OBJECTIVE: To develop a user-friendly visualization application for the automatic annotation of Human Phenotype Ontology (HPO) terms based on large language models and retrieval-augmented generation (RAG) technology, and to validate its performance in an authoritative case dataset. METHODS: By integrating the domestic open-source large language model DeepSeek-V3 with RAG technology, an interactive web application was deployed on the Streamlit cloud platform. Using only the latest official HPO dataset as the data source, the lightweight sentence-embedding model BAAI/bge-small-en-v1.5 was employed to construct a FAISS vector index. During the online phase, a four-step closed-loop process is automatically completed: multilingual translation, phenotype phrase extraction, RAG candidate retrieval, term mapping, and official database validation. 121 English case reports publicly released by BMJ Case Reports and Oxford Medical Case Reports (with a gold-standard HPO set of 1 794 terms) were selected for application validation. Precision, recall, and F1 score were calculated and compared horizontally with traditional dictionary tools, standalone large language models, and the similar application "RAG-HPO". Finally, replace the model with the more advanced ChatGPT-5 and evaluate its performance on the newly extracted dataset. RESULTS: An HPO term automatic annotation visualization application named PhenoRAG, based on large language models and RAG technology, was successfully developed. Users can access it directly via a web link. Across the 112 cases, a total of 2 150 HPO terms were generated; 2,064 (96.0%) were fully validated by the official database, with a hallucination rate of 1.3% and an HPO ID-name mismatch rate of 2.7%. After deduplication, 1,906 terms remained for testing. The overall precision was 63.65%, recall was 67.34%, and F1 was 65.44%, significantly outperforming traditional annotation tools (F1: 0.45-0.49, P < 0.001). Although PhenoRAG's F1 was lower than that of RAG-HPO (F1 = 0.78, P < 0.001), which relies on a manually constructed synonym database of 54 000 entries plus the HPO dataset, it requires no additional dictionary maintenance and can be used without any background in computer programming. Moreover, after switching to the GPT-5 model, PhenoRAG exhibited no hallucination rate on the new dataset, and its F1 score significantly increased (P = 0.038). CONCLUSION Without constructing a synonym database, the PhenoRAG achieved high-accuracy automatic mapping from clinical text to standard HPO terms. It features a low usage threshold, free access, and a Chinese-language interface, and can directly serve rare disease diagnosis, genetic counseling, and research scenarios in China and worldwide, warranting further clinical promotion and multicenter validation.
Humans ; Phenotype ; Biological Ontologies ; Language ; Software ; Large Language Models

OBJECTIVE: To explore the application value of artificial intelligence (AI)-assisted chromosomal karyotype analysis in the diagnosis of prenatal chromosomal mosaicism. METHODS: A retrospective analysis was conducted on 172 pregnant women who underwent amniocentesis at the Department of Medical Genetics and Prenatal Diagnosis, the Third Affiliated Hospital of Zhengzhou University between January 2019 and December 2024. All cases whose fetuses were diagnosed with chromosomal mosaicism via karyotype analysis and stratified into two groups based on the analytical software employed: the conventional analysis group (n = 70), which utilized Leica analysis software for karyotype image recognition and cell counting; and the AI-assisted analysis group (n = 102), which utilized AI-assisted software for the same procedures. The clinical performance of AI-assisted karyotype analysis in diagnosing chromosomal mosaicism was comprehensively evaluated by comparing the types of mosaic karyotypes, distribution of mosaic ratios, and verification outcomes of different detection modalities between the two groups. This study was approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University (Ethics No.: 2024-406-01). RESULTS: No statistically significant difference was observed in baseline characteristics (maternal age, gestational week, and indications for prenatal diagnosis) between the two groups. Regarding the detection efficacy for numerical and structural mosaicisms, no significant difference was found in the detection of numerical mosaicism. However, the conventional analysis group exhibited a significantly higher detection rate of autosomal structural mosaicism compared to the AI-assisted group (11.43% vs. 0.98%, P < 0.05). Numerical mosaicism cases were further verified using copy number variation sequencing (CNV-seq) and/or fluorescence in situ hybridization (FISH). The AI-assisted group demonstrated a significantly lower inconsistency rate (5.56% vs. 20.41%, P < 0.05) compared to the conventional group. For low-proportion (< 10%) chromosomal mosaicism, the AI-assisted group had a significantly lower detection rate (13.25% vs. 29.69%, P < 0.05). Subsequent validation of low-proportion mosaicism by CNV-seq and/or FISH showed a higher consistency rate in the AI-assisted group (81.82% vs. 54.55%), though the difference did not reach statistical significance (P = 0.360). CONCLUSION For the karyotyping analysis of prenatal chromosomal mosaicism, AI-assisted karyotype analysis shows high accuracy and consistency in identifying numerical chromosomal mosaicism, particularly in reducing the detection of low-proportion (< 10%) mosaicism while improving verification accuracy. AI-assisted analysis can significantly improve the detection accuracy of numerical mosaicism and mitigate the risk of misclassification for low-proportion (< 10%) mosaicism, thereby providing more precise clinical evidence for the prenatal diagnosis of chromosomal mosaicisms.
Humans ; Female ; Mosaicism ; Pregnancy ; Karyotyping/methods* ; Artificial Intelligence ; Prenatal Diagnosis/methods* ; Adult ; Retrospective Studies ; Chromosome Disorders/genetics* ; Amniocentesis

Academic publishing is at a critical juncture. The challenges faced by the academics are mired in controversy. Among theseare three hotly debated concerns. First is the issue of whether technological innovations such as artificial intelligence (AI)improves research efficiency or if its use sacrifices research integrity.Another is the controversy between paywall publishingand open access. Lastly, adapting an appropriate business model for sustainability is a contentious issue and the choice betweena commercial or a university-based publishing platform is a difficult one.

Traditional models of scientific investigation relied on tedious intellectual calisthenics in all aspects of research —identifying research gaps, reviewing of published literature, devising valid methodology, collecting data, analysing results, and,finally, drawing conclusions. With the advent of powerful tools employing artificial intelligence, these heavy tasks are efficientlycarried out. The dilemma lies in determining which parts of the work can be attributed to the authors and which are ascribedto the output of large language models (LLMs) and other automated assistance employed.Despite requiring adequate vettingby experts of these AI-aided output, many in the scientific community still question these methods. Can research employingAI be considered honest work? Will full disclosure answer doubts as to the integrity of the scientific work?

Indeed, LLMs just gather information that is already out there, albeit more efficiently. After all, science progresses bystanding on the shoulder of giants. AI makes such work comprehensive and efficient. Standing on those proverbial shoulders,however, require access to prior work, hence our next challenge in academic publishing--open access versus paid access.Paywalls limit the benefits of valuable research to institutions and universities with the capacity to pay. Excluded from these arethose from low resourced countries, with nations from the global south being affected disproportionately. Additionally, whilenumerous authors appreciate the features of open access as it improves their impact and visibility, many feel unduly burdenedsince the cost of publishing in this format is passed on to them.

This brings us to our third issue: who bears the cost of academic publishing? Indeed, it is a lucrative industry, generatingan annual revenue of US$19 billion and an estimated 40 percent profit margin. Many, however, find fault in this businessmodel as concerns about the profit motives of the commercial publishers far overshadow their sustainability goals.

How do we navigate this landscape of controversies? We, at the ACTA, as part of the community of scholars, would needto clarify our mission. Our goals for this publication should be consistent with our values. These values, such as scientific rigor,integrity, and accountability, should be reflected in our policies. We should be cognizant of the role we play in national scientificdiscourse while we endeavor to make an impact in the global scene. We are accountable to our stakeholders — nurturingearly career scholars, supplying evidence to health policymakers, and being accountable to those who provide resources tosustain us. This stewardship is essential so that ACTA will stand shoulder to shoulder with the giants on which science buildsupon to benefit future generations.

Artificial Intelligence ; Commerce ; Costs And Cost Analysis ; Disclosure ; Drawing ; Efficiency ; Family Characteristics ; Forecasting ; Goals ; Gymnastics ; Health ; Health Resources ; Industry ; Intelligence ; Inventions ; Language ; Literature ; Methods ; Play And Playthings ; Policy ; Publications ; Publishing ; Research ; Residence Characteristics ; Role ; Science ; Shoulder ; Social Responsibility ; Universities ; Ursidae ; Volition ; Work ; World Health Organization

OBJECTIVES: To construct an intelligent analysis model for classifying fetal orientation during intrapartum ultrasound videos based on multi-feature fusion. METHODS: The proposed model consists of the Input, Backbone Network and Classification Head modules. The Input module carries out data augmentation to improve the sample quality and generalization ability of the model. The Backbone Network was responsible for feature extraction based on Yolov8 combined with CBAM, ECA, PSA attention mechanism and AIFI feature interaction module. The Classification Head consists of a convolutional layer and a softmax function to output the final probability value of each class. The images of the key structures (the eyes, face, head, thalamus, and spine) were annotated with frames by physicians for model training to improve the classification accuracy of the anterior occipital, posterior occipital, and transverse occipital orientations. RESULTS: The experimental results showed that the proposed model had excellent performance in the tire orientation classification task with the classification accuracy reaching 0.984, an area under the PR curve (average accuracy) of 0.993, and area under the ROC curve of 0.984, and a kappa consistency test score of 0.974. The prediction results by the deep learning model were highly consistent with the actual classification results. CONCLUSIONS The multi-feature fusion model proposed in this study can efficiently and accurately classify fetal orientation in intrapartum ultrasound videos.
Humans ; Female ; Ultrasonography, Prenatal/methods* ; Pregnancy ; Fetus/diagnostic imaging* ; Neural Networks, Computer ; Video Recording

OBJECTIVES: To synthesize a temperature-responsive multimodal motion microrobot (MMMR) using temperature and magnetic field-assisted microfluidic droplet technology to achieve targeted drug delivery and controlled drug release. METHODS: Microfluidic droplet technology was utilized to synthesize the MMMR by mixing gelatin with magnetic microparticles. The microrobot possessed a magnetic anisotropy structure to allow its navigation and targeted drug release by controlling the temperature field and magnetic field. In the experiment, the MMMR was controlled to move in a wide range along a preset path by rotating a uniform magnetic field, and the local circular motion was driven by a planar rotating gradient magnetic field of different frequencies. The MMMR was loaded with simulated drugs, which were released in response to laser heating. RESULTS: Driven by a rotating magnetic field, the MMMR achieved linear motion following a predefined path. The planar gradient rotating magnetic field controlled circular motion of the MMMR with an adjustable radius, utilizing the centrifugal force generated by rotation. The drug-loaded MMMR successfully reached the target location under magnetic guidance, where the gelatin matrix was melted using laser heating for accurate drug release, after which the remaining magnetic particles were removed using magnetic field. CONCLUSIONS The MMMR possesses multimodal motion capabilities to enable precise navigation along a predefined path and dynamic regulation of drug release within the target area, thus having great potential for a wide range of biomedical applications.
Drug Delivery Systems/methods* ; Temperature ; Drug Liberation ; Magnetic Fields ; Robotics ; Gelatin/chemistry* ; Delayed-Action Preparations ; Microfluidics ; Motion

OBJECTIVES: We propose a myocardial infarction (MI) detection and localization model for improving the diagnostic accuracy for MI to provide assistance to clinical decision-making. METHODS: The proposed model was constructed based on multi-scale field residual blocks fusion modified channel attention (MSF-RB-MCA). The model utilizes lead II electrocardiogram (ECG) signals to detect and localize MI, and extracts different levels of feature information through the multi-scale field residual block. A modified channel attention for automatic adjustment of the feature weights was introduced to enhance the model's ability to focus on the MI region, thereby improving the accuracy of MI detection and localization. RESULTS: A 5-fold cross-validation test of the model was performed using the publicly available Physikalisch-Technische Bundesanstalt (PTB) dataset. For MI detection, the model achieved an accuracy of 99.96% on the test set with a specificity of 99.84% and a sensitivity of 99.99%. For MI localization, the accuracy, specificity and sensitivity were 99.81%, 99.98% and 99.65%, respectively. The performances of the model for MI detection and localization were superior to those of other comparison models. CONCLUSIONS The proposed MSF-RB-MCA model shows excellent performance in AI detection and localization based on lead II ECG signals, demonstrating its great potential for application in wearable devices.
Myocardial Infarction/diagnosis* ; Humans ; Electrocardiography/methods* ; Signal Processing, Computer-Assisted ; Algorithms ; Sensitivity and Specificity

OBJECTIVES: We propose a YOLOv11-TDSP model for improving the accuracy of dental abnormality detection on panoramic oral X-ray images. METHODS: The SHSA single-head attention mechanism was integrated with C2PSA in the backbone layer to construct a new C2PSA_SHSA attention mechanism. The computational redundancy was reduced by applying single-head attention to some input channels to enhance the efficiency and detection accuracy of the model. A small object detection layer was then introduced into the head layer to correct the easily missed and false detections of small objects. Two rounds of structured pruning were implemented to reduce the number of model parameters, avoid overfitting, and improve the average precision. Before training, data augmentation techniques such as brightness enhancement and gamma contrast adjustment were employed to enhance the generalization ability of the model. RESULTS: The experiment results showed that the optimized YOLOv11-TDSP model achieved an accuracy of 94.5%, a recall rate of 92.3%, and an average precision of 95.8% for detecting dental abnormalities. Compared with the baseline model YOLOv11n, these metrics were improved by 6.9%, 7.4%, and 5.6%, respectively. The number of parameters and computational cost of the YOLOv11-TDSP model were only 12% and 13% of those of the high-precision YOLOv11x model, respectively. CONCLUSIONS The lightweight YOLOv11-TDSP model is capable of highly accurate identification of various dental diseases on panoramic oral X-ray images.
Radiography, Panoramic/methods* ; Humans ; Algorithms ; Tooth Abnormalities/diagnostic imaging*

OBJECTIVES: To develop risk prediction models for postoperative hypothermia after transurethral holmium laser enucleation of the prostate (HoLEP) using machine learning algorithms. METHODS: We retrospectively analyzed the clinical data of 403 patients from our center (283 patients in the training set and 120in the internal validation set) and 120 patients from Weifang People's Hospital (as the external validation set). The risk prediction models were built using logistic regression, decision tree and support vector machine (SVM), and model performance was evaluated in terms of accuracy, recall, precision, F1 score and AUC. RESULTS: Operation duration, prostate weight, intraoperative irrigation volume, and being underweight were identified as the predictors of postoperative hypothermia following HoLEP. Among the 3 algorithms, SVM showed the best precision rate and accuracy in all the 3 data sets and the best area under the ROC (AUC) in the training set and validation set, followed by logistic regression, which had a similar AUC in the two data sets. SVM outperformed logistic regression and decision tree models in the validation set in precision, accuracy, recall, F1 score, and AUC, and performed well in the external validation set with better precision rate and accuracy than logistic regression and decision tree models but slightly lower recall rate, F1 index, and AUC value than the decision tree model. SVM outperformed logistic regression and decision tree models in precision, accuracy, F1 score, and AUC in the training set, but had slightly lower recall rate than the decision tree. CONCLUSIONS Among the 3 models, SVM has the best performance and generalizability for predicting post-HoLEP hypothermia risk to provide support for clinical decisions.
Humans ; Male ; Retrospective Studies ; Machine Learning ; Transurethral Resection of Prostate/adverse effects* ; Hypothermia/etiology* ; Prostatic Hyperplasia/surgery* ; Algorithms ; Lasers, Solid-State ; Risk Assessment ; Postoperative Complications ; Decision Trees ; Logistic Models ; Aged ; Middle Aged ; Support Vector Machine

OBJECTIVES: To construct a GA-BP neural network model based on the spectrum-effect relationship of Cassia seeds extract and test its performance for quality control of Cassia seeds using spectrum-effect score. METHODS: The HPLC fingerprints of Cassia seeds extract (0.1, 0.2, and 0.4 g/mL) were established. In a mouse model of 5-Fu-induced liver injury treated with 0.4, 0.8, and 1.6 g/kg of Cassia seeds extract, the pharmacodynamics parameters were measured to calculate the comprehensive efficacy using AHP-EWM. A GA-BP neural network model between the fingerprints and comprehensive efficacy was constructed, and the corresponding predicted comprehensive efficacy was obtained. The spectrum-effect relationship between the fingerprints and the measured and predicted comprehensive efficacy was established using grey correlation method followed by Gaussian fitting analysis. The spectral efficiency score was calculated using the relative peak area of the fingerprints and the correlation degree of the spectral efficiency. The reliability of the data was tested using the Z-ratio score method. The limit range of the spectral efficiency score was determined and the quality of the verification samples was evaluated. RESULTS: The error between the predicted value using the GA-BP neural network model and the measured value of the comprehensive efficacy was less than 0.2. Gaussian fitting analysis showed good fitting between the spectrum-effect relationship data of the measured and predicted comprehensive efficacy. The limit of the spectral efficiency score was 6.16-7.30. The prediction results for each verification group were consistent with the experimental results and within the limit of spectral efficiency score, and the results of Z-ratio score analysis demonstrated good data reliability. CONCLUSIONS The GA-BP neural network model can effectively predict the comprehensive efficacy of Cassia seeds extract, and the established spectrum-effect scoring method can be used for quality evaluation of samples.
Neural Networks, Computer ; Animals ; Seeds/chemistry* ; Mice ; Cassia/chemistry* ; Quality Control ; Drugs, Chinese Herbal/pharmacology* ; Plant Extracts/pharmacology* ; Male

OBJECTIVES: To develop a heterogeneous graph prediction method based on the fusion of multi-layer semantics and topological information for addressing the challenges in drug-target interaction prediction, including insufficient modeling of high-order semantic dependencies, lack of adaptive fusion of semantic paths, and over-smoothing of node features. METHODS: A heterogeneous graph network with multiple types of entities such as drugs, proteins, side effects, and diseases was constructed, and graph embedding techniques were used to obtain low-dimensional feature representations. An adaptive metapath search module was introduced to automatically discover semantic path combinations for guiding the propagation of high-order semantic information. A semantic aggregation mechanism integrating multi-head attention was designed to automatically learn the importance of each semantic path based on contextual information and achieve differentiated aggregation and dynamic fusion among paths. A structure-aware gated graph convolutional module was then incorporated to regulate the feature propagation intensity for suppressing redundant information and redcuing over-smoothing. Finally, the potential interactions between drugs and targets were predicted through an inner product operation. RESULTS: Compared with existing drug-target interaction prediction methods, the proposed method achieved an average improvement of 3.4% and 2.4%, 3.0% and 3.8% in terms of the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC) on public datasets, respectively. CONCLUSIONS The drug-target interaction prediction method developed in this study can effectively extract complex high-order semantic and topological information from heterogeneous biological networks, thereby improving the accuracy and stability of drug-target interaction prediction. This method provides technical support and theoretical foundation for precise drug target discovery and targeted treatment of complex diseases.
Semantics ; Humans ; Drug Interactions ; Neural Networks, Computer ; Algorithms