Objective To identify factors affecting and key environmental factors of the Oncomelania hupensis snail density in the Yangtze River Delta region using machine learning methods. Methods Administrative village-level O. hupensis snail survey data in the Yangtze River Delta (including Shanghai Municipality, Jiangsu Province, Zhejiang Province and Anhui Province) from 2011 to 2021 were retrieved from the Information Management System for Parasitic Disease Control of Chinese Center for Disease Control and Prevention. Environmental factor data were captured from the Google Earth Engine platform, including elevation, slope, terrain, normalized difference vegetation index (NDVI), vegetation type, soil type, total petroleum hydrocarbon (TPH), ammonium nitrogen, inorganic nitrogen, dissolved oxygen, pH of water, chemical oxygen demand (COD) and inorganic phosphorus, and climatic factor data in the study region were retrieved from the Copernicus Climate Data Store, including annual precipitation, aridity index and annual mean temperature (AMT). O. hupensis snail survey data in the Yangtze River Delta region from 2011 to 2021 were randomly divided into a training set (70%) and a test set (30%), and five machine learning models were selected for machine learning model construction and comparative analysis of the O. hupensis snail density using the software R 4.3.0, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), gradient boosting machine (GBM) and neural network (NN). The XGBoost model was employed to construct a predictive model for the O. hupensis snail density, and the impact of each environmental factor on O. hupensis snail distribution was quantified. The SHapley Additive exPlanations (SHAPs) values were calculated to estimate the average contribution of each variable to the model prediction, and the core environmental factors affecting the O. hupensis snail population density were screened. Results Among the five machine learning models, the XGBoost model exhibited the optimal comprehensive performance, with the coefficient of determination (R²) of 0.855, mean squared error (MSE) of 0.188, root mean squared error (RMSE) of 0.434 and mean absolute error (MAE) of 0.155, respectively. Analysis of factors affecting the O. hupensis snail density with the XGBoost model showed that among the 16 environmental factors, the top four high-impact factors ranked by SHAPs values included annual precipitation, elevation, aridity index and NDVI, with cumulative SHAPs contributions of 75%, which was higher than that of other environmental factors. If NDVI was higher than 0.6, the O. hupensis snail density increased with NDVI and peaked if NDVI was 0.8 (1.60 snails/0.1 m²). The O. hupensis snail density increased with elevation if the elevation ranged from 14 to 40 m, and slowly rose if the annual precipitation ranged from 900 to 1 300 mm, and then increased rapidly to the peak (1.52 snails/0.1 m²) if the annual precipitation ranged from 1 300 to 1 500 mm. In addition, the O. hupensis snail density increased rapidly to the maximum (1.60 snails/0.1 m²) if the aridity index ranged from 0.8 to 1.1, and decreased gradually if the aridity index exceeded 1.1. Conclusions The XGBoost model shows excellent performance in prediction of the O. hupensis snail density and identification of key environmental factors in the Yangtze River Delta region. Annual precipitation, elevation, aridity index and NDVI are key environmental factors affecting the distribution and density of O. hupensis snails in the Yangtze River Delta region.

Implementation science (IS) aims to systematically analyze and address the real-world gaps from evidence to practice and the influencing factors of the context. It is necessary to carry out qualitative research to gather relevant implementation outcomes. Nevertheless, traditional qualitative analysis has issues such as consuming a great deal of time and energy, and it is unable to promptly provide the crucial data required for implementation science research. The Rapid Qualitative Analysis (RQA) method, through semi-structured interviews and the adoption of techniques such as immediate data condensation and matrix analysis, can effectively shorten the cycle of qualitative data collection and data processing. RQA can promptly identify social determinants of health such as structural barriers, facilitators, and the behavioral characteristics of target groups. It provides a real-time basis for public health decision-making, the interpretation of complex social phenomena, and the process and effectiveness evaluation of research projects. Although RQA is difficult to conduct in-depth theoretical analysis based on grounded theory, its efficiency and flexibility make it the preferred tool for large-scale and time-sensitive research. Thus, it has been widely applied in implementation science research. This paper sorts out the core concepts and commonly used technical methods of RQA, as well as the differences between RQA and traditional qualitative analysis. It also explores the applications of RQA in intervention optimization, process evaluation, and implementation outcome evaluation. By integrating specific cases, this paper clarifies its application value in the field of implementation science. In the future, it is advisable to explore the integration of RQA with technologies such as artificial intelligence and big data, in order to bridge the gap between the transformation of scientific research achievements into practice. Under circumstances of limited resources or tight time constraints, RQA can be used to efficiently conduct implementation science research, providing convenient and scientific methodological and technical support for accelerating evidence-based practice.

Traditional Chinese medicine （TCM） clinical trials face challenges such as low participant compliance， insufficient geographical coverage， and cost-effectiveness imbalances. Decentralized clinical trials （DCT）， enabled by digital technology for remote data collection and monitoring， offer a new direction for TCM clinical trial research. This article systematically reviews three novel clinical trial design models. Combining the holistic concept and indivi-dualized treatment characteristics of TCM， it analyzes the challenges currently faced in TCM DCT practice， including the digitization and standardization of TCM theory， data security， privacy protection and patient engagement difficu-lties， insufficient ethical review and regulatory system adaptation， inadequate personnel training， and a shortage of interdisciplinary talent. Addressing these challenges， the article proposes methodological recommendations for DCT implementation that align with the principles of TCM diagnosis and treatment. These recommendations include promoting the intelligentization and standardization of TCM practices， constructing a full-chain data security and privacy protection system， improving the ethical framework and clarifying regulatory responsibilities， and cultivating and building interdisciplinary talent and capabilities， which provide theoretical and technical references for establishing standardized DCT practices in TCM.

Traditional Chinese medicine （TCM） clinical trials face challenges such as low participant compliance， insufficient geographical coverage， and cost-effectiveness imbalances. Decentralized clinical trials （DCT）， enabled by digital technology for remote data collection and monitoring， offer a new direction for TCM clinical trial research. This article systematically reviews three novel clinical trial design models. Combining the holistic concept and indivi-dualized treatment characteristics of TCM， it analyzes the challenges currently faced in TCM DCT practice， including the digitization and standardization of TCM theory， data security， privacy protection and patient engagement difficu-lties， insufficient ethical review and regulatory system adaptation， inadequate personnel training， and a shortage of interdisciplinary talent. Addressing these challenges， the article proposes methodological recommendations for DCT implementation that align with the principles of TCM diagnosis and treatment. These recommendations include promoting the intelligentization and standardization of TCM practices， constructing a full-chain data security and privacy protection system， improving the ethical framework and clarifying regulatory responsibilities， and cultivating and building interdisciplinary talent and capabilities， which provide theoretical and technical references for establishing standardized DCT practices in TCM.

Allergic conjunctivitis is a common ocular inflammatory disease, with intense itching being the most typical and distressing symptom for patients. In recent years, with the in-depth study of the interaction between the nervous and immune systems, significant progress has been made in understanding the mechanism of itching in allergic conjunctivitis. This review elaborates on the neurobiological basis of itching in allergic conjunctivitis, with a focus on the complex dialogue between immune cells and sensory neurons, particularly the core role of the IL-33-ST2-CGRP signaling axis in mediating itching. Additionally, this article introduces new findings in genetic susceptibility research, including the identification of susceptibility genes for allergic conjunctivitis through transcriptome-wide association studies. The sensory nervous system not only transmits itch signals but also actively participates in the formation of antigen channels related to conjunctival goblet cells, thereby regulating the local uptake of allergens and the initiation of the immune response. Moreover, targeted novel therapeutic strategies offer hope for patients with refractory allergic conjunctivitis. Exploring the molecular and cellular mechanisms of itching in allergic conjunctivitis will provide a theoretical basis for the development of more effective treatment methods.

Microalgae possess high photosynthetic efficiency, robust adaptability, and substantial biomass, serving as excellent biological resources for large-scale cultivation. Malic enzyme (ME), a ubiquitous metabolic enzyme in living organisms, catalyzes the decarboxylation of malate to produce pyruvate, CO₂, and NAD(P)H, playing a role in multiple metabolic pathways including energy metabolism, photosynthesis, respiration, and biosynthesis. In this study, we identified the Scenedesmus quadricauda malic enzyme gene (SqME) and its biological functions, aiming to provide excellent target genes for the genetic improvement of higher plants. Based on the RNA-seq data from S. quadricauda under the biofilm cultivation mode with high CO₂ and light energy transfer efficiency and small water use, a highly expressed gene (SqME) functionally annotated as ME was cloned. The physicochemical properties of the SqME-encoded protein were systematically analyzed by bioinformatics tools. The subcellular localization of SqME was determined via transient transformation in Nicotiana benthamiana leaves. The biological functions of SqME were identified via genetic transformation in Nicotiana tabacum, and the potential of SqME in the genetic improvement of higher plants was evaluated. The ORF of SqME was 1 770 bp, encoding 590 amino acid residues, and the encoded protein was located in chloroplasts. SqME was a NADP-ME, with the typical structural characteristics of ME. The ME activity in the transgenic N. tabacum plant was 1.8 folds of that in the wild-type control. Heterologous expression of SqME increased the content of chlorophyll a, chlorophyll b, and total chlorophyll by 20.9%, 26.9%, and 25.2%, respectively, compared with the control. The transgenic tobacco leaves showed an increase of 54.0% in the fluorescence parameter NPQ and a decrease of 30.1% in Fo compared with the control. Moreover, the biomass, total lipids, and soluble sugars in the transgenic tobacco leaves enhanced by 20.5%, 25.7%, and 9.5%, respectively. On the contrary, the starch and protein content in the transgenic tobacco leaves decreased by 22.4% and 12.2%, respectively. Collectively, the SqME-encoded protein exhibited a strong enzymatic activity. Heterologous expressing of SqME could significantly enhance photosynthetic protection, photosynthesis, and biomass accumulation in the host. Additionally, SqME can facilitate carbon metabolism remodeling in the host, driving more carbon flux towards lipid synthesis. Therefore, SqME can be applied in the genetic improvement of higher plants for enhancing photosynthetic carbon fixation and lipid accumulation. These findings provide scientific references for mining of functional genes from S. quadricauda and application of these genes in the genetic engineering of higher plants.
Nicotiana/genetics* ; Photosynthesis/physiology* ; Malate Dehydrogenase/biosynthesis* ; Plant Leaves/genetics* ; Scenedesmus/enzymology* ; Carbon Cycle/genetics* ; Lipid Metabolism/genetics* ; Plants, Genetically Modified/metabolism*

OBJECTIVE: To investigate the expression and possible regulatory mechanism of miR-34a in the lung tissue of neonatal rat model of bronchopulmonary dysplasia (BPD) induced by hyperoxia. METHODS: In the study, 80 newborn SD rats were randomly divided into hyperoxia group (FiO₂=60%) and air group (FiO₂=21%) within 2 hours after birth, 40 rats per group. Lung tissue samples of the SD rats in each group were extracted on the 1st, 7th, 14th and 21st days after birth, and the pathological changes of lung tissue were observed under light microscope after HE staining. The number of radial alveolar counts (RAC) and the mean alveolar diameter (MAD) and the thickness of alveolar septal thickness (AST) were measured to evaluate the development of alveoli. Real-time fluorescence quantitative PCR was used to detect the expression of miR-34a, angiopoietin-1 (Ang-1) and tyrosine kinase receptor-2 (Tie-2) in lung tissue of rats in hyperoxia group and air group at different time points. Enzyme-linked immunosorbent assay (ELISA) was used to detect the proteins expression of Ang-1 and Tie-2 in the lung tissues of the two groups at different time points. RESULTS: The weight of rats in the hyperoxia group on the 7th, 14th and 21st days after birth was significantly lower than that in the air group (P all < 0.05). With the prolongation of oxygen exposure, the number of alveoli decreased, the volume increased, the structure simplified, the alveolar cavity enlarged obviously and the alveolar septum thickened in the hyperoxia group. On the 7th, 14th and 21st days after birth, the RAC in the hyperoxia group was significantly lower than that in the air group (P all < 0.05). Compared with the air group, MAD and AST increased significantly on the 7th, 14th and 21st days after birth in the hyperoxia group, and the difference was statistically significant (P all < 0.05). The expression level of miR-34a in lung tissue of hyperoxia group was significantly higher than that of air group on the 7th, 14th and 21st days after birth, and the difference was statistically significant (P all < 0.05). Compared with the air group at the same time point, the expression levels of Ang-1 and Tie-2 mRNA and protein in the hyperoxia group were lower than those in the air group on the 14th and 21st days after birth (P all < 0.05). CONCLUSION The new BPD model of newborn SD rats can be successfully established by continuous exposure to 60% hyperoxia. The expression of miR-34a was up-regulated in the lung tissue of the new BPD model of neonatal rats. MiR-34a may play an important role in the occurrence and development of BPD by regulating Ang-1/Tie-2 signal pathway.
Animals ; MicroRNAs/metabolism* ; Bronchopulmonary Dysplasia/genetics* ; Hyperoxia/metabolism* ; Rats, Sprague-Dawley ; Animals, Newborn ; Rats ; Angiopoietin-1/genetics* ; Disease Models, Animal ; Receptor, TIE-2/genetics* ; Lung/pathology* ; Male

Glioma represents the most prevalent malignant tumor of the central nervous system, with chemotherapy serving as an essential adjunctive treatment. However, most chemotherapeutic agents exhibit limited ability to penetrate the blood-brain barrier (BBB). This study introduced a novel dual-targeting strategy for glioma therapy by modulating the formation of nanobody-driven protein coronas to enhance the brain and tumor-targeting efficiency of hydrophobic cisplatin prodrug-loaded lipid nanoparticles (C8Pt-Ls). Specifically, nanobodies (Nbs) with fibrinogen-binding capabilities were conjugated to the surface of C8Pt-Ls, resulting in the generation of Nb-C8Pt-Ls. Within the bloodstream, Nb-C8Pt-Ls could bound more fibrinogen, forming the protein corona that specifically interacted with LRP-1, a receptor highly expressed on the BBB. This interaction enabled a "Hitchhiking Effect" mechanism, facilitating efficient trans-BBB transport and promoting effective brain targeting. Additionally, the protein corona interacted with LRP-1, which is also overexpressed in glioma cells, achieving precise tumor targeting. Computational simulations and SPR detection clarified the molecular interaction mechanism of the Nb-fibrinogen-(LRP-1) complex, confirming its binding specificity and stability. Our results demonstrated that this strategy significantly enhanced C8Pt accumulation in brain tissues and tumors, induced apoptosis in glioma cells, and improved therapeutic efficacy. This study provides a novel framework for glioma therapy and underscores the potential of protein corona modulation-based dual-targeting strategies in advancing treatments for brain tumors.