Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

The traditional Chinese medicine Atractlodis Rhizoma is the dried rhizome of the Asteraceae herbal plant Atractylodes lancea, and it has the functions of drying dampness and strengthening the spleen, removing wind and dissipating cold, and brightening the eyes. The sesquiterpenoids in A. lancea are the main ingredients of its pharmacological activities in clinical practice, including atractylone, β-eudesmol, and hinesol, which possess anti-inflammation, antibacterial, antiviral, and hepatoprotective effects. This study focused on the biosynthesis of sesquiterpenoids in A. lancea, summarized the proportion of the main active ingredients in A. lancea from the genuine region and the non-genuine region, elaborated on the research progress of genes related to biosynthesis pathways, and systematically sorted out the biotic and abiotic factors affecting their biosynthesis, so as to provide a theoretical basis for further research on the biosynthetic mechanism of sesquiterpenoids in A. lancea and development of high-quality medicinal materials of A. lancea.
Atractylodes/metabolism* ; Sesquiterpenes/metabolism* ; Drugs, Chinese Herbal/pharmacology* ; Biosynthetic Pathways

Objective: To summarize the clinical characteristics of tumour necrosis factor receptor-associated periodic syndrome (TRAPS) in children. Methods: The clinical manifestations, laboratory tests, genetic testing and follow-up of 10 children with TRAPS from May 2011 to May 2021 in 6 hospitals in China were retrospectively analyzed. Results: Among the 10 patients with TRAPS, including 8 boys and 2 girls. The age of onset was 2 (1, 5) years, the age of diagnosis was (8±4) years, and the time from onset to diagnosis was 3 (1, 7) years. A total of 7 types of TNFRSF1A gene variants were detected, including 5 paternal variations, 1 maternal variation and 4 de novo variations. Six children had a family history of related diseases. Clinical manifestations included recurrent fever in 10 cases, rash in 4 cases, abdominal pain in 6 cases, joint involvement in 6 cases, periorbital edema in 1 case, and myalgia in 4 cases. Two patients had hematological system involvement. The erythrocyte sedimentation rate and C-reactive protein were significantly increased in 10 cases. All patients were negative for autoantibodies. In the course of treatment, 5 cases were treated with glucocorticoids, 7 cases with immunosuppressants, and 7 cases with biological agents. Conclusions: TRAPS is clinically characterized by recurrent fever accompanied by joint, gastrointestinal, skin, and muscle involvement. Inflammatory markers are elevated, and autoantibodies are mostly negative. Treatment mainly involves glucocorticoids, immunosuppressants, and biological agents.
Male ; Child ; Female ; Humans ; Child, Preschool ; Receptors, Tumor Necrosis Factor, Type I/genetics* ; Retrospective Studies ; Hereditary Autoinflammatory Diseases/drug therapy* ; Glucocorticoids/therapeutic use* ; Biological Factors/therapeutic use* ; Immunosuppressive Agents/therapeutic use* ; Autoantibodies ; Familial Mediterranean Fever/diagnosis* ; Mutation

Objective: To study the incidence of bloodstream infections, pathogen distribution, and antibiotic resistance profile in patients with hematological malignancies. Methods: From January 2018 to December 2021, we retrospectively analyzed the clinical characteristics, pathogen distribution, and antibiotic resistance profiles of patients with malignant hematological diseases and bloodstream infections in the Department of Hematology, Nanfang Hospital, Southern Medical University. Results: A total of 582 incidences of bloodstream infections occurred in 22,717 inpatients. From 2018 to 2021, the incidence rates of bloodstream infections were 2.79%, 2.99%, 2.79%, and 2.02%, respectively. Five hundred ninety-nine types of bacteria were recovered from blood cultures, with 487 (81.3%) gram-negative bacteria, such as Klebsiella pneumonia, Escherichia coli, and Pseudomonas aeruginosa. Eighty-one (13.5%) were gram-positive bacteria, primarily Staphylococcus aureus, Staphylococcus epidermidis, and Enterococcus faecium, whereas the remaining 31 (5.2%) were fungi. Enterobacteriaceae resistance to carbapenems, piperacillin/tazobactam, cefoperazone sodium/sulbactam, and tigecycline were 11.0%, 15.3%, 15.4%, and 3.3%, with a descending trend year on year. Non-fermenters tolerated piperacillin/tazobactam, cefoperazone sodium/sulbactam, and quinolones at 29.6%, 13.3%, and 21.7%, respectively. However, only two gram-positive bacteria isolates were shown to be resistant to glycopeptide antibiotics. Conclusions: Bloodstream pathogens in hematological malignancies were broadly dispersed, most of which were gram-negative bacteria. Antibiotic resistance rates vary greatly between species. Our research serves as a valuable resource for the selection of empirical antibiotics.
Humans ; Bacteremia/epidemiology* ; Cefoperazone ; Sulbactam ; Retrospective Studies ; Drug Resistance, Bacterial ; Microbial Sensitivity Tests ; Hematologic Neoplasms ; Sepsis ; Anti-Bacterial Agents/pharmacology* ; Gram-Negative Bacteria ; Gram-Positive Bacteria ; Piperacillin, Tazobactam Drug Combination ; Escherichia coli

This study evaluated the scientific nature and effectiveness of iterative optimization of prevention and control measures for local outbreaks caused by the BA.2 and BA.5.2 COVID-19 strains in Fujian Province in 2022,to provide a scientif-ic basis for responding to future new or recurrent respiratory infectious diseases.According to the theory of infectious disease dynamics,relevant information regarding the local epidemic situation caused by the BA.2 sub-type Omicron virus strain in March 2022 and BA.5.2 sub-type Omicron virus strain in October 2022 in Fujian Province was collected.The susceptible exposed infectious removed(SEIAR)model of COVID-19 infection with a latent period and asymptomatic infected persons was used to analyze the transmission dynam-ics of two local epidemic situations,and evaluate the preven-tion and control effects.The incubation period of the BA.2 epidemic was 3 days(1～9 days),the intergenerational inter-val was 3 days(1～5 days),and the initial Rt was 3.0(95％CI:2.7～3.3).The incubation period of the BA.5.2 epidemic was 2 days(1～6 days),the intergenerational interval was 1 day(0～2 days),and the initial R,was 1.9(95％CI:1.7～2.1).The fittingresults for the BA.2 and BA.5.2 epidemics were good,and no statistical difference was observed between the predic-ted and actual numbers of cases(x2BA.2=31.53,x2BA.5.2=27.88,P＞0.05).If an emergency response had not been initiated,the BA.2 epidemic would have continued to spread andpeak on April 7th,with an estimated 638 035 cases.The BA.5.2 epidemic would have rapidly spread,reaching a peak on November 14th,with an estimated 685 940 cases.If one incubation period were detected early,the scale of the BA.2 epidemic would have decreased by 25.73％;if two incubation periods were detected early,the scale would have decreased by 79.56％,and if one incubation period had been delayed,the scale would have expanded by 13.72％.If one incubation period had been detected early in the BA.5.2 epidemic,the scale would have decreased by 35.04％;if two incubation periods had been detected early,the scale would have decreased by 92.47％;and if one incubation period had been delayed,the scale would have increased by 19.75％.The guiding ideology,and the prevention and control measures for handling two local epidemics were optimized and iterated.Our study indicated that implementing the"four early"measures ef-fectively decreased the scale of the epidemic,and earlier detection was associated with more significant control effects.This study provides valuable information for the prevention and control of new or recurrent respiratory infectious diseases.