A new HS-SPME-GC-MS analytical method to identify and quantify compounds responsible for changes in the volatile profile in five types of meat products during aerobic storage at 4 °C.

Nowadays, it is important to monitor the freshness of meat during storage to protect consumers' health. Volatile organic compounds (VOCs) are responsible for odour and taste of food, and they give an indication about meat quality and freshness. This study had the aim to seek and select potential new markers of meat spoilage through a semi-quantitative analysis in five types of meat (beef, raw and baked ham, pork sausage and chicken) and then to develop a new quantitative analytical method to detect and quantify potential markers on five types of meat simultaneously. Firstly, a new headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) method was developed to evaluate the volatile profile of five types of meat, preserved at 4 °C for 5 days. Among the 40 compounds identified, 15 were chosen and selected as potential shelf-life markers on the basis of their presence in most of meat samples or/and for their constant increasing/decreasing trend within the sample. Afterwards, a quantitative HS-SPME-GC-MS analytical method was developed to confirm which VOCs can be considered markers of shelf-life for these meat products, stored at 4 °C for 12 days. Some of the compounds analyzed attracted attention as they can be considered markers of shelf-life for at least 4 types of meat: 1-butanol, 3-methylbutanol, 1-hexanol, 2-nonanone, nonanal, 1-octen-3-ol and linalool. In conclusion, in this study a new quantitative HS-SPME-GC-MS analytical method to quantity 15 VOCs in five types of meat was developed and it was demonstrated that some of the compounds quantified can be considered markers of shelf-life for some of the meat products analyzed.

Artificial intelligence-based classification of breast nodules: a quantitative morphological analysis of ultrasound images.

Background: Accurate classification of breast nodules into benign and malignant types is critical for the successful treatment of breast cancer. Traditional methods rely on subjective interpretation, which can potentially lead to diagnostic errors. Artificial intelligence (AI)-based methods using the quantitative morphological analysis of ultrasound images have been explored for the automated and reliable classification of breast cancer. This study aimed to investigate the effectiveness of AI-based approaches for improving diagnostic accuracy and patient outcomes. Methods: In this study, a quantitative analysis approach was adopted, with a focus on five critical features for evaluation: degree of boundary regularity, clarity of boundaries, echo intensity, and uniformity of echoes. Furthermore, the classification results were assessed using five machine learning methods: logistic regression (LR), support vector machine (SVM), decision tree (DT), naive Bayes, and K-nearest neighbor (KNN). Based on these assessments, a multifeature combined prediction model was established. Results: We evaluated the performance of our classification model by quantifying various features of the ultrasound images and using the area under the receiver operating characteristic (ROC) curve (AUC). The moment of inertia achieved an AUC value of 0.793, while the variance and mean of breast nodule areas achieved AUC values of 0.725 and 0.772, respectively. The convexity and concavity achieved AUC values of 0.988 and 0.987, respectively. Additionally, we conducted a joint analysis of multiple features after normalization, achieving a recall value of 0.98, which surpasses most medical evaluation indexes on the market. To ensure experimental rigor, we conducted cross-validation experiments, which yielded no significant differences among the classifiers under 5-, 8-, and 10-fold cross-validation (P>0.05). Conclusions: The quantitative analysis can accurately differentiate between benign and malignant breast nodules.

Development of a Simultaneous Normal-Phase HPLC Analysis of Lignans, Tocopherols, Phytosterols, and Squalene in Sesame Oil Samples.

The objective of this study was to develop a simultaneous analytical method for the determination of lignans, tocols, phytosterols, and squalene using high-performance liquid chromatography coupled with a diode array and fluorescence detector (HPLC-DAD-FLD). The method employed a VertisepTM UPS silica HPLC column (4.6 × 250 mm, 5 µm) with a mobile phase mixture of n-hexane/tetrahydrofuran/2-propanol. This approach enabled the simultaneous analysis of ten compounds within 22 min. The linear correlation (R2) exceeded 0.9901. The limit of detection (LOD) and limit of quantitation (LOQ) were up to 0.43 µg mL-1 for lignans and tocopherols and up to 326.23 µg mL-1 for phytosterol and squalene. The precision and accuracy of the intra-day and inter-day variation were less than 1.09 and 3.32% relative standard deviations (RSDs). Furthermore, the developed method was applied for the analysis of targeted compounds in twenty-eight sesame oil samples (1775-8965 µg g-1 total lignans, 29.7-687.9 µg g-1 total tocopherols, 2640-9500 µg g-1 phytosterol, and 245-4030 µg g-1 squalene). The HPLC method that has been developed was proven to be a reliable and effective tool for the determination of those functional compounds among sesame oil samples.

Chemical Relationship among Genetically Authenticated Medicinal Species of Genus Angelica.

The genus Angelica comprises various species utilized for diverse medicinal purposes, with differences attributed to the varying levels or types of inherent chemical components in each species. This study employed DNA barcode analysis and HPLC analysis to genetically authenticate and chemically classify eight medicinal Angelica species (n = 106) as well as two non-medicinal species (n = 14) that have been misused. Nucleotide sequence analysis of the nuclear internal transcribed spacer (ITS) region revealed differences ranging from 11 to 117 bp, while psbA-trnH showed variances of 3 to 95 bp, respectively. Phylogenetic analysis grouped all samples except Angelica sinensis into the same cluster, with some counterfeits forming separate clusters. Verification using the NCBI database confirmed the feasibility of species identification. For chemical identification, a robust quantitative HPLC analysis method was developed for 46 marker compounds. Subsequently, two A. reflexa-specific and seven A. biserrata-specific marker compounds were identified, alongside non-specific markers. Moreover, chemometric clustering analysis reflecting differences in chemical content between species revealed that most samples formed distinct clusters according to the plant species. However, some samples formed mixed clusters containing different species. These findings offer crucial insights for the standardization and quality control of medicinal Angelica species.

Optimization of Electrospinning Parameters for Lower Molecular Weight Polymers: A Case Study on Polyvinylpyrrolidone.

Polyvinylpyrrolidone (PVP) is a synthetic polymer that holds significance in various fields such as biomedical, medical, and electronics, due to its biocompatibility and exceptional dielectric properties. Electrospinning is the most commonly used tool to fabricate fibers because of its convenience and the wide choice of parameter optimization. Various parameters, including solution molarity, flow rate, voltage, needle gauge, and needle-to-collector distance, can be optimized to obtain the desired morphology of the fibers. Although PVP is commercially available in various molecular weights, PVP with a molecular weight of 130,000 g/mol is generally considered to be the easiest PVP to fabricate fibers with minimal challenges. However, the fiber diameter in this case is usually in the micron regime, which limits the utilization of PVP fibers in fields that require fiber diameters in the nano regime. Generally, PVP with a lower molecular weight, such as 10,000 g/mol and 55,000 g/mol, is known to present challenges in fiber preparation. In the current study, parameter optimization for PVP possessing molecular weights of 10,000 g/mol and 55,000 g/mol was carried out to obtain nanofibers. The electrospinning technique was utilized for fiber fabrication by optimizing the above-mentioned parameters. SEM analysis was performed to analyze the fiber morphology, and quantitative analysis was performed to correlate the effect of parameters on the fiber morphology. This research study will lead to various applications, such as drug encapsulation for sustained drug release and nanoparticles/nanotubes encapsulation for microwave absorption applications.

Digital Pathology for Better Clinical Practice.

(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.

Systemic cellular migration: The forces driving the directed locomotion movement of cells.

Directional motility is an essential property of cells. Despite its enormous relevance in many fundamental physiological and pathological processes, how cells control their locomotion movements remains an unresolved question. Here, we have addressed the systemic processes driving the directed locomotion of cells. Specifically, we have performed an exhaustive study analyzing the trajectories of 700 individual cells belonging to three different species (Amoeba proteus, Metamoeba leningradensis, and Amoeba borokensis) in four different scenarios: in absence of stimuli, under an electric field (galvanotaxis), in a chemotactic gradient (chemotaxis), and under simultaneous galvanotactic and chemotactic stimuli. All movements were analyzed using advanced quantitative tools. The results show that the trajectories are mainly characterized by coherent integrative responses that operate at the global cellular scale. These systemic migratory movements depend on the cooperative nonlinear interaction of most, if not all, molecular components of cells.

A versatile economic strategy by HPLC-CAD for quantification of structurally diverse markers in quality control of Shengmai Formula from raw materials to preparations.

BACKGROUND: Shengmai Formula (SMF), a classic formula in treating Qi-Yin deficiency, is composed of Ginseng Radix et Rhizoma Rubra (GRR), Ophiopogon Radix (OR), and Schisandra chinensis Fructus (SC), and has been developed into various dosage forms including Shengmai Yin Oral Liquid (SMY), Shengmai Capsules (SMC), and Shengmai Injection (SMI). The pharmacological effects of compound Chinese medicine are attributed to the integration of multiple components. Yet the quality criteria of SMF are limited to monitoring schisandrol A or ginsenosides Rg1 and Re, but none for OR. Since the complexity of raw materials and preparations, establishing a economical and unified method for SMF is challenging. It is urgent to simultaneously quantify multiple components with different structures using a universal method for quality control of SMF. Charged aerosol detector (CAD) overcame the above shortcomings owing to its characteristics of high responsiveness, nondiscrimination, and low cost. PURPOSE: We aimed to establish a versatile analysis strategy using HPLC-CAD for simultaneously quantifying the structurally diverse markers in quality control of SMF from raw materials to preparations. METHOD: By optimizing the column, mobile phase, column temperature, flow rate, and CAD parameters, a HPLC-CAD method that integrated multi-component characterization, authenticity identification, transfer information of raw materials and quantitative determination of Shengmai preparations was established. RESULTS: In total 50 components from SMF were characterized (28 in GRR, 13 in SC, and 9 in OR). The differences in raw materials between species of SC and Schisandrae sphenantherae Fructus (SS), processing methods of Ginseng Radix (GR) and GRR, and locations of OR from Sichuan (ORS) and Zhejiang (ORZ) were compared. Fourteen components in 19 batches of SMY, SMC and SMI from different manufacturers were quantified, including 11 ginsenosides and 3 lignans. The multivariate statistical analysis results further suggested that Rb1, Rg1 and Ro were the main differences among Shengmai preparations. CONCLUSION: The established versatile analysis strategy based on HPLC-CAD was proven sensitive, simple, convenient, overcoming the discriminatory effect of UV detector, revealing the composition and transfer information of SMF and applicable for authentication of the ingredient herbs and improving the quality of Shengmai preparations.

Magnetized bentonite modified rice straw biochar: qualitative and quantitative analysis of Cd(II) adsorption mechanism.

Industrialization has caused a significant global issue with cadmium (Cd) pollution. In this study, Biochar (Bc), generated through initial pyrolysis of rice straw, underwent thorough mixing with magnetized bentonite clay, followed by activation with KOH and subsequent pyrolysis. Consequently, a magnetized bentonite modified rice straw biochar (Fe3O4@B-Bc) was successfully synthesized for effective treatment and remediation of this problem. Fe3O4@B-Bc not only overcomes the challenges associated with the difficult separation of individual bentonite or biochar from water, but also exhibited a maximum adsorption capacity of Cd(II) up to 241.52 mg g-1. The characterization of Fe3O4@B-Bc revealed that its surface was rich in C, O and Fe functional groups, which enable efficient adsorption. The quantitative calculation of the contribution to the adsorption mechanism indicates that cation exchange and physical adsorption accounted for 65.87% of the total adsorption capacity. In conclusion, Fe3O4@B-Bc can be considered a low-cost and recyclable green adsorbent, with broad potential for treating cadmium-polluted water.

The Predictive Effect of Quantitative Analysis of Signal Intensity Heterogeneity on T2-Weighted MR Images for High-intensity Focused Ultrasound Treatment of Uterine Fibroids.

RATIONALE AND OBJECTIVES: To investigate whether the quantitative index of signal intensity (SI) heterogeneity on T2-weighted (T2W) magnetic resonance images can predict the difficulty and efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine fibroids. MATERIALS AND METHODS: The standard deviation (SD) of T2W image (T2WI) SI was used to quantify SI heterogeneity. The correlation between SD and the non-perfused volume ratio (NPVR) in 575 patients undergoing HIFU treatment was retrospectively analyzed, and the efficacy of SD in predicting NPVR was discussed. Three classifications were made based on the SD, and the ablation difficulty and ablation effect of different grades were compared. A total of 65 cases from another center were used as an external validation set to verify the classification performance of SD. RESULTS: The SD of SI was negatively correlated with NPVR (r = -0.460, p < 0.001). The predictive efficiency of SD for the ablation effect was higher than that of the scaled signal intensity (0.767 vs. 0.701, p = 0.006). Univariate and multivariate logistic regression analyses showed that SD was an independent predictor of ablation effect. Based on SD, the three classifications were divided into SD I: SD < 101.0, SD II: 101.0 ≤ SD < 138.7, and SD III: SD≥ 138.7. The treatment time, sonication time, treatment intensity, and total energy of SD I were lower than those of SD II and III (p < 0.05). CONCLUSION: The heterogeneity of T2WI SI of uterine fibroids is negatively correlated with NPVR. The SD of SI can be used to predict the ablation difficulty and ablation effect of HIFU.

Characterization of the chemical constituents of Croton crassifolius Geisel extract and plasma pharmacokinetics of 6 terpenoids after oral administration by UPLC-Q/TOF-MS.

INTRODUCTION: Croton crassifolius Geisel. (CCG) is a traditional Chinese medicine widely used in South China. It has various pharmacological effects and is often used in treating rheumatoid arthritis and gastric and duodenal ulcers. However, the chemical characteristics and its effective constituents are still scarcely studied. OBJECTIVE: To determine the phytochemical profile of the CCG extract and to investigate the chemical characteristics of terpenoids extracted from rat plasma following oral administration of CCG extract based on UPLC-Q/TOF-MS. Moreover, six terpenoids in CCG were quantified, and in vivo pharmacokinetic behavior after oral CCG extract was further explored. RESULTS: In total, 56 terpenoids were tentatively identified in the CCG extract and 16 terpenoids were detected in rat plasma after oral CCG extract. In addition, the contents of six terpenoids in CCG were clarified. The plasma quantification method of six terpenoids was further established, validated, and confirmed to have good sensitivity and specificity. The six analytes exhibited excellent linearity in respective concentration ranges (r ≥ 0.998). The intra-day and inter-day precisions relative standard deviation (RSD, %) were less than 11.27%, and the accuracies ranged from -7.06% to 9.91%. Stability, extraction recovery, and matrix effect in plasma were within the required limits (RSD < 15%). CONCLUSION: A total of 56 terpenoids were identified in CCG and 16 prototype components in plasma after oral CCG. The validated quantitative method was successfully applied to the simultaneous determination of six major terpenoids in plasma. The pharmacokinetic parameters are clarified, which can guide the clinical application of CCG.

Toxic element characterization against a typical high geology background: Pollution enrichment, source tracking, spatial distribution, and ecological risk assessment.

The geological environment determines the initial content of various elements in soil, while the late input of toxic elements produced through weathering and leaching is a persistent threat to food security and human health. In this study, we selected the Lou Shao Basin, a black rock system background, and combined geostatistical analysis and multivariate statistics to quantify the specific contribution of weathering of the black rock system, and to analyze the source traces, spatial distributions, and ecological risks of the soil toxicity of elements. The results show that the soils in the study area are acidic, which is related to the weathering of sulfides in the black rock system. The concentrations of most elements in the soil were determined to exceed the soil background values, and the Cd, Se and N contents, exceeded more than five times, especially Se, Mo nearly as high as 13 times. Strong positive correlation between Se, Cu, V and P, low correlation between N and Se, Cu, V, P, Ni and Cd.72.52%, 43%, 77.79%, 82%, 77%, and 44.1% of Cd, Se, Ni, Cu, B, and Mo came from the black rock system, respectively, which were greatly affected by geogenic weathering; V, Zn, Pb, and As are mainly from biomass burning sources; N and P are mainly from agricultural surface sources. Comparison found that the Cd and Se elements in the rocks in the study area were 16.78 times and 1.36 times higher than the world shale average, respectively, and need to pay attention to the weathering process of the two, and the spatial distribution of the 12 elements in soils showed a striped and centralized block distribution pattern, specifically around the distribution of carbonate and metamorphic rocks and other high-geology blocks. The ecological risk results showed that Cd was the main element causing high ecological risk, followed by Se and N, which were at moderate to high ecological risk levels, and Se and N showed similar ecological risk patterns, which may be related to the fact that selenium can promote the uptake and transformation of nitrogen. The present results add to the endogenous sources of toxic elements, quantify the source contributions of toxic elements in soils with high geologic backgrounds, fill this knowledge gap, and provide new insights for pollution control and ecological protection in areas with high geochemical backgrounds.

Dataset of media practices for informational, educational, and entertainment purposes in Peru: Uses and gratifications approach.

This database consists of the Peruvian media diet in a post-pandemic context. Specifically, it examines how Peruvians define and utilise media to create environments for information, learning, and entertainment. Since the pandemic, the relationship that users have developed with the media has intensified and changed, fostering new uses and interactions. However, our data demonstrates that the Peruvian public maintains a critical role towards mass media (broadcasting model) [1], which motivates them to seek out media and platforms that cater to their interests and expectations. In this regard, we compare three axes of analysis (information, education, and entertainment), taking socio demographic variables, to produce a baseline that is useful for specific research projects. Through a quantitative approach we explored the expectations, decisions, forms of interaction, and satisfactions that users obtain in their relationship with the media, offering new theoretical insights based on emerging data, such as the level of planning in content consumption or the role that traditional media play in different age and socioeconomic groups.

A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT.

The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.

Intraoral wear of PICN CAD-CAM composite restorations used in severe tooth wear treatment: 5-year results of a prospective clinical study using 3D profilometry.

OBJECTIVES: To evaluate, in a prospective clinical study over 5 years with ex vivo 3D profilometry analyses, the intraoral wear of Polymer-Infiltrated Ceramic Network (PICN) CAD-CAM composite restorations used in severe tooth wear treatment with the One-Step No-Prep technique. METHODS: 192 PICN (Vita Enamic) restorations on molars and premolars were included in a prospective clinical study involving patients treated according to the One-step No-prep protocol (n = 7). All patients showed clinical signs of bruxism. Replicas of restorations on molars and premolars were realized at each evaluation time (baseline and then every year up to 5 years) and scanned to perform 3D profilometry. Baseline and recall scans were superimposed with Geomagic Control software. The mean material wear was calculated for the full occlusal area (FOA) and for the occlusal contact area (OCA), respectively. Clinical evaluation of restorations was performed at recall. RESULTS: At 5 years, the estimated mean material wear for FOA was inferior to the accuracy threshold of the profilometry measurement chain. For OCA, the estimated mean wear of the material was - 27.97 µm. This material wear was shown to be significantly influenced by time (p < 0.0001) and patient (p = 0.026), while the type of tooth (molar or premolar) had no influence. At 5 years, the survival and the success rates of restorations were 99.48% and 90.62%, respectively. SIGNIFICANCE: The PICN material exhibits a low wear process in the treatment of severe tooth wear despite the presence of clinical signs of bruxism, and it constitutes a suitable material for the One-step No-prep technique.

Utility of metabolic ratios in the diagnosis of tumor Thrombus on F-18 FDG PET/CT.

BACKGROUND: This study aims to predict quantitative parameter in form of metabolic ratios to diagnose tumor thrombus on F-18 FDG PET/CT. METHODS: This is a retrospective study from Nuclear Medicine department at All India Institute of Medical Sciences, Jodhpur, India. Patients with malignancies who underwent F-18 FDG PET/CT in our department or images sent for review from February 2020 till September 2022 were screened for tumor thrombus that comprised study group. Control group had patients with malignancy and no imaging evidence of tumor thrombus. Metabolic activities (SUVmax) of tumor thrombus, liver and descending aorta in study group, and that of IVC, liver and descending aorta in control group were recorded. Metabolic ratios of tumor thrombus to liver (SUR L) and to aorta (SUR A) in study group, and IVC to liver (SUR* L) and to aorta (SUR*A) in control group were compared using receiver operator curves. RESULTS: Of 2277 studies screened, 12 had tumor thrombus. The most common primary malignant site and vessel involved were lung and IVC respectively. The median (IQR) SUR L, SUR A, SUR* L and SUR* A were 2.5 (3.25), 2.6  (6), 0.67 (0.18) and 1 (0.17) respectively. Area under ROC for SUR L and SUR A were 0.983 [95% CI: 0.955-1.0] and 0.958 [95% CI: 0.90-1.0] respectively. The ideal cut-off for SUR L was 0.953 (sensitivity 92.3%, specificity 98.0%) and for SUR A was 1.42 (sensitivity 84.6%, specificity 98.0%). CONCLUSION: Metabolic ratios of tumor thrombus to liver (SUR L) and aorta (SUR A) have good diagnostic performance and can be useful in studies with non-iodinated contrast CT.

Quantitative analysis of lesion images to evaluate the efficiency of vascular-targeted photodynamic therapy for port-wine stains: Four case reports.

SIGNIFICANCE: Vascular-targeted photodynamic therapy (V-PDT) is a clinically approved therapeutic approach for treating vascular-related diseases, such as port-wine stains (PWS). For accurate treatment, varying light irradiance is required for different lesions due to the irregularity of vascular size, shape and degree of disease, which commonly alters during different stages of V-PDT. This makes quantitative analysis of the treatment efficiency urgently needed. APPROACH: Lesion images pre- and post- V-PDT treatment of patients with PWS were used to construct a quantitative method to evaluate the differences among lesions. Image analysis techniques were applied to evaluate the V-PDT efficiency for PWS by determining the Euclidean distances and two-dimensional correlation coefficients. RESULTS: According to the image analysis, V-PDT with good treatment efficiency resulted in a larger Euclidean distance and a smaller correlation coefficient compared with the case having lower V-PDT efficiency. CONCLUSIONS: A new method to quantify the Euclidean distances and correlation coefficients has been proposed, which is promising for the quantitative analysis of V-PDT efficiency for PWS.

Improved Correlation of 18F-Flortaucipir PET SUVRs and Clinical Stages in the Alzheimer Disease Continuum with the MUBADA/PERSI-Based Analysis.

The Alzheimer disease (AD) continuum is a neurodegenerative disorder with cognitive decline and pathologic changes. Tau PET imaging can detect tau pathology, and 18F-flortaucipir PET imaging is expected to visualize progression through the stages of AD, for which quantitative assessment is essential. Two measurement methods, statistically defined multiblock barycentric discriminant analysis (MUBADA)/parametric estimation of reference signal intensity (PERSI) and anatomically defined tau meta-volume of interest (VOI)/cerebellar gray matter (CGM) for SUV ratio (SUVR), were compared in this study to assess their relationship to AD clinical stage using 2 open multicenter PET databases. Methods: Data were selected for 106 cases from 2 databases, AMED Preclinical AD study (AMED-PRE) (n = 15) and Alzheimer Disease Neuroimaging Initiative 3 (n = 91). The data of the participants were categorized into 4 groups based on the clinical criteria. Tau PET imaging was conducted using 18F-flortaucipir, and the 2 SUVR measurement methods, MUBADA/PERSI and tau meta-VOI/CGM, were compared among different clinical categories: amyloid-negative cognitively normal, preclinical AD, amyloid-negative mild cognitive impairment (MCI), and amyloid-positive MCI. Results: Significant differences were found between cognitively normal and preclinical AD, as well as between cognitively normal and amyloid-positive MCI and between amyloid-negative MCI and -positive MCI in SUVR derived by MUBADA/PERSI, whereas SUVR by tau meta-VOI/CGM did not provide significant differences between any pair. The tau meta-VOI/CGM method consistently provided higher SUVRs and larger individual variations than MUBADA/PERSI, with a mean SUVR difference of 0.136 for the studied databases. Conclusion: MUBADA/PERSI provided the SUVR of 18F-flortaucipir uptake with better association with the clinical severity of the AD continuum and with smaller variability. The results support the usefulness of MUBADA/PERSI as a quantitative measure of 18F-flortaucipir uptake in multicenter studies using different PET systems and scanning methods. However, limitations of the study include the small sample size and the unbalanced distribution among clinical categories in the AMED Preclinical AD study database.

Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis.

The study aimed to measure the carotenoid (Car) and pH contents of carrots using hyperspectral imaging. A total of 300 images were collected using a hyperspectral imaging system, covering 472 wavebands from 400 to 1000 nm. Regions of interest (ROIs) were defined to extract average spectra from the hyperspectral images (HIS). We developed two models: least squares support vector machine (LS-SVM) and partial least squares regression (PLSR) to establish a quantitative analysis between the pigment amounts and spectra. The spectra and pigment contents were predicted and correlated using these models. The selection of EWs for modeling was done using the Successive Projections Algorithm (SPA), regression coefficients (RC) from PLSR models, and LS-SVM. The results demonstrated that hyperspectral imaging could effectively evaluate the internal attributes of carrot cortex and xylem. Moreover, these models accurately predicted the Car and pH contents of the carrot parts. This study provides a valuable approach for variable selection and modeling in hyperspectral imaging studies of carrots.

Using Risk Ratios to Quantify Potential Behavior-Environment Relations.

Behavior-environment functional relations are the units of explanation in applied behavior analysis (ABA). Whether hypothesized experimentally or descriptively, quantification of putative functional relations improves our ability to predict and influence behavior. Risk ratios are an accessible, straightforward quantitative analysis that can serve this purpose. They have been employed to great effect in other fields (e.g., medicine, public health), but are rarely used within ABA. In this tutorial, we describe risk ratios and how they are calculated, discuss why risk ratios are well suited for quantifying behavior-environment relations, and illustrate their utility and applicability across five demonstrations from real clinical cases. Recommendations for the use of risk ratios in research and practice are discussed.