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 describe the distribution characteristics and trends of mortality and spatial aggregation of esophageal cancer in Shandong Province from 1970 to 2021.Methods:The mortality data of esophageal cancer were obtained from the death registration system of Shandong Province and three national all-cause mortality retrospective surveys. The crude mortality rate (CMR) and age-standardized mortality rate (ASMR, the Segi′s world standard population) were used to describe the mortality of esophageal cancer. Mortality differential decomposition was applied to quantify the contributions of demographic and non-demographic factors. The death levels of esophageal cancer in different counties (cities and districts) in Shandong Province from 1970 to 1974 and 2020 to 2021 were visualized by the ArcGIS 10.8 software, and global and local autocorrelation analyses were conducted by using the GeoDa 1.12 software.Results:The CMR of esophageal cancer in Shandong Province increased first and then decreased from 1970 to 2021. The CMR of esophageal cancer decreased from 17.59/100 000 in the period of 1970—1974 to 14.32/100 000 in the period of 2020—2021. The ASMR of esophageal cancer decreased from 20.04/100 000 in the period of 1970—1974 to 6.53/100 000 in the period of 2020—2021. Compared with the period of 1970—1974, both demographic and non-demographic factors contributed to the increase in esophageal cancer mortality rate from 1990 to 1992. However, demographic factors continued to contribute to the increase in esophageal cancer mortality rate from 2004 to 2005, 2011 to 2013, and 2020 to 2021, while non-demographic factors contributed to the continuous decrease in esophageal cancer mortality rate. The global autocorrelation analysis results showed that the Moran′s I index of ASMR of esophageal cancer in each county (city, district) of Shandong Province from 1970 to 1974 and from 2020 to 2021 were 0.67 and 0.57, respectively. Local autocorrelation analysis showed that there were 19 and 13 areas of high-high clustering of esophageal cancer in the periods of 1970—1974 and 2020—2021, respectively, with 12 overlapping counties (cities, districts). Conclusion:From 1970 to 2021, the CMR of esophageal cancer increases first and then decreases, while the ASMR of esophageal cancer gradually decreases in Shandong Province. The distribution of esophageal cancer mortality has significant spatial aggregation and changes over time.

Objective:To evaluate the effects of chlorinated organophosphate flame retardants (Cl-OPFRs) via respiratory and digestive tract exposure on multiple organs in mice.Methods:A short-term repeated exposure model of tris(2-chloroethyl) phosphate (TCEP), tris(1-chloro-2-propyl) phosphate (TCIPP) and tris(1, 3-dichloro-2-propyl) phosphate (TDCIPP) in mice was established through intratracheal instillation and oral gavage administration. The exposure doses were 0.7, 1 and 2 mg·kg -1·day -1, respectively, with continuous administration for 14 days. The organs of the heart, liver, spleen, lung, kidney, stomach, large intestine, small intestine, bladder and testis were collected and weighed to calculate the organ coefficients. The pathological and histological changes were observed by hematoxylin-eosin staining to quantitatively assess the effects of the three Cl-OPFRs on the various organs by using the pathology score. Results:Analysis of organ coefficients in tracheal drip-treated mice showed that the organ coefficients in the testes of the TCEP, TCIPP and TDCIPP groups were lower than those in the control group ( PTCEP-testis=0.045, PTCIPP-testis=0.012 and PTDCIPP-testis<0.001). The organ coefficients were lower in the lungs and small intestines of the TCEP group ( PTCEP-lung=0.006, PTCEP-small intestine=0.042). The organ coefficients for the stomach and large intestine were higher in the TDCIPP group ( PTDCIPP-stomach=0.014, PTDCIPP-large intestine=0.049). Analyses of gavage-contaminated mice showed that the organ coefficients for liver, stomach and small intestine in the TCEP and TDCIPP groups were higher than those in the control group ( PTCEP-liver=0.007, PTCEP-stomach=0.003, PTCEP-small intestine<0.001, PTDCIPP-liver=0.001, PTDCIPP-stomach=0.004, and PTDCIPP-small intestine<0.001). Histopathological analyses of the organs of tracheal drip dyed mice showed significant pathological damage in the lung tissue of the TCIPP group, mainly in the form of thickening of the interstitium, infiltration of inflammatory cells and alveolar collapse. The results of the analysis of gavage poisoned mice showed that TCIPP exposure could lead to blurring of the red and white medullary boundaries of spleen tissues, destruction of white medullary structures, etc., and induce small intestinal cryptitis. TDCIPP induced significant pathological damage to the liver tissues of mice, which mainly included cytoplasmic washout, infiltration of inflammatory cells, acute inflammation, and other injurious effects. Significant pathological damage to the intestinal tissues of mice was also observed. Conclusions:This study demonstrates that the toxic effects of Cl-OPFRs are significantly associated with exposure routes and compound specificity. Respiratory exposure predominantly induces TCIPP-mediated pulmonary injury, while digestive exposure causes TDCIPP-driven hepatointestinal toxicity. These findings provide preliminary evidence for the toxicity screening of Cl-OPFRs.

Objective:Acute kidney injury(AKI) is a common complication after cardiac surgery, and associated with increased risk of development of chronic kidney disease and mortality in the long term. Neutrophil percentage-to-albumin ratio(NPAR) is a new inflammatory marker that has been used to predict the poor prognosis of patients with cardiovascular disease or shock. However, the relationship between NPAR and AKI in patients with cardiac surgery has not been established. The aim was to evaluate the relationship between NPAR and AKI after cardiac surgery.Methods:Data of all adult patients underwent cardiac surgery with cardiopulmonary bypass from January 1, 2006 to December 31, 2018 were extracted from electronic medical record system of the Guangdong Provincial People's Hospital and retrospectively analyzed. The outcome of interest was AKI diagnosed by using the criteria of Kidney Disease Improving Global Outcomes. Logistic regression was used to assess the relationship between NPAR and postoperative AKI while adjust for potential confounders. In addition, restricted cubic spline(RCS) was utilized to provide a flexible description of the association of the preoperative NPAR and AKI. Results:Totally, 24 178 patients were analyzed. The incidence of AKI was 30.1%. Compared with patients without AKI, those with AKI were older and had higher rates of males, left ventricular ejection fraction(LVEF) less than or equal to 0.60, estimated glomerular filtration rate less than 90 ml·min -1·1.73 m -2, hypertension, diabetes, emergency surgery, preoperative critical illness, and reoperation. The baseline serum creatinine, serum uric acid, cardiopulmonary bypass time and postoperative mechanical ventilation time were higher or longer in AKI patients than those in none AKI patients. Then, patients were divided into four groups based on NPAR quartiles. After adjusting for confounding factors using logisitc regression, compared with patients with NPAR in group 3(1.551.97), respectively. The RCS showed a U-shaped nonlinear relationship( P for nonlinear less than 0.001) between NPAR and AKI after adjusting for potential confounding factors. RCS indicated the NPAR of 1.77 was the lowest risk for AKI. When NPAR less than 1.77, per 1-standard deviation(SD) increase in NPAR, the risk of AKI decreased by 23.2%( OR=0.768, 95% CI: 0.687, 0.859). Conversely, when NPAR above 1.77, per one SD increase in NPAR, the risk of AKI increased by 25.9%( OR=1.259, 95% CI: 1.166, 1.36). Conclusion:A U-shaped relationship between NPAR and AKI after cardiac surgery was shown. The appropriate value for NPAR might be 1.77. The risk of AKI might be increased, when less or above this value. NPAR might be used as an effective and economical tool to identify AKI risk among individuals with cardiac surgery, as well as to formulate individualized prevention and intervention strategy.

Objective:To explore the mediating effect of disease uncertainty on cancer-related fatigue and quality of life in postoperative thyroid cancer patients, and to provide assistance for health management of thyroid cancer patients.Methods:From January 2023 to June 2024, thyroid cancer patients who underwent surgical treatment in Xiang′an Hospital of Xiamen University were selected by convenience sampling method. Conduct a survey using The general information questionnaire, Cancer Quality of Life for Thyroid Cancer 34, Cancer Fatigue Scale, and Mishel Uncertainty in Illness Scale were used to conduct cross-sectional investigation. SPSS 22.0 was used to statistically analyze the relationship between the three factors, and AMOS 24.0 software was used to establish a structural equation model to analyze the mediating effect of disease uncertainty on cancer-related fatigue and quality of life in postoperative thyroid cancer patients.Results:Finally, 495 valid questionnaires were ultimately collected. Among 495 postoperative patients with thyroid cancer, there were 135 males and 360 females, with an age of (39.58 ± 12.97) years old. The uncertainty score of postoperative thyroid cancer patients was (60.98 ± 11.61) points, cancer-related fatigue score was (42.85 ± 7.35) points, and quality of life score was (57.51 ± 9.81) points. Pearson correlation analysis showed that cancer-related fatigue was positively correlated with disease uncertainty ( r=0.392, P<0.01) and negatively correlated with quality of life ( r=-0.465, P<0.01). Disease uncertainty was negatively correlated with quality of life ( r=-0.378, P<0.01). Disease uncertainty partially mediated the relationship between cancer-related fatigue and quality of life in postoperative thyroid cancer patients, with a mediation effect value of -0.14, accounting for 32.56% of the total effect. Conclusions:The disease uncertainty score and the quality of life score of postoperative thyroid cancer patients were at a moderate level, the cancer-related fatigue score was at an upper moderate level. Disease uncertainty is an important mediating variable between cancer-related fatigue and quality of life in postoperative thyroid cancer patients. Improving disease uncertainty in postoperative thyroid cancer patients can reduce cancer-related fatigue and improve their quality of life.

Objective:To investigate the effects of toosendanin(TSN)on the proliferation,apoptosis,migration and invasion of esophageal squamous cell carcinoma(ESCC)KYSE150 cells,and to elucidate its underlying molecular mechanisms.Methods:CCK-8 assay,colony formation assay,and EdU assay were used to assess the effects of varying TSN concentrations(0.062 5,0.125,and 0.25 μmol/L)on KYSE150 cell proliferation.The impacts of TSN on the apoptosis,migration,and invasion of KYSE150 cells were evaluated using flow cytometry,wound healing assay,and Transwell chamber assay,respectively.The expression of hypoxia-inducible factor 1 alpha(HIF1A)in esophageal cancer tissues was analyzed using the GEPIA database.qPCR was used to detect the expression level of HIF1A mRNA in human esophageal epithelial Het-1A and KYSE150 cells,and in TSN-treated KYSE150 cells.Western blot(WB)was performed to detect the effects of TSN on the upstream signaling pathway AKT/mTOR of HIF1A and the expression of downstream proteins related to cell migration,invasion,and apoptosis.Results:TSN of varying concentrations significantly inhibited proliferation,migration,and invasion of KYSE150 cells and promoted apoptosis in a dose-dependent manner(P<0.05 or P<0.01).HIF1A mRNA was highly expressed in KYSE150 cells,and its expression was significantly downregulated after TSN treatment(P<0.05 or P<0.01).TSN markedly downregulated the expression of HIF1A and key upstream signaling proteins p-AKT and p-mTOR.In addition,TSN significantly suppressed the expression of downstream proteins associated with cell migration,invasion,and apoptosis,including N-cadherin,vimentin,Bcl-2,and caspase-3,while upregulating the expression of E-cadherin(P<0.05 or P<0.01).Conclusion:TSN inhibits the proliferation,migration,and invasion,and induces apoptosis in ESCC KYSE150 cells by down-regulating HIF1A expression through suppression of the AKT/mTOR signaling pathway.

Background: Despite the fact that an increasing number of studies have focused on developing therapies for acute lung injury, managing acute respiratory distress syndrome (ARDS) remains a challenge in intensive care medicine.Whether the pathology of animal models with acute lung injury in prior studies differed from clinical symptoms of ARDS, resulting in questionable management for human ARDS. To evaluate precisely the therapeutic effect of trans‑ planted stem cells or medications on acute lung injury, we developed an animal model of severe ARDS with lower lung function, capable of keeping the experimental animals survive with consistent reproducibility. Establishing this animal model could help develop the treatment of ARDS with higher efficiency. Results: In this approach, we intratracheally delivered bleomycin (BLM, 5 mg/rat) into rats’ left trachea via a needle connected with polyethylene tube, and simultaneously rotated the rats to the left side by 60 degrees. Within sevendays after the injury, we found that arterial blood oxygen saturation ­(SpO2 ) significantly decreased to 83.7%, partial pressure of arterial oxygen ­(PaO2 ) markedly reduced to 65.3 mmHg, partial pressure of arterial carbon dioxide ­(PaCO2 )amplified to 49.2 mmHg, and the respiratory rate increased over time. Morphologically, the surface of the left lung appeared uneven on Day 1, the alveoli of the left lung disappeared on Day 2, and the left lung shrank on Day 7. A his‑ tological examination revealed that considerable cell infiltration began on Day 1 and lasted until Day 7, with a larger area of cell infiltration. Serum levels of IL-5, IL-6, IFN-γ, MCP-1, MIP-2, G-CSF, and TNF-α substantially rose on Day 7. Conclusions This modified approach for BLM-induced lung injury provided a severe, stable, and one-sided (left-lobe) ARDS animal model with consistent reproducibility. The physiological symptoms observed in this severe ARDS animal model are entirely consistent with the characteristics of clinical ARDS. The establishment of this ARDS animal model could help develop treatment for ARDS.

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index