ObjectiveTo systematically retrieve， analyze， and summarize the basic information and clinical research evidence of Chinese patent medicines （CPMs） for endometriosis via a scoping review method. MethodsCPMs indicated for EM were identified from three major drug lists， CNKI， clinical guidelines， and textbooks. Relevant clinical studies were retrieved from eight Chinese and English databases from inception to April 1， 2025. Key data from included studies were extracted to analyze characteristics of CPMs and their clinical evidence. ResultsA total of 18 CPMs were included， among which those with blood-activating and stasis-resolving effects accounted for the highest proportion. Constituent herbs predominantly exhibited sweet， bitter， and pungent flavors， with tropism to the liver and spleen meridians. Daily treatment costs varied substantially across CPMs. The 438 included studies involved 13 CPMs， among which Guizhi Fuling capsules/pills， Sanjie Zhentong capsules， and Dan'e Fukang soft extract/granules were the most extensively studied. Randomized controlled trials （RCTs） were the predominant study design. The most common comparison was between CPMs combined with Western medicine and Western medicine alone. The largest number of studies reported a sample size of 51-100 cases. The high-frequency outcome indicators covered 10 domains， including clinical response rate， adverse reactions， female hormone levels， serum CA125 levels， and recurrence status. ConclusionMultiple oral CPMs demonstrate efficacy in EM management， yet inadequate safety profiles persist in drug labeling. The annual number of clinical studies on oral CPMs for endometriosis showed an overall increasing trend. However， clinical research has shortcomings including uneven distribution of CPMs， small sample sizes， and suboptimal methodological rigor. Future efforts should focus on updating drug indications and safety information in package inserts， conducting high-quality clinical research， and generating more robust evidence to support the clinical use of oral CPMs for EM.

ObjectiveTo systematically retrieve， analyze， and summarize the basic information and clinical research evidence of Chinese patent medicines （CPMs） for endometriosis via a scoping review method. MethodsCPMs indicated for EM were identified from three major drug lists， CNKI， clinical guidelines， and textbooks. Relevant clinical studies were retrieved from eight Chinese and English databases from inception to April 1， 2025. Key data from included studies were extracted to analyze characteristics of CPMs and their clinical evidence. ResultsA total of 18 CPMs were included， among which those with blood-activating and stasis-resolving effects accounted for the highest proportion. Constituent herbs predominantly exhibited sweet， bitter， and pungent flavors， with tropism to the liver and spleen meridians. Daily treatment costs varied substantially across CPMs. The 438 included studies involved 13 CPMs， among which Guizhi Fuling capsules/pills， Sanjie Zhentong capsules， and Dan'e Fukang soft extract/granules were the most extensively studied. Randomized controlled trials （RCTs） were the predominant study design. The most common comparison was between CPMs combined with Western medicine and Western medicine alone. The largest number of studies reported a sample size of 51-100 cases. The high-frequency outcome indicators covered 10 domains， including clinical response rate， adverse reactions， female hormone levels， serum CA125 levels， and recurrence status. ConclusionMultiple oral CPMs demonstrate efficacy in EM management， yet inadequate safety profiles persist in drug labeling. The annual number of clinical studies on oral CPMs for endometriosis showed an overall increasing trend. However， clinical research has shortcomings including uneven distribution of CPMs， small sample sizes， and suboptimal methodological rigor. Future efforts should focus on updating drug indications and safety information in package inserts， conducting high-quality clinical research， and generating more robust evidence to support the clinical use of oral CPMs for EM.

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.

Gastric cancer is a high-incidence malignancy that poses a serious threat to public health in China, ranking among the top three cancers in both incidence and mortality. The majority of patients are diagnosed at an advanced stage, resulting in limited treatment options and poor prognosis. To address key challenges in gastric cancer diagnosis and treatment, a research team led by Professor Jiafu Ji at Peking University Cancer Hospital has focused on the project "Development and Dissemination of Precision Medicine Approaches in Gastric Cancer Management". Through a series of high-quality multicenter clinical studies, the team established a set of new international standards in perioperative treatment, individua-lized drug selection, intelligent noninvasive diagnostics, and novel immunotherapy strategies. These advances have significantly improved treatment efficacy and reduced surgical trauma, achieving key technological breakthroughs in diagnosis, therapy, and mechanistic understanding, and systematically enhancing outcomes for gastric cancer patients. The project ' s findings had a broad international impact, including hosting China ' s first International Gastric Cancer Congress. Through nationwide dissemination, they have promoted the development of precision diagnosis and treatment of gastric cancer as a discipline, and led the formulation of the National Health Commission's guidelines for gastric cancer diagnosis and treatment. In recognition of its achievements, the project was awarded the First Prize of the 2024 Chinese Medical Science and Technology Award.
Stomach Neoplasms/genetics* ; Humans ; Precision Medicine/methods* ; China ; Immunotherapy/methods*

This study aims to systematically review and evaluate the quality of clinical research on the treatment of adenomyosis(AM) with traditional Chinese medicine(TCM) in recent ten years, using evidence graphs. Computer searches were conducted on eight Chinese and English databases, commonly used guideline databases, and guideline-related websites, covering the period from January 1, 2014, to October 1, 2024. Two researchers independently screened, extracted information, and evaluated the quality of the evidence. The distribution and quality of the clinical research evidence were presented using both text and charts. A total of 565 articles were included in the study, comprising 523 intervention studies, 23 observational studies, 18 systematic reviews/Meta-analysis, and 1 guideline. The overall publication volume has shown a downward trend in past two years. The sample sizes of the intervention and observational studies primarily focused on 60 to 120 cases. The intervention schemes mainly involved multi-therapy combinations, including 33 classic prescriptions and 25 Chinese patent medicines. Among these, 48 studies related to 17 classic prescriptions and 45 studies related to 10 types of Chinese patent medicines involved TCM syndrome types. Randomized controlled trial(RCT) tended to focus on overall clinical efficacy and the degree of dysmenorrhea as key outcome measures. Methodological quality issues were found in 97 RCTs related to TCM decoctions and 131 RCTs related to Chinese patent medicines, primarily involving unclear explanations of some information. The AMSTAR scores for the 18 systematic reviews/Meta-analysis ranged from 1 to 8 points, with 16 studies suggesting "evidence of potential therapeutic efficacy". The recommended level for the one included guideline was B-level. TCM shows significant advantages in treating AM. Future clinical research should further standardize study designs, reference relevant reporting guidelines, improve the quality of clinical research, generate higher-level evidence-based results, and promote the high-quality development of clinical research on TCM for treating AM.
Humans ; Adenomyosis/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Female ; Medicine, Chinese Traditional ; Randomized Controlled Trials as Topic

In view of the complex structure of diabetic retinopathy and the large differences in the scales of different lesions,a novel network which integrates deformable convolution and attention mechanism is proposed for automatic diabetic retinopathy multi-lesion segmentation.Specifically,deformable convolution Haar wavelet transform encoder takes place of the original convolutional downsampling encoder to adapt to the irregular shape changes of lesions and extract effective feature information;a dense feature perception and aggregation module is introduced at the bottleneck layer to extract multi-scale features by aggregating multiple receptive fields,thus enhancing deep semantic information;and finally,in order to fully integrate the decoder output and improve the recognition accuracy of edge information,a multi scale adaptive fusion module is used to weight the decoder output of each layer for obtaining the most accurate segmentation feature map.The validation of hard percolation,bleeding point,and soft percolation segmentations on the DDR-RLS dataset reveals that the proposed network shows increases of 0.026 2,0.051 8 and 0.046 5 in IoU coefficient,0.027 1,0.058 1 and 0.050 4 in Dice coefficient,and 0.0423,0.0691 and 0.0734 in AUPR value,as compared with the original Unet.

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.

In view of the complex structure of diabetic retinopathy and the large differences in the scales of different lesions,a novel network which integrates deformable convolution and attention mechanism is proposed for automatic diabetic retinopathy multi-lesion segmentation.Specifically,deformable convolution Haar wavelet transform encoder takes place of the original convolutional downsampling encoder to adapt to the irregular shape changes of lesions and extract effective feature information;a dense feature perception and aggregation module is introduced at the bottleneck layer to extract multi-scale features by aggregating multiple receptive fields,thus enhancing deep semantic information;and finally,in order to fully integrate the decoder output and improve the recognition accuracy of edge information,a multi scale adaptive fusion module is used to weight the decoder output of each layer for obtaining the most accurate segmentation feature map.The validation of hard percolation,bleeding point,and soft percolation segmentations on the DDR-RLS dataset reveals that the proposed network shows increases of 0.026 2,0.051 8 and 0.046 5 in IoU coefficient,0.027 1,0.058 1 and 0.050 4 in Dice coefficient,and 0.0423,0.0691 and 0.0734 in AUPR value,as compared with the original Unet.

ObjectiveTo explore the effect and possible mechanism of Shenqi Pills （肾气丸） on cognitive impairment and hippocampal glucose energy metabolism in type 2 diabetes mellitus （T2DM）. MethodsSixty C57BL/6 mice were randomly divided into control group， model group， rosiglitazone group and Shenqi Pills low-， medium- and high-dose groups， with 10 mice in each group. T2DM model was induced by a high-fat diet combined with intraperitoneal injection of streptozotocin in all the groups except for the control group. After successful modeling， the high-， medium-， and low-dose Shenqi Pills groups were given 2.08， 1.04， and 0.52 g/（kg·d） of Shenqi Pills granules by gavage respectively， while the rosiglitazone group was given 3 mg/（kg·d） of rosiglitazone tablets by gavage， and the control group and model group were gavaged with 10 ml/（kg·d） of distilled water， all for 8 consecutive weeks. The body weight and fasting blood glucose （FBG） level were recorded every two weeks. The Morris water maze test was performed on the 8th week of medication. After 8-week medication， oral glucose tolerance test （OGTT） and fasting insulin level were measured， hippocampal glucose energy metabolism-related products were quantitatively detected by liquid chromatography tandem mass spectrometry， and KEGG annotation analysis was performed. ResultsCompared to those measured at the same timepoints in the control group， the body mass on week 6 and 8， as well as the FBG level on week 2， 4， 6 and 8 in the model group increased； the blood glucose level at 0， 30， 60 and 120 minutes of the OGTT test increased， while fasting insulin level after 8-week medication decreased. The escape latency of the model group was significantly prolonged on the 3rd and 4th days， and the escape latency time increased， while the total swimming distance， platform quadrant residence time and the number of platform crossings decreased （P＜0.05 or P＜0.01）. Compared to those measured at the same timepoints in the model group， the body mass on week 6 in the low-dose Shenqi Pills group， on week 6 and 8 in the medium- and high-dose groups， and on week 8 in the rosiglitazone group were significantly reduced； the FBG levels in all the Shenqi Pills groups and rosiglitazone group on week 6 and 8 decreased， while fasting insulin levels increased. In the OGTT test， blood glucose in the medium-dose group of Shenqi Pills at all timepoints decreased； in the Morris water maze test， the escape latency period of the medium- and high-dose Shenqi Pills groups was shortened on the 3rd and 4th days， while the escape latency time was reduced， and the total swimming distance， platform quadrant residence time， and number of platform crossings increased in the medium-dose Shenqi Pills group （P＜0.05 or P＜0.01）.The medium-dose Shenqi Pills showed best effect， therefore it was selected for the targeted quantitative detection of metabolites. The medium-dose Shenqi Pills group could regulate the disorder of glucose metabolism in the hippocampus of T2DM mice， and 13 differential metabolites were found，up-regulating α-ketoglutarate and 3-phosphoglyceric acid， and down-regulating fumaric acid， glutamatic acid， lactatic acid， inosine， malic acid， adenine， fructose 1,6-diphosphate and others. KEGG annotation of differential metabolites suggested that Shenqi Pills was closely related to the regulation of glucose metabolism disorder and insulin resistance in the hippocampus region of T2DM model mice， as well as neurodegenerative diseases and ABC transport， hypoxia-inducible factor 1 （HIF）， forkhead transcription factor （FoXO） and cyclic adenosine monophosphate （cAMP） signaling pathways. ConclusionShenqi Pills can improve learning and memory abilities and cognitive impairment in T2DM mice， and may act its role by regulating glucose energy metabolism in the hippocampus of T2DM.

Protein-protein interaction (PPI) plays an important role in the regulation of life. Most of the PPI interfaces are large and discontinuous, and it is difficult for small molecules to specifically bind to them. Peptides are critical in PPI surface interactions due to their higher affinity and specificity. However, peptides have some defects such as easy hydrolysis by protease and poor membrane permeability. Due to good biocompatibility and chemical diversity, cyclic peptides play an important role in drug discovery. Therefore, the development of efficient cyclic peptide construction methods has become a frontier issue in peptide drug research. In recent years, a series of new progresses have been made in the synthesis strategy and the application of cyclic peptides, providing powerful technical tools for the research and development of cyclic peptide drugs. In this review, the synthesis strategies of cyclic peptides and their application will be reviewed from four aspects: synthesis strategies, property improvement, biological activity and prospect.