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The oxidation mechanism of Kanthal AF (FeCrAl alloy) was studied in the temperature range of 800–1200 °C through isothermal and thermo-cyclic oxidation tests. At 800 °C, tests involved 12-h cycles (up to 36 cycles), while at 1000 °C and 1200 °C, they were conducted isothermally (up to 432 h). Oxidation constants, oxide scale thickness, surface microstructure, and elemental composition were analyzed using TGA, SEM-EDXS, TEM, AES, XPS, GIXRD, and ToF-SIMS. The results show that the Al2O3 layer has a multilayer structure, defined by a naturally formed thin Cr-rich oxide layer on the alloy surface, which exhibits temperature-dependent selective permeability of metal cations, influencing the oxide scale growth. At 800 °C, inward oxide diffusion dominates due to limited cation permeability in the Cr-rich layer, while at higher temperatures, outward growth of the alumina scale is facilitated by increased permeability to metal cations. Despite the low Mg concentration in the alloy (0.01 wt%), Mg diffuses into the alumina rapidly, forming Mg-Al-Fe oxides that transform into MgAl2O4 spinel, impacting scale integrity and adhesion due to associated porosity. Consequently, Mg is considered an undesirable impurity, even at trace levels. Titanium forms separate oxides in the alumina scale, while zirconium and yttrium incorporate uniformly into the alumina scale.

Abstract This review focuses on the usage of machine learning methods in sports. It closely follows the PRISMA framework for writing systematic reviews. We introduce the broader field of using sensor data for feedback in sport and cite similar reviews, that focus on other aspects of the field. With its focus on machine learning models that use signals from simple sensors, this review covers a very focused area that has not yet been covered by any other review. As described in problem definition, we use well-defined inclusion criteria, we have reviewed 72 papers. They present existing solutions, that use machine learning to extract useful information from data collected using various sensors in sports. To be included, papers had to use machine learning methods using data collected from sensors during sports, had to focus on sports-related applications and the result of machine learning had to be some information that can be used in real-time. We have found that the field is rapidly developing as 46 of the 72 included papers were from the last four years. Furthermore, we have found that the field is moving from using classical machine learning techniques to using deep learning. We analyze which data is used as input for machine learning, and we find that the most commonly used sensor is the accelerometer, closely followed by the gyroscope. The most common sensor platform is using a single wearable sensor, however, the studies that used deep learning, use multiple wearable sensors most often. Dataset sizes of sports papers are relatively small compared to other fields, but datasets are on average slightly larger in studies that use deep learning than in those that do not. We analyze the most common preprocessing methods and find that low-pass filtering and feature extraction are commonly used. We compare different machine learning models and the results of the studies that have tested multiple models on the same data, where we find that deep learning proved to be better than classical machine learning. Most studies show classification accuracy of over 90%, showing that machine learning is a useful tool for the researched problems. We end the review by researching how far the machine learning methods were implemented. Twenty of the included papers used their machine learning models in applications beyond a research paper and provided some sort of feedback back to athletes or coaches. After completing the review of the field, we propose a solution – a plan for future research. The proposed solution is to use a combination of best practices from the included paper and methods that we found are not yet implemented in the field of sports. We further elaborate, where we see the current state of the field. We conclude the article with short summary of the findings.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumour vascularity, which depends on the process of angiogenesis and affects tumour response to treatment. Our study explored the associations between DCE-MRI parameters and the expression of plasma angiogenic factors in human papilloma virus (HPV)-negative oropharyngeal cancer, as well as their predictive value for response to concurrent chemoradiotherapy (cCRT).

Diffusion tensor imaging (DTI) enables a non-invasive assessment of tissue architecture based on water diffusion. High-resolution magnetic resonance imaging (MRI) systems have enhanced the visualization of nerve compartments. This study investigates the influence of region of interest (ROI) selection on DTI indices in ex vivo peripheral nerves. Cadaveric median nerves (n=10) were harvested and immersed in fluorinated-carbon liquid, and 9-mm segments were scanned with a 9.4-T MRI system. Diffusion-weighted signals were summed, and diffusion tensors were calculated for anatomical compartments using different delineation techniques. Following the initial scan, five samples were scanned for a second time using an identical protocol. The analysis showed that the inclusion of an inert background into the ROI significantly reduces fractional anisotropy (FA). Significant differences in diffusion tensors were observed between the initial and intermediate segments, with FA and mean diffusivity (MD) differing by as much as 25.2% and 25.6%, respectively. Tracing the fascicles without the interfascicular epineurium exhibited an 8.8% lower FA compared to the delineations of the epineurium. Second MRI scans showed significant changes in dif-fusion tensors with higher eigenvalues D1-D3 and MD; along with lower FA. In conclusion, the intermediate portions of the nerve demonstrated greater consistency in DTI indices and are recommended for analysis over the initial portions. Using an inert liquid can minimize background effects, but the inherent diffusion properties of the tissue must be carefully considered. Prolonged scanning times can alter diffusion tensors, likely due to autolytic processes, underscoring the importance of consistent pre-scanning conditions and acquisition protocols. Additionally, maintaining consistency in delineations is paramount, regardless of whether the tracing method includes or excludes the interfascicular epineurium.

This retrospective study investigated the efficacy of endovascular treatment with multilayer flow modulators (MFMs) for treating aortic aneurysms in high-risk patients unsuitable for conventional treatments.

Asfiksija je kombinacija hipoksije in hipoperfuzije, ki ovira izmenjavo plinov in dovajanje energetskih substratov in s tem povzroči tkivno acidozo. Obporodna asfiksija je kljub napredku perinatalne medicine še vedno pogosto stanje, ki ima lahko pomembne dolgotrajne posledice. Najpogostejša je okvara možganov oz. hipoksično-ishemična encefalopatija. Lahko pa so pomembno prizadeti tudi drugi organski sistemi. Zato novorojenček po obporodni asfiksiji potrebuje celostno obravnavo.

Asphyxia is a combination of hypoxia and hypoperfusion that impairs gas exchange and the availability of energy substrates in tissues, causing tissue acidosis. Despite advances in perinatal care perinatal asphyxia remains a common condition that can have significant long-term consequences. The most serious consequence is brain damage or hypoxic-ischemic encephalopathy, but other organ systems can also be significantly affected, requiring a multidisciplinary approach to the management of newborns following birth asphyxia.

Abstract Trauma scientists have raised the alarm about the devastating consequences of the Ukraine war on mental health. We examined how higher education students—as indirect victims—coped with this conflict and how they emotionally reacted during 2022. We involved 2314 students from 16 countries through an online survey. A structural equation model indicated significant relations between war-related worry about military and macroeconomics domains and two coping strategies (opposition, support giving), in turn significantly linked with six emotions. The model was strongly invariant across gender, study field, and geographic area. The most frequent emotions were anger and anxiety, followed by two future-centred emotions (hopelessness and hope). Emotions were more frequent for females and students of the countries geographically close to the war region. Our findings call for evidence-based policy recommendations to be implemented by institutions to combat the negative short and long-term psychological sequelae of being witnesses of armed conflicts.

Despite the significance of neck muscles in musculoskeletal disorders, their microscopic anatomy remains poorly characterized. This study examined the splenius capitis muscle, focusing on its fiber-type composition, fiber size, and capillary network characteristics. For comparison and validation, the vastus lateralis muscle was also analyzed. Muscle samples from 13 young male subjects (mean age ± SD: 35.7 ± 8.6 years) were collected within 24-h post-mortem during autopsy. Myosin heavy chain (MyHC) isoform expression was characterized immunohistochemically in 10 μm sections, while the capillary network architecture was assessed in 100 μm sections. Immunofluorescence staining, confocal microscopy, and 3D image analysis were employed to quantify capillary tortuosity, anisotropy, branch density (Br dens), and the length of capillaries per muscle volume (LV), per muscle fiber length (LL), per fiber surface area (LS), and per fiber volume (LVf). Compared to the vastus lateralis muscle, the splenius capitis muscle had a higher percentage of type 1 fibers (51.2% vs 39.7%), fewer type 2a fibers (16.2% vs 31.4%), and smaller fiber diameters (35.5–40.9 μm vs 47–56.1 μm). It also displayed lower Br dens (P = 0.0069), higher anisotropy (P = 0.0004), and lower LL (P < 0.0001) but higher LVf (P = 0.0486). In the splenius capitis muscle, body mass index (BMI) negatively correlated with LV (P = 0.0155), LS (P = 0.0091), LVf (P = 0.0137), and anisotropy (P = 0.0425), and positively correlated with tortuosity (P = 0.0473), indicating a reduction in the capillary network. In the vastus lateralis muscle, only LV (P = 0.0161) decreased with high BMI. This study characterized the fiber-type composition, fiber size, and 3D capillary network of the splenius capitis muscle, establishing a baseline for investigations into pathological muscle alterations.

Accurate estimation of postmortem interval (PMI) is crucial in forensic medicine. The hyaline cartilage, being predominantly composed of a dense extracellular matrix and partly resistant to factors influencing protein degradation, can be utilized for analyzing PMI intervals. Various staining methods are available for cartilage staining for PMI evaluation; however, the conventional visual scoring method for assessing staining intensity is susceptible to evaluator bias. This study compared the visual scoring method with a modified Bern score with semi-automatic image analysis. The cartilage samples were obtained from three cadavers with known time of death. Forty-five histological slices were prepared and stained using Alcian blue, Safranin-O with Fast green, Safranin-O without Fast green, Masson trichrome, and Sirius red. Ten evaluators visually scored each sample on a scale of 0 to 3. A semi-automatic analysis was conducted on the same images using the deconvolution plugin of the ImageJ software. Linear regression was used to assess the correlation between the mean grey value and the mean Bern score from all evaluators. The results showed strong correlations across all evaluated staining techniques, with Masson trichrome staining exhibiting the highest correlation. Accordingly, semi-automatic image analysis can be a suitable replacement for the visual scoring method, particularly when no procedural artifacts are present.

Abstract Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical quantities. Various sensors, measuring different parameters, can produce a large amount of data at high speeds and volumes that must be stored for real-time or post-processing and analysis. In addition to sensor data, metadata is an important component and can vary between biomechanical applications. Currently however, each application typically has its own unique data flow and storage solution. In this research, we present a universal data model solution that can be applied to any sensor-based biomechanical application in sport and physical rehabilitation. Our proposed cloud platform architecture allows for the manipulation of sensor data and metadata using a combination of Big Data and conventional techniques. The main idea of this research is to develop a platform that allows a universal way for any biomechanical application to handle its data regardless of the type of data and metadata. This is achieved by creating a universal data model, and implementing this data model in a generalized architecture using a graph database. We demonstrate the benefits of this approach using examples from existing biomechanical systems and describe the development of the cloud platform architecture and the underlying data model. We also provide an example of the use of this platform in a sport shooting application. This approach is unique in that it allows data from different sources and applications to be stored and processed using the same procedures and techniques, facilitating data analysis and application development. We envision this system will expand to multiple different biomechanical applications in the future. We expect that in time, the ability to compare various data and store different biomechanical datasets will become necessity. With the advantages of modern recommender systems and utilization of artificial intelligence, huge amounts of relevant and well-prepared data with useful metadata are required thus having such system is an important advantage for future biomechanical systems development. With the increase of people’s awareness and usage of devices that increase well-being and quality of life, presented platform and similar systems will play a pivotal role in shaping the future lifestyle.

IntroductionThe global burden of diabetes mellitus is escalating, and more efficient investigative strategies are needed for a deeper understanding of underlying pathophysiological mechanisms. The crucial role of skeletal muscle in carbohydrate and lipid metabolism makes it one of the most susceptible tissues to diabetes-related metabolic disorders. In tissue studies, conventional histochemical methods have several technical limitations and have been shown to inadequately characterise the biomolecular phenotype of skeletal muscle to provide a holistic view of the pathologically altered proportions of macromolecular constituents.Materials and methodsIn this pilot study, we examined the composition of five different human skeletal muscles from male donors diagnosed with type 2 diabetes and non-diabetic controls. We analysed the lipid, glycogen, and collagen content in the muscles in a traditional manner with histochemical assays using different staining techniques. This served as a reference for comparison with the unconventional analysis of tissue composition using Fourier-transform infrared spectroscopy as an alternative methodological approach.ResultsA thorough chemometric post-processing of the infrared spectra using a multi-stage spectral decomposition allowed the simultaneous identification of various compositional details from a vibrational spectrum measured in a single experiment. We obtained multifaceted information about the proportions of the different macromolecular constituents of skeletal muscle, which even allowed us to distinguish protein constituents with different structural properties. The most important methodological steps for a comprehensive insight into muscle composition have thus been set and parameters identified that can be used for the comparison between healthy and diabetic muscles.ConclusionWe have established a methodological framework based on vibrational spectroscopy for the detailed macromolecular analysis of human skeletal muscle that can effectively complement or may even serve as an alternative to histochemical assays. As this is a pilot study with relatively small sample sets, we remain cautious at this stage in drawing definitive conclusions about diabetes-related changes in skeletal muscle composition. However, the main focus and contribution of our work has been to provide an alternative, simple and efficient approach for this purpose. We are confident that we have achieved this goal and have brought our methodology to a level from which it can be successfully transferred to a large-scale study that allows the effects of diabetes on skeletal muscle composition and the interrelationships between the macromolecular tissue alterations due to diabetes to be investigated.

Background The foramen spinosum (FS) is a pivotal passage for neurovascular structures within the skull base. We performed a detailed morphometric and morphological analysis of the FS to emphasize its clinical relevance. Materials & Methods The study was performed on dried skull specimens obtained from the anatomical collections of the Institute of Anatomy and Institute of Forensic Medicine of the University of Ljubljana. The morphometric and morphologic features of FS in 126 whole human skulls and 15 skull halves were analyzed, including dimensions, shape, and other anatomical variations, as well as relationships to surrounding structures. Measurements were done with a digital sliding caliper. Results The mean length and width of the FS were 2.45 ± 0.65 mm and 2.03 ± 0.53 mm on the right side and 2.49 ± 0.61 mm and 2.08 ± 0.48 mm on the left side. The most frequently observed shape was round (56.7%), followed by oval (28.2%), irregular (8.7%) and drop shaped (6.3%). Several anatomical variations were also noted, including FS duplication, confluences with other foramina, and FS obstruction due to marginal bony outgrowths. Conclusion The FS exhibits notable interindividual differences in anatomical characteristics which should be considered during neurosurgical procedures and radiological interventions in the skull base.

It is not well-understood how type 1 diabetes (T1DM) affects skeletal muscle histological phenotype, particularly capillarisation. This study aimed to analyze skeletal muscle myosin heavy chain (MyHC) fibre type changes and 3D capillary network characteristics in experimental T1DM mice. Female C57BL/6J-OlaHsd mice were categorized into streptozotocin (STZ)-induced diabetic (n = 12) and age-matched non-diabetic controls (n =12). The muscle fibre phenotype of the soleus, gluteus maximus, and gastrocnemius muscles were characterized based on the expression of MyHC isoforms, while capillaries of the gluteus maximus were assessed with immunofluorescence staining, confocal laser microscopy and 3D image analysis. STZ-induced diabetic mice exhibited elevated glucose levels, reduced body weight, and prolonged thermal latency, verifying the T1DM phenotype. In both T1DM and non-diabetic mice, the gluteus maximus and gastrocnemius muscles predominantly expressed fast-twitch type 2b fibers, with no significant differences noted. However, the soleus muscle in non-diabetic mice had a greater proportion of type 2a fibers and comparable type 1 fiber densities (26.2 ± 14.6% vs 21.9 ± 13.5%) relative to diabetic mice. T1DM mice showed reduced fiber diameters (P = 0.026), and the 3D capillary network analysis indicated a higher capillary length per muscle volume in the gluteus maximus of diabetic mice compared to controls (P < 0.05). Overall, T1DM induced significant changes in the skeletal muscle, including shifts in MyHC fibre types, decreased fibre diameters, and increased relative capillarisation, possibly due to muscle fibre atrophy. Our findings emphasize the superior detail provided by the 3D analytical method for characterizing skeletal muscle capillary architecture, highlighting caution in interpreting 2D data for capillary changes in T1DM.

The outbreak of the COVID-19 pandemic significantly disrupted higher education by forcing the transition to online learning, which became a mandatory teaching process during the lockdowns. Although the epidemiological situation has gradually improved since then, online learning is becoming ever more popular as it provides new learning opportunities. Therefore, the paper aims to present recent research trends concerning online learning in higher education during the COVID-19 pandemic by using selected bibliometric approaches. The bibliometric analysis is based on 8,303 documents from the Scopus database published between January 2020 and March 2022, when repeated lockdowns meant most countries were experiencing constant disruptions to the educational process. The results show that the COVID-19 pandemic increased interest in online learning research, notably in English-speaking and Asian countries, with most research being published in open-access scientific journals. Moreover, the topics most frequently discussed in the online learning research during the COVID-19 pandemic were ICT and pedagogy, technology-enhanced education, mental health and well-being, student experience and curriculum and professional development. Finally, the COVID-19 pandemic encouraged explorations of emergency remote learning approaches like e-learning, distance learning and virtual learning, which are intended to limit physical contact between teachers and students, where the specific requirements of a given field of study often guide which online learning approach is the most suitable. The findings add to the existing body of scientific knowledge and support the evidence-based policymaking needed to ensure sustainable higher education in the future.

Introduction: Local anesthetics are widely recognized pharmaceutical compounds with various clinical effects. Recent research indicates that they positively impact the antioxidant system and they may function as free radical scavengers. We hypothesize that their scavenging activity is influenced by the lipophilicity of the environment.Methods: We assessed the free radical scavenging capacity of three local anesthetics (lidocaine, bupivacaine, and ropivacaine) using ABTS, DPPH, and FRAP antioxidant assays. We also employed quantum chemistry methods to find the most probable reaction mechanism. The experiments were conducted in an aqueous environment simulating extracellular fluid or cytosol, and in a lipophilic environment (n-octanol) simulating cellular membranes or myelin sheets.Results: All local anesthetics demonstrated ABTS˙+ radical scavenging activity, with lidocaine being the most effective. Compared to Vitamin C, lidocaine exhibited a 200-fold higher half-maximal inhibitory concentration. The most thermodynamically favorable and only possible reaction mechanism involved hydrogen atom transfer between the free radical and the -C-H vicinal to the carbonyl group. We found that the antioxidant activity of all tested local anesthetics was negligible in lipophilic environments, which was further confirmed by quantum chemical calculations.Conclusion: Local anesthetics exhibit modest free radical scavenging activity in aqueous environments, with lidocaine demonstrating the highest activity. However, their antioxidant activity in lipophilic environments, such as cellular membranes, myelin sheets, and adipose tissue, appears to be negligible. Our results thus show that free radical scavenging activity is influenced by the lipophilicity of the environment.

This study represents the first detailed analysis of multi-year, near-surface turbulence observations for an urban area located in highly complex terrain. Using 4 years of eddy covariance measurements over the Alpine city of Innsbruck, Austria, the effects of the urban surface, orographic setting and mountain weather on energy and mass exchange are investigated. In terms of surface controls, the findings for Innsbruck are in accordance with previous studies at city centre sites. The available energy is partitioned mainly into net storage heat flux and sensible heat flux (each comprising about 40 % of the net radiation, Q*, during the daytime in summer). The latent heat flux is small by comparison (only about 10 % of Q*) due to the small amount of vegetation present but increases for short periods (6–12 h) following rainfall. Additional energy supplied by anthropogenic activities and heat released from the large thermal mass of the urban surface helps to support positive sensible heat fluxes in the city all year round. Annual observed CO2 fluxes (5.1 kg C m−2 yr−1) correspond well to modelled emissions and expectations based on findings at other sites with a similar proportion of vegetation. The net CO2 exchange is dominated by anthropogenic emissions from traffic in summer and building heating in winter. In contrast to previous urban observational studies, the effect of the orography is examined here. Innsbruck's location in a steep-sided valley results in marked diurnal and seasonal patterns in flow conditions. A typical valley wind circulation is observed (in the absence of strong synoptic forcing) with moderate up-valley winds during daytime, weaker down-valley winds at night (and in winter) and near-zero wind speeds around the times of the twice-daily wind reversal. Due to Innsbruck's location north of the main Alpine crest, southerly foehn events frequently have a marked effect on temperature, wind speed, turbulence and pollutant concentration. Warm, dry foehn air advected over the surface can lead to negative sensible heat fluxes both inside and outside the city. Increased wind speeds and intense mixing during foehn (turbulent kinetic energy often exceeds 5 m2 s−2) help to ventilate the city, illustrated here by low CO2 mixing ratios. Radiative exchange is also affected by the orography – incoming shortwave radiation is blocked by the terrain at low solar elevation. The interpretation of the dataset is complicated by distinct temporal patterns in flow conditions and the combined influences of the urban environment, terrain and atmospheric conditions. The analysis presented here reveals how Innsbruck's mountainous setting impacts the near-surface conditions in multiple ways, highlighting the similarities with previous studies in much flatter terrain and examining the differences in order to begin to understand interactions between urban and orographic processes.

Daylong recordings provide an ecologically valid option for analyzing language input, and have become a central method for studying child language development. However, the vast majority of this work has been conducted in North America. We harnessed a unique collection of daylong recordings from Slovenian infants (age: 16–30 months, N = 40, 18 girls), and focus our attention on manually annotated measures of parentese (infant‐directed speech with a higher pitch, slower tempo, and exaggerated intonation), conversational turns, infant words, and word combinations. Measures from daylong recordings showed large variation, but were comparable to previous studies with North American samples. Infants heard almost twice as much speech and parentese from mothers compared to fathers, but there were no differences in language input to boys and girls. Positive associations were found between the social‐interactional features of language input (parentese, turn‐taking) and infants' concurrent language production. Measures of child speech from daylong recordings were positively correlated with measures obtained through the Slovenian MacArthur‐Bates Communicative Development Inventory. These results support the notion that the social‐interactional features of parental language input are the foundation of infants' language skills, even in an environment where infants spend much of their waking hours in childcare settings, as they do in Slovenia. [ABSTRACT FROM AUTHOR]