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.

To analyze global trends and research hotspots in exercise intervention for overweight and obesity from 2010 to 2024, to provide novel perspectives for comprehensive management research on overweight and obesity.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Traditional Chinese medicine （TCM） therapy has unique clinical advantages in the treatment of atrial fibrillation， mainly reflected in five aspects， improving quality of life， enabling early diagnosis and treatment， promoting cardiac rehabilitation， making up for the limitations of Western medicine， and improving the success rate of catheter ablation. However， there is insufficient evidence in current clinical research. Based on the current status of TCM research in the treatment of atrial fibrillation， it is suggested that future studies should focus on standardized research on syndrome differentiation and classification. This can be achieved through clinical epidemiological surveys， expert consensus， and other methods to establish a unified syndrome differentiation and classification standard for atrial fibrillation. Clinical efficacy evaluation indicators should be standardized， and core outcome measures for clinical research on TCM treatment of atrial fibrillation should be developed through systematic reviews， patient interviews， and other methods. Additionally， clinical research design， implementation， and data management should be improved. By leveraging modern information technologies such as artificial intelligence， the scientific and standardized nature of TCM intervention research on atrial fibrillation can be enhanced， ultimately improving the quality of research.

The Expert Consensus on Clinical Application of Qinbaohong Zhike Oral Liquid in Treatment of Acute Bronchitis and Acute Attack of Chronic Bronchitis （GS/CACM 337-2023） was released by the China Association of Chinese Medicine on December 13^th， 2023. This expert consensus was developed by experts in methodology， pharmacy， and Chinese medicine in strict accordance with the development requirements of the China Association of Chinese Medicine （CACM） and based on the latest medical evidence and the clinical medication experience of well-known experts in the fields of respiratory medicine （pulmonary diseases） and pediatrics. This expert consensus defines the application of Qinbaohong Zhike oral liquid in the treatment of cough and excessive sputum caused by phlegm-heat obstructing lung， acute bronchitis， and acute attack of chronic bronchitis from the aspects of applicable populations， efficacy evaluation， usage， dosage， drug combination， and safety. It is expected to guide the rational drug use in medical and health institutions， give full play to the unique value of Qinbaohong Zhike oral liquid， and vigorously promote the inheritance and innovation of Chinese patent medicines.

OBJECTIVE To analyze the peak wideband acoustic immittance(WAI)values reflecting the tympanic membrane absorption characteristics during the measurement process in patients with congenital middle ear malformation.METHODS We selected 36 patients diagnosed with congenital middle ear malformation by CT as subjects and evaluated their tympanic membrane absorption characteristics by measuring their acoustic impedance peak pressure.Then,we divided these data into two groups based on the pathological malformation obseved during the surgical process,namely the auditory ossicular chain soft connection group and the auditory ossicular chain fixation group.Calculate the absorption rate values of the tympanic membrane at each frequency corresponding to the peak pressure value of the tympanogram,and the relative area of absorption rate.By conducting independent t-tests,we compared two groups-normal children and patients who had undergone ossicular chain fixation surgery-to analyze the absorption area and peak absorption rate of their tympanic peak pressure values.RESULTS Under peak pressure,there were significant differences(P<0.001)in the full frequency range tympanic membrane absorption rate and peak absorption rate between the group with fixed ossicular chain and the normal group.However,there was no difference in the full frequency range tympanic membrane absorption rate and peak absorption rate between the group with soft ossicular chain and the normal group.Under peak pressure,the absorption area of the ossicular chain fixation group was significantly lower than that of the normal group in the low frequency range of 226-1 000 Hz(P<0.001),while the absorption area of the ossicular chain soft connection group was significantly higher than that of the normal group in the low frequency range of 226-1 000 Hz(P<0.001).CONCLUSION When conducting broadband acoustic impedance test,there is a significant difference in the relative area of tympanic membrane absorption rate of acoustic impedance peak pressure between the auditory ossicular chain soft connection group and the auditory ossicular chain fixation group across 226-1 000 Hz.It has application value as a clinical rapid screening and diagnostic tool for middle ear malformations.

OBJECTIVE To investigate the auditory characteristics of patients with congenital unilateral cochlear nerve deficiency(CND).METHODS This study enrolled 21 patients(21 ears)with unilateral auditory nerve dysplasia confirmed by inner ear MRI,including 9 males and 12 females,aged 10 months to 7 years,(1.91±1.38)years.A retrospective analysis was performed on the correlation between behavioral audiometry and auditory steady-state response(ASSR)thresholds,chirp-evoked auditory brainstem response(chirp-ABR),otoacoustic emission(OAE),and other results to analyze the audiological characteristics of unilateral auditory nerve dysplasia.RESULTS In all 21 patients(21 ears)with auditory nerve dysplasia,behavioral audiometry,chirp-ABR,and ASSR results all indicated severe to profound hearing loss in the affected ears.There was a high correlation between behavioral audiometry thresholds and ASSR thresholds,with small differences observed between ASSR and behavioral audiometry results.CONCLUSION The difference between ASSR and behavioral audiometry is small in patients with CND.A-ABR can't be elicited.OAE has a certain false negative rate.The ABR often predominantly shows wave III,with prolonged latency,and a threshold indicative of profound hearing loss.These audiological characteristics can improve the early detection and diagnosis of CND and enhance the effect of intervention.

Purpose To evaluate the value of preoperative MRI-based radiomic models for assessing tumor-infiltrating CD8+T-cell expression in glioblastoma patients,and to identify the most stable and efficient radiomic feature region for predicting prognosis following immunotherapy.Materials and Methods This retrospective study included 150 patients with histopathologically confirmed glioblastoma from Lanzhou University Second Hospital(January 2018 to April 2022).Tumor-infiltrating CD8+T-cell expression was quantitatively assessed using immunohistochemical staining,with patients stratified into CD8-high and CD8-low expression groups based on overall survival.A total of 1 185 radiomic features were extracted from each patient's contrast-enhanced T1C and T2WI images,covering the original tumor region and sequentially expanded peritumoral regions(2.5 mm,5.0 mm,7.5 mm,10.0 mm,12.5 mm,15.0 mm morphological dilation of tumor core+peritumoral area).Feature selection was performed using variance threshold,minimum redundancy maximum relevance,and least absolute shrinkage and selection operator methods.XGBoost classifier was employed to construct clinical,radiomic,and clinical-radiomic multimodal combined prediction models.Diagnostic performance was evaluated using receiver operating characteristic curve analysis.Results The radiomic model based on tumor expansion of 7.5 mm(tumor+peritumoral region)demonstrated optimal predictive performance.The clinical-radiomic multimodal combined model showed superior predictive capability compared to clinical and radiomic models alone,achieving an area under the curve of 0.991 and accuracy of 99.0%in the training set,and area under the curve of 0.840 with accuracy of 80.0%in the validation set.Conclusion MRI radiomics provides a feasible approach for evaluating tumor-infiltrating CD8+T-cell expression in glioblastoma patients,offering potential for preoperative prognosis prediction.

Objective:To explore the risk factors for recurrence of thyroid cancer after radical resection via areola endoscopy,and to construct a visual risk prediction model.Methods:The clinical data of 350 thyroid cancer patients who underwent radical surgery via areola endoscopy in our hospital from January 2016 to October 2018 were retro-spectively analyzed,and they were randomly divided into the modeling group(233 cases)and the internal validation group(117 cases)in a 2:1 ratio.All patients were followed up for 3 years after surgery,and the patients of modeling group were further divided into recurrent group(51)and non recurrent group(182)according to whether they with or not recurrence.Another 163 patients with thyroid cancer who underwent laparoscopic radical mastectomy at our hos-pital from January 2019 to May 2020 were selected as the external validation group.The risk factors for recurrence of thyroid cancer after radical surgery via areola endoscopy was analyzed by using Cox regression method,and a risk prediction nomogram model was established based on this.Internal validation of the nomogram model was conducted by using the Bootstrap method,and the calibration,predictive efficacy and clinical net benefit of the nomogram model were evaluated by the calibration curve,receiver operating characteristic(ROC)curve and decision curve analysis(DCA).The external validation group data was used for external validation.Results:The recurrence rate of thyroid cancer patients after 5 years of radical surgery via areola endoscopy was 21.64％(111/513).The proportions of multiple le-sions,preoperative lymph node metastasis,TNM stages Ⅲ-Ⅳ and maximum tumor diameter,the levels of thyro-globulin(TG),triiodothyronine(T3),thyroxine(T4),free triiodothyronine(FT3),free thyroxine(FT4)and thyroid stimulating hormone(TSH)in the recurrence group were higher than those in the non recurrence group(P<0.05).The Cox regres-sion analysis results showed that the maximum tumor diameter,multiple lesions,preoperative lymph node metasta-sis,TNM stage Ⅲ-Ⅳ and TG,T3,T4,FT3,FT4 and TSH levels were all risk factors for recurrence of thyroid cancer after radical surgery via areola endoscopy(P<0.05).The risk prediction nomogram model of recurrence of thyroid cancer af-ter radical surgery under areola endoscopy was constructed based on the above influencing factors.After internal and external validation,the consistency indices of the modeling group,internal verification group and external verification group were 0.832,0.825 and 0.41 respectively,and the calibration curves of three groups were close to the standard curve.The ROC curve analysis and verification showed that the area under the curve predicted by the nomogram model of the modeling group,internal verification group and external verification group were 0.859,0.847 and 0.853 respectively.The DCA curve showed that the nomogram model had good clinical net benefits when the threshold probability of the modeling group,internal verification group and external verification group were 0.03-0.82,0.02-0.78 and 0.06-0.88 respectively.Conclusion:The maximum tumor diameter,multiple lesions,preoperative lymph node metastasis,TNM staging stage Ⅲ-Ⅳ and levels of TG,T3,T4,FT3,FT4 and TSH are all risk factors for recurrence of thy-roid cancer after radical surgery via areola endoscopy,and the risk prediction visualization nomogram model con-structed based on this is helpful for clinical screening of high-risk patients to guide early intervention and reduce the risk of recurrence.