Objective To evaluate the efficacy of Puerariae lobatae radix （PLR） and Anemarrhenae Rhizoma （AR） in preventing and treating Alzheimer’s disease （AD） and explore its potential mechanism of action by LC-MS serum metabolomics strategy. Methods The AD rat model was established by administering aluminum chloride （AlCl₃） and D-galactose （D-gal） for 20 weeks. The traditional Chinese medicine intervention group was given the PLR, AR, and PLR-AR extracts for 8 weeks by gavage. The model effect and efficacy were evaluated by Morris water maze test and biochemical indicators including SOD, NO, and MDA; Metabolomics research based on the UHPLC-Q/TOF-MS method was conducted, and relevant metabolic pathways were analyzed through the MetaboAnalyst online website. Results The learning and memory abilities of AD model rats were significantly decreased compared with the control group, and the levels of oxidative stress and lipid peroxides were significantly increased （P<0.05）, while the SOD content was decreased considerably （P<0.01）. The learning and memory abilities of AD model rats were improved, oxidative stress and lipid peroxidation levels were reversed, and serum SOD content was increased significantly after the intervention of PLR-AR, with better effects than single drugs. Through metabolomics, 70 differential metabolites were identified between the AD model group and the control group, mainly involving 10 pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism, and unsaturated fatty acid biosynthesis, et.al. The intervention of PLR-AR could adjust 47 metabolites, with 20 metabolites showing significant differences （P<0.05）. The significantly adjusted metabolites involve 6 pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis, et al. Conclusion The combination of PLR and AR could significantly improve the learning and memory abilities of AD rat models. The mechanism may be related to the improvement of oxidative stress and lipid peroxidation levels, the increase of serum SOD content, and the regulation of phenylalanine, tyrosine, and tryptophan biosynthesis pathways.

Objective To construct a glioma microfluidic chip model to simulate tumor microenvironment for evaluating the medicinal efficacy of anti-glioma traditional Chinese medicines. Methods Glioblastoma cells U251 were seeded into microfluidic chips with different culture modes, and the cell viability and tumour microenvironment within the constructed model were characterized. Fluorescence staining was used to evaluate the effects of the positive drugs temozolomide （TMZ） and docetaxel （DOC） on the cell activity and apoptosis within the model, which was applied to evaluate the medicinal efficacy of the extracts of the herb Scutellaria barbata on gliomas. Results The cells in the constructed U251 microfluidic chip model displayed high viability and were able to mimic the hypoxic microenvironment of tumor to a certain extent. The viability of the U251 cells in the microfluidic chips decreased with the increasing of the concentration of the positive drug, and the viability of the 3D cultured U251 cells was higher than that in the 2D condition （P<0.05）. The intracellular mitochondrial membrane potential decreased with the increasing of the concentration of the positive drug. And the 2 mg/ml Scutellaria barbata extract killed U251 cells to a certain extent and reduced the mitochondrial membrane potential of the cells in the model. Conclusion This study successfully constructed a microfluidic chip model of glioma that could effectively simulate the tumor microenvironment and rapidly evaluate the anti-tumor medicinal efficacy, which provided a new strategy for the medicinal efficacy evaluation and active components screening of anti-glioma traditional Chinese medicines.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Biosensor analysis technology is a kind of technology with high specificity that can convert biological reactions into optical and electrical signals. In the development of drugs for Alzheimer's disease (AD), according to different disease hypotheses and targets, this technology plays an important role in confirming targets and screening active compounds. This paper briefly describes the pathogenesis of AD and the current situation of therapeutic drugs, introduces three biosensor analysis techniques commonly used in the discovery of AD drugs, such as surface plasmon resonance (SPR), biolayer interferometry (BLI) and fluorescence analysis technology, explains its basic principle and application progress, and summarizes their advantages and limitations respectively.

Tumors continue to be a major challenge in human survival that we have yet to overcome. Despite the variety of treatment options available, we have not yet found an effective method. As more and more research is conducted, attention has been turned to a new field for tumor treatment—the tumor microenvironment (TME). This is a dynamic and complex environment consisting of various matrix cells surrounding cancer cells, including surrounding immune cells, blood vessels, extracellular matrix, fibroblasts, bone marrow-derived inflammatory cells, signaling molecules, and some specific cell types. Firstly, endothelial cells play a key role in tumor development and the immune system’s protection of tumor cells. Secondly, immune cells, such as macrophages, Treg cells, Th17 cells, are widely involved in various immune responses and activities in the human body, such as inflammation responses promoting survival carefully orchestrated by the tumor. Even though many studies have extensively researched the TME and found many research schemes, so far, no key effective method has been found to treat tumors by affecting the TME. The TME is a key interaction area between the host immune system and the tumor. Cells within the TME influence each other and interact with cancer cells to affect cancer cell invasion, tumor growth, and metastasis. This is a new direction for cancer treatment. In the complex environment of the TME, post-translational modifications (PTMs) of proteins have been proven to play an important role in the TME. PTMs are dynamic, strictly regulated changes to proteins that control their function by regulating their structure, spatial location, and interaction. Among PTMs, a reversible post-translational modification called SUMOylation is a common regulatory mechanism in cellular processes. It is a post-translational modification that targets lysine residues with a small ubiquitin-like modifier (SUMO) in a reversible post-translational modification manner. SUMOylation is widely involved in carcinogenesis, DNA damage response, cancer cell proliferation, metastasis, and apoptosis, playing a pivotal role in the TME, such as DNA damage repair, tumor metastasis, and also participates in immune cell differentiation, activation, and inhibition of immune cells. On the other hand, SUMO or sentrin-specific protease (SENP) inhibitors can interfere with the SUMOylation process, thereby affecting many biological processes, including immune response, carcinogenesis, cell cycle progression, and cell apoptosis, etc. In summary, this review aims to introduce the dynamic modification of protein SUMOylation on various immune cells and the application of various inhibitors, thereby exploring its role in the TME. This is a challenging but hopeful field, and we look forward to future research that can bring more breakthroughs. In conclusion, the TME is a complex and dynamic environment that plays a crucial role in the development and progression of tumors. Understanding the intricate interactions within the TME and the role of PTMs, particularly SUMOylation, could provide valuable insights into the mechanisms of tumor development and potentially lead to the development of novel therapeutic strategies. The study of SUMOylation and its effects on various immune cells in the TME is an exciting and promising area of research that could significantly advance our understanding of tumor biology and potentially lead to the development of more effective treatments for cancer. This is a challenging but hopeful field, and we look forward to future research that can bring more breakthroughs.

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with dysfunctions related to thinking, learning, and memory of the brain. AD has multiple pathological characteristics with complicated causes, constructing a suitable pathological model is crucial for the research of AD. Microfluidic chip technology integrates multiple functional units on a chip, which can realize microenvironmental control similar to the physiological environment. It is well applied in the construction of pathological model, early diagnosis as well as drug screening of AD. This paper focuses on the construction of AD microfluidic chips model from the perspective of cell type, culture formats and the chips structure as well as the research progress of microfluidic chips in AD application based on the pathological characteristics of AD, which will provide a reference for further elucidation of AD mechanism and drug development.

Humans ; Aged ; Lung Diseases ; Pneumonia/drug therapy* ; Adrenal Cortex Hormones/therapeutic use* ; Pulmonary Disease, Chronic Obstructive/drug therapy* ; Administration, Inhalation ; Community-Acquired Infections/drug therapy*

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing. Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing. Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing. Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.