The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Traditional Chinese medicine (TCM) serves as a treasure trove of ancient knowledge, holding a crucial position in the medical field. However, the exploration of TCM's extensive information has been hindered by challenges related to data standardization, completeness, and accuracy, primarily due to the decentralized distribution of TCM resources. To address these issues, we developed a platform for TCM knowledge discovery (TCMKD, https://cbcb.cdutcm.edu.cn/TCMKD/). Seven types of data, including syndromes, formulas, Chinese patent drugs (CPDs), Chinese medicinal materials (CMMs), ingredients, targets, and diseases, were manually proofread and consolidated within TCMKD. To strengthen the integration of TCM with modern medicine, TCMKD employs analytical methods such as TCM data mining, enrichment analysis, and network localization and separation. These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights. In addition to its analytical capabilities, a quick question and answer (Q&A) system is also embedded within TCMKD to query the database efficiently, thereby improving the interactivity of the platform. The platform also provides a TCM text annotation tool, offering a simple and efficient method for TCM text mining. Overall, TCMKD not only has the potential to become a pivotal repository for TCM, delving into the pharmacological foundations of TCM treatments, but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems, extending beyond just TCM.

AIM: To quantify early changes of macular capillary parameters in type 2 diabetic patients using optical coherence tomography angiography(OCTA).METHODS: Retrospective case study. A total of 49 healthy subjects, 52 diabetic patients without retinopathy(noDR)patients, and 43 mild nonproliferative diabetic retinopathy(mNPDR)patients were recruited. Capillary perfusion density, vessel length density(VLD), and average vessel diameter(AVD)were calculated from macular OCTA images(3 mm×3 mm)of the superficial capillary plexus after segmenting large vessels and the deep capillary plexus. Parameters were compared among control subjects, noDR, and mNPDR patients. The area under the receiver operating characteristic curve estimated the abilities of these parameters to detect early changes of retinal microvascular networks.RESULTS: Significant differences were found in the VLD and AVD among the three groups(P<0.001). Compared with the control group, the noDR group had significantly higher AVD(P<0.05). VLD of both layers in patients of mNPDR group was significant decreased compared with that of noDR group(all P<0.01). Deep AVD had a higher area under the curve(AUC)of 0.796 than other parameters to discriminate the noDR group from the healthy group. Deep AVD had the highest AUC of 0.920, followed by that of the deep VLD(AUC=0.899)to discriminate the mNPDR group from the healthy group.CONCLUSIONS: NoDR patients had wider AVD than healthy individuals and longer VLD than mNPDR patients in both layers. When compared with healthy individuals, deep AVD had a stronger ability than other parameters to detect early retinal capillary impairments in noDR patients.

The biological clock(also known as the circadian rhythm)is the fundamental reliance for all organisms on Earth to adapt and survive in the Earth's rotation environment.Circadian rhythm is the most basic regulatory mechanism of life activities,and plays a key role in maintaining normal physiological and biochemical homeostasis,disease occurrence and treatment.Recent studies have shown that the biologi-cal clock plays an important role in the development of oral tissues and in the occurrence and treatment of oral diseases.Since there is cur-rently no guiding literature on the research methods of biological clock in stomatology,researchers mainly conduct research based on pub-lished references,which has led to controversy about the research methods of biological clock in stomatology,and there are many confusions about how to rationally apply the research methods of circadia rhythms.In view of this,this expert consensus summarizes the characteristics of the biological clock and analyzes the shortcomings of the current biological clock research in stomatology,and organizes relevant experts to summarize and recommend 10 principles as a reference for the rational implementation of the biological clock in stomatology research.

Objective:To analyze the change in human resources within China′s Centers for Disease Control and Prevention (CDC) from 2010 to 2020.Methods:The self-reported information from provincial, prefectural (city), and county (district) levels of China′s CDC, covering employee counts, staff composition, professional qualifications, educational backgrounds, technical titles, and tenure, were extracted from the China Disease Prevention and Control Information System. The demographic context was provided by the annual population figures from the China Statistical Yearbook (2010-2020). The profile of CDC personnel was described, and the average annual percentage rate change (AAPC), average annual percentage rate change (APC), human resource agglomeration degree (HRAD) and the difference between HRAD and population agglomeration degree (PAD) were calculated. The Joinpoint regression model was used to analyze the time trend.Results:The decade under review witnessed a net increase of 17 300 active and 18 300 enrolled personnel in the CDC, surpassing the national population growth rate with AAPCs of 0.93% and 1.03%, respectively. This upward trajectory was statistically significant ( P<0.05). The ratio of disease control personnel per 10 000 population escalated from 1.14 to 1.21. An initial decline in active CDC workforce density (from 1.31 to 1.27 per 10 000 population between 2010 and 2017) was followed by an increase (from 1.28 to 1.37 between 2018 and 2020), with APCs of -0.40% and 3.73%, respectively. The proportion of professional and technical staff in 2019 was highest in the eastern region (86.01%), followed by the western (83.75%) and central regions (79.54%). The period also saw an enhancement in the average academic degree (from 1.91 to 2.43 points) and professional title scores (from 1.39 to 1.53 points) of CDC personnel. While the average tenure in the eastern and western regions showed a slight decline, the central region experienced an increase, with HRAD values indicating a higher concentration in the eastern and central regions compared to the western region. The HRAD-PAD discrepancy revealed a negative value in the eastern region, nearing zero in the central and western regions. Conclusion:Between 2010 and 2020, China′s CDC experienced notable growth in human resources and underwent structural optimization, albeit with significant regional disparities in concentration.

Objective:To analyze the change in human resources within China′s Centers for Disease Control and Prevention (CDC) from 2010 to 2020.Methods:The self-reported information from provincial, prefectural (city), and county (district) levels of China′s CDC, covering employee counts, staff composition, professional qualifications, educational backgrounds, technical titles, and tenure, were extracted from the China Disease Prevention and Control Information System. The demographic context was provided by the annual population figures from the China Statistical Yearbook (2010-2020). The profile of CDC personnel was described, and the average annual percentage rate change (AAPC), average annual percentage rate change (APC), human resource agglomeration degree (HRAD) and the difference between HRAD and population agglomeration degree (PAD) were calculated. The Joinpoint regression model was used to analyze the time trend.Results:The decade under review witnessed a net increase of 17 300 active and 18 300 enrolled personnel in the CDC, surpassing the national population growth rate with AAPCs of 0.93% and 1.03%, respectively. This upward trajectory was statistically significant ( P<0.05). The ratio of disease control personnel per 10 000 population escalated from 1.14 to 1.21. An initial decline in active CDC workforce density (from 1.31 to 1.27 per 10 000 population between 2010 and 2017) was followed by an increase (from 1.28 to 1.37 between 2018 and 2020), with APCs of -0.40% and 3.73%, respectively. The proportion of professional and technical staff in 2019 was highest in the eastern region (86.01%), followed by the western (83.75%) and central regions (79.54%). The period also saw an enhancement in the average academic degree (from 1.91 to 2.43 points) and professional title scores (from 1.39 to 1.53 points) of CDC personnel. While the average tenure in the eastern and western regions showed a slight decline, the central region experienced an increase, with HRAD values indicating a higher concentration in the eastern and central regions compared to the western region. The HRAD-PAD discrepancy revealed a negative value in the eastern region, nearing zero in the central and western regions. Conclusion:Between 2010 and 2020, China′s CDC experienced notable growth in human resources and underwent structural optimization, albeit with significant regional disparities in concentration.

Complex systems exist widely,including medicines from natural products,functional foods,and bio-logical samples.The biological activity of complex systems is often the result of the synergistic effect of multiple components.In the quality evaluation of complex samples,multicomponent quantitative analysis(MCQA)is usually needed.To overcome the difficulty in obtaining standard products,scholars have proposed achieving MCQA through the"single standard to determine multiple components(SSDMC)"approach.This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia.Depending on a convenient(ultra)high-performance liquid chromatography method,how can the repeatability and robustness of the MCQA method be improved?How can the chromatography conditions be optimized to improve the number of quantitative components?How can computer software technology be introduced to improve the efficiency of multicomponent analysis(MCA)?These are the key problems that remain to be solved in practical MCQA.First,this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years,as well as the method robustness and accuracy evaluation.Second,it also summarizes methods to improve peak capacity and quantitative accuracy in MCA,including column selection and two-dimensional chromatographic analysis technology.Finally,computer software technologies for predict-ing chromatographic conditions and analytical parameters are introduced,which provides an idea for intelligent method development in MCA.This paper aims to provide methodological ideas for the improvement of complex system analysis,especially MCQA.

Coronary heart disease （CHD） with atherosclerosis is a common chronic disease worldwide， and anxiety and depression are potential and crucial risk factors for adverse prognosis in CHD. Chaihu Longgu Mulitang （CLMT）， first mentioned in the Shang Han Lun （《伤寒论》）， is a classic prescription for treating Shaoyang diseases combined with disturbance of the mind and spirit， with the effects of harmonizing Shaoyang and calming the mind. Current research on mechanisms has shown that CLMT can play a role in CHD complicated with anxiety and depression through multiple pathways， including regulating related signaling pathways， inhibiting the expression of inflammatory factors， improving oxidative stress damage， modulating neurotransmitter levels， suppressing the hypothalamic-pituitary-adrenal axis， promoting mobilization of mesenchymal stem cells from the bone marrow， and inhibiting platelet activation. Clinical studies have demonstrated that CLMT significantly improves symptoms such as angina and insomnia caused by CHD complicated with anxiety and depression， effectively reduces negative emotions， improves traditional Chinese medicine （TCM） syndrome scores， and decreases levels of inflammatory factors. Furthermore， it has fewer adverse reactions and higher safety than conventional western medicine treatments. This article provides a review of the mechanisms and clinical studies of CLMT in the treatment of CHD complicated with anxiety and depression based on a comprehensive analysis of literature from the China National Knowledge Infrastructure （CNKI）， Wanfang Data， VIP， PubMed， and other databases in the past 15 years， in order to provide references for further research on the use of CLMT in the management of CHD complicated with anxiety and depression.

ObjectiveTo analyze the polarized light microscopic characteristics， the composition of physical phases and their relative contents of Maifanitum from different origins， and to establish the Fourier characteristic fingerprint of Maifanitum powder crystals by X-ray diffraction（XRD）. MethodA total of 26 batches of Maifanitum samples were selected， and the microscopic characteristics of the sample powders and grinding flakes were observed by polarized light microscopy under single polarized light and orthogonal polarized light， and the main phase compositions and their relative contents were analyzed by powder crystal XRD technique， and the XRD Fourier characteristic fingerprint of Maifanitum was established. The incident light source of XRD was Cu target Kβ radiation， the light tube voltage and light tube current were 40 kV and 40 mA， respectively， the divergence slit was 1°， the scattering slit was 1°， the receiving slit was 0.2 mm， the scanning speed was 5°·min^-1 with continuous scanning and scanning range of 5-90°（2θ）， and the step length was 0.02°. ResultThe polarized light micrographs of powders and grinding flakes of Maifanitum were obtained， and the main phases were plagioclase， potassium feldspar and quartz， and a few samples also contained illite， pyrite， iron dolomite， calcite， iron amphibole and chlorite， etc. The relative total content of feldspar phases was 61.9%-82.4%， and the relative content of quartz was 12.6%-33.6%. The XRD Fourier fingerprint analysis method of Maifanitum with 13 common peaks as the characteristic fingerprint information was established， and the similarity calculated by the mean correlation coefficient method was 0.920 9-0.997 7， the similarity calculated by the mean angle cosine method was 0.940 5-0.998 4， the similarity calculated by the median correlation coefficient method was 0.921 1-0.997 5， and the similarity calculated by the median angle cosine method was 0.947 5-0.998 2. ConclusionThe polarized light microscopic identification characteristics of Maifanitum are mainly plagioclase， quartz and potassium feldspar， and the technique of powder crystal XRD Fourier fingerprint analysis can be used for the identification of Maifanitum.

Pharmaceutical analysis is a discipline based on chemical, physical, biological, and information technologies. At present, biotechnological analysis is a short branch in pharmaceutical analysis; however, bioanalysis is the basis and an important part of medicine. Biotechnological approaches can provide information on biological activity and even clinical efficacy and safety, which are important characteristics of drug quality. Because of their advantages in reflecting the overall biological effects or functions of drugs and providing visual and intuitive results, some biotechnological analysis methods have been gradually applied to pharmaceutical analysis from raw material to manufacturing and final product analysis, including DNA super-barcoding, DNA-based rapid detection, multiplex ligation-dependent probe amplification, hyperspectral imaging combined with artificial intelligence, 3D biologically printed organoids, omics-based artificial intelligence, microfluidic chips, organ-on-a-chip, signal transduction pathway-related reporter gene assays, and the zebrafish thrombosis model. The applications of these emerging biotechniques in pharmaceutical analysis have been discussed in this review.