Tumors are the second leading cause of death worldwide. Exosomes are a type of extracellular vesicle secreted from multivesicular bodies, with particle sizes ranging from 40 to 160 nm. They regulate the tumor microenvironment, proliferation, and progression by transporting proteins, nucleic acids, and other biomolecules. Compared with other drug delivery systems, exosomes derived from different cells possess unique cellular tropism, enabling them to selectively target specific tissues and organs. This homing ability allows them to cross biological barriers that are otherwise difficult for conventional drug delivery systems to penetrate. Due to their biocompatibility and unique biological properties, exosomes can serve as drug delivery systems capable of loading various anti-tumor drugs. They can traverse biological barriers, evade immune responses, and specifically target tumor tissues, making them ideal carriers for anti-tumor therapeutics. This article systematically summarizes the methods for exosome isolation, including ultracentrifugation, ultrafiltration, size-exclusion chromatography (SEC), immunoaffinity capture, and microfluidics. However, these methods have certain limitations. A combination of multiple isolation techniques can improve isolation efficiency. For instance, combining ultrafiltration with SEC can achieve both high purity and high yield while reducing processing time. Exosome drug loading methods can be classified into post-loading and pre-loading approaches. Pre-loading is further categorized into active and passive loading. Active loading methods, including electroporation, sonication, extrusion, and freeze-thaw cycles, involve physical or chemical disruption of the exosome membrane to facilitate drug encapsulation. Passive loading relies on drug concentration gradients or hydrophobic interactions between drugs and exosomes for encapsulation. Pre-loading strategies also include genetic engineering and co-incubation methods. Additionally, we review approaches to enhance the targeting, retention, and permeability of exosomes. Genetic engineering and chemical modifications can improve their tumor-targeting capabilities. Magnetic fields can also be employed to promote the accumulation of exosomes at tumor sites. Retention time can be prolonged by inhibiting monocyte-mediated clearance or by combining exosomes with hydrogels. Engineered exosomes can also reshape the tumor microenvironment to enhance permeability. This review further discusses the current applications of exosomes in delivering various anti-tumor drugs. Specifically, exosomes can encapsulate chemotherapeutic agents such as paclitaxel to reduce side effects and increase drug concentration within tumor tissues. For instance, exosomes loaded with doxorubicin can mitigate cardiotoxicity and minimize adverse effects on healthy tissues. Furthermore, exosomes can encapsulate proteins to enhance protein stability and bioavailability or carry immunogenic cell death inducers for tumor vaccines. In addition to these applications, exosomes can deliver nucleic acids such as siRNA and miRNA to regulate gene expression, inhibit tumor proliferation, and suppress invasion. Beyond their therapeutic applications, exosomes also serve as tumor biomarkers for early cancer diagnosis. The detection of exosomal miRNA can improve the sensitivity and specificity of diagnosing prostate and pancreatic cancers. Despite their promising potential as drug delivery systems, challenges remain in the standardization and large-scale production of exosomes. This article explores the future development of engineered exosomes for targeted tumor therapy. Plant-derived exosomes hold potential due to their superior biocompatibility, lower toxicity, and abundant availability. Furthermore, the integration of exosomes with artificial intelligence may offer novel applications in diagnostics, therapeutics, and personalized medicine.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Major depressive disorder(MDD)is a prevalent con-dition associated with substantial impairment and low remission rates.Traditional antidepressants demonstrate delayed effects,low cure rate,and inadequate therapeutic effectiveness for man-aging treatment-resistant depression(TRD).Several studies have shown that ketamine,a non-selective N-methyl-D-aspartate receptor(NMDAR)antagonist,can produce rapid and sustained antidepressant effects.Ketamine has demonstrated efficacy for reducing suicidality in TRD patients.However,the pharmaco-logical mechanism for ketamine's antidepressant effects remains incompletely understood.Previous research suggests that the an-tidepressant effects of ketamine may involve the monoaminergic,glutamatergic and dopaminergic systems.This paper provides an overview of the pharmacological mechanism for ketamine's anti-depressant effects and discuss the potential directions for future research.

Glutamate,norepinephrine,and their receptors com-prise the glutamatergic and norepinephrine systems,which mu-tually affect each other and play essential roles in mediating vari-ous neuropsychiatric diseases.This paper reviews the functions of N-methyl-D-aspartate receptor(NMDA-R)and α2-adrenergic receptor(α2-AR)and their functional crosstalk at the molecular level in brain in common neuropsychiatric diseases,which would benefit our understanding of neuropathophysiology of psychiatric diseases,drug development and optimization of clinical neuro-psychopharmacology.

The aim of this study was to clarify the genetic diversity and population history of Ixodes persulcatus in some ar-eas of Inner Mongolia in order to provide accurate data for effective vector control programs and reveal the transmission mecha-nism.Samples were collected in 10 areas of Inner Mongolia during the active tick season(April 2021-July 2023)using the flag-dragging and manual sampling methods.The 16S rRNA and COI gene were sequenced to clarify genetic diversity of I.per-sulcatus.The positivity rates for the COI gene and 16S rRNA were 90.00％and 98.33％respectively.Overall,18 and 15 haplotypes were identified for the COI gene and 16S rRNA,respectively,with a total haplotype diversity＞0.762 and total nucleotide diversity＜0.005.The Tajima's values and Fu's Fs were negative for significance.A nucleotide mismatch map was shown as a single peak.The genetic differentiation index FST of the population indicates a small degree of genetic differ-entiation of the population,while analysis of molecular vari-ance indicates that the variation within populations was greater than between populations.Phylogenetic tree and haplotype network plots showed confounding distributions between hap-lotypes.I.persulcatus from the Hinggan League and Hulun-buir regions can adapt to environmental changes and possess abundant genetic diversity.Genetic differentiation is mainly concentrated within the population and no geographical isolation was observed.

Objective To investigate the predictive value of combined plasma gluconic acid(GA),fumaric acid(FA),and pseudouridine levels at admission for acute kidney injury(AKI)in elderly patients with acute myocardial infarction(AMI).Methods A total of 78 elderly AMI patients transferred from Emergency Department to Coronary Care Unit in Fuwai Hospital during Decem-ber 2021 and July 2022 were enrolled in this prospective study.They were divided into AKI group(40 cases)and non-AKI group(38 cases)according to whether they developed AKI during hospi-talization.Plasma levels of GA,FA and pseudouridine were quantitatively detected with liquid chromatography-tandem mass spectrometry.ROC curve was plotted to assess the predictive value of these three plasma metabolites for AKI in AMI patients.Multivariate logistic regression analy-sis was applied to analyze the clinical risk factors for AKI.Results There were no statistical differences in the plasma levels of GA,FA and pseudouridine between the AKI group and the non-AKI group(P＞0.05).ROC curve analysis revealed that the plasma levels of the three indicators had no predictive value for the development of AKI in elderly AMI patients(AUC=0.576,95％CI:0.449-0.704,P=0.246;AUC=0.595,95％CI:0.467--0.721,P=0.154;AUC=0.563,95％CI:0.435-0.692,P=0.337).Multivariate logistic regression analysis revealed that left ventricu-lar ejection fraction(LVEF)was an independent predictor for AKI development in elderly AMI patients(OR=0.923,95％CI:0.870-0.978,P=0.007).Conclusion Plasma GA,FA and pseud-ouridine cannot predict the development of AKI in elderly AMI patients,while,LVEF is an inde-pendent predictor for the development.

Objective:To investigate the effect of spouse smoking cessation(SSC)on the pregnancy outcome of the patients with polycystic ovary syndrome(PCOS)based on a secondary analysis of the results of a large randomized clinical trial.Methods:Basedon the inclusion and exclusion criteria,this secondary analysis included 861 female participants in the Trial of Acu-puncture Combined with Clomiphene for PCOS from 2011 to 2015.According to the smoking status of their husbands,we divided the subjects into a smoking group(n=345),a non-smoking group(n=387),and a smoking cessation group(n=129),collected their general demographics,anthropometric indexes,laboratory results and pregnancy outcomes,compared them among the three groups,and analyzed the relationship of SSC with the pregnancy outcomes of the PCOS patients.Results:The levels of total testos-terone(T)and low-density lipoprotein cholesterol(LDL-C)in the smoking cessation group were similar to those in the non-smoking group,but significantly lower than those in the smoking group(P=0.014 and P=0.011).The contents of fasting insulin,total cho-lesterol,and apolipoprotein B exhibited statistically significant differences among the three groups(P=0.029,P=0.018 and P=0.024),dramatically lower in the smoking cessation and non-smoking groups than in the smoking group,but similar between the for-mer two.The ovulation rate of the smoking cessation group was similar to that of the non-smoking group,remarkably higher than that of the smoking group(P=0.038).No statistically significant differences were observed in other indicators among the three groups(P＞0.05).Multivariate logistic regression analysis showed that SSC during pregnancy preparation was a protective factor for ovulation in the PCOS patients(OR=1.74,95％CI:1.04-2.92,P=0.035).Conclusion:SSC can improve androgen and glucolipid me-tabolisms of infertile patients with PCOS,and thereby significantly increase the ovulation rate.It is of positive significance to encourage SSC clinically for improving the pregnancy outcomes of infertile women with PCOS.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To explore the value of the classifier model based on CT radiomic characteristics in predicting the activity of pulmonary cystic echinococcosis (CE).Methods:The study was designed as cross-sectional. A retrospective analysis was performed on 81 patients diagnosed with pulmonary CE in the First Affiliated Hospital of Xinjiang Medical University from January 2010 to October 2020. The Python function divided 17 cases into an internal verification group and 64 cases into a training group with a ratio of 2∶8. In addition, 16 patients diagnosed with pulmonary CE from the Fourth Affiliated Hospital of Xinjiang Medical University from October 2020 to 2024 were included in the external validation group. All patients underwent CT examination, and radiomics features were extracted using Radcloud platform of Huimedi Huiying. The intraclass correlation coefficient was performed on the features, then feature screening was performed using the SelectKBest method, variance thresholding method, and least absolute shrinkage and selection operator. Finally, three classifiers (including support vector machine (SVM), K-neighborhood (KNN), and logistic regression (LR)) were used to build the models. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s efficiency.Results:Among 81 patients with lung CE, 58 were male, and 23 were female. twenty-eight lesions were active and 53 were inactive. A total of 11 optimal features were selected. Based on the selected features, the SVM classifier model, KNN classifier model, and LR classifier model were established. The KNN classifier model in the training group had the highest AUC value (0.93) and the highest specificity (0.98) in predicting lung CE activity. In the internal validation group, the SVM classifier model had the highest AUC value (0.92) and the highest specificity (0.91) in predicting lung CE activity. The LR classifier model performed best with the highest AUC of 0.85 for predicting lung CE activity in the external validation group, and the specificity of the three models was 0.92.Conclusion:The classifier model established based on CT radiomic features has a certain value in predicting lung CE activity, and may be helpful in clinical decision-making.