Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants that can disrupt human hepatic metabolism both directly and through alterations of the gut microbiota. However, the contribution of microbiota-mediated mechanisms to PFAS-induced hepatic dysfunction remains poorly understood. Here, we investigated how PFAS-modified gut microbial metabolites affect human hepatocyte metabolism using an in vitro colon fermentation model, supported by an in vivo mouse and in vitro human hepatocyte exposure studies. PFAS exposure altered the fecal metabolome in human colonic fermentations, particularly affecting pathways related to fatty acid, amino acid, vitamin, and mitochondrial metabolism. Fecal metabolomics from PFOA-exposed mice showed overlapping pathway-level alterations, including effects on fatty acid, bile acid, and steroid hormone metabolism, supporting the biological relevance of the in vitro findings. Exposure of HepaRG hepatocytes to control fermentation extracts markedly altered lipid profiles, confirming that gut-derived metabolites actively regulate hepatic metabolism. Notably, PFAS-exposed fermentation extracts induced distinct hepatocyte metabolic changes compared with PFAS-spiked control extracts, indicating effects driven by PFAS-modified microbial metabolites rather than direct PFAS carry-over. These changes included decreased acyl-carnitines and increased L-carnitine, consistent with altered fatty acid transport and mitochondrial β-oxidation. PFAS-modified extracts also altered bile acids, steroid metabolites, inosine, and sialic acid derivatives, suggesting broader alteration of bile acid signaling, endocrine-related metabolism, purine metabolism, glycoprotein turnover, and lipid-glucose homeostasis. These findings from our pilot study demonstrate that PFAS exposure reshapes gut microbial metabolite profiles with downstream consequences for hepatocyte metabolism. Our findings provide new mechanistic insight into how PFAS may contribute to metabolic disorders.

Chimeric antigen receptor (CAR) therapies have shown great success in hematological malignancies but remain largely ineffective against solid tumors such as pancreatic ductal adenocarcinoma (PDAC). A key obstacle among various aspects, is the dense stromal barrier formed by cancer-associated fibroblasts (CAFs), providing a rationale for simultaneously targeting stroma and tumor cells. Using immunohistochemistry of primary PDAC tumors and liver metastases, we confirmed high mesothelin (MSLN) expression on tumor cells, and CD70 expression on tumor cells and predominantly CAFs. Based on these results and the favorable safety profile of CAR natural killer (NK) cells over CAR T cells, we generated MSLN- and CD70-targeting IL-15-armored CAR NK cells. Both constructs mediated cytotoxicity against different pancreatic cancer and CAF cell lines with varying antigen expression in vitro, demonstrating that both, the CAR-molecule and IL-15 were required to increase functionality against more treatment-resistant cell lines. Interestingly, pooled MSLN- and CD70-CAR NK cells did not significantly improve cytolysis compared to monotherapies in an advanced 3D in vitro model or in vivo. Together these findings highlight the limitations of dual-targeting approaches and underscore the need for advanced engineering strategies to improve CAR NK cells beyond antigen targeting and cytokine support in the PDAC microenvironment.

Simple Summary The biodiversity of bats in Bosnia and Herzegovina has only been assessed by methods that require capture and/or the analysis of echolocation signals, thus giving and incomplete picture of the true biodiversity. To address this, we aimed to measure bat diversity by analyzing DNA from bat droppings (guano) and comparing the results of this advanced genetic method to traditional morphological measurements of captured bats. The results revealed that while the morphological method identified only 10 bat species, the DNA analysis successfully identified 13 species—including Mehely’s horseshoe bat, a species never recorded in the country before. Furthermore, DNA testing clarified uncertain morphological identifications and discovered more species at every location. The study concludes that Bosnia and Herzegovina hosts a much richer diversity of bats than previously thought, and proves that non-invasive DNA sampling from guano is a highly effective tool for routine bat monitoring and provides an easy, harmless way for conservationists to pinpoint and protect critical natural habitats, ensuring the survival of these ecologically vital animals and maintaining healthy local ecosystems.

The rapid growth of e-commerce has intensified last-mile delivery activities in urban areas, creating challenges for city logistics systems related to operational efficiency, congestion, and environmental impacts. In this context, understanding the factors that influence customer satisfaction with logistics operators is increasingly important, as mismatches between customer expectations and delivery service characteristics may lead to operational inefficiencies such as failed delivery attempts and repeated delivery rounds. This study proposes a machine learning framework for predicting customer satisfaction with postal and logistics operators in urban delivery systems using survey data on customer characteristics, preferences, and service perceptions. Several machine learning algorithms were developed and evaluated to identify the key determinants of customer satisfaction and assess their predictive performance. Beyond predictive accuracy, the study interprets customer satisfaction as an indicator of the alignment between customer expectations and delivery service configurations. Improved alignment may support service configurations that reduce delivery mismatches and repeated delivery attempts, which are recognized as a significant source of additional transport activity in urban freight systems. By identifying customer segments whose expectations are not adequately addressed by existing delivery services, the proposed framework can support more informed service design and operational decision-making. From a city logistics perspective, the potential reduction in failed deliveries and repeated delivery rounds may contribute to lower vehicle kilometers travelled, congestion, energy consumption, and emissions associated with urban freight transport, although these operational and environmental indicators were not directly measured in this study. The proposed approach therefore provides a data-driven decision-support tool that can help operators improve service quality and serve as a basis for future integration with operational and environmental indicators in sustainable last-mile delivery planning.

This retrospective study of 98 patients assessed whether hypertensive heart disease (HHD) is associated with angiographic coronary artery disease (CAD) burden, quantified by the Duke CAD Index, and whether HHD or CAD burden influenced revascularization strategy. HHD was independently associated with higher Duke CAD Index values in linear regression (B = 10.33, p = 0.038) and with greater odds of more severe CAD categories in ordinal regression (OR = 3.19, p = 0.015). However, revascularization choice did not differ by HHD status or CAD severity (p = 0.95). A model incorporating age, sex, diabetes mellitus, and HHD demonstrated moderate discriminative ability for predicting severe CAD (AUC = 0.753). These findings suggest that HHD identifies a high-risk coronary atherosclerotic phenotype, but they are hypothesis-generating and require confirmation in larger prospective studies before clinical application.

Background/Objectives: Early risk stratification remains challenging in patients with non-ST-segment elevation myocardial infarction (NSTEMI). The present study evaluated the prognostic value of 24 h high-sensitivity cardiac troponin I (hs-Troponin I) and assessed whether combining biomarkers and echocardiographic parameters improves short-term risk prediction. Methods: This prospective observational cohort study included 170 consecutive adult patients with confirmed NSTEMI who were admitted to a Medical Intensive Care Unit and prospectively enrolled between February 2022 and January 2023. Clinical, routine biochemical, inflammatory, hematological, lipid, and echocardiographic data were collected during index hospitalization. High-sensitivity cardiac troponin I was measured at admission and again 24 h after hospitalization, with the 24 h value used as the principal marker of myocardial injury in the prediction analyses. The primary endpoint was major adverse cardiovascular events (MACEs), defined as cardiovascular death, recurrent myocardial infarction, ischemic stroke, urgent coronary revascularization, or hospitalization for worsening heart failure, within 3 months. Multivariable logistic regression, Cox regression, sequential prediction modeling, and internal bootstrap validation were performed. Results: MACEs occurred in 88 patients (51.8%). Twenty-four-hour hs-Troponin I, but not admission hs-Troponin I, was independently associated with MACEs (OR 1.57, 95% CI 1.09–2.26; p = 0.015) and a shorter time to the first MACE event (HR 1.38, 95% CI 1.07–1.78; p = 0.012). Lower left ventricular ejection fraction (LVEF) was also independently associated with adverse outcomes. The addition of 24 h hs-Troponin I, LVEF, and C-reactive protein improved discrimination from an AUC of 0.665 to 0.759 (optimism-corrected AUC, 0.717), with corresponding improvements in reclassification. A simplified multimarker score was independently associated with event-free survival (HR 2.36, 95% CI 1.53–3.64; p < 0.001). Conclusions: In patients admitted to a medical intensive care unit with NSTEMI, the integration of 24 h hs-Troponin I, LVEF, and C-reactive protein improved short-term risk prediction beyond that of clinical variables alone. A practical multimarker model based on routinely available parameters identified patients at increased risk of adverse cardiovascular outcomes during early follow-up.

Background In patients with heart failure with preserved ejection fraction (HFpEF), left atrial (LA) strain represents a new indicator of mechanical atrial dysfunction and an important prognostic marker. However, the clinical significance of serial changes in LA strain during patient follow-up is still not sufficiently elucidated. Objectives To evaluate changes between baseline and one-year follow-up in LA strain during one-year follow-up in patients hospitalized with HFpEF and to investigate their association with major adverse cardiovascular events (MACE). Methods This prospective observational cohort study included 123 consecutive patients hospitalized due to acute decompensated HFpEF. Comprehensive echocardiographic evaluation was performed during index hospitalization and after 12 months. LA reservoir strain was assessed as peak atrial longitudinal strain (PALS). The primary endpoint was MACE, defined as rehospitalization due to worsening heart failure and/or cardiovascular death during one-year follow-up. Results Patients who developed MACE had lower baseline LA strain, higher LAVI, higher E/e′, higher N-terminal pro-B-type natriuretic peptide (NT-proBNP), higher right ventricular systolic pressure (RVSP), lower tricuspid annular plane systolic excursion (TAPSE), and more impaired left ventricular global longitudinal strain (LV GLS) compared with patients without MACE. Assessment at two predefined time points showed overall improvement in LA strain during follow-up, but patients with adverse outcomes remained characterized by persistently impaired LA strain and an unfavorable hemodynamic profile. Atrial fibrillation was more frequently observed among patients who developed MACE. Conclusion Persistently impaired or worsening LA strain during one-year follow-up was associated with adverse outcomes in patients hospitalized with HFpEF. Assessment of LA strain at baseline and one-year follow-up may improve longitudinal risk stratification beyond conventional echocardiographic parameters.

In this paper, the first record of the nudibranch Felimare picta (R. A. Philippi, 1836) (Gastropoda: Chromodorididae) in Bosnia and Herzegovina is reported. The specimen was observed at 7 m depth on a sandy bottom in Malostonski zaljev Bay and documented using underwater photography, expanding the known distribution of the species in the Adriatic Sea. It also emphasizes the importance of surveys in the relatively understudied marine habitats of Bosnia and Herzegovina.

For over a decade, the Shared Socioeconomic Pathways (SSPs) have served as the principal framework for quantitative modeling of the socioeconomic dimensions of global environmental change. The SSP scenarios describe many of the ecological and social processes thought to shape pandemic risk, including the emergence of novel pathogens (accelerated by processes such as deforestation, livestock intensification, and land-use change) and their subsequent spread (mediated by factors such as inequality, human mobility, and health system capacity). However, the SSP framework has not been widely incorporated into pandemic risk assessment. Here, we assess how pandemic risk is embedded in the SSP framework, and find that the framework captures most of the social-environmental drivers of pathogen spillover, and many of the social-economic drivers of pandemic spread and impacts. Because climate change and pandemics share many drivers and risk factors-- including ecosystem degradation, animal agriculture, and weak governance--SSP scenarios characterized by higher barriers to climate adaptation also generally imply lower chances of outbreak containment, and greater pandemic impacts on vulnerable populations. Pandemic risk is therefore lowest in SSP1 and highest in SSP3, but SSP5 shows that frequent spillover and effective containment can coexist. These findings suggest that pandemic risk can be understood as part of a broader polycrisis, linking climate change, biodiversity loss, and global health. We suggest that new scenario extensions, or entirely novel frameworks, will ultimately be needed to capture possible shifts in the global health landscape; however, in the meantime, scenario frameworks from the environmental sciences could be valuable tools for initiatives to quantify future pandemic risks.

Given the challenges of small and underdeveloped markets, a comprehensive approach to insurance risk analysis is essential. This paper develops and applies an actuarially justified and statistically robust methodology for motor insurance pricing in Bosnia and Herzegovina. The adequacy of Generalised Linear Models (GLMs) is assessed using a portfolio dataset covering 2011-2023, focusing on key policyholder and vehicle risk factors affecting claim frequency and severity. Comparison with existing market tariffs indicates that current premiums are generally underpriced. The proposed approach offers practical guidance for improving pricing accuracy and strengthening actuarial practices in such insurance markets.