Extent Of Degradation Research Articles

Degradation of polysorbate investigated by a high-performance liquid chromatography multi-detector system with charged aerosol and mass detection

Polysorbate 80 is widely used as a formulation component in biopharmaceutical drug products. Recent studies have shown that polysorbate 80 is readily degraded either through direct or indirect means. The degradation of polysorbate 80 causes a concern for the long-term stability of biopharmaceutical drug product, as the breakdown products of polysorbate 80 have been shown to cause adverse effects, such as formation of sub-visible and visible particles and mAb aggregation. Understanding the path and extent of degradation is of a paramount importance for the formulator during formulation development. A multi-detector HPLC system using charged aerosol and mass detection was developed and optimized for the characterization of polysorbate 80 standards. The system included a post-column make-up flow, i.e. an inverse gradient, that enabled constant eluent composition at the detectors. The inverse gradient eliminated the main source of variability for the charged aerosol detector response, thereby enabling the calculation of the mass balance between polysorbate components with different degrees of esterification. Extracted ion chromatograms of the mass detector combined with their respective retention times were used to qualitatively characterize the polysorbate samples down to the individual components. The system was applied to study the degradation of several polysorbate standards which occurred by enzymatic digestion or long-term storage. The system provided detailed information on the mechanism of degradation without the need for additional orthogonal analytical techniques.

Mapping tropical forest degradation with deep learning and Planet NICFI data

Tropical rainforests from the Brazilian Amazon are frequently degraded by logging, fire, edge effects and minor unpaved roads. However, mapping the extent of degradation remains challenging because of the lack of frequent high-spatial resolution satellite observations, occlusion of understory disturbances, quick recovery of leafy vegetation, and limitations of conventional reflectance-based remote sensing techniques. Here, we introduce a new approach to map forest degradation caused by logging, fire, and road construction based on deep learning (DL), henceforth called DL-DEGRAD, using very high spatial (4.77 m) and bi-annual to monthly temporal resolution of the Planet NICFI imagery. We applied DL-DEGRAD model over forests of the state of Mato Grosso in Brazil to map forest degradation with attributions from 2016 to 2021 at six-month intervals. A total of 73,744 images (256 × 256 pixels in size) were visually interpreted and manually labeled with three semantic classes (logging, fire, and roads) to train/validate a U-Net model. We predicted the three classes over the study area for all dates, producing accumulated degradation maps biannually. Estimates of accuracy and areas of degradation were performed using a probability design-based stratified random sampling approach (n = 2678 samples) and compared it with existing operational data products at the state level. DL-DEGRAD performed significantly better than all other data products in mapping logging activities (F1-score = 68.9) and forest fire (F1-score = 75.6) when compared with the Brazil's national maps (SIMEX, DETER, MapBiomas Fire) and global products (UMD-GFC, TMF, FireCCI, FireGFL, GABAM, MCD64). Pixel-based spatial comparison of degradation areas showed the highest agreement with DETER and SIMEX as Brazil official data products derived from visual interpretation of Landsat imagery. The U-Net model applied to NICFI data performed as closely to a trained human delineation of logged and burned forests, suggesting the methodology can readily scale up the mapping and monitoring of degraded forests at national to regional scales. Over the state of Mato Grosso, the combined effects of logging and fire are degrading the remaining intact forests at an average rate of 8443 km2 year−1 from 2017 to 2021. In 2020, a record degradation area of 13,294 km2 was estimated from DL-DEGRAD, which was two times the areas of deforestation.

Experimental study on spatial evolution of mode-I and mode-II fracture toughness of cemented paste backfill under external sulphate attack

ABSTRACT External sulphate attack (ESA) interferes with spatial changes in fracture toughness of cemented paste backfill (CPB) and affects its mechanical stability. Large-scale curing column models are developed to investigate the spatial evolution of mode-I and mode-II fracture toughness (i.e. KIC and KIIC) under seepage- and diffusion-induced ESA. It has been found that the seepage-induced ESA leads to spatial changes in KIC and KIIC of CPB at advanced ages and higher extent of degradation of fracture toughness is measured at a deeper position in CPB. Comparatively, KIc can be used as a reliable quantity to estimate KIIc under the diffusion-induced ESA.

Assessing the environmental destruction in forest ecosystems using landscape metrics and spatial analysis

Forest degradation is a serious environmental issue that has significant implications for ecological stability, biodiversity, and human well-being. Identifying the extent and severity of forest degradation is crucial for effective management and conservation of forest ecosystems. The objective of this study was to assess the ecological vulnerability of the forest in the Dadabad region using spatial analysis and landscape metrics. The land cover map of the area was divided into 13 sub-basins to quantify landscape metrics, and the severity of human activities, degradation level, and ecological vulnerability were calculated for each sub-basin. Each sub-basin was evaluated as a single landscape to determine the extent of degradation, and landscape zoning was performed based on the degradation coefficients. The study found that sub-basins 2 and 4 had the highest levels of degradation, while sub-basins 3, 7, 8, 11, and 12 were the least degraded. Over half of the Dadabad region, 37.8% for protection and 32.25% for rehabilitation, was recommended for conservation planning. The research highlights the importance of using spatial analysis of landscape metrics to assist managers and planners in protecting and conserving natural areas.

Degradation of pesticides using hybrid processes based on cavitation and photocatalysis: A review

AbstractThe production of pesticides and its subsequent consumption in agricultural fields have increased significantly also resulting in environmental concerns. The current review discusses the different aspects of two emerging techniques as cavitation and photocatalysis that can be effectively applied for the degradation of toxic pesticides. Governing mechanisms for the degradation have been presented followed by discussion on the effect of different parameters on the extent of degradation, also presenting guidelines for optimum selection. It has been also demonstrated that the obtained degradation can be synergistically intensified by using combined techniques with complimentary mechanisms. The discussion clearly revealed that cavitation and photocatalysis have great potential to degrade pesticides efficiently. Overall, it was demonstrated that combined technique under the optimized set of operating conditions is energy‐efficient, cost‐effective and promising technique for pesticide degradation.

A regional, remote sensing-based approach to mapping land degradation in the Little Karoo, South Africa

There is growing global consensus that assessments of land degradation be conducted at regional or smaller scales. Working at this scale allows for locally relevant environmental and land use conditions to be incorporated into the assessment methodology. In this paper, a recently developed regional approach to assessing land degradation in the Hardeveld bioregion of the Succulent Karoo is applied to the Little Karoo region of this biome. The methodology uses fuzzy classification statistical techniques to combine field data with multiple Sentinel-2A and Landsat vegetation indices, as well as regionally modelled soil variables. The resultant habitat condition archetype map values show strong correlation with field observations of perennial plant and bare soil cover in 96 ground-truthed plots. The archetype map indicates that heavily degraded hotspots of high bare ground cover occur throughout the project region, although there is an overall lower average habitat condition in the western half of the Little Karoo. The mean habitat condition archetype value for the entire project area is 0.54 (standard deviation = 0.13), on a continuous scale where 0 and 1 represent the most degraded and pristine extremes, respectively. Random forest regression analysis of various environmental covariates of degradation indicates a strong relationship between habitat condition and topographic as well as rainfall variables, although the limited accuracy of modelled livestock data may obscure the negative impacts of overgrazing. The 30 m resolution habitat condition archetype map builds upon previous degradation research in the Little Karoo and has the potential to inform future conservation, restoration, and rangeland management decisions. The methodology was successfully transferred to a new region and provides an opportunity to improve reporting on the extent of land degradation across South Africa.

Synergistic lignin degradation between Phanerochaete chrysosporium and Fenton chemistry is mediated through iron cycling and ligninolytic enzyme induction.

Removal of recalcitrant lignin from wastewater remains a critical bottleneck in multiple aspects relating to microbial carbon cycling ranging from incomplete treatment of biosolids during wastewater treatment to limited conversion of biomass feedstock to biofuels. Based on previous studies showing that the white rot fungus Phanerochaete chrysosporium and Fenton chemistry synergistically degrade lignin, we sought to determine optimum levels of Fenton addition and the mechanisms underlying this synergy. We tested the extent of degradation of lignin under different ratios of Fenton reagents and found that relatively low levels of H2O2 and Fe(II) enhanced fungal lignin degradation, achieving 80.4 ± 1.61 % lignin degradation at 1.5 mM H2O2 and 0.3 mM Fe(II). Using a combination of whole-transcriptome sequencing and iron speciation assays, we determined that at these concentrations, Fenton chemistry induced the upregulation of 80 differentially expressed genes in P. ch including several oxidative enzymes. This study underlines the importance of non-canonical, auxiliary lignin-degrading pathways in the synergy between white rot fungi and Fenton chemistry in lignin degradation. We also found that, relative to the abiotic control, P. ch. increases the availability of Fe(II) for the production of hydroxyl radicals in the Fenton reaction by recycling Fe(III) (p < 0.001), decreasing the Fe(II) inputs necessary for lignin degradation via the Fenton reaction.

Effect of glucose on biodegradation of hydrocarbons in crude-oil polluted soil undergoing bioremediation

Bioremediation of crude oil polluted soil can be enhanced by addition of easily assimilated carbon sources such as glucose. The aim of this study was to determine the effect of increasing concentration of glucose on biodegradation of hydrocarbons in crude-oil polluted soil undergoing bioremediation. Some hydrocarbon utilizing bacteria and fungi were used as bioremediation agents in the study. Soil artificially polluted with crude-oil was divided into 5 setups: CT (control), S1, S2, S4, and S8. Suspension of the bioremediation agents and nutrients were added to the setups. About 5, 10, 20, and 40 g glucose were added to S1, S2, S4, and S8 respectively: equivalent to 1, 2, 4, and 8 % glucose concentration. Soil moisture in the setups was maintained at 10-20 % during the experimental period. Total hydrocarbon concentration (THC), and population of total heterotrophic bacteria (THB), hydrocarbon utilizing bacteria (HUB), total fungi (TF), &amp; hydrocarbon utilizing fungi (HUF) were determined on day 1 and 21. Results obtained showed that increase in HUB occurred only in setup S1, and setup S1 had the highest extent of hydrocarbon degradation (18.93 %). Setup S8 had the lowest extent of hydrocarbon degradation (0.79 %). It is concluded that glucose concentration of 1 % and below will enhance biodegradation of hydrocarbons in crude-oil polluted soils, whereas high glucose concentration will retard hydrocarbon biodegradation. Also, the study indicates the possibility of fungi not partaking in hydrocarbon degradation when glucose is made available.

Role of Operating Modes on the Performance Durability of Solid Oxide Cells: A Comparison between Potentiostatic and Galvanostatic Operations

The commercialization of solid oxide cells for power generation or hydrogen production requires extended cell lifetime and durability. A solid oxide cell can operate either under a potentiostatic or a galvanostatic mode. It remains unclear what’s the role of those two operating modes on the performance durability of solid oxide cells, which limits our understanding and subsequently predicting service life and reliability of solid oxide cells.It is known that many degradation phenomena in solid oxide cells can be accelerated by the electrochemical operations, which manifest various distributions of oxygen chemical potential (μO2) across a solid oxide cell. The variation in the local μO2 is a central driving force for the phase transformation, demixing, and microstructure evolution, leading to a higher resistance of the cell components and decreasing cell performance. As a response to the degradation, the μO2 redistributes across the cell and the driving force for the degradation changes. The μO2 distribution differs when a cell operates at a constant current or a constant voltage. The driving force with different extents of degradation under both operation modes is compared to establish the optimum operation approach, which can shed light on identifying mitigation strategies to improve performance durability with existing cell configurations and materials. Acknowledgement We greatly appreciate the support from U.S. Department of Energy’s Office of Fossil Energy and Carbon Management under DE-FE0032110. Part of this work is supported by U.S. National Science Foundation under NSF-2119688.

Exploring Stable and Selective Anode Materials for the Electrochemical Oxidative Coupling of Methane (EOCM): A Case Study of Doped Titanates

The development of next-generation solid oxide fuel cells (SOFCs) relies on integrating materials such as La0.6Sr0.4Co0.2Fe0.8O3- δ (LSCF), which has been lauded for its fast oxygen ion and electron conduction, along with its high oxygen reduction activity.[1] However, the use of LSCF has been plagued by 1) stability issues caused by surface Sr precipitation and 2) subsequent poisoning by Cr- or S- containing species.We have previously shown that the introduction of more oxidizable cations at the electrode surface can effectively improve both the stability and the oxygen exchange kinetics of the electrodes. However, the influence of these cations, as with many dopant infiltration studies, is currently not fully understood across all possible modes of electrode surface stabilization/activity enhancement. For example, we have recently discovered that at 800°C and under air atmospheres (i.e. at typical SOFC operational conditions), these Hf/Zr surface cation dopants are mobile and diffuse into the lattice of a porous LSCF electrode as evidenced by their vanishing x-ray photoelectron signals. This is accompanied nevertheless by a noticeable electrode stabilization, suggesting a sub-surface effect of the introduced dopants.Inspired by this, in this work our aim is to uncover the mechanism responsible for the stabilization of the surface oxygen exchange kinetics. Our approach involves dense thin film LSCF as model system to investigate the temperature-dependent physicochemical properties of these surface and sub-surface dopant cations. We fabricate LSCF thin films via pulsed laser deposition onto single crystalline substrates and then introduce our targeted cations at the surface via an aqueous solution infiltration. We characterize the electrochemical performance of the thin films by performing chronoamperometric and impedance spectroscopic measurements to discern the extent of the temporal electrode performance degradation using metrics such as area-specific resistance. In parallel, we aim to rationalize the observed electrochemical performance stabilization using complementary surface science techniques. We propose investigating the cation dissolution phenomenon through a combination of x-ray photoelectron and Auger electron spectroscopies, along with atomic force microscopy, scanning/transmission electron microscopy, scanning tunnelling microscopy and grazing incidence thin film x-ray diffraction. This study aims to reveal the influence of sub-surface more oxidizable cations on the extent of Sr segregation and highlights how two major factors contribute to the observed trends: Electrode fabrication conditions, for example the dopant concentration dependence (insufficient or excessive surface dopant concentrations has previously been shown to suppress the stabilization effect).[3]Electrode operation conditions, for example the temperature profile experienced by the cell (higher temperatures lead to dopant dissolution). [1] SP Jiang, Int. J. Hydrogen Energy 44 (14), 7448 (2019).[2] N Tsvetkov et al., Nature Mat. 15 (9), 1010 (2016).[3] N Tsvetkov et al., Faraday Disc. 182 (0), 257 (2015).

A Comparative Study on Physics-Informed Machine Learning for Battery Degradation Diagnostics

Quantifying the extent of degradation in lithium-ion battery cells using non-destructive approaches can provide valuable insights into the cell’s state of health and remaining useful life. However, physics-based degradation diagnostic methods typically require collecting long-term aging data and are computationally expensive to deploy locally on a device. This work investigates combining physics-based modeling and machine learning to retain high diagnostic accuracy while mitigating the need to collect long-term degradation data. To transfer knowledge from a physics-based model to a machine learning model, we develop two new approaches: co-kriging and physics-informed neural networks. We aim to effectively diagnose cell health in the late aging stage without needing long-term degradation data. Using a small set of early-life experimental data collected from an aging experiment, we train a co-kriging machine learning model to learn the difference between the experimental data and a set of simulation data generated from a physics-based half-cell model. Additionally, we train a neural network model using a physics-informed loss function with a new term that penalizes discrepancies between neural network model prediction and physics-based model prediction. The trained models can then be used to diagnose cell capacity and three primary component-level degradation modes at a future time. The proposed methods and two existing physics-informed machine learning methods named data augmentation and delta learning are comprehensively evaluated using data from a long-term (4+ years) cycle aging experiment of 24 implantable-grade Li-ion cells cycled under two different temperatures and C-rates. Cross-validated results show that the proposed physics-informed machine learning models can significantly improve the estimation accuracy of cell capacity and three primary degradation modes compared to a purely data-driven approach. Furthermore, this work comprehensively analyzes the trends of the degradation modes and provides insight into the long-term performance of high-quality implantable-grade lithium-ion cells.

Generalized Inversion of Electrochemical Impedance Data for Battery Systems

Electrochemical Impedance Spectroscopy (EIS) is a key technique for taking a snapshot of an electrochemical system near equilibrium conditions. Due to the richness of the complex-valued dataset and its simple experimental operation, it is currently being investigated as a test to diagnosis battery health and degradation details. Nevertheless, challenges with use of EIS is in interpreting the measured impedance. Current methods for analysis involving either fitting simplified equivalent circuit models or inverting distribution of relaxation times with a singular basis function. Because of these methods' simplifications, their results sometime fail to capture both the complex and heterogeneous underlying physics.Here, we present a novel algorithm for the inversion of EIS data for battery systems, which generalizes the inversion problem to any given linear equivalent circuit model, allowing for the analysis of complicated electrochemical systems. Importantly, this algorithm provides two clear benefits. First, it allows for the analysis of both bounded and unbounded processes, something quite common to battery systems and is vital in understanding non-obvious relationships in degraded batteries. Second, the distribution of independent processes, which can correspond to different physical phenomena, are also inverted by this algorithm. These distributions contain vital features in the identification and prediction of battery state-of-health. Overall, this tool will be indispensable in the analysis of degradation extent over battery aging, and provide an algorithmic framework for interpreting EIS measurements of complicated electrochemical systems.

Developing a satellite-based combined land degradation index for monitoring environmental change: A case study in Tana-Beles watershed, Upper Blue Nile, Ethiopia

Land degradation is one of the most pressing environmental challenges due to its effect on the people and ecosystem. Early detection of land degradation could help to avoid further deterioration and work on reversing the trend. This would require an integrated approach combining indicators such as vegetation condition, soil health (i.e., soil salinity), and soil exposure (i.e., albedo) to characterize land degradation comprehensively. Therefore, this study aims at developing a satellite image-based combined land degradation index to monitor the spatial extent of land degradation in the Upper Blue Nile Basin, Ethiopia. In addition, the study also evaluates the environmental change associated with land degradation across the Tana-Beles watershed. The combined land degradation index (CLDI) was developed by integrating multiple indicators, including normalized vegetation Index (NDVI), salinity index (SI), and albedo, for two periods spanning between 1990 and 2000 (1990s) and 2010 to 2020 (2010s). The individual indicators were combined with Principal Component Analysis (PCA) by assigning weights to the individual indicators. The analysis indicated that, in the 2010s, the vegetation condition deteriorated over 98.5% of the study area, while soil salinity and albedo increased by 82% and 97% in the 1990s, respectively. The combined land degradation index developed with the PCA indicated in the 2020s a fraction of the study area ∼2% is extremely degraded, while 18%, 43%, 27%, and 10% of the site is strongly, moderately, slightly, and not degraded, respectively. Monitoring land degradation is important for countries like Ethiopia, where most of the county are occupied by semi-arid and arid climate zones.

Grassland degradation induces high dietary niche overlap between two common livestock: cattle and sheep

AbstractIntroductionDiet selection of grazing livestock is one of the critical factors affecting the diversity and composition of plant communities in grasslands, with consequent impacts for ecosystem functioning. Co‐grazing of multiple livestock species could create additive or complementary effects on plant communities due to diet overlap or differentiation among livestock species. However, there is only limited knowledge about diet overlap and differentiation of co‐occurring livestock species and whether or how these diet characteristics change with grassland degradation.Materials and MethodsWe experimentally test the diets of two commonly co‐occurring livestock species (cattle and sheep) and the dietary niche partitioning/overlap between them in non‐degraded, moderately and severely degraded grasslands.ResultsCattle exhibited broader diet than sheep, and diet overlap between species was lower in non‐degraded grasslands. As grassland degradation increased the availability of annual plants, interspecific niche overlap between cattle and sheep gradually increased, and cattle suffered a resulting dietary niche compression. Sheep maintained a relatively narrow and constant dietary breadth.ConclusionThe increasing dietary niche overlap between livestock species with increasing extent of grassland degradation indicated increased additive effects of cattle and sheep co‐grazing on plant community, and thus stronger effects on plant community composition and diversity.

Wind turbine gear reliability analysis considering dependent competing failure

AbstractTo more precisely assess the reliability of wind turbine gears, a competing failure model is established with consideration given to the degradation of internal fatigue strength and external random shocks. Firstly, the nonlinear Gamma process is adopted to depict the degradation of fatigue strength, and the strength‐stress interference theory is used to establish the threshold of degradation failure that changes over time. Also, the non‐homogeneous Poisson process is employed to depict the arrival of the shock, and classification of the shocks. Secondly, the correlation between degradation and shock is described, while the effect of shocks on degradation is demonstrated as a sudden increase in the extent of degradation. Besides, the decline rate of residual strength curve is introduced to describe the change in intensity parameters of the Poisson process, and then the effect of degradation on shocks is described. Finally, the sun gear in the wind turbine gearbox is an example. Monte Carlo simulation is used to calculate the reliability of the dependent competing failure model. This is then compared with the conventional stress‐strength interference model. The results verify that when the relationship between degradation and the shock process is disregarded, the reliability of gears may be overestimated.

Traditional ecological knowledge based indicators for monitoring rangeland conditions in Thal and Cholistan Desert, Pakistan

Sustainable range resource management relies heavily on the Traditional Ecological Knowledge (TEK) of indigenous peoples who have long inhabited the rangelands. Data gaps in rangeland monitoring could be reduced with the help of additional scientific techniques. The health of large-scale rangelands and the availability of their resources can be evaluated using a set of ecological indicators created by indigenous herders in those areas. Scientists from Pakistan's Punjab province interviewed herders in the Thal and Cholistan rangelands to learn more about the traditional ecological indicators used by locals to predict grazing resource availability. An ethnological and ecological survey was conducted through in-depth interviews. We conducted in-depth interviews with 80 herders from the Thal rangelands and focus groups with 120 herders from the Cholistan rangelands. Herders were asked what indicators they used to determine the quality of the forages, the health of the animals, and the extent of rangeland degradation. According to the results, all local herders evaluate the rangeland's resources using analogous indicators. Herders use four types of indicators: (a) plant indicators (Flora), (b) soil indicators, (c) animal indicators (Fauna), and (d) environmental indicators. When asked, 78% of respondents cited the variety of edible plants as the most critical factor in determining vegetation quality. Approximately 78.3% of the herders analysed the faeces to determine the general health of the animals in their range, revealing the rangeland's condition. The decrease in the litter was interpreted by 58.3% of the herders as a sign of land degradation; consequently, they reevaluated their grazing area and practices.

Non-destructive spectroscopic diagnostic tools for the assessment of the mechanical strength of 3D-printed PLA

Polylactic acid (PLA) is a biodegradable and biocompatible thermoplastic that is commonly used in 3D printing. Despite being widely utilized, PLA is susceptible to degradation by both UV light and hydrothermal conditions. This study investigated the effects of 3D printing temperature, UV light exposure, and hydrothermal aging on the properties of PLA. Samples were 3D printed at temperatures of 190, 205, 220 and 235 °C. The specimens were then aged under UV light and thermal cycling or hydrothermally at 80 °C for up to 6 weeks. The aged materials were then characterized using X-ray diffraction (XRD), thermos-gravimetric analysis (TGA), microscopy, mechanical testing, and ultimately Raman spectroscopy.Results showed that the PLA cristallinity increases with aging time in both hydrothermal and under UV exposure conditions, while mechanical strength decreases. Additionally, the UV exposure caused the formation of monomers and oligomers on the outermost layers. Printing temperature was shown to play only a minor role in the determination of the chemophysical properties of the 3D printed components, with faster degradation occurring on samples printed at higher temperatures, which also resulted in higher amounts of monomer when exposed to UV light for 6 weeks.The relative intensities of specific regions of the Raman spectra obtained on the aged samples could be successfully correlated with both the mechanical strength and chemo-physical properties such as the decomposition temperature. The algorithms developed in this study enable non-destructive analysis of PLA components, allowing for the determination of the extent of structural degradation due to environmental aging and the expected residual mechanical resistance.

Multiple Sclerosis Is Associated With Achalasia and Diffuse Esophageal Spasm.

Multiple sclerosis (MS) is an inflammatory disease characterized by the demyelination of primarily the central nervous system. Diffuse esophageal spasm (DES) and achalasia are both disorders of esophageal peristalsis which cause clinical symptoms of dysphagia. Mechanisms involving dysfunction of the pre- and post-ganglionic nerve fibers of the myenteric plexus have been proposed. We sought to determine whether MS confers an increased risk of developing achalasia or DES. Cohort analysis was done using the Explorys database. Univariate logistic regression was performed to determine the odds MS confers to each motility disorder studied. Comparison of proportions of dysautonomia comorbidities was performed among the cohorts. Patients with a prior diagnosis of diabetes mellitus, chronic Chagas' disease, opioid use, or CREST syndrome were excluded from the study. Odds of MS patients developing achalasia or DES were (OR, 2.09; 95% CI, 1.73-2.52; P < 0.001) and (OR, 3.15; 95% CI, 2.89-3.42; P < 0.001), respectively. In the MS/achalasia cohort, 27.27%, 18.18%, 9.09%, and 45.45% patients had urinary incontinence, gastroparesis, impotence, and insomnia, respectively. In the MS/DES cohort, 35.19%, 11.11%, 3.70%, and 55.56% had these symptoms. In MS patients without motility disorders, 12.64%, 0.79%, 2.21%, and 21.85% had these symptoms. Patients with MS have higher odds of developing achalasia or DES compared to patients without MS. MS patients with achalasia or DES have higher rates of dysautonomia comorbidities. This suggests that these patients have a more severe disease phenotype in regards to the extent of neuronal degradation and demyelination causing the autonomic dysfunction.

Bioremediation of Azo Dye Brown 703 by Pseudomonas aeruginosa: An Effective Treatment Technique for Dye-Polluted Wastewater

Dye-polluted wastewater poses a serious threat to humans’, animals’ and plants’ health, and to avoid these health risks in the future, the treatment of wastewater containing dyes is necessary before its release to environment. Herein, a biological approach is used; the textile azo dye brown 703 is degraded utilizing Pseudomonas aeruginosa. The bacterial strain was isolated from textile wastewater dumping sites in Mingora, Swat. The optimization for bacterial degradation was carried out on the nutrient broth medium, which was then subjected to a variety of environmental physicochemical conditions and nutritional source supplementation before being tested. Under micro-aerophilic circumstances, the maximum decolorization and degradation of dye occurred at a 20 ppm dye concentration within 3 days of incubation at a neutral pH and 38 °C. The decrease in the intensity of the absorbance peak in the UV–Vis spectrum was used to measure the extent of decolorization. Initially, 15 bacterial strains were isolated from the textile effluent. Out of these strains, Pseudomonas aeruginosa was found to be the most potent degrading bacteria, with a degradation extent of around 71.36% at optimum conditions. The appearance and disappearance of some new peaks in the FT-IR analysis after the degradation of brown 703 showed that the dye was degraded by Pseudomonas aeruginosa. The GC–MS analysis performed helped in identifying the degraded compounds of azo dye that were utilized in illustrating the under-study process of brown 703 degradation. The biodegradation brought about by Pseudomonas aeruginosa can be employed successfully in the future as an eco-friendly approach with far reaching results.

Arrhenius-model-based degradable oligourethane hydrogels for controlled growth factor release.

Biodegradable hydrogels are growing in demand to enable the delivery of biomolecules (e.g. growth factors) for regenerative medicine. This research investigated the resorption of an oligourethane/polyacrylic acid hydrogel, a biodegradable hydrogel which supports tissue regeneration. The Arrhenius model was used to characterize the resorption of the polymeric gels in relevant in vitro conditions, and the Flory-Rehner equation was used to correlate the volumetric swelling ratio with the extent of degradation. The study found that the swelling rate of the hydrogel follows the Arrhenius model at elevated temperatures, estimating degradation time in saline solution at 37°C to be between 5 and 13 months, serving as a preliminary approximation of degradation in vivo. The degradation products had low cytotoxicity towards endothelial cells, and the hydrogel supported stromal cell proliferation. Additionally, the hydrogels were able to release growth factors and maintain the biomolecules' bioactivity towards cell proliferation. The study of the vascular endothelial growth factor (VEGF) release from the hydrogel used a diffusion process model, showing that the electrostatic attraction between VEGF and the anionic hydrogel allowed for controlled and sustained VEGF release over three weeks. In a rat subcutaneous implant model, a selected hydrogel with desired degradation rates exhibited minimal foreign body response and supported M2a macrophage phenotype, and vascularization. The low M1 and high M2a macrophage phenotypes within the implants were associated with tissue integration. This research supports the use of oligourethane/polyacrylic acid hydrogels as a promising material for delivering growth factors and supporting tissue regeneration. STATEMENT OF SIGNIFICANCE: There is a need for degradable elastomeric hydrogels that can support the formation of soft tissues and minimize long-term foreign body responses. An Arrhenius model was used to estimate the relative breakdown of hydrogels, in-vitro. The results demonstrate that hydrogels made from a combination of poly(acrylic acid) and oligo-urethane diacrylates can be designed to resorb over defined periods ranging from months to years depending on the chemical formulation prescribed by the model. The hydrogel formulations also provided for different release profiles of growth factors, relevant to tissue regeneration. In-vivo, these hydrogels had minimal inflammatory effects and showed evidence of integration into the surrounding tissue. The hydrogel approach can help the field design a broader range of biomaterials for tissue regeneration.