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 systematically evaluate the radiation impact of radioactive sources used in online elemental analyzers in cement enterprises on the surrounding environment, and to provide a scientific basis for radiation monitoring and safety management at the application sites of this type of radioactive sources. Methods A statistical analysis was conducted on 15 cement enterprises in Guangxi Province using online elemental analyzers with ²⁵²Cf as the radioactive source. On-site investigation of radiation safety management and on-site monitoring of radiation environment were performed, followed by an evaluation based on the collected data. Results Although the gamma radiation ambient dose equivalent rate and neutron ambient dose equivalent rate increased around the sites using online elemental analyzers with ²⁵²Cf as the radioactive source, they all met the requirements of the Radiological Health Protection Requirements for Instruments with Sealed Sources (GBZ 125—2009). Conclusion Under the current usage and management conditions, the application of this type of radioactive sources has controllable radiation impact on the surrounding environment, and will not pose a threat to public health and environmental safety. However, continuous strengthening of radiation safety management measures and regular radiation monitoring work are still needed to ensure the safe use of radioactive sources, further reducing potential radiation risks and providing strong guarantees for the safe application of radioactive sources in online elemental analyzers in cement enterprises.

Polyunsaturated fatty acids (PUFAs) have diverse health-promoting effects, such as potentially protecting in immune, nervous, and cardiovascular systems by targeting a variety of sites, including most ion channels. Voltage-gated potassium channels of the K_V7 family and large-conductance Ca²⁺- and voltage-activated K⁺ (BK_Ca) channels are expressed in many tissues, therefore, their physiological importance is evident from the various disorders linked to dysfunctional K_V7 channels and BK_Ca channels. Thus, it is extremely important to learn how potassium channels are regulated by PUFAs. The aim of this review is to provide an overview of the effects of PUFAs on K_V7 channels and BK_Ca channels functions, as well as the mechanisms underlying these effects. In summarizing reported effects of PUFAs on K_V7 and BK_Ca channels mediated currents, we generally conclude that PUFAs increase the current amplitude, meanwhile, differential molecular and biophysical mechanisms are associated with the current increase. In K_V7 channels the currents increasement are associated with a shift in the voltage dependence of channel opening and increased maximum conductance in K_V7 channels, while in BK_Ca channels, they are associated with destabilization the pore domain closed conformation. Furthermore, PUFA effects are influenced by auxiliary subunits of K_V7 and BK_Ca channels, associate with channels in certain tissues. although findings are conflicting. A better understanding of how PUFAs regulate K_V7 and BK_Ca channels may offer insight into their physiological regulation and may lead to new therapeutic strategies and approaches.

Objective To construct machine learning models that can be used to predict AUC fold change(FC)using a database of existing pharmacokinetic(PK)and drug-drug interaction(DDI)information,which can be used to explore the possibility of predicting existing drug interactions and to provide certain rational recommendations for clinical drug use.Methods PK data of DDIs and AUC fold change data were extracted from FDA-approved drug labels.Peptide and pharmacodynamic(PD)information related to drug interactions were retrieved through DrugBank,and PPDT identification of relevant peptide IDs was performed using Protein Resource(UniProt),and a matrix normalization code was used to generate multidimensional vector data that were easy to analysis.The effect of PPDT on the AUC,and the resulting multiplicity change was used as the dependent variable for machine learning model construction.The model with the smallest root mean square error(RMES)value was used for model construction to train a bagged decision tree(Bagged)prediction model.The models were tested using the trained models for some of the drug tests.The models were evaluated by reviewing the available literature findings on detection of drug interaction pairs and analyzing and comparing the predicted values.Results A total of 16 pairs of model drug pairs were tested for the effects of 16 drugs on tacrolimus,and it was found that the accuracy of the prediction of the presence or absence of drug interactions was 81.25％;the prediction results were classified according to the FDA standard classification of the strong and weak for the strength of drug interactions,and the results showed that the prediction of the strength of drug interactions,with a large deviation from the larger prediction was less.Conclusion The evaluation of the model to predict the presence or absence of drug interactions was general;however,after classifying the strengths and weaknesses of drug interactions,the prediction of drug interactions was better,and the prediction results indicated that the model prediction performance has a certain reference value for potential DDI assessment before clinical trials.