Objective: To improve the efficiency and standardization of preoperative anemia diagnosis and treatment by establishing a systematic intelligent management platform for preoperative anemia. Methods: A multidisciplinary collaborative model was adopted to develop a preoperative anemia management system that integrates intelligent early warning, standardized treatment pathways, and quality control. The system utilizes natural language processing technology to automatically capture laboratory data and establish evidence-based medical decision support functions. A pre-post study design was employed to compare changes in preoperative anemia screening rates, preoperative anemia intervention rates, reasonable use of iron supplements, and perioperative red blood cell transfusion rates before and after system implementation. Results: After system implementation, the standardization of anemia diagnosis and treatment significantly improved: 1) Screening effectiveness: The anemia screening rate increased to 50.00% (an increase of 27.24%); 2) Intervention effectiveness: The anemia treatment rate rose to 56.30% (an increase of 14.02%); 3) Treatment standardization: The reasonable use rate of iron supplements increased to 55.33% (an increase of 21.02%); the red blood cell transfusion rate decreased to 18.29% (a decrease of 4.07%), and the amount of red blood cell transfusions was reduced by 291 units. Conclusion: This system achieves full-process management of preoperative anemia through information technology, significantly enhancing the standardization of diagnosis and treatment as well as intervention effectiveness, providing an effective solution for perioperative anemia management.

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

Objective To explore the dynamic changes and mechanisms of neurological and cognitive functions in mice with traumatic brain injury (TBI). Methods Totally 60 12⁃month⁃old Balb/ c mice were divided into control group (10 in group) and TBI group (50 in group). TBT model mice were divided into 5 subgroups according to the time of model construction, including model 1 day, model 1 day, model 3 day, model 7 day, model 14 days and model 28 days group with 10 in each group. At the 29th day of the experiment, neurological scores and step down tests were carried out. After the test, the mice were sacrificed for brains which were detected by immunohistochemistry staining, inflammatory cytokine tests and Western blotting. Results Compared with the control group, the neurological scores of mice in TBI group increased, and then decreased after the 7th day when the scores reached the peak. However, the latency of step down errors was lower than control group, and the number of step down errors was higher than control group which had no changes. Compared with the control group, the expression of lonized calcium⁃binding adapter molecule 1(IBA1), chemokine C⁃X3⁃C⁃motif ligand1 (CX3CL1), C⁃X3⁃C chemokine receptor 1(CX3CR1), NOD⁃like receptor thermal protein domain associated protein 3 (NLRP3), and phosphorylation nuclear factor(p⁃NF)⁃κB in TBI group increased and reached to the peak at the 7th day, and then started to decrease. At the same time, the levels of inflammatory cytokines interleukin⁃6(IL⁃6) and tumor necrosis factor⁃α(TNF⁃α) first increased to the peak, and then began to decrease. However, compared with the control group, the expression of amyloid β(Aβ) protein and p⁃Tau protein in the model group continued to increase at all time. Conclusion The TBI model caused continuous activation of microglia along with inflammatory response, which first increased and then decreased, resultsing in neurological scores changes. In addition, the inflammatory response may act as a promoter of Aβ protein deposition and Tau protein phosphorylation, leading to cognitive impairment in mice.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective As a signaling molecule,NO regulates key physiological processes and is closely related to periodontitis.To investigate the effect of flavonoid NO donor composite nanoparticles(G10@HAP/MSN@ZnO@COS)on osteogenic differentiation of periodontal ligament stem cells(PDLSCs)by regulating macrophage polarization.Methods The novel NO donor drug G10 was loaded on hydroxyapatite/mesoporous silicanant particles(HAP/MSN),filled with zinc oxide(ZnO),and then coated with chitosan(COS)to prepare composite nanoparticles(G10@HAP/MSN@ZnO@COS).The best concentration of G10@HAP/MSN@ZnO@COS was screened to promote cell proliferation by CCK-8 cell experiment.After the mouse mononuclear macrophages were stimulated by lipopo-lysaccharide,the mice were divided into four groups:Control group,G10 group,HAP/MSN@ZnO@COS group and G10@HAP/MSN@ZnO@COS group.Each group was cultured with fresh medium,5 μg/mL G10,5 μg/mL HAP/MSN@ZnO@COS and 5 μg/mL G10@HAP/MSN@ZnO@COS for 72 h respectively.ELISA and RT-qPCR were used to detect the expression of cytokines(TNF-α,IL-6,IL-1β,iNOS,IL-10)and mRNA expression in each group,and the phenotypic changes of M1/M2 were evaluated.The supernatant of each culture medium was used as conditioned medium to culture PDLSCs,and the osteogenic ability and cell miner-alization were evaluated by alkaline phosphatase activity test and alizarin red staining.Results CCK-8 experiment showed that G10@HAP/MSN@ZnO@COS of 5 μg/mL could significantly promote the proliferation of PDLSCs.The results of ELISA showed that compared with Control group,the expression of M1 type marker IL-1β,IL-6,TNF-α and iNOS in G10@HAP/MSN@ZnO@COS group was significantly decreased(P＜0.000 1),while the expression of M2 type marker IL-10 was significantly increased(P＜0.000 1).The results of RT-qPCR were consistent with those of ELISA,which showed that the expression of M1-related genes in G10@HAP/MSN@ZnO@COS group decreased significantly(P＜0.01).The results of alizarin red staining and alkaline phosphatase activity test showed that the number of mineralized nodules and alkaline phosphatase activity in G10@HAP/MSN@ZnO@COS-CM group were significantly higher than those in other groups(P＜0.000 1).Conclusion Composite nanoparticles(G10@HAP/MSN@ZnO@COS)can effectively inhibit the polarization of macrophages to M1 phenotype and promote it to M2 phenotypic polarization.The anti-inflammatory microenvironment regulated by G10@HAP/MSN@ZnO@COS can en-hance the osteogenic differentiation of PDLSCs.

ObjectiveTo examine the current status of preschoolers' eating behaviors and investigate its correlation with family cohesion and adaptability. MethodsA cross-sectional study was conducted involving 21，954 preschoolers and their families from Pingshan District， Shenzhen， between September 2021 and December 2021. A general demographic questionnaire， the Chinese version of Family Adaptability and Cohesion Evaluation Scale Ⅱ （FACESⅡ-CV） and Chinese Preschoolers’ Eating Behavior Questionnaire （CPEBQ） were used to collect the relevant information. Multiple linear regression was used to analyze the association of family cohesion and adaptability with eating behaviors of preschoolers. ResultsTypes of family cohesion and adaptability were significantly correlated with all the 7 dimensions of preschoolers' eating behaviors， including food fussiness （R²=0.252， F=114.457， P<0.001）， food responsiveness （R² = 0.111， F =24.973， P<0.001）， eating habit （R²= 0.304， F =139.658， P<0.001）， satiety responsiveness （R² = 0.259， F =105.332， P<0.001）， external eating （R² = 0.182， F =50.150， P<0.001）， emotional eating （R² = 0.234， F =91.084， P<0.001） and initiative eating （R² = 0.349， F =168.608， P<0.001）. After adjusting for confounding factors， our study showed that types of family cohesion and adaptability were independent predictors of preschoolers' eating behaviors （P<0.05）. ConclusionsTypes of family cohesion and adaptability have a significant predictive effect on the 7 dimensions of preschoolers' eating behaviors. Higher scores of family cohesion and adaptability imply stronger initiative eating ability and less poor dietary behaviors in preschoolers.

Objective To investigate the correlation between ADC value and glioma IDH-1/1p19q genotype. Methods The MRI features and molecular pathological results of 69 patients with pathologically confirmed diagnosis of WHO grade Ⅱ/Ⅲ glioma between March 2013 and December 2020 were retrospectively analyzed. The diagnostic performance of ADC values on glioma genotypes (IDH-1, 1p19q) was evaluated using the ROC curve of the subjects' working characteristics. Results The ADCmean, ADC_min, rADC_mean, and rADC_min in the IDH-1 mutation group were significantly higher than those in the IDH-1 wild group (P < 0.05, P < 0.01, P < 0.05, P < 0.01). The use of the rADC_min threshold (0.979×10³mm²/s) had the highest efficacy (AUC=0.770) for diagnosis of IDH-1 mutant and IDH-1 wild-type gliomas as well as sensitivity and specificity of 84.61% and 59.09%, respectively. Conclusion ADC can be used as an imaging biomarker for noninvasive prediction of IDH-1 mutant and wild-type Ⅱ /Ⅲ gliomas.