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 develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Aim To explore the effect of ZLY18 on angiotensin II-induced cardiac fibrosis and the underlying mechanism. Methods Ang II was used to induce cardiac fibrosis in vitro and in vivo. Cardiac fibroblasts were divided into blank control group, model group and medicine group. The medicine group was subdivided into ZLY18(L)group, ZLY18(M)group and ZLY18(H)group. Compound ZLY18 was given 1, 2, 5 μmol·L-1 respectively. C57BL/6 mice were randomly divided into control group, model group and medicine group. The medicine group were subdivided into ZLY18(L)group, ZLY18(M)group and ZLY18(H)group. Compound ZLY18 was given 10,20 and 50 mg·kg-1 respectively. Both the model group and the medicine group were given with Ang II to induce cardiac fibrosis. The changes of protein levels were detected by Western blot and immunofluorescence. The changes of cardiac function indexes in C57BL/6 mice were detected by small animal echocardiography. The morphology, cell arrangement and collagen fibers of cardiac fibroblasts were observed by tissue section staining and other methods. Results The model of Ang II-induced myocardial fibrosis was successfully established at the cell and animal levels, and ZLY18 treatment improved the elevated fibrosis-related protein caused by Ang II and abnormal cardiac function in mice. Moreover, ZLY18 was able to inhibit the increased phosphorylation of TGF-1 and Smad3 caused by Ang II and increased Smad2/3 nuclear entry, suggesting that the antifibrotic effect of ZLY18 might be related to the activation of TGF-1/Smads signaling pathway. Conclusions ZLY18 has a protective effect on Ang II-induced cardiac fibrosis. ZLY18 may inhibit TGF-β/Smads signaling pathway activation to exert anti-fibrotic effects.

OBJECTIVE: This study was aimed at investigating the carrier rate of, and molecular variation in, α- and β-globin gene mutations in Hunan Province. METHODS: We recruited 25,946 individuals attending premarital screening from 42 districts and counties in all 14 cities of Hunan Province. Hematological screening was performed, and molecular parameters were assessed. RESULTS: The overall carrier rate of thalassemia was 7.1%, including 4.83% for α-thalassemia, 2.15% for β-thalassemia, and 0.12% for both α- and β-thalassemia. The highest carrier rate of thalassemia was in Yongzhou (14.57%). The most abundant genotype of α-thalassemia and β-thalassemia was -α ^3.7/αα (50.23%) and β ^IVS-II-654/β ^N (28.23%), respectively. Four α-globin mutations [CD108 (ACC>AAC), CAP +29 (G>C), Hb Agrinio and Hb Cervantes] and six β-globin mutations [CAP +8 (C>T), IVS-II-848 (C>T), -56 (G>C), beta nt-77 (G>C), codon 20/21 (-TGGA) and Hb Knossos] had not previously been identified in China. Furthermore, this study provides the first report of the carrier rates of abnormal hemoglobin variants and α-globin triplication in Hunan Province, which were 0.49% and 1.99%, respectively. CONCLUSION Our study demonstrates the high complexity and diversity of thalassemia gene mutations in the Hunan population. The results should facilitate genetic counselling and the prevention of severe thalassemia in this region.
Humans ; beta-Thalassemia/genetics* ; alpha-Thalassemia/genetics* ; Hemoglobinopathies/genetics* ; China/epidemiology* ; High-Throughput Nucleotide Sequencing

Objective Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are major public health and social issues worldwide. The long-term follow-up of COVID-19 with pulmonary TB (PTB) survivors after discharge is unclear. This study aimed to comprehensively describe clinical outcomes, including sequela and recurrence at 3, 12, and 24 months after discharge, among COVID-19 with PTB survivors. Methods From January 22, 2020 to May 6, 2022, with a follow-up by August 26, 2022, a prospective, multicenter follow-up study was conducted on COVID-19 with PTB survivors after discharge in 13hospitals from four provinces in China. Clinical outcomes, including sequela, recurrence of COVID-19, and PTB survivors, were collected via telephone and face-to-face interviews at 3, 12, and 24 months after discharge. Results Thirty-two COVID-19 with PTB survivors were included. The median age was 52 (45, 59) years, and 23 (71.9％) were men. Among them, nearly two-thirds (62.5％) of the survivors were moderate, three (9.4％) were severe, and more than half (59.4％) had at least one comorbidity (PTB excluded). The proportion of COVID-19 survivors with at least one sequela symptom decreased from 40.6％ at 3 months to 15.8％ at 24 months, with anxiety having a higher proportion over a follow-up. Cough and amnesia recovered at the 12-month follow-up, while anxiety, fatigue, and trouble sleeping remained after 24 months. Additionally, one (3.1％) case presented two recurrences of PTB and no re-positive COVID-19 during the follow-up period. Conclusion The proportion of long symptoms in COVID-19 with PTB survivors decreased over time, while nearly one in six still experience persistent symptoms with a higher proportion of anxiety. The recurrence of PTB and the psychological support of COVID-19 with PTB after discharge require more attention.

Altitude ; China ; Ethnic Groups ; Hemoglobins ; Humans ; Microcirculation

The present study was aimed to observe the characteristics of sublingual microcirculation and the changes of humoral factors in healthy people of three different high altitude populations. Three groups of healthy subjects in Guoluo area of Qinghai province (4 100 m) were included: Tibetan group: 30 Tibetans, (45.62 ± 10.15) years old; Han group: 22 two-generation of Han immigrants, (46.23 ± 8.59) years old; migrant group: 23 migrants living at high altitude for 2-5 years, (43.45 ± 8.31) years old. Blood routine test was performed to determine white blood cell (WBC) count, red blood cell (RBC) count, hemoglobin (HGB), hematocrit (HCT), platelet (PLT) count, and neutrophil (NEUT) count. The changes of serum humoral factors including endothelin-1 (ET-1), CD31, CD34, CD105, vascular endothelial growth factor (VEGF), nitric oxide (NO) and noradrenaline (NE) were detected by ELISA. Continuous noninvasive hemodynamics monitor was used to continuously measure the changes of systemic circulation indexes: cardiac output (CO), cardiac index (CI), heart rate (HR), stroke volume (SV), pulse pressure variation (PPV), systemic vascular resistance index (SVRI), and mean arterial pressure (MAP). Blood oxygen was measured by pulse oximeter. Sublingual microcirculation indexes including total vascular density (TVD), perfused vessel density (PVD), proportion of perfused vessels (PPV), and microvascular flow index (MFI) were determined by sidestream dark field imaging. The results showed that there were no difference in systemic circulation among the 3 groups. Compared with Tibetan group, TVD and PVD of microcirculation in Han group and migrant group were significantly increased (P < 0.05). Compared with Tibetan group and Han group, WBC, RBC, HGB and HCT of migrant group were significantly increased (P < 0.05). Compared with Han group and Migrant group, PLT of Tibetan group was significantly increased (P < 0.05). Compared with the Tibetan group, the levels of serum humoral factors CD105 and VEGF were significantly higher in the migrant group (P < 0.05), while compared with Han and migration groups, NO in Tibetan group was significantly increased (P < 0.05). It is suggested that there were significant differences in microcirculation (TVD, PVD), blood routine (WBC, RBC, HGB, HCT) and humoral factors (CD105, VEGF) among different populations in high altitude area. Importantly, the increased microcirculation, erythrocytosis and increased pro-angiogenic factors due to hypoxic environment were observed in long-term residents and migrants, except for permanent residents. These physiological changes have clinical significance in the treatment of septic shock and chronic altitude sickness for different plateau populations.
Adult ; Altitude ; China ; Hemoglobins ; Humans ; Hypoxia ; Microcirculation ; Middle Aged ; Tibet ; Vascular Endothelial Growth Factor A

OBJECTIVE: To evaluate the efficacy of Chinese medicine acupoint application (CMAA) combined with Western medicine for perennial allergic rhinitis (PAR) in children. METHODS: In this prospective, parallel, randomized, placebo-controlled and single-blind trial from August to September, 2017, 180 children with PAR were randomly assigned to an integrative group (CMAA and Montelukast), CMAA group (CMAA and placebo tablet), or Montelukast group (placebo CMAA and Montelukast). Participants were applied with CMAA for 6 sessions over 2 weeks, and/or Montelukast Chewable Tablet orally once daily for 12 weeks. The changes in severity of symptoms were measured by Visual Analog Scale (VAS) and rhinitis control assessment test (RCAT) at 0, 2, 4 and 12 weeks of treatment. Blood samples were collected for serum interleukin-4, interferon gamma γ and T helper type 1 (Th1)/Th2 flow cytometric analysis at the time points of 0, 4 and 12 weeks. RESULTS: Eight cases dropped out from the trial, 3 in the integrative group, 2 in the CMAA group and 3 in the Montelukast group. The VAS scores decreased significantly while the RCAT scores increased significantly in all three groups at 4 and 12 weeks compared with baseline (P<0.01 or P<0.05). The VAS scores were significantly lower while the RCAT scores were significantly higher in the integrative and CMAA groups than the Montelukast group at 2 and 4 weeks (P<0.01 or P<0.05). At 2, 4 and 12 weeks, the scores of nasal congestion, sneezing, sleep problem, and rhinitis symptom control in the integrative and CMAA groups increased significantly compared with baseline (P<0.01 or P<0.05). The least percentages of Th2 and the most alleviated Th2 shift (highest Th1/Th2) were observed in the integrative group at 12 weeks compared with the other two groups (P<0.05). CONCLUSION The combination of CMAA with Montelukast might be more effective and appropriate than either option alone for children with PAR. (Registered at Chinese Clinical Trial Register, registration No. ChiCTR-IOR-17012434).