Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile^© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results. Full article

Injection molding technology is widely utilized across various industries for its ability to fabricate complex-shaped components with exceptional dimensional accuracy. However, challenges related to injection quality often arise, necessitating innovative approaches for improvement. This study investigates the influence of surface roughness on the efficiency of conformal cooling channels produced using additive manufacturing technologies, specifically Direct Metal Laser Sintering (DMLS) and Atomic Diffusion Additive Manufacturing (ADAM). Through a combination of experimental measurements, including surface roughness analysis, scanning electron microscopy, and cooling system flow analysis, this study elucidates the impact of surface roughness on coolant flow dynamics and pressure distribution within the cooling channels. The results reveal significant differences in surface roughness between DMLS and ADAM technologies, with corresponding effects on coolant flow behavior. Following that fact, this study shows that when cooling channels’ surface roughness is lowered up to 90%, the reduction in coolant media pressure is lowered by 0.033 MPa. Regression models are developed to quantitatively describe the relationship between surface roughness and key parameters, such as coolant pressure, Reynolds number, and flow velocity. Practical implications for the optimization of injection molding cooling systems are discussed, highlighting the importance of informed decision making in technology selection and post-processing techniques. Overall, this research contributes to a deeper understanding of the role of surface roughness in injection molding processes and provides valuable insights for enhancing cooling system efficiency and product quality. Full article

Amino-acid-fermenting bacteria are wasteful organisms within the rumens of beef cattle that remove dietary amino nitrogen by producing ammonia, which is then excreted renally. There are currently no on-label uses for the control of this microbial guild, but off-label use of broad-spectrum antimicrobials has shown efficacy, which contributes to antimicrobial resistance. Plant-derived antimicrobials supplemented into the diets of cattle may offer worthwhile alternatives. This study sought to investigate the role of cannabidiol (CBD) as a terpenophenolic antimicrobial. Ex vivo cell suspensions were harvested from the rumen fluid of Angus × Holstein steers in non-selective media with amino acid substrates. The suspensions were treated with five concentrations of CBD (860 μg mL⁻¹–0.086 μg mL⁻¹) and incubated (24 h), after which ammonia production and viable number of cells per substrate and treatment were measured. The data demonstrated a ~10–15 mM reduction in ammonia produced at the highest concentration of CBD and negligible changes in the viable number of amino-acid-fermenting bacteria. CBD does not appear to be a biologically or economically viable terpenophenolic candidate for the control of amino acid fermentation in beef cattle. Full article

Hemodynamics in intracranial aneurysm strongly depends on the non-Newtonian blood behavior due to the large number of suspended cells and the ability of red blood cells to deform and aggregate. However, most numerical investigations on intracranial hemodynamics adopt the Newtonian hypothesis to model blood flow and predict aneurysm occlusion. The aim of this study was to analyze the effect of the blood rheological model on the hemodynamics of intracranial aneurysms in the presence or absence of endovascular treatment. A numerical investigation was performed under pulsatile flow conditions in a patient-specific aneurysm with and without the insertion of an appropriately reconstructed flow diverter stent (FDS). The numerical simulations were performed using Newtonian and non-Newtonian assumptions for blood rheology. In all cases, FDS placement reduced the intra-aneurysmal velocity and increased the relative residence time (RRT) on the aneurysmal wall, indicating progressive thrombus formation and aneurysm occlusion. However, the Newtonian model largely overestimated RRT values and consequent aneurysm healing with respect to the non-Newtonian models. Due to the non-Newtonian blood properties and the large discrepancy between Newtonian and non-Newtonian simulations, the Newtonian hypothesis should not be used in the study of the hemodynamics of intracranial aneurysm, especially in the presence of endovascular treatment. Full article

Atherosclerotic plaque buildup in the coronary and carotid arteries is pivotal in the onset of acute myocardial infarctions or cerebrovascular events, leading to heightened levels of illness and death. Atherosclerosis is a complex and multistep disease, beginning with the deposition of low-density lipoproteins in the arterial intima and culminating in plaque rupture. Modern technology favors non-invasive imaging techniques to assess atherosclerotic plaque and offer insights beyond mere artery stenosis. Among these, computed tomography stands out for its widespread clinical adoption and is prized for its speed and accessibility. Nonetheless, some limitations persist. The introduction of photon-counting computed tomography (PCCT), with its multi-energy capabilities, enhanced spatial resolution, and superior soft tissue contrast with minimal electronic noise, brings significant advantages to carotid and coronary artery imaging, enabling a more comprehensive examination of atherosclerotic plaque composition. This narrative review aims to provide a comprehensive overview of the main concepts related to PCCT. Additionally, we aim to explore the existing literature on the clinical application of PCCT in assessing atherosclerotic plaque. Finally, we will examine the advantages and limitations of this recently introduced technology. Full article

Background: The ketogenic diet (KD) has been highly developed in the past for the treatment of epileptic pathological states in children and adults. Recently, the current re-emergence in its popularity mainly focuses on the therapy of cardiometabolic diseases. The KD can also have anti-inflammatory and neuroprotective activities which may be applied to the prevention and/or co-treatment of a diverse range of psychiatric disorders. Purpose: This is a comprehensive literature review that intends to critically collect and scrutinize the pre-existing research basis and clinical data of the potential advantageous impacts of a KD on stress, anxiety, depression, schizophrenia and bipolar disorder. Methods: This literature review was performed to thoroughly represent the existing research in this topic, as well as to find gaps in the international scientific community. In this aspect, we carefully investigated the ultimate scientific web databases, e.g., PubMed, Scopus, and Web of Science, to derive the currently available animal and clinical human surveys by using efficient and representative keywords. Results: Just in recent years, an increasing amount of animal and clinical human surveys have focused on investigating the possible impacts of the KD in the prevention and co-treatment of depression, anxiety, stress, schizophrenia, and bipolar disorder. Pre-existing basic research with animal studies has consistently demonstrated promising results of the KD, showing a propensity to ameliorate symptoms of depression, anxiety, stress, schizophrenia, and bipolar disorder. However, the translation of these findings to clinical settings presents a more complex issue. The majority of the currently available clinical surveys seem to be moderate, usually not controlled, and have mainly assessed the short-term effects of a KD. In addition, some clinical surveys appear to be characterized by enormous dropout rates and significant absence of compliance measurement, as well as an elevated amount of heterogeneity in their methodological design. Conclusions: Although the currently available evidence seems promising, it is highly recommended to accomplish larger, long-term, randomized, double-blind, controlled clinical trials with a prospective design, in order to derive conclusive results as to whether KD could act as a potential preventative factor or even a co-treatment agent against stress, anxiety, depression, schizophrenia, and bipolar disorder. Basic research with animal studies is also recommended to examine the molecular mechanisms of KD against the above psychiatric diseases. Full article

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward. Full article

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods. Full article

(Fe,Co)₂(P,Si) quaternary compounds combine large uniaxial magnetocrystalline anisotropy, significant saturation magnetization and tunable Curie temperature, making them attractive for permanent magnet applications. Single crystals or conventionally prepared bulk polycrystalline (Fe,Co)₂(P,Si) samples do not, however, show a significant coercivity. Here, after a ball-milling stage of elemental precursors, we optimize the sintering temperature and duration during the solid-state synthesis of bulk Fe_1.85Co_0.1P_0.8Si_0.2 compounds so as to obtain coercivity in bulk samples. We pay special attention to shortening the heat treatment in order to limit grain growth. Powder X-ray diffraction experiments demonstrate that a sintering of a few minutes is sufficient to form the desired Fe₂P-type hexagonal structure with limited secondary-phase content (~5 wt.%). Coercivity is achieved in bulk Fe_1.85Co_0.1P_0.8Si_0.2 quaternary compounds by shortening the heat treatment. Surprisingly, the largest coercivities are observed in the samples presenting large amounts of secondary-phase content (>5 wt.%). In addition to the shape of the virgin magnetization curve, this may indicate a dominant wall-pining coercivity mechanism. Despite a tenfold improvement of the coercive fields for bulk samples, the achieved performances remain modest (H_C ≈ 0.6 kOe at room temperature). These results nonetheless establish a benchmark for future developments of (Fe,Co)₂(P,Si) compounds as permanent magnets. Full article

Submerged macrophytes in eutrophic aquatic environments adapt to changes in ammonia nitrogen (NH₄-N) levels by modifying their levels of free amino acids (FAAs) and soluble carbohydrates (SCs). As symbionts of submerged macrophytes, epiphytic bacteria have obvious host specificity. In the present study, the interspecific differences in the FAA and SC contents of Hydrilla verticillata (Linn. f.) Roylep, Vallisneria natans Hara and Chara braunii Gmelin and their leaf epiphytic bacterial communities were assessed in response to increased NH₄-N concentrations. The results revealed that the response of the three submerged macrophytes to NH₄-N stress involved the consumption of SCs and the production of FAAs. The NH₄-N concentration had a greater impact on the variation in the FAA content, whereas the variation in the SC content was primarily influenced by the species. At the phylum level, the relative abundance of Nitrospirota on the leaves exhibited specific differences, with the order H. verticillata > V. natans > C. braunii. The dominant genera of epiphytic bacteria with denitrification effects on V. natans, H. verticillata and C. braunii leaves were Halomonas, Acinetobacter and Bacillus, respectively. When faced with NH₄-N stress, the variation in epiphytic bacterial populations associated with ammonia oxidation and denitrification among submerged macrophytes could contribute to their divergent responses to heightened nitrogen levels. Full article

Douchi is a Chinese traditional fermented food with a unique flavor. Methyl anthranilate (MA) plays an important role in formation of this flavor. However, the complicated relationship between the MA formation and the metabolic mechanism of the key functional microorganisms remains unclear. Here, we elucidated the response mechanism of aroma production driven by high salt stress in Trichomonascus ciferrii WLW (T. ciferrii WLW), which originates from the douchi fermentation process. The highest production of MA was obtained in a 10% NaCl environment. The enhanced expression of the key enzyme genes of the pentose phosphate pathway and shikimic acid pathway directed carbon flow toward aromatic amino acid synthesis and helped sustain an increased expression of metK to synthesize a large amount of the methyl donor S-adenosylmethionine, which promoted methyl anthranilate yield. This provides a theoretical basis for in-depth research on the applications of the flavor formation mechanisms of fermented foods. Full article

This work focuses on the study and comparison of two mixing methods for the dispersion of Alumina-Toughened Zirconia (ATZ) within the polymer matrix of Poly(ε-caprolactone) (PCL). The dry-mixing method using solvent-free impact milling (M) and the solvent casting method with chloroform (SC) were investigated. Samples were produced by 3D printing, and specimens were printed at increasing ATZ loadings (namely, 10, 20, and 40 wt.%). The chemico-physical, mechanical, and cell interaction characteristics of the materials prepared with both mixing methods were studied. Solvent mixing allowed better dispersion of the ATZ in the polymer matrix with respect to dry mixing. In addition, dry mixing affected the molecular weight of the PCL/ATZ composites much more than the solvent casting method. For these reasons, materials obtained by solid mixing exhibited the worst mechanical performance with respect to those obtained by solvent casting, which showed increased Young’s moduli with increasing ATZ amounts. The in vitro biological response elicited in a mesenchymal stem cell model seemed to be influenced by the mixing method, with a preference for the composites obtained through solvent mixing and containing 20 or 40 wt.% of ATZ. Full article

While understandings of Johannine Christianity have been many and varied, single-issue analyses no longer suffice. Things were more complex than simply inferring that synagogue-Johannine tensions, pneumatizing Gnostics, heretical secessionists, or Petrine ecclesiasts was the lone issue. Nor is a two-level reading of the Johannine narrative plausible, as there is no evidence of alien material underlying John’s story of Jesus. Thus, the early, middle, and later phases of the Johannine tradition must be taken into consideration, as an autonomous memory of Jesus is best seen as developing in a first edition, which was finalized later by the Johannine Elder after writing the Epistles. Within that perspective, Social Identity Complexity Theory is well applied as a means of understanding a number of partners in dialogue within the Johannine Situation, including the stances of Jesus remembered by the Fourth Evangelist and Johannine Elder, who addressed no fewer than seven crises over seven decades within the cosmopolitan Johannine Situation. Full article

In this article, we describe a performance comparison conducted between several digital filters intended to mitigate the intrinsic noise observed in magnetometers. The considered filters were used to smooth the control signals derived from the magnetometers, which were present in an autonomous forestry machine. Three moving average FIR filters, based on rectangular Bartlett and Hanning windows, and an exponential moving average IIR filter were selected and analyzed. The trade-off between the noise reduction factor and the latency of the proposed filters was also investigated, taking into account the crucial importance of latency on real-time applications and control algorithms. Thus, a maximum latency value was used in the filter design procedure instead of the usual filter order. The experimental results and simulations show that the linear decay moving average (LDMA) and the raised cosine moving average (RCMA) filters outperformed the simple moving average (SMA) and the exponential moving average (EMA) in terms of noise reduction, for a fixed latency value, allowing a more accurate heading angle calculation and position control mechanism for autonomous and unmanned ground vehicles (UGVs). Full article

Adenoma detection rate (ADR) is challenging to measure, given its dependency on pathology reporting. Polyp detection rate (PDR) (percentage of screening colonoscopies detecting a polyp) is a proposed alternative to overcome this issue. Overall PDR from all colonoscopies is a relatively novel concept, with no large-scale studies comparing overall PDR with screening-only PDR. The aim of the study was to compare PDR from screening, surveillance, and diagnostic indications with overall PDR and evaluate any correlation between individual endoscopist PDR by indication to determine if overall PDR can be a valuable surrogate for screening PDR. Our study analyzed a prospectively collected national endoscopy database maintained by the National Institute of Health from 2009 to 2014. Out of 354,505 colonoscopies performed between 2009–2014, 298,920 (n = 110,794 average-risk screening, n = 83,556 average-risk surveillance, n = 104,770 diagnostic) met inclusion criteria. The median screening PDR was 25.45 (IQR 13.15–39.60), comparable with the median overall PDR of 24.01 (IQR 11.46–35.86, p = 0.21). Median surveillance PDR was higher at 33.73 (IQR 16.92–47.01), and median diagnostic PDR was lower at 19.35 (IQR 9.66–29.17), compared with median overall PDR 24.01 (IQR 11.46–35.86; p < 0.01). The overall PDR showed excellent concordance with screening, surveillance, and diagnostic PDR (r > 0.85, p < 0.01, 2-tailed). The overall PDR is a reliable and pragmatic surrogate for screening PDR and can be measured in real time, irrespective of colonoscopy indication. Full article

The study investigates the structural and chemical properties of brown rice flour (WRF), black rice flour (BRF) and their mixtures in ratios of 25%, 50% and 75% to provide reference information for the gluten-free bakery industry. BRF contains higher concentrations of proteins, lipids, total minerals, crude fiber, total polyphenols, proanthocyanidins and flavonoids than WRF. A higher amylose content in BRF than in WRF resulted in flour mixtures with slower starch digestion and a lower glycemic response depending on the BRF ratio added. Differences in the chemical composition of WRF and BRF led to improved composition of the flour mixtures depending on the BRF ratio. The presence of anthocyanidins and phenolic acids in higher concentrations in the BRF resulted in a red–blue color shift within the flour mixtures. The deconvoluted FTIR spectra showed a higher proportion of α-helixes in the amide I band of BRF proteins, indicating their tighter folding. An analysis of the FTIR spectra revealed a more compact starch structure in BRF than in WRF. By processing reflection spectra, nine optically active compound groups were distinguished in rice flour, the proportion in BRF being 83.02% higher than in WRF. Due to co-pigmentation, the bathochromic shift to higher wavelengths was expressed by the proanthocyanins and phenolic acids associated with the wavelengths 380 nm to 590 nm and at 695 nm. Anthocyanins, protein–tannin complexes, methylated anthocyanins and acylated anthocyanins, associated with wavelengths 619, 644 and 668 nm, exhibited a hypsochromic effect by shifting the wavelengths to lower values. This research represents a first step in the development of rice-based products with increased nutritional value and a lower glycemic index. Full article

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists’ ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists’ scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists’ scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (<5 mm), volume measurements, kappa scores, and Bland–Altman differences that align closely with those measured by radiologists. Moreover, the inter-user variability between radiologists using the smart manual contouring platform was under 5% for T1c and under 10% for FLAIR images. These results underscore the MRIMath© platform’s low inter-user variability and the high accuracy of its T1c and FLAIR AI models. Full article

The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable system that classifies the electrical activity of three shoulder muscles in real time for actuator control, marking a milestone in the autonomy of prosthetic devices. Utilizing low-power microcontrollers, the system ensures continuous EMG signal recording, enhancing user mobility. Focusing on a case study—a 42-year-old man with left shoulder disarticulation—EMG activity was recorded over two days using a specifically designed electronic board. Data processing was performed using the Edge Impulse platform, renowned for its effectiveness in implementing AI on edge devices. The first day was dedicated to a training session with 150 repetitions spread across 30 trials and three different movements. Based on these data, the second day tested the AI model’s ability to classify EMG signals in new movement executions in real time. The results demonstrate the potential of portable AI-based systems for prosthetic control, offering accurate and swift EMG signal classification that enhances prosthetic user functionality and experience. This study not only underscores the feasibility of real-time EMG signal classification but also paves the way for future research on practical applications and improvements in the quality of life for prosthetic users. Full article

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disorder. While there are five FDA-approved drugs for treating this disease, each has only modest benefits. To design new and more effective therapies for ALS, particularly for sporadic ALS of unknown and diverse etiologies, we must identify key, convergent mechanisms of disease pathogenesis. This review focuses on the origin and effects of glutamate-mediated excitotoxicity in ALS (the cortical hyperexcitability hypothesis), in which increased glutamatergic signaling causes motor neurons to become hyperexcitable and eventually die. We characterize both primary and secondary contributions to excitotoxicity, referring to processes taking place at the synapse and within the cell, respectively. ‘Primary pathways’ include upregulation of calcium-permeable AMPA receptors, dysfunction of the EAAT2 astrocytic glutamate transporter, increased release of glutamate from the presynaptic terminal, and reduced inhibition by cortical interneurons—all of which have been observed in ALS patients and model systems. ‘Secondary pathways’ include changes to mitochondrial morphology and function, increased production of reactive oxygen species, and endoplasmic reticulum (ER) stress. By identifying key targets in the excitotoxicity cascade, we emphasize the importance of this pathway in the pathogenesis of ALS and suggest that intervening in this pathway could be effective for developing therapies for this disease. Full article

With the increase in elevator usage, more and more elevator real-time monitoring equipment is being applied to the operation of elevators. Traditional elevator monitoring equipment adopts a multi-sensor decentralized installation and layout, and the monitoring accuracy is low, which directly affects the effective alarm of the monitoring system; however, existing online monitoring systems cannot quickly alarm for faults. Aiming to solve the above problems, an elevator online monitoring system based on narrow-band Internet of Things (NB-IoT) is designed. The system is highly integrated with an STM32 main control chip, a six-axis acceleration gyroscope sensor, and an air pressure sensor to realize the edge calculation of the monitoring system. At the same time, this paper eliminates the temperature drift of the pressure sensor by using a temperature compensation algorithm and inputs the extracted characteristic parameters into the BP neural network for training to eliminate the zero drift so as to obtain the real-time height data of the elevator. The six-axis acceleration gyroscope sensor is used to calculate the posture so as to avoid the problem that a three-axis acceleration sensor or a three-axis gyroscope sensor alone cannot obtain accurate posture data. In order to further improve the monitoring accuracy, the peak-to-peak value of the signal is calculated by using a 95% confidence interval algorithm to reduce the suppression of the high-frequency components of the signal by noise and ensure that the signal has a large signal-to-noise ratio so that the obtained elevator car posture and vibration operation data are more accurate. Finally, the effectiveness of the proposed method is verified by experiments. Full article

Duchenne muscular dystrophy (DMD) is one of the most frequent and severe childhood muscle diseases. Its pathophysiology is multifaceted and still incompletely understood, but we and others have previously shown that oxidative stress plays an important role. In particular, we have demonstrated that inhibition of mitochondrial monoamine oxidases could improve some functional and biohumoral markers of the pathology. In the present study we report the use of dystrophic mdx mice to evaluate the efficacy of a dual monoamine oxidase B (MAO-B)/semicarbazide-sensitive amine oxidase (SSAO) inhibitor, PXS-5131, in reducing inflammation and fibrosis and improving muscle function. We found that a one-month treatment starting at three months of age was able to decrease reactive oxygen species (ROS) production, fibrosis, and inflammatory infiltrate in the tibialis anterior (TA) and diaphragm muscles. Importantly, we also observed a marked improvement in the capacity of the gastrocnemius muscle to maintain its force when challenged with eccentric contractions. Upon performing a bulk RNA-seq analysis, PXS-5131 treatment affected the expression of genes involved in inflammatory processes and tissue remodeling. We also studied the effect of prolonged treatment in older dystrophic mice, and found that a three-month administration of PXS-5131 was able to greatly reduce the progression of fibrosis not only in the diaphragm but also in the heart. Taken together, these results suggest that PXS-5131 is an effective inhibitor of fibrosis and inflammation in dystrophic muscles, a finding that could open a new therapeutic avenue for DMD patients. Full article

This study investigates the distribution of hyperhomocysteinemia and cardiovascular metabolic syndrome (SM) among participants, shedding light on their prevalence and co-occurrence within the study cohort. Through an analysis of demographic characteristics and health parameters, including age, gender, and body mass index (BMI), alongside nutritional data, correlations between these factors and health risks are explored. Results reveal a notable prevalence of hyperhomocysteinemia, with 45.3% of participants exhibiting this condition. Furthermore, 31.4% of the cohort does not present hyperhomocysteinemia or SM, while 23.3% shows SM without hyperhomocysteinemia. The study underscores gender-specific dietary recommendations due to significant variations in nutrient intake patterns. Additionally, inverse correlations between health risks like obesity, hypertension, and hypercholesterolemia and nutrient requirements highlight the need for tailored dietary interventions. Age-related changes in nutrient needs and the positive correlation between physical activity levels and certain nutrient demands further emphasize the importance of personalized dietary strategies. Variations in nutrient intake by gender, inverse correlations with health risks, and age-related changes underscore the need for tailored dietary strategies. These findings provide valuable insights for healthcare professionals in developing targeted nutritional interventions to mitigate disease risk and promote overall health and well-being. Full article