Microneedle arrays are a promising tool in the development of transdermal biosensing devices, and considerable research effort is being devoted to the development, microfabrication, optimization, and testing of different microneedle-based sensing platforms.[1] To date, microneedles have been fabricated from various materials in different shapes, sizes, and densities, with the aim of enhancing the performance of biosensors and developing user-friendly microneedle devices.[1] And we have demonstrated sensing of small molecules and macromolecules using both silicon and polymer based microneedle arrays [2-4]. However, one of the main challenges yet to be addressed is devices remaining fully functional and providing an accurate electrochemical response after skin insertion.[5] Such failure can mainly occur due to delamination or damage of the sensing layer during skin insertion. To address the above-mentioned challenges, we developed a microneedle array featuring recessed microcavities or 3D polymer lattices.[6-8] Those features are conductive microscale pockets located at the tip of the microneedles which can i) accommodate a biosensing layer and conduct electrochemical measurements, ii) protect the sensing layer from delamination during insertion and removal from the skin, and iii) position the sensing layer deep in the skin enabling proper access to the interstitial fluid. In our work, we illustrated that microcavities protect against delamination of the sensing layer during multiple skin applications, unlike microneedles without microcavities. The retained functionality of the sensing platform in glucose, urea and insulin sensing has been successfully demonstrated via ex vivo and in vivo tests. The aim of this work is to set the foundation for a new kind of microneedle design, involving the engineering of the microneedle surface to develop transdermal sensing devices suitable for practical application. This will not just help to advance transdermal sensing technology by overcoming challenges but also reduce the cost and duration of wearable sensor fabrication, and improve the reliability of microneedle-based diagnostics and health monitoring. References: [1] Nano Today 30 (2020) 100828. [2] Advanced Functional Materials 32 (2022) 200985. [3] Biosensors and Bioelectronics 222 (2023) 114955. [4] Biosensors & Bioelectronics, 192 (2021), 113496. [5] Nature Biomedical Engineering 5 (2021) 64-76. [6] ACS Materials Letters 5 (2023) 1851-1858 [7] ACS Sensors 9 (2024), 932–941. [8] Advanced Materials, 36 (2024), 2412999.

Inflammation, oxidative stress, and androgen activity are key features in benign prostate hyperplasia (BPH). Risks associated with the long-term use of 5α-reductase inhibitors have led to the search for alternative therapies, including food supplements. This study investigates the effectiveness of the combination of pollen extracts, namely Graminex®G96® (G) and Teupol 25P (T), towards oxidative stress and inflammation on human macrophages and benign prostate hyperplasia cells (BPH-1), both of which are LPS stimulated. The Nrf2-dependent antioxidant intracellular cascade as well as the NF-ĸB-driven inflammatory cascades were analyzed. The anti-proliferative effect of G and T, alone and in association, were evaluated on prostatic adenocarcinoma cells (PC-3) and BPH-1 cells. Finally, the inhibitory activity of GT on 5α-reductase was investigated in PC-3 cells by measuring epiandrosterone amounts, with the 5α-reductase inhibitor finasteride administered for comparison. All experiments were conducted in triplicate; data are presented as mean values ± standard deviations. Statistical analysis was performed using one-way analysis of variance. Our work demonstrates that GT promotes Nrf2-dependent antioxidant responses and counteracts the NF-ĸB-driven pathway in macrophages. GT is effective in counteracting the expression of pro-inflammatory cytokines and the generation of reactive oxygen species by promoting HO-1-dependent antioxidant responses in BPH-1 cells. GT reduces PC-3 and BPH-1 proliferation when associated with finasteride through a statistically significant inhibition of 5α-reductase activity. Data obtained in vitro and in silico demonstrate the potential efficacy of a multitargeted approach in the treatment of BPH.

There are differing results in recent literature concerning aneurysmal wall enhancement (AWE) after endovascular treatment (ET) of intracranial aneurysms (IAs). The aim of this retrospective study is to investigate if the presence of AWE of unruptured treated IAs via stent-assisted coiling (SAC) is associated with higher reperfusion rates. The clinical courses of 58 patients with IAs after ET via SAC were examined over the timespan of up to 5 years, assessing for AWE in T1 SPACE FS and T1 SE FS blood suppression sequences after contrast administration, events of reperfusion and need for retreatment. 58 patients were included (23 with AWE, 35 without). 18 of 23 patients (78.3%) with AWE showed reperfusion after treatment, compared to 15 of 35 patients (42.9%) without AWE. Reperfusion rates were significantly higher in patients with AWE, compared to those without AWE (p = 0.0139) also after propensity score matching (p = 0.0456). In patients with unruptured IAs treated exclusively with SAC, AWE on follow-up MRI was significantly associated with higher reperfusion rates. AWE may serve as an early imaging biomarker of post-treatment instability.

The analysis of carotid arteries, particularly plaques, in multi-sequence Magnetic Resonance Imaging (MRI) data is crucial for assessing the risk of atherosclerosis and ischemic stroke. In order to evaluate metrics and radiomic features, quantifying the state of atherosclerosis, accurate segmentation is important. However, the complex morphology of plaques and the scarcity of labeled data poses significant challenges. In this work, we address these problems and propose a semi-supervised deep learning-based approach designed to effectively integrate multi-sequence MRI data for the segmentation of carotid artery vessel wall and plaque. The proposed algorithm consists of two networks: a coarse localization model identifies the region of interest guided by some prior knowledge on the position and number of carotid arteries, followed by a fine segmentation model for precise delineation of vessel walls and plaques. To effectively integrate complementary information across different MRI sequences, we investigate different fusion strategies and introduce a multi-level multi-sequence version of U-Net architecture. To address the challenges of limited labeled data and the complexity of carotid artery MRI, we propose a semi-supervised approach that enforces consistency under various input transformations. Our approach is evaluated on 52 patients with arteriosclerosis, each with five MRI sequences. Comprehensive experiments demonstrate the effectiveness of our approach and emphasize the role of fusion point selection in U-Net-based architectures. To validate the accuracy of our results, we also include an expert-based assessment of model performance. Our findings highlight the potential of fusion strategies and semi-supervised learning for improving carotid artery segmentation in data-limited MRI applications.

Virtual Reality (VR) is increasingly recognized as a transformative tool for soft skills training, offering immersive and interactive environments that enhance learning outcomes. The eXcape project leverages VR to develop realistic scenarios that can be designed to practice and learn critical soft skills, such as communication, teamwork, problem-solving, adaptability, and leadership. This paper presents a systematic approach to designing these scenarios ensuring effective skill acquisition and transferability. We discuss the methodology employed in scenario creation, key pedagogical considerations, and the challenges encountered in designing immersive training experiences.

The design process for motor yachts primarily relies on the experience of designers, who draw upon their knowledge gained from working on similar hull forms. However, when a new concept is to be developed, the experience garnered from standard platforms may not suffice for achieving a successful design within a short timeframe. Designing a motor yacht involves considering multiple aspects of ship hydrodynamics, including resistance, propulsion, seakeeping, and maneuverability. While these factors have been extensively discussed for different types of ships, a comprehensive joint investigation of hulls, such as those of motor yachts, is noticeably absent in the available literature. This paper aims to fill that gap by providing guidelines for the design of motor yachts with lengths ranging from 20 to 40 m. As part of a preliminary study, a series of 15 yacht hulls were developed, starting from a reference hull form. The resistance, seakeeping and maneuverability performance of these hulls were assessed under specified environmental conditions and speeds, following the ISO 22834:2022 guidelines for comfort assessment. The calculations produced response surfaces detailing the hydrodynamic properties for this series of yachts as functions of the main dimensions of the hulls. Ultimately, these responses assist in identifying optimal design solutions for the main dimensions of a new motor yacht within the 20 to 40 m length range.

With engineering plastics increasingly replacing traditional materials in various drive and control gear systems across numerous industrial sectors, material selection for any gearwheel critically impacts its mechanical and thermal properties. This paper investigates the engagement of steel and Polyvinylidene Fluoride (PVDF) gear pairs tested under several load conditions to determine polymer gears’ characteristic service life and failure modes. Furthermore, recognizing that the application of polymer gears is limited by insufficient data on their temperature-dependent mechanical properties, this study establishes a correlation between the tribological contact, meshing temperatures, and wear coefficients of PVDF gears. The results demonstrate that the flank surface wear of the PVDF gears is directly proportional to the temperature and load level of the tested gears. Several distinct load-induced failure modes have been detected and categorized into three groups: abrasive wear resulting from the hardness disparity between the engaging surfaces, thermal failure caused by heat accumulation at higher load levels, and tooth fracture occurring due to stiffness changes induced by the compromised tooth cross-section after numerous operating cycles at a specific wear rate.

Increasing travel, climate change, spread of antimicrobial resistance and pandemics increased the need for well-trained infectious diseases (ID) specialists and qualified ID specialist training for protecting public health all over the world. In this study, we aimed to provide a comprehensive overview of ID specialty training programs for standardization and quality improvement in a large geographical area. We conducted a cross-sectional study among national respondents of 29 countries [Central Asia (Azerbaijan, Uzbekistan, the Kyrgyz Republic, Kazakhstan), the Middle East (Iran, Saudi Arabia, Jordan, Iraq, Oman, the United Arab Emirates, Qatar, Lebanon), Southeast Europe (Albania, Greece, Kosovo, Slovenia, Bosnia and Herzegovina, Serbia, the Republic of North Macedonia, Croatia), Eastern Europe (Russia, Moldova, Romania, Bulgaria), South Asia (India, Pakistan, Afghanistan), Southeast Asia (Malaysia), Türkiye] to evaluate the structure and components of ID training programs. In this study, structural variability in ID training programs was notable. 65.5% of the countries offered independent specialty program, 59% of the countries reported a required exam for entry into the ID specialization. Nearly all of the countries had a formal training curriculum; written exams were the most common used assessment method. This study provides a comprehensive overview of ID specialty training across diverse regions, highlighting major structural differences in curricula, training duration, and national standards. Its broad geographic scope and contributions from actively engaged ID educators offer a unique global perspective. The findings underscore the urgent need for harmonized training frameworks, the strengthening of national curricula, and the promotion of international collaboration and inclusive strategies, all essential for developing a skilled, competent and resilient global ID workforce.

Video-diffusion models have recently set the standard in video generation, inpainting, and domain translation thanks to their training stability and high perceptual fidelity. Building on these strengths, we repurpose conditional video diffusion as a physics surrogate for spatio-temporal fields governed by partial differential equations (PDEs). Our two-stage surrogate first applies a Sequential Deep Operator Network (S-DeepONet) to produce a coarse, physics-consistent prior from the prescribed boundary or loading conditions. The prior is then passed to a conditional video diffusion model that learns only the residual: the point-wise difference between the ground truth and the S-DeepONet prediction. By shifting the learning burden from the full solution to its much smaller residual space, diffusion can focus on sharpening high-frequency structures without sacrificing global coherence. The framework is assessed on two disparate benchmarks: (i) vortex-dominated lid-driven cavity flow and (ii) tensile plastic deformation of dogbone specimens. Across these data sets the hybrid surrogate consistently outperforms its single-stage counterpart, cutting the mean relative L2 error from 4.57% to 0.83% for the flow problem and from 4.42% to 2.94% for plasticity, a relative improvements of 81.8% and 33.5% respectively. The hybrid approach not only lowers quantitative errors but also improves visual quality, visibly recovering fine spatial details. These results show that (i) conditioning diffusion on a physics-aware prior enables faithful reconstruction of localized features, (ii) residual learning reduces the problem, accelerating convergence and enhancing accuracy, and (iii) the same architecture transfers seamlessly from incompressible flow to nonlinear elasto-plasticity without problem-specific architectural modifications, highlighting its broad applicability to nonlinear, time-dependent continua.

Hearing aid (HA) users often experience increased listening effort, particularly in noisy environments. While noise reduction (NR) algorithms aim to alleviate this, traditional electroencephalography (EEG) methods based on power analysis have limited success in assessing the listening effort in this population. This study proposes a novel method using a whole-head synchronization map analysis that uses local connectivity, a measure of statistical dependencies within localized brain regions. We use EEG electrodes to define a region based on the surrounding electrodes in the first-order neighborhood. This approach was tested using EEG data from 22 HA users with active or inactive NR engaged in a continuous speech-in-noise (SiN) task at low (3dB) and high (8dB) signal-to-noise ratio (SNR) levels. Whole-head synchronization was quantified using circular omega complexity (COC), a multivariate phase synchrony measure. Results showed increased local connectivity in the alpha band (8–12 Hz) within frontal and occipital regions during SiN condition compared to the background noise-only (NO) condition. Furthermore, NR activation impacted the synchronization map differently at the two SNRs of the experiment, with greater effect observed at low SNR, primarily in the left parietal region and alpha band. This behavior is in line with that of existing measures for listening effort, and therefore suggests that EEG local connectivity analysis holds promise as a tool for objectively assessing listening effort in HA users, especially in challenging listening environments.

In this study, we investigate integrating eye tracking with auditory attention decoding (AAD) using portable EEG devices, specifically a mobile EEG cap and cEEGrid, in a preliminary analysis with a single participant. A novel audiovisual dataset was collected using a mobile EEG system designed to simulate real-life listening environments. Our study has two main objectives: (1) to use eye tracking data to automatically infer the labels of attended and unattended speech streams, and (2) to train an AAD model using these estimated labels, evaluating its performance through speech reconstruction accuracy. The results demonstrate the feasibility of using eye tracking data to estimate attended speech labels, which were then used to train speech reconstruction models. We validated our models with varying amounts of training data and a second dataset from the same participant to assess generalization. Additionally, we examined the impact of mislabeling on AAD accuracy. These findings provide preliminary evidence that eye tracking can be used to infer speech labels, offering a potential pathway for brain-controlled hearing aids, where true labels are unknown.