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.

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.

Objective: To analyze the structural changes in whole blood and apheresis platelet donations in Chinese Society of Blood Transfusion (CSBT) sentinel units from 2021 to 2025, and to provide evidence for donor recruitment and blood service management. Methods: Aggregated data reported by sentinel sites were collected and stratified according to six indicators: age, sex, donation history, donation volume, donation site, and organization mode. Pearson χ test, Cramer′V coefficient, Cochran-Armitage trend test and simple linear regression were adopted for analysis. Results: A total of 16 256 973 whole blood donations and 1 576 546 apheresis platelet donations were included. Among whole-blood donations, the proportion of donors aged 18-22 years decreased from 27.30% to 10.60%. The proportion of male donors increased from 58.81% to 63.13%; the proportion of people aged 30 and above increased from 58.40% to 75.30%; the proportion of fixed locations rose from 28.37% to 36.57%; the proportion of social groups increased from 21.69% to 39.69%. The 400-mL donations decreased from 62.12% to 53.46%, while individually initiated voluntary donations remained above 50%. Apheresis platelet donations were predominantly from males, individuals aged 30-39 years, and repeat donors; 2-treatment-unit apheresis platelet donations increased from 67.19% to 74.93%, and individually initiated donations accounted for 92.97%-96.02%. Statistically significant overall annual compositional differences were observed for all six indicators (all P<0.001), whereas linear trends for some sub-categories were not statistically significant. Conclusion: In the participating CSBT sentinel units, among whole blood donations, the proportions of donors aged 18-22 years, university-organized group donations, and donations at blood mobiles decreased, whereas the proportions of organizational group donations and donations at fixed venues increased. The apheresis platelet donation cohort is relatively mature and is evolving toward a high-efficiency donation model. Individual voluntary donation remains the primary organization mode for both donation types. Strategies such as youth donor recruitment, optimized layout of fixed donation sites, and retention of repeat donors should be strengthened to improve the resilience of blood supply.

Objective: Data on syncopal donation-related vasovagal reaction (DRVR) collected from 74 blood centers between 2020 and 2023 was statistically analyzed to provide a reference for developing preventive strategies against syncopal DRVR. Methods: Data on blood donation adverse reactions and basic information of donors from 2020 to 2023 were collected through the information management system at monitoring sentinel sites. Statistical analysis was performed on the following aspects of syncopal DRVR: characteristics of donors who experienced syncope, reported incidence, triggers, duration, presence and occurrence time of syncope-related trauma, clinical management including outpatient and inpatient treatment, and severity grading. Results: From 2020 to 2023, 45 966 donation-related adverse reactions were recorded. Of these, 1 665 (3.72%) cases were syncopal DRVR. The incidence of syncopal DRVR decreased with age, being the highest in the 18-22 age group. Incidence was significantly higher in female donors than male donors, in first-time donors than repeat donors, and in university and individual donors than group donors (all P<0.05). There was no statistically significant difference among different blood donation locations (P>0.05). The top three triggers were tension, fatigue, and needle phobia or fear of blood. Among syncopal DRVR cases, 60.36% occurred during blood collection, 87.63% lasted for less than 60 seconds, and 5.05% were accompanied by trauma. Notably, 57.14% of these traumas occurred after donor had left the blood collection site. Syncope severity was graded based on required treatment: grade 1 (fully recovered without treatment, 95.50%); grade 2 (recovered after outpatient treatment, 4.02%); and grade 3 (recovered after inpatient treatment, 0.48%). Conclusion: By analyzing the data of syncopal DRVR cases, it is possible to provide a reference for formulating blood donor safety policies.

Objective To investigate the levels of gross α and gross β activities in different water types within a 40-kilometer radius around the Zhangzhou Nuclear Power Plant prior to its operation. Methods In 2018, drinking water samples were collected from the area surrounding the nuclear power plant during both the wet and dry seasons, including source water, treated water, tap water, and well water. The gross α and gross β activity concentrations were measured using a low-background α/β counter, followed by statistical analysis. Results A total of 80 water samples from different sources around the Zhangzhou Nuclear Power Plant were collected. The average gross α and gross β activity concentrations during the wet season were (0.110 ± 0.036) Bq/L and (0.643 ± 0.028) Bq/L, respectively, while those during the dry season were (0.124 ± 0.032) Bq/L and (0.624 ± 0.026) Bq/L, respectively. There were no significant differences in the gross α and gross β activity concentrations between the wet and dry seasons for the overall sample set (P > 0.05). However, there were statistically significant differences in the gross α and gross β activity concentrations between the wet and dry seasons for source water and well water (Z_wet = −2.005, −2.123; Z_dry = −1.943, −3.090; P < 0.05). Conclusion The radioactivity levels in different water types within various ranges around the Zhangzhou Nuclear Power Plant before its operation were determined. The measured activity concentrations were at the same level as those from previous investigations in other regions of Fujian Province.

BACKGROUND: Congenital heart disease (CHD) is a leading cause of birth defect-related mortality. However, more recent CHD mortality data for China are lacking. Additionally, limited studies have evaluated sex, rural-urban, and region-specific disparities of CHD mortality in China. METHODS: We designed a population-based study using data from the Dataset of National Mortality Surveillance in China between 2008 and 2021. We calculated age-adjusted CHD mortality using the sixth census data of China in 2010 as the standard population. We assessed the temporal trends in CHD mortality by age, sex, area, and region from 2008 to 2021 using the joinpoint regression model. RESULTS: From 2008 to 2021, 33,534 deaths were attributed to CHD. The period witnessed a two-fold decrease in the age-adjusted CHD mortality from 1.61 to 0.76 per 100,000 persons (average annual percent change [AAPC] = -5.90%). Females tended to have lower age-adjusted CHD mortality than males, but with a similar decline rate from 2008 to 2021 (females: AAPC = -6.15%; males: AAPC = -5.84%). Similar AAPC values were observed among people living in urban (AAPC = -6.64%) and rural (AAPC = -6.12%) areas. Eastern regions experienced a more pronounced decrease in the age-adjusted CHD mortality (AAPC = -7.86%) than central (AAPC = -5.83%) and western regions (AAPC = -3.71%) between 2008 and 2021. Approximately half of the deaths (46.19%) due to CHD occurred during infancy. The CHD mortality rates in 2021 were lower than those in 2008 for people aged 0-39 years, with the largest decrease observed among children aged 1-4 years (AAPC = -8.26%), followed by infants (AAPC = -7.01%). CONCLUSIONS CHD mortality in China has dramatically decreased from 2008 to 2021. The slower decrease in CHD mortality in the central and western regions than in the eastern regions suggested that public health policymakers should pay more attention to health resources and health education for central and western regions.
Humans ; Heart Defects, Congenital/mortality* ; Male ; Female ; China/epidemiology* ; Infant ; Child, Preschool ; Adult ; Child ; Adolescent ; Infant, Newborn ; Middle Aged ; Young Adult ; Aged ; Rural Population

This study aims to delve into the influences and underlying mechanisms of Banxia Xiexin Decoction(BXD) on the proliferation, apoptosis, invasion, and migration of colon cancer cells. Firstly, the components of BXD in blood were identified by UPLC-MS/MS, and subsequently the content of these components were determined by HPLC. Then, different concentrations of BXD were used to treat both the normal intestinal epithelial cells(NCM460) and the colon cancer cells(HT29 and HCT116). The cell viability and apoptosis were examined by the cell counting kit-8(CCK-8) and flow cytometry, respectively. Western blot was employed to determine the expression of the apoptosis regulators B-cell lymphoma-2(Bcl-2) and Bcl-2-associated X(Bax). The cell wound healing assay and Transwell assay were employed to measure the cell migration and invasion, respectively. Additionally, Western blot was employed to determine the expression levels of epithelial-mesenchymal transition(EMT)-associated proteins, including epithelial cadherin(E-cadherin), neural cadherin(N-cadherin), and vimentin. The protein and mRNA levels of the factors in the poly(ADP-ribose) glycohydrolase(PARG)/poly(ADP-ribose) polymerase 1(PARP1)/nuclear factor kappa-B p65(NF-κB p65) signaling pathway were determined by Western blot and RT-qPCR, respectively. The results demonstrated that following BXD intervention, the proliferation of HT29 and HCT116 cells was significantly reduced. Furthermore, BXD promoted the apoptosis, enhanced the expression of Bcl-2, and suppressed the expression of Bax in colon cancer cells. At the same time, BXD suppressed the cell migration and invasion and augmented the expression of E-cadherin while diminishing the expression of N-cadherin and vimentin. In addition, BXD down-regulated the protein and mRNA levels of PARG, PARP1, and NF-κB p65. In conclusion, BXD may inhibit the malignant phenotypes of colon cancer cells by mediating the PARG/PARP1/NF-κB signaling pathway.
Colonic Neoplasms/pathology* ; Drugs, Chinese Herbal/pharmacology* ; Phenotype ; Signal Transduction/drug effects* ; Cell Proliferation/drug effects* ; Apoptosis ; Cell Movement/drug effects* ; Neoplasm Invasiveness ; HCT116 Cells ; Proto-Oncogene Proteins c-bcl-2/biosynthesis* ; Humans ; Poly (ADP-Ribose) Polymerase-1 ; Glycoside Hydrolases ; bcl-2-Associated X Protein ; NF-kappa B p50 Subunit

This study aimed to explore the therapeutic mechanisms of Colquhounia Root Tablets(CRT) in treating diabetic kidney disease(DKD) by integrating biomolecular network mining with animal model verification. By analyzing clinical transcriptomics data, an interaction network was constructed between candidate targets of CRT and DKD-related genes. Based on the topological eigenvalues of network nodes, 101 core network targets of CRT against DKD were identified. These targets were found to be closely related to multiple pathways associated with type 2 diabetes, immune response, and metabolic reprogramming. Given that immune-inflammatory imbalance driven by metabolic reprogramming is one of the key pathogenic mechanisms of DKD, and that many core network targets of CRT are involved in this pathological process, receptor for advanced glycation end products(RAGE)-reactive oxygen species(ROS)-phosphatidylinositol 3-kinase(PI3K)-protein kinase B(AKT)-nuclear factor-κB(NF-κB)-NOD-like receptor family pyrin domain containing 3(NLRP3) signaling axis was selected as a candidate target for in-depth research. Further, a rat model of DKD induced by a high-sugar, high-fat diet and streptozotocin was established to evaluate the pharmacological effects of CRT and verify the expression of related targets. The experimental results showed that CRT could effectively correct metabolic disturbances in DKD, restore immune-inflammatory balance, and improve renal function and its pathological changes by inhibiting the activation of the RAGE-ROS-PI3K-AKT-NF-κB-NLRP3 signaling axis. In conclusion, this study reveals that CRT alleviates the progression of DKD through dual regulation of metabolic reprogramming and immune-inflammatory responses, providing strong experimental evidence for its clinical application in DKD.
Animals ; Diabetic Nephropathies/metabolism* ; Receptor for Advanced Glycation End Products/genetics* ; NF-kappa B/genetics* ; Signal Transduction/drug effects* ; Rats ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Proto-Oncogene Proteins c-akt/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Phosphatidylinositol 3-Kinases/genetics* ; Reactive Oxygen Species/metabolism* ; Humans ; Plant Roots/chemistry* ; Rats, Sprague-Dawley ; Tablets/administration & dosage*