Metabolic dysfunction-associated fatty liver disease is a common chronic liver disease worldwide， and its incidence rate continues to rise. The traditional “two-hit” theory has gradually been replaced by the “multiple-hit” hypothesis， which emphasizes the synergistic effect of multiple pathogenic factors. Necroptosis is an inflammatory form of death with both the morphological characteristics of necrosis and the signaling mechanism of apoptosis， and the core of necroptosis lies in the receptor-interacting protein kinase 1-receptor-interacting protein kinase 3-mixed lineage kinase domain-like pseudokinase signaling axis. This pathway directly leads to the death of hepatocytes， and it triggers an inflammatory response and promotes the fibrosis process by releasing damage-associated molecular patterns； the pathway is also closely associated with the metabolic reprogramming of the liver. This review summarizes the activation mechanism of necroptosis in metabolic dysfunction-associated fatty liver disease， analyzes its effect on liver inflammation， fibrosis， and metabolism， and explores its therapeutic strategies and application prospects.

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.

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.

Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.

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 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 To explore the efficiency of operating room(OR)and its influencing factors from a phenomeno-logical perspective,proposing corresponding improvement strategies.Methods A phenomenological method was adopted,invol-ving in-depth interviews with 13 surgical team members from a tertiary Traditional Chinese Medicine(TCM)orthopedic hospital in Henan Province,to collect and analyze their multifaceted understanding of OR efficiency.Results The study demonstrated eight interpretative ways of efficiency among team members.The overall framework was largely organization-oriented,with addi-tional human-centered considerations regarding the factors influencing efficiency.Conclusion A unified understanding is crucial for improving both efficiency and quality.This study suggests strategies to optimize OR processes through lean management and human-centered team development to enhance operating room efficiency.Additionally,it highlights the need for future research to incorporate more patient views.

AIM:To analyze the clinical value of predicting drug resistance in pulmonary tuberculo-sis patients based on improved machine learning models,and to build a visualization system for veri-fication.METHODS:Retrospectively selected 1 025 pulmonary tuberculosis patients hospitalized in Zhuhai Sixth People's Hospital from March 2019 to March 2024 with drug sensitivity test results as the research object.According to the definition of drug-resistant tuberculosis,the patients were divided in-to 631 sensitive groups(drug sensitivity test results showed no drug resistance),271 RR/MDR groups(meeting the definition of rifampicin resistant tu-berculosis or multi drug resistant tuberculosis,but no drug resistance to any kind of fluoroquino-lones),and 123 pre XDR groups(on the basis of multi drug resistant tuberculosis,and at the same time,drug resistance to any kind of fluoroquino-lones).Analyze clinical data based on the improved machine learning model,help build a drug resistant tuberculosis prediction model,synchronously com-plete feature screening,conduct value analysis on the screened features,and build a visual system for verification.RESULTS:Three groups of patients with baseline data comparison shows:Age,Body mass index(BMI),basic treatment of classification,lung diseases,haemoptysis,second-line drug use history,damage to lung,with empty in all statisti-cally significant difference between the three groups(P<0.05);Based on the modified ma-chine learning model,8 variables were screened,which were history of second-line drug use,BMI,treatment classification,destructive lung,underly-ing lung diseases,cavitation,hemoptysis,and age.The modified machine learning model had the high-est prediction accuracy compared with the tradi-tional model,with AUC values of 0.9322(RR/MDR prediction was positive class)and 0.9545(pre-XDR prediction was positive class).CONCLUSION:The application of the improved machine learning mod-el can help predict the occurrence of drug-resistant tuberculosis and assist the clinical formulation of more effective treatment plans.

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.