The ability of recombinant, SARS-CoV-2 Spike (S) protein to modulate the production of two COVID-19 relevant, pro-inflammatory cytokines (IL-6 and IFN-γ) in PBMC cultures of healthy, pre-Covid-19 subjects was investigated. We observed that cytokine production was largely and diversely modulated by the S protein depending on antigen or mitogen stimulation, as well as on the protein source, insect (S-in) or human (S-hu) cells. While both proteins co-stimulated cytokine production by polyclonally CD3-activated T cells, PBMC activation by the mitogenic lectin Concanavalin A (Con A) was up modulated by S-hu protein and down-modulated by S-in protein. These modulatory effects were likely mediated by the S glycans as demonstrated by direct Con A-S binding experiments and use of yeast mannan as Con A binder. While being ineffective in modulating memory antigenic T cell responses, the S proteins and mannan were able to induce IL-6 production in unstimulated PBMC cultures and upregulate the expression of Mannose Receptor (CD206), a marker of anti-inflammatory M2 macrophage. Our data point to a relevant role of N-glycans, particularly N-mannosidic chains, decorating the S protein in the immunomodulatory effects here reported. These novel biological activities of the S glycan ectodomain may add to the comprehension of Covid-19 pathology and immunity to SARS-CoV-2.

Leukemia, contributing to 3% of all cancer cases in the United States, presents a significant challenge in treatment due to its diverse subtypes, each dis-playing unique genetic and molecular abnormalities. The aggressive growth of ab-normal blood cells compromises immune function and poses challenges in developing effective therapeutic approaches. Unlike solid tumors, surgical resection is not an option, limiting treatment strategies to chemotherapy, immunotherapy, and radiation therapy, which may be nonspecific and lead to organ damage and toxicity. Anthracyclines, widely used in hematological tumors, face limitations due to severe cardiotoxicity, causing progressive cardiomyopathy and, in severe cases, congestive heart failure. Heart failure incidence is significantly higher in cancer patients treated with anthracyclines over 5, 10, and 15 years. Early detection of left ventricular dysfunction and cardiotoxicity is crucial to mitigate progression to congestive heart failure (CHF) in this population. Recent research by Bayrón-Marrero et al. reveals that lower levels of plasma soluble TREM-Like Transcript-1 (s-TLT-1), a platelet-specific receptor, correlate with left ven-tricular dysfunction in cardiovascular disease patients. Those with s-TLT-1 levels ex-ceeding 544 pg/mL exhibit a significant association with congestive heart failure. These findings suggest the potential use of s-TLT-1 as a diagnostic screening tool for early detection and treatment of anthracycline-induced CHF in leukemia patients.

The expression of non-coding RNAs varies greatly depending on the cell type and physiological state. Changes in expression affect the regulatory properties of these molecules. We investigated RNA-seq data from sorted blood cells of healthy control donors and severe COVID-19 patients. Our aim was to display a distinguishable piRNA differential expression portrait for each type of cell and to study the behavior of piRNA in the blood cells of COVID-19 patients. Overall piRNA expression in sorted control cells (erythrocytes, monocytes, lymphocytes, eosinophils, basophils, neutrophils) differs depending on cell type. Four of the most abundant piRNA were expressed relatively equally in all cell types, they included piR-49145, piR-49144, piR-49143 and piR-33151. By reviewing piRNA expression in each group of sorted cells from severe COVID-19 patients we observed 3 upregulated and 10 downregulated piRNAs in erythrocytes. Monocytes were presented with a larger amount of statistically significant piRNA, 4 upregulated and 35 downregulated. In lymphocytes, all 19 piRNAs were upregulated.

Adenosine is a multifunctional nucleoside with several roles across various levels in organisms. Beyond its intracellular involvement in cellular metabolism, extracellular adenosine potently influences both physiological and pathological processes. In relation to its blood level, adenosine impacts the cardiovascular system, such as heart beating rate and vasodilation. To exploit the adenosine levels in the blood, we employed the liquid chromatography method coupled with mass spectrometry (LC-MS). Immediately after the collection, a blood sample mixed with acetonitrile solution that is either enriched with 13C-labeled adenosine or a newly generated mixture is transferred into the tubes containing the defined amount of 13C-labeled adenosine. The 13C-enriched isotopic adenosine is used as an internal standard, allowing for more accurate quantification of adenosine. This novel protocol for LC-MS-based estimation of adenosine delivers a rapid, highly sensitive, and reproducible means for quantitative estimation of total adenosine in blood. The method also allows quantification of a few catabolites of adenosine, i.e., inosine, hypoxanthine, and xanthine. This advancement provides the analytical tool that has the potential to enhance our understanding of adenosine's systemic impact and pave the way for further investigations into its intricate regulatory mechanisms.

In the context of precision agriculture (PA), geomatic surveys exploiting UAV platforms allow the trees’ dimensional characterization and crown identification. This paper focuses on the use of low-cost UAV photogrammetry to estimate the trees’ height, as part of a project for the phytoremediation of contaminated soils. Two study areas (Area 1; Area 2) have been chosen, having different characteristics in terms of mean trees’ height (5 m; 0.7 m), to test the procedure even in a challenging context. Three campaigns have been performed in Area 1 at different altitudes (30 m, 40 m, 50 m), and one UAV flight is available in Area 2 (42 m of altitude). The implemented workflow involves the elaboration of the UAV point clouds and DSMs using the standard SfM approach, the vegetation filtering, the generation of the DTM and a GIS-based analysis to obtain the CHMs for the extraction of the trees’ heights based on a local maxima approach. UAV-derived heights have been compared with in-field measurements obtaining promising results in Area 1, confirming the applicability of the procedure for trees’ height extraction, while the application in the context of low trees was more problematic.

This paper presents the design and implementation of a remote supervision and control system for a gunpowder drum filling plant. The use of technologies such as SCADA (Ignition), IoT (Node-RED), ERP (Odoo) and digital twins (Factory I/O) to achieve remote monitoring and operation of the plant is described. The solution allows full control of the production process from any location through customized graphical interfaces. The challenges encountered in integrating multiple systems and platforms are discussed, along with the security measures implemented within the Azure cloud environment. The results demonstrate the effectiveness of 4.0 technologies in automating and digitizing critical industrial processes. This research will lay the groundwork for the adoption of these approaches in companies seeking greater efficiency, traceability and safety in their operations. The experience gained allows us to successfully face the digital transformation in modern manufacturing.

Coastal dunes worldwide are increasingly under pressure from the adverse effects of human activities. Therefore, more and more restoration measures are being taken to create conditions that help disturbed coastal dune ecosystems regenerate or recover naturally. However, many projects lack (open-access) monitoring observations needed to signal whether further actions are needed, and hence to learn by doing. This submission presents an open access data set of 37 high-resolution digital elevation models and 24 orthomosaics collected before and after the excavation of five artificial foredune trough blowouts ("notches") in winter 2012/2013 in the Dutch Zuid-Kennemerland National Park, one of the largest coastal dune restoration projects in northwest Europe. These high-resolution data provide a valuable resource for improving understanding of the biogeomorphic processes that determine the evolution of restored dune systems, as well as developing guidelines to better design future restoration efforts with foredune notching.

The goal of this article is to provide a review of the experimental techniques and procedures using vibration methods for the Structural Health Monitoring (SHM) of Polymer-Matrix Composites (PMC). It aims to be a guide for any researchers to carry out vibration experiments. The linear methods are first introduced. But as PMC is a complex material, these classic methods showed some limits, such as low accuracy for small damages, and a high environmental dependency. This is why the nonlinear methods are secondly studied, considering that the complexity of PMC induces a nonlinear behavior of the structure after damage occurrence. The different damage mechanisms are well explained in order to evaluate the potential of each vibration method to detect them.

Incretin effect helps in regulating glucose homeostasis and maintaining glycaemic control within the human body. This effect is achieved by the two naturally occurring incretin hormones: glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1). Such hormones released by specialized entero-endocrine cells of the gastrointestinal tract cause insulin augmentation after food consumption. However, incretin effects are impaired in type 2 diabetic, primarily attributed to the deterioration of GLP-1 function along with decreased GLP-1 receptor expression. Targeting this hormone has, therefore, become an important therapeutic approach to treat this diabetic condition. Native GLP-1’s brief half-life and rapid proteolytic degradation have generated significant enthusiasm in uncovering the long-acting GLP-1 receptor (GLP-1R) agonists to manage type 2 diabetes. These synthetic peptides mimic the natural GLP-1 to activate GLP-1R and effectively exert various physiological benefits in terms of weight management, blood pressure, lipid profile, obesity and major adverse cardiovascular event. This review paper provides insights into the structure, function and expression of GLP-1 and its receptor. It also summarizes existing literature regarding different types of GLP-1 receptor agonists, their effects on blood pressure and how they exert their actions. While animal models have contributed significantly to this area of study, the scarcity of human studies is noted, indicating a need for more investigation in this domain. Expanding research in humans will help in comprehending the impact of GLP-1 receptor agonists on blood pressure regulation and their underlying mechanisms.

The research aimed at the ecological assessment of the environment around uranium mining enterprises in the Turkestan region. The authors examined the state of the soil and vegetation for 2021-2023, their chemical composition and the effects of radiation. Excesses of background radiation were detected by alpha and beta studies. To study the structural organization of plant communities, we conducted floristic studies in the areas immediately adjacent to NAC “Kazatomprom” enterprises. The general habitus, size, number and shape of vegetative parts of plants were taken into account as comparison parameters. The flora growing in the study areas of the Suzak district of the region, which is distinguished by its uniqueness and narrow relevance to the ecological conditions of the area, was taken as biological objects of study. To determine the ecological state of the adjacent territories of NAC “Kazatomprom” and for physical, chemical and taxonomic analyses, 78 soil samples and 37 plant samples were obtained. The studied samples were taken near settlements N, B and S, which are closest to the territories of NAC “Kazatomprom”.

Electrospun fiber scaffolds are widely used in small diameter vascular tissue engineering because they mimick organizational structure and biological function of natural extracellular matrix. However, there is still one problem of low patency rate in the long term. The main reasons are the mismatch of compliance and the easy formation of thrombus. Therefore, it is very important to promote the rapid endothelialization of small diameter tissue engineering blood vessels by regulating the topological structure and biological properties of electrospun fiber scaffolds. Firstly, crimped polylactic acid (PLA) fibers were prepared by heat treatment to mimick the crimped structure and nonlinear mechanical properties of the elastic membrane of blood vessel wall. It improved effectively the aspect ratio of human umbilical vein endothelial cells (HUVECs) and promoted lateral alignment and growth. Then, crimped PLA fibers with three levels of pore structure were prepared by thermal induced phase separation, vapor induced phase separation and laser ablation to enhance the degree of HUVECs infiltrarion and realize patterned cell growth. Crimped PLA fibers with three levels of pore structure promote rapid endothelialization of blood vessels, which is of great significance to achieve long term patency rate of small diameter tissue engineering blood vessels.

Due to the ongoing global warming on the Earth, permafrost degradation has been extensively taking place, which poses a substantial threat to civil and industrial facilities and infrastructure elements, as well as to the utilization of natural resources in the Arctic and high-latitude regions. In order to prevent the negative consequences of permafrost thawing under the foundations of constructions, various geophysical techniques for monitoring permafrost have been proposed and applied so far: temperature, electrical, seismic and many others. At the same time, non-stationary electromagnetic methods seem to have significant potential and a number of important practical advantages. We propose a cross-borehole exploration system for the highest localization of target objects in the cryolithozone. A novel mathematical apparatus for three-dimensional modeling of transient electromagnetic (TEM) signals by the vector finite element method has been developed. The original combination of the latter, the Sumudu integral transform and artificial neural networks makes it possible to examine spatially heterogeneous objects of the cryolithozone with a high contrast of geoelectric parameters, significantly reducing computational costs. We have studied the sensitivity of the TEM signals to the boundary between thawed and frozen rocks depending on the inter-borehole distance and spatial orientations of the transmitters and receivers. Numerical simulation results of the TEM monitoring of industrial facilities located on permafrost are considered. The formation of a talik has been shown to significantly manifest itself in the measured electromagnetic responses, which enables timely prevention of industrial disasters and environmental catastrophes.

Stem cells can be used to treat diabetic mellitus and complications. ω3-docosahexaenoic acid (DHA) derived lipid mediators are inflammation-resolving and protective. This study found novel DHA-derived 7S,14R-dihydroxy-4Z,8E,10Z,12E,16Z,19Z-docosahexaenoic acid (7S,14R-diHDHA), a maresin-1 stereoisomer biosynthesized by leukocytes and related enzymes. Moreover, 7S,14R-diHDHA can enhance mesenchymal stem cell (MSC) functions in the amelioration of diabetic mellitus and retinal pericyte loss in diabetic db/db mice. MSCs treated with 7S,14R-diHDHA were delivered into db/db mice i.v. every 5 days for 35 days. Blood glucose levels in diabetic mice were lowered by 7S,14R-diHDHA-treated MSCs compared to control and untreated MSC groups, accompanied by improved glucose tolerance and higher blood insulin levels. 7S,14R-diHDHA-treated MSCs increased insulin+ β-cell ratio and decreased glucogan+ α-cell ratio in islets, as well as reduced macrophages in pancreas. 7S,14R-diHDHA induced MSC functions in promoting MIN6 β-cell viability and insulin secretion. 7S,14R-diHDHA induced MSC paracrine functions by increasing the generation of hepatocyte growth factor and vascular endothelial growth factor. Furthermore, 7S,14R-diHDHA enhanced MSC functions to ameliorate diabetes-caused pericyte loss in diabetic retinopathy by increasing their density in retina in db/db mice. Our findings provide a novel strategy for improving therapy for diabetes and diabetic retinopathy using 7S,14R-diHDHA-primed MSCs.

In drilling small holes with diameters of 1 mm or less, minute clogging and twining of chips or adhesion of the workpiece material can become factors in causing breakages of the drill bit; moreover, it can be difficult to identify the machining state. Acoustic emission (AE) sensing is a nondestructive inspection technique that measures the elastic-stress waves that are generated when a material is deformed and fractured. AE sensing permits highly sensitive measurements to be made without changing the rigidity of the experimental system, unlike force sensing of cutting resistance, etc. In the present study, attempts were made to identify the machining state and tool wear, and to predict abnormalities in small-diameter drilling by using the change in the frequency of AE signal waveforms arising from deformation and fracture. It was shown that it is possible to predict the breakage of the drill bit by detecting the high-frequency AE signals caused by adhesion. In addition, a correlation map of the AE frequency spectrum for identifying the machining state in a drilling operation is suggested.

In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using Electromagnetic Induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated CMD2 soil apparent electrical conductivity (ECa) measurements were taken and inverted to generate electromagnetic conductivity imaging (EMCI), representing soil electrical conductivity (σ) distribution through time-lapse inversion. This process involved converting EMCI data into salinity cross sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (ECe) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated the accurate prediction ability of soil salinity with R2 value of 0.81. The soil salinity cross sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmer’s income.

A crucial aspect in developing machine learning algorithms (or any type of predictive models) is the comparison of different algorithmic candidates based on evaluation criteria that measure their accuracy and practical value in terms of successfully capturing the complexity of the underlying problem and generalizing in a wide range of real-world scenarios. In the domain of diabetes mellitus machine learning techniques are widely employed in the prediction of future values of glucose concentration in the blood in order to assist the patient in avoiding deviations from the normo-glycemic value range and the consequences of hyper- and hypoglycemia. In the relative literature there is an apparent lack of a uniformly adopted evaluation metric which could combine the clarity and direct comparative nature of statistical mathematical formulas dealing with prediction error residuals (e.g., the RMSE) with the clinical insights and the visual-qualitative approach of clinical evaluation tools (e.g., Clarke's EGA). Mean Adjusted Exponent (MADEX) error metric, proposed in this paper, attempts to address this need by providing a validation tool based on an easy to implement mathematical formula, that incorporates adjustable parameters of clinical significance.

This review article focuses on the upstream pertinent pathophysiology leading to neurodegenerative disease. Specifically, the nexus appears to be blood brain barrier (BBB) leakiness resulting in a two-prong inflammatory disease spectrum damaging the microvasculature and corrupted protein synthesis and degradation with accumulating misfolded toxic proteins. The suboptimal results of removing misfolded proteins means a new approach to disease in the preclinical state is required aimed at other targets.Validated noninvasive imaging and serologic biomarkers of early preclinical disease implemented in the high-risk patient cohort along with periodic surveillance once effective treatments are developed will be required.This review discusses the physiology and pathophysiology of the BBB, new MRI imaging techniques identifying the leak and altered fluid dynamic effects in the preclinical state. The risk factors for disease development, preventative measures, and potential treatment targets are also discussed.

Structural health monitoring (SHM) is critical for ensuring the safety of infrastructures like bridges. This article presents a digital twin solution for SHM of railway bridges using low-cost wireless accelerometers and Machine Learning (ML). The system architecture combines edge on-premises computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, accelerometers stream raw vibration data, which is processed in the frequency domain and analyzed via machine learning to detect anomalies indicating potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge, where vibration data was collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks, suggesting changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warning of bridge damage and also supports the use of in-house designed sensors of lower-cost and edge computing capabilities than those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

Keywords: early childhood; education; quality; measurement; monitoring; majority world; Item Response Theory; psychometrics

Cenobamate is approved by the European Medicine Agency for the treatment of adult patients with epilepsy (PWEs) who suffer from ongoing focal-onset seizures despite appropriate treatment with at least two established antiseizure medications. Pivotal trials and post-marketing real-world observational studies have suggested a high efficacy with unusually high seizure-free rates. We sought to investigate the serum trough levels of cenobamate under steady-state conditions in seizure-free versus non-responding PWEs, and in PWEs who experienced adverse events versus those who did not. We analyzed samples from 101 PWEs treated with adjunct cenobamate under steady-state conditions. In all, 36 PWEs were seizure-free and 65 were non-responders. In 31 PWEs, adverse events were apparent, whereas in the remaining 70, no tolerability issues were reported. We found a linear correlation between the daily doses (range: 100 mg – 400 mg) and the plasma levels (3.8 mg/l – 54.6 mg/l). Neither the daily doses nor the plasma levels differed significantly between seizure-free and non-responding PWEs or between PWEs with a satisfactory tolerability and those who experienced adverse events. The main reason for this result was that the individual therapeutic ranges varied widely: seizure freedom and adverse effects were observed alongside low doses and plasma levels in some PWEs. Conversely, there were examples of PWEs who did not respond or who reported no tolerability issues at high doses or plasma levels. Still, to evaluate the individual therapeutic range and to better understand the influence of other drugs in cases where concomitant medications are used, the therapeutic drug monitoring of cenobamate may be useful.

Hypertension is one of the most common primary cardiometabolic diseases and is a major contributor to cardiac mortality and morbidity. Furthermore, cardiac risk factors such as obesity and sedentary lifestyle are often seen in hypertensive patients. Regular exercise acts against these risk factors and improves the overall health of an individual. High-intensity interval training appears to do so in a more efficient way. We used a systematic literature review (SLR) approach to provide an overview of the present literature highlighting the cardiovascular advantages of HIIT in hypertensive individuals. We reviewed 4004 articles from reputable journals published between 2013 and August 14, 2023, focusing on 7 selected papers for in-depth analysis. This systematic review revealed the protective effects of HIIT against cardiac complications in hypertension. All the reviewed articles showed a decrease in blood pressure and many other risk variables of cardiovascular diseases. However, guidelines for this exercise are needed for maximum benefits. Therefore, further research on HIIT is necessary to fully harness its potential.

In CNS drug discovery, estimation of brain exposure of lead compounds is critical for their optimization. Compounds need to cross the blood-brain barrier (BBB) to reach the pharmacological targets in the CNS. The BBB is a complex system involving passive and active mechanisms of transport and efflux transporters such as P-glycoproteins (P-gp) and breast cancer resistance protein (BCRP), which play an essential role in CNS penetration of small molecules. Several in vivo, in vitro and in silico methods are available to estimate human brain penetration. Preclinical species are used as in vivo models to understand the unbound brain exposure by deriving the Kp,uu parameter, the brain/plasma ratio of exposure corrected with the plasma and brain free fraction. MDCK-mdr1 (Madin Darby canine kidney cells transfected with the MDR1 gene encoding for the human P-gp) assay is the most commonly used in vitro assay to estimate compound permeability and human efflux. The in silico methods to predict brain exposure, such as CNS MPO, CNS BBB scores and various Machine Learning models, help save costs and speed up compound discovery and optimization at all stages. These methods enable the screening of virtual compounds and building of a CNS penetrable compounds library as well as optimization of lead molecules for CNS penetration. Therefore, it is crucial to understand these methods' reliability and ability to predict CNS penetration. We review the in silico, in vitro and in vivo data and their correlation with each other and assess published in silico approaches to predict the BBB penetrability of compounds.

Modern engineering faces challenges in ensuring technical standards for service, durability, and sustainability. Political, administrative, and budgetary factors, coupled with climate change, pose tasks to structural integrity, affecting industries and economies. Marine infrastructures represent a strategic asset of the country as it handles a large part of the economic exchanges. This article analyzes five essential factors that play a fundamental role in the performance analysis of coastal structures: Chloride-induced corrosion, degradation models, maintenance strategies, monitoring, and climate change. Starting with reinforcement corrosion, considered as the main cause of distress, particularly in coastal zones for the long-term behavior of structures. Additional pressure from the influences of climate change is becoming evident and extreme, leading to reduction in capacity. To guarantee the lifespan, degradation models contribute by estimating the long-term performance of the asset as a strategic piece to the development of effective maintenance solutions. Additionally, quantifying the condition of the structure from monitoring data, plays a crucial part that provides information on the current situation of the structure. Finally, this review summarizes the challenges associated with the maintenance of aging marine structures considering aspects such as corrosion, monitoring, and the future challenges this area will face due to climate change.

Temporal lobe epilepsy (TLE) is associated with reorganization of hippocampal neuronal net-works, gliosis, neuroinflammation, loss of integrity of the blood-brain barrier (BBB) in humans and animal models. More than 30% of epilepsies remain intractable and characterization of the molecular mechanisms involved in BBB dysfunction is essential to the identification of new thera-peutic strategies. In this work, we induced status epilepticus in rats by injection of the proconvul-sant drug pilocarpine that leads to TLE. Using RT-qPCR, double immunohistochemistry and con-focal imaging, we studied at different time points of epileptogenesis (latent phase, 3, 7, 14 days; chronic phase, 1 and 3 months) the regulation of reactive glia and vascular markers. In the hippo-campus, increased expression of mRNA encoding the glial proteins GFAP and Iba1 confirmed neu-roinflammatory status. We report for the first time the concomitant induction in endothelial cells, pericytes and basement membrane of the BBB of specific proteins CD31, PDGFR and ColIV, that peaks at the same time points as inflammation. The altered expression of these proteins occurs ear-ly in TLE, during the latent phase, suggesting that they could be associated with early rupture and pathogenicity of the BBB that will contribute to the chronic phase of epilepsy.

Wildfires are important natural drivers of forest stands dynamics, strongly influencing on their natural regeneration and ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns over the period 2000–2022 in the Middle Volga region of the Russian Federation on the base of remote sensing time series, considering the impact of cli-matic factors on forest fires. The temporal trends were assessed with the Mann-Kendall nonpara-metric statistical test and Theil-Sen’s slope estimator using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment indicated a high overall accuracy (> 84%) and F-score value (> 82%) for forest burnt area detection as evaluated against 581 test sites of ref-erence data. The results revealed that the fire occurrences in the region were mainly irregular with the highest frequency of 7.3 over a 22-year period. The total forest BA was about 280 thousand ha, which equals to 1.7% of the land surface area or 4.0% of the total forested area under study in the Middle Volga region. The coniferous forest stands are the most fire-prone ecosystems accounting for 59.0 % of the total BA; deciduous stands accounts for 25.1%; and insignificant fire occurrences were registered in young forests and shrub lands. On a seasonal scale, temperature generally has a greater impact on the BA than precipitation and wind speed.

Three different populations of sulfated polysaccharides can be found in the cell wall of the red alga Botryocladia occidentalis. In a previous work, the structures of the two more sulfated polysaccharides were revised. In this work, NMR-based structural analysis was performed on the least sulfated polysaccharide and its chemically modified derivatives. Results have revealed the presence of both 4-linked α- and 3-linked β-galactose units having the following chemical features: more than half of the total galactose units are not sulfated, the α-units occur primarily as 3,6-anhydro-galactose units either 2-O-methylated or 2-O-sulfated, and the β-galactose units can be 4-O-sulfated or 2,4-O-disulfated. SPR-based results indicated weaker binding of the least sulfated galactan to thrombin, factor Xa, and antithrombin, but stronger binding to heparin cofactor II than unfractionated heparin. This report together with our previous publication completes the structural characterization of the three polysaccharides found in the cell wall of the red alga B. occidentalis and correlates the impact of their composing chemical groups with the levels of interaction with the blood co-factors.

The Sei Dareh Bridge is a cable-stayed bridge located in West Sumatra Province, Indonesia. The bridge, which crosses the Batanghari River, has a main span of 123 meters long and 9 meters wide. Traffic load is transmitted through 24 prestressed cables to a 42.4-meter-high pylon made of concrete. The bridge deck and traffic loads are directly supported by steel box girders as main beams that are reinforced laterally with cross beams IWF 800.300.16.24 and stringers IWF 350.350.12.19. This paper discusses static and dynamic testing on the bridge which aims to assess its feasibility before it is opened to the public. Based on the test, it was concluded that a static load of 73% could not be achieved because the deflection that occurred was beyond the allowable deflection. This is exacerbated by a loud clanging sound on the ST2-X1 prestressed cable when loading to 240 tons or 58% of the targeted load. In addition, this bridge is included in the "lazy bridge" category because it takes 24 hours to return to an undeformed condition after loading. As a recommendation for this bridge, it is necessary to carry out a structural health monitoring system (SHMS) regularly on bridge slabs and cables.

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.

The study aimed to showcase how implementing a patient blood management (PBM) program effectively cut unnecessary red blood cell (RBC) transfusions in a New York City urban community teaching hospital. Analyzing seven years from 2013 to 2019, a retrospective review of RBC transfusions was conducted. Following PBM introduction, considerable improvements were observed annually. These included a drop in mean pretransfusion hemoglobin levels from 7.26g/dL (2013) to 6.58g/dL (2019), a 34% reduction in yearly RBC unit transfusions, and fewer units given to patients with pre-Hgb levels ≥7g/dL (1210 units in 2013 to 310 units in 2019). Furthermore, the study noted a decline in two-unit RBC orders when Hgb levels were ≥7g/dL from 65 orders in 2013 to merely 3 in 2019. The estimated total cost-savings attributed to the six-year PBM program duration after full implementation in 2014 amounted to $2.1 million US dollars. Overall, PBM implementation significantly decreased RBC transfusions and enhanced transfusion practices. The findings emphasize that successful PBM strategies don't always necessitate extensive resources or increased budgets but instead rely on the application of intuitive methods, as evidenced by this study.

Respiratory allergic diseases, affect over 500 million people globally and pose a substantial burden in terms of morbidity, mortality, and healthcare costs. Restrictive factors such as geographical disparities, infectious pandemics, limitations in resource, and shortage of allergy specialists in underserved areas, impede effective management. Moreover, the well-established suboptimal adherence in patients with respiratory allergies, contribute further to the accelerated loss of disease control. Telemedicine encompasses real-time visits, store-and-forward options triage, and computer-based technologies for establishing and efficient doctor-patient communication. Recent advances in digital technology, including designated applications, informative materials, digital examination devices, wearables, digital inhalers, and integrated platforms, facilitate personalized and evidence-based care delivery. The integration of telemonitoring in respiratory allergy care has shown beneficial effects in disease control, adherence, and quality of life. While the COVID-19 pandemic accelerated the adoption of telemedicine, certain concerns regarding technical requirements, platform quality, safety, reimbursement, and regulatory considerations remain unresolved. Challenges in telemonitoring implementation further include ensuring effective doctor-patient collaboration, addressing privacy concerns, and establishing cost-effective models. The integration of artificial intelligence (AI) in telemonitoring applications holds promise for data analysis, pattern recognition, and personalized treatment plans. Striking the balance between AI-enabled insights and human expertise is crucial for optimizing the benefits of telemonitoring. This overview reports the up- to- date telemedicine approaches in managing patients with respiratory allergies. While telemonitoring exhibits potential in enhancing patient care and healthcare delivery, critical considerations have to be addressed in order to ensure the successful integration of telemonitoring into the healthcare landscape.

This paper aims at achieving optimal rotor speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input-output datasets, with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the 5th order nonlinear model of the induction motor, rotor speed is estimated with the optimized EKF algorithm and the used algorithm is tested experimentally.

Sodium glucose cotransporter 2 inhibitors (SGLT2i) are currently available for the management of type 2 diabetes mellitus. SGLT2i act by inhibiting renal SGLT2, thereby increasing glucosuria and lowering serum glucose. Recent trials are emerging supporting a role for SGLT2i irrespective of the diabetic status pointing towards that SGLT2i have other mechanisms of actions beyond blood sugar control. In this review, we will shed light on the role of this group of medications that act as SGLT2i in non-diabetics focusing on pre-clinical and clinical data highlighting the mechanism of renoprotection and effects of SGLT2i in the non-diabetic kidneys.

Abstract: This review addresses the complexities of type 1 diabetes (T1D)and its associated complications, with a par-ticular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of the review is devoted to technological advances in diabetes management, particu-larly the use of Hybrid Closed Loop systems (HCLS) and to the potential of open-source HCLS systems, which could be easily adapted to different patiens’needs using big data analytics and machine learning. Personalized HCLS algorithms that integrate factors such as patient lifestyle, dietary habits, and hormonal variations are highlighted as critical to reducing the incidence of diabetes-related complications and improving patient outcomes.

A temporary monitoring system was installed on the 175-years-old historical Széchenyi chain bridge under its reconstruction process. The bridge is in the downtown area of the capital city of Hungary and plays a significant role in city life of Budapest. Six-month-long measurements were conducted during the reconstruction process of the bridge, yielding crucial insights into the structural behaviour of the historical structure. Measurement results were evaluated; the findings encompass the rotation capacity of pins between chain elements, and structural response to temperature changes. This information helped the decision-making of designers and construction company during the reconstruction made between 2021 and 2023. For instance, daily temperature fluctuations result in increased bending moments in the chain elements, rising up to 158% compared to the values observed during a proof load test in 2018. Furthermore, the measurements reveal an approximate 42% increase in normal forces compared to the proof load test, which highlights the high sensitivity of chain bridges to temperature fluctuations, where geometric stiffness plays a crucial role. Reconstruction, reducing self-weight, notably intensifies the impact on normal forces and bending moments. These outcomes strongly emphasize the dominance of dead load and self-weight in the case of chain bridges.

It is unknown whether postprandial plasma glucose measured from blood taken between 4 and 7.9 h (PPG4-7.9h) is associated with mortality from hypertension, diabetes, or cardiovascular disease (CVD). This study aimed to investigate these associations in 4,896 US adults who attended the third National Health and Nutrition Examination Survey. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of PPG4-7.9h for mortality. This cohort was followed up for 106,300 person-years (mean follow-up, 21.7 years). A 1-natural-log-unit increase in PPG4-7.9h was associated with a higher risk of mortality from hypertension (HR, 3.50; 95% CI, 2.34-5.24), diabetes (HR, 11.7; 95% CI, 6.85-20.0), and CVD (HR, 2.76; 95% CI, 2.08-3.68) after adjustment for all the tested confounders except hemoglobin A1c (HbA1c). After further adjustment for HbA1c, PPG4-7.9h remained positively associated with mortality from hypertension (HR, 2.15; 95% CI, 1.13-4.08) and CVD (HR, 1.62; 95% CI, 1.05-2.51), but was no longer associated with diabetes mortality. Subgroup analyses showed that similar results were obtained in the sub-cohort of participants without a prior diagnosis of myocardial infarction or stroke. In conclusion, PPG4-7.9h predicts mortality from hypertension and CVD independent of HbA1c.

Numerous studies have shown the multiple benefits of plant biodiversity and the crucial role of resident’s awareness in conservation and land management. Plant biodiversity is investigated in an analytical monitoring report conducted jointly with local population engaged in livestock activities and the young residents who are shifting toward hospitality and tourism. The study area, covering over 800 km2 in Sardinia, is half forested and has landscape interest. During the research, genotypes of Quercus ilex L. with sweet acorns and richer in polyphenols were discovered, the oldest ecosystem services in those rural communities. Collaborative monitoring focused attention on the benefits of plants in different habitats: 53% of these have been known to livestock farmers as food for local breeds of goats and pigs; 15% are officinal and 13% endemic. They have also been used in human nutrition within the Mediterranean diet, and they have landscape and garden interest. The study analyzes numerical data from a critical and educational perspective. These data serve as indicators of ecosystem health for the purpose of sustainable management policies, and attest to collaborative monitoring as a tool for analyzing human activities and the necessary balance between profit and biodiversity conservation, given the current challenging climate change conditions.

Intermittently Scanned Continuous Glucose Monitoring (isCGM) devices are increasingly being used in patients with type 2 diabetes mellitus (T2DM) on insulin therapy for their benefits regarding disease management. Evidence of isCGM use in patients with T2DM treated with basal insulin or oral antidiabetic drugs is lacking. The aim of this study was to assess the efficacy and safety of isCGM in this population. This was an observational, retrospective, real-world study enrolling patients with T2DM who were starting with the use of isCGM. Data from medical records (i.e. demographics, clinical characteristics, laboratory assessments, and isCGM metrics) were collected over three time periods (baseline, 3 and 6 months). The endpoints were glycated haemoglobin (HbA1c) changes, and changes in isCGM metrics as defined by the International Consensus from baseline to a 3-month and a 6-month follow-up. Overall, 132 patients were included (69% male; mean age 68.2±11.0 years; mean disease duration 19.0±9.3 years; 80% on basal insulin ± oral drugs at baseline; mean baseline HbA1c 8.1%±1.3%). The estimated mean change in HbA1c from baseline was statistically significant at three (-0.4±1.0%; p=0.003) and at six months (-0.6±1.3%; p<0.0001). No statistical differences were found in the mean change of the isCGM metrics. Intermittently Scanned Continuous Glucose Monitoring is effective and safe in improving glycaemic control in patients with type 2 diabetes treated with basal insulin or oral antidiabetic drugs.

Objective: To explore three novel fasting blood glucose (FBG)-based novel indicators, including FBG-to-albumin ratio (FAR), FBG-to-lymphocytes ratio (FLR), and FBG-to-hemoglobin ratio (FHR), in predicting prognosis and detecting metastasis for patients with pancreatic neuroendocrine neoplasms after resection. Materials and methods: A total of 178 pNEN patients underwent surgical resection were included in this study. Receiver operating characteristic (ROC) curves were used to evaluate the diagnosis values of FAR, FLR, and FHR, and the cut-off values were obtained for further analyses. Univariate and multivariate analyses were conducted to determine the independent predictors. The Kaplan-Meier method was used to evaluate the progression-free survival (PFS) and overall survival (OS) of the pNEN patients. Results: The optimal cut-off values of FAR, FLR, and FHR were 0.17, 2.85, and 0.028, respectively. As for PFS, the area under the curve (AUC) was 0.693 for FAR, 0.690 for FLR, and 0.661 for FHR, respectively. The AUC was 0.770, 0.692, and 0.715 accordingly for OS. The groups with lower FAR, FLR, and FHR were significantly associated with prolonged PFS and OS (p < 0.05). In patients with metastasis, the lower FAR group was correlated with significantly longer PFS and OS (p = 0.022 and 0.002, respectively). The FLR was an independent predictor of PFS in pNEN patients, and the FAR was a predictor of OS. FAR was an independent indicator of PFS in patients with metastasis. Conclusion: Preoperative FAR, FLR, and FHR are effective in predicting the prognosis of pNEN patients and detecting the synchronous metastases.

Understanding and predicting damage progression within thin plain-woven CFRP components is an essential issue for scheduling maintenance intervals and structural inspections, and typically require extensive test series. However, uncertainties always remain and result in conservative design and unnecessary downtimes due to the repeated inspections of a healthy structure. SHM can be used to reduce these uncertainties, as it allows to evaluate the condition of a mechanical structure during operation. Numerous SHM methods are developed , that achieve different levels of damage identification but are often investigated at small coupons with non-realistic structural damages and loading conditions on both, structure and sensor. The present research investigates the electrical impedance tomography (EIT) featuring an elastoresistive thin-film sensor to evaluate damage that propagates from a circular hole in a large plate under cyclic tensile-tensile multi-block loading. Applied fatigue loads were increased multiple times up to a total number of two million cycles. During the loading damage initiated at the hole and continuously propagated into the plate. Repeated EIT evaluations of the applied sensor and validating evaluations by means of digital image correlation clearly revealed the robustness of the hardware and the potential of the SHM method for damage evaluation of plain-woven CFRP components.

Having garnered significant attention in the scientific community over the past decade, Wire Arc Directed Energy Deposition (arc-DED) technology is at the heart of this investigation into additive manufacturing parameters. Singularly focused on invar as the selected material, the primary objective revolves around devising an indirect cord height measurement approach. This innovative methodology involves the seamless integration of internal signals and sensors, enabling the derivation of crucial measurements sans the requirement for direct physical interaction or conventional measurement methodologies. Through the course of this research, meticulous alteration of deposition parameters is undertaken to thoroughly scrutinize their influence on the ultimate cord dimensions. The fusion of cutting-edge manufacturing techniques with advanced sensor technology constitutes the essence of this endeavor, offering substantial potential to augment precision and efficiency in additive manufacturing processes. Remarkably cost-effective, this solution circumvents the need for intricate measurements and significantly contributes to the proper layer-by-layer growth process.

Tobacco smoke is an environmental mixture including polycyclic aromatic hydrocarbons and may interfere in endocrine system as an EDC in aging. Thus, we performed a molecular epidemiological approach with in-depth biological monitoring of combustible cigarette smoking among adolescents and adults in South Korea (N=620) with exposure biomarkers, i.e., exhaled CO, urinary cotinine, t,t-muconic acid (TTMA), malondialdehyde (MDA), and obtained information of their lifestyle and tobacco addiction status. We also diagnosed the genetic polymorphisms on the 96 SNPs for tobacco-metabolism, -addiction, and expression differences and compared mtDNA abnormalities in buccal and blood cells. As results, there were positive associations among the above tobacco exposure biomarkers. Man or youth smokers showed high frequency in some of mtDNA alteration, such as ‘SNPs for inconsistent bases between buccal and blood cells’. Among the SNPs, the polymorphisms on SULT1A1, DRD2, and ADH1B were related to multi-exposure biomarkers. Interestingly, youth showed similar levels of urinary TTMA and MDA to adults, although their pack-year was approx. 1/6 volume of that of adults. We also observed the negative association between urinary MDA levels and the growth rate in the adolescents (p<0.05). In conclusion, the present biological monitoring provides high susceptible population, i.e., adolescents rather than adults, and reliable genetic factors affecting tobacco exposure. The inferred environment-gene-gene interaction suggests tobacco smoking accelerats aging in adolescents as an EDC.

This paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The sensor uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations are transmitted instead of videos. The sensor is highly affordable, accessible, and versatile. It was a CES 2021 Innovation Award Honoree, and has been selected by Amazon as one of only three fall detection devices integrated into its Alexa Together urgent response service, and has received very positive reviews from Amazon customers. It has also been used in different senior care settings in about ten different countries. The paper presents the main features of the system, the evidences and lessons learned from its practical applications, and future directions.

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&lt;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&lt;0.001). Also, the average temperature was lower in the hyperbaric chamber than in the gas monitoring system (p&lt;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.

Background: Sugar laden foods and carbohydrate rich diets with high glycaemic index intake are associated with weight gain, increased incidence of diabetes and is linked with increased cardiovascular risk. A blend of mulberry leaf extract and apple peel extract (Glubloc™) may reduce postprandial blood glucose surge and insulin response following dietary carbohydrate intake by limiting its digestion and uptake from the gut. Methods: In this randomised, placebo-controlled, crossover study, thirty healthy male and female subjects were supplemented with either placebo or Glubloc™, 10 min before a carbohydrate and sugar rich meal intake. Changes in postprandial blood glucose (upto120 minutes) and insulin levels (at 60 minutes) were measured as the primary endpoints. Changes in blood glucose incremental area under the concentration curve (iAUC), maximum concentration (Cmax) and peak time (Tmax) were compared. Results: Thirty adults (mean age 31±7.2 years, BMI 24±3.8 kg/m2) completed the study and the Glubloc™ blend relative to placebo significantly reduced glucose iAUC at 0.5 h (− 38%, p < 0.0003), 1.0 h (− 30%, p = 0.0007), 1.5 h (− 29%, p = 0.0008), and 2.0 h (− 26%, p = 0.002). A statistically significant reduction in 1 h insulin levels (− 35%, p = 0.029) was observed. Conclusion: A single dose supplementation of Glubloc™ significantly reduced the postprandial blood glucose spike and serum insulin levels. The observation of a significant glucose reduction paralleled with a significant lower insulin response supports a reduced gastrointestinal glucose absorption. None of the subjects experienced any major gastrointestinal side effects. This study builds a body of evidence that the combination of white mulberry leaf and green apple peel extracts (Glubloc™) can be a useful supplement to manage postprandial glucose levels, further studies are necessary to evaluate its potential in type 2 diabetes and the regulation of dysglycaemia.

Glioblastoma multiforme (GBM) is a glioma and the most aggressive type of brain tumor with a dismal average survival time, despite the standard of care. One promising alternative therapy is boron neutron capture therapy (BNCT), which is a noninvasive therapy for treating locally invasive malignant tumors, including glioma. BNCT involves boron-10-released radiation directly into tumor cells with minimal damage to healthy tissues. This therapy lacks clinically approved targeted blood-brain-barrier-permeating delivery vehicles for therapeutic drug/ boron-10. Nanoparticles, particularly gold nanoparticles (GNPs), are selective and effective drug-delivery vehicles because of their desirable properties, facile synthesis, and biocompatibility. This review discusses historical and current GNP therapeutic applications as a drug delivery vehicle for treating glioma. Furthermore, GNPs loaded with therapeutic drugs, imaging agents, and GBM-targeting antibodies/ peptides to treat GBM will be discussed. Additionally, the importance of upper and lower limits on therapeutic efficacy will be described and explored followed by highlighting their advantages, limitations, and possible toxicity. Recent research and selected important findings for GNP-mediated pharmaceutics in GBM will also be considered.

Collecting and recording physiological parameters and forwarding this data via telemedicine or remote surveys may significantly increase the health and safety of people working under extreme environmental conditions, like firefighters, mountain rescuers, disaster control employees, military personnel, etc. First, ruggedized systems, as small, lightweight, and flexible as possible, are needed to optimize the goal of increasing occupational health and safety of persons at risk and maximizing their performance during missions. Previous systems could only record a very limited number of parameters, and a few could forward the data online to return information to the user. Therefore, in order to record several meaningful, valid, and reliable physiological parameters synchronized, an extension of existing systems is required. This review highlights this problem and gives an overview of the historical development. The actual status of the systems available was listed and described comparatively. In the end, a proven, promising, and innovative solution is presented that can solve the problem and allow people to work more safely in extreme environments, which may represent the future.

Cellular therapies have started an important new therapeutic direction for Autistic Spectrum Disease (ASD), and the ample diversity of ASD pathophysiology and the different types of cell therapies need an equally ample effort for studying the applicable ASD causes and the specific benefits of cell therapies via clinical studies.CORDUS Clinical Study is a crossover study in which autologous cord blood was administered intravenously to 56 patients between January 2019 and July 2023, and has compared its efficacy versus an individualized combination of supplements; both treatments were administered in a different sequence to all participants. Treatment efficacy was evaluated pre- and post- treatments by an independent psychotherapist with ATEC, Q-CHAT and a 16-item comparative table score, after interviews with the children’s parents and therapists.Cord blood was beneficial in the group of 3–7-year-old children (n=28; 3 out of 4 children – 78.57% - improved scores), but was much less effective in kids older than 8 years or with body weight of more than 35 kg (n=28; 1 out of 10 children improved scores). Individualized supplements were more efficacious than cord blood in 5 cases out of 28, while in 23 kids cord blood brought more improvement than supplements; 6 kids registered minor or no improvement. Statistically significant improvements were seen after cord blood infusion on areas such as verbalization and social interaction, but not on irritability or aggressive behavior; also ample individual differences were noted. No serious adverse reactions were noted during and after cord blood or supplement administration.

Keywords: COVID-19, severity, influenza, serum antibodies, blood tests

Road pavement monitoring represents the baseline to the pavement maintenance process. To quickly fix a damaged road, relevant authorities need a high-efficiency methodology that allows them to obtain data describing the current conditions of a network of roads. In urban areas, large-scale monitoring campaigns may be more expensive and not fast enough to describe how pavement degradation has evolved over time. Furthermore, at low speeds, many technologies are inadequate for monitoring the streets. In such a context, employing black box equipped vehicles to perform a routine inspection could be an excellent starting point. However, the vibration-based methodologies used to detect road anomalies are strongly affected by the speed of these vehicles. In this study, a statistical method was to analyze the effects of speed on road pavement conditions at different severity levels through the data recorded by taxi vehicles. Likewise introduced was a process that overcomes the speed effect when considering road surface conditions. The methodology proposed has succeeded in predicting the right damage severity level based on only two recorded parameters: speed and pavement deterioration index.

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.

Recreational water activities are widely recognized to have a positive impact on our physical and mental well-being. However, recreational water sources and their management are also a risk factor for human health, due to different agents, including the overgrowth of cyanobacteria and algae. These organisms have the potential to metabolize organic matter and produce thermophilic and thermotolerant toxins. Different species of algae participate in biofilm formation and impact on maintenance of equipment for water treatment. We reviewed the state of the art by providing basic definitions, taxonomy and the epidemiological and clinical issues related to the recreational uses of waters. Methods for treatments and monitoring are summarized, considering both traditional and innovative strategies. Public health and surveillance perspectives impose to focus on the detection of toxins, the formation of biofilms, the understanding of the benthonic and planktonic components as part of a larger microbial biodiversity.

This observational pilot study aimed to investigate continuous glucose monitoring (CGM) metrics in older Japanese patients with type 2 diabetes mellitus (T2DM) using a continuous glucose monitoring system (FreeStyle Libre Pro) for CGM during the first tirzepatide administration and compared the glycemic control measures before and after the treatment. The four patients re-ported a mean age of 79.5 years (standard deviation [SD]: 5.8), mean body mass index of 24.63 kg/m2 (SD: 4.7), and mean glycated hemoglobin level of 9.1% (SD: 2.1). After tirzepatide treatment, the mean of time in range, time above range, and time below range changed from 53.2% to 78.9% (p = 0.041), 45.8% to 19.7% (p = 0.038), and 1.0% to 1.5% (p = 0.206), respectively. Improved hy-perglycemia reduced the oral glucose medication in two cases and decreased the frequency of insulin injections in two cases. All cases demonstrated the CGM index improvement during short-term observation despite some differences. Future studies need to investigate the long-term impact on functional prognosis, safety, and tolerability, especially in nonobese older Asian pa-tients, to elucidate the potential benefits of tirzepatide. However, tirzepatide-associated robust glycemic improvement may simplify and reduce diabetes treatment-associated complication in older patients with T2DM.

With the constant growth of Software Defined Radio (SDR) technologies in wireless communications related fields the need for efficient ways to test and evaluate the Physical Layer (PHY) protocols developed for these technologies in real life traffic scenarios become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow feeding GNU Radio applications with test traffic in a simple way and through an interoperable interface. This makes possible the use of any traffic generator, existent one or custom built to evaluate the GNU Radio application. In addition, the paper proposes an efficient way to collect PHY layer specific monitoring data that improves the performance of the critical components of the message delivery path by employing the Protocol Buffers library. The paper considers the entire testing and evaluation ecosystem and demonstrates how PHY layer specific monitoring information is collected, handled, stored, and processed as time series allowing complex visualization and real time monitoring.

Modern gastroenterology, which occupies the pages of scientific journals without alternative, began as an initiative of manufacturers of diagnostic equipment. For widespread advertising, they selected practical doctors who were not familiar with the methodology of science and superficial knowledge of physiology. This is how a group led by DeMeester arose, which in 1976 published the DeMeester score, i.e., the normal limit for esophageal pH monitoring. pH <4, less than 4% of the time per day was considered normal, i.e., it was unreasonably claimed that such reflux is physiological. This article proves that this study was carried out with numerous methodological errors, which led to an erroneous conclusion. For a long time, pH monitoring was considered the gold standard and with its help hypotheses were proposed that form the basis of modern gastroenterology. The article proves the fallacy of 13 hypotheses, which are presented as theories. Numerous articles using pH monitoring have publicized this method. Practitioners became "great scientists", equipment manufacturers became successful businessmen. Over time, they found other groups of practitioners to promote devices for impedance ph monitoring, high-resolution manometry, and many others. At the same time, a network of reviewers who believe in the infallibility of the luminaries do not allow studies that contradict their faith to be published. Thus, fundamental science was destroyed, which alone could assess the harm of false research both for science and for patients. Conclusion This study provides a list of hypotheses, that were previously considered theories, but since scientific evidence has proven these assumptions to be false, they should be excluded from subsequent use and citation.

Generating early warning signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such early warning signal systems are still limited by the lack of data and uncertainties associated with predicting complex systems such as ecosystems. This research aims to investigate the feasibility of developing an early warning system capable of identifying an upcoming critical transition within mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land cover. To identify early warning signals, indicators such as autocorrelation (acf(1)), standard deviation (SD), and skewness (SK) are applied. The findings indicate a signal of reduced resilience by a significant increase in NDVI statistical metrics (acf(1): 0.50, SD: 0.9). Although MNDWI showed significant early warning signals in Qeshm Island (acf(1); 0.86, SD: 0.90), it provided a false alarm in the reference study site. MVWR failed to generate early warning signals of reduced resilience (acf(1); -0.100, SD: -0.07, SK: -0.21). Land-use land-cover change may explain reduced resilience in the forests. This study not only emphasizes the potential of remote sensing in monitoring the state of mangrove forests but also contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts aimed at safeguarding mangroves. This is the first report of the successful application of remote sensing in generating early warning signals of reduced resilience within mangrove forests in the Middle East.

Accurate geometric modeling of blood vessels lumen given their 3D images is a basis for vessel quantification in support of cardiovascular diseases diagnosis, treatment and monitoring. Unlike other approaches, which assume circular or elliptical vessel cross-section, the proposed method employs parametric B-splines combined with the image formation system equations to accurately localize the highly curved lumen boundaries. No image segmentation is involved which might reduce the localization accuracy in effect of spatial discretization. It is shown that the model parameters can be reliably identified by a feedforward neural network which, driven by the cross-section images, predicts the parameter values many times faster than a reference least-squares (LS) model fitting algorithm. Two example applications: modeling the lower extremities artery-vein complexes visualized in steady-state contrast-enhanced magnetic resonance images (MRI) and the coronary arteries pictured in computed tomography angiograms (CTA), are presented and discussed to demonstrate the method potential. Besides application to medical diagnosis, blood-flow simulation and vessel-phantom design, it can serve as a tool for automated annotation of image datasets required to train machine-learning algorithms.

One of the main challenges in geodetic deformation analysis is to infer whether the geometry of some engineering structures or zones of natural hazards has changed from their initial state or not. The pillar that supports such an analysis is tightly based on the fundamentals of statistical hypothesis testing. The null hypothesis model indicates that no displacement has occurred. It is tested against a class of alternative models, which stipulate different displacement patterns. In this contribution, we present an innovative geodetic displacement detection which integrates combinatorial analysis and likelihood ratio tests into a sequential procedure for the case where the differences between observations of two epochs are in play. This framework is applied and investigated to two test scenarios: a synthetic and a real simulated trilateration network. From a statistical point of view, our approach is rigorous, because the alternative model can identify simultaneously more than one unstable point. In addition, the relationship between the unknown parameters and the observations is always linear, even if the problem manifests itself as non-linear. Consequently, we avoid a potential loss of statistical test power due to the model linearization. In addition, the proposed method controls the false positive rate efficiently. One of the problems that arise here is also related to the selection of the maximum number of points to be considered in the test procedure ( ). Here, we provide an innovative methodology based on rank computation of the design matrices to define , which can even be extended to the problem of outlier detection. Determining avoids the problem of having non-separable models in identifying unstable points. The algorithms and data are available in the repository.

Extramedullary hematopoiesis is a compensatory response in chronic hemolytic anemia, such as thalassemia, and can lead to spinal cord compression. This case report describes a 36-year-old woman with β-thalassemia who presented with lower extremity motor deficiency, pelvic paresthesia, and bladder dysfunction. The patient had a history of low back pain, bilateral lower limb weakness, and poor compliance with iron chelation therapy. MRI revealed compression of the spinal cord due to extramedullary hematopoiesis. Surgical intervention was not feasible, and the patient underwent hypertransfusion and iron chelation therapy. Neurological symptoms improved, but urinary retention persisted. The patient continues to receive iron chelation treatment and undergoes transfusions. Extramedullary hematopoiesis is a common complication in thalassemia and requires an individualized treatment approach.

Multiple sources influence air quality and regional climate in complicated atmospheric emission situations like urban agglomerations. To resolve pollution plumes and source influences in polluted areas, a comprehensive chemical fingerprinting of sources utilizing non-methane hydrocarbons (NMHCs) and the identification of acceptable tracer molecules and emission ratios is required, in contrast to pristine locations, where reliance on a single or a few chemical tracers is frequently sufficient. We have characterized the prevalence of NMHCs fingerprints in the urban areas and quantified the correlation of windspeed with the concentration of these pollutants. eNose sensors and Air quality management stations provide the data to identify the emission sources of such pollutants. Based on our analysis, the average NMHC concentration in 2021 has been recorded 0.424 ppm at point 1 AQMS whereas at point 2 AQMS it was 0.256 ppm. Such outcomes could be attributed to the proximity of emissions sources, the direction and speed of the wind, or both. Moreover, traffic can be a major contributor to pollution levels in any urban area. More research based on a larger dataset is required before definitive conclusions can be drawn, and viable solutions can be proposed.

Sphingosine 1-phosphate (S1P) is a signaling lipid molecule involved in various cellular processes. It is important to develop a quantitative method for S1P to determine endogenous levels and to investigate its functions. Especially since S1P is a tiny lipid component of most biological samples, highly sensitive analysis by LC-MS/MS is required. The main challenge in S1P analysis by chromatography is peak broadening due to the presence of a polar phosphate and zwitterion formation in S1P. In this study, we used hydrogen fluoride (HF) to efficiently remove a phosphate and then analyzed the surrogate, sphingosine, as a sharp peak by LC-ESI-MS/MS. We optimized the dephosphorylation reaction in terms of reaction time, temperature, and HF concentration. Multiple reaction monitoring (MRM) for dephosphorylated form of S1P and C17-S1P as internal standard at m/z transition 300.4&gt;282.4 (quantification ion), 300.4&gt;262.4 (qualification ion), 286.3&gt;268.2 (internal standard). This method was validated by essential parameters such as specificity, linearity, range, LOQ, LOD, accuracy, precision, and repeatability. To confirm this new method, we quantified S1P levels in various serum products (100.02 ~ 284.38 nM). To apply the method in vivo, we analyzed S1P in plasma and kidney tissues obtained from a chronic kidney disease (CKD) mouse model. S1P levels were increased only in CKD kidney tissue but not in plasma. In conclusion, by applying the dephosphorylation step with HF, we established a new sensitive LC-MS/MS quantitative method for S1P that can be applied to biological samples.

One of the hallmarks of Alzheimer’s disease (AD) is the accumulation of beta-amyloid peptide (Aβ) leading to formation of soluble neurotoxic Aβ oligomers and insoluble amyloid plaques in various parts of the brain. Aβ undergoes post-translational modifications that alter its pathogenic properties. Aβ is produced not only in brain, but also in the peripheral tissues. Such Aβ, including its post-translationally modified forms, can enter the brain from circulation by binding to RAGE and contribute to the pathology of AD. However, the transport of modified forms of Aβ across the blood–brain barrier (BBB) has not been investigated. Here, we used a transwell BBB model as a controlled environment for permeability studies. We found that Aβ42 containing isomerized Asp7 residue (iso-Aβ42) and Aβ42 containing phosphorylated Ser8 residue crossed the BBB better than unmodified Aβ42, which correlated with different contribution of endocytosis mechanisms to the transport of these isoforms. Using microscale thermophoresis, we observed that RAGE binds to iso-Aβ42 an order of magnitude weaker than to Aβ42. Thus, post-translational modifications of Aβ increase the rate of its transport across the BBB and modify the mechanisms of the transport, which may be important for AD pathology and treatment.

Introduction: In recent years, platelets have been in short supply and high demand. Approximately one in five cardiac surgery patients require platelets. In our safety-net hospital, however, the incidence is over 40%. Renal dysfunction, long cardiopulmonary bypass (CPB) times, and absence of tranexamic acid (TXA) are common risk factors for requiring platelet transfusions. Methods: Our hypothesis was fewer platelets would be transfused within two hours after bypass under these three conditions: creatinine is low, predicted surgery duration is short, and TXA is uniformly administered. Our anesthesia department designed this retrospective before-and-after observational study when the surgery department decided to narrow preoperative criteria respective to these three elements. The Institutional Review Board of Louisiana State University Health Sciences Center Shreveport approved of the protocol and waived individual informed consent. We compared timing of platelet transfusions (within 7 days) in consecutive patients who had CPB during the first ninety days of 2022 to those in 2023 using t-tests in SAS 9.3. Confounders included age, weight, anticoagulants, gender, and the platelet counts and hemoglobin immediately prior to platelet transfusion. Secondary outcomes were postoperative day two creatinine and other components transfused. Results: Preoperatively creatinine tended to be higher in 2022 at 1.5 vs 1.1, reaching significance postoperatively at 1.9 vs 1.1 (p=0.02). Bypass times decreased from 174 to 124 minutes (p=0.06). TXA use increased from 18 of 28 to 26 of 29 patients. Platelet counts increased on bypass from 142 to 173 (p=0.06). Platelet and hemoglobin nadirs were higher (day 3: 8g/dL vs 8.8, p=0.04; 98 vs 138, p=0.005). Platelet units transfused per patient tended to increase (0.75+/-1 vs 0.93+/-1.5); if exposed, patients tended to receive more units (2.1+/-1.3 vs 2.5+/-1.4) sooner. Changes in transfusions for red cells were nonsignificant at 1.97 per patient to 1.21, cryoprecipitate 0.36 to 0.69, and FFP 0.93 to 0.83. Conclusions: This is the first study examining timing of platelet transfusions after bypass. In the present investigation, we measured platelet count during bypass and prothrombin hours after arrival in the intensive care. Platelets were often administered in sets of two because of scarce immediate supply, partly from lack of an agitator table. More importantly, these data support a need for point-of-care thromboelastograms intraoperatively, but the cost has been prohibitive. Some centers’ algorithms list triggers for transfusion by fibrinogen and prothrombin times, available immediately after separation from bypass.

Cardiotonic steroids (CTS) are a group of steroid compounds derived from certain plants and animals. CTS are selective inhibitors of Na,K-ATPase, so for a long time they were widely used for medical purposes to treat heart failure and some other diseases. However, over time, doctors gradually began to refuse this group of drugs due to their narrow therapeutic range and a number of serious side effects. However, in parallel with the rise and fall of interest in CTS as a drug, interest in them as a single class of high-affinity Na,K-ATPase inhibitors has only increased. Numerous data on the effect of CTS on signaling cascades and cell viability made impelling a search for a rationale for the existence of such a subtle biological regulator. A large number of studies devoted to the potential use of CTS in the treatment of neurodegenerative, oncological, immune and other diseases "revitalized" the already "written off" group of the drugs. If initially the role of exogenous (mainly plant origin) CTS was studied, then by the 90s of the XX century the study of the role of endogenous CTS and their regulatory effects became a popular trend in biology and physiology. The role of endogenous CTS was studied in details in the pathogenesis of cardiovascular diseases [1]. A large number of researchers have unanimously stated that endogenous CTS are very complex regulators of the development of pathological processes; however, the exact cause-and-effect relationship that would unambiguously explain the processes of synthesis, utilization, and the role of endogenous CTS has not yet been established. The effects of CTS in the frame of various regulatory schemes have been described, but the predominant number of studies was devoted to arterial hypertension, heart and renal failure, cancer [2–4], aging [5,6], and neuroinflammation. The effects of both endo- and exogenous CTS on blood cells and have been studied to a much lesser extent.

Background: Mounting evidence supports the role of the endocannabinoid system in neurophysiology, includ-ing blood-brain barrier (BBB) function. Recent work has demonstrated that activation of endocannabinoid re-ceptors can mitigate insults to the BBB during neurological disorders, like traumatic brain injury, cortical spreading depression and stroke. As alterations to the BBB are associated with worsening clinical outcomes in these conditions, studies herein sought to examine the impact of endocannabinoid depletion on BBB integrity. Methods: Barrier integrity was investigated in vitro via bEnd.3 cell monolayers to assess endocannabinoid syn-thesis, barrier function, calcium influx, junctional protein expression, and proteome-wide changes. Results: In-hibition of 2-AG synthesis using DAGLα inhibition and siRNA inhibition of DAGLα led to loss of barrier in-tegrity via altered expression of VE-cadherin, which could be partially rescued by exogenous application of 2-AG. Moreover, the deleterious effects of DAGLα inhibition on BBB integrity showed both calcium and PKC (protein kinase C)-dependency. Conclusions: These data indicate that disruption of 2-AG homeostasis in brain endothelial cells, in the absence of insult, is sufficient to disrupt BBB integrity thus supporting the role of the endocannabinoid system in neurovascular disorders.

This paper presents the results of experiments on the study of a hydraulic fracture interaction with a preliminary created fracture that simulates a natural fracture. A distinctive feature of the conducted experiments is the ability to use an ultrasonic sounding to measure the fracture propagation and opening simultaneously with the fluid pore pressure measurements at the several points of the porous saturated sample. It allows to obtain the pressure distributions at various experiment stages and to establish a relation between the pore pressure distribution and the hydraulic fracture propagation and its interaction with macroscopic natural fracture. The possibilities of active ultrasonic monitoring have been expanded due to preliminary calibration experiments, which make it possible to estimate the fracture opening value by attenuation of ultrasonic pulses. The experiments show that the fracture propagation is limited by the natural fracture. This is caused by the hydraulic fracturing fluid leaks into the natural fracture, so both hydraulic fracture and natural fracture compose united hydraulic system.

The previous studies on the diversion pipe of pumped storage power stations (PSPS) primarily focused on numerical simulations of pulsating pressure propagation within the pipe and its inducing factors. However, there was a lack of field monitoring and analysis of measured data to support surface ambient vibration, resulting in insufficient credibility and practicality of the numerical results. Based on ambient monitoring and data analysis, this paper examines the factors contributing to the ambient vibration of the water diversion pipeline in Changlongshan (CLS) PSPS. It is concluded that: (1) The primary source of vibration originates from units operation-induced ambient vibrations; (2) Vibration during pumping conditions exceeds that during power generation conditions; (3) The ground measuring point's vibration acceleration primarily depends on the distance from the vibration source; (4) Different types of units have varying effects on water diversion pipeline vibrations.

The Digital Twin is one of the big technology trends of the last decade. In the course of its rapid expansion into the various fields of application, many different definitions emerged, tailored to the respective applications. Taxonomies can cluster the diversity and define application-specific archetypes. This paper presents a characterization of the Digital Twin in the context of structural mechanics and lightweight design. It begins by exploring various definitions of the Digital Twin concept and its application in different industries. The paper further analyzes the product life cycle of structures, dividing it into the design and operational phases and identifying the requirements for a Digital Twin in each phase. The structural design process is examined, including the definition of a structure and the stages of the product development process. Additionally, the paper discusses the operational phase of structures, highlighting the need for continuous monitoring, evaluation, and adaptation through Structural Health Monitoring (SHM) systems. The paper proposes two archetypes of Digital Twins for structures: a structure-designing Digital Twin for the design phase and a structure-monitoring Digital Twin for the operational phase. The findings provide a framework for implementing Digital Twins in structural mechanics, enabling digital design and monitoring of structures for improved performance and maintenance strategies.

While machine learning (ML) has been quite successful in the field of structural health monitoring (SHM), its practical implementation has been limited. This is because ML model training requires data containing a variety of distinct damaged instances captured from a real structure and the experimental generation of such data is challenging. One way to tackle this issue is by generating training data through numerical simulations. However, simulated data cannot capture the bias and variance of experimental uncertainty. To overcome this problem, this work proposes a deep learning-based domain transformation method for transforming simulated data to the experimental domain. Use of this technique has been demonstrated for debonding location and size prediction of stiffened panels using a vibration-based method. The results are satisfactory for both, debonding location and size prediction. This domain transformation method can be used in any field where experimental data for training machine learning models is scarce.

Smart farm is a technology that greatly helps improve the productivity and quality of crops and is being actively introduced into indoor environments such as green houses. However, it has not yet been distributed to field-grown crops. The main reason is that, unlike indoor environments, it is very difficult to collect sensor data for monitoring the growth environment of crops in open fields where weather conditions change significantly and power supply is difficult. Additionally, because various sensors are used, the data formats of the devices are different, making it difficult to process. This paper presents a field crop growth environment monitoring and big data analysis system. The proposed system first solves power supply and data communication problems using solar power generation and LPWA technology. Additionally, based on the oneM2M architecture, data from various sensors is transmitted to the server using standardized technology. The transmitted data is stored and managed on the server as big data, and can be used to predict the production and quality of field-grown crops and take appropriate measures. The proposed system is expected to create an environment optimized for the growth of field crops and help prevent and manage diseases.

Abstract: Placenta accreta spectrum (PAS) is a severe complication of pregnancy associated with excessive invasion of cytotrophoblast cells at the sites of the endometrial-myometrial interface and the myometrium itself in cases of adherent (creta) and invasive (increta and percreta) forms, respectively. Despite advancements in ultrasound protocols and found associations of alpha-fetoprotein, PAPP-A, hCG, PLGF, sFlt-1, IL-8, and IL-33 peripheral blood levels with PAS, the condition remains undiagnosed before delivery because of the lacking clear evaluation criteria for each of the three grades of PAS. This leads to a high risk of massive blood loss, maternal hysterectomy, and preterm birth. While using deep sequencing, miRNA signatures of placental tissue, myometrium, and blood plasma from women with PAS in the third trimester of pregnancy, as well as miRNA profile in exosomes from the blood serum of women in the first trimester with physiologically progressing pregnancy, complicated by PAS or preeclampsia, were obtained. Two logistic regression models were constructed, both featuring statistically significant parameters related to the levels of miR-26a-5p, miR-17-5p, and miR-101-3p quantified by real-time PCR in native blood serum. These models demonstrated 100% sensitivity in detecting PAS during the first pregnancy screening. These miRNAs were identified as specific markers for PAS, showing significant differences in their blood serum levels during the first trimester in the PAS group compared to those in physiological pregnancies, early- or late-onset preeclampsia groups. Furthermore, these miRNAs exhibited differential expression in the PAS placenta and/or myometrium in the third trimester and, according to literature data, control angiogenesis. Significant correlations were found between extracellular hsa-miR-101-3p and nuchal translucency thickness, hsa-miR-17-5p and uterine artery pulsatility index, and hsa-miR-26a-5p and hsa-miR-17-5p with PLGF. The developed test system for early non-invasive PAS diagnosis based on the blood serum level of extracellular miR-26a-5p, miR-17-5p, and miR-101-3p can serve as an auxiliary method for 1st trimester screening pregnant women, subject to validation on independent test samples.

Traditional aquaculture systems appear to be confronted with the high total ammonia nitrogen (TAN) produced during production, which can be harmful to aquatic life. As the demand for global fish production continues to increase, farmers should adopt Recirculating Aquaculture Systems (RAS) to improve production. The biofilter plays a vital role in ammonia removal. However, the drawback of the biofilter operation is needed an automatic and controlled system with a water quality monitoring system to guarantee optimal performance. RAS utilize a monitoring and control system to maintain the water quality and control the biofilm growth in the biofilter to reduce the ammonia. Therefore, this study focuses on developing a water quality monitoring and control system to achieve optimum biofilm performance. From 35 days of the experiment, the water quality was controlled at a level save for the aquatic environment, such as dissolved oxygen, pH, temperature, and TDS. Thus, the highest TAN removal efficiency is 50% when biofilm thickness reaches 119.88 µm.

Background: Cell-free DNA (cfDNA) analysis has become a promising tool for diagnosis, prognosis and monitoring of lymphoma cases. Until now, research in this area has mainly focused in aggressive lymphomas with scanty information from other lymphoma subtypes. Methods: We selected 256 patients diagnosed with lymphomas including a large variety of B-cell and T-cell non-Hodgkin and Hodgkin lymphomas and quantified cfDNA from plasma at the time of diagnosis. We further selected 49 large B cell lymphomas (LBCL) and analyzed cfDNA levels at diagnosis (pre-therapy) and after therapy. In addition, we performed NGS in cfDNA and tissue in this cohort of LBCL. Results: Lymphoma patients showed a statistically significant higher cfDNA concentration than healthy controls (mean 53.0 ng/mL vs 5.6 ng/mL, p <0.001). The cfDNA concentration correlated with lymphoma subtype, lactate dehydrogenase, International Prognostic Index (IPI) score, Ann Arbor (AA) and B-symptoms. In 49 LBCL cases, the cfDNA concentration decreased after therapy in cases who achieved complete response (CR), and increased in non-responders. The median cfDNA at diagnosis of patients who achieved CR and later relapsed was higher (81.5 ng/mL) compared with levels of those who did not (38.6 ng/mL). A concordance of 84% was observed between NGS results in tumor and cfDNA samples. Higher VAF in cfDNA correlated with advanced stage and bulky disease. Conclusions: cfDNA analysis can be easily performed in almost all lymphoma cases. The cfDNA concentration correlated with the characteristics of aggressiveness of the lymphomas and in LBCL with the response achieved after therapy. These results support the utility of cfDNA analysis as a complementary tool in the management of lymphoma patients.

. Cucurbiturils are a family of macrocyclic oligomers capable of forming host-guest complexes with various molecules. Due to noncovalent binding to drug molecules and low toxicity, cucurbiturils has been extensively investigated as a potential carrier for drug delivery. However, the immune system interactions with different drug carriers, including cucurbiturils, are still under investigation. In this study, we focused on cucurbiturils immunosafety and immunomodulation properties in vivo. We measured blood counts, lymphocyte subpopulations in blood, spleen, and bone marrow, and assessed in vivo toxicity to spleen and bone marrow cells after intraperitoneal administration to mice. We also evaluated the effect of cucurbiturils on cellular and humoral immune responses. Cucurbiturils in high concentrations effect on the immune system in vivo. The action of various cucurbiturils differs in different homologues, which, apparently, is associated with different interactions in the internal environment of the body.

Regular weight measurement is important in fattening geese to assess their health status. Failure to gain weight may indicate a potential illness. Standard weight gain analysis involves direct contact with the animal, which can cause stress to the animal, resulting in overall negative impacts on animal welfare. The focus of this study was to design a smart solution for monitoring weight changes in the breeding of farm animals. The proposed IoT system with a weighing device equipped with RFID technology for animal registration aimed to minimise the negative aspects associated with measuring in contact with humans. The designed system aims to incorporate modern approaches in animal husbandry and utilise the acquired data for the potential development of breeding approaches for various animal breeds. The system consisted of three main components: a data acquisition system, a weighing system with RFID, and an environmental monitoring system. In this study, the RFID system accuracy for detecting geese in the weighing system environment was assessed. The entire system evaluation yielded a sensitivity of 93.87%, specificity of 99.94%, accuracy of 99.85%, and precision of 95.09%. Regression analysis revealed a good correlation between observed feeding and RFID registrations with a determination coefficient of R2 = 0.9813.

The liver health of Gilthead sea bream and European sea bass fed fishmeal-free diets including various proportions of plant proteins, insect and poultry by-product meals, was investigated through biochemical and histological analyses using a new Liver Index (LI) formula. Four isoproteic (45% DM) and isolipidic (20% DM) diets were compared: a control, plant-based diet (CV) and other three test ones where 40% of a plant protein-rich ingredient mixture was replaced by Hermetia illucens (H40) or poultry by-product (P40) meals alone or in combination (H10P30). The trials lasted 12 and 18 weeks for sea bream and sea bass, respectively. The results so far ob-tained highlighted species-specific differences in the physiological response to dietary changes. In sea bream, the biochemical and histological responses suggest favorable physiological status and liver health, with higher serum cholesterol (CHO) and triglyceride (TAG) levels and moderate hepatocyte lipid accumulation with the H10P30 diet compared to CV (p<0.05). In sea bass, all diets resulted in elevated serum TAG levels and liver lipid accumulation particularly in fish fed the P40 one (p<0.05) which resulted in the highest LI, coupled with higher frequency of severe lipid accumulation, hypertrophy, cord loss, peripheral nuclei displacement and pyknosis. In conclusion sea bream well adapted to the test diets, while sea bass exhibited altered hepatic lipid metabolism leading to incipient liver steatosis, likely due to the high lipid content of the diets including the insect and poultry meals. The LI developed in this study proved to be a reliable tool for assessing the effects of dietary changes on liver health of sea bream and sea bass, consistent with biochemical and histological findings.

Circulating miRNA-451a has recently emerged as an important biomolecule with potential clinical values as a diagnostic marker for several diseases. However, to be used as such, it is critical to accurately assay miRNA-451a in the clinic. Yet, pre-analytical factors that can affect such an error-free clinical assay of miRNA-451a have not been explored. This study aimed at investigating several of these pre-analytical factors that may affect the accurate quantification of miRNA-451a in human blood samples. We initially evaluated miRNA-451a levels in red blood cells (RBCs), white blood cells (WBCs), platelets, and plasma by droplet digital PCR (ddPCR). Next, we quantified miRNA-451a levels in whole blood or plasma stored at different temperatures for different time periods by ddPCR and used hemolyzed plasma samples to study the effects of hemolysis on plasma miRNA-451a concentration. We found that WBCs and platelets contain trace amounts of miRNA-451a and that about 99.9% of miRNA-451a in the blood are localized in RBCs. Hemolyzed plasma showed 58.5-fold increase in miRNA-451 concentration as compared to non-hemolyzed plasma. Importantly, plasma stored at room temperature (RT) or 4ºC showed a steady statistically significant decrease in miRNA-451a levels over a 7-day period whereas plasma samples stored at -20 or -80ºC for up to 7 days showed stable miRNA-451a levels. Together, our data indicate that hemolysis and blood storage at RT or 4ºC may have significant negative effects on the levels of circulating miRNA-451a.

Natural vegetation restoration can take decades to achieve and requires information on species composition and trend to inform management strategies. Here we demonstrate the potential and portability of drone, dove and Sentinel-2 imagery for species level classification and trajectory monitoring using three study sites with different revegetation histories. Drone imagery (4 cm) was classified using random forests. Dove (3 m) and Sentinel-2 (10 m) images were acquired close to the anniversary of the drone flights and converted into fractional cover maps showing the proportion of exotics, natives and other land covers using ordinary least squares (OLS) and geographically weighted regression (GWR). The time series of fractional cover maps were converted into positive and negative slope and second-order curvature and summarised as one RGB composite to illustrate trend and observe exotic expansion. Drone classification accuracies ranged from 73%-76% when applied to the full plant species list and between 93% and 97% when grouped into natives, exotics, and other land covers. GWR outperformed OLS for all fractional cover mapping and GWR with dove imagery was more accurate than with Sentinel imagery. The combination of very high-resolution drone acquired imagery with the versatility of dove acquired imagery arms land managers with highly accurate species composition and their spatio-temporal dynamics to promote timely intervention.

Blood-derived products (BP) like citrate-platelet-rich plasma (CPRP) or hyperacute serum (HAS) are known to contain many growth factors. Combined mitogenic and DNA damaging stimuli might lead to increased senescent cell burden and altered senescence-associated secretory pheno-type (SASP). Therefore, the senescent state was extensively tested by γH2AX staining, p21 Q-PCR and western blot, growth curves and senescence associated ß-galactosidase staining. Two main treatments with BP were performed early (immediately after etoposide) and late (after addition-al 11days). Effects of the BP treatment was evaluated by IL-6 and IL-8 measurement as well as collagen (COL1) and p21 mRNA expression. Additionally, XTT assays, cell size measurements, viability assays, and cell number calculations were performed. HAS treated cells in early treat-ment had the lowest observed IL-6 and IL-8. In contrast, there was a clear inflammatory response for IL-8 on HAS and CPRP treated cells in late treatment. For COL1 expression an upregulation in early treatment could be shown, meanwhile cells in the late treatment group remained unaf-fected. In CPRP treated cells, the COL1 expression decreased. All in all, BP treatment seems to have slightly positive effects regarding skin rejuvenation.

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.

The lack of accurate and feasible diagnostic tests poses a significant challenge to visceral leish-maniasis (VL) healthcare services in endemic areas. To date, various VL diagnostic tests have been or are being developed, and their diagnostic performance needs to be assessed.. In the present study, the diagnostic performance of rk39RDT, Direct Agglutination Test (DAT), Microscopy, Loop Mediated Isothermal Amplification (LAMP), and miniature direct-on-blood polymerase chain reaction - nucleic acid lateral flow immunoassay (mini-dbPCR-NALFIA) was assessed using quantitative polymerase chain reaction (qPCR) as reference test in an endemic region of Ethiopia. In this study, 235 patients VL suspected patients and 104 non-endemic healthy controls (NEHC) were recruited. Among the VL-suspected patients, 144(61.28%) tested positive with qPCR. Sensi-tivities for rk39 RDT, DAT, Microscopy, LAMP assay, and mini-dbPCR-NALFIA were 88.11%, 96.50%, 76.58%, 94.33%, and 95.80%, respectively. Specificities were 83.33%, 97.96%, 100%, 97.38%, and 98.92% for rk39 RDT, DAT, Microscopy, LAMP assay, and mini-dbPCR-NALFIA, respectively. In conclusion, Rk39 RDT and microscopy exhibited lower sensitivity, while DAT demonstrated excellent performance. LAMP and mini-dbPCR-NALFIA showed excellent perfor-mance with feasibility to implement in remote endemic areas, although the latter requires further evaluation in such regions.

(1) Background: Neonatal early-onset sepsis (EOS) is associated with important mortality and morbidity. The aims of this study were to evaluate the association between serum biomarkers with early onset neonatal sepsis in a cohort of patients with prolonged rupture of membranes (PROM), and to calculate their diagnostic accuracy. (2) Methods: A retrospective cohort study was conducted on 1355 newborns with PROM admitted between January 2017 and March 2020, who were divided in two groups: group A, with PROM ≥ 18 hours and group B, with PROM < 18 hours. Both groups were further split into subgroups: proven sepsis, probable sepsis and no sepsis. Descriptive statistics, ANOVA analysis, and a Random Effects Generalized Least Squares (GLS) regression were used to evaluate the data. (3) Results: The statistically significant predictors of neonatal sepsis were the high white blood cells count from the first (p = 0.005) and third day (p = 0.028), and high CRP values from the first day (p = 0.004). Procalcitonin (area under the curve - AUC = 0.78) and CRP (AUC = 0.76) measured on the first day had the best predictive performance for early onset neonatal sepsis. (4) Conclusions: Further studies are needed to explore other bi-omarkers of sepsis in both pregnant women and newborns to better predict EOS.

In Southeast Asia, the incidence of Leukemia, a malignant blood cancer originating from hema-topoietic progenitor cells, is on the rise, marked by a concerning 54% mortality rate. This study focuses on enhancing early-stage prediction to improve patient recovery prospects significantly. Leveraging Machine Learning and Data Science, we employ protein sequential data from frequently mutated genes such as BCL2, HSP90, PARP, and RB to predict Chronic Myeloid Leukemia (CML). Our approach relies on robust feature extraction techniques, namely Di-peptide Composition (DPC), Amino Acid Composition (AAC), and Pseudo amino acid composition (Pse-AAC), with prior attention to addressing outliers and validating feature selection through the Pearson Corre-lation Coefficient. Data augmentation ensures a well-rounded dataset for analysis. Employing a range of Machine Learning models, including Support Vector Machine (SVM), XGBoost, Random Forest (RF), K Nearest Neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR), we achieve accuracy rates spanning from 66% to 94%. These classifiers undergo comprehensive as-sessment using performance metrics such as accuracy, sensitivity, specificity, F1-score, and the confusion matrix. Our proposed solution, encompassing a user-friendly web application dashboard, presents an invaluable tool for early CML diagnosis with profound implications for practitioners, offering a deploy-able asset within healthcare institutions and hospitals.

Prediabetes, a pivotal phase in glucose metabolism between normalcy and diabetes, exerts a profound influence on the aging process and the risk of age-related diseases. This comprehensive review delves into the intricate web of blood-based biomarkers that collectively expedite senescence, marking the transition from a state of health to age-related complications. Key findings underscore the significance of diverse biomarkers, such as telomere length, p16INK4a, senescence-associated secretory phenotype (SASP) factors, DNA methylation clocks, advanced glycation end products (AGEs), inflammatory and oxidative stress markers, circulating hormones, and additional factors like folate, B12, osteocalcin, and more. Not only do these biomarkers serve as indicators of senescence, but they actively fuel chronic inflammation, oxidative stress, and metabolic dysregulation, all of which contribute to accelerated aging. The implications of this understanding are profound, as prediabetes emerges as a critical period in an individual's life, influencing various physiological systems, including the vascular and neural systems, metabolic functions, hormonal regulation, and bone health. Recognizing the profound influence of prediabetes on senescence provides a foundation for personalized intervention strategies to mitigate age-related complications and promote healthy aging. Future research directions call for a more diverse array of biomarkers, in-depth exploration of their roles, and the development of tailored precision medicine strategies to ensure a holistic understanding and effective management of prediabetes-induced senescence and its implications for aging. This knowledge has far-reaching implications for public health and clinical practice, emphasizing the need for early detection and intervention in prediabetic individuals to enhance the quality of life in an aging population with diverse needs.

Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study we trained YOLOv5 neural networks to detect Points of Interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP), compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models' precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, with high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors.

When the formation flying consists of satellites that are independent from each other and forms a virtual synthetic aperture telescope that replaces a single large telescope with all of its optical elements it is required mutual coordination between the satellites with high accuracy in order to achieve good optical observations. In this paper we consider the problem of developing a control system for a tetrahedral satellite formation flying representing a synthetic aperture telescope in the geostationary orbit for high resolution monitoring of fire in the forest area in infrared spectrum. Synthesizing the image with good quality requires to keep the configuration of formation with µm-class accuracy. Study of dynamics of passive uncontrolled motion of formation flying showed a significant deviation of the configuration from the required shape with a high frequency due to the action of gravitational forces. To keep the configuration in the required form with µm accuracy the analysis of efficiency of various controllers was carried out in the process of numerical simulation. The simulation results made it possible to highlight the features of using various approaches to the development of control system for the satellite formation flying representing a synthetic aperture telescope.

. Background: regular exercise elicits adaptive changes from several organs and physiological processes, including erythrocyte properties. Methods: in a group of 79 subjects (62 men and 17 women; mean age 31.37 +/- 10.19 years) who trained several times a week as they practiced amateur sports, we evaluated the elongation index, marker of erythrocyte deformability, the red blood cell distribution width (RDW), indicator of erythrocyte anisocytosis, hematocrit, hemoglobin and the main erythrocyte indices (MCV, MCH, MCHC) in basal conditions. Results: in comparison with a group of healthy not trained volunteers, the values of the elongation index, and not the RDW, are increased and this datum is accompanied by an increase of MCV and MCHC, likely related to an increased presence of circulating young erythrocytes in trained subjects. We also divided the same group according to the median of the VO2max, observing that the subgroup above the median shows both an increase in the elongation index values and a decrease in MCH and MCHC. Conclusions: in trained subjects there is no correlation between the values of elongation index and the RDW, while of particular interest and of a certain complexity seem the interrelations among elongation index, RDW and the main erythrocyte indices.

The increasing rate of adoption of innovative technological achievements along with the penetration of the Next Generation Internet (NGI) technologies and Artificial Intelligence (AI) in the water sector, are leading to a shift to a Water-Smart Society. New challenges have emerged in terms of data interoperability, sharing, and trustworthiness due to the rapidly increasing volume of heterogeneous data generated by multiple technologies. Hence, there is a need for efficient harmonisation and smart modeling of the data to foster advanced AI analytical processes which will lead to efficient water data management. The main objective of this work is to propose two Smart Data Models focusing on the modeling of the Satellite Imaginary data and the Flood Risk Assessment processes. The utilisation of those models reinforces the fusion and homogenisation of diverse information and data facilitating the adoption of AI technologies for flood mapping and monitoring. Furthermore, a holistic framework has been developed and evaluated via qualitative and quantitative performance indicators revealing the efficacy of the proposed models concerning the usage of the models in real cases. The framework is based on the well-known and compatible technologies on NGSI-LD standards which are customised and applicable easily to support the water data management processes effectively.

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis.

My hypothesis is that the signs and symptoms of Long COVID can be explained by a shortage of blood in the body and a resulting deficient blood flow through nearly all organs. This shortage arises through damage to the blood-producing organs during the acute phase, while the breakdown of blood continues as normal, after an initial increase. In order to ensure the perfusion of organs that are directly necessary for survival, the body takes the emergency measure of diverting blood from other organs and tissues. The perfusion of the blood-producing organs is also affected by this distribution measure, which hinders the smooth recovery of the total blood volume. The body is stuck in this vicious circle: a shortage of circulating blood hinders the recovery of blood production. This explains the long duration of Long COVID. My proposed treatment of Long COVID focuses on the recovery of the correct volume of blood in the body of the right composition by the very careful administration of donor blood products under continuous expert supervision. A trial treatment can be performed in any hospital without much additional preparations, and has a lower associated risk for the patient than analysing the total blood. A diagnosis ex juvantibus, by therapeutic response, is therefore preferable, and will result in the healing process starting earlier. Indications in blood laboratory values of a shortage of blood are a high serum ferritin due to internal breakdown of blood and values for haematocrit and albumin at reciprocal extremes of the reference ranges due to a stagnation of blood production.

Synthetic fiber ropes are frequently used as mooring lines in floaters supporting Floating Offshore Wind Turbines (FOWTs) due to their various advantages. Damages in these lines can lead to the disruption of the FOWTs normal operation and thus their early detection is crucial. Currently, this damage detection problem has been investigated exclusively for FOWT chain mooring lines. This study focuses on the damage detection in FOWT synthetic mooring lines under varying environmental-operational conditions (EOCs) via two types of vibration-based Structural Health Monitoring methods: those based on Multiple Models and those based on a single Functional Model. The methods are based on multivariate AutoRegressive or on scalar Transmittance Function (TF) AutoRegressive with eXogenous input models. All methods are evaluated through a Monte Carlo study involving 390 simulations with a 10MW wind turbine mounted on the semi-submersible OO-Star wind floater and two damage scenarios (10%, 14% stiffness reduction) to a single mooring line. The results using acceleration signals from only two measuring positions on the mooring line, indicate the great potential for robust SHM in FOWTs synthetic mooring lines with the TF model-based methods successfully detecting the considered early stage damage scenarios, even for EOCs not encountered in the methods training.

Background: Deoxycholic acid (DCA) is a secondary bile acid produced by gut bacteria. Its serum concentration increases in cardiovascular disease (CVD). We hypothesized that DCA may affect hemodynamic parameters in rats. Methods: The concentration of DCA in systemic blood was measured with liquid chromatography coupled with mass spectrometry. Arterial blood pressure (BP), heart rate (HR) and echocardiographic parameters were evaluated in anesthetized, male, 3-4-month-old Sprague-Dawley rats administered intravenously (IV) or intracerebroventricularly (ICV) with investigated compounds. Mesenteric artery (MA) reactivity was tested ex vivo. Results: The baseline plasma concentration of DCA was 0.24 ± 0.03 mg/l. The oral antibiotic treatment produced a large decrease in the concentration. Administered IV, the compound increased BP and HR in a dose-dependent manner. DCA also increased heart contractility and cardiac output. None of the tested compounds: prazosin (an alpha-blocker), propranolol (beta-adrenolytic), atropine (muscarinic receptor antagonist), glibenclamide (K-ATP inhibitor) or DY 268 (FXR antagonist), glycyrrhetinic acid (11HSD2 inhibitor) significantly diminished the DCA-induced pressor effect. ICV infusion did not exert significant HR or BP changes. DCA relaxed MAs. Systemic vascular resistance did not change significantly. Conclusions: DCA increases BP by increasing cardiac output. As a bioactive gut bacteria-derived metabolite, DCA may co the interaction between gut microbiota and the host’s circulatory system.

The Internet of Things (IoT) is experiencing exponential growth, revolutionizing various industries with its ability to connect devices and enable advanced applications. In the realm of energy production, IoT offers significant potential for optimizing processes and improving efficiency. This study presents a comprehensive monitoring and energy management strategy for the University of Cuenca's microgrid photovoltaic (PV) system, utilizing IoT and ThingSpeak as key technologies. The proposed strategy leverages intelligent environments and utilizes ThingSpeak as a robust platform for data presentation and analysis. By integrating IoT devices and sensors, real-time monitoring of PV system parameters such as solar radiation and temperature is achieved. The collected data is then analyzed using advanced algorithms and visualized through ThingSpeak, enabling effective energy management and decision-making. The developed monitoring system was rigorously tested in a laboratory microgrid setting, where the PV system is interconnected with other generation and storage systems, as well as the electrical grid. Through the seamless integration of these components, the monitoring system provides enhanced visibility and control over the microgrid's energy production. The results demonstrate the successful implementation of the monitoring system, showcasing its ability to improve the supervision, automation, and analysis of daily energy production. By leveraging IoT technologies and ThingSpeak, stakeholders can access real-time data, analyze performance trends, and optimize energy resources.

Ischemia, the inadequate blood flow, can arise from pre-existing or traumatic events, remain an undetectable issue, and impact the body's ability to provide oxygen to necessary limbs, organs, muscles, or tissue. New technology involving wearable ultrasonic devices allows medical professionals to gain real-time insight into the cardiovascular system of their patients. A non-invasive, postage-stamp-sized ultrasonic sensor can be attached to a patient, allowing for continuous monitoring of the patient’s blood pressure outside of the hospital. After reviewing 16 papers on ultrasonic sensors, this paper intends to review the current use of wearable ultrasonic sensors and the advantages and limitations of the technology used to obtain accurate blood pressure readings. This review will bring attention to the evolving and expanding world of wearable ultrasonic sensors for medical applications.

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1-5 min) but low spatial resolution (2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with Deep Learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

The soil-structure interaction represents an important parameter when analyzing the dynamic behavior of bridges, taking into account the integration between soil, foundation, and structure to ensure an adequate structural representation. In this paper, a computational modeling strategy using soil-structure interaction (SSI) methodology was applied to represent the dynamic behavior of a pedestrian bridge (LOP). The computational models were validated using monitoring results from an extensive structural health monitoring campaign. The bridge monitoring program was characterized using embedded sensors and cutting-edge technology for remote monitoring: ground-based radar interferometry and video motion amplification. The instrumentation results were compared to establish the capabilities and advantages of this novel remote monitoring technique. The experimental results obtained from free and forced vibrations in the asset served as a basis for validating the developed computational model and allowed evaluating the asset be-havior in service for different loading conditions. This paper emphasizes the SSI approach to represent the pedestrian bridge dynamic behavior more accurately, allowing computational models to represent the LOP structural responses over the course of its life-cycle.

In this paper, we redefine the boundaries of blood cell classification, expanding from 5 to 11 distinct blood cell types using the challenging 11-class PBC dataset. This shift enables a deeper understanding of blood cell diversity, overcoming previous limitations in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking "Naturalize" augmentation technique produces remarkable results. The 2K-PBC dataset generated with "Naturalize" boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The "Naturalize" technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.

The blood–brain barrier (BBB) plays pivotal roles in synaptic and neuronal functioning by sealing the space between adjacent microvascular endothelial cells. BBB breakdown is present in patients with mild cognitive impairment (MCI) or Alzheimer disease (AD). Claudin-5 (CLDN-5) is a tetra-spanning protein essential for sealing the intercellular space between adjacent endothelial cells in the BBB. In this study, we developed a blood-based assay for CLDN-5 and investigated its diagnostic utility by using 100 cognitively normal (control) subjects, 100 patients with MCI, and 100 patients with AD. Plasma CLDN-5 levels were increased in patients with AD (3.08 ng/mL) compared with controls (2.77 ng/mL). Plasma levels of phosphorylated tau (pTau181), a biomarker of pathological tau, were elevated in patients with MCI or AD (2.86 and 4.20 pg/mL, respectively) compared with control subjects (1.81 pg/mL). In patients with MCI or AD, plasma levels of CLDN-5—but not pTau181—decreased with age, suggesting some age-dependent BBB changes in MCI and AD. These findings suggest that plasma CLDN-5 may a potential biochemical marker for the diagnosis of MCI and AD.

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.

Background: Along with sarcopenia, delayed blood pressure (BP) recovery after standing and orthostatic hypotension (OH) are among key clinical challenges associated with ageing. Previous studies have reported an association between sarcopenia and delayed orthostatic BP recovery and OH. However, the haemodynamic mechanisms underlying this association remain unclear. Methods: 107 attendees to a falls and syncope clinic aged 50+ years underwent an active stand test with non-invasive beat-to-beat haemodynamic measurement. Hand grip strength and 5-chair stands time were measured. Muscle mass was estimated using bioelectrical impedance analysis, and participants were classified as non-sarcopenic and sarcopenic. Mixed effects models with linear splines were used to model the effect of sarcopenia status on Modelflow®-derived mean arterial pressure (MAP), cardiac output (CO), total peripheral resistance (TPR), stroke volume (SV) and heart rate (HR) after standing, while controlling for potential confounders. Results: Sarcopenia was associated with: attenuated recovery of MAP in the 10-20s period after standing (β -0.67, P<0.001); attenuated rise to peak (0-10s) and recovery from peak (10-20s) of CO (β -0.05, P<0.001; β 0.06, P<0.001); attenuated recovery (10-20s) of TPR from nadir (β -0.02, P<0.001) and attenuated recovery from peak (10-20s) of SV (β 0.54, P<0.001). HR was not significantly associated with sarcopenia status at any time interval after standing. Conclusion: The attenuated BP recovery after standing seen in sarcopenia appears to be driven by initial diminished peak of CO followed by attenuated recovery of CO from peak and TPR from nadir. The CO finding appears to be driven by SV rather than HR. Potential mechanisms for these findings include: cardio-sarcopenia, the effects of sarcopenia on the autonomic nervous system and/or the skeletal muscle pump.

Blood testing is a crucial application in the field of clinical studies for disease diagnosis and screening, biomarkers discovery, organ function assessment, and personalized medication. Therefore, it is of utmost importance to collect precise data in a short time. In this study, we utilized Raman spectroscopy to analyze blood samples and extracted comprehensive biological information, including the primary components and compositions present in the blood. We conducted short-wavelength (532 nm green light) Raman scattering spectroscopy on blood samples, plasma, and serum, and subsequently analyzed the biological characteristics detected in each sample type. Our results indicated that whole blood had high hemoglobin content, which suggests that hemoglobin is a major component of blood. The characteristic Raman peaks of hemoglobin were observed at 690, 989, 1015, 1182, 1233, 1315, and 1562–1649 cm−1. Analysis of plasma and serum samples indicated the presence of β-carotene, which exhibited characteristic peaks at 1013, 1172, and 1526 cm−1. Additionally, in the field of medical blood testing, the novel 3D Silicon micro-channel device technology holds immense potential. It can serve as a substrate and helps to detect various diseases and biomarkers, providing real-time data to help medical professionals and patients better understand their health conditions. Changes in biological data collected in this manner could potentially be used to diagnose clinical diseases.