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Exploratory research on drugs after lung surgery based on real-world data from the FDA adverse event reporting system database
Backgrounds Lung surgery is the most common surgery in thoracic surgery, but postoperative dyspnea, cough, palpitations, and chest pain seriously affect the quality of life of patients and hinder their postoperative recovery. Methods To reveal the potential correlation between drugs and adverse events, we used disproportionation analysis to calculate the ROR and PRR values of the top 30 reported cases of dyspnea, cough, palpitations, and chest pain, respectively. In addition, we defined high and medium ROR signaling drugs after lung surgery by generating Venn diagram and calculated the PRR of all drugs used to treat pulmonary hypertension to explore the correlation between pulmonary arterial hypertension drugs and these four symptoms. Results We identified the top 30 drugs that are most related to dyspnea, cough, palpitations, and chest pain, and defined 4 high ROR signaling drugs after lung surgery, and 10 medium ROR signaling drugs after lung surgery. Among them, 3 high ROR signaling drugs are drugs for treating pulmonary hypertension (PH). Subsequently, further analysis revealed that it is the basic drug attribute of treating PH that can easily cause these four common symptoms after lung surgery. Conclusion Our study provides a list of high and medium ROR signaling drugs drugs for postoperative patients with lung surgery and clarifies the potential correlation between the properties of drugs for treating pulmonary arterial hypertension and dyspnea, cough, palpitations, and chest pain, providing a theoretical basis for the medication of postoperative patients.
Dapsone and schistocytes: Thrombotic microangiopathy or not?
Introduction Dapsone is used in many infectious and immunological diseases. Dapsone may cause haemolysis, particularly in individuals with glucose-6-phosphate dehydrogenase (G6PD) deficiency. This hemolysis is described as oxidative and is usually not associated with the presence of schistocytes. Patients and methods We conducted a single-centre retrospective observational study at the University Hospital of Tours, France (2005–2021). Adult patients who received dapsone and had a blood smear performed during treatment or within 7 days of discontinuation were included. Data were extracted from electronic medical records. Patients were classified as schistocyte-positive (≥0.1%) or -negative. Descriptive statistics were used; no further analyses were performed due to small sample size. Results During the 2005−2021 period, 19 adult patients received dapsone in our institution. Amongst them, 10 (52.6%) had detectable schistocytes, ranging from 2 to 44/1000 erythrocytes (13 ± 14.2). Patients with schistocytosis exhibited more severe hemolytic anemia, with a larger decrease in hemoglobin (−2.7 ± 1.4 g/dL vs −1.6 ± 2.12 g/dL), higher methemoglobinemia (5.2% vs 1.9%), more frequent low haptoglobin levels (88.9% vs 60%), and elevated LDH levels (557 U/L [239–671] vs 273 U/L [208–507]) compared to patients without schistocytosis. Platelet counts were similar between the two groups. No organ failure was identified in the reviewed medical records. Schistocytosis regressed upon discontinuation of treatment after 3–18 days. Conclusion Dapsone appears to be associated with hemolytic anemia and the presence of schistocytes, suggesting mechanical intravascular haemolytic anaemia. Whether Dapsone-associated biological thrombotic microangiopathy can be associated with target organ damage requires further investigation.
Audit data governance for disability-inclusive public services: A systematic review and integrative S–A–C framework
This study consolidates interdisciplinary evidence on how audit data governance shapes accountability and service equity in data-intensive public services, with specific sensitivity to disability-inclusive delivery. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, we conducted a protocol-driven systematic literature review of peer-reviewed studies and selected institutional and standards sources published between 2010 and 2025. We searched Scopus and complemented database retrieval with targeted searches of audit, digital government, accessibility, and data-governance portals. We applied a theory-led narrative synthesis to accommodate heterogeneity in designs, domains, and outcome reporting. Synthesizing 125 records, we find that governance effects are produced through three interacting dimensions—service delivery for people with disabilities (S), audit data governance (A), and data-driven culture (C)—that jointly condition transparency, contestability, and distributive impacts. Service delivery arrangements shape where data are generated, which encounters are recorded, and whether access needs, reasonable accommodations, and complaints become organizationally legible. Audit data governance conditions the credibility and usability of audit trails through provenance, metadata, retention, audit logging, and role-appropriate oversight access, shaping both oversight feasibility and privacy risk. Data-driven culture conditions how evidence is interpreted and acted upon, including data literacy, the standing of experiential evidence, and whether audit signals support learning or only compliance. Across the corpus, recurring tensions emerge between optimization and inclusion; transparency and privacy; standardization and reasonable accommodation; automated decisioning and meaningful contestability; and compliance-oriented auditing and learning-oriented governance. Drawing these mechanisms together, we propose an integrative framework linking S, A, and C to explain how auditability and disability inclusion are co-produced across the digital service lifecycle. The framework identifies leverage points for governance reform and evaluation and outlines implications for future empirical testing and for practitioners seeking trustworthy, oversight-ready, inclusive digital public services. A review protocol was prepared before the review; the review was not registered.
An integrated method for railway fastener defect detection and geometric parameter measurement using 3D line laser sensor
Railway fasteners are key components that maintain track stability and ensure train operation safety. Automatic detection technologies for fastener defects have been widely adopted, but high-precision measurement of fastener geometric parameters still relies on manual operation, which is associated with low efficiency and significant measurement errors. This paper proposes an integrated method for railway fastener defect detection and geometric parameter measurement based on a 3D line laser sensor. A 3D imaging system is constructed to acquire RGB depth images and corresponding point clouds with a precise one-to-one mapping relationship. The YOLOv8 network is used to detect visual defects and locate intact fastener regions from RGB depth images, which are then mapped to the point clouds to extract fastener point cloud data. The PointNet++ network is adopted to segment fastener components, and the specifications of insulated blocks, the thicknesses of height adjustment pads, and bolt heights are calculated based on the spatial structure of fastener components. Experimental results demonstrate that the YOLOv8 model achieves 97.7% precision and 95.9% recall for visual defect detection, and 99.6% precision and 99.8% recall for intact fastener localization. All critical geometric measurements satisfy the corresponding railway engineering tolerances. 98.7% of HPuR measurements have errors below the 0.5 mm tolerance. HPuIP components with a 10 mm specification step exhibit a maximum measurement error of less than 2.5 mm, well below the 5 mm tolerance. 99% of insulated block measurements achieve errors below 0.5 mm tolerance at horizontal sampling intervals no greater than 0.4 mm. Bolt height measurement achieves no less than 90% precision and 91% recall for severe fastener loosening detection. The system operates at 4.32 km/h, making it suitable for hand-pushed on-site inspection. The proposed method realizes automatic defect detection and high-precision geometric parameter measurement in a unified framework, which can effectively replace manual inspection and significantly improve the intelligence and efficiency of railway track maintenance.
The dark side of streaking: Examining the backfire potential of run streaking in recreational runners who broke a long-term streak
Background Run streaking is running on consecutive days for a minimum of one mile per day. Despite its benefits for supporting habit formation and long-term behaviour change, some streak runners report potential unintended negative consequences of run streaking. The aim of the study is to examine the backfire potential of run streaking in recreational runners who ended a long-term streak. Methods Qualitative semi-structured interviews with 17 recreational adult runners (10 male, 6 female, 1 other gender). All runners ended a run streak of ³100 consecutive days. Transcripts were analyzed using a hybrid deductive–inductive thematic analysis. Results Prior to streak cessation, some runners felt streak-related inconveniences and ran with injury to prolong the streak. Immediate consequences following the end of a streak included feelings of sadness, anger, disappointment and relief. Several run streakers described a ‘grieving process’ in the weeks and months following streak cessation. Unintended negative consequences were amplified in runners with higher levels of streak attachment. All physically capable runners continued to run regularly with most starting a new streak and all voiced positive views towards run streaking despite their streak ending. Conclusion Run streaking as a behaviour change technique has small backfire potential in some runners. Ending a long-term run streak can lead to short-term negative affect which can develop into experiences of grief, particularly in those with high levels of streak attachment. No long-term negative consequences were reported. All participants perceived run streaking as positive overall and remained physically active following the end of their long-term streak.
Assessment of a modified Coronavirus Disease Optimization Algorithm for Parameter Estimation of Proton Exchange Membrane Fuel Cells
Abstract Parameter identification of a proton exchange membrane fuel cell (PEMFC) involves estimating the unknown model parameters required to build an accurate predictive representation of fuel-cell performance using optimization-based techniques. Because these parameters are often unavailable in manufacturer datasheets, their estimation is crucial for reliable performance prediction and system evaluation. In this work, a memory-based Coronavirus Disease Optimization Algorithm (mCOVIDOA) is proposed as an enhanced optimization method for extracting PEMFC parameters. Six unknown model parameters are identified and compared using six optimization approaches: mCOVIDOA, the standard Coronavirus Disease Optimization Algorithm (COVIDOA), Tunicate Swarm Algorithm (TSA), Grey Wolf Optimizer (GWO), Chimp Optimization Algorithm (ChOA), and Moth–Flame Optimizer (MFO). During the optimization process, these parameters are treated as decision variables, and the objective is to minimize the sum of squared errors (SSE) between simulated and measured cell voltages. The results show that mCOVIDOA achieves a lower SSE (1.9454) and consistently outperforms the original COVIDOA as well as GWO, TSA, ChOA, and MFO in terms of convergence quality and solution accuracy. Owing to its accurate prediction capability and faster convergence, mCOVIDOA shows strong potential for digital-twin development of fuel-cell systems and advanced control applications in automotive energy systems.
Evaluating urban oriental plane wood as a tonewood: effects of solvent extraction on acoustic performance
Abstract This research investigates the acoustic potential of mature Oriental plane wood ( Platanus orientalis L.) harvested from urban environments for use in musical instrument manufacturing. While traditionally undervalued as low grade raw material, this species offers unique aesthetic properties that could be enhanced for high value lutherie applications. The study aimed to determine if solvent extraction could improve the wood’s acoustic properties to meet standard criteria for instrument soundboxes. Forty clear, straight grained radial specimens were prepared and subjected to sequential extraction using pure water and a 1:3 acetone-ethanol solution via a Soxhlet system. Dynamic mechanical properties were evaluated using a non-contact forced vibration system. Results indicated that while solvent washing consistently reduced air-dry density and the longitudinal modulus of elasticity, the acoustic radiation coefficient remained stable or slightly increased, particularly with the ethanol-acetone treatment. However, extraction significantly increased the damping capacity, suggesting that the removed extractives, much like those in Pernambuco wood ( Caesalpinia echinata Lam.), play a structural reinforcement role within the cell wall. While plane wood requires careful impedance matching for use in resonance boxes, its extractives present a promising opportunity for impregnating other species to enhance their acoustic performance.
Neural network modelling of proton RBE values at predominant survival fractions of in vitro data
Abstract While a constant relative biological effectiveness (RBE) of 1.1 is applied in clinical proton therapy, longstanding clinical concern and in vitro evidence demonstrate a variable proton RBE. Several RBE models for protons have been developed based on general assumptions of RBE dependencies together with fitting to in vitro data, for which some modelling authors have included greater diversity of cell types than others. In this study we use neural networks (NNs) to model the proton RBE without any predefined dependencies, investigate the effect of using different biological inputs and combinations of input parameters in the modelling, and compare the results to previous approaches. A comprehensive in vitro RBE database of 431 data points was used, including parameter values for the proton linear energy transfer (LET), the (α/β) x value of the linear quadratic model parameters of the reference photon radiation, and survival fraction (SF)-specific photon doses (D x (SF)). NNs were used to model RBEs at specific SFs (RBE SF ) as a function of (1) LET, (2) LET and (α/β) x and (3) LET, (α/β) x and D x (SF). Models with inputs LET, (α/β) x and D x (SF) showed overall best performance, particularly inputs with D x (0.1) and D x (0.2), indicating that an appropriate standardized input parameter for D x (SF) could be established by choosing an SF between 0.1 and 0.2. The presented approach to assumption-free NN-based modelling of proton RBE showed similar RBE dependencies as previously published models with similar predictive power. Further, it was demonstrated that adding the photon dose sensitivity as an input parameter to modelling could increase the quality of RBE models.
Deep-penetrating geochemical techniques for concealed deposits covered by semi-humid grassland: Zhaishang gold deposit, Western Qinling Orogen, China
Beta2-adrenergic receptor activation increases susceptibility to arrhythmogenesis in aging heart by impairing repolarization reserve via Giβγ–PLC signaling pathway
Interpretable ensemble learning framework for shear strength assessment of corroded reinforced concrete beams
The relationship between medical workers’ presence of meaning in life and turnover intention: the mediating role of searching for meaning in life and professional happiness
Impact of adopting digital FinTech techniques on banks’ cyber and systemic risks
Automated orbital wall fracture detection and severity classification of ocular injuries using deep learning
Detection of season-long post-concussion motor deficits in female soccer players using a 3D goal equivalent manifold framework
Hybrid multi-scale CNN-Residual-LSTM approach for robust state-of-charge estimation in lithium-ion batteries
Synergistic effects of Graphene Nanoplatelets and Joncryl ADR on the mechanical and thermal properties of PLA/POM nanocomposites
A heterogeneous graph attention network based IPv6 target generation algorithm for nonseed prefixes
Abstract The vast scale and sparse distribution of the IPv6 address space pose considerable challenges to efficient scanning. Current IPv6 target generation algorithms primarily rely on pattern mining of seed addresses to generate target addresses, which risks failing when encountering nonseed prefixes. To address this, researchers have proposed IPv6 target generation algorithms for nonseed prefixes. However, existing approaches suffer from limitations such as single-feature exploitation, inadequate utilization of Whois auxiliary information, and inability to handle missing Whois information, resulting in low target address hit rates and prefix coverage. Therefore, we propose 6HAN, which pioneers the use of a heterogeneous graph attention network to model multi-modal correlations. 6HAN first constructs a heterogeneous graph leveraging IPv6 prefixes and their Whois metadata. Subsequently, 6HAN integrates Whois auxiliary attributes with prefix structural features to generate prefix embeddings and designs a dual-task joint self-supervised framework for model training. Finally, 6HAN completes missing Whois fields through graph neural network-based correlation prediction, retrieves similar seed prefixes for pattern migration to form nonseed prefix address patterns, and generates high-quality IPv6 target addresses. Experimental results demonstrate that when sending 50 million probe packets, 6HAN improves the hit rates by 13.81%-201.05% and prefix coverage by 17.41%-233.58% compared with HMap6 and AddrMiner-N.