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.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Chronic kidney disease(CKD)is a chronic disease characterized by renal structural damage and dysfunction.At present,there is still a lack of effective therapeutic drugs and prevention and treatment methods for CKD in clinical practice.More and more studies have shown that necroptosis,as a new type of programmed cell death,plays a vital role in the onset and progression of CKD.Targeting key molecules in the necroptosis pathway,such as RIPK1,RIPK3 and MLKL,the development of small molecule inhibitors has become an emerging strategy for the treatment of CKD,and has shown significant potential to pro-tect the kidneys and alleviate renal fibrosis in a variety of in vitro and in vivo models.Therefore,this article summarizes the re-search progress of the mechanism of necroptosis in recent years,and focuses on the potential role of necroptosis in the pathogene-sis of CKD and the therapeutic potential of targeting this path-way,providing a new perspective and research direction for the prevention and treatment of CKD in the future.

Chronic kidney disease(CKD)is a chronic disease characterized by renal structural damage and dysfunction.At present,there is still a lack of effective therapeutic drugs and prevention and treatment methods for CKD in clinical practice.More and more studies have shown that necroptosis,as a new type of programmed cell death,plays a vital role in the onset and progression of CKD.Targeting key molecules in the necroptosis pathway,such as RIPK1,RIPK3 and MLKL,the development of small molecule inhibitors has become an emerging strategy for the treatment of CKD,and has shown significant potential to pro-tect the kidneys and alleviate renal fibrosis in a variety of in vitro and in vivo models.Therefore,this article summarizes the re-search progress of the mechanism of necroptosis in recent years,and focuses on the potential role of necroptosis in the pathogene-sis of CKD and the therapeutic potential of targeting this path-way,providing a new perspective and research direction for the prevention and treatment of CKD in the future.

Objective To investigate the characteristics of changes in pain-anxiety-depression-fatigue symptom clusters and their possible associated factors in adolescents with acute lymphoblastic leukemia(ALL)during early chemotherapy.Methods A prospective longitudinal study was conducted from Nov 2019 to Oct 2021,enrolling newly diagnosed adolescent ALL patients from 5 tertiary or pediatric specialty hospitals in Shanghai,Zhejiang Province,Sichuan Province,Anhui Province and Guangdong Province.Patient-reported pain,anxiety,depression,and fatigue were collected at five time points within the first nine weeks of chemotherapy using the PROMIS Pediatric-25 instrument.Latent profile analysis(LPA)and latent transition analysis(LTA)were applied to explore the latent classes of symptom clusters,their transition probabilities over time,and possible risk or protective factors associated with class membership.Results A total of 134 ALL cases were enrolled,and symptom clusters at all the 5 time points(T1-T5)were consistently classified into three groups of mild,moderate and severe symptoms.The severe symptoms group accounted for the largest proportion at each time point(54.5%,59.7%,66.4%,49.3%,and 47.0%,respectively),while the mild and moderate symptoms groups showed an initial decline followed by an increase.Among participants,40.2%maintained the same symptom status,and 77.4%experienced at least one episode of severe symptom status during the trajectory.Religious affiliation(T5)and family monthly income>5 000 Yuan(T2,T4 and T5)served as protective factors against severe symptoms.Higher baseline fatigue(T1)was associated with membership in the severe symptoms group at subsequent time points.Conclusion Pain-anxiety-depression-fatigue symptoms in adolescents with ALL during early chemotherapy can be categorized into mild,moderate and severe symptoms with dynamic transitions over time.Higher baseline fatigue was associated with increased risk of severe symptoms,whereas higher family income and religious affiliation appeared protective effects.

Objective:To understand the coverage and information consistency rate of National Immunization Program (NIP) vaccines among children born during 2020-2021 in Zhejiang Province, Chongqing City, and Shanxi Province (3 provinces) of China .Methods:A simple random sampling method was used to randomly select 3 counties (districts) from each of the 3 provinces, 5 townships from each county (district), and 5 villages from each township. Vaccination information for seven NIP vaccines was collected for children born between 2020 and 2021 in each village. The vaccination coverage, timely coverage, and consistency rates between the survey data and the Immunization Planning Information System data were analyzed.Results:A total of 1 117 children were investigated. The vaccination coverage for each dose of NIP vaccine ranged from 99.10% to 100.00%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 99.19% to 100.00%, 98.92% to 100.00%, and 99.20% to 100.00%, respectively. The timely coverage of each dose of NIP vaccine ranged from 89.79% to 99.82%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 94.09% to 99.73%, 89.52% to 99.73%, and 78.55% to 100.00%, respectively. The consistency rate of information on each dose of NIP vaccine ranged from 94.36% to 99.91%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 97.85% to 99.73%, 98.92% to 100.00%, and 86.06% to 100.00%, respectively.Conclusions:Coverage of NIP vaccines was generally high among children born during 2020-2021 in the 3 provinces of China, but there were regional differences in the timely coverage of some vaccine doses and the vaccination information consistency rate. It is necessary to strengthen the timely vaccination of children's vaccine booster doses and optimize the management of vaccination services.

Objective To investigate the characteristics and differences of congenital endowment in senile dementia population based on the theory of five circuits and six qi.Methods Based on the cross-sectional data of China Health and Retirement Longitudinal Survey(CHARLS)in 2018,the dementia status of the population aged 60 and above in China was evaluated by using the Mini-Mental State Examination(MMSE),and the five-circuit and six-qi features at birth in the senile dementia population were analyzed by descriptive statistics and Chi-square goodness-of-fit test.Results A total of 854 patients with senile dementia were included.The five-circuit and six-qi features at birth in the senile dementia population were as follows:most of them were born at the heavenly stem of Bing while the least at the heavenly stem of Ji and Geng(P＜0.001),most of them were born at the earthly branch of Wei while the least at the earthly branch of Zi(P＜0.001),most of them were born at the yearly circuit of excessive water circuit while the least at the yearly circuit of excessive gold circuit and deficient earth circuit(P＜0.001),and most of the patients were born at sitan of taiyin damp-earth and zaiquan of taiyang cold-water while the least at sitan of jueyin wind-wood and zaiquan of shaoyang ministerial fire;no statistically significant differences were found in the dominant qi and guest qi(P＞0.05);most of the patients were born in the year of combination of circuit and qi being Shunhua while the least in the year of combination of circuit and qi being same celestial correspondence(P＜0.001),and the patients born in the year of Shunhua usually were frequently distributed in heavenly-stem and earthly-branch year of Jiawu(P＜0.001).Conclusion There is a certain relationship between the congenital endowment at birth and the incidence of senile dementia in the population of senile dementia.The circuit-qi features at birth for the prevalence of senile dementia are the yearly circuit of excessive water circuit,sitan of taiyin damp-earth and zaiquan of taiyang cold-water,and the year of the combination of circuit and qi being Shunhua.The population born at the time with the above circuit-qi features are prone to suffer the injury of the kidneys,the heart,and the spleen,and then result into illness.

Currently, the drug delivery system (DDS) based on nanomaterials has become a hot interdisciplinary research topic. One of the core issues is drug loading and controlled release, in which the key lever is carriers. Vaterite, as an inorganic porous nano-material, is one metastable structure of calcium carbonate, full of micro or nano porous. Recently, vaterite has attracted more and more attention, due to its significant advantages, such as rich resources, easy preparations, low cost, simple loading procedures, good biocompatibility and many other good points. Vaterite, gained from suitable preparation strategies, can not only possess the good drug carrying performance, like high loading capacity and stable loading efficiency, but also improve the drug release ability, showing the better drug delivery effects, such as targeting release, pH sensitive release, photothermal controlled release, magnetic assistant release, optothermal controlled release. At the same time, the vaterite carriers, with good safety itself, can protect proteins, enzymes, or other drugs from degradation or inactivation, help imaging or visualization with loading fluorescent drugs in vitro and in vivo, and play synergistic effects with other therapy approaches, like photodynamic therapy, sonodynamic therapy, and thermochemotherapy. Latterly, some renewed reports in drug loading and controlled release have led to their widespread applications in diverse fields, from cell level to clinical studies. This review introduces the basic characteristics of vaterite and briefly summarizes its research history, followed by synthesis strategies. We subsequently highlight recent developments in drug loading and controlled release, with an emphasis on the advantages, quantity capacity, and comparations. Furthermore, new opportunities for using vaterite in cell level and animal level are detailed. Finally, the possible problems and development trends are discussed.

Aim To identify the underlying target of bullatine A(BA)against rheumatoid arthritis(RA)u-sing limited proteolysis-small molecule mapping(LiP-SMap)drug target proteomics and to provide a scientif-ic basis for clinical application of Aconiti brachypodi Radix in the treatment of RA.Methods LiP-SMap drug target proteomics was employed to perform bioin-formatics analysis for comparing and validating the dif-ferential protein expression after BA intervention.A collagen-induced arthritis(CIA)model was estab-lished in DBA/1 mice using bovine type Ⅱ collagen.The mice were then divided into the CIA model group,methotrexate-positive control group(MTX group),and BA groups(10 mg·kg-1 and 20 mg·kg-1)based on their clinical scores.After drug intervention,the thera-peutic efficacy against RA was assessed by joint index scores and foot thickness measurements.Histopatholog-ical changes in the arthritic joints of CIA mice were e-valuated using hematoxylin and eosin(HE)staining.Enzyme-linked immunosorbent assay(ELISA)was employed to detect inflammatory cytokines interleukin-17(IL-17)and total IgG and IgG3 anti-collagen-spe-cific antibodies levels from the serum of CIA mice.Flow cytometry was used to detect the expression levels of intracellular Th17 cells(IL-17+CD4+T cells)and Th1 cells(IFN-γ+CD4+T cells).Fluorescent quanti-tative PCR was performed to detect the expression of genes related to differential proteins.Results The proteomic analysis identified Serpinb1a as a protein with strong binding affinity to BA,and KEGG enrich-ment analysis indicated IL-17 signaling pathway was a crucial pathway of BA in against RA.BA treatment significantly reduced clinical scores and foot thickness,improved local arthritis symptoms in CIA mice,and al-leviated inflammatory cell infiltration into arthritic joints(P＜0.05).Differential protein validation re-sults showed that BA had strong affinity with Serpinb1a(-5.92 kJ·mol-1)and downregulated the expres-sion of Serpinb1a mRNA.Furthermore,the administra-tion of BA markedly reduced serum IL-17 A levels from CIA mice,inhibited the expression of intracellular IL-17 A and IFN-γ cytokines in splenic CD4+T cells(P＜0.05),and significantly downregulated the transcrip-tional expression of IL-17F(P＜0.05).Conclusion BA exhibits therapeutic effects on collagen-induced arthritis,and its mechanism of action may involve the regulation of Serpinb1a and the IL-17 signaling path-way.