Adipose/fat tissue provides an abundant source of stromal vascular fraction (SVF) cells for immediate administration and can also give rise to a substantial number of cultured, multipotent ...adipose-derived stromal cells (ADSCs). Recently, both SVF and ADSCs have gained wide-ranging translational significance in regenerative medicine. Initially used for cosmetic breast enhancement, this mode of treatment has found use in many diseases involving immune disorders, tissue degeneration, and ischaemic conditions. In this review, we try to address several important aspects of this field, outlining the biology, technology, translation, and challenges related to SVF- and ADSC-based therapies. Starting from the basics of SVF and ADSC isolation, we touch upon recently developed technologies, addressing elements of novel methods and devices under development for point-of-care isolation of SVF. Characterisation of SVF cells and ADSCs is also an evolving area and we look into unusual expression of CD34 antigen as an interesting marker for such purposes. Based on reports involving different cells of the SVF, we draw a potential mode of action, focussing on angiogenesis since it involves multiple cells, unlike immunomodulation which is governed predominantly by ADSCs. We have looked into the latest research, experimental therapies, and clinical trials which are utilising SVF/ADSCs in conditions such as multiple sclerosis, Crohn's disease, peripheral neuropathy, osteoarthritis, diabetic foot ulcer, and so forth. However, problems have arisen with regards to the lack of proper regulatory guidelines for such therapies and, since the introduction of US Food and Drug Administration draft guidelines and the Reliable and Effective Growth for Regenerative Health Options that Improve Wellness (REGROW) Act, the debate became more public with regards to safe and efficacious use of these cells.
Formation of aluminium (Al) doped molybdenum di-silicide (MoSi2) coatings was studied to improve the high temperature oxidation behavior of TZM (Mo–0.5Ti–0.1Zr–0.02C) alloy. The pack composition of ...the halide activated pack cementation process was successfully optimized to form silicide and Al doped silicide coatings on the TZM alloy substrates. Mo(Si, Al)2 phase was found to form at the outer layer of the coating prepared by doping Al in MoSi2. A change in composition of the phases with increase in coating temperature was detected with Al doping, whereas un-doped silicide coating process was dominated by the formation and growth of MoSi2 phase. Oxidation test and the characterization studies using SEM, EDS, XRD, and micro-hardness measurements indicated the improved performance of Al doped silicide coating during high temperature oxidation in dry air due to the formation of the protective alumina scale.
► Formation of Mo(Si, Al)2 at the outer layer of coating due to Al doping in MoSi2. ► Change in phase formation in Al doped coating with increase in temperature. ► Al2O3 formed as a protective layer at the outer surface. ► Improvement of oxidation performance of the alloy at higher temperatures.
Multidrug resistance of the pathogenic microorganisms to the antimicrobial drugs has become a major impediment toward successful diagnosis and management of infectious diseases. Recent advancements ...in nanotechnology-based medicines have opened new horizons for combating multidrug resistance in microorganisms. In particular, the use of silver nanoparticles (AgNPs) as a potent antibacterial agent has received much attention. The most critical physico-chemical parameters that affect the antimicrobial potential of AgNPs include size, shape, surface charge, concentration and colloidal state. AgNPs exhibits their antimicrobial potential through multifaceted mechanisms. AgNPs adhesion to microbial cells, penetration inside the cells, ROS and free radical generation, and modulation of microbial signal transduction pathways have been recognized as the most prominent modes of antimicrobial action. On the other side, AgNPs exposure to human cells induces cytotoxicity, genotoxicity, and inflammatory response in human cells in a cell-type dependent manner. This has raised concerns regarding use of AgNPs in therapeutics and drug delivery. We have summarized the emerging endeavors that address current challenges in relation to safe use of AgNPs in therapeutics and drug delivery platforms. Based on research done so far, we believe that AgNPs can be engineered so as to increase their efficacy, stability, specificity, biosafety and biocompatibility. In this regard, three perspectives research directions have been suggested that include (1) synthesizing AgNPs with controlled physico-chemical properties, (2) examining microbial development of resistance toward AgNPs, and (3) ascertaining the susceptibility of cytoxicity, genotoxicity, and inflammatory response to human cells upon AgNPs exposure.
The phenomenology of exchange bias effects observed in structurally single-phase alloys and compounds but composed of a variety of coexisting magnetic phases such as ferromagnetic, antiferromagnetic, ...ferrimagnetic, spin-glass, cluster-glass and disordered magnetic states are reviewed. The investigations on exchange bias effects are discussed in diverse types of alloys and compounds where qualitative and quantitative aspects of magnetism are focused based on macroscopic experimental tools such as magnetization and magnetoresistance measurements. Here, we focus on improvement of fundamental issues of the exchange bias effects rather than on their technological importance.
Land subsidence caused by groundwater extraction has numerous negative consequences, such as loss of groundwater storage and damage to infrastructure. Understanding the magnitude, timing, and ...locations of land subsidence, as well as the mechanisms driving it, is crucial to implementing mitigation strategies, yet the complex, nonlinear processes causing subsidence are difficult to quantify. Physical models relating groundwater flux to aquifer compaction exist but require substantial hydrological data sets and are time consuming to calibrate. Land deformation can be measured using interferometric synthetic aperture radar (InSAR) and GPS, but the former is computationally expensive to estimate at scale and is subject to tropospheric and ionospheric errors, and the latter leaves many temporal and spatial gaps. In this study, we apply for the first time a machine learning approach that quantifies the relationships of various widely available input data, including evapotranspiration, land use, and sediment thickness, with land subsidence. We apply this method over the Western United States and estimate that from 2015 to 2016, ~2.0 km3/yr of groundwater storage was lost due to groundwater pumping‐induced compaction of sediments. Subsidence is concentrated in the Central Valley of California, and the state of California accounts for 75% of total subsidence in the Western United States. Other significant areas of subsidence occur in cultivated regions of the Basin and Range province. This study demonstrates that widely available ancillary data can be used to estimate subsidence at a larger scale than has been previously possible.
Key Points
Remote sensing and geological data sets are integrated to estimate land subsidence due to groundwater extraction
The estimates are integrated using a machine learning method called random forests
Using this method we estimate that from 2015 to 2016, ~2.0 km3/yr of groundwater storage was lost due to land subsidence in the western United States
The thymus is essential for T cell development and maturation. It is extremely sensitive to atrophy, wherein loss in cellularity of the thymus and/or disruption of the thymic architecture occur. This ...may lead to lower naïve T cell output and limited TCR diversity. Thymic atrophy is often associated with ageing. What is less appreciated is that proper functioning of the thymus is critical for reduction in morbidity and mortality associated with various clinical conditions including infections and transplantation. Therefore, therapeutic interventions which possess thymopoietic potential and lower thymic atrophy are required. These treatments enhance thymic output, which is a vital factor in generating favourable outcomes in clinical conditions. In this review, experimental studies on thymic atrophy in rodents and clinical cases where the thymus atrophies are discussed. In addition, mechanisms leading to thymic atrophy during ageing as well as during various stress conditions are reviewed. Therapies such as zinc supplementation, IL7 administration, leptin treatment, keratinocyte growth factor administration and sex steroid ablation during thymic atrophy involving experiments in animals and various clinical scenarios are reviewed. Interventions that have been used across different scenarios to reduce the extent of thymic atrophy and enhance its output are discussed. This review aims to speculate on the roles of combination therapies, which by acting additively or synergistically may further alleviate thymic atrophy and boost its function, thereby strengthening cellular T cell responses.
Toll-like receptor 4 (TLR4) has a key role in innate immunity by activating an inflammatory signaling pathway. Free fatty acids (FFAs) stimulate adipose tissue inflammation through the TLR4 pathway, ...resulting in insulin resistance. However, current evidence suggests that FFAs do not directly bind to TLR4, but an endogenous ligand for TLR4 remains to be identified. Here we show that fetuin-A (FetA) could be this endogenous ligand and that it has a crucial role in regulating insulin sensitivity via Tlr4 signaling in mice. FetA (officially known as Ahsg) knockdown in mice with insulin resistance caused by a high-fat diet (HFD) resulted in downregulation of Tlr4-mediated inflammatory signaling in adipose tissue, whereas selective administration of FetA induced inflammatory signaling and insulin resistance. FFA-induced proinflammatory cytokine expression in adipocytes occurred only in the presence of both FetA and Tlr4; removing either of them prevented FFA-induced insulin resistance. We further found that FetA, through its terminal galactoside moiety, directly binds the residues of Leu100-Gly123 and Thr493-Thr516 in Tlr4. FFAs did not produce insulin resistance in adipocytes with mutated Tlr4 or galactoside-cleaved FetA. Taken together, our results suggest that FetA fulfills the requirement of an endogenous ligand for TLR4 through which lipids induce insulin resistance. This may position FetA as a new therapeutic target for managing insulin resistance and type 2 diabetes.
R2Cu2O5 (R = Tb-Lu, Sc, In) (RCO) compounds are interesting magnetic materials due to their quasi-low dimensional crystal structure, which also lacks centro-symmetry. Previous investigations on these ...compounds indicated antiferromagnetic or canted-antiferromagnetic ground state with Néel temperatures lying below about 30 K. The present work tries to provide finer details of the intricate magnetic characteristics of the RCO compounds, which were not addressed in the previous works. These include thermomagnetic irreversibility, thermal hysteresis, hysteresis in the isothermal magnetization measurements and so on. Most interestingly, we observe large magnetocaloric effect as calculated from our magnetization data and we find that it is primarily connected to the metamagnetic transitions observed in the samples. Our work also recognizes anomalies in the dielectric constant at the magnetic transition temperatures indicating possible multiferroicity in the samples. Efforts have been made to understand the diverse magnetic properties of the RCO samples on the basis of the crystal structure and the rare-earth moment.
•A comprehensive magnetic scenario of complete R2Cu2O5 series in a single report.•Observation of sizable magnetocaloric effect in R2Cu2O5 associated with metamagnetism.•Dielectric anomaly at magnetic transition indicates the magneto-electric coupling.•Studies of thermo-magnetic irreversibility, thermal hysteresis in R2Cu2O5 oxides.
Key Points
Groundwater withdrawals are not actively monitored in most places of the world at a scale necessary to implement sustainable solutions
Various multitemporal remote sensing data are ...integrated into a machine learning framework to effectively predict groundwater withdrawals
The results over the High Plains Aquifer, Kansas, USA, show that this approach is applicable to similar regions having sparse in situ data
Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests—a state of the art machine learning algorithm—to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the Years 2002–2019. Our modeled withdrawals had high accuracy on both training and testing data sets (R2 ≈ 0.99 and R2 ≈ 0.93, respectively) during leave‐one‐out (year) cross validation with low mean absolute error (MAE) ≈ 4.31 mm and root‐mean‐square error (RMSE) ≈ 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R2 ≈ 0.84) with MAE ≈ 9.72 mm and RMSE ≈ 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices.
Plain Language Summary
Groundwater is an essential component of global water resources and is the largest source of Earth's liquid freshwater. It is extensively used for drinking water and to support global food production. Consequently, groundwater consumption has significantly increased owing to the pressing demands for water, food, and energy primarily driven by the increasing global population. Despite its critical role in the water‐food‐energy nexus, very few regions in the United States or elsewhere actively monitor their groundwater withdrawals (extraction or pumping) for implementing sustainable water management solutions. We develop a novel approach combining water balance components and land use measured using openly available remote sensing products with a machine learning model to estimate groundwater withdrawals. This framework automatically learns the interrelationships among these variables and groundwater withdrawals. Our study area is a portion of the High Plains Aquifer in Kansas (central United States) where overpumping has caused substantial groundwater storage loss. Also, a large amount of groundwater pumping data are available for validation. The results indicate good accuracy even for extreme drought years. Thus, this approach should be applicable to similar regions having sparsely or moderately available groundwater pumping data, enabling water managers to proactively implement sustainable solutions addressing water security issues.
Abstract Introduction One quarter of osteoporotic fractures occur in men. TBS, a gray-level measurement derived from lumbar spine DXA image texture, is related to microarchitecture and fracture risk ...independently of BMD. Previous studies reported the ability of spine TBS to predict osteoporotic fractures in women. Our aim was to evaluate the ability of TBS to predict clinical osteoporotic fractures in men. Methods 3620 men aged ≥ 50 (mean 67.6 years) at the time of baseline DXA (femoral neck, spine) were identified from a database (Province of Manitoba, Canada). Health service records were assessed for the presence of non-traumatic osteoporotic fracture after BMD testing. Lumbar spine TBS was derived from spine DXA blinded to clinical parameters and outcomes. We used Cox proportional hazard regression to analyze time to first fracture adjusted for clinical risk factors (FRAX without BMD), osteoporosis treatment and BMD (hip or spine). Results Mean followup was 4.5 years. 183 (5.1%) men sustain major osteoporotic fractures (MOF), 91 (2.5%) clinical vertebral fractures (CVF), and 46 (1.3%) hip fractures (HF). Correlation between spine BMD and spine TBS was modest (r = 0.31), less than correlation between spine and hip BMD (r = 0.63). Significantly lower spine TBS were found in fracture versus non-fracture men for MOF (p < 0.001), HF (p < 0.001) and CVF (p = 0.003). Area under the receiver operating characteristic curve (AUC) for incident fracture discrimination with TBS was significantly better than chance (MOF AUC = 0.59, p < 0.001; HF AUC = 0.67, p < 0.001; CVF AUC = 0.57, p = 0.032). TBS predicted MOF and HF (but not CVF) in models adjusted for FRAX without BMD and osteoporosis treatment. TBS remained a predictor of HF (but not MOF) after further adjustment for hip BMD or spine BMD. Conclusion We observed that spine TBS predicted MOF and HF independently of the clinical FRAX score, HF independently of FRAX and BMD in men. Studies with more incident fractures are needed to confirm these findings.
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