BACKGROUND Artificial intelligence (AI)-based algorithms are being implemented in breast screening to detect breast cancers on mammographic images. We aimed to apply an epidemiological approach to demonstrate how a cancer detection algorithm can be leveraged as an intermediate-term predictor of breast cancer (current and 4-year risk) to deliver greater risk-based personalisation in screening mammography. METHODS In this population cohort study, we used detection scores from an AI cancer detection algorithm (BRAIx AI Reader), which was calibrated using a training dataset of 397 648 women aged 40 years to 97 years from women who screened at BreastScreen Victoria, Australia between Jan 1, 2016, and Dec 31, 2017, to create a woman-specific mammography-based score for breast cancer risk, the BRAIx risk score. Subsequently, the BRAIx risk score was evaluated on an independent test dataset of women from BreastScreen Victoria, Australia, comprising a random population cohort of 96 348 women who screened from Jan 1, 2016, to Dec 31, 2017, aged 40 years to 74 years, and an independent, external dataset from woman screened at Karolinska University Hospital, Stockholm, Sweden. We applied logistic regression, using the BRAIx risk score to estimate risks of invasive breast cancers on the test dataset: (1) detected at cohort entry (n=525); and (2) for women given an all clear, diagnosed during the next 4 years either at future screens (n=790) or during intervals between screens (n=308). We also trained full multivariate risk models (logistic regression and elastic net) using the training dataset and evaluated their predictive performance on the test and external validation data, with assessment of familial aspects of the BRAIx risk score achieved with inference about causation from examining changes in regression coefficients in an innovative statistical analysis framework. FINDINGS In both Australian and Swedish test datasets, the BRAIx risk score predicted cancer detection at cohort entry and future cancer risk (all p<0·0001). The BRAIx risk score was the strongest tested explanatory factor for cancer detection at cohort entry (odds ratio 13·80 [95% CI 9·54-20·80] in Australian data; 8·89 [3·19-37·49] in Swedish data) and for intermediate-term cancer risk (2·29 [2·13-2.47] in Australian data; 2·15 [1·85-2·50] in Swedish data). We found that adding a thresholded binary version of the BRAIx risk score significantly improved model fit (p<2·2 × 10-16, Australian and Swedish data) and women with BRAIx risk scores of more than 2 were significantly at many-fold increased risk of intermediate-term cancer than women below that threshold (12·34 [7·33-20·91], Australia; 44·7 [11·9-184·9], Sweden; p<0·0001). For the top 2% of women given an all clear with the highest BRAIx risk score, the probability of a cancer diagnosis within 4 years was 9·7%. The BRAIx risk score explained 23% of why family history predicts 4-year risk (p<0·0001). After fitting the BRAIx risk score in a multivariate model, mammographic density was no longer significantly associated with breast cancer risk in the Australian test data (p>0·05) and became associated with lower risk for intermediate-term cancer in the external Swedish test dataset (0·83 [0·73-0·95]). INTERPRETATION The BRAIx risk score is a strong intermediate-term predictor of breast cancer (current to 4-year risk). Calibrating the score on a training dataset produces population-specific probabilities for calculating individual-specific risk scores for screening clients based on their mammogram images. These risk scores enable future development of personalised screening pathways to transform population breast cancer screening and save lives. Identification of women given an all clear but at very high risk, similar to those carrying BRCA1 and BRCA2 mutations, could reveal insights into both familial and non-familial causes of breast cancer. FUNDING Australian Government Medical Research Future Fund, the Ramaciotti Foundation, the National Breast Cancer Foundation, Cancer Australia, and the National Health and Medical Research Council.

Shigellosis remains a significant global cause of infectious colitis, increasingly complicated by multidrug-resistant strains and the microbiota-disrupting effects of broad-spectrum antibiotics. Although conventional antimicrobial therapy can reduce symptom duration and bacterial shedding, it also contributes to gut dysbiosis, loss of colonization resistance, and further selection for antimicrobial resistance. These challenges have renewed interest in precision antimicrobial strategies, particularly bacteriophage therapy, which provides strain-level specificity and preserves the gut microbiota. This narrative review evaluates the biological rationale, preclinical and early clinical evidence, safety considerations, and translational challenges associated with bacteriophage therapy targeting Shigella spp. The historical development and mechanistic basis of phage therapy are summarized, with emphasis on the advantages of obligately lytic phages, receptor-specific targeting, self-amplification at infection sites, and activity against both planktonic and biofilm-associated bacteria. Recent microbiota research indicates that shigellosis is closely associated with early and persistent disruption of gut ecology, including depletion of short-chain fatty acids-producing taxa and reduced microbial resilience. Phage-based approaches may reduce pathogen burden while preserving beneficial microbial communities. Evidence from in vitro systems, animal models, human intestinal organoids, and a Phase 1 clinical trial demonstrates targeted efficacy and favorable safety profiles for Shigella-specific phages and phage cocktails. Major barriers to clinical adoption include immune interactions, phage resistance dynamics, genomic safety screening, regulatory classification, and the need for standardized susceptibility testing. Future directions emphasize the development of personalized phage therapy platforms that integrate rapid diagnostics, phage libraries, metagenomics, and artificial intelligence-assisted matching to enable scalable, precision treatment.

Background: African swine fever virus (ASFV) causes a fatal hemorrhagic disease in domestic pigs and wild boars. While live attenuated vaccines (LAVs) provide protection, their use raises safety concerns. Therefore, the aim of the present study was to identify viral B-cell antigens associated with protection and to test their potential using highly immunogenic vaccine delivery platforms. Methods: We employed a microarray of 169 ASFV proteins expressed in a cell-free prokaryotic system to identify immunodominant antigens using sera from immune pigs. Six structural proteins were selected and formulated into AP205 virus-like particles (VLPs). Additionally, replication-defective vesicular stomatitis virus (VSV)-based vaccine candidates expressing glycosylated CD2v and EP153R proteins were generated. Three groups of specific pathogen-free pigs were immunized with either VLP- or VSV-based vaccines and challenged with the virulent ASFV Georgia 2007 strain. Control groups included pigs immunized with the attenuated ASFV Estonia 2014 strain and a naïve group. Results: Most vaccine candidates induced detectable antibody responses against target ASFV proteins. However, neither VLP- nor VSV-based vaccines provided protection, as clinical scores, hematology, cytokine responses, and viremia levels were similar to those in the negative control group. In contrast, only the ASFV Estonia 2014 strain elicited a robust T-cell response and protective immunity. Conclusions: These findings highlight the challenges in identifying protective B-cell antigens of ASFV and emphasize the pivotal role of cellular immunity in mediating protection.

In the rapidly evolving landscape of software engineering, the demand for robust and secure systems has become increasingly critical. This is especially true for self-adaptive systems due to their complexity and the dynamic environments in which they operate. To address this issue, we designed and developed the SAFT-GT toolchain that tackles the multifaceted challenges associated with ensuring both safety and security. This paper provides a comprehensive description of the toolchain's architecture and functionalities, including the Attack-Fault Trees generation and model combination approaches. We emphasize the toolchain's ability to integrate seamlessly with existing systems, allowing for enhanced safety and security analyses without requiring extensive modifications and domain knowledge. Our proposed approach can address evolving security threats, including both known vulnerabilities and emerging attack vectors that could compromise the system. As a use case for the toolchain, we integrate it into the feedback loop of self-adaptive systems. Finally, to validate the practical applicability of the toolchain, we conducted an extensive user study involving domain experts, whose insights and feedback underscore the toolchain's relevance and usability in real-world scenarios. Our findings demonstrate the toolchain's effectiveness in real-world applications while highlighting areas for future improvements. The toolchain and associated resources are available in an open-source repository to promote reproducibility and encourage further research in this field.

Geometry-based stochastic channel models with differently distributed scatterers within elliptical-shaped scattering region, become more and more popular due to their applicability for modeling different propagation scenarios in the emerging 5G networks. However, to date, their spatial and temporal characteristics are usually provided in integral forms, which are not appropriate for analytical manipulations. In this paper, it is shown that the azimuthal angles of arrival and departure for elliptical (two-dimensional) and ellipsoidal (three-dimensional) channel models, with (non)uniformly distributed scatterers and arbitrary chosen positions of the transmitter and the receiver, has the same statistics as N-dimensional channel model with homogeneously distributed scatterers within hyperellipsoidal-shaped scattering region. Thus, the azimuthal angle distributions of N-dimensional channel model with homogeneously distributed scatterers within hyperellipsoidal-shaped scattering region are derived as closed-form expressions, providing for the first time in literature the azimuthal angles of arrival and departure distributions for various existing elliptical-shaped geometry-based stochastic channel models and for a whole new class of 2-D and 3-D channel models with nonuniformly distributed scatterers.

Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare systemic vasculitis characterized by asthma, eosinophilia, and multisystem involvement. Renal manifestations are relatively uncommon but may be severe and rapidly progressive, and fatal hemorrhage from arteriovenous fistulas (AVFs) represents an uncommon yet catastrophic complication in patients with advanced kidney disease. We report a case of a 70-year-old man with long-standing asthma, chronic rhinosinusitis with nasal polyposis, marked eosinophilia, and progressive renal failure. After years of fragmented clinical manifestations, a clinical diagnosis of EGPA was considered based on clinical, laboratory, and immunological findings, supported by fulfillment of the 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology (ACR/EULAR) classification criteria in the absence of histopathological confirmation, in the setting of rapidly progressive renal dysfunction. Induction immunosuppressive therapy with high-dose corticosteroids and cyclophosphamide was initiated. Due to advanced chronic kidney disease and the anticipated need for renal replacement therapy, a left radiocephalic AVF was constructed. Seventeen days later, the patient experienced spontaneous fistula rupture at home, resulting in massive hemorrhage, refractory hemorrhagic shock, and death. This case illustrates the consequences of delayed EGPA diagnosis and highlights the possibility of fatal vascular access complications in the setting of active systemic vasculitis, underscoring the importance of careful timing of invasive procedures, heightened clinical vigilance, and structured patient education when planning vascular access in patients with active inflammatory disease.

This research aimed to examine which of the selected strategies can most effectively influence households to reduce their total municipal waste and thus protect the environment. To achieve this goal, a sample of 202 households from the Brčko District of BiH was used. Respondents evaluated six strategies against ten criteria, expressing their assessments through linguistic values. These linguistic inputs were modeled using symmetric fuzzy numbers, ensuring a consistent and mathematically robust representation of uncertainty and subjective judgment. The research used the fuzzy SiWeC (Simple Weight Calculation) method to determine the importance of the criteria, and the fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), ARAS (Additive Ratio Assessment), and SAW (Simple Additive Weighted) methods to rank the strategies. The application of several methods in decision-making helps validate results and verify the robustness of strategy selection. These methods identified “waste reduction efficiency” as the most important criterion and “Strategy 3—Packaging return machines” as the most effective overall. Furthermore, analysis of demographic subgroups revealed significant variations in the perceived value of alternative strategies. Consequently, this study concludes that to optimize municipal waste management, strategies should be tailored to specific demographic profiles. This targeted approach would enhance waste reduction at the source, divert more waste from landfills, and promote the broader implementation of circular economy principles. The use of symmetric fuzzy numbers provided a reliable and stable foundation for this multi-criteria decision-making analysis.