Inflammatory diseases (IDs) are a general term of diseases characterized by chronic inflammation as the primary pathogenetic mechanism, which seriously affect the quality of patient′s life and cause significant social and medical burden. Current drugs for IDs include nonsteroidal anti-inflammatory drugs, corticosteroids, immunomodulators, biologics, and antioxidants, but these drugs may cause gastrointestinal side effects, induce or worsen infections, and cause non-response or intolerance. Given the outstanding performance of metal polyphenol network (MPN) in the fields of drug delivery, biomedical imaging, and catalytic therapy, its application in the diagnosis and treatment of IDs has attracted much attention and significant progress has been made. In this paper, we first provide an overview of the types of IDs and their generating mechanisms, then sort out and summarize the different forms of MPN in recent years, and finally discuss in detail the characteristics of MPN and their latest research progress in the diagnosis and treatment of IDs. This research may provide useful references for scientific research and clinical practice in the related fields.

Hepatic encephalopathy is a clinical syndrome of central nervous system dysfunction caused by liver insufficiency.It severely affects the quality of life of patients and may lead to death.Accurate prediction of the risk of developing hepatic encephalopathy is crucial for early intervention and treatment.In order to identify the risk of hepatic encephalopathy in patients in advance,many studies have been devoted to efforts to develop tools and methods to identify the risk of hepatic encephalopathy as early as possible,so as to develop preventive and early management strategies.Most conventional hepatic encephalopathy risk prediction models currently assess the prob-ability of a patient developing hepatic encephalopathy by analysing factors such as clinical data and biochemical indicators,however,their accuracy,sensitivity and positive predictive value are not high.The application of artificial intelligence to clinical predictive modelling is a very hot and promising area,which can use large amounts of data and complex algorithms to improve the accuracy and efficiency of diagnosis and prognosis.To date,there have been few studies using AI techniques to predict hepatic encephalopathy.Therefore,this paper reviews the research progress of hepatic encephalopathy risk prediction models,and also discusses the prospect of AI application in hepatic encephalopathy risk prediction models.It also points out the challenges and future research directions of AI in HE risk prediction model research in order to promote the development and clinical application of hepatic encephalopathy risk prediction models.

SWOT analysis is used to identify the strengths and external opportunities of scientific research management in hospitals.It facilitates the establishment of a systematic and rational approach to scientific research project management,help-ing hospitals to mitigate internal weaknesses and address external threats.This article chooses Y Hospital to carry out a case stud-y.SWOT analysis was done to investigate the hospital's strengths,weaknesses,opportunities,and threats.Based on the analysis results,it proposes targeted management strategies of"SO,""WO,""ST,"and"WT".After the use of the strategies in the management,the number of funded scientific research projects,published papers,registered invention patents,and achievements transferred from the scientific research remarkably increases,driving the improvement of research quality.

Tumor is one of the serious problems threatening human health. There are some limitations in the delivery of commonly used tumor therapy technologies, and the therapeutic effect is not satisfactory, so new anti-tumor strategies need to be developed. The process of tumor cells using glycolysis to produce energy under aerobic conditions is called aerobic glycolysis, which is closely related to tumor growth, proliferation and metastasis, and can provide a new target spot for tumor treatment. Nano drug delivery system has been widely used in targeted tumor therapy because of its advantages of targeted drug delivery, improved anti-tumor efficacy and reduced toxic side effects. Numerous studies have shown that more and more nano drug delivery systems regulates aerobic glycolytic metabolism by targeting to potential targets such as signaling factors or reaction products of aerobic glycolytic process in tumors, and therefore enhance the anti-tumor effect. This paper reviews the application of nano drug delivery system in regulating tumor aerobic glycolysis, and provides theoretical references for realizing efficient targeted tumor therapy.

Aim To investigate the effects of ber-bamine on behavioral changes in LPS-induced chronic neuroinflammation model mice and the related mecha-nisms.Methods By injecting lipopolysaccharide in-traperitoneally for seven days in a row,berbamine was given intraperitoneally as a treatment;the behavioral studies of mice in each group were identified;Nissen staining was used to observe the changes in the patho-logical morphology of the mouse hippocampus and the expression levels of inflammation-related proteins.These procedures established a mouse neuroinflamma-tion model.Results The number of neurons in the model group's hippocampal CA1 and CA3 regions was significantly smaller than that in the control group.In the water maze experiment,as the number of training days grew,the model group's escape latency increased and its retention time in the target quadrant dropped.The immobilization period of the model group mice in-creased during the forced swimming exercise.Serum levels of inflammatory factors such as IL-1β,IL-6,and TNF-α levels were also higher.The hippocampus tis-sue of the mice in the model group had higher levels of NLRP3,ASC,caspase-1,IL-18,ROCK1,ROCK2 ex-pression,and RHOA.When compared to the model group,the administration of berbamine was a therapy intervention.In the meantime,with the number of training days increased,the target quadrant lag time increased and the escape latency gradually decreased.Additionally,the model group's mice spent less time resting during forced swimming,and the serum inflam-matory factors TNF-α,IL-1β,and IL-6 decreased in mouse hippocampal tissues.Lastly,the expression lev-els of NLRP3,caspase-1,ASC,IL-1β,ROCK1,ROCK2,and RHOA all decreased in mouse hippocam-pal tissue.Conclusions The mechanism of action of berbamine,which improves lipopolysaccharide-induced depressive-like behaviors and modifies learning memory in mice,may include the NLRP3 and RHOA/ROCK signaling pathways.

Objective Through the development of"real-time monitoring,early warning and tracking management sys-tem for infectious diseases in hospitals",real-time monitoring and early warning are realized,report cards are generated,and case tracking and management of infectious diseases are formed.Methods We selected 22 185 cases of infectious disease re-ports from April 2020 to October 2022 and 33 640 cases of infectious disease reports from November 2022 to May 2024,and com-pared the 19-month period before and after the launch of the new infectious disease early warning management system with that be-fore the launch of the original traditional infectious disease reporting management system,and compared the rate of infectious dis-ease reporting,the accuracy of infectious disease reporting,the timeliness of infectious disease reporting(time),the accuracy of infectious disease reporting,and the quality of infectious disease reporting(time),Infectious disease reporting timeliness(time),effectiveness of infectious disease tracking,and clinical medical staff's satisfaction with infectious disease reporting were compared and analyzed.Results After the use of the new hospital infectious disease early warning and tracking management sys-tem,the differences in infectious disease reporting rate,infectious disease reporting accuracy,infectious disease reporting timeli-ness,infectious disease tracking effectiveness,and clinical medical staff's satisfaction with infectious disease reporting were all sta-tistically significant(P＜0.05).Conclusion The development of"real-time monitoring,early warning and tracking management system for infectious diseases in hospitals"has significantly improved the reporting rate of infectious diseases,the accuracy of infec-tious disease reporting,the timeliness of infectious disease reporting,the effectiveness of infectious disease tracking,and the satis-faction of infectious disease reporting of clinical medical staff,and it has the characteristics of real-time,high efficiency and accura-cy,and the effect of early warning and tracking management is good,which has good value for promotion.It is characterized by re-al-time,high efficiency and accuracy,with good effect of early warning and tracking management,and has good promotion value.

Objective:To investigate the influencing factors for splenomegaly secondary to acute pancreatitis (AP) and construction of a nomogram prediction model.Methods:The retrospective case-control study was conducted. The clinicopathological data of 180 patients with AP who were admitted to Xuanwu Hospital of Capital Medical University from December 2017 to December 2021 were collected. There were 124 males and 56 females, aged (49±15) years. Among them, 60 AP patients who developed secondary splenomegaly were taken as the case group, including 48 males and 12 females, aged (47±13)years, and the rest of 120 cases of AP without secondary splenomegaly were taken as the control group, including 76 males and 44 females, aged (50±16)years. Observation indicators: (1) occurrence and clinical characteristics of splenomegaly secondary to AP; (2) influencing factors for splenomegaly secondary to AP; (3) construction and evaluation of a nomogram prediction model for splenomegaly secondary to AP. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was analyzed using the rank sum test. Count data were represented as absolute numbers, and comparison between groups was analyzed using the chi-square test or Fisher exact probability. The univariate analysis was performed using statistical methods appropriate to the data type. The optimal cut-off value was determined by the receiver operating characteristic curves. Multivariate analysis was conducted using the Logistic regression model with forward method. Based on the results of the multivariate analysis, a nomogram prediction model was constructed. The receiver operating characteristic curve was drawn, and the discrimination was evaluated using the area under curve. The consistency of the nomogram prediction model was evaluated using calibration curve, and its clinical benefit was evaluated using decision curve. Results:(1) Occurrence and clinical characteristics of splenomegaly secondary to AP. The first detection time of 60 patients with splenomegaly secondary to AP was 60(30,120)days after the onset of AP. Cases with persistent respiratory dysfunction, multiple organ failure, severity of illness as mild or moderately severe/severe, pancreatic and/or peripancreatic infection, surgery were 19, 17, 4, 56, 37, 32 for 60 patients with splenomegaly secondary to AP, versus 16, 19, 43, 77, 39, 29 for 120 patients without splenomegaly secondary to AP, respectively, showing significant differences in the above indicators between the two groups ( χ2=8.58, 3.91, 17.64, 13.95, 15.19, P<0.05). (2) Influencing factors for splenomegaly secondary to AP. Resuts of multivariate analysis showed that white blood cell count <5.775×10?/L within 24 hours of AP onset, revised computed tomography (CT) severity index >7 in 3-7 days after onset and the presence of local complications were independent risk factors influencing the splenomegaly secondary to AP ( odds ratio=3.85, 2.86, 6.40, 95% confidence interval as 1.68-8.85, 1.18-6.95, 1.56-26.35, P<0.05). (4) Construction and evaluation of a nomogram prediction model for splenomegaly secondary to AP. The nomogram prediction model was constructed based on white blood cell count within 24 hours of AP onset, revised CT severity index in 3-7 days after onset and local complications. The area under the receiver operating characteristic curve of the nomogram prediction model was 0.76 (95% confidence interval as 0.69-0.83, P<0.05), with a sensitivity of 0.87 and a specificity of 0.55. The calibration curve demonstrated consistency between the predicted rate from the nomogram prediction model and the actually observed rate. The decision curve analysis indicated that the nomogram prediction model had favorable clinical practicability. Conclusions:Patients with AP who develop secondary splenomegaly tend to have a higher severity of illness than those develop no secondary splenomegaly. White blood cell count <5.775×10?/L within 24 hours of AP onset, revised CT severity index >7 in 3-7 days after onset and presence of local complications are independent risk factors influencing splenomegaly secondary to AP, and its nomogram prediction model can predict incidence rate of splenomegaly secondary to AP.

Objectives: This study aimed to present the Asia-Pacific consensus on long-term and sequential therapy for osteoporosis, offering evidence-based recommendations for the effective management of this chronic condition.The primary focus is on achieving optimal fracture prevention through a comprehensive, individualized approach. Methods: A panel of experts convened to develop consensus statements by synthesizing the current literature and leveraging clinical expertise. The review encompassed long-term anti-osteoporosis medication goals, first-line treatments for individuals at very high fracture risk, and the strategic integration of anabolic and anti resorptive agents in sequential therapy approaches. Results: The panelists reached a consensus on 12 statements. Key recommendations included advocating for anabolic agents as the first-line treatment for individuals at very high fracture risk and transitioning to anti resorptive agents following the completion of anabolic therapy. Anabolic therapy remains an option for in dividuals experiencing new fractures or persistent high fracture risk despite antiresorptive treatment. In cases of inadequate response, the consensus recommended considering a switch to more potent medications. The consensus also addressed the management of medication-related complications, proposing alternatives instead of discontinuation of treatment. Conclusions This consensus provides a comprehensive, cost-effective strategy for fracture prevention with an emphasis on shared decision-making and the incorporation of country-specific case management systems, such as fracture liaison services. It serves as a valuable guide for healthcare professionals in the Asia-Pacific region, contributing to the ongoing evolution of osteoporosis management.

This study aims to investigate the mechanism of muscone in inhibiting the opening of mitochondrial permeability transition pore(mPTP) to alleviate the oxygen and glucose deprivation/reoxygenation(OGD/R)-induced injury of mouse hippocampal neurons(HT22). An in vitro model of HT22 cells injured by OGD/R was established. CCK-8 assay was employed to examine the viability of HT22 cells, fluorescence microscopy to measure the mitochondrial membrane potential, the content of reactive oxygen species(ROS), and the opening of mPTP in HT22 cells. Enzyme-linked immunosorbent assay was employed to determine the level of ATP and the content of cytochrome C(Cyt C) in mitochondria of HT22 cells. Flow cytometry was employed to determine the Ca~(2+) content and apoptosis of HT22 cells. The expression of Bcl-2(B-cell lymphoma-2) and Bcl-2-associated X protein(Bax) was measured by Western blot. Molecular docking and Western blot were employed to examine the binding between muscone and methyl ethyl ketone(MEK) after pronase hydrolysis of HT22 cell proteins. After the HT22 cells were treated with U0126, an inhibitor of MEK, the expression levels of MEK, p-ERK, and CypD were measured by Western blot. The results showed that compared with the OGD/R model group, muscone significantly increased the viability, mitochondrial ATP activity, and mitochondrial membrane potential, lowered the levels of ROS, Cyt C, and Ca~(2+), and reduced mPTP opening to inhibit the apoptosis of HT22 cells. In addition, muscone up-regulated the expression of MEK, p-ERK, and down-regulated that of CypD. Molecular docking showed strong binding activity between muscone and MEK. In conclusion, muscone inhibits the opening of mPTP to inhibit apoptosis, thus exerting a protective effect on OGD/R-injured HT22 cells, which is associated with the activation of MEK/ERK/CypD signaling pathway.
Mice ; Animals ; Reactive Oxygen Species/metabolism* ; Molecular Docking Simulation ; Apoptosis ; Oxygen ; Adenosine Triphosphate/pharmacology* ; Mitogen-Activated Protein Kinase Kinases/pharmacology* ; Glucose/metabolism*

Objective:To assess the effectiveness of a model created using clinical features and preoperative chest CT imaging features in predicting the chronic obstructive pulmonary disease (COPD) among patients diagnosed with lung cancer.Methods:A retrospective analysis was conducted on clinical (age, gender, smoking history, smoking index, etc.) and imaging (lesion size, location, density, lobulation sign, etc.) data from 444 lung cancer patients confirmed by pathology at the Second Affiliated Hospital of Naval Medical University between June 2014 and March 2021. These patients were randomly divided into a training set (310 patients) and an internal test set (134 patients) using a 7∶3 ratio through the random function in Python. Based on the results of pulmonary function tests, the patients were further categorized into two groups: lung cancer combined with COPD and lung cancer non-COPD. Initially, univariate analysis was performed to identify statistically significant differences in clinical characteristics between the two groups. The variables showing significance were then included in the logistic regression analysis to determine the independent factors predicting lung cancer combined with COPD, thereby constructing the clinical model. The image features underwent a filtering process using the minimum absolute value convergence and selection operator. The reliability of these features was assessed through leave-P groups-out cross-validation repeated five times. Subsequently, a radiological model was developed. Finally, a combined model was established by combining the radiological signature with the clinical features. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) curves were plotted to evaluate the predictive capability and clinical applicability of the model. The area under the curve (AUC) for each model in predicting lung cancer combined with COPD was compared using the DeLong test.Results:In the training set, there were 182 cases in the lung cancer combined with COPD group and 128 cases in the lung cancer non-COPD group. The combined model demonstrated an AUC of 0.89 for predicting lung cancer combined with COPD, while the clinical model achieved an AUC of 0.82 and the radiological model had an AUC of 0.85. In the test set, there were 78 cases in the lung cancer combined with COPD group and 56 cases in the lung cancer non-COPD group. The combined model yielded an AUC of 0.85 for predicting lung cancer combined with COPD, compared to 0.77 for the clinical model and 0.83 for the radiological model. The difference in AUC between the radiological model and the clinical model was not statistically significant ( Z=1.40, P=0.163). However, there were statistically significant differences in the AUC values between the combined model and the clinical model ( Z=-4.01, P=0.010), as well as between the combined model and the radiological model ( Z=-2.57, P<0.001). DCA showed the maximum net benifit of the combined model. Conclusion:The developed synthetic diagnostic combined model, incorporating both radiological signature and clinical features, demonstrates the ability to predict COPD in patients with lung cancer.