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.

This study explored the prescription pattern of traditional Chinese medicine(TCM) in the treatment of hypertensive left ventricular hypertrophy(LVH), so as to provide a relevant theoretical basis for the clinical diagnosis and treatment of hypertensive LVH. The study systematically searched the databases of CNKI, Wanfang, VIP, and SinoMed to screen out the qualified literature on TCM treatment of hypertensive LVH and used Microsoft Excel 2021 to establish the relevant prescription database. It also counted the frequency, property, flavor, and meridian affiliation of TCM in the prescriptions and classified their efficacy. The study used Lantern 5.0 and Rstudio software to analyze the hidden structural models and association rules of the high-frequency TCM with a frequency of >3.50% and adopted Origin 2024 software to visualize the data, so as to explore the prescription pattern of TCM in treating hypertensive LVH. The results showed that a total of 128 TCM prescriptions were included, involving 163 TCM with a total frequency of 1 242. The high-frequency TCM included Salviae Miltiorrhizae Radix et Rhizoma, Uncariae Ramulus Cum Uncis, Gastrodiae Rhizoma, Poria, and Chuanxiong Rhizoma, with the main efficacy from blood-activating and stasis-resolving herbs, tonic herbs, and liver-calming and wind-extinguishing herbs. The latent structure analysis(LSA) identified 10 latent variables, 20 latent classes, 7 comprehensive clustering models, and 23 core prescriptions. It was speculated that the common syndromes of hypertensive LVH included blood stasis obstructing the collaterals, ascending hyperactivity of liver Yang, Yin deficiency with Yang hyperactivity, and intermingled phlegm and blood stasis. The association rule analysis yielded 33 strong association rules, with the highest comprehensive association rule being Gastrodiae Rhizoma→Uncariae Ramulus Cum Uncis. Hypertensive LVH is characterized by asthenia in origin and asthenia in superficiality, with Yin deficiency and Qi deficiency as the origin and blood stasis and phlegm as the superficiality. Clinical treatment focuses on activating blood circulation, resolving stasis, tonifying Qi, and nourishing Yin, combined with syndrome-specific therapies such as calming wind and stopping convulsions, clearing heat, eliminating dampness and resolving phlegm, and promoting diuresis and reducing swelling.
Drugs, Chinese Herbal/therapeutic use* ; Data Mining ; Humans ; Hypertension/complications* ; Hypertrophy, Left Ventricular/physiopathology* ; Medicine, Chinese Traditional ; Drug Prescriptions

OBJECTIVE: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*

Objective To explore the effect of motor imagery brain-computer interface(BCI)combined with upper limb rehabilita-tion robot on upper limb function of stroke patients.Methods From May to December,2024,45 stroke inpatients from Xuzhou Rehabilitation Hospital and Xuzhou Central Hospital were recruited and randomly divided into BCI group(n=15),robot group(n=15)and combined group(n=15).All the groups received conventional rehabilitation therapy.On this basis,the BCI group received motor imagery based BCI training,the robot group received upper extremity rehabilitation robot training,and the com-bined group received BCI combined with upper extremity rehabilitation robot training.They were assessed with Fugl-Meyer Assessment-Upper Extremities(FMA-UE),Action Research Arm Test(ARAT)and modified Barthel Index(MBI)before and four weeks after treatment,while they were measured delta-alpha ratio(DAR)using the quantitative electroencephalogram.Results The intra-group effect(F>101.870,P<0.001)and interaction effect(F>7.891,P<0.001)were significant in the scores of FMA-UE,ARAT and MBI,and DAR.Post Hoc test showed that all the indexes were better in the combined group than in BCI group and the robot group(P<0.05),and there was no significant difference in the indexes between BCI group and the robot group(P>0.05).Conclusion BCI combined with upper limb rehabilitation robot can promote the recovery of upper limb function and ac-tivities of daily living of stroke patients.

Inflammatory response,immunosuppression,and drug sensitivity have been reported to have a significant correlation with the disulfidptosis levels in cancer patients.However,the value of disulfidpto-sis in colorectal cancer therapy remains unclear.Therefore,we classified the CRC cells into different cell types using single-cell sequencing data and cell-specific markers and analyzed their relationship with the cell disulfidptosis level.We found that the high disulfidptosis regions were concentrated in epithelial-like CRC cells.Further exploration using the disulfidptosis and programmed cell death 1 inhibitor therapy treated differential expression genes indicated that CRC patients with high disulfidptosis levels exhibited a lower risk profile and increased sensitivity to immunotherapy.By using the spatial transcriptomic analy-sis,we found that ubiquinol-cytochrome c reductase core protein 1(UQCRC1),a disulfidptosis-related gene,is highly expressed in epithelial-like CRC cells and co-localized with immune-infiltrated tumor re-gions.Additional bioinformatic analyses and experimental validation further confirmed that UQCRC1 was downregulated in CRC tissues.Overexpression of UQCRC1 suppressed CRC cell proliferation and migra-tion.These findings indicate that UQCRC1 is a potential target for CRC diagnosis and treatment.

AIM To study the chemical constituents from Fomes officinalis(Vill.ex Fr.)Ames and their anti-inflammatory activities.METHODS The 95％ethanol extract from F.officinalis was isolated and purified by silica gel,Sephadex LH-20,HW-40C,MCI and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.Their anti-inflammatory activities in vitro were evaluated by RAW264.7 model.RESULTS Twenty compounds were isolated and identified as asperginine A(1),laricinolic acid(2),methoxylaricinolic acid(3),fomeffic acid(4),19-acetoxy-13S-hydroxylabda-8(17),14-diene(5),bisbenzopyran(6),lariciresinol acetate(7),fomitopsin G(8),fomitopsin H(9),demalonyl fomitopsin H(10),fomlactone A(11),fomlactone B(12),fomefficinol A(13),fomefficinol B(14),laetiporins A(15),laetiporins B(16),dehydrosulphurenic acid(17),dehydroeburicoic acid(18),3-keto-dehydrosulfurenic acid(19),eburicoic acid(20).The IC50 values of compounds 7,13,20 were(4.00±1.02),(3.29±0.62),(3.22±0.94)μmol/L,respectively.CONCLUSION Compound 1 is a new compound,3,6,15,16 are isolated from this plant for the first time.Compounds 7,13,20 have strong anti-inflammatory activities.

In July 2024,the Diagnosis Related Groups(DRG)2.0 is released based on the Notice from the National Healthcare Security Administration on Issuing the DRG 2.0 and Deepening the Relevant Work.Compared with DRG 1.1,version 2.0 was established based on a wider range of suggestions regarding the Adjacent Diagnosis Related Groups(ADRG),Major Comorbidity or Complication(MCC),and Comorbidity or Complication(CC)from various institutions.A list of disease diagnoses and surgical operations that are not used as grouping rules was compiled,and grouping efficacy was further improved by upgrading the algorithms for MCC and CC with the help of AI.Meanwhile,it is necessary to pay more attention to the number of cases of ADRG,the better methods to list the MCC/CC,the suggestions of various doctors and continuously standardize the data and update the grouping scheme of DRG.

Inflammatory response,immunosuppression,and drug sensitivity have been reported to have a significant correlation with the disulfidptosis levels in cancer patients.However,the value of disulfidpto-sis in colorectal cancer therapy remains unclear.Therefore,we classified the CRC cells into different cell types using single-cell sequencing data and cell-specific markers and analyzed their relationship with the cell disulfidptosis level.We found that the high disulfidptosis regions were concentrated in epithelial-like CRC cells.Further exploration using the disulfidptosis and programmed cell death 1 inhibitor therapy treated differential expression genes indicated that CRC patients with high disulfidptosis levels exhibited a lower risk profile and increased sensitivity to immunotherapy.By using the spatial transcriptomic analy-sis,we found that ubiquinol-cytochrome c reductase core protein 1(UQCRC1),a disulfidptosis-related gene,is highly expressed in epithelial-like CRC cells and co-localized with immune-infiltrated tumor re-gions.Additional bioinformatic analyses and experimental validation further confirmed that UQCRC1 was downregulated in CRC tissues.Overexpression of UQCRC1 suppressed CRC cell proliferation and migra-tion.These findings indicate that UQCRC1 is a potential target for CRC diagnosis and treatment.

In July 2024,the Diagnosis Related Groups(DRG)2.0 is released based on the Notice from the National Healthcare Security Administration on Issuing the DRG 2.0 and Deepening the Relevant Work.Compared with DRG 1.1,version 2.0 was established based on a wider range of suggestions regarding the Adjacent Diagnosis Related Groups(ADRG),Major Comorbidity or Complication(MCC),and Comorbidity or Complication(CC)from various institutions.A list of disease diagnoses and surgical operations that are not used as grouping rules was compiled,and grouping efficacy was further improved by upgrading the algorithms for MCC and CC with the help of AI.Meanwhile,it is necessary to pay more attention to the number of cases of ADRG,the better methods to list the MCC/CC,the suggestions of various doctors and continuously standardize the data and update the grouping scheme of DRG.