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.

OBJECTIVE To compare the efficacy and safety of tildrakizumab versus secukinumab in the treatment of moderate-to-severe plaque psoriasis. METHODS A retrospective analysis was conducted on 141 patients with moderate-to-severe plaque psoriasis treated at the First Affiliated Hospital of Nanyang Medical College from January 2024 to April 2025. According to the treatment regimen，the patients were divided into tildrakizumab g roup （ n ＝61） and secukinumab group （ n ＝80）. The PASI 75，PASI 90，and PASI 100 response rates， the Psoriasis Area and Severity Index （PASI） and Dermatology Life Quality Index （DLQI） scores， skin barrier function （sebum content and stratum corneum water content）， inflammatory factor levels [interleukin-17 （IL-17）， tumor necrosis factor-α （TNF-α）， and IL-23 ] before and after treatment， and the incidence of adverse drug reactions during treatment were compared between the two groups. RESULTS After 12 weeks of treatment， the PASI 75， PASI 90， and PASI 100 response rates in the tildrakizumab group were significantly higher than those in the secukinumab group （ P ＜0.05）. After treatment， PASI and DLQI scores as well as serum levels of IL-17， TNF-α， and IL-23 in both groups were significantly reduced compared to before treatment in the same group； sebum content and stratum corneum water content were significantly increased compared to before treatment in the same group （ P ＜0.05）； the tildrakizumab group showed better results than the secukinumab group （ P ＜0.05）. The overall incidence of adverse drug reactions was also significantly lower in the tildrakizumab group compared with the secukinumab group （ P ＜0.05）. CONCLUSIONS Compared with secukinumab， tildrakizumab demonstrates superior efficacy in the treatment of moderate-to-severe plaque psoriasis， providing improved symptom relief， enhanced skin barrier function， reduced levels of inflammatory factors， and higher safety.

Background: Xiaochaihu capsules are a widely used traditional Chinese patent medicine. The extraction process, a critical step in their production, necessitates online monitoring to determine the levels of key indicators, thereby informing subsequent processing stages. Objective: The objective of this study was to establish online quantitative models for 6 key indicators (solid content, liquiritin, baicalin, wogonoside, glycyrrhizic acid, and saikosaponin B ) during the extraction process of Xiaochaihu capsules. This approach aims to provide a technical foundation for enhanced process control and to serve as a methodological reference for the application of multisource information fusion in monitoring the extraction of multi-herb traditional Chinese medicine formulations. Methods: Online quantitative models were developed based on multiple sensors, ultraviolet-visible spectroscopy (UV-Vis), and near-infrared spectroscopy (NIR) to monitor 6 indicators during the extraction process of Xiaochaihu capsules. Different preprocessing methods for spectroscopic data were optimized, and data fusion strategies were integrated to improve the predictive performance of the online quantitative models. Results: The optimal modeling strategies for the 6 indicators were as follows: for solid content, a second-order polynomial model was established using the measurement results from 2 sensors (pH and conductivity), with a predictive coefficient of determination (R ) of 0.9821. For baicalin, the optimal model was developed using UV-Vis spectra pretreated with the first derivative (1stD), achieving a predictive R of 0.9841. For wogonoside, modeling was also conducted after 1stD preprocessing of the UV-Vis spectra, yielding a predictive R of 0.9929. For liquiritin, a high-level fusion strategy combining UV-based feature extraction via the random frog (RF) algorithm and NIR-based feature extraction via competitive adaptive reweighted sampling (CARS) was used, achieving an R of 0.9406 in the validation set. For glycyrrhizic acid, the optimal approach involved a combination of UV-CARS feature extraction and NIR-RF feature extraction, coupled with high-level data fusion, resulting in an R of 0.9859 in the validation set. For saikosaponin B , the optimal scheme utilized UV-RF feature extraction combined with NIR-CARS feature extraction and high-level fusion (R = 0.7702 in the validation set). Conclusion: The integration of process analytical technologies and data fusion strategies enabled the successful establishment of online quantitative models for 6 indicators during the extraction process of Xiaochaihu capsules. Overall, the established online quantitative models effectively captured and reflected the dynamic changes of these indicators throughout the extraction process.

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.

This study innovatively incorporates on-site teaching with the International Museum of Stomatology into the curriculum to establish an immersive and intuitive teaching mode,promoting education from both theoretical and practical dimensions.The teaching effect is comprehensively evaluated to explore the pathway to optimization.Multi-dimensional questionnaires are designed to collect feedback data from students on teaching satisfaction,knowledge mastery,professional identity,and humanistic literacy perception,fol-lowed by in-depth quantitative and qualitative analyses.The results demonstrate that this teaching mode significantly enhances literacy,playing a critical role in helping stomatological students fully understand professional knowledge and humanistic connotations while sub-stantially improving their professional identity.This teaching mode gives a direction for innovative stomatological education,holds sig-nificant importance for cultivating stomatological professionals with both clinical skills and humanistic literacy,possessing substantial potential for promotion,application,and further refinement.

To address the insufficiency of domestic small-dose Chinese herbal granule dispensing equipment,this study introduces the"black box method"design concept and,on this basis,designs a lightweight,compact and highly accurate dispensing device.This device uses a two-phase four-wire brushless motor to drive the slider for displacement,thereby controlling the volume of the medicine falling into the weighing device to complete the weighing of the medicine.Finally,a two-phase hybrid stepping motor is used to drive the weighing device to transfer the medicine.This paper first elaborates in detail on the mechanical structure and weighing dispensing principle of this device.Secondly,the ANSYS finite element software is used to conduct a force analysis test on the weighing device,and it is concluded that the deformation of the weighing device during the working process is 9.98×10-5 mm,and the maximum deformation of the discharge port is 6.65×10-5 mm.Finally,the ADAMS software is used to simulate and analyze the motion process of the mechanism.The results show that,under the premise of ensuring the accuracy of weighing,there are no virtual points or interference in the motion process.On this basis,a prototype test is conducted,and the error accuracy of the dispensing machine is about 0.066 g,which is higher than the traditional dispensing accuracy.This equipment has strong adaptability and high dispensing accuracy,providing a new solution for small-dose precise quantitative equipment.

A method that integrates the features of image patches from corneal lesions and conjunctival congestion-like complications is proposed to address the limitations of manual keratitis diagnosis(i.e.,time-consuming,laborious,high subjectivity)and the generally low accuracy of automatic keratitis diagnosis based on original slit-lamp images.Specifically,samples are acquired from the corneal and conjunctival regions.A cost-sensitive convolutional neural network is then used to extract and concatenate the high-level features of these image patches.After dimensionality reduction through principal component analysis,the processed features are input into the fully connected layer for classification.Trained and evaluated on 6414 slit-lamp images collected from Ningbo Eye Hospital,the proposed method achieves accuracies of 97.8%,98.6%,and 97.0%for keratitis,normal cornea,and other abnormal corneas,respectively.This method effectively integrates relevant features and provides a feasible solution for high-accuracy keratitis diagnosis.

Objective:To investigate the intervention effect of five elements music of traditional Chinese medicine and western classical music on the sleep quality of stroke patients, and to compare the difference between the two, to provide a reference for the clinical care measures to improve the sleep quality of stroke patients.Methods:By adopting a randomized controlled trial, 75 stroke patients who were hospitalized in the rehabilitation hospital of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine from July 2022 to July 2023 were selected by convenience sampling method as the study subjects, and the patients were randomly divided the conventional treatment group, the five-element music group, and the classical music group according to the method of randomized numerical table with 25 cases in each group. On the basis of general treatment, the conventional treatment group took sleep health education, the five elements music group were given five-element music intervention based on conventional treatment, and the classical music group were given classical music intervention based on conventional treatment. The changes of Athens Insomnia Scale (AIS), Pittsburgh Sleep Quality Index (PSQI) and polysomnography (PSG) examination parameters in the 3 groups were compared.Results:There were 2 cases of shedding in the conventional treatment group, and 23 cases were finally included, 15 males and 8 females, aged (54.02 ± 7.80) years; there were no cases of shedding in the five-element music group, and 25 cases were finally included, 12 males and 13 females, aged (53.69 ± 6.02) years; and there was 1 case of shedding in the classical music group, and 24 cases were finally included, 10 males and 14 females, aged (52.34 ± 7.08) years. Comparison of AIS, PSQI and PSG scores among the 3 groups of patients before intervention showed no statistically significant differences (all P>0.05). After the intervention, the AIS scores and PSQI scores of the 3 groups were (9.48 ± 1.53) and (12.22 ± 2.94), (6.76 ± 1.36) and (7.64 ± 2.08), (7.46 ± 1.38) and (10.33 ± 2.82), respectively, and the differences were statistically significant ( F=23.21, 18.44, both P<0.05). PSG sleep structure parameters showed that the total sleep time, REM latency time and sleep efficiency of the patients in the five-element music group after the intervention were (399.89 ± 51.76) min, (136.26 ± 25.36) min, (78.87 ± 8.21)%, higher than (368.45 ± 47.88) min, (124.46 ± 26.25) min, (73.36 ± 7.86)% in the classical music group and (345.48 ± 38.69) min, (111.37 ± 23.23) min, (69.44 ± 7.88)% in the conventional treatment group, and the differences between three groups were statistically significant ( F=8.27, 5.93, and 8.49, all P<0.05); the sleep latency time, awakening time, and number of awakenings in the five-element music group were (28.86 ± 17.68) min, (54.37 ± 25.15) min, and (2.72 ± 1.19) times, respectively, which were lower than those in the classical music group (35.67 ± 16.99) min, (64.28 ± 29.34) min, and (3.67 ± 1.12) times and (42.38 ± 18.96) min, (78.38 ± 37.26) min, (4.87 ± 1.46) times in the conventional treatment group, and the differences between three groups were statistically significant ( F=3.51, 3.66, and 17.56, all P<0.05). The results of the PSG sleep progression showed that the duration of the N1 stage of sleep in the five-element music group after the intervention was (95.71 ± 15.23) min, which was higher than (83.20 ± 18.34) min in the classical music group and (80.93 ± 16.47) min in the conventional treatment group, and the difference between three groups was statistically significant ( F=5.53, P<0.01); the N3 stage sleep time and the sleep percentage of the five-element music group after the intervention were respectively (84.23 ± 20.98) min and (23.98 ± 5.89)%, which were higher than (65.33 ± 18.82) min and (18.34 ± 3.78)% in the classical music group and (45.87 ± 18.65) min and (15.03 ± 5.56)% in the conventional treatment group, and the differences between three groups were statistically significant ( F=23.08, 18.50, both P<0.05). Conclusions:Both five elements music and classical music can improve the sleep quality of stroke patients, and the effect of five elements music to improve sleep is more significant.