Objective:To evaluate the feasibility and outcomes of transnasal endoscopic clipping of the paraclival internal carotid artery (ICA) in skull base surgery.Methods:The paraclival ICA was anatomically dissected in cadaveric head specimens. The clinical data of 15 patients with skull base lesions involving the ICA who admitted to the Department of Otorhinolaryngology Head and Neck Surgery at Tianjin Huanhu Hospital from January 2021 to December 2023 were retrospectively analyzed. Among them, 4 patients underwent transnasal endoscopic clipping of the paraclival ICA and concurrent lesion resection. The surgical methods were summarized, and the key points and indications of this technique were analyzed.Results:Intraoperative clipping of the ICA was successful and hemostatic in all 4 patients. Postoperatively, 3 patients had no complications, while 1 patient developed delayed ischemic cerebral infarction. Two patients were cured, 1 patient was maintained on immunotherapy, and 1 patient died. During follow-up, the clip was in situ in 1 patient, had detached in another, and was obscured by temporal muscle coverage in the remaining 2 patients.Conclusions:Transnasal endoscopic clipping of the paraclival ICA represents a potential option for managing the ICA in skull base surgery. However, it carries significant risks and limitations, mandating careful patient selection based on specific circumstances.

Objective:Through the comparative analysis of the payment program for the therapeutic value of Traditional Chinese Medicine dominant diseases in 8 provinces,we found the shortcomings of the existing program and put forward the perfect policy suggestions.Methods:Comparative analysis of the core content of the programs in various places,the selection of disease types,efficacy evaluation indexes,the application of the payment link and the protection mechanism.Results:The fragmentation of existing programs is an obvious problem,the specific content settings of each item are quite different,reflecting differences in the understanding of the core content of the program,the existence of inconsistent understanding of Chinese medicine's therapeutic value,the failure to link the payment standard to the results of the therapeutic value evaluation,and the lack of recognition of the value of Chinese medicine's technical labor,among other problems.Conclusions:We can select disease types based on the prominent advantages of Traditional Chinese medicine,learn from the evaluation framework of western medicine value-based medical care,construct the evaluation system of Chinese medicine therapeutic value,and carry out"equal price"payment based on the"same effect"of health results,set performance indicators and payment standards in stages,realize the whole process management of disease,and enhance the Traditional Chinese Medicine's therapeutic value,and promote the development of Traditional Chinese Medicine inheritance and innovation.

Objective:Through the comparative analysis of the payment program for the therapeutic value of Traditional Chinese Medicine dominant diseases in 8 provinces,we found the shortcomings of the existing program and put forward the perfect policy suggestions.Methods:Comparative analysis of the core content of the programs in various places,the selection of disease types,efficacy evaluation indexes,the application of the payment link and the protection mechanism.Results:The fragmentation of existing programs is an obvious problem,the specific content settings of each item are quite different,reflecting differences in the understanding of the core content of the program,the existence of inconsistent understanding of Chinese medicine's therapeutic value,the failure to link the payment standard to the results of the therapeutic value evaluation,and the lack of recognition of the value of Chinese medicine's technical labor,among other problems.Conclusions:We can select disease types based on the prominent advantages of Traditional Chinese medicine,learn from the evaluation framework of western medicine value-based medical care,construct the evaluation system of Chinese medicine therapeutic value,and carry out"equal price"payment based on the"same effect"of health results,set performance indicators and payment standards in stages,realize the whole process management of disease,and enhance the Traditional Chinese Medicine's therapeutic value,and promote the development of Traditional Chinese Medicine inheritance and innovation.

Medical record homepage is a core basis for healthcare quality management, medical insurance payment, and public hospital performance evaluation.The completeness and accuracy of its data directly affect the medical quality and economic benefits of hospitals. Since July 2022, a tertiary hospital had built an auxiliary decision-making system for diagnosis omissions based on artificial intelligence technology, which was officially launched in January 2023. The system aimed to improve the quality of data on the first page of medical records and ensure reasonable payment by medical insurance. This system was built on the hospital′s electronic medical records, and integrated natural language processing, medical knowledge graphs and deep learning technologies to create three engines: diagnosis omission recognition, ICD coding and DRG grouping. The diagnosis omission recognition engine identified both explicit and implicit omitted diagnoses by using a context semantic analysis model and a contrastive learning framework for dual judgment. It also interacted with the ICD coding and DRG grouping engines to enhance the accuracy of DRG grouping. Since its launched, the system has achieved remarkable results. A comparative analysis revealed that the rate of missing diagnoses on hospital medical record homepages has decreased from 31.31% during January to September 2022 to 12.34% during the same period in 2023, and the quality control time for a single medical record had been reduced from 20 minutes to 5 minutes. Additionally, a simulation calculation showed that the system-assisted DRG grouping can increase the hospital′s medical insurance surplus. The system could provide reference and guidance for public hospitals in China to improve the quality of the homepage of medical records and better adapt to medical insurance payment reform.

Objective To investigate the differential expression genes(DEGs)related to angiogenesis in rosacea(RA)by utilizing bioinformatics analysis in order to screen the key genes and verify their mRNA expression levels.Methods The gene microarray dataset GSE65914 was retrieved from the Gene Expression Omnibus(GEO)repository.Analyzed by R programming,the dataset was refined to identify DEGs related to RA,and then cross-referenced with angiogenesis-related genes from the GeneCards database to get a subset specific to RA angiogenesis.The process of identifying key genes was augmented by employing protein-protein interaction(PPI)network analysis and Cytoscape-based computational algorithms.The mRNA expression levels of the aforementioned pivotal genes were detected by real-time fluorescent quantitative reverse transcription PCR(RT-qPCR).Results A total of 947 RA-associated DEGs were identified from GEO dataset,and then 202 genes related to RA angiogenesis were further delineated.PPI network analysis and Cytoscape algorithm finally identified 3 key genes,that is,CXCL8,IL-1B,and STAT1.The results of RT-qPCR showed that the mRNA expression levels of MIP-2,GCP-2,IL-1B and STAT1 in RA lesions were significantly higher than those in normal controls(P<0.05).Conclusion With aid of bioinformatics analysis,our study has screened and validated key genes associated with angiogenesis in RA,namely CXCL8,IL-1B,and STAT1,which providing a theoretical basis for elucidating the potential mechanisms underlying RA-induced angiogenesis and developing targeted therapeutic strategies.

Sennoside A (SA), a typical prodrug, exerts its laxative effect only after its transformation into rheinanthrone catalyzed by gut microbial hydrolases and reductases. Hydrolases have been identified, but reductases remain unknown. By linking a photoreactive group to the SA scaffold, we synthesized a photoaffinity probe to covalently label SA reductases and identified SA reductases using activity-based protein profiling (ABPP). From lysates of an active strain, Bifidobacterium pseudocatenulatum (B. pseudocatenulatum), 397 proteins were enriched and subsequently identified using mass spectrometry (MS). Among these proteins, chromate reductase/nicotinamide adenine dinucleotide (NADH) phosphate (NADPH)-dependent flavin mononucleotide (FMN) reductase/oxygen-insensitive NADPH nitroreductase (nfrA) was identified as a potent SA reductase through further bioinformatic analysis and The Universal Protein Resource (UniProt) database screening. We also determined that recombinant nfrA could reduce SA. Our study contributes to further illuminating mechanisms of SA transformation to rheinanthrone and simultaneously offers an effective method to identify gut bacterial reductases.

Accurate prediction of drug-induced adverse drug reactions (ADRs) is crucial for drug safety evaluation, as it directly impacts public health and safety. While various models have shown promising results in predicting ADRs, their accuracy still needs improvement. Additionally, many existing models often lack interpretability when linking molecular structures to specific ADRs and frequently rely on manually selected molecular fingerprints, which can introduce bias. To address these challenges, we propose ToxBERT, an efficient transformer encoder model that leverages attention and masking mechanisms for simplified molecular input line entry system (SMILES) representations. Our results demonstrate that ToxBERT achieved area under the receiver operating characteristic curve (AUROC) scores of 0.839, 0.759, and 0.664 for predicting drug-induced QT prolongation (DIQT), rhabdomyolysis, and liver injury, respectively, outperforming previous studies. Furthermore, ToxBERT can identify drug substructures that are closely associated with specific ADRs. These findings indicate that ToxBERT is not only a valuable tool for understanding the mechanisms underlying specific drug-induced ADRs but also for mitigating potential ADRs in the drug discovery pipeline.

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans

Apurinic/apyrimidinic endonuclease 1(APE 1)is a multifunctional protein that plays important roles in DNA repair and regulation of gene expression.Because APE 1 is overexpressed in various cancers,it can serve as a cancer biomarker for aiding clinical diagnosis,guiding therapy,and monitoring prognosis.On this basis,a label-free fluorescent probe was designed based on the primer exchange reaction(PER)strategy for highly sensitive detection of APE 1 activity.In the absence of APE 1,the structure of catalytic hairpin(HP)was stable and could not form G-quadruplex.Therefore,the background fluorescence of this sensing system was very low due to the dissociation of thioflavin T(ThT).In the presence of APE 1,the apurinic/apyrimidinic(AP)site of HP was cleaved by APE 1 and a short nucleic acid fragment that acted as a primer to initiate PER was generated.After PER reaction,a large number of G-quadruplex were produced,which could specifically bind with ThT and resulted in significant increase of fluorescence signal.The combination of low background design of HP and PER amplification made this biosensor had high sensitivity with a detection limit(3σ)of 0.0008 U/mL.Furthermore,the primer sequence was directly generated by the cleavage of APE 1 without additional addition,which not only increased the specificity of the reaction,but also simplified the experiment procedure.Moreover,the use of label-free fluorescence signal reduced the cost of the experiment,and realized rapid detection of APE 1.Finally,this sensor was used to detect APE 1 in human serum samples with spiked recoveries of 91%-104%,proving great potential in study of biological enzyme.

Accurate prediction of drug-induced adverse drug reactions(ADRs)is crucial for drug safety evaluation,as it directly impacts public health and safety.While various models have shown promising results in predicting ADRs,their accuracy still needs improvement.Additionally,many existing models often lack interpretability when linking molecular structures to specific ADRs and frequently rely on manually selected molecular fingerprints,which can introduce bias.To address these challenges,we propose ToxBERT,an efficient transformer encoder model that leverages attention and masking mechanisms for simplified molecular input line entry system(SMILES)representations.Our results demonstrate that ToxBERT achieved area under the receiver operating characteristic curve(AUROC)scores of 0.839,0.759,and 0.664 for predicting drug-induced QT prolongation(DIQT),rhabdomyolysis,and liver injury,respectively,outperforming previous studies.Furthermore,ToxBERT can identify drug substructures that are closely associated with specific ADRs.These findings indicate that ToxBERT is not only a valuable tool for understanding the mechanisms underlying specific drug-induced ADRs but also for mitigating potential ADRs in the drug discovery pipeline.