ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

Artificial intelligence （AI） and population pharmacokinetics （PPK） technologies have demonstrated significant potential in the personalized medication of immunosuppressants after organ transplantation, enabling precise prediction of drug dosages. This article provides a comprehensive review of the application status of AI and PPK in the individualized administration of immunosuppressants after organ transplantation， focuses on monitoring blood drug concentration， predicting efficacy/adverse reactions， and establishing individualized dosing models for organ transplant recipients after immunosuppressant administration， and analyzes and compares the application characteristics of different methods in different organ transplant patients as well as the integration and future development of AI and PPK technologies. AI and PPK technologies can not only significantly reduce the dependence on human resources， but also greatly improve the level of individualized treatment of immunosuppressants after organ transplantation， and reduce the discomfort and burden caused by frequent blood concentration monitoring to patients.

Objective : To investigate the mechanism of puerarin in the treatment of rheumatoid arthritis(RA) by network pharmacology and animal experiments. Methods : Traditional Chinese Medicine Systems Pharmcolog Database(TCMSP) and SwissTargetPrediction database were used to collect puerarin targets, and the targets of RA were obtained from GeneCards database and OMIM database. The PPI network was established by Cytoscape 3.7.2 software. Gene ontology(GO) function and Kyotoencyclopedia of genes(KEGG) enrichment analysis were performed through the Metascape database. RA rat-collagen-induced arthritis(CIA) model was reproduced using type Ⅱ collagen emulsion, 49 Wistar rats were randomly assigned to seven groups: control group, CIA model group, low-dose, medium-dose and high-dose puerarin group, methotrexate group, Tripterysium Glycosides Tablets group. Except for the control group, the other groups were given continuous gavage for 28 days after the CIA in rats model were prepared. The redness and swelling of the joints and ankle joint pathological changes were observed in each group. Western blot was used to detect the expression of Glycogen synthase kinase3β(GSK-3β), beta-catenin(β-catenin) proteins in the synovium. Real-time quantitative polymerase chain reaction(qPCR) was used to detect the expression of GSK-3β, β-catenin and c-Myc mRNA in the synovium. Results : Puerarin had 134 targets genes, RA had 5 821 target genes, and there were 102 overlapping target genes of puerarin and RA. It involved 184 signaling pathways, including JAK-STAT signaling pathway, NF-κB signaling pathway, Wnt signaling pathway, et al. The results of animal experiments showed that after the intervention of M-puerarin and MTX, the symptoms of redness and swelling of the hind foot were alleviated, the inflammatory cell infiltration in the synovium of the joint was significantly reduced, and the damage of cartilage and bone tissue was reduced. Compared with CIA group, the expressions of GSK-3β, β-catenin protein and GSK-3β, β-catenin and c-Myc mRNA in synovial tissue of rats after M-puerarin intervention decreased(P<0.05). Conclusion Puerarin has the characteristic of multi-components, multi-targets and multi-pathway intervention in RA. Puerarin may alleviate synovial hyperplasia, reduce articular cartilage erosion and bone destruction in CIA in rats by inhibiting Wnt/β-catenin signaling pathway.

Objective To explore current status and potential subtypes of elderly stroke patients' medication literacy among,and to analyze related influencing factors of different subtypes.Methods A total of 285 elderly stroke patients admitted in the First Affiliated Hospital of Zhengzhou University from November 2023 to June 2024 were selected as subjects.General Information questionnaire,medication literacy scale for elderly patients with chronic diseases,and perceived social support scale were conducted.Latent profile analysis(LPA)of elderly stroke patients' medication literacy was conducted,and Logistic regression analysis was used to explore influencing factors of different profiles.Results Score of medication literacy scale for elderly stroke patients was(48.26±12.51)points.Medication literacy among elderly stroke patients can be divided into 3 profiles,namely proactive-high literacy type(51.9％),balanced-medium literacy type(34.0％),and dependent-low literacy type(14.1％).Logistic regression analysis showed that recent medication types,current place of residence,educational level,diabetes,and social support were the influencing factors of elderly stroke patients' medication literacy(P＜0.05).Conclusion The level of medication literacy among elderly stroke patients is medium,which is characterized by 3 categories.Medical staffs targeted intervention should be adopted according to different category characteristics,so as to accurately meet their nursing needs,finally improve the level of elderly stroke patients' medication literacy.

Objective:To evaluate the effectiveness of remote management based on unmet health needs self-monitoring reports in patients with colorectal cancer.Methods:A total of 124 colorectal cancer inpatients admitted to the First Affiliated Hospital of Zhengzhou University from October 2022 to September 2023 were selected using convenience sampling and randomly divided into a control group and an intervention group (62 patients each). The control group received routine follow-up care, while the intervention group received remote management based on self-monitoring reports of unmet health needs in addition to routine follow-up, over a 3-month period. To evaluate the outcomes, the Health Literacy Management Scale (HeLMS), the Health Promoting Lifestyle Profile-II (HPLP-II), the Short Form of the Unmet Needs Survey (SF-SUNS), and the Self-Care Self-Efficacy Scale (SCSES) were compared between two groups.Results:After the intervention, the intervention group showed significantly higher scores in HeLMS, HPLP-II, and SCSES compared to their baseline values and the control group ( P<0.05). Additionally, the SF-SUNS score was significantly lower in the intervention group post-intervention than both its baseline and the control group post-intervention ( P<0.05) . Conclusions:Remote management based on unmet health needs self-monitoring reports effectively improves health literacy, promotes healthier lifestyles, enhances self-management efficacy, and reduces levels of unmet health needs in patients with colorectal cancer.

AIM To prepare the sustained-release microspheres of ginsenosides.METHODS The sustained-release microspheres were prepared by SPG membrane emulsification technology with poly(lactic-co-glycolic acid)(PLGA)as a shell carrier.With PLGA concentration,feed rate and Span 60 concentration as influencing factors,comprehensive score for appearance,drug loading and encapsulation efficiency as an evaluation indice,the preparation process was optimized by response surface method.The morphology of sustained-release microspheres was observed,after which the particle size,drug loading and encapsulation efficiency were determined,and the in vitro drug release was investigated.RESULTS The optimal conditons were determined to be 45 s for agitation time of primary emulsion,74.68 mg/mL for PLGA concentration,11％for feed rate,and 4.18 mg/mL for Span 60 concentration,the comprehensive score was 74.98.The round sustained-release microspheres demonstrated the average particle size of 4.33 μm,drug loading of(8.24±0.13)％,and encapsulation efficiency of(74.94±1.17)％,respectively.At 336 h,ginsenosides Rg1,Rb1,Rb2 displayed the accumulative release rates of 84.12％,78.04％,65.88％,respectively.CONCLUSION This reasonable and feasible method can be used for the preparation of sustained-release microspheres of ginsenosides with good appearance and high drug loading,which can provide references for the preparation of other water-soluble drug microspheres and solution of microsphere collapse problem.

Objective:To analyze the clinical characteristics and cytokines expression characteristics in infants with mild and severe respiratory syncytial virus (RSV) infection.Methods:From May 2023 to December 2023, plasma samples and clinical information were collected from 16 infants with RSV infection and 14 control infants. Cytek Aurora flow cytometry (Cytek, America) and Enzyme linked immunosorbent assay (ELISA) were used to detect the expression levels of 25 cytokines after mild and severe RSV infection.Results:Cough and nasal obstruction were the main clinical manifestations in infants with mild RSV infection, accompanied by polypnea, wheezing and other symptoms. The main symptoms of severe RSV infection were cough and rales, accompanied by fever and polypnea. In comparison with the control group, the expression levels of IL-2, IL-4, IL-5, IL-6, IL-9, IL-13, IL-22, TNF-α, IFN-α, IFN-β, MIP-1β, I-TAC, ENA-78, GROα, Eotaxin, and MCP-1 in the RSV infection group all exhibited an upregulation trend. Both IP-10 and MIP-3α demonstrated a downward trend in the RSV infection group; however, there was no statistically significant difference ( P>0.05). The levels of IL-10, IFN-γ, MIP-1α, and IL-8 in the RSV infection group were significantly higher than those in the control group, whereas the levels of MIG, TARC, and RANTES in the RSV infection group were significantly lower than those in the control group ( P<0.05). The levels of IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-13, IL-22, IFN-β, IFN-γ, TNF-α, IL-8, I-TAC, MIP-1β, Eotaxin, and MCP-1 in the mild RSV infection group were significantly higher than those in the severe RSV infection group ( P>0.05). Among these, the levels of MIG, RANTES, TARC, MIP-3α, and ENA-78 in the mild infection group were all lower than those in the severe infection group. The expressions of ENA-78 and MIP-1α in the severe infection group were significantly higher than those in the mild infection group and also higher than those in the control group. There was no significant difference in IP-10 and GROα between the mild and severe RSV infection groups ( P>0.05). Conclusions:The differences in clinical features and cytokines between infants with mild and severe RSV infection provide important data support for the prevention and treatment of RSV infection in infants.

Objective:To investigate the correlation of intracranial arterial calcification (IAC) and its different subtypes with the severity of white matter hyperintensities (WMHs) in patients with cerebral small vessel disease (CSVD).Methods:Consecutive CSVD patients admitted to the Department of Neurology, the First Affiliated Hospital of Zhengzhou University from March 2018 to March 2022 were enrolled. Baseline demographic, laboratory, and imaging data were collected. Based on a developed and validated IAC grading scale using head CT, IAC was classified into intimal and medial types, and further categorized as focal or diffuse based on the extent of involvement. The severity of WMHs on magnetic resonance imaging was assessed using the Fazekas Scale, with patients divided into those with moderate-to-severe (total score>2) and non-moderate-to-severe WMHs (total score≤2). Subgroups were stratified based on baseline characteristics (patients′ sex, age, hypertension history, stroke history, diabetes, coronary heart disease, hyperlipidemia, smoking, and alcohol consumption). Multivariate Logistic regression was used to analyze the correlation between IAC′s subtypes and the severity of WMHs, with forest plots illustrating the interaction between medial IAC and subgroup variables.Results:A total of 490 patients with CSVD who met the inclusion and exclusion criteria were ultimately included, with a age of (60.88±10.99) years, including 162 females (33.1%). Moderate-to-severe WMHs were present in 245 patients (50.0%). Among the 490 CSVD patients, 395 (80.6%) had IAC, including 335 (68.4%) with intimal IAC and 207 (42.2%) with medial IAC. Diffuse IAC was observed in 126 patients (25.7%), all of whom had medial IAC. Intracranial arterial stenosis was present in 271 patients (55.3%). Multivariate Logistic regression showed that IAC ( OR=2.073, 95% CI 1.142-3.761, P=0.016) was significantly associated with moderate-to-severe WMHs and medial IAC ( OR=3.230, 95% CI 1.800-5.797, P<0.001) and advanced age ( OR=1.046, 95% CI 1.019-1.074, P=0.001) were significantly associated with moderate-to-severe WMHs. Subgroup analysis revealed medial IAC had no significant interaction with patients′ gender, age, hypertension, diabetes, coronary heart disease, hyperlipidemia, alcohol or smoking consumption except for stroke history. Conclusion:In the CSVD patients, IAC, especially medial IAC, is significantly associated with the severity of WMHs.

Objective:To explore the correlation between the lesion imaging characteristics and the parent artery plaques detected by high-resolution magnetic resonance imaging (HR-MRI) in patients with recent small subcortical infarction (RSSI) in the lenticulostriate artery territory with mild stenosis of the parent artery.Methods:Consecutive patients with RSSI in the lenticulostriate artery territory admitted to Department of Neurology, the First Affiliated Hospital of Zhengzhou University from January 2020 to December 2022 were prospectively enrolled. Patients with stenosis of <50% in the ipsilateral middle cerebral artery (MCA) who had completed HR-MRI were included. The RSSI lesion characteristics, such as the lowest slice involved (LS), total number of slices involved (TNS) were evaluated based on serialized axial levels of diffusion weighted imaging. HR-MRI was utilized to assess the presence of dorsal superior plaques in the M1 segment of the responsible MCA. Patients were divided into plaque group and non-plaque group based on the presence or absence of plaque. Logistic regression analysis was carried out to identify the lesion characteristics associated with the presence of dorsal superior plaques in the MCA. A receiver operating characteristic (ROC) curve was employed to estimate the predictive efficacy of these parameters for MCA plaques using the area under the curve (AUC) and the optimal cut-off point.Results:A total of 112 patients were incorporated into the final analysis. The age of these patients was (57.08±11.90) years, and 78 patients (69.64%) were male. Plaques were detected on the dorsal superior wall of the MCA in 57 cases (50.89%) as the plaque group, and the other 55 cases (49.11%) were regarded as the non-plaque group. Multivariate Logistic regression revealed that LS was significantly associated with MCA plaques ( OR=0.674, 95% CI 0.485-0.937, P=0.019). The optimal cut-off point (LS≤1) was determined by ROC curve analysis (AUC=0.640, P=0.007). Conclusion:The findings suggest that in RSSI patients with mild stenosis of the parent artery, an LS≤1 is notably associated with the presence of dorsal superior plaques on MCA as detected by HR-MRI.