OBJECTIVE To compare the efficacy and safety of evolocumab and probucol in patients with ultra-high-risk atherosclerotic cardiovascular disease （ASCVD） following percutaneous coronary intervention （PCI）. METHODS A retrospective analysis was conducted on 156 ultra-high-risk ASCVD patients who underwent PCI in our institution between January 1， 2023 and December 31， 2024. According to the lipid-lowering regimen， the patients were categorized into evolocumab group （ n ＝86） and probucol group （ n ＝70）. Changes in lipid parameters [total cholesterol （TC）， low-density lipoprot ein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， triglycerides， lipoprotein （a）， and lipid goal achievement rate ] ， inflammatory markers [interleukin-6 （IL-6） and C-reactive protein （CRP） ] ， and cardiac function indices （left ventricular ejection fraction， left ventricular end-systolic diameter， left ventricular end-diastolic diameter， and N-terminal pro-B-type natriuretic peptide） were compared between two groups at baseline and after 6 months of treatment. The incidence of adverse clinical events during treatment， including acute myocardial infarction， in-stent restenosis， acute heart failure， cerebral hemorrhage， and stroke， was also evaluated. RESULTS No statistically significant differences were observed between the two groups at baseline （ P ＞0.05）. After 6 months of treatment， both groups demonstrated significant improvements in lipid profiles （except HDL-C） and inflammatory markers compared to those at baseline （ P ＜0.05）. The evolocumab group exhibited greater reductions in TC， LDL-C， IL-6， and CRP， along with a higher lipid target achievement rate， compared with the probucol group （ P ＜0.05）. There were no statistically significant differences in the cardiac function-related indicators before and after treatment between the two groups， nor in the incidence of adverse events during the treatment （ P ＞0.05）. CONCLUSIONS For ultra-high-risk ASCVD patients after PCI， both of the above treatment options are associated with improvements in blood lipid and inflammatory response， with good safety during short-term follow-up. Evolocumab shows superior efficacy in TC， LDL-C and inflammatory markers reduction and lipid target achievement， compared to probucol.

Objective To investigate the distribution characteristics of Oncomelania hupensis snails in Yunnan Province fol-lowing interruption of schistosomiasis transmission, so as to provide the evidence for assessing the risk of schistosomiasis transmission and scientifically formulating the schistosomiasis surveillance program. Methods According to the requirements of the National Schistosomiasis Surveillance Scheme (2020 Edition), O. hupensis snail surveillance data were collected from 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province from 2020 to 2024, including area of snail survey, area of snail habitats, area of re-emerging snail habitats, number of frames surveyed, number of frames with O. hupensis snails, number of O. hupensis snails captured, and number of living snails, and the occurrence of frames with snails and mean density of living snails were calculated. Changes in snail status over the 5-year period from 2020 to 2024 and the differences in snail distributions specified by epidemic intensity, environmental type, and vegetation type were analyzed. Results The areas of snail survey increased from 1 727.96 hm² in 2020 to 3 894.45 hm² in 2024 (peak) across 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province during the period from 2020 through 2024. The areas of snail habitats increased from 70.36 hm² in 2020 to a peak in 2023 (172.04 hm²), followed by a reduction to 132.36 hm² in 2024, and the areas of re-emerging snail habitats increased from 42.71 hm² in 2020 to a peak in 2022 (78.43 hm²), followed by a reduction to 40.21 hm² in 2024. The occurrence of frames with snails and mean density of living snails increased from 1.24% (3 025/244 404) and (0.033 2 ± 0.038 7) snails/0.1 m² in 2020 to peaks at 2.03% (6 231/307 563) and (0.066 9 ± 0.068 4) snails/0.1 m² in 2023, followed by reductions to 1.04% (5 829/559 941) and (0.032 6 ± 0.057 7) snails/0.1 m² in 2024, respectively. There was a significant difference in the occurrence of frames with snails over the 5-year study period (χ² = 1 962.95, P < 0.05), and the occurrence of frames with snails reduced by 48.71% in 2024 relative to in 2023 (χ² = 1 411.05, P < 0.005); however, there was no significant difference in the mean density of living snails over the 5 years (H = 5.310, P > 0.05). There were significant differences in the occurrence of frames with snails (χ² = 481.27, P < 0.05) and mean density of living snails (H = 6.872, P < 0.05) in schistosomiasis-endemic areas with different epidemic intensities. The occurrence of frames with snails (χ² = 25.32 and 38.70, both P values < 0.017) and mean density of living snails (Z = 28.55 and 49.96, both P values < 0.017) were higher in schistosomiasis transmission-interrupted and eliminated areas with snails than in schistosomiasis-eliminated areas without snails, and the occurrence of frames with snails (χ² = 453.54, P < 0.017) and mean density of living snails (Z = −56.97, P < 0.017) were higher in schistosomiasis-eliminated areas with snails than in schistosomiasis transmission-interrupted areas with snails. O. hupensis snails were mainly distributed in paddy fields, dry farmlands and ditches; however, the occurrence of frames with snails (13.40%, 424/3 164) and mean density of living snails [(0.252 8 ± 0.158 7) snails/0.1 m²] were higher in ponds/weirs than in other types of environments (both P values < 0.05). Rice, dry farmland crops and weeds were main vegetations in which O. hupensis snails were distributed, and the occurrence of frames with snails (2.29%, 7 111/310 140) and mean density of living snails [(0.072 3 ± 0.018 9) snails/0.1 m²] were higher in weeds than in other types of environments (both P values < 0.05). Conclusions O. hupensis snails have been effectively controlled in Yunnan Province following implementation of integrated schistosomiasis control measures; however, there are still risk factors for schistosomiasis transmission, including reduced attention to schistosomiasis control and snail re-emergence. Improved control efforts and surveillance system construction and timely identification of risk factors of snail status and timely management are recommended to ensure the achievement of the target of schistosomiasis elimination as scheduled.

Background/Aims: Regorafenib is recommended by guidelines and trials as a sequential second-line therapy following progression on first-line sorafenib or lenvatinib in hepatocellular carcinoma (HCC). However, efficacy is limited, highlighting the urgent need to screen suitable patients and develop sensitization strategies. Methods: Acquired sorafenib- or lenvatinib-resistant (SR or LR) HCC cell lines and organoids were established. Genome-wide CRISPR library screen was performed in SR or LR cell strains to identify synthetic lethal targets of regorafenib. RNA-seq and FITC-regorafenib efflux assay were used to elucidate ERBB3-driven downstream signaling. Preclinical mouse models of cell line- and patient-derived xenografts and clinical cohorts of HCC patients were employed to validate the efficacy of ERBB3-guided patient stratification. Results: Screening with CRISPR library, we showed that inhibition of ERBB3 was synthetic lethal with regorafenib in SR or LR cell strains and organoids. Mechanistically, SR or LR triggered feedback activation of ERBB3 signaling and mediated regorafenib efflux via ERBB3-HIF1A-ABCB1 cascade pathway, limiting sensitivity to regorafenib. Moreover, ERBB3-low tumors following SR or LR exhibited significant sensitivity to regorafenib, suggesting its potential as a predictive biomarker to screen optimal candidates for sequential therapy. Seribantumab, an ERBB3-targeting monoclonal antibody, inhibited ERBB3-HIF1A-ABCB1 cascade, and its combination with regorafenib exerted marked synergistic anti-tumor effects on ERBB3-high tumors resistant to sorafenib or lenvatinib both in vitro and in vivo. Conclusions This study revealed that ERBB3 was a key resistance factor driving limited efficacy to sequential regorafenib, but also an effective therapeutic target whose inhibition enhanced regorafenib sensitivity after SR or LR.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To investigate the prevalence of Schistosoma japonicum infections in wild murines in schistosomiasisendemic areas of Yunnan Province, so as to provide the evidence for improving the schistosomiasis surveillance and early warning capability and implementing precise interventions in the province. Methods A total of 56 natural villages were sampled from 14 schistosomiasis-endemic counties (cities, districts) in Yunnan Province as surveillance sites from 2024 to 2025. Wild murines were captured using traps at night and characterized for species. Liver samples were collected from wild murines, S. japonicum eggs and miracidia were simultaneously detected using liver tissue compression microscopy, liver tissue homogenate microscopy, and liver tissue homogenate hatching. In addition, stool samples were collected from wild murine rectum, with S. japonicum eggs detected using a modified Kato-Katz thick smear technique and S. japonicum miracidia detected using the hatching test with nylon gauzes. Results A total of 15 577 valid murine traps were recovered, and 891 wild murines belonging to 16 species were captured, with a capture rate of 5.72%(891/15 577). The captured wild murines were predominantly districted in paddy fields, dry lands, ditches, ponds and marshlands, with Apodemus chevrieri, Rattus norvegicus, Crocidura attenuata, R. rattus sladeni, R. flavipectus, and Eothenomys miletus as dominant species. No S. japonicum eggs or miradicia were in laboratory; however, other parasites were identified in 25 wild murines. The prevalence rates of Schistosoma sinensis, Capillaria hepatica and Hymenolepis nana were 2.24% (20/891), 0.90% (8/891) and 0.56% (5/891), and the prevalence of S. sinensis and C. hepatica co-infection was 0.90% (8/891), respectively. Conclusions S. sinensis, C. hepatica and H. nana are predominant parasite species in wild murines in schistosomiasis-endemic areas of Yunan Province. No S. japonicum infection was detected during the study period; however, there is still a risk of wild murines as a potential source of infection for schistosomiasis transmission. Continuous intensified surveillance of S. japonicum infections in wild murines is a critical programme in schistosomiasis surveillance and early warning in Yunnan Province.

Metabolic-associated fatty liver disease (MAFLD) and osteoporosis (OP) are two very common metabolic diseases. A growing body of experimental evidence supports a pathophysiological link between MAFLD and OP. MAFLD is often associated with the development of OP. Rutaecarpine (RUT) is one of the main active components of Chinese medicine Euodiae Fructus. Our previous studies have demonstrated that RUT has lipid-lowering, anti-inflammatory and anti-atherosclerotic effects, and can improve the OP of rats. However, whether RUT can improve both fatty liver and OP symptoms of MAFLD mice at the same time remains to be investigated. In this study, we used C57BL/6 mice fed a high-fat diet (HFD) for 4 months to construct a MAFLD model, and gave the mice a low dose (5 mg·kg^-1) and a high dose (15 mg·kg^-1) of RUT by gavage for 4 weeks. The effects of RUT on liver steatosis and bone metabolism were then evaluated at the end of the experiment [this experiment was approved by the Experimental Animal Ethics Committee of Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences (approval number: IMB-20190124D₃03)]. The results showed that RUT treatment significantly reduced hepatic steatosis and lipid accumulation, and significantly reduced bone loss and promoted bone formation. In summary, this study shows that RUT has an effect of improving fatty liver and OP in MAFLD mice.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

Fibrosis, the pathological scarring of vital organs, is a severe and often irreversible condition that leads to progressive organ dysfunction. It is particularly pronounced in organs like the liver, kidneys, lungs, and heart. Despite its clinical significance, the full understanding of its etiology and complex pathogenesis remains incomplete, posing substantial challenges to diagnosing, treating, and preventing the progression of fibrosis. Among the various molecular players involved, platelet-derived growth factor-C (PDGF-C) has emerged as a crucial factor in fibrotic diseases, contributing to the pathological transformation of tissues in several key organs. PDGF-C is a member of the PDGFs family of growth factors and is synthesized and secreted by various cell types, including fibroblasts, smooth muscle cells, and endothelial cells. It acts through both autocrine and paracrine mechanisms, exerting its biological effects by binding to and activating the PDGF receptors (PDGFRs), specifically PDGFRα and PDGFRβ. This binding triggers multiple intracellular signaling pathways, such as JAK/STAT, PI3K/AKT and Ras-MAPK pathways. which are integral to the regulation of cell proliferation, survival, migration, and fibrosis. Notably, PDGF-C has been shown to promote the proliferation and migration of fibroblasts, key effector cells in the fibrotic process, thus accelerating the accumulation of extracellular matrix components and the formation of fibrotic tissue. Numerous studies have documented an upregulation of PDGF-C expression in various fibrotic diseases, suggesting its significant role in the initiation and progression of fibrosis. For instance, in liver fibrosis, PDGF-C stimulates hepatic stellate cell activation, contributing to the excessive deposition of collagen and other extracellular matrix proteins. Similarly, in pulmonary fibrosis, PDGF-C enhances the migration of fibroblasts into the damaged areas of lungs, thereby worsening the pathological process. Such findings highlight the pivotal role of PDGF-C in fibrotic diseases and underscore its potential as a therapeutic target for these conditions. Given its central role in the pathogenesis of fibrosis, PDGF-C has become an attractive target for therapeutic intervention. Several studies have focused on developing inhibitors that block the PDGF-C/PDGFR signaling pathway. These inhibitors aim to reduce fibroblast activation, prevent the excessive accumulation of extracellular matrix components, and halt the progression of fibrosis. Preclinical studies have demonstrated the efficacy of such inhibitors in animal models of liver, kidney, and lung fibrosis, with promising results in reducing fibrotic lesions and improving organ function. Furthermore, several clinical inhibitors, such as Olaratumab and Seralutinib, are ongoing to assess the safety and efficacy of these inhibitors in human patients, offering hope for novel therapeutic options in the treatment of fibrotic diseases. In conclusion, PDGF-C plays a critical role in the development and progression of fibrosis in vital organs. Its ability to regulate fibroblast activity and influence key signaling pathways makes it a promising target for therapeutic strategies aiming at combating fibrosis. Ongoing research into the regulation of PDGF-C expression and the development of PDGF-C/PDGFR inhibitors holds the potential to offer new insights and approaches for the diagnosis, treatment, and prevention of fibrotic diseases. Ultimately, these efforts may lead to the development of more effective and targeted therapies that can mitigate the impact of fibrosis and improve patient outcomes.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.