Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*

Implementation Science is an interdisciplinary field dedicated to systematically studying how to effectively translate evidence-based research findings into practical application and implementation. In the health-related context, it focuses on enhancing the efficiency and quality of healthcare services, thereby facilitating the transition from scientific evidence to real-world practice. This article elaborates on Theories, Models, and Frameworks (TMF) within health-related Implementation Science, clarifying their basic concepts and classifications, and discussing their roles in guiding implementation processes. Furthermore, it reviews and prospects current research from three aspects: the constituent elements of TMF, their practical applications, and future directions. Five representative frameworks are emphasized, including the Consolidated Framework for Implementation Research (CFIR), the Practical Robust Implementation and Sustainability Model (PRISM), the Exploration, Preparation, Implementation, Sustainment (EPIS)framework, the Behavior Change Wheel (BCW), and the Normalization Process Theory (NPT). Additionally, resources such as the Dissemination & Implementation Models Webtool and the T-CaST tool are introduced to assist researchers in selecting appropriate TMFs based on project-specific needs.

The interactions between microorganisms and medicinal plants are crucial to the quality improvement of medicinal plants. Medicinal plants attract microorganisms to colonize by secreting specific compounds and provide niche and nutrient support for these microorganisms, with a symbiotic network formed. These microorganisms grow in the rhizosphere, phyllosphere, and endophytic tissues of plants and significantly improve the growth performance and medicinal component accumulation of medicinal plants by promoting nutrient uptake, enhancing disease resistance, and regulating the synthesis of secondary metabolites. Microorganisms are also widely used in the ecological planting of medicinal plants, and the growth conditions of medicinal plants are optimized by simulating the microbial effects in the natural environment. The interactions between microorganisms and medicinal plants not only significantly improve the yield and quality of medicinal plants but also enhance their geoherbalism, which is in line with the concept of green agriculture and eco-friendly development. This study reviewed the research results on the interactions between medicinal plants and microorganisms in recent years and focused on the analysis of the great potential of microorganisms in optimizing the growth environment of medicinal plants, regulating the accumulation of secondary metabolites, inducing systemic resistance, and promoting the ecological planting of medicinal plants. It provides a scientific basis for the research on the interactions between medicinal plants and microorganisms, the research and development of microbial agents, and the application of microorganisms in the ecological planting of medicinal plants and is of great significance for the quality improvement of medicinal plants and the green and sustainable development of TCM resources.
Plants, Medicinal/metabolism* ; Bacteria/genetics* ; Symbiosis

The traditional Chinese medicine(TCM) industry is a crucial part of China's pharmaceutical sector and plays a strategic role in ensuring public health and promoting economic and social development. In response to the practical demand for high-quality development of the TCM industry, this paper focused on the bottlenecks encountered during the digital and intelligent transformation of TCM production systems. Specifically, it explored technical strategies and methodologies for constructing the best TCM production mode. An innovative artificial intelligence(AI)-centered technical architecture for TCM production was proposed, focusing on key aspects of production management including process modeling, state evaluation, and decision optimization. Furthermore, a series of critical technologies were developed to realize the best TCM production mode. Finally, a novel AI-driven TCM production mode characterized by a closed-loop system of "measurement-modeling-decision-execution" was presented through engineering case studies. This study is expected to provide a technological pathway for developing new quality productive forces within the TCM industry.
Artificial Intelligence ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional/methods* ; Humans

Trauma is an important cause of death in young- and middle-aged people. Trauma is comprehensive and includes many surgical specialties, and the surgical techniques of these specialties have long been mature. To reduce the mortality and disability rate of trauma patients, it is necessary to improve trauma management. Trauma has attracted attention in China and trauma treatment and care developed rapidly in recent years. To decrease traumatic mortality and disability rates, our team is committed to building an efficient trauma system in Shaanxi province and has successfully developed a trauma limb salvage map to address the high rates of amputation and disability in patients with limb injuries. This article elaborates on the construction experience of a trauma limb salvage map and its application details in Shaanxi province of China.
Humans ; China ; Limb Salvage/methods* ; Wounds and Injuries/surgery* ; Male ; Extremities/injuries* ; Adult ; Amputation, Surgical ; Middle Aged ; Female

Adipose tissue is a critical energy reservoir in animals and humans, with multifaceted roles in endocrine regulation, immune response, and providing mechanical protection. Based on anatomical location and functional characteristics, adipose tissue can be categorized into distinct types, including white adipose tissue (WAT), brown adipose tissue (BAT), beige adipose tissue, and pink adipose tissue. Traditionally, adipose tissue research has centered on its morphological and functional properties as a whole. However, with the advent of single-cell transcriptomics, a new level of complexity in adipose tissue has been unveiled, showing that even under identical conditions, cells of the same type may exhibit significant variation in morphology, structure, function, and gene expression——phenomena collectively referred to as cellular heterogeneity. Single-cell transcriptomics, including techniques like single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), enables in-depth analysis of the diversity and heterogeneity of adipocytes at the single-cell level. This high-resolution approach has not only deepened our understanding of adipocyte functionality but also facilitated the discovery of previously unidentified cell types and gene expression patterns that may play key roles in adipose tissue function. This review delves into the latest advances in the application of single-cell transcriptomics in elucidating the heterogeneity and diversity within adipose tissue, highlighting how these findings have redefined the understanding of cell subpopulations within different adipose depots. Moreover, the review explores how single-cell transcriptomic technologies have enabled the study of cellular communication pathways and differentiation trajectories among adipose cell subgroups. By mapping these interactions and differentiation processes, researchers gain insights into how distinct cellular subpopulations coordinate within adipose tissues, which is crucial for maintaining tissue homeostasis and function. Understanding these mechanisms is essential, as dysregulation in adipose cell interactions and differentiation underlies a range of metabolic disorders, including obesity and diabetes mellitus type 2. Furthermore, single-cell transcriptomics holds promising implications for identifying therapeutic targets; by pinpointing specific cell types and gene pathways involved in adipose tissue dysfunction, these technologies pave the way for developing targeted interventions aimed at modulating specific adipose subpopulations. In summary, this review provides a comprehensive analysis of the role of single-cell transcriptomic technologies in uncovering the heterogeneity and functional diversity of adipose tissues.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Objective To distinguish Cremastra appendiculata(D.Don)Makino,Pleione yunnanensis Rolfe and Pleione bulbocodioides,and its easily confusing products Oreorchis patens and Iphigenia indica Kunth using the ITS sequence in DNA barcodes;To explore the genetic diversity of Cremastra appendiculata germplasm resources.Methods Three different original Cremastra appendiculata,Pleione yunnanensis Rolfe and Pleione bulbocodioides,and their easily confusing products Cremastrae Pseudobulbus of Oreorchis patens and Iphigenia indica Kunth were selected as the research objects,and the genomic DNA of the above samples were extracted by the modified CTAB method,and then the ITS sequences were amplified,sequenced and spliced by PCR technology.The Kimura 2-Parameter(K2P)model was used to calculate the genetic distance,and the phylogenetic tree was constructed with the help of neighbour joining method(NJ)for genetic relationship analysis.Results Except for the Iphigenia indica Kunth species that were not found during the BLAST search,the BLAST comparison results of the other samples were higher than 95%.At the same time,the results of phylogenetic tree showed that Cremastra appendiculata,Pleione yunnanensis Rolfe and Pleione bulbocodioides were clustered into one branch,respectively,and the easily confusing products were also respectively clustered into one branch.Conclusion The ITS sequence in DNA barcodes can be used to accurately distinguish Cremastra appendiculata,Pleione yunnanensis Rolfe and Pleione bulbocodioides,and its easily confusing products Oreorchis patens and Iphigenia indica Kunth.

OBJECTIVE To evaluate the efficacy of oral and facial muscle functional training in treating adult obstructive sleep apnea(OSA)and to identify clinical indicators influencing treatment outcomes.METHODS Through a prospective cohort study,patients diagnosed with OSA in the study unit were recruited to undergo a 3-month myofunctional therapy,including soft palate-related muscles,tongue muscles,buccal muscles,and labial muscles in multiple muscle groups,once a day,five times a week,with the use of offline clinic guidance,and the APP program video follow up training for effective training.Data were collected on multiple dimensions including physical signs,sleep breathing monitoring parameters,and airway measurements from imaging studies.Treatment efficacy was assessed by comparing subjective and objective sleep indicators before and after training.Patients were categorized into effective and ineffective groups based on treatment outcomes.Differences in baseline clinical indicators between these groups were analyzed using univariate and multivariate regression analyses.RESULTS The study finally included 58 people,51 males and 7 females,age(38.36±8.96)years,BMI(27.14±3.68)kg/m2,AHI of the enrolled patients was reduced from(31.27±22.28)times/h pre-training to(26.27±21.38)times/h post-training,the minimum oxygen saturation was increased from(78.43±10.07)%to(80.50±10.06)%,snoring index decreased from(62.80±75.20)times/h to(36.40±43.19)times/h,and ESS score decreased from 7.00±5.31 pre-training to 5.50±3.17.By comparing the effective and ineffective groups,it was found that there was a statistically significant difference in the tongue position and ESS scores between the two groups(both P<0.05),while no significant differences were found in gender,age,neck circumference,posterior soft palate area,uvula area,posterior tongue area,or posterior epiglottic area(all P>0.05).Univariate logistic regression analysis indicated that tongue position,AHI,and ESS scores were factors affecting the efficacy of oral and facial muscle function training.Multivariate regression analysis revealed that AHI was an independent prognostic factor for this training in OSA patients.CONCLUSION Oral and facial muscle function training can improve both subjective and objective sleep breathing indices in OSA patients.Tongue position,AHI,and ESS scores may serve as prognostic factors for OSA treatment,aiding in guiding subsequent individualized intervention therapies.