Objective To investigate the status of population dynamics and distribution changes of Aedes aegypti in Guangdong Province.Methods Continuous monitoring was conducted from May 2018 to July 2024 in Wushi Town and Qishui Town,Leizhou City,Zhanjiang City,Guangdong Province.Additionally,a survey of the distribution of Ae.aegypti along the Leizhou Peninsula coast was carried out.Results The density of Ae.aegypti in Zhanjiang showed a gradual decline from 2018 to 2024.The last detection of adult Ae.aegypti in Wushi Town was in September 2021,and the last larva was found in October 2023.No Ae.aegypti was detected in Qishui Town during surveys from 2021 to 2024.A survey of 18 coastal villages in the Leizhou Peninsula revealed no detections of Ae.aegypti.Conclusions This study provides a basis for understanding the distribution and population density fluctuations of Ae.aegypti,assessing its invasion risk,and scientifically conducting relevant prevention and control efforts.

Objective To introduce how to import and analyze data using the Research Electronic Data Capture(REDCap)system,taking a multi-center randomized controlled clinical research of total knee arthroplasty as an example.Methods Various tools within the REDCap system,including data import tools,data export functions,reports and statistics,project dashboards,and coding manuals,were used to systematically process and analyze the multi-center randomized controlled clinical trial data for total knee arthroplasty.Initially,electronically collected clinical data were adjusted and standardized,then uploaded in bulk to the system using the REDCap data import tool.Subsequently,the data were organized through REDCap's data export feature,and basic descriptive statistical analysis was performed using its reporting and statistical functions to ensure data quality and completeness.Results An electronic data collection and management platform for clinical research on knee osteoarthritis wase successfully created by the REDCap system.The platform enabled real-time data collection from multiple centers,and ensured data accuracy and consistency through built-in data management and quality control mechanisms.With the statistical analysis features of REDCap,the research team could monitor the progress of data in real time,conduct effective quality assessments,and perform dynamic analysis for further in-depth statistical evaluations.Conclusion The REDCap system can be used not only to build a new clinical research project,but also to import and analyze data that has been previously digitized of ongoing clinical researches into the system,which improved the scientificity of data management and research efficiency.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Soy is a vital source of plant carbohydrates.However,it poses significant allergenic risks,particularly to young children and animals.Among the various proteins in soy,β-conglycinin,which con-stitutes approximately 30％of total soy carbohydrates,is a primary allergen.Undigested β-conglycinin can lead to intestinal damage by inhibiting cell growth,disrupting the cytoskeleton,and inducing apopto-sis.It can also enter the lymphatic and circulatory systems,triggering allergic reactions.Conventional ELISA methods for detecting β-conglycinin rely on polyclonal or monoclonal antibodies,which are limited by their large molecular weight,difficulty in accessing the protein core,and sensitivity to acidic and bas-ic conditions.To address these limitations,this study aimed to develop nanobodies(Nbs)against β-con-glycinin.Nbs,derived from the variable regions of heavy-chain antibodies found in camelids,have a mo-lecular weight approximately one-tenth that of conventional antibodies.They offer advantages such as small size,stable structure,high specificity,and strong affinity.A female alpacas was immunized five times using β-conglycinin,which showed a heavy chain antibody potency of 1∶16 000 by ELISA.Pe-ripheral blood lymphocytes were subsequently isolated and total RNA was extracted.The variable region of the heavy-chain antibody was amplified via PCR,and recombinant plasmids were constructed and transformed into the E.coli competency strain ER2738.The resulting library contained about 3.5×108 CFU/mL,which increased to 1.15×1012 PFU/mL after phage rescue,with a 100％Nbs gene insertion rate,indicating high diversity.Its Nbs phage output was significantly enriched by four rounds of solid-phase elution with an enrichment rate of 155.9.Four rounds of solid-phase panning yielded 35 positive clones,all of which shared the same amino acid sequence upon sequencing.The selected Nb was ex-pressed in a prokaryotic system,and its binding ability to β-conglycinin was confirmed using Western blotting and ELISA.The results demonstrated excellent specificity and affinity.This research lays the groundwork for developing a rapid and efficient detection method for β-conglycinin using Nbs,potentially enhancing food safety and allergen management.

Objective To analyze the correlation between lesion volume,lesion mass,and maximum lesion diameter in the assessment of advanced hepatocarcinoma with lung metastasis,and to evaluate the application value of total volume response and total mass response of lung metastatic lesions in efficacy assessment.Methods A retrospective analysis was conducted on the CT imaging data of 20 patients clinically confirmed with hepatocarcinoma and lung metastases,followed by subsequent follow-up to monitor their survival outcomes.Volume measurement software was used to measure the volume of lesions before and after treatment.We recored lesion diameter,volume measurements and CT values,calculated the mass of the lesions.The correlation between lesion volume,mass and diameter was analyzed,as well as the correlation between the change rates of volume,mass and lesion diameter.Additionally,the total volume and total mass of all lesions were calculated.The correlation between the change rates of total volume/total mass and the change rate of pulmonary lesion diameter under the RECIST 1.1 criteria,as well as the correlation with changes in patients'tumor markers,were analyzed.Furthermore,the overall volume response and overall mass response of lesions were evaluated based on changes in total volume and total mass,and their consistencies with the RECIST 1.1 criteria for efficacy evaluation were analyzed.Finally,univariate Cox regression analysis was performed to explore the association between these variables and patient survival outcomes.Results There was strong correlation between lesion volume,mass and tumor diameter(r=0.771,0.775),between the rate of change in mass and the rate of change in lesion diameter(r=0.846),and between the rates of change in total volume/total mass and the rate of change in pulmonary lesion diameter under the RECIST 1.1 criteria(r=0.800,0.896).The correlation between the rates of change in total volume/total mass and patients'tumor markers was not statistically significant.There was moderate correlation between the rate of change in volume and the rate of change in lesion diameter(r=0.692).The evaluation results of total volume response and total mass response for pulmonary lesions in advanced hepatocarcinoma with lung metastasis were generally consistent with the RECIST 1.1 criteria(Kappa=0.486,0.426).Univariate Cox regression analysis revealed that total lesion volume(P=0.047)and total lesion mass(P=0.049)were independent prognostic factors for survival outcomes.Conclusion Lesion volume,mass,and diameter,as well as their respective change rates,were found to be interrelated.Furthermore,total lesion volume and total lesion mass were identified as independent prognostic factors for survival outcomes.The total volume response and total mass response are promising evaluation methods in evaluating the efficacy of lung metastasis of hepatocarcinoma,which are different from the RECIST 1.1 evaluation criteria.

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.