Multi-parameter Approaches Research Articles

Identifying Malnutrition Risk in the Elderly: A Single- and Multi-Parameter Approach.

Malnutrition is a significant concern affecting the elderly, necessitating a complex assessment. This study aims to deepen the understanding of factors associated with the assessment of malnutrition in the elderly by comparing single- and multi-parameter approaches. In this cross-sectional study, 154 individuals underwent a comprehensive geriatric assessment (CGA). Malnutrition risk was determined using the mini nutritional assessment (MNA). Additional factors assessed included sarcopenia, polypharmacy, depression, appetite, handgrip strength, and gait speed. Phase angle (PA) and body composition were measured using bioelectrical impedance analysis (BIA). The MNA identified a malnutrition risk in 36.8% of individuals. The geriatric depression scale (GDS) and PA demonstrated moderate effectiveness in assessing malnutrition risk, with AUC values of 0.69 (95% CI: 0.60-0.78) and 0.62 (95% CI: 0.54-0.72), respectively. A logistic regression model incorporating handgrip strength, skeletal muscle mass, sarcopenia, osteoporosis, depression, specific antidepressant use, mobility, appetite, and smoking achieved superior performance in predicting malnutrition risk, with an AUC of 0.84 (95% CI: 0.77-0.91). In conclusion, this study demonstrates that integrating multiple parameters into a composite model provides a more accurate and comprehensive assessment of malnutrition risk in elderly adults.

Optical Fiber Sensors Systems in Oil Tanks: Toward Structural Health Monitoring and Liquid-Level Estimation

This paper presents the development, characterization and analysis of optical fiber integrated sensors systems for structural health monitoring (SHM) in oil tanks. The sensors are based on fiber Bragg gratings (FBGs) embedded in nitrile rubber strips through a vulcanization method. Two sensors arrays (with 5 FBGs each) are fabricated and installed at different regions of the oil tank, namely the metallic side wall and polycarbonate display of the tank. The sensors were characterized as a function of the strain in the nitrile rubber strips, where all analyzed sensors presented a high linearity, since a determination coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) higher than 0.999 is obtained. Tests with different oil levels inside the tank indicate the feasibility of the sensors in the shape reconstruction and real-time strain monitoring of the structural element, where even the bending in the sidewall of the tank is estimated. Such results indicated a correlation between the wavelength shift of the FBGs and the liquid level inside the tank, which leads to the possibility of measuring liquid level from the tank strain distribution in a non-contact approach using a Random Forest algorithm for data processing. The proposed non-contact and machine learning-enable liquid level assessment resulted in errors of around 1.07%. Thus, the proposed approach indicate the feasibility of a predictive maintenance of oil tank through real-time monitoring of strain and possible structural flaws of the oil tanks in conjunction to non-contact approach of oil processing monitoring that can be useful in data fusion as well as multiparameter approaches for oil monitoring.

Multi-parameter approaches for improved ensemble prediction accuracy in hydrology and water quality modeling

Ensemble approaches can be a quick way to improve hydrological prediction accuracy by considering multiple plausible modeling representations of a system. Previous studies showed that the use of multiple types of models could increase prediction accuracy, but model selection subjectivity and parameter equifinality issues remain unsolved. This study proposes to consider multiple parameter sets identified in the model calibration process and information on their performance for improved accuracy of ensemble hydrological prediction. In this study, we investigate whether multi-parameter ensemble (MP) compared to multi-model ensemble (MM) approaches can improve the accuracy of hydrology and water quality prediction. A variety of ensemble methods, including simple averaging, median filtering, weighted averaging, Bayesian model averaging (BMA), and their variants, were tested in watershed modeling implemented to predict daily water discharge and total phosphorus (TP) loads of a study watershed. The parameter spaces of two watershed loading models, Soil and Water Assessment Tool (SWAT) and Hydrological Simulation Program-FORTRAN (HSPF), were sampled using a multi-objective heuristic optimization algorithm, namely A Multi-Algorithm Genetically Adaptive Multiobjective (AMALGAM). The modeling experiment showed that the MP approach more effectively improved modeling accuracy statistics compared to the MM of both models as well as single-model and single-parameter approaches. The ensemble approaches did not always improve modeling accuracy, and their efficiency depended on ways to determine weights for ensemble members. The BMA method outperformed the other weighting schemes tested in this study, and its performance was responsive to the number and diversity (or variance) of ensemble member candidates and the computational cost to create the candidates. The findings demonstrate how the information of parameter sets sampled in model calibration and their performance statistics could help improve hydrological prediction accuracy.

Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean

The distribution of any non-conservative variable in the deep open ocean results from the circulation and mixing of water masses (WMs) of contrasting origin and from the initial preformed composition, modified during ongoing simultaneous biological and/or geochemical processes. Estimating the contribution of the WMs composing a sample is useful to trace the distribution of each water mass and to quantitatively separate the physical (mixing) and biogeochemical components of the variability of any, non- conservative variable (e.g., dissolved organic carbon, prokaryote biomass) in the ocean. Other than potential temperature and salinity, additional semi-conservative and non-conservative variables have been used to solve the mixing of more than three water masses using Optimum Multi-Parameter (OMP) approaches. Successful application of an OMP analysis requires knowledge of the characteristics of the water masses in their source regions as well as their circulation and mixing patterns. Here, we propose the application of multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). Our model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Therefore, basic hydrographic data collected during typical research cruises or autonomous systems can be used as input variables and provide results in real time. The model can be fed with new solutions from compatible OMP analyses as well as with new water masses not previously considered in it. Our tool will provide knowledge on water mass composition and distribution to a broader community of marine scientists not specialized in OMP analysis and/or in the oceanography of the studied area. This will allow a quantitative analysis of the effect of water mass mixing on the variables or processes under study.

Solvation and Solvatochromism: An Overview#

Different examples of solvatochromic shifts are shown and the theory that explain the non-specific general solvent effects are discussed here. The use of solvatochromic shifts for the determination of excited state dipole moment has been presented in detail in connection with a brief overview of different solvent polarity functions. The lack of proper theoretical expressions to explain the specific solvent effect stimulates to introduce the concept of “empirical measures of solvent polarity”, based on well known, convenient, solvent sensitive model processes. Various solvent sensitive polarity probes are discussed in this context. So far as quantitative estimation of various parameters responsible for the observed solvatochromic shifts are concerned, introduction of multiparameter approaches, its application in recent years has been carefully analyzed. The review also focuses some of the recent developments on the theoretical calculations of various solute-solvent interaction parameters based on quantum mechanical approaches. This issue solvation interaction by the use of mixed solvents has been discussed in detail, starting from the early development of the theories related to preferential solvation to the recent scenario, with a critical survey on the application of the concept of preferential solvation in the solubilization of drugs, polymers considering the emerging ‘green approach’ in recent years.

Parametric study and optimization of a two‐body wave energy converter

The parametric study and optimization of a two-body wave energy converter (WEC) for the wave and current conditions in the region of Zhaitang Island (China) is presented. Nine parameters are considered, and their influence on the power captured by the two-body WEC is investigated following both single-parameter and multi-parameter approaches. A backpropagation neural network model is developed and applied to predict the captured power for any given values of the nine parameters and the wave frequency. Then the robust design method, also known as the Taguchi method, is implemented to study the comprehensive effects of the parameters on the power output of the device. Moreover, scale model experiments are conducted to verify and confirm the influence of the principal parameters on the power output. Combining numerical simulations, a neural network model and experimental work, this study provides an optimization programme for the main parameters of the device in the target sea region and, at a more general level, references for two-body WEC designs based on specific sea states.

A Prediction Model of Water In Situ Data Change under the Influence of Environmental Variables in Remote Sensing Validation

Validation is an essential process to evaluate the quality of waterbody remote sensing products, and the reliability and effective application of the in situ data of waterbody parameters are an important part of validation. Based on the in situ data of chlorophyll-a (Chl-a), total suspended solids (TSS) and other environmental variables (EVs) measured at the fixed station in Taihu Lake, we attempt to develop a prediction model to determine whether the in situ measurement has enough representativeness for validating waterbody remote sensing products. Key EVs that affect the changes of Chl-a and TSS are firstly identified by using correlation analysis, which participate in modeling as variables. In addition, three multi-parameter modeling approaches are selected to simulate the daily changes of Chl-a and TSS under different EVs configurations. The results indicate that the highest prediction accuracy can be achieved through the generalized regression neural network (GRNN) based model. In the all-valid dataset, the testing absolute average relative errors (AEs) of GRNN-based Chl-a and TSS prediction model are 11.4% and 11.3%, respectively, and in the sunny-day dataset, the testing AEs are 8.6% and 8.2%, respectively. Meanwhile, the application example proves that the prediction model in this paper can be effectively used to screen the in situ data and determine the time window for satellite-ground data matching.

Multi-parameter Behavioral Phenotyping of the MPP+ Model of Parkinson's Disease in Zebrafish.

Parkinson’s disease (PD) has been modeled in several animal species using the neurotoxins 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and its oxidized product 1-methyl-4-phenylpyridinium (MPP+). MPP+ selectively kills dopaminergic neurons in pars compacta of the substantia nigra, inducing parkinsonian symptoms in animals. Typically, neurotoxicity models of PD in zebrafish assess acute drug effects on locomotion. In the present study, we examined the lasting effects of MPP+ exposure and drug treatment in zebrafish larvae. Larvae were incubated in 500 μM MPP+, from 1 to 5 days post fertilization (dpf), followed by 24 h drug-free acclimation. At 6 dpf, the behavior was analyzed for locomotion, thigmotaxis, and sleep. Next, in separate assays we assessed the drug effects of brain injected glial cell-derived neurotrophic factor (GDNF) and 4-phenylbutyrate (PBA), co-incubated with MPP+. We show that MPP+ exposure consistently reduces swim distance, movement frequency, and cumulative time of movement; thus mimicking a parkinsonian phenotype of reduced movement. In contrast, MPP+ exposed larvae demonstrate reduced anxiety-like behavior and exhibit a sleep phenotype inconsistent with human PD: the larvae display longer sleep bouts, less sleep fragmentation, and more sleep. Previously reported rescuing effects of PBA were not replicated in this study. Moreover, whereas GDNF attenuated the sleep phenotype induced by MPP+, PBA augmented it. The current data suggest that MPP+ exposure generates a multifaceted phenotype in zebrafish and highlights that analyzing a narrow window of data can reveal effects that may be inconsistent with longer multi-parameter approaches. It further indicates that the model generally captures motor symptoms more faithfully than non-motor symptoms.

WEARCON: wearable home monitoring in children with asthma reveals a strong association with hospital based assessment of asthma control

BackgroundAsthma is one of the most common chronic diseases in childhood. Regular follow-up of physiological parameters in the home setting, in relation to asthma symptoms, can provide complementary quantitative insights into the dynamics of the asthma status. Despite considerable interest in asthma home-monitoring in children, there is a paucity of scientific evidence, especially on multi-parameter monitoring approaches. Therefore, the aim of this study is to investigate whether asthma control can be accurately assessed in the home situation by combining parameters from respiratory physiology sensors.MethodsSixty asthmatic and thirty non-asthmatic children were enrolled in the observational WEARCON-study. Asthma control was assessed according to GINA guidelines by the paediatrician. All children were also evaluated during a 2-week home-monitoring period with wearable devices; a physical activity tracker, a handheld spirometer, smart inhalers, and an ambulatory electrocardiography device to monitor heart and respiratory rate. Multiple logistic regression analysis was used to determine which diagnostic measures were associated with asthma control.Results24 of the 27 uncontrolled asthmatic children and 29 of the 32 controlled asthmatic children could be accurately identified with this model. The final model showed that a larger variation in pre-exercise lung function (OR = 1.34 95%-CI 1.07–1.68), an earlier wake-up-time (OR = 1.05 95%-CI 1.01–1.10), more reliever use (OR = 1.11 95%-CI 1.03–1.19) and a longer respiratory rate recovery time (OR = 1.12 95%-CI 1.05–1.20) were significant contributors to the probability of having uncontrolled asthma.ConclusionsHome-monitoring of physiological parameters correlates with paediatrician assessed asthma control. The constructed multivariate model identifies 88.9% of all uncontrolled asthmatic children, indicating a high potential for monitoring of asthma control. This may allow healthcare professionals to assess asthma control at home.Trial registrationNetherlands Trail Register, NL6087. Registered 14 February 2017.

Micromagnetic Analysis of Thermally Induced Influences on Surface Integrity Using the Burning Limit Approach

Particularly for highly stressed components, it is important to have precise knowledge of the surface and subsurface properties and, thus, of the functional properties after final grinding at the end of a complex process chain in order to avoid rejected parts. Therefore, non-destructive testing methods have been the subject of research for several years. The Barkhausen noise analysis, as a micromagnetic measuring method, has the potential to characterize the subsurface area up to an analyzing depth δ non-destructively with micromagnetic parameters. In addition to micromagnetic multiparameter approaches, which allow post-process mode clear statements about the subsurface area state, the present research work deals with the concept of a connection of a single Barkhausen noise parameter with grinding process parameters. In combination with the analytical approach of Malkin for the thermal surface and subsurface area influence, which is based on the process parameters of grinding processes, a distinction between good and rejected ground parts can be achieved. The results show that, by post-process measurements of the Barkhausen noise on case-hardened workpieces made of steel 18CrNiMo7-6 (No. 1.6587, AISI 4820) and machined by a cylindrical grinding process, incipient changes in the residual stress state up to industrial-relevant limits, which distinguish between good and rejected parts, is possible. In the future, a combination of the Malkin grinding burning limit and sufficient condition monitoring based on in-process measurements of Barkhausen noise will be investigated. The application limits of the analytical approach of Malkin as well as the measurement of the Barkhausen noise in-process have to be determined.

Towards Improved Medical Imaging Analysis for Oncology Care: Clinical Experience with Simulation Platform for Brain Tumors

Analyses of large scale medical imaging data involving machine learning are rapidly evolving to include diagnosis, treatment and prognosis in oncology care. In neuro-oncology, radiomics and similar multi-parameter analysis approaches are advancing clinical trajectories. A common challenge to robust machine learning approaches is the lack of “ground truth” datasets for supervised learning upon which to base robust prediction models. The ability to simulate the mechanistic aspects (geometry, location, physical properties etc.) of lesions/tumours may significantly boost the ability to assess, benchmark and compare competing analysis techniques for identification of the most relevant techniques for clinical care improvements. In this study, we propose and evaluate a simulation toolset for enabling the creation of large numbers of “ground truth” datasets and make the implementation available for other groups/researchers/clinicians to use. The simulation tool is developed using the Feature Manipulation Engine (Safe Software, Surrey, Canada), which enables the integration, validation, transformation and processing of over 350+ data formats. Based on clinical knowledge and experience, canonical brain tumours (both high- and low-grade gliomas) were defined by specifying location, signal intensity, shape and other characteristics. Magnetic resonance imaging (MRI) datasets with simulated brain tumours were generated by inserting tumours with prescribed properties into the template structural brain in MNI space. Blinded clinical and medical imaging experts assessed the quality and visual accuracy for outcome measures. The simulation toolset successfully incorporated the simulated tumours within specified locations in the brain MRI using an easy-to-use graphical user interface. Initial results suggest the verification of the visual realism of the simulated datasets by clinical and medical imaging experts. On-going analyses are further assessing the validity of the tool with quantitative analysis . The new simulation toolset is capable of creating simulated MR images incorporating mechanistic aspects of common brain tumours. This may enable the creation of large numbers of “ground truth” datasets for assessing medical imaging analysis techniques. Furthermore, the easy availability of the tool to everyone may spur the wide-scale use of true “ground truth” data for further development of novel techniques and assessing imaging based biomarkers for improved clinical care.

Seminal Plasma Exposures Strengthen Vaccine Responses in the Female Reproductive Tract Mucosae.

HIV-1 sexual transmission occurs mainly via mucosal semen exposures. In the female reproductive tract (FRT), seminal plasma (SP) induces physiological modifications, including inflammation. An effective HIV-1 vaccine should elicit mucosal immunity, however, modifications of vaccine responses by the local environment remain to be characterized. Using a modified vaccinia virus Ankara (MVA) as a vaccine model, we characterized the impact of HIV-1+ SP intravaginal exposure on the local immune responses of non-human primates. Multiple HIV-1+ SP exposures did not impact the anti-MVA antibody responses. However, SP exposures revealed an anti-MVA responses mediated by CD4+ T cells, which was not observed in the control group. Furthermore, the frequency and the quality of specific anti-MVA CD8+ T cell responses increased in the FRT exposed to SP. Multi-parameter approaches clearly identified the cervix as the most impacted compartment in the FRT. SP exposures induced a local cell recruitment of antigen presenting cells, especially CD11c+ cells, and CD8+ T cell recruitment in the FRT draining lymph nodes. CD11c+ cell recruitment was associated with upregulation of inflammation-related gene expression after SP exposures in the cervix. We thus highlight the fact that physiological conditions, such as SP exposures, should be taken into consideration to test and to improve vaccine efficacy against HIV-1 and other sexually transmitted infections.

Non-invasive monitoring of blood glucose using optical methods for skin spectroscopy-opportunities and recent advances.

Diabetes mellitus is a widespread disease with greatly rising patient numbers expectedin the future, not only for industrialized countries but also for regions in the developing world. There is aneed for efficient therapy, which can be via self-monitoring of blood glucose levels to provide tight glycemic control for reducing the risks of severe health complications. Advancements in diabetes technology can nowadays offer different sensor approaches, even for continuous blood glucose monitoring. Non-invasive blood glucose assays have been promised for many years and various vibrational spectroscopy-based methods of theskin are candidates for achieving this goal. Due to the small spectral signatures of the glucose hidden among a largely variable background, the largest signal-to-noise ratios and multivariate calibration are essential to provide the method applicability for self-monitoring of blood glucose. Besides multiparameter approaches, recently presented devices based on photoplethysmography with wavelengths in the visible and near-infrared range are evaluated for their potential of providing reliable blood glucose concentration predictions. Graphical abstract ᅟ.

Multi-parameter approach to evaluate the timing of memory status after 17DD-YF primary vaccination.

In this investigation, machine-enhanced techniques were applied to bring about scientific insights to identify a minimum set of phenotypic/functional memory-related biomarkers for post-vaccination follow-up upon yellow fever (YF) vaccination. For this purpose, memory status of circulating T-cells (Naïve/early-effector/Central-Memory/Effector-Memory) and B-cells (Naïve/non-Classical-Memory/Classical-Memory) along with the cytokine profile (IFN/TNF/IL-5/IL-10) were monitored before-NV(day0) and at distinct time-points after 17DD-YF primary vaccination—PV(day30-45); PV(year1-9) and PV(year10-11). A set of biomarkers (eEfCD4; EMCD4; CMCD19; EMCD8; IFNCD4; IL-5CD8; TNFCD4; IFNCD8; TNFCD8; IL-5CD19; IL-5CD4) were observed in PV(day30-45), but not in NV(day0), with most of them still observed in PV(year1-9). Deficiencies of phenotypic/functional biomarkers were observed in NV(day0), while total lack of memory-related attributes was observed in PV(year10-11), regardless of the age at primary vaccination. Venn-diagram analysis pre-selected 10 attributes (eEfCD4, EMCD4, CMCD19, EMCD8, IFNCD4, IL-5CD8, TNFCD4, IFNCD8, TNFCD8 and IL-5CD4), of which the overall mean presented moderate accuracy to discriminate PV(day30-45)&PV(year1-9) from NV(day0)&PV(year10-11). Multi-parameter approaches and decision-tree algorithms defined the EMCD8 and IL-5CD4 attributes as the top-two predictors with moderated performance. Together with the PRNT titers, the top-two biomarkers led to a resultant memory status observed in 80% and 51% of volunteers in PV(day30-45) and PV(year1-9), contrasting with 0% and 29% found in NV(day0) and PV(year10-11), respectively. The deficiency of memory-related attributes observed at PV(year10-11) underscores the conspicuous time-dependent decrease of resultant memory following17DD-YF primary vaccination that could be useful to monitor potential correlates of protection in areas under risk of YF transmission.

Theory and Modeling of Fuel Cells: Extravaganza to Panacea?

<i>Theory and Modeling of Fuel Cells: Extravaganza to Panacea?</i>

Effect of π-Extended Substituents on Photophysical Properties of BODIPY Dyes in Solutions.

Four boron-dipyrrine (BODIPY) based dyes with π-extended substituents in 8-position of dipyrrin ligand have been synthesized and characterized. Photophysical properties of the obtained compounds have been investigated in different individual solvents. Deposits of solvent polarity and viscosity were evaluated. BODIPY with 8-biphenyl substituent was found to be the fluorescent molecular rotor in contrast to more extended substituents. The complex nature of solvent-solute interactions leads to the poor applicability of standard multiparameter approaches to BODIPY solvatochromic properties. Fluorescence intensity was found to increase in case of solvent polarity growth, it is not typical for BODIPY. Taking that into account the BODIPY with π-extended substituents could be used for fluorescence viscosity measurements, and as the fluorescent media polarity indicators in analytical chemistry and biochemistry.

Recent progress in studies on polarity of ionic liquids

Although many ionic liquids have been reported, their polarity is not completely understood. Different empirical polarity scales for molecular solvents always lead to different polarity orders when they are applied on ionic liquids. Based on a literature survey, this review summarizes the recent polarity scales of ionic liquids according to the following 4 classes: (1) equilibrium and kinetic rate constants of chemical reactions; (2) empirical polar parameters of ionic liquids; (3) spectral properties of probe molecules; (4) multiparameter approaches. In addition, their interrelations are presented. A systematic understanding of the relationship between different polarity parameters of ionic liquids is of great importance for finding a universal set of parameters that can be used to predict the polarities of ionic liquids quantitatively. The potential utilization of the electron paramagnetic resonance in this field is also addressed.

Spectral collocation for multiparameter eigenvalue problems arising from separable boundary value problems

In numerous science and engineering applications a partial differential equation has to be solved on some fairly regular domain that allows the use of the method of separation of variables. In several orthogonal coordinate systems separation of variables applied to the Helmholtz, Laplace, or Schrödinger equation leads to a multiparameter eigenvalue problem (MEP); important cases include Mathieu's system, Lamé's system, and a system of spheroidal wave functions. Although multiparameter approaches are exploited occasionally to solve such equations numerically, MEPs remain less well known, and the variety of available numerical methods is not wide. The classical approach of discretizing the equations using standard finite differences leads to algebraic MEPs with large matrices, which are difficult to solve efficiently.The aim of this paper is to change this perspective. We show that by combining spectral collocation methods and new efficient numerical methods for algebraic MEPs it is possible to solve such problems both very efficiently and accurately. We improve on several previous results available in the literature, and also present a MATLAB toolbox for solving a wide range of problems.

Interpreting experimental fracture toughness results of quasi-brittle natural materials through multi-parameter approaches

Natural stones like marbles are often employed as façade panels to externally cover buildings. These natural materials tend to exhibit a quasi-brittle nonlinear fracture behaviour which, conversely to concrete counterpart, has much less been studied in the literature. In the present paper, an experimental campaign on the so-called red Verona marble is carried out, and the results are discussed together with some previously published results on the white Carrara marble. The analysis of the two marbles as a whole allows us to discuss size effect and to point out the need for additional parameters in order to describe their fracture behaviour. The study focuses on a two-parameter model which accounts for a characteristic material length. A quantitative correlation between material microstructure features, obtained from thin sections technique, and the characteristic material length is proposed.

The First Step of Amyloidogenic Aggregation.

The structural and dynamic characterization of the on-pathway intermediates involved in the mechanism of amyloid fibril formation is one of the major remaining biomedical challenges of our time. In addition to mature fibrils, various oligomeric structures are implicated in both the rate-limiting step of the nucleation process and the neuronal toxicity of amyloid deposition. Single-molecule fluorescence spectroscopy (SMFS) is an excellent tool for extracting most of the relevant information on these molecular systems, especially advanced multiparameter approaches, such as pulsed interleaved excitation (PIE). In our investigations of an amyloidogenic SH3 domain of α-spectrin, we have found dynamic oligomerization, even prior to incubation. Our single-molecule PIE experiments revealed that these species are small, mostly dimeric, and exhibit a loose and dynamic molecular organization. Furthermore, these experiments have allowed us to obtain quantitative information regarding the oligomer stability. These pre-amyloidogenic oligomers may potentially serve as the first target for fibrillization-prevention strategies.