Tumor-infiltrating lymphocytes (TILs) are a diverse population of immune cells that play a central role in tumor immunity and have emerged as critical mediators in cancer immunotherapy. This review explores the phenotypic and functional diversity of TILs—including CD8+ cytotoxic T cells, CD4+ helper T cells, regulatory T cells, B cells, and natural killer (NK) cells—and their dynamic interactions within the tumor microenvironment (TME). While TILs can drive tumor regression, their activity is often hindered by immune checkpoint signaling, metabolic exhaustion, and stromal exclusion. We highlight TIL recruitment, activation, and polarization mechanisms, focusing on chemokine gradients, endothelial adhesion molecules, and dendritic cell-mediated priming. Special emphasis is placed on preclinical models that evaluate TIL function, including 3D tumor spheroids, organoid co-cultures, syngeneic mouse models, and humanized systems. These provide valuable platforms for optimizing TIL-based therapies. Furthermore, we examine the prognostic and predictive value of TILs across cancer types, their role in adoptive cell therapy, and the challenges of translating preclinical success into clinical efficacy. Emerging technologies such as single-cell sequencing, neoantigen prediction, and biomaterial platforms are transforming our understanding of TIL biology and enhancing their therapeutic potential. Innovative strategies—ranging from genetic engineering and combination therapies to targeted modulation of the TME—are being developed to overcome resistance mechanisms and improve TIL persistence, infiltration, and cytotoxicity. This review integrates current advances in TIL research and therapy, offering a comprehensive foundation for future clinical translation. TILs hold significant promise as both biomarkers and therapeutic agents, and with continued innovation, they are poised to become a cornerstone of personalized cancer immunotherapy.

In this study, a greenhouse experiment was carried out from April to July 2024 to assess the effectiveness of four ornamental plants in removing heavy metals from the polluted soil surrounding the Zenica steel mill in Bosnia and Herzegovina. The selected ornamental plants - blue mink (Ageratum houstonianum Mill.), marigold (Tagetes erecta L.), impatiens (Impatiens walleriana Hook. f.), and begonia (Begonia semperflorens - Cultorum Group) - demonstrated potential for addressing soil contamination. These plants were cultivated in grow bags filled with soil collected from different areas surrounding the Zenica steel mill. The concentrations of heavy metals (Cu, Zn, Pb, Cd, Cr, Mn, and Fe) in both soil and plant samples were analyzed using atomic absorption spectrophotometry. The findings of this study reveal that soils adjacent to the Zenica steel mill are heavilycontaminated with Zn, Cd, and Pb and also contain notable levels of Mn and Fe. The bioaccumulation factor (BAF) and translocation factor (TF) were calculated to determine the potential of the selected ornamental plants to uptake and transport heavy metals from the soil to its aboveground parts. The BAF values for all heavy metals in all studied plant species were consistently below 1, indicating a limited capacity to remove heavy metals from the soil. This limited effectiveness can be attributed, among other factors, to the high pH levels of the tested soils. Despite the limitation, the findings revealed a significant difference in the plants’ capacity to uptake and accumulate heavy metal ions from the examined soils. Among the tested plants, blue mink demonstrated the highest ability to absorb Cu, Pb, Cr and Fe, while the highest concentrations of Zn and Cd were found in begonia

We demonstrate non-immunogenic circRNA as a tool for targeted gene regulation in plants, where it acts in an isoform- and sequence-specific manner, enabling future agronomic applications. Circular RNAs (circRNAs) are single-stranded RNA molecules characterized by their covalently closed structure and are emerging as key regulators of cellular processes in mammals, including gene expression, protein function and immune responses. Recent evidence suggests that circRNAs also play significant roles in plants, influencing development, nutrition, biotic stress resistance, and abiotic stress tolerance. However, the potential of circRNAs to modulate target protein abundance in plants remains largely unexplored. In this study, we investigated the potential of designer circRNAs to modulate target protein abundance in plants using Arabidopsis protoplasts as a model system. We show that PEG-mediated transfection with a 50-nt circRNAGFP containing a 30-nt GFP-antisense sequence results in a dose- and sequence-dependent reduction of GFP reporter target protein abundance. Notably, a single-stranded open isoform of circRNAGFP had little effect on protein abundance, indicating the importance of the closed circular structure. Additionally, circRNAGFP also reduced GFP abundance in Arabidopsis mutants defective in RNA interference (RNAi), suggesting that circRNA activity is independent of the RNAi pathway. We also show that circRNA, unlike dsRNA, does not induce pattern-triggered immunity (PTI) in plants. Findings of this proof-of-principle study together are crucial first steps in understanding the potential of circRNAs as versatile tools for modulating gene expression and offer exciting prospects for their application in agronomy, particularly for enhancing crop traits through metabolic pathway manipulation.

BACKGROUND There is uncertainty about whether patients with M2 occlusion benefit from endovascular therapy (EVT) in the late (6-24-hour) time window. We evaluated the clinical outcomes of patients with M2 occlusion selected for EVT compared with those who received medical management (MM) in the late window. METHODS This multinational cohort study was conducted at 66 sites across 10 countries (January 2014 to May 2022). We included consecutive patients with late-window stroke due to M2 occlusion, baseline National Institutes of Health Stroke Scale score of ≥5, and premorbid modified Rankin Scale score of ≤2 who received EVT or MM alone. The primary end point was 90-day ordinal shift in the modified Rankin Scale score. Safety end points were symptomatic intracranial hemorrhage and 90-day mortality. Differences in outcomes were determined using inverse probability of treatment weighting-adjusted logistic regression models. RESULTS Among 5098 patients, 496 met inclusion criteria (median [interquartile range] age, 74 years [62-81 years]; baseline National Institutes of Health Stroke Scale score, 12 [8-17]), of whom 394 (79.4%) received EVT and 102 (20.6%) MM. In inverse probability of treatment weighting adjusted analyses, there was no favorable 90-day ordinal modified Rankin Scale shift (odds ratio, 1.39 [95% CI, 0.92-2.12]) and no difference of functional independence rates (modified Rankin Scale score of 0-2; odds ratio, 1.72 [95% CI, 0.93-3.15]) with EVT compared with MM. Moreover, symptomatic intracranial hemorrhage risk (odds ratio, 3.46 [95% CI, 0.50-23.92]) and 90-day mortality (odds ratio, 1.11 [95% CI, 0.66-1.87]) were not statistically different between treatment groups. CONCLUSIONS In patients with M2 occlusion in the 6- to 24-hour time window, there was no difference in disability outcomes or symptomatic intracranial hemorrhage risk between patients treated with EVT compared with MM. Results of ongoing randomized trials will provide further insight. REGISTRATION URL: https://www.clinicaltrials.gov; Unique identifier: NCT04096248.

Despite the well-established role of human papillomavirus (HPV) as the primary cause of cervical cancer (CC) and the existence of an effective HPV vaccine, over half a million women are diagnosed with CC globally each year, with more than half of them dying from the disease. Immunotherapy has rapidly become a cornerstone of cancer treatment, offering substantial improvements in survival rates and reducing treatment-related side effects compared to traditional therapies. For the past 25 years, chemoradiotherapy (CRT) has been the standard treatment for locally advanced CC (LACC). However, while adjuvant chemotherapy has failed to improve outcomes in LACC, the integration of neoadjuvant chemotherapy (NACT) with CRT, as well as chemoimmunoradiotherapy followed by consolidation immunotherapy, has transformed treatment strategies, demonstrating superior efficacy compared to CRT alone. In the first-line treatment of CC, adding pembrolizumab to platinum-based chemotherapy, either with or without bevacizumab, has significantly improved outcomes compared to platinum-based chemotherapy and bevacizumab alone. This review explores the current landscape of immunotherapy and biomarker advancements in CC. Furthermore, we discuss promising future directions, including the potential of personalized immunotherapy approaches and novel combination therapies to further enhance treatment efficacy and improve prognoses for patients with CC.

Aim. The aim of this study is to investigate the satisfactionof primary caregivers of children and adolescentswith type 1 diabetes mellitus regarding healthcareservices.Methods. The study was conducted within the Associationof Children and Youth with Diabetes in theSarajevo Canton from October 23, 2021, to February28, 2022. The study included 50 primary caregivers,49 mothers and one father, with an average age of41.28 ± 5.64. A generic questionnaire for assessingparental satisfaction with healthcare provided totheir children, the PedsQLTM Healthcare SatisfactionGeneric Module, was used. The statistical analysesused to analyze the data included multiple linear regression,ANOVA test, and t-test.Results. Satisfaction with healthcare services wasassessed with average ratings on a scale of 0 to 100but with wide ranges of scores (59.46±24.34, rangingfrom 16.67 to 98.96). The results showed thattwo variables play a crucial role in predicting satisfactionwith healthcare services: meeting emotionalneeds (relative contribution=29.8) and satisfactionwith information (relative contribution=27.3).Conclusion. An individualized approach and a collaborationwith parents of children and adolescentswith type 1 diabetes mellitus are necessary to improvethe parental experience and their satisfactionwith healthcare services.

This study investigates the impact of thermal modification on the water resistance and colour stability of Indigenous wood species from Bosnia and Herzegovina, including Norway spruce (Picea abies), silver fir (Abies alba), oak (Quercus sp.), and walnut (Juglans regia). Thermal modification, conducted in the presence of air, enhanced the hydrophobicity of all tested wood species by reducing their water absorption and limiting their susceptibility to moisture-driven dimensional changes. The modification process also induced notable colour changes, darkening each species in line with the degradation of lignin and other wood constituents. The degree of colour change was observed to increase with higher treatment temperatures, particularly at 210 °C, as measured by the CIE Lab colour system. These findings suggest that thermal modification provides an environmentally friendly method to improve both durability and aesthetic appeal in wood, extending the material’s application range for humid or outdoor environments.

Behavior analysis across species represents a fundamental challenge in neuroscience, psychology, and ethology, typically requiring extensive expert knowledge and labor-intensive processes that limit research scalability and accessibility. We introduce BehaveAgent, an autonomous multimodal AI agent designed to automate behavior analysis from video input without retraining or manual intervention. Unlike conventional methods that require manual behavior annotation, video segmentation, task-specific model training, BehaveAgent leverages the reasoning capabilities of multimodal large language models (LLM) to generalize across novel behavioral domains without need for additional training. It integrates LLMs, vision-language models (VLMs), and large-scale visual grounding modules, orchestrated through a multimodal context memory and goal-directed attention mechanism, to enable robust zero-shot visual reasoning across species and experimental paradigms, including plants, insects, rodents, primates, and humans. Upon receiving a video input, BehaveAgent autonomously identifies the correct analysis strategy and performs end-to-end behavior analysis and interpretation without human supervision. Leveraging vision-language representations, it performs general-purpose tracking, pose estimation and segmentation. We demonstrate BehaveAgent’s universal applicability to autonomously (1) identify the behavioral paradigm and develop an action plan specialized for the identified paradigm, (2) identify relevant subjects and objects, (3) track those features, (4) identify behavioral sequences with explicit reasoning, (5) generate and execute code for targeted analysis and (6) generate comprehensive research reports that integrate behavioral findings with relevant scientific literature. Through interpretable agentic reasoning, BehaveAgent makes its internal decision-making process transparent, clarifying why particular features are tracked or behaviors inferred. By reducing the time and expertise required for behavior analysis, BehaveAgent introduces a scalable, generalizable, and explainable paradigm for advancing biological and behavioral research.

COVID-19, caused by the SARS-CoV-2, poses significant global health challenges. A key player in its pathogenesis is the nucleocapsid protein (NP), which is crucial for viral replication and assembly. While NPs from other coronaviruses, such as SARS-CoV and MERS-CoV, are known to increase inflammation and cause acute lung injury, the specific effects of the SARS-CoV-2 NP on host cells remain largely unexplored. Recent findings suggest that the NP acts as a pathogen-associated molecular pattern (PAMP) that binds to Toll-like receptor 2 (TLR2), activating NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) and MAPK (mitogen-activated protein kinase) signaling pathways. This activation is particularly pronounced in severe COVID-19 cases, leading to elevated levels of soluble ICAM-1 (intercellular adhesion molecule 1) and VCAM-1 (vascular cell adhesion molecule 1), which contribute to endothelial dysfunction and multiorgan damage. Furthermore, the NP is implicated in hyperinflammation and thrombosis—key factors in COVID-19 severity and long COVID. Its potential to bind with MASP-2 (mannan-binding lectin serine protease 2) may also be linked to persistent symptoms in long COVID patients. Understanding these mechanisms, particularly the role of the NP in thrombosis, is essential for developing targeted therapies to manage both acute and chronic effects of COVID-19 effectively. This comprehensive review aims to elucidate the multifaceted roles of the NP, highlighting its contributions to viral pathogenesis, immune evasion, and the exacerbation of thrombotic events, thereby providing insights into potential therapeutic targets for mitigating the severe and long-term impacts of COVID-19.

This paper introduces affordance-based explanations of robot navigational decisions. The rationale behind affordance-based explanations draws on the theory of affordances, a principle rooted in ecological psychology that describes potential actions the objects in the environment offer to the robot. We demonstrate how affordances can be incorporated into visual and textual explanations for common robot navigation and path-planning scenarios. Furthermore, we formalize and categorize the concept of affordance-based explanations and connect it to existing explanation types in robotics. We present the results of a user study that shows participants to be, on average, highly satisfied with visual-textual, i.e., multimodal, affordance-based explanations of robot navigation. Furthermore, we investigate the complexity of different types of textual affordance-based explanations. Our research contributes to the expanding domain of explainable robotics, focusing on explaining robot actions in navigation.

Future wireless multiple-input multiple-output (MIMO) systems will integrate both sub-6 GHz and millimeter wave (mmWave) frequency bands to meet the growing demands for high data rates. MIMO link establishment typically requires accurate channel estimation, which is particularly challenging at mmWave frequencies due to the low signal-to-noise ratio (SNR). In this paper, we propose two novel deep learning-based methods for estimating mmWave MIMO channels by leveraging out-of-band information from the sub-6 GHz band. The first method employs a convolutional neural network (CNN), while the second method utilizes a UNet architecture. We compare these proposed methods against deep-learning methods that rely solely on in-band information and with other state-of-the-art out-of-band aided methods. Simulation results show that our proposed out-of-band aided deep-learning methods outperform existing alternatives in terms of achievable spectral efficiency.

The application of additive manufacturing technologies for producing parts from polymer composite materials has gained significant attention due to the ability to create fully functional components that leverage the advantages of both polymer matrices and fiber reinforcements while maintaining the benefits of additive technology. Polymer composites are among the most advanced and widely used composite materials, offering high strength and stiffness with low mass and variable resistance to different media. This study aims to experimentally investigate the impact of selected process parameters, namely, wall thickness, raster angle, printing temperature, and build plate temperature, on the flexural properties of carbon fiber reinforced polyamide (CFrPA) fused deposition modeling (FDM) printed samples, as per ISO 178 standards. Additionally, regression and artificial neural network (ANN) models have been developed to predict these flexural properties. ANN models are developed for both normal and augmented inputs, with the architecture and hyperparameters optimized using random search technique. Response surface methodology (RSM), which is based on face centered composite design, is employed to analyze the effects of process parameters. The RSM results indicate that the raster angle and build plate temperature have the greatest impact on the flexural properties, resulting in an increase of 51% in the flexural modulus. The performance metrics of the optimized RSM and ANN models, characterized by low MSE, RMSE, MAE, and MAPE values and high R2 values, suggest that these models provide highly accurate and reliable predictions of flexural strength and modulus for the CFrPA material. The study revealed that ANN models with augmented inputs outperform both RSM models and ANN models with normal inputs in predicting these properties.