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:Degenerative scoliosis is defined as a condition that occurs in adulthood with a coronal cobb angle of the spine>10° accompanied by sagittal deformity and rotational subluxation,which often produces symptoms of spinal cord and nerve compression,such as lumbar pain,lower limb pain,numbness,weakness,and neurogenic claudication.The finite element method is a mechanical analysis technique for computer modelling,which can be used for spinal mechanics research by building digital models that can realistically restore the human spine model and design modifications. OBJECTIVE:To review the application of finite element method in the etiology and treatment of degenerative scoliosis. METHODS:The literature databases CNKI,PubMed,and Web of Science were searched for articles on the application of finite element method in degenerative scoliosis published before October 2023.Search terms were"finite element analysis,biomechanics,stress analysis,degenerative scoliosis,adult spinal deformity"in Chinese and English.Fifty-four papers were finally included. RESULTS AND CONCLUSION:(1)The biomechanical findings from the degenerative scoliosis model constructed using the finite element method were identical to those from the in vivo experimental studies,which proves that the finite element method has a high practical value in degenerative scoliosis.(2)The study of the etiology and treatment of degenerative scoliosis by the finite element method is conducive to the prevention of the occurrence of the scoliosis,slowing down the progress of the scoliosis,the development of a more appropriate treatment plan,the reduction of complications,and the promotion of the patients'surgical operation.(3)The finite element method has gradually evolved from a single bony structure to the inclusion of soft tissues such as muscle ligaments,and the small sample content is increasingly unable to meet the research needs.(4)The finite element method has much room for exploration in degenerative scoliosis.

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/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.

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.

Locomotion, a fundamental motor function encompassing various forms such as swimming, walking, running, and flying, is essential for animal survival and adaptation. The mesencephalic locomotor region (MLR), located at the midbrain-hindbrain junction, is a conserved brain area critical for controlling locomotion. This review highlights recent advances in understanding the MLR’s structure and function across species, from lampreys to mammals and birds, with a particular focus on insights gained from optogenetic studies in mammals. The goal is to uncover universal strategies for MLR-mediated locomotor control. Electrical stimulation of the MLR in species such as lampreys, salamanders, cats, and mice initiates locomotion and modulates speed and patterns. For example, in lampreys, MLR stimulation induces swimming, with increased intensity or frequency enhancing propulsive force. Similarly, in salamanders, graded stimulation transitions locomotor outputs from walking to swimming. Histochemical studies reveal that effective MLR stimulation sites colocalize with cholinergic neurons, suggesting a conserved neurochemical basis for locomotion control. In mammals, the MLR comprises two key nuclei: the cuneiform nucleus (CnF) and the pedunculopontine nucleus (PPN). Both nuclei contain glutamatergic and GABAergic neurons, with the PPN additionally housing cholinergic neurons. Optogenetic studies in mice by selectively activating glutamatergic neurons have demonstrated that the CnF and PPN play distinct roles in motor control: the CnF drives rapid escape behaviors, while the PPN regulates slower, exploratory movements. This functional specialization within the MLR allows animals to adapt their locomotion patterns and speed in response to environmental demands and behavioral objectives. Similar to findings in lampreys, the CnF and PPN in mice transmit motor commands to spinal effector circuits by modulating the activity of brainstem reticular formation neurons. However, they achieve this through distinct reticulospinal pathways, enabling the generation of specific behaviors. Further insights from monosynaptic rabies viral tracing reveal that the CnF and PPN integrate inputs from diverse brain regions to produce context-appropriate behaviors. For instance, glutamatergic neurons in the PPN receive signals from other midbrain structures, the basal ganglia, and medullary nuclei, whereas glutamatergic neurons in the CnF rarely receive inputs from the basal ganglia but instead are strongly influenced by the periaqueductal grey and inferior colliculus within the midbrain. These differential connectivity patterns underscore the specialized roles of the CnF and PPN in motor control, highlighting their unique contributions to coordinating locomotion. Birds exhibit exceptional flight capabilities, yet the avian MLR remains poorly understood. Comparative studies suggest that the pedunculopontine tegmental nucleus (PPTg) in birds is homologous to the mammalian PPN, which contains cholinergic neurons, while the intercollicular nucleus (ICo) or nucleus isthmi pars magnocellularis (ImC) may correspond to the CnF. These findings provide important clues for identifying the avian MLR and elucidating its role in flight control. However, functional validation through targeted experiments is urgently needed to confirm these hypotheses. Optogenetics and other advanced techniques in mice have greatly advanced MLR research, enabling precise manipulation of specific neuronal populations. Future studies should extend these methods to other species, particularly birds, to explore unique locomotor adaptations. Comparative analyses of MLR structure and function across species will deepen our understanding of the conserved and evolved features of motor control, revealing fundamental principles of locomotion regulation throughout evolution. By integrating findings from diverse species, we can uncover how the MLR has been adapted to meet the locomotor demands of different environments, from aquatic to aerial habitats.

Decoction is the most commonly used dosage form in the clinical treatment of traditional Chinese medicine (TCM). During boiling, the violent movement of various active ingredients in TCM creates molecular forces such as hydrogen bonding, π-π stacking, hydrophobic interactions and electrostatic interactions, which results in the formation of self-assembled aggregates in decoction (SADs), including particles, gels, fibers, etc. It was found that SADs widely existed in decoction with biological activities superior to both effective monomers and their physical mixtures, providing a new idea to reveal the pharmacodynamic material basis of Chinese herbal medicine from the perspective of component interactions-phase structure. Recently, SADs have become a novel focus of research in TCM. This paper reviewed their relevant studies in recent years and found some issues to be concerned in the research, such as the polydispersity of decoction system, instability of active ingredient interactions during boiling, uncertainty of the aggregates self-assembly rules, and stability, purity, yield of the products. In this regard, some solutions and new ideas were presented for the integrated development and clinical application of SADs.

Most chemical medicines have polymorphs. The difference of medicine polymorphs in physicochemical properties directly affects the stability, efficacy, and safety of solid medicine products. Polymorphs is incomparably important to pharmaceutical chemistry, manufacturing, and control. Meantime polymorphs is a key factor for the quality of high-end drug and formulations. Polymorph prediction technology can effectively guide screening of trial experiments, and reduce the risk of missing stable crystal form in the traditional experiment. Polymorph prediction technology was firstly based on theoretical calculations such as quantum mechanics and computational chemistry, and then was developed by the key technology of machine learning using the artificial intelligence. Nowadays, the popular trend is to combine the advantages of theoretical calculation and machine learning to jointly predict crystal structure. Recently, predicting medicine polymorphs has still been a challenging problem. It is expected to learn from and integrate existing technologies to predict medicine polymorphs more accurately and efficiently.