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

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Process analytical technology(PAT) is a key means for digital transformation and upgrading of the traditional Chinese medicine(TCM) manufacturing process, serving as an important guarantee for consistent and controllable TCM product quality. Near-infrared(NIR) spectroscopy has become the core technology for measuring the TCM manufacturing process. By incorporating NIR spectroscopy into PAT and starting from the construction of a smart platform for the TCM manufacturing process, this paper systematically described the development history and innovative application of the combination of NIR spectroscopy with chemometrics in measuring the TCM manufacturing process by the research team over the past two decades. Additionally, it explored the application of a validation method based on accuracy profile(AP) in the practice of NIR spectroscopy. Furthermore, the software development progress driven by NIR spectroscopy supported by modeling technology was analyzed, and the prospect of integrating NIR spectroscopy in smart factory control platforms was exemplified with the construction practices of related platforms. By integrating with the smart platform, NIR spectroscopy could improve production efficiency and guarantee product quality. Finally, the prospect of the smart platform application in measuring the TCM manufacturing process was projected. It is believed that the software development for NIR spectroscopy and the smart manufacturing platform will provide strong technical support for TCM digitalization and industrialization.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/analysis* ; Software ; Medicine, Chinese Traditional ; Quality Control

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To explore the impact of the two-way referral model on compliance and prognosis in patients with heart failure.Methods:This bidirectional cohort study enrolled chronic heart failure (CHF) patients treated at Qilu Hospital of Shandong University or designated primary hospitals between March 2018 and March 2022. Patients were categorized into two groups based on referral status: two-way referral group (participating in the referral model with≥1 follow-up visit at primary hospitals) and the core hospital group (receiving treatment and follow-up exclusively at Qilu Hospital). Baseline clinical characteristics were collected and compared between groups. Patients underwent followed-up, with primary endpoints including follow-up rate, drug (β-blockers, angiotension converting enzyme inhibitor (ACEI)/angiotensin Ⅱ receptor blockers (ARB)/angiotensin receptor-neprilysin inhibitor (ARNI), sodium-glucose cotransporter 2 inhibitors and mineralocorticoid receptor antagonists) utilization rate and target dose achievement rate. Secondary endpoints encompassed changes from baseline in left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDd), and N-terminal pro-brain natriuretic peptide (NT-proBNP), plus cardiovascular mortality and heart failure rehospitalization. Generalized linear mixed models analyzed longitudinal trends in LVEF, LVEDd, and NT-proBNP levels. Kaplan-Meier curves and Cox regression evaluated LVEF recovery rates, supplemented by subgroup analyses. Multivariate logistic regression was used to identify factors influencing target dose achievement rate for β-blockers and ACEI/ARB/ARNI therapies in CHF patients.Results:A total of 357 patients were enrolled, aged 53 (41, 63) years, including 256 males (71.7%). 157 patients were in the two-way referral group and 200 patients in the core hospital-treated group. Compared with the core hospital-treated group, the two-way referral group had lower baseline LVEF (28 (22, 34)% vs. 31 (23, 36)%, P=0.021) and systolic blood pressure (116 (104, 125) mmHg vs. 121 (109, 134) mmHg (1 mmHg=0.133 kPa), P=0.010). The 12-month follow-up rate of the two-way referral group was higher than the core hospital-treated group (73.8% vs. 56.0%, P=0.004). No significant between-group differences were observed in drug utilization rate of β-blockers, ACEI/ARB/ARNI, or sodium-glucose cotransporter 2 inhibitors during follow-up (all P>0.05), while mineralocorticoid receptor antagonists use showed a declining trend in both groups. Although the core hospital-treated group had higher target dose achievement rates for β-blockers (65.4% vs. 49.3%, P=0.042) and ACEI/ARB/ARNI (79.8% vs. 65.8%, P=0.046) than the two-way referral group, multivariate logistic regression indicated that the two-way referral model was not a negative predictor for these outcomes (all P>0.05). Both groups showed improved NT-proBNP, LVEDd, and LVEF from baseline (all P<0.001) with no significant difference in trends between groups (all P>0.05). There was no significant difference in the composite incidence (7.6% vs. 6.5%, P=0.674) and cumulative incidence (log-rank P=0.684) of cardiovascular death and heart failure rehospitalization at 12 months between two groups. Conclusion:The two-way referral model demonstrates advantages in improving medication adherence, drug utilization rates, and targetdoseachievement rates among CHF patients. This model not only promotes cardiac functional recovery but also reduces risks of cardiovascular mortality and heart failure rehospitalization, achieving comparable therapeutic and management outcomes to those observed in core hospital-treated patients.

Objective:To explore the impact of the two-way referral model on compliance and prognosis in patients with heart failure.Methods:This bidirectional cohort study enrolled chronic heart failure (CHF) patients treated at Qilu Hospital of Shandong University or designated primary hospitals between March 2018 and March 2022. Patients were categorized into two groups based on referral status: two-way referral group (participating in the referral model with≥1 follow-up visit at primary hospitals) and the core hospital group (receiving treatment and follow-up exclusively at Qilu Hospital). Baseline clinical characteristics were collected and compared between groups. Patients underwent followed-up, with primary endpoints including follow-up rate, drug (β-blockers, angiotension converting enzyme inhibitor (ACEI)/angiotensin Ⅱ receptor blockers (ARB)/angiotensin receptor-neprilysin inhibitor (ARNI), sodium-glucose cotransporter 2 inhibitors and mineralocorticoid receptor antagonists) utilization rate and target dose achievement rate. Secondary endpoints encompassed changes from baseline in left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDd), and N-terminal pro-brain natriuretic peptide (NT-proBNP), plus cardiovascular mortality and heart failure rehospitalization. Generalized linear mixed models analyzed longitudinal trends in LVEF, LVEDd, and NT-proBNP levels. Kaplan-Meier curves and Cox regression evaluated LVEF recovery rates, supplemented by subgroup analyses. Multivariate logistic regression was used to identify factors influencing target dose achievement rate for β-blockers and ACEI/ARB/ARNI therapies in CHF patients.Results:A total of 357 patients were enrolled, aged 53 (41, 63) years, including 256 males (71.7%). 157 patients were in the two-way referral group and 200 patients in the core hospital-treated group. Compared with the core hospital-treated group, the two-way referral group had lower baseline LVEF (28 (22, 34)% vs. 31 (23, 36)%, P=0.021) and systolic blood pressure (116 (104, 125) mmHg vs. 121 (109, 134) mmHg (1 mmHg=0.133 kPa), P=0.010). The 12-month follow-up rate of the two-way referral group was higher than the core hospital-treated group (73.8% vs. 56.0%, P=0.004). No significant between-group differences were observed in drug utilization rate of β-blockers, ACEI/ARB/ARNI, or sodium-glucose cotransporter 2 inhibitors during follow-up (all P>0.05), while mineralocorticoid receptor antagonists use showed a declining trend in both groups. Although the core hospital-treated group had higher target dose achievement rates for β-blockers (65.4% vs. 49.3%, P=0.042) and ACEI/ARB/ARNI (79.8% vs. 65.8%, P=0.046) than the two-way referral group, multivariate logistic regression indicated that the two-way referral model was not a negative predictor for these outcomes (all P>0.05). Both groups showed improved NT-proBNP, LVEDd, and LVEF from baseline (all P<0.001) with no significant difference in trends between groups (all P>0.05). There was no significant difference in the composite incidence (7.6% vs. 6.5%, P=0.674) and cumulative incidence (log-rank P=0.684) of cardiovascular death and heart failure rehospitalization at 12 months between two groups. Conclusion:The two-way referral model demonstrates advantages in improving medication adherence, drug utilization rates, and targetdoseachievement rates among CHF patients. This model not only promotes cardiac functional recovery but also reduces risks of cardiovascular mortality and heart failure rehospitalization, achieving comparable therapeutic and management outcomes to those observed in core hospital-treated patients.

Objective To investigate the expression and prognostic value of serum receptor for advanced glycation end products(RAGE)and CXC-chemokine ligand 16(CXCL16)in patients with sepsis complicated with acute respiratory distress syndrome(ARDS).Methods A total of 234 patients with sepsis diagnosed and treated in a hospital from January 2019 to January 2022 were selected as the study subjects,and were divided into 82 patients with sepsis complicated with ARDS(ARDS group)and 152 patients with sepsis without ARDS(non-ARDS group)according to whether the subjects were complicated with ARDS.ARDS group was divided into survival group(n=50)and death group(n=32)according to the survival status within 28 days of admission.Another 60 healthy subjects who underwent physical examination in the same period were se-lected as the control group.Serum RAGE and CXCL16 levels were detected by enzyme-linked immunosorbent assay.Pearson correlation analysis of serum RAGE and CXCL16 levels with sequential organ failure assess-ment(SOFA)score,acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score and oxygenation index in patients with sepsis and ARDS.Multivariate Logistic regression analysis of prognostic factors of sep-sis complicated with ARDS.The predictive value of serum RAGE and CXCL16 on the prognosis of sepsis complicated with ARDS patients was analyzed by receiver operating characteristic curve.Results The serum RAGE and CXCL16 levels in ARDS group were higher than those in non-ARDS group and control group,and the serum RAGE and CXCL16 levels in non-ARDS group were higher than those in control group,the differ-ence was statistically significant(P＜0.05).Compared with the survival group,the mechanical ventilation time,intensive care unit stay time,procalcitonin,SOFA score,APACHE Ⅱ score,serum RAGE,CXCL16 lev-els were higher in the death group,and the oxygenation index was lower,with statistical significance(all P＜0.05).The serum RAGE level in patients with sepsis complicated with ARDS was positively correlated with SOFA score and APACHE Ⅱ score(r=0.603,0.671,P＜0.05).Serum CXCL16 levels were positively corre-lated with SOFA score and APACHE Ⅱ score(r=0.655,0.707,P＜0.05).Serum RAGE and CXCL16 were negatively correlated with oxygenation index(r=-0.712,-0.683,P＜0.05).Multi-factor Logistics regres-sion analysis showed that serum RAGE and CXCL16 were independent risk factors for death within 28 days of admission in patients with sepsis complicated with ARDS.The area under the curve(AUC)of combined de-tection of serum RAGE and CXCL16 for predicting death within 28 days of admission in patients with sepsis complicated with ARDS was 0.882,which was higher than that of single index detection of serum RAGE and CXCL16,and the difference was statistically significant(Z=4.450,4.906,P＜0.05).Conclusion The com-bined detection of serum RAGE and CXCL16 is helpful to evaluate the clinical prognosis of sepsis complicated with ARDS patients.

Objectives: To construct a nomogram incorporating important prognostic factors for predicting the overall survival of patients with colorectal cancer with peritoneal metastases treated with cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC), the aim being to accurately predict such patients' survival rates. Methods: This was a retrospective observational study. Relevant clinical and follow-up data of patients with colorectal cancer with peritoneal metastases treated by CRS + HIPEC in the Department of Peritoneal Cancer Surgery, Beijing Shijitan Hospital, Capital Medical University from 2007 January to 2020 December were collected and subjected to Cox proportional regression analysis. All included patients had been diagnosed with peritoneal metastases from colorectal cancer and had no detectable distant metastases to other sites. Patients who had undergone emergency surgery because of obstruction or bleeding, or had other malignant diseases, or could not tolerate treatment because of severe comorbidities of the heart, lungs, liver or kidneys, or had been lost to follow-up, were excluded. Factors studied included: (1) basic clinicopathological characteristics; (2) details of CRS+HIPEC procedures; (3) overall survival rates; and (4) independent factors that influenced overall survival; the aim being to identify independent prognostic factors and use them to construct and validate a nomogram. The evaluation criteria used in this study were as follows. (1) Karnofsky Performance Scale (KPS) scores were used to quantitatively assess the quality of life of the study patients. The lower the score, the worse the patient's condition. (2) A peritoneal cancer index (PCI) was calculated by dividing the abdominal cavity into 13 regions, the highest score for each region being three points. The lower the score, the greater is the value of treatment. (3) Completeness of cytoreduction score (CC), where CC-0 and CC-1 denote complete eradication of tumor cells and CC-2 and CC-3 incomplete reduction of tumor cells. (4) To validate and evaluate the nomogram model, the internal validation cohort was bootstrapped 1000 times from the original data. The accuracy of prediction of the nomogram was evaluated with the consistency coefficient (C-index), and a C-index of 0.70-0.90 suggest that prediction by the model was accurate. Calibration curves were constructed to assess the conformity of predictions: the closer the predicted risk to the standard curve, the better the conformity. Results: The study cohort comprised 240 patients with peritoneal metastases from colorectal cancer who had undergone CRS+HIPEC. There were 104 women and 136 men of median age 52 years (10-79 years) and with a median preoperative KPS score of 90 points. There were 116 patients (48.3%) with PCI≤20 and 124 (51.7%) with PCI>20. Preoperative tumor markers were abnormal in 175 patients (72.9%) and normal in 38 (15.8%). HIPEC lasted 30 minutes in seven patients (2.9%), 60 minutes in 190 (79.2%), 90 minutes in 37 (15.4%), and 120 minutes in six (2.5%). There were 142 patients (59.2%) with CC scores 0-1 and 98 (40.8%) with CC scores 2-3. The incidence of Grade III to V adverse events was 21.7% (52/240). The median follow-up time is 15.3 (0.4-128.7) months. The median overall survival was 18.7 months, and the 1-, 3- and 5-year overall survival rates were 65.8%, 37.2% and 25.7%, respectively. Multivariate analysis showed that KPS score, preoperative tumor markers, CC score, and duration of HIPEC were independent prognostic factors. In the nomogram constructed with the above four variables, the predicted and actual values in the calibration curves for 1, 2 and 3-year survival rates were in good agreement, the C-index being 0.70 (95% CI: 0.65-0.75). Conclusions: Our nomogram, which was constructed with KPS score, preoperative tumor markers, CC score, and duration of HIPEC, accurately predicts the survival probability of patients with peritoneal metastases from colorectal cancer treated with cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy.
Male ; Humans ; Female ; Middle Aged ; Peritoneal Neoplasms/secondary* ; Nomograms ; Cytoreduction Surgical Procedures/adverse effects* ; Hyperthermic Intraperitoneal Chemotherapy ; Quality of Life ; Hyperthermia, Induced ; Prognosis ; Combined Modality Therapy ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Colorectal Neoplasms/pathology* ; Retrospective Studies ; Survival Rate

Animals ; SARS-CoV-2 ; Antibodies, Neutralizing ; Macaca mulatta ; COVID-19/prevention & control* ; Antibodies, Viral ; Vaccines

With the outbreak of infectious diseases, more and more attention has been paid to surveillance and early warning work. Timely and accurate monitoring data is the basis of infectious diseases prevention and control. Effective early warning methods for infectious diseases can improve the timeliness and sensitivity of early warning work. This paper briefly introduces the intelligent early warning model of infectious diseases, summarizes the emerging surveillance and early warning methods of infectious diseases, and seeks the possibility of diversified surveillance and early warning in different epidemic stages and different outbreak scenarios of infectious diseases. This paper puts forward the idea of constructing a diversified method system of infectious diseases surveillance and early warning based on multi-stages and multi-scenarios and discusses the future development trend of infectious diseases surveillance and early warning, in order to provide reference for improving the construction level of infectious diseases surveillance and early warning system in China.
Humans ; Population Surveillance/methods* ; Communicable Diseases/epidemiology* ; Disease Outbreaks/prevention & control* ; Epidemics ; China/epidemiology*