Quantitative CT analysis of idiopathic pulmonary fibrosis and correlation with lung function study.

BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease. However, the field of quantitative CT scan analysis in conjunction with pulmonary function test for IPF patients remains relatively understudied. In this study, we evaluated the diagnostic value of features derived high-resolution computed tomography (HRCT) for patients with IPF and correlated them with pulmonary function tests. METHODS: We retrospectively analyzed the chest HRCT images and pulmonary function test results of 52 patients with IPF during the same period (1 week) and selected 52 healthy individuals, matched for sex, age, and body mass index (BMI) and with normal chest HRCT as controls. HRCT scans were performed using a Philips 256-row Brilliance iCT scanner with standardized parameters. Lung function tests were performed using a Jaeger volumetric tracer for forced vital capacity (FVC), total lung capacity (TLC), forced expiratory volume in first second (FEV1), FEV1/FVC, carbon monoxide diffusing capacity (DLCO), and maximum ventilation volume (MVV) metrics. CT quantitative analysis, including tissue segmentation and threshold-based quantification of lung abnormalities, was performed using 3D-Slicer software to calculate the percentage of normal lung areas (NL%), percentage of ground-glass opacity areas (GGO%), percentage of fibrotic area (F%) and abnormal lesion area percentage (AA%). Semi-quantitative analyses were performed by two experienced radiologists to assess disease progression. The aortic-to-sternal distance (ASD) was measured on axial images as a standardized parameter. Spearman or Pearson correlation analysis and multivariate stepwise linear regression were used to analyze the relationship between the data in each group, and the ROC curve was used to determine the optimal quantitative CT metrics for identifying IPF and controls. RESULTS: ROC curve analysis showed that F% distinguished the IPF patient group from the control group with the largest area under the curve (AUC) of 0.962 (95% confidence interval: 0.85-0.96). Additionally, with F% = 4.05% as the threshold, the Youden's J statistic was 0.827, with a sensitivity of 92.3% and a specificity of 90.4%. The ASD was significantly lower in the late stage of progression than in the early stage (t = 5.691, P < 0.001), with a mean reduction of 2.45% per month. Quantitative CT indices correlated with all pulmonary function parameters except FEV1/FVC, with the highest correlation coefficients observed for F% and TLC%, FEV1%, FVC%, MVV% (r = - 0.571, - 0.520, - 0.521, - 0.555, respectively, all P-values < 0.001), and GGO% was significantly correlated with DLCO% (r = - 0.600, P < 0.001). Multiple stepwise linear regression analysis showed that F% was the best predictor of TLC%, FEV1%, FVC%, and MVV% (R2 = 0.301, 0.301, 0.300, and 0.302, respectively, all P-values < 0.001), and GGO% was the best predictor of DLCO% (R2 = 0.360, P < 0.001). CONCLUSIONS: Quantitative CT analysis can be used to diagnose IPF and assess lung function impairment. A decrease in the ASD may indicate disease progression.

Quantification of osilodrostat in horse urine using LC/ESI-HRMS to establish an elimination profile for doping control.

Aim: The use of osilodrostat, developed as a medication for Cushing's disease but categorized as an anabolic agent, is banned in horses by both the International Federation of Horseracing Authorities and the Fédération Equestre Internationale. For doping control purposes, elimination profiles of hydrolyzed osilodrostat in horse urine were established and the detectability of free forms of osilodrostat and its major metabolite, mono-hydroxylated osilodrostat (M1c), was investigated.Materials & methods: Post-administration urine samples obtained from a gelding and three mares were analyzed to establish the elimination profiles of osilodrostat using a validated method involving efficient enzymatic hydrolysis followed by LC/ESI-HRMS analysis.Results: Applying the validated quantification method with an LLOQ of 0.05 ng/ml, hydrolyzed osilodrostat could be quantified in post-administration urine samples from 48 to 72 h post-administration; by contrast, both hydrolyzed osilodrostat and M1c were detected up to 2 weeks. In addition, confirmatory analysis identified the presence of hydrolyzed osilodrostat for up to 72 h post-administration.Conclusion: For doping control purposes, we recommend monitoring both hydrolyzed M1c and osilodrostat because of the greater detectability of M1c and the availability of a reference material of osilodrostat, which is essential for confirmatory analysis.

[Box: see text].

Hue-change coupled with CRISPR-Cas12a lateral flow assay for the semiquantitative detection of Salmo salar adulteration.

Salmo salar is one of the most popular salmon species due to its meaty texture and quality protein. Oncorhynchus mykiss, which has a muscle texture similar to that of Salmo salar and is less expensive, is often used as a substitute for Salmo salar. As Salmo salar and Oncorhynchus mykiss belong to the same subfamily of Salmonidae, traditional methods are ineffective in the specific detection of the two. In this study, we combined hue-change with CRISPR/Cas12a lateral flow assay to detect the Salmo salar adulteration. This method detected S. salar genomic DNA at a vLOD of 5 copies, and was able to accurately identify adulterated samples containing 5 % w/w Salmo salar within one hour. In addition, the detection of Salmo salar in processed food products was achieved with the naked-eye at a concentration range of 0 % âˆ¼ 70 % w/w, and the detection accuracy is between 93.3 % âˆ¼ 100 %.

Optical coherence tomography angiography of the retina and choroid in systemic diseases.

Optical coherence tomography angiography (OCTA) has transformed ocular vascular imaging, revealing microvascular changes linked to various systemic diseases. This review explores its applications in diabetes, hypertension, cardiovascular diseases, and neurodegenerative diseases. While OCTA provides a valuable window into the body's microvasculature, interpreting the findings can be complex. Additionally, challenges exist due to the relative non-specificity of its findings where changes observed in OCTA might not be unique to a specific disease, variations between OCTA machines, the lack of a standardized normative database for comparison, and potential image artifacts. Despite these limitations, OCTA holds immense potential for the future. The review highlights promising advancements like quantitative analysis of OCTA images, integration of artificial intelligence for faster and more accurate interpretation, and multi-modal imaging combining OCTA with other techniques for a more comprehensive characterization of the ocular vasculature. Furthermore, OCTA's potential future role in personalized medicine, enabling tailored treatment plans based on individual OCTA findings, community screening programs for early disease detection, and longitudinal studies tracking disease progression over time is also discussed. In conclusion, OCTA presents a significant opportunity to improve our understanding and management of systemic diseases. Addressing current limitations and pursuing these exciting future directions can solidify OCTA as an indispensable tool for diagnosis, monitoring disease progression, and potentially guiding treatment decisions across various systemic health conditions.

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.

A Sensitive SERS Sensor Combined with Intelligent Variable Selection Models for Detecting Chlorpyrifos Residue in Tea.

Chlorpyrifos is one of the most widely used broad-spectrum insecticides in agriculture. Given its potential toxicity and residue in food (e.g., tea), establishing a rapid and reliable method for the determination of chlorpyrifos residue is crucial. In this study, a strategy combining surface-enhanced Raman spectroscopy (SERS) and intelligent variable selection models for detecting chlorpyrifos residue in tea was established. First, gold nanostars were fabricated as a SERS sensor for measuring the SERS spectra. Second, the raw SERS spectra were preprocessed to facilitate the quantitative analysis. Third, a partial least squares model and four outstanding intelligent variable selection models, Monte Carlo-based uninformative variable elimination, competitive adaptive reweighted sampling, iteratively retaining informative variables, and variable iterative space shrinkage approach, were developed for detecting chlorpyrifos residue in a comparative study. The repeatability and reproducibility tests demonstrated the excellent stability of the proposed strategy. Furthermore, the sensitivity of the proposed strategy was assessed by estimating limit of detection values of the various models. Finally, two-tailed paired t-tests confirmed that the accuracy of the proposed strategy was equivalent to that of gas chromatography-mass spectrometry. Hence, the proposed method provides a promising strategy for detecting chlorpyrifos residue in tea.

Comprehensive Comparison of Three Different Medicinal Parts of Eupatorium lindleyanum DC. Using the RRLC-Q-TOF-MS-Based Metabolic Profile and In Vitro Anti-Inflammatory Activity.

Eupatorium lindleyanum DC. (EL) is a traditional Chinese herb known for its phlegm-reducing, cough-relieving and asthma-calming properties. It is widely used for treating cough and bronchitis. However, preliminary experiments have revealed wide variations in the composition of its different medicinal parts (flowers, leaves and stems), and the composition and efficacy of its different medicinal parts remain largely underexplored at present. In this study, non-targeted rapid resolution liquid chromatography coupled with a quadruple time-of-flight mass spectrometry (RRLC-Q-TOF-MS)-based metabolomics approach was developed to investigate the differences in the chemical composition of different medicinal parts of EL. We identified or tentatively identified 9 alkaloids, 11 flavonoids, 14 sesquiterpene lactones, 3 diterpenoids and 24 phenolic acids. In addition, heatmap visualization, quantitative analysis by high-performance liquid chromatography (HPLC-PDA) and ultra-high-performance liquid chromatography-triple quadrupole tandem mass spectrometry (UPLC-MS/MS) showed particularly high levels of sesquiterpene lactones, flavonoids and phenolic acids in the flowers, such as eupalinolide A and B and chlorogenic acid, among others. The leaves also contained some flavonoid sesquiterpene lactones and phenolic acids, while the stems were almost absent. The findings of in vitro activity studies indicated that the flowers exhibited a notable inhibitory effect on the release of the inflammatory factors TNF-α and IL-6, surpassing the anti-inflammatory efficacy observed in the leaves. Conversely, the stems demonstrated negligible anti-inflammatory activity. The variations in anti-inflammatory activity among the flowers, leaves and stems of EL can primarily be attributed to the presence of flavonoids, phenolic acids and sesquiterpene lactones in both the flowers and leaves. Additionally, the flowers contain a higher concentration of these active components compared to the leaves. These compounds mediate their anti-inflammatory effects through distinct biochemical pathways. The results of this study are anticipated to provide a scientific basis for the rational and effective utilization of EL resources.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.