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.

OBJECTIVE To investigate the variations in taste， aroma and volatile organic compounds of Rhei Radix et Rhizoma decoction pieces derived from different sources， and to identify their origins. METHODS The flavor， odor and volatile organic compounds of Rhei Radix et Rhizoma decoction pieces from different sources were compared and analyzed by using electronic tongue， electronic nose， and gas chromatography-ion mobility spectrometry （GC-IMS）. Principal component analysis （PCA）， partial least squares-discriminant analysis （PLS-DA）， orthogonal partial least squares discriminant analysis （OPLS-DA） and Fisher discriminant analysis were employed to identify the origins of Rhei Radix et Rhizoma decoction pieces and establish the basis discrimination criteria. RESULTS The differences in taste of Rhei Radix et Rhizoma decoction pieces from 3 origins were primarily characterized by bitterness， astringency， and bitter-astringent aftertaste. In terms of smell， variations were mainly observed in inorganic sulfides， organic sulfides containing aromatic components， methane and other short-chain alkanes， alcohols， ethers， aldehydes and ketones， as well as nitrogen oxides. Differentially volatile organic compounds mainly consisted of alcohols， aldehydes and ketones. Furthermore， the samples from 8 batches could be effectively classified into 3 categories.Three types of Rhei Radix et Rhizoma decoction pieces can be effectivily identified based on the peak intensity ratio between volatile substances. For example， when the peak intensity of 2-acetylfuran was 3-19 times that of isobutyric acid [dimer]， it was considered as Rheum officinale Baill. CONCLUSIONS The discriminant models established in this study， along with the criteria for determining the origins based on the peak intensity ofcharacteristic volatile compounds， can be utilized for the identification of Rhei Radix et Rhizoma decoction pieces.

Objective:To detect itching,anxiety,depression behaviors in chronic itch models of mice and observe the activation of γ-aminobutiric acid(GABA)neurons in the ventral sector of the zona incerta(ZIv),and provide mor-phological evidence for their involvement in the modulation of itch information.Methods:Diphenylcyclopropenone(DCP)was used in glutamic acid decarboxylase 67-green fluorescent protein(GAD67-GFP)knock-in mice to establish chronic itch model.Itch behaviors were detected by video tracking system to verify whether the models were successfully established.The anxiety,depression behaviors of chronic itch model mice were detected by using elevated plus maze test(EPM)and tail suspention test(TST).By using GAD67-GFP mice,the distribution of GABAergic neurons in va-rious sectors of the zona incerta(ZI)was observed.And combined with immunofluorescence staining method,double labeling of GABAergic neurons with FOS in ZIv were observed respectively in control and DCP group mice.Results:In brain slices of GAD67-GFP mice,GABAergic neurons can be observed within all sectors of ZI and are more concentrat-ed in ZIv.Compared with control group mice,DCP group mice showed a significant increase in the bouts of scratching(P＜0.001).The time of immobility in TST was significantly higher in DCP group mice than in control group mice,which displayed depression-like behavior.The EPM test showed that the numbers of entries and proportion of time in the cross region in DCP group mice were less than in control group mice.EPM test revealed that DCP group mice exhibited anxiety-like behavior.The results of immunofluorescence staining showed that the number of FOS-positive cells in ZIv was significantly higher in DCP group mice than in control group mice,and abundant co-labeled neurons of FOS and GABAergic neurons were observed in ZIv.Conclusion:GABAergic neurons were predominantly distributed in ZI,and were more concentrated in ZIv.The activation of GABAergic neurons in ZIv of DCP group mice provides morphological evidence on the involvement of GABAergic neurons in chronic itch and associated negative emotions.

Objective To explore the impact of contrast agent concentration on the excimer laser's effect on plaque ablation.Methods Using a laser catheter with a diameter of 0.9 mm,we conducted plaque model ablation experiments employing a 308-nanometer xenon chloride excimer laser.During the excimer laser ablation process,five groups were formed based on the injected contrast agent concentrations:a saline group,25%concentration group,50%concentration group,75%concentration group,and 100%concentration group.Optical coherence tomography was utilized to assess the changes in plaque lumen area after excimer laser ablation,evaluating the impact of contrast agent concentration on the excimer laser's ablation efficacy.Simultaneously,a water manometer was used to measure the shockwave pressure generated by the excimer laser in liquids with different contrast agent concentrations,aiming to explore the correlation between the shockwave pressure of the excimer laser and its ablative effect.Results The ablation areas in the 75%concentration group and the 100%concentration group were similar(P＞0.05),both exceeding those in the 50%concentration contrast agent group,25%concentration group,and saline group(all P＜0.001).Specifically,the ablation area in the 50%concentration group was significantly larger than that in the 25%concentration group and saline group(both P＜0.001),while the 25%concentration group was larger than the saline group(P＜0.001).The influence of contrast agent concentration on the shockwave pressure of the excimer laser exhibited a similar trend.Additionally,there was a significant positive correlation between the shockwave pressure generated by the excimer laser and its ablation area(r=0.9987,P＜0.001).Conclusions The intensity of excimer laser ablation on plaque tissue can be modulated by altering the contrast agent concentration.These findings offer guidance for the application of excimer laser in conjunction with contrast agent injection techniques in the treatment of coronary artery disease.

With the reform of the medical and health system entering a critical period,public hospitals have also exposed new risks and challenges in economic operation.As an important means of hospital standardized management,internal control can better prevent and resolve the risk of hospital economic operation and ensure the sustainable operation of the hospital.By interpreting the requirements of current national policies on hospital internal control,it analyzes the functional positioning of financial and accounting supervision in hospital internal control,shares the internal control implementation path of sample hospitals from the perspective of financial and accounting supervision,and puts forward suggestions on strengthening internal control construction of public hospitals in the new era,in order to lay a good foundation for the high-quality development of hospitals.