Microarray gene expression analysis is a powerful technique used in cancer classification and research to identify and understand gene expression patterns that can differentiate between different cancer types, subtypes, and stages. However, microarray databases are highly redundant, inherently nonlinear, and noisy. Therefore, extracting meaningful information from such a huge database is a challenging one. The paper adopts the Fast Fourier Transform (FFT) and Mixture Model (MM) for dimensionality reduction and utilizes the Dragonfly optimization algorithm as the feature selection technique. The classifiers employed in this research are Nonlinear Regression, Naïve Bayes, Decision Tree, Random Forest and SVM (RBF). The classifiers' performances are analyzed with and without feature selection methods. Finally, Adaptive Moment Estimation (Adam) and Random Adaptive Moment Estimation (RanAdam) hyper-parameter tuning techniques are used as improvisation techniques for classifiers. The SVM (RBF) classifier with the Fast Fourier Transform Dimensionality Reduction method and Dragonfly feature selection achieved the highest accuracy of 98.343% with RanAdam hyperparameter tuning compared to other classifiers.

Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.

Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high seeding density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells. Using a metabolic network and flux balance analysis, we compared standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched in amino acids. We observed high glycolytic fluxes and depletion of asparagine simultaneously with the lactate shift building kinetic models around this and other observations. An ensemble of kinetic models assessed the impact of a feeding medium enriched with additional asparagine, revealing a missing lactate shift without enhancing mAb productivity in the experiments. This research provides valuable model-based insights into cellular metabolism during HSD processes, laying the foundation for refined CHO cell-based mAb production. The proposed hypothesis on the regulations guides future experimental validation and bioprocess optimization for enhanced mAb production efficiency.

Wall Shear Stress (WSS) abnormalities in carotid artery blood flow are key hemodynamic factors leading to arterial endothelial damage, atherosclerotic plaque formation, and carotid artery stenosis. Precise simulation of carotid artery blood flow WSS in vitro play an important role in understanding the underlying hemodynamic mechanism of carotid artery stenosis. This study presents a design for a microfluidic cell culture chamber with a variable cross-section and a step structure, leveraging microfluidic technology and principles of fluid dynamics. We propose an optimization strategy for microchannel dimensions based on Computational Fluid Dynamics (CFD) simulations. This strategy aims to replicate the distinct WSS waveform features found at various locations within the carotid bifurcation as observed in vivo. Our simulations indicate that with a step height of 0.09 mm, a width of 4 mm for the initial segment, and 5 mm for the subsequent wider channel, the device can effectively model the characteristics of low oscillatory WSS seen at sites of carotid sinus stenosis. Furthermore, it accurately represents the high-magnitude pulsatile WSS waveforms in the more uniform arterial sections downstream of the carotid sinus. The study also reveals that vortex formation and variations in low oscillatory WSS within the stepped section of the microchannel correlate with the step's height and width, as well as with the dimensions of the wider channel section. An increase in step width leads to a decrease in both the vortex area and the magnitude of negative WSS oscillations. When the step height is less than 0.03 mm, vortex formation is inhibited, challenging the simulation of low oscillatory WSS patterns. The variable cross-section microfluidic channel developed in this study provides a platform for simulating carotid artery WSS waveforms and facilitates research into the interplay between low oscillatory WSS at sites of carotid sinus stenosis and arterial endothelial dysfunction.

Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on eight subjects while performing 5-minute sitting and walking tasks. Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating Photoplethysmogram (PPG) and Electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls. Feature selection techniques were used to systematically select key features which were identified for their clinical relevance and significance in capturing cardiovascular dynamics and to reduce computational complexity and to decrease the chance of overfitting the ML algorithms. These features are then utilized to train and evaluate machine learning algorithms, resulting in a model with an impressive accuracy of 98%, sensitivity of 97.60%, specificity of 96.90%, and precision of 97.20%. The integrated approach outperforms single-signal strategies, showcas-ng its potential for early, precise, and non-invasive heart failure diagnosis. Furthermore, the study underscores the significance of continuous monitoring through wearables, emphasizing the benefits of integrating multiple signals for a comprehensive evaluation of cardiovascular health. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Intra Voxel Incoherent Motion (IVIM) imaging provides non-invasive perfusion measurements, eliminating the need for contrast agents. The aim of this work was to explore the feasibility of IVIM in whole brain perfusion studies, where isotropic 1mm voxel is widely accepted as a standard. This study treats on validity of time limited, precise, segmentation ready Whole Brain IVIM imaging protocol suitable for clinical reality. To assess the influence of SNR on the estimation of S0, f, D* and D IVIM parameters a series of measurements and simulations under MATLAB environment were performed for three estimation techniques, utilizing known “ground truth” and noised data. Scanner specific SNR was estimated based on healthy subject IVIM MRI study in 3T scanner. Measurements were conducted for 25,6x25,6x14,4 cm FOV with 256x256 in plane resolution and 72 slices resulting in 1x1x2 mm voxel size. Simulations were performed for 36 SNR levels around measured SNR value. Single voxel grid search estimation method mean relative error for S0 f D* D estimates at measured SNR level were: 5.00%,81.91%, 76.31%, 18.34% respectively. Analysis has shown that high resolution IVIM is possible, although there is significant variation in estimation accuracy and precision depending on SNR level and chosen estimation method.

In order to improve the yield of the therapeutic agent N-methylsansalvamide (SA), optimization of stationary liquid fermentation conditions was carried out on an endophytic strain, Fusarium sp. R1, at flask level. Using a One-Factor-At-a-Time approach, the fermentation conditions for SA production were determined as follows: fermentation time of 13 d, 24 oC, initial pH of 6.5, seed age of 24 h, inoculum size of 5.0% (v/v), loading volume of 50% (v/v), 20.0 g/L salinity, and sucrose, tryptone, and yeast extract were found to be the best sources of carbon and nitrogen. Using response surface methodology, the optimal medium compositions consist of 22.5 g/L sucrose, 16.5 g/L tryptone, and 0.024 g/L yeast extract. Verification tests suggested that the SA yield under these optimal conditions reached up to 536.77 ± 2.67 mg/L, which was increased by almost ten times the initial yield (54.05 ± 3.45 mg/L). The findings indicate that a high SA production yield can be achieved by static culture of strain R1 under the proper fermentation conditions using a low-cost medium. This study paves the way towards industrial-scale SA production by strain R1 for new drug development.

Continuum robots play the role of end-effector in various surgical robots and endoscopic devices. The robots may be categorized as rigid or soft continuum robots (SCRs). While the SCRs have proven advantages such as safety and compliance, more research and development are required to enhance their capability for specific medical scenarios. This research aims at designing a soft robot considering concepts of geometric and kinematic similarities. The chosen application is a semi-invasive medical application known as Transesophageal Echocardiography (TEE), but the methodology can be implemented in other similar applications. Since established rigid carrier construction make use of principles we find as well embodied in vertebral spines, the paragon for soft robot solution can be the phylogenetic precursor of the spine, the Chorda dorsalis. The feasibility of fabrication of a soft endoscopic device derived from that paragon within a robotics laboratory was shown empirically by producing a three-segment pneumatic SCR. The kinematic model was proposed based on the method of transformation matrices, and an algorithm based on a self-organizing map (SOM) was proposed and applied to realize kinematic similarity. The simulation results indicate that the algorithm acting as an open-loop controller forces the soft robot tip to follow the desired path which in this case is the path of the rigid probe. It is concluded that the solution provides a soft robot that can surrogate and succeed the traditional rigid counterpart owing to size, workspace, and kinematics.

The remote monitoring of clinical parameters plays a fundamental role in different situations, like pandemic health emergencies and post-surgery conditions. In these situations, the patients might be impeded in their movements and it could be difficult to have a specific health monitoring. In recent years, technological advancements in smartphones have opened up new possibilities in this landscape. The present work aims to propose a new method for respiratory kinematics monitoring via smartphone sensors. In particular, a specific application was developed to register Inertial Movement Unit (IMU) sensors data from the smartphone for respiratory kinematics measurement and to guide the user through a specific acquisition session. The session was defined to allow for the monitoring of the respiratory movement in five prescribed positions. The application and the sequence were successfully tested on a given population of 77 healthy volunteers. The resulting accelerometers and gyroscope signals were processed to evaluate the significance of differences according to participants sex, vector components and smartphone positioning and, finally, to estimate the respiratory rate. The emerged statistical differences revealed the significance of information in the different acquisition positions.

Accurate estimations of actual evapotranspiration (ETa) are fundamental for various environmental issues. Artificial Intelligence-based models are considered a promising alternative to the most common direct ETa estimation techniques and indirect methods by remote sensing (RS)-based surface energy balance models. Artificial Neural Networks (ANN) are proven to be suitable for predicting reference evapotranspiration and ETa based on RS data. The aim of this study is to develop a methodology based on ANNs for estimating daily ETa values using NDVI and land surface temperature, coupled with limited site-specific climatic variables at a large irrigation catchment. Two scenarios were implemented by the ANN model. Data from only the 38 days of satellite overpass dates was selected in Scenario-I, while the 769-day data included the satellite overpass dates and other days located between two satellite overpass acquisitions in Scenario-II. An irrigation scheme in the Mediterranean region of Turkiye was selected, and a total of 38 Landsat images, and local climatic data were used in 2021 and 2022. Results showed that R2 values in Scenario-I and II were acknowledgeably high for training (0.79 and 0.86), testing (0.75 and 0.81), and the entire dataset (0.76 and 0.84), respectively. Results of the new model in two scenarios showed acceptable agreement with ETa-METRIC values. The proposed ANN model demonstrated the potential of obtaining daily ETa using limited climatic data and RS imagery.

Oncology has emerged as a crucial field of study and treatment in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation on computed tomography scans, and evaluates their accuracy and efficiency. It is noteworthy that although studies have been conducted on liver segmentation in computed tomography scans, they often lack an intuitive and visual component that allows healthcare professionals to manipulate and observe the results obtained, thereby limiting interaction with the outcomes. From the literature review, challenges such as under-segmentation, over-segmentation, and poor boundary detection, as well as the selection of methods to improve the accuracy and efficiency of liver segmentation in computed tomography scanners, are highlighted as needs to be addressed. The importance of future research in understanding the essential features for the study, generating more datasets, improving segmentation efficiency, and developing lightweight artificial intelligence frameworks for liver segmentation is outlined.

We propose a new methodology for long-time biopotential recording based on MEMS multisensor integrated platform featuring a commercial electrostatic charge transfer sensor. That family of sensors was originally intended for presence tracking in automotive, so the existing setup has been engineered for the acquisition of electrocardiogram, electroencephalogram, electrooculogram and electromyography, designing a dedicated front-end and writing a proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in domestic environment will be discussed in detail. The excellent results candidate this technology to provide a low power, unexplored solution to biopotentials acquisition. The technology breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, they open to additional possibilities for wearable sensing and strengthen the role of MEMS technology in medical wearable applications for long time synchronous acquisition of a wide range of signals.

In recent times, using image-based deep learning techniques has been very useful for classifying fruits. However, mango is a particular challenge because its shelf life is directly related to the degree of maturity at the time of harvest, which is determined mainly through the visual assessment of the operators when sorting them, doing it incorrectly, coupled with the blows and compressions exposed during storage and transport, trigger the deterioration in their quality, hence the importance of this operation. In this sense, the purpose of this study focuses on designing a computational model based on Deep Learning that uses a convolutional neural network (CNN) parameterized to select mangoes according to their color tone based on their maturity stage. A dataset was established from 201 labeled images, then a CNN architecture was built, obtaining a model on which 3 criteria were evaluated: the number of convolution and grouping layers, the resolution of the input images and the number of training epochs required. As a result, an input image resolution of 32 x 32 pixels was achieved, the process included a convolution layer followed by pooling that fed another layer that processed the results and ended in an output layer with 2 neurons under the One Hot Encoding representation. The model required 20 epochs to perfect its training, achieving 96.04 % accuracy in mango image selection. This study supports the potential of CNNs in quality analysis through mango color selection, even in uncertainty scenarios.

Abstract Background Context Pelvic ring fractures are becoming more common in the aging population and can prove to be fatal, having mortality rates between 10% and 16%. Stabilization of these fractures is challenging and often require immediate internal fixation. Therefore, it is necessary to have a biomechanical understanding of the different fixation techniques for pelvic ring fractures. Methods A previously validated three-dimensional finite element model of the lumbar spine, pelvis, and femur was used for this study. A unilateral pelvic ring fracture was simulated by resecting the left side of the sacrum and pelvis. Five different fixation techniques were used to stabilize the fracture. A compressive follower load and pure moment was applied to compare different biomechanical parameters including range of motion (contralateral sacroiliac joint, L1-S1 segment, L5-S1 segment), and stresses (L5-S1 nucleus stresses, instrument stresses) between different fixation techniques. Results Transiliac -trans sacral screw fixation at S1 and S2 showed the highest stabilization for horizontal and vertical displacement at the sacral fracture site and reduction of contralateral sacroiliac joint for bending and flexion range of motion by 165% and 121%, respectively. DTSF (Double transiliac rod and screw fixation) model showed highest stabilization in horizontal displacement at the pubic rami fracture site, while the L5_PF_W_CC (L5-Ilium posterior screw fixation with cross connectors) and L5_PF_WO_CC ( L5-Ilium posterior screw fixation without cross connectors) showed higher rod stresses, reduced L1-S1 (approximately 28%), and L5-S1 (approximately 90%) range of motion. Conclusions Longer sacral screw fixations were superior in stabilizing sacral and contralateral sacroiliac joint range of motion. Lumbopelvic fixations displayed a higher degree of stabilization in the horizontal displacement compared to vertical displacement of pubic rami fracture, while also indicating the highest rod stresses. When determining the surgical approach for pelvic ring fractures, patient-specific factors should be accounted for to weigh the advantages and disadvantages for each technique.

In this study, different configurations based on linear fiber lasers were proposed and experimentally demonstrated to measure the concentration of paracetamol liquid solutions in a range of 52.61 to 201.33 g/kg. The optical gain was provided by a commercial bidirectional Erbium-Doped Fiber Amplifier (EDFA) and the linear cavity was obtained using two commercial Fiber Bragg Gratings (FBGs). The main difference of each configuration was the coupling ratio of the optical coupler used to extract the system signal. The sensing head corresponded to a Single-Mode Fiber (SMF) tip that worked as an intensity sensor. The results reveal that, despite the optical coupler used (50:50, 60:40, 70:30 or 80:20), all the configurations reached the laser condition, however, the concentration sensing was only possible using a laser drive current near to the threshold value. The configurations using a 70:30 and an 80:20 optical coupler allowed to perform paracetamol concentration measurements with a higher sensitivity of (-3.00±0.24) pW/(g/kg). In terms of resolution, the highest value obtained was 1.75 g/kg, when it was extracted 20% of the output power to the linear cavity fiber laser configuration.

The orbitofrontal cortex (OFC) area plays a critical role in human brain functions. However, susceptibility differences between paranasal sinuses and nasal cavity tissues often cause local field variations in the OFC during MRI, resulting in image distortions and signal loss. In this paper, we introduce a subject-friendly neck passive shim solution aimed at enhancing field homogeneity in the brain, specifically in the OFC region. Utilizing 3D gradient-echo pulse sequences, main magnetic field (B0) maps of four subjects’ brains were acquired at 4T MRI. Subsequently, these maps were decomposed into spherical harmonic coefficients and averaged. Optimal positions for placing shim elements on a semi-cylindrical surface, positioned slightly above the neck and below the chin, were computed. The findings indicate a substantial enhancement in B0 homogeneity, particularly within the OFC region, through the integration of this semi-cylindrical passive shim system in conjunction with first and second-order active shimming.

Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid-liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited because fixed recipes are usually applied to run the processes. Therefore, a digital twin has been developed to optimize an industrial brownfield DCF plant. The core of the digital twin is a mechanistic-empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation of selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes.

There are different ways to analyse energy absorbance (EA) in the human auditory system. In previous research, we developed a complete finite element model (FEM) of the human auditory system. In this mentioned work, the external auditory canal (EAC), middle ear, and inner ear (spiral cochlea, vestibule, and semi-circular canals) were modelled based on human temporal bone histological sections. Multiple acoustic, structure and fluid-coupled analyses were conducted using the FEM to perform harmonic analyses in the 0.1–10 kHz range. Once the FEM had been validated with published experimental data, the FEM numerical results were used to calculate the EA or energy reflected (ER) by the tympanic membrane. This EA was also measured in clinical audiology tests which were used as a diagnostic parameter. A mathematical approach was developed to calculate the EA and ER, with numerical and experimental results showing adequate correlation up to 1 kHz. Another published FEM had adapted its boundary conditions to replicate experimental results. Here, we recalculated those numerical results by applying the natural boundary conditions of human hearing and found that the results almost totally agreed with our FEM. This boundary problem is frequent and problematic in experimental hearing test protocols: the more invasive they are, the more the results are affected. One of the main objectives of using FEMs is to explore how the experimental test conditions influence the results. Further work will still be required to uncover the relationship between the middle ear structure and EA to clarify how to best use FEMs. Moreover, the FEM boundary conditions must be more representative in future work to ensure their adequate interpretation.

Microspheres, synthesized from diverse natural or synthetic polymers, are readily utilized in biomedical tissue engineering to improve the healing of various tissues. Their ability to encapsulate growth factors, therapeutics, and natural biomolecules, which can aid tissue regeneration, makes microspheres invaluable for future clinical therapies. While microsphere-supplemented scaffolds have been investigated, a pure microsphere-based scaffold with an optimized architecture has been challenging to create via 3D printing methods due to issues that prevent consistent deposition of microsphere-based materials and their ability to maintain the shape of the 3D-printed structure. Utilizing the extrusion printing process, we established a methodology which not only allows to create large microsphere-based scaffolds but also multicomposite matrices into which cells, growth factors, and therapeutics encapsulated in microspheres can be directly deposited during the printing process. Our 3D-McMap method addresses issues with scaffold shape fidelity during and after printing. Carefully timed breaks, minuscule drying steps, and adjustments to extrusion parameters, generated an evenly layered large microsphere-based scaffold that retains its internal architecture. Such scaffolds are superior to other microsphere-containing scaffolds, as they can release biomolecules in a highly controlled spatiotemporal manner. This capability permits to study cell responses to the delivered signals, to develop scaffolds that precisely modulate new tissue formation.

The development of sensitive and specific exosome detection devices is highly desirable because it is believed to deliver specific important information for cancer screening, diagnosis, and monitoring. Among many detection techniques, nanoplasmonic sensors are optical detection methods that offer advantages in sensitivity and detection speed and this makes the sample analysis process faster and more accurate. This paper summarizes the development of nanoplasmonic biosensors, especially Surface Plasmon Resonance (SPR) and Localized Surface Plasmon Resonance (LSPR)-based biosensors for exosome detection. There are three main parts in this paper. The first part will focus on introducing the theoretical background regarding the working principles of SPR and LSPR biosensors. After that, we will introduce several methods that can be used for exosome isolation. This article will close by explaining the engineering of nanoplasmonic sensors both in terms of transducer modification to amplify the signal and functionalization strategies to increase sensor specificity. In this section, the detection mechanism, the type of exosomes detected, and the resulting detection limits are some of the points highlighted in this paper.

Background: The protocol described in this paper is part of a research project coordinated 1 between three Spanish universities where a technology aimed at improving the quality of life of 2 people suffering from cerebral palsy will be developed. Part of the technology developed will consist 3 of developing an interface and a series of applications to increase motivation for daily physical activity. 4 The basis of these developments is based on the measurement of the emotional state of the subjects. 5 Methods: The experimental protocol is designed with two objectives, on the one hand to identify the 6 emotional state through physiological signals, and on the other to determine whether music can be 7 a motivating factor to promote physical activity. It has been specifically designed for subjects with 8 cerebral palsy taking into account the special characteristics of this population. These are people with 9 whom it is difficult to use questionnaires to have a basis to contrast with the measured physiological 10 signals, so measurements must be taken in carefully chosen daily life situations Discussion: As results 11 we hope to obtain which physiological parameters are the most robust to measure the emotional state 12 and how to design rehabilitation and physical activity promotion routines that are motivating, in 13 addition to being able to avoid risk factors during the performance of these routines. Trial registration: 14 NCT05621057. https://clinicaltrials.gov/ct2/show/NCT05621057

The central nervous system (CNS) controls movements and regulates joint stiffness with muscle coactivation, but until now few studies have examined muscle pairs during running. This study aims to investigate differences in lower limb muscle coactivation during gait at different speeds from walking to running. Nineteen healthy runners walked and ran at speeds ranging from 0.8 km/h to 9.3 km/h. Twelve lower limb muscles' coactivation was calculated using the time-varying multi-muscle coactivation function (TMCf) with global, flexor-extension, and rostro-caudal approaches. Spatiotemporal and kinematic parameters were also measured. We found that TMCf, spatiotemporal and kinematic parameters were significantly affected by gait speed for all approaches. Significant differences were observed in the main parameters of each coactivation approach, and in the spatiotemporal, and kinematic parameters at the transition between walking and running. Positive and negative strong correlations were found between global coactivation parameters and center of mass displacements, as well as some spatiotemporal parameters, regardless of gait speed. Our findings suggest that walking and running have different coactivation patterns and kinematic characteristics, with the whole-limb stiffness exerted more synchronously and stably in running. The coactivation indexes and kinematic parameters could be the result of global coactivation, which is a sensory-control integration process used by the CNS to deal with more demanding and potentially unstable tasks like running.

Streptomyces sp. HNS054, which was previously isolated from a marine sponge, demonstrates vigorous growth and facile genetic modification. This study explores its potential as a marine ac-tinomycete chassis through complete genome sequencing and genetic engineering. With a 7.5 Mb genome containing 6678 predicted genes, 21 secondary metabolic biosynthetic gene clusters (BGCs), and 8 common site-specific recombination (SSR) system attB loci, HNS054 shows a close phylogenetic relation to S. griseoincarnatus strain RB7AG. Utilizing multiplexed site-specific ge-nome engineering (MSGE) and the CRISPR/Cas9 method, engineered strains derived from HNS054 (with three copies of the target BGCs) produce higher amounts of aborycin and ac-tinorhodin than S. coelicolor M1346 (containing three copies of the same BGCs). HNS054 stands out for its remarkable characteristics, including salt tolerance, rapid growth, and compatibility with synthetic biology tools, and positions it as a promising candidate for developing marine ac-tinomycete hosts to enhance secondary metabolite production.

(1) Background: Myelodysplastic syndromes (MDS) are a group of diseases that affect the morphology and function of neutrophils. Rapid diagnosis of MDS is crucial for the initiation of treatment that can vastly improve disease outcome. (2) methods: In this work, we present a new approach for detecting morphological differences between neutrophils isolated from blood samples of high risk MDS patients and that for patients and blood bank donors (BBDs). First, using fluorescent flow cytometry CD66b positive cells were stained with 2′,7′-dichlorofluorescin diacetate (DCF) and Hoechst. Secondly, we imaged the cells using interferometric phase microscopy (IPM) without utilizing cell staining. (3) Results: flow cytometry results show MDS patients present different dispersion patterns compared to BBDs. BBDs present two distinct subpopulations (one of these are the H cells constituting 16% for neutrophils) whereas MDS patients present only 4% of this subpopulation. IPM results are consistent with this pattern: ~20% of BBDs neutrophils were characterized as H cells and whereas MDS patients present only ~4% of these cells. (5) Conclusion- IPM can be utilized for diagnosis of complex hematological pathologies such as MDS.

Transcutaneous nerve stimulation has several neuromodulatory applications, including pain management, sensory restoration, and motor recovery. Transcutaneous stimulation delivered via electrode arrays offers ease of use and reconfigurability through non-invasive means. Also, employing electrode arrays allows regulation of the permeated electric fields for greater control over the stimulation performance. Field shaping involves the regulation of permeated electric fields and is a well-established practice for deep brain stimulation; however, its applicability to peripheral nerve stimulation still needs exploration. Adopting such techniques can help to overcome current limitations with transcutaneous stimulation, like poor targetability, early onset of fatigue, and perceived discomfort. This article provides a perspective on the applicability of field-shaping techniques in improving the overall stimulation outcome.

Ultrasound A-scan is an important tool for quantitative assessment of ocular lesions. However, its usability is limited by difficulty to accurately localize the ultrasound probe to a lesion of interest. In this study, a transparent LiNbO3 single crystal ultrasound transducer was fabricated, and integrated with a widefield fundus camera to guide the ultrasound local position. The novel fundus camera guided ultrasound probe was validated for in vivo measurement of rat eyes. Anterior and posterior segments of the rat eye could be unambiguously differentiated with the fundus photography guided ultrasound measurement. A model eye was also used to verify the clinical potential of the prototype device.

(1) Background: Intracortical microelectrodes (IMEs) are an important part of interfacing with the central nervous system (CNS) and recording neural signals. However, recording electrodes have shown a characteristic steady decline in recording performance owing to chronic neuroinflammation. The topography of implanted devices has been explored to mimic the nanoscale three-dimensional architecture of the extracellular matrix. Our previous work used histology to study the implant sites of non-recording probes and showed that a nanoscale topography at the probe surface mitigated the neuroinflammatory response compared to probes with smooth surfaces. Here, we hypothesized that the improvement in the neuroinflammatory response for probes with nanoscale surface topography would extend to improved recording performance. (2) Methods: A novel design modification was implemented on planar silicon-based neural probes by etching nanopatterned grooves (with a 500 nm pitch) into the probe shank. To assess the hypothesis, two groups of rats were implanted with either nanopatterned (n=6) or smooth control (n=6) probes, and their recording performance was assessed over 4 weeks. Postmortem gene expression analysis was performed to compare the neuroinflammatory response from the two groups. (3) Results: Nanopatterned probes demonstrated increased impedance and noise floor compared to controls. However, the recording performance of the nanopatterned and smooth probes was similar, with active electrode yield for control probes and nanopatterned probes being approximately 50% and 45%, respectively by 4 weeks post-implantation. Gene expression analysis showed 1 gene, Sirt1, differentially expressed out of 152 in the panel. (4) Conclusions: This study provides a foundation for investigating novel nanoscale topographies on neural probes.

Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures and technology used to detect and identify weeds in different crops; 61 articles applying CNN architectures and technology were analyzed in the last five years (2019-2023). The results show the used of different devices to acquire the image for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

The development of new medical monitoring applications requires precise modeling of the human body effects as well as simulating and emulating realistic scenarios and conditions. The first aim of this paper is to develop realistic and adjustable human body 3D emulation platforms which could be used for evaluating emerging microwave-based medical monitoring/sensing applications such as the detection of brain tumors, strokes, breast cancers as well as for capsule endoscopy studies. The new phantom recipes are developed for microwave ranges for the realistic shaped phantom molds. The second aim is to validate the feasibility and reliability of the phantoms for practical scenarios with electromagnetic simulations using tissue layer models and biomedical antennas. The third aim is to investigate the impact of the water temperature in the phantom cooking phase on the dielectric properties of the stabilized phantom. The evaluations show that the dielectric properties of the developed phantoms correspond closely to those of real human tissue. The error in dielectric properties varies between 0.5-8%. In the practical scenario simulations, the differences obtained with phantoms-based simulations in S21 parameters are 0.1-13dB. However, the differences are smaller in the frequency ranges targeted for medical applications.

Purpose: this paper evaluates the effect of thermo-formed aligners with complementary biomechanical attachments (CBA) on periodontal ligaments (PDL) during the expansion process of the maxillary arch using simulations based on the finite element method (FEM). Methods: four types of teeth (canine 3, premolar 4, premolar 5 and molar 6) were modeled using 3D CAD. In addition, aligners, CBAs and PDL were also 3D modeled. With this information, a FEM model was generated, and a clinical rehabilitation scenario was simulated. For the validation of the results, a comparison with medical records was performed. Results: for canine 3, the displacement was 0.134 mm with a maximum stress of 4.822 x10-3MPa in the amelocemental junction. For premolar 4, the displacement was 0.132 mm at a maximum stress of 3.273x10-3MPa in the amelocemental junction. Premolar 5 had a displacement of 0.129 mm and a stress of 1.358 x10-3MPa at 1 mm from the amelocemental junction. Molar 6 had a displacement of 0.124 mm and a maximum stress of 2.440 x10-3MPa. Conclusions: The simulations showed that the use of CBA reduces tooth tipping. The model with Vestibular CBA presented the best results in terms of the dental movement of expansion of the maxillary arch with the use of thermoformed aligners. Significance: Considering the difficulty of performing in vivo tests that evaluate the configuration effectiveness of the different CBA alternatives in thermoformed aligners, FEM techniques were used to reduce time and costs, and get accurate results with minimal errors.

Adults with obesity experience high rates of disability and rapid functional decline. Identifying movement dysfunction early can direct intervention and disrupt disability development, however subtle changes in movement are difficult to detect with the naked eye. This study evaluated how a portable, inertial measurement unit (IMU)-based motion capture system compares to a laboratory-based optokinetic motion capture (OMC) system for evaluating gait kinematics in adults with obesity. Ten adults with obesity performed overground walking while equipped with the OMC and IMU systems. Fifteen gait cycles for each participant were extracted for 150 total cycles analyzed. Kinematics were compared between OMC and IMU across the gait cycles (coefficient of multiple correlations), at clinically significant time points (interclass correlations), and over clinically relevant ranges (Bland Altman plots). Sagittal plane kinematics were most similar between systems, especially at the knee. Sagittal plane joint angles at clinically meaningful timepoints were poorly associated except for ankle dorsiflexion at heel strike (ρ=0.38) and minimum angle (ρ=0.83). Most motions had more than 5° difference between systems across the range of angles measured. IMU-based motion capture shows promise for detecting subtle gait changes in adults with obesity. However, more work is needed before this method can replace traditional OMC.

3D bioprinting, like 3D printing, is a process that builds structures by depositing material layer by layer. In the specific case of bioprinting, these layers are composed of a biocompatible material together with living cells, allowing the creation of three-dimensional structures that recreate functional tissues and organs. Bioprinting cell-laden structures is complicated by the high rate of cell damage and stress that occurs during the process, caused by the high pressures and stresses to which they are subjected. To minimise damage during the process, it is important to study and optimise certain bioprinting conditions beforehand and to analyse how they affect the cells by means of computational simulations. In this review, both quantitative and qualitative data are collected to improve the nozzle geometry through computational simulation studies. Optimal ranges for nozzle diameter (0.2-1mm) and length (8-10mm; 300-900μm) have been defined, and recommendations for improving nozzle performance during bioprinting, such as the provision of an inner angle of 20-30°, an inner EDTA coating and a shear stress of less than 10kPa, have been gathered. Finally, based on the data collected, a design of experiments is proposed to obtain an optimal bioprinting configuration for a particular bioink.

This article describes a novel electrochemical on-chip biosensor that utilizes anti-PSA antibody (Ab) and silver nanoparticles (AgNPs) to enhance the sensing and detection capability of prostate specific antigen (PSA) in the blood. The AgNPs are prepared, characterized, and applied onto a silicon photonic on-chip biosensing receptor platform designed to enhance the accurate detection of PSA. The AgNPs were synthesized by a chemical reduction method using silver nitrate (AgNO₃) as the precursor. Transmission electron microscopy (TEM), selected area electron diffraction (SAED), energy dispersion X-ray spectroscopy (EDS), small angle X-ray scattering (SAXS), X-ray diffraction (XRD), and light microscopy were among the methods used in the characterization and analysis of the AgNPs. Each stage of the immunosensor fabrication was characterized using cyclic voltammetry. The proposed immunosensor was applied in the detection of PSA, a prostate cancer biomarker, with a high sensitivity and a limit of detection of 0.17 ng/mL over a linear concentration range of 2.5 to 11.0 ng/mL. The immunosensor displayed good stability and was selective in the presence of interfering species like immunoglobulin (Ig) in human serum, ascorbic acid (AA), and diclofenac (Dic). The detectivity and sensitivity are significantly higher than previous reports on similar or related technologies.

Tissue engineered heart valves can grow, repair, and remodel after implantation, presenting a more favorable long-term solution compared to mechanical and porcine valves. Achieving functional engineered valve tissue requires the maturation of human cells seeded onto valve scaffolds under favorable growth conditions in bioreactors. The mechanical stress and strain on developing valve tissue caused by different pressure and flow conditions in bioreactors are currently unknown. The aim of this study is to quantify the wall shear stress (WSS) magnitude in heart valve prostheses under different valve geometries and bioreactor flow rates. To achieve this, the study used fluid-structure interaction simulations to obtain the valve's opening geometries during the systolic phase. These geometries were then used in computational fluid dynamics simulations with refined near-wall mesh elements and ranges of prescribed inlet flow rates. The data obtained included histograms and regression curves that characterized the distribution, peak, and median WSS for various flow rates and opening configurations. The study also found that the upper region of the valve near the commissures experienced higher WSS magnitudes than the rest of the valve.

Hyperbaric oxygen therapy (HBOT) is a well-established treatment for carbon monoxide (CO) poisoning. HBOT is performed in a hyperbaric chamber, which increases the atmosphere absolute (ATA) and oxygen concentration in the chamber. To ensure patient safety during HBOT, a gas monitoring system is used to monitor the oxygen levels, ATA, and other parameters. This study aims to perform the safety evaluation of the monoplace hyperbaric chamber compared to a gas monitoring system in the treatment of CO poisoning. In addition, it examined the clinical characteristics of the patients diagnosed with CO poisoning who performed HBOT. In the safety evaluation of the monoplace chamber, it was compared the values of oxygen (O2), carbon dioxide (CO2), humidity, and temperature were measured in the hyperbaric chamber and gas monitoring system when operating at 2.0 ATA and 3.0 ATA. Moreover, in the clinical characteristics of patients with CO poisoning who performed HBOT, 1,006 patients were analyzed by using the data registry. Under the condition at 3.0 ATA, the average CO2 was higher in the hyperbaric chamber than in the gas monitoring system (p<0.001). Also, the average humidity was higher in the hyperbaric chamber than in the gas monitoring system (p=0.004). Under the condition at 2.0 ATA, the average humidity was higher in the hyperbaric chamber than in the gas monitoring system (p<0.001). Also, the average temperature was lower in the hyperbaric chamber than in the gas monitoring system (p<0.001). The mean age of the 1,006 CO poisoning patients who received HBOT was 44.93±19.78 years. According to the source of CO, 680 patients (67.6%) visited ER most commonly due to charcoal. The median maximal CO exposure time and time from rescue to ER were 4.0 hours and 3.09 hours, respectively. The median GCS score at the scene or ER was 15. According to the underlying comorbidities, psychiatric diseases (19.5%) were the most common, followed by hypertension (18.1%) and diabetes mellitus (11.5%). In the emergency room, some patients had appeared symptoms such as loss of consciousness (59.8%), shock (6.7%), or seizure (8.3%). The results of this study could be expected that HBOT can be effectively performed by monitoring the gas concentration change inside the hyperbaric chamber.

Biofilm serves as the residence for bacteria, wherein bacteria attach to surfaces and produce extracellular polymeric substances, including proteins, polysaccharides, and extracellular DNA. In recent years, biofilm reactors have gained increasing significance, playing a crucial role, for instance, in immobilizing heavy metals from waterways. This paper employs a bibliographic study to analyze the most important keywords and organizations in the field of biofilm reactors, identifying recent significant papers. Additionally, the paper discusses the opportunities for the future development of biofilm reactors. Through this study, a better understanding of key factors in biofilm reactor research is achieved, providing valuable guidance for future studies and applications in this field.

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an es-sential biomarker for directly assessing tissue viability and function in neurologic disorders. For quantitative mapping of OEF, an integrative model of quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) was recently pro-posed. However, QQ assumes Gaussian noise in both susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low sig-nal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic noise in mGRE complex signals. The proposed mcQQ was implemented using a deep learning framework (mcQQ-NET) and compared with the existing deep learning-based QQ (QQ-NET) in simulations, ischemic stroke patients, and healthy subjects. Both mcQQ-NET and QQ-NET used identical training and testing datasets and schemes for a fair comparison. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the sub-acute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaf-fected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR. Therefore, mcQQ-NET improves OEF accuracy by better reflecting realistic data noise characteristics.

Surface Plasmon Resonance (SPR) technology has been known as a powerful tool for studying biomolecular interactions because it offers real-time and label-free multiparameter analysis with high sensitivity. This article summarizes the results that have been obtained from the use of SPR technology in studying the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations. This paper will begin by introducing the working principle of SPR and the kinetic parameters of the sensorgram which include the association rate constant (ka), dissociation rate constant (kd), equilibrium association constant (KA), and equilibrium dissociation constant (KD). At the end of the section, we will summarize kinetic data on the interaction of SARS-CoV-2 with different variants with various ligands which were obtained from the results of SPR signal analysis. These affinity-related kinetic data are very useful both in drug discovery for therapeutic purposes and in the detection of SARS-CoV-2.

Near-infrared spectroscopy (NIRS) has become a key modality in medical imaging, finding application in both brain and breast imaging. This paper discusses the current trends in NIRS for brain and breast imaging, exploring advances in multi-modal integration with modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Challenges related to spatial resolution, depth sensitivity, and the impact of extracerebral tissues on signal specificity are examined. In addition, ongoing efforts to enhance hemodynamic measurements’ quantitative accuracy. Challenges, including limited spatial resolution and tissue heterogeneity, are discussed. The discussion extends to diffuse optical tomography and ongoing instrumentation development, clinical trials and studies validating NIRS diagnostic efficacy in breast imaging. The paper emphasizes the need for standardization, integration into routine clinical practice, and motivates for future work.

Genetic engineering and directed evolution are effective methods to address the low yield and poor industrialization level of microbial target products. The current research focus is on how to efficiently and rapidly screen beneficial mutants from constructed large-scale mutation libraries. Traditional screening methods such as plate screening and well plate screening are severely limited in their development and application due to their low efficiency and high costs. In the past decade, microfluidic technology has become an important high-throughput screening technology due to its fast speed, low cost, high automation, and high screening throughput, and it has developed rapidly. Droplet-based microfluidic high-throughput screening has been widely used in various fields such as strain/enzyme activity screening, pathogen detection, single-cell analysis, drug discovery, and chemical synthesis, and has been widely applied in industries such as materials, food, chemicals, textiles, and biomedicine. In particular, in the field of enzyme research, droplet-based microfluidic high-throughput screening has shown excellent performance in discovering enzymes with new functions, improved catalytic efficiency or stability, acid-base tolerance, etc. Currently, droplet-based microfluidic high-throughput screening technology has achieved high-throughput screening of enzymes such as glycosidase, lipase, peroxidase, protease, amylase, oxidase, and transaminase, as well as high-throughput detection of products such as riboflavin, coumarin, 3-dehydroquinate, lactic acid, and ethanol. This article reviews the application of droplet-based microfluidics in high-throughput screening, with a focus on high-throughput screening strategies based on UV, visible, and fluorescence spectroscopy, including labeled optical signal detection screening, as well as label-free electrochemical detection, mass spectrometry, Raman spectroscopy, nuclear magnetic resonance, etc. Furthermore, the research progress and development trends of droplet-based microfluidic technology in enzyme modification and strain screening are also introduced.

Lower urinary tract dysfunction, such as incontinence or urinary retention, is one of the leading consequences of neurological diseases. This significantly impacts the quality of life for those affected, with implications extending not only to humans but also to clinical veterinary care. Having motor and sensory fibers, the pudendal nerve is an optimal candidate for neuromodulation therapies using bidirectional intraneural prostheses, paving the way towards the restoration of a more physiological urination cycle: bladder state can be detected from recorded neural signals, then an electrical current can be injected to the nerve based on the bladder real-time need. To develop such prostheses and investigate this novel approach, animal studies are still required since the morphology of the target nerve is fundamental to optimize the prosthesis design. This study aims to describe the porcine pudendal nerve as a model for neuromodulation studies aiming at restoring lower urinary tract dysfunction. Five male farm pigs were involved in the study. First, a surgical procedure to access the porcine pudendal nerve without muscle resection was developed. Then, an intraneural interface was implanted to confirm the presence of fibers innervating the external urethral sphincter by measuring its electromyographic activity. Finally, the morpho-physiology of the porcine pudendal nerve at the level of surgical exposure was described by using histological and immunohistochemical characterization. This analysis confirmed the nerve fasciculate nature and the presence of mixed fibers with a spatial and functional organization. These achievements pave the way for further pudendal neuromodulation studies by using a clinically relevant animal model with the potential for translating the findings into clinical applications.

In the previous work, the pure fatigue behavior of AM60 magnesium alloy (PF-AM60) was compared with the corrosion fatigue behavior of these specimens (CF-AM60). In this research, in continuation of the previous job, the pure fatigue behaviors of AM60 with polylactic acid (PLA) coating (PF-AM60-PLA) and also, corrosion fatigue behaviors of magnesium alloy with PLA coating (CF-AM60-PLA) were evaluated. Polymer coating was made by fused deposition modeling (FDM) with a 3D printer and attached to standard fatigue test specimens with a glue. Then, they were immersed in the simulated body fluid (SBF) for 27 days. In the end, a high-cycle bending fatigue test was performed on samples. The fracture surface of the samples was also observed using the field emission scanning electron microscopy (FESEM). Due to corrosion, the weight of the specimens redcued by an average of 35%. The corrosion rate decreased in the first 7 days and then increased. PF samples with coating had an average of 49% increase in fatigue lifetime. Regarding the CF samples, despite the use of a 10 times stronger solution, the fatigue lifetime of these samples decreased by only 35%. FESEM results also showed cleavage plates and striations. In addition, the separation of the glue from the coating and Mg was observed. Corrosion products including microcracks and holes were seen on the fracture surface of CF specimens, which caused stress concentration and crack growth. Holes caused by the release of gases were also observed in polymer coatings.

Atrial Fibrillation (AF) is known as one of the dominant cardiac disease that is associated with severe consequences such as risk of strokes. This cardiac arrhythmia can be efficiently diagnosed using EEG signals. However, AF episodes in EEG records are not always permanent for all subjects. In addition, lengthy subject preparation and ECG recording sessions makes this practice time consuming and not attractive for efficient diagnosis. One attractive solution for this problem will be the use of wearable Internet of Medical Things IoMT devices for EEG recording and AF detection. From another side the use machine learning for efficient diagnosis could provide high accurate diagnosis. However, the deployment of these wireless wearable devices faces the problems of limited processing capabilities and of energy scarcity. In this context, the design of energy efficient accurate on-sensor AF detection scheme using machine learning algorithms still requires a focused research effort. This paper proposes a wearable solution for ECG-based processing scheme for embedded AF detection. The proposed method achieved 100% sensitivity and 94.5% of specificity and accuracy of around 97.4 % for wide range of subjects while showing low energy-consumption. The obtained results show the efficiency of using Logistic Regression algorithm for accurate AF detection and attest about practical feasibility of the proposed approach in wearable IoMT devices.

Electroencephalography (EEG) enabled earbuds represent a promising frontier in evaluating brain activity beyond traditional laboratory settings. However, these devices need more comprehensive characterization before widespread health related usage. A toolbox was developed to facilitate and expand the assessment of ear-EEG devices. The first component of this toolbox is a desktop application (“EaR-P Lab”) that integrates EEG validation paradigms. This application is designed to be used with EEG amplifiers compatible with the Lab Streaming Layer (LSL) protocol. The second element of the toolbox introduces an adaptation of the phantom testing concept, to the domain of ear-EEG by using 3D ear scans of the tested individuals, allowing controlled assessment of ear-EEG devices. The EEG paradigms were validated for scalp acquisitions and applied on ear-EEG measurements, under wet ear electrode conditions. The ear-EEG phantom was successful in measuring useful metrics in EEG system characterization, highlighting performance differences within the electrodes on the tested earpieces. This knowledge was factored in to select an optimal electrode reference which resulted in increased response power on the auditory steady-state response (ASSR). Through this work, an ear-EEG evaluation toolkit is made available with the intention to enable future and systematic assessment of novel ear-EEG devices from the hardware to the neural signals.

Breast cancer is a global health concern, necessitating continuous advancements in diagnostic medical imaging technologies for early detection and improvement of treatment. This paper presents an overview of the past, present and future of breast cancer imaging using light and some results from the current studies. The paper specifically presents the role of Diffuse Optical Tomography (DOT) in breast cancer imaging. Conventional methods, such as X-ray mammography, face limitations. Diffuse Optical Tomography, a non-invasive light-based imaging modality can provide quantitative descriptive and architectural information about breast tissue. The paper discusses the key challenges and recent developments in breast cancer imaging using DOT to overcome some key challenges. This paper aims to contribute to the understanding of the evolving technological landscape in breast cancer imaging using light for improving early detection and patient outcomes.

This paper explores the application of machine learning techniques for automated object grasping. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms. In this study, three reinforcement learning algorithms, namely Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), are employed to train agents for automated grasping tasks. The results indicate that the SAC algorithm achieves the highest success rate of 99% among the three algorithms at just under 200,000 timesteps. This research also shows that object’s physical characteristics can affect the agent’s ability to learn an optimal policy. Moreover, the findings highlight the potential of the SAC algorithm in developing intelligent prosthetic hands with automatic object gripping capabilities.

Epilepsy is a chronic neurological disorder characterized by recurring seizures resulting from abnormal neuronal hyperexcitability. In the case of pharmacoresistant epilepsy requiring resection surgery, identifying the epileptogenic zone (EZ) is crucial. Fast ripples (FRs; 200-600 Hz) are one of the promising biomarkers that can aid in EZ delineation. However, recording FRs requires small surface electrodes. These microelectrodes suffer from high impedance, which significantly impacts FR observability and detection. In this study, we explored the potential of a conductive polymer coating to enhance FR observability. We employed biophysical modeling to compare two types of microelectrodes: Gold (Au) and Au coated with the conductive polymer poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (Au/PEDOT:PSS). These electrodes were then implanted in the CA1 hippocampal neural network of epileptic mice to record FRs during epileptogenesis. Results showed that polymer-coated electrodes demonstrate two-order lower impedance and a higher transfer function amplitude and cut-off frequency. Consequently, FRs recorded with the PEDOT:PSS coated microelectrode yielded significantly higher signal energy compared to the uncoated one. The coating with PEDOT:PSS improved the observability of recorded FRs and therefore their detection. This work paves the way for the development of signal-specific microelectrodes design allowing better targeting of pathological biomarkers.

A typical solid-state quantum sensor can be developed based on negatively charged nitrogen-vacancy (NV-) centers in diamond. The electron spin state of NV- can be controlled and read at room temperature. Through optical detection magnetic resonance (ODMR) technology, temperature measurement inside living cells can be achieved at the nanoscale. The key to ODMR technology is to apply microwave resonance to manipulate the electron spin state of NV-. Therefore, the microwave field characteristics formed near the NV- have a crucial impact on the sensitivity of ODMR measurement. This article mainly focuses on the temperature situation in cellular applications, and simulates the influence of structural parameters of double open loop resonant (DOLR) microwave antennas and broadband large-area (BLA) microwave antennas on the microwave field’s resonance frequency, quality factor Q, magnetic field strength, uniformity, etc. The parameters are optimized to have sufficient bandwidth, high signal-to-noise ratio, low power loss, and high magnetic field strength in the temperature range of 36 ℃ to 42.5 ℃. Finally, the ODMR spectra are used for effect comparison, and the signal-to-noise ratio and Q values of the ODMR spectra are compared when using different antennas. We have provided an optimization method for the design of microwave antennas and it is concluded that the DOLR microwave antenna is more suitable for living cell temperature measurement in our study.

The application of chitosan and alginate coatings for Ready-to-Eat (RTE) baked fish product were studied. An experimental design was used to investigate the effect of coating polysaccharide concentration and glycerol addition on the safety (microbial growth), and quality (water loss and lipid oxidation) of RTE seafood product under optimal, and abused storage conditions. The results showed that a chitosan coating with 1% (w/v) chitosan in 1% (v/v) acetic acid, and 15% (w/w chitosan) glycerol, or 1% (w/v) alginate coating with no glycerol, and no crosslinking showed the best performance in controlling the tested safety and quality parameters. Desirability method was used to identify the shelf life of chitosan, alginate and double coated RTE products. Whereby, chitosan coated samples showed the best performance with a third fold shelf life extension compared to uncoated products.

This pioneering study presents an in-depth biomechanical examination of soccer's diving header, aiming to quantify its impact on ball speed enhancement (BSE) and effective offensive range (EOR). Despite the diving header's widespread acclaim and historical significance, there remains a dearth of scientific scrutiny into its biomechanical intricacies. Employing an innovative research design featuring a static hanging ball at varied offensive distances and heights, the study replicates diverse header scenarios. The results of 3D motion quantification have shown that a physically excellent player (identified through the maximal standing long jump test) could reach an EOR around 2.64 times his body height. Furthermore, the study unveils that proficient players could attain BSE surpassing 9 m/s, signifying the diving header's heightened efficacy compared to traditional heading techniques, which could only result in 4.5 m/s. Correlation analyses unveil noteworthy relationships, highlighting the pivotal role of head speed at impact and the influence of minimizing speed-drop and temporal disparities for amplified effectiveness. Considerations for optimizing diving header execution are introduced, emphasizing the necessity for targeted training programs. Despite acknowledged limitations inherent to its pilot nature, this exploration furnishes foundational knowledge to guide subsequent research and practical applications, providing valuable insights into soccer training and skill development through a biomechanical lens.

Muscle fatigue caused by using staplers affects the surgical procedures of surgeons. Compared to manual staplers, electric staplers decrease the muscle fatigue by replacing repeated gripping and releasing with automatic model. Still powered by the battery, the electric staplers are confronted with new operation fatigue due to the extra weight of the battery. The weight and manual operation of the electric stapler can cause user's muscle fatigue. To tackle the unsettled fatigue issue, the study is to propose a new design method to offset the influence of extra weight of the battery attached to the electrical stapler by changing the opening and closing of the stapler end clamp to electric control. The purpose of this study is to verify whether the design of this electric stapler can offset the impact of weight. Experiments are set up to test the feasibility of the design on 12 male Chinese surgeons. The muscle fatigue is indicated by measuring the EMG (electromyography) signal, and the stapler's working effect is indicated by measuring the end movement of the stapler. The result shows the new-designed stapler reduce the operation fatigue, counteracting the impact of the battery weight.

The surface electromyogram (sEMG)--based gesture recognition to the development of intelligent prostheses has emerged as a promising avenue for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. Cross-session calibration and training data indicate the capacity to reduce this variation. The impact of varying calibration and training data amounts on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of training data volume on benchmark performance. Two amputees who had undergone amputations years prior were recruited and seven sessions of data were collected for analysis from each of them. The experimental results show that the calibration data improved the average accuracy per subject by 3.03% and 6.16%, respectively, compared to the baseline results. Moreover, it was found that to collect training data for modeling, boosting the number of training sessions proves more efficient than increasing the number of trials within a single session. Two potentially effective strategies are proposed in light of this research to enhance the cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, as well as offer effective strategies for maximizing the utilization of the entire dataset.

The characteristics of exhaled aerosols outside the human respiratory airway are of significant importance in understanding virus transmission, yet they remain poorly understood. In order to effectively prevent and control the spread of respiratory infectious diseases, this study numerically investigates the exhaling characteristics of aerosols exhaled from the bronchus or larynx of a human upper airway model. This is achieved using the Euler-Lagrange method and considering various aerosol diameters (dp=0.1, 0.3, 0.5, and 1-20 μm) as well as five expiratory flow intensities (Q=15, 30, 60, 90, and 120 L/min). The important findings of this study are as follows: (1) Expiratory airflow exhibits complex flow phenomena, including jet-flow, flow separations, and vortex structures, with their characteristics being influenced by the expiratory flow intensities. (2) The exhaling characteristics of aerosols vary depending on the combined effects of expiratory flow intensities, virus aerosol diameters, and initial exhaled locations from either the bronchus or larynx. (3) A critical diameter (dc) is identified to represent the size at which aerosols can effectively exit the respiratory airway and potentially pose a transmission risk. This critical diameter is identical for aerosols exhaled from both the bronchus and larynx under the same expiratory flow intensity, but it decreases as the expiratory flow intensity increases. In conclusion, expiratory flow intensity is the most critical factor in determining whether aerosols can be expelled from the respiratory airway, as well as influencing the critical diameter (dc) for aerosols initially located in/after the larynx.

The brain being arguably the most complex organ in the human body, its detailed functioning is yet to be fully deciphered, let alone accurately modeled. Nonetheless, the medical community has gathered quite a remarkable amount of studies about the consequences of illnesses and injuries on its development and functioning. This bibliographic review aims to cover mostly the findings related to the changes in the physical distribution of neurons and their connections - the connectome -, both structural and functional, as well as their modelling. It does not intend to offer an extensive medical description of all injuries and diseases affecting the brain, rather presenting the most common ones succinctly so the need for accurate brain modelling can be fully understood and pondered, offering propositions to this aim.

The research focuses on the electrochemical behavior of TiVN trilayers in a liquid that simulates a physiological environment (blood plasma). TiVN films are obtained through Physical Direct Current Vapor (PVD) Deposition on titanium bases, depositing TiN and VN layers as an intermediate layer during the procedure. Impedance measurements are carried out before and during the wear test to determine the impact on the sample. The results show that the TiVN trilayer enhances corrosion resistance according to the time of exposure to the electrolyte. Initially, the load resistance (Rct) was 10.50 Ohms, but after 16 days, the Rct rose markedly to 13.87 Ohms, signaling an advance in corrosion resistance, a valuable property for wide applications.

Jalapeño peppers (Capsicum annuum L.) are an important agricultural product worldwide. Despite its high demand in recent years, there are few studies on its production under adverse conditions caused by environmental phenomena. Crops in protected environments such as aeroponics offer greater control of these phenomena and enable efficient use of resources. However, it is necessary to validate the cultivation technique for which new technologies are being used. One of these is the analysis of images captured in the visible and near and far infrared spectrum which involves using non-invasive techniques in order to characterize the growth of a plant and diagnose if it presents any type of stress. This study presents the characterization of vegetative growth and fruiting of jalapeño pepper plants in an aeroponic system, where the root, leaf development parameters and fruits were measured in four jalapeño pepper crops through images of plants captured in the visible (VIS), near infrared (NIR) and far infrared (IR) spectrums. Four crops of thirty jalapeño pepper plants were sown to obtain a total of one hundred and twenty plants which were characterized in the different phases of growth and fruiting. Each of the four jalapeño pepper crops were monitored for sixty days in an aeroponic system in a greenhouse. The first crop was intended to carry out tests to establish the appropriate fertigation times, the next three crops were grown under favorable conditions. Algorithms were developed in Matlab to obtain, over ten image capture sessions, the morphometric and thermal parameters of the roots (perimeter, area, length and average temperature), plants (perimeter, area, height and average temperature), and fruiting. (yield and number of fruits). The statistical analysis was carried out using the ANOVA and Tukey tests considering a value of p ≤ 0.05. The results obtained indicate that there is no significant difference between the characterizations of the four crops. This statement is also supported by the visual analysis of the growth curves parameters of the four crops. In addition, the temperature inside the aeroponic system was contrasted with the ambient temperature and it was verified that the temperature to which the roots are exposed was in the range of 10°C – 20°C. The thermal analysis determined that a plant that presents water stress and is also exposed to high temperatures has an average leaf temperature of 3.7 to 5 °C above the optimal condition for the plant, while a plant with stress at normal temperatures was 1.3 °C higher than the plant without stress.

This study investigates the suitability of the timestamp approach algorithm for predicting blood pressure (BP) using wearable and comfortable devices, showcasing its accuracy even with low-frequency sampled signals. Developed within the SINTEC European project, the algorithm utilizes Electrocardiogram (ECG) and Photoplethysmogram (PPG) sensors, demonstrating promising results in both laboratory and hospital settings. The research emphasizes the algorithm’s potential to transform continuous and non-invasive BP monitoring through wearable technology, offering frequent measurements with minimal discomfort. While the study shows promise, further exploration is needed, focusing on optimizing machine learning coefficients for long-term monitoring applications and addressing potential changes over time. Technological improvements, including the implementation of edge computing algorithms, hold the key to enhancing BP accuracy, denoising techniques, and integrating alarm systems for future wearable solutions. This study emphasizes the adaptability of the algorithm for diverse healthcare environments, paving the way for broader applications in convenient and reliable BP monitoring.

To investigate the variation patterns of spectral characteristics of main branches in fruit trees due to uneven distribution of growth, pruning, and fruit quality, as well as to determine the optimal fruit vi-bration harvesting, a vibration differential model of tree branches was constructed using the concen-trated mass method. A three-dimensional model of the fruit tree was built using finite element software. The spectral characteristics of each branch were obtained using the linear sweep frequency method. The method of changing the mass of each branch was employed to simulate the variation in branch mass. The frequency of occurrence, mean acceleration, and coefficient of variation of the inherent frequencies were used as evaluation indicators. The results revealed that when the vibration frequency of the fruit tree was at the peak frequency in the tree trunk spectrum, the frequency of occurrence and mean accel-eration were optimal. And the coefficient of variation of branch acceleration was less than 1, indicating a moderate dispersion. Additionally, reducing the branch mass resulted in an increase in the peak fre-quency of the tree trunk spectrum. Therefore, the growth, pruning, and uneven distribution of the branches in fruit trees can alter the spectral characteristics. When harvesting fruit by vibration, selecting the peak frequency in the trunk vibration spectrum as the excitation force frequency yields the best fruit detachment.

When a limb suffers a fracture, rupture, or dislocation, it is traditionally immobilized with plaster. This may induce discomfort in the patient, excessive itching and sweating, which creates the growth of bacteria, leading to an unhygienic and difficulty to keep clean from treatment. Furthermore, if the plaster remains for a long period, it may cause lesions in the joints and ligaments. To overcome all these disadvantages, orthoses have emerged as important medical devices to help patients in rehabilitation, as well as for self-care of deficiencies in clinics and daily life. Traditionally, these devices are produced manually, which becomes time-consuming and error prone. From another point-of-view, it is possible to use imageology (X-ray or computed tomography) to scan the human body; a process that may help orthoses manufacturing but induces radiation to the patient. To overcome this great disadvantage, several types of 3D scanners, without any kind of radiation have emerged. This article describes the use of various types of scanners capable of digitizing the human body, to produce custom orthoses. Studies have shown that photogrammetry is the most used and most suitable 3D scanner for the acquisition of the human body in 3D. With this evolution of technology, it is possible to decrease the scanning time and it will be possible to introduce this technology in clinical environment.

We developed a novel machine learning algorithm to augment clinical diagnosis of prostate cancer utilizing first and second-order texture analysis metrics and novel application of machine learning radiomics analysis. We successfully discriminated between significant prostate cancers versus non-tumor regions and provided accurate prediction between Gleason score cohorts with statistical sensitivity of greater than 0.91 sensitivity across three separate cohorts. Tumor heterogeneity and prediction between GS cohorts were quantified using two feature selection approaches and two classifiers with tuned hyperparameters (including grid search and randomized search). There was a total of 71 patients analyzed in this study. Multiparametric MRI, incorporating T2WI and the ADC maps, were used to derive radiomics features. Recursive feature elimination (RFE), the least absolute shrinkage and selection operator (LASSO), and two classification approaches, including a support vector machine (SVM) (with grid search) and random forest (RF) (with randomized search), were utilized to differentiate between non-tumor regions versus significant cancer and prediction among Gleason score groups. In T2WI images, the RFE feature selection approach combined with RF and SVM classifiers outperformed LASSO with SVM and RF classifiers regarding performance. The detection precision of the radiomics framework depending on multiple imaging modalities was lower than the diagnostic accuracy of single imaging modality model with every machine learning approach assessed. The best performance was achieved by combining LASSO and SVM into a model that used both T2WI and ADC. This model had an area under the curve (AUC) of 0.91. Radiomic features computed from ADC and T2WI images were used to predict three groups of Gleason score using two kinds of feature selection methods (RFE and LASSO), RF and SVM classifier models with tuned hyperparameters. Using combined sequences (T2WI and ADC map images) and combined radiomics (1st and GLCM features), LASSO, with a feature selection method with RF, was able to predict G3 with the highest sensitivity at a level AUC of 0.92. To predict G3 for single sequence (T2WI images) using GLCM features, LASSO with SVM achieved the highest sensitivity with an AUC of 0.92.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores into cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

In this study, the efficiency of coating (alginate and carrageenan) and osmotic dehydration pre-treatment (NaCl and sucrose) on decreasing oil uptake of potato strips was assessed. It was examined how various pre-treatments affected the sample’s texture, morphology, color and density properties as well as their moisture content and effective moisture diffusion coefficient (Deff). Results showed that NaCl osmotic dehydration pre-treatment and sucrose solution decreased oil uptake by 30.76% and 41.75%. This is attributed to moisture removal increasing caused by improving dielectric properties. Osmotic dehydration and coating pre-treatment frequently enhanced product shrinkage, browning index, a* and ∆E. Conversely, density, lightness, breaking force, Deff and b* decreased. In control samples, SEM revealed a severely damaged internal structure in comparison to a tissue homogenous layer produced by coating. For moisture loss and oil uptake, the coefficient of determination (R2) and root mean square error (RMSE) of eight empirical models were compared. Finally, the Dehghannya and Krokida model (moisture loss) and the Movahhed and Naghavi model (oil uptake) were found the best fit to the experimental data. Although coating pre-treatment and osmotic dehydration can be used as an alternate de-oiling approach for potato chips, the mechanism of oil uptake reduction differ.

Bacteriotherapy is emerging as a strategic and effective approach to treat infections by providing putatively harmless bacteria (i.e., probiotics) as antagonists to pathogens. A proper delivery of probiotics or their metabolites (i.e., post-biotics) can avail itself of biomaterial encapsulation by innovative manufacturing technologies. This review paper aims at providing the most recent biomaterial-assisted strategies proposed to treat infections or disbiosis using bacteriotherapy. We revised the encapsulation processes across multiscale biomaterial approaches, which could be ideal to target different tissues and suit diverse therapeutic opportunities. Hydrogels, and in particular polysaccharides, are the focus of this review, as they have been reported to better sustain vitality of the live cells incorporated. Specifically, the approaches for fabricating hydrogel-based devices with increasing dimensionality (D), namely, 0D (i.e., particles), 1D (i.e., fibers), 2D (i.e., fiber meshes), and 3D (i.e., scaffolds) endowed with probiotics, were detailed by describing their advantages and challenges, along with a future overlook in the field. Electrospinning, electrospray and 3D bioprinting were investigated as new biofabrication methods for probiotic encapsulation within multidimensional matrices. Finally, examples of biomaterial-based systems for cell and post-biotic release were reported.

In this observational study, we compared continuous physiological signals during an active stand test in adults aged 50 years and over, characterized as frail by three different criteria, using data from The Irish Longitudinal Study on Ageing (TILDA). Four independent groups were identified: those characterized as frail only by one of the frailty tools used (the physical Frailty Phenotype (FP), the 32-item Frailty Index (FI), or the Clinical Frailty Scale (CFS) classification tree), and a fourth group where participants were not characterized as frail by any of these tools. Continuous non-invasive physiological signals were collected during an active stand test: systolic (sBP) and diastolic (dBP) blood pressure, as well as heart rate (HR), using digital artery photoplethysmography. Additionally, frontal lobe cerebral oxygenation (Oxy), deoxygenation (Deoxy), and tissue saturation index (TSI) were also non-invasively measured using near-infrared spectroscopy (NIRS). The signals were visualized across frailty groups and statistically compared using one-dimensional statistical parametric mapping (SPM). A total of 1,124 participants (mean age 63.5 years, 50.2% women) were included: 23 were characterized as frail only by FP, 97 by FI, 38 by CFS, and 966 by none of these criteria. SPM analyses revealed that only the group characterized as frail by FI had significantly different signals (p<0.05) compared to the non-frail group. Specifically, they exhibited a lesser increase in HR between 5-15 seconds post-stand and larger deficits in sBP and dBP between 15-40 seconds post-stand, and possibly a lower Delta TSI at 20 seconds post-stand. In this comparison, the FI proved more adept at capturing distinct physiological responses to standing, likely due to its direct inclusion of cardiovascular morbidities in its definition.

Particle-mediated elasticity/viscoelasticity imaging has potential to expand the elasticity imaging field, as it can provide accurate and local tissue elastic properties as well as density, Poisson’s ratio, and viscosity. Here, we investigated elasticity imaging based on the use of small particles located within the tissue and at the tissue interface exposed to a static and dynamic external load. First, we discuss elasticity/viscoelasticity imaging methods based on the use of particles (bubbles and rigid spheres) located within the tissue. Elasticity/viscoelasticity imaging techniques based on the use of particles (bubbles, rigid and soft spheres) located at the tissue interface are then presented. Based on new advances, we updated some of the models for the responses of the particles located within the tissue and at the tissue interface available in the literature. Finally, we compared the mathematical models for the particles located within the tissue and at the tissue interface and evaluated the elasticity/viscoelasticity imaging methods based on the use of small particles. Therefore, instead of the use of the term elasticity imaging, we suggest the use of the term viscoelasticity imaging.

Abstract: This study presents a methodological framework for AD and MCI diagnosis using radiomic analysis of 18FDG-PET imaging and conducts non-invasive predictions and in-depth analysis of AD and MCI and the associated small number of regions and features. Our methodology follows a structured process commencing with data preprocessing and labeling, facilitating segmentation through FastSurfer, a tool that efficiently segments the brain into 95 ROIs using the DKT-atlas. Subsequently, Feature extraction was carried out using PyRadiomics, calculating 120 features for each of the 95 ROIs (11,400 per image). These extracted features form the foundation of our radiomics analysis, primarily for early diagnostic purposes. In the feature selection phase, we explored a set of eight commonly employed techniques, including ANOVA, PCA, and LASSO, originating from the four main categories, namely filtered, embedded, wrapper, and hybrid methods, to identify a pertinent subset of features. Our evaluation assessed the performance of nine classification methods, such as GradientBoosting, RandomForest, and GaussianNB, in conjunction with eight feature selection techniques. The choice of feature selection method and classifiers was predicated on their ability to achieve the best area under the ROC curve with independent data. For all three predictions AD vs. CN, AD vs. MCI, and CN vs. MCI the Random Forest (RF) classifier with LASSO feature selection demonstrated the highest accuracy with an AUC of 0.976 for AD vs CN, AUC=0.917 for AD vs MCI, and AUC=0.877 for MCI vs CN. In conclusion, our RAB-PET platform enables efficient AD and MCI diagnosis from FDG-PET images using a radiomics pipeline. It also offers a general hardware and software tool for the investigation of other brain disorders.

This review article conducts an in-depth analysis of the role of next-generation technologies in soilless vegetable production, highlighting their groundbreaking potential to revolutionize yield, efficiency, and sustainability. These technologies, such as AI-driven monitoring systems and precision farming methods, offer unparalleled accuracy in monitoring critical variables like nutrient concentrations and pH levels. However, the paper also addresses the multifaceted challenges that hinder the widespread adoption of these technologies. The high initial investment costs pose a significant barrier, particularly for small and medium-scale farmers, thereby risking the creation of a technological divide in the industry. Additionally, the technical complexity of these systems demands specialized expertise, potentially exacerbating knowledge gaps among farmers. Ethical considerations are scrutinized, including data privacy concerns and potential job displacement due to automation. Regulatory challenges, such as international trade regulations and policy frameworks, are discussed as they may need revision to accommodate these new technologies. The paper concludes by emphasizing that while these sustainable technologies offer transformative benefits, their potential for broad adoption is constrained by a complex interplay of financial, technical, ethical, regulatory, and social factors. Comprehensive, multi-faceted solutions are therefore essential for their ethical and equitable implementation.

Hip joint surgeries, including total hip replacement or resurfacing, have increased tremendously in the last decades. Number of patients of all ages, replace their hip, due to severe injuries or trauma problems, so as to have a better life. According to these factors, new material and processes in implants manufacturing are imported to medicine, through research programs, every year. In the scope of one of these programs, that has to do with ceramic coatings on metallic resurfacing implants, a new 3axis Hip simulator was designed and manufactured in the Laboratory of Manufacturing Technology of the National Technical University of Athens. The scope of this paper is to present the main parts, materials and process used to manufacture the simulator as well as the way the simulator is working and how its movement in 3axis is accomplished. Furthermore, the evaluation of the machine and its use to examine resurfacing implants is presented.

Regenerative Medicine and Tissue Engineering are interdisciplinary fields that combine cell biology, material science, and bioengineering to develop therapies for tissue repair and regeneration. This field has made significant advances in recent years, particularly in the use of stem cells and biomaterials. Advancements in cell-based therapies have involved the use of cells to repair or replace damaged tissues and organs. Tissue engineering involves using scaffolds and growth factors to promote tissue regeneration. However, a major challenge in this field is the formation of adhesions, which can cause complications, such as intestinal obstruction and chronic pain. Adhesion barriers are used to prevent adhesion of tissues and organs during recovery, thereby reducing the risk of complications. Anti-adhesion materials, such as polysaccharides, proteins, and synthetic polymers, are used to create a physical barrier between tissues and prevent adhesion. These materials can be used alone or in combination, but their effectiveness varies.

Nanotechnology has become an increasingly promising medical field, particularly scar removal. Scar removal is a complex process involving regenerating damaged skin tissue, and nanotechnology presents unique solutions to this issue. One potential application of nanotechnology is using nanofibers as scaffolds to support the growth of new skin tissue. These fibres can also be loaded with drugs or growth factors to promote tissue regeneration and reduce scarring. Another potential application is nanocarriers for drug delivery to specific body areas, which can promote tissue regeneration and reduce scarring.Additionally, nanotechnology has been utilized to create new materials for skin regeneration, such as "nano skin" that mimics the structure of natural skin. Nanoprobes have also been developed for the detection of scar tissue and the monitoring of its progression. These potential nanotechnology applications offer exciting possibilities for the future of scar removal and skin repair. With further research and development, nanotechnology has the potential to revolutionize scar removal and provide more effective solutions for tissue regeneration.

The study conducts a comparative life cycle assessment (LCA) analysis to assess the environmental impact of two different manufacturing processes used to produce transparent dental aligners. The former method consists of thermoforming a polymeric disc over 3D printed, customized models, while the second, more innovative approach involves the direct printing of aligners using additive manufacturing (AM), specifically applying digital light processing (DLP) technology. The analysis results highlight how adopting direct printing through AM brings significant advantages in terms of environmental sustainability, thanks to the substantial reduction in raw materials and electricity consumption. These drops translate into decreased potential environmental impacts across all impact categories considered within the EF 3.1 method. Furthermore, lowering the amount of raw materials needed in the direct printing process contributes to a notable decrease in the overall volume of waste generated, emphasising the environmental benefits of this innovative technique.

Purpose: The objective of the study was to qualitatively assess micro-gap dimensions, connecting fitting surface profile and bacterial leakage of machined and cast high gold alloy UCLA abutments, with or without electrodeposited gold coatings attached to external hexagon implants. Materials and methods: 16 plastic UCLA (PUCLA) and 5 machined cast-to UCLA (GUCLA) abutments were cast with a high gold content alloy. 10 were electrolytically gold plated (8 - PUCLA, 2 - GUCLA). All abutments were attached to implants giving 21 implant-abutment combinations (IACs). External perimeter micro-gaps measured with SEM under different illumination and magnification conditions were averaged over three regions. The IACs were examined for E.coli leakage following an initial sterility test. Disassembled combinations were examined with SEM and surface profiles qualitatively assessed. Results: External micro-gap measurements did not reflect the variable connecting surface profiles but average values < 3.5 μm were observed for all IACs. Bacterial transfer was observed in 3 of 5 PUCLA plated and 2 of 5 PUCLA non-plated IACs. No transfer occurred in the 3 GUCLA non-plated or 2 GUCLA plated IACs. Abutment connecting surfaces, both Au plated and not Au plated, showed plastic deformation (smearing) in variable mosaic patterns across the micro-gap. External micro-gap dimensions although not truly reflective of surface connecting profiles averaged < 3.5 μm measured under shadow eliminating silhouette illumination for both cast and pre-machined external hexagon abutments with and without Au plating. Conclusions: Micro-gap dimensions < 5 μm were obtained with both high noble metal cast and pre-machined external hexagon abutments with and without Au electrodeposited on the abutment connecting surface. Regions of intimate contact due to plastic deformation (smearing) of these surfaces was observed. A continuous smeared region around the circumference of the surfaces can provide an effective barrier to egress of E.coli bacteria from the internal regions of the implant under static loading.

(1) Background: OCT imaging has been used to assess enamel demineralization in dental research but is not yet developed enough to qualify as a diagnostic technique in clinics. The current capabilities of most commercial acquisition software allow for visual and qualitative assessment. There is a need for a fast and verified batch-processing algorithm for segmenting and analyzing the de-mineralized enamel. This study suggests a GUI MATLAB algorithm for processing and quantitative analysis of the demineralized enamel. (2) Methods: A group of artificially demineralized human enamels was in vitro scanned under the OCT, and ROI frames were extracted. By using a selected intensity threshold colormap, inter- (Ie) and intra- (Ia) prismatic demineralization can be segmented. A set of quantitative measurements for average demineralized depth, average line profile, and integrated reflectivity can be obtained for an accurate assessment. Real and simulated OCT frames were used for algorithm verification. (3) Results: A strong correlation between automated and known Excel measurements for the average demineralization depth was found (R² > 0.97). (4) Conclusions: OCT image segmentation and quantification of the enamel demoralization zones are possible. The algorithm can assess the future development of a real-time assessment of dental diagnostics using an oral probe OCT.

Compton cameras detect scattered gamma rays and estimate the distribution of gamma-ray sources. Nonetheless, crafting a camera tailored to a specific application presents formidable challenges, often necessitating the implementation of diverse image reconstruction techniques. Delving into the factors influencing these cameras can pave the way for design optimization and performance enhancement. This study introduces an inventive detector design for Compton imaging systems, building upon the achievements of prior designs. The proposed system contains eight scatterer detectors and a semiconductor absorber detector, spaced at 1 mm and 30 mm intervals, respectively. The source-to-first-scatterer-detector distance is 5 mm, with scatterer and absorber detector plates measuring 70× 70× 2.125 mm³ and 70× 70× 10 mm³, respectively. Geant4 simulation toolkit models the Compton imaging system, and an analytical method reconstructs Compton camera images. Unlike more straightforward techniques, the analytical method directly reconstructs Compton camera images by solving the equation relating to the reflected image data. This approach is implemented in the C++ programming language. The study's findings reveal that the analytical method discerns optimal conditions and parameters that significantly influence efficiency, yielding a full width at half maximum (FWHM) of 3.7 mm with an angular uncertainty of approximately 2.7 degrees at an energy level of 0.662 MeV. Compared to another experimental design employing the analytical image reconstruction approach, the FWHM value decreased by 0.7 mm. This study presents an innovative detector design and an analytical reconstruction method for Compton imaging systems, showcasing improved efficiency and accuracy.

: Nanotechnology has expanded what can be achieved in our approach to cancer treatment. The ability to produce and engineer functional nanoparticle formulations to elicit higher incidences of tumor cell radiolysis has resulted in substantial improvements in cancer cell eradication while also permitting multi-modal biomedical functionalities. These radiosensitive nanomaterials utilize material characteristics, such as radio- blocking/absorbing high-Z atomic number elements, to mediate localized effects from therapeutic irradiation. These materials thereby allow for subsequent scattered or emitted radiation to produce direct (e.g., damage to genetic materials) or indirect (e.g., protein oxidation, reactive oxygen species formation) damage to tumor cells. Using nanomaterials that activate under certain physiologic conditions such as the tumor micro-environment can selectively target tumor cells. These characteristics combined with biological interactions that can target the tumor environment allow for localized, radio-sensitization while mitigating damage to healthy cells. This review explores the various nanomaterial formulations utilized in cancer radiosensitivity research. Emphasis on inorganic nanomaterials showcase the specific material characteristics that enable higher incidences of radiation while ensuring localized cancer targeting based on tumor micro-environment activation. The aim of this review is to guide future research in cancer radiosensitization using nanomaterial formulations and to detail common approaches to radiosensization, as well their relations to commonly implemented radiotherapy techniques.

In this paper we report on the use of an in-house developed solid-state hybrid microwave and radiofrequency generator operating at frequencies of 2.45 GHz and/or 480 kHz for cancer ablation in various tissues. The generator has demonstrated increased accuracy due to its custom-designed hardware, which incorporates the latest technologies and algorithms to control the emitted power versus temperature profile of the treated sample. In particular, the hybrid generator incorporates control systems based on impedance or reflected power measurements that allow controlled ablation without causing unwanted carbonisation and without including areas where tissue damage is not desired. Tests performed ex-vivo on chicken breast and bovine liver show that radiofrequency ablation (RFA) is very effective for performing controlled ablations with minimally invasive probes, such as cardiac pathologies, small lesions, tissues with particular composition, while microwave ablation (MWA) is optimal for performing large ablations in highly vascularised tissues, such as liver cancer, where it is necessary to achieve higher temperatures.

Biomedical devices made from high-modulus and hardness materials play a critical role in enhancing the quality of life for individuals with bone-related ailments. While these materials have been successfully used in orthopedic applications, concerns including stress-shielding have necessitated the exploration of alternative solutions. An ideal biomedical implant requires a delicate balance of mechanical performance, corrosion resistance, tissue biocompatibility, and other properties such as tribological performance and osseointegration. This review explores the suitability of biodegradable magnesium (Mg) alloys as a promising material for biomedical implants. It delves into the essential properties of biomedical implants, emphasizing the importance of matching mechanical characteristics with human bone properties to mitigate stress shielding. The corrosion properties of implant materials are discussed, highlighting the need for controlled degradation to ensure the safety and longevity of implants. The focus then shifts to the potential of magnesium alloys as biomedical implants, examining their benefits, limitations, and the challenges associated with their high degradation rates and less-than-satisfactory mechanical properties. Alloying with elements such as aluminum, zinc, and others is explored to improve magnesium alloys' mechanical performance and corrosion resistance. Furthermore, this review discusses surface modification techniques, including chemical conversion coatings and biomimetic deposition, as effective strategies to enhance the corrosion resistance and biocompatibility of magnesium and its alloys. These modifications offer opportunities to improve the long-term performance of magnesium-based biomedical implants. This paper provides a comprehensive overview of the properties, challenges, and potential solutions associated with biodegradable magnesium alloys as a promising material for biomedical implants. It underscores the importance of addressing issues related to mechanical performance, corrosion resistance, and biocompatibility to advance the development of safe and effective biomedical implant materials.

Low-complexity ultrasound systems are increasingly desired for wearable point-of-care ultra-sound and massive-channel ultrasound for 3-D matrix imaging. However, the system complexity is closely associated with the imaging capabilities, remaining as a challenge. To resolve this limitation, this study revisits the general structures of analog and digital beamformers and in-troduces a hybrid approach to implement efficient ultrasound systems. The suggested hybrid beamforming takes two stages, where the first analog stage partially beamforms M-channel RF signals to N sum-out data (M-to-N beamforming), and the second digital stage beamforms N partial sums to single final beamformed data (N-to-1 beamforming). Our approach was systematically designed and implemented, which was demonstrated with a customized 64-channel 1-D phased array using a tissue mimicking phantom. The demonstrated results indicate that the analog-digital hybrid beamforming can be applied to any kind of arrays for sophisticated 3-D imaging and tiny wearable ultrasound applications.

Microbial cell factories offer an eco-friendly alternative for transforming raw materials into commercially valuable products, primarily because of their reduced carbon impact compared to conventional industrial procedures. These systems often depend on lignocellulosic materials, mainly pentose and hexose sugars, richly available carbon reservoirs. Nevertheless, these resources might not always be efficient due to the limited supply, significant cost, and other considerable obstacles. One major hurdle when working with sugars derived from lignocellulosic biomass, especially glucose, is balancing carbon allocation to satisfy energy, cofactor, and other essential component needs for cellular proliferation while maintaining a robust yield. Furthermore, nearly half or more of this carbon is inevitably lost as CO2 during the biosynthesis processes, which serves to generate the energy necessary for maintaining cell growth and other regular metabolic activities. This carbon loss lowers the theoretical production yield and compromises the benefit of reducing greenhouse gas emissions – a fundamental advantage of biomanufacturing. This review paper posits the perspectives of using CO2 from atmosphere, industrial wastes, or the exhausted gases generated in microbial fermentation as a feedstock for biomanufacturing. Both one-step direct CO2 fixation or two-step indirect CO2 fixation and conversion strategies are discussed, which can minimize the carbon loss, significantly increase the carbon yield, and eventually achieve the carbon-neutral or -negative goals. The one-step strategy uses novel metabolic pathway design and engineering approaches to directly fix the CO2 via the pathway toward the synthesis of the desired fermentation products. Due to the limitation of the yield and efficiency in one-step CO2 fixation, the two-step strategy aims to completely avoid carbon loss in biomanufacturing by integrating a first electrochemical fixation of the exhausted CO2 into C1/C2 products such as formate, methanol, acetate, and ethanol and a second fermentation unit to utilize the CO2-derived C1/C2 chemicals or co-utilize both C5/C6 sugars and C1/C2 chemicals for product formation. The great potentials and challenges of using CO2 as a feedstock for further biomanufacturing are also discussed.

Encapsulated nanofibers have emerged as a promising approach for the treatment of acne, owing to their ability to provide controlled release, targeted delivery, increased efficacy, and improved stability. Electrospinning is a well-established method for producing encapsulated nanofibers and has been shown to be effective for encapsulating various active ingredients. However, there are still several challenges that need to be addressed in the development of encapsulated nanofibers for acne treatment. One major challenge is the need for comprehensive in vitro and in vivo studies to evaluate the safety and efficacy of these treatments. The cost and scalability of production also need to be considered to make these treatments accessible and affordable for patients. In addition, the long-term stability of encapsulated active ingredients is another challenge in the development of encapsulated nanofibers for acne treatment. Regulatory frameworks need to be developed to ensure the safety and efficacy of these treatments. Future research may focus on developing multifunctional nanofibers that combine active ingredients with other properties, such as antimicrobial, anti-inflammatory, and wound-healing properties, to provide a comprehensive approach to acne treatment. Moreover, the development of nanofiber-based skincare products may have a significant impact on the cosmetic industry. Overall, while there are still challenges to overcome, the potential benefits of encapsulated nanofibers for acne treatment make them an exciting and promising area of research for the future. In particular, the integration of smart drug delivery systems and responsive materials may enable the development of more personalized and effective treatments for acne. The development of new materials and encapsulation techniques, as well as the exploration of combination therapies that target multiple aspects of acne pathogenesis, are also future perspectives for encapsulated nanofibers in acne treatment.

Collaborative robots (cobots) have largely replaced conventional industrial robots in today’s workplaces, particularly in manufacturing setups due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human-robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots' irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker’s performance in a human-robot collaborative environment. In this study, factory workers’ mental workload has been assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals have been collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot’s movement speed, and cobot’s payload capacity on the mental stress of a human worker have been observed, for a task designed in the context of a smart factory. Task complexity and cobot’s speed have proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually, they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) have been utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression has performed better for most of the targets and the best correlation (rsq-adj = 0.654146) has been achieved for predicting missed beeps, behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm has been used to evaluate the accuracy of correlation between traditional measures and physiological variables, with highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters.

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to hu-man error. Recognizing this limitation, the rise of deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain alongside pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using auto-encoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: remarkable 100% accuracy in most of the binary classifications, impressive performance exceeding 95% for three-class and four-class challenges, and a commendable rate exceeding 93.5% for the five-class classification.

Perceptual and statistical evidence has underlined voice characteristics of individuals affected by genetic syndromes different from that of normophonic subjects. In this paper we propose a procedure for the systematic collection of such pathological voices and the development of AI-based automatic tools to support differential diagnosis. Guidelines are provided concerning most suitable recording devices, vocal tasks and acoustical parameters, in order to simplify, speed up and make the whole procedure homogenous and replicable. The proposed procedure was applied to a set of 56 subjects, affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS) and Smith-Magenis syndrome (SMS). The whole database has been divided into three groups, respectively called: paediatric subjects (PS, individuals &lt; 12 years of age), female adults (FA) and male adults (MA) subjects. In line with literature results, the Kruskal-Wallis test and post-hoc analysis with Dunn-Bonferroni test highlighted several significant differences in acoustical features not only between healthy subjects and patients, but also across syndromes within PS, FA and MA groups. Machine learning provided for the PS group a k-nearest neighbour classifier with 75% accuracy, for the FA group a support vector machine (SVM) model with 84% accuracy and for the MA group a SVM model with 97% accuracy. These preliminary results suggest that the proposed procedure, based on acoustical analysis and AI, might be helpful for an effective non-invasive automatic characterization of genetic syndromes.

Geotechnical investigation is an important site characterization process in engineering construction. Excavation pits and boreholes are popular geotechnical investigation methods for accessing subsurface soil and rock characteristics. Although they are accurate, they are limited to the discrete locations where tests are carried out. The demand for 3D representation has been increasing as an alternative for improved visualization of geotechnical investigations. This study developed a procedure for 3D modelling of geotechnical investigations using open-source software. It improves 3D visualization of geotechnical investigations and characterization of accuracy and uncertainty assessment. Presently, most geotechnical investigations do not regularly report uncertainty assessment despite its importance in risk analysis of the investigations. The procedure developed in this study was tested in a proposed site for construction of an Inland Container Depot where it depicted geotechnical soil properties with more than 80% accuracy on holdout samples. It was able to model both horizontal and vertical variations of the soil properties with quantification of uncertainty. More testing and wide applications are recommended.

The article was devoted to topics related to the courier market, electromobility and renewable energy sources. This is the direction in which the car market is currently heading. The current situation of transport in Poland and the electrification trends of the courier market in Poland and beyond were presented and discussed. The article presents the conducted research and analyzes it for a selected company, which at the same time constituted the achievement of the goal and the solution of the research problem. Much attention was focused on conducting AHP analysis and simulating route changes for selected regions. The study contains an introduction and a summary discussing the most important conclusions.

The bionic polarization imaging navigation sensor (BPINS) is a navigation sensor that provides absolute heading, and it is of practical engineering significance to model the measurement error of BPINS. The existing BPINSs are still modeled using photodiode-based measurements rather than imaging measurements and are not modeled systematically enough. This paper proposes a measurement model of BPINS that takes into account the geometric and polarization errors of the optical system. Firstly, the key error factors affecting the measurement accuracy of BPINS are systematically investigated and the Stokes vector-based measurement error model of BPINS is established. Secondly, based on its measurement error model, the effect of the error source on the measurement performance of BPINS is quantitatively analyzed by using Rayleigh scattering to generate scattered sunlight as a known incident light source. The experimental results show that the coordinate deviation of the principal point has a greater impact, followed by grayscale response inconsistency of CMOS and integration angle error of micro-polarization array, and finally lens attenuation. This finding can guide the subsequent calibration of BPINS, and the quantitative results provide an important reference for its optimal design.

Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging comparatively to other industries. A promising approach is to combine Deep Neural Networks (DNN) with prior knowledge in Hybrid Neural Network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It revealed that HNNs were applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs were mainly applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies combined shallow Feedforward Neural Networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and Physics Informed Neural Networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.

This paper presents new perspectives of photonic technologies for capsule endoscopy. It is firstly presented a review about conventional endoscopy (upper endoscopy and colonoscopy), followed by capsule endoscopy (CE), as well as their techniques, advantages, and drawbacks. The technologies for CEs presented in this paper have the potential of integration on the existing endoscopic systems commercially available. Such technologies include narrow band imaging (NBI), photodynamic therapy (PDT), confocal laser endomicroscopy (CLE), Optical coherence tomography (OCT) and Spectroscopy in order to improve the performance of the gastrointestinal (GI) tract examination. In the context of NBI, two optical filters were designed and fabricated for integration into endoscopic capsules, allowing the visualization of light centered at the 415 nm and 540 nm. These optical filters are based on the principle of Fabry-Perot and were made of thin-films of titanium dioxide (TiO2) and silicon dioxide (SiO2). Moreover, strategies and aid solutions for the adaptation of ECs for PDT are also discussed.

Our image recognition system mainly judges whether the left upper limb in the image is the left upper limb or the right upper limb through our deep learning model in the image. The doctor then could give the correct surgical position. From the experimental results, it could be found that the precision rate and recall rate of the intelligent image recognition system proposed in this paper for preventing the upper limb dislocation surgery could reach 98% and 93%, respectively. It proved that our artificial intelligent image recognition system, AIIRS, could indeed assist orthopedic surgeons to prevent the occurrence of left and right dislocation in upper limb surgery. At the same time, this paper also completes the IRB application approval through the prototype experimental results and will conduct the second phase of human trials in the future. It showed that the research results of this paper will be of great benefit and research value to upper limb orthopedic surgery.

The number of people affected by eye conditions is growing every year, due to the popularity of electronic devices and an aging population. In the world of medicine, accomplishing eye medication administration has always been a difficult task. Despite the fact that there are many eye drops on the market, most of them have important limitations due to quick clearance mechanisms and ocular barriers. One solution with tremendous potential is the contact lens used as medication delivery vehicle to bypass this constraint. Therapeutic contact lenses for ocular medication delivery have attracted a lot of attention because they have the potential to improve ocular bioavailability, patient compliance, and reduce side effects. However, it is essential not to compromise essential features such as water content, optical transparency, and modulus in order to target successful in vitro and in vivo sustained drug delivery profile from impregnated contact lenses. Aside from difficulties like drug stability and burst release, the changing of lens physical and chemical properties caused by therapeutic or non-therapeutic components can limit the commercialization potential of pharmaceutical-loaded lenses. Research has progressed towards bioinspired techniques and smart materials to improve the efficacy of drug-eluting contact lenses. The bioinspired method uses bioinspired polymeric materials to improve biocompatibility, a specialized molecule recognition technique called molecular imprinting, or a stimuli-responsive system to improve biocompatibility and support drug delivery efficacy of drug-eluting contact lenses. This review encompasses strategies of material design, lens manufacturing and drug impregnation under the current auspices in ophthalmic therapies and projects an outlook onto future opportunities in the field of eye conditions management by means of active principles-eluting contact lens.

To study and monitor the adverse health consequences from using electronic cigarettes, a user’s puff topography, which are quantification parameters of the user’s vaping habits, plays a central role. In this work, we introduce a topography sensor to measure the mass of total particulate matter generated in every puff and to estimate the nicotine yield. The sensor is compact and low-cost, and is integrated into the electronic cigarette device to promptly and conveniently monitor the user’s daily puff topography. The topography sensor is comprised of a photometric sensor and a pressure sensor. The photometric sensor measures the mass concentration of the aerosol, based on scattering of near-infrared light from airborne particles, while the pressure sensor measures the flow rate. The topography sensor was experimented under various conditions with a wide range of atomizer power, puff duration, and inhalation pressure. The sensor’s accuracy was validated by comparing the sensor’s readings with reference measurements, and the results matched closely with the trends reported by existing studies on electronic cigarettes. An example application of tracking a user’s puff topography was also demonstrated. Our topography sensor holds great promise in mitigating health risks of vaping, and in promoting quality control of electronic cigarette products.

The introduction of exoskeletons in the industry has focused on improving worker safety. Exoskeletons have the objective of decreasing the risk of injury or fatigue when performing physically demanding tasks. The exoskeleton’s effects on the muscles is one of the most common focus in the assessments. The present study aims to analyse the muscle interactions generated by using a passive lumbar exoskeleton during load-handling tasks in laboratory conditions with and without an exoskeleton. Electromyographic data of the muscles involved in the task were recorded from twelve participants performing load-handling tasks. Correlation Coefficient, Coherence Coefficient, Mutual Information, and Multivariate Sample Entropy were calculated to determine if there were significant differences in muscle interactions between the two test conditions. Results showed statistically significant differences for all pairs of muscles and indicated that the use of the exoskeleton implied more constant values throughout the exercise. The Directed Conditional Granger Causality was obtained to study the directionality of the interactions, with significant differences in two muscle pairs Gluteus-Quadriceps and Gluteus-Lumbar in the gravity-positive direction in both cases. In conclusion, EMG parameters chosen appear to be appropriate measurements for studying the exoskeleton effects over muscle couplings.

It is generally accepted that the human abdominal wall is comprised of skin, subcutaneous tissues, muscles and their aponeuroses, and the parietal peritoneum. Understanding these layers and their mechanical properties provides valuable information to those designing procedural skills trainers, supporting surgical procedures (hernia repair), and engineering-based work (in silico simulation). However, there is little literature available on the mechanical properties of the abdominal wall in layers or as a composite. This work characterizes the tensile properties of the human abdominal wall by layer and as a partial composite. Tissues were collected from fresh-never-frozen and fresh-frozen cadavers and tested in uniaxial tension at a rate of 5 mm/min until failure. Stress-strain curves were created for each sample, and the values for elastic moduli, ultimate tensile strength, and strain at failure were obtained. The experimental outcomes from this study demonstrated variation of tensile properties within and between tissues. The data also suggest that the tensile properties of composite abdominal walls are not additive. Ultimately, this body of work contributes to a deeper comprehension of these mechanical properties and will serve to enhance patient care, refine surgical interventions, and assist with more sophisticated engineering solutions.

To maintain a healthy lifestyle, adults rely on their ability to walk while simultaneously managing multiple tasks that challenge their coordination. This study investigates the impact of cognitive dual-task on lower limb muscle activities in 21 healthy young adults during both gait initiation and steady-state gait. We utilized wireless electromyography sensors to measure muscle activities, along with a 3D motion capture system and force plates to detect the phases of gait initiation and steady-state gait. Participants were asked to walk at their self-selected pace, and we compared single-task and dual-task conditions. We analyzed mean muscle activation and co-contraction in the biceps femoris, vastus lateralis, gastrocnemius, and tibialis anterior muscles. The findings revealed that during gait initiation with the dual-task condition, there was a decrease in mean muscle activation and an increase in mean muscle c-contraction between the swing and stance limb compared to the single-task condition. In steady-state gait, there was also a decrease in mean muscle activation in the dual-task condition compared to the single-task condition. When participants performed dual-task activities during gait initiation, early indicators of reduced balance capability were observed. Additionally, during dual-task steady-state gait, knee stabilizer muscles exhibited signs of altered activation, contributing to balance instability.

Deep Neutral Networks (DNNs) were initially proposed towards the midst of the 20th century, motivated by the neural structure and mechanism of the human brain. Thanks to major ad-vancements in computational resources, during the past decade DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and gen-eralizable networks is a major challenge, especially in critical applications, such as medical Computer Aided Diagnostics (CAD) and other medical imaging tasks, including classification, regression and reconstruction. Within this context, a line of physics-driven approaches for deep learning has recently emerged, aimed at improving the stability and generalization capacity of DNNs for medical imaging applications. In this paper, we review recent work focused on phys-ics-driven or prior-information learning for a variety of imaging modalities and medical applica-tions. We discuss how the inclusion of domain-related knowledge into the learning process and networks’ design supports their stability and generalization capability. In addition, we highlight current and future challenges within this scope.

Diffuse optical tomography (DOT) technology enables a differentiation between oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the sensory and motor cerebral gyri, resulting in greater sensitivity for cerebral activation compared to functional magnetic resonance imaging (fMRI). Here, we introduce a novel approach where functional regions of interest (ROIs) are created based on the specific signal behavior observed in DOT measurements, in contrast to the conventional use of structural-ROIs obtained from anatomical information. The generation of cerebral activation maps involves using the general linear model (GLM) to compare the outcomes obtained from both the functional and structural ROI approaches. DOT-derived maps are then compared with maps derived from fMRI datasets considered the gold standard for assessing functional brain activity. The results obtained demonstrate the effectiveness of employing functional ROIs to improve the spatial location of functional activations in the sensory and motor cerebral gyri by leveraging the neural synchronization data provided by DOT. Furthermore, this methodology simplifies data processing in animal models, where anatomical differences compared to the human head can pose challenges. By incorporating functional ROIs prior to GLM application, this study offers enhancements to DOT analysis techniques and broadens its applicability in both human and animal models.

The purpose of this study was to investigate the functional load capacity of the periodontal ligament (PDL) in a full arch maxilla and mandible model using numerical simulation. The goal of was to determine the functional load pattern in multi- and single rooted teeth with full and reduced periodontal support. CBCT data were used to create 3D-models of a maxilla and mandible. The DICOM dataset was used to create a CAD-model. For a precise description of the surfaces of each structure (enamel, dentin, cementum, pulp, PDL, gingiva, bone), each tooth was segmented separately, and the biomechanical characteristics were considered. A Finite-Element-Analysis (FEA) software computed the biomechanical behavior of stepwise increased force of 700N in cranial and 350N in ventral direction of the muscle approach of the Masseter muscle. The periodontal attachment (cementum-PDL-bone contact) was subsequently reduced in 1mm increments and the simulation repeated. Quantitative (pressure, tension, and deformation) and qualitative (color-coded images) data were recorded and descriptively analyzed. The teeth with the highest load capacities were the upper and lower molars (0.4-0.6MPa), followed by the premolars (0.4-0.5MPa) and canines (0.3-0.4MPa) when vertically loaded. Qualitative data showed that the area with the highest stress in the PDL were for single rooted teeth the cervical and apical area and for molars additionally the furcation roof. In both, single- and multi rooted teeth the gradual reduction of the bone levels caused an increase of the load on the remaining PDL. Cervical and apical areas as well as the furcation roof are the zones with the highest functional stress. The more bone loss, the higher the mechanical load on the residual periodontal supporting structures.

Aborycin is a type I lasso peptide with a stable interlocked structure, offering a favorable framework for drug development. The aborycin biosynthetic gene cluster gul from marine sponge-associated Streptomyces sp. HNS054 was cloned and integrated into the chromosome of S. coelicolor hosts with different copies. The 3-copy gul-integration strains S. coelicolor M1346::3gul showed better production than one-copy or 2-copy gul-integration strains, and the total titer reached approximately 10.4 mg/L, i.e., 2.1 times that of the native strain. Then, five regulatory genes, phoU (SCO4228), wblA (SCO3579), SCO1712, orrA (SCO3008) and gntR (SCO1678), which were reported to have negative effects on secondary metabolism, were further knocked out from the M1346::3gul genome by CRISPR/Cas9 technology. While the ΔSCO1712 mutant showed a significant decrease (4.6 mg/L) and the ΔphoU mutant showed no significant improvement (12.1 mg/L) in aborycin production, the ΔwblA, ΔorrA and ΔgntR mutations significantly improved the aborycin titers to approximately 23.6 mg/L, 56.3 mg/L and 48.2 mg/L, respectively, which were among the highest heterologous yields for lasso peptides in both Escherichia coli systems and Streptomyces systems. Thus, this study provided important clues for future studies on enhancing antibiotic production in Streptomyces systems.

Effective oral care is a critical requirement to maintain high quality of life. Most oral diseases are caused by plaque (oral biofilm), which is also correlated with systemic diseases. A common method to remove biofilm is brushing teeth with a toothpaste. However, 3.5 billion people in the world have oral diseases, meaning that more efficient methods of removing biofilms are needed. We have developed a toothbrush applying bioelectric effect (BE) utilizing an electric force for biofilm removal. It demonstrated significantly higher biofilm removal efficiency than non-BE manual toothbrushes. Tests were performed in saline and toothpaste conditions using various pressure. Results showed that the BE toothbrush had a significantly higher biofilm removal efficiency in saline (0.5 N: 215.43 ± 89.92 %, 2.5 N: 116.77 ± 47.02 %) and in a toothpaste slurry (0.5 N: 104.96 ± 98.93 %, 2.5 N: 96.23 ± 35.16 %) than non-BE manual toothbrushes. Results also showed that BE toothbrushes were less dependent on toothpaste. This study suggests that the application of BE can be a new solution to plaque problems in oral care.