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.

Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of pulmonary adenocarcinoma, accounting for approximately 2%-10% of all pulmonary adenocarcinoma cases. IMA is characterized by a high frequency of intrapulmonary metastasis and a generally poor prognosis. Clinically, IMA often presents with nonspecific pneumonia-like symptoms. Radiologically, it manifests as multilobar and multifocal consolidations and ground-glass opacities, which significantly overlap with the radiologic features of pneumonia and other benign or malignant tumors. Consequently, IMA is frequently misdiagnosed in clinical practice, and a definitive diagnosis is nearly impossible upon initial presentation, leading to delayed treatment and a poor prognosis. Currently, there is a lack of systematic reviews correlating the clinical manifestations, pathological characteristics, and radiologic features of IMA. This review aims to systematically elucidate the clinical, pathological, and radiologic features of IMA, and thoroughly analyze its diagnostic challenges and key points for differential diagnosis, in order to provide a theoretical basis and practical guidance for the early diagnosis of this disease.

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of pulmonary adenocarcinoma, accounting for approximately 2%-10% of all pulmonary adenocarcinoma cases. IMA is characterized by a high frequency of intrapulmonary metastasis and a generally poor prognosis. Clinically, IMA often presents with nonspecific pneumonia-like symptoms. Radiologically, it manifests as multilobar and multifocal consolidations and ground-glass opacities, which significantly overlap with the radiologic features of pneumonia and other benign or malignant tumors. Consequently, IMA is frequently misdiagnosed in clinical practice, and a definitive diagnosis is nearly impossible upon initial presentation, leading to delayed treatment and a poor prognosis. Currently, there is a lack of systematic reviews correlating the clinical manifestations, pathological characteristics, and radiologic features of IMA. This review aims to systematically elucidate the clinical, pathological, and radiologic features of IMA, and thoroughly analyze its diagnostic challenges and key points for differential diagnosis, in order to provide a theoretical basis and practical guidance for the early diagnosis of this disease.

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of pulmonary adenocarcinoma, accounting for approximately 2%-10% of all pulmonary adenocarcinoma cases. IMA is characterized by a high frequency of intrapulmonary metastasis and a generally poor prognosis. Clinically, IMA often presents with nonspecific pneumonia-like symptoms. Radiologically, it manifests as multilobar and multifocal consolidations and ground-glass opacities, which significantly overlap with the radiologic features of pneumonia and other benign or malignant tumors. Consequently, IMA is frequently misdiagnosed in clinical practice, and a definitive diagnosis is nearly impossible upon initial presentation, leading to delayed treatment and a poor prognosis. Currently, there is a lack of systematic reviews correlating the clinical manifestations, pathological characteristics, and radiologic features of IMA. This review aims to systematically elucidate the clinical, pathological, and radiologic features of IMA, and thoroughly analyze its diagnostic challenges and key points for differential diagnosis, in order to provide a theoretical basis and practical guidance for the early diagnosis of this disease.

[Objective] To investigate and summarize the clinicopathological features, diagnosis, treatment and prognosis of adrenocortical carcinoma with rhabdoid features. [Methods] The clinical diagnosis and treatment of a case of adrenocortical carcinoma with rhabdoid features admitled to Department of Urology, Weifang People's Hospital were reported.The clinical manifestations, pathological features, diagnosis and prognosis of the disease were analyzed in combination with relevant literature. [Results] A 34-year-old male patient was admitted due to scrotal distension and pain that had persisted for 6 months.Imaging examination showed a huge soft tissue tumor in the left adrenal region of the retroperitoneum with compression displacement of the left kidney, leading to obstruction of venous return in the left spermatic vein, which in turn caused varicose veins.The levels of serum renin, angiotensin, aldosterone, cortisol, and catecholamine were within normal ranges.Surgical resection of the tumor was performed, and postoperative pathological examination revealed that the tumor tissue was predominantly composed of rhabdoid cells, exhibiting positive immunohistochemical staining for INI 1, Syn, Calretinin and Vimentin.Genetic testing did not identify any deletion of SMARCB1 and SMARCA4 mutations.Therefore, the diagnosis was adrenocortical carcinoma with rhabdoid features.At the current 20-month follow-up, no recurrence or metastasis was observed.A review of the literature found that only 7 cases of this disease had been reported. [Conclusion] Adrenocortical carcinoma with rhabdoid features is a rare disease, and a definitive diagnosis is dependent upon pathological examination.Surgical resection remains the primary treatment.Long-term follow-up is essential, and further research is needed to evaluate the impact of adjuvant therapy.

Objective: To systematically evaluate the prevalence and influencing factors of screen exposure among preschool children in China, so as to provide evidence for formulating scientific intervention strategies. Methods: Retrieve relevant studies on screen exposure among preschool children from PubMed, Web of Science, Embase, Cochrane Library, China Biomedical Literature Database, CNKI, VIP, and Wanfang databases from the time of estaldishment to June 29, 2025. Meta analysis was performed using Stata 16.0 software. Results: A total of 43 studies were included. Meta analysis showed that the prevalence of screen exposure among preschool children in China was 46.0% (95% CI = 38.9 %-53.1%, P <0.01). Girls, non only child, father s age<35 years, both parents having an educational level of high school or below, being cared for by grandparents, rural residence, parents having no exercise habit, parental support for the use of screen devices, and parental screen time>1 h/d were influencing factors for screen exposure among preschool children [ OR (95% CI ) were 0.85(0.78-0.92), 1.09(1.04-1.15), 1.45(1.22-1.71), 1.38(1.24- 1.54 ), 1.78(1.32-2.40), 1.39(1.16-1.65), 1.38(1.13-1.69), 1.67(1.40-1.98), 1.70(1.38-2.10), 1.59(1.04-2.43), all P <0.05]. Conclusion The prevalence of screen exposure among preschool children in China is relatively high, and relevant child health promotion strategies should be formulated to reduce its occurrence.

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

This study aims to investigate the correlations of the appearance traits, total antioxidant capacity, and component content of Forsythiae Fructus processed by different methods, explore the effects of different processing methods on the abovementioned three aspects of Forsythiae Fructus, and screen out the internal and external indicators that have important effects on its quality. It determined the length, diameter, stem length, chroma value L~*, a~*, b~*, and other appearance indexes and antioxidant activity of Forsythiae Fructus processed by different methods. The content of forsythiaside A, rutin, forsythin, pinoresinol, and phillygenin was determined by ultra performance liquid chromatography(UPLC). Correlation analysis, principal component analysis(PCA), orthogonal partial least squares discriminant analysis(OPLS-DA), and independent sample t-test analysis were performed on the appearance indexes and the component content. The correlation analysis showed that there were differences in the appearance traits and the component content. L~* and E~* had highly significant negative correlations with pinoresinol and phillygenin(P<0.01) and significant positive correlations with forsythiaside A(P<0.05). There were a highly significant negative correlation between a~* and forsythiaside A(P<0.01) and highly significant positive correlations of a~* with pinoresinol and phillygenin(P<0.01). There were a highly significant positive correlation between b~* and forsythiaside A(P<0.01) and highly significant negative correlations of b~* with pinoresinol and phillygenin(P<0.01). The total antioxidant capacity had highly significant negative correlations with pinoresinol and phillygenin(P<0.01). The PCA results showed that there were differences among Forsythiae Fructus samples processed by different methods. OPLS-DA marked five important indicators, which were forsythiaside A, stem length, E~*, L~*, and b~*. The results of independent sample t-test showed that the content of forsythiaside A, pinoresinol, and phillygenin, the total antioxidant capacity, and the appearance traits such as L~*, a~*, b~*, and E~* were significantly different between the Forsythiae Fructus samples processed by steaming and boiling(P<0.05). According to content determination and a related biological activity analysis, steaming is a good choice from the perspective of improving the stability of chemical constituents and antioxidant activity of Forsythiae Fructus. From the point of view of improving the stability of chemical constituents and anti-inflammatory and anti-cancer activities of Forsythiae Fructus, it is recommended to use boiling as the processing method. Based on the above analysis methods, the main indexes for the appearance traits of Forsythiae Fructus processed by different methods are powder chroma value(L~*, a~*, b~*, E~*), stem length, and total antioxidant capacity, and those for chemical constituents are the content of forsythiaside A, pinoresinol, and phillygenin. This study provides reference for seeking scientific processing methods of Forsythiae Fructus.
Forsythia/chemistry* ; Drugs, Chinese Herbal/isolation & purification* ; Fruit/chemistry* ; Antioxidants/analysis* ; Chromatography, High Pressure Liquid ; Glycosides/analysis* ; Principal Component Analysis ; Furans ; Lignans