OBJECTIVE To conduct a comprehensive clinical evaluation of four nucleoside （acid） analogues that have been approved and marketed in China， such as entecavir， tenofovir disoproxil fumarate， tenofovir alafenamide fumarate， and tenofovir amibufenamide. METHODS According to the Guideline for the Administration of Clinical Comprehensive Evaluation of Drugs （2021 edition， trial implementation）， a comprehensive search was conducted across databases including CNKI， Wanfang Data， VIP， PubMed， the Cochrane Library， Embase， as well as relevant official websites. Drug package inserts， guidelines， consensus statements， and relevant literature for the four drugs were collected and subjected to a comprehensive evaluation across six dimensions： safety， efficacy， cost-effectiveness， innovativeness， suitability， and accessibility. RESULTS The scores for entecavir in terms of safety， efficacy， cost-effectiveness， innovativeness， suitability， and accessibility-along with its comprehensive score-were 13， 14， 13， 10， 18， and 6， totaling 74 points. For tenofovir disoproxil fumarate， the respective scores were 13， 17， 18， 8， 18， and 7， totaling 81 points. For tenofovir alafenamide fumarate， the scores were 14， 20， 12， 8， 18， and 5， totaling 77 points. Finally， for tenofovir amibufenamide， the scores were 10.5， 17， 10， 6， 15， and 4， totaling 62.5 points. CONCLUSIONS Tenofovir disoproxil fumarate， with the highest score， is recommended as the first-line option， suitable for adults， children， and pregnant women. However， caution is warranted for potential renal impairment. Tenofovir alafenamide fumarate is recommended as a second-line alternative， particularly for individuals at high risk for bone and renal damage. Entecavir has a score similar to tenofovir alafenamide fumarate but requires dosing on an empty stomach and dose adjustment based on renal function of patients. Tenofovir amibufenamide received the lowest score and is considered a weak recommendation. The clinical application of these nucleoside （acid） analogues should be individualized based on the patient’s age， physiological status， and risk factors.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

ObjectiveTo collate and compare the characteristics and differences in the methods for constructing animal models of different subtypes of gastroesophageal reflux disease （GERD） based on literature， providing a reference for researchers in this field regarding animal model construction. MethodsExperimental studies related to GERD including reflux esophagitis （RE）， nonerosive reflux disease （NERD） and Barrett's esophagus （BE） model construction from January 1， 2014 to January 27， 2024， were retrieved from databases such as CNKI， Wanfang， VIP， Web of Science， and Pubmed. Information on animal strains， genders， modeling methods including disease-syndrome combination models， modeling cycles were extracted； for studies with model evaluation， the methods of model evaluation were also extracted； then analyzing all those information. ResultsA total of 182 articles were included. SD rats were most frequently selected when inducing animal models of RE （88/148， 59.46%） and NERD （9/14， 64.29%）. For BE， C57BL/6 mice were most commonly used （11/20， 55.00%）. Male animals （RE： 111/135， 82.22%； NERD： 11/14， 78.57%； BE： 10/12， 83.33%） were the most common gender among the three subtypes. The key to constructing RE animal models lies in structural damage to the esophageal mucosal layer， gastric content reflux， or mixed reflux， among which forestomach ligation + incomplete pylorus ligation （42/158， 26.58%） was the most common modeling method； the key to constructing NERD animal models lies in micro-inflammation of the esophageal mucosa， visceral hypersensitivity， and emotional problems， and intraperitoneal injection of a mixed suspension of ovalbumin and aluminum hydroxide combined with acid perfusion in the lower esophagus （8/14， 57.14%） was the most common modeling method； the key to constructing BE animal models lies in long-term inflammatory stimulation of the esophageal mucosa and bile acid reflux， and constructing interleukin 2-interleukin 1β transgenic mice （7/25， 28.00%） was the most common modeling method. Adverse psychological stress was the most common method for inducing liver depression. ConclusionsThe construction key principles and methodologies for RE， NERD， and BE animal models exhibit significant differences. Researchers should select appropriate models based on subtype characteristics （e.g.， RE focusing on structural damage， NERD emphasizing visceral hypersensitivity）. Current studies show insufficient exploration of traditional Chinese medicine disease-syndrome combination models. Future research needs to optimize syndrome modeling approaches （e.g.， composite etiology simulation） and establish integrated Chinese-Western medicine evaluation systems to better support mechanistic investigations of traditional Chinese medicine.

BackgroundPrenatal depression has an important impact on maternal health and pregnancy outcomes. Previous studies have shown that maternal prenatal depression is associated with social support， and social support is related to fear of childbirth. However， there is limited research on the relationship among maternal prenatal depression， social support and fear of childbirth， and no studies have specifically explored the influence of social support and fear of childbirth on prenatal depression in primiparous women. ObjectiveTo investigate the current status of prenatal depression among primiparous women， and to analyze the correlation between social support and fear of childbirth， and to further explore the influence of social support and fear of childbirth on prenatal depression in this population， so as to provide references for improving their mental health. MethodsA total of 380 primiparous women admitted to the inpatient department of Chengdu Wenjiang District People's Hospital from December 2022 to September 2023 were enrolled as study subjects. A self-made questionnaire， Edinburgh Postnatal Depression Scale （EPDS）， Social Support Rating Scale （SSRS） and Childbirth Attitudes Questionnaire （CAQ） were used to conduct the survey. Pearson correlation analysis was employed to examine the relationships between scale scores. Multiple linear regression analysis was conducted to identify influencing factors of prenatal depression. ResultsA total of 380 questionnaires were distributed， with 372 （97.89%） valid responses collected. Among the participants， 222 cases （59.68%） were identified with prenatal depression. Pearson correlation analysis revealed that EPDS score was negatively correlated with SSRS score （r=-0.283， P<0.01） and positively correlated with CAQ score （r=0.341， P<0.01）. Multiple linear regression analysis indicated that social support （β=-0.166， P<0.01） and fear of childbirth （β=0.269， P<0.01） were influencing factors of prenatal depression in primiparous women. ConclusionThe prevalence of prenatal depression among primiparous women is concerning， with depression levels showing significant associations with both social support and fear of childbirth.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Objective To analyze the characteristics and risk factors associated with postoperative deep vein thrombosis(DVT)of the lower extremities in patients undergoing surgery for lumbar degenerative diseases.Methods A retrospective analysis was conducted on clinical data from 298 patients who were hospitalized for lumbar degenerative diseases and underwent lumbar spine surgery treatment in the First Affiliated Hospital of Nanchang University from October 1,2022 to April 15,2023.Patients were divided into DVT group(n=71)and non-DVT group(n=227)according to whether DVT of the lower limbs occurred within 1 week postoperatively.The incidence and distribution characteristics of postoperative DVT were analyzed.Univariate and binary logistic regression analyses were performed to identify risk factors for DVT,and receiver operating characteristic(ROC)curves were used to determine cut-off values for relevant risk factors.Results A total of 298 patients were included,among whom 159 were males(53.4%)and 139 were females(46.6%),with an average age of(64.5±9.8)years.DVT occurred in 71 patients,and the incidence of lower extremity DVT was 23.8%.In the DVT group,there were 49 cases(69.0%)of intermuscular vein thrombosis,and 22 cases of other types of thrombosis(7 cases of peroneal vein thrombosis,4 cases of posterior tibial vein thrombosis,3 cases of common femoral vein thrombosis,1 case of anterior tibial vein thrombosis,and 7 cases of multiple thrombosis);58 cases(81.7%)had DVT in one lower extremity,and 13 cases(18.3%)had DVT in both lower extremities.Univariate analysis results showed that age,body mass index(BMI),length of hospital stay,history of hypertension,operative time,and intraoperative blood loss were associated with the occurrence of lower extremity DVT after surgery for lumbar degenerative diseases(P<0.05).Binary logistic regression analysis results indicated that older age(OR=1.079,P<0.01),higher BMI(OR=1.130,P=0.01),history of hypertension(OR=2.992,P<0.01),and larger intraoperative blood loss(OR=1.002,P=0.03)were independent risk factors for the occurrence of lower extremity DVT.ROC curve analysis demonstrated that patients with age>58.5 years,BMI>24.01 kg/m2,history of hypertension,and intraoperative blood loss>550 ml had a significantly increased risk of postoperative lower limb DVT.Conclusions The incidence of lower extremity DVT after surgery for lumbar degenerative disease is high,and intermuscular venous thrombosis is more common.Older age,higher BMI,history of hypertension,and larger intraoperative blood loss are independent risk factors for the occurrence of lower extremity DVT after surgery.