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.

Moyamoya vasculopathy (MMV) refers to a chronic cerebrovascular disease characterized by progressive stenosis or occlusion of the arteries around the circle of Willis, accompanied by the formation of collateral vessels. In traditional Chinese medicine (TCM), MMV falls into the categories of “stroke”, “vertigo”, and “headache”, among others. Its core etiology is related to congenital insufficiency and acquired malnourishment, with wind, fire, phlegm, deficiency, and stasis as the key pathogenic factors. Current Western medical diagnosis and treatment of MMV remain suboptimal, and TCM pattern differentiation has yet to be standardized. The most common clinical patterns are liver yang hyperactivity (associated with hemorrhagic symptoms) and qi deficiency with blood stasis (associated with ischemic symptoms). Treatment modalities include acupuncture, Chinese herbal formulas, and TCM–Western medicine integrated therapy. Clinical trial results have confirmed that integrated therapy offers certain benefits for MMV; however, there is a lack of relevant diagnostic and treatment guidelines as well as in-depth mechanistic studies. This article reviews the progress in TCM and Western medicine for the diagnosis and treatment of MMV and proposes a trinity diagnostic and therapeutic approach integrating “disease differentiation–pattern differentiation–staging”, which provides a reference for TCM–Western medicine integration management.

Kounis syndrome is an acute coronary syndrome triggered by an allergic reaction, which is clinically rare and frequently subject to misdiagnosis or missed diagnosis. This article presents a case report of a 70-year-old male patient who developed a rash, pruritus, and chest pain following colon polyp resection. Coronary angiography revealed occlusion of the left anterior descending artery, and blood flow was restored after stent implantation. However, the patient experienced recurrent symptoms accompanied by loss of consciousness. Drug skin tests confirmed positive reactions to chlorhexidine and fondaparinux sodium, leading to a diagnosis of type Ⅱ Kounis syndrome. By avoiding allergenic drugs and combining antihistamines with symptomatic treatment to correct myocardial ischemia, the patient′s clinical symptoms significantly improved, and he eventually recovered and was discharged from the hospital. This case underscores the importance of maintaining vigilance for this syndrome in patients with allergies accompanied by chest pain and promptly identifying and avoiding allergens.

ObjectiveThe U6 promoter is an essential element for driving sgRNA expression in the clustered regularly interspaced short palindromic repeat sequences/CRISPR-associated protein 9（CRISPR/Cas9）gene editing system in dicotyledonous plants. Endogenous U6 promoters typically exhibit higher transcriptional activity， which can significantly improve gene editing efficiency. This study aims to identify endogenous U6 promoters in Artemisia annua to optimize its CRISPR/Cas9 gene editing system， which holds significant importance for its molecular breeding. MethodsOn the basis of the highly conserved U6 snRNA sequences in Arabidopsis thaliana， endogenous U6 promoters were screened in the A. annua genome. Expression vectors were constructed with candidate AaU6 promoter driving the firefly luciferase （LUC） reporter gene， and then transiently transformed into Nicotiana benthamiana. Transcriptional activities of the promoters were measured and compared by in vivo imaging and the Dual Luciferase Reporter assay. ResultsEight endogenous U6 promoters were successfully cloned from A. annua. Sequences alignment revealed that all these promoters contained the two conserved cis-acting elements， upstream sequence element （USE） and TATA-box， which affected their transcriptional activity. Dual-luciferase activity assays indicated that the transcriptional activities of AaU6-3， AaU6-1， and AaU6-5 were significantly higher than that of the Arabidopsis AtU6-26 promoter， with AaU6-3 exhibiting the highest activity. ConclusionThis study identified three endogenous AaU6 promoters with high transcriptional activity in A. annua， providing key functional elements for establishing an efficient gene editing system in A. annua. These findings will contribute to advancing precision molecular breeding and high-quality germplasm innovation in A. annua.

Sennoside A (SA), a typical prodrug, exerts its laxative effect only after its transformation into rheinanthrone catalyzed by gut microbial hydrolases and reductases. Hydrolases have been identified, but reductases remain unknown. By linking a photoreactive group to the SA scaffold, we synthesized a photoaffinity probe to covalently label SA reductases and identified SA reductases using activity-based protein profiling (ABPP). From lysates of an active strain, Bifidobacterium pseudocatenulatum (B. pseudocatenulatum), 397 proteins were enriched and subsequently identified using mass spectrometry (MS). Among these proteins, chromate reductase/nicotinamide adenine dinucleotide (NADH) phosphate (NADPH)-dependent flavin mononucleotide (FMN) reductase/oxygen-insensitive NADPH nitroreductase (nfrA) was identified as a potent SA reductase through further bioinformatic analysis and The Universal Protein Resource (UniProt) database screening. We also determined that recombinant nfrA could reduce SA. Our study contributes to further illuminating mechanisms of SA transformation to rheinanthrone and simultaneously offers an effective method to identify gut bacterial reductases.

OBJECTIVE: The study aim was to investigate the effects of exposure to multiple environmental organic pollutants on cardiopulmonary health with a focus on the potential mediating role of oxidative stress. METHODS: A repeated-measures randomized crossover study involving healthy college students in Beijing was conducted. Biological samples, including morning urine and venous blood, were collected to measure concentrations of 29 typical organic pollutants, including hydroxy polycyclic aromatic hydrocarbons (OH-PAHs), bisphenol A and its substitutes, phthalates and their metabolites, parabens, and five biomarkers of oxidative stress. Health assessments included blood pressure measurements and lung function indicators. RESULTS: Urinary concentrations of 2-hydroxyphenanthrene (2-OH-PHE) ( β = 4.35% [95% confidence interval ( CI): 0.85%, 7.97%]), 3-hydroxyphenanthrene ( β = 3.44% [95% CI: 0.19%, 6.79%]), and 4-hydroxyphenanthrene (4-OH-PHE) ( β = 5.78% [95% CI: 1.27%, 10.5%]) were significantly and positively associated with systolic blood pressure. Exposures to 1-hydroxypyrene (1-OH-PYR) ( β = 3.05% [95% CI: -4.66%, -1.41%]), 2-OH-PHE ( β = 2.68% [95% CI: -4%, -1.34%]), and 4-OH-PHE ( β = 3% [95% CI: -4.68%, -1.29%]) were negatively associated with the ratio of forced expiratory volume in the first second to forced vital capacity. These findings highlight the adverse effects of exposure to multiple pollutants on cardiopulmonary health. Biomarkers of oxidative stress, including 8-hydroxy-2'-deoxyguanosine and extracellular superoxide dismutase, mediated the effects of multiple OH-PAHs on blood pressure and lung function. CONCLUSION Exposure to multiple organic pollutants can adversely affect cardiopulmonary health. Oxidative stress is a key mediator of the effects of OH-PAHs on blood pressure and lung function.
Humans ; Oxidative Stress/drug effects* ; Male ; Cross-Over Studies ; Female ; Young Adult ; Environmental Pollutants/toxicity* ; Environmental Exposure/adverse effects* ; Biomarkers/blood* ; Adult ; Blood Pressure/drug effects* ; Polycyclic Aromatic Hydrocarbons/urine* ; Beijing

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective To explore and analyze the characteristics and transmission routes of carbapenem-resistant Klebsiella pneumoniae(CRKP)strains in intensive care unit(ICU).Methods From January to October 2023,17 clinical infection isolates(clinical infection group),5 active screening isolates(active screening group),and 7 envi-ronmental isolates(environmental group)of CRKP in the liver surgery ICU of a hospital were selected and analyzed by whole-genome sequencing.The differences in resistance genes,virulence genes,and sequence typing(ST)were compared,and transmission routes were analyzed based on the phylogenetic tree.Results 29 strains of CRKP car-ried 4-18 resistance genes and 52-98 virulence genes,respectively.There were no statistically significant diffe-rences in genotype distribution of resistance genes,the number of virulence genes,and gene types among three groups of CRKP(all P＞0.05).ST showed that 29 CRKP strains mainly consisted of two categories:ST11 and ST15.Based on the phylogenetic tree constructed from the core genome,there were 7 highly homologous groups of CRKP,among which 4 groups had clear epidemiological associations.Conclusion CRKP in ICU carries more re-sistance and virulence genes,and some strains are highly homologous in ST and phylogenetic tree,which may lead to cross transmission.In the future,prevention and control measures should be strengthened to reduce the trans-mission of CRKP.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.