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.

To address the constrains imposed by insufficient hydrogen peroxide (H2O2) and high glutathione (GSH) expression in tumor cells on the efficacy of chemodynamic therapy (CDT), zeolitic imidazolate framework-8 (ZIF-8) loaded with disulfiram (DSF) and 3-amino-1,2,4-triazole (3-AT) was synthesized via a one-pot approach. Subsequently, hyaluronic acid (HA)-modified cupric peroxide (CuO2) was in-situ grown on its surface through biomineralization to construct a multifunctional copper-based nanozyme ADZCH (3-AT/DSF@ZIF-8@CuO2-HA). This nanoplatform disrupts the intratumoral H2O2 homeostasis, depletes GSH, and synchronously delivers DSF and Cu2+ via cascade catalysis, thereby enhancing CDT and sensitizing tumors to DSF-based chemotherapy. The results of physicochemical characterization indicated that ADZCH presented a uniform core-shell structure with favorable dispersibility. Its particle size and Zeta potential were 196.5 nm and −19.5 mV, respectively. It possessed a microporous structure with a specific surface area of 81.8600 m2/g, and demonstrated efficient loading capacity for DSF and 3-AT, achieving drug loading efficiencies of 5.91% and 45.07%, respectively. Moreover, ADZCH can continuously and slowly release drugs in an acidic environment and maintain good stability under diverse physiological conditions. In vitro functional assays verified that ADZCH catalytically generated H2O2 and hydroxyl radicals while concurrently depleting GSH in a concentration- and incubation time-dependent manner. Cellular uptake experiments showed that HA modification significantly improved the uptake of nanoparticles by 4T1 cells. Cytotoxicity tests showed that 80 μg/mL ADZCH had a significant cytotoxic effect on 4T1 cells but no significant toxicity on L929 cells. DCFH-DA probe detection indicated that ADZCH could significantly induce intracellular reactive oxygen species (ROS) generation, thereby enhancing CDT efficacy. Live/dead staining experiments showed that ADZCH efficiently induced apoptosis, with the proportion of dead cells reaching 94.74%, demonstrating its promising potential for anti-tumor applications.This study provides new research ideas and experimental basis for overcoming the tumor microenvironment barrier and enhancing the anti-tumor effect of CDT combined with chemotherapy.

Yttrium-90 microsphere selective internal radiation therapy (SIRT), also known as transarterial radioembolization, has become one of the pivotal treatments for liver cancer, particularly for selected advantageous patient groups. This review summarizes the characteristics of different patients with liver cancer that could obtain maximum benefit from SIRT and discusses key factors affecting efficacy and safety, including tumor characteristics, liver function, patient performance status, and treatment intent. In evaluating appropriate candidates, mapping serves as a crucial simulation procedure to assess tumor vascular anatomy, predict lung shunting, and guide catheter positioning and dose planning. This procedure substantially enhances therapeutic precision while minimizing the risk of nontarget radiation-related adverse events, such as radiation-induced pneumonitis and gastrointestinal toxicity. Several studies have suggested that SIRT is not only suitable for patients with early or limited hepatocellular carcinoma but can also be used as a bridging therapy for liver transplantation and conversion therapy for unresectable liver cancers. In combination with systemic treatments, SIRT has demonstrated survival benefits in patients with unresectable liver cancer. This review also highlights the importance of further optimizing patient screening through personalized dosimetry and mapping to ensure the precision and safety of treatment. A thorough review of relevant literature and clinical practice offers clinicians comprehensive suggestions on patient screening and clarifies the promise of SIRT in the liver cancer population.

Background and aims The mechanism of cholestatic liver injury (CLI) is unclear, and effective therapies are lacking. While peroxisome proliferator-activated receptor alpha (PPARα) agonists show potential hepatoprotective effect and pyroptosis is implicated in hepatocellular damage, how PPARα activation mitigates lithocholic acid (LCA)-induced pyroptosis remains unknown. Methods The hepatoprotective effect of PPARα agonists was evaluated in a mouse model of intrahepatic cholestasis induced by LCA. Liver injury was assessed via serum biochemistry, hematoxylin and eosin and TUNEL staining, and electron microscopy. Pyroptosis pathways were analyzed using real-time quantitative polymerase chain reaction, Western blot, and co-immunoprecipitation. Results Combined morphological, histopathological, and biochemical analyses confirmed that PPARα activation protects against CLI. Compared with LCA treatment alone, PPARα activation significantly attenuated the elevation of serum lactate dehydrogenase (LDH), the increased TUNEL-positive cells, and the formation of hepatocyte membrane pores. Mechanistically, PPARα activation suppressed both NOD-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis and apoptosis protease-activating factor-1 (APAF-1)/CASPASE-3/GSDME-mediated pyroptosis. Furthermore, PPARα agonist pretreatment inhibited activation of the nuclear factor-kappa B (NF-κB) and forkhead box O1 (FOXO1) signaling pathways. Conclusions PPARα protects against LCA-induced CLI by inhibiting both NLRP3 inflammasome-mediated pyroptosis associated with NF-κB and APAF-1/CASPASE-3/GSDME-mediated pyroptosis associated with the FOXO1 signaling pathway.

Objective To screen for key genes and important pathways common for different etiologies of acute kidney injury(AKI)by pathway-associated deep neural network and multiple machine learning algorithms.Methods AKI microarray datasets GSE30718,GSE37838,GSE53769,GSE108113,GSE125779,GSE99325,and GSE174020 downloaded from the Gene Expression Omnibus(GEO)database were merged,including 60 kidney samples from AKI patients and 79 kidney samples from healthy controls.They were divided(8∶2)into training sets and test sets,and were used to train and evaluate pathway-associated deep neural network and 4 machine learning algorithms,including least absolute shrinkage and selection operator(LASSO),random forest(RF),support vector machine-recursive feature elimination(SVM-RFE),and extreme gradient boosting(XgBoost),to screen for common key genes and pathways of different etiologies of AKI.The downloaded datasets GSE99340 and GSE1563 were merged,including 43 kidney samples from AKI patients and 36 kidney samples from healthy controls,which were used as external validation sets for LASSO model and nomogram performance test based on the final screened genes.The pathway-associated deep neural network and machine learning algorithms were evaluated using receiver operating characteristic curves,precision,recall,accuracy,and F1-score.The immune cell infiltration characteristics were explored in AKI via cell-type identification by estimating relative subsets of RNA transcripts(CIBERSORT),and Pearson correlation coefficients were used to evaluate the correlation between the final screened common key genes and immune cell infiltration levels.Results The pathway-associated deep neural network trained by 5-fold cross validation produced an area under curve(AUC)of 0.914 5±0.007 0,a precision of 0.750 0±0.044 0,a recall of 0.923 1±0.048 0,an accuracy of 0.838 7±0.016 0,and an F1-score of 0.827 6±0.020 0 in the test set,yielding a robust and highly accurate classification performance for AKI,and identified key pathways and a subset of candidate genes.The 4 machine learning algorithms all achieved high discriminative performance for AKI in the test set with AUC≥0.860,precision≥0.750,recall≥0.800,and F1-score≥0.774,and screened 7 common key genes for AKI with different etiologies,including CD86,C-X-C motif chemokine ligand 10(CXCL10),dynamin 2(DNM2),proto-oncogene FOS,transcription factor 12(TCF12),VGF nerve growth factor inducible(VGF),and A kinase anchoring protein 5(AKAP5).Based on the final screened common key genes,the LASSO model had an AUC of 0.940 4 for the test set and an AUC of 0.944 4 for the external validation,and the model showed a very high discriminatory ability for the AKI,which demonstrated the overall regulatory performance of the genes.The nomogram constructed based on the screened 7 genes demonstrated the highest classification performance with an AUC of 0.928 9,validating the outstanding contribution and overall action performance of the screened individual genes.Immune cell infiltration analysis showed that there were significant differences in B cells na?ve,mast cells activated,monocytes,macrophages M1,B cells memory,and dendritic cells activated between AKI samples and healthy control samples(all P＜0.05).Macrophages M1 and monocytes were positively correlated with CD86 and CXCL10,mast cells activated were positively correlated with FOS,and B cells na?ve were negatively correlated with CD86 and CXCL10(all P＜0.01).Mast cells activated were positively correlated with VGF and negatively correlated with CD86 and TCF12,while memory B cells were positively correlated with CD86(all P＜0.05).Conclusion Strategy combining pathway-associated deep neural network and multiple machine learning classifiers can mine high-value key genes from high-dimensional,complex and heterogeneous transcriptomic data as potential targets for therapeutic interventions in AKI.

Objective To prepare an advanced irrigation device equipped with a specialized nozzle designed to access the pericoronal blind sac of impacted wisdom teeth,and evaluate its efficacy in plaque removal.Methods 3D-printer was utilized to develop pericoronal models simulating mandibular wisdom teeth with high vertical impaction and varying soft tissue coverage(0%,25%,50%,and 75%,respectively)and personalized pericoronal irrigator.The obtained pericoronal models were randomly divided into 3 groups,treated with personalized precision pericoronal flushing,routine flushing and brushing,respectively,and,under a pressures of 45 psi or 75 psi(1 psi=6 894.76 Pa),were performed to clean the test surfaces of plaque-biofilm-obstructed wisdom teeth,respectively.Images of the tooth surfaces were analyzed before and after cleaning,and the cleaning rate was calculated using Python software.Results For wisdom teeth with 50%and 75%pericoronal soft tissue coverage,at a pressure of 45 psi,the precision pericoronal flushing group demonstrated significantly higher cleaning rate when compared to the brushing and routine flushing groups(P<0.05).At 0%and 25%pericoronal soft tissue coverage,the precision pericoronal flushing and brushing groups exhibited significantly higher cleaning rates than the routine flushing group(P<0.05).Furthermore,at 75 psi,the precision pericoronal flushing group achieved a significantly higher cleaning rate across all 4 levels of pericoronal soft tissue coverage than both the routine flushing and brushing groups(P<0.05).Notably,the precision pericoronal flushing group at 75 psi obtained significantly higher cleaning rate than at 45 psi across 0%,25%,and 50%pericoronal soft tissue coverage(P<0.05).Conclusion The precision pericoronal irrigation device demonstrates significantly superior cleaning efficacy for vertically impacted mandibular third molars compared to conventional irrigation devices and manual tooth brushing.It is an effective device for preventing and treating pericoronitis.

Ischemic stroke (IS) is a globally life-threatening disease. Presently, few therapeutic medicines are available for treating IS, and rt-PA is the only drug approved by the US Food and Drug Administration (FDA) in the US. In fact, many agents showing excellent neuroprotection but no blood flow-improving activity in animals have not achieved ideal clinical efficacy, while thrombolytic drugs only improving blood flow without neuroprotection have limited their wider application. To address these challenges and meet the huge unmet clinical need, we have designed and identified a novel compound AAPB with dual effects of neuroprotection and cerebral blood flow improvement. AAPB significantly reduced cerebral infarction and neural function deficit in tMCAO rats, pMCAO rats, and IS rhesus monkeys, as well as displayed exceptional safety profiles and excellent pharmacokinetic properties in rats and dogs. AAPB has now entered phase I of clinical trials fighting IS in China.

Peptide receptor radionuclide therapy (PRRT) with radiolabeled SSTR2 agonists is a treatment option that is highly effective in controlling metastatic and progressive neuroendocrine tumors (NETs). Previous studies have shown that an SSTR2 agonist combined with albumin binding moiety Evans blue (denoted as ¹⁷⁷Lu-EB-TATE) is characterized by a higher tumor uptake and residence time in preclinical models and in patients with metastatic NETs. This study aimed to enhance the in vivo stability, pharmacokinetics, and pharmacodynamics of ¹⁷⁷Lu-EB-TATE by replacing the maleimide-thiol group with a polyethylene glycol chain, resulting in a novel EB conjugated SSTR2-targeting radiopharmaceutical, ¹⁷⁷Lu-LNC1010, for PRRT. In preclinical studies, ¹⁷⁷Lu-LNC1010 exhibited good stability and SSTR2-binding affinity in AR42J tumor cells and enhanced uptake and prolonged retention in AR42J tumor xenografts. Thereafter, we presented the first-in-human dose escalation study of ¹⁷⁷Lu-LNC1010 in patients with advanced/metastatic NETs. ¹⁷⁷Lu-LNC1010 was well-tolerated by all patients, with minor adverse effects, and exhibited significant uptake and prolonged retention in tumor lesions, with higher tumor radiation doses than those of ¹⁷⁷Lu-EB-TATE. Preliminary PRRT efficacy results showed an 83% disease control rate and a 42% overall response rate after two ¹⁷⁷Lu-LNC1010 treatment cycles. These encouraging findings warrant further investigations through multicenter, prospective, and randomized controlled trials.

Ovarian tumor (OT) is the most lethal form of gynecologic malignancy, with minimal improvements in patient outcomes over the past several decades. Metastasis is the leading cause of ovarian cancer-related deaths, yet the underlying mechanisms remain poorly understood. Psychological stress is known to activate the glucocorticoid receptor (NR3C1), a factor associated with poor prognosis in OT patients. However, the precise mechanisms linking NR3C1 signaling and metastasis have yet to be fully elucidated. In this study, we demonstrate that chronic restraint stress accelerates epithelial-mesenchymal transition (EMT) and metastasis in OT through an NR3C1-dependent mechanism involving nuclear protein 1 (NUPR1). Mechanistically, NR3C1 directly regulates the transcription of NUPR1, which in turn increases the expression of snail family transcriptional repressor 2 (SNAI2), a key driver of EMT. Clinically, elevated NR3C1 positively correlates with NUPR1 expression in OT patients, and both are positively associated with poorer prognosis. Overall, our study identified the NR3C1/NUPR1 axis as a critical regulatory pathway in psychological stress-induced OT metastasis, suggesting a potential therapeutic target for intervention in OT metastasis.