Extrinsic Calibration for a Modular 3D Scanning Quality Validation Platform with a 3D Checkerboard

Optical 3D scanning applications are increasingly used in various medical fields. Setups involving multiple adjustable systems require repeated extrinsic calibration between patients. Existing calibration solutions are either not applicable to the medical field or require a time-consuming process with multiple captures and target poses. Here, we present an application with a 3D checkerboard (3Dcb) for extrinsic calibration with a single capture. The 3Dcb application can register captures with a reference to validate measurement quality. Furthermore, it can register captures from camera pairs for point-cloud stitching of static and dynamic scenes. Registering static captures from TIDA-00254 to its reference from a Photoneo MotionCam-3D resulted in an error (root mean square error ± standard deviation) of 0.02 mm ± 2.9 mm. Registering a pair of Photoneo MotionCam-3D cameras for dynamic captures resulted in an error of 2.2 mm ± 1.4 mm. These results show that our 3Dcb implementation provides registration for static and dynamic captures that is sufficiently accurate for clinical use. The implementation is also robust and can be used with cameras with comparatively low accuracy. In addition, we provide an extended overview of extrinsic calibration approaches and the application's code for completeness and service to fellow researchers.

Growth Control of Listeria monocytogenes in Raw Sausage via Bacteriocin-Producing Leuconostoc carnosum DH25

The current study addresses the critical issue of Listeria monocytogenes growth in raw sausage/meat products leading to human infections, most commonly listeriosis, which is known for its high fatality rate. This research focuses on the isolation, identification, and screening of lactic acid bacteria from various meat and fish products in Switzerland. In total, 274 lactic acid bacteria strains were isolated from 30 different products and were screened for their ability to inhibit Listeria monocytogenes growth, with 51 isolates demonstrating anti-Listeria activity at 8 °C, 15 °C, 25 °C, and 37 °C. Further experiments, using a meat model and a raw sausage challenge test, demonstrated that Leuconostoc carnosum DH25 significantly inhibited Listeria monocytogenes growth during the ripening and storage of the tested meat/sausage. This inhibitory effect was found to be attributed to the bacteriocins produced by Leuconostoc carnosum DH25 rather than factors like pH or water activity. The stability of the anti-Listeria substances was examined, revealing their resistance to temperature and pH changes, making Leuconostoc carnosum DH25 a promising protective culture for raw sausages. The genome sequencing of this strain confirms its safety, with no antibiotic resistance genes or virulence factors detected, and reveals the presence of the structural genes for the production of the bacteriocin LeucocinB-Ta11a. This study underscores the potential of LAB strains and their bacteriocins as effective tools for enhancing food safety and preventing Listeria monocytogenes growth in meat products, offering valuable insights into biocontrol strategies in the food industry.

Can Wrist-Worn Medical Devices Correctly Identify Ovulation?

(1) Background: Hormonal fluctuations across the menstrual cycle lead to multiple changes in physiological parameters such as body temperature, cardiovascular function, respiratory rate and perfusion. Electronic wearables analyzing those parameters might present a convenient alternative to urinary ovulation tests for predicting the fertile window. (2) Methods: We conducted a prospective observational study including women aged 18-45 years without current hormonal therapy who used a wrist-worn medical device and urinary ovulation tests for a minimum of three cycles. We analyzed the accuracy of both the retrospective and prospective algorithms using a generalized linear mixed-effects model. The findings were compared to real-world data from bracelet users who also reported urinary ovulation tests. (3) Results: A total of 61 study participants contributing 205 cycles and 6081 real-life cycles from 3268 bracelet users were included in the analysis. The mean error in identifying ovulation with the wrist-worn medical device retrospective algorithm in the clinical study was 0.31 days (95% CI -0.13 to 0.75). The retrospective algorithm identified 75.4% of fertile days, and the prospective algorithm identified 73.8% of fertile days correctly within the pre-specified equivalence limits (±2 days). The quality of the retrospective algorithm in the clinical study could be confirmed by real-world data. (4) Conclusion: Our data indicate that wearable sensors may be used to accurately detect the periovulatory period.

A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart Homes

Photovoltaic installations can be environmentally beneficial to a greater or lesser extent, depending on the conditions. If the energy produced is not used, it is redirected to the grid, otherwise a battery with a high ecological footprint is needed to store it. To alleviate this problem, an innovative recommender system is proposed for residents of smart homes equipped with battery-free solar panels to optimise the energy produced. Using artificial intelligence, the system is designed to predict the energy produced and consumed for the day ahead using three data sources: sensor logs from the home automation solution, data collected by the solar inverter, and weather data. Based on these predictions, recommendations are then generated and ranked by relevance. Data collected over 76 days were used to train two variants of the system, considering or without considering energy consumption. Recommendations selected by the system over 14 days were randomly picked to be evaluated for relevance, ranking, and diversity by 11 people. The results show that it is difficult to predict residents' consumption based solely on sensor logs. On average, respondents reported that 74% of the recommendations were relevant, while the values contained in them (i.e., accuracy of times of day and kW energy) were accurate in 66% (variant 1) and 77% of cases (variant 2). Also, the ranking of the recommendations was considered logical in 91% and 88% of cases. Overall, residents of such solar-powered smart homes might be willing to use such a system to optimise the energy produced. However, further research should be conducted to improve the accuracy of the values contained in the recommendations.

An Evaluation of Parylene Thin Films to Prevent Encrustation for a Urinary Bladder Pressure MEMS Sensor System

Recent developments in urological implants have focused on preventive strategies to mitigate encrustation and biofilm formation. Parylene, a conformal, pinhole-free polymer coating, has gained attention due to its high biocompatibility and chemical resistance, excellent barrier properties, and low friction coefficient. This study aims to evaluate the effectiveness of parylene C in comparison to a parylene VT4 grade coating in preventing encrustation on a urinary bladder pressure MEMS sensor system. Additionally, silicon oxide (SiOx) applied as a finish coating was investigated for further improvements. An in vitro encrustation system mimicking natural urine flow was used to quantify the formation of urinary stones. These stones were subsequently analyzed using Fourier transform infrared spectrometry (FTIR). Encrustation results were then discussed in relation to coating surface chemical properties. Parylene C and VT4 grades demonstrated a very low encrustation mass, making them attractive options for encrustation prevention. The best performance was achieved after the addition of a hydrophilic SiOx finish coating on parylene VT4 grade. Parylene-based encapsulation proved to be an outstanding solution to prevent encrustation for urological implants.

Deep-Learning-Based Dose Predictor for Glioblastoma-Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process.

Changes in Heart Rate, Heart Rate Variability, Breathing Rate, and Skin Temperature throughout Pregnancy and the Impact of Emotions-A Longitudinal Evaluation Using a Sensor Bracelet

(1) Background: Basic vital signs change during normal pregnancy as they reflect the adaptation of maternal physiology. Electronic wearables like fitness bracelets have the potential to provide vital signs continuously in the home environment of pregnant women. (2) Methods: We performed a prospective observational study from November 2019 to November 2020 including healthy pregnant women, who recorded their wrist skin temperature, heart rate, heart rate variability, and breathing rate using an electronic wearable. In addition, eight emotions were assessed weekly using five-point Likert scales. Descriptive statistics and a multivariate model were applied to correlate the physiological parameters with maternal emotions. (3) Results: We analyzed data from 23 women using the electronic wearable during pregnancy. We calculated standard curves for each physiological parameter, which partially differed from the literature. We showed a significant association of several emotions like feeling stressed, tired, or happy with the course of physiological parameters. (4) Conclusions: Our data indicate that electronic wearables are helpful for closely observing vital signs in pregnancy and to establish modern curves for the physiological course of these parameters. In addition to physiological adaptation mechanisms and pregnancy disorders, emotions have the potential to influence the course of physiological parameters in pregnancy.

Rework Potential of Soy and Pea Protein Isolates in High-Moisture Extrusion

High-moisture extrusion (HME) is an effective process to make fibrous products that can be used as meat analogues. In this study, the effect of extrusion of already extruded products (i.e., re-extrusion) was tested with the aim to explore the potential of rework in HME. The rework of material is important because it is a route to reduce waste, which is always produced, for example during the start or at the end of a production run. Pea and soy protein isolates (PPI and SPI) were first extruded, then freeze-dried and ground, and extruded again. The visual and textural properties of the fibrous products were evaluated. Also, the rheological properties, solubility, and water-holding capacity (WHC) of the ingredients and the products after the first and second extrusion were quantified. The obtained freeze-dried powders after the first HME cycle had a reduction in solubility of 15% for PPI and 74% for SPI. Furthermore, WHC was reduced by 65% and 17% for PPI and SPI, respectively. After the second HME cycle, the reduction in solubility and WHC was augmented to 22% and 90% for PPI, and 79% and 63% for SPI. No effect on stock and loss moduli after heating and cooling were found, even after two HME cycles. SPI fibrous products did not differ in cutting strength, anisotropy index, or visual appearance after re-extrusion. Only, a decrease in hardness was detected, from 62.0 N to 51.1 N. For PPI, re-extrusion did reduce the cutting force and hardness but not the anisotropy index. It was concluded that even though HME induces a loss of solubility and WHC, this did not affect the fibrous texture formation of the protein. This means that the texture formed during HME does not depend on the process history and that rework is thus possible for fibrous products.

Exploring Fully Biobased Adhesives: Sustainable Kraft Lignin and 5-HMF Adhesive for Particleboards

Most adhesives used in the wood-based panel (WBP) industry are petroleum-based and are associated with environmental impact and price fluctuations. Furthermore, most have potential adverse health impacts, such as formaldehyde emissions. This has led to interest from the WBP industry in developing adhesives with bio-based and/or non-hazardous components. This research focuses on the replacement of phenol-formaldehyde resins by Kraft lignin for phenol substitution and 5-hydroxymethylfurfural (5-HMF) for formaldehyde substitution. Resin development and optimization was carried out regarding varying parameters such as molar ratio, temperature or pH. The adhesive properties were analyzed using a rheometer, gel timer and a differential scanning calorimeter (DSC). The bonding performances were evaluated using an Automated Bonding Evaluation System (ABES). Particleboards were produced using a hot press, and their internal bond strength (IB) was evaluated according to SN EN 319. Hardening of the adhesive could be achieved at low temperatures by increasing or decreasing the pH. The most promising results were obtained at pH 13.7. The adhesive performances were improved by adding filler and extender (up to 28.6% based on dry resin) and several boards were produced reaching P1 requirements. A particleboard achieved a mean IB of 0.29 N/mm², almost reaching almost P2 requirements. However, adhesive reactivity and strength must be improved for industrial use.

Injectable Hyaluronan-Based Thermoresponsive Hydrogels for Dermatological Applications

Most marketed HA-based dermal fillers use chemical cross-linking to improve mechanical properties and extend their lifetime in vivo; however, stiffer products with higher elasticity require an increased extrusion force for injection in clinical practice. To balance longevity and injectability, we propose a thermosensitive dermal filler, injectable as a low viscosity fluid that undergoes gelation in situ upon injection. To this end, HA was conjugated via a linker to poly(N-isopropylacrylamide) (pNIPAM), a thermosensitive polymer using "green chemistry", with water as the solvent. HA-L-pNIPAM hydrogels showed a comparatively low viscosity (G' was 105.1 and 233 for Candidate1 and Belotero Volume^®, respectively) at room temperature and spontaneously formed a stiffer gel with submicron structure at body temperature. Hydrogel formulations exhibited superior resistance against enzymatic and oxidative degradation and could be administered using a comparatively lower injection force (49 N and >100 N for Candidate 1 and Belotero Volume^®, respectively) with a 32G needle. Formulations were biocompatible (viability of L929 mouse fibroblasts was >100% and ~85% for HA-L-pNIPAM hydrogel aqueous extract and their degradation product, respectively), and offered an extended residence time (up to 72 h) at the injection site. This property could potentially be exploited to develop sustained release drug delivery systems for the management of dermatologic and systemic disorders.

Development of a Water Transmission Rate (WTR) Measurement System for Implantable Barrier Coatings

While water vapor transmission rate (WVTR) measurement is standardly used to assess material permeability, a system able to quantify liquid water transmission rate (WTR) measurement is highly desirable for implantable thin film barrier coatings. Indeed, since implantable devices are in contact or immersed in body fluids, liquid WTR was carried out to obtain a more realistic measurement of the barrier performance. Parylene is a well-established polymer which is often the material of choice for biomedical encapsulation applications due to its flexibility, biocompatibility, and attractive barrier properties. Four grades of parylene coatings were tested with a newly developed permeation measurement system based on a quadrupole mass spectrometer (QMS) detection method. Successful measurements of gas and water vapor and the water transmission rates of thin parylene films were performed and validated, comparing the results with a standardized method. In addition, the WTR results allowed for the extraction of an acceleration transmission rate factor from the vapor-to-liquid water measurement mode, which varies from 4 to 4.8 between WVTR and WTR. With a WTR of 72.5 µm g m^-2 day^-1, parylene C displayed the most effective barrier performance.

Early Molecular Insights into Thanatin Analogues Binding to A. baumannii LptA

The cationic antimicrobial ß-hairpin, thanatin, was recently developed into drug-like analogues active against carbapenem-resistant Enterobacteriaceae (CRE). The analogues represent new antibiotics with a novel mode of action targeting LptA in the periplasm and disrupting LPS transport. The compounds lose antimicrobial efficacy when the sequence identity to E. coli LptA falls below 70%. We wanted to test the thanatin analogues against LptA of a phylogenetic distant organism and investigate the molecular determinants of inactivity. Acinetobacter baumannii (A. baumannii) is a critical Gram-negative pathogen that has gained increasing attention for its multi-drug resistance and hospital burden. A. baumannii LptA shares 28% sequence identity with E. coli LptA and displays an intrinsic resistance to thanatin and thanatin analogues (MIC values > 32 µg/mL) through a mechanism not yet described. We investigated the inactivity further and discovered that these CRE-optimized derivatives can bind to LptA of A. baumannii in vitro, despite the high MIC values. Herein, we present a high-resolution structure of A. baumannii LptAm in complex with a thanatin derivative 7 and binding affinities of selected thanatin derivatives. Together, these data offer structural insights into why thanatin derivatives are inactive against A. baumannii LptA, despite binding events in vitro.

Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction

Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification. Trained on N = 111 and tested on N = 60 diagnostic T1-weighted MRI of 76 rotator cuff tear patients acquired from 19 centers, a nnU-Net segmented the anatomy with an average Dice coefficient of 0.91 ± 0.06. For the automatic identification of inaccurate segmentations during the inference procedure, the nnU-Net framework was adapted to allow for the estimation of label-specific network uncertainty directly from its subnetworks. The average Dice coefficient of segmentation results from the subnetworks identified labels requiring segmentation correction with an average sensitivity of 1.0 and a specificity of 0.94. The presented automatic methods facilitate the use of 3D diagnosis in clinical routine by eliminating the need for time-consuming manual segmentation and slice-by-slice segmentation verification.

The Effect of Prebiotics on Human Iron Absorption: A Review

Iron deficiency remains the most common nutritional deficiency. Oral iron supplementation is the recommended first-line treatment and used as a preventive measure as well. Enhancers of iron absorption are highly sought after to improve supplementation outcomes. Evidence from animal and human studies exists that prebiotics can enhance iron absorption. The purpose of this present narrative review of the literature is to summarize the existing evidence on the effects of prebiotics on human iron absorption. Relevant articles were identified from PUBMED, Scopus, and Web of Science from inception to November 2021. Only human trials investigating the effect of prebiotics on iron absorption were included. Eleven articles were identified and included for review. There are promising findings supporting an enhancing effect of certain prebiotics, but inconsistencies between the studies and results exist. The most convincing evidence exists for the prebiotics galacto-oligosaccharides and fructo-oligosaccharides combined with the commonly used iron compound ferrous fumarate, from studies in adult women with low iron stores and in anemic infants. Many factors seem to play a role in the enhancing effect of prebiotics on iron absorption such as type of prebiotic, dose, acute (single-dose) or chronic (long-term) prebiotic consumption, iron compound, iron status, inflammatory status, and age of the population studied. More research investigating the optimal combination of prebiotic, iron compound, and dose as well as the effect of long-term application on iron status outcomes is needed.

Topical Wound Treatment with a Nitric Oxide-Releasing PDE5 Inhibitor Formulation Enhances Blood Perfusion and Promotes Healing in Mice

Chronic, non-healing wounds constitute a major health problem, and the current therapeutic options are limited. Therefore, pharmaceuticals that can be locally applied to complicated wounds are urgently needed. Such treatments should directly target the underlying causes, which include diabetes mellitus, chronic local pressure and/or vascular insufficiency. A common consequence of these disorders is impaired wound angiogenesis. Here, we investigated the effect of topical application of a nitric oxide-releasing phosphodiesterase 5 inhibitor (TOP-N53)-containing liquid hydrogel on wound repair in mice. The drug-loaded hydrogel promoted re-epithelialization and angiogenesis in wounds of healthy and healing-impaired diabetic mice. Using a non-invasive label-free optoacoustic microscopy approach combined with automated vessel analysis, we show that the topical application of TOP-N53 formulation increases the microvascular network density and promotes the functionality of the newly formed blood vessels, resulting in enhanced blood perfusion of the wounds. These results demonstrate a remarkable healing-stimulating activity of topically applied TOP-N53 formulation, supporting its further development as a wound therapeutic.