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.

ObjectiveThe proteome of biological evidence contains rich genetic information, namely single amino acid polymorphisms (SAPs) in protein sequences. However, due to the lack of efficient and convenient analysis tools, the application of SAP in public security still faces many challenges. This paper aims to meet the application requirements of SAP analysis for forensic biological evidence’s proteome data. MethodsThe software is divided into three modules. First, based on a built-in database of common non-synonymous single nucleotide polymorphisms (nsSNPs) and SAPs in East Asian populations, the software integrates and annotates newly identified exonic nsSNPs as SAPs, thereby constructing a customized SAP protein sequence database. It then utilizes a pre-installed search engine—either pFind or MaxQuant—to perform analysis and output SAP typing results, identifying both reference and variant types, along with their corresponding imputed nsSNPs. Finally, SAPTyper compares the proteome-based typing results with the individual’s exome-derived nsSNP profile and outputs the comparison report. ResultsSAPTyper accepts proteomic DDA mass spectrometry raw data (DDA acquisition mode) and exome sequencing results of nsSNPs as input and outputs the report of SAPs result. The pFind and Maxquant search engines were used to test the proteome data of 2 hair shafts of2 individuals, and both obtained SAP results. It was found that the results of the Maxquant search engine were slightly less than those of pFind. This result shows that SAPTyper can achieve SAP fingding function. Moreover, the pFind search engine was used to test the proteome data of 3 hair shafts from 1 European person and 1 African person in the literature. Among the sites fully matched by the literature method, sites detected by SAPTyper are also included; for semi-matching sites, that is, nsSNPs are heterozygous, both literature method and SAPTyper method had the risk of missing detection for one type of the allele. Comparing the analysis results of SAPTyper with the SAP test results reported in the literature, it was found that some imputed nsSNP sites identified by the literature method but not detected by SAPTyper had a MAF of less than 0.1% in East Asian populations, and therefore they were not included in the common nsSNP database of East Asian populations constructed by this software. Since the database construction of this software is based on the genetic variation information of East Asian populations, it is currently unable to effectively identify representative unique common variation sites in European or African populations, but it can still identify SAP sites shared by these populations and East Asian populations. ConclusionAn automated SAP analysis algorithm was developed for East Asian populations, and the software named SAPTyper was developed. This software provides a convenient and efficient analysis tool for the research and application of forensic proteomic SAP and has important application prospects in individual identification and phenotypic inference based on SAP.

Objective: To investigate the association of sleep and circadian rhythm disruption indicators (including chronotype, sleep duration, and social jetlag) with co-occurring depressive and anxiety symptoms among primary and secondary school students, so as to provide a reference for promoting their mental health. Methods: In October 2023, a total of 15 944 primary and secondary school students were recruited from Nanjing, using a stratified cluster random sampling method. The Morning and Evening Questionnaire-5, Center for Epidemiological Studies Depression, and Generalized Anxiety Disorder-7 were used for the survey. Chi-square test was employed for intergroup comparisons, and Logistic regression model was applied to analyze the independent and joint effects of sleep related factors on comorbid symptoms of depressive and anxiety among primary and middle school students. Results: The prevalence of co-occurring depressive and anxiety symptoms among primary and secondary school students in Nanjing was 16.9%. After adjusting for covariates, Logistic regression analysis revealed significant independent associations between evening chronotype ( OR=6.55, 95%CI =5.59-7.68), insufficient sleep duration ( OR=3.05, 95%CI =2.60-3.59), and social jetlag ≥2 h ( OR= 2.09 , 95%CI =1.85-2.37) with comorbid symptoms of depressive and anxiety among students (all P <0.05). Concurrent of evening chronotype and insufficient sleep ( OR=7.54, 95%CI =3.55-16.01), as well as evening chronotype and social jetlag ≥2 h ( OR=4.18, 95%CI =3.01-5.81), were associated with an increased risk of co-occurring depressive and anxiety symptoms (both P < 0.05 ). In the female and high school student subgroups, the combination of evening chronotype and insufficient sleep or social jetlag ≥2 h showed stronger joint effects on co-occurring depressive and anxiety symptoms [ OR (95% CI )=8.46(3.25-22.04) and 15.90(3.66-69.08); 7.87(4.90-12.65) and 4.85(3.10-7.59), respectively; all P <0.05]. Conclusions Evening chronotype, insufficient sleep, and social jetlag≥2 h may serve as risk factors for comorbid symptoms of depressive and anxiety in school aged populations. Paying attention to the coexistence of multiple sleep related risk factors may help mitigate the occurrence of emotional disorders in this demographic.

Objective To explore the mechanism of kaempferol in improving airway inflammation in chronic obstructive pulmonary disease(COPD)rats.Methods Twenty-for male SD rats were randomly divided into control group,model group(exposed to second-hand smoke for 36 weeks)and experimental group(intragastric administration of 20 mg·kg-1 kaempferol from the 7th day of exposure to secondhand smoke).Pulmonary function was measured by small animal pulmonary function test system,lung index was calculated by body weight and lung weight,inflammatory infiltration and fibrosis changes in lung tissue were observed histologically,the expression of interleukin-1(IL-1),interleukin-6(IL-6)and tumor necrosis factor-α(TNF-α)in bronchoalveolar lavage fluid was detected by enzyme linked immunosorbent assay(ELISA);the expression of superoxide dismutase(SOD)and malondialdehyde(MDA)was detected by the detection kit;and the protein expression of Toll-like receptor 4(TLR4)and nuclear factor-κB(NF-κB)p65 in lung tissue was detected by Western blotting.Results The peak expiratory flow in the control group,model group and experimental group were(19.48±1.61),(15.86±1.00)and(18.07±0.83)mL·s-1;valsalva maneuver were(231.22±10.26),(244.94±9.32)and(225.71±3.80)mL·min-1;lung compliance were(0.53±0.08),(0.29±0.02)and(0.48±0.03)mL·cm-1 H2O;total airway resistance were(0.19±0.12),(0.21±0.01)and(0.20±0.01)cmH2O·mL-1;running economy were(0.19±0.01),(0.26±0.03)and(0.21±0.01)mL·s-1;lung weight were(2.76±0.10),(2.94±0.18)and(2.29±0.26)g;body weight were(375.13±23.55),(243.00±26.75)and(325.38±23.80)g;lung index were 0.74±0.02,1.22±0.09 and 0.70±0.05;the expression levels of cytokines IL-1 in bronchoalveolar lavage fluid(BALF)were(32.39±3.37),(161.88±9.02)and(66.44±6.81)pg·mL-1;IL-6 were(75.07±8.87),(129.58±13.29)and(99.94±9.92)pg·mL-1;TNF-α were(441.76±9.92),(814.47±122.02)and(656.70±70.06)pg·mL-1;MDA expression were(135.73±6.20),(179.05±15.18)and(149.40±13.83)nmol·mL-1;SOD expression were(4 258.55±384.12),(1 232.24±237.08)and(2 134.33±197.99)U·mL-1;TLR4 protein expression level in lung tissue were 0.98±0.02,1.55±0.04,1.34±0.02;NF-κB p65 protein expression level were 0.98±0.02,1.61±0.03,1.09±0.03;the above indicators,control group compared with model group,experimental compared with and model group,the differences were all statistically significant(all P＜0.05).Conclusion Kaempferol ameliorates lung injury induced by airway inflammation in COPD rats by inhibiting TLR4/NF-κB signaling pathway.

Objective To observe the multi-system toxicity of Tripterygium glycosides tablets in rats with type Ⅱ collagen-induced arthritis(CIA).Methods Healthy SD rats were randomly divided into normal group,model group and experimental group,with 10 rats in each group.In addition to the normal group,the other groups were established collagen-induced arthritis model.After the first immunization,the normal group and the model group were given an equal volume of 0.3％sodium carboxymethyl cellulose solution,and the experimental group was given 72 mg·kg-1·d-1 Tripterygium glycosides solution,once a day for 6 weeks.On the 42 nd day,the blood routine of each group was detected and the organ index was calculated.The levels of liver,kidney function and sex hormones in rats were detected by enzyme-linked immunosorbent assay.The histopathological changes of liver,kidney,ovary and testis were observed by hematoxylin-eosin(HE)staining.Results The testicular indexes of the normal group,the model group and the experimental group were(0.81±0.05)％,(0.97±0.06)％and(0.81±0.12)％;the estradiol contents were(63.90±16.19),(55.42±7.23)and(40.04±5.97)pg·mL-1;the testosterone contents were(1 290.96±257.94),(1 198.43±190.77)and(912.62±61.72)pg·mL-1,respectively.There were statistically significant differences in the above indexes between the model group and the experimental group(P＜0.01,P＜0.05).HE pathological results showed that Tripterygium glycosides tablets could cause abnormal histopathological changes of ovary and testis in CIA model rats.Conclusion Continuous administration of 8-fold clinical equivalent dose of Tripterygium glycosides tablets for 6 weeks can cause damage to the reproductive system of CIA rats.

Objective To observe the effect of ultrasound-guided anterior quadratus lumborum block at lateral supra-arcuate ligament(QLB-LSAL)and transversus abdominis plane block(TAPB)on analgesia and recovery quality after laparoscopic partial hepatectomy(LPH).Methods Fifty-eight patients underwent elective LPH were selected and divided into the quadratus lumborum group or the transversus abdominis group randomly,with 29 patients in each group.The quadratus lumborum group received bilateral QLB-LSAL,and the transversus abdominis group received bilateral subcostal TAPB block before surgery.Both groups received 20 mL of 0.33%ropivacaine on each side.All patients used patient-controlled intravenous analgesia(PCIA)postoperatively.The numeric rating scale(NRS)scores for rest and movement were recorded at 2,4,6,12,24 and 48 hours postoperatively,as well as the Quality of Recovery-15(QoR-15)scores at 1 day preoperatively,1 and 3 days postoperatively.The perioperative anesthetic agent consumption,PCIA pressing frequency,remedial analgesia use in 48 h,postoperative nausea and vomiting(PONV)incidence and time of first out-of-bed mobilization were also recorded.Results Compared with the transversus abdominis group,the quadratus lumborum group had lower movement NRS scores at 2,4,6,12,24 and 48 hours postoperatively,and lower rest NRS scores at 2,4,6,12 and 24 hours postoperatively(P＜0.05).The quadratus lumborum group had higher QoR-15 scores at 1 and 3 days postoperatively(P＜0.05).Patients in the quadratus lumborum group had reduced perioperative remifentanil and sufentanil consumption,postoperative 48-hour rescue analgesia use,PCIA pressing frequency,PONV incidence and time of first out-of-bed mobilization(P＜0.05).Conclusion QLB-LSAL block provides superior analgesic effects and recovery quality compared to TAPB block after LPH.