Objective To modify the YOLOv5 algorithm with similarity attention mechanism(SimAM)to enhance the recognition and segmentation accuracy of CT images for cerebral hemorrhage.Methods A basic framework was established with a YOLOv5 algorithm consisting of a backbone network(Backbone),a neck module(Neck)and a head module(Head),and then SimAM was introduced at the end of Backbone to form a YOLOv5-Sim-B algorithm and at the end of Neck to construct a YOLOv5-Sim-N algorithm.The YOLOv5-Sim-B and YOLOv5-Sim-N algorithms were trained and validated using the CT image dataset for cerebral hemorrhage publicly available on the Kaggle competition platform,and compared with the traditional YOLOv5 algorithm for recognizing and segmenting cerebral hemorrhagic lesions in CT images.Results In case the value of IoU-T was 0.6,the mean average precision(mAP)was 0.967 for YOLOv5-Sim-B algorithm,0.960 for the YOLOv5-Sim-N algorithm and 0.964 for the traditional YOLOv5 algorithm during the recognition and segmentation of cerebral hemorrhagic lesions in CT images.Conclusion The proposed algorithm gains advantages in detection accuracy and robustness,and can efficiently identify and segment cerebral hemorrhage foci in CT images.[Chinese Medical Equipment Journal,2025,46(5):1-8]

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To propose an improved YOLOv8 algorithm based on polarized self-attention(PSA)and deformable attention(DAT)so as to enhance the detection of pulmonary nodules in CT images.Methods A basic framework was established with a YOLOv8 model consisting of a backbone network(Backbone),a neck module(Neck)and a head module(Head).PSA was introduced into the end of the spatial pyramid pooling-fast(SPPF)of Backbone to construct a YOLOv8-PSA algorithm,and DAT was involved in the medium-scale feature layer P4 in Head to form a YOLOv8-DAT algorithm.The YOLOv8-PSA and YOLOv8-DAT algorithms were trained and validated using the CT image dataset of pulmonary nodules from public platforms,and compared with the original YOLOv8 algorithm for the detection of pulmonary nodule lesions in CT images.Results When used for pulmonary module detection of CT images,the YOLOv8-DAT algorithm had the mean average precision(mAP)in case of intersection over union threshold of 0.5(mAP50),mAP in case of intersection over union threshold of 0.5 to 0.95(mAP50-95)and precision ratio being 0.918,0.588 and 0.960 respectively,which gained advantages over the YOLOv8-PSA algorithm with mAP50,mAP50-95 and precision ratio being 0.914,0.583 and 0.945 respectively,and over the original YOLOv8 algorithm with mAP50,mAP50-95 and precision ratio being 0.911,0.564 and 0.952 respectively.Conclusion The YOLOv8-DAT algorithm detects pulmonary modules in CT images effectively,and facilitates early screening and diagnosis of pulmonary modules clinically.[Chinese Medical Equipment Journal,2025,46(8):1-10]

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To propose an improved YOLOv8 algorithm based on polarized self-attention(PSA)and deformable attention(DAT)so as to enhance the detection of pulmonary nodules in CT images.Methods A basic framework was established with a YOLOv8 model consisting of a backbone network(Backbone),a neck module(Neck)and a head module(Head).PSA was introduced into the end of the spatial pyramid pooling-fast(SPPF)of Backbone to construct a YOLOv8-PSA algorithm,and DAT was involved in the medium-scale feature layer P4 in Head to form a YOLOv8-DAT algorithm.The YOLOv8-PSA and YOLOv8-DAT algorithms were trained and validated using the CT image dataset of pulmonary nodules from public platforms,and compared with the original YOLOv8 algorithm for the detection of pulmonary nodule lesions in CT images.Results When used for pulmonary module detection of CT images,the YOLOv8-DAT algorithm had the mean average precision(mAP)in case of intersection over union threshold of 0.5(mAP50),mAP in case of intersection over union threshold of 0.5 to 0.95(mAP50-95)and precision ratio being 0.918,0.588 and 0.960 respectively,which gained advantages over the YOLOv8-PSA algorithm with mAP50,mAP50-95 and precision ratio being 0.914,0.583 and 0.945 respectively,and over the original YOLOv8 algorithm with mAP50,mAP50-95 and precision ratio being 0.911,0.564 and 0.952 respectively.Conclusion The YOLOv8-DAT algorithm detects pulmonary modules in CT images effectively,and facilitates early screening and diagnosis of pulmonary modules clinically.[Chinese Medical Equipment Journal,2025,46(8):1-10]

Hepatocellular carcinoma(HCC)is difficult to detect in its early stages and current treatment methods are associated with significant side effects and a high risk of developing drug resistance.This study aims to investigate the effect of phycocyanin(PC)on the apoptosis of human HCC HepG2 cells and its potential mechanism.HepG2 cells were treated with PC at concentrations of 0.1,0.25,0.5,1,2.5,5,and 10 μg/mL for 12 h,and with 10 μg/mL PC and 2.5 μmol/L Wip1 inhibitor(Wip1i)alone or in combination for 12 and 24 h,respectively.Cell proliferation levels were assessed using the CCK-8 cell proliferation-toxicity assay kit.Apoptosis levels were measured by Annexin V-FITC/Propidium Iodide double staining combined with flow cytometry.TMT(Tandem Mass Tag)proteomics quantitative technol-ogy was applied to analyze differential protein expression.Western blotting was used to detect the expres-sion levels of Wip1,p53,and phosphorylated-p53(Ser15)proteins.The CCK-8 assay revealed that PC effectively inhibited HepG2 cell proliferation in a concentration-dependent manner,with a half-maximal inhibitory concentration(IC50)of 19.37 μg/mL.Flow cytometry results showed that PC significantly in-duced apoptosis,with an apoptosis rate of 30.40％.Quantitative proteomics analysis indicated that PC induced activation of the p53 pathway.The CCK-8 assay showed that Wip1i enhanced the cytotoxic effect of PC on HepG2 cells.Western blotting confirmed that PC inhibited Wip1 expression,induced p53 pro-tein phosphorylation,and promoted the expression of total p53 protein.Additionally,Wip1i further en-hanced PC-mediated activation of the p53 pathway,increasing the expression of p53 and pP53(S15).In conclusion,PC may induce apoptosis by inhibiting the activity of the p53 negative regulator Wip1,thereby promoting apoptosis through the Wip1/p53 pathway.

Objective To modify the YOLOv5 algorithm with similarity attention mechanism(SimAM)to enhance the recognition and segmentation accuracy of CT images for cerebral hemorrhage.Methods A basic framework was established with a YOLOv5 algorithm consisting of a backbone network(Backbone),a neck module(Neck)and a head module(Head),and then SimAM was introduced at the end of Backbone to form a YOLOv5-Sim-B algorithm and at the end of Neck to construct a YOLOv5-Sim-N algorithm.The YOLOv5-Sim-B and YOLOv5-Sim-N algorithms were trained and validated using the CT image dataset for cerebral hemorrhage publicly available on the Kaggle competition platform,and compared with the traditional YOLOv5 algorithm for recognizing and segmenting cerebral hemorrhagic lesions in CT images.Results In case the value of IoU-T was 0.6,the mean average precision(mAP)was 0.967 for YOLOv5-Sim-B algorithm,0.960 for the YOLOv5-Sim-N algorithm and 0.964 for the traditional YOLOv5 algorithm during the recognition and segmentation of cerebral hemorrhagic lesions in CT images.Conclusion The proposed algorithm gains advantages in detection accuracy and robustness,and can efficiently identify and segment cerebral hemorrhage foci in CT images.[Chinese Medical Equipment Journal,2025,46(5):1-8]

Objective:To investigate the longitudinal patterns and influencing factors of platelet counts among healthy adults in Sichuan Province from 2010 to 2021, and to inform the establishment of region-specific reference intervals for platelet counts.Methods:This study is a retrospective study. A total of 7 808 healthy adults who underwent annual physical examinations at West China Hospital, Sichuan University, between January 2010 and December 2021 were included. All participants were permanent Chengdu residents and completed consecutive complete blood count tests. Group-based trajectory modeling (GBTM) was used to identify distinct trajectories of platelet count over the ten-year period. One-way analyses were then conducted to compare baseline demographic characteristics (sex and age) among the different trajectory groups.Results:Among 7 808 participants, 4 589 (58.8%) were male and 3 219 (41.2%) were female. Four platelet count trajectories were identified by GBTM: steadily increasing group [27.4% (2 139/7 808)], early increase-plateau group [44.1% (3 445/7 808)], early decrease-subsequent increase group [5.4% (422/7 808)], and steadily decreasing group [23.1% (1 802/7 808)], with an average growth rate of 3.3%, 1.6%, 0.7%, and -0.6%, respectively. There were statistically significant differences in both sex and age distributions among the four trajectory groups. Sex-distribution differed significantly across the four trajectory groups ( χ2=73.3, P<0.001). The male proportions in the four trajectory groups were 59.6% (1 275/2 139), 62.8% (2 165/3 445), 48.1% (203/422), and 52.5% (946/1 802), respectively. The baseline ages were 45 (36, 55), 43 (35, 53), 50 (40, 60), and 47 (39, 58) years, respectively (H=121.0, P<0.001). Conclusions:Healthy adults in Sichuan Province exhibit four longitudinal trajectories of platelet counts: steadily increasing, early increase-plateau, early decrease-subsequent increase, and steadily decreasing. The two trajectories characterized by rising platelet counts (steadily increasing group and early increase-plateau group) exhibited higher male predominance and lower median ages, whereas the early decrease-subsequent increase group and the steadily decreasing group exhibited lower male proportions and higher median ages. Therefore, while establishing reference intervals and developing health management strategies for platelet counts, it is essential to account for the sex, age characteristics and the population′s dynamic changes.

Hepatocellular carcinoma(HCC)is difficult to detect in its early stages and current treatment methods are associated with significant side effects and a high risk of developing drug resistance.This study aims to investigate the effect of phycocyanin(PC)on the apoptosis of human HCC HepG2 cells and its potential mechanism.HepG2 cells were treated with PC at concentrations of 0.1,0.25,0.5,1,2.5,5,and 10 μg/mL for 12 h,and with 10 μg/mL PC and 2.5 μmol/L Wip1 inhibitor(Wip1i)alone or in combination for 12 and 24 h,respectively.Cell proliferation levels were assessed using the CCK-8 cell proliferation-toxicity assay kit.Apoptosis levels were measured by Annexin V-FITC/Propidium Iodide double staining combined with flow cytometry.TMT(Tandem Mass Tag)proteomics quantitative technol-ogy was applied to analyze differential protein expression.Western blotting was used to detect the expres-sion levels of Wip1,p53,and phosphorylated-p53(Ser15)proteins.The CCK-8 assay revealed that PC effectively inhibited HepG2 cell proliferation in a concentration-dependent manner,with a half-maximal inhibitory concentration(IC50)of 19.37 μg/mL.Flow cytometry results showed that PC significantly in-duced apoptosis,with an apoptosis rate of 30.40％.Quantitative proteomics analysis indicated that PC induced activation of the p53 pathway.The CCK-8 assay showed that Wip1i enhanced the cytotoxic effect of PC on HepG2 cells.Western blotting confirmed that PC inhibited Wip1 expression,induced p53 pro-tein phosphorylation,and promoted the expression of total p53 protein.Additionally,Wip1i further en-hanced PC-mediated activation of the p53 pathway,increasing the expression of p53 and pP53(S15).In conclusion,PC may induce apoptosis by inhibiting the activity of the p53 negative regulator Wip1,thereby promoting apoptosis through the Wip1/p53 pathway.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.