Objective: To investigate the accuracy of the mandibular curve of Spee and mandibular curve of Wilson curve, mandibular posterior crown inclination, maxillary and mandibular arch width, and mandibular incisor labial inclination changes in the invisible orthodontic treatment of extracted and non-extracted patients to provide a basis for the study of the therapeutic efficacy of the invisible aligner. Methods: This study was approved by the Unit Medical Ethics Committee. Adult bony Class I patients treated with invisible aligners between 2016 and 2023 were selected and categorized into 32 cases in a group with four first premolar extractions and 33 cases in a non-extraction group. The initial scanning model (T1), the orthodontic plan design model (T2), and the scanning model after alignment and leveling (T3) were collected, and the scanning data at different time points were exported to STL format files. They were then analyzed and measured using GOM Inspect 2019 software; the clinical effect (T1-T3) was defined as AC, and the expected result (T1-T2) was defined as CC. To explore the mandibular curve of Spee, the leveling accuracy of the mandibular Wilson curve (AC/CC×100%), the change in mandibular posterior crown inclination, the change in maxillary and mandibular arch width and the change in mandibular incisor labial inclination. The results were tallied using R4.3.2 software. Results: The leveling accuracy of the mandibular curve of Spee was 3.2% and 10.1% in the extraction and non-extraction groups, respectively; the leveling accuracy of the mandibular first molar in the extraction and non-extraction groups was 9.5% and 4.2%, respectively, and the leveling accuracy of the mandibular second premolar was 32.8% and 25%, respectively. The leveling accuracy of the mandibular curve of Wilson was 126% in the extraction group compared to 704% in the non-extraction group. The maximum values of AC for crown inclination of the mandibular posterior teeth were all found in the first molar and the minimum values in the second premolar (P< 0.05); CC was the greatest in the second premolar and the least in the first molar in the extraction group (P< 0.05), and in the second molar and the least in the second premolar in the non-extraction group (P< 0.05). There was no significant difference in the change of maxillary and mandibular arch widths in the extraction group (P> 0.05); the AC of the arch widths at the maxillary first molar and at the mandibular second premolar and first molar in the non-extraction group was significantly greater than the CC (P< 0.05). The AC of the mandibular second premolar crown inclination was significantly greater in the non-extraction group than in the extraction group (P < 0.05), showing a more pronounced buccal inclination of the crowns; in contrast, there was no significant difference between the mandibular molar crown inclination AC and CC in the two groups (P > 0.05). The CC of mandibular curve of Spee in the extraction group was significantly greater than that in the non-extraction group (P < 0.05), whereas there was no significant difference in AC between the two groups (P > 0.05). In addition, the AC of the labial inclination of the mandibular incisors in the extraction group and the non-extraction group was smaller than the CC, but both were positive, indicating an increase in the degree of labial inclination of the mandibular incisors. Conclusion The invisible aligner mandibular curve of Spee leveling was poorly expressed, and there was a tendency for overexpression of the mandibular curve of Wilson leveling. Increased labial inclination of the mandibular incisors was observed in patients. The extraction group was accurately predicted in arch width control, there was overexpansion in the non-extraction group.

Real-world study (RWS), based on multi-source data from real medical environments, is gradually becoming an important supplement to traditional randomized controlled trials, and its application in the field of transfusion medicine is becoming increasingly widespread. This article systematically reviews the definition and methodological system of RWS, examines its application cases in clinical blood transfusion research, and discusses the advantages, limitations, and future research directions of RWS, aiming to provide a reference for evidence-based research in blood transfusion medicine.

Real-world study (RWS), based on multi-source data from real medical environments, is gradually becoming an important supplement to traditional randomized controlled trials, and its application in the field of transfusion medicine is becoming increasingly widespread. This article systematically reviews the definition and methodological system of RWS, examines its application cases in clinical blood transfusion research, and discusses the advantages, limitations, and future research directions of RWS, aiming to provide a reference for evidence-based research in blood transfusion medicine.

Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model's applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.

Objective:To investigate the gene expression level of fructose-bisphosphate aldolase A (ALDOA) in non-small cell lung cancer (NSCLC) tissues and the effect of ALDOA on NSCLC prognosis and immune cell infiltration in tumor microenvironment (TME).Methods:The clinical data of patients with lung adenocarcinoma and squamous cell carcinoma, the data of corresponding gene in tumor tissues and corresponding normal lung tissues in The Cancer Genome Atlas (TCGA) database, and the data of healthy population in the Genotype Tissue Expression (GTEx) database were collected in June 2023. Gene Expression Profile Interaction Analysis (GEPIA) 2.0 online tool was used to explore the expression differences of ALDOA at transcription level in NSCLC tissues and normal lung tissues, as well as NSCLC tissues of different stages; the expression of ALDOA was obtained through immunohistochemical staining in different pathological types of NSCLC and normal lung tissues from the Human Protein Atlas (HPA) database; the survival of patients with high and low expression of ALDOA in NSCLC tissues in TCGA database was evaluated by plotting Kaplan-Meier survival curves (differentiated by the median expression of ALDOA at transcription level in NSCLC samples), and log-rank test was used for comparing survival between groups. Using the Tumor Immune Estimation Resource (TIMER) 2.0 database, the CIBERSORT algorithm was applied to evaluate the correlation between ALDOA expression and immune cell infiltration levels in NSCLC tissues from the TCGA database. Further analysis of the correlation between ALDOA expression levels and programmed death receptor ligand 1 (PD-L1), matrix metalloproteinase 9 (MMP-9), vascular endothelial growth factor (VEGF), and various inflammatory factors in NSCLC tissues was conducted using the GEPIA 2.0 online tool to evaluate the impact of ALDOA expression levels on the tumor immune microenvironment.Results:The expression of ALDOA at transcription level in lung adenocarcinoma (483 cases) and lung squamous cell carcinoma (486 cases) tissues were higher than those in corresponding normal lung tissues (347 and 338 cases, respectively), and the differences were statistically significant (all P < 0.01). There was a statistically significant difference in the expression of ALDOA at transcription level among TNM stage Ⅰ, Ⅱ, Ⅲ, and Ⅳ NSCLC tissues ( F = 4.55, P = 0.004), and the expression level of ALDOA gradually increased with the increase of stage. In the HPA database, ALDOA protein stained by immunohistochemistry showed mild to moderate staining in normal lung tissues, while it showed severe staining in lung adenocarcinoma and squamous cell carcinoma tissue samples. Kaplan-Meier survival analysis showed that the overall survival of lung adenocarcinoma patients (239 cases) with high ALDOA expression was worse than that of patients with low ALDOA expression (239 cases) ( P < 0.001), and there was no statistically significant difference in disease-free survival between the two groups ( P = 0.140). There was no statistically significant difference in overall survival and disease-free survival between patients with high expression (241 cases) and low expression (241 cases) of ALDOA in lung squamous cell carcinoma (both P > 0.05). TIMER 2.0 database analysis shows that in lung adenocarcinoma, the expression of ALDOA at transcription level was negatively correlated with the levels of activated mast cells ( Rho = -0.209) and memory B cells ( Rho = -0.133), and positively correlated with the levels of resting mast cells ( Rho = 0.210) and resting natural killer cells ( Rho = 0.110), with statistically significant differences (all P < 0.05). In lung squamous cell carcinoma, the expression of ALDOA at transcription level was negatively correlated with the levels of activated mast cells ( Rho = -0.105), memory B cells ( Rho = -0.213) and CD8 + T cells ( Rho = -0.148), and positively correlated with the levels of resting mast cells ( Rho = 0.173), plasma B cells ( Rho = 0.174) and resting natural killer cells ( Rho = 0.136), with statistically significant differences (all P < 0.05). In NSCLC tissues, the expression of ALDOA at transcription level was negatively correlated with PD-L1 ( R = -0.11), interleukin (IL)-2 ( R = -0.31), IL-4 ( R = -0.14), IL-5 ( R = -0.10), IL-6 ( R = -0.12), and IL-10 ( R = -0.24) levels (all P < 0.001), positively correlated with MMP-9 ( R = 0.11) and VEGF ( R = 0.18) levels (both P < 0.001), and positively correlated with IL-9 ( R = 0.11) and VEGF ( R = 0.18) levels (both P < 0.001), but it was not correlated with IL-17 level ( R = -0.02, P = 0.540). Conclusions:The expression level of ALDOA is elevated in NSCLC tissues, and high ALDOA level in lung adenocarcinoma may indicate poor survival of patients. ALDOA may affect the levels of immune cell infiltration, tumor markers and inflammatory factors in TME, and it may be a potential biomarker for prognosis.

Objective To measure and analyze through cone-beam computed tomography(CBCT)of the incidence of alveolar bone fenestration and dehiscence,as well as the thickness and height of the alveolar bone in the maxillary canine region of adult patients with different sagittal skeletal patterns.Methods A total of 174 adult patients meeting the criteria were selected.In the first step,the inci-dence rates of fenestration and dehiscence were measured and statistically analyzed after grouping according to ANB.In the second step,the patients with bone fenestration and bone dehiscence were excluded,and the thickness of the alveolar bone at the apical,mid-dle and cervical regions on the labial and palatal sides of the maxillary canine area and the height of the alveolar crest were measured in the remaining samples.Then,the three groups of patients were further divided into three groups according to the FMA for comparison and analysis.Results The incidence of bone fenestration and bone dehiscence in the maxillary canine area was higher in adult patients with skeletal Class Ⅱ.The thickness of the labial alveolar bone at the apical region in skeletal Class Ⅲ was greater than that in skeletal Class Ⅱ and skeletal Class Ⅰ.The thickness of the palatal alveolar bone at the middle root in skeletal Class Ⅱ:average angle＞low angle＞high angle.In skeletal Class Ⅲ,the thickness of the palatal alveolar bone at the cervical root:low angle＞average angle＞high angle.Conclusion The incidence of bone fenestration and bone dehiscence was the highest in adult patients with skeletal ClassⅡ.The thickness of the labial alveolar bone at the apical region was greater in skeletal Class Ⅲ than in skeletal Class Ⅱ and skeletal Class Ⅰ.The thickness of the palatal alveolar bone at the middle root in skeletal Class Ⅱ high angle patients was thinner than that in the average angle and low angle groups,and the thickness of the palatal alveolar bone at the cervical region in skeletal Class Ⅲ was thinner in the high angle group than in the average angle and low angle groups.

Objective To address the national requirements for high-quality development of public hospitals issued by the National Health Commission(NHC),this study establishes a knowledge-value-oriented performance management system tailored to dental specialty characteristics,aiming to synergistically enhance disciplinary advancement and operational efficiency.Methods Guided by policies such as Guidelines on Strengthening Operational Management in Public Hospitals,we integrated resources in oral and maxillofacial surgery through data-driven decision-making and multidimensional investigations.A"2-3-3"performance manage-ment model was developed,featuring dual objectives(public welfare and economic efficiency),a three-tier allocation mechanism(hospital-department-individual),and triple support strategies(key disciplines,young talents,and under-resourced projects).Im-plementation involved organizational restructuring,business-finance integration,and digital transformation.Results Post-reform outcomes demonstrated a 14％year-on-year increase in outpatient volume and a 9％growth in bed-to-surgery ratio.Both medical quality indicators and revenue and expenditure structures were optimized,achieving better equilibrium between public welfare and economic sustainability.Conclusion This model replaces the egalitarian distribution approach with a value-driven incentive mech-anism,effectively promoting disciplinary specialization and talent engagement.It provides a replicable framework for performance reform in dental specialty hospitals,aligning with the NHC's policy orientation on refined public hospital management.

Recently, diffusion models have emerged as a promising paradigm for molecular design and optimization. However, most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geometries, with limited research on molecular sequence diffusion models. The International Union of Pure and Applied Chemistry (IUPAC) names are more akin to chemical natural language than the Simplified Molecular Input Line Entry System (SMILES) for organic compounds. In this work, we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language (SMILES) and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language. We propose DiffIUPAC, a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings. Evaluation results demonstrate that our model outperforms existing methods and successfully captures the semantic rules of both chemical languages. Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints. Additionally, to illustrate the model's applicability in drug design, we conducted case studies in functional group editing, analogue design and linker design.

Objective: Through analyzing the current handling capabilities for complex cases in blood transfusion compatibility detection and the application status of artificial intelligence-assisted (AI-assisted) technologies, this study explores the establishment of an effective AI-augmented protocol for managing challenging blood transfusion compatibility detection. Methods: This research systematically analyzes, designs, and explores an AI-augmented operational workflow for resolving challenging blood transfusion compatibility detection cases. Through three representative case studies, we evaluate its effectiveness, accuracy, and efficiency in addressing real-world diagnostic challenges. Results: The AI-augmented operational model demonstrates significant efficacy in resolving complex blood transfusion compatibility challenges, including complex blood typing, antibody specificity identification, challenging cross-matching, and transfusion strategy formulation. Conclusion: AI-augmented technologies demonstrate immense potential in resolving complex blood transfusion compatibility detections. By enabling intelligent, automated, precise, and standardized solutions, they significantly enhance diagnostic accuracy and operational efficiency, which is critical for ensuring transfusion safety and advancing personalized transfusion medicine. This study delineates both the advantages and existing limitations of AI implementation, explores future developmental trajectories, and provides a theoretical framework and practical implementation pathways for deeper integration of AI in transfusion medicine.

Objective To address the national requirements for high-quality development of public hospitals issued by the National Health Commission(NHC),this study establishes a knowledge-value-oriented performance management system tailored to dental specialty characteristics,aiming to synergistically enhance disciplinary advancement and operational efficiency.Methods Guided by policies such as Guidelines on Strengthening Operational Management in Public Hospitals,we integrated resources in oral and maxillofacial surgery through data-driven decision-making and multidimensional investigations.A"2-3-3"performance manage-ment model was developed,featuring dual objectives(public welfare and economic efficiency),a three-tier allocation mechanism(hospital-department-individual),and triple support strategies(key disciplines,young talents,and under-resourced projects).Im-plementation involved organizational restructuring,business-finance integration,and digital transformation.Results Post-reform outcomes demonstrated a 14％year-on-year increase in outpatient volume and a 9％growth in bed-to-surgery ratio.Both medical quality indicators and revenue and expenditure structures were optimized,achieving better equilibrium between public welfare and economic sustainability.Conclusion This model replaces the egalitarian distribution approach with a value-driven incentive mech-anism,effectively promoting disciplinary specialization and talent engagement.It provides a replicable framework for performance reform in dental specialty hospitals,aligning with the NHC's policy orientation on refined public hospital management.