Digital biopsy for liver diseases is characterized by the deep integration of artificial intelligence （AI） technologies and large-scale liver disease data， through which intelligent analytics are applied to support clinical decision-making and full-cycle management. This article reviews the AI technical framework based on standardized data governance and centered on multimodal large medical models， covering the application of natural language processing， knowledge map， generative AI， and large language models in the establishment of databases for specialty diseases， diagnosis， prognosis prediction， treatment， and automated medical documentation. This article also discusses the application prospects of this framework in medical education， scientific research， and healthcare management. Although this technique shows broad application potential， it still faces challenges in areas such as multi-center data integration， model interpretability， ethics， and data security. In the future， a smart ecosystem with closed-loop optimization and human-AI collaboration should be established to promote the comprehensive implementation of digital biopsy in the whole process of medicine， education， research， and management， thereby providing help for the precise prevention and control and holistic health management of liver diseases.

Drug literacy is one of the important components of health literacy,and in recent years,the research on drug literacy has gradually deepened.The polypharmacy treatment and irrational drug use in the patients with HIV/AIDS has increased the burden of drug use,therefore improving the level of drug literacy is the key to maintain the medication efficacy,drug self-administered and safe medication.This paper summa-rized the definition,current status of assessment tools,influencing factors and interventional measures of drug literacy of HIV/AIDS patients in domestic and foreign research in order to provide reference for the develop-ment of assessment tools for drug literacy of HIV/AIDS patients,improve the level of drug literacy of pa-tients,and promote safe and rational use of drugs.

Objective:To explore the prognostic value of systemic immune-inflammatory index(SII)combined with tumor burden score (TBS) (hereinafter referred to as STS) in patients with intrahepatic cholangiocarcinoma (ICC) after radical resection, and to construct a nomogram model.Methods:The clinical data (including the degree of tumor differentiation, vascular cancer thrombus, and lymph node metastasis, etc.) of 258 ICC patients who received radical resection at People′s Hospital of Zhengzhou University (170 cases, training set) and Cancer Hospital of Zhengzhou University (88 cases, validation set) from January 1, 2016 to January 31, 2020 were retrospectively analyzed and graded by SII, TBS and STS. Multivariate Cox regression analysis were used to identify independent factors affecting the prognosis of patients with ICC. Kaplan-Meier survival curve and receiver operating characteristic curve (ROC) were drawn to evaluate the predictive efficiency of SII, TBS and STS in the overall survival of patients with ICC after radical resection. The nomogram prediction model was constructed and evaluate the performance of nomogram model using consistency index (C-index) and calibration curve.Results:Among 170 ICC patients in the training set, there were 106 cases of SII grade 1 and 64 cases of SII grade 2; 137 cases of TBS grade 1 and 33 cases of TBS grade 2; and 98 cases of STS grade 1, 47 cases of STS grade 2, and 25 cases of STS grade 3. Among 88 ICC patients in the validation set, there were 33 cases of SII grade 1 and 55 cases of SII grade 2; 66 cases of TBS grade 1 and 22 cases of TBS grade 2; and 30 case of STS grade 1, 39 cases of TBS grade 2, and 19 cases of TBS grade 3.The multivariate Cox regression analysis showed that tumor differentiation degree (highly differentiated vs. moderately differentiated HR=0.157, 95% confidence interval(95% CI) 0.045 to 0.546, highly differentiated vs. poorly differentiated HR=0.452, 95% CI 0.273 to 0.750), STS (grade 3 vs. grade 2 HR=1.966, 95% CI 1.148 to 3.469; grade 3 vs. grade 1 HR=1.405, 95% CI 0.890 to 2.216), vascular cancer thrombus ( HR=2.006, 95% CI 1.313 to 3.066), nerve invasion ( HR=1.865, 95% CI 1.221 to 2.850), and lymph node metastasis ( HR=1.802, 95% CI 1.121 to 2.896) were independent influencing factors of overall survival in ICC patients after radical resection (all P<0.05). The Kaplan-Meier survival curve showed that SII, TBS, and STS were independent influencing factors of overall survival in ICC patients (all P<0.05). The results of ROC analysis showed that the areas under the curve of SII, TBS and STS in predicting overall survival of ICC patients after radical resection were 0.566 (95% CI 0.479 to 0.652), 0.585 (95% CI 0.499 to 0.672), and 0.657 (95% CI 0.522 to 0.692), respectively. Tumor differentiation, vascular tumor thrombus, nerve invassion, lymph node metastasis, and STS were included to constract the nomogram model. The C-indexes of the training set and validation set based on the nomogram model were 0.792 (95% CI 0.699 to 0.825) and 0.776 (95% CI 0.716 to 0.833), respectively. The calibration curves of the survival rate of the training set and the validation set were close to the reference lines, and the nomogram model had better predictive ability in both the training set and the validation set. Conclusions:Preoperative STS grading is an effective and practical predictor of overall survival in ICC patients after radical section. Compared with SII and TBS alone, it has better predictive value for the prognosis of patients with ICC.

Objective:To research the quality control and testing methods of polysomnography and to ensure its safe and effective performance.Methods:A quality control testing method was designed for the main indicators of EEG signal,EMG signal,ECG signal,and pulse oxygen saturation of polysomnography.In August 2023,two polysomnography instruments of the same brand and different models(marked as Test Equipment A and Test Equipment B)in clinical use in Daping Hospital,Army Medical University were selected.The quality control testing of polysomnography instrument was conducted by using electroencephalogram calibration instrument and vital sign simulator to evaluate the reliability of the performance of polysomnography.Results:A quality control testing method was developed for quality control testing of polysomnography aiming at the repeatability of the indicated values of EEG signals,EMG signals,ECG signals,and pulse oxygen saturation of polysomnography.Except for the output value of 2 mV of the voltage measurement simulator of test equipment B,the relative error of the recorded data was-11%,and the parameters were out of tolerance,and the rest of the test data of test equipment A and test equipment B met the maximum limit output value of the national metrology verification regulations and the technical requirements of the equipment manufacturer.Conclusion:The quality control detection method of polysomnography can evaluate the performance parameters of the selected testing equipment A and testing equipment B,and provide technical support for the quality control detection and safe use of such equipment.

Bedridden patients mostly use the back cushion object,raise the bedside position,and other methods to obtain the semi-decumbent position.However,the existing methods have shortcomings such as insufficient execution,wrong angle estimation,weak consciousness,forgetting,decreased comfort,easy to cause pressure sore and aspiration pneumonia.To solve the shortcomings of the existing eating position placement method,the department of geriatric medicine team of Tongxiang City Hospital of Traditional Chinese Medicine designed a bed-rest pillow for bed patients to eat or other requirements of semi-lying position,and obtained the National Utility Model Patent of China(patent number:ZL 202122859891.4).This device places the pillow on a flat bed,with the back of the pillow next to the head of the bed for support and to prevent sliding.The patient was placed in a retainer slot,head in the first retainer slot,shoulder and back in the second retainer slot,waist and abdomen in the third retainer slot,and hands on the armrests on both sides of the pillow.The use of pillows for bedridden patients is simple and easy to learn,convenient,economical and practical,time-saving and labor-saving,and convenient supervision and inspection,which can reduce complications such as aspiration and pressure ulcers,thereby reducing the economic burden of the patient,improving the quality of life,and improving the satisfaction of patients and their families,and is worthy of clinical promotion and use.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To construct and validate a machine learning model for preoperative prediction of perineural invasion (PNI) status in intrahepatic cholangiocarcinoma (ICC).Methods:Clincial data of 329 patients, including 245 admitted to Zhengzhou University People's Hospital from January 2018 to June 2023 and 84 admitted to the Affiliated Cancer Hospital of Zhengzhou University from January 2013 to January 2020 were retrospectively analyzed. Patients were divided into a training set ( n=231) and a validation set ( n=98). Clinicopathological data including age, gender, hepatitis B virus (HBV) infection status were collected. Predictive variables were determined using least absolute shrinkage and selection operator (LASSO) regression analysis. Six machine learning algorithms including random forest (RF), logistic regression, and linear kernel-based support vector machine were selected to construct the preoperative prediction model for PNI in ICC. Performance metrics of the model were calculated using a confusion matrix, and the final model was selected. The model performance was evaluated in the validation set. Calibration curves were plotted to evaluate the final model, and a Pareto chart was used to visualize the importance of predictive variables. Results:LASSO regression identified nine predictive variables included in the prediction model, including carbohydrate antigen 19-9 (CA19-9), HBV infection status, alkaline phosphatase, alanine aminotransferase, prothrombin time, total bilirubin, albumin, neutrophil times gamma-glutamyl transferase to lymphocyte ratio, and tumor burden score. Among the trained six models, the area under the curve (AUC) of the RF model was 0.909, with a sensitivity of 0.842 and an accuracy of 0.870. Compared with the AUC of the RF model, the AUCs of the other 5 models were lower (all P<0.05). The AUC of the RF model for predicting PNI in ICC in validation set was 0.736. Calibration curves showed good fit of the RF model's prediction of PNI in ICC in both training and validation sets. The Pareto chart showed that CA19-9 was the most important predictive variable in the model, followed by HBV infection status. Conclusion:The machine learning model based on the RF algorithm has a high accuracy in preoperative prediction of PNI status in ICC.

Objective:To develop a predictive model for the intervention efficacy of lower extremity atherosclerotic occlusive disease (LEASO) and evaluate its performance to predict the outcomes of intervention therapy for patients with lower extremity atherosclerotic occlusive disease.Methods:This study retrospectively analyzed data from 238 patients with lower extremity atherosclerotic occlusive disease (LEASO), including 188 males and 50 females, aged between 35 and 88 years with a mean age of 68 years. These patients were randomly divided in a 7∶3 ratio into a training set ( n=166) and a testing set ( n=72) based on adverse outcomes, both training and test sets were divided into MALEs and non-MALEs groups. The training set had 67 MALEs and 99 non-MALEs, while the test set had 26 MALEs and 46 non-MALEs. Important variables related to outcome events were selected using LASSO regression in the training set and incorporated into a multifactorial logistic regression model to construct a predictive model. The model was visualized using forest plots and its performance was evaluated using data from both the training and testing sets. Results:Through LASSO regression, SIIRI(Systemic immune inflammatory response index, SIIRI), Rutherford >4, IP(Infrapopliteal, IP)>1, and P(Pedal, P)≥1 were selected as predictive indicators for the model. The area under the curve, sensitivity, and specificity of the model in the training set and testing set were 0.813, 80.6%, 72.7%, and 0.764, 65.4%, 80.4%. The calibration curve was consistent with expectations. The decision curves of the model had the highest accuracy, net benefit rate for clinical application of the model when the threshold probabilities of the training set and test set were in the range of 0~0.79 and 0~0.66.Conclusions:The predictive model built using preoperative Rutherford classification, IP classification, P classification, and SIIRI can identify high-risk individuals for early detection of MALEs and provide targeted intensified treatment. This model has practical significance in improving the prognosis of such patients and can be applied in clinical practice.

With the development of population aging, the incidence of lower limb artery ischemic diseases is gradually increasing. Although various treatments such as medication and endovascular surgery are currently available, patients with compromised microcirculation in the distal limbs and poor outflow pathways often do not achieve satisfactory results. Additionally, these treatments can be costly, and long-term patency rates are not ideal. The amendment in situ autologous great saphenous vein arterialization surgery utilizes the patient′s great saphenous vein to provide arterial blood in a retrograde manner and re-establishes blood supply to the tissues through the venous microcirculation system in the distal foot. This approach can achieve good limb salvage results and long-term patency. Therefore, this article aims to elaborate on the methods and value of amendment in situ autologous great saphenous vein arterialization surgery.

Objective:To explore the relevant risk factors and prognosis of patients with intrahepatic cholangiocarcinoma (ICC) who experienced recurrence within 6 months after surgeryMethods:This retrospective study included a total of 259 patients with ICC a treated at He'nan Provincial People's Hospital and He'nan Cancer Hospital from Jan 2018 to Jan 2020. The clinical and pathological data ,differences between the group with recurrence within 6 months and the group without recurrence within 6 months were compared using the chi-square test. Logistic regression analysis was used to determine the relevant risk factors for recurrence within 6 months. Kaplan-Meier method was used to construct survival and recurrence curves, and survival rates were calculated.Results:The overall survival and recurrence-free survival of patients in the group with recurrence within 6 months were significantly shorter. CA19-9, tumor longitudinal diameter, microvascular invasion, and neural invasion were identified as independent risk factors for recurrence within 6 months after ICC surgery ( P<0.001). Conclusion:The patient population experiencing recurrence within 6 months after ICC surgery has an extremely poor prognosis and possesses a specific tumor microenvironment. CA19-9, tumor longitudinal diameter, microvascular invasion, and neural invasion were identified as independent risk factors for recurrence within 6 months after ICC surgery.