Background: Chinese herbal pieces are an essential component of traditional Chinese medicine. Accurate identification and classification of these materials are crucial in clinical practice. Objective: This study aims to enhance the recognition efficiency of Chinese herbal pieces using deep learning technology, while addressing the limitations of traditional manual classification methods in terms of both quality and efficiency. Methods: A comprehensive dataset containing 201 types of Chinese herbal pieces was established. Based on Real-time Detection Transformer (RT-DETR), we designed and integrated a Feature-focused Diffusion Network (FDN), resulting in an improved model termed RT-DETR-FDN. The proposed FDN includes a Feature-focus Module and a feature diffusion mechanism, enabling the model to capture more extensive feature information from Chinese herbal pieces and diffuse it across multiple detection scales. Results: Experimental results show that RT-DETR-FDN achieved a precision of 0.925, a recall of 0.943, and an mAP50-95 of 0.851. In addition, the model was compared with representative You Only Look Once series models commonly used in object detection. Compared with these models, RT-DETR-FDN achieved higher recognition accuracy while maintaining a lightweight architecture. Conclusion: This study integrates deep learning with traditional Chinese medicine, providing a more effective solution for the recognition of Chinese herbal pieces.

GPR126, also known as ADGRG6, is one of the most deeply studied aGPCRs. Initially, GPR126 was thought to be a receptor associated with muscle development and was primarily expressed in the muscular and skeletal systems. With the deepening of research, it was found that GPR126 is expressed in multiple mammalian tissues and organs, and is involved in many biological processes such as embryonic development, nervous system development, and extracellular matrix interactions. Compared with other aGPCRs proteins, GPR126 has a longer N-terminal domain, which can bind to ligands one-to-one and one-to-many. Its N-terminus contains five domains, a CUB (complement C1r/C1s, Uegf, Bmp1) domain, a PTX (Pentraxin) domain, a SEA (Sperm protein, Enterokinase, and Agrin) domain, a hormone binding (HormR) domain, and a conserved GAIN domain. The GAIN domain has a self-shearing function, which is essential for the maturation, stability, transport and function of aGPCRs. Different SEA domains constitute different GPR126 isomers, which can regulate the activation and closure of downstream signaling pathways through conformational changes. GPR126 has a typical aGPCRs seven-transmembrane helical structure, which can be coupled to Gs and Gi, causing cAMP to up- or down-regulation, mediating transmembrane signaling and participating in the regulation of cell proliferation, differentiation and migration. GPR126 is activated in a tethered-stalk peptide agonism or orthosteric agonism, which is mainly manifested by self-proteolysis or conformational changes in the GAIN domain, which mediates the rapid activation or closure of downstream pathways by tethered agonists. In addition to the tethered short stem peptide activation mode, GPR126 also has another allosteric agonism or tunable agonism mode, which is specifically expressed as the GAIN domain does not have self-shearing function in the physiological state, NTF and CTF always maintain the binding state, and the NTF binds to the ligand to cause conformational changes of the receptor, which somehow transmits signals to the GAIN domain in a spatial structure. The GAIN domain can cause the 7TM domain to produce an activated or inhibited signal for signal transduction, For example, type IV collagen interacts with the CUB and PTX domains of GPR126 to activate GPR126 downstream signal transduction. GPR126 has homology of 51.6%-86.9% among different species, with 10 conserved regions between different species, which can be traced back to the oldest metazoans as well as unicellular animals.In terms of diseases, GPR126 dysfunction involves the pathological process of bone, myelin, embryo and other related diseases, and is also closely related to the occurrence and development of malignant tumors such as breast cancer and colon cancer. However, the biological function of GPR126 in various diseases and its potential as a therapeutic target still needs further research. This paper focuses on the structure, interspecies differences and conservatism, signal transduction and biological functions of GPR126, which provides ideas and references for future research on GPR126.

In view of the few studies on the influence of Armillaria spp. infection on the content of the chemical components in different parts of Polyporus umbellatus sclerotia, this study determined the biomass of P. umbellatus sclerotia and the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in the separated cavity wall of the sclerotia and the uninfected part of the sclerotia in different harvesting years under the conditions of A. gallica and A. mellea infection respectively. According to the difference of content and dynamic changes of the polysaccharide and the steroid substances, the superior Armillaria sp. was screened to obtain the best harvest years of P. umbellatus. Using HPLC and UV-VIS spectrophotometry methods, the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in P. umbellatus sclerotia infected by the two Armillaria spp. in different years were determined. In addition, the differentially expressed genes related to P. umbellatus polysaccharide synthesis were screened according to the transcriptomic data of different parts of P. umbellatus after A. mellea infection. With the increase of years, the biomass of sclerotia infected by different Armillaria spp. had significant differences, and there were significant differences in the four components of sclerotia. The four components of the separated cavity wall of the sclerotia were significantly higher than those of the uninfected part. The best harvest time was the third year after cultivation. Transcriptomic analysis showed that the infection of Armillaria spp. could significantly promote polysaccharide synthesis, which provided a basis for polysaccharide content determination at the molecular level. The study clarified the influence of different Armillaria spp. infection on the accumulation of chemical components of P. umbellatus sclerotia, laying a foundation for exploring the symbiosis mechanism and provided a scientific clue for screening superior Armillaria sp. and guiding the artificial cultivation of P. umbellatus sclerotia.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

BACKGROUND:Large deep burn wounds are often accompanied by scar hyperplasia after healing,requiring transplantation of medium-thickness skin for repair,and the medium-thickness skin slices taken generally reach below the papillary layer of the dermis.If not handled correctly,complications such as delayed healing,ulceration,and post-healing pain and itching in the donor area can easily occur.Therefore,the repair of wounds in the donor area should be emphasized.OBJECTIVE:To observe the safety and practicability of autologous scalp repair of skin donor area in patients with deep burns and scarring.METHODS:Sixty patients with deep burn and scar hyperplasia admitted to the Burn Department of Zhengzhou First People's Hospital from January 2021 to September 2023 were selected as the study subjects.They all needed medium-thickness skin transplantation and repair,and were divided into study group(n=30)and control group(n=30)according to random systematic sampling method.The skin was taken from the patient's own medium-thickness skin on the leg or back.In the study group,the skin donor area was repaired with self-blade thick scalp replantation,and in the control group,the skin donor area was repaired with absorbent dressing.The wound healing rate and the time to complete epithelialization of the wound were observed and compared in the two groups 6 days after surgery.The pain of dressing change at 3 and 6 days after surgery and scar hyperplasia in the skin donor area at 6 months after surgery were compared between two groups.RESULTS AND CONCLUSION:Compared with the control group,the time to complete epithelialization of the wound was significantly lower in the study group(P<0.05),and the wound healing rate was significantly higher in the study group(P<0.05),the pain score for dressing change at 3 and 6 days after surgery was significantly lower in the study group(P<0.05),and the scar hyperplasia rate,scar score and itch score were also significantly lower in the study group(P<0.05)at 6 months after surgery.In conclusion,autologous scalp repair of the medium-thickness skin donor area can accelerate wound healing and reduce scar hyperplasia.

The aim of this study is to identify the linear Fc-binding epitope for IgG1 on bovine IgG Fc receptor Ⅱ(boFcγRⅡ)to understand the molecular basis of IgG-Fcγ interaction.The boFcγRⅡ molecules were expressed on cell surface of the boFcγR Ⅱ-transfected COS-7 cells.The extracel-lular domain of boFcγRⅡ was expressed in NS0 cells,and the boFcγRⅡ recombinant protein was purified from ascites by Ni-chelation chromatography.Peptides derived from the membrane-distal extracellular domain(EC2)of boFcγR Ⅱ were synthesized and conjugated to a carrier protein of IgG-free bovine serum albumin(BSA).Binding of bovine IgG1 to the different peptides was tested by dot-blot assay,and the IgG-binding peptide was further modified by truncation and mutation to identify the Fc-binding epitope as well as its key amino acids for Fc-binding.The inhibition effect of the Fc-binding peptide was determined by competitive ELISA and Fc-rosetting inhibition assay,re-spectively.The results showed that boFcγR Ⅱ molecules were stably expressed on surface of the transfected COS-7 cells,which showed about 90％rosetting with IgG1-RBCs.The soluble boFcγRⅡ recombinant protein specifically bound to bovine IgG1.The minimal effective peptide of 122FYQDRKSKIF131 of boFcγRⅡ was able to bind bovine IgG1 specifically,suggesting it repre-sents a linear Fc-binding epitope located in the putative C-C'loop of the EC2 domain on the recep-tor.The Ala-substitution of Phe122,Tyr123,Arg126,Lys127,Ser128,Lys129 or Phe131 within the linear epitope led to a complete loss of its IgG1-binding capability,indicating those residues are critical for IgG1-binding on boFcγRⅡ.The Fc-binding peptide inhibited bovine IgG1 binding to the soluble recombinant protein of boFcγRⅡ with IC50 of 20.05 μmol/L,and inhibited the rosette formation of bovine IgG1-sensitized RBCs on the boFcγRⅡ transfected cells with IC50 of 80.15 μmol/L.The re-sults indicate that boFcγRⅡ possesses the linear epitope for Fc-binding,and the Fc-binding pep-tide showed well capability of regulating boFcγR Ⅱ-IgG1 interaction on cell surface,thereby provi-ding a research foundation for understanding the IgG-Fcγ interaction.

BACKGROUND:Three-dimensional(3D)Motion Capture Technology can build accurate,objective,and quantized medical virtual simulation model,which is conducive to clinical learners'precise and in-depth understanding and mastery of various traditional therapies.The virtual simulation model of traditional Chinese medicine based on the 3D Motion Capture Technology has been reported,but such a system of traditional Mongolian medicine therapy has not been reported.OBJECTIVE:To construct an interactive 3D visualization virtual simulation model based on the 3D Motion Capture Technology.METHODS:Motion capture data of the professor of Mongolian Medicine Department were collected using the 3D optical motion capture system(Motion Analysis)and Plantar Force Platform.The 3D motion model of brain vibration therapy was constructed using Motion Builder software,and the role model was constructed using Maya software matched with the action model.Unity3D software was used to build a virtual simulation system of Mongolian medical brain vibration therapy.The system integrated information on 3D animation,kinematic and dynamic parameters of Mongolian medical brain vibration therapy.RESULTS AND CONCLUSION:By using 3D Motion Capture Technology and Computer Simulation Technology to reconstruct the operation of Mongolian medical brain vibration therapy,it can display the posture of the operator and subject and record the key parameters of spatial position and changes of joint motion to obtain kinematic and dynamic parameters.The interactive 3D virtual simulation technology is used to realize the visual presentation of 3D virtual simulation of Mongolian medical brain vibration therapy.It lays a foundation for the standardization,digitization and visualization of Mongolian medical brain vibration therapy.