Background: Influenza A virus (IAV) is a negative-sense RNA virus of the Orthomyxoviridae family and is the etiological agent of a highly contagious acute respiratory disease that can lead to acute lung injury. Objective: To elucidate the molecular mechanisms of IAV infection, an integrative research approach combining gene expression profiling, multinetwork analysis, and in vivo experimental validations was employed. Methods: First, a series of network-based analyses were performed, including protein-protein interaction network construction, weighted gene co-expression network analysis, and subsequent gene set enrichment analysis, to identify the major underlying mechanisms of IAV infection. Following gene expression analysis, core targets, both direct and indirect regulators, were screened. An IAV (H1N1) strain A/PR/8/34-induced acute lung injury mouse model was constructed for in vivo validations. Batch one included two groups to evaluate findings from the multi-network analysis: Mock (n = 10; 5 males and 5 females) and IAV (n = 10; 5 males and 5 females). Batch two included three groups to assess the role of toll-like receptor 3 (TLR3) in IAV infection: Mock (n = 6; 3 males and 3 females), IAV (n = 6; 3 males and 3 females), and TLR3 inhibitor (n = 6; 3 males and 3 females). Body weight was measured on days 0, 3, and 5 after infection. On day 5, lung tissues were collected to assess viral load and histopathological changes. Key targets were examined using enzyme-linked immunosorbent assay, Western blotting, and immunofluorescence staining, both in sera and lung tissues. Results: IAV infection was significantly associated with dysregulation of the immune-inflammation system, such as the LTR, nucle-otide-binding oligomerization domain-(NOD) like receptor, retinoic acid-inducible gene I-like receptor, and nuclear factor kappa-B signaling pathways. Gene set enrichment analysis further indicated that the TLR and necroptosis signaling pathways played crucial roles in the progression of IAV infection (TLR signaling pathway normalized enrichment score = 2.3941, P = 1.00 × 10 ⁻¹⁰; necroptosis normalized enrichment score = 1.9421, P = 6.21 × 10 ⁻⁷). Among the core targets, TLR3 and mixed lineage kinase domain-like protein (MLKL) may regulate gene expression at the transcriptional level (all P < 0.05). In vivo validation using an IAV (PR8) infected acute lung injury mouse model demonstrated increased viral load and lung index, alveolar structural damage, and inflammatory cell infiltration. Immunofluorescence staining exhibited large gaps in Lamin B1 staining and breaches in Emerin signals following IAV-PR8 infection. Expression levels of TLR3, p-receptor-interacting serine/threonine-protein kinase 3 (RIPK3)/RIPK3, and p-mixed lineage kinase domain-like protein (MLKL)/MLKL proteins in lung tissues, as well as proinflammatory factors and mediators in sera, were significantly elevated after IAV infection. Moreover, enhanced neutrophil infiltration (myeloperoxidase) and citrullinated histone H3 (a neutrophil extracellular trap-specific marker), both established indicators of neutrophil extracellular trap formation, were observed. Notably, treatment with a TLR3 inhibitor significantly ameliorated IAV-induced acute lung injury by regulating necroptosis-related targets. Conclusion: Our study provides network-based in vivo evidence that TLR3-receptor-interacting serine/threonine-protein kinase 3-MLKL-mediated necroptosis may underlie IAV-induced acute lung injury and could serve as a potential therapeutic target in severe influenza cases.

Objective To investigate the prevalence of cognitive frailty and its associated factors among middle-aged and elderly patients with chronic obstructive pulmonary disease (COPD) in Gusu District, Suzhou City, to provide a basis for intervention measures. Methods COPD patients managed at Pingjiang New Town Community Health Service Center in Gusu District from January to December 2024 were selected. Data were collected using the Fried Frailty Phenotype, Mini-Mental State Examination, and a general information questionnaire. Chi-square tests, one-way analysis of variance, and multivariate Logistic regression were used for data analysis. Results A total of 1 388 patients were included, with 916 males (65.99%) and 472 females (34.01%). Cognitive frailty was observed in 554 cases (prevalence: 39.91%). Univariate analysis showed significant differences in gender, education, visual impairment, hearing impairment, sleep disorders, and disease duration between the cognitive frailty and non-cognitive frailty groups (all P<0.05). Multivariate Logistic regression indicated that education was negatively associated with cognitive frailty (illiteracy OR=6.969, P<0.001; primary school OR=1.499, P=0.005), while visual impairment (OR=2.347, P<0.001), hearing impairment (OR=1.705, P<0.001), sleep disorders (OR=2.488, P<0.001), and disease duration (≤5 years OR=0.329, P<0.001; 6-10 years OR=0.487, P<0.001) were positively associated with cognitive frailty. Conclusion The prevalence of cognitive frailty among middle-aged and elderly COPD patients in Gusu District, Suzhou City, is high (39.91%). Epositively associated with cognitive frailtyducation, sensory impairments, sleep disorders, and disease duration are associated factors. Clinical attention to high-risk groups and implementation of early screening and targeted interventions are recommended.

Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveTo investigate the association between insulin resistance （IR） and cardiovascular metabolic indicators， including blood pressure， blood lipids， and serum uric acid， in a middle-aged population. MethodsBased on the baseline survey of the Hong Sifang cohort of China National Salt Industry Group， a total of 575 participants were included. According to the homeostasis model assessment of insulin resistance， the participants were divided into an IR group （n=116） and a non-IR group （n=459）. Differences between the two groups were compared using the t-test and chi-square test. Multivariate logistic regression analysis was performed to evaluate the associations between IR and abnormalities in metabolic indicators. ResultsA total of 575 participants were included， including 390 men and 185 women， with a mean age of （48.58 ± 5.14） years and a mean body mass index （BMI） of （24.16 ± 2.97） kg/m². Blood pressure， serum uric acid， and blood lipid levels were significantly higher in the IR group than in the non-IR group （P<0.001）. After adjusting for potential confounders including age， sex， and BMI， IR was independently associated with hypertension， dyslipidemia， and hyperuricemia， with odds ratios （OR） and 95% confidence intervals （CI） of 2.57 （1.61-4.09）， 3.81 （2.36-6.17）， and 2.68 （1.53-4.71）， respectively （all P<0.001）. ConclusionInsulin resistance is significantly associated with multiple cardiovascular metabolic risk factors in the middle-aged population. Monitoring IR may contribute to the early prevention and control of cardiovascular diseases.

Objective To define the conceptual connotation of network dynamic collaboration capacity in medical surge response and construct its theoretical model.Methods A mixed concept analysis method was employed,integrating multidisciplinary literature and collecting empirical evidence through semi-structured expert interviews to extract the concept of network dynamic collaboration capacity in medical surge response.By integrating complex systems,network science,synergetics,and dynamic capability theory,and combining the interview results,the study used the analogy of flood control in hydraulic engineering to develop a"network-dynamic-collaboration"triangular capacity theoretical model.Results It reveals one antecedents(sudden external shocks have led to an abnormal and continuous surge in medical demand),six core attributes(information interconnection accessibility,dynamic resource adaptability,risk perception responsiveness,multi-party collaborative interactivity,service process adaptability elasticity,and learning iterative evolution),and four consequences(mitigation of crowding risk,protection of service continuity,minimization of crisis spillover,and enhancement of system resilience)for the network dynamic collaboration capacity in medical surge response.The theoretical model elucidates the coupling mechanisms among network structural resilience,dynamic regulation processes,and collaborative co-evolution in resisting medical surge.Conclusion The new concept and theoretical model proposed in this study deepen the understanding of medical surge response system mechanisms and offer a theoretical framework and practical guidance for strengthening the full-chain resilience of health emergency systems.

Objective:To establish a rapid method for urinary iodine determination based on arsenic-cerium catalytic spectrophotometry using semiconductor water bath rapid cooling digestion technology (rapid cooling method) and an automated iodine analyzer.Methods:Urine samples were collected from three staff members randomly selected at the Gansu Provincial Center for Disease Control and Prevention. The samples were mixed and divided into three equal portions. Using rapid cooling method instead of traditional natural cooling method, the mixed urine sample was lowered from 100 ℃ to the measurement temperature (30 ℃), and then the urine iodine was determined by arsenic cerium catalytic spectrophotometry using a fully automatic iodine analyzer. The differences between the two cooling methods were compared. Additionally, the rapid cooling-arsenic-cerium catalytic spectrophotometry method was validated in terms of the linear equation of the standard curve, precision, accuracy (using urinary iodine reference materials GBW09110e and GBW09109m, and spike recovery experiments), and detection limit.Results:Using the rapid cooling method, the temperature of urine samples reached 30 ℃ in 40 minutes, while the natural cooling method required 120 minutes to reach the same temperature. Urinary iodine levels determined by the rapid cooling method in the three samples were (158.3 ± 1.9), (133.7 ± 2.7), and (219.2 ± 3.1) μg/L, respectively, while those determined by the natural cooling method were (155.5 ± 2.7), (136.2 ± 2.3), and (215.1 ± 3.9) μg/L, respectively. There was no statistically significant difference between the results of the two methods ( t = 1.43, P = 0.192). In the validation experiments for the rapid cooling method, the linear equations for the low-concentration (0 - 300 μg/L) and high-concentration (300 - 1 200 μg/L) iodine standard series were y = - 0.003 0 x + 0.117 5 ( r = - 0.999 7) and y = - 0.001 5 x + 0.649 3 ( r = - 0.999 8), respectively. The precision experiments showed that the relative deviation values of the repeated tests for two different iodine concentrations (300 and 600 μg/L) were 1.99% and 1.90%, respectively. The accuracy experiments demonstrated that the repeated test results for the national urinary iodine reference materials (GBW09110e and GBW09109m) were 216.8 and 134.7 μg/L, respectively, with relative standard deviations of 1.04% and 1.35%, respectively, all within the reference ranges[(218 ± 15) and (135 ± 10) μg/L]. The detection limit was 1.20 μg/L. The spike recovery rate ranged from 92.90% to 99.10%, with an average recovery rate of 96.77%. Conclusions:The rapid cooling-arsenic-cerium catalytic spectrophotometry method not only shortens the time required for urinary iodine determination and improves the speed of measurement, but also provides accurate results, which can be applied when a large number of urine samples need to be measured in a short period of time.

Obstructive sleep apnea(OSA)is a common sleep disorder characterized by repeated episodes of upper airway collapse and obstruction during sleep.Polysomnography is the gold standard for diagnosing OSA,but it is expensive,time-consuming,and can cause discomfort for patients.In recent years,acoustic-based approaches for detecting OSA have emerged as a research focus.This review summarizes recent advances in OSA automatic detection techniques based on snoring and speech signals,and systematically examines their applications in diagnosis,severity assessment,and localization of obstruction sites.Findings indicate that the acoustic features of snoring and speech signals hold significant value for OSA screening,and when combined with machine learning and deep learning models,it can achieve high diagnostic accuracy.Future research should focus on elucidating the relationship between acoustic features and the pathophysiological mechanisms of OSA,integrating multimodal information,and advancing the clinical application of wearable devices,with the aim of promoting intelligent,non-invasive,and cost-effective screening technologies for OSA.

BACKGROUND:Exosomes derived from human umbilical cord mesenchymal stem cells are widely used for bone repair and reconstruction,significantly enhancing osteogenesis and promoting angiogenesis.OBJECTIVE:To explore the mechanisms of exosomes derived from human umbilical cord mesenchymal stem cells in the treatment of osteoporotic fractures.METHODS:H uman umbilical cord mesenchymal stem cells were extracted using tissue block culture method.Exosomes derived from human umbilical cord mesenchymal stem cells were extracted using ultracentrifugation method for identification.Thirty 12-week-old female SD rats were randomly divided into sham-operated group(n=6)and ovariectomized group(n=24).Osteoporosis model was established by castration in the ovariectomized group.12 weeks after modeling,6 rats in the ovariectomized group and 6 rats in the sham-operated group were randomly selected for Micro-CT and hematoxylin-eosin staining to verify the models.After verification,the remaining 18 rats in the ovariectomized group were randomly assigned to three groups to establish osteoporotic fracture models.The fracture end was separately injected with PBS(PBS group),exosomes at 1.5×1011 particles/mL(low-concentration exosome group),and 3×1011 particles/mL(high-concentration exosome group).Four weeks after operation,fracture healing and bone angiogenesis were evaluated by imaging and histological observations.RESULTS AND CONCLUSION:(1)In the gross specimens,compared with the PBS group,the exosome group had faster fracture healing and more callus formation.(2)The X-ray score of fracture healing in the exosome group was significantly higher than that in the PBS group,and the difference was significant(P＜0.05).(3)Micro-CT three-dimensional imaging:Compared with the PBS group,the fracture healing in the exosome group was accelerated and the callus formation was significantly increased;the bone volume fraction in the exosome group was significantly higher than that in the PBS group,and the difference was significant(P＜0.01).(4)Hematoxylin-eosin staining and Masson's trichrome staining showed that bone trabeculae and the new bone tissue in the exosome group were more than those in the PBS group.(5)Immunohistochemical staining showed that the expression of CD31 and osteocalcin in the exosome group was significantly higher than that in the PBS group.The high-concentration exosome group had a higher density of new blood vessels,more callus formation,and faster fracture healing than the low-concentration exosome group,showing a concentration-dependent manner.The results show that exosomes derived from human umbilical cord mesenchymal stem cells can promote the repair of osteoporotic fracture by enhancing the angiogenesis and osteogenesis.The mechanism may be related to the increased expression of CD31 and osteocalcin.

Objective:To explore the influence of Stat1 on Foxp3 expression and production of Treg.Methods:C57BL/6 mice were used and separated into normal control group and Stat1 specific inhibitor Fludarabine(Flud)treatment group.Ratio of CD4+Foxp3+Treg and expression of Foxp3 in spleen,lymph nodes and peripheral blood of mice in each group were detected by flow cy-tometry.Human Stat1 overexpression plasmid was constructed and transfected into human breast cancer MCF-7 cells,and expression changes of Foxp3 was detected by RT-qPCR and Western blot.Results:Compared with mice in normal control group,proportion of Treg and expression of Foxp3 in lymph nodes and peripheral blood of mice in Flud treatment group were increased,while Stat1 overex-pression resulted in decreased Foxp3 mRNA and protein expression in MCF-7 cells.Conclusion:Stat1 inhibits expression of Foxp3 and production of Tregs.