Decoding the bitter taste of Idesia polycarpa var. vestita Diels fruit: Bitterness contribution and mechanisms.

To comprehensively explore the contribution and mechanisms of identified low-threshold bitter substances in Idesia polycarpa var. vestita Diels (I. vestita) fruit, we performed quantification and elucidated their interactions with main bitter taste receptors through molecular docking. The established method for quantifying bitter compounds in I. vestita fruit was validated, yielding satisfactory parameters for linearity, stability, and accuracy. Idescarpin (17.71-101.05 mg/g) and idesin (7.88-77.14 mg/g) were the predominant bitter compounds in terms of content. Taste activity values (TAVs) exceeded 10 for the bitter substances, affirming their pivotal role as major contributors to overall bitterness of I. vestita fruit. Notably, idescarpin with the highest TAV, played a crucial role in generating the bitterness of I. vestita fruit. Hydrogen bonds and hydrophobic interactions were the main driving forces. This study holds potential implications for industrial development of I. vestita fruit by providing novel insights into the mechanism underlying its bitterness formation.

Evaluating the Efficacy of CortexID Quantitative Analysis in Localization of the Epileptogenic Zone in Patients with Temporal Lobe Epilepsy.

INTRODUCTION: There remains a critical need for precise localization of the epileptogenic foci in individuals with drug-resistant epilepsy (DRE). 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging can reveal hypometabolic regions during the interval between seizures in patients with epilepsy. However, visual-based qualitative analysis is time-consuming and strongly influenced by physician experience. CortexID Suite is a quantitative analysis software that helps to evaluate PET imaging of the human brain. Therefore, we aimed to evaluate the efficacy of CortexID quantitative analysis in the localization of the epileptogenic zone in patients with temporal lobe epilepsy (TLE). METHODS: A total of 102 patients with epilepsy who underwent 18F-FDG-PET examinations were included in this retrospective study. The PET visual analysis was interpreted by two nuclear medicine physicians, and the quantitative analysis was performed automatically using CortexID analysis software. The assumed epileptogenic zone was evaluated comprehensively by two skilled neurologists in the preoperative assessment of epilepsy. The accuracy of epileptogenic zone localization in PET visual analysis was compared with that in CortexID quantitative analysis. RESULTS: The diagnostic threshold for the difference in the metabolic Z-score between the right and left sides of medial temporal lobe epilepsy (MTLE) was calculated as 0.87, and that for lateral temporal lobe epilepsy (LTLE) was 2.175. In patients with MTLE, the area under the curve (AUC) was 0.922 for PET visual analysis, 0.853 for CortexID quantitative analysis, and 0.971 for the combined diagnosis. In patients with LTLE, the AUC was 0.842 for PET visual analysis, 0.831 for CortexID quantitative analysis, and 0.897 for the combined diagnosis. These results indicate that the diagnostic efficacy of CortexID quantitative analysis is not inferior to PET visual analysis (p > 0.05), while combined analysis significantly increases diagnostic efficacy (p < 0.05). Among the 23 patients who underwent surgery, the sensitivity and specificity of PET visual analysis for localization were 95.4% and 66.7%, and the sensitivity and specificity of CortexID quantitative analysis were 100% and 50%. CONCLUSION: The diagnostic efficacy of CortexID quantitative analysis is comparable to PET visual analysis in the localization of the epileptogenic zone in patients with TLE. CortexID quantitative analysis combined with visual analysis can further improve the accuracy of epileptogenic zone localization.

Dynamic functional network connectivity in patients with a mismatch between white matter hyperintensity and cognitive function.

Objective: White matter hyperintensity (WMH) in patients with cerebral small vessel disease (CSVD) is strongly associated with cognitive impairment. However, the severity of WMH does not coincide fully with cognitive impairment. This study aims to explore the differences in the dynamic functional network connectivity (dFNC) of WMH with cognitively matched and mismatched patients, to better understand the underlying mechanisms from a quantitative perspective. Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive function scale assessment of the patients were acquired. Preprocessing of the rs-fMRI data was performed, and this was followed by dFNC analysis to obtain the dFNC metrics. Compared the dFNC and dFNC metrics within different states between mismatch and match group, we analyzed the correlation between dFNC metrics and cognitive function. Finally, to analyze the reasons for the differences between the mismatch and match groups, the CSVD imaging features of each patient were quantified with the assistance of the uAI Discover system. Results: The 149 CSVD patients included 20 cases of "Type I mismatch," 51 cases of Type I match, 38 cases of "Type II mismatch," and 40 cases of "Type II match." Using dFNC analysis, we found that the fraction time (FT) and mean dwell time (MDT) of State 2 differed significantly between "Type I match" and "Type I mismatch"; the FT of States 1 and 4 differed significantly between "Type II match" and "Type II mismatch." Correlation analysis revealed that dFNC metrics in CSVD patients correlated with executive function and information processing speed among the various cognitive functions. Through quantitative analysis, we found that the number of perivascular spaces and bilateral medial temporal lobe atrophy (MTA) scores differed significantly between "Type I match" and "Type I mismatch," while the left MTA score differed between "Type II match" and "Type II mismatch." Conclusion: Different mechanisms were implicated in these two types of mismatch: Type I affected higher-order networks, and may be related to the number of perivascular spaces and brain atrophy, whereas Type II affected the primary networks, and may be related to brain atrophy and the years of education.

Surrogacy in the United States: analysis of sociodemographic profiles and motivations of surrogates.

RESEARCH QUESTION: What is the profile of women in the USA who become surrogates, and what is their power of decision and motivations? DESIGN: This quantitative study was performed with 231 participants in the USA, given the country's long history of surrogacy, to help clarify the profile of women who become surrogates, their power of decision and motivations. RESULTS: Descriptive and multivariate cluster analyses showed that women who become surrogates earn above the average income for their state of residency, have a high level of education, have health insurance, are employed, and decide to become a surrogate for prosocial/altruistic reasons. CONCLUSIONS: In contrast to the premise of both radical feminism and ultra-conservative Catholicism, this study found that altruism and empathy are the primary motivations for participating in surrogacy processes, and that a woman's decision to become a surrogate is not motivated by social conditioning relating to poverty or social status.

Effective strategy for distinguishing raw and vinegar Schisandrae Chinensis Fructus based on electronic eye and electronic tongue combined with chemometrics.

INTRODUCTION: Schisandrae Chinensis Fructus (SCF), a traditional Chinese medicine, has been used in treating virtual injury and strain since ancient times. The Chinese Pharmacopoeia reveals that SCF includes raw (RSCF) and vinegar-processed (VSCF) decoction pieces. OBJECTIVE: This study developed an effective method combining the electronic eye (e-eye), electronic tongue (e-tongue), and chemometrics to discriminate RSCF and VSCF from the perspective of chemical composition, color, and taste. MATERIAL AND METHODS: First, RSCF were collected and processed into VSCF, and their color parameters, e-tongue sensory properties, high-performance liquid chromatography (HPLC) and ultra-HPLC (UPLC) characteristic fingerprints, and nominal ingredients were determined. Multivariate statistical analyses, including principal component, linear discriminant, similarity, and partial least squares discriminant analyses, were conducted. RESULTS: HPLC and UPLC fingerprints were established, demonstrating a > 0.900 similarity. The content determination indicated increased schisantherin A, schisantherin B, and schisandrin A contents in VSCF. The e-eye data demonstrated a > 1.5 total color difference before and after processing ΔE*ab, indicating the significantly changed sample color and appearance before and after processing. The e-tongue technology was used to quantitatively characterize the taste of RSCF and VSCF. The t-test revealed significantly reduced sourness, aftertaste-bitter, and aftertaste-astringent values of SCF after vinegar processing. Principal component and partial least squares discriminant analyses indicated that e-eye and e-tongue realize the rapid RSCF and VSCF identification. CONCLUSION: The proposed comprehensive strategy of electronic eye and electronic tongue combined with chemometrics demonstrated satisfactory results with high efficiency, accuracy, and reliability. This can be developed into a novel and accurate method for discriminating RSCF and VSCF.

Machine learning quantification of Amyloid-ß deposits in the temporal lobe of 131 brain bank cases.

Accurate and scalable quantification of amyloid-ß (Aß) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Disease (AD). This multidisciplinary study addresses the current limitations on neuropathology by leveraging a machine learning (ML) pipeline to perform a granular quantification of Aß deposits and assess their distribution in the temporal lobe. Utilizing 131 whole-slide-images from consecutive autopsied cases at the University of California Davis Alzheimer Disease Research Center, our objectives were threefold: (1) Validate an automatic workflow for Aß deposit quantification in white matter (WM) and gray matter (GM); (2) define the distributions of different Aß deposit types in GM and WM, and (3) investigate correlates of Aß deposits with dementia status and the presence of mixed pathology. Our methodology highlights the robustness and efficacy of the ML pipeline, demonstrating proficiency akin to experts' evaluations. We provide comprehensive insights into the quantification and distribution of Aß deposits in the temporal GM and WM revealing a progressive increase in tandem with the severity of established diagnostic criteria (NIA-AA). We also present correlations of Aß load with clinical diagnosis as well as presence/absence of mixed pathology. This study introduces a reproducible workflow, showcasing the practical use of ML approaches in the field of neuropathology, and use of the output data for correlative analyses. Acknowledging limitations, such as potential biases in the ML model and current ML classifications, we propose avenues for future research to refine and expand the methodology. We hope to contribute to the broader landscape of neuropathology advancements, ML applications, and precision medicine, paving the way for deep phenotyping of AD brain cases and establishing a foundation for further advancements in neuropathological research.

Proteomic Identification and Label-Free Quantification of Proteins Implicated in Neurite and Spine Formation.

The molecular mechanisms underlying neurite formation include multiple crosstalk between pathways such as membrane trafficking, intracellular signaling, and actin cytoskeletal rearrangement. To study the proteins involved in such complex pathways, we present a detailed workflow of the sample preparation for mass spectrometry-based proteomics and data analysis. We have also included steps to perform label-free quantification of proteins that will help researchers quantify changes in the expression levels of key regulators of neuronal morphogenesis on a global scale.

A novel approach to the quantitative analysis of the particulate matter in conventional cigarette smoke and heated tobacco product aerosols.

The particulate and soluble matter present in aerosols from combustible cigarettes (CCs) and Heated Tobacco Products (HTPs) was collected in liquid water. These liquids, yellowish in the experiments with cigarettes and colourless after using HTPs, were analysed by Laser Diffraction (LD) and by Transmission Electron Microscopy coupled to Energy Dispersive X-ray spectroscopy (TEM-EDX) to study the amount, size, composition, and other features of the particulate matter (PM) present in the collected aerosols. The particulate matter concentration in HTPs samples is below the limit of quantification for LD, and only samples from cigarettes show a particulate matter concentration above such limit. TEM analysis has revealed that the liquid samples (from both, cigarettes and HTPs experiments) contain particulate matter, mainly composed of carbon (C) and oxygen (O), but also of traces of inorganic elements. The TEM electron beam results in the evaporation of the particulate matter derived from HTPs, but not of that derived from cigarettes, highlighting the different nature of the particulate matter in both systems, i.e. liquid particulate matter present in the HTPs aerosols and solid particulate matter in the cigarettes smoke. A protocol for the quantitative comparison of the particulate matter present in aerosols has been applied over sixteen TEM images for each sample, confirming important differences from the point of view of the amount of particulate matter and particle size ranges. Thus, the amount of particulate matter for HTPs aerosol samples is more than one order of magnitude lower than for cigarettes smoke.

Rapid detection of fertilizer information based on Raman spectroscopy and machine learning.

The rapid detection of fertilizer nutrient information is a crucial element in enabling intelligent and precise variable fertilizer application. However, traditional detection methods possess limitations, such as the difficulty in quantifying multiple components and cross-contamination. In this study, a rapid detection method was proposed, leveraging Raman spectroscopy combined with machine learning, to identify five types of fertilizers: K2SO4, (CO(NH2)2, KH2PO4, KNO3, and N:P:K (15-15-15), along with their concentrations. Qualitative and quantitative models of fertilizers were constructed using three machine learning algorithms combined with five spectral preprocessing methods. Two variable selection methods were used to optimize the quantitative model. The results showed that the classification accuracy of the five fertilizer solutions obtained by random forest (RF) was 100 %. Moreover, in terms of regression, partial least squares regression (PLSR) outperformed extreme learning machine (ELM) and least squares support vector machine (LSSVM), yielding prediction Rp2 within the range of 0.9843-0.9990 and a root mean square error in the range of 0.0486-0.1691. In addition, this study evaluated the impact of different water types (deionized water, well water, and industrial transition water) on the detection of fertilizer information via Raman spectroscopy. The results showed that while different water types did not notably affect the identification of fertilizer nutrients, they did exert a pronounced effect on the quantification of concentrations. This study highlights the efficacy of combining Raman spectroscopy with machine learning in detecting fertilizer nutrients and their concentration information effectively.

An integrated CBLA-Net with fractional discrete wavelet transform and frequency-based CARS to predict heavy metal elements by XRF.

BACKGROUND: X-ray fluorescence (XRF) emerges as a promising technique for estimating heavy metal elements. However, XRF spectra typically contain a significant amount of environmental information and signal noise, and the relationship between spectral intensity and element concentration is difficult to quantify using a single model, thereby reducing the predictive performance for low concentration elements. RESULTS: This paper proposed a comprehensive framework for predicting elemental concentrations, encompassing preprocessing, variable selection, decision-making, to enable fast, non-destructive, and accurate estimation of element concentrations in soil. Firstly, an optimal denoising method based on fractional discrete wavelet transform (FDWT) was introduced to enhance signal quality. Furthermore, the frequency-based competitive adaptive reweighted sampling (FCARS) algorithm was employed for feature selection of XRF spectral variables, allowing extraction of the most informative features from the complex spectral data. Finally, a novel deep learning network, called ConvBiLSTM-Attention (CBLA-Net), was designed to achieve precise estimation of heavy metal elements concentration. Compared with other advanced algorithms, The CBLA-Net demonstrated the highest accuracy for V, Cr, Mn, Zn, Cd, and Pb, achieving the coefficient of determination (R2) of 0.9730, 0.9874, 0.9952, 0.9921, 0.9518, and 0.9741, respectively. The CBLA-Net not only effectively extract local features and capture global information, but also combines attention mechanism to focus on key information. SIGNIFICANCE: The proposed novel deep learning quantitative framework, including preprocessing, feature selection, and CBLA-Net decision-making, significantly enhances the accuracy of elemental content prediction. It provides a new approach for accurately assessing the concentration of heavy metal elements in soil.

Power of a Few Vagrant Words: Effects of Direct and Indirect Suggestions for Self-Representation in Art-Based Online Interventions.

This study compares two groups of university students with differing instructions participating in an object drawing task as a part of an art therapy-based self-help online intervention. The intervention aimed to help participants enhance positive mood and subjective feeling of self-control and reduce negative mood. The object task contained suggestive elements in the instructions similar to self-hypnosis with an indirect and a direct way of formulation. Quantitative (positive affect and negative affect scale and Self-Assessment Manikin scale) and qualitative methods (text and picture rating) were used to investigate the difference between the outcome effects on the two groups. The results found a significant decrease in negative mood for indirect suggestion, while a significant increase of positive mood for the direct suggestion condition. Based on qualitative analyses, findings indicated that hidden implications in the art-making instructions modified the chosen imaginary and emotional changes related to art-making. Suggestions in the instructions can make a difference in one's mood and this should also be considered while designing guidelines for self-hypnosis.

Quantitative and Aesthetic Analysis of Changes in Eyebrow Position After Subbrow Blepharoplasty.

BACKGROUND: Narrowing of the distance between the eyes and eyebrows is commonly observed after subbrow blepharoplasty. The purpose of this study was to quantify the changes in brow-lid distance after subbrow blepharoplasty in Asian women. METHODS: We observed and standardized the pre- and postoperative photographs of 63 patients who underwent subbrow blepharoplasty from January 2020 to October 2022. We measured the distances from the medial and lateral eyebrow to the lower lid margin on the right side of the face and then analyzed the changes in postoperative brow-lid distance using the standard iris diameter for Asian women of 11.5 mm as a reference. RESULTS: Photographs of 63 patients were included in the study. All 63 patients were females. The average postoperative distance of the lateral eyebrow to the lower lid margin was 30.08 ± 2.74 mm, a significant decrease in comparison to the preoperative distance (31.84 ± 2.65 mm) (P < 0.001). The mean postoperative distance of the medial eyebrow to the lower lid margin was 25.84 ± 2.87 mm, compared with that of the preoperative distance (27.59 ± 2.94 mm), which was a significant decrease (P<0.001). All 63 patients (100%) had a decrease in the lateral eyebrow distance, while 59 (93.65%) had a decreased medial eyebrow distance. CONCLUSIONS: There was a statistically significant change in brow position, consistent with our observations. Subbrow blepharoplasty can cause a decrease in the distance between the eyebrows and eyes. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Smartphone based lateral flow immunoassay quantifications.

Lateral Flow Immunoassay (LFI) is a disposable tool designed to detect target substances using minimal resources. For qualitative analysis, LFI does not require a device (i.e., reader) to interpret test results. However, various studies have been conducted to implement quantitative analysis using LFI systems, incorporating LFI along with electrical/electronic readers, to overcome the limitations associated with qualitative LFI analysis. The reader used for the quantitative analysis of LFI should ensure mobility for easy on-site diagnostics and inspections, be user-friendly in operation, and have a fast processing speed until the results are obtained. Due to these requirements, smartphones are increasingly utilized as readers in quantitative analysis of LFI. Among the various components constituting a smartphone, high-performance cameras can serve as sensors converting visual signals into electrical signals. With powerful processing units, large storage capacity, and network capabilities for transmitting analysis results, smartphones are also utilized as interfaces for quantitative analysis. Absolutely, the widespread global use of smartphones is a key advantage, leading to their utilization as diagnostic devices for acquiring, analyzing, storing, and transmitting assay test results. This paper summarizes research cases where smartphones are utilized as readers for quantitative LFI systems used in confirming contamination in food or the environment, detecting drugs, and diagnosing diseases in humans or animals. The systems are classified based on the types of label particles used in the assay, and efforts to improve the quantitative analysis performance for each are examined. Cases where smartphones were used as LFI readers for the diagnosis of the 2019 Coronavirus Disease (COVID-19), which has recently caused significant global damage, have also been investigated.

When National Origins Equal Socio-economic Background: The Effect of the Ethno-class Parental Background on the Education of Children Coming of Age in Switzerland.

The educational outcomes of the descendants of migrants are important indicators of migrants' incorporation into host societies and an indicator of intergenerational social im/mobility. This paper examines this relationship using data from a survey that follows a cohort of young adults, born between 1988 and 1997, who grew up in Switzerland. It looks at the relationship between the educational output of respondents and their parental migratory background, with the theoretical consideration that the family's social capital is a starting point in the descendants' trajectories. The paper is based on secondary data and exploratory cross-sectional quantitative analyses. The results highlight first a correspondence between migrant parents' national origins and their socio-economic status-in other words, an 'ethno-class'. Second, they show differences in educational outcomes between migrants' descendants and native Swiss as well as between the migrants' descendants themselves-which indicates a segmented incorporation process for both the first and the second generation, in confirmation of previous research. Third, results show that parental background and language region of residence are statistically significant in determining the level of education achieved by the migrants' descendants, especially those with a low socio-economic status. Their social mobility is 'limited', and they remain mostly in vocational education. The paper concludes that the Swiss school system still fails to include the most unprivileged and that a glass ceiling remains for them.

Evaluation of oxalic acid extraction and quantification methods in the different purslane (Portulaca oleracea L.) matrices and spinach (Spinacea oleracea).

Purslane (Portulaca oleracea) and spinach (Spinacea oleracea) are species with elevated levels of oxalic acid, an antinutrient that interferes in the bioaccessibility of minerals such as calcium and iron. Evaluating methods to determine oxalic acid content with reduced matrix interference, such as employing Flame Atomic Absorption Spectrometry (FAAS), can enhance the specificity of determinations. The different matrices of purslane (whole plant, leaves, and juice) and spinach (whole plant) were tested using three extraction methods (M1, M2, and M3). The oxalic acid content was evaluated by UV-vis spectrophotometry and FAAS (Flame Atomic Absorption Spectrometry). The absence of the precipitation step in M1 resulted in high levels of oxalic acid in the investigated matrices. The quantification of oxalic acid by FAAS for M2 (6M HCl for 1 hour at 100°C) and M3 (0.25N HCl for 15 minutes at 100°C) in the samples of purslane leaves and spinach whole plants yielded statistically similar results. However, the analysis by UV-vis spectrophotometry for M2 and M3 showed significant discrepancies in all evaluated samples, suggesting interference from colored compounds in the food matrix.•Comparison of methods of extraction•Comparison of UV-vis spectrophotometer and FAAS in the quantification of oxalic acid•Analysis of antinutrients in plant matrices.

An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy.

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Mathematical modeling and quantitative analysis of phenotypic plasticity during tumor evolution based on single-cell data.

Tumor is a complex and aggressive type of disease that poses significant health challenges. Understanding the cellular mechanisms underlying its progression is crucial for developing effective treatments. In this study, we develop a novel mathematical framework to investigate the role of cellular plasticity and heterogeneity in tumor progression. By leveraging temporal single-cell data, we propose a reaction-convection-diffusion model that effectively captures the spatiotemporal dynamics of tumor cells and macrophages within the tumor microenvironment. Through theoretical analysis, we obtain the estimate of the pulse wave speed and analyze the stability of the homogeneous steady state solutions. Notably, we employe the AddModuleScore function to quantify cellular plasticity. One of the highlights of our approach is the introduction of pulse wave speed as a quantitative measure to precisely gauge the rate of cell phenotype transitions, as well as the novel implementation of the high-plasticity cell state/low-plasticity cell state ratio as an indicator of tumor malignancy. Furthermore, the bifurcation analysis reveals the complex dynamics of tumor cell populations. Our extensive analysis demonstrates that an increased rate of phenotype transition is associated with heightened malignancy, attributable to the tumor's ability to explore a wider phenotypic space. The study also investigates how the proliferation rate and the death rate of tumor cells, phenotypic convection velocity, and the midpoint of the phenotype transition stage affect the speed of tumor cell phenotype transitions and the progression to adenocarcinoma. These insights and quantitative measures can help guide the development of targeted therapeutic strategies to regulate cellular plasticity and control tumor progression effectively.

Recent progress in quantitative analysis of self-assembled peptides.

Self-assembled peptides have been among the important biomaterials due to its excellent biocompatibility and diverse functions. Over the past decades, substantial progress and breakthroughs have been made in designing self-assembled peptides with multifaceted biomedical applications. The techniques for quantitative analysis, including imaging-based quantitative techniques, chromatographic technique and computational approach (molecular dynamics simulation), are becoming powerful tools for exploring the structure, properties, biomedical applications, and even supramolecular assembly processes of self-assembled peptides. However, a comprehensive review concerning these quantitative techniques remains scarce. In this review, recent progress in techniques for quantitative investigation of biostability, cellular uptake, biodistribution, self-assembly behaviors of self-assembled peptide etc., are summarized. Specific applications and roles of these techniques are highlighted in detail. Finally, challenges and outlook in this field are concluded. It is believed that this review will provide technical guidance for researchers in the field of peptide-based materials and pharmaceuticals, and facilitate related research for newcomers in this field.

Optimization of penalization function in Bayesian penalized likelihood reconstruction algorithm for [18F]flutemetamol amyloid PET images.

Point-spread-function (PSF) correction is not recommended for amyloid PET images due to Gibbs artifacts. Q.Clear™, a Bayesian Penalized Likelihood (BPL) reconstruction method without incorporating PSF correction reduces these artifacts but degrades image contrast by our previous findings. The present study aimed to recover lost contrast by optimizing reconstruction parameters in time-of-flight (TOF) BPL reconstruction of amyloid PET images without PSF correction. We selected candidate conditions based on a phantom study and then determined which were optimal in a clinical study. Phantom images were reconstructed under conditions of 1‒9 iterations, ß 300-1000 and γ factors from 2 to 10 in TOF-BPL without PSF correction. We evaluated the %contrast and the coefficients of variation (CV, %). Standardized uptake value ratios (SUVr) and Centiloid scales (CL) were calculated from PET images acquired from 71 participants after an [18F]flutemetamol injection. Both %contrast and CV were independent of iterations, whereas a trade-off was found between γ factors and ß. We selected a γ factors of 5 without PSF correction (iterations, 1; ß, 500) and of 10 without PSF correction (iterations, 1; ß, 800) as candidates for clinical investigation. The SUVr and CL remained stable across various conditions, and CL scales effectively discriminated amyloid PET using measured values. The optimal reconstruction parameters of TOF-BPL for [18F]flutemetamol PET images were γ factor 10, iterations 1 and ß 800, without PSF correction.

Signal interference between drugs and metabolites in LC-ESI-MS quantitative analysis and its evaluation strategy.

Liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS) is a widely utilized technique for in vivo pharmaceutical analysis. Ionization interference within electrospray ion source, occurring between drugs and metabolites, can lead to signal variations, potentially compromising quantitative accuracy. Currently, method validation often overlooks this type of signal interference, which may result in systematic errors in quantitative results without matrix-matched calibration. In this study, we conducted an investigation using ten different groups of drugs and their corresponding metabolites across three LC-ESI-MS systems to assess the prevalence of signal interference. Such interferences can potentially cause or enhance nonlinearity in the calibration curves of drugs and metabolites, thereby altering the relationship between analyte response and concentration for quantification. Finally, we established an evaluation scheme through a step-by-step dilution assay and employed three resolution methods: chromatographic separation, dilution, and stable labeled isotope internal standards correction. The above strategies were integrated into the method establishment process to improve quantitative accuracy.