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

BACKGROUND AND OBJECTIVES

data-mce-style="text-align: justify;">The global pandemic caused by Coronavirus Disease 2019 (COVID-19) has affected millions, with growing evidence of the potential role of ocular tissues in viral transmission. At the time of writing, local data regarding the phenomenon was limited. This study investigated external ocular manifestations in patients with COVID-19 at a referral center in the Philippines, examined correlations between demographics, systemic manifestations, and laboratory results with ocular manifestations, and determined their timing relative to systemic symptoms.

METHODS

data-mce-style="text-align: justify;">This single-center, descriptive cross-sectional study was carried out from December 8 to 18, 2020 at the adult COVID-19 wards of the Philippine General Hospital involving 72 participants. Data collection involved relevant clinical history taking and performing gross eye examination. The prevalence of ocular manifestations was described with 95% confidence intervals. Correlations between ocular manifestations and quantitative variables were analyzed with point-biserial correlation, and associations with qualitative variables were tested using chi-square or Fisher’s exact tests.

RESULTS

data-mce-style="text-align: justify;">Among participants, 31.9% presented with ocular manifestations with foreign body sensation as the most prevalent ocular symptom (11.1%) and conjunctival hyperemia as the most prevalent ocular finding (19.4%). The median age of patients with ocular manifestations was 41 years old with a higher prevalence in the male population (73.9%, CI=95%, p=0.001). No significant correlation was observed between presence of external ocular manifestations and the different systemic and ocular co-morbidities as well as with COVID-19 clinical classification. Among those who experienced symptoms, majority (29.2%) of the patients experienced systemic symptoms prior to the onset of ocular symptoms. Ocular complaints may present as the sole manifestation (13.9%). Several laboratory parameters were measured and only temperature and AST levels showed a low positive correlation with the presence of ocular manifestations. 

CONCLUSION

data-mce-style="text-align: justify;">Ocular manifestations occur in roughly one third of patients with COVID-19 based on this study population. With some individuals presenting with ocular signs or symptoms as the initial and sole manifestation, healthcare practitioners must exercise caution and remain vigilant in managing patients who present as such. At the time of writing, this is the first local study investigating the different external ocular manifestations in patients with COVID-19. There is a need to pursue more robust studies and conduct more local investigations which will guide both ophthalmologists and other practitioners in strengthening existing guidelines regarding precautionary practices, clinical diagnosis, and management of COVID-19 patients.

Human ; Sars-cov-2 ; Covid-19 ; Philippines ; Adult ; Association ; Classification ; Collection ; Confidence Intervals ; Coronavirus ; Cross-sectional Studies ; Data Collection ; Demography ; Diagnosis ; Disease ; Exercise ; Eye ; Foreign Bodies ; History ; Hospitals ; Hospitals, General ; Hyperemia ; Laboratories ; Male ; Morbidity ; Ophthalmologists ; Pandemics ; Patients ; Population ; Prevalence ; Referral And Consultation ; Role ; Sensation ; Temperature ; Time ; Tissues ; Volition ; World Health Organization ; Writing

OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks

BACKGROUND: PANoptosis has the features of pyroptosis, apoptosis, and necroptosis. Numerous studies have confirmed the diverse roles of various types of cell death in acute liver failure (ALF), but limited attention has been given to the crosstalk among them. In this study, we aimed to explore the role of PANoptosis in ALF and uncover new targets for its prevention or treatment. METHODS: Three ALF-related datasets (GSE14668, GSE62029, and GSE74000) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Hub genes were identified through intersecting DEGs, genes obtained from weighted gene co-expression network analysis (WGCNA), and genes related to PANoptosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein‍‒‍protein interaction (PPI) analyses and gene set enrichment analysis (GSEA) were performed to determine functional roles. Verification was performed using an ALF mouse model. RESULTS: Our results showed that expression of seven hub genes (B-cell lymphoma-2-modifying factor (BMF), B-cell lymphoma-2-interacting protein 3-like (BNIP3L), Caspase-1 (CASP1), receptor-interacting protein kinase 3 (RIPK3), uveal autoantigen with coiled-coil domains and ankyrin repeats protein (UACA), uncoordinated-5 homolog B receptor (UNC5B), and Z-DNA-binding protein 1 (ZBP1)) was up-regulated in liver samples of patients. However, in the ALF mouse model, the expression of BNIP3L, RIPK3, phosphorylated RIPK3 (P-RIPK3), UACA, and cleaved caspase-1 was up-regulated, while the expression of CASP1 and UNC5B was down-regulated. The expression of ZBP1 and BMF increased only during the development of ALF, and there was no significant change in the end stage. Immunofluorescence of mouse liver tissue showed that macrophages expressed all seven markers. Western blot results showed that pyroptosis, apoptosis, and necroptosis were always involved in lipopolysaccharide (LPS)/ d-galactosamine (d-gal)‍-induced ALF mice. The ALF cell model showed that bone marrow-derived macrophages (BMDMs) form PANoptosomes after LPS stimulation. CONCLUSIONS: Our results suggest that PANoptosis of macrophages promotes the development of ALF. The seven new ALF biomarkers identified and validated in this study may contribute to further investigation of diagnostic markers or novel therapeutic targets of ALF.
Animals ; Liver Failure, Acute/genetics* ; Computational Biology ; Mice ; Pyroptosis/genetics* ; Humans ; Protein Interaction Maps ; Apoptosis/genetics* ; Necroptosis/genetics* ; Gene Regulatory Networks ; Gene Ontology ; Gene Expression Profiling ; Disease Models, Animal

Gut microbial communities are likely remodeled in tandem with accumulated physiological decline during aging, yet there is limited understanding of gut microbiome variation in advanced age. Here, we performed a metagenomics-based enterotype analysis in a geographically homogeneous cohort of 367 enrolled Chinese individuals between the ages of 60 and 94 years, with the goal of characterizing the gut microbiome of elderly individuals and identifying factors linked to enterotype variations. In addition to two adult-like enterotypes dominated by Bacteroides (ET-Bacteroides) and Prevotella (ET-Prevotella), we identified a novel enterotype dominated by Escherichia (ET-Escherichia), whose prevalence increased in advanced age. Our data demonstrated that age explained more of the variance in the gut microbiome than previously identified factors such as type 2 diabetes mellitus (T2DM) or diet. We characterized the distinct taxonomic and functional profiles of ET-Escherichia, and found the strongest cohesion and highest robustness of the microbial co-occurrence network in this enterotype, as well as the lowest species diversity. In addition, we carried out a series of correlation analyses and co-abundance network analyses, which showed that several factors were likely linked to the overabundance of Escherichia members, including advanced age, vegetable intake, and fruit intake. Overall, our data revealed an enterotype variation characterized by Escherichia enrichment in the elderly population. Considering the different age distribution of each enterotype, these findings provide new insights into the changes that occur in the gut microbiome with age and highlight the importance of microbiome-based stratification of elderly individuals.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Bacteroides ; China ; Diabetes Mellitus, Type 2/microbiology* ; Escherichia coli/classification* ; Gastrointestinal Microbiome/genetics* ; Metagenomics ; East Asian People

Objective:The clinical significance of aural fullness in patients with vestibular migraine（VM） remains unclear, and it is yet to be determined whether VM with aural fullness represents a distinct subtype of VM; this study aimed to compare differences in demographic characteristics, clinical manifestations, audiological findings, and vestibular function tests between VM patients with and without aural fullness, and explore whether the former is a subtype of VM and whether it requires differentiated treatment. Methods:A total of 174 VM patients were enrolled, including 75 with aural fullness（aural fullness group） and 99 without aural fullness（non-aural fullness group）; demographic data, vertigo characteristics, medical history, family history, pure-tone audiometry, and vestibular function tests were thoroughly recorded, and independent samples t-test and chi-square test were used for inter-group comparisons. Results:①Regarding demographic characteristics, the age of the aural fullness group was significantly lower than that of the non-aural fullness group[（44.08±13.97） years vs. （49.45±16.05） years, P=0.020）, while the two groups showed consistent gender distribution（more females than males） with no statistically significant difference. ②For aural fullness characteristics, unilateral aural fullness accounted for 65.0% in the aural fullness group, significantly higher than bilateral aural fullness（35.0%, P<0.001）. ③In terms of vertigo characteristics, there were no statistically significant inter-group differences in the nature of attacks（rotational vertigo: 36.0% vs. 41.4%, P=0.463; dizziness: 21.3% vs. 11.1%, P=0.064; rotational vertigo or dizziness: 29.3% vs. 25.3%, P=0.548; dizziness with unsteady gait: 9.3% vs. 11.1%, Fisher P=0.806; visual oscillation with unsteady gait: 4.0% vs. 11.1%, Fisher P=0.086）, duration（several hours: 34.7% vs. 33.3%, P=0.841; several minutes: 22.7% vs. 21.2%, P=0.808; several seconds: 5.3% vs. 8.1%, Fisher P=0.557; several days: 9.3% vs. 9.1%, Fisher P=1.000; multiple combined patterns: 17.3% vs. 15.2%, P=0.686）, or incidence of nausea and vomiting（84.0% vs. 72.7%, P=0.071, no statistical significance）. ④No statistically significant inter-group differences were found in medical history and family history, including motion sickness history（8.0% vs. 4.0%, Fisher P=0.337）, headache history（22.7% vs. 34.3%, P=0.084）, and family history of dizziness（12.0% vs. 14.1%, P=0.666）. ⑤For audiological characteristics, 21.3%（16/75） of patients in the aural fullness group had low-frequency hearing loss, significantly higher than 5.1% in the non-aural fullness group（χ²=10.66, P=0.001）; among patients with unilateral aural fullness, 28.6%（14/49） had ipsilateral low-frequency hearing loss, significantly higher than 7.7%（2/26） of those with bilateral aural fullness（χ²=4.41, P=0.036）; however, there was no statistically significant difference in the rate of bilateral high-frequency hearing loss between the two groups（54.7%[41/75]vs. 50.5%[50/99], χ²=0.30, P=0.586）. ⑥In vestibular function tests, no statistically significant inter-group differences were observed in smooth pursuit type Ⅲ/Ⅳ（12.5% vs. 13.1%, P=0.913）, caloric test with CP>25%（31.2% vs. 37.4%, P=0.411）, abnormal video head impulse test（vHIT） rate（30.8% vs. 32.6%, P=0.865）, or abnormal vestibular evoked myogenic potential（VEMP） rate（53.8% vs. 38.9%, Fisher P=0.484）. Conclusion:VM patients with aural fullness have an earlier age of onset, with nearly 1/4 accompanied by low-frequency hearing loss; VM patients with and without aural fullness are highly consistent in gender distribution, nature/duration of vertigo, vestibular function impairment, and presence of bilateral high-frequency hearing loss, suggesting that the core clinical phenotypes of the two groups are consistent, while the former has an earlier age of onset and a higher proportion of unilateral hearing loss, which may be related to the pathological mechanism of VM and inner ear microcirculation disorders.
Humans ; Female ; Male ; Middle Aged ; Adult ; Migraine Disorders/classification* ; Young Adult ; Vertigo ; Age of Onset ; Aged ; Hearing Loss

The prevalence of Class III malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore, early orthodontic treatment for Class III malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class III malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class III malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class III malocclusion through early orthodontic treatment.
Humans ; Malocclusion, Angle Class III/classification* ; Orthodontics, Corrective/methods* ; Consensus ; Child

Identification of disease-specific cell subtypes (DSCSs) has profound implications for understanding disease mechanisms, preoperative diagnosis, and precision therapy. However, achieving unified annotation of DSCSs in heterogeneous single-cell datasets remains a challenge. In this study, we developed the gPRINT algorithm (generalized approach for cell subtype identification with single cell's voicePRINT). Inspired by the principles of speech recognition in noisy environments, gPRINT transforms gene position and gene expression information into voiceprints based on ordered and clustered gene expression phenomena, obtaining unique "gene print" patterns for each cell. Then, we integrated neural networks to mitigate the impact of background noise on cell identity label mapping. We demonstrated the reproducibility of gPRINT across different donors, single-cell sequencing platforms, and disease subtypes, and its utility for automatic cell subtype annotation across datasets. Moreover, gPRINT achieved higher annotation accuracy of 98.37% when externally validated based on the same tissue, surpassing other algorithms. Furthermore, this approach has been applied to fibrosis-associated diseases in multiple tissues throughout the body, as well as to the annotation of fibroblast subtypes in a single tissue, tendon, where fibrosis is prevalent. We successfully achieved automatic prediction of tendinopathy-specific cell subtypes, key targets, and related drugs. In summary, gPRINT provides an automated and unified approach for identifying DSCSs across datasets, facilitating the elucidation of specific cell subtypes under different disease states and providing a powerful tool for exploring therapeutic targets in diseases.
Humans ; Algorithms ; Single-Cell Analysis ; Databases, Genetic ; Molecular Sequence Annotation

Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated host response to infection. Due to different infection sources, pathogens and basic conditions of patients, there is significant heterogeneity in clinical manifestations, response to treatment and prognosis of patients with sepsis. Accurate classification and individualized treatment of sepsis will help to further improve the prognosis of patients with sepsis. In recent years, the integration of artificial intelligence and bioinformatics has brought new opportunities for the research of sepsis classification. This review systematically introduces a variety of sepsis classification methods and their clinical application value. The clinical data in the electronic medical record, such as the dynamic changes of vital signs such as body temperature, can be used as the basis for sepsis classification. Different subtypes of body temperature trajectories have differences in physiological characteristics and prognosis, which contributes to predict the prognosis of patients and guide fluid management strategies. Biomarker classification can more comprehensively reflect the pathophysiological state of patients. Immune index classification is helpful to identify immunocompromised patients so as to carry out targeted immunotherapy. Transcriptome data and genotyping reveal the heterogeneity of sepsis at the molecular level and provide a new perspective for precision medicine. In addition, a detailed systematic review of sepsis-related organ function damage, such as acute respiratory distress syndrome (ARDS), acute kidney injury (AKI), and acute liver injury, has also been conducted, which is helpful to develop targeted organ protection and treatment strategies. These typing methods have shown good application prospects in clinical practice. However, there are still limitations in the current research, such as typing stability and biomarker selection, which need to be further explored. Future research should focus on the development of stable and efficient typing tools to achieve precise treatment of sepsis and improve the prognosis of patients.
Humans ; Sepsis/classification* ; Multiple Organ Failure/classification* ; Prognosis ; Artificial Intelligence ; Biomarkers ; Computational Biology ; Respiratory Distress Syndrome

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny