Isoelectric point determination by icIEF as quality control for structural and functional characterization of HPV16 virus-like particles.

Ag+ modified paper-based SERS combined with SiPLS for quantitative detection of soluble As3+ in aqueous realgar solutions.

NIR and MIR spectroscopy to predict quality parameters in pink pepper (Schinus terebinthifolius Raddi) by chemometrics.

Selective electrochemical detection of cannabidiol (CBD) and tetrahydrocannabinol (THC) at molecular-imprinted mesoporous Pt-Ir surfaces.

Ultrasound-assisted gram-scale synthesis of nano-tubular ag-MOF and its hybridization with yellow/green carbon quantum dots: membrane-based turn-on fluorescence recognition for procaterol.

Danshen-Honghua herb pair: A review on chemical constituents, pharmacology, clinical application, quality control, and t-copula function analysis.

Harnessing the application of artificial intelligence in identification of traditional Chinese medicines.

Ilex cornuta: A review of botany, quality control, phytochemistry, and multimodal pharmacological actions.

Approximately one-third of fresh food is wasted globally throughout the supply chain. Machine learning (ML), a key branch of artificial intelligence, enhances postharvest logistics and preservation of fresh food by enabling intelligent sensing, precise evaluation, and adaptive control. However, its application is challenged by data standardization, sensor limitations, product variability, and limited model generalizability. This review summarizes current advanced ML applications in the food supply chain, emphasizing their transformative potential for quality control. We explore ML's ability to integrate multi-omics data for deeper insights into molecular changes during transportation and storage, enabling the development and evaluation of management strategies. Practical applications in grading, sensor technology, and intelligent preservation materials are also evaluated. ML models, such as support vector machine (SVM) and convolutional neural network (CNN), enhance precise grading and quality prediction by analyzing sensory attributes and chemical composition. By capturing complex molecular interactions, ML enables innovative sensor surface design with enhanced sensitivity and specificity. ML-driven sensors further support real-time environmental monitoring, while intelligent packaging materials powered by ML maintain freshness and reduce spoilage through adaptive responses to internal conditions. To ensure model robustness and generalizability, appropriate validation strategies such as cross-validation and external validation are essential. Despite its substantial potential, the widespread adoption of ML still faces challenges, including limited varietal and regional generalization, decision-making transparency, computational demands, limited data availability, and algorithm selection. Addressing these issues is critical for achieving effective and sustainable ML integration in postharvest quality control systems.

Consciousness: The control of quality

Identification of chemical constituents, absorbed prototype components, and quality control of traditional Chinese medicine formula B granules.

With the advancement of minimally invasive surgery and anesthesiology, ambulatory surgery has become a pivotal model for enhancing healthcare efficiency and optimizing resource allocation. This guideline seeks to establish a systematic, operable, and standardized framework for implementing ambulatory surgery in pediatric otorhinolaryngology and head and neck surgery across healthcare institutions in China. Formulated based on evidence-based medicine principles and integrating the latest literature and multidisciplinary expert consensus, it provides detailed recommendations pertaining to organizational structure, hardware configuration, information system development, multidisciplinary team building, patient and procedure selection criteria, perioperative process management, and quality control. The primary objectives are to ensure surgical safety and quality, and to promote the standardized and sustainable development of ambulatory surgery within this pediatric subspecialty.

Rapid discriminate and determination of chemical constituents of Canarii Fructus (Chinese olive) and Chebulae Fructus Immaturus using digital image-based color histograms and chemometrics.

Analysis of different cultivars and origins of Chinese cigar tobacco leaves based on headspace solid phase microextraction-gas chromatography-high resolution mass spectrometry combined with multivariate statistical methods.

The impact of storage conditions on quality and shelf life of fresh-cut jackfruit: a metabolomics perspective.

The progress of research on the herbology, geographical distribution, phytochemistry, pharmacological effects and gene regulation of snow lotus: A review.

Recent advancements in electrochemical sensing have significantly improved the detection of vanillin, an essential flavor compound in food products. This review discusses notable innovations, including developing 3D hybrid sensors that combine Silver-Palladium (Ag- Pd) bimetallic nanoparticles with graphene oxide. These sensors offer a broad detection range, low detection limits, and high recovery rates. Other advancements feature carbon paste electrodes (CPE) modified with cadmium oxide nanoparticles and single-walled carbon nanotubes, manganese dioxide nanowire hybrid electrodes with reduced graphene oxide, and various nanocomposite sensors such as poly (glutamic acid) with multiwalled carbon nanotubes and molybdenum disulfide-polyaniline-graphitic carbon nitride. These technologies demonstrate exceptional sensitivity, selectivity, and reliability, with detection limits as low as 0.0032 μM and broad dynamic ranges. These technologies' superior sensitivity and reliability should reassure and instill confidence in the potential of electrochemical sensing in vanillin detection. Despite these improvements, several challenges persist, including issues related to long-term stability, reproducibility, specificity in complex real-world samples, and scalability for commercial production. Addressing these challenges is essential for advancing the practical application of electrochemical sensors in vanillin detection. Future research is crucial to address these challenges and further enhance the field of electrochemical sensing. Future research should focus on improving sensor durability, expanding testing across diverse matrices, and exploring cost-effective manufacturing methods to ensure these advanced sensors can be widely implemented in food safety and quality control.

A multidimensional strategy for exploring quality markers of Xue-Fu-Zhu-Yu decoction and its three processed products based on intestinal absorption and pharmacodynamics.

Dendritic cell (DC) maturation is traditionally triggered by pathogen-associated molecular patterns (PAMPs) through Toll-like receptors. However, emerging evidence suggests that the cytokine interleukin-1β (IL-1β) can act independently of PAMPs to drive DC activation and T cell priming. This study aimed to perform a comparative transcriptomic analysis of IL-1β- and R848-induced maturation in human DCs to uncover shared and distinct molecular signatures underlying these activation pathways. RNA-seq data from dataset GSE108526 were analyzed following normalization and quality control. Differential gene expression was assessed using a linear model incorporating both treatment type and timepoint as factors. Functional enrichment analyses were conducted to identify pathways and biological processes enriched under each condition. Both IL-1β and R848 activated core immune response programs, yet exhibited distinct transcriptional emphases. IL-1β preferentially upregulated genes associated with cytokine regulation, surface receptor signaling, and cell communication, whereas R848 induced stronger interferon-stimulated gene networks and antiviral defense pathways. Comparative visualization of key maturation, cytokine, and IL-2 receptor genes revealed overlapping but stimulus-specific expression profiles. The findings highlight that IL-1β can substitute for PAMPs in inducing DC activation, yet orchestrates a unique immune transcriptional landscape. This suggests a specialized immunomodulatory potential for IL-1β distinct from classical TLR-mediated pathways.

Cerebrovascular reactivity (CVR) mapping based on resting-state BOLD fMRI can be widely available for research of vascular health not only in clinical studies but also in open databases. However, as it utilizes spontaneous CO2 fluctuations of blood as endogenous stimuli, resting-state CVR may be prone to low SNR and reproducibility if the CO2 fluctuation of an individual is small. The automatic identification of such poor-quality CVR datasets is crucial for large-scale research. Thus, in this work, we developed an automatic quality control algorithm for resting-state CVR mapping. Utilizing a total of 51 resting-state CVR maps acquired with three scanning protocols in each healthy participant, quality control parameters reflecting common characteristics of poor-quality CVR, including pooled variance of different tissue types, proportion of negative voxels in gray matter, and the sensitivity of the BOLD signal to CVR, were extracted and then combined into one comprehensive quality evaluation index (QEI). We further evaluated its performance by leave-one-out cross-validation and correlation analyses with test-retest reproducibility. Leave-one-out cross-validation showed that QEI was significantly correlated with the reference standard of quality evaluation in all left-out cases (r = 0.766). Correlation analyses with test-retest reproducibility revealed significant positive correlations between the worse QEI and similarity index of CVR maps from two tests (r = 0.809, 0.890, 0.396, and 0.654 for data from four open databases). The proposed QEI performed not only in good agreement with visual inspection but can also adapt in resting-state CVR from multiple age groups and scanning protocols, paving the way for the clinical applications of resting-state CVR mapping technology.