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.

Objective:To explore the role and possible mechanism of ACOT11 in renal fibrosis model mice.Methods:A mouse model of renal fibrosis was established by unilateral ureteral obstruction(UUO)(Sham group and UUO7 group),and the expression of ACOT11 in the kidneys of UUO induced fibrosis mouse models was detected by protein immunoblotting and real-time fluorescence quantitative PCR(qRT-PCR).Subsequently,immunohistochemistry,Masson staining,H&E staining,PAS staining,and other experimental methods were used to detect the expression levels of fibrosis biomarkers fibronectin,α-SMA,and COL-1 in the kidneys of control and experimental group mice.In addition,by constructing ACOT11 gene knockout model mice and using the gene knockout model mice to construct a renal fibrosis model,the expression levels of fibrosis biomarkers such as fibronectin,α-SMA,COL-1,as well as fibrosis mechanism pathway related indicators TGF-β and Smad2 in the kidneys of each group of mice were further detected.Results:The results of WB and qRT-PCR experiments showed that the expression of ACOT11 in the kidney tissue of UUO model mice was significantly reduced compared to the Sham group.After knocking out the ACOT11 gene,H&E staining,PAS staining,and Masson staining showed that pathological inflammatory reactions such as abnormal glomerular and tubular structures,inflammatory cell infiltration and interstitial fibrous tissue proliferation in mice were significantly aggravated compared to the control group,and the expression of fibrosis markers Fibronectin,α-SMA,and COL-1 was significantly higher than that of the control group.Conclusion:ACOT11 plays a protective role in mice with unilateral ureteral obstruction model.After ACOT11 gene knockout,the fibrosis biomarkers of the mouse kidney increases and the degree of fibrosis worsens.

Aberrant expression of Colgalt1 is closely associated with tumorigenesis and tumor progres-sion;however,the mechanism by which it regulates macrophages to influence tumor development remains poorly understood.This study aimed to establish a macrophage-specific Colgalt1 gene knockout mouse model to delve into the mechanisms through which Colgalt1 modulates macrophage function and subse-quently affects the occurrence and progression of tumor-related diseases.Initially,Colgalt1flox+mice were generated using gene editing techniques,followed by crossing with Lyz2-Cre+mice,which exhibit tissue-specific expression in the myeloid lineage(including monocytes and mature macrophages).Through this strategy,mice with the genotype Colgalt1-/-Lyz2-Cre+were successfully obtained,achieving conditional knockout of the Colgalt1 gene in macrophages.Colgalt1flox/flox Lyz2-Cre-mice were used as control.PCR and agarose gel electrophoresis were employed to identify the Flox and Cre genotypes of the knockout mice.RT-qPCR and Western Blot techniques were utilized to detect the expression levels of Colgalt1 in BMDMs from knockout mice at both the mRNA and protein levels,respectively.Western Blot results re-vealed a significant downregulation of Colgaltl expression in BMDMs from knockout mice compared to controls(P＜0.01).RT-qPCR results demonstrated a significant reduction in Colgalt1 mRNA levels in BMDMs from knockout mice compared to contro1s(P＜0.001),while no significant differences in Col-galt1 mRNA expression were observed in liver,lung,or spleen tissues between the two groups.Addition-ally,immunohistochemistry was employed to detect Colgalt1 expression in liver-specific macrophages,re-vealing an absence of Colgalt l-positive staining in liver macrophages from knockout mice.HE staining was used to observe cellular morphology in liver tissues from both groups of mice,showing no significant differences in cellular morphology or obvious pathological changes in tissues and organs.Moreover,the o-verall survival of the mice was not affected.Finally,RT-qPCR was used to assess the expression of mac-rophage-related inflammatory factors in BMDMs from both groups of mice.The results indicated that com-pared to controls,knockout mice exhibited downregulated expression of TNF-α(P＜0.05)and signifi-cantly upregulated expression of IL-10(P＜0.01),Arginase1(P＜0.001),and CD206(P＜0.001)in BMDMs,suggesting an anti-inflammatory trend and M2 polarization of macrophages following Colgalt 1 knockout.In summary,this study successfully established a macrophage-specific Colgalt1 gene knockout mouse model,providing a more reliable experimental animal model for in-depth exploration of the specific roles of Colgalt1 in macrophage functional regulation and the pathogenesis of tumor-related diseases.This model holds promise for identifying novel therapeutic targets and strategies for tumors and other diseases.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

This study aimed to explore the therapeutic mechanisms of Colquhounia Root Tablets(CRT) in treating diabetic kidney disease(DKD) by integrating biomolecular network mining with animal model verification. By analyzing clinical transcriptomics data, an interaction network was constructed between candidate targets of CRT and DKD-related genes. Based on the topological eigenvalues of network nodes, 101 core network targets of CRT against DKD were identified. These targets were found to be closely related to multiple pathways associated with type 2 diabetes, immune response, and metabolic reprogramming. Given that immune-inflammatory imbalance driven by metabolic reprogramming is one of the key pathogenic mechanisms of DKD, and that many core network targets of CRT are involved in this pathological process, receptor for advanced glycation end products(RAGE)-reactive oxygen species(ROS)-phosphatidylinositol 3-kinase(PI3K)-protein kinase B(AKT)-nuclear factor-κB(NF-κB)-NOD-like receptor family pyrin domain containing 3(NLRP3) signaling axis was selected as a candidate target for in-depth research. Further, a rat model of DKD induced by a high-sugar, high-fat diet and streptozotocin was established to evaluate the pharmacological effects of CRT and verify the expression of related targets. The experimental results showed that CRT could effectively correct metabolic disturbances in DKD, restore immune-inflammatory balance, and improve renal function and its pathological changes by inhibiting the activation of the RAGE-ROS-PI3K-AKT-NF-κB-NLRP3 signaling axis. In conclusion, this study reveals that CRT alleviates the progression of DKD through dual regulation of metabolic reprogramming and immune-inflammatory responses, providing strong experimental evidence for its clinical application in DKD.
Animals ; Diabetic Nephropathies/metabolism* ; Receptor for Advanced Glycation End Products/genetics* ; NF-kappa B/genetics* ; Signal Transduction/drug effects* ; Rats ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Proto-Oncogene Proteins c-akt/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Phosphatidylinositol 3-Kinases/genetics* ; Reactive Oxygen Species/metabolism* ; Humans ; Plant Roots/chemistry* ; Rats, Sprague-Dawley ; Tablets/administration & dosage*

This study aims to systematically characterize the targeted effects of Quanduzhong Capsules on cartilage lesions in knee osteoarthritis by integrating spatial transcriptomics data mining and animal experiments validation, thereby elucidating the related molecular mechanisms. A knee osteoarthritis model was established using Sprague-Dawley(SD) rats, via a modified Hulth method. Hematoxylin and eosin(HE) staining was employed to detect knee osteoarthritis-associated pathological changes in knee cartilage. Candidate targets of Quanduzhong Capsules were collected from the HIT 2.0 database, followed by bioinformatics analysis of spatial transcriptomics datasets(GSE254844) from cartilage tissues in clinical knee osteoarthritis patients to identify spatially specific disease genes. Furthermore, a "formula candidate targets-spatially specific genes in cartilage lesions" interaction network was constructed to explore the effects and major mechanisms of Quanduzhong Capsules in distinct cartilage regions. Experimental validation was conducted through immunohistochemistry using animal-derived biospecimens. The results indicated that Quanduzhong Capsules effectively inhibited the degenerative changes in the cartilage of affected joints in rats, which was associated with the regulation of Quanduzhong Capsules on the thioredoxin-interacting protein(TXNIP)-NOD-like receptor family pyrin domain containing 3(NLRP3)-bone morphogenetic protein receptor type 2(BMPR2)-fibronectin 1(FN1)-matrix metallopeptidase 2(MMP2) signal axis in the articular cartilage surface and superficial zones, subsequently inhibiting cartilage matrix degradation leading to oxidative stress and inflammatory diffusion. In summary, this study clarifies the spatially specific targeted effects and protective mechanisms of Quanduzhong Capsules within pathological cartilage regions in knee osteoarthritis, providing theoretical and experimental support for the clinical application of this drug in the targeted therapy on the inflamed cartilage.
Animals ; Osteoarthritis, Knee/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Rats ; Male ; Humans ; Capsules ; Female ; Disease Models, Animal

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Aberrant expression of Colgalt1 is closely associated with tumorigenesis and tumor progres-sion;however,the mechanism by which it regulates macrophages to influence tumor development remains poorly understood.This study aimed to establish a macrophage-specific Colgalt1 gene knockout mouse model to delve into the mechanisms through which Colgalt1 modulates macrophage function and subse-quently affects the occurrence and progression of tumor-related diseases.Initially,Colgalt1flox+mice were generated using gene editing techniques,followed by crossing with Lyz2-Cre+mice,which exhibit tissue-specific expression in the myeloid lineage(including monocytes and mature macrophages).Through this strategy,mice with the genotype Colgalt1-/-Lyz2-Cre+were successfully obtained,achieving conditional knockout of the Colgalt1 gene in macrophages.Colgalt1flox/flox Lyz2-Cre-mice were used as control.PCR and agarose gel electrophoresis were employed to identify the Flox and Cre genotypes of the knockout mice.RT-qPCR and Western Blot techniques were utilized to detect the expression levels of Colgalt1 in BMDMs from knockout mice at both the mRNA and protein levels,respectively.Western Blot results re-vealed a significant downregulation of Colgaltl expression in BMDMs from knockout mice compared to controls(P＜0.01).RT-qPCR results demonstrated a significant reduction in Colgalt1 mRNA levels in BMDMs from knockout mice compared to contro1s(P＜0.001),while no significant differences in Col-galt1 mRNA expression were observed in liver,lung,or spleen tissues between the two groups.Addition-ally,immunohistochemistry was employed to detect Colgalt1 expression in liver-specific macrophages,re-vealing an absence of Colgalt l-positive staining in liver macrophages from knockout mice.HE staining was used to observe cellular morphology in liver tissues from both groups of mice,showing no significant differences in cellular morphology or obvious pathological changes in tissues and organs.Moreover,the o-verall survival of the mice was not affected.Finally,RT-qPCR was used to assess the expression of mac-rophage-related inflammatory factors in BMDMs from both groups of mice.The results indicated that com-pared to controls,knockout mice exhibited downregulated expression of TNF-α(P＜0.05)and signifi-cantly upregulated expression of IL-10(P＜0.01),Arginase1(P＜0.001),and CD206(P＜0.001)in BMDMs,suggesting an anti-inflammatory trend and M2 polarization of macrophages following Colgalt 1 knockout.In summary,this study successfully established a macrophage-specific Colgalt1 gene knockout mouse model,providing a more reliable experimental animal model for in-depth exploration of the specific roles of Colgalt1 in macrophage functional regulation and the pathogenesis of tumor-related diseases.This model holds promise for identifying novel therapeutic targets and strategies for tumors and other diseases.