degradation in forecasting accuracy, as measured by MAPE, RMSE, and MAE, demonstrating stronger attack effectiveness. For defense, the proposed DAE-based preprocessing strategy effectively mitigates adversarial perturbations, significantly reducing forecasting errors while preserving the original accuracy on clean data. Moreover, it consistently outperforms adversarial training in terms of robustness and usability.

Effect of aging and Varroa parasitism on the paracellular and transcellular permeability of the honeybee blood-brain barrier

Honeybees ( Apis mellifera ) provide crucial pollination services to agricultural systems globally, however, their healthspan in these contexts is constantly at risk. Agricultural environments impose a variety of sublethal stressors onto honeybees, including parasites, pathogens, pesticides, and poor nutrition. Synergies between age, age-associated tasks, and these stressors are believed to underlie colony failure trends of the past decade. Identifying the mechanisms by which age and stressors impact honeybee physiology is an important priority in protecting honeybee and other pollinator populations. An underexplored physiological structure in honeybees is the blood-brain barrier, a protective layer of cells that surrounds the brain. Here, we assess key dimensions of blood-brain barrier function; paracellular and transcellular permeability to molecules in the hemolymph. We measured these modes of permeability across worker groups that differ in age and foraging experience, as well as in bees exposed to varying levels of infestation by the parasitic mite Varroa destructor during development. Our results demonstrate that the paracellular permeability of the honeybee blood-brain barrier is stable across these age groups and upon Varroa exposure. In contrast, we found that transcellular permeability is increased in honeybees exposed to a high Varroa load. Together, these results demonstrate that age-related variation and parasitic stress differentially impact a primary protective structure of the honeybee central nervous system, which may lead to targeted interventions for protecting honeybee healthspan. The assay developed here may be easily applied to different aging- and stress-related contexts, further enabling studies focused on understanding maintenance and decline of the honeybee blood-brain barrier.

Drug–food and drug–alcohol interactions: Pharmacists’ knowledge gaps and patient safety concerns in the United Arab Emirates

Pharmacists play a critical role in promoting medication safety through patient counselling and identification of potential interactions. However, knowledge regarding drug–food interactions (DFIs) and drug–alcohol interactions (DAIs) among community pharmacists in the United Arab Emirates (UAE) has not been previously examined. This cross-sectional study assessed pharmacists’ knowledge of DFIs, DAIs, and drug–food administration timing, and identified factors associated with higher knowledge levels using a validated self-administered 24-items questionnaires. The survey assessed sociodemographic and professional characteristics, knowledge of DFIs, DAIs, and drug-food timing. Descriptive statistics and multiple linear regression analyses were performed using SPSS v28.0 to determine predictors of total knowledge scores. Pharmacists demonstrated moderate knowledge, with mean scores of 8.62/12 for DFIs, 5.84/12 for drug–food timing, and 4.70/6 for DAIs (total score 19.16/30). High awareness was observed for interactions such as caffeine–diazepam (76.3%) and methotrexate–alcohol (81.0%), whereas only 25.9% identified potassium-rich foods’ interaction with spironolactone. Higher knowledge scores were associated with being male (p < 0.001), aged 20–29 years (p = 0.041), having ≥6 years of experience (p = 0.004), and higher self-perceived knowledge (p < 0.001). These findings highlight important knowledge gaps that may affect patient safety. Targeted continuing education and integration of DFI/DAI content into pharmacy curricula are recommended to strengthen counselling practices and improve medication safety outcomes.

Correction: Ultrashort-T2* mapping at 7 tesla using an optimized pointwise encoding time reduction with radial acquisition (PETRA) sequence at standard and extended echo times

Daily briefing: Suck-up chatbots can encourage real-life rudeness

Diagnostic accuracy of AI-assisted chest radiographs in tuberculosis screening: A Ghanaian clinical study

Background Tuberculosis remains a major global health challenge, particularly in resource-limited settings where access to expert radiological interpretation is constrained. Artificial intelligence offers a promising solution to enhance diagnostic accuracy and efficiency in TB screening. Aim This study aimed to evaluate the diagnostic performance of an AI-based system compared to a radiologist in screening for TB using chest X-rays from 1,010 patients. Methods Patients were adults ≥18 years with suspected TB in a high-burden setting. GeneXpert MTB/RIF served as reference to assess accuracy, sensitivity, specificity, PPV, NPV, and AUC for radiologist and AI TB predictions. Comparisons used McNemar’s test and Cohen’s kappa to evaluate agreement and significance of differences. Results The AI system demonstrated superior performance with an accuracy of 91%, sensitivity of 86%, specificity of 93%, PPV of 85%, NPV of 94%, and AUC of 0.90. In contrast, the radiologist achieved an accuracy of 86%, sensitivity of 84%, specificity of 87%, PPV of 76%, NPV of 92%, and AUC of 0.86. McNemar’s test revealed a statistically significant difference between the two modalities (p = 0.0021). Cohen’s kappa indicated substantial agreement between AI and GeneXpert MTB/RIF result (κ = 0.79), moderate agreement for the radiologist and GeneXpert MTB/RIF result (κ = 0.69), and moderate agreement between radiologist and AI predictions (κ = 0.53). Conclusion The AI system outperformed the radiologist in TB screening, demonstrating higher diagnostic accuracy and agreement with GeneXpert MTB/RIF result. These findings support the integration of AI into TB screening workflows, particularly in settings with limited access to expert radiological interpretation.

High-flux hemodialysis combined with nutritional intervention: Effects on the nutritional status and clinical outcomes of patients with renal failure

Background To evaluate the association between high-flux–based hemodialysis prescription combined with nutritional intervention and nutritional status and clinical outcomes in renal failure patients. Methods Patients admitted between January 2020 and August 2024 were divided into an observation group (high-flux hemodialysis, n = 115) and a control group (Conventional blood purification, n = 115). Renal function [β2-microglobulin (β2-MG), serum creatinine (Scr), blood urea nitrogen (BUN)], inflammatory markers [interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor-α (TNF-α)], and nutritional indices [serum albumin (ALB), prealbumin (PA), transferrin (SF)] were compared. Cox multivariate analysis identified prognostic factors. Results Both groups exhibited post-treatment reductions in uremic markers (β2-MG: P < 0.05; Scr: P < 0.05; BUN: P < 0.05) and inflammation (IL-6: P < 0.05; CRP: P < 0.05; TNF-α:P < 0.05), with no baseline differences (P > 0.05). The observation group demonstrated significantly greater reductions in uremic markers and inflammatory markers versus controls (P < 0.05). Nutritional parameters (ALB, PA, SF) increased post-treatment in both groups (P < 0.05). Survival rates were higher in the observation group (P < 0.05). Deceased patients were predominantly≥60 years old, had cardiovascular comorbidities, and exhibited higher conventional blood purification utilization (P < 0.05).Lower ALB (P < 0.05) and elevated IL-6, CRP, TNF-α (P < 0.05) correlated with mortality.A reduced Cox model identified IL-6 (HR: 1.107, 95%CI: 1.063–1.153) and TNF-α (HR: 1.069, 95%CI: 1.012–1.130) as independent predictors of poor outcomes (P < 0.05). Conclusions High-flux-based hemodialysis prescription with nutritional support was associated with more effective clearance of uremic markers, improvements in nutritional status and inflammatory response, as well as better survival in renal failure patients. Elevated IL-6 and TNF-α levels were identified as significant predictors of mortality in the reduced Cox model.

Briefing Chat: ‘Zombie cells’ resurrected with new genes

Bacillus megaterium strain KGA3 increases saline–alkaline tolerance of maize by recruiting keystone taxa in rhizosphere soil

Geographical barriers and multimorbidity in quilombola territories of the amazon region

Background Quilombola communities in the Brazilian Amazon face persistent social and territorial inequities that shape health outcomes and access to care. Geographic isolation, limited transportation, centralization of specialized services, and socioeconomic disadvantages contribute to unequal opportunities for timely diagnosis and treatment. Understanding how these determinants interact with patterns of multimorbidity is essential for guiding equiTable health policies and strengthening primary care in remote territories. Methods A cross-sectional epidemiological study was conducted with 518 adults from nine quilombola communities in Santarém, Pará. Data were collected through household surveys addressing sociodemographics, self-reported diseases, service utilization and resolvability. Geographic coordinates of communities and health services were mapped to classify accessibility as high, medium or low. Diseases were converted into a binary matrix to estimate prevalence and identify multimorbidity (≥2 conditions). Statistical analyses included chi-square tests, ANOVA, Spearman correlations and heatmap visualization. A Composite Access Index (CAI) integrating geographic distance, epidemiological burden and service-use indicators was developed. A Random Forest model was used to identify conditions most strongly associated with multimorbidity. Results Communities showed marked territorial heterogeneity. Pérola do Maicá had the highest accessibility, while Ituqui, Tiningu and Murumuru presented substantial geographic and logistical barriers. Service utilization ranged from 42.9% to 95.0%, and most communities relied on care outside their territory (70–95%). Complete problem resolution was reported by 72.5% of participants, though with variation among communities. The CAI identified Ituqui (0.550), Tiningu (0.480) and Murumurutuba (0.331) as the most vulnerable territories. The Random Forest model achieved 93.6% accuracy, with hypertension, diabetes, musculoskeletal diseases, arthritis/rheumatism and heart disease emerging as key predictors of multimorbidity. Discussion Findings indicate that social and territorial determinants are strongly associated with inequities in access to health services, continuity of care, and disease burden across quilombola communities. Conclusions Geographic barriers and the distribution of health services are associated with distinct patterns of multimorbidity and health service access among quilombola populations. Strengthening primary care, transportation, and diagnostic support may help mitigate inequities and improve health conditions in remote Amazonian territories.

PCB-YOLOV8X: a network for detecting micro-sized defects on PCB surfaces based on enhanced feature information

Exploring the association between Frailty Index and Knee osteoarthritis in middle-aged and older Chinese adults: A cross-sectional analysis of data from the China Health and Retirement Longitudinal Study

Objective To explore the relationship between Frailty Index (FI) and Knee osteoarthritis (KOA) in Chinese people aged 45 and above. Methods Data on symptomatic KOA, FI, and over ten covariates were from the China Health and Retirement Longitudinal Study (CHARLS). Logistic regression and restricted cubic splines (RCS) were used to analyze the correlation. Subgroup and interaction analyses were also conducted, and FI was divided into quartiles to assess result stability. Six machine learning constructs were used to build predictive models, which were then evaluated. Results Among 29,105 participants, 5,012 had symptomatic KOA. Weighted multivariate logistic results showed a correlation between KOA and frailty (OR 3.63(3.48,3.78)), present across quartiles. Subgroup analyses revealed potential effects of various factors on their association, with significant interactions found for gender, education, place of residence, alcohol consumption, hypertension, and dyslipidemia. RCS results indicated a logarithmic non-linear relationship between symptomatic KOA and frailty. The area under the curve of the Random Forest Machine model is 0.90, and it performs best on the calibration curve and decision curve analysis. Conclusion This study showed a nonlinear relationship between FI and KOA in middle-aged and older Chinese adults, with higher FI scores linked to higher KOA prevalence and a significant inflection point, informing focused KOA interventions in aging populations.

Cross-cultural adaptation, validity, and reliability of the Turkish version of the athlete disability index

Building an intelligent brain platform for small and medium-sized enterprises using ChatGLM and Multi-Agent Systems

Large language models (LLMs) have demonstrated strong capabilities in semantic understanding and text generation. However, their direct application in the segmented and specialized domains of small and medium-sized enterprises (SMEs) presents several challenges. These include semantic overgeneralization, poor alignment with enterprise-specific knowledge, and insufficient domain expertise. To address these limitations, this study proposes an “Enterprise Intelligent Brain” platform tailored to the business needs of SMEs. The platform is built upon Chat Global Language Model (ChatGLM) and is enhanced through a multi-agent coordination mechanism and structured support from enterprise knowledge graphs. The study centers on improving the platform’s semantic adaptability and intelligent responsiveness in real-world enterprise scenarios. It begins by identifying the core semantic demands of typical SME operations—such as policy consultation, customer service, and business process execution—and constructs a triadic system architecture that integrates three key components: semantic parsing, task scheduling, and knowledge support. Methodologically, the platform applies domain-specific fine-tuning to the ChatGLM model to enhance relevance and precision. It also incorporates a multi-agent task allocation framework and utilizes knowledge graph reasoning to improve contextual accuracy and domain knowledge integration. The effectiveness of the proposed system is evaluated using three public datasets: Baidu DuReader-Enterprise, the E-commerce Dialogue Dataset, and the Enterprise Knowledge Graph-Based Q&A Dataset. Experimental results confirmed that the optimized system significantly outperformed the baseline model across multiple metrics. Notably, it achieved a task completion rate of up to 99.904%, an average response time as low as 0.858 seconds, a context retention score of up to 0.953, and a user satisfaction rating of up to 4.767. Additionally, the system demonstrated strong performance in knowledge invocation coverage and error recovery, indicating its robustness in complex and dynamic SME environments. Therefore, this study provides a practical and scalable framework for deploying LLMs in domain-specific SME contexts. It offers both a technical solution and theoretical insights for developing enterprise-grade semantic intelligence platforms capable of supporting intelligent decision-making and service automation.

Nationwide assessment of community pharmacists’ practices and atorvastatin–drug interactions in Egypt

Abstract Atorvastatin (ATV) is widely prescribed in Egypt, where cardiovascular disease remains the leading cause of mortality; however, it is prone to clinically significant drug–drug interactions (DDIs). This national cross-sectional study evaluated community pharmacists’ (CPs’) knowledge, practice behaviors, and reported rates of ATV-related DDIs across Egypt. A total of 973 licensed pharmacists completed a validated survey. The mean cumulative practice score was 7.2 ± 1.91, indicating high adherence to recommended safety practices. Frequently reported roles included targeting polypharmacy patients (80.1%), initiating single-drug therapy when appropriate (82.2%), counseling patients about adverse effects (85.1%), and using web-based drug-information resources (88.2%), whereas structured DDI screening tool use was less common (46.1%). Compared with male pharmacists, female pharmacists presented significantly higher cumulative practice scores (7.52 ± 1.59 vs. 7.08 ± 1.99; p  = 0.016), and diploma holders scored higher than Ph.D. holders did (7.47 ± 1.81 vs. 6.82 ± 1.87; adjusted p  = 0.02). High-frequency encounters were reported for major DDIs with cyclosporine (49.3%) and clarithromycin (45.8%) and for moderate DDIs with digoxin (42.2%), phenytoin (41.0%), and azithromycin (40.5%). Regional variation showed higher interaction frequencies in Upper Egypt and the Delta. Strengthening pharmacist stewardship programs and expanding standardized DDI screening approaches may improve ATV safety.

Identification of BmSP25 gene in Bombyx mori with antiviral function against BmNPV

Bombyx mori serine protease (BmSP) is constitute a gene family of proteolytic enzymes characterized by serine residues at their active sites and play critical roles in physiological processes, including digestion, growth and development, and immune responses. As a member of the BmSP family, BmSP25 exhibits differential expression following Bombyx mori nucleopolyhedrovirus ( BmNPV ) infection. Our result demonstrated that BmNPV infection upregulated BmSP25 expression in both resistant (SuN) and susceptible (P50) silkworm strains, with a more pronounced response observed in SuN than in P50. To further investigate its function, siRNA-mediated knockdown of BmSP25 in BmN cells promoted the proliferation of recombinant BV-EGFP virus, whereas overexpression of BmSP25 significantly suppressed BmNPV replication. To validate its antiviral activity at the organismal level, transgenic silkworm strains overexpressing BmSP25 ( BmSP25 -OE) and BmSP25 knockout strains ( BmSP25 -KO) were generated. Following oral inoculation with BmNPV , viral proliferation was significantly inhibited in the BmSP25 -OE strain, whereas viral replication was notably enhanced in the BmSP25 -KO strain. This study is the first to clearly demonstrate the anti- BmNPV function of BmSP25 in silkworms, providing a foundation for further elucidation of its role in host immune defense mechanisms and identifying a potential genetic target for molecular breeding aimed at improving disease resistance in silkworms.

Isolation of soil cellulolytic bacteria and their temperature- and pH-dependent decomposition of carboxymethylcellulose-based hydrogels

Gastrointestinal adverse events associated with tirzepatide: A bibliometric and pharmacovigilance analysis

Background This study examines gastrointestinal adverse events (GIAEs) associated with tirzepatide using bibliometric and pharmacovigilance analyses. Research design and methods A bibliometric analysis of Web of Science data identified research trends in tirzepatide-related adverse events, while a pharmacovigilance analysis of FAERS data (Q2 2022–Q2 2024) assessed real-world GIAEs patterns. Disproportionality, time-to-onset, univariate, and comparative analyses were conducted to evaluate reporting odds ratios (RORs), onset timing, and subgroup differences. Results Among 110 studies, cardiovascular outcomes predominated as the research focus. FAERS data showed that nausea (27.7%) and diarrhea (12.8%) were the most frequently reported events, whereas eructation and impaired gastric emptying had the highest disproportionality. GIAEs were more common in older adults, males, and patients receiving concomitant medications, and most occurred within 3 months (median onset: 16 days). Tirzepatide had a lower ROR for GIAEs than GLP-1 receptor agonists but a higher ROR than non-GLP-1 drugs, with a greater risk in patients with type 2 diabetes (T2DM) than in those using tirzepatide for weight loss. Conclusion Tirzepatide is associated with an increased risk of GIAEs, particularly among patients with T2DM, males, older adults, and those using concomitant medications. FAERS-based real-world evidence complements clinical trial data and highlights the need for individualized patient monitoring and management strategies.