The gut microbiota is regarded as the "eighth organ" of the human body and plays a critical regulatory role in the occurrence and progression of various diseases. Ulcerative colitis （UC） is a chronic inflammatory bowel disease with a complex etiology and a tendency toward recurrent episodes. In recent years， studies have shown that gut microbiota dysbiosis plays a key role in its pathological processes. Meanwhile， an increasing number of studies have demonstrated that imbalances in the gut microbiota and abnormalities in its metabolites are closely associated with the development of atrial fibrillation （AF）. Although UC and AF belong to diseases of the digestive system and cardiovascular system， respectively， both exhibit systemic inflammatory characteristics and are often accompanied by gut microbiota dysregulation and abnormal metabolic products. However， systematic investigations into the mechanisms by which gut microbiota-derived metabolites act in these two diseases remain limited. Based on this， the present study adopts literature review and theoretical analysis methods， taking the "gut microbiota-gut-heart" axis as the entry point， to systematically summarize the signaling networks of three key classes of metabolites， i.e.， short-chain fatty acids （SCFAs）， bile acids （BAs）， and trimethylamine N-oxide （TMAO）， in the comorbidity mechanism of UC and AF. The findings indicate that these metabolites may activate key inflammatory pathways， such as NF-κB and NLRP3， thereby synergistically mediating intestinal barrier dysfunction and systemic inflammation and constructing a potential comorbidity network. On this basis， potential intervention strategies for the treatment of UC-AF comorbidity， including probiotic intervention and fecal microbiota transplantation， are further discussed. This study aims to provide new theoretical evidence and research perspectives for prevention and treatment strategies of cross-system diseases.

AIM: To detect the distribution and expression of vascular endothelial growth factor(VEGF)in radiation retinopathy(RR)through fluorescence targeted imaging.METHODS:Covalent binding of fluorescein FITC with VEGF antibody ranibizumab to prepare targeted fluorescent imaging probe ranibizumab-FITC. SD rats were randomly divided into three groups based on the principle of weight balance: a normal control group(Con group), a low-dose radiation group(10 Gy group), and a high-dose radiation group(30 Gy group). Medical linear accelerators and lead blocks were used to locally irradiate the rat eyeballs for modeling. Western blot and qRT-PCR were used to detect the expression levels of VEGF-A in each group and to screen for appropriate modeling dose. The inverted fluorescence microscope and the confocal microscope were used to observe the distribution of VEGF and imaging probes in the retinas of control and RR model group rats, and to verify the effectiveness of targeted probes.RESULTS:The expression level of VEGF-A in the retina of rats in the high-dose radiation group(30 Gy group)was higher than that in the normal control group(Con group). In early RR, VEGF expression was observed to be associated with microaneurysms and abnormal microvessels in the retina. VEGF accumulation was observed at the site of capillary wall damage. When retinal capillary endothelial damage occurred, targeted probes gathered on the outer surface of the vessel wall.CONCLUSION:The expression level of VEGF in the retina of RR model rats is elevated, and fluorescent targeted molecular imaging probes can detect the spatial distribution of VEGF at the microvascular lesions in the retina of RR rats.

This paper proposes a two-stage method integrating visual foundation models （VFM） and diffusion models. The segment anything model （SAM） as VFM is combined with the SegRefiner diffusion model to construct the SAM-SegRefiner framework for automated segmentation of edema， inflammation， and thrombus regions in histopathological images of hemorrhoidal tissue， providing a reproducible technical tool for the objective quantification of pathological morphology and its application in traditional Chinese medicine （TCM） syndrome research. Trained and validated on multi-center retrospective data， the SAM-SegRefiner model achieved an average pixel accuracy of 95.32% and a mean intersection over union （mIoU） of 66.81% on an independent test set， significantly outperfor-ming comparative models such as U-Net， MixU-Net， and SAM-Med2D， and also demonstrating robust cross-center generalization capability. Furthermore， by correlating the quantitatively segmented results from the model with the patients' TCM syndrome types， the potential associations between pathomorphological features and TCM syndrome differentiation have been explored. The analysis revealed no statistically significant differences in the degree of inflammatory infiltration and thrombus formation among different syndrome types， suggesting a complex relationship between local pathological changes and systemic syndrome manifestations.

BACKGROUND:Gradient artificial bone repair scaffolds can mimic unique anatomical features in musculoskeletal tissues,showing great potential for repairing injured musculoskeletal tissues. OBJECTIVE:To review the latest research advances in gradient artificial bone repair scaffolds for tissue engineering in the musculoskeletal system and describe their advantages and fabrication strategies. METHODS:The first author of the article searched the Web of Science and PubMed databases for articles published from 2000 to 2023 with search terms"gradient,bone regeneration,scaffold".Finally,76 papers were analyzed and summarized after the screening. RESULTS AND CONCLUSION:(1)As an important means of efficient and high-quality repair of skeletal system tissues,gradient artificial bone repair scaffolds are currently designed bionically for the natural gradient characteristics of bone tissue,bone-cartilage,and tendon-bone tissue.These scaffolds can mimic the extracellular matrix of native tissues to a certain extent in terms of structure and composition,thus promoting cell adhesion,migration,proliferation,differentiation,and regenerative recovery of damaged tissues to their native state.(2)Advanced manufacturing technology provides more possibilities for gradient artificial bone repair scaffold preparation:Gradient electrospun fiber scaffolds constructed by spatially differentiated fiber arrangement and loading of biologically active substances have been developed;gradient 3D printed scaffolds fabricated by layered stacking,graded porosity,and bio-3D printing technology;gradient hydrogel scaffolds fabricated by in-situ layered injections,simple layer-by-layer stacking,and freeze-drying method;and in addition,there are also scaffolds made by other modalities or multi-method coupling.These scaffolds have demonstrated good biocompatibility in vitro experiments,were able to accelerate tissue regeneration in small animal tests,and were observed to have significantly improved histological structure.(3)The currently developed gradient artificial bone repair scaffolds have problems such as mismatch of gradient scales,unclear material-tissue interactions,and side effects caused by degradation products,which need to be further optimized by combining the strengths of related disciplines and clinical needs in the future.

OBJECTIVE To optimize the management model of drugs used in investigator-initiated trial （IIT）. METHODS With benchmarking analysis， based on the practical work experience of a tertiary specialized hospital in the field of IIT drug management in Shanghai， a thorough review was conducted， involving relevant laws， regulations， and academic literature to establish benchmark criteria and the evaluation standards. Starting from the initiation of IIT projects， a detailed comparative analysis of key processes was carried out， such as the receipt， storage， distribution， use and recycling of drugs for trial. The deficiencies in the current management of IIT drugs were reviewed in detail and a series of optimization suggestions were put forward. RESULTS It was found that the authorized records of drug management were missing， the training before project implementation was insufficient， and the records of receipt and acceptance of IIT drugs were incomplete. In light of these existing problems， improvement measures were put forward， including strengthening the training of drug administrators and stipulating that only drug administrators with pharmacist qualifications be eligible to inspect and accept drugs， etc. The related systems were improved， and 17 key points of quality control for the management of IIT drugs were developed. CONCLUSIONS A preliminary IIT drug management system for medical institutions has been established， which helps to improve the institutional X2023076） framework of medical institutions in this field.

Objective Based on the radiomics features extracted from the unenhanced CT images of the lower abdomen, a variety of machine learning models were constructed to explore their application value in the assessment of split renal function. Methods A retrospective analysis was conducted on the unenhanced CT images from 240 single kidneys in patients with clinically suspected renal dysfunction. Based on the results of single-photon emission computed tomography renal dynamic imaging, the cases were classified into the normal glomerular filtration rate group (n=118) and the decreased glomerular filtration rate group (n=122). The region of interest was outlined on the unenhanced CT images and the radiomics features were extracted. The features were selected by correlation analysis and least absolute shrinkage and selection operator, and the machine learning models were constructed based on the algorithms of decision tree, support vector machine, random forest, logistic regression, and extreme gradient boosting. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated to compare the performance of different models. Results Sixteen radiomics features were selected for constructing the machine learning models. The support vector machine model showed relatively high performance for the assessment of split renal function on the test set, with an area under the receiver operating characteristic curve value of 0.883 (95% confidence interval: 0.804-0.961), an accuracy of 0.778, a sensitivity of 0.811, and a specificity of 0.743. Conclusion The machine learning models constructed based on unenhanced CT radiomics can be used to preliminarily assess split renal function, which provides an innovative, convenient, and safe method for clinical diagnosis and has positive significance for treatment.

Background Coal workers' pneumoconiosis is a serious occupational disease in China. Exhaled volatile organic compounds (VOCs) can serve as the "breath fingerprint" of internal pathological processes, which provides a theoretical basis for exhaled VOCs to be used as potential non-invasive biomarkers for early diagnosis of coal workers' pneumoconiosis. Objective To screen out the characteristic VOCs and important characteristic VOCs of exhaled air in patients with coal workers' pneumoconiosis, and to explore the potential of these VOCs as biomarkers for early non-invasive diagnosis of the disease. Methods In this study, 27 VOCs in the exhaled breath of 22 patients with stage I coal workers' pneumoconiosis, 77 workers exposed to dust, and 92 healthy controls were quantitatively detected by thermal desorption gas chromatography-mass spectrometry (TD-GC-MS). Substances with P<0.05 in univariate analysis and variable importance projection (VIP) >1 in supervised orthogonal partial least squares discriminant analysis (OPLS-DA) model were selected as the characteristic VOCs for early diagnosis of coal workers' pneumoconiosis. Age was included in the LASSO regression model as a covariate to screen out important characteristic VOCs, and the diagnostic performance was evaluated by receiver operating characteristic (ROC) curve. Spearman correlation was further used to explore the correlation between important characteristic VOCs and clinical lung function indicators. Results Through univariate analysis and OPLS-DA modeling, 8 VOCs were selected, including 2-methylpentane, 3-methylpentane, n-hexane, methylcyclopentane, n-heptane, methylcyclohexane, 4-methyl-2-pentanone, and 2-hexanone, in exhaled breath of patients with coal workers' pneumoconiosis. The concentrations of 4 VOCs, including 3-methylpentane, n-hexane, 4-methyl-2-pentanone, and 2-hexanone, showed a decreasing trend with the increase of dust exposure years. By LASSO regression, the important characteristic VOCs of the coal workers' pneumoconiosis group and the dust exposure group were n-hexane, methylcyclohexane and 4-methyl-2-pentanone, and the important characteristic VOCs of the coal workers' pneumoconiosis group and the healthy group were 2-methyl-pentane and 4-methyl-2-pentanone. The ROC analysis showed that the area under the curve (AUC) of n-hexane, methylcyclohexane, and 4-methyl-2-pentanone were 0.969, 0.909, and 0.956, respectively, and the AUC of combined diagnosis was 0.988 and its Youden index was 0.961, suggesting that these results can serve as a valuable reference for further research on early diagnosis. The Correlation analysis found that there was a positive correlation between n-hexane and lung function indicators in the important characteristic VOCs, indicating that it could indirectly reflect the obstruction of lung function ventilation, further proving that important characteristic VOCs have the potential to monitor lung function decline. Conclusion Three important characteristic VOCs selected in this study have the potential to be used as non-invasive biomarkers for early diagnosis and disease monitoring of coal workers' pneumoconiosis, and are worthy of further study and verification.

Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

Understory planting of medicinal plants is a new planting mode that connects Chinese herbal medicine(CHM) with forest resources.The complex and variable understory environmental factors will inevitably affect the yield and quality of understory CHM.This research summarized the research progress on understory planting of medicinal plants based on forest types and environmental factors within the forest from the perspectives of understory light, air temperature and humidity, soil characteristics, and the interaction between crops within the forest.The results showed that the complex and variable light, temperature and humidity, and soil factors(such as fertility, acidity and alkalinity, and microorganisms) under the forest could affect the yield and quality of medicinal plants to varying degrees through physiological activities such as photosynthesis and respiration, resulting in a significant increase or decrease in yield and quality compared to open field cultivation.In addition, the competition or mutual benefit between different crops within the forest could lead to differences in the yield and quality of understory medicinal plants compared to open field cultivation.A reasonable combination of planting could achieve resource sharing and complementary advantages.Therefore, conducting systematic research on the effects of understory environmental factors on the yield and content of medicinal plants with different growth and development characteristics can provide theoretical guidance and technical references for formulating comprehensive strategies for understory planting of medicinal plants, such as selecting suitable medicinal plant varieties, optimizing planting density, and conducting reasonable forest management, thus contributing to the sustainable development and ecological protection of CHM.
Plants, Medicinal/growth & development* ; Forests ; Soil/chemistry* ; Environment ; Ecosystem ; Temperature