ObjectiveThis paper aims to investigate the mechanism of Jinyang Dingtong plaster in improving the peripheral pain sensitization and synovial fibrosis in rats with knee osteoarthritis （KOA） by blocking the ion channels of transient receptor potentials （TRPs）. MethodsThe active components in the transdermal absorption solution of Jinyang Dingtong plaster were identified by using ultra-high performance liquid chromatography-electrospray ionization-quadrupole ion trap tandem mass spectrometry （UPLC-MS/MS） technology. A KOA rat model was established through intra-articular injection of monoiodoacetic acid. The rats were randomly divided into blank control group， KOA group， compound Nanxing Zhitong plaster Group， and Jinyang Dingtong plaster group， with eight rats per group. Among them， the rats in the compound Nanxing Zhitong plaster group and the Jinyang Dingtong plaster group were intervened with external application treatment. After the intervention period， the cold and mechanical stimulus pain thresholds of rats in each group were detected， and the transverse diameter of the knee joint was measured. The levels of inflammatory factors in the serum such as interleukin-1β （IL-1β）， tumor necrosis factor-α （TNF-α）， nerve growth factor （NGF）， and calcitonin gene-related peptide （CGRP） were determined by enzyme-linked immunosorbent assay （ELISA）. Protein expression levels of transient receptor potential ankyrin 1 （TRPA1）， transient receptor potential melastatin 8 （TRPM8）， transient receptor potential vanilloid 1 （TRPV1）， transient receptor potential vanilloid 4 （TRPV4）， transforming growth factor-β （TGF-β）， and vascular endothelial growth factor （VEGF） in synovial tissue were detected by Western blot. Histopathological changes in synovial tissue were observed by using hematoxylin and eosin （HE）， Masson， and Sirius red staining， while the expression of type Ⅰ collagen and alpha-smooth muscle actin （α-SMA） was detected by multiplex immunofluorescence. ResultsA total of 35 active components in the transdermal absorption solution of Jinyang Dingtong plaster were identified by UPLC-MS/MS， including phenolic acids， flavonoids， quinones， alkaloids， terpenes， lignans， and coumarins. Among them， the constituents such as berberine， paeoniflorin， ferulic acid， and caffeic acid exhibit clear anti-inflammatory， analgesic， and anti-fibrotic pharmacological effects. Compared to the blank control group， rats in the KOA group showed a significant decrease in cold and mechanical stimuli pain thresholds （P<0.01）. After 14 and 28 days of Jinyang Dingtong plaster intervention， the pain threshold in this group was significantly increased compared to that in KOA group （P<0.01）， showing no significant difference from that in compound Nanxing Analgesic plaster group. Additionally， Jinyang Dingtong plaster reduced the levels of IL-1β， TNF-α， NGF， and CGRP in the serum of KOA rats （P<0.01）， lowered the expression of TRPA1， TRPM8， TRPV1， TRPV4， TGF-β， and VEGF proteins in synovial tissue （P<0.01）， improved synovial pathological damage in KOA rats， and significantly decreased fluorescence intensity of type Ⅰ collagen and α-SMA （P<0.01）. ConclusionJinyang Dingtong plaster can improve the peripheral pain sensitization and synovial fibrosis in KOA rats by downregulating the expression of ion channels of TRPs and related inflammatory and fibrotic factors.

ObjectiveThis paper aims to investigate the mechanism of Jinyang Dingtong plaster in improving the peripheral pain sensitization and synovial fibrosis in rats with knee osteoarthritis （KOA） by blocking the ion channels of transient receptor potentials （TRPs）. MethodsThe active components in the transdermal absorption solution of Jinyang Dingtong plaster were identified by using ultra-high performance liquid chromatography-electrospray ionization-quadrupole ion trap tandem mass spectrometry （UPLC-MS/MS） technology. A KOA rat model was established through intra-articular injection of monoiodoacetic acid. The rats were randomly divided into blank control group， KOA group， compound Nanxing Zhitong plaster Group， and Jinyang Dingtong plaster group， with eight rats per group. Among them， the rats in the compound Nanxing Zhitong plaster group and the Jinyang Dingtong plaster group were intervened with external application treatment. After the intervention period， the cold and mechanical stimulus pain thresholds of rats in each group were detected， and the transverse diameter of the knee joint was measured. The levels of inflammatory factors in the serum such as interleukin-1β （IL-1β）， tumor necrosis factor-α （TNF-α）， nerve growth factor （NGF）， and calcitonin gene-related peptide （CGRP） were determined by enzyme-linked immunosorbent assay （ELISA）. Protein expression levels of transient receptor potential ankyrin 1 （TRPA1）， transient receptor potential melastatin 8 （TRPM8）， transient receptor potential vanilloid 1 （TRPV1）， transient receptor potential vanilloid 4 （TRPV4）， transforming growth factor-β （TGF-β）， and vascular endothelial growth factor （VEGF） in synovial tissue were detected by Western blot. Histopathological changes in synovial tissue were observed by using hematoxylin and eosin （HE）， Masson， and Sirius red staining， while the expression of type Ⅰ collagen and alpha-smooth muscle actin （α-SMA） was detected by multiplex immunofluorescence. ResultsA total of 35 active components in the transdermal absorption solution of Jinyang Dingtong plaster were identified by UPLC-MS/MS， including phenolic acids， flavonoids， quinones， alkaloids， terpenes， lignans， and coumarins. Among them， the constituents such as berberine， paeoniflorin， ferulic acid， and caffeic acid exhibit clear anti-inflammatory， analgesic， and anti-fibrotic pharmacological effects. Compared to the blank control group， rats in the KOA group showed a significant decrease in cold and mechanical stimuli pain thresholds （P<0.01）. After 14 and 28 days of Jinyang Dingtong plaster intervention， the pain threshold in this group was significantly increased compared to that in KOA group （P<0.01）， showing no significant difference from that in compound Nanxing Analgesic plaster group. Additionally， Jinyang Dingtong plaster reduced the levels of IL-1β， TNF-α， NGF， and CGRP in the serum of KOA rats （P<0.01）， lowered the expression of TRPA1， TRPM8， TRPV1， TRPV4， TGF-β， and VEGF proteins in synovial tissue （P<0.01）， improved synovial pathological damage in KOA rats， and significantly decreased fluorescence intensity of type Ⅰ collagen and α-SMA （P<0.01）. ConclusionJinyang Dingtong plaster can improve the peripheral pain sensitization and synovial fibrosis in KOA rats by downregulating the expression of ion channels of TRPs and related inflammatory and fibrotic factors.

Cancer stands as a significant global public health challenge, and cancer screening serves as a pivotal strategy for reducing its mortality. Presently, only a limited number of cancer types have appropriate screening methods available. Traditional single-cancer screening approaches are fraught with limitations, including invasiveness, low accuracy, and poor patient compliance. Multi-cancer early detection (MCED) leveraging liquid biopsy technology enables non-invasive and efficient early detection of multiple cancers by analyzing biomarkers such as cell-free DNA, cell-free RNA, proteins, and metabolites in blood and other bodily fluids. This innovative approach substantially broadens the spectrum of detectable cancers and enhances population coverage, showcasing immense potential for improving existing cancer screening strategies. This expert consensus comprehensively reviews the progress of liquid biopsy-based MCED, biomarker selection and detection technologies, the criteria for cancer type selection, research design and clinical utility evaluation, as well as implementation pathways. The overarching goal of this consensus is to offer scientific guidance for further research and the widespread adoption of MCED, thereby facilitating the continuous optimization of cancer screening strategies.

Cancer stands as a significant global public health challenge,and cancer screening serves as a pivotal strategy for reducing its mortality.Presently,only a limited number of cancer types have appropriate screening methods available.Traditional single-cancer screen-ing approaches are fraught with limitations,including invasiveness,low accuracy,and poor patient compliance.Multi-cancer early detection(MCED)leveraging liquid biopsy technology enables non-invasive and efficient early detection of multiple cancers by analyzing biomarkers such as cell-free DNA,cell-free RNA,proteins,and metabolites in blood and other bodily fluids.This innovative approach substantially broadens the spectrum of detectable cancers and enhances population coverage,showcasing immense potential for improving existing can-cer screening strategies.This expert consensus comprehensively reviews the progress of liquid biopsy-based MCED,biomarker selection and detection technologies,the criteria for cancer type selection,research design and clinical utility evaluation,as well as implementation path-ways.The overarching goal of this consensus is to offer scientific guidance for further research and the widespread adoption of MCED,thereby facilitating the continuous optimization of cancer screening strategies.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.