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.

Background: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

Radar-based non-contact health monitoring technology (RBNHMT) has emerged as a transformative paradigm in continuous health sensing, enabling non-invasive and continuous monitoring of physiological parameters and behavioral patterns by transmitting electromagnetic waves, analyzing the reflected signals, and detecting subtle bodily movements—ranging from millimeter-scale chest wall displacements due to respiration to micro-scale vibrations associated with cardiac activity—ultimately transforming them into quantifiable health data. Distinguished by its non-contact operation, inherent privacy preservation, and adaptability to diverse scenarios, RBNHMT exhibits stronger resistance to environmental interference than conventional contact-based monitoring, and has solidified its position as a prominent and dynamic research focus in the field of non-contact health monitoring. Currently, significant and multifaceted progress has been made across several key areas. In human activity recognition (HAR), systems leveraging micro-Doppler signatures or point cloud sequences achieve high-precision detection of gait, gestures, and fall events, with state-of-the-art deep learning-based models achieving accuracy rates exceeding 99% in controlled experimental settings. For vital sign and sleep monitoring, it not only tracks respiratory and heart rates continuously but also extracts clinically relevant metrics such as heart rate variability (HRV) for autonomic nervous system assessment and estimates blood pressure through indirect methods like pulse transit time analysis, while maintaining robustness in dynamic settings through advanced motion compensation algorithms. In sleep monitoring, it further enables sleep posture classification and apnea event detection. In emotion and stress recognition, it provides a non-intrusive approach for psychological assessment by analyzing autonomic-response physiological signal patterns or behavioral features. Furthermore, its applications in auxiliary medical diagnosis have expanded to promising interdisciplinary areas such as non-contact heart sound auscultation, radar-based screening for obstructive sleep apnea (OSA), and emerging research into breast cancer detection using microwave and millimeter-wave imaging techniques. However, several challenges impede its practical deployment. Signal quality is significantly compromised by multipath interference in complex indoor environments and clutter from static objects, and by motion artifacts in dynamic scenarios where gross body movements obscure the subtle physiological signals. Algorithmically, separating signals from multiple targets in close proximity and calibrating for substantial individual physiological differences, such as body habitus, baseline vital signs, remain difficult and limit generalizability. Hardware design also faces the challenge of balancing power consumption, cost, integration, and performance, often requiring trade-offs that constrain miniaturization, battery life, or measurement sensitivity. Future advancement, therefore, requires collaborative and targeted innovation across multiple dimensions. Algorithmically, developing adaptive signal processing models based on emerging paradigms such as few-shot learning (for user-specific calibration with minimal data) and reinforcement learning (for dynamic noise suppression) is essential. At the hardware level, highly integrated radar SoCs with embedded processing capabilities and advanced packaging technologies are crucial for achieving the dual goals of device miniaturization and cost reduction without sacrificing performance. At the system level, fusing radar data with complementary modalities such as infrared and acoustic sensing can create a synergistic, multi-modal framework that significantly enhances perceptual robustness and reliability in complex, real-world environments. This review provides a comprehensive synthesis that systematically summarizes the relevant theoretical foundations and application progress, and offers an in-depth analysis of the current technical bottlenecks. It aims to provide a clear development path and a foundational academic reference for the in-depth integration and practical application of RBNHMT in critical scenarios including rehabilitation engineering, smart elderly care, in-vehicle health monitoring, and beyond, thereby offering innovative technical support for the vision of universal, proactive, and personalized health management.

Objective To evaluate the levels and changes in occupational individual external radiation dose in non-medical nuclear utilization units in Nanning City, and to provide a basis for radiation protection in such units. Methods Thermoluminescent dosimeters were used to monitor individual radiation doses among radiation workers in 38 non-medical nuclear utilization units in Nanning City. The results were subjected to statistical analysis. Results From 2021 to 2023, a total of 3724 person-times were monitored in 38 non-medical nuclear utilization units in Nanning City. The mean annual effective dose was 0.101 mSv in 2021, 0.060 mSv in 2022, and 0.094 mSv in 2023, with a three-year average of 0.085 mSv, all well below the occupational exposure limit of 1.0 mSv. Significant differences in mean annual effective doses were observed across occupations and sexes (P<0.05). Female workers received significantly lower mean annual effective dose than male workers. Workers in the cement manufacturing industry had a significantly higher mean annual effective dose compared to those in government institutions and industrial flaw detection practitioners (F = 2.913, P<0.05). No significant differences were found among workers exposed to unsealed radioactive sources, sealed radioactive sources, and radiation-generating equipment (F = 0.585, P>0.05). Conclusion The occupational individual external radiation doses in non-medical nuclear utilization units in Nanning City are at low levels. There are no significant differences in mean annual effective dose across different nuclear utilization units. However, differences in occupational roles between males and females significantly affect the mean annual effective dose.

ObjectiveTo investigate the clinical and pathological features of intrahepatic intraductal papillary neoplasm of the bile duct （IPNB）， and to improve the understanding， diagnosis， and treatment of this disease. MethodsA retrospective analysis was performed for four patients who underwent surgical resection in Beijing Chaoyang Hospital， Capital Medical University， from January 2010 to December 2025 and were diagnosed with intrahepatic IPNB based on pathological examination. The clinical， imaging， and pathological features of these patients were summarized， and a literature review was performed for related articles in China and globally. ResultsAmong the four patients， there were three female patients and one male patient， with a median age of 65 years， and clinical manifestations included jaundice， abdominal pain or diarrhea， and asymptomatic findings on physical examination. The main imaging manifestation was bile duct dilatation or solid-cystic space-occupying lesion. Pathological typing revealed one case each of the gastric， intestinal， pancreatobiliary， and oncocytic types， and there was one patient with low-grade intraepithelial neoplasia and three patients with high-grade intraepithelial neoplasia， among whom one patient also had invasive carcinoma. ConclusionIntrahepatic IPNB is a rare precancerous lesion of the liver with diverse clinical， imaging， and pathological manifestations， and radical surgical resection remains the cornerstone of treatment.

Objective:To explore the risk factors of pulmonary infection in patients with non-small cell lung cancer (NSCLC) after thoracoscopic lobectomy and evaluate their predictive value.Methods:A retrospective analysis was conducted on the clinical data of 152 NSCLC patients who underwent thoracoscopic lobectomy at the Central Hospital of Tongchuan Mining Bureau in Shaanxi Province from May 2021 to May 2024. Among them, 45 cases were diagnosed with pulmonary infection by postoperative lung CT examination (postoperative infection group), while the remaining 107 cases did not develop pulmonary infection (non infection group). The levels of serum procalcitonin, serum amyloid A (SAA), and soluble triggering receptor expressed on myeloid cell-1 (sTREM-1) between two groups of patients before and after surgery were compared, and conduct a multivariate logistic regression analysis on the risk factors of pulmonary infection in NSCLC patients undergoing thoracoscopic lobectomy was conducted to evaluate the predictive value of each indicator for pulmonary infection in NSCLC patients after thoracoscopic lobectomy. The normally distributed quantitative data was represented by xˉ± s. Independent sample t-test was used for comparison between two groups, and paired t-test was used for comparison before and after treatment; The count data was presented as an example (%), and the comparison between groups was conducted using the chi square test. The risk factors for postoperative pulmonary infection in NSCLC patients were analyzed using multiple logistic regression analysis. Receiver operating characteristic (ROC) curves were plotted to analyze the predictive value of each indicator for pulmonary infection after thoracoscopic lobectomy. Results:No difference were found in the levels of serum procalcitonin, SAA, and sTREM-1 between two groups of patients before surgery(all P>0.05); 24 hours after surgery, the levels of serum procalcitonin, SAA, and sTREM-1 in both groups of patients were higher than before surgery [Postoperative infection group: (1.16±0.29) μg/L compared to (0.34±0.09) μg/L, (10.2±1.8) mg/L compared to (7.3±0.9) mg/L, (20.3±4.8) ng/L compared to (8.0±1.2) ng/L, t-values were 20.70, 9.70, and 16.89, respectively, all P<0.001; The uninfected group (0.84±0.14) μg/L compared to (0.32±0.08) μg/L, (8.2±1.1) mg/L compared to (7.4±0.9) mg/L, and (13.5±6.3) ng/L compared to (8.1±1.2) ng/L, with t-values of 33.36, 6.13, and 8.73, respectively, all P<0.001, and the infected group was higher than the uninfected group ( t-values of 9.18, 8.32, and 6.52, respectively, all P<0.001). The results of multivariate logistic regression analysis showed that serum procalcitonin, SAA, and sTREM-1 levels at 24 hours after surgery were all risk factors for pulmonary infection in NSCLC patients undergoing thoracoscopic lobectomy (odds ratios of 2.40, 1.61, 1.60, 95% confidence intervals: 1.14~5.04, 1.09~2.38, 1.13~2.26, P values of 0.021, 0.017, 0.009, respectively). The area under curve(AUC) of serum procalcitonin, SAA, and sTREM-1 predicting pulmonary infection after thoracoscopic lobectomy in NSCLC patients 24 hours after surgery were 0.84, 0.74, and 0.79, respectively, with cutoff values of 1.03 μg/L, 9.74 mg/L, and 16.85 ng/L, respectively. Conclusions:The levels of serum procalcitonin, SAA, and sTREM-1 24 hours after surgery are all risk factors for pulmonary infection in NSCLC patients undergoing thoracoscopic lobectomy, and all three had a high predictive value for postoperative pulmonary infection.

The glymphatic system(GS)is a unique toxic sub-stance clearance system in brain,which is very important for maintaining the microenvironment stability of the central nervous system.The polarization distribution of aquaporin 4(AQP4)lo-cated in the terminal foot of astrocytes affects the function of GS and participates in the pathological progress of many neurodegen-erative diseases,but the detailed regulation mechanism of AQP4 polarization distribution has not been systematically summarized.Therefore,this paper systematically combs the mechanism of reg-ulating the polarization distribution of AQP4 from the perspective of the composition integrity of dystrophin-glycoprotein complex(DGC)and basement membrane foot complex,and summarizes the potential drug and non-drug therapies for targeted regulation of AQP4 polarization distribution at present,aiming at providing new target reference and theoretical basis for targeted regulation of AQP4 polarization to prevent and treat neurodegenerative dis-eases.

Objective To propose a novel method for immunohistochemically positive cell counting based on the improved YOLOv5s.Methods Regarding the small target characteristics of positive cells,a small target detection layer was added to refine feature extraction.Then,a bidirectional weighted feature pyramid network was used to replace path aggregation network(PANet)in the neck network for realizing multi-scale feature fusion.Additionally,the method used coordinate attention mechanism to make the model pay more attention to small target characteristics,and replaced the original GIoU with EIoU loss function for enhancing the detection performance.Results The model was trained on the self-built immunohistochemical image dataset.The average accuracy of the improved model was 89.3%,which was 4.0%higher than the original model and surpassed mainstream target detection models.The 5-year survival prediction model constructed with the method achieved an average accuracy of 76.8%and an average area under the curve of 0.81,demonstrating its superior prediction ability.Conclusion The proposed model can quickly detect the number of immunohistochemically positive cells and effectively assist doctors in survival prediction.

Objective: This study aimed to explore the correlation between balance function and core muscle activation in patients with adolescent idiopathic scoliosis (AIS), compared to healthy individuals. Methods: A total of 24 AIS patients and 25 healthy controls were recruited. The limits of stability (LOS) test were conducted to assess balance function, while surface electromyography was used to measure the activity of core muscles, including the internal oblique, external oblique, and multifidus. Diaphragm thickness was measured using ultrasound during different postural tasks. Center of pressure (COP) displacement and trunk inclination distance were also recorded during the LOS test. Results: AIS patients showed significantly greater activation of superficial core muscles, such as the internal and external oblique muscles, compared to the control group (p < 0.05). Diaphragm activation was lower in AIS patients during balance tasks (p < 0.01). Although no significant difference was observed in COP displacement between the groups, trunk inclination was significantly greater in the AIS group during certain tasks (p < 0.05). Conclusion These findings suggest distinct postural control patterns in AIS patients, highlighting the importance of targeted interventions to improve balance and core muscle function in this population.