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 understand the dynamic temporal evolution pathways of Internet addiction among university students and to identify the core driving nodes, so as to provide theoretical evidences for the precise implementation of targeted interventions. Methods: Using a convenient cluster sampling method, a total of 1 066 full time freshmen and sophomores were recruited from three universities in Guizhou, Jiangxi, and Guangdong Provinces for a follow up survey (T1:January-March 2024; T2:January-March 2025). The Revised Chen Internet Addiction Scale (CIAS-R) was employed to assess the status of Internet addiction among university students, and cross sectional as well as cross lagged panel network models were constructed to analyze Internet addiction and its multidimensional influencing factors. Results: The T1 network comprised 19 nodes and 114 non zero edges, while the T2 network comprised 19 nodes and 126 non zero edges. Cross sectional network analysis revealed the strongest association between "insufficient sleep" and "daytime fatigue"; the core nodes were "first thought upon waking for going online" and "feeling low after disconnection" (characteristics of psychological dependence) at T1, while the core nodes shifted to "impaired health" and "excitement when online" (characteristics of functional impairment and addictive psychodynamic features) at T2. Cross lagged network analysis further indicated that "reduced leisure" directly predicted "sleep compression", and a bidirectional relationship was observed between "needing more time to achieve satisfaction" and "academic decline". Conclusions Internet addiction among university students exhibits dynamic evolutionary characteristics. Stage specific targeted interventions focusing on core driving nodes are needed, integrating behavioral regulation and academic support to break the vicious cycle and enhancing the ability to cope with real life demands.

Based on the interleukin-17(IL-17) signaling pathway, this study explores the effect and mechanism of Bufei Decoction on Klebsiella pneumoniae pneumonia in rats. SD rats were randomly divided into the control group, model group, Bufei Decoction low-dose group(6.68 g·kg~(-1)·d~(-1)), Bufei Decoction high-dose group(13.36 g·kg~(-1)·d~(-1)), and dexamethasone group(1.04 mg·kg~(-1)·d~(-1)), with 10 rats in each group. A pneumonia model was established by tracheal drip injection of K. pneumoniae. After successful model establishment, the improvement in lung tissue damage was observed following drug administration. Core targets and signaling pathways were screened using transcriptomics techniques. Real-time fluorescence quantitative polymerase chain reaction was used to detect the mRNA expression of core targets interleukin-6(IL-6), interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and chemokine CXC ligand 6(CXCL6). Western blot was used to assess key proteins in the IL-17 signaling pathway, including interleukin-17A(IL-17A), nuclear transcription factor-κB activator 1(Act1), tumor necrosis factor receptor-associated factor 6(TRAF6), and downstream phosphorylated p38 mitogen-activated protein kinase(p-p38 MAPK), and phosphorylated nuclear factor-κB p65(p-NF-κB p65). Apoptosis of lung tissue cells was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling(TUNEL). The results showed that, compared with the control group, the model group exhibited significant pathological damage in lung tissue. The mRNA expression of IL-6, IL-1β, TNF-α, and CXCL6, as well as the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly increased, and the number of apoptotic cells was notably higher, indicating successful model establishment. Compared with the model group, both low-and high-dose groups of Bufei Decoction showed reduced pathological damage in lung tissue. The mRNA expression levels of IL-6, IL-1β, TNF-α, and CXCL6, and the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly decreased, with a significant reduction in apoptotic cells in the high-dose group. In conclusion, Bufei Decoction can effectively improve lung tissue damage and reduce inflammation in rats with K. pneumoniae. The mechanism may involve the regulation of the IL-17 signaling pathway and the reduction of apoptosis.
Animals ; Interleukin-17/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Rats ; Male ; Klebsiella pneumoniae/physiology* ; Klebsiella Infections/immunology* ; Humans ; Lung/drug effects*

In the clinical stage, suspected hemolytic plasma may cause hemolysis illness, manifesting as symptoms such as heart failure, severe anemia, etc. Applying a deep learning method to plasma images significantly improves recognition accuracy, so that this paper proposes a plasma quality detection model based on improved "You Only Look Once" 5th version (YOLOv5). Then the model presented in this paper and the evaluation system ‌were introduced‌ into the plasma datasets, and ‌the average accuracy of the final classification reached 98.7%‌. The results of this paper's experiment were obtained through the combination of several key algorithm modules including‌ omni-dimensional dynamic convolution, pooling with separable kernel attention, residual bi-fusion feature pyramid network, ‌and‌ re-parameterization convolution. The method of this paper‌ obtains the feature information of spatial mapping efficiently, and enhances the average recognition accuracy of plasma quality detection. This paper presents a high-efficiency detection method for plasma images, aiming to provide a practical approach to prevent hemolysis illnesses caused by external factors.
Algorithms ; Humans ; Hemolysis ; Plasma ; Deep Learning ; Image Processing, Computer-Assisted/methods*

Coronary artery calcification often appears a variety of complex lesions,increasing coronary intervention of the difficulty of treatment,especially the severe calcification lesions,usually cannot be fully dilated,resulting in a reduced success rate of surgery,an increased rate of acute stent thrombosis and restenosis,and even a serious impact on the prognosis of patients.Intravascular lithotripsy(IVL)is increasingly used in calcified lesions.There is more and more evidence of using in stable angina pectoris and unstable angina pectoris,but its use in acute ST-segment elevation myocardial infarction is limited,and only a few cases have been reported abroad.Moreover,the consensus of Chinese experts in the diagnosis and treatment of coronary artery calcification in 2021 edition lists thrombotic lesions as contraindications of shock wave balloon.This case is the first time in China to report the use of shock wave balloon in patients with acute ST elevation myocardial infarction complicated with thrombus.In this case,the patient with acute ST elevation myocardial infarction complicated with thrombus was severely under expanded stent after stent implantation,and obtain good curative effect using shockwave balloon at selected time in hospital after intensive anticoagulant therapy.

Objective:To explore the influencing factors of evacuation associated pulmonary edema (WIPE) in patients with mechanical ventilation of cardiogenic respiratory failure, and to build a risk warning model based on independent influencing factors.Methods:A total of 220 patients with cardiogenic respiratory failure who were treated and received mechanical ventilation in Chengbei Campus of Hangzhou First People′s Hospital from April 2021 to December 2023 were retrospectively selected by cross-sectional investigation method, and were divided into WIPE group (34 cases) and non WIPE group (186 cases) according to whether the patients had WIPE or not. Clinical data of the patients were analyzed using the hospital electronic medical record system. The influencing factors of WIPE were determined by univariate analysis and multivariate Logistic regression analysis, and the risk early warning model was constructed based on regression analysis. The corresponding nomogram was drawn by R language software, and the predictive efficiency of the model was tested by receiver operating characteristic curve and calibration curve.Results:WIPE group included 18 males and 16 females, aged (65.12±9.28) years. Non WIPE group included 107 males and 79 females, aged (60.25±8.40) years. Multivariate Logistic regression analysis showed that age ( OR=1.072), smoking history ( OR=3.412), acute physiology and chronic health evaluationⅡ( OR=1.184), cardiac function classification ( OR=4.043), shallow rapid breathing index ( OR=1.100), mechanical ventilation time ( OR=1.540), hypertension ( OR=4.903), left ventricular diastolic dysfunction ( OR=5.151) and chronic obstructive pulmonary disease ( OR= 5.536) were independent influencing factors (all P < 0.05). The area under the curve of the risk early warning model constructed based on the above 9 independent influencing factors was 0.938, and the sensitivity and specificity corresponding to the optimal cutoff value of 0.620 were 0.971 and 0.801, respectively, indicating good differentiation ability. The calibration curve results show that the average absolute error was 0.020, the calibration curve fits the ideal curve, and the model calibration performance was good. Conclusions:WIPE in patients with cardiogenic respiratory failure induced by mechanical ventilation is affected by cardiac function status, mechanical ventilation parameters and other factors. The risk early warning model based on the above 9 independent influencing factors has good predictive efficacy, and can provide reference for clinical prevention of WIPE.

Objective:To investigate the correlation between oral cleanliness and secondary Pulmonary infection in patients with severe chronic obstructive pulmonary disease (COPD) in mechanical ventilation, and to investigate the predictive effect of oral cleanliness on the risk of secondary pulmonary infection.Methods:Using the cross-sectional survey method, the purposeful sampling method was adopted to select 216 patients with severe COPD who were hospitalized in Hangzhou First People′s Hospital from June 2020 to December 2023 and received mechanical ventilation. The oral cleanliness index and general clinical data of patients at admission were collected using the hospital electronic medical record system. The independent influencing factors of secondary lung infection were analyzed by univariate analysis and multivariate Logisitic regression. The predictive value of oral cleanliness index on secondary lung infection was analyzed by patient operating characteristic (ROC) curve.Results:216 patients with severe COPD who underwent mechanical ventilation were included.Patients aged 37-84 (66.81 ± 8.98) years were included, including 125 males and 91 females.Among them, 89 cases developed secondary pulmonary infection, with an infection rate of 41.20%.Univariate analysis and multivariate Logistic regression analysis showed that, Beck Oral Rating Scale (BOAS) score ( OR = 1.371), visual simulation score of oral odor ( OR = 1.405), gum index ( OR = 3.508), plaque index ( OR = 14.357), smoking history ( OR = 6.772), duration of disease ( OR = 1.391), COPD assessment test score ( OR = 1.269) and mechanical ventilation time ( OR = 1.302) were independent factors for secondary pulmonary infection (all P<0.05). ROC curve analysis showed that oral cleanliness index combined with infection prediction was effective (the area under the ROC curve was 0.833) . Conclusions:Oral cleanliness was closely related to secondary pulmonary infection in patients with severe COPD with mechanical ventilation. BOAS score, visual simulation score of oral odor, gingival index and plaque index could predict secondary pulmonary infection independently, and combined test could predict secondary pulmonary infection.

Objective:To explore the influencing factors of evacuation associated pulmonary edema (WIPE) in patients with mechanical ventilation of cardiogenic respiratory failure, and to build a risk warning model based on independent influencing factors.Methods:A total of 220 patients with cardiogenic respiratory failure who were treated and received mechanical ventilation in Chengbei Campus of Hangzhou First People′s Hospital from April 2021 to December 2023 were retrospectively selected by cross-sectional investigation method, and were divided into WIPE group (34 cases) and non WIPE group (186 cases) according to whether the patients had WIPE or not. Clinical data of the patients were analyzed using the hospital electronic medical record system. The influencing factors of WIPE were determined by univariate analysis and multivariate Logistic regression analysis, and the risk early warning model was constructed based on regression analysis. The corresponding nomogram was drawn by R language software, and the predictive efficiency of the model was tested by receiver operating characteristic curve and calibration curve.Results:WIPE group included 18 males and 16 females, aged (65.12±9.28) years. Non WIPE group included 107 males and 79 females, aged (60.25±8.40) years. Multivariate Logistic regression analysis showed that age ( OR=1.072), smoking history ( OR=3.412), acute physiology and chronic health evaluationⅡ( OR=1.184), cardiac function classification ( OR=4.043), shallow rapid breathing index ( OR=1.100), mechanical ventilation time ( OR=1.540), hypertension ( OR=4.903), left ventricular diastolic dysfunction ( OR=5.151) and chronic obstructive pulmonary disease ( OR= 5.536) were independent influencing factors (all P < 0.05). The area under the curve of the risk early warning model constructed based on the above 9 independent influencing factors was 0.938, and the sensitivity and specificity corresponding to the optimal cutoff value of 0.620 were 0.971 and 0.801, respectively, indicating good differentiation ability. The calibration curve results show that the average absolute error was 0.020, the calibration curve fits the ideal curve, and the model calibration performance was good. Conclusions:WIPE in patients with cardiogenic respiratory failure induced by mechanical ventilation is affected by cardiac function status, mechanical ventilation parameters and other factors. The risk early warning model based on the above 9 independent influencing factors has good predictive efficacy, and can provide reference for clinical prevention of WIPE.

Objective:To investigate the correlation between oral cleanliness and secondary Pulmonary infection in patients with severe chronic obstructive pulmonary disease (COPD) in mechanical ventilation, and to investigate the predictive effect of oral cleanliness on the risk of secondary pulmonary infection.Methods:Using the cross-sectional survey method, the purposeful sampling method was adopted to select 216 patients with severe COPD who were hospitalized in Hangzhou First People′s Hospital from June 2020 to December 2023 and received mechanical ventilation. The oral cleanliness index and general clinical data of patients at admission were collected using the hospital electronic medical record system. The independent influencing factors of secondary lung infection were analyzed by univariate analysis and multivariate Logisitic regression. The predictive value of oral cleanliness index on secondary lung infection was analyzed by patient operating characteristic (ROC) curve.Results:216 patients with severe COPD who underwent mechanical ventilation were included.Patients aged 37-84 (66.81 ± 8.98) years were included, including 125 males and 91 females.Among them, 89 cases developed secondary pulmonary infection, with an infection rate of 41.20%.Univariate analysis and multivariate Logistic regression analysis showed that, Beck Oral Rating Scale (BOAS) score ( OR = 1.371), visual simulation score of oral odor ( OR = 1.405), gum index ( OR = 3.508), plaque index ( OR = 14.357), smoking history ( OR = 6.772), duration of disease ( OR = 1.391), COPD assessment test score ( OR = 1.269) and mechanical ventilation time ( OR = 1.302) were independent factors for secondary pulmonary infection (all P<0.05). ROC curve analysis showed that oral cleanliness index combined with infection prediction was effective (the area under the ROC curve was 0.833) . Conclusions:Oral cleanliness was closely related to secondary pulmonary infection in patients with severe COPD with mechanical ventilation. BOAS score, visual simulation score of oral odor, gingival index and plaque index could predict secondary pulmonary infection independently, and combined test could predict secondary pulmonary infection.