Many cyberattacks succeed because they exploit flaws at the human level. To address this problem, organizations rely on security awareness programs, which aim to make employees more resilient against social engineering. While some works have, implicitly or explicitly, suggested that such programs should account for contextual relevance, the common praxis in research is to adopt a "general" viewpoint. For instance, instead of focusing on department-specific issues, prior user studies sought to provide organization-wide conclusions by treating all participants equally. Such a protocol may lead to overlooking vulnerabilities that affect only specific subsets of an organization, and which can be (or are) exploited by real-world attackers.In this paper, we tackle such an oversight. First, through a systematic literature review encompassing over 1k papers, we provide factual evidence that prior literature poorly accounted for department-specific needs. Then, building on this (worrying) finding, we carry out a multi-company and mixed-methods study focusing on two pivotal departments of modern organizations: human resources (HR) and accounting. We explore three dimensions: what specific threats are faced by these departments; what topics should be covered in the security-awareness campaigns delivered to these departments; and which delivery methods would maximize the effectiveness of such campaigns for these departments. We begin by interviewing 16 employees of a multinational enterprise, and then use these results as a scaffold to design a structured survey through which we collect the responses of over 90 HR/accounting members of 9 organizations of varying size. We find that HR and accounting departments face distinct threats: HR is targeted through job applications containing mal-ware and executive impersonation, while accounting is exposed to invoice fraud, credential theft, and ransomware. Current training is often viewed as too generic, with employees preferring shorter, scenario-based formats like videos and simulations. These preferences contradict the common industry practice of lengthy, annual sessions. Based on these insights, we propose practical recommendations for designing awareness programs tailored to departmental needs and workflows.

Plasma proteomics technologies are advancing rapidly, offering new opportunities for biomarker discovery and precision medicine. Direct comparisons of available technologies are needed to understand how platform selection affects downstream findings. We compared the performance of a peptide fractionation-based mass spectrometry method (HiRIEF LC-MS/MS) and the Olink Explore 3072 proximity extension assays on 88 plasma samples, analyzing 1129 proteins with both methods. The platforms exhibited complementary proteome coverage, high precision, and concordance in estimating sex differences in protein levels. Quantitative agreement between platforms was moderate (median correlation 0.59, interquartile range 0.33-0.75), mainly influenced by technical factors. Finally, we present a publicly available tool for peptide-level analysis of platform agreement and demonstrate its utility in clarifying cross-platform discrepancies in protein and proteoform measurements. Our findings provide insights for platform selection and study design, and highlight the value of combining mass spectrometry and affinity-based approaches for more comprehensive and reliable plasma proteome profiling. Advancements in plasma proteomics have opened new avenues for biomarker discovery, necessitating a clear understanding of technological capabilities. Here, the authors compare HiRIEF LC-MS/MS and Olink Explore 3072, revealing complementary strengths and moderate quantitative agreement, and introduce PeptAffinity, a resource facilitating detailed peptide-level exploration of differences in protein quantification between platforms.

The aviation industry operates as a complex, dynamic system generating vast volumes of data from aircraft sensors, flight schedules, and external sources. Managing this data is critical for mitigating disruptive and costly events such as mechanical failures and flight delays. This paper presents a comprehensive application of predictive analytics and machine learning to enhance aviation safety and operational efficiency. We address two core challenges: predictive maintenance of aircraft engines and forecasting flight delays. For maintenance, we utilise NASA’s C-MAPSS simulation dataset to develop and compare models, including one-dimensional convolutional neural networks (1D CNNs) and long short-term memory networks (LSTMs), for classifying engine health status and predicting the Remaining Useful Life (RUL), achieving classification accuracy up to 97%. For operational efficiency, we analyse historical flight data to build regression models for predicting departure delays, identifying key contributing factors such as airline, origin airport, and scheduled time. Our methodology highlights the critical role of Exploratory Data Analysis (EDA), feature selection, and data preprocessing in managing high-volume, heterogeneous data sources. The results demonstrate the significant potential of integrating these predictive models into aviation Business Intelligence (BI) systems to transition from reactive to proactive decision-making. The study concludes by discussing the integration challenges within existing data architectures and the future potential of these approaches for optimising complex, networked transportation systems.

Cervical cancer is one of the leading causes of cancer in women, worldwide. Infection with humanpapillomavirus (HPV) has been accepted as the primary cause for the development of invasive cervicalcancer and its precursor lesions. Despite HPV infection has been proposed as an indispensable factor forcervical cancer development, only a subset of neoplastic lesions with HPV infection persist and progress toinvasive cancer. This suggests us that other molecular events are also involved in cancer progression. Aimof this study was to extract mRNA from cytobrush-collected healthy and HPV infected cervical epithelialcells and investigate various RNA extraction and purification protocols for assessment of RNA yield andquality. Taking into consideration that cervical cancer screening is based on the cytology basedPapanicolaou test (Pap test), main challenge is to investigate whether the samples obtained by regular Paptesting can be used for gene expression analysis. For this purpose, a total of 68 cervical specimens werepreviously tested for HPV infection. Following HPV testing, samples were submitted to RNA extractionand compared to the products after additional purification step involving DNase I. Products obtained afterdifferent RNA extraction and purification methods were visualized using 2% agarose gel electrophoresis.In conclusion, DNase I based RNA purification represents a necessary step for the assurance of a high-quality extracted RNA used for gene expression analysis studies. Reliance on commercial kits for RNAextraction only, without performing additional purification step can lead to errors in drawing finalconclusions and/or to false negative gene expression profiling, affecting the overall diagnostic procedure.According to obtained results, the type of sampling used in this study was not suitable for the subsequentgene expression analysis.

Abstract To preserve resources for future generations and promote rural development, supporting ecotourism is essential. This paper provides guidelines for developing ecotourism, highlighting its role in environmental conservation. While mass tourism benefits rural communities, it can cause significant environmental harm. Therefore, this research promotes ecotourism as a sustainable alternative. In rural areas, ecotourism supports development by responsibly using natural resources. The study focuses on the potential of rural settlements in the Semberija region of Bosnia and Herzegovina, assessing their capacity for ecotourism to aid local development. A decision model was developed, considering four main criteria - natural, infrastructure, socio-cultural, and economic - and their sub-criteria. This model evaluates six rural communities’ ecotourism potential. To determine the importance of each criterion, a fuzzy weighting method with the Bonferroni mean operator was used, revealing economic factors as the most influential. The fuzzy ranking method then ranked the settlements, with Amajlije identified as having the highest ecotourism potential. The findings suggest that promoting ecotourism in Amajlije and similar communities can support sustainable rural development, balancing environmental preservation with economic growth.

Background Acute cholecystitis (AC) is one of the most common surgical emergencies with a wide range of clinical outcomes. Early identification of patients at risk for postoperative complications is essential for optimizing surgical decision-making and resource allocation. Hemogram-derived indices such as the systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR), in addition to biochemical markers, may provide prognostic value beyond traditional risk factors. Materials and methods This retrospective single-center study included 210 patients admitted to the University Clinical Center Tuzla with AC between January 2024 and January 2025. Demographic, clinical, and laboratory data were collected. Receiver operating characteristic (ROC) analysis was performed to identify optimal cut-off values for predicting complications. Multivariate logistic regression was adjusted for age, sex, diabetes mellitus, hypertension, and other baseline comorbidities, in addition to SII, NLR, glucose, and creatinine. Results Four variables emerged as independent predictors of complications: SII > 950 remained an independent predictor after full adjustment (p = 0.002) with a sensitivity of 78% and specificity of 72%. It yielded the highest discriminatory accuracy among the evaluated markers, with an area under the curve (AUC) of 0.81 (95% confidence interval (CI) 0.75-0.87). No formal comparison with TG18 grading was performed. In contrast, baseline comorbidities such as diabetes mellitus and hypertension did not retain significance after adjustment. Conclusion SII, NLR, glucose, and creatinine independently predicted complications in AC, with SII emerging as the strongest predictor among the evaluated variables. These findings suggest that incorporating hemogram-derived indices into preoperative assessment may enhance risk stratification. However, the retrospective single-center design and potential confounding related to the surgical approach warrant cautious interpretation.

Dexketoprofen/tramadol is a fixed-dose multimodal combination analgesic that significantly controls multiple acute pain states, and may have an important clinical application in providing pain control adequate to prevent the transition from acute to chronic postsurgical and low back pain. A consensus is needed to quantify and define the actual burden of postsurgical pain (PSP) and low back pain (LBP), which can support efforts toward effective approaches to manage potential pain chronification. This study utilized a modified Delphi approach. A Scientific Committee set forth 28 statements on six themes about the burden of acute PSP and LBP, their potential transition to chronic pain, their pathophysiology, therapeutic approaches to stop this transition, and the role of multimodal analgesia in this context, specifically a fixed-dose combination oral product of dexketoprofen/tramadol. An international panel of healthcare professionals from various regions and relevant medical specialties participated in a Delphi study and were surveyed for consensus on a 5-point Likert scale with consensus defined as > 70% concordance. A round of online voting lasting 3 months and using an online survey platform was permitted for each participant. A total of 100 experts completed the Delphi survey. All the 28 proposed statements reached consensus > 70% in the first round of voting. A fixed-dose combination product, specifically dexketoprofen/tramadol was recognized as a multimodal analgesic which could effectively relieve acute pain and act to prevent its transition to chronic pain. The high global burden of chronic PSP (CPSP) and chronic LBP (CLBP) was identified as well. Healthcare professionals who deal with pain recognize the burden of acute pain, the risks of acute pain transitioning to chronic pain, and inspire to avert the transition by providing effective multimodal control of acute pain. The role of fixed-dose combination analgesics, in particular dexketoprofen/tramadol, was recognized by consensus as an efficacious and safe therapy option for these acute pain syndromes. 7KDL7wDHZ5GDvGppW1iD89 A Video Abstract is available for this article. To view, please see the online version of the manuscript or follow the ‘Digital Features’ link. A Video Abstract for The Role of Dexketoprofen/Tramadol in Multimodal Therapy to Prevent Acute Postsurgical and Acute Low Back Pain from Developing into Chronic Pain: A Delphi Consensus Study (MP4 112565 KB) A Video Abstract is available for this article. To view, please see the online version of the manuscript or follow the ‘Digital Features’ link. A Video Abstract for The Role of Dexketoprofen/Tramadol in Multimodal Therapy to Prevent Acute Postsurgical and Acute Low Back Pain from Developing into Chronic Pain: A Delphi Consensus Study (MP4 112565 KB)

Per- and polyfluoroalkyl substances (PFAS) are of increasing concern due to their environmental persistence, bioaccumulative nature, and association with adverse health outcomes. The growing need for large-scale monitoring and long-term exposure assessment studies necessitates the development of high-throughput, sustainable analytical methodologies. In this work, a solid-phase microextraction-microfluidic open interface-mass spectrometry (SPME-MOI-MS) platform was developed for the rapid screening of 18 PFAS compounds in human plasma. By bypassing the liquid chromatography separation, the method achieves high-throughput performance with an average analysis time of 3.7 min per sample. A novel SPME coating, comprising hydrophilic-lipophilic balanced mixed-mode weak anion exchange sorbent (HLB-WAX) particles embedded in a polyacrylonitrile (PAN) binder, enabled efficient extraction and effective cleanup of complex biological matrices, facilitating direct MS analysis. The method demonstrated excellent linearity (1-100 ng/mL) and low limits of detection (0.11-0.86 ng/mL) across target PFAS compounds. For practical application, PFOA and PFNA were detected in human plasma samples during these initial investigations, demonstrating the potential of the SPME-MOI-MS approach for large-scale PFAS biomonitoring and exposure assessment.