ObjectiveTo conduct a comparative quality analysis of Inulae Flos， fuzz of Inulae Flos and calyx of Inulae Flos， elucidating the scientific connotation of the "removing calyx" process in the traditional processing of Inulae Flos. MethodsInulae Flos decoction pieces were collected， and the fuzz and calyx of Inulae Flos were prepared according to the experiences of old medicine workers. Subsequently， according to the methods under the "Inulae Flos" item in the 2025 edition of the Pharmacopoeia of the People's Republic of China， the appearance characteristics and thin-layer chromatography（TLC） identification of these samples were tested， and the moisture content， total ash content， extract content were also measured. The characteristic fingerprint patterns of Inulae Flos and fuzz of Inulae Flos were established by high-performance liquid chromatography（HPLC）， followed by similarity evaluation， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA）. The contents of cryptochlorogenic acid， caffeic acid， 1，3-O-dicaffeoylqunic acid， 1，5-O-dicaffeoylqunic acid， and 1-O-acetyl britannilactone were determined to compare the quality differences of Inulae Flos， fuzz of Inulae Flos， and calyx of Inulae Flos. ResultsThe moisture content of Inulae Flos， fuzz of Inulae Flos， and calyx of Inulae Flos was all<10%. The determination results of total ash content were as follows：Calyx of Inulae Flos>Inulae Flos>fuzz of Inulae Flos， and the determination results of alcohol-soluble extract content were as follows：Fuzz of Inulae Flos>Inulae Flos>calyx of Inulae Flos. HPLC fingerprint patterns of Inulae Flos and fuzz of Inulae Flos were established， and 22 common peaks were identified. The similarity analysis and PCA showed that the overall quality of Inulae Flos and fuzz of Inulae Flos was similar， while the overall quality of calyx of Inulae Flos differed significantly from that of Inulae Flos and fuzz of Inulae Flos. PLS-DA results showed that Inulae Flos， fuzz of Inulae Flos， and calyx of Inulae Flos clustered into distinct groups， indicating significant differences among them. Cryptochlorogenic acid and caffeic acid had relatively high contents in calyx of Inulae Flos， the contents of 1，3-O-dicaffeoylqunic acid and 1，5-O-dicaffeoylqunic acid in Inulae Flos and fuzz of Inulae Flos were higher than those in calyx of Inulae Flos. The order of 1-O-acetyl britannilactone content was determined as follows：fuzz of Inulae Flos>Inulae Flos>calyx of Inulae Flos. ConclusionThe scientific nature of "Removing Calyx" process in the cleansing of Inulae Flos by old medicine workers is demonstrated by the resulting fuzz of Inulae Flos decoction pieces exhibiting enhanced cleanliness and higher content of the index component 1-O-acetyl britannilactone. This study provides a reference basis for further improving and enhancing the processing method and quality control standards of Inulae Flos.

Objective: To analyze the etiological surveillance results of influenza-like illness (ILI) cases in Jiangsu Province, and investigate the distribution characteristics of different influenza virus types, so as to provide the evidence for improving influenza prevention and control measures. Methods: Influenza laboratory testing data for sentinel surveillance of ILI cases in Jiangsu Province from 2019 to 2024 were collected through the China Influenza Surveillance Information System. The positive detection rate of influenza virus was calculated, and descriptive analysis was performed to characterize the distribution of different influenza virus types. Using the farthest neighbor linkage method, influenza virus positive detection rates clustering was analyzed by year and week. Clusters were defined based on inter-cluster distance, and the intensity of the positive detection rate was visualized through color gradients in the clustering heatmap. Results: From 2019 to 2024, a total of 183 878 ILI specimens were collected in Jiangsu Province. Among them, 20 059 specimens tested positive for influenza virus, corresponding to an overall positive detection rate of 10.91%, and an average annual positive detection rate of 10.89%. The primary circulating influenza virus types were influenza A H3N2 subtype, accounting for 40.92%, followed by influenza B Victoria linage at 34.00%, and influenza A H1N1 subtype at 24.80%. Influenza B Yamagata linage was not detected throughout the five-year period. Influenza A H3N2 subtype predominated during two distinct periods: from January to March 2019, and from June 2022 to December 2023. Influenz B Victoria linage was the dominant type from April 2019 to May 2022 and again from January to April 2024. Influenza A H1N1 subtype emerged as the primary type from May to December 2024. Year-based clustering analysis grouped the annual positive detection rates from 2019 to 2024 into three clusters. The closest cluster distance was observed between 2019 and 2024. The highest annual positive detection rate occurred in 2023. Both influenza A H3N2 and H1N1 subtype each formed a single cluster, with their peak positive detection rates also recorded in 2023. Influenza B Victoria lineage was separated into two clusters, with its highest positive detection rate occurring in 2020. Week-based clustering analysis revealed that influenza virus detection was concentrated in weeks 47 to 52 and weeks 1 to 15. More specifically, the positive detection rates for influenza A H3N2 subtype peaked during weeks 30 to 34 and weeks 42 to 52; for influenza A H1N1 subtype, during weeks 9 to 15 and weeks 51 to 52; and for influenza B Victoria lineage, during weeks 1 to 11 and weeks 50 to 52. Conclusions From 2019 to 2024, the average annual positive detection rate of influenza virus in Jiangsu Province remained relatively low. Influenza activity characterized by the alternating circulation of influenza A H1N1 subtype, influenza A H3N2 subtype, and influenza B Victoria linage. It is necessary to maintain the surveillance sensitivity for the influenza B Yamagata lineage.

ObjectiveTo establish fingerprint profiles and a quantitative determination method for Lycii Cortex， providing a scientific basis for the formulation of quality standards for Lycii Cortex and its roasted products. MethodsHigh performance liquid chromatography（HPLC） was developed for the quantitative method for determining kukoamine B in Lycii Cortex and its roasted products on an Alphasil XD-C₁₈ CH column（4.6 mm×250 mm， 5 μm）. HPLC fingerprint profiles were established for 10 batches of Lycii Cortex and its roasted products， and ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS） was used to identify the common peaks based on reference standards， literature and MS information. Quality evaluation indicators included yield of decoction pieces， appearance properties， content of kukoamine B， and fingerprint profiles. The temperature and time of the roasting process were investigated to select the optimal preparation process， which was then verified. Additionally， chemical pattern recognition was combined to assess the differences in the chemical composition of Lycii Cortex before and after roasting， as well as among samples from different origins. ResultsQuantitative analysis indicated that the contents of kukoamine B in Lycii Cortex and its roasted products were 0.35%-5.51% and 0.24%-4.15%， respectively. The transfer rate of kukoamine B was 58.6%-78.9% after roasting. The fingerprint profile analysis demonstrated that the method established in this study effectively separated kukoamine B from other components in the samples and distinctly differentiated it from its impurity peak， cis-N-caffeoylputrescine. The HPLC fingerprint profiles of Lycii Cortex and its roasted products showed high similarity（all above 0.95）， with 7 common peaks identified and five common components， including kukoamine B， cis-N-caffeoylputrescine， N-coumaroyl tyramine， feruloyltyramine， and glucosyringic acid， confirmed. Process optimization confirmed that baking at 110 ℃ for 20 min was a stable and feasible method for roasting Lycii Cortex. Principal component analysis and cluster analysis showed that there was little difference in the chemical composition between raw and roasted Lycii Cortex， but the quality of Lycii Cortex from different origins differed greatly. ConclusionThis study successfully established the fingerprint profiles and a quantitative method for the effective component kukoamine B in Lycii Cortex and roasted Lycii Cortex. The qualitative and quantitative analyses clarified that the impact of the roasting process on the chemical composition of Lycii Cortex was less significant than the variations due to its geographical origin. The findings of this study offer a reference for the development of quality evaluation methods and the establishment of quality standards for Lycii Cortex and its processed products.

The Maillard reaction is a complex process in which amine compounds such as amino acids， peptides， and proteins undergo condensation， polymerization， and other reactions with carbonyl compounds such as reducing sugars， ketones， and aldehydes at room temperature or under heating conditions， ultimately producing substances such as melanoidins and aromatic compounds. The processing of traditional Chinese medicine（TCM） often involves heating and the addition of auxiliary materials， providing complete conditions for the occurrence of the Maillard reaction. The Maillard reaction is affected by various factors such as temperature， pH， moisture， substrate， reaction time and pressure， the progress of the reaction also affected by different processing technologies of TCM and the addition of different excipients. The Maillard reaction involves multiple substances， most of which have significant physiological activity or toxicity， affecting the efficacy and pharmacological effects of TCM. It can also produce various flavor substances and browning products that change the flavor and color of TCM. The Maillard reaction mechanism， influencing factors， related components， and the impact of Maillard reaction on various aspects of TCM processing are reviewed from multiple perspectives in this article， providing reference for the further improvement of processing mechanism and quality control of TCM.

OBJECTIVE To provide a reference for the quality control of Hypericum perforatum. METHODS High- performance liquid chromatography （HPLC） was used to establish fingerprints for 20 batches of H. perforatum and determine the contents of its main components： chlorogenic acid， rutin， hyperin， isoquercitrin， avicularin， quercitrin and quercetin. Cluster analysis was conducted using SPSS 26.0 software. The chromaticity values （luminance value L*， red-green value a*， and yellow- blue value b*） of H. perforatum powder were measured using electronic eye. A prediction model for the contents of seven components in H. perforatum based on its appearance chromaticity values was established using machine learning algorithms. The predictive performance of the models was evaluated using root-mean-square-error （RMSE）. RESULTS A total of 16 common peaks were calibrated in the fingerprints of 20 batches of H. perforatum， and 9 peaks were identified， which were chlorogenic acid， rutin， hyperin， isoquercitrin， avicularin， quercitrin， quercetin， hypericin and hyperforin； the similarities of the 20 batches of samples and reference fingerprint ranged from 0.889-0.987. The contents of chlorogenic acid， rutin， hyperin， isoquercitrin， avicularin， quercitrin and quercetin were 0.025%-0.166%， 0.048%-0.339%， 0.082%-0.419%， 0.017%-0.209%， 0.011%-0.134%， 0.020%-0.135%， 0.041%-0.235%， respectively. Cluster analysis results showed that 18 batches of qualified H. perforatum were grouped into three categories， when the Euclidean distance was set to 1.4. L* of the 20 batches of H. perforatum ranged from 62.814 to 75.668， a* ranged from 1.409 to 3.490， and b* ranged from 25.249 to 30.759. RMSE of three prediction models， namely XGBoost， LightGBM， and AdaBoost， ranged from 0.008 to 0.070， indicating good fitting performance. XGBoost model predicted the contents of the other six components with high accuracy， except for rutin. CONCLUSIONS The established fingerprints and content determination methods are accurate， reproducible， and reliable. The content prediction model based on appearance chromaticity values， combined with machine learning algorithms， can be used for the quality control of H. perforatum.

Taste is a sensation produced by the reaction of substances in the mouth with taste receptor cells， and a normal taste function is essential for our daily life and health. As receivers of taste molecules， taste receptors include sour， bitter， sweet， salty， and umami receptors， which are mainly distributed in the oral cavity， gastrointestinal tract， respiratory tract epithelium and other organs and play a physiological role. Traditional Chinese medicine （TCM） has five flavors （sour， bitter， sweet， pungent， and salty）， which are closely related to the efficacy. Except the pungent flavor and umami taste receptors， the other five taste receptors correspond to the five flavors in the TCM theory， while the correlations between them have not been studied， such as those between bitter receptors and bitter TCM and between sweet receptors and sweet TCM. This article reviews the research reports on taste receptors in recent years. By analyzing the relationships of taste receptors with five flavors of TCM， signaling mechanisms， and diseases based on "receptor-TCM" correlations， this article puts forward the possibility of combining the TCM theory of five flavors with modern biomedical research， providing a reference for the research on "flavors" in TCM and the correlations between TCM and taste receptors.

Taste is a sensation produced by the reaction of substances in the mouth with taste receptor cells， and a normal taste function is essential for our daily life and health. As receivers of taste molecules， taste receptors include sour， bitter， sweet， salty， and umami receptors， which are mainly distributed in the oral cavity， gastrointestinal tract， respiratory tract epithelium and other organs and play a physiological role. Traditional Chinese medicine （TCM） has five flavors （sour， bitter， sweet， pungent， and salty）， which are closely related to the efficacy. Except the pungent flavor and umami taste receptors， the other five taste receptors correspond to the five flavors in the TCM theory， while the correlations between them have not been studied， such as those between bitter receptors and bitter TCM and between sweet receptors and sweet TCM. This article reviews the research reports on taste receptors in recent years. By analyzing the relationships of taste receptors with five flavors of TCM， signaling mechanisms， and diseases based on "receptor-TCM" correlations， this article puts forward the possibility of combining the TCM theory of five flavors with modern biomedical research， providing a reference for the research on "flavors" in TCM and the correlations between TCM and taste receptors.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Ecological cultivation is an important way for the sustainable production of traditional Chinese medicine in the context of the carbon peaking and carbon neutrality goals. Facility cultivation and simulative habitat cultivation modes have been developed and applied to develop the endangered Dendrobium huoshanense on the basis of protection. However, the differences in the greenhouse gas emissions and global warming potential of these cultivation modes remain unexplored, which limits the accurate assessment of carbon-friendly ecological cultivation modes of D. huoshanense. Greenhouse gas emission flux monitoring based on the static chamber method provides an effective way to solve this problem. Therefore, this study conducted a field experiment in the facility cultivation and simulative habitat cultivation modes at a D. huoshanense cultivation base in Dabie Mountains, Anhui Province. From April 2023 to March 2024, samples of greenhouse gases were collected every month, and the concentrations of CO_2, CH_4, and N_2O of the samples were then detected by gas chromatography. The greenhouse gas emission fluxes, cumulative emissions, and global warming potential were further calculated, and the following results were obtained.(1)The two cultivation modes of D. huoshanense showed significant differences in greenhouse gas emission fluxes, especially the CO_2 emission flux, with a pattern of facility cultivation>simulative habitat cultivation [(35.60±11.70)mg·m~(-2)·h~(-1) vs(2.10±4.59)mg·m~(-2)·h~(-1)].(2) The annual cumulative CO_2 emission flux in the case of facility cultivation was significantly higher than that of simulative habitat cultivation[(3 077.00±842.00)kg·hm~(-2) vs(221.00±332.00)kg·hm~(-2)], while no significant difference was found in annual cumulative CH_4 and N_2O emission fluxes.(3) The facility cultivation mode had a significantly higher global warming potential than the simulative habitat cultivation mode [(3 053.00±847.00)kg·hm~(-2) vs(196.00±362.00)kg·hm~(-2)]. Overall, the simulative habitat cultivation of D. huoshanense has obvious carbon-friendly characteristics compared with facility cultivation, which is in line with the concept of ecological cultivation of medicinal plants. This study is of great reference significance for the implementation and promotion of the ecological cultivation mode of D. huoshanense under carbon peaking and carbon neutrality goals.
Dendrobium/chemistry* ; Greenhouse Gases/metabolism* ; Carbon/analysis* ; Ecosystem ; Carbon Dioxide/metabolism* ; China ; Global Warming