Distant metastasis after hepatectomy for hepatocellular carcinoma (HCC) significantly impairs long-term outcome. This study aimed to identify patterns, risk factors, and develop a prediction model for distant metastasis at first recurrence following HCC resection. This multi-center retrospective study included patients undergoing curative hepatectomy for HCC. Risk factors for distant metastasis were identified using Cox regression. A nomogram was constructed and validated using the concordance index (C-index) and calibration curves. Among 2,705 patients, 1,507 experienced recurrence, with 342 (22.7 per cent) developing distant metastasis. Common metastatic sites included extrahepatic vessels (36.2 per cent), lungs (26.0 per cent), and lymph nodes (20.8 per cent). Patients with distant metastasis had significantly worse 5-year overall survival compared to those with intrahepatic recurrence (9.1 versus 41.1 per cent, p < 0.001). Independent risk factors included preoperative tumor rupture, tumor size over 5.0 cm, multiple tumors, satellite nodules, macro- and microvascular invasion, narrow resection margin, and intraoperative blood transfusion. The nomogram demonstrated excellent discrimination (C-index > 0.85) and accurately stratified patients into three risk categories. In conclusion, distant metastasis at first recurrence following HCC resection was associated with poor prognosis. The proposed nomogram facilitates accurate prediction of distant metastasis, potentially informing personalized postoperative monitoring and interventions for high-risk patients.

Dermatologic toxicities associated with targeted therapies may impact drug intolerance and predict drug response, among which rash is most frequently reported and well delineated. However, the profile and effect of non-rash dermatologic toxicity are not fully understood. We identified stage-IV non-small cell lung cancer patients diagnosed at Mayo Clinic in 2006-2019 and systematically analyzed demographics, targeted agents, toxicity, response, and survival outcomes of patients who received targeted therapy. Five toxicity subgroups-none, only non-rash dermatologic, concurrent non-rash and rash (concurrent) dermatologic, only rash, and others-were compared; multivariable survival analyses employed Cox Proportional Hazard models. This study included 533 patients who had taken targeted therapies: 36 (6.8%) had no toxicity, 26 (4.9%) only non-rash dermatologic, 193 (36.2%) only rash, 134 (25.1%) concurrent dermatologic, 144 (27.0%) other toxicities. Non-rash dermatologic toxicities predominately included xerosis (12.8%), pruritus (8.5%), paronychia (7.0%). Rash was the most frequent (59.4%) and the earliest occurring (21 median onset days [MOD]) dermatologic toxicity; paronychia was the latest (69 MOD) occurring. In 329 epidermal growth factor receptor inhibitors-treated patients with dermatologic toxicity, mild toxicity occurred the most frequently in patients with only non-rash (81.8%), then those with only rash (64.8%), and the least in the concurrent (50.4%, P=0.013). Patients with concurrent dermatologic toxicities had a significantly higher response rate (67.9%) than those with only non-rash (53.8%) or only rash (41.1%, p < 0.001). Multivariable analysis demonstrated concurrent dermatologic toxicity independently predicted a lower risk of death (harzard ratio [HR] 0.48 [0.30-0.77], p < 0.001). Compared to rash, non-rash dermatologic toxicity might be a stronger predictor of better treatment response and longer survival in patients who received targeted therapy.

Immune checkpoint inhibitors (ICIs) have been widely used in various types of cancer, but they have also led to a significant number of adverse events, including ICI-induced immune-mediated hepatitis (IMH). This study aimed to explore the risk factors for IMH in patients treated with ICIs and to develop and validate a new nomogram model to predict the risk of IMH. Detailed information was collected between January 1, 2020, and December 31, 2023. Univariate logistic regression analysis was used to assess the impact of each clinical variable on the occurrence of IMH, followed by stepwise multivariate logistic regression analysis to determine independent risk factors for IMH. A nomogram model was constructed based on the results of the multivariate analysis. The performance of the nomogram model was evaluated via the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) analysis. A total of 216 (8.82%) patients developed IMH. According to stepwise multivariate logistic analysis, hepatic metastasis, the TNM stage, the WBC count, LYM, ALT, TBIL, ALB, GLB, and ADA were identified as risk factors for IMH. The AUC for the nomogram model was 0.817 in the training set and 0.737 in the validation set. The calibration curves, DCA results, and CIC results indicated that the nomogram model had good predictive accuracy and clinical utility. The nomogram model is intuitive and straightforward, making it highly suitable for rapid assessment of the risk of IMH in patients receiving ICI therapy in clinical practice. Implementing this model enables early adoption of preventive and therapeutic strategies, ultimately reducing the likelihood of immune-related adverse events (IRAEs), and especially IMH.

Parkinson's disease (PD) is a progressive neurodegenerative disorder for which reliable blood biomarkers to predict disease progression remain elusive. Plasma extracellular vesicles (EVs) have gained attention as a promising biomarker platform due to their stability and ability to cross the blood-brain barrier. This study explored the potential of EV-cargo proteins, specifically α-synuclein, tau, and β-amyloid, as biomarkers of PD progression. A cohort of 55 people with PD (PwP) and 58 healthy controls (HCs) underwent annual assessments of plasma EV proteins, cognition, and motor symptoms. EVs were isolated and validated using standardized methods, with pathognomonic proteins quantified via immunomagnetic reduction assays. Associations between biomarker changes and clinical symptom progression were analyzed. Over an average of 3.96 visits for PwP and 2.25 visits for HCs, PwP exhibited a distinct pattern of plasma EV protein changes linked to motor symptom progression, particularly in the Unified PD Rating Scale (UPDRS) part II score. Notably, changes in plasma EV α-synuclein levels were significantly correlated with changes in motor and cognitive symptoms, suggesting its central role in disease progression. These findings highlight the potential of plasma EV biomarkers, especially α-synuclein, as indicators of ongoing pathogenesis and as candidates for evaluating α-synuclein-targeted therapies in PD.

Swallowing is a complex process requiring precise coordination of numerous muscles in the head and neck to smoothly guide ingested material from the mouth to the stomach. Animal and human studies have revealed a complex network of neurons in the brainstem, cortex, and cerebellum that coordinate normal swallowing. The interactions between these regions ensure smooth and efficient swallowing. However, the current understanding of the neurophysiological mechanisms involved in post-stroke dysphagia (PSD) is incomplete, and complete functional connectivity for swallowing recovery remains understudied and requires further exploration. In this review, we discussed the neuroanatomy of swallowing and the pathogenesis of PSD and summarized the factors affecting PSD recovery. We also described the plasticity of neural networks affecting PSD, including enhancing activation of neural pathways, cortical reorganization, regulation of extracellular matrix dynamics and its components, modulation of neurotransmitter delivery, and identification of potential therapeutic targets for functional recovery in PSD. Finally, we discussed the therapeutic strategies based on functional compensation and motor learning. This review aimed to provide a reference for clinicians and researchers to promote the optimization of PSD treatments and explore future research directions.

In human-computer interaction, gesture recognition based on physiological signals offers advantages such as a more natural and fast interaction mode and less constrained by the environment than visual-based. Surface electromyography-based gesture recognition has significantly progressed. However, since individuals have physical differences, researchers must collect data multiple times from each user to train the deep learning model. This data acquisition process can be particularly burdensome for non-healthy users. Researchers are currently exploring transfer learning and data augmentation techniques to enhance the accuracy of small-sample gesture recognition models. However, challenges persist, such as negative transfer and limited diversity in training samples, leading to suboptimal recognition performance. Therefore, We introduce motion information into sEMG-based recognition and propose a multimodal optimal matching and augmentation method for small sample gesture recognition, achieving efficient gesture recognition with only one acquisition per gesture. Firstly, this method utilizes the optimal matching signal selection module to select the most similar signals from the existing data to the new user as the training set, reducing inter-domain differences. Secondly, the similarity calculation augmentation module enhances the diversity of the training set. Finally, the Modal-type embedding enhances the information interaction between each mode signal. We evaluated the effectiveness on Self-collected Stroke Patient, the Ninapro DB1 dataset and the Ninapro DB5 dataset and achieved accuracies of 93.69%, 91.65% and 98.56%, respectively. These results demonstrate that the method achieved performance comparable to traditional recognition models while significantly reducing the collected data.

Cognitive impairment refers to the impairment of higher brain functions such as perception, thinking or memory that affects the individual's ability to perform daily or social activities. Studies have found that changes in neuronal activity during tasks in patients with cognitive impairment are closely related to changes in cerebral cortical hemodynamics. Functional near-infrared spectroscopy is an indirect method to measure neural activity based on changes in blood oxygen concentration in the cerebral cortex. Due to its strong anti-motion interference, high compatibility, and almost no restriction on participants and environment, it has shown great potential in the research field of cognitive impairment. Recognizing these benefits, this comprehensive review systematically elucidates the rationale, historical development, advantages and disadvantages of functional near-infrared spectroscopy, and also discusses the applications of combining functional near-infrared spectroscopy with other detection techniques. Additionally, this review summarized how functional near-infrared spectroscopy can be applied to cognitive impairment caused by different diseases, ultimately aiding the study of neural mechanisms of cognitive activities, which is crucial for the diagnosis, differentiation and treatment of cognitive impairment.

Alzheimer's disease (AD) is the most common type of dementia. Its incidence is rising rapidly as the global population ages, leading to a significant social and economic burden. AD involves complex pathologies, including amyloid plaque accumulation, synaptic dysfunction, and neuroinflammation. This study explores the therapeutic potential of N -((perfluorophenyl)amino)glutamine (RA-PF), a derivative of γ-glutamyl-N'-(2-hydroxyphenyl)hydrazide (Ramalin), a compound with antioxidant and anti-inflammatory properties. Administration of RA-PF to 5xFAD mice decreases BACE1, reduces Aβ plaque deposition, inhibits microglial activation, restores synaptic transmission, and improves mitochondrial motility, leading to the recovery of cognitive function. Additionally, RA-PF treatment in 3xTg-AD mice alleviates anxiety-like behaviors, tau phosphorylation via inactivating GSK-3β, and BACE1 expression. Further transcriptomic analysis reveals RA-PF treatment in AD mice models recovers phagosome, inflammation, NOD-like receptor, presynaptic membrane, and postsynaptic membrane related signaling pathways. These findings suggest that RA-PF effectively targets multiple aspects of AD pathology, offering a novel multi-target approach for AD treatment.

Parkinson's disease (PD) is a progressive disease that requires effective staging management. The role of intestinal microbiota in PD has been studied, but its changes at different stages are not clear. In this study, meta- analysis, bioinformatics analysis and in vivo simulation were used to explore the intestinal microbiota distribution of PD patients and models at different stages. Two PD models at different stages were established in rotenone-treated rats and MPTP-induced mice. The differences in the intestinal microbiota among the different stages of PD patients or models were compared and analyzed. There were significant differences between PD patients and controls, including Actinobacteriota, Deltaproteobacteria, Clostridiales, Lachnospiraceae, Parabacteroides, etc. Through bioinformatics analysis, we revealed significant differences between PD patients at different stages and controls, including Actinobacteriota, Methanobacteria, Erysipelotrichales, Prevotellaceae, Parabacteroides, Parabacteroides gordonii, etc. Through meta-analysis, we found that Actinobacteriota and Erysipelotrichaceae had significantly increased in the chronic MPTP model, while Prevotellaceae had significantly decreased. PD rats and mice presented significant damage to motor function, coordination, autonomous activity ability and gastrointestinal function, and the damage in the late group was greater than that in the early group. There were significant differences in intestinal microbiota between PD patients or models at different stages and the control groups. In the early stage, the dominant microbiota are Akkermansia, Alistipes, Anaerotruncus, Bilophila, Rikenellaceae, Verrucomicrobia and Verrucomicrobiae, whereas in the late stage, the dominant microbiota are Actinobacteriota and Erysipelotrichaceae. These differences can lay a foundation for subsequent research on the treatment and mechanism of PD at different stages.

Growth and differentiation factor 15 (GDF15), a member of the transforming growth factor-βsuperfamily, is considered a stress response factor and has garnered increasing attention in recent years due to its roles in neurological diseases. Although many studies have suggested that GDF15 expression is elevated in patients with neurodegenerative diseases (NDDs), glioma, and ischemic stroke, the effects of increased GDF15 expression and the potential underlying mechanisms remain unclear. Notably, many experimental studies have shown the multidimensional beneficial effects of GDF15 on NDDs, and GDF15 overexpression is able to rescue NDD-associated pathological changes and phenotypes. In glioma, GDF15 exerts opposite effects, it is both protumorigenic and antitumorigenic. The causes of these conflicting findings are not comprehensively clear, but inhibiting GDF15 is helpful for suppressing tumor progression. GDF15 is also regarded as a biomarker of poor clinical outcomes in ischemic stroke patients, and targeting GDF15 may help prevent this disease. Thus, we systematically reviewed the synthesis, transcriptional regulation, and biological functions of GDF15 and its related signaling pathways within the brain. Furthermore, we explored the potential of GDF15 as a therapeutic target and assessed its clinical applicability in interventions for brain diseases. By integrating the latest research findings, this study provides new insights into the future treatment of neurological diseases.

Alzheimer's disease (AD), the leading cause of dementia, significantly impacts global public health, with cases expected to exceed 150 million by 2050. Late-onset Alzheimer's disease (LOAD), predominantly influenced by the APOE-ε4 allele, exhibits complex pathogenesis involving amyloid-β (Aβ) plaques, neurofibrillary tangles (NFTs), neuroinflammation, and blood-brain barrier (BBB) disruption. Proteomics has emerged as a pivotal technology in uncovering molecular mechanisms and identifying biomarkers for early diagnosis and intervention in AD. This paper reviews the genetic and molecular roles of APOE-ε4 in the pathology of AD, including its effects on Aβ aggregation, tau phosphorylation, neuroinflammation, and BBB integrity. Additionally, it highlights recent advances in serum proteomics, revealing APOE-ε4-dependent and independent protein signatures with potential as early biomarkers for AD. Despite technological progress, challenges such as population diversity, standardization, and distinguishing AD-specific biomarkers remain. Directions for future research emphasize multicenter longitudinal studies, multi-omics integration, and the clinical translation of proteomic findings to enable early detection of AD and personalized treatment strategies. Proteomics advances in AD research hold the promise of improving patient outcomes and reducing the global disease burden.

This study aimed at analyzing and comparing the clinical efficacy and prognosis of repeat laparoscopic hepatectomy (r-LH) and radiofrequency ablation (RFA) in treating recurrent hepatocellular carcinoma (RHCC). Clinicopathological data of RHCC patients who underwent r-LH or RFA as treatment from three medical centers were retrospectively reviewed. Baseline characteristics at the recurrence time after initial hepatectomy and clinical outcomes following treatment of RHCC were compared between the two groups. Using the Kaplan-Meier method, survival curves for the two groups of patients were generated, and the log-rank test was used to compare survival differences. Propensity score matching (PSM) analysis was used to match patients of the r-LH and RFA groups in a 1:1 ratio. A total of 272 patients were enrolled, including 133 patients who underwent r-LH and 139 patients who received RFA. After PSM, 76 patients were matched in each study group. Compared with the r-LH group, the RFA group had shorter hospitalization and fewer postoperative complications. However, the r-LH group had significantly better overall survival (OS) and disease-free survival (DFS) than the RFA group before and after PSM. Subgroup analysis demonstrated that RHCC patients with solitary tumor or those with tumors located near the diaphragm, visceral surface or vessels, had survival benefits from r-LH. When tumor diameter ≤ 5 cm, r-LH appears to be an effective priority to RFA with a significantly higher OS and DFS rate in treating RHCC patients, especially for patients with solitary tumor and those with tumors located near the diaphragm, visceral surface or vessels.

The current state of systemic therapy for advanced biliary tract cancer (BTC) has undergone significant changes. Currently, there are no clinical trials directly comparing various first-line systemic therapy regimens to each other, and these trials are unlikely to be conducted in the future. In this systematic review, after various abstracts and full-text articles published from the establishment of the database until October 2024 were searched, we included and analysed phase 3 clinical trials to evaluate the efficacy of different first-line systemic treatment regimens in advanced BTC. We used the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines and a random effects model to pool the overall effects. Finally, seven low-risk-of-bias trials (with all of the trials representing first-line trials) were included. A total of 4033 patients were included in seven first-line trials. In terms of progression-free survival (PFS), network meta-analysis revealed that durvalumab + gemcitabine + cisplatin (GemCis) triple therapy, S-1 + GemCis triple therapy, and pembrolizumab + GemCis triple therapy were superior to GemCis. In terms of overall survival (OS), network meta-analysis revealed that durvalumab + GemCis triple therapy and pembrolizumab + GemCis triple therapy outperformed GemCis. According to the ranking of the P scores, durvalumab + GemCis triple therapy ranked first in PFS and second in OS. Therefore, the advantages of molecular immunotherapy have gradually become known, which suggests that future trials should focus on other potential combinations and molecular immunotargeted therapies.

Syphilis, a chronic infection caused by Treponema pallidum, is experiencing a global resurgence, posing significant public health challenges. This study examined the escalating trends of syphilis in the United States, China, and some other countries highlighting the impact of the COVID-19 pandemic, changes in sexual behavior, coinfection with the other infectious diseases such as AIDs, and the role of public health funding. The analysis revealed a stark increase in syphilis cases, particularly among high-risk groups such as men who have sex with men (MSM). China's National Syphilis Control Program (NSCP), initiated in 2010, is a comprehensive approach to syphilis management that incorporates health education, access to testing and treatment, partner notification, safe sex promotion, community interventions, and stigma reduction. The success of the NSCP in reducing early syphilis incidence rates and congenital syphilis in Guangdong Province, that may be served as a model for international syphilis control efforts. This study highlights the necessity for targeted public health interventions and the importance of robust healthcare infrastructure in combating the syphilis epidemic.

In recent years, machine learning, and particularly deep learning, has shown remarkable potential in various fields, including medicine. Advanced techniques like convolutional neural networks and transformers have enabled high-performance predictions for complex problems, making machine learning a valuable tool in medical decision-making. From predicting postoperative complications to assessing disease risk, machine learning has been actively used to analyze patient data and assist healthcare professionals. However, the "black box" problem, wherein the internal workings of machine learning models are opaque and difficult to interpret, poses a significant challenge in medical applications. The lack of transparency may hinder trust and acceptance by clinicians and patients, making the development of explainable AI (XAI) techniques essential. XAI aims to provide both global and local explanations for machine learning models, offering insights into how predictions are made and which factors influence these outcomes. In this article, we explore various applications of machine learning in medicine, describe commonly used algorithms, and discuss explainable AI as a promising solution to enhance the interpretability of these models. By integrating explainability into machine learning, we aim to ensure its ethical and practical application in healthcare, ultimately improving patient outcomes and supporting personalized treatment strategies.

Recently, a systemic therapy for advanced hepatocellular carcinoma (HCC) has been developed. The regimen for unresectable HCC varies and includes single or multi-tyrosine kinase inhibitors, monoclonal antibodies, immune checkpoint inhibitors, or their combinations. Treatment with these agents begins with sorafenib as the first-line drug for unresectable HCC. Subsequently, several systemic therapies, including lenvatinib, ramucirumab, cabozantinib, and regorafenib have been investigated and established. With advances in systemic therapy for unresectable HCC, the prognosis of patients with unresectable HCC has improved significantly than previously. Conversion surgery, consisting of systemic therapy and surgery, showed the possibility of improving the prognosis than systemic therapy alone. Although a combination of atezolizumab and bevacizumab is mostly used for initially unresectable HCC to conduct conversion surgery because of the high response rate and fewer adverse events compared to others, many trials are being conducted to assess their efficacy for initially unresectable HCC. However, the appropriate timing of surgery and interval between systemic therapy and surgery remain controversial. To address these issues, a multidisciplinary team can play a vital role in determining the strategies for treating unresectable HCC. This review describes previous and current trends in the treatment of HCC, with a particular focus on conversion surgery for initially unresectable HCC.

The prognosis following radical surgery for intrahepatic cholangiocarcinoma (ICC) is poor, and optimal follow-up strategies remain unclear, with ongoing debates regarding anatomic resection (AR) versus non-anatomic resection (NAR). This study included 680 patients from five hospitals, comparing a combination of eight feature screening methods and 11 machine learning algorithms to predict prognosis and construct integrated models. These models were assessed using nested cross-validation and various datasets, benchmarked against TNM stage and performance status. Evaluation metrics such as area under the curve (AUC) were applied. Prognostic models incorporating screened features showed superior performance compared to unselected models, with AR emerging as a key variable. Treatment recommendation models for surgical approaches, including DeepSurv, neural network multitask logistic regression (N-MTLR), and Kernel support vector machine (SVM), indicated that N-MTLR's recommendations were associated with survival benefits. Additionally, some patients identified as suitable for NAR were within groups previously considered for AR. In conclusion, three robust clinical models were developed to predict ICC prognosis and optimize surgical decisions, improving patient outcomes and supporting shared decision-making for patients and surgeons.

The role of laparoscopic liver resection (LLR) for intrahepatic cholangiocarcinoma (ICC) remains debated. This study aimed to evaluate the short- and long-term outcomes of LLR vs. open liver resection (OLR) in ICC stratified by tumor burden score (TBS). ICC patients who underwent LLR or OLR were included from a multicenter database between July 2009 and October 2022. Patients were stratified into two cohorts based on whether the TBS was > 5.3. A 1:3 propensity score matching (PSM) analysis was performed between LLR and OLR in each cohort. Cox regression analysis was used to identify prognostic factors for ICC. A total of 626 patients were included in this study, 304 and 322 patients were classified into the low- and high-TBS groups, respectively. In the low-TBS group, after PSM, LLR was associated with less blood loss, lower CCI, fewer complications and shorter hospital stay (all p < 0.05). Kaplan-Meier curves revealed that LLR had better OS (p = 0.032). Multivariate Cox regression analysis showed that surgical procedure was an independent prognostic factor for ICC (HR: 0.445; 95% CI: 0.235-0.843; p = 0.013). In the high-TBS group, after PSM, LLR were associated with reduced blood loss, lower CCI, fewer complications and shorter hospital stay (all p < 0.05), while OS (p = 0.98) and DFS (p = 0.24) were similar between the two groups. TBS is an important prognostic factor for ICC. LLR is a safe and feasible option for ICC and leads to faster postoperative recovery. LLR can offer ICC a comparable and even better long-term prognosis than OLR.

Up to half of hepatocellular carcinoma (HCC) cases are diagnosed at an advanced stage, for which effective treatment options are lacking, resulting in a poor prognosis. Over the past few years, the combination of immune checkpoint inhibitors and anti-angiogenic targeted therapy has proven highly efficacious in treating advanced HCC, significantly extending patients' survival and providing a potential for sequential curative surgery. After sequential curative hepatectomy or liver transplantation following conversion therapy, patients can receive long-term survival benefits. In order to improve the long-term survival rate of the overall population with liver cancer and achieve the goal of a 15% increase in the overall 5-year survival rate outlined in the Healthy China 2030 blueprint, the Professional Committee for Prevention and Control of Hepatobiliary and Pancreatic Diseases of Chinese Preventive Medicine Association, Chinese Society of Liver Cancer, and the Liver Study Group of Surgery Committee of Beijing Medical Association organized in-depth discussions among relevant domestic experts in the field. These discussions focused on the latest progress since the release of the Chinese expert consensus on conversion therapy of immune checkpoint inhibitors combined antiangiogenic targeted drugs for advanced hepatocellular carcinoma (2021 Edition) and resulted in a new consensus on the modifications and supplements to related key points. This consensus aims to further guide clinical practice, standardize medical care, and promote the development of the discipline.

Water-based therapy has been gaining attention in recent years and is being widely used in clinical settings. Hydrotherapy is the most important area of water-based therapy, and it has distinct advantages and characteristics compared to conventional land-based exercises. Several new techniques and pieces of equipment are currently emerging with advances in computer technologies. However, comprehensive reviews of hydrotherapy are insufficient. Hence, this study reviewed the status quo, mechanisms, adverse events and contraindications, and future prospects of the use of hydrotherapy. This study aims to comprehensively review the latest information regarding the application of hydrotherapy to musculoskeletal diseases, neurological diseases, and COVID-19. We have attempted to provide a "take-home message" regarding the clinical applications and mechanisms of hydrotherapy based on the latest evidence available.

Hepatocellular carcinoma (HCC) represents a significant global health burden, particularly in the Asia-Pacific region, where it is a leading cause of cancer-related mortality. In China alone, HCC accounts for approximately 367,700 new cases and 316,500 deaths annually; over 50% of patients are diagnosed at an advanced stage, limiting curative treatment options and resulting in poor survival outcomes. Systemic therapies combining immune checkpoint inhibitors (ICIs) with antiangiogenic targeted drugs have shown promise in converting unresectable HCC into resectable cases, potentially transforming clinical outcomes. The Chines expert consensus on sequential surgery following conversion therapy based on combination of immune checkpoint inhibitors and antiangiogenic targeted drugs for advanced hepatocellular carcinoma (2024 edition) provides an updated, multidisciplinary framework emphasizing sequential surgery post-conversion therapy. The consensus highlights treatment protocols, efficacy evaluation, and innovative adjuvant strategies to refine clinical practice and enhance survival outcomes in advanced HCC.