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The PDMS-based microfluidic organ-on-chip platform represents an exciting paradigm that has enjoyed a rapid rise in popularity and adoption. A particularly promising element of this platform is its amenability to rapid manufacturing strategies, which can enable quick adaptations through iterative prototyping. These strategies, however, come with challenges; fluid flow, for example, a core principle of organs-on-chip and the physiology they aim to model, necessitates robust, leak-free channels for potentially long (multi-week) culture durations. In this report, we describe microfluidic chip fabrication methods and strategies that are aimed at overcoming these difficulties; we employ a subset of these strategies to a blood–brain-barrier-on-chip, with others applied to a small-airway-on-chip. Design approaches are detailed with considerations presented for readers. Results pertaining to fabrication parameters we aimed to improve (e.g., the thickness uniformity of molded PDMS), as well as illustrative results pertaining to the establishment of cell cultures using these methods will also be presented.

Clinical observations are highly inconsistent with the use of the antidiabetic rosiglitazone regarding its associated increased risk of myocardial infarction. This may be due to its hidden cardiotoxic properties that have only become evident during post-marketing studies. Therefore, we aimed to investigate the hidden cardiotoxicity of rosiglitazone in ischemia/reperfusion (I/R) injury models. Rats were treated orally with either 0.8 mg/kg/day rosiglitazone or vehicle for 28 days and subjected to I/R with or without cardioprotective ischemic preconditioning (IPC). Rosiglitazone did not affect mortality, arrhythmia score, or infarct size during I/R. However, rosiglitazone abolished the antiarrhythmic effects of IPC. To investigate the direct effect of rosiglitazone on cardiomyocytes, we utilized adult rat cardiomyocytes (ARCMs), AC16, and differentiated AC16 (diffAC16) human cardiac cell lines. These were subjected to simulated I/R in the presence of rosiglitazone. Rosiglitazone improved cell survival of ARCMs at 0.3 μM. At 0.1 and 0.3 μM, rosiglitazone improved cell survival of AC16s but not that of diffAC16s. This is the first demonstration that chronic administration of rosiglitazone does not result in major hidden cardiotoxic effects in myocardial I/R injury models. However, the inhibition of the antiarrhythmic effects of IPC may have some clinical relevance that needs to be further explored.

Perinatal brain injury secondary to hypoxia-ischemia and/or infection/inflammation remains a major cause of disability. Therapeutic hypothermia significantly improves outcomes, but in randomized controlled trials nearly half of infants still died or survived with disability, showing that additional interventions are needed. There is growing evidence that brain injury spreads over time from injured to previously uninjured regions of the brain. At least in part, this spread is related to opening of connexin hemichannels and pannexin channels, both of which are large conductance membrane channels found in many brain cells. Opening of these membrane channels releases adenosine triphosphate (ATP), and other neuroactive molecules, into the extracellular space. ATP has an important role in normal signaling, but pathologically can trigger the assembly of the multi-protein inflammasome complex. The inflammasome complex promotes activation of inflammatory caspases, and release of inflammatory cytokines. Overall, the connexin hemichannel appears to play a primary role in propagation of injury and chronic disease, and connexin hemichannel blockade has been shown to be neuroprotective in multiple animal models. Thus, there is potential for some blockers of connexin or pannexin channels to be developed into targeted interventions that could be used in conjunction with or separate to therapeutic hypothermia.

Combinatory flooding techniques evolved over the years to mitigate various limitations associated with unitary flooding techniques and to enhance their performance as well. This study investigates the potential of a combination of 1-hexadecyl-3-methyl imidazolium bromide (C16mimBr) and monoethanolamine (ETA) as an alkali–surfactant (AS) formulation for enhanced oil recovery. The study is conducted comparative to a conventional combination of cetyltrimethylammonium bromide (CTAB) and sodium metaborate (NaBO2). The study confirmed that C16mimBr and CTAB have similar aggregation behaviors and surface activities. The ETA–C16mimBr system proved to be compatible with brine containing an appreciable concentration of divalent cations. Studies on interfacial properties showed that the ETA–C16mimBr system exhibited an improved IFT reduction capability better than the NaBO2–CTAB system, attaining an ultra-low IFT of 7.6 × 10−3 mN/m. The IFT reduction performance of the ETA–C16mimBr system was improved in the presence of salt, attaining an ultra-low IFT of 2.3 × 10−3 mN/m. The system also maintained an ultra-low IFT even in high salinity conditions of 15 wt% NaCl concentration. Synergism was evident for the ETA–C16mimBr system also in altering the carbonate rock surface, while the wetting power of CTAB was not improved by the addition of NaBO2. Both the ETA–C16mimBr and NaBO2–CTAB systems proved to form stable emulsions even at elevated temperatures. This study, therefore, reveals that a combination of surface-active ionic liquid and organic alkali has excellent potential in enhancing the oil recovery in carbonate reservoirs at high salinity, high-temperature conditions in carbonate formations.

In this study, we demonstrate that Raman microscopy combined with computational analysis is a useful approach to discriminating accurately between brain tumor bio-specimens and to identifying structural changes in glioblastoma (GBM) bio-signatures after nordihydroguaiaretic acid (NDGA) administration. NDGA phenolic lignan was selected as a potential therapeutic agent because of its reported beneficial effects in alleviating and inhibiting the formation of multi-organ malignant tumors. The current analysis of NDGA’s impact on GBM human cells demonstrates a reduction in the quantity of altered protein content and of reactive oxygen species (ROS)-damaged phenylalanine; results that correlate with the ROS scavenger and anti-oxidant properties of NDGA. A novel outcome presented here is the use of phenylalanine as a biomarker for differentiating between samples and assessing drug efficacy. Treatment with a low NDGA dose shows a decline in abnormal lipid-protein metabolism, which is inferred by the formation of lipid droplets and a decrease in altered protein content. A very high dose results in cell structural and membrane damage that favors transformed protein overexpression. The information gained through this work is of substantial value for understanding NDGA’s beneficial as well as detrimental bio-effects as a potential therapeutic drug for brain cancer.

In the past decade, pervasive environments have progressed from promising research concepts to available products present in our everyday lives. By connecting multiple smart objects, device ensembles can be formed to assist users in performing tasks. Furthermore, smart objects can be used to control applications, that, in turn, can be used to control other smart objects. As manual configuration is often time-consuming, an automatic connection of the components may present a useful tool, which should take various aspects into account. While dynamically connecting these components allows for solutions tailored to the needs and respective tasks of a user, it obfuscates the handling and ultimately may decrease usability. Self-descriptions have been proposed to overcome this issue for ensembles of smart objects. For a more extensive approach, descriptions of applications in pervasive environments need to be addressed as well. Based on previous research in the context of self-explainability of smart objects, we propose a description language as well as a framework to support self-explaining ambient applications (applications that are used within smart environments). The framework can be used to manually or automatically connect smart objects as well as ambient applications and to realize self-explainability for these interconnected device and application ensembles.

Gravitational waves are detected using resonant optical cavity interferometers. The mirror coatings’ inherent thermal noise and photon scattering limit sensitivity. Crystals within the reflective coating may be responsible for either or both noise sources. In this study, we explored crystallization reduction in zirconia through nano-layering with silica. We used X-ray diffraction (XRD) to monitor crystal growth between successive annealing cycles. We observed crystal formation at higher temperatures in thinner zirconia layers, indicating that silica is a successful inhibitor of crystal growth. However, the thinnest barriers break down at high temperatures, thus allowing crystal growth beyond each nano-layer. In addition, in samples with thicker zirconia layers, we observe that crystallization saturates with a significant portion of amorphous material remaining.

Poly(lactic acid) (PLA) belongs to the 3D printable materials which show shape-memory properties, i.e., which can recover their original shape after a deformation if they are heated above the glass transition temperature. This makes PLA quite an interesting material for diverse applications, such as bumpers, safety equipment for sports, etc. After investigating the influence of the infill design and degree, as well as the pressure orientation on the recovery properties of 3D printed PLA cubes in previous studies, here we report on differences between different PLA materials as well as on the impact of post-treatments after 3D printing by solvents or by heat. Our results show not only large differences between materials from different producers, but also a material-dependent impact of the post treatments. Generally, it is possible to tailor the mechanical and recovery properties of 3D printed PLA parts by choosing the proper material in combination with a chemical or temperature post-treatment.

To characterize ischemia reperfusion injury (IRI)-induced acute kidney injury (AKI) in C57BL/6 (B6) and CD1-mice by longitudinal functional MRI-measurement of edema formation (T2-mapping) and inflammation (diffusion weighted imaging (DWI)). IRI was induced with unilateral right renal pedicle clamping for 35min. 7T-MRI was performed 1 and 14 days after surgery. DWI (7 b-values) and multiecho TSE sequences (7 TE) were acquired. Parameters were quantified in relation to the contralateral kidney on day 1 (d1). Renal MCP-1 and IL-6-levels were measured by qPCR and serum-CXCL13 by ELISA. Immunohistochemistry for fibronectin and collagen-4 was performed. T2-increase on d1 was higher in the renal cortex (127 ± 5% vs. 94 ± 6%, p < 0.01) and the outer stripe of the outer medulla (141 ± 9% vs. 111 ± 9%, p < 0.05) in CD1, indicating tissue edema. Medullary diffusivity was more restricted in CD1 than B6 (d1: 73 ± 3% vs. 90 ± 2%, p < 0.01 and d14: 77 ± 5% vs. 98 ± 3%, p < 0.01). Renal MCP-1 and IL-6-expression as well as systemic CXCL13-release were pronounced in CD1 on d1 after IRI. Renal fibrosis was detected in CD1 on d14. T2-increase and ADC-reduction on d1 correlated with kidney volume loss on d14 (r = 0.7, p < 0.05; r = 0.6, p < 0.05) and could serve as predictive markers. T2-mapping and DWI evidenced higher susceptibility to ischemic AKI in CD1 compared to B6.

The lungs are affected by illnesses including asthma, chronic obstructive pulmonary disease, and infections such as influenza and SARS-CoV-2. Physiologically relevant models for respiratory conditions will be essential for new drug development. The composition and structure of the lung extracellular matrix (ECM) plays a major role in the function of the lung tissue and cells. Lung-on-chip models have been developed to address some of the limitations of current two-dimensional in vitro models. In this review, we describe various ECM substitutes utilized for modeling the respiratory system. We explore the application of lung-on-chip models to the study of cigarette smoke and electronic cigarette vapor. We discuss the challenges and opportunities related to model characterization with an emphasis on in situ characterization methods, both established and emerging. We discuss how further advancements in the field, through the incorporation of interstitial cells and ECM, have the potential to provide an effective tool for interrogating lung biology and disease, especially the mechanisms that involve the interstitial elements.

In recent years, the need for sophisticated human in vitro models for integrative biology has motivated the development of organ-on-a-chip platforms. Organ-on-a-chip devices are engineered to mimic the mechanical, biochemical and physiological properties of human organs; however, there are many important considerations when selecting or designing an appropriate device for investigating a specific scientific question. Building microfluidic Brain-on-a-Chip (BoC) models from the ground-up will allow for research questions to be answered more thoroughly in the brain research field, but the design of these devices requires several choices to be made throughout the design development phase. These considerations include the cell types, extracellular matrix (ECM) material(s), and perfusion/flow considerations. Choices made early in the design cycle will dictate the limitations of the device and influence the end-point results such as the permeability of the endothelial cell monolayer, and the expression of cell type-specific markers. To better understand why the engineering aspects of a microfluidic BoC need to be influenced by the desired biological environment, recent progress in microfluidic BoC technology is compared. This review focuses on perfusable blood–brain barrier (BBB) and neurovascular unit (NVU) models with discussions about the chip architecture, the ECM used, and how they relate to the in vivo human brain. With increased knowledge on how to make informed choices when selecting or designing BoC models, the scientific community will benefit from shorter development phases and platforms curated for their application.

The goal in multiobjective optimization is to determine the so-called Pareto set. Our optimization problem is governed by a parameter-dependent semi-linear elliptic partial differential equation (PDE). To solve it, we use a gradient-based set-oriented numerical method. The numerical solution of the PDE by standard discretization methods usually leads to high computational effort. To overcome this difficulty, reduced-order modeling (ROM) is developed utilizing the reduced basis method. These model simplifications cause inexactness in the gradients. For that reason, an additional descent condition is proposed. Applying a modified subdivision algorithm, numerical experiments illustrate the efficiency of our solution approach.

The minimum information requirements needed to guarantee high-quality surface analysis data of nanomaterials are described with the aim to provide reliable and traceable information about size, shape, elemental composition and surface chemistry for risk assessment approaches. The widespread surface analysis methods electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) were considered. The complete analysis sequence from sample preparation, over measurements, to data analysis and data format for reporting and archiving is outlined. All selected methods are used in surface analysis since many years so that many aspects of the analysis (including (meta)data formats) are already standardized. As a practical analysis use case, two coated TiO2 reference nanoparticulate samples, which are available on the Joint Research Centre (JRC) repository, were selected. The added value of the complementary analysis is highlighted based on the minimum information requirements, which are well-defined for the analysis methods selected. The present paper is supposed to serve primarily as a source of understanding of the high standardization level already available for the high-quality data in surface analysis of nanomaterials as reliable input for the nanosafety community.

In this study, the response of fiber Bragg gratings (FBGs) embedded in cast aluminum parts under thermal and mechanical load were investigated. Several types of FBGs in different types of fibers were used in order to verify general applicability. To monitor a temperature-induced strain, an embedded regenerated FBG (RFBG) in a cast part was placed in a climatic chamber and heated up to 120 ∘C within several cycles. The results show good agreement with a theoretical model, which consists of a shrink-fit model and temperature-dependent material parameters. Several cast parts with different types of FBGs were machined into tensile test specimens and tensile tests were executed. For the tensile tests, a cyclic procedure was chosen, which allowed us to distinguish between the elastic and plastic deformation of the specimen. An analytical model, which described the elastic part of the tensile test, was introduced and showed good agreement with the measurements. Embedded FBGs - integrated during the casting process - showed under all mechanical and thermal load conditions no hysteresis, a reproducible sensor response, and a high reliable operation, which is very important to create metallic smart structures and packaged fiber optic sensors for harsh environments.

Polyacrylonitrile (PAN) nanofiber mats are typical precursors for carbon nanofibers. They can be fixed or even elongated during stabilization and subsequent carbonization to gain straight, mechanically robust carbon nanofibers. These processes necessitate additional equipment or are—if the nanofiber mats are just fixed at the edges—prone to resulting in the specimens breaking, due to an uneven force distribution. Hence, we showed in a previous study that electrospinning PAN on aluminum foils and stabilizing them fixed on these substrates, is a suitable solution to keep the desired morphology after stabilization and incipient carbonization. Here, we report on the influence of different metallic and semiconductor substrates on the physical and chemical properties of the nanofiber mats after stabilization and carbonization at temperatures up to 1200 °C. For stabilization on a metal substrate, an optimum stabilization temperature of slightly above 240 °C was found, approached with a heating rate of 0.25 K/min. Independent from the substrate material, SEM images revealed less defect fibers in the nanofiber mats stabilized and incipiently carbonized on a metal foil. Finally, high-temperature carbonization on different substrates is shown to allow for producing metal/carbon nano-composites.

Alongside efficiency, long-term stability of dye-sensitized solar cells (DSSCs) is a key factor regarding their commercialization. One suitable and cost-effective method to increase the long-term stability is to prevent leakage and evaporation of the electrolyte by gelling it with polymers such as poly(ethylene oxide) (PEO) and gaining a gel polymer electrolyte (GPE). In this study, a GPE based on PEO and glycerol is investigated for the first time as electrolyte for environmentally friendly DSSCs with natural dyes. To evaluate the novel glycerol/PEO GPE, the ionic conductivity and resulting efficiency progressions of DSSCs were measured for 75 days. Different molecular weights (MWs) of PEO and blending with poly(vinylidene fluoride) (PVDF) had negligible impact on efficiencies. 17 wt% PEO was found to be more suitable than lower concentrations and resulted in a relatively high efficiency over 75 days. A glycerol electrolyte without PEO had higher ionic conductivity and achieved higher efficiencies as well but leaked from the unsealed DSSCs. In addition, the reproducibility was examined especially, which appeared to be reduced by considerable differences between identical DSSCs and between measurements of the same DSSC at different times. This emphasizes the relevance of studying multiple DSSC per sample to ensure reliable results.

Polyacrylonitrile (PAN) nanofibers, prepared by electrospinning, are often used as a precursor for carbon nanofibers. The thermal carbonization process necessitates a preceding oxidative stabilization, which is usually performed thermally, i.e., by carefully heating the electrospun nanofibers in an oven. One of the typical problems occurring during this process is a strong deformation of the fiber morphologies—the fibers become thicker and shorter, and show partly undesired conglutinations. This problem can be solved by stretching the nanofiber mat during thermal treatment, which, on the other hand, can lead to breakage of the nanofiber mat. In a previous study, we have shown that the electrospinning of PAN on aluminum foils and the subsequent stabilization of this substrate is a simple method for retaining the fiber morphology without breaking the nanofiber mat. Here, we report on the impact of different aluminum foils on the physical and chemical properties of stabilized PAN nanofibers mats, and on the following incipient carbonization process at a temperature of max. 600 °C, i.e., below the melting temperature of aluminum.

The Karlsruhe Tritium Neutrino (KATRIN) experiment aims at measuring the effective electron neutrino mass with a sensitivity of 0.2 eV/c2, i.e., improving on previous measurements by an order of magnitude. Neutrino mass data taking with KATRIN commenced in early 2019, and after only a few weeks of data recording, analysis of these data showed the success of KATRIN, improving on the known neutrino mass limit by a factor of about two. This success very much could be ascribed to the fact that most of the system components met, or even surpassed, the required specifications during long-term operation. Here, we report on the performance of the laser Raman (LARA) monitoring system which provides continuous high-precision information on the gas composition injected into the experiment’s windowless gaseous tritium source (WGTS), specifically on its isotopic purity of tritium—one of the key parameters required in the derivation of the electron neutrino mass. The concentrations cx for all six hydrogen isotopologues were monitored simultaneously, with a measurement precision for individual components of the order 10−3 or better throughout the complete KATRIN data taking campaigns to date. From these, the tritium purity, εT, is derived with precision of −3 and trueness of −3, being within and surpassing the actual requirements for KATRIN, respectively.

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic−ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia−ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80−120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms.

Accurate clinical evaluation of renal osteodystrophy (ROD) is currently accomplished using invasive in vivo transiliac bone biopsy, followed by in vitro histomorphometry. In this study, we demonstrate that an alternative method for ROD assessment is through a fast, label-free Raman recording of multiple biomarkers combined with computational analysis for predicting the minimally required number of spectra for sample classification at defined accuracies. Four clinically relevant biomarkers: the mineral-to-matrix ratio, the carbonate-to-matrix ratio, phenylalanine, and calcium contents were experimentally determined and simultaneously considered as input to a linear discriminant analysis (LDA). Additionally, sample evaluation was performed with a linear support vector machine (LSVM) algorithm, with a 300 variable input. The computed probabilities based on a single spectrum were only marginally different (~80% from LDA and ~87% from LSVM), both providing an unacceptable classification power for a correct sample assignment. However, the Type I and Type II assignment errors confirm that a relatively small number of independent spectra (7 spectra for Type I and 5 spectra for Type II) is necessary for a p < 0.05 error probability. This low number of spectra supports the practicality of future in vivo Raman translation for a fast and accurate ROD detection in clinical settings.

Vertical farming is one of the suggested avenues for producing food for the growing world population. Concentrating the cultivation of crops such as herbs in large indoor farms makes food production susceptible to technical, biological or other problems that might destroy large amounts of food at once. Thus, there is a trend towards locally, self-sufficient food production in vertical systems on a small scale. Our study examined whether conventional knitted fabrics, such as patches of worn jackets, can be used for hydroponics instead of the specialized nonwoven materials used in large-scale indoor systems. To this end, seed germination and seedling growth of 14 different crop plant species on knitted fabrics with three different stitch sizes were compared. Our results showed that hydroponic culture on knitted fabrics are indeed possible and allow for growing a broad spectrum of plant species, suggesting recycling of old textile fabrics for this purpose. Among the 14 plant species studied, differences in germination success, average fresh and dry masses, as well as water contents were found, but these parameters were not affected by knitted fabric stitch size.

A novel technique for strain and temperature decoupling with surface-glued fiber Bragg gratings (FBGs) is presented and applied for strain-independent temperature measurements in a temperature range between −30 °C and 110 °C with uncertainties below 4 °C over the entire measurement range. The influence of temperature-dependent glue-induced transversal forces on the fiber sensor could be eliminated with a sensor element consisting of two FBGs in identical polarization-maintaining fibers that were spliced perpendicular to each other. After aligning and gluing the sensor element with its optical axes parallel and perpendicular to the specimen, the averaged Bragg wavelength shifts of both FBGs were proven to be independent of the glue’s influence and therefore independent of any change in the material characteristics of the glue, such as aging or creeping behavior. For the first time, this methodology enables temperature measurements with surface-attached bare FBGs independently of arbitrary longitudinal and glue-induced strains. This is of great value for all applications that rely on a fully glued sensor design, e.g., in applications with high electromagnetic fields, on rotating parts, or in vacuum for space applications.

With the goal of accurately detecting and quantifying the amounts of dopamine (DA) and serotonin (5-HT) in mixtures of these neurotransmitters without using any labelling, we present a detailed, comparative computational and Raman experimental study. Although discrimination between these two analytes is achievable in such mixtures for concentrations in the millimolar range, their accurate quantification remains unattainable. As shown for the first time in this work, the formation of a new composite resulting from their interactions with each other is the main reason for this lack of quantification. While this new hydrogen-bonded complex further complicates potential analyte discrimination and quantification at concentrations characteristic of physiological levels (i.e., nanomolar concentrations), it can also open new avenues for its use in drug delivery and pharmaceutical research. This remark is based not only on chemical interactions analyzed here from both theoretical and experimental approaches, but also on biological relationship, with consideration of both functional and neural proximity perspectives. Thus, this research constitutes an important contribution toward better understanding of neural processes, as well as toward possible future development of label-free biosensors.

The purpose of this article is to review conventional and advanced neuroimaging techniques performed in the setting of traumatic brain injury (TBI). The primary goal for the treatment of patients with suspected TBI is to prevent secondary injury. In the setting of a moderate to severe TBI, the most appropriate initial neuroimaging examination is a noncontrast head computed tomography (CT), which can reveal life-threatening injuries and direct emergent neurosurgical intervention. We will focus much of the article on advanced neuroimaging techniques including perfusion imaging and diffusion tensor imaging and discuss their potentials and challenges. We believe that advanced neuroimaging techniques may improve the accuracy of diagnosis of TBI and improve management of TBI.

To better understand detection and monitoring of the important neurotransmitter adenosine at physiological levels, this study combines quantum chemical density functional modeling and ultrasensitive surface-enhanced Raman spectroscopic (SERS) measurements. Combined simulation results and experimental data for an analyte concentration of about 10−11 molar indicate the presence of all known molecular forms resulting from adenosine’s complex redox-reaction. Detailed analysis presented here, besides assessing potential Raman signatures of these adenosinic forms, also sheds light on the analytic redox process and voltammetric detection. Examples of adenosine Raman fingerprints for different molecular orientations with respect to the SERS substrate are the vibrational line around 920 ± 10 cm−1 for analyte physisorption through the carbinol moiety and around 1600 ± 20 cm−1 for its fully oxidized form. However, both hydroxyl/oxygen sites and NH2/nitrogen sites contribute to molecule’s interaction with the SERS environment. Our results also reveal that contributions of partially oxidized adenosine forms and of the standard form are more likely to be detected with the first recorded voltammetric oxidation peak. The fully oxidized adenosine form contributes mostly to the second peak. Thus, this comparative theoretical–experimental investigation of adenosine’s vibrational signatures provides significant insights for advancing its detection, and for future development of opto-voltammetric biosensors.

Defining the pathogenesis of renal osteodystrophy (ROD) and its treatment efficacy are difficult, since many factors potentially affect bone quality. In this study, confocal Raman microscopy and parallel statistical analysis were used to identify differences in bone composition between healthy and ROD bone tissues through direct visualization of three main compositional parametric ratios, namely, calcium content, mineral-to-matrix, and carbonate-to-matrix. Besides the substantially lower values found in ROD specimens for these representative ratios, an obvious accumulation of phenylalanine is Raman spectroscopically observed for the first time in ROD samples and reported here. Thus, elevated phenylalanine could also be considered as an indicator of the disease. Since the image results are based on tens of thousands of spectra per sample, not only are the average ratios statistically significantly different for normal and ROD bone, but the method is clearly powerful in distinguishing between the two types of samples. Furthermore, the statistical outcomes demonstrate that only a relatively small number of spectra need to be recorded in order to classify the samples. This work thus opens the possibility of future development of in vivo Raman sensors for assessment of bone structure, remodeling, and mineralization, where different biomarkers are simultaneously detected with unprecedented accuracy.

A combined theoretical and experimental analysis of dopamine (DA) is presented in this work with the objective of achieving more accurate detection and monitoring of this neurotransmitter at very low concentrations, specific to physiological levels. Surface-enhanced Raman spectroscopy on silver nanoparticles was employed for recording DA concentrations as low as 10−11 molar. Quantum chemical density functional calculations were carried out using Gaussian-09 analytical suite software. Relatively good agreement between the simulated and experimentally determined results indicates the presence of different DA molecular forms, such as uncharged DA±, anionic DA−, and dopaminequinone. Disappearance of the strongest bands of dopamine around 750 cm−1 and 790 cm−1, which suggests its adsorption onto the metallic surface, is not only consistent with all of these DA configurations, but also provides additional information about the analyte’s redox process and voltammetric detection. On the other hand, occurrence of the abovementioned Raman lines could indicate the formation of multilayers of DA or its presence in a cationic DA+ form. Thus, through coordinated experiment and theory, valuable insights into changes observed in the vibrational signatures of this important neurotransmitter can be achieved for a better understanding of its detection at physiological levels, which is crucial if further optovoltammetric medical device development is envisioned.

Electroencephalographic (EEG) monitoring is important for the diagnosis of hypoxic-ischemic (HI) brain injury in high-risk preterm infants. EEG monitoring is limited by the reliance on expert clinical observation. However, high-risk preterm infants often do not present observable symptoms due to their frailty. Thus, there is an urgent need to find better ways to automatically quantify changes in the EEG these high-risk babies. This article is a first step towards this goal. This innovative study demonstrates the effectiveness of deep Convolutional Neural Networks (CNN) pattern classifiers, trained on spectrally-detailed Wavelet Scalograms (WS) images derived from neonatal EEG sharp waves—a potential translational HI biomarker, at birth. The WS-CNN classifiers exhibit outstanding performance in identifying HI sharp waves within an exclusive clinical EEG recordings dataset of preterm infants immediately after birth. The work has impact as it demonstrates exceptional high accuracy of 99.34 ± 0.51% cross-validated across 13,624 EEG patterns over 48 h raw EEG at low 256 Hz clinical sampling rates. Furthermore, the WS-CNN pattern classifier is able to accurately identify the sharp-waves within the most critical first hours of birth (n = 8, 4:36 ± 1:09 h), regardless of potential morphological changes influenced by different treatments/drugs or the evolutionary ‘timing effects’ of the injury. This underscores its reliability as a tool for the identification and quantification of clinical EEG sharp-wave biomarkers at bedside.

Glycoside hydrolases have been implicated in a wide range of human conditions including lysosomal storage diseases. Consequently, many researchers have directed their efforts towards identifying new classes of glycoside hydrolase inhibitors, both synthetic and from natural sources. A large percentage of such inhibitors are reversible competitive inhibitors that bind in the active site often due to them possessing structural features, often a protonatable basic nitrogen atom, that mimic the enzymatic transition state. We report that mechanism-based small molecule galacto-like configured cyclohexenyl carbasugars form reversible covalent complexes with both α-galactosidase and β-galactosidase. In addition, we show that the β-galactosidase from Aspergillus oryzae reacts with three different carbasugar inhibitors, with three different second-order rate constants (kinact/Ki), to give the same enzyme–carbasugar covalent intermediate. The surprising observation that the α-galacto-configured inhibitor covalently labels the A. oryzae β-galactosidase highlights the catalytic versatility of glycoside hydrolases. We expect that cyclohexenyl covalent inhibitors will become an important class of compounds in the chemical biologist’s tool box.

Reliable prognostic biomarkers are needed to support the early diagnosis of brain injury in extremely preterm infants, and to develop effective neuroprotective protocols that are tailored to the progressing phases of injury. Experimental and clinical research shows that severity of neuronal damage is correlated with changes in the electroencephalogram (EEG) after hypoxic-ischemia (HI). We have previously reported that micro-scale sharp-wave EEG waveforms have prognostic utility within the early hours of post-HI recordings in preterm fetal sheep, before injury develops. This article aims to investigate whether these subtle EEG patterns are translational in the early hours of life in clinical recordings from extremely preterm newborns. This work evaluates the existence and morphological similarity of the sharp-waves automatically identified throughout the entire duration of EEG data from a cohort of fetal sheep 6 h after HI (n = 7, at 103 ± 1 day gestation) and in recordings commencing before 6 h of life in extremely preterm neonates (n = 7, 27 ± 2.0 weeks gestation). We report that micro-scale EEG waveforms with similar morphology and characteristics (r = 0.94) to those seen in fetal sheep after HI are also present after birth in recordings started before 6 h of life in extremely preterm neonates. This work further indicates that the post-HI sharp-waves show rapid morphological evolution, influenced by age and/or severity of neuronal loss, and thus that automated algorithms should be validated against such signal variations. Finally, this article discusses the need for more focused research on the early assessment of EEG changes in preterm infants to help determine the timing of brain injury to identify biomarkers that could assist in targeting novel therapies for particular phases of injury.

The advent of fiber optic technology in geophysics exploration has grown in its use in the exploration, production, and monitoring of subsurface environments, revolutionizing the way data are gathered and interpreted critically to speed up decision-making and reduce expense and time. Distributed Acoustic Sensing (DAS) has been increasingly utilized to build relationships in complex geophysics environments by utilizing continuous measurement along fiber optic cables with high spatial resolution and a frequency response of up to 10 KHz. DAS, as fiber optic technology examining backscattered light from a laser emitted inside the fiber and measuring strain changes, enables the performance of subsurface imaging in terms of real-time monitoring for Vertical Seismic Profiling (VSP), reservoir monitoring, and microseismic event detection. This review examines the most widely used fiber optic cables employed for DAS acquisition, namely Single-Mode Fiber (SMF) and Multi-Mode Fiber (MMF), with the different deployments and scopes of data used in geophysics exploration. Over the years, SMF has emerged as a preferred type of fiber optic cable utilized for DAS acquisition and, in most applications examined in this review, outperformed MMF. On the other side, MMF has proven to be preferable when used to measure distributed temperature. Finally, the fiber optic cable deployment technique and acquisition parameters constitute a pivotal preliminary step in DAS data preprocessing, offering a pathway to improve imaging resolution based on DAS measurement as a future scope of work.

Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia–ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI–normothermia term (n = 7), HI–hypothermia term (n = 14), sham–normothermia term (n = 5), and HI–normothermia preterm (n = 14) groups, with a total of >17,300 h of recordings. Algorithms were trained and tested using leave-one-out cross-validation and k-fold cross-validation approaches. The accuracy of the term-trained seizure detectors was consistently excellent for HI–normothermia preterm data (accuracy = 99.5%, area under curve (AUC) = 99.2%). Conversely, when the HI–normothermia preterm data were used in training, the performance on HI–normothermia term and HI–hypothermia term data fell (accuracy = 98.6%, AUC = 96.5% and accuracy = 96.9%, AUC = 89.6%, respectively). Findings suggest that HI–normothermia preterm seizures do not contain all the spectral features seen at term. Nevertheless, an average 5-fold cross-validated accuracy of 99.7% (AUC = 99.4%) was achieved from all seizure detectors. This significant advancement highlights the reliability of the proposed deep-learning algorithms in identifying clinically translatable post-HI stereotypic seizures in 256Hz recordings, regardless of maturity and with minimal impact from hypothermia.

The IoT encompasses objects, sensors, and everyday items not typically considered computers. IoT devices are subject to severe energy, memory, and computation power constraints. Employing NDN for the IoT is a recent approach to accommodate these issues. To gain a deeper insight into how different network parameters affect energy consumption, analyzing a range of parameters using hyperparameter optimization seems reasonable. The experiments from this work’s ndnSIM-based hyperparameter setup indicate that the data packet size has the most significant impact on energy consumption, followed by the caching scheme, caching strategy, and finally, the forwarding strategy. The energy footprint of these parameters is orders of magnitude apart. Surprisingly, the packet request sequence influences the caching parameters’ energy footprint more than the graph size and topology. Regarding energy consumption, the results indicate that data compression may be more relevant than expected, and caching may be more significant than the forwarding strategy. The framework for ndnSIM developed in this work can be used to simulate NDN networks more efficiently. Furthermore, the work presents a valuable basis for further research on the effect of specific parameter combinations not examined before.

Brakes are essential parts of any system that involves motion. A few seconds are spent applying the brake, accumulating enough force to retard a system. In case of emergency stops, we need an efficient braking system that acts quickly and safely to avoid any mishaps. This work aims to develop a prototype of an instantaneous braking system, which can stop the motion of a rotating member. The operating principle of the device is the electromagnetic actuation of a solenoid. The actuation of a solenoid is simple and instantaneous. As a result, we could achieve immediate braking of a rotating member.

A high prevalence of both squamous (ESGD) and glandular (EGGD) ulcers was previously found in, mainly young, Icelandic horses coming into training for the first time. This study evaluated risk factors for gastric ulcers in Icelandic riding horses at various ages and stages of training. The horses (n = 211) were gastroscoped from 21 equine establishments across Iceland. A variety of morphometric, clinical, behavioural and management factors were evaluated as potential risk factors for gastroscopically significant (grade ≥ 2/4: found in 27% of horses) or gastroscopically severe (grade 3 or 4/4: found in ~10% of horses) ESGD or gastroscopically significant EGGD (grade ≥ 1/2: found in 46.4%). Body condition score (BCS), cresty neck score (CNS), stable/turnout behaviour, exercise intensity/frequency and age were not significantly associated with ESGD or EGGD ulcer score. However, having come off the pasture into training for 4 weeks or less was a significant risk factor for gastroscopically significant and severe ESGD compared to 5 weeks or more. For both EGGD and ESGD, “region” was important. Gastroscopically significant EGGD and gastroscopically severe ESGD were more prevalent in those showing clinical signs often associated with ulcers. Geldings were more likely to have gastroscopically significant ESGD than both mares and stallions and more EGGD than stallions. Being stabled, but spending >2 h/day out in the paddock, compared with 4 weeks was protective for gastroscopically significant and gastroscopically severe ESGD but not EGGD. This study confirms the relatively low prevalence of ESGD in Icelandic horses being kept in training establishments and fed low NSC diets but highlights the high prevalence of EGGD.

The maternal cardiovascular-circulatory system undergoes profound changes almost from the conception of a pregnancy until the postpartum period to support the maternal adaptions required for pregnancy and lactation. Maintenance of cardiovascular homeostasis requires changes in the cardiovascular autonomic responses. Here, we present a longitudinal study of the maternal cardiovascular autonomic responses to pregnancy and maternal position. Over a normal gestation, in the left lateral position there are significant changes in both time and frequency domain parameters reflecting heart rate variability. We show that cardiovascular autonomic responses to physiological stressors (standing and supine positions in late pregnancy) became significantly different with advancing gestation. In the third trimester, 60% of the subjects had an unstable heart rate response on standing, and these subjects had a significantly reduced sample entropy evident in their heart rate variability data. By 6 weeks, postpartum function returned to near the non-pregnant state, but there were consistent differences in high-frequency power when compared to nulligravid cases. Finally, we review complementary evidence, in particular from magnetic resonance imaging, that provides insights into the maternal and fetal impacts of positioning in pregnancy. This demonstrates a clear relationship between supine position and maternal hemodynamic parameters, which relates to compression of the inferior vena cava (p = 0.05). Together, these studies demonstrate new understanding of the physiology of physiological stressors related to position.

Racehorse training and racing schedules in many parts of the United States and Canada were interrupted or otherwise reduced during the first three to six months of 2020. This was an indirect consequence of mitigations to prevent the spread of the pandemic virus COVID-19. Data from the Equine Injury Database, a census-level survey of all race starts made in the USA and Canada, were used to analyse the incidence of fatalities in 2009–2022 among three age cohorts of racehorses within each year. There was a statistically significant increase in the incidence of musculoskeletal fatalities among 2-year-old Thoroughbreds in 2020, compared to the period 2009–2019. In 2021 and 2022, the training schedules of 2-year-old horses returned to pre-2020 levels, as did the incidence of fatalities. The delayed start to training for 2-year-old horses was associated with an increase in risk of fatal musculoskeletal injury for those horses during 2020, but the risk for the same horses in 2021—when they were 3 years old—was not significantly different to the risk for 3-year-olds in any other year. The increased risk of fatal musculoskeletal injury in 2020 was only found among horses that were 2 years old in 2020—horses aged 3 years or more in 2020 were not at increased risk.

Our objective is to develop a model for the prediction of minimum fetal blood pressure (FBP) during fetal heart rate (FHR) decelerations. Experimental data from umbilical occlusions in near-term fetal sheep (2698 occlusions from 57 near-term lambs) were used to train a convolutional neural network. This model was then used to estimate FBP for decelerations extracted from the final 90 min of 53,445 human FHR signals collected using cardiotocography. Minimum sheep FBP was predicted with a mean absolute error of 6.7 mmHg (25th, 50th, 75th percentiles of 2.3, 5.2, 9.7 mmHg), mean absolute percentage errors of 17.3% (5.5%, 12.5%, 23.9%) and a coefficient of determination R2=0.36. While the model was unable to clearly predict severe compromise at birth in humans, there is positive evidence that such a model could predict human FBP with further development. The neural network is capable of predicting FBP for many of the sheep decelerations accurately but performed far from satisfactory at identifying FHR segments that correspond to the highest or lowest minimum FBP. These results indicate that with further work and a larger, more variable training dataset, the model could achieve higher accuracy.

Atomic force microscopy (AFM) belongs to the high-resolution surface morphology investigation methods. Since it can, in many cases, be applied in air, samples can more easily be inspected than by a scanning electron microscope (SEM). In addition, several special modes exist which enable examination of the mechanical and other physical parameters of the specimen, such as friction, adhesion between tip and sample, elastic modulus, etc. In tapping mode, e.g., phase imaging can be used to qualitatively distinguish between different materials on the surface. This is especially interesting for polymers, for which the evaluation by energy-dispersive X-ray spectroscopy (EDS) is mostly irrelevant. Here we give an overview of phase imaging experiments on different filaments used for 3D printing by fused deposition modeling (FDM). Furthermore, the acrylonitrile butadiene styrene (ABS), especially different poly(lactide acids) (PLAs) with special features, such as thermochromic or photochromic properties, are investigated and compared with SEM images.

The English surveillance programme for antimicrobial utilisation and resistance (ESPAUR) antimicrobial resistance (AMR) chapter reports on bacterial, viral, and fungal AMR trends between 2017 and 2021 in England. A 10.8% increase in patient episodes of bacteraemia or fungaemia was observed, and the estimated burden of resistance decreased by 4.2%. Individuals with an antimicrobial-resistant strain (resistant to ≥1 key AMR burden-defined antibiotics) had a higher crude case fatality rate (18.1%) compared to those with a susceptible strain (16.3%). The effect of deprivation on carbapenemase-producing organisms (CPO) incidence, and the impact of the AMR burden across ethnic groups, have been described for the first time. Understanding the impact of ethnicity, deprivation, regional divergence, and potential confounders remains a crucial avenue of enquiry to target appropriate AMR interventions. These findings were presented at the ESPAUR Report webinar on 23 November 2022.