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.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

BACKGROUND:In recent years,with the development of biological scaffold materials and bioprinting technology,tissue-engineered bone has become a research hotspot in bone defect repair. OBJECTIVE:To summarize the current treatment methods for bone defects,summarize the biomaterials and bioprinting technology for preparing tissue-engineered bone scaffolds,and explore the application of biomaterials and printing technology in tissue engineering and the current challenges. METHODS:Search terms were"bone defect,tissue engineering,biomaterials,3D printing technology,4D printing technology,bioprinting,biological scaffold,bone repair"in Chinese and English.Relevant documents published from January 1,2009 to December 1,2022 were retrieved on CNKI,PubMed and Web of Science databases.After being screened by the first author,high-quality references were added.A total of 93 articles were included for review. RESULTS AND CONCLUSION:The main treatment methods for bone defects include bone transplantation,membrane-guided regeneration,gene therapy,bone tissue engineering,etc.The best treatment method is still uncertain.Bone tissue engineering technology is a new technology for the treatment of bone defects.It has become the focus of current research by constructing three-dimensional structures that can promote the proliferation and differentiation of osteoblasts and enhance the ability of bone formation.Biological scaffold materials are diverse,with their characteristics,advantages and disadvantages.A single biological material cannot meet the demand for tissue-engineered bone for the scaffold.Usually,multiple materials are combined to complement each other,which is to meet the demand for mechanical properties while taking into account the biological properties of the scaffold.Bioprinting technology can adjust the pore of the scaffold,build a complex spatial structure,and is more conducive to cell adhesion,proliferation and differentiation.The emerging 4D printing technology introduces"time"as the fourth dimension to make the prepared scaffold dynamic.With the synchronous development of smart materials,4D printing technology provides the possibility of efficient repair of bone defects in the future.

Nephrotic syndrome (NS) has a variety of classifications, pathogenesis and pathological types. Clinical diagnosis primarily relies on serum biochemistry, while the specific classification necessitates renal puncture for biopsy, which is hindered by poor patient compliance. Therefore, it is of great significance for clinical diagnosis to find a non-invasive and rapid method to reflect the classification and progression of nephrotic syndrome. In this study, LC-MS metabolomics combined with receiver operating characteristic (ROC) and multiple linear regression analysis was used to screen and identify potential biomarkers capable of reflecting the typing and progression of nephrotic syndrome. According to the statistical parameters VIP>1, P<0.05 and AUC>0.5 obtained from the orthogonal partial least squares discriminant analysis (OPLS-DA) model, five potential classification markers were screened to distinguish membranous nephropathy (MN) from IgA nephropathy (IgAN), including indoleacetic acid, isoleucine proline, DL-indole-3-lactic acid, D-phenylalanine and L-tryptophan. Furthermore, using estimated glomerular filtration rate (eGFR) as the dependent variable, a multiple linear regression analysis was conducted to identify the potential progression markers capable of reflecting the progression of MN to uremia. These metabolites included alanylleucine, 9-capryloylcarnitine, gluconic acid, caprylyl glycine and sebacic acid. Potential markers of progression of IgA nephropathy to uremia comprised alanylleucine, 9-capryloylcarnitine, caprylyl glycine, and sebacic acid. This study provides a theoretical basis for the discovery of potential classification and progression biomarkers of kidney disease, and also offers a methodological reference for future research in this area. The protocol was approved by the Ethics Committee of Shanxi Provincial People's Hospital [(2020) Provincial Medical Ke Lun Shen Zi No. 30].

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

Objective The leptin receptor,encoded by the LEPR gene,is involved in tumorigenesis.A potential functional variant of LEPR,rs1137101(Gln223Arg),has been extensively investigated for its contribution to the risk of digestive system(DS)cancers,but results remain conflicting rather than conclusive.Here,we performed a case-control study and subsequent meta-analysis to examine the association between rs1137101 and DS cancer risk. Methods A total of 1,727 patients with cancer(gastric/liver/colorectal:460/480/787)and 800 healthy controls were recruited.Genotyping of rs1137101 was conducted using a polymerase chain reaction-restriction fragment length polymorphism(PCR-RFLP)assay and confirmed using Sanger sequencing.Twenty-four eligible studies were included in the meta-analysis. Results After Bonferroni correction,the case-control study revealed that rs1137101 was significantly associated with the risk of liver cancer in the Hubei Chinese population.The meta-analysis suggested that rs1137101 is significantly associated with the risk of overall DS,gastric,and liver cancer in the Chinese population. Conclusion The LEPR rs1137101 variant may be a genetic biomarker for susceptibility to DS cancers(especially liver and gastric cancer)in the Chinese population.

Objective To explore the relationship between basic motor skills and social interaction ability in school-age children with moderate autism,and the mediating role of executive function and the realization path.Methods A cross-sectional design was used to investigate 117 school-age children with autism from Sep.to Dec.2020.The level of basic motor skills was assessed by the test of gross motor development(TGMD),the impairment of executive function was assessed by the behavior rating inventory of executive function(BRIEF),and the social disorder was assessed by the social responsive scale-second edition(SRS-2).Pearson correlation analysis was used to explore the interrelationship,and structural equation modeling was applied to explore the path relationship.Results There was a significant negative correlation between the level of basic motor skills and SRS-2 scores(r=-0.312,P＜0.001).There were significant negative correlations between the level of basic motor skills and the BRIEF scores of inhibition(r=-0.336,P＜0.001),switching(r=-0.325,P＜0.001),affective control(r=-0.338,P＜0.001),task initiation(r=-0.240,P=0.009),working memory(r=-0.278,P=0.002),and planning(r=-0.224,P=0.015).The SRS-2 score was positively correlated with the BRIEF scores of inhibition(r=0.378,P＜0.001),switching(r=0.299,P=0.001),affective control(r=0.417,P＜0.001),task initiation(r=0.246,P=0.007),working memory(r=0.409,P＜0.001),and monitoring(r=0.258,P=0.005).Executive function played a complete intermediary role between basic motor skills and social interaction ability(B=-1.912,95％confidence interval:-3.116 to-1.069).Conclusion In school-age children with moderate autism,executive function and social interaction ability change positively with the level of basic motor skills.Basic motor skills can affect social interaction ability through the mediating role of executive function,and inhibition and affective control are important pathways to achieve this.

Objective To explore the clinical characteristics of pediatric patients with cerebral palsy(CP)who also have comorbid epilepsy.Methods A retrospective analysis was conducted on the clinical data of 155 pediatric patients with CP and comorbid epilepsy admitted to the Third Affiliated Hospital of Zhengzhou University from January 2019 to December 2022.Patients were divided into 4 groups based on CP subtype:spastic diplegia group(n=29),spastic hemiplegia group(n=33),spastic quadriplegia group(n=73),and non-spastic group(n=20).Differences in sex,season of birth,birth weight,gestational age,and the relationship between gestational age and weight were compared among the groups.Additionally,the relationships between perinatal risk factors,MRI classification system(MRICS),gross motor function classification system(GMFCS),and the age of the first onset of epilepsy with respect to CP subtype were analyzed.Results Among the 155 patients,101 were male and 54 were female.A lower proportion of patients with spastic hemiplegia was observed with a gestational age of 28-31+6 weeks compared with those with spastic diplegia and spastic quadriplegia(P=0.009).The proportion of patients with a history of asphyxia in spastic hemiplegia group was significantly lower than that in the other 3 groups,and the proportion of patients with hypoxic-ischemic encephalopathy(HIE)in spastic hemiplegia group was significantly lower than in that both spastic quadriplegia group and non-spastic group(P<0.05).The proportion of patients in spastic quadriplegia group who had their first seizure at an age of<1 year was significantly higher than that in spastic diplegic group(P=0.041).The spastic diplegia group exhibited a higher percentage of white matter damage compared with the other 3 groups,and had a lower percentage of gray matter damage compared with both spastic hemiplegic group and non-spastic group(P=0.001).The proportion of patients with GMFCS levels Ⅳ-Ⅴ in spastic quadriplegia group was higher than those in the other 3 groups(P<0.001),and the proportion of patients with levels Ⅰ-Ⅲ in spastic hemiplegia group was significantly higher than those in spastic quadriplegia group and non-spastic group(P<0.001).Conclusion Significant differences were observed among pediatric patients with different subtypes of CP and comorbid epilepsy in factors such as gestational age,history of asphyxia,HIE history,age of first seizure,MRICS classification and GMFCS levels.