ObjectiveExosomes are microvesicles which could be secreted by all cell types with diameters between 30 and 150 nm. It was widely distributed in body fluids including blood, urine, and breast milk. Exosomes are considered as potential biomarkers and drug carriers by reason of containing nucleic acids, lipids, proteins and other bioactive molecules. Milk-derived exosomes have been widely used as drug delivery carriers to treat targeted diseases with a lower cost, higher biocompatibility and lower immunogenicity. Until now, there is no research about the milk-derived exosomes phosphorylation to reveal the difference of protein phosphorylation in different species of milk. To investigate the pathways and proteins with specific functions, phosphorylated proteomic analysis of milk-derived exosomes from different species is performed, and provide new ideas for exploring diversified treatments of disease. MethodsWhey and exosomes derived from bovine, porcine and caprine milk were performed for proteomics and phosphoproteomics analysis. The relationship between milk exosome proteins from different species and signaling pathways were analyzed using bioinformatics tools. ResultsA total of 4 191 global proteins, 1 640 phosphoproteins and 4 064 phosphosites were identified from 3 species of milk-derived exosomes, and the exosome proteins and phosphoproteins from different species were significantly higher than those of whey. Meanwhile, some special pathways were enriched like Fcγ-mediated phagocytosis from bovine exosomes, pathways related with neural and immune system from caprine exosomes, positive and negative regulation of multiple activities from porcine exosomes. ConclusionIn this study, the proteomic and phosphoproteomic analyses of exosomes and whey from bovine, porcine and caprine milk were carried out to reveal the difference of composition and related signaling pathways of milk exosome from different species. These results provided powerful support for the application of exosomes from different milk sources in the field of disease treatment.

Viral myocarditis(VMC)is the leading cause of dilated cardiomyopathy,which can lead to heart failure and sudden cardiac death.With the development of high-throughput sequencing technology,non-coding RNA(ncRNA)plays an important role in the occurrence and development of VMC.ncRNA promotes the occurrence and development of VMC by regulating viral replication,immune cell function,myocardial cell death,myocardial interstitial fibrosis,and other pathological processes.This article reviews the research progress of ncRNA in VMC and provides new ideas for the pathogenesis,diagnosis,and treatment of VMC.

Objective As a new type of N-methyl-D-aspartic acid(NMDA)receptor antagonist,esmolamine has the same pharmacological mechanism and site as ketamine,but its affinity to NMDA receptor is 3-4 times that of ketamine,and its analgesic effect is 2-2.5 times that of ketamine.Postoperative cognitive dysfunction is the most common complication in elderly patients undergoing anesthesia and surgery.The incidence is higher in elderly patients over 65 years old,which seriously affects the quality of postoperative recovery and increases the medical burden.It is one of the clinical problems to be solved urgently.In recent years,with the clinical application of esmolamine,it has been found that intraoperative use of esmolamine can make the hemodynamics of surgical patients more stable,reduce the release of inflammatory factors and reduce the incidence of postoperative cognitive impairment.This article reviews the research progress of the effect of esmolamine on postoperative cognitive dysfunction in the field of clinical and basic research at home and abroad,in order to provide clinical reference.

Connectomics, a research field in neuroscience studying the synaptic connectivity patterns between neurons across different brain regions, is crucial for understanding neural computations underlying complex functions such as emotion, learning, and cognition. Specifically, micrometer-resolution mesoscale connectomics has become the most widely used technology in rodent neuroscience due to its unique advantages, and it also has the potential to transform brain research in non-human primates. Traditional mesoscale connectome techniques typically use fluorescence labeling and optical imaging to perform anterograde or retrograde tracing of neural circuits. To achieve single-cell resolution, methods for sparse labeling of neurons have been developed. However, it remains challenging to trace neurons in high throughput in individual animals and integrate multi-omics data across modalities. In the past decade, high-throughput mesoscale connectome technologies based on DNA barcoding have made significant progress. These technologies have provided novel tools to map single cell connectome, with higher throughput, lower cost, and multi-omics compatibility. Here we review several mature mesoscale connectome technologies based on DNA barcoding, discussing their principles, applications, advantages and disadvantages. We also propose future directions for barcoding-based connectomics.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Objective:To explore the potential causal relationship between gut microbiota and teratozoospermia.Methods:We searched the database of Genome-Wide Association Study(GWAS)for gut microbiota-and teratozoospermia-related data.We used gut microbiota as an exposure factor,determined the instrumental variables according to the GWAS data on 18 340 participants released by the MiBioGen Alliance,and derived the outcome variables from the European data on teratozoospermia,with a sample size of 85 716,including 915 cases and 209 006 controls.Using inverse-variance weighting(IVW),MR-Egger regression and the weighted median estimator(WME),we performed two-sample Mendelian randomization(MR)analysis on the retrieved data,and estimated the causal relationship between gut microbiota and teratozoospermia based on the β value.Results:Two-sample MR analysis indicated that the class Erysipelotrichia,family Erysipelotrichaceae,family Streptococcaceae,genus Coprococcusl,genus Ruminococcaceae UCG009,genus Streptococcus,order Clostridialesm and order Erysipelotrichales were causally related with the increased risk,while the family Porphyromonadaceae with the decreased risk of teratozoospermia.Conclusion:The class Erysipelotrichia,family Erysipe-lotrichaceae,family Streptococcaceae,genus Coprococcusl,genus Ruminococcaceae UCG009,genus Streptococcus,order Clostridia-lesm and order Erysipelotrichales are one of the causes of teratozoospermia,related to the increased risk of the condition,while the family Porphyromonadaceae has a protective effect on sperm morphology,reducing the risk of teratozoospermia.

Objective To investigate the clinical characteristics and prognosis of pneumococcal meningitis(PM),and drug sensitivity of Streptococcus pneumoniae(SP)isolates in Chinese children.Methods A retrospective analysis was conducted on clinical information,laboratory data,and microbiological data of 160 hospitalized children under 15 years old with PM from January 2019 to December 2020 in 33 tertiary hospitals across the country.Results Among the 160 children with PM,there were 103 males and 57 females.The age ranged from 15 days to 15 years,with 109 cases(68.1% )aged 3 months to under 3 years.SP strains were isolated from 95 cases(59.4% )in cerebrospinal fluid cultures and from 57 cases(35.6% )in blood cultures.The positive rates of SP detection by cerebrospinal fluid metagenomic next-generation sequencing and cerebrospinal fluid SP antigen testing were 40% (35/87)and 27% (21/78),respectively.Fifty-five cases(34.4% )had one or more risk factors for purulent meningitis,113 cases(70.6% )had one or more extra-cranial infectious foci,and 18 cases(11.3% )had underlying diseases.The most common clinical symptoms were fever(147 cases,91.9% ),followed by lethargy(98 cases,61.3% )and vomiting(61 cases,38.1% ).Sixty-nine cases(43.1% )experienced intracranial complications during hospitalization,with subdural effusion and/or empyema being the most common complication[43 cases(26.9% )],followed by hydrocephalus in 24 cases(15.0% ),brain abscess in 23 cases(14.4% ),and cerebral hemorrhage in 8 cases(5.0% ).Subdural effusion and/or empyema and hydrocephalus mainly occurred in children under 1 year old,with rates of 91% (39/43)and 83% (20/24),respectively.SP strains exhibited complete sensitivity to vancomycin(100% ,75/75),linezolid(100% ,56/56),and meropenem(100% ,6/6).High sensitivity rates were also observed for levofloxacin(81% ,22/27),moxifloxacin(82% ,14/17),rifampicin(96% ,25/26),and chloramphenicol(91% ,21/23).However,low sensitivity rates were found for penicillin(16% ,11/68)and clindamycin(6% ,1/17),and SP strains were completely resistant to erythromycin(100% ,31/31).The rates of discharge with cure and improvement were 22.5% (36/160)and 66.2% (106/160),respectively,while 18 cases(11.3% )had adverse outcomes.Conclusions Pediatric PM is more common in children aged 3 months to under 3 years.Intracranial complications are more frequently observed in children under 1 year old.Fever is the most common clinical manifestation of PM,and subdural effusion/emphysema and hydrocephalus are the most frequent complications.Non-culture detection methods for cerebrospinal fluid can improve pathogen detection rates.Adverse outcomes can be noted in more than 10% of PM cases.SP strains are high sensitivity to vancomycin,linezolid,meropenem,levofloxacin,moxifloxacin,rifampicin,and chloramphenicol.[Chinese Journal of Contemporary Pediatrics,2024,26(2):131-138]

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.

Objective To explore the risk factors for surgical site infection(SSI)after transforaminal lumbar interbody fusion(TLIF)for the treatment of lumbar degenerative diseases.Methods A total of 1 000 patients who underwent TLIF for lumbar degenerative diseases in our hospital were included and divided into the infection group(n=23)and the non-infection group(n=977)according to whether the surgical incision was infected.General data,surgical and laboratory indicators of patients were collected,and potential risk factors of SSI were screened by univariate analysis and multivariate regression analysis,a nomogram model was established,and its predictive efficiency was validated by the receive operating characteristic(ROC)curve.Results The incidence of SSI in patients after TLIF was 2.3％.The results of univariate analysis showed that age,operative time,intraoperative blood loss,preoperative C-reactive protein(CRP),smoking,and diabetes mellitus were the significant risk factors for the occurrence of SSI.Multivariate regression analysis showed that older age,longer operation time,more intraoperative blood loss,smoking and diabetes mellitus were the independent risk factors for postoperative SSI.ROC curve showed that the nomogram model established in this study has good predictive efficiency.Conclusion Older age,longer operation time,more intraoperative blood loss,smoking,and diabetes mellitus were independent risk factors for postoperative SSI.For patients with these high risk factors,corresponding intervention measures should be taken before operation to reduce the incidence of SSI.