Abstract: Lung cancer is the leading cause of cancer-related mortality worldwide, accounting for more deaths than any other ma-lignancy. Despite advances in treatment, it remains highly lethal, with 5-year survival rates showing minimal improve-ment over the past several decades, highlighting a critical unmet clinical need. Macrophage Migration Inhibitory Factor (MIF) is a multifunctional cytokine that contributes to inflammation and cancer, promoting tumor growth, progression, and metastasis through modulation of the tumor microenvironment, stimulation of angiogenesis, and regulation of im-mune responses. Polymorphisms in the promoter region of MIF, such as high-expression CATT repeats, influence MIF expression and susceptibility to a range of inflammatory, autoimmune, and malignant disorders, yet their role in lung cancer remains largely unexplored. Therapeutic strategies targeting MIF, including small-molecule inhibitors, antibod-ies, and peptide-based agents, have shown promise in preclinical models, although their clinical translation is still lim-ited. This review discusses the dual role of MIF in inflammation and oncology, summarizes current therapeutic devel-opments, and emphasizes the potential of MIF-targeted interventions in lung cancer. It discusses the significance of ge-netic predisposition, particularly high-expression MIF alleles, in guiding personalized treatment strategies for lung can-cer and optimizing clinical outcomes in patients most likely to benefit from MIF inhibition.

Abstract: Background/Objectives: Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely recognition of ST-elevation myocardial infarction (STEMI) and initiation of reperfusion therapy in the emergency department (ED). We evaluated the diagnostic performance of a real-time artificial intelligence (AI) ECG interpretation system and its pragmatic impact when integrated into routine ED workflows. Methods: This prospective, single-center pragmatic observational study was conducted in a regional emergency medical center ED in Busan, Republic of Korea (1 January–31 December 2024). Consecutive adults (≥18 years) undergoing 12-lead ECG for cardiovascular-related symptoms were enrolled (N = 1524). A predefined alternating-day protocol allocated visits to physician-only interpretation days (physician-days, n = 763) or AI-output disclosure days (AI-days, n = 761). Diagnostic performance for STEMI was assessed using paired ECG-level comparisons between physician-alone interpretation and AI output against a blinded expert-panel reference standard; clinical impact outcomes included reperfusion-related time metrics, hospital length of stay (LOS), and in-hospital mortality. Results: Against the expert reference standard, AI showed higher STEMI sensitivity than physician-alone interpretation (96.7% vs. 68.3%; McNemar p = 0.027), while specificity was lower (75.9% vs. 84.5%; p = 0.018). In pragmatic day-level comparisons, door-to-balloon time was shorter on AI-days (40.0 ± 19.81 vs. 47.34 ± 21.90 min; p = 0.001), and time to PCI was significantly reduced among patients with atypical presentations (42.3 ± 18.21 vs. 57.1 ± 20.11 min; p = 0.013). Among admitted patients, hospital LOS was shorter on AI-days (13 ± 9.21 vs. 17 ± 10.31 days; p = 0.010), whereas in-hospital mortality did not differ significantly between groups (17.0% vs. 16.77%; p = 0.191). Conclusions: Real-time AI-ECG integration in the ED was associated with improved STEMI detection sensitivity and shorter reperfusion-related time metrics, particularly in atypical presentations, and with reduced hospital LOS among admitted patients. Short-term mortality was comparable between groups. Further multicenter studies are warranted to confirm generalizability and to balance benefits against potential false-positive–related operational impacts.

Abstract: Microbial biosurfactants, derived from diverse aquatic and extreme ecosystems, offer a sustainable and environmentally compatible strategy for enhanced oil recovery by fundamentally altering subsurface rock wettability. These biologically produced amphiphiles can efficiently transform oil-wet rock surfaces into water-wet states, thereby mobilizing otherwise trapped crude oil. This review delineates their core mechanisms: wettability alteration, interfacial tension reduction, and emulsification. Major classes of biosurfactants, including glycolipids, lipopeptides, and polymeric bioemulsifiers are surveyed, focusing particularly on their relevance to challenging reservoir conditions. A detailed assessment is devoted to persistent hurdles such as stability under high temperature and salinity, adsorption onto rock formations, and economic scalability. Future prospects center on three key approaches: advancing synergistic “bio-hybrid” systems that integrate biosurfactants with complementary agents such as biopolymers and nanomaterials; achieving cost-effective production through the valorization of waste feedstocks; and expanding targeted bioprospecting of microbial diversity from extreme aquatic environments. Together, these strategies will drive the advancement of robust, green microbial enhanced oil recovery (MEOR) technologies.

Abstract: Currently, research aimed at optimizing the power rating and energy capacity of electrical energy storage (EES) systems while accounting for multiple sources of uncertainty remains underrepresented in the scientific literature, due to the complexity of solving multidimensional uncertainty problems in microgrids. Regarding the comprehensive assessment of EES parameters considering the influence of various factors, despite numerous studies dedicated to the evaluation and rational selection of EES parameters, this task remains largely unresolved. This paper proposes a methodology for selecting EES parameters that accounts for the uncertainty of wind power plant (WPP) generation and electric vehicle charging station (EVCS) load, EES performance degradation, as well as the reliability and cost of microgrid implementation to ensure uninterrupted operation of EV supply equipment within a distribution network with limited available power capacity. The developed method and EVCS load profile model enable the generation of a time-based power profile under input data uncertainty. The work presents a mathematical model of microgrid operation that considers the integrated performance of EES, WPP, and EVCS. The EES parameter selection methodology is demonstrated using examples of various system configuration scenarios.

Abstract: Dynamic fire-safety systems are no longer futuristic; they are a viable alternative to rigid, prescriptive approaches to occupant protection and evacuation. This paper analyses three case studies where Building Information Modelling (BIM), a Digital Twin (DT), Internet-of-Things (IoT) sensor networks, Artificial Intelligence (AI) control algorithms, and dynamic evacuation signage were integrated to support a Dynamic Fire-Safety System (DFS). Secondary research covered a university building in Lille, the Beijing Capital Airport Emergency Center, and a shopping mall in the Taipei 101 high-rise complex. All facilities meet formal requirements, yet a BIM/DT/IoT/AI layer suggests better performance under fire conditions. Methods included a structured literature review, BIM-based fire modelling in Fire Dynamics Simulator (FDS), evacuation simulations, and comparison of static versus dynamic paradigms. The workflow reconstructs fire–evacuation scenarios to assess time-dependent tenability, exit viability, and congestion-driven bottlenecks. In Lille, DFS serves as a computational laboratory for design decisions; in Beijing, as a decision-support core controlling signage in near real time; and in Taipei 101, as an optimisation-driven strategy for multi-storey occupant populations. Across the cases, DFS-oriented solutions are reported to shorten evacuation time and/or increase the probability of successful evacuation relative to static arrangements. Reported benefits depend on clear cue visibility and timely actuation of guidance signals. Implications for Poland are discussed: prescriptive rules should remain a baseline, while complex facilities may adopt performance-based solutions grounded in BIM/DT/IoT/AI, provided equivalence to conventional protection is demonstrated.

Abstract: Repeated measures ANOVA is the statistical method for comparing means of the same sample measured at least two different times, or two different contexts. It may also be used to compare means between two or more related groups. This paper serves as a tutorial for repeated measures ANOVA using R. It will introduce readers to parametric, nonparametric and robust one-way repeated measures ANOVA using the rstatix, afex, WRS2, and ARTool packages.

Abstract: Paramedics and other public safety personnel experience a high prevalence of mental health conditions, yet stigma and access barriers often limit engagement with professional care. As part of a post-pandemic mental health study, we evaluated the feasibility and utility of providing voluntary, personalized mental health feedback reports to paramedics in two Ontario services. During compulsory continuing medical education sessions in the fall of 2024, 995 paramedics (96% of eligible) completed a survey with screening tools for various mental disorders. Participants could choose to receive a confidential report summarizing their screening results. Overall, 58% opted to receive a report, of whom 38% completed a follow-up survey approximately two months later. Participants who screened positive for posttraumatic stress disorder, major depressive disorder, or generalized anxiety disorder were significantly more likely to report contacting a mental health professional than those screening negative (27% vs. 7.8%; Odds Ratio 4.35, p<0.001), corresponding to an estimated pseudo-number needed to treat of five. Qualitative comments indicated that feedback reports increased awareness, validated symptoms, and, in some cases, prompted help-seeking or behavior change. These findings suggest that voluntary, low-burden mental health check-ins are a feasible strategy to identify at-risk paramedics and facilitate connection to care.

Abstract: Let $A\in\C^{d\times d}$ and let $W(A)$ denote its numerical range. For a bounded convex domain $\Omega\subset\C$ with $C^1$ boundary containing $\spec(A)$, consider the operator-valued boundary kernel \[ P_{\Omega}(\sigma,A)\;:=\;\Real\!\Bigl(n_{\Omega}(\sigma)\,(\sigma\Id-A)^{-1}\Bigr), \qquad \sigma\in\partial\Omega, \] where $n_{\Omega}(\sigma)$ is the outward unit normal at $\sigma$. For convex $\Omega$ with $W(A)\subset\Omega$ this kernel is strictly positive definite on $\partial\Omega$ and underlies boundary-integral functional calculi on convex domains. We analyze the opposite limiting regime $\Omega\downarrow W(A)$. Along any $C^1$ convex exhaustion $\Omega_\varepsilon\downarrow W(A)$, if $\sigma_\varepsilon\in\partial\Omega_\varepsilon$ approaches $\sigma_0\in\partial W(A)$ with convergent outward normals and $\sigma_0\notin\spec(A)$, then $\lambda_{\min}(P_{\Omega_\varepsilon}(\sigma_\varepsilon,A))\to 0$ and the corresponding min-eigenvectors converge (up to subsequences and phases) to the canonical subspace $(\sigma_0\Id-A)\mathcal M(n)$ determined by the maximal eigenspace of $H(n)=\Real(\overline{n}A)$. Quantitatively, we obtain two-sided bounds in terms of an explicit support-gap scalar, yielding a linear degeneracy rate under bounded-resolvent hypotheses and an explicit rate for outer offsets $W(A)+\varepsilon\mathbb{D}$. For normal matrices we compute the eigenvalues of $P_{\Omega}(\sigma,A)$ explicitly, showing that degeneracy may fail at spectral support points unless the supporting face contains multiple eigenvalues.

Abstract: HER2 status determination is a crucial task in breast cancer prognosis and treatment,1 yet traditional diagnostic methods such as immunohistochemistry (IHC) and fluorescence in situ2 hybridization (FISH) are invasive, time-consuming, and costly. Motivated by the need for scalable3 and data-driven predictive approaches, we propose a hybrid machine learning framework that4 integrates ensemble learning with fuzzy modeling for HER2 prediction using routinely available5 clinical and immunohistochemical data. A dataset comprising 624 breast cancer patients from6 Mahdieh Clinic (Kermanshah, Iran) was analyzed, with extensive feature engineering, scaling, and7 class balancing applied. We developed an ensemble framework based on tree-based learners (Random8 Forest, XGBoost, and LightGBM), combined through ensemble strategies and enhanced using fuzzy9 feature representations and decision threshold optimization. The proposed hybrid model achieved10 an accuracy of 0.816, an F1-score of 0.814, and an area under the ROC curve (AUC) of 0.862 on11 the held-out test set, demonstrating strong discriminative capability and balanced classification12 performance. This work highlights the potential of hybrid fuzzy–ensemble learning for uncertainty-13 aware predictive analytics in biomedical decision support, aligning with the journal’s focus on14 information processes, intelligent systems, and data mining.

Abstract:

Background: The inferior subluxation of the proximal part of the fibula have been reported to occur with distraction osteogenesis of the tibia with unilateral external fixation in achondroplasia. The purpose of this study was to evaluate the clinical and radiologic effects when the fibula head is not fixed to the tibia during the tibial lengthening in achondroplasic patients. Methods:We retrospectively reviewed all the achondroplasic patients who were underwent bilateral tibial lengthening by distraction osteogenesis with use of a unilateral external fixation between 2007 and 2016. Clinically and radiographically data related to mean age at the time of the surgery, amount of lengthening, proximal fibular migration, mean mechanical axis deviation were collected. The mean duration of follow-up was 7.4 years (range, 1.96-11years). Results:80 tibial lengthening procedures in 40 achondroplasic patients were evaluated. The mean age at the time of the surgery was 11.97 years old (range 9.30-16.40 years). The mean amount of lengthening was 15.03 +/- 1.56 cm (range 11.40-18.50 cm) and the mean percentage lengthening was 84.64 % (range 62.21 to 138%). The mean proximal fibular migration (PFM) was 19.09 ± 4.57mm (range, 10.48 to 29.37mm). The mean mechanical axis deviation (MAD) of 14.40 ± 11.95 mm (range, -22.45 to 59.74mm) preoperatively and 8.2 ±15,25mm (range, -21.91 to 49.88mm) at the final follow-up. We observed that the degree of proximal fibular migration was linearly correlated with the amount of tibial lengthening. The proximal fibular migration was not associated with the valgus deformity of the knee (p> 0.05). Our study showed a relation between the presence of valgus deformity and the magnitude of lengthening. We observe that the proximal fibular migration and the magnitude of lengthening were not associated with the presence of complications. Conclusions:The proximal fibular migration is common in patients undergoing bilateral tibial lengthening using unilateral external fixation. However, no valgus deformity or presence of major complications was found. These findings indicate that the fixation of the proximal tibiofibular joint is not required in bilateral tibial lengthening with unilateral external fixation in achondroplasic patients.

Abstract: Orodispersible tablet is a patient-friendly alternative for delivering the drug to patients having problems with swallowing and sensitive to the bitterness of the drug. This study focuses on taste masking of cetirizine dihydrochloride by applying coating technique. Taste masked orodispersible tablet (ODT) was formulated using superdisintegrants and apply hydrophilic/hydrophopic polymer/lipid coating. Formulations were designed by 22 factorial central composite design, in which stearic acid and compritol 888 ATO were taken as independent variables. The taste masking was evaluated using the small-volume shake flask method, as an alternative to testing with a human panel. The concentration of stearic acid and compritol 888 ATO in the optimized formulation obtained from the Minitab software response optimization was nearly as same as that of formulation F3. Thus, formulation F3 was considered to be formulation of choice. The weight variation, disintegration time, friability, and dissolution were found to be satisfactory. The coating of API with compritol 888 ATO exhibited effective taste-masking properties while maintaining rapid disintegration and acceptable mechanical strength while masking the bitterness of the drug molecule. Taste masking of cetirizine dihydrochloride was successfully achieved using a lipid-based coating approach, with Compritol® 888 ATO and stearic acid as coating materials. Thus, the study successfully developed a taste-masked orodispersible tablet of cetirizine dihydrochloride using a lipid-based coating technique.

Abstract: This paper proposes a self-supervised modeling framework based on contrastive time-series representation learning to address the complexity of backend system performance anomaly prediction in cloud computing and microservice environments. The method constructs a time-varying service dependency graph and a temporal encoding mechanism to achieve joint representation of spatial structural features and temporal dynamic features, enabling the unsupervised identification of potential performance degradation patterns. The model consists of four main components: a dynamic graph construction module, a graph convolution feature extraction module, a time-series encoding module, and a contrastive learning optimization module. The dynamic graph module captures the evolving dependencies among services, while the time-series encoding module extracts multi-scale temporal features. The contrastive learning module builds positive and negative sample pairs to achieve representation aggregation and differentiation in the latent space. Extensive experiments on real backend system monitoring datasets, along with sensitivity analyses on learning rate, optimizer, temperature coefficient, and data missing rate, demonstrate that the proposed model outperforms mainstream methods in accuracy, precision, recall, and AUC, showing strong generalization and robustness. This study provides a new technical approach for early identification of performance anomalies in complex distributed systems and offers practical, theoretical, and methodological support for intelligent operation and performance assurance in cloud platforms.

Abstract: The whole framework of host innate and adaptive immunological pathways is proposed. In the innate immunity, γδ T cells can be categorized into several groups. Clonal anergy and tolerance pathway is related to Vγ2 chain γδ T cells. Host innate immunological pathway against viruses is related to Vγ8 chain γδ T cells. Host innate immunological pathway against intracellular micro-organisms is related to Vγ9 chain γδ T cells. Host innate immunological pathway against extracellular micro-organisms is related to Vγ4 chain γδ T cells. Host innate immunological pathway against helminths is related to Vγ3 chain γδ T cells. Host innate immunological pathway against insects is related to Vγ5 chain γδ T cells. In the adaptive immunity, there are five eradicable immune reactions and four tolerable immune reactions. In the tolerable immune reactions, TH3 is related to interleukin-35 producing CD4 T cells, and TH4 is related to interleukin-32 producing CD4 T cells.

Abstract: For decades, competitive debate has been hailed as the "ultimate mental gym," sharpening critical thinking, research skills, and public speaking. However, it has often remained an elitist activity, confined to schools with the budget for specialized coaches and extensive travel. The emergence of Large Language Models (LLMs) like Gemini and GPT-4 represents a seismic shift, offering a way to democratize high-level dialectical training. While some fear that AI might encourage "intellectual laziness," I argue that, if implemented correctly, LLMs can serve as the ultimate "Digital Socrates"—an infinitely patient, remarkably well-read sparring partner for the next generation of thinkers.

Abstract: This paper features an empirical evaluation of Yang and Zhang’s (2000) short-cut method for calculating drift-independent realised volatility, based on high, low, open, and close prices using a historical time series. They suggest that this method is unbiased in the continuous limit, independent of the drift, and consistent when faced with opening price jumps. Souto and Moradi (2024) published Python script to implement this method and claimed that this was the first open-source code made available, that automates its estimation from high-frequency intraday stock data. We convert their code to R script and undertake an evaluation of the method using high-frequency hourly data on Bitcoin, originally sourced from Binance, for a period from January 1, 2018, to May 29, 2025. The method is benchmarked against the Bipower variation method of Barndorff-Nielsen and Shephard (2004a, 2004b). The ordinary least squares regression analysis reveals a close relationship between the methods. We further explore the persistence of volatility estimates by regressing the absolute values of daily Bitcoin returns on lags of themselves. The results suggest significant persistence out to seven daily lags. This finding supports the range enhanced GARCH model for modelling cryptocurrencies of Fiszeder et al. (2024). (The R code developed is attached in the Appendix).

Abstract: Artificial intelligence (AI) is at the vanguard of transforming radiology in several ways, including augmenting diagnoses, improving workflows, and increasing operational efficiency. Several integration challenges, including concerns over privacy, clinical usability, and workflow compatibility, still remain. This review discusses the foundations and current trends of clinical AI in radiology to provide essential context for ongoing developments. We then outline four key use cases from the Moffitt Cancer Center: (1) local deployment of large language models (LLMs) for restructuring and streamlining radiology reports, improving clarity and consistency without relying on external resources; (2) multimodal AI frameworks combining CT images, clinical data, laboratory biomarkers, and LLM-extracted features from clinical notes for early detection of cachexia in pancreatic cancer; (3) privacy-preserving federated learning (FL) infrastructure enabling collaborative AI model development across institutions without sharing raw patient data; and (4) an uncertainty-aware de-identification pipeline for removing Protected Health Information (PHI) from radiology images and clinical reports to support secure data analysis and sharing. We further discuss emerging opportunities for tumor board decision support, clinical trial matching, radiology report quality assurance, and the development of an imaging complexity index. Our experience highlights the critical importance of local deployment, multimodal reasoning, privacy preservation, and human-in-the-loop oversight in translating AI models from research to oncology radiology practice.

Abstract: Immersive virtual environments are increasingly investigated as tools capable of modulating conscious experience, yet the specific contribution of graded immersion to altered states of consciousness (ASC), time perception, and cognition remains unclear. The present study examined how different levels of immersion during videogame play influence subjective experience and post-experience cognitive performance. Seventy-two participants played an identical 35-minute segment of the videogame Half-Life: Alyx under one of three conditions: desktop PC (low immersion), head-mounted virtual reality (VR; medium immersion), or VR combined with full-body locomotion via an omnidirectional treadmill (high immersion). Following gameplay, participants completed validated measures of presence (IPQ), immersion (IEQ), ASC (5D-ASC), retrospective time estimation, and cognitive flexibility (Stroop task and Alternative Uses Test). Presence was selectively enhanced in VR relative to desktop play, whereas immersion was highest in the VR plus treadmill condition. Specific ASC dimensions related to embodiment and self-experience (disembodiment, depersonalization, derealization, and altered perception of time and space) were significantly elevated in immersive conditions. Retrospective time estimation accuracy was reduced in the highest immersion condition, indicating increased temporal distortion. Cognitive flexibility measures showed no broad modulation by immersion, with only subtle differences in Stroop accuracy. Overall, the findings indicate that increasing immersion during videogame play selectively reshapes specific dimensions of conscious experience, particularly embodiment- and time-related aspects, without globally altering executive function.

Abstract: The acquisition of liquid energy sources and basic chemicals from washing water via Kolbe electrolysis is of great significance for achieving the goal of carbon-neutrality. However, the oleophilic products tend to adhere to Platinum (Pt) electrode, which results in a shortened working life of the Kolbe electrolysis. To address these issues, a novel method for endowing carbon fiber paper electrodes with underwater superoleophobic properties through simple electrodeposition is reported herein. The underwater superoleophobic electrodes improve the efficiency of the Kolbe electrolysis reaction, as oleophilic products can be easily removed from the electrode surface, thereby exposing more active reaction sites. Importantly, the underwater superoleophobic electrodes have fully demonstrated their advantages of excellent electrochemical performance, stability and durability. This work provides a novel approach for the design of high-performance electrodes in organic electro-catalysis.

Abstract: This study developed a novel antimicrobial air filter by functionalizing polypropylene nonwoven fabrics with bovine lactoferrin peptide to control indoor bioaerosols. The filtration performance was evaluated against E. coli and  virus under various environmental conditions. Results demonstrated broad-spectrum inactivation efficacy, with the 2.0 mg coating achieving the highest performance in a dose-dependent manner. A critical breakthrough was the environmental stability of the lactoferrin coating; unlike traditional biopolymers, its antimicrobial efficiency remained consistent across 30–70% relative humidity (p > 0.05). Furthermore, a field test conducted in a dental clinic validated its practical feasibility, achieving an 83.3% reduction in bacterial bioaerosols over a 210-min operation. These findings suggest that lactoferrin-functionalized filters offer a robust, moisture-resistant, and safe solution for improving indoor air quality in high-risk environments.

Abstract: To address the issues of insufficient restoration of texture details in deblurred images and inadequate learning of frequency domain features, an image deblurring algorithm based on frequency domain feature enhancement and convolutional neural networks is proposed. First, a Fourier residual module with a parallel structure is constructed to achieve collaborative learning and modeling of spatial and frequency domain features. By introducing the Fourier transform, the frequency domain feature learning is enhanced to improve the restoration of texture details. Second, after a gated feed-forward unit is applied to Fourier residual module, its nonlinear representation capability is further improved. In addition, a supervised attention module is introduced at the decoder stage to promote more effective extraction of key features essential for image reconstruction. Experimental results have demonstrated that the proposed algorithm effectively removes blur while better preserving image details.