Objective:Through the analysis of the detection results of hepatitis B virus (HBV) serological markers in the population admitted to Shengjing Hospital of China Medical University in recent five years, the prevalence and characteristics of HBV infection in our hospital were clarified.Methods:Cross-sectional study was conducted. A total of 1 017 030 patients who underwent HBV serological markers testing in Shengjing Hospital of China Medical University from January 1, 2019 to December 31, 2023 were enrolled as the research objects. The included cases were divided into 14 districts in Liaoning: Shenyang (345 346), Dalian (13 219), Anshan (38 536), Fushun (40 067), Benxi (28 883), Dandong (34 284), Jinzhou (35 827), Yingkou (40 573), Fuxin (30 675), Liaoyang (26 282), Panjin (21 008), Tieling (74 632), Chaoyang (43 858) and Huludao (20 949). The included cases were divided into 7 groups according to age: 0-10 years old (162 457), 11-20 years old (33 657), 21-30 years old (129 791), 31-40 years old (235 378), 41-50 years old (124 925), 51-60 years old (143 361), and≥61 years old (187 461). According to different time points of the implementation of vaccination policies, cases born from January 1, 1992 to December 31, 2023 were divided into three groups: the group from January 1, 1992 to December 31, 2001 (94 194), the group from January 1, 2002 to May 31, 2005 (70 428), and the group from June 1, 2005 to December 31, 2023 (87 057). The general data and HBV serological markers results were collected for statistical analysis. The comparison of intergroup rates was conducted using the Chi-square test, and the trend test was conducted using the Cochran Armitage test. Results:In recent five years, the total positivity of serum hepatitis B surface antigen (HBsAg), hepatitis B surface antibody (HBsAb), hepatitis B e antigen (HBeAg), hepatitis B e antibody (HBeAb) and hepatitis B core antibody (HBcAb) were 6.68% (67 938/1 017 030), 51.69% (485 627/939 498), 2.37% (21 961/926 626), 15.00% (138 853/925 759) and 27.86% (257 344/923 703), respectively. According to gender, the total positivity of HBV serological markers in men was significantly higher than that in women, and the difference was statistically significant (HBsAg: χ 2=5 599.286, P<0.001; HBsAb: χ 2=5.065, P=0.024; HBeAg: χ 2=2 451.420, P<0.001; HBeAb: χ 2=1 066.145, P<0.001; HBcAb: χ 2=4 013.618, P<0.001). According to the age group, the peak of HBsAg positivity was mainly distributed in the age group of 41-50 years old, with a positivity of 10.48% (2 864/27 324). The positivity of HBsAb decreased with age.The positivity of HBsAb decreased with age increase, highest in the 0-10 years old group, and a decrese in the 11-20 years old group ( Z=18 915.453, P<0.001). The positivity of HBcAb increased with age ( Z=27 493.853, P<0.001). According to the regional groups, the positivity of HBsAg and HBeAg in Shenyang City were 5.01% (3 996/76 974) and 1.45% (944/69 117) respectively. The positivity of HBsAb in patients from Liaoyang district was the highest [55.89% (3 362/6 150)]. The highest positivity of HBeAb and HBcAb were in patients from Dandong district, 19.90% (3 362/6 150) and 37.37% (3 119/8 680) respectively. According to the different time points of the implementation of the vaccination policies, the positivity of HBsAg in cases born from June 1, 2005 and December 31, 2023 decreased to 0.30% (259/87 057) compared with cases born before this time point (χ 2=5 777.47, P<0.001). Conclusions:The analysis of HBV serological laboratory characteristics showed that the positivity of contagious HBV serological markers was still high. The positivity of male was higher than that of female. HBsAb levels decreased significantly in the 11-20 years old group, suggesting the necessity to strengthen hepatitis B prevention in this age group. There were regional differences in the prevalence of hepatitis B, suggesting the necessity to optimize and improve HBV prevention and control strategies.

Red blood cell (RBC) antibodies refer to antibodies targeting erythrocyte surface antigens, which can be classified by their origin and characteristics into autoantibodies (warm and cold antibodies), alloantibodies, drug-induced antibodies, and irregular antibodies. Among them, the autoantibody can induce and mediate the autoimmune hemolytic anemia (AIHA) which represents one of the most common acquired hemolytic disorders in clinical practice, characterized by accelerated RBC destruction and shortened erythrocyte lifespan. AIHA is primarily categorized into warm antibody type, cold antibody type, and mixed type based on its optimal reaction temperature.With advancements in immunology and molecular biology research, and clinical laboratory technologies, the clinical testing methods for autoantibodies have evolved from traditional agglutination tests to high-sensitivity techniques such as enzyme-linked immunosorbent assays (ELISA), flow cytometry, and molecular diagnostics. These technological improvements have not only enhanced the detection rate of anti-RBC antibodies but also significantly expanded their clinical applications.This article provides a critical review of the clinical significance of warm and cold antibodies in AIHA, encompassing their detection methods, pathogenic mechanisms, and therapeutic progress, aiming to serve as a reference for both clinicians and laboratory physicians.

Objective:To conduct consistency analysis of antinuclear antibody (ANA) anti-cell type 2 (AC-2) fluorescence pattern based on HEp-2 cells and monkey liver matrix, and to investigate the clinical significance of the AC-2 and anti-dense fine speckled 70 (DFS70) antibody.Methods:This study is a cross-sectional study. Retrospective analysis of the results of 33 921 ANA tests detected by indirect immunofluorescence assay (IIFA) from January 2024 to December 2024 in Shengjing Hospital of China Medical University. A total of 457 patients with AC-2 positive were taken from the cohort as the research objectives, then the positive rate of AC-2 in ANA among the 457 patients and its distribution in different diseases were counted. One hundred cases were selected from 457 AC-2 positive cases, whose AC-2 fluorescence pattern were analyzed using HEp-2 cells and HEp-2 cells combined with monkey liver matrix and the anti-DFS70 antibody was detected to help identifying the AC-2 fluorescence pattern. Finally, the consistency of the two methods in identifying the AC-2 fluorescence pattern was compared and the clinical significance of the AC-2 and anti-DFS70 antibody was discussed. Spearman correlation analysis was used to compare the correlation between the reciprocals of ANA titer of AC-2 positive samples and the levels of anti-DFS70 antibody.Results:AC-2 positive cases accounted for 1.15% (457/33 921) and 3.64% (457/12 555) of the all cases and ANA positive cases, respectively. The positive rate of AC-2 in females (394/457) was higher than that in males (63/457), P<0.01. Totally 87.09% of the AC-2 positive patients aged 40 and below. Moreover, the most positive cases of AC-2 occured in female infertility, adverse pregnancy history, pregnancy status and patients with connective tissue disease or joint pain/arthritis, accounting for 57.33% of the total positive cases. AC-2 positive patients were mostly distributed in women with infertility, bad pregnancy history, pregnancy status, connective tissue disease or patients manifested with joint pain/arthritis, accounting for 57.33% (262/457) of the total positive cases.The consistency analysis of AC-2 fluorescence pattern based on HEp-2 cells and HEp-2 cells combined with monkey liver matrix showed that among 100 serum samples, 97 cases were unanimously determined to be AC-2 fluorescence pattern, suggesting that HEp-2 cells can identify most AC-2 fluorescent pattern.The other three cases were identified as AC-1 and AC-5, and these AC-2 miscible pattern could be identified by monkey liver matrix and DFS70 antibody. In addition, the positive rate of anti-DFS70 antibody was 92.78% (90/97) in AC-2 fluorescent pattern, and the titer of ANA was positively correlated with the concentration of anti-DFS70 antibody in 62 cases with single positive anti-DFS70 antibody ( r=0.441, P<0.05). Among 62 cases of single positive anti-DFS70 antibody, 5 patients with connective tissue disease and 57 patients with non-autoimmune diseases; among the 25 cases of positive DFS70 antibody combined with non-positive ANA spectrum antibody, 4 cases were pregnant, and 16 cases had infertility or bad pregnancy history. All of them were combined with anti-thyroid globulin or thyroid peroxidase antibody positive, lupus anticoagulant positive, phospholipid antibody and herpes virus, rubella virus or cytomegalovirus antibody positive. Two cases were arthritis patients with positive anti-cyclic citrulline colypeptide antibody. One patient was Hashimoto′s thyroid, and 1 case was antiphospholipid syndrome. Three autoimmune diseased patients with positive DFS70 antibody were combined with positive ANA spectrum antibody, which were anti-Sjogren′s syndrome A antigen, polymyositis-scleroderma antigen antibody. Conclusion:Most AC-2 fluorescent pattern can be identified by HEp-2 cells, monkey liver matrix and anti-DFS70 antibody are helpful to identify AC-2, AC-2 fluorescent pattern and DFS70 antibodies can be positive alone or combined with ANA spectrum antibodies and other non-ANA spectrum antibodies in different diseases, which account for a high proportion of female infertility patients, patients with bad pregnancy history and pregnancy status.

Tunnel peripherally inserted central catheter (TPICC) catheterization technology is a new type of PICC catheterization that applies subcutaneous tunnel technology to traditional PICC catheterization. It has significant advantages in reducing catheter-related complications and improving patient comfort. According to the different methods of creating subcutaneous tunnels, it can be divided into one-needle TPICC catheterization technology and two-needle TPICC catheterization technology. This article reviews the overview, clinical application status and application effects of two TPICC catheterization techniques, and puts forward existing problems and prospects, in order to provide reference for clinical practice and related research of the two.

Objective:To explore the expressions of long noncoding RNA (lncRNA) homeobox A11 antisense RNA (HOXA11-AS) and lymphoid enhancer-binding factor 1 antisense RNA 1 (LEF1-AS1) in hypopharyngeal carcinoma tissues and their relationships with prognosis.Methods:Prospectively, 80 patients with hypopharyngeal carcinoma who were treated in Handan Central Hospital from February 2019 to February 2021 were selected. The hypopharyngeal carcinoma tissues resected surgically and the adjacent normal tissues (more than 2 cm away from the edge of the cancer tissue) were obtained. The expressions of HOXA11-AS and LEF1-AS1 were detected by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). The expressions of HOXA11-AS and LEF1-AS1 in hypopharyngeal carcinoma tissues and adjacent normal tissues were compared. The relationships between their expressions and clinicopathological features were analyzed. The Kaplan-Meier method was used to analyze the relationships between high/low expressions of HOXA11-AS and LEF1-AS1 and the prognosis of patients with hypopharyngeal carcinoma.Results:The expressions of HOXA11-AS and LEF1-AS1 in hypopharyngeal carcinoma tissues were higher than those in adjacent normal tissues (all P<0.05). The expressions of HOXA11-AS and LEF1-AS1 in hypopharyngeal carcinoma tissues were related to tumor node metastasis (TNM) stage, degree of differentiation, and lymph node metastasis (all P<0.05). The 3-year overall survival rates of patients with high expressions of HOXA11-AS and LEF1-AS1 in hypopharyngeal carcinoma tissues were lower than those of patients with low expressions (all P<0.05). Conclusions:The expressions of HOXA11-AS and LEF1-AS1 are increased in hypopharyngeal carcinoma tissues, which are related to poor prognosis of patients with hypopharyngeal carcinoma.

Objective:To explore the value of non-invasive habitat imaging (HI) multi-parametric MRI (mpMRI) in predicting the risk of prostate cancer (PCa).Methods:In this cross-sectional study, 220 patients with PCa confirmed by radical prostatectomy (RP) who underwent multi-parametric MRI (mpMRI) scanning at Xijing Hospital, Air Force Military Medical University from January 2018 to May 2024 were retrospectively collected. Patients were divided into a training set (154 cases) and a test set (66 cases) by simple random sampling in a 7∶3 ratio. Based on mpMRI imaging, the apparent diffusion coefficient (ADC), perfusion fraction (f), and mean kurtosis (MK) of each voxel were integrated. The K-means clustering algorithm was used to divide the PCa target lesions into habitat subregions, generate habitat maps, and calculate the proportion of each habitat subregion in the entire lesion. According to the 2019 International Society of Urological Pathology (ISUP) guidelines, patients were categorized into a low-risk group (ISUP≤2, 65 cases) and a high-risk group (ISUP≥3, 155 cases). The RP specimens were matched with the habitat map to identify corresponding habitat subregions, and the ISUP grade of each subregion was individually evaluated to calculate the detection rate of high-risk PCa patients. The logistic regression analysis was applied to identify the independent risk factors associated with PCa risk, and the HI-clinical imaging model and clinical imaging model were constructed. The efficacy of the models was assessed using receiver operating characteristic curve.Results:Based on the optimal cluster number, the habitat was divided into three subregions. Habitat 1 had lower ADC and f values and higher MK values, while habitat 2 had the opposite characteristics, and habitat 3 was intermediate. The proportion of habitat 1 in the high-risk group was 28.8%, in the low-risk group was 8.9%. In the training set, the comparison of habitat subregions with pathological results showed that the detection rate of high-risk lesions was 66.9% (103/154) in habitat 1, 25.3% (39/154) in habitat 2, and 47.4% (73/154) in habitat 3. The logistic regression analysis indicated that the proportion of habitat 1 ( OR=3.03, 95% CI 1.77-5.18, P<0.001), prostate-specific antigen ( OR=1.66, 95% CI 1.04-2.66, P=0.034), and the prostate imaging reporting and data system score ( OR=1.65, 95% CI 1.00-2.70, P=0.048) as independent risk factors for high-risk PCa. In the training set, the area under the curve (AUC) for predicting PCa risk was 0.854 (95% CI 0.789-0.920) for the HI-clinical imaging model and 0.779 (95% CI 0.701-0.856) for the clinical imaging model. In the test set, the AUC values were 0.809 (95% CI 0.693-0.895) and 0.738 (95% CI 0.619-0.856), respectively. Conclusion:HI based on mpMRI can effectively predict the risk of PCa.

Temporomandibular joint osteoarthritis (TMJ-OA) is a common disease often accompanied by pain, seriously affecting physical and mental health of patients. Abnormal innervation at the osteochondral junction has been considered as a predominant origin of arthralgia, while the specific mechanism mediating pain remains unclear. To investigate the underlying mechanism of TMJ-OA pain, an abnormal joint loading model was used to induce TMJ-OA pain. We found that during the development of TMJ-OA, the increased innervation of sympathetic nerve of subchondral bone precedes that of sensory nerves. Furthermore, these two types of nerves are spatially closely associated. Additionally, it was discovered that activation of sympathetic neural signals promotes osteoarthritic pain in mice, whereas blocking these signals effectively alleviates pain. In vitro experiments also confirmed that norepinephrine released by sympathetic neurons promotes the activation and axonal growth of sensory neurons. Moreover, we also discovered that through releasing norepinephrine, regional sympathetic nerves of subchondral bone were found to regulate growth and activation of local sensory nerves synergistically with other pain regulators. This study identified the role of regional sympathetic nerves in mediating pain in TMJ-OA. It sheds light on a new mechanism of abnormal innervation at the osteochondral junction and the regional crosstalk between peripheral nerves, providing a potential target for treating TMJ-OA pain.
Animals ; Osteoarthritis/physiopathology* ; Mice ; Sympathetic Nervous System/physiopathology* ; Temporomandibular Joint Disorders/physiopathology* ; Arthralgia ; Sensory Receptor Cells ; Disease Models, Animal ; Norepinephrine ; Male ; Temporomandibular Joint/physiopathology* ; Pain Measurement

Large language models (LLMs) such as ChatGPT, Claude, Llama, and Qwen are emerging as transformative technologies for the diagnosis and treatment of various diseases. With their exceptional long-context reasoning capabilities, LLMs are proficient in clinically relevant tasks, particularly in medical text analysis and interactive dialogue. They can enhance diagnostic accuracy by processing vast amounts of patient data and medical literature and have demonstrated their utility in diagnosing common diseases and facilitating the identification of rare diseases by recognizing subtle patterns in symptoms and test results. Building on their image-recognition abilities, multimodal LLMs (MLLMs) show promising potential for diagnosis based on radiography, chest computed tomography (CT), electrocardiography (ECG), and common pathological images. These models can also assist in treatment planning by suggesting evidence-based interventions and improving clinical decision support systems through integrated analysis of patient records. Despite these promising developments, significant challenges persist regarding the use of LLMs in medicine, including concerns regarding algorithmic bias, the potential for hallucinations, and the need for rigorous clinical validation. Ethical considerations also underscore the importance of maintaining the function of supervision in clinical practice. This paper highlights the rapid advancements in research on the diagnostic and therapeutic applications of LLMs across different medical disciplines and emphasizes the importance of policymaking, ethical supervision, and multidisciplinary collaboration in promoting more effective and safer clinical applications of LLMs. Future directions include the integration of proprietary clinical knowledge, the investigation of open-source and customized models, and the evaluation of real-time effects in clinical diagnosis and treatment practices.
Humans ; Large Language Models ; Tomography, X-Ray Computed

Purpose To explore the value of contrast-enhanced CT habitat imaging(HI)in preoperative non-invasive visualization for predicting pathological grading of clear cell renal carcinoma(ccRCC).Materials and Methods A retrospective analysis was conducted on enhanced CT images and clinical data from 240 patients with pathologically confirmed ccRCC at Xijing Hospital,the Fourth Military Medical University from January 2020 to December 2023.All patients were randomly divided into training and test sets at a 7:3 ratio and classified into low-grade group(International Society of Urological Pathology Ⅰ-Ⅱ)and high-grade group(International Society of Urological Pathology Ⅲ-Ⅳ)based on postoperative pathology.Using wash-in and wash-out parametric maps,the tumors were segmented into three perfusion-based habitat subregions(low,medium and high)via K-means clustering,and the volume fraction of each subregion was calculated.Predictive factors were selected from habitat features and clinical variables(including sex,age,tumor size,etc.)using Logistic regression.Three models were constructed:a clinical model,a habitat imaging model and a combined clinical-habitat model.Model performance was evaluated using receiver operating characteristic curve,calibration curve and decision curve analysis.Results Habitat 3 exhibited higher wash-in and wash-out gradients compared to Habitats 1 and 2,indicating hyper perfusion.Its proportion was significantly higher in the low-grade group than in the high-grade group(Z=-7.71,-5.11,both P<0.01).Multivariate Logistic regression identified hypertension,maximum tumor diameter and platelet-to-lymphocyte ratio as independent risk factors for high-grade ccRCC,while the proportion of Habitat 3 was a protective factor(OR=0.297,95％CI 0.184-0.479).The combined clinical-habitat model demonstrated the highest predictive performance[area under the curve(AUC)=0.938],significantly outperforming the clinical model(AUC=0.801,Z=-3.832,P<0.01)and the habitat imaging model(AUC=0.895,Z=-2.157,P=0.031).Conclusion The clinical-habitat imaging model achieves the highest predictive performance for ccRCC pathological grading.Contrast-enhanced CT habitat imaging provides significant incremental value in predicting ccRCC pathological grading,showing potential to guide precision medicine in clinical practice.

Purpose To explore the value of contrast-enhanced CT habitat imaging(HI)in preoperative non-invasive visualization for predicting pathological grading of clear cell renal carcinoma(ccRCC).Materials and Methods A retrospective analysis was conducted on enhanced CT images and clinical data from 240 patients with pathologically confirmed ccRCC at Xijing Hospital,the Fourth Military Medical University from January 2020 to December 2023.All patients were randomly divided into training and test sets at a 7:3 ratio and classified into low-grade group(International Society of Urological Pathology Ⅰ-Ⅱ)and high-grade group(International Society of Urological Pathology Ⅲ-Ⅳ)based on postoperative pathology.Using wash-in and wash-out parametric maps,the tumors were segmented into three perfusion-based habitat subregions(low,medium and high)via K-means clustering,and the volume fraction of each subregion was calculated.Predictive factors were selected from habitat features and clinical variables(including sex,age,tumor size,etc.)using Logistic regression.Three models were constructed:a clinical model,a habitat imaging model and a combined clinical-habitat model.Model performance was evaluated using receiver operating characteristic curve,calibration curve and decision curve analysis.Results Habitat 3 exhibited higher wash-in and wash-out gradients compared to Habitats 1 and 2,indicating hyper perfusion.Its proportion was significantly higher in the low-grade group than in the high-grade group(Z=-7.71,-5.11,both P<0.01).Multivariate Logistic regression identified hypertension,maximum tumor diameter and platelet-to-lymphocyte ratio as independent risk factors for high-grade ccRCC,while the proportion of Habitat 3 was a protective factor(OR=0.297,95％CI 0.184-0.479).The combined clinical-habitat model demonstrated the highest predictive performance[area under the curve(AUC)=0.938],significantly outperforming the clinical model(AUC=0.801,Z=-3.832,P<0.01)and the habitat imaging model(AUC=0.895,Z=-2.157,P=0.031).Conclusion The clinical-habitat imaging model achieves the highest predictive performance for ccRCC pathological grading.Contrast-enhanced CT habitat imaging provides significant incremental value in predicting ccRCC pathological grading,showing potential to guide precision medicine in clinical practice.