Component analysis using UPLC-Q-TOF/MS and quality evaluation using fingerprinting and chemometrics for hops.

Hops, extensively cultivated in China for their food and medicinal applications, currently lack well-defined chemical markers to evaluate variations in their quality. The study aimed to explore variations in the quality of Chinese hops by the chemical characteristics of hops, employing UPLC-Q-TOF/MS, integrated with chemical fingerprinting and chemometrics. The results indicated that Chinese hops are abundant in polyphenols and bitter acids. The integration of UPLC-Q-TOF/MS, Chemical fingerprinting, and chemometrics revealed to be an accurate and effective approach for assessing the quality of Chinese hops. In this study, ten important chemical markers were found to be useful in differentiating various hop varieties. Moreover, the support vector machine showed a prediction accuracy of 92.3077% in identifying Chinese hop varieties. The strategy of the study lays the groundwork for classifying Chinese hop varieties and serves as a prerequisite for future quality control studies, particularly focusing on chemical compositions.

A comparative analysis of the nutrient and phytochemical richness among different varieties of quinoa in China.

Quinoa is a nutrient-dense pseudocereal that has garnered global attention for its potential to bolster food security and nutrition. Despite its celebrated status, the detailed nutritional profiles of various quinoa varieties remain poorly understood, which poses a significant barrier to the strategic cultivation and utilization of quinoa's genetic diversity to combat malnutrition. The impetus for this research lies in the urgent need to identify superior quinoa strains that can be tailored to meet specific nutritional requirements and adapt to diverse agro-ecological zones. Our findings reveal substantial variation in nutrient content across different quinoa varieties, highlighting the variety ZLZX-8 as a particularly nutrient-rich strain with the highest levels of protein, fat, essential fatty acids, amino acids, and key minerals such as Mg, K, and Zn. Moreover, ZLZX-8's exceptional antioxidant capacity suggests it may have additional health benefits beyond its macronutrient profile. In contrast, ZLZX-7 stands out for its dietary fiber and phenolic content, which are critical for digestive health and disease prevention, respectively. Meanwhile, ZLZX-5, with its high starch content, could be better suited for energy production in dietary applications. Notably, the study also uncovers a correlation between grain color and nutrient profile, with colored quinoa varieties exhibiting superior fiber, inositol, phenolic content, and antioxidant activity compared to their white counterparts. This work lays the groundwork for an informed selection of quinoa varieties that can enhance dietary quality, support local and global food systems, and contribute to the fight against malnutrition.

Quality Evaluation of Lonicerae Flos Produced in Southwest China Based on HPLC Analysis and Antioxidant Activity.

Lonicera macranthoides, the main source of traditional Chinese medicine Lonicerae Flos, is extensively cultivated in Southwest China. However, the quality of L. macranthoides produced in this region significantly varies due to its wide distribution and various cultivation breeds. Herein, 50 Lonicerae Flos samples derived from different breeds of L. macranthoides cultivated in Southwest China were collected for quality evaluation. Six organic acids and three saponin compounds were quantitatively analyzed using HPLC. Furthermore, the antioxidant activity of a portion of samples was conducted with 2,2'-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) and 1,1-diphenyl-2-picryl-hydrazyl (DPPH) radical scavenging experiments. According to the quantitative results, all samples met the quality standards outlined in the Chinese Pharmacopoeia. The samples from Guizhou, whether derived from unopened or open wild-type breeds, exhibited high quality, while the wild-type samples showed relatively significant fluctuation in quality. The samples from Chongqing and Hunan demonstrated similar quality, whereas those from Sichuan exhibited relatively lower quality. These samples demonstrated significant abilities in clearing ABTS and DPPH radicals. The relationship between HPLC chromatograms and antioxidant activity, as elucidated by multivariate analysis, indicated that chlorogenic acid, isochlorogenic acid A, isochlorogenic acid B, and isochlorogenic acid C are active components and can serve as Q-markers for quality evaluation.

The future of patient education: A study on AI-driven responses to urinary incontinence inquiries.

OBJECTIVE: To evaluate the effectiveness of ChatGPT in providing insights into common urinary incontinence concerns within urogynecology. By analyzing the model's responses against established benchmarks of accuracy, completeness, and safety, the study aimed to quantify its usefulness for informing patients and aiding healthcare providers. METHODS: An expert-driven questionnaire was developed, inviting urogynecologists worldwide to assess ChatGPT's answers to 10 carefully selected questions on urinary incontinence (UI). These assessments focused on the accuracy of the responses, their comprehensiveness, and whether they raised any safety issues. Subsequent statistical analyses determined the average consensus among experts and identified the proportion of responses receiving favorable evaluations (a score of 4 or higher). RESULTS: Of 50 urogynecologists that were approached worldwide, 37 responded, offering insights into ChatGPT's responses on UI. The overall feedback averaged a score of 4.0, indicating a positive acceptance. Accuracy scores averaged 3.9 with 71% rated favorably, whereas comprehensiveness scored slightly higher at 4 with 74% favorable ratings. Safety assessments also averaged 4 with 74% favorable responses. CONCLUSION: This investigation underlines ChatGPT's favorable performance across the evaluated domains of accuracy, comprehensiveness, and safety within the context of UI queries. However, despite this broadly positive reception, the study also signals a clear avenue for improvement, particularly in the precision of the provided information. Refining ChatGPT's accuracy and ensuring the delivery of more pinpointed responses are essential steps forward, aiming to bolster its utility as a comprehensive educational resource for patients and a supportive tool for healthcare practitioners.

nnSegNeXt: A 3D Convolutional Network for Brain Tissue Segmentation Based on Quality Evaluation.

Accurate and automated segmentation of brain tissue images can significantly streamline clinical diagnosis and analysis. Manual delineation needs improvement due to its laborious and repetitive nature, while automated techniques encounter challenges stemming from disparities in magnetic resonance imaging (MRI) acquisition equipment and accurate labeling. Existing software packages, such as FSL and FreeSurfer, do not fully replace ground truth segmentation, highlighting the need for an efficient segmentation tool. To better capture the essence of cerebral tissue, we introduce nnSegNeXt, an innovative segmentation architecture built upon the foundations of quality assessment. This pioneering framework effectively addresses the challenges posed by missing and inaccurate annotations. To enhance the model's discriminative capacity, we integrate a 3D convolutional attention mechanism instead of conventional convolutional blocks, enabling simultaneous encoding of contextual information through the incorporation of multiscale convolutional features. Our methodology was evaluated on four multi-site T1-weighted MRI datasets from diverse sources, magnetic field strengths, scanning parameters, temporal instances, and neuropsychiatric conditions. Empirical evaluations on the HCP, SALD, and IXI datasets reveal that nnSegNeXt surpasses the esteemed nnUNet, achieving Dice coefficients of 0.992, 0.987, and 0.989, respectively, and demonstrating superior generalizability across four distinct projects with Dice coefficients ranging from 0.967 to 0.983. Additionally, extensive ablation studies have been implemented to corroborate the effectiveness of the proposed model. These findings represent a notable advancement in brain tissue analysis, suggesting that nnSegNeXt holds the promise to significantly refine clinical workflows.

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms.

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

Rapid and comprehensive quality evaluation of Huang-qin from different origins by FT-IR and NIR spectroscopy combined with chemometrics.

INTRODUCTION: Quality evaluation of Huang-qin is significant to ensure its clinical efficacy. OBJECTIVE: This study aims to establish an accurate, rapid and comprehensive Huang-qin quality evaluation method to overcome the time-consuming and laborious shortcomings of traditional herbal medicine quality assessment methods. METHODS: The contents of baicalin, baicalein and scutellarin in Huang-qin from five different origins were analyzed by FT-IR and NIR spectra combined with multivariate data technology. The quality of Huang-qin from different origins was evaluated by TOPSIS and consistency analysis based on the content of three active ingredients. The correlation between ecological factors and the accumulation of active ingredients was explored. RESULTS: Satisfactory prediction results of PLS models were obtained. Relatively, the model based on FT-IR combined with the PLS regression method has higher R2 and smaller RMSE than the NIR combined with the PLS method. TOPSIS and consistency analysis results showed that the quality of Huang-qin from different geographical origins was significantly different. The results showed that the quality of Huang-qin produced in Shanxi Province was the best among the five origins studied. The results also found that the quality of Huang-qin in different growing areas of the same origin was not completely consistent. The correlation study showed that altitude, sunshine duration and rainfall were the main factors that caused the quality difference of medicinal materials in different geographical origins. CONCLUSION: This study provides a reference for the rapid quantitative analysis of the active components of herbal medicine and the quality evaluation of them.

Quality evaluation of chicken soup based on entropy weight method and grey correlation degree method.

This study aimed to develop an assessment framework for evaluating the quality of different chicken soup variants. Three types of chicken soup, traditional chicken soup (TCS), concentrated chicken soup (CCS), and blended chicken soup (BCS), were prepared and analyzed for various physicochemical parameters, including gross protein content, crude fat content, pH level, solid content, viscosity, and chromatic aberration value. Sensory evaluation was also conducted to assess overall quality. Correlation analysis helped identify three key evaluation indicators: gross protein content, L* value (lightness), and b* value (chromatic aberration). The weight assigned to gross protein content was the highest using the entropy weight method (EWM). Moreover, the grey correlation degree method was comprehensively applied to evaluate the chicken soup's quality. This analysis identified TCS and CCS as varieties with superior overall quality, showing a positive correlation with sensory evaluation, consistent with the results of nuclear magnetic resonance (NMR) used in this paper. These results provide theoretical support for assessing comprehensive quality and selecting chicken soup varieties.

Research on the prognostic value of adjusting intraperitoneal three-dimensional quality evaluation mode in laparoscopic cholecystectomy patients.

BACKGROUND: Benign gallbladder diseases have become a high-prevalence condition not only in China but also worldwide. The main types of benign gallbladder diseases include gallbladder polyps, acute and chronic cholecystitis, and gallstones, with gallstones being the most common, accounting for over 70% of cases. Although the mortality rate of benign gallbladder diseases is low, they carry obvious potential risks. Studies have shown that an increased incidence of benign gallbladder diseases can increase the risk of cardiovascular diseases and gallbladder cancer, resulting in a substantial disease burden on patients and their families. AIM: To assess the medical utility of the Configuration-Procedure-Consequence (CPC) three-dimensional quality evaluation model in modulating the prognosis of laparoscopic cholecystectomy patients. METHODS: A total of 98 patients who underwent laparoscopic cholecystectomy in our hospital from February 2020 to January 2022 were selected as the subjects. According to the random number table method, they were divided into a study group and a control group, with 49 patients in each group. The control group received routine perioperative care, while the study group had the addition of the CPC three-dimensional quality evaluation. The postoperative recovery-related indicators (time to first flatus, time to oral intake, time to ambulation, hospital stay), stress indicators (cortisol and adrenaline levels), distinctions in anxiety and depression status, and the incidence of perioperative complications were compared. RESULTS: The time to first flatus, time to oral intake, time to ambulation, and hospital stay of the study group patients were obviously lower than those of the control group patients, with statistical significance (P < 0.05). On the 1st day after admission, there were no obvious distinctions in cortisol and adrenaline levels in blood samples, as well as in the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) scores between the study group and the control group (P > 0.05). However, on the 3rd day after surgery, the cortisol and adrenaline levels, as well as SAS and SDS scores of the study group patients, were obviously lower than those of the control group patients (P < 0.05). The study group had 2 cases of incisional infection and 1 case of pulmonary infection, with a total incidence of complications of 6.12% (3/49), which was obviously lower than the 20.41% (10/49) in the control group (P < 0.05). CONCLUSION: Implementing the CPC three-dimensional quality evaluation model for patients undergoing laparoscopic cholecystectomy can help accelerate their perioperative recovery process, alleviate perioperative stress symptoms, mitigate anxiety, depression, and other adverse emotions, and to some extent, reduce the incidence of perioperative complications.

Rhizosphere microbial markers (micro-markers): A new physical examination indicator for traditional Chinese medicines.

Rhizosphere microorganisms, as one of the most important components of the soil microbiota and plant holobiont, play a key role in the medicinal plant-soil ecosystem, which are closely related to the growth, adaptability, nutrient absorption, stress tolerance and pathogen resistance of host plants. In recent years, with the wide application of molecular biology and omics technologies, the outcomes of rhizosphere microorganisms on the health, biomass production and secondary metabolite biosynthesis of medicinal plants have received extensive attention. However, whether or to what extent rhizosphere microorganisms can contribute to the construction of the quality evaluation system of Chinese medicinal materials is still elusive. Based on the significant role of rhizosphere microbes in the survival and quality formation of medicinal plants, this paper proposed a new concept of rhizosphere microbial markers (micro-markers), expounded the relevant research methods and ideas of applying the new concept, highlighted the importance of micro-markers in the quality evaluation and control system of traditional Chinese medicines (TCMs), and introduced the potential value in soil environmental assessment, plant pest control and quality assessment of TCMs. It provides reference for developing ecological planting of TCMs and ensuring the production of high quality TCMs by regulating rhizosphere microbial communities.

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning.

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC's full potential.

Evaluating the Quality of Serial EM Sections with Deep Learning.

Automated image acquisition can significantly improve the throughput of serial section scanning electron microscopy (ssSEM). However, image quality can vary from image to image depending on autofocusing and beam stigmation. Automatically evaluating the quality of images is, therefore, important for efficiently generating high-quality serial section scanning electron microscopy (ssSEM) datasets. We tested several convolutional neural networks for their ability to reproduce user-generated evaluations of ssSEM image quality. We found that a modification of ResNet-50 that we term quality evaluation Network (QEN) reliably predicts user-generated quality scores. Running QEN in parallel to ssSEM image acquisition therefore allows users to quickly identify imaging problems and flag images for retaking. We have publicly shared the Python code for evaluating images with QEN, the code for training QEN, and the training dataset.

A systematic review on reporting quality of economic evaluations for negotiated glucose-lowering drugs in China national reimbursement drug list.

BACKGROUND: This study aimed to examine the reporting quality of existing economic evaluations for negotiated glucose-lowering drugs (GLDs) included in China National Reimbursement Drug List (NRDL) using the Consolidated Health Economic Evaluation Reporting Standards 2013 (CHEERS 2013). METHODS: We performed a systematic literature research through 7 databases to identify published economic evaluations for GLDs included in the China NRDL up to March 2021. Reporting quality of identified studies was assessed by two independent reviewers based on the CHEERS checklist. The Kruskal-Wallis test and Mann-Whitney U test were performed to examine the association between reporting quality and characteristics of the identified studies. RESULTS: We have identified 24 studies, which evaluated six GLDs types. The average score rate of the included studies was 77.41% (SD:13.23%, Range 47.62%-91.67%). Among all the required reporting items, characterizing heterogeneity (score rate = 4.17%) was the least satisfied item. Among six parts of CHEERS, results part scored least at 0.55 (score rate = 54.79%) because of the incompleteness of characterizing uncertainty. Results from the Kruskal-Wallis test and Mann-Whitney U test showed that model choice, journal type, type of economic evaluations, and study perspective were associated with the reporting quality of the studies. CONCLUSIONS: There remains room to improve the reporting quality of economic evaluations for GLDs in NRDL. Checklists such as CHEERS should be widely used to improve the reporting quality of economic researches in China.

Unraveling patterns, causes, and nature-based remediation strategy for non-grain production on farmland in hilly regions.

The surge in non-grain production on farmland (NGPF) poses significant threats to food security and land sustainability, particularly in hilly regions. However, there remains a lack of clarity on how to effectively balance grain and non-grain production in relation to land remediation. Using Wannian County as a case study, we investigate the evolution of this by leveraging high-precision land surveys and satellite imagery. Through the application of bootstrapped partial linear regression models, we identify key influencers behind each type of NGPF. In proposing land remediation solutions, we integrate the results of NGPF and land quality evaluations to identify mismatches between non-grain production and land attributes (i.e., topography, geology, soil, and land use). Our findings reveal a substantial growth in NGPF, expanding from 3838.72 ha to 5659.64 ha (2010-2020), and predominantly occurring on farmland with favorable natural conditions and connected locations such as proximity to roads, town centers, and industrial plants. Surprisingly, the basic farmland protection policy shows limited effectiveness in curbing NGPF, except for garden operations. We identify 1674 NGPF patches suitable for conversion to grain production and provide land remediation suggestions tailored to low-quality farmland with specific natural barriers, thus complementing the demand for regional non-grain production. This study thereby innovatively proposes nature-based land remediation strategies to address the non-grain production dilemma by tailoring NGPF and land quality, offering valuable insights for sustainable farmland management in China and beyond.

Effect of continuous microwave processing on enzymes and quality attributes of bael beverage.

Bael (Aegle marmelos) beverage was pasteurized using continuous-microwave (MW) and traditional thermal processing and the activity of native enzymes, pulp-hydrolyzing enzymes, bioactive, physicochemical, and sensory properties were analyzed. First-order and linear biphasic models fitted well (R2 ≥ 0.90) for enzyme inactivation and bioactive alteration kinetics, respectively. For the most resistant enzyme, polyphenoloxidase (PPO), the inactivation target of ≥ 90 % was achieved at 90 °C TMW (final temperature under MW) and 95 °C for 5 min (conventional thermal). MW treatment displayed faster enzyme inactivation and better retention of TPC and AOC. MW treatment at 90 °C TMW showed 5.3 min D-value, 90% total carotenoid content, 3.42 crisp sensory score (out of 5), and no or minor change in physicochemical attributes. Thermal and MW treatment caused the loss of 14 and 10 bioactive compounds, respectively. The secondary and tertiary structural modifications of PPO enzyme-protein revealed MW's lethality primarily due to its thermal effects.

Quality Evaluation of Polygonatum cyrtonema Hua Based on UPLC-Q-Exactive Orbitrap MS and Electronic Sensory Techniques with Different Numbers of Steaming Cycles.

In this study, electronic sensory techniques were employed to comprehensively evaluate the organoleptic quality, chemical composition and content change rules for Polygonatum cyrtonema Hua (PCH) during the steaming process. The results were subjected to hierarchical cluster analysis (HCA), principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). These analyses revealed, from a sensory product perspective, overall differences in colour, odour and taste among the samples of PCH with different numbers of steaming cycles. Using the UPLC-Q-Exactive Orbitrap MS technique, 64 chemical components, including polysaccharides, organic acids, saponins and amino acids were detected in PCH before and after steaming. The sensory traits were then correlated with the chemical composition. From the perspectives of sensory traits, chemical composition, and multi-component index content, it was preliminarily deduced that carrying out five cycles of steaming and sun-drying was optimal, providing evidence for the quality evaluation of PCH during the steaming process.

Separation, identification, and fingerprinting of antioxidant components in persimmon (Diospyros kaki) leaves by offline two-dimensional liquid chromatography with electrochemical detection and tandem mass spectrometry.

In this work, the antioxidant components in persimmon (Diospyros kaki) leaves were separated by offline two-dimensional liquid chromatography-electrochemical detection (LC×LC-ECD) and identified by LC-tandem mass spectrometry (LC-MS/MS). A total of 33 antioxidants, mainly proanthocyanidins, and glycosides of kaempferol and quercetin, were identified. The antioxidant assays demonstrated that the fractions collected from the first-dimension LC (1D-LC) possessed considerable radical scavenging capabilities, with correlation coefficients of peak area versus radical scavenging capability of 1,1-diphenyl-2-picrylhydrazyl and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) being 0.9335 and 0.9116, respectively. The fingerprinting showed that 37 peaks were present in all samples. The major antioxidant components of persimmon leaves were the glycosides of kaempferol and quercetin. Finally, fourteen antioxidants were quantitatively assessed. Offline LC×LC provided high peak capacity and separation; ECD enabled specific screening and detection of antioxidant components; and MS/MS provided excellent identification capability. In this study, the combination of the three approaches was utilized to screen for antioxidant components in persimmon leaves, with satisfactory findings. In conclusion, this technique is an effective means for rapid analysis of antioxidant components and quality control of medicinal plants, achieving rapid separation of congeners and facilitating more accurate qualitative and quantitative analyses.

Evaluation of Cordyceps sinensis Quality in 15 Production Areas Using Metabolomics and the Membership Function Method.

Cordyceps sinensis is a precious medicinal and edible fungus, which is widely used in body health care and disease prevention. The current research focuses on the comparison of metabolite characteristics between a small number of samples and lacks a comprehensive evaluation of the quality of C. sinensis in a large-scale space. In this study, LC-MS/MS, principal component analysis (PCA), hierarchical cluster analysis (HCA), and the membership function method were used to comprehensively evaluate the characteristics and quality of metabolites in 15 main producing areas of C. sinensis in China. The results showed that a total of 130 categories, 14 supercategories, and 1718 metabolites were identified. Carboxylic acids and derivatives, fatty acyls, organo-oxygen compounds, benzene and substituted derivatives, prenol lipids, and glycerophospholipids were the main components of C. sinensis. The HCA analysis and KEGG pathway enrichment analysis of 559 differentially accumulated metabolites (DAMs) showed that the accumulation models of fatty acids and conjugates and carbohydrates and carbohydrate conjugates in glycerophospholipid metabolism and arginine and proline metabolism may be one of the reasons for the quality differences in C. sinensis in different producing areas. In addition, a total of 18 biomarkers were identified and validated, which had a significant discrimination effect on the samples (p < 0.05). Overall, YS, BR, and ZD, with the highest membership function values, are rich and balanced in nutrients. They are excellent raw materials for the development of functional foods and provide scientific guidance for consumers to nourish health care.

Evaluation of the Quality and Readability of Online Information about Alzheimer's Disease in China.

Background: With the increasing popularity of the internet, a growing number of patients and their companions are actively seeking health-related information online. Objective: The aim of this study was to assess the quality and readability of online information about Alzheimer's disease (AD) in China. Methods: A total of 263 qualified AD-related web pages from different businesses, governments, and hospitals were obtained. The quality of the web pages was assessed using the DISCERN tool, and the readability of the web pages was assessed using a readability measurement website suitable for the Chinese language. The differences in readability and quality between different types of web pages were investigated, and the correlation between quality and readability was analyzed. Results: The mean overall DISCERN score was 40.93±7.5. The government group scored significantly higher than the commercial and hospital groups. The mean readability score was 12.74±1.27, and the commercial group had the lowest readability score. There was a positive correlation between DISCERN scores and readability scores. Conclusions: This study presents an evaluation of the quality and readability of health information pertaining to AD in China. The findings indicate that there is a need to enhance the quality and readability of web pages about AD in China. Recommendations for improvement are proposed in light of these findings.

Effects of Different Varieties on Physicochemical Properties, Browning Characteristics, and Quality Attributes of Mume fructus (Wumei).

The dried Mume fructus (MF) is a special food and herbal medicine with a long history of processing and application. The browning index (BI) of Prunus mume (PM) is pivotal in determining the medicinal value and edible quality of MF. Exploring the BI of PM holds significant importance for both the selection of PM varieties and understanding the formation mechanism of high-quality MF. This study systematically analyzed the physicochemical properties, BI, and quality indicators of four PM varieties (Qingzhu Mei, Yesheng Mei, Nangao Mei, and Zhaoshui Mei) after processing into MF. The results showed significant differences in eight physicochemical indicators among the four PM varieties (p < 0.05). Notably, Qingzhu Mei exhibited the highest titratable acid content, Nangao Mei had the most prominent soluble solid and soluble sugar content, and Zhaoshui Mei showed outstanding performance in reducing sugar, soluble protein, and free amino acids. Regarding drying characteristics, Yesheng Mei and Nangao Mei required a shorter drying time. In terms of BI, Nangao Mei exhibited the greatest degree of browning and its color appearance was darker. When considering quality evaluation, Nangao Mei excelled in rehydration ability and extract content, while Zhaoshui Mei demonstrated outstanding levels of total phenols, total flavonoids, and total antioxidant capacity. Overall, the evaluation suggested that the Nangao Mei variety was more suitable for MF processing. These research results provide a valuable theoretical foundation for understanding the BI of PM varieties and serve as a reference for the selection of PM varieties as raw materials for processing MF.