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.

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 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 explore the characteristics of high-frequency users of pediatric telemedicine outpatient services and analyze the factors influencing the frequency of use,providing theoretical support for the promotion and broader application of pediatric telemedicine outpatient services.Methods 197 parents of patients were randomly selected for telephone questionnaire survey by cross-sectional research method,and were divided into high frequency and low frequency users according to their frequency of use.Based on annual usage frequency,participants were divided into high-frequency and low-frequency groups.Mann-Whitney U test and x2 test or Fisher exact probability method were used for univariate analysis,and multiple logistic regression model was used to analyze the independent influencing fac-tors of the difference in the frequency of pediatric outpatient telemedicine use.Results A total of 165 questionnaires were completed.Among high-frequency users,the proportion of provinces outside Beijing was higher than that of low-frequency users,the overall knowledge level of information was better than that of low-frequency users,the overall satisfaction evaluation was better than that of low-frequency users,and the proportion of gastroenterology pa-tients was higher than that of low-frequency users.Conclusion Pediatric medical institutions need to strengthen the pub-licity of the platform,improve service quality,improve user experience by optimizing technical means,further pro-mote Internet outpatient services,and expand its application potential in children's health management.

Alzheimer′s disease(AD), as a common cognitive impairment disorder in the elderly, has imposed a significant burden on the global public health system.The latest international diagnostic criteria emphasize defining AD from a biological perspective and viewing it as a continuous pathological and physiological change process.This article, guided by the principle of ′early diagnosis-precision intervention′, systematically elaborates on the current research status of AD regarding diagnostic criteria, biomarkers, new drug development, and the establishment of China-specific AD cohorts.It also provides an in-depth outlook on the future research directions and clinical application prospects of AD.

Objective:We aimed to analyze the spatio-temporal trend of breast cancer incidence and mortality in Shandong Province from 2012 to 2023 and predict the development trend from 2024 to 2030.Methods:Data on the incidence and mortality of breast cancer in Shandong Province from 2012 to 2023 were obtained from the Shandong Cancer Registry. The incidence, age-specific incidence, mortality, and age-specific mortality in different years, as well as in urban and rural areas, were calculated, and the rates were standardized based on the age composition of the Chinese standard population in 2000. The average annual percent change (AAPC) rate was calculated using Joinpoint 4.8.0.1 software. The global and local spatial autocorrelation analysis were performed using GeoDa 1.12 software. The Bayesian age-period-cohort model was used to predict the trend of breast cancer incidence and mortality from 2024 to 2030.Results:From 2012 to 2023, the breast cancer age-standardized incidence rate (ASIR) showed an increasing trend. The ASIR increased from 30.48/100 000 in 2012 to 39.94/100 000 in 2023 (AAPC=2.59%, P<0.001). The ASIR of urban and rural females also showed an upward trend. Additionally, the ASIR in rural areas (AAPC=3.33%, P<0.001) increased more than that in urban areas (AAPC=1.83%, P=0.002). The incidence peak of breast cancer mainly concentrated in population aged 45-64 years, and with the increase of years, the incidence peak gradually moved forward. The age-standardized mortality rate (ASMR) showed a downward trend. The ASMR decreased from 6.89/100 000 in 2012 to 4.93/100 000 in 2023 (AAPC=-3.12%, P<0.001). The ASMR of urban and rural females also showed a downward trend (urban: AAPC=-3.56%, P=0.007; rural: AAPC=-2.72%, P<0.001). The spatial analysis showed that from 2015 to 2023, the clustering areas of breast cancer incidence and mortality in Shandong had changed significantly. In 2015, the "High-high clusters" of ASIR mainly included Wendeng District in Weihai City, Dongying District, Kenli District, Lijin County, Guangrao County in Dongying City, Tianqiao District, Shizhong District in Jinan City; In 2023, the "High-high clusters" mainly included Jiaxiang County, Liangshan County, Jinxiang County, Wenshang County, Rencheng District in Jining City, Hedong District in Linyi City, Guangrao County in Dongying City. In 2015, the "High-high clusters" of ASMR only included Wenshang County in Jining City. In 2023, the "High-high clusters" mainly included Laizhou County in Yantai City, Junan County and Yishui County in Linyi City, Gaotang County in Liaocheng City, Dongping County and Ningyang County in Taian City. The Bayesian age-period-cohort model predicted that the ASIR trend of breast cancer in Shandong tended to be smooth (AAPC=0.33%, P=0.001). However, the ASMR remained decreasing (AAPC=-4.68%, P<0.001). Conclusions:The breast cancer incidence in Shandong showed an increasing trend, and it is expected to be smooth by 2030. However, the mortality showed a continuous downward trend. The incidence peak was mainly in the population aged 45-64 years, with obvious regional differences. Targeted prevention and control measures should be taken for high-risk groups and areas in Shandong Province.