ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Malignant insulinoma is a kind of rare and challenging neuroendocrine tumor. It is often accompanied by distant metastasis, among which liver metastasis is most common, and the prognosis is often non-promising. In this paper, we report a case of multiple liver metastases from malignant insulinoma. The patient, a 70-year-old male, was admitted to the hospital due to "episodic consciousness disorder for more than four months." Blood glucose monitoring revealed recurrent hypoglycemia in the early morning, after meals, and at night. Pancreatic perfusion CT and dynamic enhanced MRI of the liver revealed a mass in the uncinate process of the pancreatic head and multiple liver metastases. Percutaneous liver biopsy confirmed the diagnosis of insulinoma. After multidisciplinary discussions, hepatic artery embolization and radiofrequency ablation were performed in stages, in combination with everolimus treatment. Thereafter, the enhanced CT demonstrated that some liver metastases shrank. The patient had regular meals, and the blood sugar gradually increased and remained normal thereafter. This article discusses this case's clinical characteristics and multidisciplinary collaborative diagnosis and treatment, aiming to provide experience for the comprehensive clinical diagnosis and treatment of malignant insulinoma patients.

Objective:To investigate the impact of rosacea on patients＇ quality of life and mental health, and to analyze variations across different subtypes, gender, sun exposure, smoking status, ethnicity, and educational levels, as well as patients＇ demands for treatment.Methods:It was a cross-sectional study from October 2022 to January 2024, 177 rosacea patients from the Department of Dermatology, the First Affiliated Hospital of Kunming Medical University were surveyed. The cohort comprised 36 males and 141 females, with a median age of 35 (29, 44) years. Data collected through questionnaires included general information, Dermatology Life Quality Index (DLQI), Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and Patient Need Questionnaire (PNQ). Statistical analysis was conducted on the collected data.Results:The median DLQI score was 10 (3, 15), indicating moderate impact. The median GAD-7 score was 5 (2, 7), suggesting mild anxiety; while the median PHQ-9 score was 6 (2, 9), indicating mild depression. Female patients scored higher in both DLQI and GAD-7 than males ( P<0.05). Papulopustular subtypes had higher DLQI and GAD-7 scores compared to other subtypes ( P<0.01). Patients with lower education levels scored higher in DLQI and GAD-7 than those with higher education ( P<0.01), and non-smokers had higher GAD-7 scores than smokers ( P<0.05). Significant positive correlations were found between DLQI and PHQ-9 ( r=0.39, P<0.01), and DLQI and GAD-7 ( r=0.51, P<0.01), as well as between PHQ-9 and GAD-7 ( r=0.59, P<0.01). Regarding treatment needs, 159 patients (89.8%) expressed a strong desire for a clear diagnosis and treatment plan, and the same number wished for a complete resolution of all skin lesions. Female patients consistently demonstrated significantly stronger demands in all 28 treatment need items (all P<0.05). Conclusions:Rosacea significantly affects patients＇ quality of life and mental health, particularly among females, individuals with papulopustular subtypes, and those with lower education levels. There is a strong demand for definitive diagnostic and therapeutic plans, especially among female patients.

Objective:To compare the predictive performance of different machine learning models using pre-hospital data to predict adverse inhospital outcome in patients with severe trauma.Methods:A retrospective cohort study was conducted to analyze the clinical data of 100 135 patients with severe trauma from the National Trauma Data Bank (NTDB) from January 2017 to December 2018. There were 69 644 males and 30 480 females apart from 11 patients with missing gender information, with the range age of 16-89 years [(50.1±21.1)years]. Clinical characteristics included demographic information (sex and age), trauma type (blunt or penetrating trauma), pre-hospital time [emergency medical services (EMS) response time, EMS scene time, and EMS transport time], pre-hospital vital signs (systolic blood pressure, pulse rate, respiratory rate, and oxygen saturation), trauma score [Glasgow coma score (GCS) and injury severity score (ISS)]. The original data were divided into the training set (in the year 2017) and the testing set (in the year 2018) according to the year of admission, including 50 429 patients in the training set and 49 706 patients in the testing set. The patients were classified into non-adverse outcome group ( n=94 526) and adverse outcome group ( n=5 609), according to whether they had an adverse outcome or not. There were 2 808 patients with adverse outcome in the training set and 2 801 patients with adverse outcome in the testing set. All models were built based on the training set. Eight machine learning algorithms consisting of neural network (NNET), naive Bayes (NB), gradient boosting machine (GBM), adaptive boosting (Ada), random forest (RF), bagging tree (BT), categorical boosting (CatBoost) and extreme gradient boosting (XGB) were used to construct prediction models for clinical outcomes among patients with severe trauma based on their clinical features. Models were evaluated according to the sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC) and Hosmer-Lemeshow goodness-of-fit test. Results:Of the NNET, NB, GBM, Ada, RF, BT, CatBoost and XGB models in the testing set, the sensitivity was 0.84, 0.83, 0.27, 0.79, 0.83, 0.81, 0.62 and 0.78, respectively; the specificity was 0.79, 0.76, 0.81, 0.79, 0.79, 0.74, 0.83 and 0.79, respectively; the AUC was 0.89 (95% CI 0.88, 0.90), 0.86 (95% CI 0.85, 0.87), 0.54 (95% CI 0.53, 0.55), 0.86 (95% CI 0.85, 0.87), 0.88 (95% CI 0.88, 0.90), 0.83 (95% CI 0.82, 0.85), 0.77 (95% CI 0.76, 0.79) and 0.86 (95% CI 0.85, 0.87), respectively. The NNET model had the best differentiation. In terms of calibration degree, both NNET and NB showed good performance ( P>0.05 for Hosmer-Lemeshow goodness-of-fit test). Conclusion:The NNET model has a favorable predictive performance for adverse inhospital outcome in patients with severe trauma, which may provide a reference for the rapid prediction of prognosis in patients with severe trauma.

Cell aging is an extremely complex process, which is characterized by mitochondrial structural dysfunction, telomere shortening, inflammatory microenvironment, protein homeostasis imbalance, epigenetic changes, abnormal DNA damage and repair, etc. Aging is usually accompanied by structural and functional damage of tissues and organs which further induces the occurrence and development of aging-related diseases. Aging includes physiological aging caused by increased age and pathological aging induced by a variety of factors. Noteworthy, as a target organ directly contacting with the outside air, lung is more prone to various stimuli, causing pathological premature aging which is lung aging. Studies have found that there is a certain proportion of senescent cells in the lungs of most chronic respiratory diseases. However, the underlying mechanism by which these senescent cells induce lung senescence and their role in chronic respiratory diseases is still obscure. This paper focuses on the causes and classification of lung aging, the internal mechanism of lung aging involved in chronic respiratory diseases, and the application of anti-aging treatments in chronic respiratory diseases. We hope to provide new research ideas and theoretical basis for the clinical prevention and treatment in chronic respiratory diseases.
Aging/pathology* ; Cellular Senescence ; Humans ; Lung/pathology* ; Lung Diseases/pathology* ; Respiration Disorders/pathology* ; Telomere ; Telomere Shortening

Objective: To retrospectively analyze the data of Chinese patients with newly diagnosed acute promyelocytic leukemia (APL) to preliminarily discuss the clinical and cytogenetic characteristics. Methods: From February 2004 to June 2020, patients with newly diagnosed APL aged ≥ 15 years who were admitted to the Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Science & Peking Union Medical College were chosen. Clinical and laboratory features were retrospectively analyzed. Results: A total of 790 cases were included, with a male to female ratio of 1.22. The median age of the patients was 41 (15-76) years. Patients aged between 20 and 59 predominated, with 632 patients (80%) of 790 patients classified as low and intermediate risk and 158 patients (20%) of 790 patients classified as high risk. The white blood cell, platelet, and hemoglobin levels at diagnosis were 2.3 (0.1-176.1) ×10(9)/L, 29.5 (2.0-1220.8) ×10(9)/L, and 89 (15-169) g/L, respectively, and 4.8% of patients were complicated with psoriasis. The long-form type of PML-RARα was most commonly seen in APL, accounting for 58%. Both APTT extension (10.3%) and creatinine>14 mg/L (1%) are rarely seen in patients at diagnosis. Cytogenetics was performed in 715 patients with newly diagnosed APL. t (15;17) with additional chromosomal abnormalities were found in 155 patients, accounting for 21.7%; among which, +8 was most frequently seen. A complex karyotype was found in 64 (9.0%) patients. Next-generation sequencing was performed in 178 patients, and 113 mutated genes were discovered; 75 genes had an incidence rate>1%. FLT3 was the most frequently seen, which accounted for 44.9%, and 20.8% of the 178 patients present with FLT3-ITD. Conclusions: Patients aged 20-59 years are the most common group with newly diagnosed APL. No obvious difference was found in the ratio of males to females. In terms of risk stratification, patients divided into low and intermediate risk predominate. t (15;17) with additional chromosomal abnormalities accounted for 21% of 715 patients, in which +8 was most commonly seen. The long-form subtype was most frequently seen in PML-RARα-positive patients, and FLT3 was most commonly seen in the mutation spectrum of APL.
Adult ; Aged ; Chromosome Aberrations ; Cytogenetics ; Female ; Humans ; Leukemia, Promyelocytic, Acute/genetics* ; Male ; Middle Aged ; Mutation ; Oncogene Proteins, Fusion/genetics* ; Retrospective Studies ; Young Adult

OBJECTIVES: To investigate the clinical treatment outcomes and the changes of the outcomes over time in extremely preterm twins in Guangdong Province, China. METHODS: A retrospective analysis was performed for 269 pairs of extremely preterm twins with a gestational age of <28 weeks who were admitted to the department of neonatology in 26 grade A tertiary hospitals in Guangdong Province from January 2008 to December 2017. According to the admission time, they were divided into two groups: 2008-2012 and 2013-2017. Besides, each pair of twins was divided into the heavier infant and the lighter infant subgroups according to birth weight. The perinatal data of mothers and hospitalization data of neonates were collected. The survival rate of twins and the incidence rate of complications were compared between the 2008-2012 and 2013-2017 groups. RESULTS: Compared with the 2008-2012 group, the 2013-2017 group (both the heavier infant and lighter infant subgroups) had lower incidence rates of severe asphyxia and smaller head circumference at birth (P<0.05). The mortality rates of both of the twins, the heavier infant of the twins, and the lighter infant of the twins were lower in the 2013-2017 group compared with the 2008-2012 group (P<0.05). Compared with the 2008-2012 group, the 2013-2017 group (both the heavier infant and lighter infant subgroups) had lower incidence rates of pulmonary hemorrhage, patent ductus arteriosus (PDA), periventricular-intraventricular hemorrhage (P-IVH), and neonatal respiratory distress syndrome (NRDS) and a higher incidence rate of bronchopulmonary dysplasia (P<0.05). CONCLUSIONS There is a significant increase in the survival rate over time in extremely preterm twins with a gestational age of <28 weeks in the 26 grade A tertiary hospitals in Guangdong Province. The incidences of severe asphyxia, pulmonary hemorrhage, PDA, P-IVH, and NRDS decrease in both the heavier and lighter infants of the twins, but the incidence of bronchopulmonary dysplasia increases. With the improvement of diagnosis and treatment, the multidisciplinary collaboration between different fields of fetal medicine including prenatal diagnosis, obstetrics, and neonatology is needed in the future to jointly develop management strategies for twin pregnancy.
Bronchopulmonary Dysplasia/epidemiology* ; Female ; Gestational Age ; Humans ; Infant ; Infant, Extremely Premature ; Infant, Newborn ; Pregnancy ; Respiratory Distress Syndrome, Newborn/epidemiology* ; Retrospective Studies ; Treatment Outcome

Objective: To investigate the application effect of augmented reality and mixed reality navigation technology in three-dimensional(3D) laparoscopic narrow right hepatectomy(LRH). Methods: A retrospective analysis was performed on the clinical data of 5 patients with hepatic malignancy admitted to the First Department of Hepatobiliary Surgery,Zhujiang Hospital,Southern Medical University from September 2020 to June 2021,all of whom were males,aged from 42 to 74 years.Preoperative evaluation was performed using the self-developed 3D abdominal medical image visualization system; if all the 5 patients were to receive right hemihepatectomy,the remnant liver volume would be insufficient,so LRH were planned.During the operation,the independently developed 3D laparoscopic augmented reality and mixed reality surgical navigation system was used to perform real-time multi-modal image fusion and interaction between the preoperative 3D model and 3D laparoscopic scene.Meanwhile,intraoperative ultrasound assisted indocyanine green fluorescence was used to determine the surgical path.In this way,the LRH under the guidance of augmented reality and mixed reality navigation was completed.The predicted liver resection volume was evaluated before surgery,actual resected liver volume,surgical indicators and postoperative complications were analyzed. Results: All the 5 patients completed LRH under the guidance of augmented reality and mixed reality navigation technology,with no conversion to laparotomy.The median operative time was 300 minutes(range:270 to 360 minutes),no intraoperative blood transfusion was performed,and the median postoperative hospital stay was 8 days(range:7 to 9 days).There were no perioperative deaths,or postoperative complications such as liver failure,bleeding,or biliary fistula. Conclusion: For patients who need to undergo LRH,the use of augmented and mixed reality navigation technology can safely and effectively guide the implementation of surgery,retain more functional liver volume,improve surgical safety,and reduce postoperative complications.
Adult ; Aged ; Augmented Reality ; Hepatectomy/methods* ; Humans ; Imaging, Three-Dimensional ; Laparoscopy/methods* ; Liver Neoplasms/surgery* ; Male ; Middle Aged ; Retrospective Studies ; Technology

Prostate cancer (PCa) is the second-most common cancer among men. Both active surveillance or watchful waiting (AS/WW) and focal laser ablation (FLA) can avoid the complications caused by radical treatment. How to make the choice between these options in clinical practice needs further study. Therefore, this study aims to compare and analyze their effects based on overall survival (OS) and cancer-specific survival (CSS) to obtain better long-term benefits. We included patients with low-risk PCa from the Surveillance Epidemiology and End Results database of 2010-2016. Multivariate Cox proportional hazard analyses were conducted for OS and CSS in the two groups. To eliminate bias, this study applied a series of sensitivity analyses. Moreover, Kaplan-Meier curves were plotted to obtain survival status. A total of 18 841 patients with low-risk PCa were included, with a median of 36-month follow-up. According to the multivariate Cox proportional hazard regression, the FLA group presented inferior survival benefits in OS than the AS/WW group (hazard ratio [HR]: 2.13, 95% confidence interval [CI]: 1.37-3.33, P < 0.05). After adjusting for confounders, the result persisted (HR: 1.69, 95% CI: 1.02-2.81, P < 0.05). According to the results of the sensitivity analysis, the inverse probability of the treatment weighing model indicated the same result in OS. In conclusion, AS/WW and FLA have the advantage of fewer side effects and the benefit of avoiding overtreatment compared with standard treatment. Our study suggested that AS/WW provides more survival benefits for patients with low-risk PCa. More relevant researches and data will be needed for further clarity.
Humans ; Laser Therapy ; Male ; Proportional Hazards Models ; Prostatectomy ; Prostatic Neoplasms ; Risk ; Watchful Waiting