Objective:To provide novel genetic biomarkers for the diagnosis and treatment of sepsis,bioinformatics analysis was used to screen differentially expressed genes and identify Hub genes in sepsis.Methods:Gene Expression Omnibus(GEO)database was used to retrieve gene expression datasets of sepsis and screen for differentially expressed genes(DEGs).Protein-protein interaction(PPI)network analysis,Gene Ontology(GO)analysis,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were used to clarify the molecular mechanism of DEGs,and Hub genes were screened.Results:A total of 361 DEGs were identified,including 163 up-regulated genes and 198 down-regulated genes.Enrichment analysis revealed that these DEGs were primarily involved in antigen processing and presentation,T cell biology,cell adhesion molecules,and T cell receptor signaling pathways.CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1 were determined as optimal diagnostic biomarkers for sepsis.Conclusions:This study elucidated 10 Hub genes(CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1)as potential biomarkers for the diagnosis and treatment of sepsis.However,since the the generalizability of these Hub genes in patients with sepsis remains unvalidated,further experimental verification is still needed in the future.

Objective:To provide novel genetic biomarkers for the diagnosis and treatment of sepsis,bioinformatics analysis was used to screen differentially expressed genes and identify Hub genes in sepsis.Methods:Gene Expression Omnibus(GEO)database was used to retrieve gene expression datasets of sepsis and screen for differentially expressed genes(DEGs).Protein-protein interaction(PPI)network analysis,Gene Ontology(GO)analysis,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were used to clarify the molecular mechanism of DEGs,and Hub genes were screened.Results:A total of 361 DEGs were identified,including 163 up-regulated genes and 198 down-regulated genes.Enrichment analysis revealed that these DEGs were primarily involved in antigen processing and presentation,T cell biology,cell adhesion molecules,and T cell receptor signaling pathways.CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1 were determined as optimal diagnostic biomarkers for sepsis.Conclusions:This study elucidated 10 Hub genes(CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1)as potential biomarkers for the diagnosis and treatment of sepsis.However,since the the generalizability of these Hub genes in patients with sepsis remains unvalidated,further experimental verification is still needed in the future.

Integrated metabolomic and lipidomic profiling,utilizing liquid chromatography coupled with high-resolution mass spectrometry(LC-HRMS),has emerged as a pivotal strategy for biomarker discovery.However,the inherent polarity disparity between metabolites and lipids complicates simultaneous analysis.To address this,a dual-stationary phase polarity-extended liquid chromatography(PELC)system was developed,which surpassed conventional one-dimensional LC(1D-LC)by enabling comprehensive coverage of both polar and non-polar compounds within a single injection.This system enhanced chromatographic resolution,peak capacity,and throughput while minimizing analytical variability.Extracellular vesicles(EVs),lipid bilayer-enclosed nanoparticles ubiquitously present in biofluids,had gained prominence as reservoirs of cancer biomarkers due to their cargo stability and pathophysiological relevance.Herein,the application of PELC-HRMS for concurrent metabolome-lipidome profiling in EVs was pioneered.A total of 193 metabolites were identified using this technique coupled with MS-DIAL software and Human Metabolome Database.Subsequently,this technique was employed to explore potential biomarkers for prostate cancer(PCa).Multivariate analysis identified 17 differentially abundant metabolites in PCa,implicating dysregulated pathways including purine metabolism,starch and sucrose metabolism,galactose metabolism,cysteine and methionine metabolism,and biosynthesis of unsaturated fatty acids.Notably,creatine(AUC=0.92)and DG 42:5(AUC=0.80)demonstrated robust diagnostic efficacy,attributable to their broad polarity ranges and EV-specific enrichment.This study established PELC as a high-fidelity platform for multi-omics integration in complex biospecimens,advancing mechanistic insights into metabolic rewiring and disease pathophysiology.

OBJECTIVE: To explore the clinical significance of circulating tumor DNA (ctDNA) in response evaluation and relapse monitoring for patients with primary mediastinal large B-cell lymphoma (PMBCL). METHODS: The clinical characteristics, efficacy and survival of 38 PMBCL patients in our hospital from January 2010 to April 2020 were retrospectively analyzed. The ctDNA monitoring was conducted by targeted next-generation sequencing (NGS). RESULTS: Among the 38 patients, 26 cases were female, and 32 cases were diagnosed with Ann Arbor stage I-II. The 5-year overall survival (OS) rate and progression-free survival (PFS) rate were 74.7% and 61.7%, respectively. Males and those with high aaIPI scores (3 points) had a relatively poor prognosis. The NGS results of 23 patients showed that STAT6 (65.2%), SOCS1 (56.5%), and TNFAIP3 (56.5%) were the most common mutated genes. Patients with stable disease (SD)/progressive disease (PD) exhibited enrichment in cell cycle, FoxO, and TNF signaling pathways. A total of 29 patients underwent end-of-treatment PET/CT (EOT PET/CT), and 16 of them received ctDNA monitoring with 12 negative. Among 6 patients with EOT PET/CT positive (Deauville 4), 4 underwent ctDNA monitoring, and 3 of them were negative, being still in continuous remission without any subsequent anti-tumor therapy. CONCLUSION CtDNA may be combined with PET/CT to assess efficacy, monitor relapse, and guide treatment of PMBCL.
Humans ; Circulating Tumor DNA/blood* ; Female ; Mediastinal Neoplasms ; Male ; Retrospective Studies ; High-Throughput Nucleotide Sequencing ; Prognosis ; Lymphoma, Large B-Cell, Diffuse/genetics* ; Middle Aged ; Adult ; Aged ; Neoplasm Recurrence, Local ; Mutation

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Accurate timing of myelination is crucial for the proper functioning of the central nervous system. Here, we identified a de novo heterozygous mutation in TMEM63A (c.1894G>A; p. Ala632Thr) in a 7-year-old boy exhibiting hypomyelination. A Ca²⁺ influx assay suggested that this is a loss-of-function mutation. To explore how TMEM63A deficiency causes hypomyelination, we generated Tmem63a knockout mice. Genetic deletion of TMEM63A resulted in hypomyelination at postnatal day 14 (P14) arising from impaired differentiation of oligodendrocyte precursor cells (OPCs). Notably, the myelin dysplasia was transient, returning to normal levels by P28. Primary cultures of Tmem63a^-/- OPCs presented delayed differentiation. Lentivirus-based expression of TMEM63A but not TMEM63A_A632T rescued the differentiation of Tmem63a^-/- OPCs in vitro and myelination in Tmem63a^-/- mice. These data thus support the conclusion that the mutation in TMEM63A is the pathogenesis of the hypomyelination in the patient. Our study further demonstrated that TMEM63A-mediated Ca²⁺ influx plays critical roles in the early development of myelin and oligodendrocyte differentiation.
Animals ; Cell Differentiation/physiology* ; Oligodendroglia/metabolism* ; Mice, Knockout ; Mice ; Male ; Myelin Sheath/metabolism* ; Humans ; Child ; Cells, Cultured ; Oligodendrocyte Precursor Cells/metabolism*

This study aimed to explore the pharmacological mechanism of Yourenji Capsules(YRJ) in improving osteoporosis by combining network pharmacology and proteomics technologies. The SD rats were randomly divided into a blank control group and a 700 mg·kg~(-1) YRJ group. The rats were subjected to gavage administration with the corresponding drugs, and the blank serum, drug-containing serum, and YRJ samples were compared using ultra performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS/MS) to analyze the main components absorbed into blood. Network pharmacology analysis was conducted based on the YRJ components absorbed into blood to obtain related targets of the components and target genes involved in osteoporosis, and Venn diagrams were used to identify the intersection of drug action targets and disease targets. The STRING database was used for protein-protein interaction(PPI) network analysis of potential target proteins to construct a PPI network. Gene Ontology(GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment were performed using Enrichr to investigate the potential mechanism of action of YRJ. Ovariectomy(OVX) was performed to establish a rat model of osteoporosis, and the rats were divided into a sham group, a model group, and a 700 mg·kg~(-1) YRJ group. The rats were given the corresponding drugs by gavage. The femurs of the rats were subjected to label-free proteomics analysis to detect differentially expressed proteins, and GO functional enrichment and KEGG pathway enrichment analyses were performed on the differentially expressed proteins. With the help of network pharmacology and proteomics results, the mechanism by which YRJ improves osteoporosis was predicted. The analysis of the YRJ components absorbed into blood revealed 23 bioactive components of YRJ, and network pharmacology results indicated that key targets involved include tumor necrosis factor(TNF), tumor protein p53(TP53), protein kinase(AKT1), and matrix metalloproteinase 9(MMP9). These targets are mainly involved in osteoclast differentiation, estrogen signaling pathways, and nuclear factor-kappa B(NF-κB) signaling pathways. Additionally, the proteomics analysis highlighted important pathways such as peroxisome proliferator-activated receptor(PPAR) signaling pathways, mitogen-activated protein kinase(MAPK) signaling pathways, and β-alanine metabolism. The combined approaches of network pharmacology and proteomics have revealed that the mechanism by which YRJ improves osteoporosis may be closely related to the regulation of inflammation, osteoblast, and osteoclast metabolic pathways. The main pathways involved include the NF-κB signaling pathways, MAPK signaling pathways, and PPAR signaling pathways, among others.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Osteoporosis/metabolism* ; Proteomics ; Rats ; Rats, Sprague-Dawley ; Network Pharmacology ; Female ; Protein Interaction Maps/drug effects* ; Capsules ; Humans ; Signal Transduction/drug effects*

Objective:To analyze the epidemic characteristics of reported tuberculosis incidence in Kashgar from 2015 to 2022, and use the seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence, providing references for the local control of pulmonary tuberculosis.Methods:The reported incidence data of tuberculosis in the Kashgar area of Xinjiang from January 2015 to August 2023 were collected through the"Infectious Disease Monitoring System", a subsystem of the "Chinese Disease Prevention and Control Information System". The epidemic characteristics of reported incidence in this area from 2015 to 2022 were analyzed. Two SARIMA models of monthly reported incidence number and rate were established. The prediction performance of the two models was evaluated using the reported incidence data of tuberculosis from January 2023 to August 2023. The χ2 test was used to analyze population characteristics, and the Cochran-Armitage trend test was used to analyze annual incidence. Results:From 2015 to 2022, 133 972 cases of pulmonary tuberculosis were reported in Kashgar, with a yearly reported incidence rate of 383.64/100 000, showing a rising trend ( TCA=77.03, P<0.001) and then a declining trend ( TCA=176.16, P<0.001). The proportion of pathogenic positive pulmonary tuberculosis had increased yearly ( TCA=132.66, P<0.001). The reported onset time was concentrated from January to June each year, with a peak in April. Yengisar County, Zepu County and Yopurga County had the highest reported incidence rate in Kashgar. The sex ratio of men to women was 1.03∶1, and the reported incidence rate of men was higher than that of women ( χ2=27.04, P<0.001). The reported incidence rate of the group aged 60 years and older was the highest. The patient′s occupation was mainly farmers (84.99%). The average relative errors of the SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model in predicting the reported monthly incidence number and rate were 11.67% and -9.81%, respectively. Both models had good prediction accuracy (MAPE=33.55%, MAPE=38.22%). Conclusion:The average reported incidence rate of pulmonary tuberculosis in the Kashgar area shows a rising trend first and then a declining trend. The patients are mainly men and farmers, and attention should be paid to the prevention and control of tuberculosis among the elderly in winter and spring. The SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model can fit the trend of reported tuberculosis incidence in the Kashgar area well and have good predictive performance.

Objective:To analyze the epidemic characteristics of reported tuberculosis incidence in Kashgar from 2015 to 2022, and use the seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence, providing references for the local control of pulmonary tuberculosis.Methods:The reported incidence data of tuberculosis in the Kashgar area of Xinjiang from January 2015 to August 2023 were collected through the"Infectious Disease Monitoring System", a subsystem of the "Chinese Disease Prevention and Control Information System". The epidemic characteristics of reported incidence in this area from 2015 to 2022 were analyzed. Two SARIMA models of monthly reported incidence number and rate were established. The prediction performance of the two models was evaluated using the reported incidence data of tuberculosis from January 2023 to August 2023. The χ2 test was used to analyze population characteristics, and the Cochran-Armitage trend test was used to analyze annual incidence. Results:From 2015 to 2022, 133 972 cases of pulmonary tuberculosis were reported in Kashgar, with a yearly reported incidence rate of 383.64/100 000, showing a rising trend ( TCA=77.03, P<0.001) and then a declining trend ( TCA=176.16, P<0.001). The proportion of pathogenic positive pulmonary tuberculosis had increased yearly ( TCA=132.66, P<0.001). The reported onset time was concentrated from January to June each year, with a peak in April. Yengisar County, Zepu County and Yopurga County had the highest reported incidence rate in Kashgar. The sex ratio of men to women was 1.03∶1, and the reported incidence rate of men was higher than that of women ( χ2=27.04, P<0.001). The reported incidence rate of the group aged 60 years and older was the highest. The patient′s occupation was mainly farmers (84.99%). The average relative errors of the SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model in predicting the reported monthly incidence number and rate were 11.67% and -9.81%, respectively. Both models had good prediction accuracy (MAPE=33.55%, MAPE=38.22%). Conclusion:The average reported incidence rate of pulmonary tuberculosis in the Kashgar area shows a rising trend first and then a declining trend. The patients are mainly men and farmers, and attention should be paid to the prevention and control of tuberculosis among the elderly in winter and spring. The SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model can fit the trend of reported tuberculosis incidence in the Kashgar area well and have good predictive performance.

Objective:To explore the mechanisms of Qianlie Jindan Tablets(QLJD)acting on chronic nonbacterial prostatitis(CNP)in rats based on non-targeted urine metabolomics.Methods:According to the body mass index,we equally randomized 30 eight-week-old male SD rats into a blank control,a CNP model control and a QLJD medication group.We established the CNP model in the latter groups and,from the 4th day of modeling,treated the rats in the blank and model control groups intragastrically with nor-mal saline and those in the QLJD medication group with QLJD suspension,qd,for 30 successive days.Then we detected the changes in the metabolites of the rats by ultra-high-performance liquid chromatography-tandem mass spectrometry,and identified the differential metabolites in different groups by multivariate statistical analysis,followed by functional annotation of the differential metabolites.Results:Eight common metabolites were identified by metabolomics analysis,of which 5 were decreased in the CNP model controls and increased in the QLJD medication group,while the other 3 increased in the former and decreased in the latter group.Creatinine and genistein were important differential metabolites,and the arginine and proline metabolic pathways and isoflavone biosynthesis pathways were the main ones for QLJD acting on CNP.Compared with the blank controls,the model controls showed up-regulated arginine and proline metabolic pathways,increased production of creatinine,down-regulated isoflavone biosynthetic pathway and decreased produc-tion of genistein.The above changes in the model controls were all reversed in the QLJD medication group.Conclusion:QLJD acts effectively on CNP in male rats by regulating L-arginine and proline metabolic pathways,as well as the isoflavone biosynthesis pathway and naringenin metabolism.