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.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Exercise-induced metabolic remodeling is a fundamental adaptive process whereby the body reorganizes systemic and cellular metabolism to meet the dynamic energy demands posed by physical activity. Emerging evidence reveals that such remodeling not only enhances energy homeostasis but also profoundly influences immune function through complex molecular interactions involving glucose, lipid, and protein metabolism. This review presents an in-depth synthesis of recent advances, elucidating how exercise modulates immune regulation via metabolic reprogramming, highlighting key molecular mechanisms, immune-metabolic signaling axes, and the authors’ academic perspective on the integrated “exercise-metabolism-immunity” network. In the domain of glucose metabolism, regular exercise improves insulin sensitivity and reduces hyperglycemia, thereby attenuating glucose toxicity-induced immune dysfunction. It suppresses the formation of advanced glycation end-products (AGEs) and interrupts the AGEs-RAGE-inflammation positive feedback loop in innate and adaptive immune cells. Importantly, exercise-induced lactate, traditionally viewed as a metabolic byproduct, is now recognized as an active immunomodulatory molecule. At high concentrations, lactate can suppress immune function through pH-mediated effects and GPR81 receptor activation. At physiological levels, it supports regulatory T cell survival, promotes macrophage M2 polarization, and modulates gene expression via histone lactylation. Additionally, key metabolic regulators such as AMPK and mTOR coordinate immune cell energy balance and phenotype; exercise activates the AMPK-mTOR axis to favor anti-inflammatory immune cell profiles. Simultaneously, hypoxia-inducible factor-1α (HIF-1α) is transiently activated during exercise, driving glycolytic reprogramming in T cells and macrophages, and shaping the immune landscape. In lipid metabolism, exercise alleviates adipose tissue inflammation by reducing fat mass and reshaping the immune microenvironment. It promotes the polarization of adipose tissue macrophages from a pro-inflammatory M1 phenotype to an anti-inflammatory M2 phenotype. Moreover, exercise alters the secretion profile of adipokines—raising adiponectin levels while reducing leptin and resistin—thereby influencing systemic immune balance. At the circulatory level, exercise improves lipid profiles by lowering pro-inflammatory free fatty acids (particularly saturated fatty acids) and triglycerides, while enhancing high-density lipoprotein (HDL) function, which has immunoregulatory properties such as endotoxin neutralization and macrophage cholesterol efflux. Regarding protein metabolism, exercise triggers the expression of heat shock proteins (HSPs) that act as intracellular chaperones and extracellular immune signals. Exercise also promotes the secretion of myokines (e.g., IL-6, IL-15, irisin, FGF21) from skeletal muscle, which modulate immune responses, facilitate T cell and macrophage function, and support immunological memory. Furthermore, exercise reshapes amino acid metabolism, particularly of glutamine, arginine, and branched-chain amino acids (BCAAs), thereby influencing immune cell proliferation, biosynthesis, and signaling. Leucine-mTORC1 signaling plays a key role in T cell fate, while arginine metabolism governs macrophage polarization and T cell activation. In summary, this review underscores the complex, bidirectional relationship between exercise and immune function, orchestrated through metabolic remodeling. Future research should focus on causative links among specific metabolites, signaling pathways, and immune phenotypes, as well as explore the epigenetic consequences of exercise-induced metabolic shifts. This integrated perspective advances understanding of exercise as a non-pharmacological intervention for immune regulation and offers theoretical foundations for individualized exercise prescriptions in health and disease contexts.

Platycodonis Radix is the dry root of Platycodon grandiflorum of Campanulaceae, which has a variety of pharmacological effects and is a commonly used bulk Chinese medicine. In this study, the chloroplast genome sequences of six P. grandiflorum from different producing areas has been sequenced with Illumina HiSeq X Ten platform. The specific DNA barcodes were screened, and the germplasm resources and genetic diversity were analyzed according to the specific barcodes. The total length of the chloroplast genome of 6 P. grandiflorum samples was 172 260-172 275 bp, and all chloroplast genomes showed a typical circular tetrad structure and encoded 141 genes. The comparative genomics analysis and results of amplification efficiency demonstrated that trnG-UCC and ndhG_ndhF were the potential specific DNA barcodes for identification the germplasm resources of P. grandiflorum. A total of 305 P. grandiflorum samples were collected from 15 production areas in 9 provinces, for which the fragments of trnG-UCC and ndhG_ndhF were amplificated and the sequences were analyzed. The results showed that trnG-UCC and ndhG_ndhF have 5 and 11 mutation sites, respectively, and 5 and 7 haplotypes were identified, respectively. The combined analysis of the two sequences formed 13 haplotypes (named Hap1-Hap13), and Hap4 is the main genotype, followed by Hap1. The unique haplotypes possessed by the three producing areas can be used as DNA molecular tags in this area to distinguish from the germplasm resources of P. grandiflorum from other areas. The haplotype diversity, nucleotide diversity and genetic distance were 0.94, 4.79×10^-3 and 0.000 0-0.020 3, respectively, suggesting that the genetic diversity was abundant and intraspecific kinship was relatively close. This study laid a foundation for the identification of P. grandiflorum, the protection and utilization of germplasm resources, and molecular breeding.

Humans ; Vascular Stiffness ; Coal Mining ; Occupational Diseases/epidemiology* ; Risk Factors ; Coal ; Miners

ObjectiveThe absorption of substances into blood is mainly dependent on the mesenteric lymphatic pathway and the portal venous pathway. The substances transported via the portal venous pathway can be metabolized by the biotransformation in the liver. On the contrary, the substances in the mesenteric lymph fluid enter the blood circulation without biotransformation and can affect the body directly. Alcohol consumption is strongly linked to global health risk. Previous reports have analyzed the changes of metabolites in plasma, serum, urine, liver and feces after alcohol consumption. Whether alcohol consumption affects the metabolites in lymph fluid is still unknown. Therefore, it is particularly important to explore the changes of substances transported via the mesenteric lymphatic pathway and analyze their harmfulness after alcohol drinking. MethodsIn this study, male Wistar rats were divided into high, medium, and low-dosage alcohol groups (receiving Chinese Baijiu at 56%, 28% and 5.6% ABV, respectively) and water groups. The experiment was conducted by alcohol gavage lasting 10 d, 10 ml·kg^-1·d^-1. Then mesenteric lymph fluid was collected for non-targeted metabolomic analysis by using liquid chromatography-mass spectrometry (LC-MS) and bioinformatic analysis. Principal component analysis and hierarchical clustering were performed by using Biodeep. Meanwhile, KEGG enrichment analysis of the differential metabolites was also performed by Biodeep. MetaboAnalyst was used to analyze the relationship between the differential metabolites and diseases. ResultsThe metabolites in the mesenteric lymph fluid of the high-dosage alcohol group change the most. Based on the KEGG enrichment analysis, the pathways of differential metabolites between the high-dosage alcohol group and the control group are mainly enriched in the central carbon metabolism in cancer, bile secretion, linoleic acid metabolism, biosynthesis of unsaturated fatty acids, etc. Interestingly, in the biosynthesis of unsaturated fatty acids category, the content of arachidonic acid is increased by 7.25 times, whereas the contents of palmitic acid, oleic acid, stearic acid, arachidic acid and erucic acid all decrease, indicating lipid substances in lymph fluid are absorbed selectively after alcohol intake. It’s worth noting that arachidonic acid is closely related to inflammatory response. Furthermore, the differential metabolites are mainly related with schizophrenia, Alzheimer’s disease and lung cancer. The differential metabolites between the medium-dosage alcohol and the control group were mainly enriched in phenylalanine metabolism, valine, leucine and isoleucine biosynthesis, linoleic acid metabolism and cholesterol metabolism. The differential metabolites are mainly related to schizophrenia, Alzheimer’s disease, lung cancer and Parkinson’s disease. As the dose of alcohol increases, the contents of some metabolites in lymph fluid increase, including cholesterol, L-leucine, fumaric acid and mannitol, and the number of metabolites related to schizophrenia also tends to increase, indicatingthat some metabolites absorbed by the intestine-lymphatic pathway are dose-dependent on alcohol intake. ConclusionAfter alcohol intake, the metabolites transported via the intestinal-lymphatic pathway are significantly changed, especially in the high-dosage group. Some metabolites absorbed via the intestinal-lymphatic pathway are dose-dependent on alcohol intake. Most importantly, alcohol intake may cause inflammatory response and the occurrence of neurological diseases, psychiatric diseases and cancer diseases. High-dosage drinking may aggravate or accelerate the occurrence of related diseases. These results provide new insights into the pathogenesis of alcohol-related diseases based on the intestinal-lymphatic pathway.