OBJECTIVE To explore optimization pathways for the drug traceability code management model in outpatient pharmacy workflows， providing practical evidence for enhancing the efficiency of pharmaceutical service. METHODS Taking the outpatient pharmacy of the First Affiliated Hospital of Sun Yat-sen University as the research subject， a comprehensive drug traceability system was established through three key interventions： upgrading the information system architecture [including integration of the hospital information system （HIS） with the traceability platform]， workflow optimization （reorganizing the inventory-dispensing-verification tripartite process）， and designing a dual-mode traceability data collection mechanism （primary data capture at dispensing stations and supplementary capture at verification stations）. Operational efficiency differences before and after implementation were analyzed using the medical insurance data and service timeliness metrics in September 2024. RESULTS After the implementation of drug traceability code management， in terms of data collection: Mode Ⅰ （verification-stage capture） uploaded 26 144 records， while Mode Ⅲ （inventory-as-sales capture） uploaded 443 061 records， totaling 469 205 entries； in terms of time efficiency： average drug dispensing time increased from 28.74 s to 43.37 s （enhanced by 51%）. Through dynamic staffing adjustments， patient wait time only extended from 8.04 min to 8.67 min （enhanced by 8%）. CONCLUSIONS Drug traceability code management can be effectively implemented via a “system reconstruction-process reengineering-human-machine collaboration” trinity strategy， leveraging informatization （e.g.， dual-mode data capture） to offset manual operation delays， which validates the feasibility of balancing national traceability demands with service efficiency in outpatient pharmacies.

Objective To establish a chronic rejection (CR) model of mouse heart transplantation and analyze its characteristics. Methods Allogeneic BALB/c and C57BL/6 mice were used as donor and recipient for heart transplantation, and intraperitoneal injection of cytotoxic T lymphocyte-associated antigen 4-immunoglobulin (CTLA4-Ig) was given 1 and 2 days after surgery. Graft survival time, donor specific antibody (DSA) level, graft pathology and inflammatory cell infiltration were observed. Results In allogeneic transplantation model, graft survival time was prolonged after CTLA4-Ig treatment [(28.2±4.1) d vs. (7.0±0.7) d, P < 0.01]. The level of serum DSA-IgG increased at 2, 3 and 4 weeks after surgery, while the level of DSA-IgM remained unchanged. Myocardial cell injury, inflammatory cell infiltration, interstitial fibrosis and C4d deposition in capillaries were aggravated 3 weeks after operation and worsened 4 weeks after operation. The infiltrated immune cells were mainly macrophages, T cells and plasma cells. Conclusions Mouse allogeneic heart transplantation combined with CTLA4-Ig successfully establishes a CR model, which provides a basis for subsequent studies on the pathogenesis and intervention of CR.

ObjectiveTo study the clinical features of viral encephalitis with isolated dizziness，and to analyze the diagnostic efficacy of vestibular function examination and cerebrospinal fluid cytology in these patients. MethodsTotally 37 cases of viral encephalitis with isolated dizziness and 10 healthy volunteers were included. Clinical data ［dizziness handicap inventory （DHI） score，head imaging，electroencephalogram，vestibular function test，cerebrospinal fluid routine，biochemistry，cell morphology，etiology second-generation sequencing，misdiagnosis］ were collected. The area under the ROC curve（AUC）of diagnostic value of each type of test was analyzed. The changes of each examination before and after treatment were compared. ResultsWe found 89.19%（33/37）of the patients were misdiagnosed. Vestibular function smooth follow-up test indicated vestibular central lesion （AUC value：0.82）in 64.86%（24/37）of the patients. The number of CSF transformed lymphocytes increased in 86.49%（32/37）of the patients（AUC value：0.93），the CSF large lymphocytes increased in 97.30% （36/37）of the patients （AUC value：0.99），and the mononucleosis was activated in 94.59%（35/37）of the patients（AUC value：0.97）. Furthermore，18.92%（7/37）of the patients had increased EEG slow wave（AUC value：0.60），while 13.51%（5/37） of the patients showed cortical swelling on head MR （AUC：0.60）. After antiviral treatment，dizziness grade decreased（Z=-4.899，P<0.001），smooth tracking abnormalities decreased（Z=-4.583，P<0.001），the proportion of CSF transformed lymphocytes decreased（t=4.281，P<0.001），and the proportion of large lymphocytes decreased（t=6.905，P<0.001）. ConclusionThe misdiagnosis rate of viral encephalitis with isolated dizziness is high. Incorporating into diagnosis the increased large lymphocytes， transformed lymphocytes，activated monocytes in CSF cytology with smooth follow-up test may improve diagnostic efficiency .

Liver transplantation has currently become an important treatment for patients with end-stage liver disease or hepatocellular carcinoma (HCC), significantly improving patients’ prognosis. However, liver transplantation still facing many challenges, such as donor sources, liver preservation technology, transplant rejection, biliary complications and postoperative tumor recurrence after HCC liver transplantation, which urgently need to be solved and optimized. With the development of new technologies, liver transplantation in our country is facing new opportunities and challenges. Domestic research teams actively respond to the needs of the times and continuously promote innovation and breakthroughs in the basic research of liver transplantation. This article reviews the cutting-edge progress in the field of basic liver transplantation research in 2024 and evaluates the important research achievements obtained by Chinese research teams in this field. The systematic sorting out of these research advances not only helps to promote the integration of Chinese characteristic liver transplantation research into the international academic system and the docking of Chinese liver transplantation research with the global forefront, but also helps researchers and clinical surgeons to fully understand the current status of basic liver transplantation research in China, provides a clear direction for future basic research, and thus promotes the vigorous development of Chinese liver transplantation cause.

In recent years, significant progress has been made in the field of liver transplantation in terms of donor expansion, technological innovation and perioperative management. Machine perfusion technology, through dynamic repair and assessment of donor liver quality, can effectively reduce postoperative complications and increase the utilization rate of marginal donor livers. The optimization of split liver transplantation technology combined with normothermic perfusion further alleviates the shortage of donors, but its promotion is still limited by technical barriers. Xenotransplantation has achieved preclinical breakthroughs in the field of genetically modified pig livers, but ethical and immune barrier issues need to be urgently resolved. In the field of liver cancer liver transplantation, the focus is on neoadjuvant treatment with immune checkpoint inhibitors and the development of recurrence prediction models, which promotes precise treatment. For perioperative management, the optimization of individualized immunosuppressive regimens, artificial liver support, and strategies for the prevention and control of vascular complications has significantly improved patients’ survival rates. Personalized treatment for children, elderly recipients, and recipients with multiple comorbidities provides new ideas for liver transplantation in special populations. In the future, liver transplantation research may focus on the integration of multidisciplinary approaches, individualized treatment and emerging technologies to advance the global liver transplantation cause to new heights.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.