Acute-on-chronic liver failure （ACLF） is a syndrome of acute liver function decompensation in patients with chronic liver disease and is characterized by a high short-term mortality rate. Liver transplantation is an effective radical treatment method for ACLF and can significantly improve the survival rate of patients. However， in the context of a shortage of donor organs globally， how to achieve a balance between saving lives and avoiding futile transplantation while optimizing organ utilization is a critical challenge in clinical decision-making. This article systematically reviews the evolution of the evaluation and decision-making system for liver transplantation in ACLF patients and points out s that the core of this system has shifted from relying solely on the MELD score to a model based on multidimensional prognostic assessment， dynamic evaluation， and multidisciplinary collaboration. In the future， the integration of Eastern and Western criteria， the application of artificial intelligence for precise prediction， and the establishment of a more equitable priority and allocation system will be the key directions for development in this field.

Objective To establish a risk prediction model for cerebral edema after mechanical thrombectomy in acute cerebral infarction (ACI) based on multimodal computed tomography (CT) parameters. Methods A total of 220 ACI patients who underwent mechanical thrombectomy at The People’s Hospital of Bozhou between January 2022 and June 2025 were enrolled. All patients underwent preoperative conventional non-contrast CT, CT perfusion imaging, and multiphase CT angiography. Based on the presence or absence of cerebral edema at 72 hours postoperatively, patients were divided into cerebral edema and non-cerebral edema groups. General data and multimodal CT parameters were compared between the two groups. Multivariable logistic regression model was used to analyze the influencing factors of postoperative cerebral edema. A nomogram prediction model was constructed based on the multivariable analysis results, and the efficacy of the model was validated. Results The incidence of postoperative cerebral edema was 23.64% (52/220). Multivariable logistic regression analysis showed that time from onset to recanalization (OR=1.081, 95%CI=1.017-1.149), cerebral blood flow (OR=0.903, 95%CI=0.828-0.984), time to peak (OR=1.171, 95%CI=1.041-1.317), mean transit time (OR=2.815, 95%CI=1.823-6.178), and collateral circulation score (OR=0.960, 95%CI=0.930-0.990) were influencing factors for cerebral edema after mechanical thrombectomy in ACI patients (P<0.05). A nomogram model was constructed based on these factors. The calibration curve indicated good model fit, and the decision curve demonstrated net benefit. The area under the receiver operating characteristic curve for predicting cerebral edema after mechanical thrombectomy in patients with ACI was 0.961, with a sensitivity of 88.46% and a specificity of 91.07%. The Hosmer–Lemeshow goodness-of-fit test showed good model fit (χ²=1.766, P=0.962). Conclusion A risk prediction model for cerebral edema after mechanical thrombectomy in ACI patients was constructed using multimodal CT parameters. The model demonstrated high sensitivity and specificity.

Objective To establish a risk prediction model for cerebral edema after mechanical thrombectomy in acute cerebral infarction (ACI) based on multimodal computed tomography (CT) parameters. Methods A total of 220 ACI patients who underwent mechanical thrombectomy at The People’s Hospital of Bozhou between January 2022 and June 2025 were enrolled. All patients underwent preoperative conventional non-contrast CT, CT perfusion imaging, and multiphase CT angiography. Based on the presence or absence of cerebral edema at 72 hours postoperatively, patients were divided into cerebral edema and non-cerebral edema groups. General data and multimodal CT parameters were compared between the two groups. Multivariable logistic regression model was used to analyze the influencing factors of postoperative cerebral edema. A nomogram prediction model was constructed based on the multivariable analysis results, and the efficacy of the model was validated. Results The incidence of postoperative cerebral edema was 23.64% (52/220). Multivariable logistic regression analysis showed that time from onset to recanalization (OR=1.081, 95%CI=1.017-1.149), cerebral blood flow (OR=0.903, 95%CI=0.828-0.984), time to peak (OR=1.171, 95%CI=1.041-1.317), mean transit time (OR=2.815, 95%CI=1.823-6.178), and collateral circulation score (OR=0.960, 95%CI=0.930-0.990) were influencing factors for cerebral edema after mechanical thrombectomy in ACI patients (P<0.05). A nomogram model was constructed based on these factors. The calibration curve indicated good model fit, and the decision curve demonstrated net benefit. The area under the receiver operating characteristic curve for predicting cerebral edema after mechanical thrombectomy in patients with ACI was 0.961, with a sensitivity of 88.46% and a specificity of 91.07%. The Hosmer–Lemeshow goodness-of-fit test showed good model fit (χ²=1.766, P=0.962). Conclusion A risk prediction model for cerebral edema after mechanical thrombectomy in ACI patients was constructed using multimodal CT parameters. The model demonstrated high sensitivity and specificity.

Objective To establish a risk prediction model for cerebral edema after mechanical thrombectomy in acute cerebral infarction (ACI) based on multimodal computed tomography (CT) parameters. Methods A total of 220 ACI patients who underwent mechanical thrombectomy at The People’s Hospital of Bozhou between January 2022 and June 2025 were enrolled. All patients underwent preoperative conventional non-contrast CT, CT perfusion imaging, and multiphase CT angiography. Based on the presence or absence of cerebral edema at 72 hours postoperatively, patients were divided into cerebral edema and non-cerebral edema groups. General data and multimodal CT parameters were compared between the two groups. Multivariable logistic regression model was used to analyze the influencing factors of postoperative cerebral edema. A nomogram prediction model was constructed based on the multivariable analysis results, and the efficacy of the model was validated. Results The incidence of postoperative cerebral edema was 23.64% (52/220). Multivariable logistic regression analysis showed that time from onset to recanalization (OR=1.081, 95%CI=1.017-1.149), cerebral blood flow (OR=0.903, 95%CI=0.828-0.984), time to peak (OR=1.171, 95%CI=1.041-1.317), mean transit time (OR=2.815, 95%CI=1.823-6.178), and collateral circulation score (OR=0.960, 95%CI=0.930-0.990) were influencing factors for cerebral edema after mechanical thrombectomy in ACI patients (P<0.05). A nomogram model was constructed based on these factors. The calibration curve indicated good model fit, and the decision curve demonstrated net benefit. The area under the receiver operating characteristic curve for predicting cerebral edema after mechanical thrombectomy in patients with ACI was 0.961, with a sensitivity of 88.46% and a specificity of 91.07%. The Hosmer–Lemeshow goodness-of-fit test showed good model fit (χ²=1.766, P=0.962). Conclusion A risk prediction model for cerebral edema after mechanical thrombectomy in ACI patients was constructed using multimodal CT parameters. The model demonstrated high sensitivity and specificity.

Acute-on-chronic liver failure （ACLF） is an acute decompensation syndrome that develops on the background of chronic liver disease and is characterized by high mortality， rapid disease progression， complex clinical phenotypes， and a narrow window for reversibility. At present， there is still a lack of a unified standardized diagnostic system for ACLF globally， and there are significant differences between different diagnostic criteria in terms of liver failure， extrahepatic organ failure， and high-risk states within specific etiological contexts. To achieve individualized risk stratification of ACLF patients and optimize the allocation of clinical resources， it is urgently needed to establish a scientifically sound and precise classification system. Traditional static diagnostic models are confined only to baseline assessment and mortality prediction and fail to reflect dynamic disease evolution or effectively distinguish treatment response across different groups of patients. This article systematically reviews the major definitions and classification systems of ACLF at the current stage， analyzes the application value and limitations of various classification frameworks in clinical treatment decision-making， prognostic risk assessment， and clinical trial design， and proposes that the clinical classification system for ACLF should move beyond the single baseline evaluation approach toward a dynamic， treatment-oriented， and cross-etiology integrated assessment model， in order to provide a more precise theoretical basis for clinical diagnosis and treatment regarding artificial liver support， intensive care management， liver regeneration therapies， and determination of indications for liver transplantation.

ObjectiveTo establish and validate a new prediction model for the risk of death in patients with acute-on-chronic liver failure （ACLF） based on sarcopenia and other clinical indicators， and to improve the accuracy of prognostic assessment for ACLF patients. MethodsA total of 380 patients with ACLF who were admitted to Beijing YouAn Hospital， Capital Medical University， from January 2019 to January 2022 were enrolled， and they were divided into training group with 228 patients and testing group with 152 patients in a ratio of 6∶4 using the stratified random sampling method. For the training group， CT images were used to measure the cross-sectional area of the skeletal muscle at the third lumbar vertebra （L3）， and L3 skeletal muscle index （L3-SMI） was calculated. Sarcopenia was diagnosed based on the previously established L3-SMI reference values for healthy adults in northern China. Univariate and multivariable Cox regression analyses were used to establish a sarcopenia-ACLF model which integrated sarcopenia and clinical risk factors， and a nomogram was developed for presentation. The area under the ROC curve （AUC） was used to assess the predictive performance of the model， the calibration curve was used to assess the degree of calibration， and a decision curve analysis was used to investigate the clinical application value of the model. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. The Kaplan-Meier method was used to plot survival curves， and the Log-rank test was used for comparison between groups. The DeLong test was used for comparison of AUC between different models. ResultsThe multivariate Cox regression analysis showed that sarcopenia （hazard ratio ［HR］=1.962， 95% confidence interval ［CI］： 1.185‍ ‍—‍ ‍3.250， P=0.009）， total bilirubin （HR=1.003， 95%CI： 1.002‍ ‍—‍ ‍1.005， P<0.001）， international normalized ratio （HR=1.997， 95%CI： 1.674‍ ‍—‍ ‍2.382， P<0.001）， and lactic acid （HR=1.382， 95%CI： 1.170‍ ‍—‍ ‍1.632， P<0.001） were included in the sarcopenia-ACLF model. In the training cohort， the sarcopenia-ACLF model had a larger AUC than MELD-Na score in predicting 90-day mortality in patients with ACLF （0.80 vs 0.73， Z=1.97， P=0.049）. In the test cohort， the sarcopenia-ACLF model had a significantly larger AUC than MELD score （0.79 vs 0.69， Z=2.70， P=0.007） and MELD-Na score （0.79 vs 0.68， Z=2.92， P=0.004）. The calibration curve showed that the model had good calibration ability， with a relatively good consistency between the predicted risk of mortality and the observed results. The DCA results showed that within a reasonable range of threshold probabilities， the sarcopenia-ACLF model showed a greater net benefit than MELD and MELD-Na scores in both the training cohort and the test cohort. ConclusionThe sarcopenia-ACLF model developed in this study provides a more accurate tool for predicting the risk of 90-day mortality in ACLF patients， which provides support for clinical decision-making and helps to optimize treatment strategies.

Lung cancer （LC） is a significant global public health issue， with both its incidence and mortality rates ranking among the highest worldwide. The age-standardized incidence and mortality rates are increasing annually， posing a serious threat to the life and health of LC patients. Radical surgical resection is the primary treatment for malignant lung tumors. However， postoperative multidimensional functional impairments， including respiratory， mucosal， and psychological functions， are common. These impairments not only reduce patients' quality of life and affect their treatment tolerance and duration， but also negatively correlate with prognosis， facilitating disease recurrence and metastasis. At present， postoperative functional dysfunction after LC surgery remains a key clinical challenge that urgently needs to be addressed. There is a lack of standardized and regulated postoperative rehabilitation treatment management and traditional Chinese medicine （TCM） differentiation and treatment strategies for LC. Focusing on the core underlying pathogenesis of "Qi sinking" after LC surgery， and guided by the classical TCM theory of "lungs governing Qi"， this study， based on the core concept of the "five perspectives on treatment" theory， innovatively proposes the respiratory dysfunction as the core pathogenesis of "Qi sinking in the chest" during the rapid rehabilitation phase， mucosal dysfunction as the core pathogenesis of "Yin deficiency and Qi sinking" during the postoperative adjuvant treatment phase， and the psychological dysfunction as the core pathogenesis of "Qi sinking with emotional constraint" during the consolidation phase. Accordingly， stage-specific dynamic functional protection strategies are constructed. In the rapid rehabilitation phase， the strategy emphasizes tonifying Qi and uplifting sinking Qi， with differentiation and treatment based on the principle of ''descending before ascending''. In the adjuvant treatment phase， the approach focuses on nourishing Yin and uplifting Qi， with prescription combinations that integrate unblocking and tonification. In the consolidation phase， the strategy aims to resolve constraint and uplift Qi， with clinical treatment emphasizing a combination of dynamic and static methods. At each stage of functional rehabilitation， clinical differentiation and treatment should support healthy Qi and eliminate pathogenic factors simultaneously. This study is the first to propose the concept of postoperative functional protection in TCM， offering a new approach for TCM differentiation and treatment in the full-cycle， stage-based， and dynamic protection of postoperative function in LC patients. It is expected to contribute to the construction and development of an integrated TCM-Western medicine comprehensive program for cancer prevention and treatment in China.

Tumor treatment-related adverse reactions are a major focus of clinical concern， among which recurrent aphthous oral ulcers （RAU） associated with targeted therapy for lung cancer （LC） are among the most painful and distressing for patients. Currently， modern medical interventions show limited efficacy， and there is an urgent need for more effective treatment strategies. This study differentiates RAU associated with targeted therapy for LC from chemotherapy-related and ordinary oral ulcers， elucidates the pathophysiological basis of such ulcers， and traces the theoretical origin of "Yin deficiency and Qi collapse". Based on the new system of "five perspectives on diagnosis and treatment" for tumor prevention and treatment， with a focus on the core and symptom perspectives and rooted in the traditional concept of "lung dominating Qi"， we innovatively propose the concept of "medicine-induced ulcer" and are the first to introduce the theory of "Yin deficiency and Qi collapse" into the syndrome differentiation and treatment of RAU associated with targeted therapy for LC （i.e.， medicine-induced ulcer）. We propose that "Yin deficiency and Qi collapse" is the core pathogenesis of medicine-induced ulcers， in which the collapse of formless Qi is the key to their onset， while the deficiency and stasis of tangible Yin and blood constitute the root of recurrence. A hierarchical strategy for syndrome differentiation and treatment is established： first treating the collapse of formless Qi， then replenishing tangible deficiencies， and concurrently preventing recurrence. We emphasize that treatment should address both root and manifestation， with appropriate prioritization. In the acute phase， while relieving symptoms and promoting ulcer healing by nourishing Qi， uplifting collapse， and generating body fluids， attention should also be paid to nourishing spleen Yin， facilitating the circulation of nutritive Qi， and alleviating stasis to target the root pathogenesis and reduce recurrence. A verified case is presented to support this approach. This study enriches the theoretical framework and clinical methods of traditional Chinese medicine （TCM） in the treatment of RAU associated with targeted therapy for LC， promotes symptom management of treatment-related adverse reactions through integrated TCM and Western medicine， and provides theoretical support for the construction and development of a comprehensive differentiation and treatment system for lung cancer prevention， treatment， and rehabilitation.

ObjectiveTo investigate the dynamic trajectories of prognostic scores and key clinical indicators in hepatitis B virus-related acute-on-chronic liver failure （HBV-ACLF） patients without liver cirrhosis， to clarify their association with outcomes， and to provide new evidence for individualized prognostic assessment. MethodsA prospective study was conducted for the data of 154 non-cirrhotic HBV-ACLF patients who attended Beijing YouAn Hospital of Capital Medical University from January 2016 to December 2023， including prognostic scores and key biochemical indicators on Days 3， 7， 14， 21， and 28 of the disease course. According to the outcome of patients at 1 year， they were divided into death/liver transplantation group with 43 patients， liver cirrhosis group with 23 patients， and non-liver cirrhosis group with 88 patients， and the trajectory heterogeneity of different outcome subgroups was analyzed. A one-way analysis of variance was used for comparison of normally distributed continuous data among the three groups； the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data among the three groups； the Wilcoxon test was used between two groups. the chi-square test was used for comparison of categorical data between groups. The mean and its 95% confidence interval （CI） were calculated for each indicator at difference time points； the linear interpolation method was used to connect the means at adjacent time points and construct the group-specific longitudinal trend curve； the 95%CI was visualized using the semi-transparent ribbon area， with the transparency parameter （α=0.2） optimized to enhance the visual discrimination of overlapping intervals across multiple groups. A linear mixed-effects model was used to compare the longitudinal changing trend of each indicator between the patients with different outcomes； likelihood ratio was used to evaluate the significance of the interaction effect between time and group， and in case of the significant interaction effect， the slope based on the estimated marginal mean was used for comparison between two groups. ResultsThere were significant differences between the three groups in the incidence rates of ascites and grade Ⅲ — Ⅳ hepatic encephalopathy， MELD score， MELD-Na score， CLIF-C ACLF score， COSSH-ACLF Ⅱ score， total bilirubin （TBil）， international normalized ratio （INR）， alpha-fetoprotein， blood sodium， alanine aminotransferase， and procalcitonin at the baseline（all P0.05）. The analysis of dynamic trajectories showed that the death/liver transplantation group had high levels of prognostic scores and the biochemical parameters of TBil and INR （TBil400 μmol/L， INR2.5）， as well as a low level of platelet count （PLT） （100×10⁹/L）. The non-liver cirrhosis group had rapid improvements in indicators， with TBil200 μmol/L， INR1.5， and PLT100×10⁹/L by day 28， while the liver cirrhosis group showed a trend of recovery， with TBil200 μmol/L， INR2.0， and PLT 100×10⁹/L on day 28， with significant global heterogeneity in the temporal trends of the above indicators across the three groups （all P0.01）. ConclusionDynamic monitoring of prognostic scores and key clinical indicators can effectively stratify the 1-year outcomes of non-cirrhotic patients with HBV-ACLF. Patients with poor prognosis were typically characterized by INR 2.5 and TBil 400 μmol/L. Among those who survived beyond 1 year， individuals who subsequently progressed to cirrhosis were frequently identified by the presence of INR 1.5， TBil 200 μmol/L， and PLT 100×10⁹/L at day 28.