ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.

The etiology of tumors is complex and influenced by multiple factors, including the host and environmental conditions. Allergy is primarily driven by the immune response of helper T cell 2 (Th2). Research has shown that the Th2 immune response is closely related to tumors, which is specifically manifested through Th2 antibodies, allergy-related effector cells and mediators within the tumors, as well as tumor immune-related functions. This internal interaction mechanism will increase the complexity and challenges associated with the clinical diagnosis and treatment of tumors and allergy. The formation of allergic constitution is shaped by both congenital and acquired factors, and its physical state is closely linked to the occurrence and progression of allergic diseases. Therefore, this paper aims to explore the relationship between tumors and allergic reactions from the perspective of traditional Chinese medicine (TCM) constitution theory. Based on the four basic principles of the TCM constitution, including endowment inheritance theory, environment constraint theory, body-spirit composition theory, and life process theory, this exploration will focus on four aspects: genetic factors and internal disease causes, inflammatory environments and functional regulation, psychological disorders and emotional pathogenesis, as well as age structure and disease risk. Furthermore, from the perspective of constitution-disease relation of chronic disease prevention, this paper will discuss the significant importance of adjusting allergic constitution to improve both subjective symptoms and objective indicators of allergic reactions in tumor patients.

Promoting the construction of a “Healthy China” is essential to building a great modern socialist country. Health workers in every era have their historical missions and they are the “benevolent doctors” of their own era. Therefore， clarifying the basic connotation and times requirements of “benevolent doctors” has become the first question to be answered in cultivating “benevolent doctors”. The basic connotation of “benevolent doctor” should reflect not only the comprehensive development of moral， intellectual， physical， aesthetic， and labor education in fostering virtue and nurturing talents， but also embody the people-centered development philosophy， promote social equity and justice， and reflect the strategic needs of building a “Healthy China.” Specifically in the practice of medical education， emphasizing both medical science spirit and medical humanities spirit has become an important path to cultivate “benevolent doctors” in the new era.

OBJECTIVE To establish HPLC fingerprint for Jianpi hewei formula （JPHWF）， conduct chemical pattern recognition analysis， and determine the contents of seven components in the formula， aiming to provide a scientific basis for quality control and further research of JPHWF. METHODS Taking 15 batches of standard decoctions of JPHWF as samples， the HPLC fingerprint was established using the Similarity Evaluation System of TCM Chromatographic Fingerprint （2012 edition）. Subsequently， similarity evaluation， as well as identification and attribution analysis of chromatographic peaks， were conducted. Using the common peak areas from the 15 batches of samples as variables， chemical pattern recognition analyses were performed on the samples through hierarchical cluster analysis， principal component analysis， and orthogonal partial least squares-discriminant analysis. The contents of adenine， 5-hydroxymethylfurfural， tetrahydropalmatine， naringin， dehydrocorydaline， neohesperidin and glycyrrhizic acid in 15 batches of samples were determined by HPLC. RESULTS There were 19 common peaks in the characteristic chromatograms for 15 batches of samples with the similarities of more than 0.95. Results of chemical pattern recognition analysis showed that 15 batches of samples could be clustered into 3 categories， and 3 differential compounds were found [peak 7 （5- hydroxymethylfurfural）， peak 17 （neohesperidin）， and peak 15 （naringin）]. The 7 components were linearly good in the respective concentration ranges （R2≥0.999 4）； RSDs of precision， stability and repeatability tests were less than 2% （n＝6）； the average recovery rate of 98.95%-103.81%， RSD of 0.61%-2.75% （n＝6）； the contents of them were 0.031-0.106， 0.267-0.824， 0.089- 0.144， 1.344-2.091， 0.089-0.178， 1.328-2.028， 0.040-0.150 mg/g， respectively. CONCLUSIONS Established HPLC fingerprinting method coupled with multi-index content determination is validated to be accurate and reliable， and its combination with chemical pattern recognition analysis can be applied to the quality control of JPHWF.

ObjectiveBased on functional near-infrared spectroscopy (fNIRS), we investigated the brain activity characteristics of gross motor tasks in children with autism spectrum disorder (ASD) and motor dysfunctions (MDs) to provide a theoretical basis for further understanding the mechanism of MDs in children with ASD and designing targeted intervention programs from a central perspective. MethodsAccording to the inclusion and exclusion criteria, 48 children with ASD accompanied by MDs were recruited into the ASD group and 40 children with typically developing (TD) into the TD group. The fNIRS device was used to collect the information of blood oxygen changes in the cortical motor-related brain regions during single-handed bag throwing and tiptoe walking, and the differences in brain activation and functional connectivity between the two groups of children were analyzed from the perspective of brain activation and functional connectivity. ResultsCompared to the TD group, in the object manipulative motor task (one-handed bag throwing), the ASD group showed significantly reduced activation in both left sensorimotor cortex (SMC) and right secondary visual cortex (V2) (P<0.05), whereas the right pre-motor and supplementary motor cortex (PMC&SMA) had significantly higher activation (P<0.01) and showed bilateral brain region activity; in terms of brain functional integration, there was a significant decrease in the strength of brain functional connectivity (P<0.05) and was mainly associated with dorsolateral prefrontal cortex (DLPFC) and V2. In the body stability motor task (tiptoe walking), the ASD group had significantly higher activation in motor-related brain regions such as the DLPFC, SMC, and PMC&SMA (P<0.05) and showed bilateral brain region activity; in terms of brain functional integration, the ASD group had lower strength of brain functional connectivity (P<0.05) and was mainly associated with PMC&SMA and V2. ConclusionChildren with ASD exhibit abnormal brain functional activity characteristics specific to different gross motor tasks in object manipulative and body stability, reflecting insufficient or excessive compensatory activation of local brain regions and impaired cross-regions integration, which may be a potential reason for the poorer gross motor performance of children with ASD, and meanwhile provides data support for further unraveling the mechanisms underlying the occurrence of MDs in the context of ASD and designing targeted intervention programs from a central perspective.

OBJECTIVE To provide a detailed report and interpretation of the method and results for determining the weights of the technical indicators from the “multi-dimensional and multi-criteria comprehensive evaluation index system （first edition）” stated in Guideline for Multi-dimensional and Multi-criteria Comprehensive Evaluation of Chinese Patent Medicine. METHODS Normalization calculations were performed on the comprehensive weight values calculated by the analytic hierarchy process and expert weighting method to obtain the objective weights of the indicators. RESULTS The weight results of the six primary dimensions in the current comprehensive evaluation indicator system of Chinese patent medicine showed effectiveness dimension＞ safety dimension＞standard dimension＞application dimension＞scientific dimension＞economic dimension， with weight values of 0.281 0， 0.268 5， 0.195 8， 0.107 3， 0.096 1 and 0.051 3 respectively， consistent with the results of most researches currently. CONCLUSIONS The process of weight determination in this indicator system is scientifically reasonable， with clear methods and clear interpretations， and is worthy of further optimization and widespread application.

Aim To investigate the role of metabolites of eicosapentaenoic acid (EPA) in promoting the transdifferentiation of pancreatic α cells to β cells. Methods Male C57BL/6J mice were injected intraperitoneally with 60 mg/kg streptozocin (STZ) for five consecutive days to establish a type 1 diabetes (T1DM) mouse model. After two weeks, they were randomly divided into model groups and 97% EPA diet intervention group, 75% fish oil (50% EPA +25% DHA) diet intervention group, and random blood glucose was detected every week; after the model expired, the regeneration of pancreatic β cells in mouse pancreas was observed by immunofluorescence staining. The islets of mice (obtained by crossing GCG

In this study, we designed and synthesized 12 novel aloperine derivatives with different core structures. Among them, compound 3 with a ten-membered ring core was obtained through a special ring expansion reaction after γ-H Huffman elimination of quaternary ammonium salt, and the structure was verified by X-single crystal diffraction. Furthermore, their antiviral activity against human β-coronavirus HCoV-OC43 was evaluated by CCK-8 assay. Quaternary ammonium salt 2a and 3 had a good inhibitory effect against HCoV-OC43, and 2a had the highest anti-HCoV-OC43 activity with an EC₅₀ values of 3.77 μmol·L^-1 and a SI value of over 53.1. Schrӧdinger molecular docking results showed that both 2a and 3 might display their anti-HCoV-OC43 activity by directly acting on host TMPRSS2 and SR-B1. The results expanded the structural types of endocyclic aloperine and the function against coronavirus, and provided useful scientific data for the development of pharmaceutical applications of these compounds.

People frequently struggle to juggle their work, family, and social life in today’s fast-paced environment, which can leave them exhausted and worn out. The development of technologies for detecting fatigue while driving is an important field of research since driving when fatigued poses concerns to road safety. In order to throw light on the most recent advancements in this field of research, this paper provides an extensive review of fatigue driving detection approaches based on electroencephalography (EEG) data. The process of fatigue driving detection based on EEG signals encompasses signal acquisition, preprocessing, feature extraction, and classification. Each step plays a crucial role in accurately identifying driver fatigue. In this review, we delve into the signal acquisition techniques, including the use of portable EEG devices worn on the scalp that capture brain signals in real-time. Preprocessing techniques, such as artifact removal, filtering, and segmentation, are explored to ensure that the extracted EEG signals are of high quality and suitable for subsequent analysis. A crucial stage in the fatigue driving detection process is feature extraction, which entails taking pertinent data out of the EEG signals and using it to distinguish between tired and non-fatigued states. We give a thorough rundown of several feature extraction techniques, such as topology features, frequency-domain analysis, and time-domain analysis. Techniques for frequency-domain analysis, such wavelet transform and power spectral density, allow the identification of particular frequency bands linked to weariness. Temporal patterns in the EEG signals are captured by time-domain features such autoregressive modeling and statistical moments. Furthermore, topological characteristics like brain area connection and synchronization provide light on how the brain’s functional network alters with weariness. Furthermore, the review includes an analysis of different classifiers used in fatigue driving detection, such as support vector machine (SVM), artificial neural network (ANN), and Bayesian classifier. We discuss the advantages and limitations of each classifier, along with their applications in EEG-based fatigue driving detection. Evaluation metrics and performance assessment are crucial aspects of any detection system. We discuss the commonly used evaluation criteria, including accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. Comparative analyses of existing models are conducted, highlighting their strengths and weaknesses. Additionally, we emphasize the need for a standardized data marking protocol and an increased number of test subjects to enhance the robustness and generalizability of fatigue driving detection models. The review also discusses the challenges and potential solutions in EEG-based fatigue driving detection. These challenges include variability in EEG signals across individuals, environmental factors, and the influence of different driving scenarios. To address these challenges, we propose solutions such as personalized models, multi-modal data fusion, and real-time implementation strategies. In conclusion, this comprehensive review provides an extensive overview of the current state of fatigue driving detection based on EEG signals. It covers various aspects, including signal acquisition, preprocessing, feature extraction, classification, performance evaluation, and challenges. The review aims to serve as a valuable resource for researchers, engineers, and practitioners in the field of driving safety, facilitating further advancements in fatigue detection technologies and ultimately enhancing road safety.

ObjectiveTo investigate the genetic polymorphism and structure of 47 autosomal microhaplotypes in the Han population in Changshu City, Jiangsu Province, and to evaluate the forensic efficiencies and forensic parameters. MethodsThe DNA library of unrelated individual samples was prepared according to MHSeqTyper47 kit manual and sequenced on the MiSeq FGx platform. Microhaplotype genotyping and sequencing depth statistics were processed using MHTyper. The genetic information of samples was then evaluated. The fixation index and genetic distance between the Jiangsu Changshu population and the reference populations in the 1000 Genomes Project phase 3 (1KG) were calculated, and forensic parameters were evaluated. ResultsThe fixation index and genetic distance between the Han population in Changshu, Jiangsu, and the CHB (Han Chinese in Beijing, China) reference population in 1KG were the lowest. The effective allele number (A_e) of each locus is also the closest between the two populations. The combined matching probability (CMP) of the Changshu Han population is close to the 5 populations of the East Asian reference super-population in 1KG, which is 1.25×10^-36, and the combined probability of exclusion reached 0.999 999 999 964 1. ConclusionThis study reported the genetic polymorphism and allele frequency of 47 microhaplotypes in a Han population in Changshu City, Jiangsu Province. This information provides a data basis for 47 microhaplotypes in forensic applications. In addition, the polymorphism differences between the 1KG reference population and the Han population in Changshu, Jiangsu were compared, and the genetic structure of 47 microhaplotypes in the Han population in Changshu, Jiangsu was revealed. In general, the reference data of the East Asian super-population in 1KG is more in line with the genetic characteristics of Han population in Changshu, Jiangsu.