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.

The tumor microenvironment is a crucial factor in tumor occurrence and progression. The hypoxic microenvironment is widely present in tumor tissue and is a key endogenous factor accelerating tumor deterioration. The "blood stasis toxin" theory, as an emerging perspective in tumor research, is regarded as the unique "soil" in tumor progression from the perspective of traditional Chinese medicine(TCM) due to its dynamic evolution mechanism, which closely resembles the formation of the hypoxic microenvironment. Scientifically integrating TCM theories with the biological characteristics of tumors and exploring precise syndrome differentiation and treatment strategies are key to achieving comprehensive tumor prevention and control. This article focused on the hypoxic microenvironment of the tumor, elucidating its formation mechanisms and evolutionary processes and carefully analyzing the internal relationship between the "blood stasis toxin" theory and the hypoxic microenvironment. Additionally, it explored the interaction among blood stasis, toxic pathogens, and hypoxic environment and proposed micro-level prevention and treatment strategies targeting the hypoxic microenvironment based on the "blood stasis toxin" theory, aiming to provide TCM-based theoretical support and therapeutic approaches for precise regulation of the hypoxic microenvironment.
Humans ; Tumor Microenvironment/drug effects* ; Neoplasms/therapy* ; Animals ; Medicine, Chinese Traditional ; Disease Progression ; Drugs, Chinese Herbal

Gegen Qinlian Decoction(GQD) is a classic prescription for the clinical treatment of ulcerative colitis(UC). This study, based on the differences in efficacy observed in UC mice under different level of bile acids treated with GQD, aims to clarify the impact of bile acids on UC and its therapeutic effects. It further investigates the expression of bile acid receptors in the liver of UC mice, and preliminarily reveals the mechanism through which GQD affects bile acid synthesis in the treatment of UC. A UC mouse model was established using dextran sulfate sodium(DSS) induction. The efficacy of GQD was evaluated by assessing the general condition, disease activity index(DAI) score, colon length, and histopathological changes in colon tissue via hematoxylin and eosin(HE) staining. ELISA and Western blot were used to evaluate the inflammatory response in colon tissue. The total bile acid(TBA) level and liver damage were quantified using an automatic biochemistry analyzer. The expression levels of bile acid receptors and bile acid synthetases in liver tissue were detected by Western blot and RT-qPCR. The results showed that compared with the model group, GQD treatment significantly improved the DAI score, colon shortening, and histopathological damage in UC mice. The levels of pro-inflammatory factors TNF-α and IL-6 in the colon were significantly reduced. Serum TBA levels were significantly decreased, while alkaline phosphatase(ALP) levels significantly increased. After administration of cholic acid(CA), UC symptoms in the CA + GQD group were significantly aggravated compared with the GQD group. The DAI score, degree of weight loss, colon injury, serum TBA, and liver injury markers all increased significantly. However, compared with the CA group, the CA + GQD group showed a marked reduction in TBA levels and a significant improvement in UC-related symptoms, indicating that GQD can alleviate UC damage exacerbated by CA. Further investigation into the expression of bile acid receptors and synthetases in the liver showed that under GQD treatment, the expression of farnesoid X receptor(FXR) and small heterodimer partner(SHP) significantly increased, while the expression of G protein-coupled receptor 5(TGR5) and cholesterol 7α-hydroxylase(Cyp7A1) significantly decreased. These findings suggest that GQD may affect bile acid receptors and synthetases, inhibiting bile acid synthesis through the FXR/SHP pathway to treat UC.
Animals ; Colitis, Ulcerative/genetics* ; Bile Acids and Salts/biosynthesis* ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Male ; Humans ; Receptors, Cytoplasmic and Nuclear/metabolism* ; Colon/metabolism* ; Disease Models, Animal ; Liver/metabolism* ; Mice, Inbred C57BL

OBJECTIVES: Color doppler flow imaging (CDFI) and cone-beam computed tomography (CBCT) were utilized to evaluate changes in mucosal vascular parameters and the osteogenic effects following guided bone regeneration (GBR) in the maxillary anterior region using trapezoidal or modified triangular flaps. METHODS: Patients undergoing single maxillary anterior dental implant surgery with GBR were randomly allocated into two groups: a trapezoidal flap group and a modified triangular flap group. After GBR surgery, the mucosal vascular parameters at the surgical site were assessed at various time intervals (preoperative, 2 h, 1 and 3 days, and 1, 2, and 4 weeks postoperative) using CDFI. In addition, the effects of bone augmentation were evaluated through the analysis of CBCT images obtained preoperatively, 2 h, and 6 months postoperative. RESULTS: The buccal mucosa in the edentulous area had a lower blood flow rate than the corresponding tooth in the same jaw, and the difference was statistically significant (P<0.001). The mucosal blood flow rate in the surgical area increased compared with that in the preoperative period. The peak flow rate was recorded at 2 weeks postoperatively and then decreased to levels comparable to those of the reference tooth. A statistically significant difference was observed between the two groups (P<0.05). The buccal alveolar ridge width of the implant platform was reduced by (1.3±0.9) mm in the trapezoidal flap group and (0.9±0.7) mm in the modified triangular flap group, respectively, at 6 months postoperatively, compared with 2 h postoperative. The buccal alveolar ridge width of the 5 mm from the implant platform was reduced by (0.9±0.6) mm and (0.3±0.6) mm, respectively. The buccal alveolar ridge width of the 10 mm from the implant platform was reduced by (0.6±0.8) mm and (0.2±0.6) mm, respectively. The height of the alveolar ridge was reduced by (1.9±1.4 ) mm and (1.4±1.3) mm. The change in graft volume was (136±78 ) mm³ and (114±85) mm³. However, the differences between the two groups were not statistically significant (P>0.05). CONCLUSIONS When a tooth is missing, blood flow to the buccal mucosa on the side of the missing tooth is reduced. The modified triangular flap group demonstrated superior microcirculation of blood flow in the operative area after GBR of the maxillary anterior teeth. Trapezoidal and modified triangular flaps achieved the anticipated bone augmentation during bone augmentation surgery in the maxillary anterior region, with no considerable effect on the changes in alveolar bone size parameters.
Humans ; Surgical Flaps/blood supply* ; Bone Regeneration ; Mouth Mucosa/blood supply* ; Cone-Beam Computed Tomography ; Osteogenesis ; Maxilla/surgery* ; Male ; Female ; Guided Tissue Regeneration, Periodontal/methods*

To explore the regulatory mechanism of drought stress on the synthesis of chlorogenic acid and luteoloside in Lonicera japonica, we designed five drought gradients (soil water contents of 30%, 24%, 17%, 14%, and 10%) and screened and verified the differentially expressed genes (DEGs) by RNA sequencing (RNA-seq) and reverse transcription quantitative polymerase chain reaction (RT-qPCR). Furthermore, we employed HPLC to systematically measure the content changes of chlorogenic acid and luteoloside. The results revealed that drought significantly reduced the accumulation of secondary metabolites, and severe drought led to more obvious reductions. Under extreme drought (soil water content of 10%), the content of chlorogenic acid and luteoloside decreased significantly to 25.73 mg/g and 11.33 mg/g (with the decrease rates of 37.85% and 9.58%, respectively). A total of 77 454 genes were identified via transcriptome analysis, among which the number of DEGs reached 1 128 under the extraordinary drought. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses revealed that the DEGs were mainly involved in flavonoid synthesis, secondary metabolite biosynthesis, plant hormone signal transduction and the plant-pathogen interaction pathways, and the expression of key genes regulating the synthesis of chlorogenic acid and luteoloside was significantly downregulated. RT-qPCR verified the accuracy of the RNA-seq data. This study revealed that drought stress reduced the content of chlorogenic acid and luteoloside, the main secondary metabolites, by inhibiting the expression of key genes in the secondary metabolism pathways. The findings provide candidate gene resources for molecular breeding of drought-tolerant Lonicera japonica.
Lonicera/physiology* ; Chlorogenic Acid/metabolism* ; Droughts ; Stress, Physiological ; Gene Expression Regulation, Plant ; Glucosides/metabolism* ; Luteolin

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

The traditional medical quality management model has many problems,such as excessive reliance on experience,poor coordination among departments,and lagging supervision,innovating medical quality management models has become an essential issue for the high-quality development of hospitals.A tertiary Grade A general hospital built a"data element multiplier"ecological chain for medical quality management,and established a complete closed-loop management system for medical quality management under the"Internet plus"model.Finally,the mobile medical quality management platform was built.Jointly driven by"Internet plus"and"data element multiplier",the level of lean management has been significantly improved,providing practical reference for hospitals at the same level.