OBJECTIVE To optimize the preparation technology of Jineijin danggui ruchuang ointment and to establish its quality standard. METHODS Using oil phase dosage， emulsifier dosage and emulsification temperature as the indicators， the preparation technology of Jineijin danggui ruchuang ointment was optimized by response surface method. Gallus gallus domesticus and Angelica sinensis in the ointment were identified by TLC. The property of the ointment was observed， and its particle size and deliverable volume were inspected according to Chinese Pharmacopoeia. The contents of uridine， ferulic acid and ligustilide in the ointment were determined by high-performance liquid chromatography. RESULTS The optimal preparation technology of Jineijin danggui ruchuang ointment was as follows： 4.0 g of white vaseline， 7.0 g of liquid paraffin， 5.0 g of lanolin （oil phase）， 0.44 g of triethanolamine as the emulsifier， and emulsification temperature of 78 ℃ . Jineijin danggui ruchuang ointment prepared according to the optimal preparation method was a beige paste， and its particle size and deliverable volume inspection all met the requirements of the Chinese Pharmacopoeia. The identification of TLC for G. gallus domesticus and A. sinensis obtained satisfactory results. The linear ranges of uridine， ferulic acid and ligustilide were 1.6-25.6 μg/mL， 0.003 15-0.100 8 mg/mL， 0.006- 0.192 mg/mL （all r＞0.999）. RSD for the inspection， stability， reproducibility and recovery tests were all less than 2%（ n＝6）. The average contents of uridine， ferulic acid and ligustilide were 0.081 2， 0.100 0 and 0.396 9 mg/g， respectively. CONCLUSIONS The optimal preparation technology can be used for the production of the in-hospital preparation of Jineijin danggui ruchuang ointment； the content determination method can be used for the quality control of the ointment.

Objective To investigate the relationship between brain white matter lesions (WML) and triglyceride glucose (TyG) index in non-diabetic elderly individuals based on magnetic resonance imaging. Methods A total of 523 non-diabetic elderly individuals aged ≥ 60 years were selected from Jinan, Shandong Province, China from June 2018 to December 2019. According to the quartiles of TyG index, there were 133 participants in the first quartile (Q1) group, 127 in the second quartile (Q2) group, 132 in the third quartile (Q3) group, and 131 in the fourth quartile (Q4) group. All participants underwent brain magnetic resonance imaging to evaluate paraventricular, deep, and total WML volumes, as well as Fazekas scores. Results Compared with Q1, Q2, and Q3 groups, Q4 group showed significant increase in periventricular, deep, and total WML volumes (P < 0.05). The proportion of participants with a Fazekas score ≥ 2 in the periventricular, deep, and total WML was higher in the Q4 group compared with the Q1 and Q2 groups (P < 0.05). The proportion of participants with a Fazekas score ≥ 2 in deep WML was higher in Q4 group than in Q3 group (P < 0.05). TyG index was significantly positively correlated with periventricular, deep, and total WML volumes (r = 0.401, 0.405, and 0.445, P < 0.001). After adjusting for confounding factors, TyG index was still significantly positively correlated with periventricular, deep, and total WML volumes (P < 0.001). Logistic regression analysis showed that compared with Q1 group, the risk of Fazekas score ≥ 2 in periventricular WML was 1.950-fold (95% confidence interval [CI]: 1.154-3.294, P = 0.013) in Q3 group and 3.411-fold (95% CI: 1.984-5.863, P < 0.001) in Q4 group, the risk of Fazekas score ≥ 2 in total WML was 2.529-fold (95%CI: 1.444-4.430, P = 0.001) in Q3 group and 4.486-fold (95%CI: 2.314-8.696, P < 0.001) in Q4 group. The risk of Fazekas score ≥ 2 in deep WML was 2.953-fold (95%CI: 1.708-5.106, P < 0.001) in Q4 group compared with Q1 group. Conclusion Increased TyG index is an independent risk factor for WML in non-diabetic elderly individuals.

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.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

The present study employed UPLC-MS/MS to analyze and identify compounds in the raw powder of Tongluo Shenggu capsules. An HPLC method for the determination of vitexin content was established. The analysis of this drug was performed on a 30 ℃ thermostatic Acquity UPLC® BEH C18 (2.1 mm×100 mm,1.7 μm) column, with the mobile phase comprising 0.2% formic acid-methanol flowing at 0.3 mL /min in a gradient elution manner. Mass spectrometry was detected by ESI sources in both positive and negative ion modes for qualitative identification of chemical constituents. 12 flavonoid and 3 stilbenes compounds in the raw powder of Tongluo Shenggu capsules were successfully identified. Additionally, an HPLC method for the determination of vitexin content was established using a XBridge C18 column (4.6 mm × 250 mm, 5 µm) with a mobile phase of 0.05% glacial acetic acid in methanol for gradient elution, at a column temperature of 30 °C, a flow rate of 1.0 mL/min, and an injection volume of 20 μL. The method demonstrated good linearity in the concentration range of 10 µg/mL to 40 µg/mL (R=1.000) with an average recovery rate of 96.7%. The establishment of these methods provides a scientific basis for the quality control and development of the raw powder of Tongluo Shenggu capsules.

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.

Objective To investigate the risk factors for re-fracture after percutaneous kyphoplasty (PKP) in elderly patients with osteoporotic thoracolumbar compression fractures and to construct a line graph prediction model. Methods One hundred and eighty-two elderly patients with osteoporotic thoracolumbar compression fractures treated with PKP from January 2016 to November 2019 were selected for the study‚ and the patients were continuously followed up for 3 years after surgery. Clinical data were collected from both groups; Receiver operating characteristic (ROC) curve analysis was performed on the measures; Logistic regression analysis was performed to determine the independent risk factors affecting postoperative re-fracture in PKP; the R language software 4. 0 “rms” package was used to construct a predictive model for the line graph‚ and the calibration and decision curves were used to internally validate the predictive model for the line graph and for clinical evaluation of predictive performance. Results The differences between the two groups were statistically significant (P<0. 05) in terms of bone mineral density (BMD)‚ number of injured vertebrae‚ single-segment cement injection‚ type of cement distribution‚ cement leakage‚ difference in vertebral body height before and after PKP‚ and change in posterior convexity angle. The area under the curve (AUC) for BMD‚ number of injured vertebrae‚ single-segment cement injection volume‚ cement leakage‚ pre-and post-PKP vertebral height difference‚ and posterior convexity change were 0. 772‚ 0. 732‚ 0. 722‚ 0. 801‚ and 0. 813‚ respectively‚ and the best cutoff values were -3. 1‚ 2‚ 3. 9 ml‚ 0. 4 mm‚ and 8. 7°‚ respectively. BMD‚ number of injured vertebrae‚ single-segment cement injection volume‚ cement leakage‚ pre-and post-PKP vertebral height difference‚ and posterior convexity change were independent risk factors for re-fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fractures. The calibration curve of the column line graph prediction model was close to the original curve and the ideal curve with a C-index of 0. 818 (95% CI: 0. 762-0. 883)‚ and the model fit was good; the threshold value of the column line graph prediction model was >0. 22‚ which could provide a net clinical benefit‚ and the net clinical benefit was higher than the independent predictors. Conclusion BMD‚ number of injured vertebrae‚ single-segment cement injection‚ cement leakage‚ pre-and post-PKP vertebral height difference‚ and posterior convexity angle change are independent risk factors affecting the recurrent fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fracture‚ and this study constructs a column line graph model to predict the recurrent fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fracture as a predictor for clinical. This study provides an important reference for clinical prevention and treatment‚ and has clinical application value.

ObjectiveTo explore the molecular mechanism of Sanhuang Xiexintang （SHXXT） in protecting stress gastric ulcer （SGU） in rats through network pharmacology， molecular docking， and animal experiments. MethodThe active ingredients and corresponding targets in SHXXT were collected and screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP）， Traditional Chinese Medicine Information Database （TCMID）， Bioinformation Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine （BATMAN-TCM）， and Swiss Target Prediction database. SGU-related targets were screened from the Online Mendelian Inheritance in Man （OMIM）， Therapeutic Target Database （TTD）， GeneCards database， and PharmGKB database. Herbal-ingredient-target （H-C-T） network was constructed by using Cytoscape 3.9.1 software. Protein-protein interaction （PPI） of drug and disease intersection targets was analyzed by using the Protein Interaction Platform （STRING） database. Gene ontology （GO） enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway analysis were conducted through the Database for Annotation Visualization and Integrated Discovery （DAVID）. The active ingredients and key targets were validated using AutodockVina 1.2.2 molecular docking software， and the experimental results were further validated through animal experiments. ResultThe 55 active ingredients were screened， and 255 potential target genes for SHXXT treatment of SGU were predicted. The PPI analysis showed that protein kinase B （Akt）， phosphatase and tensin homolog deleted on chromosome ten （PTEN）， tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and cyclooxygenase-2 （COX-2） are the core targets of SHXXT for protecting SGU. GO and KEGG analyses showed that SHXXT may affect the development of SGU by regulating various biological processes such as the phosphoinositide 3-kinase （PI3K）/Akt signaling pathway and inflammatory processes. The molecular docking results showed that both the active ingredients and key targets had good binding ability. Animal experiments showed that compared with the blank group， the ulcer index （UI） of the model group was significantly increased （P<0.01）， and the serum levels of TNF-α and IL-1β significantly increased （P<0.01）. The phosphorylation level of PTEN in gastric mucosal tissue was significantly down-regulated （P<0.05）. The phosphorylation levels of PI3K， Akt， and nuclear factor kappa-B （NF-κB） were significantly up-regulated （P<0.05）. Compared with the model group， the UI of the treatment group was significantly reduced （P<0.01）， and the serum levels of TNF-α and IL-1β were significantly reduced （P<0.01）. The phosphorylation level of PTEN in gastric mucosal tissue was significantly up-regulated （P<0.01）， and the phosphorylation levels of PI3K， Akt， and NF-κB were significantly downregulated （P<0.01）. ConclusionThe application of network pharmacology prediction， molecular docking simulation， and animal experimental validation confirms that SHXXT regulates the PI3K/Akt/NF-κB signaling pathway to regulate the inflammatory response of rats and thus protects the gastric mucosa of SGU rats.